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6,067
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fix tqdm lock
|
close https://github.com/huggingface/datasets/issues/6066
|
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6067",
"html_url": "https://github.com/huggingface/datasets/pull/6067",
"diff_url": "https://github.com/huggingface/datasets/pull/6067.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6067.patch",
"merged_at": "2023-07-25T09:54:12"
}
| true
|
[
"_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) | 0.006578 / 0.011353 (-0.004775) | 0.003953 / 0.011008 (-0.007055) | 0.084417 / 0.038508 (0.045908) | 0.076729 / 0.023109 (0.053620) | 0.315369 / 0.275898 (0.039471) | 0.347012 / 0.323480 (0.023533) | 0.005299 / 0.007986 (-0.002686) | 0.003321 / 0.004328 (-0.001007) | 0.063954 / 0.004250 (0.059704) | 0.055810 / 0.037052 (0.018758) | 0.317651 / 0.258489 (0.059162) | 0.352603 / 0.293841 (0.058762) | 0.031355 / 0.128546 (-0.097192) | 0.008493 / 0.075646 (-0.067153) | 0.287295 / 0.419271 (-0.131977) | 0.052716 / 0.043533 (0.009183) | 0.316410 / 0.255139 (0.061271) | 0.328893 / 0.283200 (0.045693) | 0.024005 / 0.141683 (-0.117678) | 1.520333 / 1.452155 (0.068178) | 1.601268 / 1.492716 (0.108552) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.205144 / 0.018006 (0.187138) | 0.459160 / 0.000490 (0.458670) | 0.000321 / 0.000200 (0.000121) | 0.000054 / 0.000054 (-0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027503 / 0.037411 (-0.009908) | 0.081476 / 0.014526 (0.066950) | 0.096759 / 0.176557 (-0.079798) | 0.157888 / 0.737135 (-0.579247) | 0.094592 / 0.296338 (-0.201746) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.384762 / 0.215209 (0.169553) | 3.843503 / 2.077655 (1.765849) | 1.921685 / 1.504120 (0.417565) | 1.752441 / 1.541195 (0.211246) | 1.822105 / 1.468490 (0.353615) | 0.480243 / 4.584777 (-4.104534) | 3.577220 / 3.745712 (-0.168492) | 5.047560 / 5.269862 (-0.222302) | 2.988008 / 4.565676 (-1.577669) | 0.056430 / 0.424275 (-0.367845) | 0.007180 / 0.007607 (-0.000427) | 0.458113 / 0.226044 (0.232069) | 4.584096 / 2.268929 (2.315168) | 2.395307 / 55.444624 (-53.049317) | 2.080530 / 6.876477 (-4.795947) | 2.239000 / 2.142072 (0.096927) | 0.575822 / 4.805227 (-4.229405) | 0.133303 / 6.500664 (-6.367361) | 0.059449 / 0.075469 (-0.016020) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.256496 / 1.841788 (-0.585291) | 19.651614 / 8.074308 (11.577306) | 14.232480 / 10.191392 (4.041088) | 0.146461 / 0.680424 (-0.533963) | 0.018632 / 0.534201 (-0.515569) | 0.399844 / 0.579283 (-0.179439) | 0.411225 / 0.434364 (-0.023139) | 0.458203 / 0.540337 (-0.082135) | 0.669916 / 1.386936 (-0.717020) |\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) | 0.006463 / 0.011353 (-0.004890) | 0.003898 / 0.011008 (-0.007110) | 0.064037 / 0.038508 (0.025529) | 0.071982 / 0.023109 (0.048873) | 0.361936 / 0.275898 (0.086038) | 0.393165 / 0.323480 (0.069685) | 0.005207 / 0.007986 (-0.002779) | 0.003231 / 0.004328 (-0.001098) | 0.064318 / 0.004250 (0.060068) | 0.055776 / 0.037052 (0.018724) | 0.383087 / 0.258489 (0.124598) | 0.402428 / 0.293841 (0.108587) | 0.031587 / 0.128546 (-0.096959) | 0.008527 / 0.075646 (-0.067119) | 0.070495 / 0.419271 (-0.348777) | 0.048806 / 0.043533 (0.005273) | 0.369932 / 0.255139 (0.114793) | 0.385268 / 0.283200 (0.102068) | 0.023183 / 0.141683 (-0.118500) | 1.491175 / 1.452155 (0.039020) | 1.534191 / 1.492716 (0.041475) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224526 / 0.018006 (0.206520) | 0.445460 / 0.000490 (0.444970) | 0.003612 / 0.000200 (0.003412) | 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) | 0.029829 / 0.037411 (-0.007583) | 0.087951 / 0.014526 (0.073425) | 0.100069 / 0.176557 (-0.076487) | 0.154944 / 0.737135 (-0.582192) | 0.101271 / 0.296338 (-0.195067) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.412385 / 0.215209 (0.197175) | 4.108038 / 2.077655 (2.030384) | 2.163578 / 1.504120 (0.659459) | 2.031934 / 1.541195 (0.490740) | 2.155857 / 1.468490 (0.687367) | 0.481132 / 4.584777 (-4.103645) | 3.620868 / 3.745712 (-0.124844) | 5.222175 / 5.269862 (-0.047687) | 3.115637 / 4.565676 (-1.450039) | 0.056480 / 0.424275 (-0.367795) | 0.007761 / 0.007607 (0.000154) | 0.483553 / 0.226044 (0.257509) | 4.830087 / 2.268929 (2.561159) | 2.629919 / 55.444624 (-52.814705) | 2.327551 / 6.876477 (-4.548926) | 2.539934 / 2.142072 (0.397861) | 0.587963 / 4.805227 (-4.217265) | 0.131085 / 6.500664 (-6.369579) | 0.060807 / 0.075469 (-0.014662) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.350003 / 1.841788 (-0.491785) | 19.491713 / 8.074308 (11.417405) | 14.030429 / 10.191392 (3.839037) | 0.174762 / 0.680424 (-0.505662) | 0.018523 / 0.534201 (-0.515678) | 0.394946 / 0.579283 (-0.184337) | 0.407652 / 0.434364 (-0.026712) | 0.465806 / 0.540337 (-0.074531) | 0.605417 / 1.386936 (-0.781519) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cc85979df3a39657079fdf0844c7e64547507f1a \"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) | 0.006235 / 0.011353 (-0.005118) | 0.003675 / 0.011008 (-0.007333) | 0.080680 / 0.038508 (0.042171) | 0.064378 / 0.023109 (0.041268) | 0.394312 / 0.275898 (0.118414) | 0.428143 / 0.323480 (0.104663) | 0.004794 / 0.007986 (-0.003191) | 0.002899 / 0.004328 (-0.001429) | 0.062592 / 0.004250 (0.058342) | 0.050957 / 0.037052 (0.013904) | 0.396831 / 0.258489 (0.138342) | 0.438280 / 0.293841 (0.144439) | 0.027743 / 0.128546 (-0.100804) | 0.008068 / 0.075646 (-0.067578) | 0.262541 / 0.419271 (-0.156730) | 0.060837 / 0.043533 (0.017304) | 0.397941 / 0.255139 (0.142802) | 0.417012 / 0.283200 (0.133813) | 0.030153 / 0.141683 (-0.111530) | 1.477115 / 1.452155 (0.024960) | 1.516642 / 1.492716 (0.023926) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.178032 / 0.018006 (0.160026) | 0.445775 / 0.000490 (0.445286) | 0.004275 / 0.000200 (0.004075) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025025 / 0.037411 (-0.012386) | 0.074113 / 0.014526 (0.059587) | 0.083814 / 0.176557 (-0.092743) | 0.148860 / 0.737135 (-0.588275) | 0.085408 / 0.296338 (-0.210931) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.393714 / 0.215209 (0.178505) | 3.936589 / 2.077655 (1.858934) | 1.910501 / 1.504120 (0.406381) | 1.729670 / 1.541195 (0.188475) | 1.777647 / 1.468490 (0.309156) | 0.499532 / 4.584777 (-4.085245) | 3.002385 / 3.745712 (-0.743327) | 2.906916 / 5.269862 (-2.362945) | 1.883321 / 4.565676 (-2.682356) | 0.057546 / 0.424275 (-0.366730) | 0.006492 / 0.007607 (-0.001115) | 0.463605 / 0.226044 (0.237560) | 4.620215 / 2.268929 (2.351287) | 2.399021 / 55.444624 (-53.045603) | 2.182962 / 6.876477 (-4.693514) | 2.357344 / 2.142072 (0.215272) | 0.583946 / 4.805227 (-4.221282) | 0.124644 / 6.500664 (-6.376021) | 0.060831 / 0.075469 (-0.014638) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.276412 / 1.841788 (-0.565375) | 18.462522 / 8.074308 (10.388214) | 13.877375 / 10.191392 (3.685983) | 0.150584 / 0.680424 (-0.529840) | 0.016675 / 0.534201 (-0.517526) | 0.331711 / 0.579283 (-0.247573) | 0.366659 / 0.434364 (-0.067705) | 0.396400 / 0.540337 (-0.143938) | 0.555418 / 1.386936 (-0.831518) |\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) | 0.005995 / 0.011353 (-0.005358) | 0.003610 / 0.011008 (-0.007399) | 0.061802 / 0.038508 (0.023294) | 0.059265 / 0.023109 (0.036156) | 0.392628 / 0.275898 (0.116730) | 0.413143 / 0.323480 (0.089663) | 0.004687 / 0.007986 (-0.003299) | 0.002843 / 0.004328 (-0.001486) | 0.061932 / 0.004250 (0.057682) | 0.049466 / 0.037052 (0.012413) | 0.402718 / 0.258489 (0.144229) | 0.415039 / 0.293841 (0.121198) | 0.027352 / 0.128546 (-0.101194) | 0.007965 / 0.075646 (-0.067682) | 0.067456 / 0.419271 (-0.351815) | 0.042336 / 0.043533 (-0.001196) | 0.405543 / 0.255139 (0.150404) | 0.403209 / 0.283200 (0.120010) | 0.021459 / 0.141683 (-0.120224) | 1.442861 / 1.452155 (-0.009293) | 1.491213 / 1.492716 (-0.001503) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.248225 / 0.018006 (0.230219) | 0.434174 / 0.000490 (0.433684) | 0.001973 / 0.000200 (0.001773) | 0.000070 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025475 / 0.037411 (-0.011936) | 0.077865 / 0.014526 (0.063339) | 0.086980 / 0.176557 (-0.089577) | 0.143682 / 0.737135 (-0.593453) | 0.088634 / 0.296338 (-0.207705) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417591 / 0.215209 (0.202382) | 4.168700 / 2.077655 (2.091045) | 2.335743 / 1.504120 (0.831623) | 2.208174 / 1.541195 (0.666980) | 2.256658 / 1.468490 (0.788168) | 0.503164 / 4.584777 (-4.081613) | 3.026667 / 3.745712 (-0.719045) | 4.496675 / 5.269862 (-0.773187) | 2.741049 / 4.565676 (-1.824628) | 0.057781 / 0.424275 (-0.366494) | 0.006810 / 0.007607 (-0.000797) | 0.490803 / 0.226044 (0.264759) | 4.914369 / 2.268929 (2.645441) | 2.594250 / 55.444624 (-52.850375) | 2.274552 / 6.876477 (-4.601925) | 2.397529 / 2.142072 (0.255456) | 0.593008 / 4.805227 (-4.212220) | 0.126194 / 6.500664 (-6.374470) | 0.062261 / 0.075469 (-0.013208) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.357561 / 1.841788 (-0.484227) | 18.622995 / 8.074308 (10.548687) | 14.142569 / 10.191392 (3.951177) | 0.146527 / 0.680424 (-0.533897) | 0.016863 / 0.534201 (-0.517338) | 0.336219 / 0.579283 (-0.243064) | 0.348650 / 0.434364 (-0.085714) | 0.385958 / 0.540337 (-0.154380) | 0.517958 / 1.386936 (-0.868978) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f3da7a5a7d0d0415476ecebb0458e7c60df24445 \"CML watermark\")\n"
] |
6,066
|
AttributeError: '_tqdm_cls' object has no attribute '_lock'
|
### Describe the bug
```python
File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/site-packages/datasets/load.py", line 1034, in get_module
data_files = DataFilesDict.from_patterns(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/site-packages/datasets/data_files.py", line 671, in from_patterns
DataFilesList.from_patterns(
File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/site-packages/datasets/data_files.py", line 586, in from_patterns
origin_metadata = _get_origin_metadata(data_files, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/site-packages/datasets/data_files.py", line 502, in _get_origin_metadata
return thread_map(
^^^^^^^^^^^
File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/site-packages/tqdm/contrib/concurrent.py", line 70, in thread_map
return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/site-packages/tqdm/contrib/concurrent.py", line 48, in _executor_map
with ensure_lock(tqdm_class, lock_name=lock_name) as lk:
File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/contextlib.py", line 144, in __exit__
next(self.gen)
File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/site-packages/tqdm/contrib/concurrent.py", line 25, in ensure_lock
del tqdm_class._lock
^^^^^^^^^^^^^^^^
AttributeError: '_tqdm_cls' object has no attribute '_lock'
```
### Steps to reproduce the bug
Happens ocasionally.
### Expected behavior
I added a print in tqdm `ensure_lock()`, got a `ensure_lock <datasets.utils.logging._tqdm_cls object at 0x16dddead0> ` print.
According to the code in https://github.com/tqdm/tqdm/blob/master/tqdm/contrib/concurrent.py#L24
```python
@contextmanager
def ensure_lock(tqdm_class, lock_name=""):
"""get (create if necessary) and then restore `tqdm_class`'s lock"""
print("ensure_lock", tqdm_class, lock_name)
old_lock = getattr(tqdm_class, '_lock', None) # don't create a new lock
lock = old_lock or tqdm_class.get_lock() # maybe create a new lock
lock = getattr(lock, lock_name, lock) # maybe subtype
tqdm_class.set_lock(lock)
yield lock
if old_lock is None:
del tqdm_class._lock # <-- It tries to del the `_lock` attribute from tqdm_class.
else:
tqdm_class.set_lock(old_lock)
```
But, huggingface datasets `datasets.utils.logging._tqdm_cls` does not have the field `_lock`: https://github.com/huggingface/datasets/blob/main/src/datasets/utils/logging.py#L205
```python
class _tqdm_cls:
def __call__(self, *args, disable=False, **kwargs):
if _tqdm_active and not disable:
return tqdm_lib.tqdm(*args, **kwargs)
else:
return EmptyTqdm(*args, **kwargs)
def set_lock(self, *args, **kwargs):
self._lock = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*args, **kwargs)
def get_lock(self):
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
```
### Environment info
Python 3.11.4
tqdm '4.65.0'
datasets master
|
[] |
{
"url": null,
"html_url": null,
"diff_url": null,
"patch_url": null,
"merged_at": null
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| false
|
[
"Hi ! I opened https://github.com/huggingface/datasets/pull/6067 to add the missing `_lock`\r\n\r\nWe'll do a patch release soon, but feel free to install `datasets` from source in the meantime",
"I have tested the latest main, it does not work.\r\n\r\nI add more logs to reproduce this issue, it looks like a multi threading bug:\r\n\r\n```python\r\n@contextmanager\r\ndef ensure_lock(tqdm_class, lock_name=\"\"):\r\n \"\"\"get (create if necessary) and then restore `tqdm_class`'s lock\"\"\"\r\n import os\r\n import threading\r\n print(os.getpid(), threading.get_ident(), \"ensure_lock\", tqdm_class, lock_name)\r\n old_lock = getattr(tqdm_class, '_lock', None) # don't create a new lock\r\n lock = old_lock or tqdm_class.get_lock() # maybe create a new lock\r\n lock = getattr(lock, lock_name, lock) # maybe subtype\r\n tqdm_class.set_lock(lock)\r\n print(os.getpid(), threading.get_ident(), \"set_lock\")\r\n yield lock\r\n if old_lock is None:\r\n print(os.getpid(), threading.get_ident(), \"del tqdm_class\")\r\n del tqdm_class._lock\r\n else:\r\n tqdm_class.set_lock(old_lock)\r\n```\r\noutput\r\n```\r\n64943 8424758784 ensure_lock <datasets.utils.logging._tqdm_cls object at 0x2aa7fb250> \r\n64943 8424758784 set_lock\r\n64943 8424758784 del tqdm_class\r\n64943 8424758784 ensure_lock <datasets.utils.logging._tqdm_cls object at 0x2aa7fb250> \r\n64943 8424758784 set_lock\r\n64943 8424758784 del tqdm_class\r\n64943 11638370304 ensure_lock <datasets.utils.logging._tqdm_cls object at 0x2aa7fb250> \r\n64943 11638370304 set_lock\r\n64943 11568967680 ensure_lock <datasets.utils.logging._tqdm_cls object at 0x2aa7fb250> \r\n64943 11568967680 set_lock\r\n64943 11638370304 del tqdm_class\r\n64943 11638370304 ensure_lock <datasets.utils.logging._tqdm_cls object at 0x2aa7fb250> \r\n64943 11638370304 set_lock\r\n64943 11638370304 del tqdm_class\r\n64943 11568967680 del tqdm_class\r\n```\r\n\r\nThread `11638370304` del the _lock from tqdm_class first, then thread `11568967680` del _lock failed.",
"Maybe it is a bug of tqdm? I think simply use `try ... except AttributeError ...` wraps `del tqdm_class._lock` should work.",
"Yes it looks like a bug on their end indeed, do you want to open a PR on tqdm ?\r\n\r\nLet me see if I can find a workaround in the meantime",
"I opened https://github.com/huggingface/datasets/pull/6068 if you want to try it out",
"> I opened #6068 if you want to try it out\r\n\r\nThis fix works! Thanks.",
"Awesome ! closing this then :)\r\nWe'll do a patch release today or tomorrow"
] |
6,065
|
Add column type guessing from map return function
|
As discussed [here](https://github.com/huggingface/datasets/issues/5965), there are some cases where datasets is unable to automatically promote columns during mapping. The fix is to explicitly provide a `features` definition so pyarrow can configure itself with the right column types from the outset.
This PR provides an alternative approach, which is functionally equivalent to specifying features but a bit cleaner within a larger mapping pipeline. It allows clients to typehint the return variable coming from the mapper function - if we find one of these type annotations specified, and no explicit features have been passed in, we'll try to convert it into a Features map. If the map function runs and casting is unable to succeed, it will raise a DatasetTransformationNotAllowedError that indicates the typehint may be to blame. It works for batched and non-batched mapping functions.
Currently supported column types:
- builtins primitives: string, int, float, bool
- dictionaries, lists (nested and one-deep)
- Optional types and None-Unions (synonymous with optional types)
It's used like:
```python
class DatasetTyped(TypedDict):
texts: list[str]
def dataset_typed_map(batch) -> DatasetTyped:
return {"texts": [text.split() for text in batch["raw_text"]]}
dataset = {"raw_text": ["", "This is a test", "This is another test"]}
with Dataset.from_dict(dataset) as dset:
new_dataset = dset.map(
dataset_typed_map,
batched=True,
batch_size=1,
num_proc=1,
)
```
Open questions:
- Should logging indicate we have automatically guessed these types? Or proceed quietly until we hit an error (as is the current implementation).
|
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6065",
"html_url": "https://github.com/huggingface/datasets/pull/6065",
"diff_url": "https://github.com/huggingface/datasets/pull/6065.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6065.patch",
"merged_at": null
}
| true
|
[
"Thanks for working on this. However, having thought about this issue a bit more, supporting this doesn't seem like a good idea - it's better to be explicit than implicit, according to the Zen of Python 🙂. Also, I don't think many users would use this, so this raises the question of whether this is something we want to maintain.\r\n\r\ncc @lhoestq for the 2nd opinion",
"@mariosasko I was going to quote the Zen of Python in the other direction :) To me, this actually is much more explicit than the current behavior of guessing pyarrow types based on the raw dictionary return values. Explicit typehinting is increasingly the de facto way to deal with this dynamic type serialization - plus it feels like a clearer fit to me than separating out the mapper function from the feature column definition in the call to the actual `.map()`. Another benefit is providing typehinting support for clients that use mypy or other static typecheckers to detect return mismatches.\r\n\r\nBut will leave it to you and @lhoestq to see if it's something you'd like in core versus a support package.",
"I meant that explicitly specifying the target features (the `features` param) is cleaner (easier to track) than relying on type hints.",
"Passing features= to `map()` is richer and more explicit. Also I don't think users would guess that such API exist.\r\n\r\nOther libraries like dask also infer the type from the output or requires the typing to be specified using the `meta` argument",
"Point about discoverability is a fair one, would certainly need some docs around it. All good! Will close this out and keep in our extension utilities."
] |
6,064
|
set dev version
| null |
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6064",
"html_url": "https://github.com/huggingface/datasets/pull/6064",
"diff_url": "https://github.com/huggingface/datasets/pull/6064.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6064.patch",
"merged_at": "2023-07-24T15:56:10"
}
| true
|
[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6064). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006704 / 0.011353 (-0.004649) | 0.004208 / 0.011008 (-0.006800) | 0.085895 / 0.038508 (0.047387) | 0.079303 / 0.023109 (0.056193) | 0.353430 / 0.275898 (0.077532) | 0.390814 / 0.323480 (0.067334) | 0.006565 / 0.007986 (-0.001420) | 0.003588 / 0.004328 (-0.000740) | 0.065249 / 0.004250 (0.060999) | 0.059772 / 0.037052 (0.022720) | 0.356315 / 0.258489 (0.097826) | 0.404812 / 0.293841 (0.110971) | 0.031127 / 0.128546 (-0.097419) | 0.008656 / 0.075646 (-0.066991) | 0.288734 / 0.419271 (-0.130537) | 0.053157 / 0.043533 (0.009625) | 0.354651 / 0.255139 (0.099512) | 0.370590 / 0.283200 (0.087391) | 0.024944 / 0.141683 (-0.116738) | 1.472393 / 1.452155 (0.020238) | 1.548946 / 1.492716 (0.056229) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223430 / 0.018006 (0.205424) | 0.567359 / 0.000490 (0.566870) | 0.006744 / 0.000200 (0.006544) | 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) | 0.030174 / 0.037411 (-0.007237) | 0.084865 / 0.014526 (0.070339) | 0.098986 / 0.176557 (-0.077571) | 0.161458 / 0.737135 (-0.575678) | 0.099198 / 0.296338 (-0.197141) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.404324 / 0.215209 (0.189115) | 4.043744 / 2.077655 (1.966090) | 1.972834 / 1.504120 (0.468714) | 1.801634 / 1.541195 (0.260439) | 1.891198 / 1.468490 (0.422708) | 0.488511 / 4.584777 (-4.096266) | 3.566890 / 3.745712 (-0.178823) | 3.369415 / 5.269862 (-1.900447) | 2.054995 / 4.565676 (-2.510682) | 0.057225 / 0.424275 (-0.367050) | 0.007360 / 0.007607 (-0.000247) | 0.471813 / 0.226044 (0.245769) | 4.734397 / 2.268929 (2.465468) | 2.526585 / 55.444624 (-52.918039) | 2.230535 / 6.876477 (-4.645942) | 2.434403 / 2.142072 (0.292330) | 0.630090 / 4.805227 (-4.175137) | 0.138544 / 6.500664 (-6.362120) | 0.060099 / 0.075469 (-0.015370) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.260951 / 1.841788 (-0.580837) | 20.051513 / 8.074308 (11.977204) | 14.675938 / 10.191392 (4.484546) | 0.169535 / 0.680424 (-0.510889) | 0.018574 / 0.534201 (-0.515627) | 0.394255 / 0.579283 (-0.185028) | 0.412713 / 0.434364 (-0.021651) | 0.475891 / 0.540337 (-0.064446) | 0.658223 / 1.386936 (-0.728713) |\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) | 0.006969 / 0.011353 (-0.004384) | 0.004417 / 0.011008 (-0.006591) | 0.064399 / 0.038508 (0.025891) | 0.082928 / 0.023109 (0.059819) | 0.402285 / 0.275898 (0.126387) | 0.440032 / 0.323480 (0.116552) | 0.005896 / 0.007986 (-0.002090) | 0.003580 / 0.004328 (-0.000749) | 0.065340 / 0.004250 (0.061090) | 0.060363 / 0.037052 (0.023311) | 0.417413 / 0.258489 (0.158924) | 0.448527 / 0.293841 (0.154686) | 0.032238 / 0.128546 (-0.096308) | 0.008820 / 0.075646 (-0.066826) | 0.071516 / 0.419271 (-0.347755) | 0.050614 / 0.043533 (0.007081) | 0.406565 / 0.255139 (0.151426) | 0.422527 / 0.283200 (0.139328) | 0.025866 / 0.141683 (-0.115817) | 1.512256 / 1.452155 (0.060101) | 1.568433 / 1.492716 (0.075717) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.266521 / 0.018006 (0.248515) | 0.564524 / 0.000490 (0.564034) | 0.005236 / 0.000200 (0.005036) | 0.000085 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031998 / 0.037411 (-0.005413) | 0.090754 / 0.014526 (0.076229) | 0.105954 / 0.176557 (-0.070602) | 0.164506 / 0.737135 (-0.572629) | 0.108792 / 0.296338 (-0.187546) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422044 / 0.215209 (0.206835) | 4.204449 / 2.077655 (2.126795) | 2.232060 / 1.504120 (0.727940) | 2.060389 / 1.541195 (0.519194) | 2.152723 / 1.468490 (0.684233) | 0.488456 / 4.584777 (-4.096321) | 3.591102 / 3.745712 (-0.154611) | 5.250401 / 5.269862 (-0.019461) | 3.060259 / 4.565676 (-1.505417) | 0.057558 / 0.424275 (-0.366717) | 0.007881 / 0.007607 (0.000274) | 0.508631 / 0.226044 (0.282587) | 5.064857 / 2.268929 (2.795928) | 2.719068 / 55.444624 (-52.725556) | 2.389992 / 6.876477 (-4.486485) | 2.595073 / 2.142072 (0.453000) | 0.590179 / 4.805227 (-4.215048) | 0.136149 / 6.500664 (-6.364515) | 0.062546 / 0.075469 (-0.012923) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.369252 / 1.841788 (-0.472535) | 20.637580 / 8.074308 (12.563272) | 14.217129 / 10.191392 (4.025737) | 0.195464 / 0.680424 (-0.484960) | 0.018452 / 0.534201 (-0.515749) | 0.397044 / 0.579283 (-0.182239) | 0.401127 / 0.434364 (-0.033237) | 0.465033 / 0.540337 (-0.075305) | 0.613484 / 1.386936 (-0.773452) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d9f1651128e50e7887f5e8eaaf6b55fe4cd84fdc \"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) | 0.006793 / 0.011353 (-0.004559) | 0.004374 / 0.011008 (-0.006635) | 0.084958 / 0.038508 (0.046450) | 0.080440 / 0.023109 (0.057331) | 0.317951 / 0.275898 (0.042053) | 0.376133 / 0.323480 (0.052653) | 0.005775 / 0.007986 (-0.002211) | 0.003644 / 0.004328 (-0.000684) | 0.064823 / 0.004250 (0.060573) | 0.059442 / 0.037052 (0.022390) | 0.319636 / 0.258489 (0.061147) | 0.389668 / 0.293841 (0.095827) | 0.031181 / 0.128546 (-0.097365) | 0.008725 / 0.075646 (-0.066921) | 0.288514 / 0.419271 (-0.130757) | 0.053466 / 0.043533 (0.009933) | 0.323131 / 0.255139 (0.067992) | 0.345276 / 0.283200 (0.062076) | 0.025046 / 0.141683 (-0.116637) | 1.491659 / 1.452155 (0.039504) | 1.562105 / 1.492716 (0.069389) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.286325 / 0.018006 (0.268319) | 0.578021 / 0.000490 (0.577531) | 0.007240 / 0.000200 (0.007040) | 0.000095 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030163 / 0.037411 (-0.007248) | 0.082100 / 0.014526 (0.067574) | 0.098331 / 0.176557 (-0.078225) | 0.160517 / 0.737135 (-0.576618) | 0.098479 / 0.296338 (-0.197859) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.401782 / 0.215209 (0.186573) | 4.006330 / 2.077655 (1.928675) | 2.033841 / 1.504120 (0.529721) | 1.853248 / 1.541195 (0.312053) | 1.980046 / 1.468490 (0.511556) | 0.480636 / 4.584777 (-4.104141) | 3.684482 / 3.745712 (-0.061231) | 5.601940 / 5.269862 (0.332079) | 3.369683 / 4.565676 (-1.195993) | 0.057105 / 0.424275 (-0.367170) | 0.007462 / 0.007607 (-0.000145) | 0.474860 / 0.226044 (0.248815) | 4.749624 / 2.268929 (2.480695) | 2.492084 / 55.444624 (-52.952540) | 2.157985 / 6.876477 (-4.718491) | 2.420997 / 2.142072 (0.278925) | 0.574718 / 4.805227 (-4.230509) | 0.134672 / 6.500664 (-6.365992) | 0.061677 / 0.075469 (-0.013792) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.284151 / 1.841788 (-0.557637) | 20.186823 / 8.074308 (12.112515) | 14.247024 / 10.191392 (4.055632) | 0.171606 / 0.680424 (-0.508818) | 0.018619 / 0.534201 (-0.515582) | 0.394156 / 0.579283 (-0.185127) | 0.424684 / 0.434364 (-0.009679) | 0.476056 / 0.540337 (-0.064281) | 0.668751 / 1.386936 (-0.718185) |\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) | 0.006807 / 0.011353 (-0.004546) | 0.004142 / 0.011008 (-0.006867) | 0.065503 / 0.038508 (0.026995) | 0.083232 / 0.023109 (0.060122) | 0.378278 / 0.275898 (0.102380) | 0.410191 / 0.323480 (0.086711) | 0.005660 / 0.007986 (-0.002326) | 0.003486 / 0.004328 (-0.000842) | 0.066109 / 0.004250 (0.061859) | 0.059654 / 0.037052 (0.022601) | 0.375965 / 0.258489 (0.117476) | 0.420046 / 0.293841 (0.126205) | 0.031587 / 0.128546 (-0.096959) | 0.008693 / 0.075646 (-0.066953) | 0.071121 / 0.419271 (-0.348151) | 0.049468 / 0.043533 (0.005935) | 0.373785 / 0.255139 (0.118646) | 0.395577 / 0.283200 (0.112377) | 0.024138 / 0.141683 (-0.117545) | 1.465451 / 1.452155 (0.013297) | 1.547565 / 1.492716 (0.054849) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.325241 / 0.018006 (0.307234) | 0.532415 / 0.000490 (0.531925) | 0.004755 / 0.000200 (0.004555) | 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) | 0.033472 / 0.037411 (-0.003939) | 0.090574 / 0.014526 (0.076048) | 0.106712 / 0.176557 (-0.069845) | 0.164353 / 0.737135 (-0.572783) | 0.109344 / 0.296338 (-0.186994) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420161 / 0.215209 (0.204952) | 4.192334 / 2.077655 (2.114679) | 2.178181 / 1.504120 (0.674061) | 2.017405 / 1.541195 (0.476211) | 2.182783 / 1.468490 (0.714293) | 0.484037 / 4.584777 (-4.100740) | 3.641911 / 3.745712 (-0.103801) | 5.543874 / 5.269862 (0.274013) | 3.440084 / 4.565676 (-1.125593) | 0.056662 / 0.424275 (-0.367614) | 0.007773 / 0.007607 (0.000166) | 0.498357 / 0.226044 (0.272313) | 4.951315 / 2.268929 (2.682386) | 2.656732 / 55.444624 (-52.787892) | 2.370566 / 6.876477 (-4.505910) | 2.682289 / 2.142072 (0.540217) | 0.598479 / 4.805227 (-4.206749) | 0.151546 / 6.500664 (-6.349118) | 0.063278 / 0.075469 (-0.012191) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.385897 / 1.841788 (-0.455891) | 20.961851 / 8.074308 (12.887543) | 14.465688 / 10.191392 (4.274296) | 0.166156 / 0.680424 (-0.514268) | 0.018848 / 0.534201 (-0.515353) | 0.401712 / 0.579283 (-0.177571) | 0.416674 / 0.434364 (-0.017690) | 0.471834 / 0.540337 (-0.068503) | 0.622463 / 1.386936 (-0.764473) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7e3ab9bc6ae8cc42f7e7d01afbd2637d51c3faf6 \"CML watermark\")\n"
] |
6,063
|
Release: 2.14.0
| null |
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6063",
"html_url": "https://github.com/huggingface/datasets/pull/6063",
"diff_url": "https://github.com/huggingface/datasets/pull/6063.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6063.patch",
"merged_at": "2023-07-24T15:47:51"
}
| true
|
[
"_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) | 0.007703 / 0.011353 (-0.003650) | 0.004699 / 0.011008 (-0.006309) | 0.090195 / 0.038508 (0.051687) | 0.119165 / 0.023109 (0.096056) | 0.361435 / 0.275898 (0.085537) | 0.404429 / 0.323480 (0.080949) | 0.006172 / 0.007986 (-0.001814) | 0.003932 / 0.004328 (-0.000397) | 0.068384 / 0.004250 (0.064133) | 0.066730 / 0.037052 (0.029678) | 0.360978 / 0.258489 (0.102489) | 0.401301 / 0.293841 (0.107460) | 0.032836 / 0.128546 (-0.095710) | 0.010821 / 0.075646 (-0.064825) | 0.294526 / 0.419271 (-0.124745) | 0.068751 / 0.043533 (0.025218) | 0.368427 / 0.255139 (0.113288) | 0.376969 / 0.283200 (0.093770) | 0.040538 / 0.141683 (-0.101145) | 1.509966 / 1.452155 (0.057811) | 1.564885 / 1.492716 (0.072169) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.292243 / 0.018006 (0.274237) | 0.662067 / 0.000490 (0.661577) | 0.004966 / 0.000200 (0.004766) | 0.000103 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029050 / 0.037411 (-0.008361) | 0.099880 / 0.014526 (0.085354) | 0.109277 / 0.176557 (-0.067280) | 0.167877 / 0.737135 (-0.569258) | 0.110770 / 0.296338 (-0.185569) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.395742 / 0.215209 (0.180533) | 3.944152 / 2.077655 (1.866498) | 1.875295 / 1.504120 (0.371175) | 1.705088 / 1.541195 (0.163893) | 1.884443 / 1.468490 (0.415953) | 0.497243 / 4.584777 (-4.087534) | 3.749287 / 3.745712 (0.003575) | 4.418826 / 5.269862 (-0.851035) | 2.481149 / 4.565676 (-2.084528) | 0.058260 / 0.424275 (-0.366015) | 0.007744 / 0.007607 (0.000137) | 0.472531 / 0.226044 (0.246486) | 4.716022 / 2.268929 (2.447094) | 2.480446 / 55.444624 (-52.964179) | 2.163098 / 6.876477 (-4.713379) | 2.217555 / 2.142072 (0.075482) | 0.601965 / 4.805227 (-4.203262) | 0.139364 / 6.500664 (-6.361301) | 0.067097 / 0.075469 (-0.008372) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.330537 / 1.841788 (-0.511251) | 22.176270 / 8.074308 (14.101962) | 16.224981 / 10.191392 (6.033589) | 0.173708 / 0.680424 (-0.506715) | 0.019402 / 0.534201 (-0.514799) | 0.401994 / 0.579283 (-0.177289) | 0.432597 / 0.434364 (-0.001767) | 0.489933 / 0.540337 (-0.050404) | 0.672334 / 1.386936 (-0.714602) |\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) | 0.008622 / 0.011353 (-0.002731) | 0.004609 / 0.011008 (-0.006399) | 0.067791 / 0.038508 (0.029283) | 0.112770 / 0.023109 (0.089661) | 0.380939 / 0.275898 (0.105041) | 0.416940 / 0.323480 (0.093460) | 0.006170 / 0.007986 (-0.001815) | 0.003876 / 0.004328 (-0.000452) | 0.066227 / 0.004250 (0.061976) | 0.073132 / 0.037052 (0.036080) | 0.390120 / 0.258489 (0.131631) | 0.420893 / 0.293841 (0.127052) | 0.033235 / 0.128546 (-0.095311) | 0.009659 / 0.075646 (-0.065987) | 0.072668 / 0.419271 (-0.346604) | 0.051333 / 0.043533 (0.007801) | 0.393828 / 0.255139 (0.138689) | 0.412376 / 0.283200 (0.129176) | 0.027760 / 0.141683 (-0.113923) | 1.494369 / 1.452155 (0.042214) | 1.592862 / 1.492716 (0.100145) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.345376 / 0.018006 (0.327369) | 0.609399 / 0.000490 (0.608909) | 0.000546 / 0.000200 (0.000346) | 0.000061 / 0.000054 (0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035601 / 0.037411 (-0.001810) | 0.106527 / 0.014526 (0.092001) | 0.114388 / 0.176557 (-0.062168) | 0.175607 / 0.737135 (-0.561529) | 0.113009 / 0.296338 (-0.183329) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417237 / 0.215209 (0.202028) | 4.136329 / 2.077655 (2.058675) | 2.147134 / 1.504120 (0.643014) | 2.009501 / 1.541195 (0.468306) | 2.139499 / 1.468490 (0.671009) | 0.491593 / 4.584777 (-4.093184) | 3.766734 / 3.745712 (0.021022) | 5.652446 / 5.269862 (0.382585) | 3.021654 / 4.565676 (-1.544022) | 0.058458 / 0.424275 (-0.365817) | 0.008271 / 0.007607 (0.000664) | 0.488229 / 0.226044 (0.262184) | 4.861343 / 2.268929 (2.592415) | 2.694142 / 55.444624 (-52.750482) | 2.489130 / 6.876477 (-4.387346) | 2.679376 / 2.142072 (0.537304) | 0.589959 / 4.805227 (-4.215268) | 0.137939 / 6.500664 (-6.362725) | 0.066833 / 0.075469 (-0.008636) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.444871 / 1.841788 (-0.396916) | 22.874961 / 8.074308 (14.800653) | 15.842130 / 10.191392 (5.650738) | 0.175529 / 0.680424 (-0.504895) | 0.019024 / 0.534201 (-0.515177) | 0.406551 / 0.579283 (-0.172732) | 0.430335 / 0.434364 (-0.004029) | 0.475750 / 0.540337 (-0.064587) | 0.624836 / 1.386936 (-0.762100) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dabbb7467f49fd22ae1a43cc577eb43008d63ee8 \"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) | 0.006068 / 0.011353 (-0.005285) | 0.003694 / 0.011008 (-0.007315) | 0.080321 / 0.038508 (0.041813) | 0.061738 / 0.023109 (0.038629) | 0.329675 / 0.275898 (0.053777) | 0.364008 / 0.323480 (0.040528) | 0.004722 / 0.007986 (-0.003263) | 0.002857 / 0.004328 (-0.001471) | 0.062447 / 0.004250 (0.058197) | 0.047006 / 0.037052 (0.009953) | 0.335730 / 0.258489 (0.077241) | 0.373047 / 0.293841 (0.079206) | 0.027273 / 0.128546 (-0.101274) | 0.007979 / 0.075646 (-0.067667) | 0.262693 / 0.419271 (-0.156579) | 0.045416 / 0.043533 (0.001883) | 0.340774 / 0.255139 (0.085635) | 0.359667 / 0.283200 (0.076468) | 0.020848 / 0.141683 (-0.120835) | 1.450110 / 1.452155 (-0.002045) | 1.489511 / 1.492716 (-0.003206) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.185090 / 0.018006 (0.167084) | 0.429823 / 0.000490 (0.429334) | 0.000703 / 0.000200 (0.000503) | 0.000058 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024398 / 0.037411 (-0.013013) | 0.072983 / 0.014526 (0.058457) | 0.084012 / 0.176557 (-0.092544) | 0.146160 / 0.737135 (-0.590975) | 0.084068 / 0.296338 (-0.212270) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.432204 / 0.215209 (0.216995) | 4.320593 / 2.077655 (2.242939) | 2.261260 / 1.504120 (0.757140) | 2.087148 / 1.541195 (0.545954) | 2.144520 / 1.468490 (0.676029) | 0.501477 / 4.584777 (-4.083300) | 3.119557 / 3.745712 (-0.626156) | 3.572527 / 5.269862 (-1.697335) | 2.208836 / 4.565676 (-2.356840) | 0.057232 / 0.424275 (-0.367043) | 0.006494 / 0.007607 (-0.001113) | 0.508135 / 0.226044 (0.282091) | 5.090416 / 2.268929 (2.821488) | 2.739800 / 55.444624 (-52.704824) | 2.416105 / 6.876477 (-4.460372) | 2.616037 / 2.142072 (0.473965) | 0.583730 / 4.805227 (-4.221497) | 0.124312 / 6.500664 (-6.376352) | 0.060760 / 0.075469 (-0.014709) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.256097 / 1.841788 (-0.585691) | 18.326073 / 8.074308 (10.251765) | 13.859173 / 10.191392 (3.667781) | 0.143639 / 0.680424 (-0.536785) | 0.016649 / 0.534201 (-0.517552) | 0.331671 / 0.579283 (-0.247612) | 0.365370 / 0.434364 (-0.068994) | 0.392753 / 0.540337 (-0.147584) | 0.549302 / 1.386936 (-0.837634) |\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) | 0.006054 / 0.011353 (-0.005299) | 0.003641 / 0.011008 (-0.007367) | 0.063109 / 0.038508 (0.024601) | 0.060482 / 0.023109 (0.037372) | 0.404047 / 0.275898 (0.128149) | 0.425436 / 0.323480 (0.101956) | 0.004603 / 0.007986 (-0.003382) | 0.002905 / 0.004328 (-0.001423) | 0.063207 / 0.004250 (0.058956) | 0.048248 / 0.037052 (0.011196) | 0.404325 / 0.258489 (0.145836) | 0.432652 / 0.293841 (0.138811) | 0.027630 / 0.128546 (-0.100916) | 0.008062 / 0.075646 (-0.067584) | 0.068367 / 0.419271 (-0.350905) | 0.042169 / 0.043533 (-0.001364) | 0.384903 / 0.255139 (0.129764) | 0.418617 / 0.283200 (0.135417) | 0.020767 / 0.141683 (-0.120915) | 1.463606 / 1.452155 (0.011451) | 1.512081 / 1.492716 (0.019365) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229601 / 0.018006 (0.211594) | 0.417878 / 0.000490 (0.417388) | 0.000373 / 0.000200 (0.000173) | 0.000053 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026354 / 0.037411 (-0.011057) | 0.078100 / 0.014526 (0.063574) | 0.087122 / 0.176557 (-0.089434) | 0.140017 / 0.737135 (-0.597118) | 0.089923 / 0.296338 (-0.206415) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422405 / 0.215209 (0.207196) | 4.237383 / 2.077655 (2.159728) | 2.161104 / 1.504120 (0.656984) | 1.982337 / 1.541195 (0.441142) | 2.050216 / 1.468490 (0.581726) | 0.499281 / 4.584777 (-4.085496) | 2.996953 / 3.745712 (-0.748759) | 5.027069 / 5.269862 (-0.242792) | 2.804703 / 4.565676 (-1.760974) | 0.057707 / 0.424275 (-0.366568) | 0.006809 / 0.007607 (-0.000798) | 0.495196 / 0.226044 (0.269152) | 4.946593 / 2.268929 (2.677665) | 2.598965 / 55.444624 (-52.845660) | 2.349871 / 6.876477 (-4.526606) | 2.451665 / 2.142072 (0.309593) | 0.592314 / 4.805227 (-4.212913) | 0.125685 / 6.500664 (-6.374979) | 0.063252 / 0.075469 (-0.012217) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.325422 / 1.841788 (-0.516366) | 18.521059 / 8.074308 (10.446751) | 14.046757 / 10.191392 (3.855365) | 0.133009 / 0.680424 (-0.547415) | 0.017097 / 0.534201 (-0.517104) | 0.339804 / 0.579283 (-0.239479) | 0.345464 / 0.434364 (-0.088900) | 0.387623 / 0.540337 (-0.152714) | 0.519880 / 1.386936 (-0.867056) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#88896a7b28610ace95e444b94f9a4bc332cc1ee3 \"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) | 0.008671 / 0.011353 (-0.002682) | 0.004681 / 0.011008 (-0.006327) | 0.107517 / 0.038508 (0.069008) | 0.078846 / 0.023109 (0.055737) | 0.449745 / 0.275898 (0.173847) | 0.504075 / 0.323480 (0.180596) | 0.005837 / 0.007986 (-0.002148) | 0.004031 / 0.004328 (-0.000297) | 0.092021 / 0.004250 (0.087771) | 0.065954 / 0.037052 (0.028902) | 0.442082 / 0.258489 (0.183593) | 0.529349 / 0.293841 (0.235508) | 0.052527 / 0.128546 (-0.076019) | 0.013854 / 0.075646 (-0.061792) | 0.367315 / 0.419271 (-0.051956) | 0.068731 / 0.043533 (0.025199) | 0.494733 / 0.255139 (0.239594) | 0.472801 / 0.283200 (0.189601) | 0.036791 / 0.141683 (-0.104892) | 1.877648 / 1.452155 (0.425493) | 1.928399 / 1.492716 (0.435683) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231910 / 0.018006 (0.213904) | 0.553464 / 0.000490 (0.552974) | 0.011915 / 0.000200 (0.011715) | 0.000378 / 0.000054 (0.000324) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028232 / 0.037411 (-0.009179) | 0.091441 / 0.014526 (0.076916) | 0.110394 / 0.176557 (-0.066162) | 0.187638 / 0.737135 (-0.549497) | 0.111810 / 0.296338 (-0.184529) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.599987 / 0.215209 (0.384778) | 6.008709 / 2.077655 (3.931054) | 2.518769 / 1.504120 (1.014650) | 2.197029 / 1.541195 (0.655834) | 2.217165 / 1.468490 (0.748675) | 0.894939 / 4.584777 (-3.689837) | 5.001217 / 3.745712 (1.255505) | 4.636482 / 5.269862 (-0.633379) | 3.237613 / 4.565676 (-1.328063) | 0.104227 / 0.424275 (-0.320048) | 0.008504 / 0.007607 (0.000897) | 0.750190 / 0.226044 (0.524145) | 7.514571 / 2.268929 (5.245642) | 3.358003 / 55.444624 (-52.086621) | 2.585649 / 6.876477 (-4.290827) | 2.731129 / 2.142072 (0.589056) | 1.088828 / 4.805227 (-3.716400) | 0.217308 / 6.500664 (-6.283356) | 0.076410 / 0.075469 (0.000941) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.620087 / 1.841788 (-0.221701) | 23.145743 / 8.074308 (15.071435) | 20.583403 / 10.191392 (10.392011) | 0.225467 / 0.680424 (-0.454956) | 0.029063 / 0.534201 (-0.505138) | 0.480563 / 0.579283 (-0.098720) | 0.539083 / 0.434364 (0.104719) | 0.563787 / 0.540337 (0.023449) | 0.782902 / 1.386936 (-0.604034) |\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) | 0.010113 / 0.011353 (-0.001239) | 0.004997 / 0.011008 (-0.006011) | 0.082974 / 0.038508 (0.044466) | 0.090375 / 0.023109 (0.067266) | 0.440273 / 0.275898 (0.164375) | 0.476939 / 0.323480 (0.153459) | 0.005955 / 0.007986 (-0.002031) | 0.004375 / 0.004328 (0.000046) | 0.080459 / 0.004250 (0.076209) | 0.061787 / 0.037052 (0.024734) | 0.477211 / 0.258489 (0.218722) | 0.487164 / 0.293841 (0.193323) | 0.054198 / 0.128546 (-0.074348) | 0.013945 / 0.075646 (-0.061701) | 0.093006 / 0.419271 (-0.326266) | 0.062685 / 0.043533 (0.019152) | 0.461373 / 0.255139 (0.206234) | 0.475766 / 0.283200 (0.192567) | 0.032059 / 0.141683 (-0.109623) | 1.857989 / 1.452155 (0.405834) | 1.837993 / 1.492716 (0.345277) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.243048 / 0.018006 (0.225042) | 0.535850 / 0.000490 (0.535360) | 0.007204 / 0.000200 (0.007004) | 0.000104 / 0.000054 (0.000049) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032584 / 0.037411 (-0.004827) | 0.098151 / 0.014526 (0.083625) | 0.109691 / 0.176557 (-0.066866) | 0.172803 / 0.737135 (-0.564333) | 0.110469 / 0.296338 (-0.185869) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.635086 / 0.215209 (0.419877) | 6.500864 / 2.077655 (4.423210) | 2.996727 / 1.504120 (1.492607) | 2.537218 / 1.541195 (0.996023) | 2.572310 / 1.468490 (1.103820) | 0.870868 / 4.584777 (-3.713909) | 4.989744 / 3.745712 (1.244032) | 4.422174 / 5.269862 (-0.847687) | 2.935874 / 4.565676 (-1.629803) | 0.097118 / 0.424275 (-0.327157) | 0.009360 / 0.007607 (0.001753) | 0.790447 / 0.226044 (0.564403) | 7.859519 / 2.268929 (5.590591) | 3.975616 / 55.444624 (-51.469009) | 3.018271 / 6.876477 (-3.858206) | 3.111173 / 2.142072 (0.969101) | 1.085577 / 4.805227 (-3.719651) | 0.225719 / 6.500664 (-6.274945) | 0.080576 / 0.075469 (0.005107) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.802284 / 1.841788 (-0.039504) | 23.487921 / 8.074308 (15.413613) | 20.595171 / 10.191392 (10.403779) | 0.196610 / 0.680424 (-0.483814) | 0.027483 / 0.534201 (-0.506718) | 0.485840 / 0.579283 (-0.093443) | 0.542661 / 0.434364 (0.108297) | 0.580602 / 0.540337 (0.040265) | 0.768195 / 1.386936 (-0.618741) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#88896a7b28610ace95e444b94f9a4bc332cc1ee3 \"CML watermark\")\n"
] |
6,062
|
Improve `Dataset.from_list` docstring
| null |
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6062",
"html_url": "https://github.com/huggingface/datasets/pull/6062",
"diff_url": "https://github.com/huggingface/datasets/pull/6062.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6062.patch",
"merged_at": "2023-07-24T14:34:43"
}
| true
|
[
"_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) | 0.008340 / 0.011353 (-0.003013) | 0.005053 / 0.011008 (-0.005955) | 0.103294 / 0.038508 (0.064786) | 0.069417 / 0.023109 (0.046308) | 0.436922 / 0.275898 (0.161024) | 0.461348 / 0.323480 (0.137868) | 0.006030 / 0.007986 (-0.001955) | 0.003727 / 0.004328 (-0.000601) | 0.076384 / 0.004250 (0.072134) | 0.056742 / 0.037052 (0.019689) | 0.439996 / 0.258489 (0.181507) | 0.469417 / 0.293841 (0.175577) | 0.044343 / 0.128546 (-0.084203) | 0.012634 / 0.075646 (-0.063013) | 0.359746 / 0.419271 (-0.059525) | 0.064842 / 0.043533 (0.021309) | 0.425960 / 0.255139 (0.170821) | 0.458568 / 0.283200 (0.175368) | 0.039802 / 0.141683 (-0.101881) | 1.687320 / 1.452155 (0.235165) | 1.806212 / 1.492716 (0.313496) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.255484 / 0.018006 (0.237478) | 0.563039 / 0.000490 (0.562549) | 0.000445 / 0.000200 (0.000245) | 0.000076 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027511 / 0.037411 (-0.009900) | 0.089185 / 0.014526 (0.074659) | 0.098397 / 0.176557 (-0.078160) | 0.163897 / 0.737135 (-0.573238) | 0.099905 / 0.296338 (-0.196434) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.612737 / 0.215209 (0.397528) | 6.209948 / 2.077655 (4.132294) | 2.756060 / 1.504120 (1.251940) | 2.402115 / 1.541195 (0.860920) | 2.422665 / 1.468490 (0.954175) | 0.834799 / 4.584777 (-3.749977) | 5.251699 / 3.745712 (1.505986) | 5.554141 / 5.269862 (0.284280) | 3.254699 / 4.565676 (-1.310977) | 0.095697 / 0.424275 (-0.328578) | 0.009406 / 0.007607 (0.001799) | 0.729025 / 0.226044 (0.502980) | 7.195521 / 2.268929 (4.926593) | 3.360264 / 55.444624 (-52.084361) | 2.696764 / 6.876477 (-4.179713) | 2.702796 / 2.142072 (0.560724) | 0.974420 / 4.805227 (-3.830808) | 0.195215 / 6.500664 (-6.305450) | 0.069754 / 0.075469 (-0.005715) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.553458 / 1.841788 (-0.288330) | 21.972436 / 8.074308 (13.898128) | 20.027392 / 10.191392 (9.836000) | 0.216950 / 0.680424 (-0.463474) | 0.032196 / 0.534201 (-0.502005) | 0.449884 / 0.579283 (-0.129399) | 0.586213 / 0.434364 (0.151849) | 0.537227 / 0.540337 (-0.003111) | 0.751022 / 1.386936 (-0.635914) |\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) | 0.007859 / 0.011353 (-0.003493) | 0.004762 / 0.011008 (-0.006246) | 0.086023 / 0.038508 (0.047515) | 0.069218 / 0.023109 (0.046109) | 0.449312 / 0.275898 (0.173414) | 0.481687 / 0.323480 (0.158207) | 0.006318 / 0.007986 (-0.001668) | 0.004063 / 0.004328 (-0.000266) | 0.076917 / 0.004250 (0.072667) | 0.058034 / 0.037052 (0.020981) | 0.474265 / 0.258489 (0.215775) | 0.497736 / 0.293841 (0.203895) | 0.044587 / 0.128546 (-0.083959) | 0.013880 / 0.075646 (-0.061766) | 0.089233 / 0.419271 (-0.330038) | 0.058760 / 0.043533 (0.015227) | 0.439515 / 0.255139 (0.184376) | 0.473246 / 0.283200 (0.190047) | 0.042968 / 0.141683 (-0.098715) | 1.802647 / 1.452155 (0.350493) | 1.778563 / 1.492716 (0.285847) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.343741 / 0.018006 (0.325735) | 0.567409 / 0.000490 (0.566919) | 0.029727 / 0.000200 (0.029527) | 0.000147 / 0.000054 (0.000092) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031021 / 0.037411 (-0.006390) | 0.096659 / 0.014526 (0.082133) | 0.103341 / 0.176557 (-0.073215) | 0.169893 / 0.737135 (-0.567242) | 0.103280 / 0.296338 (-0.193058) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.584724 / 0.215209 (0.369515) | 5.792596 / 2.077655 (3.714941) | 2.683133 / 1.504120 (1.179013) | 2.367837 / 1.541195 (0.826643) | 2.378567 / 1.468490 (0.910076) | 0.803427 / 4.584777 (-3.781350) | 5.179017 / 3.745712 (1.433305) | 4.446323 / 5.269862 (-0.823538) | 2.771731 / 4.565676 (-1.793945) | 0.100943 / 0.424275 (-0.323332) | 0.009875 / 0.007607 (0.002268) | 0.725260 / 0.226044 (0.499216) | 7.149728 / 2.268929 (4.880800) | 3.646438 / 55.444624 (-51.798187) | 2.793858 / 6.876477 (-4.082618) | 2.971966 / 2.142072 (0.829894) | 0.998147 / 4.805227 (-3.807080) | 0.198004 / 6.500664 (-6.302660) | 0.072581 / 0.075469 (-0.002888) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.696737 / 1.841788 (-0.145051) | 22.615193 / 8.074308 (14.540884) | 20.272421 / 10.191392 (10.081029) | 0.237459 / 0.680424 (-0.442965) | 0.034774 / 0.534201 (-0.499427) | 0.484649 / 0.579283 (-0.094634) | 0.590263 / 0.434364 (0.155899) | 0.547833 / 0.540337 (0.007495) | 0.762109 / 1.386936 (-0.624827) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4bc3628b5a8f71ad7cfc014d8ba5e798f26becb7 \"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) | 0.011183 / 0.011353 (-0.000170) | 0.005267 / 0.011008 (-0.005741) | 0.108506 / 0.038508 (0.069997) | 0.083541 / 0.023109 (0.060431) | 0.452189 / 0.275898 (0.176291) | 0.496229 / 0.323480 (0.172749) | 0.004951 / 0.007986 (-0.003035) | 0.004452 / 0.004328 (0.000124) | 0.085133 / 0.004250 (0.080883) | 0.061291 / 0.037052 (0.024239) | 0.450453 / 0.258489 (0.191964) | 0.506456 / 0.293841 (0.212616) | 0.049784 / 0.128546 (-0.078762) | 0.014738 / 0.075646 (-0.060908) | 0.372603 / 0.419271 (-0.046669) | 0.065223 / 0.043533 (0.021690) | 0.467872 / 0.255139 (0.212733) | 0.500062 / 0.283200 (0.216862) | 0.040911 / 0.141683 (-0.100772) | 1.852970 / 1.452155 (0.400816) | 2.016996 / 1.492716 (0.524280) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.262620 / 0.018006 (0.244614) | 0.593925 / 0.000490 (0.593435) | 0.000413 / 0.000200 (0.000213) | 0.000085 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035713 / 0.037411 (-0.001698) | 0.111403 / 0.014526 (0.096878) | 0.117259 / 0.176557 (-0.059298) | 0.201545 / 0.737135 (-0.535590) | 0.133111 / 0.296338 (-0.163228) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.597318 / 0.215209 (0.382109) | 5.882691 / 2.077655 (3.805036) | 2.572203 / 1.504120 (1.068083) | 2.248016 / 1.541195 (0.706821) | 2.359103 / 1.468490 (0.890613) | 0.852023 / 4.584777 (-3.732754) | 5.270831 / 3.745712 (1.525119) | 4.712915 / 5.269862 (-0.556947) | 3.124295 / 4.565676 (-1.441381) | 0.092045 / 0.424275 (-0.332230) | 0.007834 / 0.007607 (0.000227) | 0.695711 / 0.226044 (0.469666) | 7.011760 / 2.268929 (4.742831) | 3.333300 / 55.444624 (-52.111325) | 2.745889 / 6.876477 (-4.130587) | 3.153458 / 2.142072 (1.011385) | 1.011089 / 4.805227 (-3.794139) | 0.207467 / 6.500664 (-6.293197) | 0.079802 / 0.075469 (0.004333) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.703784 / 1.841788 (-0.138003) | 24.414340 / 8.074308 (16.340032) | 22.534528 / 10.191392 (12.343136) | 0.276129 / 0.680424 (-0.404295) | 0.027954 / 0.534201 (-0.506247) | 0.484261 / 0.579283 (-0.095022) | 0.605316 / 0.434364 (0.170952) | 0.557219 / 0.540337 (0.016882) | 0.802209 / 1.386936 (-0.584727) |\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) | 0.009109 / 0.011353 (-0.002244) | 0.005376 / 0.011008 (-0.005632) | 0.085141 / 0.038508 (0.046633) | 0.100560 / 0.023109 (0.077450) | 0.482673 / 0.275898 (0.206775) | 0.551582 / 0.323480 (0.228103) | 0.006756 / 0.007986 (-0.001229) | 0.004171 / 0.004328 (-0.000158) | 0.084184 / 0.004250 (0.079933) | 0.069283 / 0.037052 (0.032230) | 0.517722 / 0.258489 (0.259233) | 0.542641 / 0.293841 (0.248801) | 0.047790 / 0.128546 (-0.080756) | 0.014063 / 0.075646 (-0.061583) | 0.110591 / 0.419271 (-0.308680) | 0.064373 / 0.043533 (0.020840) | 0.496636 / 0.255139 (0.241497) | 0.551906 / 0.283200 (0.268707) | 0.046187 / 0.141683 (-0.095496) | 1.864836 / 1.452155 (0.412681) | 1.923765 / 1.492716 (0.431049) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.286558 / 0.018006 (0.268552) | 0.610353 / 0.000490 (0.609863) | 0.012647 / 0.000200 (0.012447) | 0.000162 / 0.000054 (0.000107) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037099 / 0.037411 (-0.000313) | 0.108608 / 0.014526 (0.094082) | 0.120386 / 0.176557 (-0.056170) | 0.183450 / 0.737135 (-0.553686) | 0.124860 / 0.296338 (-0.171479) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.629006 / 0.215209 (0.413797) | 6.309206 / 2.077655 (4.231551) | 2.878558 / 1.504120 (1.374438) | 2.616093 / 1.541195 (1.074898) | 2.668096 / 1.468490 (1.199606) | 0.865732 / 4.584777 (-3.719045) | 5.312433 / 3.745712 (1.566721) | 4.799352 / 5.269862 (-0.470509) | 3.142207 / 4.565676 (-1.423469) | 0.099591 / 0.424275 (-0.324684) | 0.009159 / 0.007607 (0.001552) | 0.730999 / 0.226044 (0.504954) | 7.486442 / 2.268929 (5.217513) | 3.657699 / 55.444624 (-51.786925) | 3.080094 / 6.876477 (-3.796383) | 3.320976 / 2.142072 (1.178904) | 1.089324 / 4.805227 (-3.715904) | 0.222831 / 6.500664 (-6.277833) | 0.083976 / 0.075469 (0.008507) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.793181 / 1.841788 (-0.048607) | 25.307444 / 8.074308 (17.233136) | 21.321713 / 10.191392 (11.130321) | 0.216326 / 0.680424 (-0.464098) | 0.034298 / 0.534201 (-0.499903) | 0.497173 / 0.579283 (-0.082110) | 0.643550 / 0.434364 (0.209186) | 0.581213 / 0.540337 (0.040876) | 0.830973 / 1.386936 (-0.555963) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#24875bb8494c3a7803182b08c70747b1b1a6bf4d \"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) | 0.006886 / 0.011353 (-0.004467) | 0.004267 / 0.011008 (-0.006741) | 0.086182 / 0.038508 (0.047674) | 0.083405 / 0.023109 (0.060296) | 0.313717 / 0.275898 (0.037819) | 0.351476 / 0.323480 (0.027996) | 0.005702 / 0.007986 (-0.002284) | 0.003802 / 0.004328 (-0.000526) | 0.065759 / 0.004250 (0.061508) | 0.060056 / 0.037052 (0.023003) | 0.315871 / 0.258489 (0.057382) | 0.364520 / 0.293841 (0.070679) | 0.032067 / 0.128546 (-0.096479) | 0.008679 / 0.075646 (-0.066967) | 0.294968 / 0.419271 (-0.124303) | 0.054684 / 0.043533 (0.011152) | 0.314124 / 0.255139 (0.058985) | 0.337312 / 0.283200 (0.054113) | 0.025051 / 0.141683 (-0.116632) | 1.505242 / 1.452155 (0.053087) | 1.608263 / 1.492716 (0.115547) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.266562 / 0.018006 (0.248556) | 0.579887 / 0.000490 (0.579397) | 0.004161 / 0.000200 (0.003961) | 0.000090 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031153 / 0.037411 (-0.006258) | 0.087703 / 0.014526 (0.073177) | 0.103864 / 0.176557 (-0.072693) | 0.159032 / 0.737135 (-0.578104) | 0.102482 / 0.296338 (-0.193857) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.405805 / 0.215209 (0.190596) | 4.050669 / 2.077655 (1.973014) | 2.064384 / 1.504120 (0.560264) | 1.892825 / 1.541195 (0.351630) | 2.001083 / 1.468490 (0.532593) | 0.478174 / 4.584777 (-4.106603) | 3.542580 / 3.745712 (-0.203132) | 3.319205 / 5.269862 (-1.950656) | 2.075868 / 4.565676 (-2.489808) | 0.057345 / 0.424275 (-0.366930) | 0.007459 / 0.007607 (-0.000148) | 0.483564 / 0.226044 (0.257520) | 4.827746 / 2.268929 (2.558818) | 2.579541 / 55.444624 (-52.865083) | 2.205125 / 6.876477 (-4.671352) | 2.489206 / 2.142072 (0.347133) | 0.575843 / 4.805227 (-4.229384) | 0.133010 / 6.500664 (-6.367654) | 0.061082 / 0.075469 (-0.014387) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.286059 / 1.841788 (-0.555729) | 20.575173 / 8.074308 (12.500865) | 14.351692 / 10.191392 (4.160300) | 0.150401 / 0.680424 (-0.530022) | 0.018678 / 0.534201 (-0.515523) | 0.397860 / 0.579283 (-0.181423) | 0.419474 / 0.434364 (-0.014890) | 0.474492 / 0.540337 (-0.065846) | 0.659510 / 1.386936 (-0.727426) |\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) | 0.006948 / 0.011353 (-0.004405) | 0.004305 / 0.011008 (-0.006703) | 0.064220 / 0.038508 (0.025712) | 0.083251 / 0.023109 (0.060142) | 0.388148 / 0.275898 (0.112250) | 0.417834 / 0.323480 (0.094354) | 0.005762 / 0.007986 (-0.002224) | 0.003803 / 0.004328 (-0.000525) | 0.066365 / 0.004250 (0.062114) | 0.061808 / 0.037052 (0.024756) | 0.390889 / 0.258489 (0.132400) | 0.430619 / 0.293841 (0.136778) | 0.031777 / 0.128546 (-0.096770) | 0.008781 / 0.075646 (-0.066865) | 0.070844 / 0.419271 (-0.348427) | 0.050552 / 0.043533 (0.007019) | 0.378420 / 0.255139 (0.123281) | 0.403273 / 0.283200 (0.120074) | 0.024578 / 0.141683 (-0.117105) | 1.494790 / 1.452155 (0.042636) | 1.549408 / 1.492716 (0.056692) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.302668 / 0.018006 (0.284662) | 0.542235 / 0.000490 (0.541746) | 0.001847 / 0.000200 (0.001647) | 0.000092 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031947 / 0.037411 (-0.005465) | 0.092220 / 0.014526 (0.077694) | 0.104525 / 0.176557 (-0.072031) | 0.162000 / 0.737135 (-0.575135) | 0.106795 / 0.296338 (-0.189543) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.412035 / 0.215209 (0.196826) | 4.106527 / 2.077655 (2.028872) | 2.111529 / 1.504120 (0.607409) | 1.953201 / 1.541195 (0.412006) | 2.079258 / 1.468490 (0.610768) | 0.479562 / 4.584777 (-4.105215) | 3.606256 / 3.745712 (-0.139456) | 5.175250 / 5.269862 (-0.094612) | 3.292465 / 4.565676 (-1.273212) | 0.057726 / 0.424275 (-0.366549) | 0.008247 / 0.007607 (0.000640) | 0.486143 / 0.226044 (0.260098) | 4.859051 / 2.268929 (2.590123) | 2.675629 / 55.444624 (-52.768995) | 2.267448 / 6.876477 (-4.609029) | 2.567639 / 2.142072 (0.425567) | 0.580822 / 4.805227 (-4.224406) | 0.134942 / 6.500664 (-6.365722) | 0.063825 / 0.075469 (-0.011644) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.334421 / 1.841788 (-0.507367) | 20.481428 / 8.074308 (12.407120) | 14.227943 / 10.191392 (4.036551) | 0.170711 / 0.680424 (-0.509713) | 0.018212 / 0.534201 (-0.515989) | 0.397212 / 0.579283 (-0.182071) | 0.411934 / 0.434364 (-0.022430) | 0.478019 / 0.540337 (-0.062319) | 0.645434 / 1.386936 (-0.741502) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ef3d3f10886e23a65cce3bfd939b8ec0d5a5c2c1 \"CML watermark\")\n"
] |
6,061
|
Dill 3.7 support
|
Adds support for dill 3.7.
|
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6061",
"html_url": "https://github.com/huggingface/datasets/pull/6061",
"diff_url": "https://github.com/huggingface/datasets/pull/6061.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6061.patch",
"merged_at": "2023-07-24T14:04:36"
}
| true
|
[
"_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) | 0.007700 / 0.011353 (-0.003653) | 0.004680 / 0.011008 (-0.006328) | 0.098812 / 0.038508 (0.060304) | 0.085062 / 0.023109 (0.061952) | 0.371472 / 0.275898 (0.095574) | 0.412552 / 0.323480 (0.089072) | 0.004700 / 0.007986 (-0.003285) | 0.003765 / 0.004328 (-0.000564) | 0.074267 / 0.004250 (0.070017) | 0.063003 / 0.037052 (0.025951) | 0.391842 / 0.258489 (0.133353) | 0.436955 / 0.293841 (0.143114) | 0.035291 / 0.128546 (-0.093255) | 0.009309 / 0.075646 (-0.066338) | 0.313097 / 0.419271 (-0.106174) | 0.060098 / 0.043533 (0.016565) | 0.350726 / 0.255139 (0.095587) | 0.402692 / 0.283200 (0.119493) | 0.029321 / 0.141683 (-0.112361) | 1.671806 / 1.452155 (0.219651) | 1.743760 / 1.492716 (0.251044) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242281 / 0.018006 (0.224275) | 0.505054 / 0.000490 (0.504564) | 0.006595 / 0.000200 (0.006395) | 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) | 0.032174 / 0.037411 (-0.005238) | 0.094483 / 0.014526 (0.079957) | 0.108527 / 0.176557 (-0.068030) | 0.178983 / 0.737135 (-0.558152) | 0.113766 / 0.296338 (-0.182572) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419764 / 0.215209 (0.204555) | 4.282650 / 2.077655 (2.204995) | 2.075325 / 1.504120 (0.571205) | 1.897668 / 1.541195 (0.356473) | 2.027109 / 1.468490 (0.558619) | 0.519983 / 4.584777 (-4.064794) | 4.134603 / 3.745712 (0.388891) | 6.586711 / 5.269862 (1.316849) | 3.811726 / 4.565676 (-0.753951) | 0.058628 / 0.424275 (-0.365647) | 0.007586 / 0.007607 (-0.000021) | 0.502180 / 0.226044 (0.276136) | 5.101588 / 2.268929 (2.832660) | 2.534295 / 55.444624 (-52.910330) | 2.220170 / 6.876477 (-4.656307) | 2.441110 / 2.142072 (0.299038) | 0.644775 / 4.805227 (-4.160452) | 0.144716 / 6.500664 (-6.355948) | 0.067018 / 0.075469 (-0.008451) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.431279 / 1.841788 (-0.410508) | 21.947814 / 8.074308 (13.873506) | 15.548236 / 10.191392 (5.356844) | 0.174774 / 0.680424 (-0.505650) | 0.021182 / 0.534201 (-0.513019) | 0.441320 / 0.579283 (-0.137963) | 0.476685 / 0.434364 (0.042321) | 0.506277 / 0.540337 (-0.034060) | 0.809943 / 1.386936 (-0.576993) |\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) | 0.007172 / 0.011353 (-0.004181) | 0.004358 / 0.011008 (-0.006650) | 0.068604 / 0.038508 (0.030096) | 0.083956 / 0.023109 (0.060847) | 0.402579 / 0.275898 (0.126681) | 0.444714 / 0.323480 (0.121235) | 0.005940 / 0.007986 (-0.002046) | 0.003607 / 0.004328 (-0.000722) | 0.073134 / 0.004250 (0.068883) | 0.061722 / 0.037052 (0.024669) | 0.410957 / 0.258489 (0.152468) | 0.458819 / 0.293841 (0.164978) | 0.033710 / 0.128546 (-0.094836) | 0.010230 / 0.075646 (-0.065417) | 0.084678 / 0.419271 (-0.334593) | 0.058203 / 0.043533 (0.014670) | 0.444972 / 0.255139 (0.189833) | 0.470962 / 0.283200 (0.187763) | 0.029222 / 0.141683 (-0.112461) | 1.671460 / 1.452155 (0.219306) | 1.759471 / 1.492716 (0.266754) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.238894 / 0.018006 (0.220888) | 0.493605 / 0.000490 (0.493115) | 0.001979 / 0.000200 (0.001780) | 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) | 0.036498 / 0.037411 (-0.000913) | 0.095245 / 0.014526 (0.080719) | 0.112147 / 0.176557 (-0.064409) | 0.171128 / 0.737135 (-0.566007) | 0.115295 / 0.296338 (-0.181044) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.461067 / 0.215209 (0.245858) | 4.723932 / 2.077655 (2.646277) | 2.432697 / 1.504120 (0.928578) | 2.237302 / 1.541195 (0.696107) | 2.351320 / 1.468490 (0.882830) | 0.509963 / 4.584777 (-4.074813) | 4.194817 / 3.745712 (0.449105) | 6.689529 / 5.269862 (1.419667) | 3.351198 / 4.565676 (-1.214478) | 0.064563 / 0.424275 (-0.359712) | 0.008605 / 0.007607 (0.000998) | 0.575590 / 0.226044 (0.349546) | 5.644179 / 2.268929 (3.375250) | 3.021375 / 55.444624 (-52.423249) | 2.595305 / 6.876477 (-4.281172) | 2.839228 / 2.142072 (0.697156) | 0.657148 / 4.805227 (-4.148079) | 0.144831 / 6.500664 (-6.355834) | 0.067882 / 0.075469 (-0.007587) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.595580 / 1.841788 (-0.246208) | 22.431609 / 8.074308 (14.357301) | 15.700845 / 10.191392 (5.509453) | 0.164675 / 0.680424 (-0.515749) | 0.021322 / 0.534201 (-0.512879) | 0.455270 / 0.579283 (-0.124013) | 0.451547 / 0.434364 (0.017183) | 0.520955 / 0.540337 (-0.019383) | 0.687803 / 1.386936 (-0.699133) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7d19574e9f44bd3b59a3e47ca7c4ea66305a8e6b \"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) | 0.008171 / 0.011353 (-0.003182) | 0.005563 / 0.011008 (-0.005445) | 0.102265 / 0.038508 (0.063757) | 0.074755 / 0.023109 (0.051646) | 0.431317 / 0.275898 (0.155419) | 0.472179 / 0.323480 (0.148699) | 0.006153 / 0.007986 (-0.001833) | 0.003832 / 0.004328 (-0.000496) | 0.078480 / 0.004250 (0.074230) | 0.056250 / 0.037052 (0.019197) | 0.432938 / 0.258489 (0.174449) | 0.480983 / 0.293841 (0.187142) | 0.048861 / 0.128546 (-0.079685) | 0.016252 / 0.075646 (-0.059394) | 0.343508 / 0.419271 (-0.075763) | 0.065057 / 0.043533 (0.021524) | 0.468418 / 0.255139 (0.213279) | 0.463692 / 0.283200 (0.180492) | 0.032912 / 0.141683 (-0.108771) | 1.795194 / 1.452155 (0.343039) | 1.833047 / 1.492716 (0.340331) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.197980 / 0.018006 (0.179974) | 0.500662 / 0.000490 (0.500172) | 0.007380 / 0.000200 (0.007181) | 0.000110 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028323 / 0.037411 (-0.009089) | 0.089817 / 0.014526 (0.075291) | 0.102923 / 0.176557 (-0.073633) | 0.173851 / 0.737135 (-0.563284) | 0.104006 / 0.296338 (-0.192333) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.580277 / 0.215209 (0.365068) | 5.878739 / 2.077655 (3.801085) | 2.404673 / 1.504120 (0.900553) | 2.071765 / 1.541195 (0.530571) | 2.106024 / 1.468490 (0.637534) | 0.855217 / 4.584777 (-3.729560) | 4.918602 / 3.745712 (1.172890) | 5.354984 / 5.269862 (0.085122) | 3.141288 / 4.565676 (-1.424389) | 0.099553 / 0.424275 (-0.324723) | 0.008152 / 0.007607 (0.000545) | 0.709857 / 0.226044 (0.483813) | 7.144602 / 2.268929 (4.875673) | 3.137637 / 55.444624 (-52.306987) | 2.379851 / 6.876477 (-4.496626) | 2.346426 / 2.142072 (0.204353) | 1.033416 / 4.805227 (-3.771811) | 0.213120 / 6.500664 (-6.287544) | 0.076037 / 0.075469 (0.000568) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.597742 / 1.841788 (-0.244046) | 21.745366 / 8.074308 (13.671058) | 20.830698 / 10.191392 (10.639306) | 0.238727 / 0.680424 (-0.441697) | 0.027923 / 0.534201 (-0.506278) | 0.466073 / 0.579283 (-0.113210) | 0.548647 / 0.434364 (0.114283) | 0.549245 / 0.540337 (0.008908) | 0.977148 / 1.386936 (-0.409788) |\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) | 0.008252 / 0.011353 (-0.003101) | 0.004653 / 0.011008 (-0.006356) | 0.084012 / 0.038508 (0.045504) | 0.077418 / 0.023109 (0.054309) | 0.440748 / 0.275898 (0.164850) | 0.464279 / 0.323480 (0.140799) | 0.005762 / 0.007986 (-0.002224) | 0.004909 / 0.004328 (0.000581) | 0.086441 / 0.004250 (0.082190) | 0.057883 / 0.037052 (0.020831) | 0.466655 / 0.258489 (0.208166) | 0.479751 / 0.293841 (0.185910) | 0.047166 / 0.128546 (-0.081380) | 0.014480 / 0.075646 (-0.061166) | 0.092599 / 0.419271 (-0.326672) | 0.062454 / 0.043533 (0.018921) | 0.449753 / 0.255139 (0.194614) | 0.461876 / 0.283200 (0.178676) | 0.034828 / 0.141683 (-0.106855) | 1.752249 / 1.452155 (0.300095) | 1.865449 / 1.492716 (0.372732) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.245028 / 0.018006 (0.227022) | 0.509564 / 0.000490 (0.509074) | 0.003930 / 0.000200 (0.003730) | 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) | 0.034746 / 0.037411 (-0.002665) | 0.096563 / 0.014526 (0.082037) | 0.107581 / 0.176557 (-0.068975) | 0.184952 / 0.737135 (-0.552184) | 0.108747 / 0.296338 (-0.187591) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.613091 / 0.215209 (0.397882) | 5.994985 / 2.077655 (3.917330) | 2.711276 / 1.504120 (1.207156) | 2.415862 / 1.541195 (0.874668) | 2.391055 / 1.468490 (0.922565) | 0.868723 / 4.584777 (-3.716054) | 4.953992 / 3.745712 (1.208280) | 4.606542 / 5.269862 (-0.663319) | 2.942162 / 4.565676 (-1.623515) | 0.102737 / 0.424275 (-0.321538) | 0.008634 / 0.007607 (0.001027) | 0.722122 / 0.226044 (0.496078) | 7.245097 / 2.268929 (4.976168) | 3.428232 / 55.444624 (-52.016393) | 2.709539 / 6.876477 (-4.166938) | 2.857956 / 2.142072 (0.715884) | 1.045594 / 4.805227 (-3.759634) | 0.213344 / 6.500664 (-6.287320) | 0.073601 / 0.075469 (-0.001868) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.651954 / 1.841788 (-0.189834) | 22.458646 / 8.074308 (14.384338) | 19.583203 / 10.191392 (9.391811) | 0.246932 / 0.680424 (-0.433492) | 0.025730 / 0.534201 (-0.508471) | 0.473475 / 0.579283 (-0.105808) | 0.521411 / 0.434364 (0.087047) | 0.562038 / 0.540337 (0.021700) | 0.767673 / 1.386936 (-0.619263) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3869d99628329c696f6975377f65e625dd8ef3e0 \"CML watermark\")\n",
"The CI error is unrelated.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006649 / 0.011353 (-0.004703) | 0.003963 / 0.011008 (-0.007045) | 0.084564 / 0.038508 (0.046056) | 0.075668 / 0.023109 (0.052559) | 0.314233 / 0.275898 (0.038335) | 0.343320 / 0.323480 (0.019841) | 0.005405 / 0.007986 (-0.002581) | 0.003356 / 0.004328 (-0.000973) | 0.065094 / 0.004250 (0.060844) | 0.058774 / 0.037052 (0.021722) | 0.320772 / 0.258489 (0.062283) | 0.353546 / 0.293841 (0.059705) | 0.030921 / 0.128546 (-0.097625) | 0.008463 / 0.075646 (-0.067184) | 0.287490 / 0.419271 (-0.131781) | 0.053188 / 0.043533 (0.009656) | 0.324023 / 0.255139 (0.068884) | 0.337828 / 0.283200 (0.054628) | 0.024764 / 0.141683 (-0.116918) | 1.458028 / 1.452155 (0.005873) | 1.521615 / 1.492716 (0.028899) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209360 / 0.018006 (0.191353) | 0.461331 / 0.000490 (0.460841) | 0.000386 / 0.000200 (0.000186) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028405 / 0.037411 (-0.009006) | 0.081074 / 0.014526 (0.066548) | 0.094868 / 0.176557 (-0.081689) | 0.151050 / 0.737135 (-0.586085) | 0.095854 / 0.296338 (-0.200484) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.393957 / 0.215209 (0.178748) | 3.938649 / 2.077655 (1.860994) | 1.938190 / 1.504120 (0.434070) | 1.766458 / 1.541195 (0.225263) | 1.818028 / 1.468490 (0.349538) | 0.483926 / 4.584777 (-4.100851) | 3.641957 / 3.745712 (-0.103755) | 4.883845 / 5.269862 (-0.386016) | 2.960300 / 4.565676 (-1.605377) | 0.057227 / 0.424275 (-0.367048) | 0.007285 / 0.007607 (-0.000322) | 0.475928 / 0.226044 (0.249884) | 4.756757 / 2.268929 (2.487828) | 2.502659 / 55.444624 (-52.941966) | 2.178067 / 6.876477 (-4.698410) | 2.378298 / 2.142072 (0.236226) | 0.578639 / 4.805227 (-4.226588) | 0.132512 / 6.500664 (-6.368152) | 0.059656 / 0.075469 (-0.015813) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.272673 / 1.841788 (-0.569115) | 19.266884 / 8.074308 (11.192576) | 14.272930 / 10.191392 (4.081538) | 0.165897 / 0.680424 (-0.514527) | 0.018436 / 0.534201 (-0.515765) | 0.395177 / 0.579283 (-0.184107) | 0.420134 / 0.434364 (-0.014229) | 0.460781 / 0.540337 (-0.079557) | 0.645376 / 1.386936 (-0.741560) |\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) | 0.006504 / 0.011353 (-0.004849) | 0.003942 / 0.011008 (-0.007066) | 0.064936 / 0.038508 (0.026428) | 0.075015 / 0.023109 (0.051905) | 0.396871 / 0.275898 (0.120973) | 0.423448 / 0.323480 (0.099968) | 0.005239 / 0.007986 (-0.002747) | 0.003265 / 0.004328 (-0.001063) | 0.064910 / 0.004250 (0.060660) | 0.055006 / 0.037052 (0.017953) | 0.392818 / 0.258489 (0.134329) | 0.429735 / 0.293841 (0.135894) | 0.031847 / 0.128546 (-0.096699) | 0.008626 / 0.075646 (-0.067021) | 0.071591 / 0.419271 (-0.347681) | 0.049006 / 0.043533 (0.005473) | 0.384913 / 0.255139 (0.129774) | 0.408969 / 0.283200 (0.125769) | 0.023573 / 0.141683 (-0.118110) | 1.490271 / 1.452155 (0.038117) | 1.564620 / 1.492716 (0.071904) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225917 / 0.018006 (0.207911) | 0.450369 / 0.000490 (0.449880) | 0.000375 / 0.000200 (0.000175) | 0.000055 / 0.000054 (0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031196 / 0.037411 (-0.006215) | 0.090486 / 0.014526 (0.075960) | 0.102326 / 0.176557 (-0.074231) | 0.157483 / 0.737135 (-0.579653) | 0.103670 / 0.296338 (-0.192668) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417577 / 0.215209 (0.202368) | 4.170798 / 2.077655 (2.093143) | 2.123689 / 1.504120 (0.619569) | 1.948231 / 1.541195 (0.407037) | 2.040277 / 1.468490 (0.571787) | 0.497919 / 4.584777 (-4.086858) | 3.633270 / 3.745712 (-0.112442) | 4.851698 / 5.269862 (-0.418164) | 2.691992 / 4.565676 (-1.873684) | 0.058641 / 0.424275 (-0.365634) | 0.007719 / 0.007607 (0.000112) | 0.500652 / 0.226044 (0.274607) | 4.988657 / 2.268929 (2.719728) | 2.604488 / 55.444624 (-52.840136) | 2.329829 / 6.876477 (-4.546648) | 2.468239 / 2.142072 (0.326167) | 0.598724 / 4.805227 (-4.206503) | 0.135959 / 6.500664 (-6.364706) | 0.061088 / 0.075469 (-0.014381) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.352107 / 1.841788 (-0.489681) | 19.973976 / 8.074308 (11.899668) | 14.292812 / 10.191392 (4.101420) | 0.163855 / 0.680424 (-0.516568) | 0.018402 / 0.534201 (-0.515799) | 0.393128 / 0.579283 (-0.186155) | 0.407379 / 0.434364 (-0.026985) | 0.462324 / 0.540337 (-0.078013) | 0.607501 / 1.386936 (-0.779435) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ae126ac974cad3050f90106e5909232140786811 \"CML watermark\")\n"
] |
6,060
|
Dataset.map() execute twice when in PyTorch DDP mode
|
### Describe the bug
I use `torchrun --standalone --nproc_per_node=2 train.py` to start training. And write the code following the [docs](https://huggingface.co/docs/datasets/process#distributed-usage). The trick about using `torch.distributed.barrier()` to only execute map at the main process doesn't always work. When I am training model, it will map twice. When I am running a test for dataset and dataloader (just print the batches), it can work. Their code about loading dataset are same.
And on another server with 30 CPU cores, I use 2 GPUs and it can't work neither.
I have tried to use `rank` and `local_rank` to check, they all didn't make sense.
### Steps to reproduce the bug
use `torchrun --standalone --nproc_per_node=2 train.py` or `torchrun --standalone train.py` to run
This is my code:
```python
if args.distributed and world_size > 1:
if args.local_rank > 0:
print(f"Rank {args.rank}: Gpu {args.gpu} waiting for main process to perform the mapping", force=True)
torch.distributed.barrier()
print("Mapping dataset")
dataset = dataset.map(lambda x: cut_reorder_keys(x, num_stations_list=args.num_stations_list, is_pad=True, is_train=True), num_proc=8, desc="cut_reorder_keys")
dataset = dataset.map(lambda x: random_shift(x, shift_range=(-160, 0), feature_scale=16), num_proc=8, desc="random_shift")
dataset_test = dataset_test.map(lambda x: cut_reorder_keys(x, num_stations_list=args.num_stations_list, is_pad=True, is_train=False), num_proc=8, desc="cut_reorder_keys")
if args.local_rank == 0:
print("Mapping finished, loading results from main process")
torch.distributed.barrier()
```
### Expected behavior
Only the main process will execute `map`, while the sub process will load cache from disk.
### Environment info
server with 64 CPU cores (AMD Ryzen Threadripper PRO 5995WX 64-Cores) and 2 RTX 4090
- `python==3.9.16`
- `datasets==2.13.1`
- `torch==2.0.1+cu117`
- `22.04.1-Ubuntu`
server with 30 CPU cores (Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz) and 2 RTX 4090
- `python==3.9.0`
- `datasets==2.13.1`
- `torch==2.0.1+cu117`
- `Ubuntu 20.04`
|
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[
"Sorry for asking a duplicate question about `num_proc`, I searched the forum and find the solution.\r\n\r\nBut I still can't make the trick with `torch.distributed.barrier()` to only map at the main process work. The [post on forum]( https://discuss.huggingface.co/t/slow-processing-with-map-when-using-deepspeed-or-fairscale/7229/7) didn't help.",
"If it does the `map` twice then it means the hash of your map function is not some same between your two processes.\r\n\r\nCan you make sure your map functions have the same hash in different processes ?\r\n\r\n```python\r\nfrom datasets.fingerprint import Hasher\r\n\r\nprint(Hasher.hash(lambda x: cut_reorder_keys(x, num_stations_list=args.num_stations_list, is_pad=True, is_train=True)))\r\nprint(Hasher.hash(lambda x: random_shift(x, shift_range=(-160, 0), feature_scale=16)))\r\n```\r\n\r\nYou can also set the fingerprint used to reload the resulting dataset by passing `new_finegrprint=` in `map`, see https://huggingface.co/docs/datasets/v2.13.1/en/about_cache#the-cache. This will force the different processes to use the same fingerprint used to locate the resulting dataset in the cache.",
"Thanks for help! I find the fingerprint between processes don't have same hash:\r\n```\r\nRank 0: Gpu 0 cut_reorder_keys fingerprint c7f47f40e9a67657\r\nRank 0: Gpu 0 random_shift fingerprint 240a0ce79831e7d4\r\n\r\nRank 1: Gpu 1 cut_reorder_keys fingerprint 20edd3d9cf284001\r\nRank 1: Gpu 1 random_shift fingerprint 819f7c1c18e7733f\r\n```\r\nBut my functions only process the example one by one and don't need rank or other arguments. After all it can work in the test for dataset and dataloader.\r\nI'll try to set `new_fingerprint` to see if it works and figure out the reason of different hash.",
"I finally figure it out. The fingerprint of the function will change if other key-value pairs change in `args` even the `args.num_stations_list` is not changed.\r\n\r\n```python\r\nlambda x: cut_reorder_keys(x, num_stations_list=args.num_stations_list, is_pad=True, is_train=True)\r\n```\r\n\r\nMy `args` contains the key `rank` which refers the rank of its GPU, so the fingerprints change among the GPUs.\r\nI use `partial` in `functools` to generate a partial function that fixs the argument `num_stations_list=args.num_stations_list`, and the fingerprint of this partial function keeps among the GPUs. Finally I can reuse the mapped cache."
] |
6,059
|
Provide ability to load label mappings from file
|
### Feature request
My task is classification of a dataset containing a large label set that includes a hierarchy. Even ignoring the hierarchy I'm not able to find an example using `datasets` where the label names aren't hard-coded. This works find for classification of a handful of labels but ideally there would be a way of loading the name/id mappings required for `datasets.features.ClassLabel` from a file.
It is possible to pass a file to ClassLabel but I cannot see an easy way of using this with `GeneratorBasedBuilder` since `self._info` is called before the `dl_manager` is constructed so even if my dataset contains say `label_mappings.json` there's no way of loading it in order to construct the `datasets.DatasetInfo`
I can see other uses to accessing the `download_manager` from `self._info` - i.e. if the files contain a schema (i.e. `arrow` or `parquet` files) the `datasets.DatasetInfo` could be inferred.
The workaround that was suggested in the forum is to generate a `.py` file from the `label_mappings.json` and import it.
```
class TestDatasetBuilder(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.Value("string"),
"label": datasets.features.ClassLabel(names=["label_1", "label_2"]),
}
),
task_templates=[TextClassification(text_column="text", label_column="label")],
)
def _split_generators(self, dl_manager):
train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
]
def _generate_examples(self, filepath):
"""Generate AG News examples."""
with open(filepath, encoding="utf-8") as csv_file:
csv_reader = csv.DictReader(csv_file)
for id_, row in enumerate(csv_reader):
yield id_, row
```
### Motivation
Allow `datasets.DatasetInfo` to be generated based on the contents of the dataset.
### Your contribution
I'm willing to work on a PR with guidence.
|
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[
"I would like this also as I have been working with a dataset with hierarchical classes. In fact, I encountered this very issue when trying to define the dataset with a script. I couldn't find a work around and reverted to hard coding the class names in the readme yaml.\r\n\r\n@david-waterworth do you envision also being able to define the hierarchical structure of the classes?",
"@danielduckworth yes I did need to do that (but I ended up ditching datasets as it looks like this is a \"wont fix\"). ",
"@david-waterworth Hmm, that's a shame. What are you using now? Also, I’m curious to know about the work you’re doing that involves hierarchical classes, if you don’t mind sharing."
] |
6,058
|
laion-coco download error
|
### Describe the bug
The full trace:
```
/home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/datasets/load.py:1744: FutureWarning: 'ignore_verifications' was de
precated in favor of 'verification_mode' in version 2.9.1 and will be removed in 3.0.0.
You can remove this warning by passing 'verification_mode=no_checks' instead.
warnings.warn(
Downloading and preparing dataset parquet/laion--laion-coco to /home/bian/.cache/huggingface/datasets/laion___parquet/laion--
laion-coco-cb4205d7f1863066/0.0.0/bcacc8bdaa0614a5d73d0344c813275e590940c6ea8bc569da462847103a1afd...
Downloading data: 100%|█| 1.89G/1.89G [04:57<00:00,
Downloading data files: 100%|█| 1/1 [04:59<00:00, 2
Extracting data files: 100%|█| 1/1 [00:00<00:00, 13
Generating train split: 0 examples [00:00, ? examples/s]<_io.BufferedReader
name='/home/bian/.cache/huggingface/datasets/downlo
ads/26d7a016d25bbd9443115cfa3092136e8eb2f1f5bcd4154
0cb9234572927f04c'>
Traceback (most recent call last):
File "/home/bian/data/ZOC/download_laion_coco.py", line 4, in <module>
dataset = load_dataset("laion/laion-coco", ignore_verifications=True)
File "/home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/datasets/load.py", line 1791, in load_dataset
builder_instance.download_and_prepare(
File "/home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/datasets/builder.py", line 891, in download_and_prepare
self._download_and_prepare(
File "/home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/datasets/builder.py", line 986, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/datasets/builder.py", line 1748, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/datasets/builder.py", line 1842, in _prepare_split_single
generator = self._generate_tables(**gen_kwargs)
File "/home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py", line 67, in
_generate_tables
parquet_file = pq.ParquetFile(f)
File "/home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/pyarrow/parquet/core.py", line 323, in __init__
self.reader.open(
File "pyarrow/_parquet.pyx", line 1227, in pyarrow._parquet.ParquetReader.open
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file
.
```
I have carefully followed the instructions in #5264 but still get the same error.
Other helpful information:
```
ds = load_dataset("parquet", data_files=
...: "https://huggingface.co/datasets/laion/l
...: aion-coco/resolve/d22869de3ccd39dfec1507
...: f7ded32e4a518dad24/part-00000-2256f782-1
...: 26f-4dc6-b9c6-e6757637749d-c000.snappy.p
...: arquet")
Found cached dataset parquet (/home/bian/.cache/huggingface/datasets/parquet/default-a02eea00aeb08b0e/0.0.0/bb8ccf89d9ee38581ff5e51506d721a9b37f14df8090dc9b2d8fb4a40957833f)
100%|██████████████| 1/1 [00:00<00:00, 4.55it/s]
```
### Steps to reproduce the bug
```
from datasets import load_dataset
dataset = load_dataset("laion/laion-coco", ignore_verifications=True/False)
```
### Expected behavior
Properly load Laion-coco dataset
### Environment info
datasets==2.11.0 torch==1.12.1 python 3.10
|
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| false
|
[
"This can also mean one of the files was not downloaded correctly.\r\n\r\nWe log an erroneous file's name before raising the reader's error, so this is how you can find the problematic file. Then, you should delete it and call `load_dataset` again.\r\n\r\n(I checked all the uploaded files, and they seem to be valid Parquet files, so I don't think this is a bug on their side)\r\n"
] |
6,057
|
Why is the speed difference of gen example so big?
|
```python
def _generate_examples(self, metadata_path, images_dir, conditioning_images_dir):
with open(metadata_path, 'r') as file:
metadata = json.load(file)
for idx, item in enumerate(metadata):
image_path = item.get('image_path')
text_content = item.get('text_content')
image_data = open(image_path, "rb").read()
yield idx, {
"text": text_content,
"image": {
"path": image_path,
"bytes": image_data,
},
"conditioning_image": {
"path": image_path,
"bytes": image_data,
},
}
```
Hello,
I use the above function to deal with my local data set, but I am very surprised that the speed at which I generate example is very different. When I start a training task, **sometimes 1000examples/s, sometimes only 10examples/s.**
I'm not saying that speed is changing all the time. I mean, the reading speed is different in different training, which will cause me to start training over and over again until the speed of this generation of examples is normal.
|
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|
[
"Hi!\r\n\r\nIt's hard to explain this behavior without more information. Can you profile the slower version with the following code\r\n```python\r\nimport cProfile, pstats\r\nfrom datasets import load_dataset\r\n\r\nwith cProfile.Profile() as profiler:\r\n ds = load_dataset(...)\r\n\r\nstats = pstats.Stats(profiler).sort_stats(\"cumtime\")\r\nstats.print_stats()\r\n```\r\nand share the output?"
] |
6,056
|
Implement proper checkpointing for dataset uploading with resume function that does not require remapping shards that have already been uploaded
|
Context: issue #5990
In order to implement the checkpointing, I introduce a metadata folder that keeps one yaml file for each set that one is uploading. This yaml keeps track of what shards have already been uploaded, and which one the idx of the latest one was. Using this information I am then able to easily get the push_to_hub function to retrieve on demand past history of uploads and continue mapping and uploading from where it was left off.
|
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| true
|
[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6056). All of your documentation changes will be reflected on that endpoint.",
"@lhoestq Reading the filenames is something I tried earlier, but I decided to use the yaml direction because:\r\n\r\n1. The yaml file name is constructed to retain information about the shard_size, and total number of shards, hence ensuring that the files uploaded are not just files that have the same name but actually represent a different configuration of shard_size, and total number of shards. \r\n2. Remembering the total file size is done easily in the yaml, whereas alternatively I am not sure how one could access the file size of the uploaded files without downloading them.\r\n3. I also had an issue earlier with the hashes not being consistent with which the yaml helped -- but this is no longer an issue as I found a way around it. \r\n\r\nIf 1 and 2 can be achieved without an additional yaml, then I would be willing to make those changes. Let me know of any ideas. 1. could be done by changing the data file names, but I'd rather not do that as to prevent breaking existing datasets that try to upload updates to their data. ",
"If the file name depends on the shard's fingerprint **before** mapping then we can know if a shard has been uploaded before mapping and without requiring an extra YAML file. It should do the job imo\r\n\r\n> I also had an issue earlier with the hashes not being consistent with which the yaml helped -- but this is no longer an issue as I found a way around it.\r\n\r\nwhat was the issue ?",
"> If the file name depends on the shard's fingerprint **before** mapping then we can know if a shard has been uploaded before mapping and without requiring an extra YAML file. It should do the job imo\r\n> \r\n> > I also had an issue earlier with the hashes not being consistent with which the yaml helped -- but this is no longer an issue as I found a way around it.\r\n> \r\n> what was the issue ?\r\n\r\nYou are right. I was having some other issue earlier that I need more input from you guys to overcome, and when I overcame it the yaml was just legacy from before. I'll update the PR. ",
"> If the file name depends on the shard's fingerprint **before** mapping then we can know if a shard has been uploaded before mapping and without requiring an extra YAML file. It should do the job imo\r\n> \r\n> > I also had an issue earlier with the hashes not being consistent with which the yaml helped -- but this is no longer an issue as I found a way around it.\r\n> \r\n> what was the issue ?\r\n\r\nI remembered what it was, and why I needed the yaml. I needed it so it could remember the progress for a particular num_shards setup, as different num_shards would lead to different number of splits, and a user might switch between them while uploading, and I did not want the index to be conflated with one of another num_shards setup. \r\n\r\nAny idea how we deal with that without a yaml?",
"If the user changes the num_shards parameters then we should re-upload everything.\r\n\r\nIt happens that the num_shards is part of the parquet file names, so it restarts the upload from scratch without having to write additional logic :)"
] |
6,055
|
Fix host URL in The Pile datasets
|
### Describe the bug
In #3627 and #5543, you tried to fix the host URL in The Pile datasets. But both URLs are not working now:
`HTTPError: 404 Client Error: Not Found for URL: https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst`
And
`ConnectTimeout: HTTPSConnectionPool(host='mystic.the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(, 'Connection to mystic.the-eye.eu timed out. (connect timeout=10.0)'))`
### Steps to reproduce the bug
```
from datasets import load_dataset
# This takes a few minutes to run, so go grab a tea or coffee while you wait :)
data_files = "https://mystic.the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst"
pubmed_dataset = load_dataset("json", data_files=data_files, split="train")
pubmed_dataset
```
Result:
`ConnectTimeout: HTTPSConnectionPool(host='mystic.the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(, 'Connection to mystic.the-eye.eu timed out. (connect timeout=10.0)'))`
And
```
from datasets import load_dataset
# This takes a few minutes to run, so go grab a tea or coffee while you wait :)
data_files = "https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst"
pubmed_dataset = load_dataset("json", data_files=data_files, split="train")
pubmed_dataset
```
Result:
`HTTPError: 404 Client Error: Not Found for URL: https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst`
### Expected behavior
Downloading as normal.
### Environment info
Environment info
`datasets` version: 2.9.0
Platform: Windows
Python version: 3.9.13
|
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6,054
|
Multi-processed `Dataset.map` slows down a lot when `import torch`
|
### Describe the bug
When using `Dataset.map` with `num_proc > 1`, the speed slows down much if I add `import torch` to the start of the script even though I don't use it.
I'm not sure if it's `torch` only or if any other package that is "large" will also cause the same result.
BTW, `import lightning` also slows it down.
Below are the progress bars of `Dataset.map`, the only difference between them is with or without `import torch`, but the speed varies by 6-7 times.
- without `import torch` 
- with `import torch` 
### Steps to reproduce the bug
Below is the code I used, but I don't think the dataset and the mapping function have much to do with the phenomenon.
```python3
from datasets import load_from_disk, disable_caching
from transformers import AutoTokenizer
# import torch
# import lightning
def rearrange_datapoints(
batch,
tokenizer,
sequence_length,
):
datapoints = []
input_ids = []
for x in batch['input_ids']:
input_ids += x
while len(input_ids) >= sequence_length:
datapoint = input_ids[:sequence_length]
datapoints.append(datapoint)
input_ids[:sequence_length] = []
if input_ids:
paddings = [-1] * (sequence_length - len(input_ids))
datapoint = paddings + input_ids if tokenizer.padding_side == 'left' else input_ids + paddings
datapoints.append(datapoint)
batch['input_ids'] = datapoints
return batch
if __name__ == '__main__':
disable_caching()
tokenizer = AutoTokenizer.from_pretrained('...', use_fast=False)
dataset = load_from_disk('...')
dataset = dataset.map(
rearrange_datapoints,
fn_kwargs=dict(
tokenizer=tokenizer,
sequence_length=2048,
),
batched=True,
num_proc=8,
)
```
### Expected behavior
The multi-processed `Dataset.map` function speed between with and without `import torch` should be the same.
### Environment info
- `datasets` version: 2.13.1
- Platform: Linux-3.10.0-1127.el7.x86_64-x86_64-with-glibc2.31
- Python version: 3.10.11
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.1
|
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[
"A duplicate of https://github.com/huggingface/datasets/issues/5929"
] |
6,053
|
Change package name from "datasets" to something less generic
|
### Feature request
I'm repeatedly finding myself in situations where I want to have a package called `datasets.py` or `evaluate.py` in my code and can't because those names are being taken up by Huggingface packages. While I can understand how (even from the user's perspective) it's aesthetically pleasing to have nice terse library names, ultimately a library hogging simple names like this is something I find short-sighted, impractical and at my most irritable, frankly rude.
My preference would be a pattern like what you get with all the other big libraries like numpy or pandas:
```
import huggingface as hf
# hf.transformers, hf.datasets, hf.evaluate
```
or things like
```
import huggingface.transformers as tf
# tf.load_model(), etc
```
If this isn't possible for some technical reason, at least just call the packages something like `hf_transformers` and so on.
I realize this is a very big change that's probably been discussed internally already, but I'm making this issue and sister issues on each huggingface project just to start the conversation and begin tracking community feeling on the matter, since I suspect I'm not the only one who feels like this.
Sorry if this has been requested already on this issue tracker, I couldn't find anything looking for terms like "package name".
Sister issues:
- [transformers](https://github.com/huggingface/transformers/issues/24934)
- **datasets**
- [evaluate](https://github.com/huggingface/evaluate/issues/476)
### Motivation
Not taking up package names the user is likely to want to use.
### Your contribution
No - more a matter of internal discussion among core library authors.
|
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[
"This would break a lot of existing code, so we can't really do this."
] |
6,052
|
Remove `HfFileSystem` and deprecate `S3FileSystem`
|
Remove the legacy `HfFileSystem` and deprecate `S3FileSystem`
cc @philschmid for the SageMaker scripts/notebooks that still use `datasets`' `S3FileSystem`
|
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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) | 0.006658 / 0.011353 (-0.004695) | 0.004347 / 0.011008 (-0.006661) | 0.084179 / 0.038508 (0.045671) | 0.080842 / 0.023109 (0.057733) | 0.321642 / 0.275898 (0.045744) | 0.348758 / 0.323480 (0.025278) | 0.005624 / 0.007986 (-0.002362) | 0.003479 / 0.004328 (-0.000850) | 0.065125 / 0.004250 (0.060875) | 0.057624 / 0.037052 (0.020572) | 0.323643 / 0.258489 (0.065154) | 0.360939 / 0.293841 (0.067098) | 0.031005 / 0.128546 (-0.097541) | 0.008618 / 0.075646 (-0.067028) | 0.287443 / 0.419271 (-0.131828) | 0.052640 / 0.043533 (0.009107) | 0.316947 / 0.255139 (0.061808) | 0.330292 / 0.283200 (0.047093) | 0.024393 / 0.141683 (-0.117289) | 1.476734 / 1.452155 (0.024579) | 1.534505 / 1.492716 (0.041789) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.273808 / 0.018006 (0.255802) | 0.591146 / 0.000490 (0.590656) | 0.000322 / 0.000200 (0.000122) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029992 / 0.037411 (-0.007419) | 0.086654 / 0.014526 (0.072129) | 0.098590 / 0.176557 (-0.077967) | 0.157225 / 0.737135 (-0.579910) | 0.101816 / 0.296338 (-0.194522) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.382578 / 0.215209 (0.167368) | 3.803576 / 2.077655 (1.725922) | 1.875136 / 1.504120 (0.371016) | 1.704207 / 1.541195 (0.163012) | 1.765146 / 1.468490 (0.296656) | 0.482802 / 4.584777 (-4.101975) | 3.571772 / 3.745712 (-0.173940) | 3.245626 / 5.269862 (-2.024235) | 2.051612 / 4.565676 (-2.514064) | 0.056539 / 0.424275 (-0.367736) | 0.007199 / 0.007607 (-0.000408) | 0.462445 / 0.226044 (0.236401) | 4.623800 / 2.268929 (2.354872) | 2.318948 / 55.444624 (-53.125677) | 1.971442 / 6.876477 (-4.905035) | 2.225444 / 2.142072 (0.083371) | 0.575205 / 4.805227 (-4.230022) | 0.129243 / 6.500664 (-6.371421) | 0.059036 / 0.075469 (-0.016433) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.266827 / 1.841788 (-0.574960) | 20.323419 / 8.074308 (12.249110) | 14.577603 / 10.191392 (4.386210) | 0.162131 / 0.680424 (-0.518293) | 0.018529 / 0.534201 (-0.515672) | 0.395046 / 0.579283 (-0.184237) | 0.410870 / 0.434364 (-0.023494) | 0.455782 / 0.540337 (-0.084556) | 0.662851 / 1.386936 (-0.724085) |\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) | 0.006867 / 0.011353 (-0.004486) | 0.004197 / 0.011008 (-0.006811) | 0.066060 / 0.038508 (0.027552) | 0.084145 / 0.023109 (0.061036) | 0.366740 / 0.275898 (0.090842) | 0.402362 / 0.323480 (0.078882) | 0.005785 / 0.007986 (-0.002200) | 0.003551 / 0.004328 (-0.000778) | 0.066177 / 0.004250 (0.061926) | 0.061521 / 0.037052 (0.024468) | 0.377807 / 0.258489 (0.119318) | 0.413490 / 0.293841 (0.119649) | 0.031918 / 0.128546 (-0.096628) | 0.008767 / 0.075646 (-0.066879) | 0.071437 / 0.419271 (-0.347835) | 0.049237 / 0.043533 (0.005704) | 0.365929 / 0.255139 (0.110790) | 0.393545 / 0.283200 (0.110346) | 0.024054 / 0.141683 (-0.117628) | 1.524599 / 1.452155 (0.072445) | 1.576592 / 1.492716 (0.083876) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.315181 / 0.018006 (0.297174) | 0.535501 / 0.000490 (0.535011) | 0.000410 / 0.000200 (0.000210) | 0.000054 / 0.000054 (-0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032915 / 0.037411 (-0.004497) | 0.089310 / 0.014526 (0.074784) | 0.105136 / 0.176557 (-0.071421) | 0.158572 / 0.737135 (-0.578563) | 0.106850 / 0.296338 (-0.189489) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419343 / 0.215209 (0.204134) | 4.200166 / 2.077655 (2.122511) | 2.180234 / 1.504120 (0.676114) | 2.016885 / 1.541195 (0.475690) | 2.131480 / 1.468490 (0.662990) | 0.484681 / 4.584777 (-4.100096) | 3.613535 / 3.745712 (-0.132177) | 5.762111 / 5.269862 (0.492249) | 3.190590 / 4.565676 (-1.375086) | 0.057403 / 0.424275 (-0.366872) | 0.007862 / 0.007607 (0.000255) | 0.490857 / 0.226044 (0.264813) | 4.911241 / 2.268929 (2.642313) | 2.650787 / 55.444624 (-52.793838) | 2.317060 / 6.876477 (-4.559416) | 2.579677 / 2.142072 (0.437605) | 0.587388 / 4.805227 (-4.217840) | 0.148109 / 6.500664 (-6.352555) | 0.061435 / 0.075469 (-0.014034) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.322181 / 1.841788 (-0.519606) | 20.647184 / 8.074308 (12.572875) | 14.907635 / 10.191392 (4.716243) | 0.156330 / 0.680424 (-0.524094) | 0.018719 / 0.534201 (-0.515482) | 0.397636 / 0.579283 (-0.181647) | 0.414107 / 0.434364 (-0.020257) | 0.460812 / 0.540337 (-0.079526) | 0.609568 / 1.386936 (-0.777368) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#74398c95b81a08a51457a2bef56efb7e608bded2 \"CML watermark\")\n",
"This would mean when i update my examples to newer datasets version i need to make a change right? nothing backward breaking? ",
"what would be the change i need to make? ",
"@philschmid You just need to replace the occurrences of `datasets.filesystems.S3FileSystem` with `s3fs.S3FileSystem`. From the moment it was added until now, `datasets.filesystems.S3FileSystem` is a \"dummy\" subclass of `s3fs.S3FileSystem` that only changes its docstring.\r\n\r\n\r\n",
"The CI is failing because I updated the YAML validation for https://github.com/huggingface/datasets/pull/6044.\r\nIt will be fixed once https://github.com/huggingface/datasets/pull/6044 is merged",
"I just merged the other PR so you should be good now",
"<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) | 0.006303 / 0.011353 (-0.005049) | 0.003746 / 0.011008 (-0.007262) | 0.081083 / 0.038508 (0.042575) | 0.067973 / 0.023109 (0.044864) | 0.322221 / 0.275898 (0.046323) | 0.359432 / 0.323480 (0.035952) | 0.004891 / 0.007986 (-0.003095) | 0.002988 / 0.004328 (-0.001341) | 0.064068 / 0.004250 (0.059818) | 0.052042 / 0.037052 (0.014990) | 0.323387 / 0.258489 (0.064898) | 0.390416 / 0.293841 (0.096575) | 0.028090 / 0.128546 (-0.100457) | 0.008009 / 0.075646 (-0.067638) | 0.262288 / 0.419271 (-0.156984) | 0.044986 / 0.043533 (0.001453) | 0.322319 / 0.255139 (0.067180) | 0.345323 / 0.283200 (0.062123) | 0.021798 / 0.141683 (-0.119885) | 1.417259 / 1.452155 (-0.034895) | 1.490050 / 1.492716 (-0.002667) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.195902 / 0.018006 (0.177896) | 0.490808 / 0.000490 (0.490318) | 0.002969 / 0.000200 (0.002770) | 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) | 0.025221 / 0.037411 (-0.012190) | 0.075341 / 0.014526 (0.060815) | 0.086703 / 0.176557 (-0.089853) | 0.146953 / 0.737135 (-0.590182) | 0.086610 / 0.296338 (-0.209728) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434890 / 0.215209 (0.219681) | 4.352283 / 2.077655 (2.274629) | 2.293098 / 1.504120 (0.788979) | 2.123023 / 1.541195 (0.581829) | 2.179722 / 1.468490 (0.711232) | 0.503851 / 4.584777 (-4.080926) | 3.087991 / 3.745712 (-0.657721) | 2.898689 / 5.269862 (-2.371173) | 1.902813 / 4.565676 (-2.662864) | 0.058079 / 0.424275 (-0.366196) | 0.006600 / 0.007607 (-0.001007) | 0.509243 / 0.226044 (0.283199) | 5.080204 / 2.268929 (2.811275) | 2.753594 / 55.444624 (-52.691030) | 2.417385 / 6.876477 (-4.459091) | 2.635470 / 2.142072 (0.493398) | 0.591059 / 4.805227 (-4.214168) | 0.126360 / 6.500664 (-6.374304) | 0.062108 / 0.075469 (-0.013361) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254398 / 1.841788 (-0.587390) | 18.866729 / 8.074308 (10.792420) | 14.120008 / 10.191392 (3.928616) | 0.152388 / 0.680424 (-0.528035) | 0.016997 / 0.534201 (-0.517204) | 0.336435 / 0.579283 (-0.242848) | 0.364612 / 0.434364 (-0.069752) | 0.391434 / 0.540337 (-0.148903) | 0.567180 / 1.386936 (-0.819756) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006477 / 0.011353 (-0.004876) | 0.003723 / 0.011008 (-0.007285) | 0.062712 / 0.038508 (0.024204) | 0.069380 / 0.023109 (0.046271) | 0.393394 / 0.275898 (0.117496) | 0.446903 / 0.323480 (0.123423) | 0.004833 / 0.007986 (-0.003153) | 0.002946 / 0.004328 (-0.001382) | 0.062076 / 0.004250 (0.057826) | 0.051589 / 0.037052 (0.014537) | 0.388536 / 0.258489 (0.130047) | 0.451406 / 0.293841 (0.157565) | 0.027824 / 0.128546 (-0.100722) | 0.008040 / 0.075646 (-0.067606) | 0.067085 / 0.419271 (-0.352187) | 0.042269 / 0.043533 (-0.001264) | 0.363419 / 0.255139 (0.108280) | 0.435201 / 0.283200 (0.152001) | 0.021275 / 0.141683 (-0.120408) | 1.439838 / 1.452155 (-0.012316) | 1.477279 / 1.492716 (-0.015437) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229667 / 0.018006 (0.211661) | 0.434101 / 0.000490 (0.433611) | 0.000652 / 0.000200 (0.000452) | 0.000060 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026141 / 0.037411 (-0.011271) | 0.078950 / 0.014526 (0.064424) | 0.090143 / 0.176557 (-0.086413) | 0.143941 / 0.737135 (-0.593195) | 0.090465 / 0.296338 (-0.205873) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.432042 / 0.215209 (0.216833) | 4.322134 / 2.077655 (2.244479) | 2.242897 / 1.504120 (0.738777) | 2.076351 / 1.541195 (0.535157) | 2.166739 / 1.468490 (0.698249) | 0.500833 / 4.584777 (-4.083944) | 3.140117 / 3.745712 (-0.605595) | 4.383050 / 5.269862 (-0.886812) | 2.548245 / 4.565676 (-2.017432) | 0.057521 / 0.424275 (-0.366754) | 0.006946 / 0.007607 (-0.000662) | 0.509613 / 0.226044 (0.283569) | 5.114052 / 2.268929 (2.845123) | 2.682112 / 55.444624 (-52.762512) | 2.362385 / 6.876477 (-4.514092) | 2.531787 / 2.142072 (0.389715) | 0.595085 / 4.805227 (-4.210142) | 0.130198 / 6.500664 (-6.370466) | 0.064057 / 0.075469 (-0.011412) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.346254 / 1.841788 (-0.495534) | 19.036911 / 8.074308 (10.962603) | 14.478689 / 10.191392 (4.287297) | 0.147541 / 0.680424 (-0.532883) | 0.016851 / 0.534201 (-0.517350) | 0.333901 / 0.579283 (-0.245382) | 0.380012 / 0.434364 (-0.054352) | 0.396021 / 0.540337 (-0.144317) | 0.540612 / 1.386936 (-0.846324) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#02dd4ccaf7971cd71d658ce9f62bc0c5cfc1e3ad \"CML watermark\")\n",
"CI failure is unrelated. Merging.",
"<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) | 0.009498 / 0.011353 (-0.001855) | 0.005639 / 0.011008 (-0.005369) | 0.108731 / 0.038508 (0.070223) | 0.094052 / 0.023109 (0.070943) | 0.454375 / 0.275898 (0.178477) | 0.486852 / 0.323480 (0.163372) | 0.006627 / 0.007986 (-0.001359) | 0.004712 / 0.004328 (0.000383) | 0.082006 / 0.004250 (0.077756) | 0.079394 / 0.037052 (0.042342) | 0.450982 / 0.258489 (0.192493) | 0.502659 / 0.293841 (0.208818) | 0.049741 / 0.128546 (-0.078806) | 0.014482 / 0.075646 (-0.061164) | 0.362661 / 0.419271 (-0.056611) | 0.068225 / 0.043533 (0.024692) | 0.456219 / 0.255139 (0.201080) | 0.483919 / 0.283200 (0.200719) | 0.044490 / 0.141683 (-0.097193) | 1.809420 / 1.452155 (0.357265) | 1.908859 / 1.492716 (0.416143) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.267350 / 0.018006 (0.249344) | 0.600403 / 0.000490 (0.599913) | 0.003665 / 0.000200 (0.003465) | 0.000162 / 0.000054 (0.000107) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032499 / 0.037411 (-0.004912) | 0.104829 / 0.014526 (0.090303) | 0.115809 / 0.176557 (-0.060747) | 0.191561 / 0.737135 (-0.545574) | 0.113454 / 0.296338 (-0.182885) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.599165 / 0.215209 (0.383956) | 5.802947 / 2.077655 (3.725292) | 2.477330 / 1.504120 (0.973210) | 2.231147 / 1.541195 (0.689952) | 2.365688 / 1.468490 (0.897197) | 0.853912 / 4.584777 (-3.730865) | 5.529472 / 3.745712 (1.783760) | 6.145286 / 5.269862 (0.875424) | 3.415871 / 4.565676 (-1.149805) | 0.099889 / 0.424275 (-0.324386) | 0.008933 / 0.007607 (0.001325) | 0.704643 / 0.226044 (0.478598) | 7.178101 / 2.268929 (4.909173) | 3.367120 / 55.444624 (-52.077504) | 2.795177 / 6.876477 (-4.081300) | 2.796798 / 2.142072 (0.654726) | 1.039097 / 4.805227 (-3.766130) | 0.232784 / 6.500664 (-6.267881) | 0.083608 / 0.075469 (0.008138) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.646827 / 1.841788 (-0.194961) | 25.003419 / 8.074308 (16.929111) | 22.165746 / 10.191392 (11.974354) | 0.246179 / 0.680424 (-0.434245) | 0.029304 / 0.534201 (-0.504897) | 0.500767 / 0.579283 (-0.078516) | 0.606501 / 0.434364 (0.172137) | 0.564092 / 0.540337 (0.023755) | 0.857568 / 1.386936 (-0.529368) |\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) | 0.009206 / 0.011353 (-0.002146) | 0.005084 / 0.011008 (-0.005925) | 0.081402 / 0.038508 (0.042894) | 0.088028 / 0.023109 (0.064919) | 0.539509 / 0.275898 (0.263611) | 0.590759 / 0.323480 (0.267280) | 0.006527 / 0.007986 (-0.001459) | 0.004375 / 0.004328 (0.000047) | 0.082327 / 0.004250 (0.078076) | 0.065442 / 0.037052 (0.028390) | 0.548254 / 0.258489 (0.289765) | 0.598388 / 0.293841 (0.304547) | 0.049409 / 0.128546 (-0.079137) | 0.014366 / 0.075646 (-0.061280) | 0.094568 / 0.419271 (-0.324703) | 0.063685 / 0.043533 (0.020152) | 0.545359 / 0.255139 (0.290220) | 0.573358 / 0.283200 (0.290159) | 0.036864 / 0.141683 (-0.104819) | 1.817985 / 1.452155 (0.365830) | 1.925188 / 1.492716 (0.432472) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.303205 / 0.018006 (0.285199) | 0.620981 / 0.000490 (0.620491) | 0.004910 / 0.000200 (0.004710) | 0.000104 / 0.000054 (0.000050) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033791 / 0.037411 (-0.003620) | 0.114974 / 0.014526 (0.100448) | 0.117682 / 0.176557 (-0.058875) | 0.177188 / 0.737135 (-0.559947) | 0.126425 / 0.296338 (-0.169914) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.636932 / 0.215209 (0.421723) | 6.289054 / 2.077655 (4.211399) | 2.920772 / 1.504120 (1.416652) | 2.672080 / 1.541195 (1.130885) | 2.712271 / 1.468490 (1.243781) | 0.889305 / 4.584777 (-3.695472) | 5.536018 / 3.745712 (1.790306) | 4.687564 / 5.269862 (-0.582298) | 3.204239 / 4.565676 (-1.361437) | 0.095546 / 0.424275 (-0.328729) | 0.008838 / 0.007607 (0.001231) | 0.714584 / 0.226044 (0.488540) | 7.482663 / 2.268929 (5.213735) | 3.621392 / 55.444624 (-51.823232) | 2.987777 / 6.876477 (-3.888700) | 3.312636 / 2.142072 (1.170564) | 1.033721 / 4.805227 (-3.771506) | 0.206292 / 6.500664 (-6.294372) | 0.079423 / 0.075469 (0.003953) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.798645 / 1.841788 (-0.043143) | 25.544329 / 8.074308 (17.470021) | 23.041318 / 10.191392 (12.849926) | 0.259067 / 0.680424 (-0.421357) | 0.029839 / 0.534201 (-0.504362) | 0.495583 / 0.579283 (-0.083700) | 0.598755 / 0.434364 (0.164391) | 0.574864 / 0.540337 (0.034527) | 0.831160 / 1.386936 (-0.555776) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4200443045e694a045446950e8b235c7beb6239e \"CML watermark\")\n"
] |
6,051
|
Skipping shard in the remote repo and resume upload
|
### Describe the bug
For some reason when I try to resume the upload of my dataset, it is very slow to reach the index of the shard from which to resume the uploading.
From my understanding, the problem is in this part of the code:
arrow_dataset.py
```python
for index, shard in logging.tqdm(
enumerate(itertools.chain([first_shard], shards_iter)),
desc="Pushing dataset shards to the dataset hub",
total=num_shards,
disable=not logging.is_progress_bar_enabled(),
):
shard_path_in_repo = path_in_repo(index, shard)
# Upload a shard only if it doesn't already exist in the repository
if shard_path_in_repo not in data_files:
```
In particular, iterating the generator is slow during the call:
```python
self._select_contiguous(start, length, new_fingerprint=new_fingerprint)
```
I wonder if it is possible to avoid calling this function for shards that are already uploaded and just start from the correct shard index.
### Steps to reproduce the bug
1. Start the upload
```python
dataset = load_dataset("imagefolder", data_dir=DATA_DIR, split="train", drop_labels=True)
dataset.push_to_hub("repo/name")
```
2. Stop and restart the upload after hundreds of shards
### Expected behavior
Skip the uploaded shards faster.
### Environment info
- `datasets` version: 2.5.1
- Platform: Linux-4.18.0-193.el8.x86_64-x86_64-with-glibc2.17
- Python version: 3.8.16
- PyArrow version: 12.0.1
- Pandas version: 2.0.2
|
[] |
{
"url": null,
"html_url": null,
"diff_url": null,
"patch_url": null,
"merged_at": null
}
| false
|
[
"Hi! `_select_contiguous` fetches a (zero-copy) slice of the dataset's Arrow table to build a shard, so I don't think this part is the problem. To me, the issue seems to be the step where we embed external image files' bytes (a lot of file reads). You can use `.map` with multiprocessing to perform this step before `push_to_hub` in a faster manner and cache it to disk:\r\n```python\r\nfrom datasets.table import embed_table_storage\r\n# load_dataset(...)\r\nformat = dataset.format\r\ndataset = dataset.with_format(\"arrow\")\r\ndataset = dataset.map(embed_table_storage, batched=True)\r\ndataset = dataset.with_format(**format)\r\n# push_to_hub(...)\r\n```\r\n\r\n(In Datasets 3.0, these external bytes will be written to an Arrow file when generating a dataset to avoid this \"embed\" step)",
"Hi, thanks, this solution saves some time.\r\nBut can't we avoid embedding all external image files bytes with each push, skipping the images that have already been pushed into the repo?\r\n\r\nEdit: Ok I missed the part of cache it manually on the disk the first time, this solves the problem. Thank you"
] |
6,049
|
Update `ruff` version in pre-commit config
|
so that it corresponds to the one that is being run in CI
|
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6049",
"html_url": "https://github.com/huggingface/datasets/pull/6049",
"diff_url": "https://github.com/huggingface/datasets/pull/6049.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6049.patch",
"merged_at": null
}
| true
|
[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6049). All of your documentation changes will be reflected on that endpoint.",
"I've updated the `ruff`'s pre-commit version as part of https://github.com/huggingface/datasets/pull/6434, so feel free to close this PR."
] |
6,048
|
when i use datasets.load_dataset, i encounter the http connect error!
|
### Describe the bug
`common_voice_test = load_dataset("audiofolder", data_dir="./dataset/",cache_dir="./cache",split=datasets.Split.TEST)`
when i run the code above, i got the error as below:
--------------------------------------------
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.3.2/datasets/audiofolder/audiofolder.py (ConnectionError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.3.2/datasets/audiofolder/audiofolder.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f299ed082e0>: Failed to establish a new connection: [Errno 101] Network is unreachable'))")))
--------------------------------------------------
My all data is on local machine, why does it need to connect the internet? how can i fix it, because my machine cannot connect the internet.
### Steps to reproduce the bug
1
### Expected behavior
no error when i use the load_dataset func
### Environment info
python=3.8.15
|
[] |
{
"url": null,
"html_url": null,
"diff_url": null,
"patch_url": null,
"merged_at": null
}
| false
|
[
"The `audiofolder` loader is not available in version `2.3.2`, hence the error. Please run the `pip install -U datasets` command to update the `datasets` installation to make `load_dataset(\"audiofolder\", ...)` work."
] |
6,047
|
Bump dev version
|
workaround to fix an issue with transformers CI
https://github.com/huggingface/transformers/pull/24867#discussion_r1266519626
|
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6047",
"html_url": "https://github.com/huggingface/datasets/pull/6047",
"diff_url": "https://github.com/huggingface/datasets/pull/6047.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6047.patch",
"merged_at": "2023-07-18T10:15:52"
}
| true
|
[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6047). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006384 / 0.011353 (-0.004969) | 0.003872 / 0.011008 (-0.007136) | 0.083454 / 0.038508 (0.044946) | 0.069120 / 0.023109 (0.046011) | 0.312573 / 0.275898 (0.036675) | 0.345814 / 0.323480 (0.022334) | 0.005729 / 0.007986 (-0.002257) | 0.003225 / 0.004328 (-0.001103) | 0.063950 / 0.004250 (0.059700) | 0.053998 / 0.037052 (0.016946) | 0.316492 / 0.258489 (0.058003) | 0.350738 / 0.293841 (0.056897) | 0.030770 / 0.128546 (-0.097776) | 0.008474 / 0.075646 (-0.067173) | 0.286989 / 0.419271 (-0.132282) | 0.052473 / 0.043533 (0.008940) | 0.314361 / 0.255139 (0.059222) | 0.335170 / 0.283200 (0.051970) | 0.022885 / 0.141683 (-0.118798) | 1.465430 / 1.452155 (0.013275) | 1.527799 / 1.492716 (0.035083) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209377 / 0.018006 (0.191371) | 0.455583 / 0.000490 (0.455094) | 0.003352 / 0.000200 (0.003152) | 0.000080 / 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) | 0.026284 / 0.037411 (-0.011127) | 0.080710 / 0.014526 (0.066185) | 0.091741 / 0.176557 (-0.084816) | 0.147602 / 0.737135 (-0.589534) | 0.091173 / 0.296338 (-0.205166) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.386592 / 0.215209 (0.171383) | 3.856665 / 2.077655 (1.779011) | 1.835745 / 1.504120 (0.331625) | 1.671814 / 1.541195 (0.130619) | 1.711224 / 1.468490 (0.242734) | 0.484704 / 4.584777 (-4.100073) | 3.649239 / 3.745712 (-0.096473) | 3.784051 / 5.269862 (-1.485810) | 2.241195 / 4.565676 (-2.324482) | 0.056613 / 0.424275 (-0.367662) | 0.007140 / 0.007607 (-0.000467) | 0.464585 / 0.226044 (0.238540) | 4.616537 / 2.268929 (2.347609) | 2.371969 / 55.444624 (-53.072656) | 1.977754 / 6.876477 (-4.898723) | 2.083385 / 2.142072 (-0.058687) | 0.582330 / 4.805227 (-4.222897) | 0.132744 / 6.500664 (-6.367920) | 0.059822 / 0.075469 (-0.015647) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.259566 / 1.841788 (-0.582221) | 18.990166 / 8.074308 (10.915858) | 13.992069 / 10.191392 (3.800677) | 0.160001 / 0.680424 (-0.520423) | 0.018622 / 0.534201 (-0.515579) | 0.392921 / 0.579283 (-0.186362) | 0.418225 / 0.434364 (-0.016139) | 0.471252 / 0.540337 (-0.069086) | 0.653227 / 1.386936 (-0.733709) |\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) | 0.006641 / 0.011353 (-0.004712) | 0.003738 / 0.011008 (-0.007271) | 0.064053 / 0.038508 (0.025545) | 0.069467 / 0.023109 (0.046357) | 0.360625 / 0.275898 (0.084727) | 0.394291 / 0.323480 (0.070811) | 0.005236 / 0.007986 (-0.002750) | 0.003304 / 0.004328 (-0.001024) | 0.064078 / 0.004250 (0.059827) | 0.054605 / 0.037052 (0.017552) | 0.374567 / 0.258489 (0.116078) | 0.411227 / 0.293841 (0.117386) | 0.031614 / 0.128546 (-0.096933) | 0.008323 / 0.075646 (-0.067324) | 0.070616 / 0.419271 (-0.348656) | 0.050077 / 0.043533 (0.006544) | 0.362229 / 0.255139 (0.107090) | 0.388310 / 0.283200 (0.105110) | 0.024053 / 0.141683 (-0.117630) | 1.508913 / 1.452155 (0.056759) | 1.562140 / 1.492716 (0.069423) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230172 / 0.018006 (0.212165) | 0.449363 / 0.000490 (0.448873) | 0.002374 / 0.000200 (0.002174) | 0.000097 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029813 / 0.037411 (-0.007598) | 0.087298 / 0.014526 (0.072772) | 0.096712 / 0.176557 (-0.079845) | 0.152864 / 0.737135 (-0.584271) | 0.098204 / 0.296338 (-0.198135) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.408664 / 0.215209 (0.193455) | 4.075068 / 2.077655 (1.997414) | 2.096365 / 1.504120 (0.592245) | 1.936096 / 1.541195 (0.394901) | 1.961872 / 1.468490 (0.493382) | 0.483383 / 4.584777 (-4.101394) | 3.686926 / 3.745712 (-0.058787) | 4.798824 / 5.269862 (-0.471037) | 2.652279 / 4.565676 (-1.913398) | 0.056695 / 0.424275 (-0.367580) | 0.007592 / 0.007607 (-0.000016) | 0.484710 / 0.226044 (0.258665) | 4.842153 / 2.268929 (2.573225) | 2.636828 / 55.444624 (-52.807796) | 2.243666 / 6.876477 (-4.632811) | 2.375972 / 2.142072 (0.233899) | 0.578544 / 4.805227 (-4.226683) | 0.132579 / 6.500664 (-6.368085) | 0.061287 / 0.075469 (-0.014182) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.360287 / 1.841788 (-0.481501) | 19.464110 / 8.074308 (11.389802) | 14.530875 / 10.191392 (4.339483) | 0.149479 / 0.680424 (-0.530944) | 0.018471 / 0.534201 (-0.515730) | 0.395399 / 0.579283 (-0.183884) | 0.412897 / 0.434364 (-0.021467) | 0.465194 / 0.540337 (-0.075144) | 0.611752 / 1.386936 (-0.775184) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79a535de98b590da7bc223a6498c59790882f14a \"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) | 0.008986 / 0.011353 (-0.002367) | 0.005104 / 0.011008 (-0.005905) | 0.108371 / 0.038508 (0.069863) | 0.091655 / 0.023109 (0.068546) | 0.430183 / 0.275898 (0.154285) | 0.481387 / 0.323480 (0.157907) | 0.006662 / 0.007986 (-0.001324) | 0.004681 / 0.004328 (0.000353) | 0.089325 / 0.004250 (0.085075) | 0.065096 / 0.037052 (0.028044) | 0.435021 / 0.258489 (0.176532) | 0.478635 / 0.293841 (0.184794) | 0.047628 / 0.128546 (-0.080918) | 0.013496 / 0.075646 (-0.062150) | 0.389661 / 0.419271 (-0.029611) | 0.082260 / 0.043533 (0.038727) | 0.474165 / 0.255139 (0.219026) | 0.464877 / 0.283200 (0.181677) | 0.039784 / 0.141683 (-0.101899) | 1.874694 / 1.452155 (0.422539) | 1.980183 / 1.492716 (0.487467) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.254044 / 0.018006 (0.236038) | 0.631495 / 0.000490 (0.631005) | 0.000628 / 0.000200 (0.000428) | 0.000086 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038773 / 0.037411 (0.001362) | 0.103681 / 0.014526 (0.089156) | 0.125081 / 0.176557 (-0.051476) | 0.198345 / 0.737135 (-0.538790) | 0.122217 / 0.296338 (-0.174121) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.611677 / 0.215209 (0.396468) | 6.220790 / 2.077655 (4.143135) | 2.729858 / 1.504120 (1.225739) | 2.351944 / 1.541195 (0.810749) | 2.449137 / 1.468490 (0.980647) | 0.896842 / 4.584777 (-3.687935) | 5.537491 / 3.745712 (1.791778) | 8.480182 / 5.269862 (3.210320) | 5.251404 / 4.565676 (0.685728) | 0.100449 / 0.424275 (-0.323826) | 0.009008 / 0.007607 (0.001401) | 0.750060 / 0.226044 (0.524016) | 7.390940 / 2.268929 (5.122011) | 3.478256 / 55.444624 (-51.966369) | 2.883597 / 6.876477 (-3.992880) | 3.082256 / 2.142072 (0.940183) | 1.114339 / 4.805227 (-3.690889) | 0.225389 / 6.500664 (-6.275275) | 0.083972 / 0.075469 (0.008503) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.741522 / 1.841788 (-0.100266) | 25.674700 / 8.074308 (17.600392) | 24.324412 / 10.191392 (14.133020) | 0.257878 / 0.680424 (-0.422546) | 0.038384 / 0.534201 (-0.495817) | 0.508302 / 0.579283 (-0.070981) | 0.612979 / 0.434364 (0.178615) | 0.584366 / 0.540337 (0.044029) | 0.881115 / 1.386936 (-0.505821) |\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) | 0.009114 / 0.011353 (-0.002239) | 0.005333 / 0.011008 (-0.005675) | 0.094944 / 0.038508 (0.056436) | 0.099178 / 0.023109 (0.076068) | 0.529813 / 0.275898 (0.253915) | 0.551282 / 0.323480 (0.227802) | 0.006442 / 0.007986 (-0.001543) | 0.004283 / 0.004328 (-0.000045) | 0.084257 / 0.004250 (0.080007) | 0.067557 / 0.037052 (0.030504) | 0.514733 / 0.258489 (0.256244) | 0.568200 / 0.293841 (0.274359) | 0.050969 / 0.128546 (-0.077577) | 0.014495 / 0.075646 (-0.061151) | 0.097089 / 0.419271 (-0.322182) | 0.063142 / 0.043533 (0.019609) | 0.513327 / 0.255139 (0.258188) | 0.520593 / 0.283200 (0.237394) | 0.036824 / 0.141683 (-0.104859) | 1.954875 / 1.452155 (0.502720) | 1.976307 / 1.492716 (0.483591) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.304070 / 0.018006 (0.286063) | 0.611073 / 0.000490 (0.610583) | 0.005027 / 0.000200 (0.004827) | 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) | 0.037993 / 0.037411 (0.000582) | 0.115876 / 0.014526 (0.101350) | 0.118087 / 0.176557 (-0.058469) | 0.186437 / 0.737135 (-0.550699) | 0.129883 / 0.296338 (-0.166456) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.658292 / 0.215209 (0.443083) | 6.618257 / 2.077655 (4.540602) | 3.203786 / 1.504120 (1.699667) | 2.858714 / 1.541195 (1.317519) | 2.940974 / 1.468490 (1.472484) | 0.856238 / 4.584777 (-3.728538) | 5.427708 / 3.745712 (1.681996) | 4.810048 / 5.269862 (-0.459813) | 3.120006 / 4.565676 (-1.445671) | 0.098098 / 0.424275 (-0.326177) | 0.010077 / 0.007607 (0.002470) | 0.790890 / 0.226044 (0.564845) | 7.956679 / 2.268929 (5.687750) | 3.955710 / 55.444624 (-51.488914) | 3.446419 / 6.876477 (-3.430057) | 3.541228 / 2.142072 (1.399156) | 1.013420 / 4.805227 (-3.791808) | 0.213741 / 6.500664 (-6.286923) | 0.080857 / 0.075469 (0.005388) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.813265 / 1.841788 (-0.028522) | 25.965199 / 8.074308 (17.890891) | 21.892761 / 10.191392 (11.701369) | 0.257843 / 0.680424 (-0.422580) | 0.029388 / 0.534201 (-0.504813) | 0.510609 / 0.579283 (-0.068674) | 0.626579 / 0.434364 (0.192215) | 0.576865 / 0.540337 (0.036528) | 0.826610 / 1.386936 (-0.560326) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a1a9c00249b330f97f66ceb86c2939261091f4fe \"CML watermark\")\n"
] |
6,046
|
Support proxy and user-agent in fsspec calls
|
Since we switched to the new HfFileSystem we no longer apply user's proxy and user-agent.
Using the HTTP_PROXY and HTTPS_PROXY environment variables works though since we use aiohttp to call the HF Hub.
This can be implemented in `_prepare_single_hop_path_and_storage_options`.
Though ideally the `HfFileSystem` could support passing at least the proxies
|
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[
"hii @lhoestq can you assign this issue to me?\r\n",
"You can reply \"#self-assign\" to this issue to automatically get assigned to it :)\r\nLet me know if you have any questions or if I can help",
"#2289 ",
"Actually i am quite new to figure it out how everything goes and done \r\n\r\n> You can reply \"#self-assign\" to this issue to automatically get assigned to it :)\r\n> Let me know if you have any questions or if I can help\r\n\r\nwhen i wrote #self-assign it automatically got converted to some number is it correct or i have done it some wrong way, I am quite new to open source thus wanna try to learn and explore it",
"#2289 #self-assign ",
"Ah yea github tries to replace the #self-assign with an issue link. I guess you can try to copy-paste instead to see if it works\r\n\r\nAnyway let me assign you manually",
"thanks a lot @lhoestq ! though i have a very lil idea of the issue, i am new. as i said before, but gonna try my best shot to do it.\r\ncan you please suggest some tips or anything from your side, how basically we approach it will be really helpfull.\r\nWill try my best!",
"The HfFileSystem from the `huggingface_hub` package can already read the HTTP_PROXY and HTTPS_PROXY environment variables. So the remaining thing missing is the `user_agent` that the user may include in a `DownloadConfig` object. The user agent can be used for regular http calls but also calls to the HfFileSystem.\r\n\r\n- for http, the `user_agent` isn't passed from `DownloadConfig` to `get_datasets_user_agent` in `_prepare_single_hop_path_and_storage_options` in `streaming_download_manager.py` so we need to include it\r\n- for HfFileSystem I think it requires a PR in https://github.com/huggingface/huggingface_hub to include it in the `HfFileSystem.__init__`"
] |
6,045
|
Check if column names match in Parquet loader only when config `features` are specified
|
Fix #6039
|
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6045",
"html_url": "https://github.com/huggingface/datasets/pull/6045",
"diff_url": "https://github.com/huggingface/datasets/pull/6045.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6045.patch",
"merged_at": "2023-07-24T14:35:03"
}
| true
|
[
"_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) | 0.006557 / 0.011353 (-0.004796) | 0.004096 / 0.011008 (-0.006913) | 0.083577 / 0.038508 (0.045069) | 0.072092 / 0.023109 (0.048983) | 0.319192 / 0.275898 (0.043294) | 0.351845 / 0.323480 (0.028365) | 0.005475 / 0.007986 (-0.002511) | 0.003419 / 0.004328 (-0.000910) | 0.064562 / 0.004250 (0.060311) | 0.057930 / 0.037052 (0.020878) | 0.326085 / 0.258489 (0.067596) | 0.368316 / 0.293841 (0.074475) | 0.030502 / 0.128546 (-0.098044) | 0.008504 / 0.075646 (-0.067142) | 0.287217 / 0.419271 (-0.132054) | 0.052337 / 0.043533 (0.008804) | 0.319011 / 0.255139 (0.063872) | 0.352711 / 0.283200 (0.069511) | 0.023278 / 0.141683 (-0.118405) | 1.482578 / 1.452155 (0.030423) | 1.553391 / 1.492716 (0.060675) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.199628 / 0.018006 (0.181622) | 0.464571 / 0.000490 (0.464081) | 0.003512 / 0.000200 (0.003312) | 0.000072 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029109 / 0.037411 (-0.008302) | 0.082203 / 0.014526 (0.067677) | 0.096223 / 0.176557 (-0.080333) | 0.155598 / 0.737135 (-0.581537) | 0.097738 / 0.296338 (-0.198600) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.386135 / 0.215209 (0.170926) | 3.837157 / 2.077655 (1.759502) | 1.836869 / 1.504120 (0.332750) | 1.680592 / 1.541195 (0.139398) | 1.769456 / 1.468490 (0.300966) | 0.493150 / 4.584777 (-4.091627) | 3.589797 / 3.745712 (-0.155915) | 3.330000 / 5.269862 (-1.939861) | 2.059856 / 4.565676 (-2.505821) | 0.057951 / 0.424275 (-0.366324) | 0.007340 / 0.007607 (-0.000267) | 0.463203 / 0.226044 (0.237159) | 4.631514 / 2.268929 (2.362585) | 2.329887 / 55.444624 (-53.114738) | 2.008815 / 6.876477 (-4.867662) | 2.199067 / 2.142072 (0.056995) | 0.591417 / 4.805227 (-4.213810) | 0.137154 / 6.500664 (-6.363510) | 0.061326 / 0.075469 (-0.014143) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.269676 / 1.841788 (-0.572111) | 19.375167 / 8.074308 (11.300858) | 13.945419 / 10.191392 (3.754027) | 0.146482 / 0.680424 (-0.533942) | 0.018257 / 0.534201 (-0.515944) | 0.391684 / 0.579283 (-0.187599) | 0.411454 / 0.434364 (-0.022910) | 0.466260 / 0.540337 (-0.074077) | 0.655571 / 1.386936 (-0.731365) |\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) | 0.006619 / 0.011353 (-0.004734) | 0.004102 / 0.011008 (-0.006907) | 0.064848 / 0.038508 (0.026340) | 0.074822 / 0.023109 (0.051713) | 0.366535 / 0.275898 (0.090637) | 0.395873 / 0.323480 (0.072394) | 0.005315 / 0.007986 (-0.002670) | 0.003270 / 0.004328 (-0.001059) | 0.064829 / 0.004250 (0.060578) | 0.056094 / 0.037052 (0.019042) | 0.370355 / 0.258489 (0.111866) | 0.406837 / 0.293841 (0.112996) | 0.031634 / 0.128546 (-0.096912) | 0.008569 / 0.075646 (-0.067077) | 0.071126 / 0.419271 (-0.348145) | 0.048629 / 0.043533 (0.005096) | 0.365175 / 0.255139 (0.110036) | 0.385234 / 0.283200 (0.102034) | 0.023295 / 0.141683 (-0.118388) | 1.466907 / 1.452155 (0.014752) | 1.523118 / 1.492716 (0.030401) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227872 / 0.018006 (0.209866) | 0.451573 / 0.000490 (0.451083) | 0.000379 / 0.000200 (0.000179) | 0.000055 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029496 / 0.037411 (-0.007915) | 0.086614 / 0.014526 (0.072088) | 0.098165 / 0.176557 (-0.078392) | 0.152218 / 0.737135 (-0.584917) | 0.101215 / 0.296338 (-0.195123) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.407519 / 0.215209 (0.192310) | 4.074704 / 2.077655 (1.997049) | 2.113185 / 1.504120 (0.609065) | 1.947461 / 1.541195 (0.406266) | 1.998521 / 1.468490 (0.530031) | 0.487463 / 4.584777 (-4.097313) | 3.465423 / 3.745712 (-0.280289) | 3.376498 / 5.269862 (-1.893363) | 2.001533 / 4.565676 (-2.564144) | 0.057052 / 0.424275 (-0.367223) | 0.007325 / 0.007607 (-0.000283) | 0.485648 / 0.226044 (0.259604) | 4.860191 / 2.268929 (2.591262) | 2.550340 / 55.444624 (-52.894284) | 2.231136 / 6.876477 (-4.645341) | 2.262539 / 2.142072 (0.120467) | 0.591422 / 4.805227 (-4.213805) | 0.132875 / 6.500664 (-6.367789) | 0.062154 / 0.075469 (-0.013315) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.321834 / 1.841788 (-0.519954) | 19.734750 / 8.074308 (11.660442) | 14.681049 / 10.191392 (4.489657) | 0.148894 / 0.680424 (-0.531530) | 0.018414 / 0.534201 (-0.515787) | 0.393377 / 0.579283 (-0.185906) | 0.402795 / 0.434364 (-0.031569) | 0.478624 / 0.540337 (-0.061714) | 0.656767 / 1.386936 (-0.730169) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a5a84a1fa226a4cafb3bb4387dc4b212a46caf31 \"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) | 0.007012 / 0.011353 (-0.004341) | 0.004120 / 0.011008 (-0.006888) | 0.083720 / 0.038508 (0.045212) | 0.083105 / 0.023109 (0.059996) | 0.323803 / 0.275898 (0.047905) | 0.340345 / 0.323480 (0.016865) | 0.005872 / 0.007986 (-0.002113) | 0.003528 / 0.004328 (-0.000801) | 0.065185 / 0.004250 (0.060935) | 0.063092 / 0.037052 (0.026040) | 0.314900 / 0.258489 (0.056411) | 0.349251 / 0.293841 (0.055410) | 0.031612 / 0.128546 (-0.096934) | 0.008541 / 0.075646 (-0.067105) | 0.289865 / 0.419271 (-0.129407) | 0.055264 / 0.043533 (0.011731) | 0.309152 / 0.255139 (0.054013) | 0.332625 / 0.283200 (0.049425) | 0.024306 / 0.141683 (-0.117377) | 1.489191 / 1.452155 (0.037037) | 1.562447 / 1.492716 (0.069731) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236681 / 0.018006 (0.218675) | 0.567767 / 0.000490 (0.567277) | 0.003022 / 0.000200 (0.002822) | 0.000218 / 0.000054 (0.000164) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028698 / 0.037411 (-0.008714) | 0.081681 / 0.014526 (0.067155) | 0.099109 / 0.176557 (-0.077447) | 0.154381 / 0.737135 (-0.582754) | 0.098691 / 0.296338 (-0.197648) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.397985 / 0.215209 (0.182776) | 3.962499 / 2.077655 (1.884844) | 1.936158 / 1.504120 (0.432038) | 1.762339 / 1.541195 (0.221144) | 1.837451 / 1.468490 (0.368961) | 0.485655 / 4.584777 (-4.099122) | 3.538341 / 3.745712 (-0.207371) | 5.110095 / 5.269862 (-0.159767) | 3.066152 / 4.565676 (-1.499524) | 0.057505 / 0.424275 (-0.366770) | 0.007334 / 0.007607 (-0.000273) | 0.475622 / 0.226044 (0.249578) | 4.754091 / 2.268929 (2.485162) | 2.431379 / 55.444624 (-53.013246) | 2.106178 / 6.876477 (-4.770298) | 2.364305 / 2.142072 (0.222232) | 0.614038 / 4.805227 (-4.191190) | 0.148530 / 6.500664 (-6.352134) | 0.061033 / 0.075469 (-0.014436) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.242345 / 1.841788 (-0.599443) | 19.017266 / 8.074308 (10.942958) | 13.477782 / 10.191392 (3.286390) | 0.158513 / 0.680424 (-0.521911) | 0.018757 / 0.534201 (-0.515444) | 0.393773 / 0.579283 (-0.185510) | 0.416933 / 0.434364 (-0.017431) | 0.460012 / 0.540337 (-0.080326) | 0.637010 / 1.386936 (-0.749926) |\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) | 0.006689 / 0.011353 (-0.004664) | 0.004168 / 0.011008 (-0.006840) | 0.065009 / 0.038508 (0.026501) | 0.073766 / 0.023109 (0.050657) | 0.369585 / 0.275898 (0.093687) | 0.407945 / 0.323480 (0.084465) | 0.005583 / 0.007986 (-0.002403) | 0.003494 / 0.004328 (-0.000835) | 0.065032 / 0.004250 (0.060782) | 0.057166 / 0.037052 (0.020114) | 0.370656 / 0.258489 (0.112166) | 0.428381 / 0.293841 (0.134540) | 0.031653 / 0.128546 (-0.096893) | 0.008731 / 0.075646 (-0.066915) | 0.071624 / 0.419271 (-0.347648) | 0.049364 / 0.043533 (0.005832) | 0.361824 / 0.255139 (0.106685) | 0.387615 / 0.283200 (0.104415) | 0.023228 / 0.141683 (-0.118455) | 1.476204 / 1.452155 (0.024049) | 1.553522 / 1.492716 (0.060806) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.266955 / 0.018006 (0.248948) | 0.556566 / 0.000490 (0.556076) | 0.000399 / 0.000200 (0.000199) | 0.000056 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033104 / 0.037411 (-0.004307) | 0.088067 / 0.014526 (0.073541) | 0.103333 / 0.176557 (-0.073224) | 0.157061 / 0.737135 (-0.580074) | 0.105007 / 0.296338 (-0.191331) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420826 / 0.215209 (0.205617) | 4.201656 / 2.077655 (2.124001) | 2.208336 / 1.504120 (0.704216) | 2.043780 / 1.541195 (0.502585) | 2.156215 / 1.468490 (0.687725) | 0.490485 / 4.584777 (-4.094292) | 3.611446 / 3.745712 (-0.134267) | 5.293140 / 5.269862 (0.023279) | 2.739778 / 4.565676 (-1.825899) | 0.058175 / 0.424275 (-0.366100) | 0.007633 / 0.007607 (0.000026) | 0.500773 / 0.226044 (0.274729) | 5.000900 / 2.268929 (2.731971) | 2.721200 / 55.444624 (-52.723424) | 2.349381 / 6.876477 (-4.527095) | 2.386261 / 2.142072 (0.244188) | 0.583174 / 4.805227 (-4.222053) | 0.134558 / 6.500664 (-6.366106) | 0.062157 / 0.075469 (-0.013312) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.351087 / 1.841788 (-0.490701) | 20.305703 / 8.074308 (12.231395) | 14.548518 / 10.191392 (4.357126) | 0.173720 / 0.680424 (-0.506704) | 0.018100 / 0.534201 (-0.516101) | 0.395187 / 0.579283 (-0.184097) | 0.414619 / 0.434364 (-0.019745) | 0.462515 / 0.540337 (-0.077823) | 0.617822 / 1.386936 (-0.769114) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#033d0a9de5c825fc9a6a9ce3c3d80eaab3493720 \"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) | 0.006909 / 0.011353 (-0.004444) | 0.003954 / 0.011008 (-0.007054) | 0.084329 / 0.038508 (0.045821) | 0.074919 / 0.023109 (0.051809) | 0.319350 / 0.275898 (0.043451) | 0.347264 / 0.323480 (0.023785) | 0.005326 / 0.007986 (-0.002660) | 0.003323 / 0.004328 (-0.001006) | 0.064286 / 0.004250 (0.060036) | 0.054748 / 0.037052 (0.017696) | 0.324784 / 0.258489 (0.066295) | 0.361445 / 0.293841 (0.067605) | 0.031239 / 0.128546 (-0.097308) | 0.008361 / 0.075646 (-0.067286) | 0.287482 / 0.419271 (-0.131789) | 0.052093 / 0.043533 (0.008560) | 0.321454 / 0.255139 (0.066315) | 0.337999 / 0.283200 (0.054800) | 0.025807 / 0.141683 (-0.115876) | 1.501838 / 1.452155 (0.049683) | 1.574484 / 1.492716 (0.081767) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.193220 / 0.018006 (0.175214) | 0.448105 / 0.000490 (0.447615) | 0.002949 / 0.000200 (0.002749) | 0.000071 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028517 / 0.037411 (-0.008894) | 0.087281 / 0.014526 (0.072755) | 0.098295 / 0.176557 (-0.078262) | 0.156972 / 0.737135 (-0.580163) | 0.101250 / 0.296338 (-0.195088) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.383734 / 0.215209 (0.168525) | 3.821293 / 2.077655 (1.743638) | 1.866487 / 1.504120 (0.362367) | 1.722195 / 1.541195 (0.181000) | 1.843762 / 1.468490 (0.375272) | 0.484813 / 4.584777 (-4.099964) | 3.535381 / 3.745712 (-0.210331) | 5.502338 / 5.269862 (0.232477) | 3.256078 / 4.565676 (-1.309599) | 0.057312 / 0.424275 (-0.366963) | 0.007305 / 0.007607 (-0.000302) | 0.461523 / 0.226044 (0.235479) | 4.611828 / 2.268929 (2.342899) | 2.337180 / 55.444624 (-53.107445) | 2.040956 / 6.876477 (-4.835521) | 2.241233 / 2.142072 (0.099160) | 0.583727 / 4.805227 (-4.221500) | 0.132427 / 6.500664 (-6.368237) | 0.060306 / 0.075469 (-0.015163) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.282223 / 1.841788 (-0.559565) | 19.439745 / 8.074308 (11.365437) | 13.627657 / 10.191392 (3.436265) | 0.158975 / 0.680424 (-0.521449) | 0.018599 / 0.534201 (-0.515601) | 0.391136 / 0.579283 (-0.188147) | 0.410947 / 0.434364 (-0.023417) | 0.453889 / 0.540337 (-0.086448) | 0.620928 / 1.386936 (-0.766008) |\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) | 0.006428 / 0.011353 (-0.004925) | 0.003980 / 0.011008 (-0.007028) | 0.065006 / 0.038508 (0.026498) | 0.076541 / 0.023109 (0.053432) | 0.358518 / 0.275898 (0.082620) | 0.394397 / 0.323480 (0.070917) | 0.005845 / 0.007986 (-0.002140) | 0.003258 / 0.004328 (-0.001071) | 0.064436 / 0.004250 (0.060186) | 0.056691 / 0.037052 (0.019639) | 0.367369 / 0.258489 (0.108880) | 0.420345 / 0.293841 (0.126504) | 0.031047 / 0.128546 (-0.097499) | 0.008430 / 0.075646 (-0.067216) | 0.071280 / 0.419271 (-0.347991) | 0.048872 / 0.043533 (0.005339) | 0.360073 / 0.255139 (0.104934) | 0.384150 / 0.283200 (0.100951) | 0.023189 / 0.141683 (-0.118494) | 1.500251 / 1.452155 (0.048096) | 1.545910 / 1.492716 (0.053194) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224861 / 0.018006 (0.206855) | 0.439901 / 0.000490 (0.439411) | 0.000372 / 0.000200 (0.000172) | 0.000054 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029914 / 0.037411 (-0.007497) | 0.086916 / 0.014526 (0.072390) | 0.099527 / 0.176557 (-0.077029) | 0.153031 / 0.737135 (-0.584104) | 0.100008 / 0.296338 (-0.196330) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420305 / 0.215209 (0.205096) | 4.198224 / 2.077655 (2.120569) | 2.223807 / 1.504120 (0.719687) | 2.058475 / 1.541195 (0.517280) | 2.140405 / 1.468490 (0.671915) | 0.481224 / 4.584777 (-4.103553) | 3.593767 / 3.745712 (-0.151945) | 5.536710 / 5.269862 (0.266849) | 3.162048 / 4.565676 (-1.403629) | 0.056662 / 0.424275 (-0.367614) | 0.007301 / 0.007607 (-0.000306) | 0.507494 / 0.226044 (0.281450) | 5.047824 / 2.268929 (2.778896) | 2.715167 / 55.444624 (-52.729458) | 2.334916 / 6.876477 (-4.541560) | 2.406615 / 2.142072 (0.264543) | 0.572761 / 4.805227 (-4.232466) | 0.131248 / 6.500664 (-6.369416) | 0.062401 / 0.075469 (-0.013068) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.375896 / 1.841788 (-0.465892) | 19.836638 / 8.074308 (11.762329) | 14.246645 / 10.191392 (4.055253) | 0.164975 / 0.680424 (-0.515449) | 0.018293 / 0.534201 (-0.515908) | 0.394196 / 0.579283 (-0.185087) | 0.405895 / 0.434364 (-0.028469) | 0.459221 / 0.540337 (-0.081116) | 0.609898 / 1.386936 (-0.777038) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f89210ad839c2225b64822dfa248f68ab29ad46f \"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) | 0.008463 / 0.011353 (-0.002890) | 0.004754 / 0.011008 (-0.006254) | 0.103574 / 0.038508 (0.065066) | 0.083541 / 0.023109 (0.060432) | 0.402498 / 0.275898 (0.126600) | 0.434944 / 0.323480 (0.111465) | 0.005766 / 0.007986 (-0.002219) | 0.003823 / 0.004328 (-0.000505) | 0.078433 / 0.004250 (0.074183) | 0.056948 / 0.037052 (0.019895) | 0.392539 / 0.258489 (0.134050) | 0.447226 / 0.293841 (0.153385) | 0.045845 / 0.128546 (-0.082701) | 0.014043 / 0.075646 (-0.061603) | 0.355768 / 0.419271 (-0.063503) | 0.065492 / 0.043533 (0.021960) | 0.408047 / 0.255139 (0.152908) | 0.468313 / 0.283200 (0.185113) | 0.033779 / 0.141683 (-0.107904) | 1.772198 / 1.452155 (0.320043) | 1.889127 / 1.492716 (0.396411) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.207107 / 0.018006 (0.189101) | 0.533261 / 0.000490 (0.532771) | 0.000864 / 0.000200 (0.000664) | 0.000105 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032139 / 0.037411 (-0.005272) | 0.102002 / 0.014526 (0.087476) | 0.108780 / 0.176557 (-0.067777) | 0.202857 / 0.737135 (-0.534278) | 0.110378 / 0.296338 (-0.185960) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.582814 / 0.215209 (0.367605) | 5.870683 / 2.077655 (3.793028) | 2.510290 / 1.504120 (1.006171) | 2.146337 / 1.541195 (0.605142) | 2.239278 / 1.468490 (0.770788) | 0.861205 / 4.584777 (-3.723572) | 5.177394 / 3.745712 (1.431682) | 8.550713 / 5.269862 (3.280852) | 4.867715 / 4.565676 (0.302038) | 0.096665 / 0.424275 (-0.327610) | 0.008702 / 0.007607 (0.001095) | 0.748908 / 0.226044 (0.522863) | 7.302815 / 2.268929 (5.033887) | 3.205045 / 55.444624 (-52.239580) | 2.743914 / 6.876477 (-4.132562) | 2.831240 / 2.142072 (0.689167) | 1.103912 / 4.805227 (-3.701315) | 0.246075 / 6.500664 (-6.254589) | 0.092092 / 0.075469 (0.016623) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.591331 / 1.841788 (-0.250457) | 23.085848 / 8.074308 (15.011540) | 22.887963 / 10.191392 (12.696571) | 0.212735 / 0.680424 (-0.467689) | 0.027400 / 0.534201 (-0.506801) | 0.493822 / 0.579283 (-0.085461) | 0.574485 / 0.434364 (0.140121) | 0.574873 / 0.540337 (0.034536) | 0.826178 / 1.386936 (-0.560758) |\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) | 0.009155 / 0.011353 (-0.002198) | 0.004976 / 0.011008 (-0.006032) | 0.079308 / 0.038508 (0.040799) | 0.093959 / 0.023109 (0.070850) | 0.449110 / 0.275898 (0.173212) | 0.493356 / 0.323480 (0.169876) | 0.006317 / 0.007986 (-0.001669) | 0.004179 / 0.004328 (-0.000150) | 0.076991 / 0.004250 (0.072740) | 0.061977 / 0.037052 (0.024924) | 0.493823 / 0.258489 (0.235333) | 0.491609 / 0.293841 (0.197768) | 0.049552 / 0.128546 (-0.078994) | 0.015174 / 0.075646 (-0.060472) | 0.090431 / 0.419271 (-0.328841) | 0.061597 / 0.043533 (0.018064) | 0.467672 / 0.255139 (0.212533) | 0.490542 / 0.283200 (0.207342) | 0.035048 / 0.141683 (-0.106635) | 1.807939 / 1.452155 (0.355784) | 1.854859 / 1.492716 (0.362142) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236672 / 0.018006 (0.218666) | 0.542236 / 0.000490 (0.541746) | 0.016334 / 0.000200 (0.016134) | 0.000220 / 0.000054 (0.000165) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032051 / 0.037411 (-0.005360) | 0.115352 / 0.014526 (0.100826) | 0.125115 / 0.176557 (-0.051441) | 0.173670 / 0.737135 (-0.563466) | 0.117832 / 0.296338 (-0.178507) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.631513 / 0.215209 (0.416304) | 6.371688 / 2.077655 (4.294033) | 2.867240 / 1.504120 (1.363120) | 2.454907 / 1.541195 (0.913713) | 2.518860 / 1.468490 (1.050370) | 0.879973 / 4.584777 (-3.704804) | 5.170263 / 3.745712 (1.424551) | 7.986429 / 5.269862 (2.716567) | 4.828095 / 4.565676 (0.262418) | 0.097808 / 0.424275 (-0.326468) | 0.010541 / 0.007607 (0.002934) | 0.745601 / 0.226044 (0.519557) | 7.631683 / 2.268929 (5.362755) | 3.524255 / 55.444624 (-51.920369) | 2.866199 / 6.876477 (-4.010278) | 2.982483 / 2.142072 (0.840410) | 1.148957 / 4.805227 (-3.656270) | 0.217067 / 6.500664 (-6.283598) | 0.074357 / 0.075469 (-0.001112) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.714917 / 1.841788 (-0.126871) | 24.151348 / 8.074308 (16.077040) | 21.993604 / 10.191392 (11.802212) | 0.234883 / 0.680424 (-0.445541) | 0.028182 / 0.534201 (-0.506019) | 0.474050 / 0.579283 (-0.105233) | 0.557012 / 0.434364 (0.122648) | 0.537823 / 0.540337 (-0.002514) | 0.741488 / 1.386936 (-0.645448) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c2e5a7a01a952a17d0424e93c3be2b4a5ffca7da \"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) | 0.007640 / 0.011353 (-0.003713) | 0.004776 / 0.011008 (-0.006232) | 0.101582 / 0.038508 (0.063074) | 0.085113 / 0.023109 (0.062003) | 0.376000 / 0.275898 (0.100102) | 0.421117 / 0.323480 (0.097637) | 0.006095 / 0.007986 (-0.001891) | 0.003884 / 0.004328 (-0.000445) | 0.077263 / 0.004250 (0.073013) | 0.065262 / 0.037052 (0.028210) | 0.384041 / 0.258489 (0.125552) | 0.442229 / 0.293841 (0.148388) | 0.035706 / 0.128546 (-0.092840) | 0.009996 / 0.075646 (-0.065651) | 0.344925 / 0.419271 (-0.074346) | 0.062358 / 0.043533 (0.018825) | 0.371738 / 0.255139 (0.116599) | 0.407093 / 0.283200 (0.123894) | 0.026996 / 0.141683 (-0.114687) | 1.762705 / 1.452155 (0.310550) | 1.846777 / 1.492716 (0.354061) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.219660 / 0.018006 (0.201653) | 0.521795 / 0.000490 (0.521305) | 0.005344 / 0.000200 (0.005145) | 0.000098 / 0.000054 (0.000044) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036027 / 0.037411 (-0.001385) | 0.100309 / 0.014526 (0.085784) | 0.113041 / 0.176557 (-0.063515) | 0.190037 / 0.737135 (-0.547099) | 0.114552 / 0.296338 (-0.181786) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.466364 / 0.215209 (0.251154) | 4.638745 / 2.077655 (2.561090) | 2.317875 / 1.504120 (0.813755) | 2.099241 / 1.541195 (0.558046) | 2.149827 / 1.468490 (0.681337) | 0.578913 / 4.584777 (-4.005864) | 4.281866 / 3.745712 (0.536154) | 3.778453 / 5.269862 (-1.491408) | 2.411704 / 4.565676 (-2.153972) | 0.068556 / 0.424275 (-0.355719) | 0.008779 / 0.007607 (0.001172) | 0.553165 / 0.226044 (0.327121) | 5.524520 / 2.268929 (3.255591) | 2.848444 / 55.444624 (-52.596181) | 2.468591 / 6.876477 (-4.407885) | 2.652117 / 2.142072 (0.510045) | 0.694124 / 4.805227 (-4.111103) | 0.157087 / 6.500664 (-6.343577) | 0.070706 / 0.075469 (-0.004763) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.492031 / 1.841788 (-0.349757) | 23.086596 / 8.074308 (15.012288) | 16.791351 / 10.191392 (6.599959) | 0.203932 / 0.680424 (-0.476492) | 0.021736 / 0.534201 (-0.512464) | 0.468344 / 0.579283 (-0.110939) | 0.493790 / 0.434364 (0.059426) | 0.563226 / 0.540337 (0.022889) | 0.780384 / 1.386936 (-0.606553) |\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) | 0.007980 / 0.011353 (-0.003373) | 0.004696 / 0.011008 (-0.006312) | 0.076712 / 0.038508 (0.038204) | 0.095915 / 0.023109 (0.072805) | 0.433615 / 0.275898 (0.157717) | 0.482477 / 0.323480 (0.158997) | 0.007029 / 0.007986 (-0.000957) | 0.003842 / 0.004328 (-0.000487) | 0.076331 / 0.004250 (0.072081) | 0.069755 / 0.037052 (0.032703) | 0.458914 / 0.258489 (0.200425) | 0.486155 / 0.293841 (0.192314) | 0.036966 / 0.128546 (-0.091580) | 0.010082 / 0.075646 (-0.065564) | 0.083886 / 0.419271 (-0.335385) | 0.059329 / 0.043533 (0.015796) | 0.453782 / 0.255139 (0.198643) | 0.459508 / 0.283200 (0.176308) | 0.028400 / 0.141683 (-0.113283) | 1.796406 / 1.452155 (0.344251) | 1.881161 / 1.492716 (0.388445) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235053 / 0.018006 (0.217047) | 0.501907 / 0.000490 (0.501417) | 0.005211 / 0.000200 (0.005011) | 0.000101 / 0.000054 (0.000046) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037752 / 0.037411 (0.000341) | 0.107299 / 0.014526 (0.092773) | 0.120307 / 0.176557 (-0.056250) | 0.187542 / 0.737135 (-0.549593) | 0.121805 / 0.296338 (-0.174533) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.490039 / 0.215209 (0.274830) | 4.919169 / 2.077655 (2.841515) | 2.520610 / 1.504120 (1.016490) | 2.324473 / 1.541195 (0.783279) | 2.421195 / 1.468490 (0.952705) | 0.576314 / 4.584777 (-4.008463) | 4.304752 / 3.745712 (0.559040) | 3.881151 / 5.269862 (-1.388710) | 2.409777 / 4.565676 (-2.155900) | 0.067400 / 0.424275 (-0.356875) | 0.009235 / 0.007607 (0.001627) | 0.586601 / 0.226044 (0.360556) | 5.850080 / 2.268929 (3.581152) | 3.064859 / 55.444624 (-52.379766) | 2.701734 / 6.876477 (-4.174743) | 2.926190 / 2.142072 (0.784117) | 0.698511 / 4.805227 (-4.106716) | 0.158273 / 6.500664 (-6.342392) | 0.074530 / 0.075469 (-0.000939) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.607113 / 1.841788 (-0.234674) | 23.499279 / 8.074308 (15.424971) | 17.049509 / 10.191392 (6.858117) | 0.175689 / 0.680424 (-0.504735) | 0.021762 / 0.534201 (-0.512439) | 0.491450 / 0.579283 (-0.087833) | 0.487557 / 0.434364 (0.053193) | 0.570104 / 0.540337 (0.029766) | 0.761527 / 1.386936 (-0.625409) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7096c59e6a8f4d5b16f3b906075f9e2ed83bbb25 \"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) | 0.008725 / 0.011353 (-0.002628) | 0.005156 / 0.011008 (-0.005852) | 0.095147 / 0.038508 (0.056639) | 0.084916 / 0.023109 (0.061807) | 0.390769 / 0.275898 (0.114871) | 0.434716 / 0.323480 (0.111237) | 0.005982 / 0.007986 (-0.002004) | 0.004323 / 0.004328 (-0.000006) | 0.074712 / 0.004250 (0.070461) | 0.058889 / 0.037052 (0.021837) | 0.403997 / 0.258489 (0.145508) | 0.443361 / 0.293841 (0.149520) | 0.045908 / 0.128546 (-0.082639) | 0.013562 / 0.075646 (-0.062085) | 0.330683 / 0.419271 (-0.088588) | 0.064821 / 0.043533 (0.021288) | 0.407202 / 0.255139 (0.152063) | 0.409930 / 0.283200 (0.126730) | 0.032693 / 0.141683 (-0.108990) | 1.630181 / 1.452155 (0.178026) | 1.729680 / 1.492716 (0.236963) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.261240 / 0.018006 (0.243234) | 0.581850 / 0.000490 (0.581360) | 0.002997 / 0.000200 (0.002797) | 0.000107 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029279 / 0.037411 (-0.008133) | 0.085004 / 0.014526 (0.070478) | 0.127782 / 0.176557 (-0.048774) | 0.168852 / 0.737135 (-0.568283) | 0.098697 / 0.296338 (-0.197641) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.546417 / 0.215209 (0.331208) | 5.602186 / 2.077655 (3.524531) | 2.597049 / 1.504120 (1.092930) | 2.384880 / 1.541195 (0.843685) | 2.444516 / 1.468490 (0.976026) | 0.796562 / 4.584777 (-3.788214) | 5.239440 / 3.745712 (1.493727) | 7.087768 / 5.269862 (1.817906) | 4.308476 / 4.565676 (-0.257200) | 0.091215 / 0.424275 (-0.333060) | 0.007942 / 0.007607 (0.000335) | 0.690059 / 0.226044 (0.464015) | 6.727809 / 2.268929 (4.458880) | 3.294522 / 55.444624 (-52.150103) | 2.604088 / 6.876477 (-4.272389) | 2.786970 / 2.142072 (0.644898) | 0.918817 / 4.805227 (-3.886410) | 0.191451 / 6.500664 (-6.309213) | 0.069557 / 0.075469 (-0.005912) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.486377 / 1.841788 (-0.355411) | 22.363470 / 8.074308 (14.289162) | 19.963684 / 10.191392 (9.772292) | 0.204161 / 0.680424 (-0.476263) | 0.034570 / 0.534201 (-0.499631) | 0.467937 / 0.579283 (-0.111346) | 0.564870 / 0.434364 (0.130506) | 0.511133 / 0.540337 (-0.029204) | 0.777084 / 1.386936 (-0.609852) |\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) | 0.008612 / 0.011353 (-0.002741) | 0.004993 / 0.011008 (-0.006015) | 0.080769 / 0.038508 (0.042261) | 0.075923 / 0.023109 (0.052814) | 0.442271 / 0.275898 (0.166373) | 0.495625 / 0.323480 (0.172146) | 0.006467 / 0.007986 (-0.001518) | 0.004001 / 0.004328 (-0.000328) | 0.077309 / 0.004250 (0.073059) | 0.063466 / 0.037052 (0.026414) | 0.452460 / 0.258489 (0.193971) | 0.494063 / 0.293841 (0.200223) | 0.045751 / 0.128546 (-0.082796) | 0.013402 / 0.075646 (-0.062245) | 0.085760 / 0.419271 (-0.333511) | 0.056532 / 0.043533 (0.012999) | 0.440596 / 0.255139 (0.185457) | 0.459540 / 0.283200 (0.176340) | 0.035897 / 0.141683 (-0.105786) | 1.728264 / 1.452155 (0.276109) | 1.808142 / 1.492716 (0.315426) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.285094 / 0.018006 (0.267088) | 0.598440 / 0.000490 (0.597950) | 0.003476 / 0.000200 (0.003276) | 0.000103 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035106 / 0.037411 (-0.002305) | 0.091724 / 0.014526 (0.077198) | 0.122803 / 0.176557 (-0.053754) | 0.182114 / 0.737135 (-0.555022) | 0.116196 / 0.296338 (-0.180143) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.585420 / 0.215209 (0.370211) | 5.790370 / 2.077655 (3.712715) | 2.833247 / 1.504120 (1.329127) | 2.627949 / 1.541195 (1.086755) | 2.643050 / 1.468490 (1.174560) | 0.792036 / 4.584777 (-3.792741) | 5.145084 / 3.745712 (1.399372) | 4.423679 / 5.269862 (-0.846182) | 2.802778 / 4.565676 (-1.762898) | 0.093983 / 0.424275 (-0.330292) | 0.009260 / 0.007607 (0.001652) | 0.720302 / 0.226044 (0.494258) | 7.116959 / 2.268929 (4.848031) | 3.574782 / 55.444624 (-51.869843) | 3.009330 / 6.876477 (-3.867147) | 3.126488 / 2.142072 (0.984415) | 0.949144 / 4.805227 (-3.856083) | 0.195143 / 6.500664 (-6.305521) | 0.072490 / 0.075469 (-0.002979) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.626368 / 1.841788 (-0.215419) | 23.683021 / 8.074308 (15.608713) | 20.085297 / 10.191392 (9.893905) | 0.267057 / 0.680424 (-0.413367) | 0.028306 / 0.534201 (-0.505894) | 0.478448 / 0.579283 (-0.100835) | 0.597619 / 0.434364 (0.163256) | 0.544737 / 0.540337 (0.004399) | 0.761805 / 1.386936 (-0.625131) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7ac53b590916c8d859fabcc2ef23c12add7f22f7 \"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) | 0.009359 / 0.011353 (-0.001994) | 0.004848 / 0.011008 (-0.006160) | 0.099471 / 0.038508 (0.060963) | 0.079483 / 0.023109 (0.056373) | 0.375281 / 0.275898 (0.099383) | 0.415566 / 0.323480 (0.092086) | 0.006317 / 0.007986 (-0.001669) | 0.005145 / 0.004328 (0.000817) | 0.080345 / 0.004250 (0.076094) | 0.064540 / 0.037052 (0.027487) | 0.385897 / 0.258489 (0.127408) | 0.432576 / 0.293841 (0.138735) | 0.055109 / 0.128546 (-0.073437) | 0.014166 / 0.075646 (-0.061480) | 0.350870 / 0.419271 (-0.068402) | 0.087483 / 0.043533 (0.043950) | 0.402288 / 0.255139 (0.147149) | 0.391997 / 0.283200 (0.108798) | 0.045233 / 0.141683 (-0.096450) | 1.795002 / 1.452155 (0.342847) | 1.839063 / 1.492716 (0.346347) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220851 / 0.018006 (0.202845) | 0.513391 / 0.000490 (0.512901) | 0.003740 / 0.000200 (0.003540) | 0.000107 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035287 / 0.037411 (-0.002124) | 0.090670 / 0.014526 (0.076144) | 0.115651 / 0.176557 (-0.060905) | 0.180469 / 0.737135 (-0.556667) | 0.106955 / 0.296338 (-0.189384) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.632381 / 0.215209 (0.417172) | 6.185151 / 2.077655 (4.107497) | 2.548263 / 1.504120 (1.044143) | 2.194931 / 1.541195 (0.653737) | 2.368685 / 1.468490 (0.900194) | 0.956467 / 4.584777 (-3.628310) | 5.280904 / 3.745712 (1.535192) | 4.783057 / 5.269862 (-0.486805) | 3.218493 / 4.565676 (-1.347184) | 0.103545 / 0.424275 (-0.320730) | 0.008424 / 0.007607 (0.000817) | 0.736303 / 0.226044 (0.510259) | 7.354305 / 2.268929 (5.085376) | 3.280670 / 55.444624 (-52.163954) | 2.478628 / 6.876477 (-4.397848) | 2.623290 / 2.142072 (0.481217) | 1.033064 / 4.805227 (-3.772163) | 0.206496 / 6.500664 (-6.294168) | 0.066449 / 0.075469 (-0.009020) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.508756 / 1.841788 (-0.333031) | 21.866012 / 8.074308 (13.791704) | 21.887761 / 10.191392 (11.696369) | 0.231415 / 0.680424 (-0.449008) | 0.028917 / 0.534201 (-0.505284) | 0.468761 / 0.579283 (-0.110522) | 0.568236 / 0.434364 (0.133872) | 0.550156 / 0.540337 (0.009818) | 0.783197 / 1.386936 (-0.603739) |\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) | 0.009413 / 0.011353 (-0.001939) | 0.004951 / 0.011008 (-0.006058) | 0.071402 / 0.038508 (0.032893) | 0.068455 / 0.023109 (0.045346) | 0.425216 / 0.275898 (0.149318) | 0.431928 / 0.323480 (0.108448) | 0.006477 / 0.007986 (-0.001509) | 0.003891 / 0.004328 (-0.000437) | 0.076898 / 0.004250 (0.072647) | 0.057522 / 0.037052 (0.020470) | 0.449585 / 0.258489 (0.191096) | 0.431356 / 0.293841 (0.137515) | 0.049728 / 0.128546 (-0.078818) | 0.014456 / 0.075646 (-0.061190) | 0.084618 / 0.419271 (-0.334653) | 0.064482 / 0.043533 (0.020949) | 0.456377 / 0.255139 (0.201238) | 0.433949 / 0.283200 (0.150749) | 0.036577 / 0.141683 (-0.105106) | 1.819742 / 1.452155 (0.367588) | 1.694691 / 1.492716 (0.201975) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224610 / 0.018006 (0.206604) | 0.494586 / 0.000490 (0.494096) | 0.004506 / 0.000200 (0.004307) | 0.000119 / 0.000054 (0.000065) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033172 / 0.037411 (-0.004239) | 0.100562 / 0.014526 (0.086036) | 0.116499 / 0.176557 (-0.060058) | 0.153717 / 0.737135 (-0.583418) | 0.140047 / 0.296338 (-0.156291) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.635922 / 0.215209 (0.420713) | 6.359792 / 2.077655 (4.282137) | 2.689083 / 1.504120 (1.184963) | 2.330574 / 1.541195 (0.789380) | 2.583535 / 1.468490 (1.115044) | 0.902737 / 4.584777 (-3.682040) | 5.136586 / 3.745712 (1.390874) | 4.570824 / 5.269862 (-0.699037) | 3.029953 / 4.565676 (-1.535724) | 0.103961 / 0.424275 (-0.320314) | 0.007908 / 0.007607 (0.000301) | 0.723290 / 0.226044 (0.497246) | 7.678599 / 2.268929 (5.409671) | 3.342522 / 55.444624 (-52.102102) | 2.774659 / 6.876477 (-4.101817) | 2.966496 / 2.142072 (0.824423) | 1.025395 / 4.805227 (-3.779832) | 0.222246 / 6.500664 (-6.278418) | 0.072455 / 0.075469 (-0.003014) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.603637 / 1.841788 (-0.238151) | 21.387722 / 8.074308 (13.313414) | 22.855221 / 10.191392 (12.663829) | 0.222147 / 0.680424 (-0.458277) | 0.030763 / 0.534201 (-0.503438) | 0.472586 / 0.579283 (-0.106697) | 0.560161 / 0.434364 (0.125797) | 0.551941 / 0.540337 (0.011604) | 0.711254 / 1.386936 (-0.675682) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#85cf123e553ff282b43ad1d1877ba2c40d206d52 \"CML watermark\")\n"
] |
6,044
|
Rename "pattern" to "path" in YAML data_files configs
|
To make it easier to understand for users.
They can use "path" to specify a single path, <s>or "paths" to use a list of paths.</s>
Glob patterns are still supported though
|
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6044",
"html_url": "https://github.com/huggingface/datasets/pull/6044",
"diff_url": "https://github.com/huggingface/datasets/pull/6044.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6044.patch",
"merged_at": "2023-07-19T16:48:06"
}
| true
|
[
"_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) | 0.006543 / 0.011353 (-0.004809) | 0.004085 / 0.011008 (-0.006924) | 0.083989 / 0.038508 (0.045481) | 0.074733 / 0.023109 (0.051623) | 0.310839 / 0.275898 (0.034941) | 0.333540 / 0.323480 (0.010060) | 0.005566 / 0.007986 (-0.002419) | 0.003461 / 0.004328 (-0.000868) | 0.065194 / 0.004250 (0.060943) | 0.057007 / 0.037052 (0.019954) | 0.325633 / 0.258489 (0.067144) | 0.351665 / 0.293841 (0.057824) | 0.030561 / 0.128546 (-0.097985) | 0.008579 / 0.075646 (-0.067068) | 0.287457 / 0.419271 (-0.131815) | 0.063554 / 0.043533 (0.020021) | 0.309182 / 0.255139 (0.054043) | 0.327809 / 0.283200 (0.044609) | 0.034470 / 0.141683 (-0.107213) | 1.452098 / 1.452155 (-0.000057) | 1.527130 / 1.492716 (0.034414) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.241736 / 0.018006 (0.223729) | 0.552432 / 0.000490 (0.551943) | 0.004085 / 0.000200 (0.003885) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027290 / 0.037411 (-0.010121) | 0.081250 / 0.014526 (0.066724) | 0.094739 / 0.176557 (-0.081818) | 0.150424 / 0.737135 (-0.586711) | 0.095488 / 0.296338 (-0.200851) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.377245 / 0.215209 (0.162036) | 3.781021 / 2.077655 (1.703366) | 1.820092 / 1.504120 (0.315972) | 1.654420 / 1.541195 (0.113225) | 1.751256 / 1.468490 (0.282766) | 0.475161 / 4.584777 (-4.109616) | 3.603462 / 3.745712 (-0.142251) | 5.437837 / 5.269862 (0.167975) | 3.305598 / 4.565676 (-1.260079) | 0.055856 / 0.424275 (-0.368419) | 0.007259 / 0.007607 (-0.000348) | 0.454205 / 0.226044 (0.228161) | 4.544157 / 2.268929 (2.275229) | 2.296776 / 55.444624 (-53.147848) | 1.951017 / 6.876477 (-4.925459) | 2.128759 / 2.142072 (-0.013313) | 0.590354 / 4.805227 (-4.214873) | 0.129974 / 6.500664 (-6.370690) | 0.059506 / 0.075469 (-0.015963) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.285866 / 1.841788 (-0.555921) | 19.419446 / 8.074308 (11.345138) | 13.985108 / 10.191392 (3.793716) | 0.146803 / 0.680424 (-0.533620) | 0.018176 / 0.534201 (-0.516025) | 0.392345 / 0.579283 (-0.186938) | 0.405394 / 0.434364 (-0.028970) | 0.454649 / 0.540337 (-0.085688) | 0.633075 / 1.386936 (-0.753861) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006497 / 0.011353 (-0.004855) | 0.004092 / 0.011008 (-0.006916) | 0.064908 / 0.038508 (0.026400) | 0.073494 / 0.023109 (0.050385) | 0.382227 / 0.275898 (0.106329) | 0.407320 / 0.323480 (0.083840) | 0.005653 / 0.007986 (-0.002332) | 0.003500 / 0.004328 (-0.000829) | 0.064570 / 0.004250 (0.060320) | 0.058733 / 0.037052 (0.021681) | 0.385702 / 0.258489 (0.127213) | 0.426463 / 0.293841 (0.132622) | 0.031073 / 0.128546 (-0.097473) | 0.008710 / 0.075646 (-0.066936) | 0.071378 / 0.419271 (-0.347893) | 0.050141 / 0.043533 (0.006608) | 0.377769 / 0.255139 (0.122630) | 0.395016 / 0.283200 (0.111816) | 0.025158 / 0.141683 (-0.116525) | 1.470503 / 1.452155 (0.018348) | 1.532742 / 1.492716 (0.040026) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.214249 / 0.018006 (0.196243) | 0.583580 / 0.000490 (0.583090) | 0.004027 / 0.000200 (0.003828) | 0.000104 / 0.000054 (0.000050) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030186 / 0.037411 (-0.007226) | 0.086927 / 0.014526 (0.072401) | 0.102060 / 0.176557 (-0.074497) | 0.156281 / 0.737135 (-0.580855) | 0.100825 / 0.296338 (-0.195514) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419942 / 0.215209 (0.204733) | 4.183797 / 2.077655 (2.106142) | 2.205079 / 1.504120 (0.700959) | 2.071219 / 1.541195 (0.530024) | 2.194047 / 1.468490 (0.725557) | 0.478768 / 4.584777 (-4.106009) | 3.584864 / 3.745712 (-0.160848) | 3.371635 / 5.269862 (-1.898227) | 2.022134 / 4.565676 (-2.543542) | 0.056553 / 0.424275 (-0.367722) | 0.007231 / 0.007607 (-0.000376) | 0.493158 / 0.226044 (0.267113) | 4.934370 / 2.268929 (2.665441) | 2.699593 / 55.444624 (-52.745031) | 2.396371 / 6.876477 (-4.480105) | 2.438052 / 2.142072 (0.295979) | 0.589578 / 4.805227 (-4.215649) | 0.147234 / 6.500664 (-6.353430) | 0.062049 / 0.075469 (-0.013420) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.318246 / 1.841788 (-0.523542) | 19.829025 / 8.074308 (11.754717) | 14.314825 / 10.191392 (4.123433) | 0.168309 / 0.680424 (-0.512115) | 0.018596 / 0.534201 (-0.515605) | 0.397540 / 0.579283 (-0.181743) | 0.421280 / 0.434364 (-0.013084) | 0.479917 / 0.540337 (-0.060421) | 0.643494 / 1.386936 (-0.743442) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5be59becaa65f1fa08129091b8c778823e4a50ac \"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) | 0.008349 / 0.011353 (-0.003004) | 0.005362 / 0.011008 (-0.005646) | 0.100777 / 0.038508 (0.062269) | 0.078719 / 0.023109 (0.055609) | 0.398105 / 0.275898 (0.122207) | 0.444189 / 0.323480 (0.120709) | 0.006834 / 0.007986 (-0.001152) | 0.004642 / 0.004328 (0.000314) | 0.076284 / 0.004250 (0.072034) | 0.062738 / 0.037052 (0.025685) | 0.409532 / 0.258489 (0.151043) | 0.447218 / 0.293841 (0.153377) | 0.052996 / 0.128546 (-0.075550) | 0.012977 / 0.075646 (-0.062669) | 0.347687 / 0.419271 (-0.071585) | 0.068076 / 0.043533 (0.024543) | 0.394526 / 0.255139 (0.139387) | 0.434110 / 0.283200 (0.150910) | 0.041719 / 0.141683 (-0.099963) | 1.759109 / 1.452155 (0.306955) | 1.866049 / 1.492716 (0.373333) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.287633 / 0.018006 (0.269627) | 0.611540 / 0.000490 (0.611051) | 0.005388 / 0.000200 (0.005188) | 0.000096 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027394 / 0.037411 (-0.010017) | 0.089796 / 0.014526 (0.075270) | 0.106931 / 0.176557 (-0.069625) | 0.173560 / 0.737135 (-0.563575) | 0.106948 / 0.296338 (-0.189391) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.575156 / 0.215209 (0.359947) | 5.674170 / 2.077655 (3.596516) | 2.463090 / 1.504120 (0.958971) | 2.128245 / 1.541195 (0.587050) | 2.118982 / 1.468490 (0.650492) | 0.876976 / 4.584777 (-3.707801) | 5.238229 / 3.745712 (1.492517) | 4.548788 / 5.269862 (-0.721074) | 2.905243 / 4.565676 (-1.660433) | 0.090750 / 0.424275 (-0.333525) | 0.008266 / 0.007607 (0.000659) | 0.693305 / 0.226044 (0.467260) | 7.126970 / 2.268929 (4.858041) | 3.152131 / 55.444624 (-52.292494) | 2.532118 / 6.876477 (-4.344359) | 2.678442 / 2.142072 (0.536369) | 0.932745 / 4.805227 (-3.872483) | 0.196290 / 6.500664 (-6.304374) | 0.074082 / 0.075469 (-0.001387) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.599636 / 1.841788 (-0.242152) | 23.271435 / 8.074308 (15.197127) | 19.696709 / 10.191392 (9.505317) | 0.222668 / 0.680424 (-0.457756) | 0.029088 / 0.534201 (-0.505113) | 0.492477 / 0.579283 (-0.086806) | 0.580578 / 0.434364 (0.146214) | 0.558852 / 0.540337 (0.018514) | 0.762083 / 1.386936 (-0.624853) |\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) | 0.009021 / 0.011353 (-0.002332) | 0.005011 / 0.011008 (-0.005997) | 0.076504 / 0.038508 (0.037996) | 0.077303 / 0.023109 (0.054193) | 0.480660 / 0.275898 (0.204762) | 0.493944 / 0.323480 (0.170464) | 0.006339 / 0.007986 (-0.001646) | 0.004302 / 0.004328 (-0.000026) | 0.076228 / 0.004250 (0.071978) | 0.060805 / 0.037052 (0.023753) | 0.477539 / 0.258489 (0.219050) | 0.496799 / 0.293841 (0.202958) | 0.049495 / 0.128546 (-0.079052) | 0.013333 / 0.075646 (-0.062313) | 0.087217 / 0.419271 (-0.332055) | 0.061451 / 0.043533 (0.017918) | 0.485169 / 0.255139 (0.230030) | 0.487348 / 0.283200 (0.204149) | 0.035874 / 0.141683 (-0.105809) | 1.829137 / 1.452155 (0.376982) | 1.906151 / 1.492716 (0.413435) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.304526 / 0.018006 (0.286520) | 0.627499 / 0.000490 (0.627009) | 0.003786 / 0.000200 (0.003586) | 0.000098 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035512 / 0.037411 (-0.001899) | 0.096684 / 0.014526 (0.082158) | 0.111879 / 0.176557 (-0.064678) | 0.171489 / 0.737135 (-0.565647) | 0.112175 / 0.296338 (-0.184164) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.604791 / 0.215209 (0.389582) | 6.089137 / 2.077655 (4.011482) | 2.883237 / 1.504120 (1.379117) | 2.561109 / 1.541195 (1.019914) | 2.542400 / 1.468490 (1.073910) | 0.852828 / 4.584777 (-3.731949) | 5.236812 / 3.745712 (1.491100) | 4.756429 / 5.269862 (-0.513432) | 2.885660 / 4.565676 (-1.680016) | 0.095643 / 0.424275 (-0.328632) | 0.008403 / 0.007607 (0.000796) | 0.727707 / 0.226044 (0.501663) | 7.428002 / 2.268929 (5.159074) | 3.816051 / 55.444624 (-51.628573) | 2.971057 / 6.876477 (-3.905420) | 2.915965 / 2.142072 (0.773893) | 1.006553 / 4.805227 (-3.798674) | 0.201840 / 6.500664 (-6.298824) | 0.080795 / 0.075469 (0.005326) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.794951 / 1.841788 (-0.046837) | 23.624556 / 8.074308 (15.550248) | 21.856195 / 10.191392 (11.664802) | 0.253043 / 0.680424 (-0.427381) | 0.031201 / 0.534201 (-0.503000) | 0.461641 / 0.579283 (-0.117642) | 0.577789 / 0.434364 (0.143425) | 0.569197 / 0.540337 (0.028860) | 0.780111 / 1.386936 (-0.606825) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4904f14459c862f0ab525ec034a636177be5dee4 \"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) | 0.007646 / 0.011353 (-0.003707) | 0.004750 / 0.011008 (-0.006258) | 0.097981 / 0.038508 (0.059473) | 0.088989 / 0.023109 (0.065880) | 0.377732 / 0.275898 (0.101834) | 0.406805 / 0.323480 (0.083325) | 0.006389 / 0.007986 (-0.001597) | 0.003854 / 0.004328 (-0.000474) | 0.073977 / 0.004250 (0.069727) | 0.066497 / 0.037052 (0.029444) | 0.371498 / 0.258489 (0.113009) | 0.417352 / 0.293841 (0.123511) | 0.036326 / 0.128546 (-0.092220) | 0.009876 / 0.075646 (-0.065770) | 0.330142 / 0.419271 (-0.089130) | 0.062423 / 0.043533 (0.018890) | 0.369375 / 0.255139 (0.114236) | 0.406048 / 0.283200 (0.122848) | 0.026564 / 0.141683 (-0.115119) | 1.713295 / 1.452155 (0.261140) | 1.797493 / 1.492716 (0.304777) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231889 / 0.018006 (0.213882) | 0.512497 / 0.000490 (0.512007) | 0.000390 / 0.000200 (0.000190) | 0.000069 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033978 / 0.037411 (-0.003433) | 0.100117 / 0.014526 (0.085592) | 0.112460 / 0.176557 (-0.064097) | 0.179936 / 0.737135 (-0.557200) | 0.114277 / 0.296338 (-0.182061) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.461320 / 0.215209 (0.246111) | 4.563180 / 2.077655 (2.485526) | 2.249474 / 1.504120 (0.745354) | 2.100450 / 1.541195 (0.559255) | 2.231080 / 1.468490 (0.762590) | 0.567907 / 4.584777 (-4.016870) | 4.117233 / 3.745712 (0.371521) | 4.943159 / 5.269862 (-0.326703) | 3.112299 / 4.565676 (-1.453377) | 0.065500 / 0.424275 (-0.358775) | 0.008407 / 0.007607 (0.000800) | 0.545928 / 0.226044 (0.319883) | 5.508058 / 2.268929 (3.239129) | 2.834645 / 55.444624 (-52.609980) | 2.440328 / 6.876477 (-4.436148) | 2.680483 / 2.142072 (0.538410) | 0.697191 / 4.805227 (-4.108036) | 0.176646 / 6.500664 (-6.324018) | 0.073608 / 0.075469 (-0.001861) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.451865 / 1.841788 (-0.389922) | 22.752595 / 8.074308 (14.678287) | 15.543338 / 10.191392 (5.351946) | 0.214644 / 0.680424 (-0.465780) | 0.022050 / 0.534201 (-0.512151) | 0.463898 / 0.579283 (-0.115385) | 0.481691 / 0.434364 (0.047327) | 0.549715 / 0.540337 (0.009378) | 0.773595 / 1.386936 (-0.613341) |\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) | 0.007541 / 0.011353 (-0.003812) | 0.004715 / 0.011008 (-0.006293) | 0.076782 / 0.038508 (0.038274) | 0.086242 / 0.023109 (0.063133) | 0.458053 / 0.275898 (0.182155) | 0.503097 / 0.323480 (0.179617) | 0.006262 / 0.007986 (-0.001724) | 0.003882 / 0.004328 (-0.000447) | 0.075669 / 0.004250 (0.071419) | 0.066004 / 0.037052 (0.028952) | 0.469439 / 0.258489 (0.210950) | 0.529744 / 0.293841 (0.235903) | 0.037228 / 0.128546 (-0.091319) | 0.009794 / 0.075646 (-0.065852) | 0.082464 / 0.419271 (-0.336808) | 0.058797 / 0.043533 (0.015264) | 0.452069 / 0.255139 (0.196930) | 0.488246 / 0.283200 (0.205046) | 0.029324 / 0.141683 (-0.112359) | 1.742237 / 1.452155 (0.290082) | 1.839676 / 1.492716 (0.346959) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228106 / 0.018006 (0.210100) | 0.491632 / 0.000490 (0.491142) | 0.004993 / 0.000200 (0.004793) | 0.000114 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035413 / 0.037411 (-0.001999) | 0.104617 / 0.014526 (0.090091) | 0.121948 / 0.176557 (-0.054609) | 0.186233 / 0.737135 (-0.550902) | 0.121574 / 0.296338 (-0.174764) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.473849 / 0.215209 (0.258640) | 4.788312 / 2.077655 (2.710657) | 2.470535 / 1.504120 (0.966415) | 2.270393 / 1.541195 (0.729198) | 2.361096 / 1.468490 (0.892606) | 0.556184 / 4.584777 (-4.028593) | 4.216852 / 3.745712 (0.471140) | 3.901718 / 5.269862 (-1.368143) | 2.355209 / 4.565676 (-2.210467) | 0.066708 / 0.424275 (-0.357567) | 0.008709 / 0.007607 (0.001102) | 0.571714 / 0.226044 (0.345669) | 5.663150 / 2.268929 (3.394221) | 3.025769 / 55.444624 (-52.418855) | 2.652554 / 6.876477 (-4.223923) | 2.750555 / 2.142072 (0.608483) | 0.681536 / 4.805227 (-4.123691) | 0.157187 / 6.500664 (-6.343477) | 0.073533 / 0.075469 (-0.001936) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.604630 / 1.841788 (-0.237158) | 22.735629 / 8.074308 (14.661321) | 16.762347 / 10.191392 (6.570955) | 0.175514 / 0.680424 (-0.504910) | 0.021497 / 0.534201 (-0.512704) | 0.461438 / 0.579283 (-0.117845) | 0.476184 / 0.434364 (0.041820) | 0.571048 / 0.540337 (0.030710) | 0.747086 / 1.386936 (-0.639850) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6ea38fc40ee2b10d3b5c6df09b09ad05e02a2cff \"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) | 0.006889 / 0.011353 (-0.004464) | 0.004241 / 0.011008 (-0.006767) | 0.084542 / 0.038508 (0.046034) | 0.080484 / 0.023109 (0.057374) | 0.309356 / 0.275898 (0.033458) | 0.338548 / 0.323480 (0.015068) | 0.004904 / 0.007986 (-0.003082) | 0.005220 / 0.004328 (0.000892) | 0.065501 / 0.004250 (0.061251) | 0.062095 / 0.037052 (0.025043) | 0.317332 / 0.258489 (0.058843) | 0.364797 / 0.293841 (0.070956) | 0.030492 / 0.128546 (-0.098054) | 0.008991 / 0.075646 (-0.066656) | 0.288274 / 0.419271 (-0.130998) | 0.052582 / 0.043533 (0.009049) | 0.310838 / 0.255139 (0.055699) | 0.346304 / 0.283200 (0.063104) | 0.027968 / 0.141683 (-0.113715) | 1.509727 / 1.452155 (0.057573) | 1.577410 / 1.492716 (0.084694) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.269725 / 0.018006 (0.251719) | 0.627685 / 0.000490 (0.627195) | 0.000419 / 0.000200 (0.000219) | 0.000060 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031022 / 0.037411 (-0.006389) | 0.081858 / 0.014526 (0.067332) | 0.099477 / 0.176557 (-0.077080) | 0.162981 / 0.737135 (-0.574154) | 0.101987 / 0.296338 (-0.194351) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.386297 / 0.215209 (0.171088) | 3.845321 / 2.077655 (1.767666) | 1.834446 / 1.504120 (0.330326) | 1.699730 / 1.541195 (0.158536) | 1.764342 / 1.468490 (0.295852) | 0.486423 / 4.584777 (-4.098354) | 3.527595 / 3.745712 (-0.218117) | 4.137034 / 5.269862 (-1.132827) | 2.590457 / 4.565676 (-1.975219) | 0.057598 / 0.424275 (-0.366677) | 0.007318 / 0.007607 (-0.000289) | 0.460775 / 0.226044 (0.234730) | 4.627576 / 2.268929 (2.358647) | 2.402566 / 55.444624 (-53.042059) | 2.011392 / 6.876477 (-4.865085) | 2.223915 / 2.142072 (0.081842) | 0.623217 / 4.805227 (-4.182011) | 0.148875 / 6.500664 (-6.351789) | 0.059799 / 0.075469 (-0.015671) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.290768 / 1.841788 (-0.551020) | 20.455083 / 8.074308 (12.380775) | 13.469846 / 10.191392 (3.278454) | 0.170329 / 0.680424 (-0.510095) | 0.018409 / 0.534201 (-0.515792) | 0.394356 / 0.579283 (-0.184927) | 0.422685 / 0.434364 (-0.011679) | 0.476241 / 0.540337 (-0.064096) | 0.662682 / 1.386936 (-0.724254) |\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) | 0.006724 / 0.011353 (-0.004629) | 0.004508 / 0.011008 (-0.006500) | 0.065304 / 0.038508 (0.026796) | 0.080243 / 0.023109 (0.057133) | 0.384545 / 0.275898 (0.108647) | 0.415234 / 0.323480 (0.091754) | 0.006361 / 0.007986 (-0.001624) | 0.004193 / 0.004328 (-0.000135) | 0.065940 / 0.004250 (0.061689) | 0.063633 / 0.037052 (0.026581) | 0.392799 / 0.258489 (0.134310) | 0.443618 / 0.293841 (0.149777) | 0.031134 / 0.128546 (-0.097412) | 0.009058 / 0.075646 (-0.066588) | 0.071051 / 0.419271 (-0.348221) | 0.049096 / 0.043533 (0.005563) | 0.379526 / 0.255139 (0.124387) | 0.403370 / 0.283200 (0.120171) | 0.026378 / 0.141683 (-0.115305) | 1.457879 / 1.452155 (0.005724) | 1.562890 / 1.492716 (0.070174) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.304416 / 0.018006 (0.286410) | 0.626046 / 0.000490 (0.625557) | 0.000469 / 0.000200 (0.000269) | 0.000057 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032979 / 0.037411 (-0.004433) | 0.086769 / 0.014526 (0.072243) | 0.108188 / 0.176557 (-0.068369) | 0.163077 / 0.737135 (-0.574058) | 0.106276 / 0.296338 (-0.190062) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.406922 / 0.215209 (0.191713) | 4.052828 / 2.077655 (1.975174) | 2.084802 / 1.504120 (0.580682) | 1.927263 / 1.541195 (0.386069) | 1.956078 / 1.468490 (0.487587) | 0.480110 / 4.584777 (-4.104667) | 3.553022 / 3.745712 (-0.192691) | 3.554450 / 5.269862 (-1.715411) | 2.082681 / 4.565676 (-2.482995) | 0.056711 / 0.424275 (-0.367564) | 0.007374 / 0.007607 (-0.000234) | 0.480555 / 0.226044 (0.254510) | 4.795851 / 2.268929 (2.526923) | 2.606675 / 55.444624 (-52.837949) | 2.249964 / 6.876477 (-4.626512) | 2.274234 / 2.142072 (0.132162) | 0.571767 / 4.805227 (-4.233461) | 0.133312 / 6.500664 (-6.367352) | 0.061703 / 0.075469 (-0.013766) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.354308 / 1.841788 (-0.487479) | 20.959352 / 8.074308 (12.885044) | 14.158420 / 10.191392 (3.967028) | 0.197959 / 0.680424 (-0.482465) | 0.018412 / 0.534201 (-0.515789) | 0.394307 / 0.579283 (-0.184976) | 0.402455 / 0.434364 (-0.031909) | 0.463314 / 0.540337 (-0.077024) | 0.621050 / 1.386936 (-0.765886) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d7298d4d1b169442a8d0bc8c1667298bb89ca501 \"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) | 0.007179 / 0.011353 (-0.004174) | 0.004318 / 0.011008 (-0.006690) | 0.085209 / 0.038508 (0.046701) | 0.089989 / 0.023109 (0.066880) | 0.328188 / 0.275898 (0.052290) | 0.346027 / 0.323480 (0.022547) | 0.005711 / 0.007986 (-0.002275) | 0.003703 / 0.004328 (-0.000625) | 0.065419 / 0.004250 (0.061169) | 0.065354 / 0.037052 (0.028301) | 0.314531 / 0.258489 (0.056042) | 0.354357 / 0.293841 (0.060516) | 0.030918 / 0.128546 (-0.097628) | 0.008632 / 0.075646 (-0.067015) | 0.286817 / 0.419271 (-0.132455) | 0.065267 / 0.043533 (0.021735) | 0.310918 / 0.255139 (0.055779) | 0.330497 / 0.283200 (0.047298) | 0.035695 / 0.141683 (-0.105988) | 1.471101 / 1.452155 (0.018947) | 1.538658 / 1.492716 (0.045942) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.254314 / 0.018006 (0.236308) | 0.591413 / 0.000490 (0.590923) | 0.006082 / 0.000200 (0.005882) | 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) | 0.031843 / 0.037411 (-0.005568) | 0.089968 / 0.014526 (0.075442) | 0.101838 / 0.176557 (-0.074718) | 0.164401 / 0.737135 (-0.572734) | 0.103785 / 0.296338 (-0.192554) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.380486 / 0.215209 (0.165277) | 3.798868 / 2.077655 (1.721213) | 1.824645 / 1.504120 (0.320525) | 1.660804 / 1.541195 (0.119610) | 1.784793 / 1.468490 (0.316303) | 0.487222 / 4.584777 (-4.097555) | 3.560580 / 3.745712 (-0.185132) | 5.392662 / 5.269862 (0.122800) | 3.295327 / 4.565676 (-1.270350) | 0.057699 / 0.424275 (-0.366576) | 0.007559 / 0.007607 (-0.000048) | 0.459655 / 0.226044 (0.233611) | 4.587583 / 2.268929 (2.318654) | 2.304845 / 55.444624 (-53.139779) | 1.966433 / 6.876477 (-4.910044) | 2.254591 / 2.142072 (0.112519) | 0.582978 / 4.805227 (-4.222250) | 0.133455 / 6.500664 (-6.367210) | 0.061924 / 0.075469 (-0.013546) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.275685 / 1.841788 (-0.566103) | 20.814545 / 8.074308 (12.740237) | 13.753567 / 10.191392 (3.562175) | 0.164076 / 0.680424 (-0.516348) | 0.018768 / 0.534201 (-0.515433) | 0.390991 / 0.579283 (-0.188293) | 0.404417 / 0.434364 (-0.029947) | 0.457522 / 0.540337 (-0.082815) | 0.624654 / 1.386936 (-0.762282) |\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) | 0.007435 / 0.011353 (-0.003918) | 0.004255 / 0.011008 (-0.006754) | 0.066134 / 0.038508 (0.027626) | 0.086035 / 0.023109 (0.062925) | 0.364688 / 0.275898 (0.088790) | 0.403895 / 0.323480 (0.080415) | 0.005868 / 0.007986 (-0.002117) | 0.003634 / 0.004328 (-0.000694) | 0.065803 / 0.004250 (0.061553) | 0.065113 / 0.037052 (0.028061) | 0.370057 / 0.258489 (0.111568) | 0.412634 / 0.293841 (0.118793) | 0.031660 / 0.128546 (-0.096886) | 0.008699 / 0.075646 (-0.066947) | 0.070618 / 0.419271 (-0.348654) | 0.050814 / 0.043533 (0.007281) | 0.362320 / 0.255139 (0.107181) | 0.383863 / 0.283200 (0.100663) | 0.027980 / 0.141683 (-0.113703) | 1.486389 / 1.452155 (0.034234) | 1.595534 / 1.492716 (0.102817) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.300991 / 0.018006 (0.282985) | 0.565265 / 0.000490 (0.564775) | 0.000400 / 0.000200 (0.000200) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034942 / 0.037411 (-0.002470) | 0.092498 / 0.014526 (0.077972) | 0.106737 / 0.176557 (-0.069819) | 0.165400 / 0.737135 (-0.571735) | 0.107809 / 0.296338 (-0.188529) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.412156 / 0.215209 (0.196947) | 4.116747 / 2.077655 (2.039092) | 2.199612 / 1.504120 (0.695492) | 2.049310 / 1.541195 (0.508115) | 2.174342 / 1.468490 (0.705852) | 0.482794 / 4.584777 (-4.101983) | 3.561344 / 3.745712 (-0.184368) | 3.465935 / 5.269862 (-1.803926) | 2.076595 / 4.565676 (-2.489081) | 0.056242 / 0.424275 (-0.368033) | 0.007371 / 0.007607 (-0.000236) | 0.489135 / 0.226044 (0.263091) | 4.895691 / 2.268929 (2.626763) | 2.626936 / 55.444624 (-52.817688) | 2.306658 / 6.876477 (-4.569818) | 2.421705 / 2.142072 (0.279633) | 0.599547 / 4.805227 (-4.205680) | 0.133627 / 6.500664 (-6.367037) | 0.063830 / 0.075469 (-0.011639) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.383039 / 1.841788 (-0.458748) | 21.005346 / 8.074308 (12.931038) | 14.911083 / 10.191392 (4.719691) | 0.190995 / 0.680424 (-0.489429) | 0.018510 / 0.534201 (-0.515691) | 0.396346 / 0.579283 (-0.182937) | 0.411496 / 0.434364 (-0.022868) | 0.470972 / 0.540337 (-0.069366) | 0.615670 / 1.386936 (-0.771266) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d6d2ba47759d8acbf3d750b1cc4d89b195b1f9c9 \"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) | 0.007249 / 0.011353 (-0.004104) | 0.004261 / 0.011008 (-0.006747) | 0.100645 / 0.038508 (0.062137) | 0.078522 / 0.023109 (0.055413) | 0.423526 / 0.275898 (0.147628) | 0.439541 / 0.323480 (0.116061) | 0.005812 / 0.007986 (-0.002173) | 0.003615 / 0.004328 (-0.000713) | 0.075908 / 0.004250 (0.071658) | 0.062490 / 0.037052 (0.025437) | 0.414941 / 0.258489 (0.156452) | 0.447267 / 0.293841 (0.153426) | 0.035127 / 0.128546 (-0.093419) | 0.009642 / 0.075646 (-0.066004) | 0.354093 / 0.419271 (-0.065179) | 0.060970 / 0.043533 (0.017437) | 0.418579 / 0.255139 (0.163440) | 0.427972 / 0.283200 (0.144772) | 0.025838 / 0.141683 (-0.115845) | 1.778349 / 1.452155 (0.326194) | 1.845965 / 1.492716 (0.353249) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227304 / 0.018006 (0.209298) | 0.571833 / 0.000490 (0.571343) | 0.001328 / 0.000200 (0.001128) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031343 / 0.037411 (-0.006068) | 0.096400 / 0.014526 (0.081875) | 0.106881 / 0.176557 (-0.069676) | 0.175449 / 0.737135 (-0.561686) | 0.108751 / 0.296338 (-0.187588) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.480204 / 0.215209 (0.264995) | 4.622063 / 2.077655 (2.544408) | 2.211505 / 1.504120 (0.707385) | 2.065154 / 1.541195 (0.523959) | 2.159446 / 1.468490 (0.690956) | 0.584571 / 4.584777 (-4.000206) | 4.392449 / 3.745712 (0.646737) | 4.790166 / 5.269862 (-0.479695) | 2.840615 / 4.565676 (-1.725062) | 0.070845 / 0.424275 (-0.353430) | 0.009112 / 0.007607 (0.001505) | 0.580251 / 0.226044 (0.354207) | 5.660311 / 2.268929 (3.391382) | 2.836136 / 55.444624 (-52.608489) | 2.412859 / 6.876477 (-4.463618) | 2.556710 / 2.142072 (0.414637) | 0.691946 / 4.805227 (-4.113282) | 0.160123 / 6.500664 (-6.340541) | 0.072593 / 0.075469 (-0.002876) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.547339 / 1.841788 (-0.294448) | 21.724793 / 8.074308 (13.650485) | 16.315304 / 10.191392 (6.123912) | 0.188733 / 0.680424 (-0.491690) | 0.022109 / 0.534201 (-0.512092) | 0.481623 / 0.579283 (-0.097660) | 0.464316 / 0.434364 (0.029952) | 0.557953 / 0.540337 (0.017615) | 0.756023 / 1.386936 (-0.630913) |\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) | 0.008637 / 0.011353 (-0.002716) | 0.005286 / 0.011008 (-0.005723) | 0.091387 / 0.038508 (0.052879) | 0.114092 / 0.023109 (0.090983) | 0.457547 / 0.275898 (0.181649) | 0.506878 / 0.323480 (0.183398) | 0.006849 / 0.007986 (-0.001137) | 0.004255 / 0.004328 (-0.000073) | 0.079556 / 0.004250 (0.075306) | 0.077729 / 0.037052 (0.040677) | 0.454094 / 0.258489 (0.195605) | 0.515812 / 0.293841 (0.221971) | 0.038271 / 0.128546 (-0.090275) | 0.010110 / 0.075646 (-0.065536) | 0.094254 / 0.419271 (-0.325017) | 0.065392 / 0.043533 (0.021860) | 0.459749 / 0.255139 (0.204610) | 0.489829 / 0.283200 (0.206629) | 0.040393 / 0.141683 (-0.101290) | 1.810414 / 1.452155 (0.358259) | 1.913212 / 1.492716 (0.420496) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236898 / 0.018006 (0.218891) | 0.513118 / 0.000490 (0.512628) | 0.004432 / 0.000200 (0.004232) | 0.000115 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035074 / 0.037411 (-0.002337) | 0.102384 / 0.014526 (0.087858) | 0.117326 / 0.176557 (-0.059231) | 0.182596 / 0.737135 (-0.554539) | 0.116384 / 0.296338 (-0.179955) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.514544 / 0.215209 (0.299335) | 5.152930 / 2.077655 (3.075275) | 2.624477 / 1.504120 (1.120357) | 2.363209 / 1.541195 (0.822014) | 2.436060 / 1.468490 (0.967570) | 0.592523 / 4.584777 (-3.992254) | 4.209668 / 3.745712 (0.463956) | 6.284372 / 5.269862 (1.014511) | 3.667303 / 4.565676 (-0.898374) | 0.067017 / 0.424275 (-0.357259) | 0.008607 / 0.007607 (0.001000) | 0.600840 / 0.226044 (0.374796) | 5.992630 / 2.268929 (3.723701) | 3.114532 / 55.444624 (-52.330093) | 2.693242 / 6.876477 (-4.183235) | 2.767187 / 2.142072 (0.625115) | 0.687591 / 4.805227 (-4.117636) | 0.158477 / 6.500664 (-6.342187) | 0.075504 / 0.075469 (0.000034) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.605039 / 1.841788 (-0.236749) | 21.524730 / 8.074308 (13.450422) | 17.014643 / 10.191392 (6.823251) | 0.201580 / 0.680424 (-0.478843) | 0.023028 / 0.534201 (-0.511173) | 0.483801 / 0.579283 (-0.095482) | 0.490221 / 0.434364 (0.055857) | 0.589292 / 0.540337 (0.048955) | 0.758532 / 1.386936 (-0.628404) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8c9c24d1d90f0c2db043ae2bc39f7c292454a58c \"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) | 0.008080 / 0.011353 (-0.003273) | 0.004859 / 0.011008 (-0.006149) | 0.101895 / 0.038508 (0.063387) | 0.091168 / 0.023109 (0.068059) | 0.378914 / 0.275898 (0.103016) | 0.417172 / 0.323480 (0.093692) | 0.006314 / 0.007986 (-0.001672) | 0.004069 / 0.004328 (-0.000259) | 0.076566 / 0.004250 (0.072315) | 0.070986 / 0.037052 (0.033934) | 0.380935 / 0.258489 (0.122446) | 0.417131 / 0.293841 (0.123290) | 0.036343 / 0.128546 (-0.092203) | 0.009996 / 0.075646 (-0.065650) | 0.346386 / 0.419271 (-0.072886) | 0.063162 / 0.043533 (0.019630) | 0.372620 / 0.255139 (0.117481) | 0.404902 / 0.283200 (0.121702) | 0.028217 / 0.141683 (-0.113466) | 1.793875 / 1.452155 (0.341721) | 1.836284 / 1.492716 (0.343568) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223830 / 0.018006 (0.205823) | 0.503643 / 0.000490 (0.503153) | 0.004957 / 0.000200 (0.004757) | 0.000107 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035455 / 0.037411 (-0.001957) | 0.108015 / 0.014526 (0.093489) | 0.116887 / 0.176557 (-0.059669) | 0.188174 / 0.737135 (-0.548961) | 0.117217 / 0.296338 (-0.179121) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.471681 / 0.215209 (0.256472) | 4.694509 / 2.077655 (2.616855) | 2.369539 / 1.504120 (0.865419) | 2.176839 / 1.541195 (0.635644) | 2.300536 / 1.468490 (0.832045) | 0.575689 / 4.584777 (-4.009088) | 4.232765 / 3.745712 (0.487053) | 4.766775 / 5.269862 (-0.503087) | 2.864667 / 4.565676 (-1.701010) | 0.069390 / 0.424275 (-0.354885) | 0.008822 / 0.007607 (0.001214) | 0.559620 / 0.226044 (0.333576) | 5.580401 / 2.268929 (3.311472) | 2.920293 / 55.444624 (-52.524331) | 2.552166 / 6.876477 (-4.324311) | 2.795890 / 2.142072 (0.653818) | 0.687863 / 4.805227 (-4.117364) | 0.159129 / 6.500664 (-6.341535) | 0.073475 / 0.075469 (-0.001994) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.505892 / 1.841788 (-0.335896) | 24.127650 / 8.074308 (16.053342) | 16.758238 / 10.191392 (6.566846) | 0.200555 / 0.680424 (-0.479869) | 0.021596 / 0.534201 (-0.512605) | 0.480668 / 0.579283 (-0.098615) | 0.483528 / 0.434364 (0.049164) | 0.571241 / 0.540337 (0.030903) | 0.790547 / 1.386936 (-0.596390) |\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) | 0.007997 / 0.011353 (-0.003356) | 0.004842 / 0.011008 (-0.006166) | 0.077190 / 0.038508 (0.038681) | 0.092765 / 0.023109 (0.069656) | 0.457475 / 0.275898 (0.181577) | 0.523914 / 0.323480 (0.200434) | 0.006349 / 0.007986 (-0.001637) | 0.003902 / 0.004328 (-0.000427) | 0.075860 / 0.004250 (0.071609) | 0.069708 / 0.037052 (0.032656) | 0.459612 / 0.258489 (0.201123) | 0.555028 / 0.293841 (0.261187) | 0.036854 / 0.128546 (-0.091692) | 0.010078 / 0.075646 (-0.065568) | 0.083871 / 0.419271 (-0.335400) | 0.061221 / 0.043533 (0.017689) | 0.435737 / 0.255139 (0.180598) | 0.509700 / 0.283200 (0.226500) | 0.038091 / 0.141683 (-0.103592) | 1.777161 / 1.452155 (0.325006) | 1.859603 / 1.492716 (0.366886) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.250020 / 0.018006 (0.232014) | 0.486198 / 0.000490 (0.485708) | 0.007080 / 0.000200 (0.006880) | 0.000114 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038163 / 0.037411 (0.000751) | 0.110812 / 0.014526 (0.096286) | 0.122489 / 0.176557 (-0.054068) | 0.188215 / 0.737135 (-0.548920) | 0.122375 / 0.296338 (-0.173963) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.484534 / 0.215209 (0.269325) | 4.828654 / 2.077655 (2.751000) | 2.545102 / 1.504120 (1.040982) | 2.368867 / 1.541195 (0.827672) | 2.458042 / 1.468490 (0.989552) | 0.576372 / 4.584777 (-4.008404) | 4.814033 / 3.745712 (1.068321) | 6.175972 / 5.269862 (0.906110) | 4.033422 / 4.565676 (-0.532254) | 0.068544 / 0.424275 (-0.355731) | 0.008906 / 0.007607 (0.001299) | 0.581767 / 0.226044 (0.355723) | 5.808623 / 2.268929 (3.539695) | 3.120312 / 55.444624 (-52.324313) | 2.774834 / 6.876477 (-4.101642) | 2.770413 / 2.142072 (0.628340) | 0.692715 / 4.805227 (-4.112512) | 0.158883 / 6.500664 (-6.341782) | 0.075894 / 0.075469 (0.000425) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.631250 / 1.841788 (-0.210538) | 24.693250 / 8.074308 (16.618942) | 17.434790 / 10.191392 (7.243398) | 0.196456 / 0.680424 (-0.483968) | 0.022505 / 0.534201 (-0.511696) | 0.474788 / 0.579283 (-0.104495) | 0.500947 / 0.434364 (0.066583) | 0.553596 / 0.540337 (0.013259) | 0.737767 / 1.386936 (-0.649169) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f87d6e6394bf4b390ccc82235eb7667f874e5d43 \"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) | 0.006629 / 0.011353 (-0.004724) | 0.004115 / 0.011008 (-0.006894) | 0.083934 / 0.038508 (0.045426) | 0.074952 / 0.023109 (0.051843) | 0.313069 / 0.275898 (0.037171) | 0.345878 / 0.323480 (0.022398) | 0.006034 / 0.007986 (-0.001952) | 0.003413 / 0.004328 (-0.000916) | 0.065130 / 0.004250 (0.060880) | 0.057363 / 0.037052 (0.020310) | 0.314483 / 0.258489 (0.055994) | 0.352626 / 0.293841 (0.058785) | 0.031325 / 0.128546 (-0.097221) | 0.008577 / 0.075646 (-0.067069) | 0.288137 / 0.419271 (-0.131135) | 0.053651 / 0.043533 (0.010118) | 0.313006 / 0.255139 (0.057867) | 0.338668 / 0.283200 (0.055468) | 0.023709 / 0.141683 (-0.117974) | 1.481209 / 1.452155 (0.029054) | 1.559801 / 1.492716 (0.067085) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211543 / 0.018006 (0.193537) | 0.452185 / 0.000490 (0.451696) | 0.003177 / 0.000200 (0.002977) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028821 / 0.037411 (-0.008591) | 0.083290 / 0.014526 (0.068765) | 0.097478 / 0.176557 (-0.079079) | 0.153506 / 0.737135 (-0.583629) | 0.097054 / 0.296338 (-0.199284) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.385847 / 0.215209 (0.170638) | 3.835629 / 2.077655 (1.757974) | 1.880938 / 1.504120 (0.376819) | 1.711848 / 1.541195 (0.170653) | 1.785099 / 1.468490 (0.316609) | 0.486256 / 4.584777 (-4.098521) | 3.629026 / 3.745712 (-0.116686) | 3.321578 / 5.269862 (-1.948283) | 2.024314 / 4.565676 (-2.541363) | 0.058097 / 0.424275 (-0.366179) | 0.007724 / 0.007607 (0.000117) | 0.458293 / 0.226044 (0.232249) | 4.581314 / 2.268929 (2.312386) | 2.314379 / 55.444624 (-53.130246) | 1.966089 / 6.876477 (-4.910387) | 2.203824 / 2.142072 (0.061752) | 0.611581 / 4.805227 (-4.193647) | 0.149166 / 6.500664 (-6.351498) | 0.059825 / 0.075469 (-0.015644) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.235546 / 1.841788 (-0.606242) | 19.747439 / 8.074308 (11.673131) | 14.628383 / 10.191392 (4.436991) | 0.193074 / 0.680424 (-0.487350) | 0.020327 / 0.534201 (-0.513874) | 0.397051 / 0.579283 (-0.182232) | 0.418491 / 0.434364 (-0.015873) | 0.462055 / 0.540337 (-0.078282) | 0.637524 / 1.386936 (-0.749412) |\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) | 0.007069 / 0.011353 (-0.004284) | 0.004106 / 0.011008 (-0.006902) | 0.065818 / 0.038508 (0.027310) | 0.077101 / 0.023109 (0.053991) | 0.363323 / 0.275898 (0.087425) | 0.399463 / 0.323480 (0.075983) | 0.005540 / 0.007986 (-0.002446) | 0.003480 / 0.004328 (-0.000849) | 0.065176 / 0.004250 (0.060926) | 0.060867 / 0.037052 (0.023815) | 0.365763 / 0.258489 (0.107273) | 0.407789 / 0.293841 (0.113949) | 0.032018 / 0.128546 (-0.096528) | 0.008550 / 0.075646 (-0.067096) | 0.071750 / 0.419271 (-0.347521) | 0.050625 / 0.043533 (0.007092) | 0.361434 / 0.255139 (0.106295) | 0.384799 / 0.283200 (0.101599) | 0.026104 / 0.141683 (-0.115579) | 1.496093 / 1.452155 (0.043938) | 1.592909 / 1.492716 (0.100193) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.185794 / 0.018006 (0.167787) | 0.453379 / 0.000490 (0.452890) | 0.004365 / 0.000200 (0.004165) | 0.000092 / 0.000054 (0.000038) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031666 / 0.037411 (-0.005746) | 0.088323 / 0.014526 (0.073798) | 0.104602 / 0.176557 (-0.071954) | 0.159827 / 0.737135 (-0.577308) | 0.103725 / 0.296338 (-0.192614) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.413509 / 0.215209 (0.198300) | 4.126071 / 2.077655 (2.048416) | 2.137088 / 1.504120 (0.632968) | 1.981034 / 1.541195 (0.439839) | 2.063660 / 1.468490 (0.595170) | 0.478798 / 4.584777 (-4.105979) | 3.642801 / 3.745712 (-0.102911) | 3.428994 / 5.269862 (-1.840867) | 2.031902 / 4.565676 (-2.533774) | 0.056244 / 0.424275 (-0.368032) | 0.007365 / 0.007607 (-0.000242) | 0.484371 / 0.226044 (0.258327) | 4.838537 / 2.268929 (2.569608) | 2.559497 / 55.444624 (-52.885127) | 2.251863 / 6.876477 (-4.624614) | 2.339227 / 2.142072 (0.197155) | 0.607228 / 4.805227 (-4.198000) | 0.133877 / 6.500664 (-6.366787) | 0.062049 / 0.075469 (-0.013420) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.350389 / 1.841788 (-0.491399) | 20.060359 / 8.074308 (11.986051) | 14.305675 / 10.191392 (4.114283) | 0.165642 / 0.680424 (-0.514782) | 0.018206 / 0.534201 (-0.515994) | 0.396907 / 0.579283 (-0.182376) | 0.431896 / 0.434364 (-0.002468) | 0.475778 / 0.540337 (-0.064559) | 0.644688 / 1.386936 (-0.742248) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8f6fa96ae5de873a49ef28739e8f64edf8b18cae \"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) | 0.009048 / 0.011353 (-0.002305) | 0.005787 / 0.011008 (-0.005221) | 0.111617 / 0.038508 (0.073109) | 0.087603 / 0.023109 (0.064494) | 0.446481 / 0.275898 (0.170583) | 0.491726 / 0.323480 (0.168247) | 0.007052 / 0.007986 (-0.000934) | 0.004481 / 0.004328 (0.000152) | 0.084331 / 0.004250 (0.080081) | 0.072006 / 0.037052 (0.034953) | 0.454238 / 0.258489 (0.195749) | 0.496749 / 0.293841 (0.202908) | 0.049027 / 0.128546 (-0.079520) | 0.014005 / 0.075646 (-0.061641) | 0.372550 / 0.419271 (-0.046722) | 0.071414 / 0.043533 (0.027881) | 0.459432 / 0.255139 (0.204293) | 0.467332 / 0.283200 (0.184133) | 0.037539 / 0.141683 (-0.104144) | 1.869179 / 1.452155 (0.417024) | 1.983641 / 1.492716 (0.490925) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.265426 / 0.018006 (0.247419) | 0.672527 / 0.000490 (0.672037) | 0.001152 / 0.000200 (0.000953) | 0.000181 / 0.000054 (0.000127) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032967 / 0.037411 (-0.004445) | 0.103023 / 0.014526 (0.088497) | 0.115978 / 0.176557 (-0.060578) | 0.191698 / 0.737135 (-0.545438) | 0.117867 / 0.296338 (-0.178471) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.602208 / 0.215209 (0.386999) | 6.147784 / 2.077655 (4.070129) | 2.768933 / 1.504120 (1.264813) | 2.415619 / 1.541195 (0.874424) | 2.456159 / 1.468490 (0.987669) | 0.836270 / 4.584777 (-3.748507) | 5.447754 / 3.745712 (1.702042) | 7.751825 / 5.269862 (2.481963) | 4.591892 / 4.565676 (0.026215) | 0.108269 / 0.424275 (-0.316006) | 0.009626 / 0.007607 (0.002019) | 0.719260 / 0.226044 (0.493216) | 7.313442 / 2.268929 (5.044514) | 3.490739 / 55.444624 (-51.953885) | 2.743543 / 6.876477 (-4.132934) | 3.035071 / 2.142072 (0.892999) | 1.042791 / 4.805227 (-3.762436) | 0.217080 / 6.500664 (-6.283584) | 0.084286 / 0.075469 (0.008817) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.655427 / 1.841788 (-0.186361) | 25.386536 / 8.074308 (17.312228) | 21.740666 / 10.191392 (11.549274) | 0.246388 / 0.680424 (-0.434036) | 0.029723 / 0.534201 (-0.504478) | 0.491537 / 0.579283 (-0.087746) | 0.603495 / 0.434364 (0.169131) | 0.573938 / 0.540337 (0.033600) | 0.981875 / 1.386936 (-0.405061) |\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) | 0.009664 / 0.011353 (-0.001689) | 0.006446 / 0.011008 (-0.004562) | 0.085113 / 0.038508 (0.046605) | 0.094533 / 0.023109 (0.071424) | 0.498388 / 0.275898 (0.222490) | 0.540127 / 0.323480 (0.216647) | 0.007316 / 0.007986 (-0.000670) | 0.004252 / 0.004328 (-0.000077) | 0.086292 / 0.004250 (0.082041) | 0.067956 / 0.037052 (0.030903) | 0.507664 / 0.258489 (0.249175) | 0.554324 / 0.293841 (0.260483) | 0.050107 / 0.128546 (-0.078439) | 0.014277 / 0.075646 (-0.061370) | 0.098838 / 0.419271 (-0.320433) | 0.066053 / 0.043533 (0.022521) | 0.491090 / 0.255139 (0.235951) | 0.537432 / 0.283200 (0.254232) | 0.035937 / 0.141683 (-0.105746) | 1.820715 / 1.452155 (0.368561) | 1.996268 / 1.492716 (0.503552) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.300859 / 0.018006 (0.282852) | 0.610958 / 0.000490 (0.610468) | 0.000474 / 0.000200 (0.000274) | 0.000098 / 0.000054 (0.000044) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036372 / 0.037411 (-0.001039) | 0.109115 / 0.014526 (0.094589) | 0.122802 / 0.176557 (-0.053755) | 0.187092 / 0.737135 (-0.550044) | 0.123432 / 0.296338 (-0.172906) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.646979 / 0.215209 (0.431770) | 6.577713 / 2.077655 (4.500058) | 3.004606 / 1.504120 (1.500486) | 2.661183 / 1.541195 (1.119989) | 2.726717 / 1.468490 (1.258227) | 0.889497 / 4.584777 (-3.695280) | 5.485055 / 3.745712 (1.739343) | 4.852043 / 5.269862 (-0.417819) | 3.177392 / 4.565676 (-1.388285) | 0.099796 / 0.424275 (-0.324479) | 0.009868 / 0.007607 (0.002261) | 0.819919 / 0.226044 (0.593874) | 7.911255 / 2.268929 (5.642326) | 3.839877 / 55.444624 (-51.604747) | 3.088663 / 6.876477 (-3.787813) | 3.371184 / 2.142072 (1.229112) | 1.072762 / 4.805227 (-3.732466) | 0.224536 / 6.500664 (-6.276128) | 0.083415 / 0.075469 (0.007946) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.754426 / 1.841788 (-0.087361) | 25.546690 / 8.074308 (17.472382) | 22.998252 / 10.191392 (12.806860) | 0.258019 / 0.680424 (-0.422405) | 0.030104 / 0.534201 (-0.504097) | 0.518406 / 0.579283 (-0.060877) | 0.605753 / 0.434364 (0.171389) | 0.599630 / 0.540337 (0.059292) | 0.819042 / 1.386936 (-0.567894) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#350f4fd6caabbdfacb5fbf9193ab255c3d0daa4c \"CML watermark\")\n"
] |
6,043
|
Compression kwargs have no effect when saving datasets as csv
|
### Describe the bug
Attempting to save a dataset as a compressed csv file, the compression kwargs provided to `.to_csv()` that get piped to panda's `pandas.DataFrame.to_csv` do not have any effect - resulting in the dataset not getting compressed.
A warning is raised if explicitly providing a `compression` kwarg, but no warnings are raised if relying on the defaults. This can lead to datasets secretly not getting compressed for users expecting the behaviour to match panda's `.to_csv()`, where the compression format is automatically inferred from the destination path suffix.
### Steps to reproduce the bug
```python
# dataset is not compressed (but at least a warning is emitted)
import datasets
dataset = datasets.load_dataset("rotten_tomatoes", split="train")
dataset.to_csv("uncompressed.csv")
print(os.path.getsize("uncompressed.csv")) # 1008607
dataset.to_csv("compressed.csv.gz", compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1})
print(os.path.getsize("compressed.csv.gz")) # 1008607
```
```shell
>>>
RuntimeWarning: compression has no effect when passing a non-binary object as input.
csv_str = batch.to_pandas().to_csv(
```
```python
# dataset is not compressed and no warnings are emitted
dataset.to_csv("compressed.csv.gz")
print(os.path.getsize("compressed.csv.gz")) # 1008607
# compare with
dataset.to_pandas().to_csv("pandas.csv.gz")
print(os.path.getsize("pandas.csv.gz")) # 418561
```
---
I think that this is because behind the scenes `pandas.DataFrame.to_csv` is always called with a buf-like `path_or_buf`, but users that are providing a path-like to `datasets.Dataset.to_csv` are likely not to expect / know that - leading to a mismatch in their understanding of the expected behaviour of the `compression` kwarg.
### Expected behavior
The dataset to be saved as a compressed csv file when providing a `compression` kwarg, or when relying on the default `compression='infer'`
### Environment info
`datasets == 2.13.1`
|
[] |
{
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"html_url": null,
"diff_url": null,
"patch_url": null,
"merged_at": null
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| false
|
[
"Hello @exs-avianello, I have reproduced the bug successfully and have understood the problem. But I am confused regarding this part of the statement, \"`pandas.DataFrame.to_csv` is always called with a buf-like `path_or_buf`\".\r\n\r\nCan you please elaborate on it?\r\n\r\nThanks!",
"Hi @aryanxk02 ! Sure, what I actually meant is that when passing a path-like `path_or_buf` here\r\n\r\nhttps://github.com/huggingface/datasets/blob/14f6edd9222e577dccb962ed5338b79b73502fa5/src/datasets/arrow_dataset.py#L4708-L4714 \r\n\r\nit gets converted to a file object behind the scenes here\r\n\r\nhttps://github.com/huggingface/datasets/blob/14f6edd9222e577dccb962ed5338b79b73502fa5/src/datasets/io/csv.py#L92-L94\r\n\r\nand the eventual pandas `.to_csv()` calls that write to it always get `path_or_buf=None`, making pandas ignore the `compression` kwarg in the `to_csv_kwargs`\r\n\r\nhttps://github.com/huggingface/datasets/blob/14f6edd9222e577dccb962ed5338b79b73502fa5/src/datasets/io/csv.py#L107-L109",
"@exs-avianello When `path_or_buf` is set to None, the `to_csv()` method will return the CSV data as a string instead of saving it to a file. Hence the compression doesn't take place. I think setting `path_or_buf=self.path_or_buf` should work. What you say?"
] |
6,042
|
Fix unused DatasetInfosDict code in push_to_hub
| null |
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6042",
"html_url": "https://github.com/huggingface/datasets/pull/6042",
"diff_url": "https://github.com/huggingface/datasets/pull/6042.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6042.patch",
"merged_at": "2023-07-18T16:08:42"
}
| true
|
[
"_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) | 0.008634 / 0.011353 (-0.002719) | 0.005147 / 0.011008 (-0.005861) | 0.102865 / 0.038508 (0.064357) | 0.080245 / 0.023109 (0.057136) | 0.401288 / 0.275898 (0.125390) | 0.419708 / 0.323480 (0.096228) | 0.006342 / 0.007986 (-0.001644) | 0.003998 / 0.004328 (-0.000330) | 0.078880 / 0.004250 (0.074630) | 0.068199 / 0.037052 (0.031147) | 0.389573 / 0.258489 (0.131084) | 0.417292 / 0.293841 (0.123451) | 0.048856 / 0.128546 (-0.079691) | 0.014165 / 0.075646 (-0.061481) | 0.348063 / 0.419271 (-0.071209) | 0.067547 / 0.043533 (0.024014) | 0.402251 / 0.255139 (0.147112) | 0.419478 / 0.283200 (0.136278) | 0.034846 / 0.141683 (-0.106837) | 1.773493 / 1.452155 (0.321338) | 1.930546 / 1.492716 (0.437830) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211835 / 0.018006 (0.193829) | 0.545311 / 0.000490 (0.544821) | 0.006766 / 0.000200 (0.006566) | 0.000104 / 0.000054 (0.000050) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035406 / 0.037411 (-0.002006) | 0.100769 / 0.014526 (0.086243) | 0.108667 / 0.176557 (-0.067890) | 0.193099 / 0.737135 (-0.544036) | 0.113539 / 0.296338 (-0.182799) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.586935 / 0.215209 (0.371726) | 5.895245 / 2.077655 (3.817591) | 2.528375 / 1.504120 (1.024255) | 2.228617 / 1.541195 (0.687423) | 2.295799 / 1.468490 (0.827309) | 0.859272 / 4.584777 (-3.725505) | 5.033434 / 3.745712 (1.287722) | 7.546587 / 5.269862 (2.276726) | 4.457137 / 4.565676 (-0.108539) | 0.099626 / 0.424275 (-0.324649) | 0.009296 / 0.007607 (0.001689) | 0.713498 / 0.226044 (0.487454) | 7.409385 / 2.268929 (5.140456) | 3.361418 / 55.444624 (-52.083206) | 2.681111 / 6.876477 (-4.195366) | 2.849598 / 2.142072 (0.707526) | 1.114863 / 4.805227 (-3.690364) | 0.215494 / 6.500664 (-6.285170) | 0.075807 / 0.075469 (0.000338) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.606458 / 1.841788 (-0.235330) | 23.751096 / 8.074308 (15.676788) | 21.279110 / 10.191392 (11.087718) | 0.220785 / 0.680424 (-0.459639) | 0.032688 / 0.534201 (-0.501513) | 0.530948 / 0.579283 (-0.048335) | 0.630056 / 0.434364 (0.195693) | 0.572743 / 0.540337 (0.032405) | 0.771853 / 1.386936 (-0.615083) |\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) | 0.008693 / 0.011353 (-0.002660) | 0.004750 / 0.011008 (-0.006259) | 0.079764 / 0.038508 (0.041256) | 0.082096 / 0.023109 (0.058987) | 0.467198 / 0.275898 (0.191300) | 0.532361 / 0.323480 (0.208881) | 0.005836 / 0.007986 (-0.002149) | 0.004333 / 0.004328 (0.000005) | 0.080444 / 0.004250 (0.076194) | 0.065883 / 0.037052 (0.028831) | 0.464871 / 0.258489 (0.206382) | 0.575026 / 0.293841 (0.281185) | 0.057807 / 0.128546 (-0.070739) | 0.017462 / 0.075646 (-0.058185) | 0.093667 / 0.419271 (-0.325605) | 0.071466 / 0.043533 (0.027933) | 0.495846 / 0.255139 (0.240707) | 0.526100 / 0.283200 (0.242900) | 0.034852 / 0.141683 (-0.106831) | 1.884152 / 1.452155 (0.431998) | 1.922681 / 1.492716 (0.429965) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.250969 / 0.018006 (0.232963) | 0.504979 / 0.000490 (0.504489) | 0.000466 / 0.000200 (0.000266) | 0.000083 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032411 / 0.037411 (-0.005000) | 0.093184 / 0.014526 (0.078658) | 0.110798 / 0.176557 (-0.065759) | 0.165741 / 0.737135 (-0.571394) | 0.111022 / 0.296338 (-0.185317) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.661284 / 0.215209 (0.446075) | 6.622388 / 2.077655 (4.544733) | 3.095705 / 1.504120 (1.591585) | 2.745698 / 1.541195 (1.204503) | 2.694103 / 1.468490 (1.225612) | 0.862154 / 4.584777 (-3.722623) | 5.109985 / 3.745712 (1.364273) | 5.040362 / 5.269862 (-0.229499) | 3.072837 / 4.565676 (-1.492840) | 0.110421 / 0.424275 (-0.313854) | 0.008476 / 0.007607 (0.000869) | 0.910020 / 0.226044 (0.683975) | 8.123626 / 2.268929 (5.854698) | 3.813811 / 55.444624 (-51.630813) | 3.017244 / 6.876477 (-3.859232) | 3.061222 / 2.142072 (0.919150) | 1.073548 / 4.805227 (-3.731680) | 0.216327 / 6.500664 (-6.284338) | 0.072977 / 0.075469 (-0.002492) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.722482 / 1.841788 (-0.119305) | 23.706716 / 8.074308 (15.632407) | 23.192134 / 10.191392 (13.000742) | 0.276733 / 0.680424 (-0.403691) | 0.033538 / 0.534201 (-0.500663) | 0.602083 / 0.579283 (0.022799) | 0.578718 / 0.434364 (0.144354) | 0.558311 / 0.540337 (0.017974) | 0.740341 / 1.386936 (-0.646595) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7ac575b8ed57dac60d7ba33a616894f38601f84a \"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) | 0.006862 / 0.011353 (-0.004491) | 0.004223 / 0.011008 (-0.006786) | 0.085931 / 0.038508 (0.047423) | 0.081437 / 0.023109 (0.058328) | 0.349542 / 0.275898 (0.073644) | 0.379881 / 0.323480 (0.056401) | 0.005651 / 0.007986 (-0.002334) | 0.003662 / 0.004328 (-0.000666) | 0.065251 / 0.004250 (0.061001) | 0.061599 / 0.037052 (0.024547) | 0.359681 / 0.258489 (0.101192) | 0.392502 / 0.293841 (0.098661) | 0.031300 / 0.128546 (-0.097246) | 0.008591 / 0.075646 (-0.067055) | 0.288577 / 0.419271 (-0.130694) | 0.062920 / 0.043533 (0.019388) | 0.348989 / 0.255139 (0.093850) | 0.362769 / 0.283200 (0.079569) | 0.030087 / 0.141683 (-0.111596) | 1.480748 / 1.452155 (0.028594) | 1.580413 / 1.492716 (0.087697) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.205804 / 0.018006 (0.187798) | 0.455386 / 0.000490 (0.454897) | 0.003134 / 0.000200 (0.002934) | 0.000077 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030252 / 0.037411 (-0.007159) | 0.087566 / 0.014526 (0.073041) | 0.098209 / 0.176557 (-0.078347) | 0.155816 / 0.737135 (-0.581319) | 0.098938 / 0.296338 (-0.197401) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.386688 / 0.215209 (0.171479) | 3.852777 / 2.077655 (1.775123) | 1.938688 / 1.504120 (0.434568) | 1.779234 / 1.541195 (0.238039) | 1.864262 / 1.468490 (0.395772) | 0.482472 / 4.584777 (-4.102305) | 3.658060 / 3.745712 (-0.087652) | 5.206489 / 5.269862 (-0.063373) | 3.262498 / 4.565676 (-1.303179) | 0.057523 / 0.424275 (-0.366752) | 0.007365 / 0.007607 (-0.000242) | 0.466886 / 0.226044 (0.240841) | 4.671026 / 2.268929 (2.402097) | 2.380357 / 55.444624 (-53.064268) | 2.096590 / 6.876477 (-4.779887) | 2.274415 / 2.142072 (0.132342) | 0.579705 / 4.805227 (-4.225522) | 0.134522 / 6.500664 (-6.366142) | 0.062232 / 0.075469 (-0.013237) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.245965 / 1.841788 (-0.595823) | 20.115180 / 8.074308 (12.040872) | 14.602983 / 10.191392 (4.411591) | 0.146890 / 0.680424 (-0.533533) | 0.018424 / 0.534201 (-0.515777) | 0.393941 / 0.579283 (-0.185342) | 0.413785 / 0.434364 (-0.020579) | 0.453344 / 0.540337 (-0.086993) | 0.655446 / 1.386936 (-0.731490) |\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) | 0.006807 / 0.011353 (-0.004546) | 0.004083 / 0.011008 (-0.006925) | 0.065389 / 0.038508 (0.026881) | 0.081056 / 0.023109 (0.057947) | 0.362823 / 0.275898 (0.086925) | 0.401928 / 0.323480 (0.078448) | 0.005452 / 0.007986 (-0.002533) | 0.003413 / 0.004328 (-0.000915) | 0.065238 / 0.004250 (0.060987) | 0.057264 / 0.037052 (0.020211) | 0.375713 / 0.258489 (0.117224) | 0.407858 / 0.293841 (0.114017) | 0.031580 / 0.128546 (-0.096966) | 0.008643 / 0.075646 (-0.067003) | 0.071693 / 0.419271 (-0.347578) | 0.049392 / 0.043533 (0.005859) | 0.370194 / 0.255139 (0.115055) | 0.384647 / 0.283200 (0.101447) | 0.024805 / 0.141683 (-0.116877) | 1.509511 / 1.452155 (0.057356) | 1.560193 / 1.492716 (0.067477) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.234442 / 0.018006 (0.216436) | 0.458818 / 0.000490 (0.458329) | 0.000407 / 0.000200 (0.000207) | 0.000060 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031661 / 0.037411 (-0.005750) | 0.093143 / 0.014526 (0.078618) | 0.102205 / 0.176557 (-0.074352) | 0.155850 / 0.737135 (-0.581286) | 0.104345 / 0.296338 (-0.191994) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419641 / 0.215209 (0.204432) | 4.200808 / 2.077655 (2.123153) | 2.218227 / 1.504120 (0.714107) | 2.052604 / 1.541195 (0.511409) | 2.150611 / 1.468490 (0.682121) | 0.482665 / 4.584777 (-4.102112) | 3.606541 / 3.745712 (-0.139172) | 3.310637 / 5.269862 (-1.959224) | 2.070200 / 4.565676 (-2.495476) | 0.056586 / 0.424275 (-0.367689) | 0.007826 / 0.007607 (0.000218) | 0.491037 / 0.226044 (0.264992) | 4.901538 / 2.268929 (2.632610) | 2.676402 / 55.444624 (-52.768223) | 2.363935 / 6.876477 (-4.512542) | 2.587813 / 2.142072 (0.445741) | 0.579302 / 4.805227 (-4.225926) | 0.132792 / 6.500664 (-6.367873) | 0.061865 / 0.075469 (-0.013604) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.354315 / 1.841788 (-0.487473) | 20.874516 / 8.074308 (12.800208) | 14.863559 / 10.191392 (4.672167) | 0.183635 / 0.680424 (-0.496789) | 0.018636 / 0.534201 (-0.515565) | 0.395317 / 0.579283 (-0.183966) | 0.410598 / 0.434364 (-0.023766) | 0.476485 / 0.540337 (-0.063853) | 0.643246 / 1.386936 (-0.743690) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4472a8795c603a95eef7c8f15cb04f1290cc8d11 \"CML watermark\")\n"
] |
6,041
|
Flatten repository_structure docs on yaml
|
To have Splits, Configurations and Builder parameters at the same doc level
|
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6041",
"html_url": "https://github.com/huggingface/datasets/pull/6041",
"diff_url": "https://github.com/huggingface/datasets/pull/6041.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6041.patch",
"merged_at": "2023-07-17T10:16:22"
}
| true
|
[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6041). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007587 / 0.011353 (-0.003766) | 0.004469 / 0.011008 (-0.006540) | 0.098028 / 0.038508 (0.059520) | 0.086378 / 0.023109 (0.063269) | 0.412290 / 0.275898 (0.136392) | 0.449912 / 0.323480 (0.126432) | 0.004769 / 0.007986 (-0.003217) | 0.003708 / 0.004328 (-0.000621) | 0.075541 / 0.004250 (0.071290) | 0.063821 / 0.037052 (0.026768) | 0.417213 / 0.258489 (0.158724) | 0.471954 / 0.293841 (0.178113) | 0.036243 / 0.128546 (-0.092303) | 0.009540 / 0.075646 (-0.066106) | 0.339043 / 0.419271 (-0.080228) | 0.061853 / 0.043533 (0.018320) | 0.418510 / 0.255139 (0.163371) | 0.462372 / 0.283200 (0.179173) | 0.027328 / 0.141683 (-0.114355) | 1.745114 / 1.452155 (0.292959) | 1.879839 / 1.492716 (0.387123) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211042 / 0.018006 (0.193035) | 0.512865 / 0.000490 (0.512375) | 0.008744 / 0.000200 (0.008544) | 0.000121 / 0.000054 (0.000066) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032493 / 0.037411 (-0.004918) | 0.096472 / 0.014526 (0.081946) | 0.110340 / 0.176557 (-0.066216) | 0.183195 / 0.737135 (-0.553940) | 0.112829 / 0.296338 (-0.183510) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.478040 / 0.215209 (0.262830) | 4.743776 / 2.077655 (2.666121) | 2.389770 / 1.504120 (0.885650) | 2.168468 / 1.541195 (0.627274) | 2.238154 / 1.468490 (0.769663) | 0.572308 / 4.584777 (-4.012469) | 4.154783 / 3.745712 (0.409071) | 3.771509 / 5.269862 (-1.498353) | 2.384828 / 4.565676 (-2.180848) | 0.068122 / 0.424275 (-0.356153) | 0.008573 / 0.007607 (0.000965) | 0.560300 / 0.226044 (0.334256) | 5.591163 / 2.268929 (3.322235) | 2.929660 / 55.444624 (-52.514965) | 2.517721 / 6.876477 (-4.358756) | 2.762285 / 2.142072 (0.620213) | 0.687193 / 4.805227 (-4.118034) | 0.157839 / 6.500664 (-6.342825) | 0.071862 / 0.075469 (-0.003607) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.484788 / 1.841788 (-0.357000) | 21.696071 / 8.074308 (13.621763) | 15.476166 / 10.191392 (5.284774) | 0.185034 / 0.680424 (-0.495390) | 0.021181 / 0.534201 (-0.513020) | 0.463324 / 0.579283 (-0.115959) | 0.502455 / 0.434364 (0.068091) | 0.559880 / 0.540337 (0.019543) | 0.767281 / 1.386936 (-0.619655) |\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) | 0.007572 / 0.011353 (-0.003781) | 0.004331 / 0.011008 (-0.006677) | 0.075023 / 0.038508 (0.036515) | 0.085474 / 0.023109 (0.062365) | 0.464900 / 0.275898 (0.189002) | 0.503348 / 0.323480 (0.179868) | 0.006885 / 0.007986 (-0.001101) | 0.003647 / 0.004328 (-0.000681) | 0.074874 / 0.004250 (0.070623) | 0.071076 / 0.037052 (0.034024) | 0.465495 / 0.258489 (0.207006) | 0.506418 / 0.293841 (0.212577) | 0.038900 / 0.128546 (-0.089647) | 0.009467 / 0.075646 (-0.066180) | 0.082547 / 0.419271 (-0.336724) | 0.058457 / 0.043533 (0.014924) | 0.459114 / 0.255139 (0.203975) | 0.484872 / 0.283200 (0.201673) | 0.027443 / 0.141683 (-0.114240) | 1.713996 / 1.452155 (0.261841) | 1.893639 / 1.492716 (0.400922) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.248693 / 0.018006 (0.230687) | 0.488805 / 0.000490 (0.488315) | 0.000421 / 0.000200 (0.000221) | 0.000067 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034886 / 0.037411 (-0.002525) | 0.103215 / 0.014526 (0.088689) | 0.116422 / 0.176557 (-0.060134) | 0.182789 / 0.737135 (-0.554346) | 0.117788 / 0.296338 (-0.178550) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.482782 / 0.215209 (0.267573) | 4.802895 / 2.077655 (2.725241) | 2.489823 / 1.504120 (0.985703) | 2.324005 / 1.541195 (0.782810) | 2.457674 / 1.468490 (0.989184) | 0.566980 / 4.584777 (-4.017797) | 4.117359 / 3.745712 (0.371647) | 3.841180 / 5.269862 (-1.428681) | 2.322410 / 4.565676 (-2.243266) | 0.066367 / 0.424275 (-0.357908) | 0.008501 / 0.007607 (0.000894) | 0.561453 / 0.226044 (0.335408) | 5.694861 / 2.268929 (3.425932) | 3.129829 / 55.444624 (-52.314796) | 2.647375 / 6.876477 (-4.229102) | 2.673071 / 2.142072 (0.530998) | 0.676120 / 4.805227 (-4.129108) | 0.153483 / 6.500664 (-6.347181) | 0.070797 / 0.075469 (-0.004672) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.575697 / 1.841788 (-0.266091) | 22.447462 / 8.074308 (14.373154) | 15.964906 / 10.191392 (5.773514) | 0.218343 / 0.680424 (-0.462081) | 0.021051 / 0.534201 (-0.513150) | 0.466079 / 0.579283 (-0.113204) | 0.493190 / 0.434364 (0.058826) | 0.565929 / 0.540337 (0.025592) | 0.768638 / 1.386936 (-0.618298) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#104bafffef7ddc775ec2d0b10b2b262466041eb7 \"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) | 0.006268 / 0.011353 (-0.005085) | 0.003715 / 0.011008 (-0.007293) | 0.080628 / 0.038508 (0.042120) | 0.070294 / 0.023109 (0.047185) | 0.404749 / 0.275898 (0.128851) | 0.434130 / 0.323480 (0.110650) | 0.005533 / 0.007986 (-0.002452) | 0.002980 / 0.004328 (-0.001349) | 0.063016 / 0.004250 (0.058766) | 0.051667 / 0.037052 (0.014615) | 0.403859 / 0.258489 (0.145370) | 0.437913 / 0.293841 (0.144073) | 0.027518 / 0.128546 (-0.101029) | 0.007991 / 0.075646 (-0.067655) | 0.260723 / 0.419271 (-0.158548) | 0.046580 / 0.043533 (0.003047) | 0.405453 / 0.255139 (0.150314) | 0.428390 / 0.283200 (0.145190) | 0.022774 / 0.141683 (-0.118909) | 1.488204 / 1.452155 (0.036049) | 1.536557 / 1.492716 (0.043841) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.185864 / 0.018006 (0.167858) | 0.431388 / 0.000490 (0.430898) | 0.003743 / 0.000200 (0.003543) | 0.000065 / 0.000054 (0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024062 / 0.037411 (-0.013350) | 0.075749 / 0.014526 (0.061224) | 0.083519 / 0.176557 (-0.093037) | 0.147965 / 0.737135 (-0.589170) | 0.085635 / 0.296338 (-0.210703) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400455 / 0.215209 (0.185246) | 4.084294 / 2.077655 (2.006640) | 1.928795 / 1.504120 (0.424675) | 1.743205 / 1.541195 (0.202010) | 1.811233 / 1.468490 (0.342743) | 0.504976 / 4.584777 (-4.079801) | 3.073134 / 3.745712 (-0.672578) | 2.816357 / 5.269862 (-2.453505) | 1.857462 / 4.565676 (-2.708214) | 0.058329 / 0.424275 (-0.365946) | 0.006850 / 0.007607 (-0.000757) | 0.466017 / 0.226044 (0.239973) | 4.660158 / 2.268929 (2.391230) | 2.396614 / 55.444624 (-53.048010) | 2.007491 / 6.876477 (-4.868986) | 2.206997 / 2.142072 (0.064925) | 0.592233 / 4.805227 (-4.212994) | 0.125364 / 6.500664 (-6.375300) | 0.061166 / 0.075469 (-0.014303) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.290148 / 1.841788 (-0.551640) | 18.317462 / 8.074308 (10.243154) | 13.465142 / 10.191392 (3.273750) | 0.149696 / 0.680424 (-0.530728) | 0.017120 / 0.534201 (-0.517081) | 0.334818 / 0.579283 (-0.244465) | 0.363976 / 0.434364 (-0.070388) | 0.388271 / 0.540337 (-0.152066) | 0.542383 / 1.386936 (-0.844553) |\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) | 0.006029 / 0.011353 (-0.005324) | 0.003656 / 0.011008 (-0.007352) | 0.063518 / 0.038508 (0.025010) | 0.058214 / 0.023109 (0.035105) | 0.435987 / 0.275898 (0.160089) | 0.442769 / 0.323480 (0.119289) | 0.004675 / 0.007986 (-0.003310) | 0.002911 / 0.004328 (-0.001418) | 0.063020 / 0.004250 (0.058769) | 0.049422 / 0.037052 (0.012369) | 0.435521 / 0.258489 (0.177032) | 0.478251 / 0.293841 (0.184411) | 0.027294 / 0.128546 (-0.101252) | 0.008073 / 0.075646 (-0.067574) | 0.068397 / 0.419271 (-0.350875) | 0.044796 / 0.043533 (0.001263) | 0.416646 / 0.255139 (0.161507) | 0.435021 / 0.283200 (0.151821) | 0.024686 / 0.141683 (-0.116997) | 1.495650 / 1.452155 (0.043496) | 1.495846 / 1.492716 (0.003130) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211205 / 0.018006 (0.193199) | 0.414497 / 0.000490 (0.414007) | 0.001704 / 0.000200 (0.001504) | 0.000073 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025237 / 0.037411 (-0.012174) | 0.077291 / 0.014526 (0.062765) | 0.085736 / 0.176557 (-0.090821) | 0.141059 / 0.737135 (-0.596076) | 0.087620 / 0.296338 (-0.208719) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.421995 / 0.215209 (0.206786) | 4.158503 / 2.077655 (2.080849) | 2.313598 / 1.504120 (0.809479) | 2.183553 / 1.541195 (0.642359) | 2.279656 / 1.468490 (0.811166) | 0.500146 / 4.584777 (-4.084631) | 3.092654 / 3.745712 (-0.653059) | 4.371616 / 5.269862 (-0.898245) | 2.605096 / 4.565676 (-1.960581) | 0.057658 / 0.424275 (-0.366617) | 0.006574 / 0.007607 (-0.001033) | 0.491455 / 0.226044 (0.265411) | 4.926730 / 2.268929 (2.657801) | 2.635749 / 55.444624 (-52.808875) | 2.255780 / 6.876477 (-4.620697) | 2.305547 / 2.142072 (0.163474) | 0.589027 / 4.805227 (-4.216200) | 0.126229 / 6.500664 (-6.374435) | 0.063268 / 0.075469 (-0.012201) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.299102 / 1.841788 (-0.542686) | 18.547417 / 8.074308 (10.473109) | 13.860030 / 10.191392 (3.668638) | 0.145482 / 0.680424 (-0.534942) | 0.016543 / 0.534201 (-0.517658) | 0.330788 / 0.579283 (-0.248496) | 0.362020 / 0.434364 (-0.072344) | 0.380635 / 0.540337 (-0.159703) | 0.517375 / 1.386936 (-0.869561) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bf602e0193baca21e283babbac9622ae36d1e6b6 \"CML watermark\")\n"
] |
6,040
|
Fix legacy_dataset_infos
|
was causing transformers CI to fail
https://circleci.com/gh/huggingface/transformers/855105
|
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6040",
"html_url": "https://github.com/huggingface/datasets/pull/6040",
"diff_url": "https://github.com/huggingface/datasets/pull/6040.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6040.patch",
"merged_at": "2023-07-17T10:16:03"
}
| true
|
[
"_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) | 0.006087 / 0.011353 (-0.005265) | 0.003567 / 0.011008 (-0.007442) | 0.079668 / 0.038508 (0.041160) | 0.063647 / 0.023109 (0.040538) | 0.323082 / 0.275898 (0.047184) | 0.348679 / 0.323480 (0.025199) | 0.004726 / 0.007986 (-0.003259) | 0.002955 / 0.004328 (-0.001373) | 0.062724 / 0.004250 (0.058473) | 0.050194 / 0.037052 (0.013142) | 0.321407 / 0.258489 (0.062918) | 0.355053 / 0.293841 (0.061212) | 0.026992 / 0.128546 (-0.101554) | 0.007994 / 0.075646 (-0.067653) | 0.260562 / 0.419271 (-0.158710) | 0.050933 / 0.043533 (0.007400) | 0.316644 / 0.255139 (0.061505) | 0.336759 / 0.283200 (0.053560) | 0.022581 / 0.141683 (-0.119101) | 1.481259 / 1.452155 (0.029104) | 1.535191 / 1.492716 (0.042475) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.194111 / 0.018006 (0.176104) | 0.448146 / 0.000490 (0.447656) | 0.000321 / 0.000200 (0.000121) | 0.000056 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023908 / 0.037411 (-0.013503) | 0.073316 / 0.014526 (0.058790) | 0.085588 / 0.176557 (-0.090968) | 0.145377 / 0.737135 (-0.591759) | 0.084788 / 0.296338 (-0.211550) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.439327 / 0.215209 (0.224118) | 4.384833 / 2.077655 (2.307179) | 2.322943 / 1.504120 (0.818823) | 2.147737 / 1.541195 (0.606542) | 2.226725 / 1.468490 (0.758235) | 0.502957 / 4.584777 (-4.081820) | 3.098106 / 3.745712 (-0.647606) | 4.194642 / 5.269862 (-1.075220) | 2.598820 / 4.565676 (-1.966856) | 0.057942 / 0.424275 (-0.366333) | 0.006857 / 0.007607 (-0.000750) | 0.511517 / 0.226044 (0.285472) | 5.121797 / 2.268929 (2.852868) | 2.756506 / 55.444624 (-52.688118) | 2.424602 / 6.876477 (-4.451875) | 2.608342 / 2.142072 (0.466270) | 0.589498 / 4.805227 (-4.215729) | 0.126065 / 6.500664 (-6.374600) | 0.061456 / 0.075469 (-0.014013) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.239928 / 1.841788 (-0.601860) | 18.423532 / 8.074308 (10.349224) | 13.935148 / 10.191392 (3.743756) | 0.129913 / 0.680424 (-0.550511) | 0.016744 / 0.534201 (-0.517457) | 0.333468 / 0.579283 (-0.245815) | 0.359615 / 0.434364 (-0.074749) | 0.383678 / 0.540337 (-0.156659) | 0.533007 / 1.386936 (-0.853929) |\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) | 0.005980 / 0.011353 (-0.005373) | 0.003640 / 0.011008 (-0.007368) | 0.062500 / 0.038508 (0.023992) | 0.059843 / 0.023109 (0.036733) | 0.360993 / 0.275898 (0.085095) | 0.401981 / 0.323480 (0.078501) | 0.005495 / 0.007986 (-0.002490) | 0.002862 / 0.004328 (-0.001467) | 0.062491 / 0.004250 (0.058240) | 0.050778 / 0.037052 (0.013726) | 0.371007 / 0.258489 (0.112518) | 0.405154 / 0.293841 (0.111313) | 0.027390 / 0.128546 (-0.101156) | 0.008042 / 0.075646 (-0.067604) | 0.067590 / 0.419271 (-0.351681) | 0.042485 / 0.043533 (-0.001048) | 0.361305 / 0.255139 (0.106166) | 0.388669 / 0.283200 (0.105469) | 0.024143 / 0.141683 (-0.117540) | 1.451508 / 1.452155 (-0.000647) | 1.490431 / 1.492716 (-0.002285) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.175976 / 0.018006 (0.157970) | 0.428923 / 0.000490 (0.428434) | 0.002099 / 0.000200 (0.001899) | 0.000068 / 0.000054 (0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026346 / 0.037411 (-0.011065) | 0.078084 / 0.014526 (0.063558) | 0.087287 / 0.176557 (-0.089269) | 0.144179 / 0.737135 (-0.592957) | 0.088286 / 0.296338 (-0.208053) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.450436 / 0.215209 (0.235227) | 4.488801 / 2.077655 (2.411146) | 2.479303 / 1.504120 (0.975184) | 2.305396 / 1.541195 (0.764201) | 2.370370 / 1.468490 (0.901879) | 0.502355 / 4.584777 (-4.082422) | 3.094733 / 3.745712 (-0.650979) | 4.062367 / 5.269862 (-1.207495) | 2.587506 / 4.565676 (-1.978170) | 0.058245 / 0.424275 (-0.366030) | 0.006487 / 0.007607 (-0.001120) | 0.524147 / 0.226044 (0.298102) | 5.236876 / 2.268929 (2.967947) | 2.897134 / 55.444624 (-52.547490) | 2.574631 / 6.876477 (-4.301846) | 2.620307 / 2.142072 (0.478235) | 0.586963 / 4.805227 (-4.218265) | 0.125761 / 6.500664 (-6.374903) | 0.062264 / 0.075469 (-0.013205) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.299668 / 1.841788 (-0.542120) | 19.004441 / 8.074308 (10.930133) | 13.841867 / 10.191392 (3.650475) | 0.159674 / 0.680424 (-0.520750) | 0.016699 / 0.534201 (-0.517502) | 0.331868 / 0.579283 (-0.247415) | 0.344604 / 0.434364 (-0.089760) | 0.379391 / 0.540337 (-0.160947) | 0.514790 / 1.386936 (-0.872146) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#47a006a90e9711b33db70b0ef2d2cefaadfa2179 \"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) | 0.005792 / 0.011353 (-0.005561) | 0.003519 / 0.011008 (-0.007489) | 0.079133 / 0.038508 (0.040625) | 0.057858 / 0.023109 (0.034749) | 0.314206 / 0.275898 (0.038308) | 0.346939 / 0.323480 (0.023459) | 0.004583 / 0.007986 (-0.003403) | 0.002824 / 0.004328 (-0.001504) | 0.061652 / 0.004250 (0.057402) | 0.048520 / 0.037052 (0.011467) | 0.318018 / 0.258489 (0.059529) | 0.350350 / 0.293841 (0.056509) | 0.026284 / 0.128546 (-0.102262) | 0.007827 / 0.075646 (-0.067819) | 0.259624 / 0.419271 (-0.159647) | 0.052318 / 0.043533 (0.008786) | 0.317400 / 0.255139 (0.062261) | 0.340530 / 0.283200 (0.057331) | 0.025181 / 0.141683 (-0.116501) | 1.459208 / 1.452155 (0.007053) | 1.529158 / 1.492716 (0.036442) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.169692 / 0.018006 (0.151686) | 0.432638 / 0.000490 (0.432148) | 0.003675 / 0.000200 (0.003475) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022956 / 0.037411 (-0.014456) | 0.071860 / 0.014526 (0.057334) | 0.082159 / 0.176557 (-0.094398) | 0.142560 / 0.737135 (-0.594576) | 0.082333 / 0.296338 (-0.214006) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.397923 / 0.215209 (0.182714) | 3.958757 / 2.077655 (1.881102) | 1.925837 / 1.504120 (0.421717) | 1.758114 / 1.541195 (0.216919) | 1.808845 / 1.468490 (0.340354) | 0.501116 / 4.584777 (-4.083661) | 3.007739 / 3.745712 (-0.737973) | 3.295755 / 5.269862 (-1.974106) | 2.123843 / 4.565676 (-2.441833) | 0.057174 / 0.424275 (-0.367101) | 0.006426 / 0.007607 (-0.001182) | 0.468196 / 0.226044 (0.242152) | 4.677392 / 2.268929 (2.408464) | 2.334179 / 55.444624 (-53.110446) | 1.989283 / 6.876477 (-4.887194) | 2.140091 / 2.142072 (-0.001981) | 0.590700 / 4.805227 (-4.214527) | 0.124066 / 6.500664 (-6.376598) | 0.059931 / 0.075469 (-0.015538) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.224547 / 1.841788 (-0.617240) | 17.866979 / 8.074308 (9.792671) | 13.142009 / 10.191392 (2.950617) | 0.147081 / 0.680424 (-0.533343) | 0.016777 / 0.534201 (-0.517424) | 0.327766 / 0.579283 (-0.251517) | 0.343988 / 0.434364 (-0.090376) | 0.383268 / 0.540337 (-0.157070) | 0.528109 / 1.386936 (-0.858827) |\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) | 0.006145 / 0.011353 (-0.005208) | 0.003634 / 0.011008 (-0.007374) | 0.062887 / 0.038508 (0.024379) | 0.062659 / 0.023109 (0.039550) | 0.362962 / 0.275898 (0.087064) | 0.405149 / 0.323480 (0.081669) | 0.004821 / 0.007986 (-0.003164) | 0.002888 / 0.004328 (-0.001441) | 0.062982 / 0.004250 (0.058732) | 0.051929 / 0.037052 (0.014877) | 0.366825 / 0.258489 (0.108336) | 0.409830 / 0.293841 (0.115989) | 0.027263 / 0.128546 (-0.101283) | 0.007972 / 0.075646 (-0.067674) | 0.067413 / 0.419271 (-0.351858) | 0.044233 / 0.043533 (0.000700) | 0.365087 / 0.255139 (0.109948) | 0.393845 / 0.283200 (0.110646) | 0.027740 / 0.141683 (-0.113943) | 1.497896 / 1.452155 (0.045741) | 1.549419 / 1.492716 (0.056703) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225510 / 0.018006 (0.207503) | 0.417054 / 0.000490 (0.416564) | 0.002184 / 0.000200 (0.001984) | 0.000075 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025503 / 0.037411 (-0.011908) | 0.076164 / 0.014526 (0.061638) | 0.086110 / 0.176557 (-0.090446) | 0.140387 / 0.737135 (-0.596748) | 0.086956 / 0.296338 (-0.209382) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.469667 / 0.215209 (0.254458) | 4.689915 / 2.077655 (2.612261) | 2.685000 / 1.504120 (1.180880) | 2.516160 / 1.541195 (0.974965) | 2.531733 / 1.468490 (1.063243) | 0.501675 / 4.584777 (-4.083102) | 3.000579 / 3.745712 (-0.745133) | 2.853376 / 5.269862 (-2.416486) | 1.810677 / 4.565676 (-2.754999) | 0.057632 / 0.424275 (-0.366643) | 0.006390 / 0.007607 (-0.001217) | 0.543986 / 0.226044 (0.317941) | 5.432837 / 2.268929 (3.163908) | 3.138797 / 55.444624 (-52.305827) | 2.813141 / 6.876477 (-4.063336) | 2.803681 / 2.142072 (0.661609) | 0.588736 / 4.805227 (-4.216491) | 0.125696 / 6.500664 (-6.374968) | 0.062492 / 0.075469 (-0.012977) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.337163 / 1.841788 (-0.504624) | 18.611715 / 8.074308 (10.537407) | 13.953016 / 10.191392 (3.761624) | 0.154670 / 0.680424 (-0.525754) | 0.016523 / 0.534201 (-0.517678) | 0.333898 / 0.579283 (-0.245385) | 0.336520 / 0.434364 (-0.097844) | 0.389032 / 0.540337 (-0.151305) | 0.529202 / 1.386936 (-0.857734) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#01d4b3330f2cc243a3f3b0cd61ec5558466c40fd \"CML watermark\")\n"
] |
6,039
|
Loading column subset from parquet file produces error since version 2.13
|
### Describe the bug
`load_dataset` allows loading a subset of columns from a parquet file with the `columns` argument. Since version 2.13, this produces the following error:
```
Traceback (most recent call last):
File "/usr/lib/python3.10/site-packages/datasets/builder.py", line 1879, in _prepare_split_single
for _, table in generator:
File "/usr/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py", line 68, in _generate_tables
raise ValueError(
ValueError: Tried to load parquet data with columns '['sepal_length']' with mismatching features '{'sepal_length': Value(dtype='float64', id=None), 'sepal_width': Value(dtype='float64', id=None), 'petal_length': Value(dtype='float64', id=None), 'petal_width': Value(dtype='float64', id=None), 'species': Value(dtype='string', id=None)}'
```
This seems to occur because `datasets` is checking whether the columns in the schema exactly match the provided list of columns, instead of whether they are a subset.
### Steps to reproduce the bug
```python
# Prepare some sample data
import pandas as pd
iris = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')
iris.to_parquet('iris.parquet')
# ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']
print(iris.columns)
# Load data with datasets
from datasets import load_dataset
# Load full parquet file
dataset = load_dataset('parquet', data_files='iris.parquet')
# Load column subset; throws error for datasets>=2.13
dataset = load_dataset('parquet', data_files='iris.parquet', columns=['sepal_length'])
```
### Expected behavior
No error should be thrown and the given column subset should be loaded.
### Environment info
- `datasets` version: 2.13.0
- Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.35
- Python version: 3.10.9
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 1.5.3
|
[] |
{
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"html_url": null,
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}
| false
|
[] |
6,038
|
File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/builder.py", line 992, in _download_and_prepare if str(split_generator.split_info.name).lower() == "all": AttributeError: 'str' object has no attribute 'split_info'. Did you mean: 'splitlines'?
|
Hi, I use the code below to load local file
```
def _split_generators(self, dl_manager):
# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
# urls = _URLS[self.config.name]
data_dir = dl_manager.download_and_extract(_URLs)
print(data_dir)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir["train"]),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir["dev"]),
"split": "dev",
},
),
]
```
and error occured
```
Traceback (most recent call last):
File "/home/zhizhou/data1/zhanghao/huggingface/FineTuning_Transformer/load_local_dataset.py", line 2, in <module>
dataset = load_dataset("./QA_script.py",data_files='/home/zhizhou/.cache/huggingface/datasets/conversatiom_corps/part_file.json')
File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/load.py", line 1809, in load_dataset
builder_instance.download_and_prepare(
File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/builder.py", line 909, in download_and_prepare
self._download_and_prepare(
File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/builder.py", line 1670, in _download_and_prepare
super()._download_and_prepare(
File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/builder.py", line 992, in _download_and_prepare
if str(split_generator.split_info.name).lower() == "all":
AttributeError: 'str' object has no attribute 'split_info'. Did you mean: 'splitlines'?
```
Could you help me?
|
[] |
{
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"html_url": null,
"diff_url": null,
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| false
|
[
"Instead of writing the loading script, you can use the built-in loader to [load JSON files](https://huggingface.co/docs/datasets/loading#json):\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset(\"json\", data_files={\"train\": os.path.join(data_dir[\"train\"]), \"dev\": os.path.join(data_dir[\"dev\"])})\r\n```"
] |
6,037
|
Documentation links to examples are broken
|
### Describe the bug
The links at the bottom of [add_dataset](https://huggingface.co/docs/datasets/v1.2.1/add_dataset.html) to examples of specific datasets are all broken, for example
- text classification: [ag_news](https://github.com/huggingface/datasets/blob/master/datasets/ag_news/ag_news.py) (original data are in csv files)
### Steps to reproduce the bug
Click on links to examples from latest documentation
### Expected behavior
Links should be up to date - it might be more stable to link to https://huggingface.co/datasets/ag_news/blob/main/ag_news.py
### Environment info
dataset v1.2.1
|
[] |
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|
[
"These docs are outdated (version 1.2.1 is over two years old). Please refer to [this](https://huggingface.co/docs/datasets/dataset_script) version instead.\r\n\r\nInitially, we hosted datasets in this repo, but now you can find them [on the HF Hub](https://huggingface.co/datasets) (e.g. the [`ag_news`](https://huggingface.co/datasets/ag_news/blob/main/ag_news.py) script)",
"Sorry I thought I'd selected the latest version."
] |
6,036
|
Deprecate search API
|
The Search API only supports Faiss and ElasticSearch as vector stores, is somewhat difficult to maintain (e.g., it still doesn't support ElasticSeach 8.0, difficult testing, ...), does not have the best design (adds a bunch of methods to the `Dataset` class that are only useful after creating an index), the usage doesn't seem to be significant and is not integrated with the Hub. Since we have no plans/bandwidth to improve it and better alternatives such as `langchain` and `docarray` exist, I think it should be deprecated (and eventually removed).
If we decide to deprecate/remove it, the following usage instances need to be addressed:
* [Course](https://github.com/huggingface/course/blob/0018bb434204d9750a03592cb0d4e846093218d8/chapters/en/chapter5/6.mdx#L342 ) and [Blog](https://github.com/huggingface/blog/blob/4897c6f73d4492a0955ade503281711d01840e09/image-search-datasets.md?plain=1#L252) - calling the FAISS API directly should be OK in these instances as it's pretty simple to use for basic scenarios. Alternatively, we can use `langchain`, but this adds an extra dependency
* [Transformers](https://github.com/huggingface/transformers/blob/50726f9ea7afc6113da617f8f4ca1ab264a5e28a/src/transformers/models/rag/retrieval_rag.py#L183) - we can use the FAISS API directly and store the index as a separate attribute (and instead of building the `wiki_dpr` index each time the dataset is generated, we can generate it once and push it to the Hub repo, and then read it from there
cc @huggingface/datasets @LysandreJik for the opinion
|
[] |
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| true
|
[
"<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) | 0.005746 / 0.011353 (-0.005607) | 0.003461 / 0.011008 (-0.007548) | 0.078672 / 0.038508 (0.040164) | 0.056800 / 0.023109 (0.033691) | 0.312853 / 0.275898 (0.036955) | 0.346715 / 0.323480 (0.023235) | 0.004516 / 0.007986 (-0.003469) | 0.002872 / 0.004328 (-0.001457) | 0.061264 / 0.004250 (0.057013) | 0.046606 / 0.037052 (0.009553) | 0.320080 / 0.258489 (0.061591) | 0.350390 / 0.293841 (0.056550) | 0.026445 / 0.128546 (-0.102101) | 0.007710 / 0.075646 (-0.067936) | 0.259519 / 0.419271 (-0.159752) | 0.043935 / 0.043533 (0.000402) | 0.320015 / 0.255139 (0.064876) | 0.339799 / 0.283200 (0.056599) | 0.018638 / 0.141683 (-0.123044) | 1.463393 / 1.452155 (0.011239) | 1.496977 / 1.492716 (0.004261) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.185175 / 0.018006 (0.167168) | 0.420734 / 0.000490 (0.420245) | 0.002569 / 0.000200 (0.002369) | 0.000067 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022335 / 0.037411 (-0.015077) | 0.071686 / 0.014526 (0.057161) | 0.079906 / 0.176557 (-0.096650) | 0.140386 / 0.737135 (-0.596749) | 0.079712 / 0.296338 (-0.216627) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.392643 / 0.215209 (0.177434) | 3.917934 / 2.077655 (1.840279) | 1.906808 / 1.504120 (0.402688) | 1.729564 / 1.541195 (0.188369) | 1.751533 / 1.468490 (0.283043) | 0.496810 / 4.584777 (-4.087967) | 3.047405 / 3.745712 (-0.698307) | 4.361766 / 5.269862 (-0.908095) | 2.660845 / 4.565676 (-1.904832) | 0.056951 / 0.424275 (-0.367324) | 0.006277 / 0.007607 (-0.001330) | 0.466357 / 0.226044 (0.240312) | 4.660457 / 2.268929 (2.391529) | 2.328590 / 55.444624 (-53.116034) | 1.986140 / 6.876477 (-4.890337) | 2.096182 / 2.142072 (-0.045891) | 0.581685 / 4.805227 (-4.223542) | 0.123643 / 6.500664 (-6.377021) | 0.060286 / 0.075469 (-0.015183) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.237024 / 1.841788 (-0.604763) | 17.778533 / 8.074308 (9.704225) | 13.202205 / 10.191392 (3.010813) | 0.141301 / 0.680424 (-0.539123) | 0.016453 / 0.534201 (-0.517748) | 0.329173 / 0.579283 (-0.250110) | 0.349945 / 0.434364 (-0.084419) | 0.375319 / 0.540337 (-0.165018) | 0.530394 / 1.386936 (-0.856542) |\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) | 0.005863 / 0.011353 (-0.005489) | 0.003578 / 0.011008 (-0.007430) | 0.062719 / 0.038508 (0.024211) | 0.056192 / 0.023109 (0.033082) | 0.422812 / 0.275898 (0.146914) | 0.454316 / 0.323480 (0.130836) | 0.004446 / 0.007986 (-0.003540) | 0.002808 / 0.004328 (-0.001521) | 0.062819 / 0.004250 (0.058569) | 0.046243 / 0.037052 (0.009190) | 0.445858 / 0.258489 (0.187369) | 0.463750 / 0.293841 (0.169909) | 0.027504 / 0.128546 (-0.101042) | 0.007897 / 0.075646 (-0.067749) | 0.068248 / 0.419271 (-0.351024) | 0.041921 / 0.043533 (-0.001612) | 0.413314 / 0.255139 (0.158175) | 0.441619 / 0.283200 (0.158419) | 0.019246 / 0.141683 (-0.122437) | 1.457069 / 1.452155 (0.004914) | 1.524168 / 1.492716 (0.031452) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237785 / 0.018006 (0.219779) | 0.418455 / 0.000490 (0.417965) | 0.002301 / 0.000200 (0.002101) | 0.000065 / 0.000054 (0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025630 / 0.037411 (-0.011781) | 0.076673 / 0.014526 (0.062147) | 0.084877 / 0.176557 (-0.091680) | 0.137528 / 0.737135 (-0.599607) | 0.085261 / 0.296338 (-0.211077) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419040 / 0.215209 (0.203831) | 4.183022 / 2.077655 (2.105368) | 2.157852 / 1.504120 (0.653732) | 1.966177 / 1.541195 (0.424982) | 2.019612 / 1.468490 (0.551122) | 0.497415 / 4.584777 (-4.087362) | 3.102873 / 3.745712 (-0.642839) | 4.526336 / 5.269862 (-0.743525) | 2.991503 / 4.565676 (-1.574174) | 0.057235 / 0.424275 (-0.367040) | 0.006735 / 0.007607 (-0.000872) | 0.498255 / 0.226044 (0.272211) | 4.957364 / 2.268929 (2.688435) | 2.632643 / 55.444624 (-52.811981) | 2.249788 / 6.876477 (-4.626688) | 2.289134 / 2.142072 (0.147062) | 0.583581 / 4.805227 (-4.221646) | 0.126046 / 6.500664 (-6.374618) | 0.062966 / 0.075469 (-0.012504) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.295215 / 1.841788 (-0.546573) | 18.554020 / 8.074308 (10.479711) | 13.683273 / 10.191392 (3.491881) | 0.132266 / 0.680424 (-0.548158) | 0.016376 / 0.534201 (-0.517825) | 0.334495 / 0.579283 (-0.244788) | 0.347106 / 0.434364 (-0.087258) | 0.387531 / 0.540337 (-0.152806) | 0.525745 / 1.386936 (-0.861191) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f22aa0bf179602e4bf3f44a9de5d180579bb377e \"CML watermark\")\n",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6036). All of your documentation changes will be reflected on that endpoint.",
"I don't think `transformers` should have any dataset indexing code. So before deprecating I'd be in favor of finding a suitable replacement. Not sure about the stats of the RAG model that uses `datasets` indexing though",
"The RAG downloads stats are decent (over 20k downloads last month).\r\n\r\nI think it's suboptimal to maintain an API that only a single model uses. One option is to put this code into a separate lib. However, `langchain` and `docarray` already provide a unified interface to vector stores, so I don't see this as an impactful project. Considering how specific this model is, I think we should go with the simplest solution and combine an index with a dataset in Transformers (this wouldn't require too much code).",
"What about migrating to the [datasets-server](https://github.com/huggingface/datasets-server) search feature instead? Would make more sense from a product perspective ",
"I don't think it's a good idea:\r\n- using datasets-server would require to upload the data and to not control the indexing, whereas the current feature is about using a local index that you control\r\n- faiss indexes are vector indexes that are not supported by datasets-server, and they are also very customised. For instance RAG uses DPR embeddings and cosine similarity\r\n- FTS is only done for the first 5GB of data for now in datasets-server\r\n\r\nI think a better option would be to integrate with open source search tools such as docarray.\r\nAnd if we want to make the datasets-server search available in python we can build an integration in docarray and/or in huggingface_hub.",
"`llama_index` is another popular tool in this space.\r\n\r\n@lhoestq \r\n> I think a better option would be to integrate with open source search tools such as docarray.\r\nAnd if we want to make the datasets-server search available in python we can build an integration in docarray and/or in huggingface_hub.\r\n\r\nI don't think these integrations would be popular unless we integrate them with the Hub \"UI-wise\" (e.g., through a widget), so they can wait IMO. Also, FAISS supports `fsspec` already with the callback reader/writer, so this doesn't require a specific integration. ",
"After discussing it a bit with @lhoestq, do we need to deprecate the search API? While I understand it's imperfect, it looks like this will result in significant work to update it everywhere, so I'd favor keeping it until there's an obviously better alternative; this way we can focus on different things in the meantime.",
"FAISS/ES are simple to use (probably the main reason why they are so popular), so creating \"better alternatives\" is not easy - they usually add more complexity (as is the case here, `langchain`, etc.)\r\n\r\nSo, instead of waiting for better alternatives, IMO it makes more sense to wait for the RAG model to be deprecated in Transformers (less than 1,000 cumulated downloads over all checkpoints in the past 30 days) before deprecating this API here.\r\n\r\nIn the meantime, we should make it clear that the vector search API is in maintenance mode (no new features, etc.).\r\n\r\nHow does that sound?"
] |
6,035
|
Dataset representation
|
__repr__ and _repr_html_ now both are similar to that of Polars
|
[] |
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"url": "https://api.github.com/repos/huggingface/datasets/pulls/6035",
"html_url": "https://github.com/huggingface/datasets/pull/6035",
"diff_url": "https://github.com/huggingface/datasets/pull/6035.diff",
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"merged_at": null
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| true
|
[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6035). All of your documentation changes will be reflected on that endpoint."
] |
6,034
|
load_dataset hangs on WSL
|
### Describe the bug
load_dataset simply hangs. It happens once every ~5 times, and interestingly hangs for a multiple of 5 minutes (hangs for 5/10/15 minutes). Using the profiler in PyCharm shows that it spends the time at <method 'connect' of '_socket.socket' objects>. However, a local cache is available so I am not sure why socket is needed. ([profiler result](https://ibb.co/0Btbbp8))
It only happens on WSL for me. It works for native Windows and my MacBook. (cache quickly recognized and loaded within a second).
### Steps to reproduce the bug
I am using Ubuntu 22.04.2 LTS (GNU/Linux 5.15.90.1-microsoft-standard-WSL2 x86_64)
Python 3.10.10 (main, Mar 21 2023, 18:45:11) [GCC 11.2.0] on linux
>>> import datasets
>>> datasets.load_dataset('ai2_arc', 'ARC-Challenge') # hangs for 5/10/15 minutes
### Expected behavior
cache quickly recognized and loaded within a second
### Environment info
Please let me know if I should provide more environment information.
|
[] |
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| false
|
[
"Even if a dataset is cached, we still make requests to check whether the cache is up-to-date. [This](https://huggingface.co/docs/datasets/v2.13.1/en/loading#offline) section in the docs explains how to avoid them and directly load the cached version.",
"Thanks - that works! However it doesn't resolve the original issue (but I am not sure if it is a WSL problem)",
"We use `requests` to make HTTP requests (and `aiohttp` in the streaming mode), so I don't think we can provide much help regarding the socket issue (it probably has something to do with WSL). "
] |
6,033
|
`map` function doesn't fully utilize `input_columns`.
|
### Describe the bug
I wanted to select only some columns of data.
And I thought that's why the argument `input_columns` exists.
What I expected is like this:
If there are ["a", "b", "c", "d"] columns, and if I set `input_columns=["a", "d"]`, the data will have only ["a", "d"] columns.
But it doesn't select columns.
It preserves existing columns.
The main cause is `update` function of `dictionary` type `transformed_batch`.
https://github.com/huggingface/datasets/blob/682d21e94ab1e64c11b583de39dc4c93f0101c5a/src/datasets/iterable_dataset.py#L687-L691
`transformed_batch` gets all the columns by `transformed_batch = dict(batch)`.
Even `function_args` selects `input_columns`, `update` preserves columns other than `input_columns`.
I think it should take a new dictionary with columns in `input_columns` like this:
```
# transformed_batch = dict(batch)
# transformed_batch.update(self.function(*function_args, **self.fn_kwargs)
# This is what I think correct.
transformed_batch = self.function(*function_args, **self.fn_kwargs)
```
Let me know how to use `input_columns`.
### Steps to reproduce the bug
Described all above.
### Expected behavior
Described all above.
### Environment info
datasets: 2.12
python: 3.8
|
[] |
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| false
|
[] |
6,032
|
DownloadConfig.proxies not work when load_dataset_builder calling HfApi.dataset_info
|
### Describe the bug
```python
download_config = DownloadConfig(proxies={'https': '<my proxy>'})
builder = load_dataset_builder(..., download_config=download_config)
```
But, when getting the dataset_info from HfApi, the http requests not using the proxies.
### Steps to reproduce the bug
1. Setup proxies in DownloadConfig.
2. Call `load_dataset_build` with download_config.
3. Inspect the call stack in HfApi.dataset_info.
### Expected behavior
DownloadConfig.proxies works for getting dataset_info.
### Environment info
https://github.com/huggingface/datasets/commit/406b2212263c0d33f267e35b917f410ff6b3bc00
Python 3.11.4
|
[] |
{
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| false
|
[
"`HfApi` comes from the `huggingface_hub` package. You can use [this](https://huggingface.co/docs/huggingface_hub/v0.16.3/en/package_reference/utilities#huggingface_hub.configure_http_backend) utility to change the `huggingface_hub`'s `Session` proxies (see the example).\r\n\r\nWe plan to implement https://github.com/huggingface/datasets/issues/5080 and make this behavior more consistent eventually.",
"> this\r\n\r\nThanks. I will try `huggingface_hub.configure_http_backend` to change session's config.",
"@mariosasko are you saying if I do the following:\r\n\r\n```\r\ndef backend_factory() -> requests.Session:\r\n session = requests.Session()\r\n session.proxies = {\r\n \"https\": \"127.0.0.1:8887\",\r\n \"http\": \"127.0.0.1:8887\",\r\n }\r\n session.verify = \"/etc/ssl/certs/ca-certificates.crt\"\r\n return session\r\n\r\n# Set it as the default session factory\r\nconfigure_http_backend(backend_factory=backend_factory)\r\n```\r\n\r\nwhich works nicely with transformer library:\r\n\r\n```\r\ndef download_gpt_2_model():\r\n tokenizer = GPT2Tokenizer.from_pretrained(\r\n \"gpt2\", force_download=True, resume_download=False\r\n )\r\n text = \"Replace me by any text you'd like.\"\r\n encoded_input = tokenizer(text, return_tensors=\"pt\")\r\n print(encoded_input)\r\n\r\n model = GPT2Model.from_pretrained(\r\n \"gpt2\", force_download=True, resume_download=False\r\n )\r\n output = model(**encoded_input)\r\n```\r\n\r\nshould work for datasets library as well ?\r\n\r\nIn my case if I just do:\r\n\r\n```\r\ndef download_sts12_sts_dataset():\r\n dataset = load_dataset(\r\n \"mteb/sts12-sts\",\r\n download_mode=\"force_redownload\",\r\n verification_mode=\"basic_checks\",\r\n revision=\"main\",\r\n )\r\n\r\n```\r\nI am getting:\r\n`ConnectionError: Couldn't reach https://huggingface.co/datasets/mteb/sts12-sts/resolve/main/dataset_infos.json (ConnectTimeout(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /datasets/mteb/sts12-sts/resolve/main/dataset_infos.json (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7f429e87a3a0>, 'Connection to huggingface.co timed out. (connect timeout=100)'))\")))`\r\n\r\nwhich is typical when the proxy server is not defined. Looks like what is set in configure_http_backend(backend_factory=backend_factory) is ignore.\r\n\r\nIf I use env variable instead, it is working \r\n```\r\ndef download_sts12_sts_dataset():\r\n\r\n os.environ[\"https_proxy\"] = \"127.0.0.1:8887\"\r\n os.environ[\"http_proxy\"] = \"127.0.0.1:8887\"\r\n os.environ[\"REQUESTS_CA_BUNDLE\"] = \"/etc/ssl/certs/ca-certificates.crt\"\r\n\r\n dataset = load_dataset(\r\n \"mteb/sts12-sts\",\r\n download_mode=\"force_redownload\",\r\n verification_mode=\"basic_checks\",\r\n revision=\"main\",\r\n )\r\n```\r\n\r\nShould I add something ?\r\n\r\nI am using `huggingface_hub 0.15.1`, `datasets 2.13.0`, `transformers 4.30.2`",
"`huggingface_hub.configure_http_backend` works for `transformers` because they only use the `huggingface_hub` lib for downloads. Our download logic is a bit more complex (e.g., we also support downloading non-Hub files), so we are not aligned with them yet. In the meantime, it's best to use the env vars.",
"@mariosasko I fully understand that the logic for dataset is different. I see 2 issues with the current implementation of the env variables:\r\n\r\n- having the same https_proxy/http_prox/no_proxy env variables for all tools is not good in some case. For example I have 2 differents proxy server. In 2019 we had discussion with the Tensorflow teams and they recommended to do the following: TFDS_HTTP_PROXY, TFDS_HTTPS_PROXY ...\r\n- with recent version of requests, it is not possible to deactivate TLS interception (verify=false) by using env variable. This is useful to debug things and in some case TLS is not working and you need to ignore verifying the SSL certificate (probably not recommended) \r\n\r\nOne of the best way is to able to pass our requests.Session() directly\r\n```\r\nimport openai\r\nsession = requests.Session()\r\nsession.cert = CERT\r\nsession.verify = False\r\nopenai.requestssession = session\r\n```\r\n\r\nMy 2 cents in this discussion"
] |
6,031
|
Argument type for map function changes when using `input_columns` for `IterableDataset`
|
### Describe the bug
I wrote `tokenize(examples)` function as an argument for `map` function for `IterableDataset`.
It process dictionary type `examples` as a parameter.
It is used in `train_dataset = train_dataset.map(tokenize, batched=True)`
No error is raised.
And then, I found some unnecessary keys and values in `examples` so I added `input_columns` argument to `map` function to select keys and values.
It gives me an error saying
```
TypeError: tokenize() takes 1 positional argument but 3 were given.
```
The code below matters.
https://github.com/huggingface/datasets/blob/406b2212263c0d33f267e35b917f410ff6b3bc00/src/datasets/iterable_dataset.py#L687
For example, `inputs = {"a":1, "b":2, "c":3}`.
If `self.input_coluns` is `None`,
`inputs` is a dictionary type variable and `function_args` becomes a `list` of a single `dict` variable.
`function_args` becomes `[{"a":1, "b":2, "c":3}]`
Otherwise, lets say `self.input_columns = ["a", "c"]`
`[inputs[col] for col in self.input_columns]` results in `[1, 3]`.
I think it should be `[{"a":1, "c":3}]`.
I want to ask if the resulting format is intended.
Maybe I can modify `tokenize()` to have 2 parameters in this case instead of having 1 dictionary.
But this is confusing to me.
Or it should be fixed as `[{col:inputs[col] for col in self.input_columns}]`
### Steps to reproduce the bug
Run `map` function of `IterableDataset` with `input_columns` argument.
### Expected behavior
`function_args` looks better to have same format.
I think it should be `[{"a":1, "c":3}]`.
### Environment info
dataset version: 2.12
python: 3.8
|
[] |
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|
[
"Yes, this is intended."
] |
6,030
|
fixed typo in comment
|
This mistake was a bit confusing, so I thought it was worth sending a PR over.
|
[] |
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"merged_at": "2023-07-14T14:13:38"
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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) | 0.005855 / 0.011353 (-0.005498) | 0.003556 / 0.011008 (-0.007452) | 0.079430 / 0.038508 (0.040922) | 0.056754 / 0.023109 (0.033645) | 0.311718 / 0.275898 (0.035820) | 0.346731 / 0.323480 (0.023251) | 0.004414 / 0.007986 (-0.003571) | 0.002835 / 0.004328 (-0.001493) | 0.062138 / 0.004250 (0.057888) | 0.044259 / 0.037052 (0.007206) | 0.314681 / 0.258489 (0.056192) | 0.359802 / 0.293841 (0.065961) | 0.026684 / 0.128546 (-0.101862) | 0.008023 / 0.075646 (-0.067623) | 0.260148 / 0.419271 (-0.159123) | 0.043734 / 0.043533 (0.000202) | 0.312081 / 0.255139 (0.056942) | 0.340004 / 0.283200 (0.056805) | 0.019559 / 0.141683 (-0.122124) | 1.488758 / 1.452155 (0.036604) | 1.510828 / 1.492716 (0.018111) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.181376 / 0.018006 (0.163370) | 0.441726 / 0.000490 (0.441236) | 0.001722 / 0.000200 (0.001522) | 0.000066 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023760 / 0.037411 (-0.013651) | 0.071847 / 0.014526 (0.057321) | 0.082642 / 0.176557 (-0.093915) | 0.145555 / 0.737135 (-0.591580) | 0.084554 / 0.296338 (-0.211784) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.401688 / 0.215209 (0.186479) | 4.000994 / 2.077655 (1.923339) | 2.047109 / 1.504120 (0.542989) | 1.891874 / 1.541195 (0.350679) | 1.970599 / 1.468490 (0.502109) | 0.500646 / 4.584777 (-4.084131) | 3.006623 / 3.745712 (-0.739089) | 4.248359 / 5.269862 (-1.021503) | 2.613946 / 4.565676 (-1.951730) | 0.057921 / 0.424275 (-0.366354) | 0.006407 / 0.007607 (-0.001200) | 0.470676 / 0.226044 (0.244631) | 4.722280 / 2.268929 (2.453352) | 2.448530 / 55.444624 (-52.996095) | 2.175841 / 6.876477 (-4.700635) | 2.352287 / 2.142072 (0.210214) | 0.589049 / 4.805227 (-4.216179) | 0.125145 / 6.500664 (-6.375519) | 0.060829 / 0.075469 (-0.014640) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.189225 / 1.841788 (-0.652563) | 16.753085 / 8.074308 (8.678777) | 13.086512 / 10.191392 (2.895120) | 0.132371 / 0.680424 (-0.548052) | 0.016933 / 0.534201 (-0.517268) | 0.328258 / 0.579283 (-0.251025) | 0.344074 / 0.434364 (-0.090290) | 0.374042 / 0.540337 (-0.166296) | 0.515307 / 1.386936 (-0.871629) |\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) | 0.005963 / 0.011353 (-0.005390) | 0.003484 / 0.011008 (-0.007525) | 0.062618 / 0.038508 (0.024110) | 0.057217 / 0.023109 (0.034108) | 0.426760 / 0.275898 (0.150862) | 0.464422 / 0.323480 (0.140942) | 0.005276 / 0.007986 (-0.002709) | 0.002872 / 0.004328 (-0.001456) | 0.062636 / 0.004250 (0.058385) | 0.045953 / 0.037052 (0.008900) | 0.433221 / 0.258489 (0.174732) | 0.475087 / 0.293841 (0.181246) | 0.027217 / 0.128546 (-0.101329) | 0.007965 / 0.075646 (-0.067681) | 0.067749 / 0.419271 (-0.351522) | 0.041235 / 0.043533 (-0.002298) | 0.425424 / 0.255139 (0.170285) | 0.453390 / 0.283200 (0.170190) | 0.020217 / 0.141683 (-0.121466) | 1.436354 / 1.452155 (-0.015801) | 1.492372 / 1.492716 (-0.000345) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226896 / 0.018006 (0.208889) | 0.411935 / 0.000490 (0.411445) | 0.000356 / 0.000200 (0.000156) | 0.000054 / 0.000054 (-0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024705 / 0.037411 (-0.012706) | 0.076232 / 0.014526 (0.061706) | 0.086949 / 0.176557 (-0.089608) | 0.141867 / 0.737135 (-0.595269) | 0.088199 / 0.296338 (-0.208140) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419748 / 0.215209 (0.204539) | 4.198597 / 2.077655 (2.120942) | 2.338477 / 1.504120 (0.834357) | 2.195741 / 1.541195 (0.654547) | 2.278145 / 1.468490 (0.809655) | 0.502365 / 4.584777 (-4.082412) | 2.987773 / 3.745712 (-0.757939) | 2.896526 / 5.269862 (-2.373336) | 1.841610 / 4.565676 (-2.724067) | 0.058032 / 0.424275 (-0.366243) | 0.006470 / 0.007607 (-0.001137) | 0.496969 / 0.226044 (0.270925) | 4.960984 / 2.268929 (2.692056) | 2.648615 / 55.444624 (-52.796009) | 2.286846 / 6.876477 (-4.589631) | 2.320176 / 2.142072 (0.178104) | 0.600550 / 4.805227 (-4.204678) | 0.125652 / 6.500664 (-6.375012) | 0.062177 / 0.075469 (-0.013292) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.293063 / 1.841788 (-0.548725) | 18.294204 / 8.074308 (10.219896) | 13.720502 / 10.191392 (3.529110) | 0.146480 / 0.680424 (-0.533944) | 0.016965 / 0.534201 (-0.517236) | 0.330137 / 0.579283 (-0.249146) | 0.352051 / 0.434364 (-0.082313) | 0.381754 / 0.540337 (-0.158584) | 0.517935 / 1.386936 (-0.869001) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#269fcd31a2e759c65ffd5952ecef13e6a0d92574 \"CML watermark\")\n"
] |
6,029
|
[docs] Fix link
|
Fixes link to the builder classes :)
|
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"merged_at": "2023-07-13T17:38:59"
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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) | 0.007039 / 0.011353 (-0.004314) | 0.004175 / 0.011008 (-0.006833) | 0.085426 / 0.038508 (0.046918) | 0.079818 / 0.023109 (0.056709) | 0.321924 / 0.275898 (0.046026) | 0.345482 / 0.323480 (0.022002) | 0.005510 / 0.007986 (-0.002475) | 0.003452 / 0.004328 (-0.000877) | 0.065158 / 0.004250 (0.060907) | 0.058843 / 0.037052 (0.021791) | 0.316280 / 0.258489 (0.057791) | 0.351666 / 0.293841 (0.057825) | 0.031190 / 0.128546 (-0.097357) | 0.008500 / 0.075646 (-0.067147) | 0.289595 / 0.419271 (-0.129676) | 0.053798 / 0.043533 (0.010265) | 0.315804 / 0.255139 (0.060665) | 0.334957 / 0.283200 (0.051757) | 0.024350 / 0.141683 (-0.117332) | 1.515753 / 1.452155 (0.063599) | 1.556215 / 1.492716 (0.063499) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210378 / 0.018006 (0.192372) | 0.469309 / 0.000490 (0.468820) | 0.002890 / 0.000200 (0.002690) | 0.000086 / 0.000054 (0.000031) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030214 / 0.037411 (-0.007197) | 0.088492 / 0.014526 (0.073966) | 0.098684 / 0.176557 (-0.077873) | 0.156077 / 0.737135 (-0.581058) | 0.098814 / 0.296338 (-0.197525) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.404548 / 0.215209 (0.189339) | 4.026173 / 2.077655 (1.948518) | 2.043216 / 1.504120 (0.539096) | 1.880997 / 1.541195 (0.339802) | 1.975205 / 1.468490 (0.506715) | 0.489395 / 4.584777 (-4.095382) | 3.684097 / 3.745712 (-0.061615) | 5.126934 / 5.269862 (-0.142928) | 3.092153 / 4.565676 (-1.473524) | 0.057668 / 0.424275 (-0.366607) | 0.007372 / 0.007607 (-0.000235) | 0.479647 / 0.226044 (0.253603) | 4.780207 / 2.268929 (2.511278) | 2.533457 / 55.444624 (-52.911168) | 2.182126 / 6.876477 (-4.694351) | 2.431834 / 2.142072 (0.289761) | 0.591760 / 4.805227 (-4.213467) | 0.135450 / 6.500664 (-6.365214) | 0.063218 / 0.075469 (-0.012251) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.262053 / 1.841788 (-0.579734) | 20.246992 / 8.074308 (12.172684) | 14.638222 / 10.191392 (4.446830) | 0.150021 / 0.680424 (-0.530403) | 0.018680 / 0.534201 (-0.515521) | 0.395215 / 0.579283 (-0.184068) | 0.421270 / 0.434364 (-0.013094) | 0.458845 / 0.540337 (-0.081492) | 0.634488 / 1.386936 (-0.752448) |\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) | 0.007080 / 0.011353 (-0.004273) | 0.004112 / 0.011008 (-0.006896) | 0.066426 / 0.038508 (0.027918) | 0.090088 / 0.023109 (0.066978) | 0.400191 / 0.275898 (0.124293) | 0.429614 / 0.323480 (0.106134) | 0.005428 / 0.007986 (-0.002558) | 0.003501 / 0.004328 (-0.000827) | 0.065056 / 0.004250 (0.060806) | 0.061643 / 0.037052 (0.024590) | 0.398619 / 0.258489 (0.140130) | 0.445497 / 0.293841 (0.151657) | 0.031703 / 0.128546 (-0.096843) | 0.008708 / 0.075646 (-0.066938) | 0.071561 / 0.419271 (-0.347711) | 0.050684 / 0.043533 (0.007151) | 0.385361 / 0.255139 (0.130222) | 0.409349 / 0.283200 (0.126149) | 0.027388 / 0.141683 (-0.114295) | 1.473021 / 1.452155 (0.020866) | 1.525246 / 1.492716 (0.032529) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237710 / 0.018006 (0.219704) | 0.468719 / 0.000490 (0.468230) | 0.000385 / 0.000200 (0.000185) | 0.000054 / 0.000054 (-0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032539 / 0.037411 (-0.004872) | 0.095324 / 0.014526 (0.080798) | 0.102248 / 0.176557 (-0.074308) | 0.156096 / 0.737135 (-0.581039) | 0.103458 / 0.296338 (-0.192881) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.416226 / 0.215209 (0.201017) | 4.141044 / 2.077655 (2.063389) | 2.143732 / 1.504120 (0.639612) | 2.001020 / 1.541195 (0.459825) | 2.091194 / 1.468490 (0.622704) | 0.489977 / 4.584777 (-4.094800) | 3.579615 / 3.745712 (-0.166097) | 3.438082 / 5.269862 (-1.831780) | 2.069031 / 4.565676 (-2.496645) | 0.056994 / 0.424275 (-0.367281) | 0.007362 / 0.007607 (-0.000245) | 0.493077 / 0.226044 (0.267033) | 4.922622 / 2.268929 (2.653694) | 2.627083 / 55.444624 (-52.817541) | 2.301141 / 6.876477 (-4.575336) | 2.356794 / 2.142072 (0.214722) | 0.583792 / 4.805227 (-4.221436) | 0.133707 / 6.500664 (-6.366958) | 0.062892 / 0.075469 (-0.012577) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.364908 / 1.841788 (-0.476880) | 20.641219 / 8.074308 (12.566911) | 14.848528 / 10.191392 (4.657136) | 0.174207 / 0.680424 (-0.506217) | 0.018206 / 0.534201 (-0.515995) | 0.413742 / 0.579283 (-0.165541) | 0.419940 / 0.434364 (-0.014424) | 0.458543 / 0.540337 (-0.081794) | 0.616518 / 1.386936 (-0.770418) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#18b2202c3e7cdde05920078f01864964556427da \"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) | 0.006875 / 0.011353 (-0.004478) | 0.003489 / 0.011008 (-0.007519) | 0.082077 / 0.038508 (0.043569) | 0.103011 / 0.023109 (0.079902) | 0.370572 / 0.275898 (0.094674) | 0.416400 / 0.323480 (0.092920) | 0.004048 / 0.007986 (-0.003938) | 0.003563 / 0.004328 (-0.000765) | 0.062666 / 0.004250 (0.058416) | 0.063664 / 0.037052 (0.026612) | 0.374206 / 0.258489 (0.115717) | 0.425590 / 0.293841 (0.131749) | 0.028174 / 0.128546 (-0.100373) | 0.007906 / 0.075646 (-0.067741) | 0.266251 / 0.419271 (-0.153020) | 0.045923 / 0.043533 (0.002390) | 0.376746 / 0.255139 (0.121607) | 0.401950 / 0.283200 (0.118750) | 0.024628 / 0.141683 (-0.117054) | 1.441903 / 1.452155 (-0.010252) | 1.537494 / 1.492716 (0.044777) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.214696 / 0.018006 (0.196690) | 0.425626 / 0.000490 (0.425137) | 0.003370 / 0.000200 (0.003170) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023133 / 0.037411 (-0.014279) | 0.072374 / 0.014526 (0.057848) | 0.081255 / 0.176557 (-0.095301) | 0.146960 / 0.737135 (-0.590175) | 0.081748 / 0.296338 (-0.214590) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.390683 / 0.215209 (0.175473) | 3.893166 / 2.077655 (1.815511) | 1.884321 / 1.504120 (0.380201) | 1.701899 / 1.541195 (0.160704) | 1.737839 / 1.468490 (0.269349) | 0.497008 / 4.584777 (-4.087769) | 3.041211 / 3.745712 (-0.704501) | 3.519947 / 5.269862 (-1.749915) | 2.015085 / 4.565676 (-2.550592) | 0.057685 / 0.424275 (-0.366590) | 0.006415 / 0.007607 (-0.001192) | 0.465565 / 0.226044 (0.239520) | 4.635224 / 2.268929 (2.366295) | 2.297941 / 55.444624 (-53.146683) | 1.946670 / 6.876477 (-4.929807) | 2.078527 / 2.142072 (-0.063546) | 0.584101 / 4.805227 (-4.221126) | 0.126488 / 6.500664 (-6.374176) | 0.060819 / 0.075469 (-0.014650) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.223400 / 1.841788 (-0.618388) | 17.960923 / 8.074308 (9.886615) | 13.187683 / 10.191392 (2.996291) | 0.129258 / 0.680424 (-0.551166) | 0.016601 / 0.534201 (-0.517600) | 0.330028 / 0.579283 (-0.249255) | 0.353861 / 0.434364 (-0.080503) | 0.376022 / 0.540337 (-0.164315) | 0.518145 / 1.386936 (-0.868791) |\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) | 0.006015 / 0.011353 (-0.005338) | 0.003605 / 0.011008 (-0.007403) | 0.062169 / 0.038508 (0.023661) | 0.056094 / 0.023109 (0.032985) | 0.353085 / 0.275898 (0.077187) | 0.393744 / 0.323480 (0.070265) | 0.004672 / 0.007986 (-0.003313) | 0.002859 / 0.004328 (-0.001469) | 0.062992 / 0.004250 (0.058742) | 0.049767 / 0.037052 (0.012714) | 0.356850 / 0.258489 (0.098361) | 0.403731 / 0.293841 (0.109890) | 0.026664 / 0.128546 (-0.101882) | 0.008026 / 0.075646 (-0.067621) | 0.067944 / 0.419271 (-0.351327) | 0.042133 / 0.043533 (-0.001400) | 0.353865 / 0.255139 (0.098726) | 0.383461 / 0.283200 (0.100261) | 0.021250 / 0.141683 (-0.120433) | 1.428102 / 1.452155 (-0.024053) | 1.481061 / 1.492716 (-0.011655) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223552 / 0.018006 (0.205546) | 0.402390 / 0.000490 (0.401900) | 0.000721 / 0.000200 (0.000521) | 0.000059 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025065 / 0.037411 (-0.012347) | 0.075537 / 0.014526 (0.061011) | 0.083519 / 0.176557 (-0.093037) | 0.137068 / 0.737135 (-0.600068) | 0.084165 / 0.296338 (-0.212173) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420176 / 0.215209 (0.204967) | 4.206226 / 2.077655 (2.128571) | 2.168089 / 1.504120 (0.663969) | 1.987299 / 1.541195 (0.446104) | 2.029489 / 1.468490 (0.560999) | 0.495822 / 4.584777 (-4.088955) | 3.106580 / 3.745712 (-0.639132) | 3.833215 / 5.269862 (-1.436647) | 2.450450 / 4.565676 (-2.115226) | 0.056979 / 0.424275 (-0.367296) | 0.006514 / 0.007607 (-0.001093) | 0.503646 / 0.226044 (0.277601) | 5.035035 / 2.268929 (2.766106) | 2.608245 / 55.444624 (-52.836379) | 2.245492 / 6.876477 (-4.630985) | 2.262868 / 2.142072 (0.120795) | 0.590736 / 4.805227 (-4.214491) | 0.124637 / 6.500664 (-6.376027) | 0.061442 / 0.075469 (-0.014027) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.316736 / 1.841788 (-0.525052) | 17.948635 / 8.074308 (9.874327) | 13.752442 / 10.191392 (3.561050) | 0.144107 / 0.680424 (-0.536317) | 0.017112 / 0.534201 (-0.517089) | 0.336537 / 0.579283 (-0.242746) | 0.347832 / 0.434364 (-0.086532) | 0.392944 / 0.540337 (-0.147393) | 0.534455 / 1.386936 (-0.852481) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#406b2212263c0d33f267e35b917f410ff6b3bc00 \"CML watermark\")\n"
] |
6,028
|
Use new hffs
|
Thanks to @janineguo 's work in https://github.com/huggingface/datasets/pull/5919 which was needed to support HfFileSystem.
Switching to `HfFileSystem` will help implementing optimization in data files resolution
## Implementation details
I replaced all the from_hf_repo and from_local_or_remote in data_files.py to only use a new `from_patterns` which works for any fsspec path, including hf:// paths, https:// URLs and local paths. This simplifies the codebase since there is no logic duplication anymore when it comes to data files resolution.
I added `_prepare_path_and_storage_options` which returns the right storage_options to use given a path and a `DownloadConfig`. This is the only place where the logic depends on the filesystem type that must be used.
I also removed the `get_metadata_data_files_list ` and `get_patterns_and_data_files` functions added recently, since data files resolution is now handled using a common interface.
## New features
hf:// paths are now supported in data_files
## Breaking changes
DataFilesList and DataFilesDict:
- use `str` paths instead of `Union[Path, Url]`
- require posix paths for windows paths
close https://github.com/huggingface/datasets/issues/6017
|
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6028",
"html_url": "https://github.com/huggingface/datasets/pull/6028",
"diff_url": "https://github.com/huggingface/datasets/pull/6028.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6028.patch",
"merged_at": "2023-07-17T17:01:00"
}
| true
|
[
"_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) | 0.006665 / 0.011353 (-0.004688) | 0.004376 / 0.011008 (-0.006633) | 0.085529 / 0.038508 (0.047021) | 0.076372 / 0.023109 (0.053263) | 0.310019 / 0.275898 (0.034121) | 0.341404 / 0.323480 (0.017924) | 0.005666 / 0.007986 (-0.002320) | 0.003763 / 0.004328 (-0.000566) | 0.064678 / 0.004250 (0.060427) | 0.059283 / 0.037052 (0.022231) | 0.316194 / 0.258489 (0.057704) | 0.349397 / 0.293841 (0.055557) | 0.031199 / 0.128546 (-0.097347) | 0.008724 / 0.075646 (-0.066923) | 0.300236 / 0.419271 (-0.119035) | 0.068872 / 0.043533 (0.025339) | 0.308521 / 0.255139 (0.053382) | 0.331292 / 0.283200 (0.048092) | 0.028236 / 0.141683 (-0.113447) | 1.501365 / 1.452155 (0.049211) | 1.554334 / 1.492716 (0.061618) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.238291 / 0.018006 (0.220285) | 0.565069 / 0.000490 (0.564580) | 0.001626 / 0.000200 (0.001426) | 0.000070 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029777 / 0.037411 (-0.007634) | 0.082873 / 0.014526 (0.068347) | 0.099619 / 0.176557 (-0.076937) | 0.156572 / 0.737135 (-0.580563) | 0.099887 / 0.296338 (-0.196452) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.401017 / 0.215209 (0.185808) | 3.827192 / 2.077655 (1.749537) | 1.861554 / 1.504120 (0.357434) | 1.699869 / 1.541195 (0.158674) | 1.720043 / 1.468490 (0.251553) | 0.486757 / 4.584777 (-4.098020) | 3.638125 / 3.745712 (-0.107587) | 5.844959 / 5.269862 (0.575097) | 3.454901 / 4.565676 (-1.110775) | 0.057650 / 0.424275 (-0.366625) | 0.007341 / 0.007607 (-0.000266) | 0.462698 / 0.226044 (0.236654) | 4.633472 / 2.268929 (2.364544) | 2.287607 / 55.444624 (-53.157017) | 2.057318 / 6.876477 (-4.819159) | 2.203657 / 2.142072 (0.061584) | 0.598136 / 4.805227 (-4.207091) | 0.134012 / 6.500664 (-6.366653) | 0.060824 / 0.075469 (-0.014645) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.277752 / 1.841788 (-0.564036) | 20.013398 / 8.074308 (11.939089) | 14.372993 / 10.191392 (4.181601) | 0.169991 / 0.680424 (-0.510433) | 0.018344 / 0.534201 (-0.515857) | 0.396985 / 0.579283 (-0.182299) | 0.416289 / 0.434364 (-0.018075) | 0.458658 / 0.540337 (-0.081680) | 0.692980 / 1.386936 (-0.693956) |\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) | 0.006689 / 0.011353 (-0.004664) | 0.004393 / 0.011008 (-0.006615) | 0.064069 / 0.038508 (0.025561) | 0.080717 / 0.023109 (0.057607) | 0.370090 / 0.275898 (0.094191) | 0.400432 / 0.323480 (0.076952) | 0.005613 / 0.007986 (-0.002372) | 0.003641 / 0.004328 (-0.000687) | 0.064771 / 0.004250 (0.060520) | 0.057555 / 0.037052 (0.020502) | 0.392156 / 0.258489 (0.133667) | 0.409842 / 0.293841 (0.116001) | 0.031500 / 0.128546 (-0.097047) | 0.008786 / 0.075646 (-0.066860) | 0.070342 / 0.419271 (-0.348929) | 0.048646 / 0.043533 (0.005113) | 0.360914 / 0.255139 (0.105775) | 0.387626 / 0.283200 (0.104426) | 0.022787 / 0.141683 (-0.118896) | 1.508915 / 1.452155 (0.056761) | 1.539719 / 1.492716 (0.047002) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.257985 / 0.018006 (0.239979) | 0.550990 / 0.000490 (0.550501) | 0.000407 / 0.000200 (0.000207) | 0.000057 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030183 / 0.037411 (-0.007228) | 0.086882 / 0.014526 (0.072356) | 0.102382 / 0.176557 (-0.074175) | 0.154745 / 0.737135 (-0.582390) | 0.104008 / 0.296338 (-0.192331) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426284 / 0.215209 (0.211075) | 4.240812 / 2.077655 (2.163158) | 2.261240 / 1.504120 (0.757120) | 2.085905 / 1.541195 (0.544710) | 2.160374 / 1.468490 (0.691883) | 0.481126 / 4.584777 (-4.103651) | 3.516234 / 3.745712 (-0.229478) | 3.325322 / 5.269862 (-1.944539) | 2.043307 / 4.565676 (-2.522369) | 0.056663 / 0.424275 (-0.367612) | 0.007786 / 0.007607 (0.000179) | 0.497614 / 0.226044 (0.271570) | 4.974529 / 2.268929 (2.705600) | 2.700018 / 55.444624 (-52.744606) | 2.393778 / 6.876477 (-4.482699) | 2.628202 / 2.142072 (0.486130) | 0.594316 / 4.805227 (-4.210911) | 0.147092 / 6.500664 (-6.353572) | 0.062207 / 0.075469 (-0.013262) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.315676 / 1.841788 (-0.526112) | 20.749251 / 8.074308 (12.674943) | 14.371553 / 10.191392 (4.180160) | 0.170249 / 0.680424 (-0.510175) | 0.018478 / 0.534201 (-0.515722) | 0.395710 / 0.579283 (-0.183573) | 0.409706 / 0.434364 (-0.024658) | 0.463454 / 0.540337 (-0.076884) | 0.615657 / 1.386936 (-0.771279) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c5a752d8e8ca0a6ed118b024ba03c1b4a2881177 \"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) | 0.007224 / 0.011353 (-0.004129) | 0.004506 / 0.011008 (-0.006503) | 0.096729 / 0.038508 (0.058221) | 0.082394 / 0.023109 (0.059284) | 0.390954 / 0.275898 (0.115056) | 0.416647 / 0.323480 (0.093167) | 0.005894 / 0.007986 (-0.002092) | 0.003756 / 0.004328 (-0.000572) | 0.075800 / 0.004250 (0.071549) | 0.062683 / 0.037052 (0.025631) | 0.398959 / 0.258489 (0.140470) | 0.436624 / 0.293841 (0.142783) | 0.034650 / 0.128546 (-0.093896) | 0.009655 / 0.075646 (-0.065991) | 0.315761 / 0.419271 (-0.103511) | 0.060957 / 0.043533 (0.017424) | 0.385649 / 0.255139 (0.130510) | 0.394022 / 0.283200 (0.110822) | 0.024601 / 0.141683 (-0.117082) | 1.729586 / 1.452155 (0.277431) | 1.724153 / 1.492716 (0.231437) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.207070 / 0.018006 (0.189063) | 0.466502 / 0.000490 (0.466012) | 0.010739 / 0.000200 (0.010540) | 0.000214 / 0.000054 (0.000160) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031633 / 0.037411 (-0.005779) | 0.095345 / 0.014526 (0.080819) | 0.105399 / 0.176557 (-0.071157) | 0.174173 / 0.737135 (-0.562962) | 0.104207 / 0.296338 (-0.192132) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.435312 / 0.215209 (0.220103) | 4.265600 / 2.077655 (2.187946) | 2.056500 / 1.504120 (0.552380) | 1.848023 / 1.541195 (0.306828) | 1.946156 / 1.468490 (0.477666) | 0.557788 / 4.584777 (-4.026989) | 4.070289 / 3.745712 (0.324577) | 3.608027 / 5.269862 (-1.661835) | 2.214556 / 4.565676 (-2.351121) | 0.062623 / 0.424275 (-0.361652) | 0.008083 / 0.007607 (0.000476) | 0.491782 / 0.226044 (0.265738) | 4.989963 / 2.268929 (2.721035) | 2.575867 / 55.444624 (-52.868757) | 2.208045 / 6.876477 (-4.668431) | 2.364184 / 2.142072 (0.222112) | 0.633925 / 4.805227 (-4.171302) | 0.144323 / 6.500664 (-6.356341) | 0.067505 / 0.075469 (-0.007965) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.467219 / 1.841788 (-0.374569) | 22.334967 / 8.074308 (14.260659) | 15.715747 / 10.191392 (5.524355) | 0.175443 / 0.680424 (-0.504980) | 0.026165 / 0.534201 (-0.508036) | 0.490675 / 0.579283 (-0.088608) | 0.509211 / 0.434364 (0.074847) | 0.586303 / 0.540337 (0.045965) | 0.785052 / 1.386936 (-0.601884) |\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) | 0.007893 / 0.011353 (-0.003460) | 0.004577 / 0.011008 (-0.006431) | 0.075781 / 0.038508 (0.037273) | 0.095492 / 0.023109 (0.072382) | 0.433259 / 0.275898 (0.157361) | 0.469386 / 0.323480 (0.145906) | 0.006317 / 0.007986 (-0.001669) | 0.003708 / 0.004328 (-0.000621) | 0.074417 / 0.004250 (0.070167) | 0.068605 / 0.037052 (0.031552) | 0.448701 / 0.258489 (0.190212) | 0.469131 / 0.293841 (0.175290) | 0.036647 / 0.128546 (-0.091899) | 0.010077 / 0.075646 (-0.065570) | 0.082457 / 0.419271 (-0.336815) | 0.063255 / 0.043533 (0.019722) | 0.428144 / 0.255139 (0.173005) | 0.451872 / 0.283200 (0.168672) | 0.033953 / 0.141683 (-0.107730) | 1.781752 / 1.452155 (0.329597) | 1.869014 / 1.492716 (0.376297) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223596 / 0.018006 (0.205590) | 0.470307 / 0.000490 (0.469818) | 0.005059 / 0.000200 (0.004859) | 0.000104 / 0.000054 (0.000049) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038804 / 0.037411 (0.001393) | 0.117879 / 0.014526 (0.103353) | 0.140701 / 0.176557 (-0.035855) | 0.194672 / 0.737135 (-0.542463) | 0.132806 / 0.296338 (-0.163533) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.510109 / 0.215209 (0.294900) | 4.729457 / 2.077655 (2.651803) | 2.512113 / 1.504120 (1.007993) | 2.302553 / 1.541195 (0.761358) | 2.420462 / 1.468490 (0.951972) | 0.531682 / 4.584777 (-4.053095) | 4.061208 / 3.745712 (0.315496) | 3.588542 / 5.269862 (-1.681320) | 2.203187 / 4.565676 (-2.362489) | 0.065791 / 0.424275 (-0.358484) | 0.008839 / 0.007607 (0.001232) | 0.562041 / 0.226044 (0.335997) | 5.702340 / 2.268929 (3.433412) | 3.127609 / 55.444624 (-52.317015) | 2.823060 / 6.876477 (-4.053417) | 2.898675 / 2.142072 (0.756603) | 0.659589 / 4.805227 (-4.145638) | 0.148798 / 6.500664 (-6.351866) | 0.070787 / 0.075469 (-0.004682) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.478317 / 1.841788 (-0.363471) | 21.995400 / 8.074308 (13.921092) | 16.770729 / 10.191392 (6.579337) | 0.226333 / 0.680424 (-0.454091) | 0.021835 / 0.534201 (-0.512366) | 0.460373 / 0.579283 (-0.118910) | 0.479494 / 0.434364 (0.045130) | 0.529470 / 0.540337 (-0.010868) | 0.718066 / 1.386936 (-0.668870) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9a717b8eb80b0e50b25818127f79a35e0866fb14 \"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) | 0.007824 / 0.011353 (-0.003529) | 0.004601 / 0.011008 (-0.006407) | 0.100025 / 0.038508 (0.061517) | 0.096046 / 0.023109 (0.072936) | 0.376226 / 0.275898 (0.100328) | 0.410905 / 0.323480 (0.087425) | 0.006048 / 0.007986 (-0.001938) | 0.003817 / 0.004328 (-0.000511) | 0.076624 / 0.004250 (0.072374) | 0.066390 / 0.037052 (0.029338) | 0.380098 / 0.258489 (0.121609) | 0.413603 / 0.293841 (0.119762) | 0.036546 / 0.128546 (-0.092001) | 0.009881 / 0.075646 (-0.065765) | 0.344338 / 0.419271 (-0.074934) | 0.061882 / 0.043533 (0.018350) | 0.368568 / 0.255139 (0.113429) | 0.397133 / 0.283200 (0.113934) | 0.027255 / 0.141683 (-0.114428) | 1.795099 / 1.452155 (0.342945) | 1.852443 / 1.492716 (0.359727) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.247436 / 0.018006 (0.229430) | 0.494119 / 0.000490 (0.493629) | 0.004359 / 0.000200 (0.004159) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034765 / 0.037411 (-0.002647) | 0.104541 / 0.014526 (0.090015) | 0.113898 / 0.176557 (-0.062659) | 0.183634 / 0.737135 (-0.553501) | 0.116423 / 0.296338 (-0.179916) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.458747 / 0.215209 (0.243538) | 4.555740 / 2.077655 (2.478085) | 2.217240 / 1.504120 (0.713121) | 2.039879 / 1.541195 (0.498684) | 2.088581 / 1.468490 (0.620091) | 0.588063 / 4.584777 (-3.996714) | 4.238226 / 3.745712 (0.492514) | 4.768060 / 5.269862 (-0.501802) | 2.857117 / 4.565676 (-1.708560) | 0.068742 / 0.424275 (-0.355533) | 0.008667 / 0.007607 (0.001059) | 0.549294 / 0.226044 (0.323249) | 5.464635 / 2.268929 (3.195706) | 2.744435 / 55.444624 (-52.700189) | 2.347660 / 6.876477 (-4.528816) | 2.616816 / 2.142072 (0.474743) | 0.703701 / 4.805227 (-4.101526) | 0.159749 / 6.500664 (-6.340915) | 0.071990 / 0.075469 (-0.003479) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.486599 / 1.841788 (-0.355188) | 22.745438 / 8.074308 (14.671130) | 16.822332 / 10.191392 (6.630940) | 0.184730 / 0.680424 (-0.495694) | 0.021267 / 0.534201 (-0.512934) | 0.467108 / 0.579283 (-0.112176) | 0.472674 / 0.434364 (0.038311) | 0.548094 / 0.540337 (0.007756) | 0.735885 / 1.386936 (-0.651051) |\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) | 0.007746 / 0.011353 (-0.003607) | 0.004585 / 0.011008 (-0.006423) | 0.076943 / 0.038508 (0.038435) | 0.087473 / 0.023109 (0.064363) | 0.480099 / 0.275898 (0.204201) | 0.495271 / 0.323480 (0.171791) | 0.006348 / 0.007986 (-0.001638) | 0.003902 / 0.004328 (-0.000426) | 0.077586 / 0.004250 (0.073335) | 0.066467 / 0.037052 (0.029415) | 0.468741 / 0.258489 (0.210252) | 0.506778 / 0.293841 (0.212937) | 0.036877 / 0.128546 (-0.091669) | 0.010102 / 0.075646 (-0.065545) | 0.084419 / 0.419271 (-0.334852) | 0.058721 / 0.043533 (0.015188) | 0.453633 / 0.255139 (0.198494) | 0.481171 / 0.283200 (0.197971) | 0.028716 / 0.141683 (-0.112967) | 1.853048 / 1.452155 (0.400893) | 1.885847 / 1.492716 (0.393130) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.192136 / 0.018006 (0.174130) | 0.484481 / 0.000490 (0.483991) | 0.002951 / 0.000200 (0.002751) | 0.000098 / 0.000054 (0.000044) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037949 / 0.037411 (0.000538) | 0.108364 / 0.014526 (0.093838) | 0.119542 / 0.176557 (-0.057014) | 0.188542 / 0.737135 (-0.548593) | 0.122011 / 0.296338 (-0.174327) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.483135 / 0.215209 (0.267926) | 4.849715 / 2.077655 (2.772060) | 2.497736 / 1.504120 (0.993616) | 2.314243 / 1.541195 (0.773048) | 2.412739 / 1.468490 (0.944249) | 0.564137 / 4.584777 (-4.020639) | 4.242273 / 3.745712 (0.496561) | 6.337843 / 5.269862 (1.067982) | 3.923250 / 4.565676 (-0.642426) | 0.066464 / 0.424275 (-0.357811) | 0.009217 / 0.007607 (0.001610) | 0.575667 / 0.226044 (0.349623) | 5.746187 / 2.268929 (3.477258) | 3.069655 / 55.444624 (-52.374969) | 2.674798 / 6.876477 (-4.201679) | 2.956535 / 2.142072 (0.814463) | 0.701043 / 4.805227 (-4.104185) | 0.157241 / 6.500664 (-6.343423) | 0.073175 / 0.075469 (-0.002294) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.609943 / 1.841788 (-0.231844) | 23.478594 / 8.074308 (15.404286) | 17.454437 / 10.191392 (7.263045) | 0.186422 / 0.680424 (-0.494002) | 0.021703 / 0.534201 (-0.512498) | 0.471704 / 0.579283 (-0.107579) | 0.480553 / 0.434364 (0.046189) | 0.552881 / 0.540337 (0.012544) | 0.722515 / 1.386936 (-0.664421) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#84645f80049cd00d9e0d4908faf3c3203fdcf21d \"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) | 0.007542 / 0.011353 (-0.003811) | 0.004692 / 0.011008 (-0.006316) | 0.099155 / 0.038508 (0.060647) | 0.089365 / 0.023109 (0.066256) | 0.370870 / 0.275898 (0.094972) | 0.422152 / 0.323480 (0.098673) | 0.006223 / 0.007986 (-0.001763) | 0.003852 / 0.004328 (-0.000476) | 0.075438 / 0.004250 (0.071188) | 0.065973 / 0.037052 (0.028921) | 0.381513 / 0.258489 (0.123024) | 0.416196 / 0.293841 (0.122355) | 0.035483 / 0.128546 (-0.093063) | 0.009884 / 0.075646 (-0.065762) | 0.341290 / 0.419271 (-0.077982) | 0.060546 / 0.043533 (0.017014) | 0.365101 / 0.255139 (0.109962) | 0.391058 / 0.283200 (0.107859) | 0.026325 / 0.141683 (-0.115358) | 1.815168 / 1.452155 (0.363013) | 1.834711 / 1.492716 (0.341994) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222177 / 0.018006 (0.204171) | 0.501151 / 0.000490 (0.500662) | 0.010202 / 0.000200 (0.010002) | 0.000102 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034043 / 0.037411 (-0.003368) | 0.097884 / 0.014526 (0.083358) | 0.114022 / 0.176557 (-0.062534) | 0.186200 / 0.737135 (-0.550935) | 0.115555 / 0.296338 (-0.180783) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.485857 / 0.215209 (0.270648) | 4.959263 / 2.077655 (2.881608) | 2.501085 / 1.504120 (0.996965) | 2.234660 / 1.541195 (0.693465) | 2.238585 / 1.468490 (0.770095) | 0.645431 / 4.584777 (-3.939345) | 4.434311 / 3.745712 (0.688599) | 4.771491 / 5.269862 (-0.498371) | 2.778963 / 4.565676 (-1.786714) | 0.075615 / 0.424275 (-0.348660) | 0.009502 / 0.007607 (0.001895) | 0.546539 / 0.226044 (0.320495) | 5.464242 / 2.268929 (3.195314) | 2.894101 / 55.444624 (-52.550524) | 2.513761 / 6.876477 (-4.362715) | 2.719843 / 2.142072 (0.577770) | 0.678828 / 4.805227 (-4.126399) | 0.157839 / 6.500664 (-6.342825) | 0.071305 / 0.075469 (-0.004164) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.496879 / 1.841788 (-0.344909) | 22.214452 / 8.074308 (14.140144) | 17.707541 / 10.191392 (7.516149) | 0.197008 / 0.680424 (-0.483416) | 0.024883 / 0.534201 (-0.509318) | 0.493611 / 0.579283 (-0.085672) | 0.500677 / 0.434364 (0.066313) | 0.569381 / 0.540337 (0.029044) | 0.773950 / 1.386936 (-0.612986) |\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) | 0.007337 / 0.011353 (-0.004015) | 0.004572 / 0.011008 (-0.006436) | 0.091123 / 0.038508 (0.052615) | 0.079762 / 0.023109 (0.056652) | 0.450527 / 0.275898 (0.174629) | 0.525097 / 0.323480 (0.201617) | 0.005873 / 0.007986 (-0.002112) | 0.003797 / 0.004328 (-0.000532) | 0.076259 / 0.004250 (0.072009) | 0.062745 / 0.037052 (0.025692) | 0.465553 / 0.258489 (0.207064) | 0.546026 / 0.293841 (0.252186) | 0.035638 / 0.128546 (-0.092909) | 0.010086 / 0.075646 (-0.065560) | 0.109269 / 0.419271 (-0.310002) | 0.056765 / 0.043533 (0.013233) | 0.440887 / 0.255139 (0.185748) | 0.513325 / 0.283200 (0.230125) | 0.027206 / 0.141683 (-0.114476) | 1.863564 / 1.452155 (0.411409) | 1.918206 / 1.492716 (0.425490) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.266479 / 0.018006 (0.248473) | 0.487971 / 0.000490 (0.487481) | 0.012246 / 0.000200 (0.012046) | 0.000119 / 0.000054 (0.000065) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035281 / 0.037411 (-0.002130) | 0.102991 / 0.014526 (0.088465) | 0.114638 / 0.176557 (-0.061919) | 0.184117 / 0.737135 (-0.553018) | 0.117943 / 0.296338 (-0.178396) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.497897 / 0.215209 (0.282688) | 4.973806 / 2.077655 (2.896151) | 2.596146 / 1.504120 (1.092026) | 2.419694 / 1.541195 (0.878499) | 2.525784 / 1.468490 (1.057294) | 0.568021 / 4.584777 (-4.016756) | 4.296431 / 3.745712 (0.550719) | 3.690682 / 5.269862 (-1.579179) | 2.345965 / 4.565676 (-2.219712) | 0.066859 / 0.424275 (-0.357416) | 0.009093 / 0.007607 (0.001486) | 0.582616 / 0.226044 (0.356571) | 5.826528 / 2.268929 (3.557600) | 3.253222 / 55.444624 (-52.191403) | 2.798447 / 6.876477 (-4.078030) | 3.054609 / 2.142072 (0.912537) | 0.678816 / 4.805227 (-4.126411) | 0.157966 / 6.500664 (-6.342698) | 0.073797 / 0.075469 (-0.001672) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.599480 / 1.841788 (-0.242308) | 23.249738 / 8.074308 (15.175430) | 16.965406 / 10.191392 (6.774014) | 0.171390 / 0.680424 (-0.509034) | 0.021810 / 0.534201 (-0.512391) | 0.483339 / 0.579283 (-0.095944) | 0.496615 / 0.434364 (0.062251) | 0.583786 / 0.540337 (0.043448) | 0.741699 / 1.386936 (-0.645237) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7935cd2e564f5d1c66ed1acf731703724ba7a287 \"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) | 0.006054 / 0.011353 (-0.005299) | 0.003706 / 0.011008 (-0.007302) | 0.080060 / 0.038508 (0.041552) | 0.061479 / 0.023109 (0.038370) | 0.327981 / 0.275898 (0.052083) | 0.356930 / 0.323480 (0.033450) | 0.004671 / 0.007986 (-0.003315) | 0.002901 / 0.004328 (-0.001428) | 0.062425 / 0.004250 (0.058174) | 0.046310 / 0.037052 (0.009258) | 0.323657 / 0.258489 (0.065168) | 0.370130 / 0.293841 (0.076289) | 0.027151 / 0.128546 (-0.101395) | 0.007850 / 0.075646 (-0.067797) | 0.262300 / 0.419271 (-0.156971) | 0.045456 / 0.043533 (0.001923) | 0.325569 / 0.255139 (0.070430) | 0.352962 / 0.283200 (0.069762) | 0.020156 / 0.141683 (-0.121527) | 1.429404 / 1.452155 (-0.022750) | 1.615032 / 1.492716 (0.122316) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.187309 / 0.018006 (0.169303) | 0.428848 / 0.000490 (0.428358) | 0.003599 / 0.000200 (0.003399) | 0.000069 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023260 / 0.037411 (-0.014151) | 0.072467 / 0.014526 (0.057941) | 0.082398 / 0.176557 (-0.094159) | 0.142573 / 0.737135 (-0.594562) | 0.082570 / 0.296338 (-0.213768) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426503 / 0.215209 (0.211294) | 4.267875 / 2.077655 (2.190220) | 2.189762 / 1.504120 (0.685642) | 2.027992 / 1.541195 (0.486798) | 2.053211 / 1.468490 (0.584721) | 0.503850 / 4.584777 (-4.080927) | 3.086444 / 3.745712 (-0.659268) | 3.319492 / 5.269862 (-1.950370) | 2.070714 / 4.565676 (-2.494962) | 0.057591 / 0.424275 (-0.366684) | 0.006407 / 0.007607 (-0.001200) | 0.501145 / 0.226044 (0.275100) | 5.017753 / 2.268929 (2.748825) | 2.643145 / 55.444624 (-52.801479) | 2.327440 / 6.876477 (-4.549037) | 2.460250 / 2.142072 (0.318178) | 0.589397 / 4.805227 (-4.215830) | 0.124948 / 6.500664 (-6.375716) | 0.060450 / 0.075469 (-0.015020) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.279870 / 1.841788 (-0.561918) | 18.115908 / 8.074308 (10.041600) | 13.570032 / 10.191392 (3.378640) | 0.132981 / 0.680424 (-0.547442) | 0.016942 / 0.534201 (-0.517259) | 0.333591 / 0.579283 (-0.245692) | 0.358844 / 0.434364 (-0.075520) | 0.395748 / 0.540337 (-0.144590) | 0.546213 / 1.386936 (-0.840723) |\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) | 0.006062 / 0.011353 (-0.005291) | 0.003673 / 0.011008 (-0.007336) | 0.064726 / 0.038508 (0.026218) | 0.061854 / 0.023109 (0.038745) | 0.385343 / 0.275898 (0.109445) | 0.441284 / 0.323480 (0.117805) | 0.004830 / 0.007986 (-0.003156) | 0.002909 / 0.004328 (-0.001420) | 0.063874 / 0.004250 (0.059624) | 0.049331 / 0.037052 (0.012278) | 0.418484 / 0.258489 (0.159995) | 0.451397 / 0.293841 (0.157556) | 0.027665 / 0.128546 (-0.100881) | 0.008088 / 0.075646 (-0.067558) | 0.069625 / 0.419271 (-0.349646) | 0.043437 / 0.043533 (-0.000095) | 0.359789 / 0.255139 (0.104650) | 0.430206 / 0.283200 (0.147007) | 0.022308 / 0.141683 (-0.119375) | 1.461030 / 1.452155 (0.008875) | 1.513683 / 1.492716 (0.020966) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230958 / 0.018006 (0.212952) | 0.417553 / 0.000490 (0.417063) | 0.000802 / 0.000200 (0.000602) | 0.000066 / 0.000054 (0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025421 / 0.037411 (-0.011991) | 0.077156 / 0.014526 (0.062630) | 0.087533 / 0.176557 (-0.089024) | 0.138048 / 0.737135 (-0.599087) | 0.089358 / 0.296338 (-0.206981) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.439172 / 0.215209 (0.223963) | 4.409509 / 2.077655 (2.331854) | 2.491270 / 1.504120 (0.987150) | 2.308446 / 1.541195 (0.767252) | 2.378440 / 1.468490 (0.909950) | 0.499834 / 4.584777 (-4.084943) | 3.083168 / 3.745712 (-0.662544) | 2.867543 / 5.269862 (-2.402318) | 1.876354 / 4.565676 (-2.689323) | 0.057092 / 0.424275 (-0.367183) | 0.006955 / 0.007607 (-0.000653) | 0.513799 / 0.226044 (0.287754) | 5.126660 / 2.268929 (2.857731) | 2.917348 / 55.444624 (-52.527277) | 2.508035 / 6.876477 (-4.368441) | 2.698089 / 2.142072 (0.556016) | 0.586828 / 4.805227 (-4.218399) | 0.124740 / 6.500664 (-6.375924) | 0.062276 / 0.075469 (-0.013193) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.291624 / 1.841788 (-0.550164) | 18.199968 / 8.074308 (10.125660) | 13.888139 / 10.191392 (3.696747) | 0.162955 / 0.680424 (-0.517469) | 0.017343 / 0.534201 (-0.516858) | 0.334683 / 0.579283 (-0.244600) | 0.352708 / 0.434364 (-0.081656) | 0.400629 / 0.540337 (-0.139708) | 0.539497 / 1.386936 (-0.847439) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e7976db7fe22c6b93a869488d07b8137ea6a0db4 \"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) | 0.007500 / 0.011353 (-0.003853) | 0.004498 / 0.011008 (-0.006510) | 0.100239 / 0.038508 (0.061731) | 0.083424 / 0.023109 (0.060315) | 0.366664 / 0.275898 (0.090766) | 0.406641 / 0.323480 (0.083161) | 0.004577 / 0.007986 (-0.003409) | 0.004809 / 0.004328 (0.000480) | 0.076898 / 0.004250 (0.072647) | 0.064021 / 0.037052 (0.026969) | 0.375836 / 0.258489 (0.117347) | 0.413008 / 0.293841 (0.119167) | 0.036010 / 0.128546 (-0.092537) | 0.009655 / 0.075646 (-0.065991) | 0.342595 / 0.419271 (-0.076677) | 0.061846 / 0.043533 (0.018313) | 0.376543 / 0.255139 (0.121404) | 0.395858 / 0.283200 (0.112659) | 0.026792 / 0.141683 (-0.114891) | 1.775569 / 1.452155 (0.323414) | 1.865077 / 1.492716 (0.372360) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221521 / 0.018006 (0.203514) | 0.474604 / 0.000490 (0.474114) | 0.004354 / 0.000200 (0.004154) | 0.000090 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032947 / 0.037411 (-0.004464) | 0.100454 / 0.014526 (0.085928) | 0.111955 / 0.176557 (-0.064602) | 0.179752 / 0.737135 (-0.557383) | 0.114282 / 0.296338 (-0.182056) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.458261 / 0.215209 (0.243052) | 4.563536 / 2.077655 (2.485881) | 2.231928 / 1.504120 (0.727808) | 2.036751 / 1.541195 (0.495556) | 2.170413 / 1.468490 (0.701923) | 0.570825 / 4.584777 (-4.013952) | 4.505762 / 3.745712 (0.760050) | 5.033461 / 5.269862 (-0.236401) | 2.704989 / 4.565676 (-1.860687) | 0.067011 / 0.424275 (-0.357264) | 0.008568 / 0.007607 (0.000961) | 0.545151 / 0.226044 (0.319106) | 5.438984 / 2.268929 (3.170055) | 2.771818 / 55.444624 (-52.672806) | 2.393082 / 6.876477 (-4.483395) | 2.467173 / 2.142072 (0.325101) | 0.678849 / 4.805227 (-4.126379) | 0.160480 / 6.500664 (-6.340184) | 0.073681 / 0.075469 (-0.001788) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.532272 / 1.841788 (-0.309516) | 22.548741 / 8.074308 (14.474433) | 17.091044 / 10.191392 (6.899652) | 0.172100 / 0.680424 (-0.508324) | 0.022220 / 0.534201 (-0.511981) | 0.467871 / 0.579283 (-0.111412) | 0.491135 / 0.434364 (0.056771) | 0.548433 / 0.540337 (0.008096) | 0.733340 / 1.386936 (-0.653596) |\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) | 0.007593 / 0.011353 (-0.003760) | 0.004656 / 0.011008 (-0.006352) | 0.076940 / 0.038508 (0.038431) | 0.085183 / 0.023109 (0.062073) | 0.447178 / 0.275898 (0.171280) | 0.469545 / 0.323480 (0.146065) | 0.006023 / 0.007986 (-0.001962) | 0.003808 / 0.004328 (-0.000520) | 0.076767 / 0.004250 (0.072517) | 0.065713 / 0.037052 (0.028661) | 0.445573 / 0.258489 (0.187084) | 0.481689 / 0.293841 (0.187848) | 0.036893 / 0.128546 (-0.091654) | 0.009976 / 0.075646 (-0.065670) | 0.084443 / 0.419271 (-0.334829) | 0.058829 / 0.043533 (0.015297) | 0.429291 / 0.255139 (0.174152) | 0.454016 / 0.283200 (0.170816) | 0.027289 / 0.141683 (-0.114394) | 1.806786 / 1.452155 (0.354632) | 1.887680 / 1.492716 (0.394964) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.241012 / 0.018006 (0.223006) | 0.470629 / 0.000490 (0.470139) | 0.003213 / 0.000200 (0.003013) | 0.000107 / 0.000054 (0.000052) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036896 / 0.037411 (-0.000515) | 0.106932 / 0.014526 (0.092406) | 0.120333 / 0.176557 (-0.056223) | 0.186271 / 0.737135 (-0.550865) | 0.121581 / 0.296338 (-0.174758) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.507782 / 0.215209 (0.292573) | 5.062932 / 2.077655 (2.985278) | 2.689539 / 1.504120 (1.185419) | 2.482978 / 1.541195 (0.941784) | 2.561320 / 1.468490 (1.092830) | 0.570664 / 4.584777 (-4.014113) | 4.346051 / 3.745712 (0.600339) | 6.479374 / 5.269862 (1.209513) | 4.096483 / 4.565676 (-0.469194) | 0.067564 / 0.424275 (-0.356711) | 0.009147 / 0.007607 (0.001540) | 0.596059 / 0.226044 (0.370015) | 5.963223 / 2.268929 (3.694295) | 3.201039 / 55.444624 (-52.243585) | 2.816581 / 6.876477 (-4.059896) | 3.047821 / 2.142072 (0.905748) | 0.687749 / 4.805227 (-4.117478) | 0.158174 / 6.500664 (-6.342490) | 0.073329 / 0.075469 (-0.002140) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.601346 / 1.841788 (-0.240441) | 23.712210 / 8.074308 (15.637902) | 16.567272 / 10.191392 (6.375880) | 0.224745 / 0.680424 (-0.455679) | 0.021662 / 0.534201 (-0.512539) | 0.471427 / 0.579283 (-0.107856) | 0.498751 / 0.434364 (0.064387) | 0.572047 / 0.540337 (0.031710) | 0.821868 / 1.386936 (-0.565068) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#34d0c9027c750adc89f3d04a6bf2e9cb95915da4 \"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) | 0.006371 / 0.011353 (-0.004981) | 0.003749 / 0.011008 (-0.007259) | 0.084155 / 0.038508 (0.045647) | 0.072450 / 0.023109 (0.049340) | 0.308002 / 0.275898 (0.032104) | 0.340471 / 0.323480 (0.016991) | 0.005054 / 0.007986 (-0.002931) | 0.003176 / 0.004328 (-0.001152) | 0.064867 / 0.004250 (0.060616) | 0.054305 / 0.037052 (0.017252) | 0.321047 / 0.258489 (0.062558) | 0.345999 / 0.293841 (0.052158) | 0.030507 / 0.128546 (-0.098039) | 0.008299 / 0.075646 (-0.067347) | 0.287682 / 0.419271 (-0.131590) | 0.052048 / 0.043533 (0.008515) | 0.308322 / 0.255139 (0.053183) | 0.333220 / 0.283200 (0.050020) | 0.022698 / 0.141683 (-0.118985) | 1.474033 / 1.452155 (0.021879) | 1.544790 / 1.492716 (0.052074) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.200612 / 0.018006 (0.182606) | 0.450934 / 0.000490 (0.450445) | 0.005383 / 0.000200 (0.005183) | 0.000200 / 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) | 0.027759 / 0.037411 (-0.009652) | 0.080935 / 0.014526 (0.066409) | 0.093041 / 0.176557 (-0.083516) | 0.148643 / 0.737135 (-0.588492) | 0.093463 / 0.296338 (-0.202876) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.381653 / 0.215209 (0.166444) | 3.810699 / 2.077655 (1.733044) | 1.866858 / 1.504120 (0.362738) | 1.716985 / 1.541195 (0.175790) | 1.788071 / 1.468490 (0.319581) | 0.481130 / 4.584777 (-4.103647) | 3.529798 / 3.745712 (-0.215914) | 3.982037 / 5.269862 (-1.287824) | 2.324866 / 4.565676 (-2.240811) | 0.056767 / 0.424275 (-0.367508) | 0.007306 / 0.007607 (-0.000301) | 0.459472 / 0.226044 (0.233428) | 4.602808 / 2.268929 (2.333879) | 2.332014 / 55.444624 (-53.112610) | 2.044858 / 6.876477 (-4.831619) | 2.204165 / 2.142072 (0.062093) | 0.577946 / 4.805227 (-4.227281) | 0.130900 / 6.500664 (-6.369764) | 0.059054 / 0.075469 (-0.016415) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.245211 / 1.841788 (-0.596576) | 19.176397 / 8.074308 (11.102089) | 13.995280 / 10.191392 (3.803888) | 0.171743 / 0.680424 (-0.508681) | 0.018038 / 0.534201 (-0.516163) | 0.392338 / 0.579283 (-0.186945) | 0.419370 / 0.434364 (-0.014994) | 0.477829 / 0.540337 (-0.062508) | 0.677409 / 1.386936 (-0.709527) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006513 / 0.011353 (-0.004840) | 0.003984 / 0.011008 (-0.007024) | 0.064516 / 0.038508 (0.026008) | 0.070504 / 0.023109 (0.047395) | 0.384509 / 0.275898 (0.108611) | 0.410564 / 0.323480 (0.087084) | 0.005310 / 0.007986 (-0.002675) | 0.003268 / 0.004328 (-0.001061) | 0.064684 / 0.004250 (0.060433) | 0.055367 / 0.037052 (0.018315) | 0.399108 / 0.258489 (0.140619) | 0.422740 / 0.293841 (0.128900) | 0.031624 / 0.128546 (-0.096922) | 0.008617 / 0.075646 (-0.067030) | 0.070929 / 0.419271 (-0.348342) | 0.049146 / 0.043533 (0.005613) | 0.385492 / 0.255139 (0.130353) | 0.407434 / 0.283200 (0.124234) | 0.021972 / 0.141683 (-0.119711) | 1.496135 / 1.452155 (0.043980) | 1.533739 / 1.492716 (0.041023) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226218 / 0.018006 (0.208211) | 0.443176 / 0.000490 (0.442686) | 0.000376 / 0.000200 (0.000176) | 0.000055 / 0.000054 (0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030315 / 0.037411 (-0.007097) | 0.086416 / 0.014526 (0.071890) | 0.097725 / 0.176557 (-0.078831) | 0.150407 / 0.737135 (-0.586728) | 0.099914 / 0.296338 (-0.196424) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.409807 / 0.215209 (0.194598) | 4.099086 / 2.077655 (2.021431) | 2.103160 / 1.504120 (0.599040) | 1.927927 / 1.541195 (0.386733) | 1.977751 / 1.468490 (0.509261) | 0.476995 / 4.584777 (-4.107781) | 3.521835 / 3.745712 (-0.223877) | 3.237695 / 5.269862 (-2.032167) | 1.995953 / 4.565676 (-2.569724) | 0.056208 / 0.424275 (-0.368068) | 0.007660 / 0.007607 (0.000053) | 0.483537 / 0.226044 (0.257492) | 4.833974 / 2.268929 (2.565046) | 2.589115 / 55.444624 (-52.855510) | 2.228076 / 6.876477 (-4.648401) | 2.395271 / 2.142072 (0.253198) | 0.577534 / 4.805227 (-4.227694) | 0.131432 / 6.500664 (-6.369232) | 0.060999 / 0.075469 (-0.014471) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.356043 / 1.841788 (-0.485745) | 19.470401 / 8.074308 (11.396093) | 14.091266 / 10.191392 (3.899874) | 0.166809 / 0.680424 (-0.513615) | 0.018782 / 0.534201 (-0.515419) | 0.394916 / 0.579283 (-0.184367) | 0.411378 / 0.434364 (-0.022986) | 0.466886 / 0.540337 (-0.073451) | 0.617369 / 1.386936 (-0.769567) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#601ae6c7baff33a600fd10b12940966024fd2221 \"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) | 0.007590 / 0.011353 (-0.003762) | 0.004068 / 0.011008 (-0.006941) | 0.105479 / 0.038508 (0.066971) | 0.085614 / 0.023109 (0.062505) | 0.384325 / 0.275898 (0.108427) | 0.467867 / 0.323480 (0.144387) | 0.004652 / 0.007986 (-0.003333) | 0.005445 / 0.004328 (0.001117) | 0.079604 / 0.004250 (0.075353) | 0.066031 / 0.037052 (0.028978) | 0.426184 / 0.258489 (0.167695) | 0.480712 / 0.293841 (0.186871) | 0.037837 / 0.128546 (-0.090709) | 0.009765 / 0.075646 (-0.065882) | 0.351316 / 0.419271 (-0.067955) | 0.063634 / 0.043533 (0.020101) | 0.420297 / 0.255139 (0.165158) | 0.449169 / 0.283200 (0.165969) | 0.030947 / 0.141683 (-0.110736) | 1.840184 / 1.452155 (0.388029) | 1.934074 / 1.492716 (0.441357) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223483 / 0.018006 (0.205477) | 0.521086 / 0.000490 (0.520596) | 0.000379 / 0.000200 (0.000179) | 0.000065 / 0.000054 (0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032011 / 0.037411 (-0.005400) | 0.101474 / 0.014526 (0.086948) | 0.108652 / 0.176557 (-0.067904) | 0.173340 / 0.737135 (-0.563796) | 0.114186 / 0.296338 (-0.182153) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.478020 / 0.215209 (0.262811) | 4.645400 / 2.077655 (2.567746) | 2.590763 / 1.504120 (1.086643) | 2.383002 / 1.541195 (0.841807) | 2.482550 / 1.468490 (1.014060) | 0.572417 / 4.584777 (-4.012360) | 4.233436 / 3.745712 (0.487724) | 4.858823 / 5.269862 (-0.411038) | 2.838913 / 4.565676 (-1.726764) | 0.070010 / 0.424275 (-0.354265) | 0.009602 / 0.007607 (0.001995) | 0.538735 / 0.226044 (0.312691) | 5.534340 / 2.268929 (3.265411) | 2.915006 / 55.444624 (-52.529619) | 2.625132 / 6.876477 (-4.251345) | 2.537838 / 2.142072 (0.395766) | 0.667870 / 4.805227 (-4.137357) | 0.146330 / 6.500664 (-6.354334) | 0.071631 / 0.075469 (-0.003838) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.594686 / 1.841788 (-0.247101) | 22.311113 / 8.074308 (14.236804) | 17.603983 / 10.191392 (7.412591) | 0.195995 / 0.680424 (-0.484428) | 0.022254 / 0.534201 (-0.511947) | 0.479661 / 0.579283 (-0.099622) | 0.463626 / 0.434364 (0.029262) | 0.483465 / 0.540337 (-0.056873) | 0.676141 / 1.386936 (-0.710795) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006146 / 0.011353 (-0.005207) | 0.004856 / 0.011008 (-0.006152) | 0.067506 / 0.038508 (0.028998) | 0.073968 / 0.023109 (0.050859) | 0.470013 / 0.275898 (0.194115) | 0.479022 / 0.323480 (0.155542) | 0.005972 / 0.007986 (-0.002014) | 0.003846 / 0.004328 (-0.000483) | 0.075141 / 0.004250 (0.070890) | 0.058597 / 0.037052 (0.021544) | 0.481454 / 0.258489 (0.222965) | 0.515634 / 0.293841 (0.221793) | 0.034979 / 0.128546 (-0.093567) | 0.010385 / 0.075646 (-0.065261) | 0.072649 / 0.419271 (-0.346622) | 0.058183 / 0.043533 (0.014650) | 0.462138 / 0.255139 (0.206999) | 0.476093 / 0.283200 (0.192893) | 0.032918 / 0.141683 (-0.108765) | 1.820530 / 1.452155 (0.368375) | 1.626360 / 1.492716 (0.133644) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208970 / 0.018006 (0.190964) | 0.492478 / 0.000490 (0.491988) | 0.005487 / 0.000200 (0.005287) | 0.000140 / 0.000054 (0.000086) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037896 / 0.037411 (0.000484) | 0.089752 / 0.014526 (0.075227) | 0.107445 / 0.176557 (-0.069111) | 0.181260 / 0.737135 (-0.555876) | 0.105700 / 0.296338 (-0.190639) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.495031 / 0.215209 (0.279821) | 4.806939 / 2.077655 (2.729284) | 2.227928 / 1.504120 (0.723808) | 2.067117 / 1.541195 (0.525922) | 2.348982 / 1.468490 (0.880492) | 0.567201 / 4.584777 (-4.017576) | 4.166592 / 3.745712 (0.420880) | 3.654329 / 5.269862 (-1.615533) | 2.331092 / 4.565676 (-2.234584) | 0.062212 / 0.424275 (-0.362063) | 0.008775 / 0.007607 (0.001168) | 0.515413 / 0.226044 (0.289369) | 5.449300 / 2.268929 (3.180371) | 3.206574 / 55.444624 (-52.238050) | 2.600455 / 6.876477 (-4.276022) | 3.041162 / 2.142072 (0.899089) | 0.681899 / 4.805227 (-4.123328) | 0.155400 / 6.500664 (-6.345265) | 0.073933 / 0.075469 (-0.001537) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.572329 / 1.841788 (-0.269459) | 23.638519 / 8.074308 (15.564211) | 17.145663 / 10.191392 (6.954271) | 0.232690 / 0.680424 (-0.447734) | 0.028620 / 0.534201 (-0.505581) | 0.488105 / 0.579283 (-0.091178) | 0.490365 / 0.434364 (0.056001) | 0.599501 / 0.540337 (0.059164) | 0.708101 / 1.386936 (-0.678835) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4a761315900880a25b347ad19b78bd567cfce1f0 \"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) | 0.005947 / 0.011353 (-0.005406) | 0.003577 / 0.011008 (-0.007431) | 0.081631 / 0.038508 (0.043122) | 0.058651 / 0.023109 (0.035541) | 0.342742 / 0.275898 (0.066843) | 0.384130 / 0.323480 (0.060650) | 0.004620 / 0.007986 (-0.003366) | 0.002885 / 0.004328 (-0.001444) | 0.063698 / 0.004250 (0.059448) | 0.048953 / 0.037052 (0.011901) | 0.367880 / 0.258489 (0.109391) | 0.407050 / 0.293841 (0.113209) | 0.027242 / 0.128546 (-0.101305) | 0.007914 / 0.075646 (-0.067733) | 0.262156 / 0.419271 (-0.157116) | 0.044750 / 0.043533 (0.001218) | 0.351613 / 0.255139 (0.096474) | 0.380284 / 0.283200 (0.097084) | 0.020080 / 0.141683 (-0.121603) | 1.498101 / 1.452155 (0.045946) | 1.543608 / 1.492716 (0.050892) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.180014 / 0.018006 (0.162008) | 0.436172 / 0.000490 (0.435682) | 0.003694 / 0.000200 (0.003494) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024389 / 0.037411 (-0.013022) | 0.072874 / 0.014526 (0.058348) | 0.083469 / 0.176557 (-0.093088) | 0.144600 / 0.737135 (-0.592536) | 0.084229 / 0.296338 (-0.212110) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.391636 / 0.215209 (0.176427) | 3.906941 / 2.077655 (1.829286) | 1.901944 / 1.504120 (0.397825) | 1.762702 / 1.541195 (0.221507) | 1.817970 / 1.468490 (0.349480) | 0.500345 / 4.584777 (-4.084432) | 3.011351 / 3.745712 (-0.734361) | 4.417763 / 5.269862 (-0.852098) | 2.689744 / 4.565676 (-1.875933) | 0.057765 / 0.424275 (-0.366511) | 0.006412 / 0.007607 (-0.001195) | 0.468156 / 0.226044 (0.242112) | 4.664975 / 2.268929 (2.396047) | 2.323355 / 55.444624 (-53.121270) | 1.984280 / 6.876477 (-4.892197) | 2.165215 / 2.142072 (0.023142) | 0.586950 / 4.805227 (-4.218278) | 0.124363 / 6.500664 (-6.376301) | 0.060702 / 0.075469 (-0.014767) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.238870 / 1.841788 (-0.602917) | 18.587360 / 8.074308 (10.513052) | 13.831674 / 10.191392 (3.640282) | 0.143542 / 0.680424 (-0.536882) | 0.016913 / 0.534201 (-0.517288) | 0.332314 / 0.579283 (-0.246969) | 0.345419 / 0.434364 (-0.088945) | 0.381257 / 0.540337 (-0.159081) | 0.537844 / 1.386936 (-0.849092) |\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) | 0.006294 / 0.011353 (-0.005059) | 0.003714 / 0.011008 (-0.007294) | 0.062684 / 0.038508 (0.024176) | 0.063520 / 0.023109 (0.040411) | 0.389591 / 0.275898 (0.113693) | 0.444278 / 0.323480 (0.120798) | 0.004825 / 0.007986 (-0.003160) | 0.003010 / 0.004328 (-0.001318) | 0.062767 / 0.004250 (0.058517) | 0.051739 / 0.037052 (0.014686) | 0.434299 / 0.258489 (0.175810) | 0.452003 / 0.293841 (0.158162) | 0.027375 / 0.128546 (-0.101171) | 0.008135 / 0.075646 (-0.067511) | 0.067401 / 0.419271 (-0.351871) | 0.042752 / 0.043533 (-0.000780) | 0.367633 / 0.255139 (0.112494) | 0.433039 / 0.283200 (0.149840) | 0.021086 / 0.141683 (-0.120597) | 1.488024 / 1.452155 (0.035870) | 1.507767 / 1.492716 (0.015050) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230046 / 0.018006 (0.212040) | 0.428085 / 0.000490 (0.427595) | 0.002188 / 0.000200 (0.001988) | 0.000070 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026705 / 0.037411 (-0.010706) | 0.082466 / 0.014526 (0.067940) | 0.089378 / 0.176557 (-0.087179) | 0.147287 / 0.737135 (-0.589849) | 0.090426 / 0.296338 (-0.205913) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430882 / 0.215209 (0.215672) | 4.296224 / 2.077655 (2.218569) | 2.229982 / 1.504120 (0.725862) | 2.048506 / 1.541195 (0.507311) | 2.129514 / 1.468490 (0.661024) | 0.502964 / 4.584777 (-4.081813) | 3.048125 / 3.745712 (-0.697587) | 4.208636 / 5.269862 (-1.061226) | 2.594015 / 4.565676 (-1.971661) | 0.057967 / 0.424275 (-0.366308) | 0.006875 / 0.007607 (-0.000732) | 0.513872 / 0.226044 (0.287828) | 5.126435 / 2.268929 (2.857506) | 2.691278 / 55.444624 (-52.753346) | 2.361723 / 6.876477 (-4.514754) | 2.511213 / 2.142072 (0.369141) | 0.593558 / 4.805227 (-4.211670) | 0.129332 / 6.500664 (-6.371332) | 0.064051 / 0.075469 (-0.011418) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.289049 / 1.841788 (-0.552739) | 18.912363 / 8.074308 (10.838055) | 14.226500 / 10.191392 (4.035108) | 0.131392 / 0.680424 (-0.549032) | 0.016750 / 0.534201 (-0.517451) | 0.330078 / 0.579283 (-0.249205) | 0.347588 / 0.434364 (-0.086776) | 0.383234 / 0.540337 (-0.157103) | 0.510967 / 1.386936 (-0.875969) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d7892beb30bab0633b84398c5ea43d7e69fe38cc \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005974 / 0.011353 (-0.005379) | 0.003691 / 0.011008 (-0.007317) | 0.079410 / 0.038508 (0.040902) | 0.061769 / 0.023109 (0.038660) | 0.323310 / 0.275898 (0.047412) | 0.354325 / 0.323480 (0.030845) | 0.004794 / 0.007986 (-0.003191) | 0.002899 / 0.004328 (-0.001430) | 0.062104 / 0.004250 (0.057854) | 0.048973 / 0.037052 (0.011921) | 0.326497 / 0.258489 (0.068008) | 0.361347 / 0.293841 (0.067506) | 0.026741 / 0.128546 (-0.101805) | 0.007936 / 0.075646 (-0.067710) | 0.259168 / 0.419271 (-0.160104) | 0.044859 / 0.043533 (0.001327) | 0.319342 / 0.255139 (0.064203) | 0.343711 / 0.283200 (0.060511) | 0.022298 / 0.141683 (-0.119384) | 1.451595 / 1.452155 (-0.000560) | 1.573730 / 1.492716 (0.081014) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.173086 / 0.018006 (0.155080) | 0.432400 / 0.000490 (0.431910) | 0.003739 / 0.000200 (0.003539) | 0.000073 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024477 / 0.037411 (-0.012934) | 0.073463 / 0.014526 (0.058937) | 0.083410 / 0.176557 (-0.093146) | 0.144760 / 0.737135 (-0.592376) | 0.084199 / 0.296338 (-0.212140) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.388251 / 0.215209 (0.173042) | 3.875375 / 2.077655 (1.797720) | 1.875515 / 1.504120 (0.371395) | 1.729282 / 1.541195 (0.188087) | 1.784732 / 1.468490 (0.316242) | 0.496985 / 4.584777 (-4.087792) | 3.030276 / 3.745712 (-0.715436) | 2.813192 / 5.269862 (-2.456669) | 1.868647 / 4.565676 (-2.697030) | 0.057376 / 0.424275 (-0.366899) | 0.006463 / 0.007607 (-0.001144) | 0.462153 / 0.226044 (0.236108) | 4.586583 / 2.268929 (2.317654) | 2.287730 / 55.444624 (-53.156894) | 1.972177 / 6.876477 (-4.904299) | 2.151592 / 2.142072 (0.009520) | 0.587169 / 4.805227 (-4.218058) | 0.127063 / 6.500664 (-6.373601) | 0.060297 / 0.075469 (-0.015172) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.267651 / 1.841788 (-0.574136) | 18.426011 / 8.074308 (10.351703) | 14.050470 / 10.191392 (3.859078) | 0.148063 / 0.680424 (-0.532361) | 0.017112 / 0.534201 (-0.517089) | 0.330051 / 0.579283 (-0.249232) | 0.358730 / 0.434364 (-0.075634) | 0.392365 / 0.540337 (-0.147972) | 0.534650 / 1.386936 (-0.852286) |\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) | 0.005936 / 0.011353 (-0.005417) | 0.003652 / 0.011008 (-0.007356) | 0.063066 / 0.038508 (0.024558) | 0.060617 / 0.023109 (0.037507) | 0.388293 / 0.275898 (0.112395) | 0.411422 / 0.323480 (0.087942) | 0.004691 / 0.007986 (-0.003295) | 0.002857 / 0.004328 (-0.001472) | 0.064198 / 0.004250 (0.059947) | 0.049124 / 0.037052 (0.012071) | 0.403601 / 0.258489 (0.145112) | 0.413619 / 0.293841 (0.119778) | 0.027279 / 0.128546 (-0.101267) | 0.008072 / 0.075646 (-0.067575) | 0.067890 / 0.419271 (-0.351381) | 0.041866 / 0.043533 (-0.001667) | 0.393438 / 0.255139 (0.138299) | 0.402865 / 0.283200 (0.119666) | 0.023381 / 0.141683 (-0.118302) | 1.496324 / 1.452155 (0.044170) | 1.538080 / 1.492716 (0.045364) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212065 / 0.018006 (0.194059) | 0.410511 / 0.000490 (0.410021) | 0.001236 / 0.000200 (0.001036) | 0.000067 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026012 / 0.037411 (-0.011399) | 0.076592 / 0.014526 (0.062066) | 0.085963 / 0.176557 (-0.090594) | 0.137803 / 0.737135 (-0.599332) | 0.087594 / 0.296338 (-0.208745) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434283 / 0.215209 (0.219074) | 4.345478 / 2.077655 (2.267824) | 2.400954 / 1.504120 (0.896834) | 2.282024 / 1.541195 (0.740829) | 2.414247 / 1.468490 (0.945757) | 0.501855 / 4.584777 (-4.082922) | 3.059433 / 3.745712 (-0.686279) | 2.811288 / 5.269862 (-2.458574) | 1.856839 / 4.565676 (-2.708838) | 0.058017 / 0.424275 (-0.366258) | 0.006844 / 0.007607 (-0.000763) | 0.515376 / 0.226044 (0.289332) | 5.148775 / 2.268929 (2.879847) | 2.930807 / 55.444624 (-52.513817) | 2.520532 / 6.876477 (-4.355944) | 2.746299 / 2.142072 (0.604227) | 0.590102 / 4.805227 (-4.215125) | 0.125747 / 6.500664 (-6.374917) | 0.061873 / 0.075469 (-0.013597) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.306247 / 1.841788 (-0.535541) | 18.366048 / 8.074308 (10.291740) | 13.855617 / 10.191392 (3.664225) | 0.150124 / 0.680424 (-0.530300) | 0.017189 / 0.534201 (-0.517012) | 0.336285 / 0.579283 (-0.242998) | 0.344985 / 0.434364 (-0.089379) | 0.397973 / 0.540337 (-0.142364) | 0.536142 / 1.386936 (-0.850794) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1ae24cf12054b4a512f198979b1ca7707bb99d56 \"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) | 0.006401 / 0.011353 (-0.004952) | 0.003789 / 0.011008 (-0.007219) | 0.079516 / 0.038508 (0.041008) | 0.068279 / 0.023109 (0.045170) | 0.295691 / 0.275898 (0.019793) | 0.327208 / 0.323480 (0.003728) | 0.005070 / 0.007986 (-0.002915) | 0.003044 / 0.004328 (-0.001285) | 0.061411 / 0.004250 (0.057161) | 0.053227 / 0.037052 (0.016175) | 0.297368 / 0.258489 (0.038879) | 0.334740 / 0.293841 (0.040899) | 0.029459 / 0.128546 (-0.099087) | 0.008080 / 0.075646 (-0.067566) | 0.267344 / 0.419271 (-0.151927) | 0.049877 / 0.043533 (0.006344) | 0.293853 / 0.255139 (0.038714) | 0.319819 / 0.283200 (0.036620) | 0.022593 / 0.141683 (-0.119089) | 1.459054 / 1.452155 (0.006900) | 1.471250 / 1.492716 (-0.021466) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.194326 / 0.018006 (0.176320) | 0.443565 / 0.000490 (0.443075) | 0.003745 / 0.000200 (0.003545) | 0.000075 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026640 / 0.037411 (-0.010772) | 0.077630 / 0.014526 (0.063104) | 0.089364 / 0.176557 (-0.087192) | 0.147327 / 0.737135 (-0.589809) | 0.089603 / 0.296338 (-0.206735) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.373758 / 0.215209 (0.158549) | 3.746778 / 2.077655 (1.669123) | 1.814991 / 1.504120 (0.310871) | 1.645650 / 1.541195 (0.104455) | 1.690752 / 1.468490 (0.222262) | 0.472117 / 4.584777 (-4.112660) | 3.457346 / 3.745712 (-0.288367) | 3.138869 / 5.269862 (-2.130993) | 1.934924 / 4.565676 (-2.630753) | 0.055709 / 0.424275 (-0.368566) | 0.006680 / 0.007607 (-0.000927) | 0.446874 / 0.226044 (0.220829) | 4.458409 / 2.268929 (2.189480) | 2.253932 / 55.444624 (-53.190693) | 2.007240 / 6.876477 (-4.869237) | 2.081687 / 2.142072 (-0.060386) | 0.563379 / 4.805227 (-4.241848) | 0.128694 / 6.500664 (-6.371970) | 0.057409 / 0.075469 (-0.018060) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.212231 / 1.841788 (-0.629556) | 18.519121 / 8.074308 (10.444813) | 13.582243 / 10.191392 (3.390851) | 0.142488 / 0.680424 (-0.537936) | 0.017421 / 0.534201 (-0.516780) | 0.366864 / 0.579283 (-0.212419) | 0.401467 / 0.434364 (-0.032897) | 0.443659 / 0.540337 (-0.096679) | 0.618854 / 1.386936 (-0.768082) |\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) | 0.006121 / 0.011353 (-0.005232) | 0.003690 / 0.011008 (-0.007318) | 0.060340 / 0.038508 (0.021832) | 0.067215 / 0.023109 (0.044106) | 0.382846 / 0.275898 (0.106948) | 0.415774 / 0.323480 (0.092294) | 0.004868 / 0.007986 (-0.003118) | 0.003108 / 0.004328 (-0.001221) | 0.060572 / 0.004250 (0.056321) | 0.050453 / 0.037052 (0.013401) | 0.400494 / 0.258489 (0.142005) | 0.424368 / 0.293841 (0.130527) | 0.030279 / 0.128546 (-0.098267) | 0.008151 / 0.075646 (-0.067495) | 0.066707 / 0.419271 (-0.352564) | 0.046118 / 0.043533 (0.002585) | 0.386697 / 0.255139 (0.131558) | 0.410156 / 0.283200 (0.126957) | 0.020688 / 0.141683 (-0.120995) | 1.418162 / 1.452155 (-0.033993) | 1.463057 / 1.492716 (-0.029659) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216081 / 0.018006 (0.198075) | 0.440541 / 0.000490 (0.440051) | 0.000371 / 0.000200 (0.000171) | 0.000054 / 0.000054 (-0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027763 / 0.037411 (-0.009648) | 0.082316 / 0.014526 (0.067791) | 0.094086 / 0.176557 (-0.082471) | 0.144738 / 0.737135 (-0.592398) | 0.094837 / 0.296338 (-0.201501) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.396277 / 0.215209 (0.181068) | 3.958791 / 2.077655 (1.881136) | 2.021367 / 1.504120 (0.517247) | 1.860112 / 1.541195 (0.318917) | 1.886032 / 1.468490 (0.417541) | 0.468536 / 4.584777 (-4.116241) | 3.417950 / 3.745712 (-0.327762) | 4.849991 / 5.269862 (-0.419871) | 2.773935 / 4.565676 (-1.791742) | 0.055813 / 0.424275 (-0.368462) | 0.007053 / 0.007607 (-0.000554) | 0.470167 / 0.226044 (0.244122) | 4.702969 / 2.268929 (2.434041) | 2.474161 / 55.444624 (-52.970464) | 2.171256 / 6.876477 (-4.705220) | 2.315373 / 2.142072 (0.173301) | 0.589195 / 4.805227 (-4.216032) | 0.128237 / 6.500664 (-6.372427) | 0.058641 / 0.075469 (-0.016828) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.292947 / 1.841788 (-0.548841) | 18.851300 / 8.074308 (10.776992) | 14.089764 / 10.191392 (3.898372) | 0.164853 / 0.680424 (-0.515571) | 0.017281 / 0.534201 (-0.516920) | 0.359112 / 0.579283 (-0.220171) | 0.386696 / 0.434364 (-0.047668) | 0.428222 / 0.540337 (-0.112115) | 0.568659 / 1.386936 (-0.818277) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#563864ded894b468e2ba3f677ef79c5ab3fe65df \"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) | 0.006051 / 0.011353 (-0.005301) | 0.003654 / 0.011008 (-0.007355) | 0.080081 / 0.038508 (0.041572) | 0.062925 / 0.023109 (0.039815) | 0.358097 / 0.275898 (0.082199) | 0.405728 / 0.323480 (0.082248) | 0.005359 / 0.007986 (-0.002627) | 0.002820 / 0.004328 (-0.001508) | 0.063108 / 0.004250 (0.058858) | 0.049627 / 0.037052 (0.012575) | 0.397870 / 0.258489 (0.139381) | 0.437157 / 0.293841 (0.143316) | 0.027707 / 0.128546 (-0.100839) | 0.007911 / 0.075646 (-0.067735) | 0.260991 / 0.419271 (-0.158280) | 0.044771 / 0.043533 (0.001238) | 0.340230 / 0.255139 (0.085091) | 0.384925 / 0.283200 (0.101725) | 0.021369 / 0.141683 (-0.120314) | 1.431439 / 1.452155 (-0.020715) | 1.478794 / 1.492716 (-0.013922) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.182626 / 0.018006 (0.164620) | 0.435551 / 0.000490 (0.435061) | 0.003015 / 0.000200 (0.002815) | 0.000064 / 0.000054 (0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024703 / 0.037411 (-0.012708) | 0.073640 / 0.014526 (0.059114) | 0.084598 / 0.176557 (-0.091959) | 0.145810 / 0.737135 (-0.591325) | 0.085125 / 0.296338 (-0.211213) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.394539 / 0.215209 (0.179330) | 3.945882 / 2.077655 (1.868227) | 1.947166 / 1.504120 (0.443046) | 1.763305 / 1.541195 (0.222111) | 1.816208 / 1.468490 (0.347718) | 0.498880 / 4.584777 (-4.085897) | 3.098283 / 3.745712 (-0.647429) | 2.823474 / 5.269862 (-2.446388) | 1.873993 / 4.565676 (-2.691684) | 0.058097 / 0.424275 (-0.366179) | 0.006488 / 0.007607 (-0.001119) | 0.466711 / 0.226044 (0.240667) | 4.671520 / 2.268929 (2.402592) | 2.363381 / 55.444624 (-53.081243) | 2.052092 / 6.876477 (-4.824385) | 2.209212 / 2.142072 (0.067140) | 0.594650 / 4.805227 (-4.210577) | 0.125604 / 6.500664 (-6.375060) | 0.061511 / 0.075469 (-0.013958) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.226564 / 1.841788 (-0.615224) | 18.583605 / 8.074308 (10.509297) | 13.993091 / 10.191392 (3.801699) | 0.146185 / 0.680424 (-0.534239) | 0.016839 / 0.534201 (-0.517362) | 0.334116 / 0.579283 (-0.245167) | 0.360780 / 0.434364 (-0.073584) | 0.386008 / 0.540337 (-0.154329) | 0.643278 / 1.386936 (-0.743658) |\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) | 0.006174 / 0.011353 (-0.005179) | 0.003658 / 0.011008 (-0.007350) | 0.063250 / 0.038508 (0.024742) | 0.063542 / 0.023109 (0.040433) | 0.366845 / 0.275898 (0.090947) | 0.409794 / 0.323480 (0.086314) | 0.005678 / 0.007986 (-0.002308) | 0.003061 / 0.004328 (-0.001268) | 0.063561 / 0.004250 (0.059311) | 0.052648 / 0.037052 (0.015596) | 0.378096 / 0.258489 (0.119607) | 0.410706 / 0.293841 (0.116865) | 0.027668 / 0.128546 (-0.100878) | 0.008045 / 0.075646 (-0.067601) | 0.068290 / 0.419271 (-0.350981) | 0.042602 / 0.043533 (-0.000930) | 0.364976 / 0.255139 (0.109837) | 0.395599 / 0.283200 (0.112400) | 0.022733 / 0.141683 (-0.118950) | 1.522473 / 1.452155 (0.070319) | 1.515891 / 1.492716 (0.023175) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.232554 / 0.018006 (0.214547) | 0.420702 / 0.000490 (0.420213) | 0.002161 / 0.000200 (0.001961) | 0.000064 / 0.000054 (0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026276 / 0.037411 (-0.011135) | 0.078504 / 0.014526 (0.063978) | 0.088989 / 0.176557 (-0.087567) | 0.144044 / 0.737135 (-0.593091) | 0.091074 / 0.296338 (-0.205265) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420189 / 0.215209 (0.204980) | 4.189596 / 2.077655 (2.111941) | 2.316425 / 1.504120 (0.812305) | 2.186877 / 1.541195 (0.645682) | 2.259065 / 1.468490 (0.790575) | 0.502827 / 4.584777 (-4.081950) | 3.135266 / 3.745712 (-0.610446) | 2.838808 / 5.269862 (-2.431053) | 1.876519 / 4.565676 (-2.689158) | 0.057802 / 0.424275 (-0.366473) | 0.006824 / 0.007607 (-0.000784) | 0.500213 / 0.226044 (0.274168) | 4.999798 / 2.268929 (2.730869) | 2.627713 / 55.444624 (-52.816911) | 2.344263 / 6.876477 (-4.532214) | 2.415449 / 2.142072 (0.273376) | 0.593082 / 4.805227 (-4.212145) | 0.125787 / 6.500664 (-6.374877) | 0.062699 / 0.075469 (-0.012770) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.308219 / 1.841788 (-0.533569) | 18.703099 / 8.074308 (10.628791) | 13.976234 / 10.191392 (3.784842) | 0.144037 / 0.680424 (-0.536387) | 0.016592 / 0.534201 (-0.517609) | 0.333078 / 0.579283 (-0.246206) | 0.342317 / 0.434364 (-0.092047) | 0.396837 / 0.540337 (-0.143500) | 0.532641 / 1.386936 (-0.854295) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#14f6edd9222e577dccb962ed5338b79b73502fa5 \"CML watermark\")\n"
] |
6,027
|
Delete `task_templates` in `IterableDataset` when they are no longer valid
|
Fix #6025
|
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6027",
"html_url": "https://github.com/huggingface/datasets/pull/6027",
"diff_url": "https://github.com/huggingface/datasets/pull/6027.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6027.patch",
"merged_at": "2023-07-13T13:57:35"
}
| true
|
[
"_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) | 0.008698 / 0.011353 (-0.002655) | 0.005250 / 0.011008 (-0.005758) | 0.104101 / 0.038508 (0.065593) | 0.085021 / 0.023109 (0.061912) | 0.426653 / 0.275898 (0.150755) | 0.460449 / 0.323480 (0.136969) | 0.005222 / 0.007986 (-0.002763) | 0.006280 / 0.004328 (0.001951) | 0.083458 / 0.004250 (0.079207) | 0.066132 / 0.037052 (0.029079) | 0.433416 / 0.258489 (0.174927) | 0.482718 / 0.293841 (0.188877) | 0.048872 / 0.128546 (-0.079675) | 0.013699 / 0.075646 (-0.061948) | 0.365660 / 0.419271 (-0.053611) | 0.071008 / 0.043533 (0.027475) | 0.428688 / 0.255139 (0.173549) | 0.443554 / 0.283200 (0.160354) | 0.035901 / 0.141683 (-0.105782) | 1.829296 / 1.452155 (0.377141) | 1.862351 / 1.492716 (0.369635) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236284 / 0.018006 (0.218278) | 0.584075 / 0.000490 (0.583585) | 0.004634 / 0.000200 (0.004434) | 0.000125 / 0.000054 (0.000070) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034723 / 0.037411 (-0.002688) | 0.100989 / 0.014526 (0.086464) | 0.113722 / 0.176557 (-0.062834) | 0.187659 / 0.737135 (-0.549477) | 0.113937 / 0.296338 (-0.182401) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.587500 / 0.215209 (0.372291) | 5.847371 / 2.077655 (3.769716) | 2.599691 / 1.504120 (1.095571) | 2.246187 / 1.541195 (0.704992) | 2.419126 / 1.468490 (0.950636) | 0.847327 / 4.584777 (-3.737450) | 5.230438 / 3.745712 (1.484726) | 7.539021 / 5.269862 (2.269160) | 4.617473 / 4.565676 (0.051797) | 0.103620 / 0.424275 (-0.320655) | 0.009195 / 0.007607 (0.001588) | 0.714247 / 0.226044 (0.488203) | 7.331621 / 2.268929 (5.062693) | 3.416575 / 55.444624 (-52.028049) | 2.649467 / 6.876477 (-4.227009) | 2.928091 / 2.142072 (0.786018) | 1.002155 / 4.805227 (-3.803072) | 0.210790 / 6.500664 (-6.289874) | 0.081303 / 0.075469 (0.005834) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.655431 / 1.841788 (-0.186357) | 24.069595 / 8.074308 (15.995287) | 20.923766 / 10.191392 (10.732374) | 0.232021 / 0.680424 (-0.448403) | 0.026355 / 0.534201 (-0.507846) | 0.496830 / 0.579283 (-0.082453) | 0.582620 / 0.434364 (0.148257) | 0.551227 / 0.540337 (0.010890) | 0.756389 / 1.386936 (-0.630547) |\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) | 0.009329 / 0.011353 (-0.002024) | 0.005045 / 0.011008 (-0.005964) | 0.082116 / 0.038508 (0.043608) | 0.082420 / 0.023109 (0.059311) | 0.502513 / 0.275898 (0.226615) | 0.526098 / 0.323480 (0.202618) | 0.007468 / 0.007986 (-0.000517) | 0.005477 / 0.004328 (0.001148) | 0.082617 / 0.004250 (0.078367) | 0.070292 / 0.037052 (0.033239) | 0.503290 / 0.258489 (0.244801) | 0.541631 / 0.293841 (0.247790) | 0.050826 / 0.128546 (-0.077721) | 0.014699 / 0.075646 (-0.060948) | 0.094441 / 0.419271 (-0.324830) | 0.065034 / 0.043533 (0.021501) | 0.486778 / 0.255139 (0.231639) | 0.516907 / 0.283200 (0.233707) | 0.045140 / 0.141683 (-0.096543) | 1.831676 / 1.452155 (0.379521) | 1.910865 / 1.492716 (0.418149) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.286818 / 0.018006 (0.268812) | 0.558621 / 0.000490 (0.558131) | 0.002830 / 0.000200 (0.002630) | 0.000148 / 0.000054 (0.000094) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036716 / 0.037411 (-0.000696) | 0.107830 / 0.014526 (0.093305) | 0.116368 / 0.176557 (-0.060188) | 0.178401 / 0.737135 (-0.558734) | 0.124729 / 0.296338 (-0.171609) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.633557 / 0.215209 (0.418348) | 6.423135 / 2.077655 (4.345480) | 2.981883 / 1.504120 (1.477763) | 2.755592 / 1.541195 (1.214398) | 2.769337 / 1.468490 (1.300847) | 0.836219 / 4.584777 (-3.748558) | 5.302030 / 3.745712 (1.556318) | 7.463960 / 5.269862 (2.194098) | 4.427254 / 4.565676 (-0.138422) | 0.095990 / 0.424275 (-0.328285) | 0.009264 / 0.007607 (0.001657) | 0.770642 / 0.226044 (0.544597) | 7.779667 / 2.268929 (5.510739) | 3.799115 / 55.444624 (-51.645509) | 3.212560 / 6.876477 (-3.663917) | 3.281657 / 2.142072 (1.139584) | 1.044981 / 4.805227 (-3.760246) | 0.210693 / 6.500664 (-6.289971) | 0.079466 / 0.075469 (0.003997) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.793155 / 1.841788 (-0.048632) | 24.691127 / 8.074308 (16.616819) | 22.083150 / 10.191392 (11.891758) | 0.242246 / 0.680424 (-0.438178) | 0.028001 / 0.534201 (-0.506200) | 0.494061 / 0.579283 (-0.085222) | 0.599288 / 0.434364 (0.164924) | 0.552101 / 0.540337 (0.011764) | 0.784093 / 1.386936 (-0.602843) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cd429c39604af34bc3a3ba1f463329b23fcbc1e3 \"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) | 0.006658 / 0.011353 (-0.004695) | 0.004044 / 0.011008 (-0.006965) | 0.085844 / 0.038508 (0.047336) | 0.077147 / 0.023109 (0.054038) | 0.344387 / 0.275898 (0.068489) | 0.376718 / 0.323480 (0.053238) | 0.005537 / 0.007986 (-0.002448) | 0.003452 / 0.004328 (-0.000876) | 0.065326 / 0.004250 (0.061076) | 0.057639 / 0.037052 (0.020587) | 0.352363 / 0.258489 (0.093873) | 0.378939 / 0.293841 (0.085098) | 0.031259 / 0.128546 (-0.097287) | 0.008464 / 0.075646 (-0.067183) | 0.289076 / 0.419271 (-0.130195) | 0.052991 / 0.043533 (0.009459) | 0.346053 / 0.255139 (0.090914) | 0.362761 / 0.283200 (0.079561) | 0.023501 / 0.141683 (-0.118182) | 1.478312 / 1.452155 (0.026157) | 1.545437 / 1.492716 (0.052721) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.202964 / 0.018006 (0.184957) | 0.534793 / 0.000490 (0.534303) | 0.006025 / 0.000200 (0.005825) | 0.000225 / 0.000054 (0.000171) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029418 / 0.037411 (-0.007993) | 0.084297 / 0.014526 (0.069771) | 0.096702 / 0.176557 (-0.079855) | 0.157355 / 0.737135 (-0.579781) | 0.097858 / 0.296338 (-0.198480) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.380728 / 0.215209 (0.165519) | 3.787712 / 2.077655 (1.710057) | 1.836393 / 1.504120 (0.332273) | 1.678415 / 1.541195 (0.137220) | 1.781800 / 1.468490 (0.313310) | 0.478677 / 4.584777 (-4.106100) | 3.614080 / 3.745712 (-0.131632) | 3.255637 / 5.269862 (-2.014225) | 2.063642 / 4.565676 (-2.502035) | 0.056470 / 0.424275 (-0.367805) | 0.007408 / 0.007607 (-0.000199) | 0.459155 / 0.226044 (0.233111) | 4.586679 / 2.268929 (2.317750) | 2.305737 / 55.444624 (-53.138888) | 1.954755 / 6.876477 (-4.921721) | 2.190809 / 2.142072 (0.048737) | 0.572426 / 4.805227 (-4.232802) | 0.130349 / 6.500664 (-6.370315) | 0.059346 / 0.075469 (-0.016124) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.253671 / 1.841788 (-0.588117) | 19.509015 / 8.074308 (11.434706) | 13.951349 / 10.191392 (3.759957) | 0.171038 / 0.680424 (-0.509386) | 0.018826 / 0.534201 (-0.515375) | 0.394642 / 0.579283 (-0.184642) | 0.419614 / 0.434364 (-0.014750) | 0.470931 / 0.540337 (-0.069406) | 0.643858 / 1.386936 (-0.743078) |\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) | 0.006765 / 0.011353 (-0.004587) | 0.003955 / 0.011008 (-0.007053) | 0.064377 / 0.038508 (0.025869) | 0.076980 / 0.023109 (0.053871) | 0.368675 / 0.275898 (0.092777) | 0.403746 / 0.323480 (0.080267) | 0.005303 / 0.007986 (-0.002683) | 0.003257 / 0.004328 (-0.001072) | 0.064154 / 0.004250 (0.059903) | 0.056975 / 0.037052 (0.019923) | 0.376718 / 0.258489 (0.118229) | 0.416291 / 0.293841 (0.122450) | 0.031444 / 0.128546 (-0.097102) | 0.008532 / 0.075646 (-0.067115) | 0.070455 / 0.419271 (-0.348816) | 0.049032 / 0.043533 (0.005499) | 0.361413 / 0.255139 (0.106274) | 0.384648 / 0.283200 (0.101448) | 0.024050 / 0.141683 (-0.117633) | 1.514330 / 1.452155 (0.062176) | 1.585424 / 1.492716 (0.092708) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.214701 / 0.018006 (0.196695) | 0.447706 / 0.000490 (0.447216) | 0.000373 / 0.000200 (0.000173) | 0.000058 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031007 / 0.037411 (-0.006404) | 0.090545 / 0.014526 (0.076019) | 0.100611 / 0.176557 (-0.075945) | 0.154847 / 0.737135 (-0.582289) | 0.102864 / 0.296338 (-0.193475) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.427740 / 0.215209 (0.212531) | 4.273143 / 2.077655 (2.195488) | 2.294906 / 1.504120 (0.790786) | 2.138460 / 1.541195 (0.597265) | 2.274126 / 1.468490 (0.805636) | 0.486559 / 4.584777 (-4.098218) | 3.565554 / 3.745712 (-0.180158) | 3.377659 / 5.269862 (-1.892202) | 2.029883 / 4.565676 (-2.535793) | 0.057303 / 0.424275 (-0.366972) | 0.007314 / 0.007607 (-0.000293) | 0.504263 / 0.226044 (0.278219) | 5.041196 / 2.268929 (2.772268) | 2.819273 / 55.444624 (-52.625351) | 2.421479 / 6.876477 (-4.454998) | 2.503063 / 2.142072 (0.360991) | 0.581467 / 4.805227 (-4.223760) | 0.133532 / 6.500664 (-6.367132) | 0.062504 / 0.075469 (-0.012965) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.328765 / 1.841788 (-0.513022) | 20.131672 / 8.074308 (12.057363) | 14.312895 / 10.191392 (4.121503) | 0.191199 / 0.680424 (-0.489225) | 0.018522 / 0.534201 (-0.515679) | 0.393121 / 0.579283 (-0.186162) | 0.413122 / 0.434364 (-0.021242) | 0.469312 / 0.540337 (-0.071026) | 0.633140 / 1.386936 (-0.753796) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dbf6c103f5844de40431478e7e4a64fbf2c2c067 \"CML watermark\")\n"
] |
6,026
|
Fix style with ruff 0.0.278
| null |
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6026",
"html_url": "https://github.com/huggingface/datasets/pull/6026",
"diff_url": "https://github.com/huggingface/datasets/pull/6026.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6026.patch",
"merged_at": "2023-07-13T12:37:01"
}
| true
|
[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6026). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006444 / 0.011353 (-0.004909) | 0.003768 / 0.011008 (-0.007240) | 0.079625 / 0.038508 (0.041117) | 0.064490 / 0.023109 (0.041381) | 0.313858 / 0.275898 (0.037960) | 0.350810 / 0.323480 (0.027330) | 0.004804 / 0.007986 (-0.003182) | 0.002904 / 0.004328 (-0.001425) | 0.061728 / 0.004250 (0.057477) | 0.052265 / 0.037052 (0.015213) | 0.321246 / 0.258489 (0.062757) | 0.353873 / 0.293841 (0.060032) | 0.027510 / 0.128546 (-0.101036) | 0.007942 / 0.075646 (-0.067704) | 0.260518 / 0.419271 (-0.158754) | 0.045686 / 0.043533 (0.002153) | 0.316821 / 0.255139 (0.061682) | 0.337086 / 0.283200 (0.053886) | 0.022188 / 0.141683 (-0.119495) | 1.427345 / 1.452155 (-0.024810) | 1.476059 / 1.492716 (-0.016657) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.189640 / 0.018006 (0.171634) | 0.429724 / 0.000490 (0.429235) | 0.005314 / 0.000200 (0.005114) | 0.000076 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024412 / 0.037411 (-0.013000) | 0.073488 / 0.014526 (0.058962) | 0.083843 / 0.176557 (-0.092714) | 0.147849 / 0.737135 (-0.589286) | 0.085465 / 0.296338 (-0.210873) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.405314 / 0.215209 (0.190105) | 4.071471 / 2.077655 (1.993816) | 1.916252 / 1.504120 (0.412132) | 1.721616 / 1.541195 (0.180422) | 1.807187 / 1.468490 (0.338697) | 0.498045 / 4.584777 (-4.086732) | 3.057526 / 3.745712 (-0.688187) | 4.451424 / 5.269862 (-0.818437) | 2.764020 / 4.565676 (-1.801656) | 0.057665 / 0.424275 (-0.366610) | 0.006679 / 0.007607 (-0.000928) | 0.485733 / 0.226044 (0.259688) | 4.844367 / 2.268929 (2.575438) | 2.435359 / 55.444624 (-53.009265) | 2.111478 / 6.876477 (-4.764999) | 2.377448 / 2.142072 (0.235375) | 0.587997 / 4.805227 (-4.217230) | 0.125545 / 6.500664 (-6.375120) | 0.061509 / 0.075469 (-0.013960) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.229210 / 1.841788 (-0.612577) | 18.553994 / 8.074308 (10.479686) | 14.037877 / 10.191392 (3.846485) | 0.144230 / 0.680424 (-0.536194) | 0.016891 / 0.534201 (-0.517310) | 0.329039 / 0.579283 (-0.250244) | 0.357269 / 0.434364 (-0.077095) | 0.384222 / 0.540337 (-0.156115) | 0.521292 / 1.386936 (-0.865644) |\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) | 0.006359 / 0.011353 (-0.004994) | 0.003721 / 0.011008 (-0.007287) | 0.062047 / 0.038508 (0.023539) | 0.065267 / 0.023109 (0.042158) | 0.360164 / 0.275898 (0.084266) | 0.402292 / 0.323480 (0.078812) | 0.005603 / 0.007986 (-0.002382) | 0.002966 / 0.004328 (-0.001363) | 0.062580 / 0.004250 (0.058330) | 0.053634 / 0.037052 (0.016582) | 0.362210 / 0.258489 (0.103721) | 0.404285 / 0.293841 (0.110444) | 0.027567 / 0.128546 (-0.100979) | 0.008119 / 0.075646 (-0.067528) | 0.067577 / 0.419271 (-0.351694) | 0.042867 / 0.043533 (-0.000666) | 0.361576 / 0.255139 (0.106437) | 0.389061 / 0.283200 (0.105862) | 0.021923 / 0.141683 (-0.119760) | 1.446259 / 1.452155 (-0.005895) | 1.490724 / 1.492716 (-0.001992) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206433 / 0.018006 (0.188427) | 0.424178 / 0.000490 (0.423688) | 0.002340 / 0.000200 (0.002140) | 0.000069 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024955 / 0.037411 (-0.012456) | 0.077446 / 0.014526 (0.062920) | 0.088540 / 0.176557 (-0.088017) | 0.141225 / 0.737135 (-0.595910) | 0.089747 / 0.296338 (-0.206592) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.443738 / 0.215209 (0.228529) | 4.208887 / 2.077655 (2.131233) | 2.155127 / 1.504120 (0.651007) | 2.028178 / 1.541195 (0.486983) | 2.084903 / 1.468490 (0.616413) | 0.497530 / 4.584777 (-4.087247) | 3.069012 / 3.745712 (-0.676700) | 3.025184 / 5.269862 (-2.244678) | 1.904687 / 4.565676 (-2.660990) | 0.057526 / 0.424275 (-0.366749) | 0.006482 / 0.007607 (-0.001125) | 0.494692 / 0.226044 (0.268647) | 4.944437 / 2.268929 (2.675508) | 2.655989 / 55.444624 (-52.788635) | 2.331677 / 6.876477 (-4.544800) | 2.382396 / 2.142072 (0.240324) | 0.582019 / 4.805227 (-4.223209) | 0.125866 / 6.500664 (-6.374799) | 0.062908 / 0.075469 (-0.012561) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.294612 / 1.841788 (-0.547176) | 19.016152 / 8.074308 (10.941844) | 14.088828 / 10.191392 (3.897436) | 0.160842 / 0.680424 (-0.519582) | 0.017054 / 0.534201 (-0.517146) | 0.333647 / 0.579283 (-0.245636) | 0.348094 / 0.434364 (-0.086270) | 0.394970 / 0.540337 (-0.145367) | 0.551141 / 1.386936 (-0.835795) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9e9cfe886792b30b5000808072a0f91ec8536749 \"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) | 0.007442 / 0.011353 (-0.003911) | 0.004302 / 0.011008 (-0.006707) | 0.087159 / 0.038508 (0.048651) | 0.095094 / 0.023109 (0.071985) | 0.315422 / 0.275898 (0.039524) | 0.346672 / 0.323480 (0.023192) | 0.005811 / 0.007986 (-0.002174) | 0.003597 / 0.004328 (-0.000731) | 0.066400 / 0.004250 (0.062150) | 0.065947 / 0.037052 (0.028894) | 0.323269 / 0.258489 (0.064780) | 0.353309 / 0.293841 (0.059468) | 0.032268 / 0.128546 (-0.096278) | 0.008696 / 0.075646 (-0.066950) | 0.291486 / 0.419271 (-0.127786) | 0.054609 / 0.043533 (0.011076) | 0.321061 / 0.255139 (0.065922) | 0.336907 / 0.283200 (0.053707) | 0.027338 / 0.141683 (-0.114345) | 1.496442 / 1.452155 (0.044287) | 1.576946 / 1.492716 (0.084229) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229140 / 0.018006 (0.211134) | 0.487500 / 0.000490 (0.487010) | 0.002425 / 0.000200 (0.002225) | 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) | 0.029351 / 0.037411 (-0.008060) | 0.089610 / 0.014526 (0.075084) | 0.097880 / 0.176557 (-0.078676) | 0.155947 / 0.737135 (-0.581189) | 0.098593 / 0.296338 (-0.197745) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.382911 / 0.215209 (0.167702) | 3.820363 / 2.077655 (1.742708) | 1.866385 / 1.504120 (0.362265) | 1.712910 / 1.541195 (0.171716) | 1.813863 / 1.468490 (0.345373) | 0.484884 / 4.584777 (-4.099893) | 3.678911 / 3.745712 (-0.066801) | 5.249908 / 5.269862 (-0.019953) | 3.099614 / 4.565676 (-1.466063) | 0.057449 / 0.424275 (-0.366826) | 0.007728 / 0.007607 (0.000120) | 0.462123 / 0.226044 (0.236078) | 4.603942 / 2.268929 (2.335014) | 2.380957 / 55.444624 (-53.063668) | 2.059621 / 6.876477 (-4.816856) | 2.293764 / 2.142072 (0.151691) | 0.636471 / 4.805227 (-4.168756) | 0.150112 / 6.500664 (-6.350552) | 0.063705 / 0.075469 (-0.011764) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.358099 / 1.841788 (-0.483689) | 20.193750 / 8.074308 (12.119442) | 14.297350 / 10.191392 (4.105958) | 0.164477 / 0.680424 (-0.515947) | 0.018259 / 0.534201 (-0.515942) | 0.399010 / 0.579283 (-0.180273) | 0.417306 / 0.434364 (-0.017058) | 0.456961 / 0.540337 (-0.083377) | 0.631068 / 1.386936 (-0.755868) |\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) | 0.007324 / 0.011353 (-0.004028) | 0.004463 / 0.011008 (-0.006545) | 0.066148 / 0.038508 (0.027640) | 0.093909 / 0.023109 (0.070799) | 0.399122 / 0.275898 (0.123224) | 0.430226 / 0.323480 (0.106746) | 0.005505 / 0.007986 (-0.002481) | 0.003579 / 0.004328 (-0.000749) | 0.066529 / 0.004250 (0.062278) | 0.063471 / 0.037052 (0.026418) | 0.406351 / 0.258489 (0.147862) | 0.439987 / 0.293841 (0.146146) | 0.032640 / 0.128546 (-0.095906) | 0.008770 / 0.075646 (-0.066877) | 0.072592 / 0.419271 (-0.346680) | 0.050429 / 0.043533 (0.006896) | 0.390873 / 0.255139 (0.135734) | 0.412438 / 0.283200 (0.129239) | 0.027113 / 0.141683 (-0.114570) | 1.458281 / 1.452155 (0.006126) | 1.536819 / 1.492716 (0.044103) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228309 / 0.018006 (0.210303) | 0.454042 / 0.000490 (0.453552) | 0.000387 / 0.000200 (0.000187) | 0.000055 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029573 / 0.037411 (-0.007838) | 0.086433 / 0.014526 (0.071907) | 0.097992 / 0.176557 (-0.078565) | 0.152464 / 0.737135 (-0.584671) | 0.099901 / 0.296338 (-0.196437) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.413807 / 0.215209 (0.198598) | 4.126395 / 2.077655 (2.048740) | 2.113544 / 1.504120 (0.609424) | 1.967829 / 1.541195 (0.426635) | 2.037123 / 1.468490 (0.568633) | 0.489403 / 4.584777 (-4.095374) | 3.689508 / 3.745712 (-0.056204) | 3.503909 / 5.269862 (-1.765952) | 2.113812 / 4.565676 (-2.451864) | 0.057988 / 0.424275 (-0.366287) | 0.007336 / 0.007607 (-0.000271) | 0.490840 / 0.226044 (0.264795) | 4.885040 / 2.268929 (2.616112) | 2.627864 / 55.444624 (-52.816760) | 2.231467 / 6.876477 (-4.645010) | 2.251307 / 2.142072 (0.109235) | 0.577370 / 4.805227 (-4.227857) | 0.131770 / 6.500664 (-6.368895) | 0.061313 / 0.075469 (-0.014156) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.362052 / 1.841788 (-0.479735) | 21.332694 / 8.074308 (13.258386) | 15.562019 / 10.191392 (5.370627) | 0.170874 / 0.680424 (-0.509550) | 0.019226 / 0.534201 (-0.514975) | 0.400311 / 0.579283 (-0.178972) | 0.423060 / 0.434364 (-0.011304) | 0.469946 / 0.540337 (-0.070391) | 0.647745 / 1.386936 (-0.739191) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aec567c2f224f192e6e1f9799e3afc755eb517b2 \"CML watermark\")\n"
] |
6,025
|
Using a dataset for a use other than it was intended for.
|
### Describe the bug
Hi, I want to use the rotten tomatoes dataset but for a task other than classification, but when I interleave the dataset, it throws ```'ValueError: Column label is not present in features.'```. It seems that the label_col must be there in the dataset for some reason?
Here is the full stacktrace
```
File "/home/suryahari/Vornoi/tryage-handoff-other-datasets.py", line 276, in create_dataloaders
dataset = interleave_datasets(dsfold, stopping_strategy="all_exhausted")
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py", line 134, in interleave_datasets
return _interleave_iterable_datasets(
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1833, in _interleave_iterable_datasets
info = DatasetInfo.from_merge([d.info for d in datasets])
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 275, in from_merge
dataset_infos = [dset_info.copy() for dset_info in dataset_infos if dset_info is not None]
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 275, in <listcomp>
dataset_infos = [dset_info.copy() for dset_info in dataset_infos if dset_info is not None]
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 378, in copy
return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()})
File "<string>", line 20, in __init__
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 208, in __post_init__
self.task_templates = [
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 209, in <listcomp>
template.align_with_features(self.features) for template in (self.task_templates)
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/tasks/text_classification.py", line 20, in align_with_features
raise ValueError(f"Column {self.label_column} is not present in features.")
ValueError: Column label is not present in features.
```
### Steps to reproduce the bug
Delete the column `labels` from the `rotten_tomatoes` dataset. Try to interleave it with other datasets.
### Expected behavior
Should let me use the dataset with just the `text` field
### Environment info
latest datasets library? I don't think this was an issue in earlier versions.
|
[] |
{
"url": null,
"html_url": null,
"diff_url": null,
"patch_url": null,
"merged_at": null
}
| false
|
[
"I've opened a PR with a fix. In the meantime, you can avoid the error by deleting `task_templates` with `dataset.info.task_templates = None` before the `interleave_datasets` call.\r\n` "
] |
6,024
|
Don't reference self in Spark._validate_cache_dir
|
Fix for https://github.com/huggingface/datasets/issues/5963
|
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6024",
"html_url": "https://github.com/huggingface/datasets/pull/6024",
"diff_url": "https://github.com/huggingface/datasets/pull/6024.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6024.patch",
"merged_at": "2023-07-13T12:37:09"
}
| true
|
[
"Ptal @lhoestq :) I tested this manually on a multi-node Databricks cluster",
"Hm looks like the check_code_quality failures are unrelated to me change... https://github.com/huggingface/datasets/actions/runs/5536162850/jobs/10103451883?pr=6024",
"_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) | 0.005952 / 0.011353 (-0.005400) | 0.003585 / 0.011008 (-0.007424) | 0.079163 / 0.038508 (0.040655) | 0.057926 / 0.023109 (0.034817) | 0.326647 / 0.275898 (0.050749) | 0.383485 / 0.323480 (0.060005) | 0.004530 / 0.007986 (-0.003456) | 0.002821 / 0.004328 (-0.001508) | 0.062071 / 0.004250 (0.057820) | 0.048023 / 0.037052 (0.010971) | 0.329368 / 0.258489 (0.070879) | 0.390877 / 0.293841 (0.097036) | 0.026959 / 0.128546 (-0.101588) | 0.007911 / 0.075646 (-0.067735) | 0.259956 / 0.419271 (-0.159315) | 0.044582 / 0.043533 (0.001049) | 0.320537 / 0.255139 (0.065398) | 0.373814 / 0.283200 (0.090614) | 0.020275 / 0.141683 (-0.121408) | 1.532128 / 1.452155 (0.079973) | 1.595031 / 1.492716 (0.102315) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.186127 / 0.018006 (0.168120) | 0.428586 / 0.000490 (0.428097) | 0.005180 / 0.000200 (0.004980) | 0.000069 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024876 / 0.037411 (-0.012536) | 0.072169 / 0.014526 (0.057643) | 0.082015 / 0.176557 (-0.094542) | 0.147467 / 0.737135 (-0.589668) | 0.082769 / 0.296338 (-0.213570) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.410625 / 0.215209 (0.195416) | 4.116742 / 2.077655 (2.039088) | 2.172291 / 1.504120 (0.668171) | 2.022462 / 1.541195 (0.481268) | 2.048142 / 1.468490 (0.579651) | 0.503152 / 4.584777 (-4.081625) | 3.019135 / 3.745712 (-0.726577) | 3.589451 / 5.269862 (-1.680410) | 2.206876 / 4.565676 (-2.358801) | 0.057687 / 0.424275 (-0.366588) | 0.006560 / 0.007607 (-0.001047) | 0.475585 / 0.226044 (0.249541) | 4.784344 / 2.268929 (2.515416) | 2.506322 / 55.444624 (-52.938302) | 2.168251 / 6.876477 (-4.708225) | 2.324453 / 2.142072 (0.182381) | 0.590609 / 4.805227 (-4.214618) | 0.124178 / 6.500664 (-6.376486) | 0.059197 / 0.075469 (-0.016272) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.212359 / 1.841788 (-0.629429) | 17.915843 / 8.074308 (9.841535) | 13.128330 / 10.191392 (2.936938) | 0.144805 / 0.680424 (-0.535618) | 0.016889 / 0.534201 (-0.517312) | 0.344056 / 0.579283 (-0.235227) | 0.359370 / 0.434364 (-0.074994) | 0.404199 / 0.540337 (-0.136138) | 0.549117 / 1.386936 (-0.837819) |\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) | 0.005914 / 0.011353 (-0.005439) | 0.003565 / 0.011008 (-0.007443) | 0.061575 / 0.038508 (0.023067) | 0.057677 / 0.023109 (0.034568) | 0.359753 / 0.275898 (0.083855) | 0.394135 / 0.323480 (0.070655) | 0.004648 / 0.007986 (-0.003338) | 0.002795 / 0.004328 (-0.001534) | 0.061877 / 0.004250 (0.057626) | 0.049673 / 0.037052 (0.012621) | 0.363120 / 0.258489 (0.104631) | 0.402685 / 0.293841 (0.108844) | 0.027021 / 0.128546 (-0.101525) | 0.008006 / 0.075646 (-0.067641) | 0.067398 / 0.419271 (-0.351874) | 0.044442 / 0.043533 (0.000909) | 0.364851 / 0.255139 (0.109712) | 0.387219 / 0.283200 (0.104019) | 0.027267 / 0.141683 (-0.114416) | 1.466675 / 1.452155 (0.014520) | 1.512607 / 1.492716 (0.019891) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206156 / 0.018006 (0.188150) | 0.410877 / 0.000490 (0.410387) | 0.003061 / 0.000200 (0.002861) | 0.000068 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024869 / 0.037411 (-0.012542) | 0.075736 / 0.014526 (0.061210) | 0.083922 / 0.176557 (-0.092634) | 0.139510 / 0.737135 (-0.597626) | 0.087685 / 0.296338 (-0.208654) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.414473 / 0.215209 (0.199264) | 4.150633 / 2.077655 (2.072979) | 2.132892 / 1.504120 (0.628773) | 1.964072 / 1.541195 (0.422878) | 2.003353 / 1.468490 (0.534863) | 0.498012 / 4.584777 (-4.086765) | 3.010135 / 3.745712 (-0.735577) | 2.841130 / 5.269862 (-2.428732) | 1.826013 / 4.565676 (-2.739664) | 0.057443 / 0.424275 (-0.366832) | 0.006374 / 0.007607 (-0.001234) | 0.490337 / 0.226044 (0.264292) | 4.889628 / 2.268929 (2.620700) | 2.575626 / 55.444624 (-52.868998) | 2.246522 / 6.876477 (-4.629955) | 2.276183 / 2.142072 (0.134110) | 0.581465 / 4.805227 (-4.223763) | 0.123877 / 6.500664 (-6.376787) | 0.060339 / 0.075469 (-0.015130) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.333202 / 1.841788 (-0.508585) | 18.363558 / 8.074308 (10.289250) | 14.109356 / 10.191392 (3.917964) | 0.147358 / 0.680424 (-0.533066) | 0.016813 / 0.534201 (-0.517388) | 0.334815 / 0.579283 (-0.244468) | 0.366576 / 0.434364 (-0.067788) | 0.397223 / 0.540337 (-0.143115) | 0.547893 / 1.386936 (-0.839043) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#67ac60bcbebe9ddac70264951b1d584c93003cdf \"CML watermark\")\n"
] |
6,023
|
Fix `ClassLabel` min max check for `None` values
|
Fix #6022
|
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6023",
"html_url": "https://github.com/huggingface/datasets/pull/6023",
"diff_url": "https://github.com/huggingface/datasets/pull/6023.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6023.patch",
"merged_at": "2023-07-12T16:18:04"
}
| true
|
[
"_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) | 0.007108 / 0.011353 (-0.004245) | 0.004446 / 0.011008 (-0.006562) | 0.084013 / 0.038508 (0.045505) | 0.084271 / 0.023109 (0.061162) | 0.324496 / 0.275898 (0.048598) | 0.347783 / 0.323480 (0.024303) | 0.004382 / 0.007986 (-0.003604) | 0.005200 / 0.004328 (0.000872) | 0.065117 / 0.004250 (0.060866) | 0.063368 / 0.037052 (0.026316) | 0.328731 / 0.258489 (0.070242) | 0.356676 / 0.293841 (0.062835) | 0.031155 / 0.128546 (-0.097392) | 0.008672 / 0.075646 (-0.066975) | 0.287573 / 0.419271 (-0.131698) | 0.053692 / 0.043533 (0.010160) | 0.308796 / 0.255139 (0.053657) | 0.330521 / 0.283200 (0.047321) | 0.025010 / 0.141683 (-0.116672) | 1.498968 / 1.452155 (0.046813) | 1.552096 / 1.492716 (0.059380) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.263580 / 0.018006 (0.245574) | 0.559765 / 0.000490 (0.559275) | 0.003450 / 0.000200 (0.003250) | 0.000079 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029403 / 0.037411 (-0.008008) | 0.088154 / 0.014526 (0.073628) | 0.100372 / 0.176557 (-0.076185) | 0.157777 / 0.737135 (-0.579359) | 0.102273 / 0.296338 (-0.194066) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.387027 / 0.215209 (0.171818) | 3.854260 / 2.077655 (1.776605) | 1.875159 / 1.504120 (0.371039) | 1.703734 / 1.541195 (0.162539) | 1.814305 / 1.468490 (0.345815) | 0.482524 / 4.584777 (-4.102253) | 3.463602 / 3.745712 (-0.282110) | 4.004766 / 5.269862 (-1.265095) | 2.406751 / 4.565676 (-2.158925) | 0.057069 / 0.424275 (-0.367206) | 0.007448 / 0.007607 (-0.000159) | 0.465801 / 0.226044 (0.239757) | 4.636700 / 2.268929 (2.367771) | 2.329475 / 55.444624 (-53.115150) | 1.998330 / 6.876477 (-4.878146) | 2.264617 / 2.142072 (0.122544) | 0.577998 / 4.805227 (-4.227230) | 0.130846 / 6.500664 (-6.369818) | 0.059713 / 0.075469 (-0.015756) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.275931 / 1.841788 (-0.565857) | 20.396288 / 8.074308 (12.321980) | 13.875242 / 10.191392 (3.683850) | 0.164367 / 0.680424 (-0.516057) | 0.018573 / 0.534201 (-0.515628) | 0.397516 / 0.579283 (-0.181767) | 0.398977 / 0.434364 (-0.035387) | 0.462386 / 0.540337 (-0.077951) | 0.610129 / 1.386936 (-0.776807) |\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) | 0.006912 / 0.011353 (-0.004441) | 0.004212 / 0.011008 (-0.006797) | 0.065707 / 0.038508 (0.027199) | 0.090435 / 0.023109 (0.067325) | 0.380539 / 0.275898 (0.104641) | 0.412692 / 0.323480 (0.089212) | 0.005545 / 0.007986 (-0.002441) | 0.003657 / 0.004328 (-0.000672) | 0.065380 / 0.004250 (0.061130) | 0.062901 / 0.037052 (0.025848) | 0.385931 / 0.258489 (0.127442) | 0.416272 / 0.293841 (0.122431) | 0.031974 / 0.128546 (-0.096572) | 0.008783 / 0.075646 (-0.066863) | 0.071424 / 0.419271 (-0.347847) | 0.049454 / 0.043533 (0.005921) | 0.374231 / 0.255139 (0.119092) | 0.386530 / 0.283200 (0.103331) | 0.025404 / 0.141683 (-0.116279) | 1.469869 / 1.452155 (0.017715) | 1.548629 / 1.492716 (0.055913) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218413 / 0.018006 (0.200406) | 0.573863 / 0.000490 (0.573373) | 0.004156 / 0.000200 (0.003956) | 0.000097 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032610 / 0.037411 (-0.004801) | 0.088270 / 0.014526 (0.073744) | 0.106821 / 0.176557 (-0.069735) | 0.164498 / 0.737135 (-0.572638) | 0.106881 / 0.296338 (-0.189457) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.433730 / 0.215209 (0.218520) | 4.323902 / 2.077655 (2.246247) | 2.308607 / 1.504120 (0.804487) | 2.138888 / 1.541195 (0.597693) | 2.246760 / 1.468490 (0.778269) | 0.486863 / 4.584777 (-4.097914) | 3.561826 / 3.745712 (-0.183886) | 5.592685 / 5.269862 (0.322824) | 3.318560 / 4.565676 (-1.247116) | 0.057348 / 0.424275 (-0.366927) | 0.007434 / 0.007607 (-0.000174) | 0.506767 / 0.226044 (0.280723) | 5.083097 / 2.268929 (2.814168) | 2.780618 / 55.444624 (-52.664006) | 2.456924 / 6.876477 (-4.419553) | 2.564184 / 2.142072 (0.422112) | 0.580693 / 4.805227 (-4.224534) | 0.134471 / 6.500664 (-6.366194) | 0.062883 / 0.075469 (-0.012586) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.346618 / 1.841788 (-0.495169) | 20.547998 / 8.074308 (12.473690) | 14.404159 / 10.191392 (4.212767) | 0.176612 / 0.680424 (-0.503812) | 0.018372 / 0.534201 (-0.515829) | 0.395636 / 0.579283 (-0.183647) | 0.410661 / 0.434364 (-0.023703) | 0.468782 / 0.540337 (-0.071555) | 0.637476 / 1.386936 (-0.749460) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0172d4dac0ca823e8bd293cfd4d28e78d92efe42 \"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) | 0.009896 / 0.011353 (-0.001457) | 0.004658 / 0.011008 (-0.006351) | 0.101185 / 0.038508 (0.062677) | 0.075480 / 0.023109 (0.052371) | 0.410620 / 0.275898 (0.134722) | 0.470639 / 0.323480 (0.147159) | 0.007042 / 0.007986 (-0.000943) | 0.003909 / 0.004328 (-0.000419) | 0.079676 / 0.004250 (0.075425) | 0.066921 / 0.037052 (0.029869) | 0.423624 / 0.258489 (0.165135) | 0.473008 / 0.293841 (0.179167) | 0.048492 / 0.128546 (-0.080054) | 0.012833 / 0.075646 (-0.062813) | 0.335286 / 0.419271 (-0.083985) | 0.083506 / 0.043533 (0.039973) | 0.401918 / 0.255139 (0.146779) | 0.467975 / 0.283200 (0.184775) | 0.050025 / 0.141683 (-0.091658) | 1.679392 / 1.452155 (0.227237) | 1.852812 / 1.492716 (0.360095) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.248067 / 0.018006 (0.230061) | 0.584818 / 0.000490 (0.584328) | 0.021558 / 0.000200 (0.021358) | 0.000104 / 0.000054 (0.000050) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028572 / 0.037411 (-0.008839) | 0.097212 / 0.014526 (0.082686) | 0.121675 / 0.176557 (-0.054881) | 0.186597 / 0.737135 (-0.550538) | 0.122285 / 0.296338 (-0.174053) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.586279 / 0.215209 (0.371070) | 5.634402 / 2.077655 (3.556747) | 2.560648 / 1.504120 (1.056528) | 2.288796 / 1.541195 (0.747601) | 2.402580 / 1.468490 (0.934090) | 0.801453 / 4.584777 (-3.783324) | 5.036654 / 3.745712 (1.290942) | 8.319972 / 5.269862 (3.050110) | 4.665620 / 4.565676 (0.099944) | 0.107292 / 0.424275 (-0.316983) | 0.009206 / 0.007607 (0.001599) | 0.766505 / 0.226044 (0.540461) | 7.333784 / 2.268929 (5.064856) | 3.601875 / 55.444624 (-51.842749) | 2.886388 / 6.876477 (-3.990089) | 3.231797 / 2.142072 (1.089725) | 1.179509 / 4.805227 (-3.625718) | 0.224656 / 6.500664 (-6.276008) | 0.084749 / 0.075469 (0.009280) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.772345 / 1.841788 (-0.069443) | 24.138788 / 8.074308 (16.064480) | 20.712416 / 10.191392 (10.521024) | 0.254655 / 0.680424 (-0.425769) | 0.028858 / 0.534201 (-0.505343) | 0.499314 / 0.579283 (-0.079969) | 0.605797 / 0.434364 (0.171433) | 0.567628 / 0.540337 (0.027290) | 0.752288 / 1.386936 (-0.634648) |\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) | 0.010134 / 0.011353 (-0.001219) | 0.004630 / 0.011008 (-0.006378) | 0.082282 / 0.038508 (0.043774) | 0.081722 / 0.023109 (0.058613) | 0.465018 / 0.275898 (0.189120) | 0.516392 / 0.323480 (0.192912) | 0.006618 / 0.007986 (-0.001368) | 0.004310 / 0.004328 (-0.000018) | 0.078990 / 0.004250 (0.074739) | 0.077729 / 0.037052 (0.040677) | 0.464892 / 0.258489 (0.206403) | 0.510551 / 0.293841 (0.216710) | 0.050750 / 0.128546 (-0.077796) | 0.014402 / 0.075646 (-0.061244) | 0.092587 / 0.419271 (-0.326685) | 0.074769 / 0.043533 (0.031237) | 0.468591 / 0.255139 (0.213452) | 0.508138 / 0.283200 (0.224938) | 0.047774 / 0.141683 (-0.093909) | 1.798354 / 1.452155 (0.346199) | 1.851431 / 1.492716 (0.358714) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.282528 / 0.018006 (0.264522) | 0.588286 / 0.000490 (0.587797) | 0.004892 / 0.000200 (0.004692) | 0.000136 / 0.000054 (0.000082) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037048 / 0.037411 (-0.000364) | 0.101513 / 0.014526 (0.086987) | 0.133238 / 0.176557 (-0.043319) | 0.234799 / 0.737135 (-0.502336) | 0.120636 / 0.296338 (-0.175703) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.615377 / 0.215209 (0.400168) | 6.225717 / 2.077655 (4.148062) | 2.974137 / 1.504120 (1.470018) | 2.642168 / 1.541195 (1.100973) | 2.706051 / 1.468490 (1.237561) | 0.837171 / 4.584777 (-3.747606) | 5.143368 / 3.745712 (1.397656) | 4.560241 / 5.269862 (-0.709621) | 2.838375 / 4.565676 (-1.727301) | 0.092505 / 0.424275 (-0.331770) | 0.008962 / 0.007607 (0.001355) | 0.726361 / 0.226044 (0.500317) | 7.323998 / 2.268929 (5.055070) | 3.650531 / 55.444624 (-51.794094) | 2.960886 / 6.876477 (-3.915591) | 3.003889 / 2.142072 (0.861816) | 0.979264 / 4.805227 (-3.825963) | 0.204531 / 6.500664 (-6.296133) | 0.078285 / 0.075469 (0.002816) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.774225 / 1.841788 (-0.067563) | 26.399536 / 8.074308 (18.325228) | 22.312890 / 10.191392 (12.121498) | 0.244651 / 0.680424 (-0.435773) | 0.026950 / 0.534201 (-0.507251) | 0.493037 / 0.579283 (-0.086246) | 0.620399 / 0.434364 (0.186036) | 0.748985 / 0.540337 (0.208648) | 0.799766 / 1.386936 (-0.587170) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a49ac2864177ec4fb34c43b59a6e49de1f21f973 \"CML watermark\")\n"
] |
6,022
|
Batch map raises TypeError: '>=' not supported between instances of 'NoneType' and 'int'
|
### Describe the bug
When mapping some datasets with `batched=True`, datasets may raise an exeception:
```python
Traceback (most recent call last):
File "/Users/codingl2k1/Work/datasets/venv/lib/python3.11/site-packages/multiprocess/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/src/datasets/utils/py_utils.py", line 1328, in _write_generator_to_queue
for i, result in enumerate(func(**kwargs)):
File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_dataset.py", line 3483, in _map_single
writer.write_batch(batch)
File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_writer.py", line 549, in write_batch
array = cast_array_to_feature(col_values, col_type) if col_type is not None else col_values
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/src/datasets/table.py", line 1831, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/src/datasets/table.py", line 1831, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/src/datasets/table.py", line 2063, in cast_array_to_feature
return feature.cast_storage(array)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/src/datasets/features/features.py", line 1098, in cast_storage
if min_max["max"] >= self.num_classes:
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: '>=' not supported between instances of 'NoneType' and 'int'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/Users/codingl2k1/Work/datasets/t1.py", line 33, in <module>
ds = ds.map(transforms, num_proc=14, batched=True, batch_size=5)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/src/datasets/dataset_dict.py", line 850, in map
{
File "/Users/codingl2k1/Work/datasets/src/datasets/dataset_dict.py", line 851, in <dictcomp>
k: dataset.map(
^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_dataset.py", line 577, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_dataset.py", line 542, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_dataset.py", line 3179, in map
for rank, done, content in iflatmap_unordered(
File "/Users/codingl2k1/Work/datasets/src/datasets/utils/py_utils.py", line 1368, in iflatmap_unordered
[async_result.get(timeout=0.05) for async_result in async_results]
File "/Users/codingl2k1/Work/datasets/src/datasets/utils/py_utils.py", line 1368, in <listcomp>
[async_result.get(timeout=0.05) for async_result in async_results]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/venv/lib/python3.11/site-packages/multiprocess/pool.py", line 774, in get
raise self._value
TypeError: '>=' not supported between instances of 'NoneType' and 'int'
```
### Steps to reproduce the bug
1. Checkout the latest main of datasets.
2. Run the code:
```python
from datasets import load_dataset
def transforms(examples):
# examples["pixel_values"] = [image.convert("RGB").resize((100, 100)) for image in examples["image"]]
return examples
ds = load_dataset("scene_parse_150")
ds = ds.map(transforms, num_proc=14, batched=True, batch_size=5)
print(ds)
```
### Expected behavior
map without exception.
### Environment info
Datasets: https://github.com/huggingface/datasets/commit/b8067c0262073891180869f700ebef5ac3dc5cce
Python: 3.11.4
System: Macos
|
[] |
{
"url": null,
"html_url": null,
"diff_url": null,
"patch_url": null,
"merged_at": null
}
| false
|
[
"Thanks for reporting! I've opened a PR with a fix."
] |
6,021
|
[docs] Update return statement of index search
|
Clarifies in the return statement of the docstring that the retrieval score is `IndexFlatL2` by default (see [PR](https://github.com/huggingface/transformers/issues/24739) and internal Slack [convo](https://huggingface.slack.com/archives/C01229B19EX/p1689105179711689)), and fixes the formatting because multiple return values are not supported.
|
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6021",
"html_url": "https://github.com/huggingface/datasets/pull/6021",
"diff_url": "https://github.com/huggingface/datasets/pull/6021.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6021.patch",
"merged_at": "2023-07-12T17:03:00"
}
| true
|
[
"_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) | 0.007697 / 0.011353 (-0.003656) | 0.004233 / 0.011008 (-0.006776) | 0.087890 / 0.038508 (0.049382) | 0.065305 / 0.023109 (0.042196) | 0.366919 / 0.275898 (0.091020) | 0.399656 / 0.323480 (0.076176) | 0.006753 / 0.007986 (-0.001232) | 0.003428 / 0.004328 (-0.000900) | 0.070180 / 0.004250 (0.065930) | 0.054164 / 0.037052 (0.017112) | 0.377130 / 0.258489 (0.118641) | 0.403456 / 0.293841 (0.109615) | 0.042639 / 0.128546 (-0.085907) | 0.012396 / 0.075646 (-0.063250) | 0.314235 / 0.419271 (-0.105036) | 0.061976 / 0.043533 (0.018443) | 0.376959 / 0.255139 (0.121820) | 0.433313 / 0.283200 (0.150113) | 0.031253 / 0.141683 (-0.110430) | 1.555749 / 1.452155 (0.103594) | 1.643905 / 1.492716 (0.151189) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208630 / 0.018006 (0.190624) | 0.519532 / 0.000490 (0.519042) | 0.003719 / 0.000200 (0.003519) | 0.000099 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027403 / 0.037411 (-0.010008) | 0.080990 / 0.014526 (0.066464) | 0.090424 / 0.176557 (-0.086133) | 0.153922 / 0.737135 (-0.583213) | 0.098156 / 0.296338 (-0.198183) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.519453 / 0.215209 (0.304244) | 5.100089 / 2.077655 (3.022434) | 2.212165 / 1.504120 (0.708045) | 1.894405 / 1.541195 (0.353210) | 1.922914 / 1.468490 (0.454424) | 0.762443 / 4.584777 (-3.822334) | 4.669214 / 3.745712 (0.923502) | 5.016066 / 5.269862 (-0.253796) | 3.128821 / 4.565676 (-1.436856) | 0.091541 / 0.424275 (-0.332734) | 0.007582 / 0.007607 (-0.000026) | 0.652753 / 0.226044 (0.426709) | 6.601375 / 2.268929 (4.332446) | 3.076948 / 55.444624 (-52.367677) | 2.250544 / 6.876477 (-4.625933) | 2.404059 / 2.142072 (0.261987) | 0.994917 / 4.805227 (-3.810311) | 0.200318 / 6.500664 (-6.300346) | 0.069354 / 0.075469 (-0.006115) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.482559 / 1.841788 (-0.359229) | 20.722092 / 8.074308 (12.647784) | 17.703217 / 10.191392 (7.511825) | 0.215370 / 0.680424 (-0.465053) | 0.028208 / 0.534201 (-0.505993) | 0.425992 / 0.579283 (-0.153291) | 0.492785 / 0.434364 (0.058421) | 0.474154 / 0.540337 (-0.066183) | 0.644599 / 1.386936 (-0.742337) |\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) | 0.008372 / 0.011353 (-0.002981) | 0.004543 / 0.011008 (-0.006465) | 0.070564 / 0.038508 (0.032056) | 0.066855 / 0.023109 (0.043746) | 0.386724 / 0.275898 (0.110826) | 0.432184 / 0.323480 (0.108704) | 0.005250 / 0.007986 (-0.002736) | 0.003630 / 0.004328 (-0.000698) | 0.069310 / 0.004250 (0.065060) | 0.055759 / 0.037052 (0.018707) | 0.375789 / 0.258489 (0.117299) | 0.417335 / 0.293841 (0.123494) | 0.043424 / 0.128546 (-0.085122) | 0.013106 / 0.075646 (-0.062541) | 0.087836 / 0.419271 (-0.331436) | 0.057770 / 0.043533 (0.014237) | 0.396694 / 0.255139 (0.141555) | 0.439350 / 0.283200 (0.156150) | 0.031660 / 0.141683 (-0.110023) | 1.571339 / 1.452155 (0.119185) | 1.667169 / 1.492716 (0.174452) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.180534 / 0.018006 (0.162528) | 0.540027 / 0.000490 (0.539537) | 0.003573 / 0.000200 (0.003373) | 0.000141 / 0.000054 (0.000086) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031380 / 0.037411 (-0.006032) | 0.083762 / 0.014526 (0.069236) | 0.098166 / 0.176557 (-0.078390) | 0.160761 / 0.737135 (-0.576374) | 0.097683 / 0.296338 (-0.198656) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.568074 / 0.215209 (0.352865) | 5.660544 / 2.077655 (3.582889) | 2.416698 / 1.504120 (0.912578) | 2.177096 / 1.541195 (0.635901) | 2.206178 / 1.468490 (0.737688) | 0.844864 / 4.584777 (-3.739912) | 4.793636 / 3.745712 (1.047923) | 7.062387 / 5.269862 (1.792525) | 4.201228 / 4.565676 (-0.364449) | 0.091997 / 0.424275 (-0.332279) | 0.007881 / 0.007607 (0.000274) | 0.679466 / 0.226044 (0.453422) | 6.580268 / 2.268929 (4.311340) | 3.229907 / 55.444624 (-52.214717) | 2.524877 / 6.876477 (-4.351600) | 2.463796 / 2.142072 (0.321723) | 0.975627 / 4.805227 (-3.829600) | 0.186670 / 6.500664 (-6.313994) | 0.065307 / 0.075469 (-0.010163) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.501447 / 1.841788 (-0.340340) | 21.231037 / 8.074308 (13.156729) | 17.591671 / 10.191392 (7.400279) | 0.212745 / 0.680424 (-0.467679) | 0.026100 / 0.534201 (-0.508101) | 0.428391 / 0.579283 (-0.150892) | 0.535268 / 0.434364 (0.100904) | 0.506733 / 0.540337 (-0.033604) | 0.660832 / 1.386936 (-0.726104) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#962537d7ee9191438ef47a4185d0ba626b2ee949 \"CML watermark\")\n"
] |
6,020
|
Inconsistent "The features can't be aligned" error when combining map, multiprocessing, and variable length outputs
|
### Describe the bug
I'm using a dataset with map and multiprocessing to run a function that returned a variable length list of outputs. This output list may be empty. Normally this is handled fine, but there is an edge case that crops up when using multiprocessing. In some cases, an empty list result ends up in a dataset shard consisting of a single item. This results in a `The features can't be aligned` error that is difficult to debug because it depends on the number of processes/shards used.
I've reproduced a minimal example below. My current workaround is to fill empty results with a dummy value that I filter after, but this was a weird error that took a while to track down.
### Steps to reproduce the bug
```python
import datasets
dataset = datasets.Dataset.from_list([{'idx':i} for i in range(60)])
def test_func(row, idx):
if idx==58:
return {'output': []}
else:
return {'output' : [{'test':1}, {'test':2}]}
# this works fine
test1 = dataset.map(lambda row, idx: test_func(row, idx), with_indices=True, num_proc=4)
# this fails
test2 = dataset.map(lambda row, idx: test_func(row, idx), with_indices=True, num_proc=32)
>ValueError: The features can't be aligned because the key output of features {'idx': Value(dtype='int64', id=None), 'output': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None)} has unexpected type - Sequence(feature=Value(dtype='null', id=None), length=-1, id=None) (expected either [{'test': Value(dtype='int64', id=None)}] or Value("null").
```
The error occurs during the check
```python
_check_if_features_can_be_aligned([dset.features for dset in dsets])
```
When the multiprocessing splitting lines up just right with the empty return value, one of the `dset` in `dsets` will have a single item with an empty list value, causing the error.
### Expected behavior
Expected behavior is the result would be the same regardless of the `num_proc` value used.
### Environment info
Datasets version 2.11.0
Python 3.9.16
|
[] |
{
"url": null,
"html_url": null,
"diff_url": null,
"patch_url": null,
"merged_at": null
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| false
|
[
"This scenario currently requires explicitly passing the target features (to avoid the error): \r\n```python\r\nimport datasets\r\n\r\n...\r\n\r\nfeatures = dataset.features\r\nfeatures[\"output\"] = = [{\"test\": datasets.Value(\"int64\")}]\r\ntest2 = dataset.map(lambda row, idx: test_func(row, idx), with_indices=True, num_proc=32, features=features)\r\n```",
"I just encountered the same error in the same situation (multiprocessing with variable length outputs).\r\n\r\nThe funny (or dangerous?) thing is, that this error only showed up when testing with a small test dataset (16 examples, ValueError with `num_proc` >1) but the same code works fine for the full dataset (~70k examples).\r\n\r\n@mariosasko Any idea on how to do that with a nested feature with lists of variable lengths containing dicts?\r\n\r\nEDIT: Was able to narrow it down: >200 Examples: no error, <150 Examples: Error. \r\nNow idea what to make of this but pretty obvious that this is a bug....",
"This error also occurs while concatenating the datasets."
] |
6,019
|
Improve logging
|
Adds the StreamHandler (as `hfh` and `transformers` do) to the library's logger to log INFO messages and logs the messages about "loading a cached result" (and some other warnings) as INFO
(Also removes the `leave=False` arg in the progress bars to be consistent with `hfh` and `transformers` - progress bars serve as an indicator that a result is not cached, so it makes more sense not to delete them)
Fix #2832, fix https://github.com/huggingface/datasets/issues/1948, fix https://github.com/huggingface/datasets/issues/5444
|
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6019",
"html_url": "https://github.com/huggingface/datasets/pull/6019",
"diff_url": "https://github.com/huggingface/datasets/pull/6019.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6019.patch",
"merged_at": "2023-07-12T17:19:28"
}
| true
|
[
"_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) | 0.007782 / 0.011353 (-0.003571) | 0.004451 / 0.011008 (-0.006557) | 0.099928 / 0.038508 (0.061420) | 0.081534 / 0.023109 (0.058425) | 0.379382 / 0.275898 (0.103484) | 0.410652 / 0.323480 (0.087172) | 0.005967 / 0.007986 (-0.002019) | 0.003702 / 0.004328 (-0.000627) | 0.076359 / 0.004250 (0.072109) | 0.066721 / 0.037052 (0.029669) | 0.383595 / 0.258489 (0.125106) | 0.423854 / 0.293841 (0.130013) | 0.032796 / 0.128546 (-0.095750) | 0.009728 / 0.075646 (-0.065918) | 0.344347 / 0.419271 (-0.074925) | 0.056320 / 0.043533 (0.012788) | 0.379974 / 0.255139 (0.124835) | 0.401294 / 0.283200 (0.118094) | 0.024110 / 0.141683 (-0.117572) | 1.804194 / 1.452155 (0.352039) | 1.860240 / 1.492716 (0.367523) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.233803 / 0.018006 (0.215797) | 0.506893 / 0.000490 (0.506404) | 0.003894 / 0.000200 (0.003694) | 0.000090 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033328 / 0.037411 (-0.004083) | 0.098661 / 0.014526 (0.084136) | 0.114971 / 0.176557 (-0.061586) | 0.186815 / 0.737135 (-0.550321) | 0.115490 / 0.296338 (-0.180848) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422590 / 0.215209 (0.207381) | 4.277189 / 2.077655 (2.199535) | 2.095565 / 1.504120 (0.591445) | 2.040825 / 1.541195 (0.499630) | 2.162562 / 1.468490 (0.694072) | 0.578602 / 4.584777 (-4.006175) | 4.203474 / 3.745712 (0.457762) | 6.674595 / 5.269862 (1.404734) | 3.913251 / 4.565676 (-0.652426) | 0.067777 / 0.424275 (-0.356498) | 0.008716 / 0.007607 (0.001109) | 0.548704 / 0.226044 (0.322660) | 5.162120 / 2.268929 (2.893192) | 2.600250 / 55.444624 (-52.844374) | 2.232730 / 6.876477 (-4.643747) | 2.485617 / 2.142072 (0.343544) | 0.650872 / 4.805227 (-4.154355) | 0.148022 / 6.500664 (-6.352642) | 0.064795 / 0.075469 (-0.010674) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.399439 / 1.841788 (-0.442349) | 22.438959 / 8.074308 (14.364651) | 16.447831 / 10.191392 (6.256439) | 0.202003 / 0.680424 (-0.478421) | 0.026200 / 0.534201 (-0.508001) | 0.472966 / 0.579283 (-0.106317) | 0.491621 / 0.434364 (0.057257) | 0.551580 / 0.540337 (0.011242) | 0.751420 / 1.386936 (-0.635516) |\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) | 0.007241 / 0.011353 (-0.004112) | 0.004434 / 0.011008 (-0.006574) | 0.075872 / 0.038508 (0.037364) | 0.080094 / 0.023109 (0.056985) | 0.459244 / 0.275898 (0.183346) | 0.492482 / 0.323480 (0.169002) | 0.005791 / 0.007986 (-0.002194) | 0.003657 / 0.004328 (-0.000671) | 0.075214 / 0.004250 (0.070964) | 0.064208 / 0.037052 (0.027156) | 0.464195 / 0.258489 (0.205706) | 0.497809 / 0.293841 (0.203968) | 0.036301 / 0.128546 (-0.092245) | 0.009855 / 0.075646 (-0.065791) | 0.080826 / 0.419271 (-0.338445) | 0.056700 / 0.043533 (0.013167) | 0.452850 / 0.255139 (0.197711) | 0.490738 / 0.283200 (0.207538) | 0.024145 / 0.141683 (-0.117538) | 1.689911 / 1.452155 (0.237757) | 1.789803 / 1.492716 (0.297087) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.247741 / 0.018006 (0.229735) | 0.486769 / 0.000490 (0.486279) | 0.000418 / 0.000200 (0.000218) | 0.000060 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036317 / 0.037411 (-0.001094) | 0.104943 / 0.014526 (0.090417) | 0.120972 / 0.176557 (-0.055585) | 0.188461 / 0.737135 (-0.548674) | 0.120926 / 0.296338 (-0.175412) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.465788 / 0.215209 (0.250579) | 4.662369 / 2.077655 (2.584714) | 2.442241 / 1.504120 (0.938121) | 2.266328 / 1.541195 (0.725133) | 2.438998 / 1.468490 (0.970508) | 0.531384 / 4.584777 (-4.053393) | 4.125286 / 3.745712 (0.379574) | 3.920912 / 5.269862 (-1.348950) | 2.292149 / 4.565676 (-2.273528) | 0.070146 / 0.424275 (-0.354129) | 0.008887 / 0.007607 (0.001280) | 0.598181 / 0.226044 (0.372137) | 5.726454 / 2.268929 (3.457526) | 3.081836 / 55.444624 (-52.362788) | 2.683508 / 6.876477 (-4.192969) | 2.587350 / 2.142072 (0.445278) | 0.604736 / 4.805227 (-4.200491) | 0.141303 / 6.500664 (-6.359362) | 0.065020 / 0.075469 (-0.010449) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.481850 / 1.841788 (-0.359938) | 22.259592 / 8.074308 (14.185284) | 16.304290 / 10.191392 (6.112898) | 0.173514 / 0.680424 (-0.506909) | 0.021590 / 0.534201 (-0.512611) | 0.471753 / 0.579283 (-0.107531) | 0.472132 / 0.434364 (0.037768) | 0.563344 / 0.540337 (0.023007) | 0.738509 / 1.386936 (-0.648427) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1cb7ae56dbd814945a4982c63bf0e50859a7b93a \"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) | 0.005910 / 0.011353 (-0.005443) | 0.004372 / 0.011008 (-0.006636) | 0.081583 / 0.038508 (0.043075) | 0.069598 / 0.023109 (0.046488) | 0.346360 / 0.275898 (0.070462) | 0.360733 / 0.323480 (0.037254) | 0.004725 / 0.007986 (-0.003261) | 0.003106 / 0.004328 (-0.001222) | 0.059916 / 0.004250 (0.055666) | 0.053242 / 0.037052 (0.016189) | 0.353551 / 0.258489 (0.095062) | 0.373052 / 0.293841 (0.079211) | 0.029036 / 0.128546 (-0.099510) | 0.007894 / 0.075646 (-0.067753) | 0.284131 / 0.419271 (-0.135140) | 0.049348 / 0.043533 (0.005815) | 0.347409 / 0.255139 (0.092270) | 0.355029 / 0.283200 (0.071830) | 0.022511 / 0.141683 (-0.119171) | 1.454495 / 1.452155 (0.002340) | 1.439551 / 1.492716 (-0.053166) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218889 / 0.018006 (0.200883) | 0.478734 / 0.000490 (0.478244) | 0.003758 / 0.000200 (0.003558) | 0.000083 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025759 / 0.037411 (-0.011653) | 0.082511 / 0.014526 (0.067985) | 0.087578 / 0.176557 (-0.088979) | 0.137760 / 0.737135 (-0.599375) | 0.093312 / 0.296338 (-0.203027) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.378963 / 0.215209 (0.163754) | 3.645846 / 2.077655 (1.568191) | 1.741135 / 1.504120 (0.237015) | 1.599166 / 1.541195 (0.057972) | 1.610817 / 1.468490 (0.142327) | 0.459209 / 4.584777 (-4.125568) | 3.484857 / 3.745712 (-0.260855) | 3.928109 / 5.269862 (-1.341752) | 2.419784 / 4.565676 (-2.145892) | 0.051987 / 0.424275 (-0.372288) | 0.006495 / 0.007607 (-0.001112) | 0.427311 / 0.226044 (0.201267) | 4.226378 / 2.268929 (1.957450) | 2.212331 / 55.444624 (-53.232293) | 1.916213 / 6.876477 (-4.960264) | 1.978809 / 2.142072 (-0.163263) | 0.547351 / 4.805227 (-4.257876) | 0.121110 / 6.500664 (-6.379554) | 0.054163 / 0.075469 (-0.021306) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.228594 / 1.841788 (-0.613193) | 19.410901 / 8.074308 (11.336593) | 13.014722 / 10.191392 (2.823330) | 0.156449 / 0.680424 (-0.523975) | 0.021032 / 0.534201 (-0.513169) | 0.403976 / 0.579283 (-0.175307) | 0.413885 / 0.434364 (-0.020479) | 0.470465 / 0.540337 (-0.069873) | 0.641322 / 1.386936 (-0.745614) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007210 / 0.011353 (-0.004143) | 0.003824 / 0.011008 (-0.007185) | 0.058227 / 0.038508 (0.019719) | 0.076211 / 0.023109 (0.053102) | 0.336626 / 0.275898 (0.060728) | 0.420542 / 0.323480 (0.097062) | 0.006178 / 0.007986 (-0.001808) | 0.003332 / 0.004328 (-0.000997) | 0.058073 / 0.004250 (0.053823) | 0.062485 / 0.037052 (0.025432) | 0.386175 / 0.258489 (0.127686) | 0.415659 / 0.293841 (0.121818) | 0.031264 / 0.128546 (-0.097282) | 0.007502 / 0.075646 (-0.068144) | 0.072079 / 0.419271 (-0.347192) | 0.055860 / 0.043533 (0.012327) | 0.343508 / 0.255139 (0.088369) | 0.437844 / 0.283200 (0.154645) | 0.032852 / 0.141683 (-0.108831) | 1.409241 / 1.452155 (-0.042913) | 1.623949 / 1.492716 (0.131233) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.207511 / 0.018006 (0.189504) | 0.464149 / 0.000490 (0.463660) | 0.003248 / 0.000200 (0.003048) | 0.000226 / 0.000054 (0.000172) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030767 / 0.037411 (-0.006645) | 0.079169 / 0.014526 (0.064643) | 0.093111 / 0.176557 (-0.083445) | 0.153369 / 0.737135 (-0.583767) | 0.092939 / 0.296338 (-0.203400) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.375602 / 0.215209 (0.160392) | 3.968612 / 2.077655 (1.890957) | 2.081749 / 1.504120 (0.577629) | 1.899772 / 1.541195 (0.358577) | 1.847923 / 1.468490 (0.379433) | 0.442867 / 4.584777 (-4.141910) | 3.646664 / 3.745712 (-0.099048) | 5.870600 / 5.269862 (0.600739) | 3.356698 / 4.565676 (-1.208979) | 0.051422 / 0.424275 (-0.372853) | 0.006006 / 0.007607 (-0.001601) | 0.442439 / 0.226044 (0.216395) | 4.466256 / 2.268929 (2.197328) | 2.483832 / 55.444624 (-52.960792) | 2.105612 / 6.876477 (-4.770865) | 2.060650 / 2.142072 (-0.081422) | 0.531119 / 4.805227 (-4.274108) | 0.123436 / 6.500664 (-6.377228) | 0.059838 / 0.075469 (-0.015632) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.283042 / 1.841788 (-0.558746) | 19.688251 / 8.074308 (11.613943) | 13.346386 / 10.191392 (3.154994) | 0.197463 / 0.680424 (-0.482961) | 0.018484 / 0.534201 (-0.515717) | 0.391727 / 0.579283 (-0.187556) | 0.425061 / 0.434364 (-0.009303) | 0.448177 / 0.540337 (-0.092160) | 0.653694 / 1.386936 (-0.733242) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#01604752fe89d290479fa406b1a24ac1f346826e \"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) | 0.008966 / 0.011353 (-0.002387) | 0.005195 / 0.011008 (-0.005813) | 0.102879 / 0.038508 (0.064371) | 0.090902 / 0.023109 (0.067792) | 0.434397 / 0.275898 (0.158498) | 0.454013 / 0.323480 (0.130534) | 0.008507 / 0.007986 (0.000521) | 0.005000 / 0.004328 (0.000671) | 0.075789 / 0.004250 (0.071538) | 0.067608 / 0.037052 (0.030555) | 0.435091 / 0.258489 (0.176602) | 0.469411 / 0.293841 (0.175570) | 0.050859 / 0.128546 (-0.077687) | 0.013560 / 0.075646 (-0.062086) | 0.345473 / 0.419271 (-0.073799) | 0.094974 / 0.043533 (0.051441) | 0.429626 / 0.255139 (0.174487) | 0.434290 / 0.283200 (0.151090) | 0.052269 / 0.141683 (-0.089413) | 1.700549 / 1.452155 (0.248395) | 1.890693 / 1.492716 (0.397976) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.296618 / 0.018006 (0.278612) | 0.613908 / 0.000490 (0.613419) | 0.000484 / 0.000200 (0.000284) | 0.000086 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034346 / 0.037411 (-0.003065) | 0.096836 / 0.014526 (0.082310) | 0.113332 / 0.176557 (-0.063224) | 0.194464 / 0.737135 (-0.542671) | 0.111732 / 0.296338 (-0.184606) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.624954 / 0.215209 (0.409745) | 6.442193 / 2.077655 (4.364538) | 2.818331 / 1.504120 (1.314211) | 2.529607 / 1.541195 (0.988413) | 2.549026 / 1.468490 (1.080536) | 0.967367 / 4.584777 (-3.617410) | 5.446885 / 3.745712 (1.701173) | 6.259099 / 5.269862 (0.989237) | 3.652936 / 4.565676 (-0.912740) | 0.106420 / 0.424275 (-0.317855) | 0.011293 / 0.007607 (0.003686) | 0.772026 / 0.226044 (0.545982) | 7.823986 / 2.268929 (5.555057) | 3.725328 / 55.444624 (-51.719297) | 2.851489 / 6.876477 (-4.024988) | 3.013722 / 2.142072 (0.871649) | 1.045090 / 4.805227 (-3.760137) | 0.213174 / 6.500664 (-6.287490) | 0.077104 / 0.075469 (0.001635) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.657135 / 1.841788 (-0.184652) | 24.547604 / 8.074308 (16.473296) | 19.989533 / 10.191392 (9.798141) | 0.257139 / 0.680424 (-0.423285) | 0.028448 / 0.534201 (-0.505753) | 0.490801 / 0.579283 (-0.088482) | 0.628072 / 0.434364 (0.193708) | 0.584873 / 0.540337 (0.044536) | 0.825258 / 1.386936 (-0.561678) |\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) | 0.009258 / 0.011353 (-0.002095) | 0.005660 / 0.011008 (-0.005348) | 0.080577 / 0.038508 (0.042069) | 0.095786 / 0.023109 (0.072676) | 0.473334 / 0.275898 (0.197436) | 0.527962 / 0.323480 (0.204482) | 0.006537 / 0.007986 (-0.001449) | 0.004411 / 0.004328 (0.000083) | 0.080702 / 0.004250 (0.076452) | 0.077020 / 0.037052 (0.039968) | 0.483205 / 0.258489 (0.224716) | 0.556916 / 0.293841 (0.263076) | 0.047670 / 0.128546 (-0.080877) | 0.016647 / 0.075646 (-0.058999) | 0.090653 / 0.419271 (-0.328619) | 0.062122 / 0.043533 (0.018589) | 0.498326 / 0.255139 (0.243187) | 0.546572 / 0.283200 (0.263372) | 0.037525 / 0.141683 (-0.104157) | 1.869520 / 1.452155 (0.417365) | 1.915335 / 1.492716 (0.422619) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.248287 / 0.018006 (0.230281) | 0.611440 / 0.000490 (0.610950) | 0.004102 / 0.000200 (0.003902) | 0.000132 / 0.000054 (0.000078) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038228 / 0.037411 (0.000817) | 0.103510 / 0.014526 (0.088984) | 0.114337 / 0.176557 (-0.062219) | 0.189662 / 0.737135 (-0.547473) | 0.119078 / 0.296338 (-0.177260) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.606622 / 0.215209 (0.391413) | 6.053900 / 2.077655 (3.976246) | 2.857972 / 1.504120 (1.353852) | 2.549756 / 1.541195 (1.008561) | 2.584557 / 1.468490 (1.116067) | 0.930431 / 4.584777 (-3.654346) | 5.524077 / 3.745712 (1.778365) | 7.858406 / 5.269862 (2.588545) | 4.890697 / 4.565676 (0.325020) | 0.095356 / 0.424275 (-0.328919) | 0.008614 / 0.007607 (0.001007) | 0.774227 / 0.226044 (0.548182) | 7.470215 / 2.268929 (5.201287) | 3.784820 / 55.444624 (-51.659805) | 3.199364 / 6.876477 (-3.677113) | 3.212002 / 2.142072 (1.069929) | 1.054104 / 4.805227 (-3.751123) | 0.226044 / 6.500664 (-6.274620) | 0.092237 / 0.075469 (0.016768) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.801054 / 1.841788 (-0.040734) | 24.220404 / 8.074308 (16.146096) | 21.652936 / 10.191392 (11.461544) | 0.247004 / 0.680424 (-0.433420) | 0.029651 / 0.534201 (-0.504550) | 0.475702 / 0.579283 (-0.103581) | 0.621121 / 0.434364 (0.186757) | 0.570489 / 0.540337 (0.030151) | 0.768840 / 1.386936 (-0.618096) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b2fc21eda345643fb57d1d1167ebed9043310911 \"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) | 0.009223 / 0.011353 (-0.002130) | 0.005750 / 0.011008 (-0.005258) | 0.105264 / 0.038508 (0.066756) | 0.088478 / 0.023109 (0.065369) | 0.461119 / 0.275898 (0.185221) | 0.481115 / 0.323480 (0.157636) | 0.006366 / 0.007986 (-0.001619) | 0.004515 / 0.004328 (0.000186) | 0.079296 / 0.004250 (0.075045) | 0.063483 / 0.037052 (0.026430) | 0.444490 / 0.258489 (0.186001) | 0.496474 / 0.293841 (0.202634) | 0.048568 / 0.128546 (-0.079978) | 0.013574 / 0.075646 (-0.062073) | 0.379213 / 0.419271 (-0.040059) | 0.086464 / 0.043533 (0.042932) | 0.437526 / 0.255139 (0.182387) | 0.447117 / 0.283200 (0.163917) | 0.049502 / 0.141683 (-0.092180) | 1.749146 / 1.452155 (0.296992) | 1.831082 / 1.492716 (0.338365) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.268205 / 0.018006 (0.250199) | 0.627406 / 0.000490 (0.626917) | 0.005439 / 0.000200 (0.005239) | 0.000128 / 0.000054 (0.000074) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030564 / 0.037411 (-0.006848) | 0.096365 / 0.014526 (0.081840) | 0.117484 / 0.176557 (-0.059072) | 0.189104 / 0.737135 (-0.548032) | 0.118073 / 0.296338 (-0.178266) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.618229 / 0.215209 (0.403019) | 6.437853 / 2.077655 (4.360199) | 2.789946 / 1.504120 (1.285826) | 2.339245 / 1.541195 (0.798050) | 2.588779 / 1.468490 (1.120289) | 0.921008 / 4.584777 (-3.663769) | 5.402940 / 3.745712 (1.657227) | 4.818783 / 5.269862 (-0.451078) | 3.162259 / 4.565676 (-1.403417) | 0.108501 / 0.424275 (-0.315774) | 0.009384 / 0.007607 (0.001777) | 0.766811 / 0.226044 (0.540766) | 7.624629 / 2.268929 (5.355701) | 3.442420 / 55.444624 (-52.002204) | 2.759967 / 6.876477 (-4.116510) | 3.049644 / 2.142072 (0.907572) | 1.113308 / 4.805227 (-3.691919) | 0.223923 / 6.500664 (-6.276741) | 0.079156 / 0.075469 (0.003687) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.683318 / 1.841788 (-0.158470) | 25.062141 / 8.074308 (16.987833) | 21.777131 / 10.191392 (11.585739) | 0.266939 / 0.680424 (-0.413485) | 0.029670 / 0.534201 (-0.504531) | 0.476761 / 0.579283 (-0.102522) | 0.622080 / 0.434364 (0.187716) | 0.601781 / 0.540337 (0.061443) | 0.785126 / 1.386936 (-0.601811) |\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) | 0.010198 / 0.011353 (-0.001155) | 0.005777 / 0.011008 (-0.005231) | 0.083003 / 0.038508 (0.044495) | 0.093411 / 0.023109 (0.070302) | 0.496178 / 0.275898 (0.220280) | 0.554670 / 0.323480 (0.231190) | 0.008351 / 0.007986 (0.000365) | 0.004678 / 0.004328 (0.000350) | 0.083631 / 0.004250 (0.079381) | 0.075538 / 0.037052 (0.038485) | 0.492410 / 0.258489 (0.233921) | 0.545209 / 0.293841 (0.251368) | 0.048365 / 0.128546 (-0.080181) | 0.014219 / 0.075646 (-0.061427) | 0.100749 / 0.419271 (-0.318523) | 0.063431 / 0.043533 (0.019898) | 0.511115 / 0.255139 (0.255976) | 0.532965 / 0.283200 (0.249765) | 0.037968 / 0.141683 (-0.103715) | 1.940268 / 1.452155 (0.488113) | 2.032934 / 1.492716 (0.540217) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.238179 / 0.018006 (0.220172) | 0.605767 / 0.000490 (0.605277) | 0.004033 / 0.000200 (0.003833) | 0.000125 / 0.000054 (0.000071) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036436 / 0.037411 (-0.000975) | 0.108034 / 0.014526 (0.093509) | 0.118624 / 0.176557 (-0.057933) | 0.183079 / 0.737135 (-0.554056) | 0.121739 / 0.296338 (-0.174600) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.630538 / 0.215209 (0.415329) | 6.552184 / 2.077655 (4.474529) | 3.003412 / 1.504120 (1.499292) | 2.669026 / 1.541195 (1.127832) | 2.791109 / 1.468490 (1.322619) | 0.884003 / 4.584777 (-3.700774) | 5.538660 / 3.745712 (1.792947) | 5.126708 / 5.269862 (-0.143154) | 3.120825 / 4.565676 (-1.444852) | 0.101178 / 0.424275 (-0.323097) | 0.009027 / 0.007607 (0.001420) | 0.785914 / 0.226044 (0.559869) | 7.994720 / 2.268929 (5.725792) | 4.061996 / 55.444624 (-51.382629) | 3.263230 / 6.876477 (-3.613247) | 3.288622 / 2.142072 (1.146550) | 1.141867 / 4.805227 (-3.663360) | 0.255287 / 6.500664 (-6.245378) | 0.100637 / 0.075469 (0.025168) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.769821 / 1.841788 (-0.071967) | 24.994008 / 8.074308 (16.919700) | 21.765971 / 10.191392 (11.574579) | 0.268493 / 0.680424 (-0.411931) | 0.028047 / 0.534201 (-0.506154) | 0.489472 / 0.579283 (-0.089811) | 0.594809 / 0.434364 (0.160445) | 0.613578 / 0.540337 (0.073241) | 0.879360 / 1.386936 (-0.507576) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b85b1154aef2a9ab4d558f60d91623f2cc1583c4 \"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) | 0.006003 / 0.011353 (-0.005350) | 0.003590 / 0.011008 (-0.007418) | 0.084657 / 0.038508 (0.046149) | 0.057884 / 0.023109 (0.034775) | 0.318347 / 0.275898 (0.042449) | 0.345976 / 0.323480 (0.022496) | 0.004706 / 0.007986 (-0.003279) | 0.002921 / 0.004328 (-0.001407) | 0.061850 / 0.004250 (0.057600) | 0.050558 / 0.037052 (0.013505) | 0.320877 / 0.258489 (0.062388) | 0.356062 / 0.293841 (0.062222) | 0.027511 / 0.128546 (-0.101035) | 0.007954 / 0.075646 (-0.067693) | 0.260290 / 0.419271 (-0.158981) | 0.051207 / 0.043533 (0.007674) | 0.334423 / 0.255139 (0.079284) | 0.338575 / 0.283200 (0.055375) | 0.022330 / 0.141683 (-0.119353) | 1.445446 / 1.452155 (-0.006709) | 1.500626 / 1.492716 (0.007910) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.192440 / 0.018006 (0.174433) | 0.428455 / 0.000490 (0.427965) | 0.000318 / 0.000200 (0.000118) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022933 / 0.037411 (-0.014478) | 0.072795 / 0.014526 (0.058269) | 0.081149 / 0.176557 (-0.095407) | 0.142941 / 0.737135 (-0.594195) | 0.082410 / 0.296338 (-0.213928) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.405220 / 0.215209 (0.190011) | 4.048585 / 2.077655 (1.970931) | 2.027908 / 1.504120 (0.523788) | 1.887828 / 1.541195 (0.346633) | 2.131780 / 1.468490 (0.663290) | 0.502847 / 4.584777 (-4.081930) | 3.069498 / 3.745712 (-0.676215) | 4.094774 / 5.269862 (-1.175088) | 2.544004 / 4.565676 (-2.021673) | 0.059540 / 0.424275 (-0.364735) | 0.006501 / 0.007607 (-0.001106) | 0.477218 / 0.226044 (0.251173) | 4.764961 / 2.268929 (2.496032) | 2.434594 / 55.444624 (-53.010030) | 2.104833 / 6.876477 (-4.771644) | 2.263059 / 2.142072 (0.120987) | 0.591755 / 4.805227 (-4.213472) | 0.131167 / 6.500664 (-6.369497) | 0.061808 / 0.075469 (-0.013661) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.345364 / 1.841788 (-0.496424) | 18.122584 / 8.074308 (10.048276) | 13.318689 / 10.191392 (3.127297) | 0.144526 / 0.680424 (-0.535898) | 0.016997 / 0.534201 (-0.517204) | 0.336036 / 0.579283 (-0.243247) | 0.359532 / 0.434364 (-0.074832) | 0.386945 / 0.540337 (-0.153392) | 0.538659 / 1.386936 (-0.848277) |\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) | 0.006088 / 0.011353 (-0.005265) | 0.003684 / 0.011008 (-0.007324) | 0.062340 / 0.038508 (0.023832) | 0.058461 / 0.023109 (0.035352) | 0.360134 / 0.275898 (0.084236) | 0.393298 / 0.323480 (0.069818) | 0.004664 / 0.007986 (-0.003322) | 0.002909 / 0.004328 (-0.001420) | 0.062668 / 0.004250 (0.058418) | 0.050145 / 0.037052 (0.013092) | 0.361897 / 0.258489 (0.103408) | 0.402008 / 0.293841 (0.108167) | 0.027491 / 0.128546 (-0.101055) | 0.008113 / 0.075646 (-0.067534) | 0.068114 / 0.419271 (-0.351157) | 0.043303 / 0.043533 (-0.000230) | 0.360569 / 0.255139 (0.105430) | 0.387144 / 0.283200 (0.103944) | 0.020194 / 0.141683 (-0.121489) | 1.418066 / 1.452155 (-0.034089) | 1.475640 / 1.492716 (-0.017076) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.200291 / 0.018006 (0.182285) | 0.432298 / 0.000490 (0.431809) | 0.003303 / 0.000200 (0.003103) | 0.000075 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027749 / 0.037411 (-0.009662) | 0.081890 / 0.014526 (0.067364) | 0.094319 / 0.176557 (-0.082238) | 0.148646 / 0.737135 (-0.588490) | 0.091830 / 0.296338 (-0.204509) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.433546 / 0.215209 (0.218337) | 4.326855 / 2.077655 (2.249200) | 2.230186 / 1.504120 (0.726066) | 2.052524 / 1.541195 (0.511329) | 2.117270 / 1.468490 (0.648779) | 0.500331 / 4.584777 (-4.084446) | 3.113662 / 3.745712 (-0.632050) | 2.931540 / 5.269862 (-2.338322) | 1.853615 / 4.565676 (-2.712062) | 0.058250 / 0.424275 (-0.366025) | 0.006546 / 0.007607 (-0.001061) | 0.508850 / 0.226044 (0.282806) | 5.081809 / 2.268929 (2.812880) | 2.687037 / 55.444624 (-52.757588) | 2.369317 / 6.876477 (-4.507160) | 2.383549 / 2.142072 (0.241477) | 0.587039 / 4.805227 (-4.218188) | 0.125858 / 6.500664 (-6.374806) | 0.062522 / 0.075469 (-0.012947) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.294929 / 1.841788 (-0.546858) | 18.056312 / 8.074308 (9.982004) | 13.755117 / 10.191392 (3.563725) | 0.132037 / 0.680424 (-0.548387) | 0.016866 / 0.534201 (-0.517335) | 0.339040 / 0.579283 (-0.240243) | 0.364371 / 0.434364 (-0.069993) | 0.399533 / 0.540337 (-0.140804) | 0.564524 / 1.386936 (-0.822412) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#64b811c13a7982015d7e078e3d693ce5359a05a2 \"CML watermark\")\n",
"@lhoestq This bar comes from: https://github.com/huggingface/datasets/blob/b8067c0262073891180869f700ebef5ac3dc5cce/src/datasets/builder.py#L1156-L1166\r\n\r\nDo you prefer not showing it or, e.g., having `desc=\"Generating splits\"`?",
"No strong opinion. Since there is a \"Generating\" progress bar already, maybe it can be \"Preparing splits\" (ref to download_and_prepare)",
"<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) | 0.006348 / 0.011353 (-0.005005) | 0.003721 / 0.011008 (-0.007287) | 0.084039 / 0.038508 (0.045531) | 0.067627 / 0.023109 (0.044517) | 0.308372 / 0.275898 (0.032474) | 0.335131 / 0.323480 (0.011652) | 0.005157 / 0.007986 (-0.002829) | 0.003266 / 0.004328 (-0.001062) | 0.065374 / 0.004250 (0.061124) | 0.055550 / 0.037052 (0.018498) | 0.314001 / 0.258489 (0.055512) | 0.350510 / 0.293841 (0.056669) | 0.030859 / 0.128546 (-0.097688) | 0.008286 / 0.075646 (-0.067361) | 0.287122 / 0.419271 (-0.132149) | 0.051494 / 0.043533 (0.007961) | 0.309868 / 0.255139 (0.054729) | 0.325845 / 0.283200 (0.042645) | 0.022622 / 0.141683 (-0.119061) | 1.468730 / 1.452155 (0.016575) | 1.547871 / 1.492716 (0.055155) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.202763 / 0.018006 (0.184757) | 0.456403 / 0.000490 (0.455914) | 0.003116 / 0.000200 (0.002916) | 0.000079 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027297 / 0.037411 (-0.010114) | 0.081204 / 0.014526 (0.066678) | 0.094274 / 0.176557 (-0.082282) | 0.154391 / 0.737135 (-0.582744) | 0.094312 / 0.296338 (-0.202026) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.387382 / 0.215209 (0.172173) | 3.865597 / 2.077655 (1.787943) | 1.855959 / 1.504120 (0.351839) | 1.685411 / 1.541195 (0.144216) | 1.732127 / 1.468490 (0.263637) | 0.482230 / 4.584777 (-4.102547) | 3.664947 / 3.745712 (-0.080765) | 5.114379 / 5.269862 (-0.155482) | 3.102803 / 4.565676 (-1.462873) | 0.056509 / 0.424275 (-0.367766) | 0.007230 / 0.007607 (-0.000377) | 0.456788 / 0.226044 (0.230744) | 4.575831 / 2.268929 (2.306902) | 2.335249 / 55.444624 (-53.109375) | 2.003805 / 6.876477 (-4.872672) | 2.141788 / 2.142072 (-0.000285) | 0.577501 / 4.805227 (-4.227726) | 0.130264 / 6.500664 (-6.370400) | 0.058889 / 0.075469 (-0.016580) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.252673 / 1.841788 (-0.589115) | 18.676897 / 8.074308 (10.602589) | 13.988101 / 10.191392 (3.796709) | 0.151376 / 0.680424 (-0.529048) | 0.018104 / 0.534201 (-0.516097) | 0.388413 / 0.579283 (-0.190870) | 0.414841 / 0.434364 (-0.019523) | 0.456078 / 0.540337 (-0.084259) | 0.641715 / 1.386936 (-0.745221) |\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) | 0.006315 / 0.011353 (-0.005038) | 0.003847 / 0.011008 (-0.007162) | 0.063989 / 0.038508 (0.025481) | 0.068244 / 0.023109 (0.045135) | 0.416201 / 0.275898 (0.140303) | 0.438446 / 0.323480 (0.114966) | 0.005820 / 0.007986 (-0.002166) | 0.003165 / 0.004328 (-0.001163) | 0.064143 / 0.004250 (0.059892) | 0.056529 / 0.037052 (0.019477) | 0.414916 / 0.258489 (0.156427) | 0.450771 / 0.293841 (0.156930) | 0.030611 / 0.128546 (-0.097935) | 0.008289 / 0.075646 (-0.067357) | 0.070725 / 0.419271 (-0.348546) | 0.047998 / 0.043533 (0.004465) | 0.405609 / 0.255139 (0.150470) | 0.421895 / 0.283200 (0.138696) | 0.022135 / 0.141683 (-0.119548) | 1.444238 / 1.452155 (-0.007916) | 1.515823 / 1.492716 (0.023107) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227043 / 0.018006 (0.209037) | 0.439732 / 0.000490 (0.439242) | 0.001267 / 0.000200 (0.001067) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029082 / 0.037411 (-0.008329) | 0.086201 / 0.014526 (0.071675) | 0.098653 / 0.176557 (-0.077903) | 0.152574 / 0.737135 (-0.584561) | 0.100696 / 0.296338 (-0.195642) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.411243 / 0.215209 (0.196034) | 4.100170 / 2.077655 (2.022515) | 2.118310 / 1.504120 (0.614190) | 1.935646 / 1.541195 (0.394451) | 1.970798 / 1.468490 (0.502307) | 0.478635 / 4.584777 (-4.106142) | 3.589396 / 3.745712 (-0.156316) | 3.312462 / 5.269862 (-1.957399) | 1.963081 / 4.565676 (-2.602595) | 0.056392 / 0.424275 (-0.367883) | 0.007134 / 0.007607 (-0.000473) | 0.485131 / 0.226044 (0.259086) | 4.838946 / 2.268929 (2.570017) | 2.624550 / 55.444624 (-52.820075) | 2.223046 / 6.876477 (-4.653431) | 2.230642 / 2.142072 (0.088570) | 0.594892 / 4.805227 (-4.210335) | 0.130523 / 6.500664 (-6.370141) | 0.059585 / 0.075469 (-0.015884) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.329941 / 1.841788 (-0.511847) | 19.199057 / 8.074308 (11.124748) | 14.166009 / 10.191392 (3.974617) | 0.190595 / 0.680424 (-0.489829) | 0.018419 / 0.534201 (-0.515782) | 0.392031 / 0.579283 (-0.187252) | 0.409395 / 0.434364 (-0.024969) | 0.475930 / 0.540337 (-0.064408) | 0.654412 / 1.386936 (-0.732524) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#42fdfbd567674d075c3a9148ec3c95221eb62cfe \"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) | 0.007500 / 0.011353 (-0.003853) | 0.004328 / 0.011008 (-0.006681) | 0.086718 / 0.038508 (0.048209) | 0.098638 / 0.023109 (0.075529) | 0.335308 / 0.275898 (0.059409) | 0.369163 / 0.323480 (0.045683) | 0.005733 / 0.007986 (-0.002253) | 0.003738 / 0.004328 (-0.000590) | 0.066452 / 0.004250 (0.062202) | 0.066245 / 0.037052 (0.029192) | 0.337609 / 0.258489 (0.079120) | 0.388584 / 0.293841 (0.094744) | 0.031742 / 0.128546 (-0.096804) | 0.008721 / 0.075646 (-0.066925) | 0.290820 / 0.419271 (-0.128452) | 0.053323 / 0.043533 (0.009790) | 0.329192 / 0.255139 (0.074053) | 0.350560 / 0.283200 (0.067360) | 0.025402 / 0.141683 (-0.116281) | 1.476174 / 1.452155 (0.024020) | 1.578194 / 1.492716 (0.085478) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.256160 / 0.018006 (0.238154) | 0.560315 / 0.000490 (0.559825) | 0.005287 / 0.000200 (0.005088) | 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) | 0.029164 / 0.037411 (-0.008247) | 0.084881 / 0.014526 (0.070356) | 0.100979 / 0.176557 (-0.075577) | 0.156539 / 0.737135 (-0.580597) | 0.101510 / 0.296338 (-0.194828) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.381138 / 0.215209 (0.165929) | 3.791573 / 2.077655 (1.713918) | 1.841954 / 1.504120 (0.337834) | 1.672463 / 1.541195 (0.131268) | 1.785769 / 1.468490 (0.317279) | 0.483263 / 4.584777 (-4.101514) | 3.617391 / 3.745712 (-0.128322) | 5.607794 / 5.269862 (0.337933) | 3.359530 / 4.565676 (-1.206147) | 0.056826 / 0.424275 (-0.367449) | 0.007375 / 0.007607 (-0.000232) | 0.455853 / 0.226044 (0.229809) | 4.548965 / 2.268929 (2.280037) | 2.412716 / 55.444624 (-53.031908) | 1.991456 / 6.876477 (-4.885021) | 2.242851 / 2.142072 (0.100778) | 0.573070 / 4.805227 (-4.232157) | 0.134658 / 6.500664 (-6.366006) | 0.061539 / 0.075469 (-0.013930) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.278306 / 1.841788 (-0.563481) | 20.634317 / 8.074308 (12.560009) | 15.164246 / 10.191392 (4.972854) | 0.167487 / 0.680424 (-0.512937) | 0.019006 / 0.534201 (-0.515195) | 0.394617 / 0.579283 (-0.184666) | 0.423385 / 0.434364 (-0.010979) | 0.469968 / 0.540337 (-0.070370) | 0.630058 / 1.386936 (-0.756878) |\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) | 0.006793 / 0.011353 (-0.004559) | 0.004260 / 0.011008 (-0.006748) | 0.065398 / 0.038508 (0.026890) | 0.077850 / 0.023109 (0.054741) | 0.371754 / 0.275898 (0.095855) | 0.400652 / 0.323480 (0.077172) | 0.005729 / 0.007986 (-0.002256) | 0.003660 / 0.004328 (-0.000669) | 0.065119 / 0.004250 (0.060869) | 0.060714 / 0.037052 (0.023661) | 0.384592 / 0.258489 (0.126103) | 0.412806 / 0.293841 (0.118965) | 0.031865 / 0.128546 (-0.096681) | 0.008807 / 0.075646 (-0.066839) | 0.071156 / 0.419271 (-0.348115) | 0.049571 / 0.043533 (0.006038) | 0.367381 / 0.255139 (0.112242) | 0.386713 / 0.283200 (0.103513) | 0.024838 / 0.141683 (-0.116845) | 1.492986 / 1.452155 (0.040831) | 1.559243 / 1.492716 (0.066526) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.269737 / 0.018006 (0.251730) | 0.565177 / 0.000490 (0.564687) | 0.000404 / 0.000200 (0.000204) | 0.000060 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031631 / 0.037411 (-0.005780) | 0.087289 / 0.014526 (0.072764) | 0.102798 / 0.176557 (-0.073759) | 0.158977 / 0.737135 (-0.578158) | 0.105495 / 0.296338 (-0.190843) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425067 / 0.215209 (0.209858) | 4.243121 / 2.077655 (2.165466) | 2.234567 / 1.504120 (0.730447) | 2.070810 / 1.541195 (0.529615) | 2.176802 / 1.468490 (0.708312) | 0.484987 / 4.584777 (-4.099790) | 3.647000 / 3.745712 (-0.098712) | 3.574843 / 5.269862 (-1.695019) | 2.092581 / 4.565676 (-2.473095) | 0.057299 / 0.424275 (-0.366976) | 0.007480 / 0.007607 (-0.000128) | 0.507838 / 0.226044 (0.281794) | 5.076594 / 2.268929 (2.807666) | 2.718858 / 55.444624 (-52.725766) | 2.362793 / 6.876477 (-4.513684) | 2.451962 / 2.142072 (0.309890) | 0.581355 / 4.805227 (-4.223872) | 0.133723 / 6.500664 (-6.366941) | 0.061896 / 0.075469 (-0.013573) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.325814 / 1.841788 (-0.515974) | 20.614502 / 8.074308 (12.540194) | 14.769422 / 10.191392 (4.578029) | 0.193797 / 0.680424 (-0.486627) | 0.018379 / 0.534201 (-0.515822) | 0.394153 / 0.579283 (-0.185130) | 0.409585 / 0.434364 (-0.024779) | 0.479107 / 0.540337 (-0.061231) | 0.668397 / 1.386936 (-0.718539) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b2d892237169bad5512c91cae453d257ebefc201 \"CML watermark\")\n",
"In the end, I decided to remove the progress bar to avoid having it displayed when loading a cached dataset.",
"<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) | 0.006673 / 0.011353 (-0.004680) | 0.004162 / 0.011008 (-0.006846) | 0.084017 / 0.038508 (0.045509) | 0.079536 / 0.023109 (0.056426) | 0.313594 / 0.275898 (0.037695) | 0.349200 / 0.323480 (0.025720) | 0.005544 / 0.007986 (-0.002441) | 0.003472 / 0.004328 (-0.000857) | 0.064742 / 0.004250 (0.060491) | 0.056857 / 0.037052 (0.019805) | 0.318635 / 0.258489 (0.060146) | 0.354378 / 0.293841 (0.060537) | 0.030856 / 0.128546 (-0.097690) | 0.008759 / 0.075646 (-0.066887) | 0.287760 / 0.419271 (-0.131511) | 0.052307 / 0.043533 (0.008775) | 0.316396 / 0.255139 (0.061257) | 0.351408 / 0.283200 (0.068208) | 0.024914 / 0.141683 (-0.116769) | 1.484592 / 1.452155 (0.032437) | 1.560662 / 1.492716 (0.067945) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.280938 / 0.018006 (0.262932) | 0.580236 / 0.000490 (0.579747) | 0.003369 / 0.000200 (0.003169) | 0.000090 / 0.000054 (0.000036) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028736 / 0.037411 (-0.008675) | 0.082916 / 0.014526 (0.068390) | 0.097761 / 0.176557 (-0.078796) | 0.153515 / 0.737135 (-0.583620) | 0.099282 / 0.296338 (-0.197057) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.401244 / 0.215209 (0.186035) | 4.019866 / 2.077655 (1.942211) | 2.029642 / 1.504120 (0.525522) | 1.849591 / 1.541195 (0.308396) | 1.946829 / 1.468490 (0.478339) | 0.479750 / 4.584777 (-4.105027) | 3.482822 / 3.745712 (-0.262890) | 3.955859 / 5.269862 (-1.314003) | 2.370747 / 4.565676 (-2.194930) | 0.056905 / 0.424275 (-0.367370) | 0.007319 / 0.007607 (-0.000288) | 0.485310 / 0.226044 (0.259266) | 4.858228 / 2.268929 (2.589299) | 2.500476 / 55.444624 (-52.944148) | 2.171156 / 6.876477 (-4.705320) | 2.427266 / 2.142072 (0.285194) | 0.570199 / 4.805227 (-4.235029) | 0.130855 / 6.500664 (-6.369809) | 0.060269 / 0.075469 (-0.015200) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.258044 / 1.841788 (-0.583743) | 20.218657 / 8.074308 (12.144349) | 13.597970 / 10.191392 (3.406578) | 0.167656 / 0.680424 (-0.512768) | 0.018137 / 0.534201 (-0.516064) | 0.395309 / 0.579283 (-0.183975) | 0.406325 / 0.434364 (-0.028039) | 0.467457 / 0.540337 (-0.072880) | 0.613636 / 1.386936 (-0.773300) |\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) | 0.006846 / 0.011353 (-0.004507) | 0.004207 / 0.011008 (-0.006802) | 0.064525 / 0.038508 (0.026017) | 0.081329 / 0.023109 (0.058220) | 0.399838 / 0.275898 (0.123940) | 0.431305 / 0.323480 (0.107825) | 0.005859 / 0.007986 (-0.002127) | 0.003568 / 0.004328 (-0.000760) | 0.065262 / 0.004250 (0.061011) | 0.064796 / 0.037052 (0.027744) | 0.406858 / 0.258489 (0.148369) | 0.440971 / 0.293841 (0.147130) | 0.031421 / 0.128546 (-0.097125) | 0.008777 / 0.075646 (-0.066870) | 0.071418 / 0.419271 (-0.347853) | 0.049263 / 0.043533 (0.005730) | 0.384279 / 0.255139 (0.129140) | 0.410745 / 0.283200 (0.127546) | 0.024467 / 0.141683 (-0.117216) | 1.522379 / 1.452155 (0.070224) | 1.581636 / 1.492716 (0.088920) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.276161 / 0.018006 (0.258155) | 0.548842 / 0.000490 (0.548352) | 0.004523 / 0.000200 (0.004324) | 0.000098 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030747 / 0.037411 (-0.006664) | 0.087493 / 0.014526 (0.072967) | 0.106563 / 0.176557 (-0.069993) | 0.162949 / 0.737135 (-0.574186) | 0.105303 / 0.296338 (-0.191036) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425854 / 0.215209 (0.210645) | 4.244797 / 2.077655 (2.167142) | 2.269006 / 1.504120 (0.764886) | 2.097428 / 1.541195 (0.556234) | 2.181038 / 1.468490 (0.712548) | 0.477286 / 4.584777 (-4.107491) | 3.591452 / 3.745712 (-0.154260) | 3.481281 / 5.269862 (-1.788580) | 2.066895 / 4.565676 (-2.498782) | 0.056576 / 0.424275 (-0.367699) | 0.007409 / 0.007607 (-0.000199) | 0.498411 / 0.226044 (0.272367) | 4.994873 / 2.268929 (2.725945) | 2.749148 / 55.444624 (-52.695476) | 2.378544 / 6.876477 (-4.497932) | 2.452859 / 2.142072 (0.310786) | 0.571340 / 4.805227 (-4.233887) | 0.132174 / 6.500664 (-6.368490) | 0.061507 / 0.075469 (-0.013962) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.370773 / 1.841788 (-0.471015) | 20.493342 / 8.074308 (12.419034) | 14.809886 / 10.191392 (4.618494) | 0.175730 / 0.680424 (-0.504693) | 0.018617 / 0.534201 (-0.515583) | 0.393808 / 0.579283 (-0.185476) | 0.416419 / 0.434364 (-0.017945) | 0.477183 / 0.540337 (-0.063155) | 0.668060 / 1.386936 (-0.718876) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2de7a2a4af5d94b0f98a7a6db94e78984af40602 \"CML watermark\")\n",
"Nice one :)"
] |
6,018
|
test1
| null |
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6018",
"html_url": "https://github.com/huggingface/datasets/pull/6018",
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"patch_url": "https://github.com/huggingface/datasets/pull/6018.patch",
"merged_at": null
}
| true
|
[
"We no longer host datasets in this repo. You should use the HF Hub instead."
] |
6,017
|
Switch to huggingface_hub's HfFileSystem
|
instead of the current datasets.filesystems.hffilesystem.HfFileSystem which can be slow in some cases
related to https://github.com/huggingface/datasets/issues/5846 and https://github.com/huggingface/datasets/pull/5919
|
[
{
"id": 1935892871,
"node_id": "MDU6TGFiZWwxOTM1ODkyODcx",
"url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement",
"name": "enhancement",
"color": "a2eeef",
"default": true,
"description": "New feature or request"
}
] |
{
"url": null,
"html_url": null,
"diff_url": null,
"patch_url": null,
"merged_at": null
}
| false
|
[] |
6,016
|
Dataset string representation enhancement
|
my attempt at #6010
not sure if this is the right way to go about it, I will wait for your feedback
|
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6016",
"html_url": "https://github.com/huggingface/datasets/pull/6016",
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}
| true
|
[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6016). All of your documentation changes will be reflected on that endpoint.",
"It we could have something similar to Polars, that would be great.\r\n\r\nThis is what Polars outputs: \r\n* `__repr__`/`__str__` :\r\n```\r\nshape: (67_349, 3)\r\n┌───────┬───────────────────────────────────┬───────┐\r\n│ idx ┆ sentence ┆ label │\r\n│ --- ┆ --- ┆ --- │\r\n│ i32 ┆ str ┆ i64 │\r\n╞═══════╪═══════════════════════════════════╪═══════╡\r\n│ 0 ┆ hide new secretions from the par… ┆ 0 │\r\n│ 1 ┆ contains no wit , only labored g… ┆ 0 │\r\n│ 2 ┆ that loves its characters and co… ┆ 1 │\r\n│ 3 ┆ remains utterly satisfied to rem… ┆ 0 │\r\n│ … ┆ … ┆ … │\r\n│ 67345 ┆ anguish , anger and frustration ┆ 0 │\r\n│ 67346 ┆ at achieving the modest , crowd-… ┆ 1 │\r\n│ 67347 ┆ a patient viewer ┆ 1 │\r\n│ 67348 ┆ this new jangle of noise , mayhe… ┆ 0 │\r\n└───────┴───────────────────────────────────┴───────┘\r\n```\r\n\r\n* `_repr_html_`:\r\n<img width=\"251\" alt=\"Screenshot 2023-07-12 at 18 25 58\" src=\"https://github.com/huggingface/datasets/assets/47462742/5d04519d-f302-4411-9fbc-7445bdf53b23\">\r\n\r\n"
] |
6,015
|
Add metadata ui screenshot in docs
| null |
[] |
{
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"html_url": "https://github.com/huggingface/datasets/pull/6015",
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"merged_at": "2023-07-11T15:56:46"
}
| true
|
[
"_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) | 0.007633 / 0.011353 (-0.003720) | 0.004666 / 0.011008 (-0.006343) | 0.097768 / 0.038508 (0.059260) | 0.085153 / 0.023109 (0.062044) | 0.400315 / 0.275898 (0.124417) | 0.452903 / 0.323480 (0.129423) | 0.006227 / 0.007986 (-0.001759) | 0.003814 / 0.004328 (-0.000515) | 0.074586 / 0.004250 (0.070336) | 0.064295 / 0.037052 (0.027242) | 0.408082 / 0.258489 (0.149593) | 0.446921 / 0.293841 (0.153080) | 0.034593 / 0.128546 (-0.093953) | 0.009191 / 0.075646 (-0.066456) | 0.337099 / 0.419271 (-0.082173) | 0.075320 / 0.043533 (0.031787) | 0.403488 / 0.255139 (0.148349) | 0.435309 / 0.283200 (0.152109) | 0.035675 / 0.141683 (-0.106008) | 1.732642 / 1.452155 (0.280487) | 1.770238 / 1.492716 (0.277522) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235879 / 0.018006 (0.217873) | 0.500330 / 0.000490 (0.499841) | 0.005221 / 0.000200 (0.005021) | 0.000150 / 0.000054 (0.000096) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032479 / 0.037411 (-0.004933) | 0.095873 / 0.014526 (0.081348) | 0.107118 / 0.176557 (-0.069438) | 0.173809 / 0.737135 (-0.563326) | 0.109832 / 0.296338 (-0.186507) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.444342 / 0.215209 (0.229133) | 4.459010 / 2.077655 (2.381355) | 2.209687 / 1.504120 (0.705567) | 2.007556 / 1.541195 (0.466362) | 2.113683 / 1.468490 (0.645193) | 0.544281 / 4.584777 (-4.040496) | 4.037151 / 3.745712 (0.291439) | 4.852644 / 5.269862 (-0.417217) | 3.134126 / 4.565676 (-1.431550) | 0.066815 / 0.424275 (-0.357460) | 0.008836 / 0.007607 (0.001229) | 0.560904 / 0.226044 (0.334859) | 5.302760 / 2.268929 (3.033832) | 2.750182 / 55.444624 (-52.694442) | 2.322595 / 6.876477 (-4.553882) | 2.547486 / 2.142072 (0.405414) | 0.665766 / 4.805227 (-4.139461) | 0.151613 / 6.500664 (-6.349051) | 0.071155 / 0.075469 (-0.004314) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.473717 / 1.841788 (-0.368071) | 22.584179 / 8.074308 (14.509871) | 15.888001 / 10.191392 (5.696609) | 0.181073 / 0.680424 (-0.499351) | 0.021395 / 0.534201 (-0.512806) | 0.452693 / 0.579283 (-0.126590) | 0.447709 / 0.434364 (0.013345) | 0.529599 / 0.540337 (-0.010738) | 0.699241 / 1.386936 (-0.687695) |\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) | 0.007917 / 0.011353 (-0.003436) | 0.004544 / 0.011008 (-0.006464) | 0.074566 / 0.038508 (0.036058) | 0.087530 / 0.023109 (0.064421) | 0.419753 / 0.275898 (0.143854) | 0.452352 / 0.323480 (0.128872) | 0.005882 / 0.007986 (-0.002104) | 0.003904 / 0.004328 (-0.000425) | 0.073539 / 0.004250 (0.069289) | 0.071320 / 0.037052 (0.034267) | 0.432899 / 0.258489 (0.174409) | 0.470365 / 0.293841 (0.176524) | 0.036198 / 0.128546 (-0.092348) | 0.009342 / 0.075646 (-0.066304) | 0.080970 / 0.419271 (-0.338301) | 0.058769 / 0.043533 (0.015236) | 0.413397 / 0.255139 (0.158258) | 0.448362 / 0.283200 (0.165162) | 0.034177 / 0.141683 (-0.107506) | 1.706217 / 1.452155 (0.254063) | 1.776743 / 1.492716 (0.284026) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198779 / 0.018006 (0.180773) | 0.499862 / 0.000490 (0.499372) | 0.003891 / 0.000200 (0.003692) | 0.000108 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034671 / 0.037411 (-0.002740) | 0.103165 / 0.014526 (0.088639) | 0.115813 / 0.176557 (-0.060744) | 0.177407 / 0.737135 (-0.559728) | 0.117733 / 0.296338 (-0.178606) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.476859 / 0.215209 (0.261650) | 4.823063 / 2.077655 (2.745409) | 2.524133 / 1.504120 (1.020013) | 2.374482 / 1.541195 (0.833288) | 2.518047 / 1.468490 (1.049557) | 0.559131 / 4.584777 (-4.025646) | 4.126213 / 3.745712 (0.380501) | 6.488570 / 5.269862 (1.218708) | 3.816540 / 4.565676 (-0.749137) | 0.064742 / 0.424275 (-0.359533) | 0.008476 / 0.007607 (0.000869) | 0.576432 / 0.226044 (0.350387) | 5.835133 / 2.268929 (3.566205) | 3.237833 / 55.444624 (-52.206791) | 2.726596 / 6.876477 (-4.149880) | 2.799212 / 2.142072 (0.657139) | 0.661628 / 4.805227 (-4.143599) | 0.153997 / 6.500664 (-6.346667) | 0.070621 / 0.075469 (-0.004848) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.648505 / 1.841788 (-0.193282) | 22.454019 / 8.074308 (14.379711) | 16.077098 / 10.191392 (5.885706) | 0.217875 / 0.680424 (-0.462549) | 0.021285 / 0.534201 (-0.512916) | 0.459837 / 0.579283 (-0.119446) | 0.476211 / 0.434364 (0.041847) | 0.525903 / 0.540337 (-0.014435) | 0.717224 / 1.386936 (-0.669712) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b767e9c3ef30f9da30d47cfcaccf9a7ac2500c43 \"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) | 0.008929 / 0.011353 (-0.002424) | 0.004188 / 0.011008 (-0.006820) | 0.097030 / 0.038508 (0.058522) | 0.071363 / 0.023109 (0.048254) | 0.333116 / 0.275898 (0.057218) | 0.371272 / 0.323480 (0.047792) | 0.006430 / 0.007986 (-0.001555) | 0.003689 / 0.004328 (-0.000639) | 0.068666 / 0.004250 (0.064416) | 0.057562 / 0.037052 (0.020510) | 0.347208 / 0.258489 (0.088719) | 0.390514 / 0.293841 (0.096673) | 0.050560 / 0.128546 (-0.077987) | 0.013372 / 0.075646 (-0.062275) | 0.311345 / 0.419271 (-0.107927) | 0.068990 / 0.043533 (0.025457) | 0.363026 / 0.255139 (0.107887) | 0.379793 / 0.283200 (0.096593) | 0.036891 / 0.141683 (-0.104792) | 1.583481 / 1.452155 (0.131327) | 1.688727 / 1.492716 (0.196011) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209777 / 0.018006 (0.191771) | 0.507267 / 0.000490 (0.506777) | 0.003637 / 0.000200 (0.003438) | 0.000105 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029309 / 0.037411 (-0.008102) | 0.088386 / 0.014526 (0.073861) | 0.104974 / 0.176557 (-0.071582) | 0.171999 / 0.737135 (-0.565137) | 0.110797 / 0.296338 (-0.185542) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.543465 / 0.215209 (0.328256) | 5.361491 / 2.077655 (3.283836) | 2.348712 / 1.504120 (0.844592) | 2.012527 / 1.541195 (0.471332) | 2.069776 / 1.468490 (0.601286) | 0.874262 / 4.584777 (-3.710515) | 4.877317 / 3.745712 (1.131605) | 5.327459 / 5.269862 (0.057597) | 3.336823 / 4.565676 (-1.228854) | 0.100456 / 0.424275 (-0.323819) | 0.008503 / 0.007607 (0.000895) | 0.692009 / 0.226044 (0.465965) | 6.912731 / 2.268929 (4.643802) | 3.110548 / 55.444624 (-52.334076) | 2.443665 / 6.876477 (-4.432811) | 2.528713 / 2.142072 (0.386641) | 1.076358 / 4.805227 (-3.728869) | 0.220352 / 6.500664 (-6.280312) | 0.080293 / 0.075469 (0.004824) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.538444 / 1.841788 (-0.303344) | 21.121221 / 8.074308 (13.046913) | 19.810609 / 10.191392 (9.619216) | 0.225406 / 0.680424 (-0.455018) | 0.026652 / 0.534201 (-0.507549) | 0.430372 / 0.579283 (-0.148911) | 0.510722 / 0.434364 (0.076358) | 0.514347 / 0.540337 (-0.025991) | 0.686050 / 1.386936 (-0.700886) |\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) | 0.007675 / 0.011353 (-0.003678) | 0.004542 / 0.011008 (-0.006466) | 0.069655 / 0.038508 (0.031147) | 0.069338 / 0.023109 (0.046229) | 0.436505 / 0.275898 (0.160607) | 0.481806 / 0.323480 (0.158326) | 0.005315 / 0.007986 (-0.002670) | 0.004455 / 0.004328 (0.000127) | 0.072674 / 0.004250 (0.068424) | 0.058088 / 0.037052 (0.021035) | 0.445825 / 0.258489 (0.187336) | 0.501706 / 0.293841 (0.207865) | 0.047123 / 0.128546 (-0.081424) | 0.012943 / 0.075646 (-0.062703) | 0.093491 / 0.419271 (-0.325780) | 0.060169 / 0.043533 (0.016637) | 0.436530 / 0.255139 (0.181391) | 0.466873 / 0.283200 (0.183674) | 0.040453 / 0.141683 (-0.101230) | 1.586438 / 1.452155 (0.134283) | 1.671081 / 1.492716 (0.178365) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.180607 / 0.018006 (0.162601) | 0.520145 / 0.000490 (0.519655) | 0.004824 / 0.000200 (0.004624) | 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) | 0.029308 / 0.037411 (-0.008103) | 0.093652 / 0.014526 (0.079126) | 0.102332 / 0.176557 (-0.074224) | 0.162414 / 0.737135 (-0.574721) | 0.098017 / 0.296338 (-0.198321) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.583949 / 0.215209 (0.368740) | 6.035191 / 2.077655 (3.957536) | 2.801274 / 1.504120 (1.297155) | 2.566150 / 1.541195 (1.024955) | 2.437122 / 1.468490 (0.968632) | 0.865038 / 4.584777 (-3.719739) | 4.841727 / 3.745712 (1.096015) | 4.683919 / 5.269862 (-0.585943) | 2.941240 / 4.565676 (-1.624437) | 0.104888 / 0.424275 (-0.319387) | 0.007747 / 0.007607 (0.000140) | 0.780041 / 0.226044 (0.553997) | 7.771314 / 2.268929 (5.502385) | 3.680814 / 55.444624 (-51.763811) | 2.938472 / 6.876477 (-3.938004) | 2.981740 / 2.142072 (0.839668) | 1.065411 / 4.805227 (-3.739816) | 0.222265 / 6.500664 (-6.278399) | 0.082428 / 0.075469 (0.006959) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.626774 / 1.841788 (-0.215014) | 21.618284 / 8.074308 (13.543976) | 20.596743 / 10.191392 (10.405351) | 0.240969 / 0.680424 (-0.439454) | 0.025630 / 0.534201 (-0.508570) | 0.481981 / 0.579283 (-0.097302) | 0.547914 / 0.434364 (0.113550) | 0.522296 / 0.540337 (-0.018041) | 0.729174 / 1.386936 (-0.657762) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b8067c0262073891180869f700ebef5ac3dc5cce \"CML watermark\")\n"
] |
6,014
|
Request to Share/Update Dataset Viewer Code
|
Overview:
The repository (huggingface/datasets-viewer) was recently archived and when I tried to run the code, there was the error message "AttributeError: module 'datasets.load' has no attribute 'prepare_module'". I could not resolve the issue myself due to lack of documentation of that attribute.
Request:
I kindly request the sharing of the code responsible for the dataset preview functionality or help with resolving the error. The dataset viewer on the Hugging Face website is incredibly useful since it is compatible with different types of inputs. It allows users to find datasets that meet their needs more efficiently. If needed, I am willing to contribute to the project by testing, documenting, and providing feedback on the dataset viewer code.
Thank you for considering this request, and I look forward to your response.
|
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"color": "cfd3d7",
"default": true,
"description": "This issue or pull request already exists"
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{
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|
[
"Hi ! The huggingface/dataset-viewer code was not maintained anymore because we switched to a new dataset viewer that is deployed available for each dataset the Hugging Face website.\r\n\r\nWhat are you using this old repository for ?",
"I think these parts are outdated:\r\n\r\n* https://github.com/huggingface/datasets-viewer/blob/8efad8eae313a891f713469983bf4c744786f26e/run.py#L126-L131\r\n* https://github.com/huggingface/datasets-viewer/blob/8efad8eae313a891f713469983bf4c744786f26e/run.py#L145-L150\r\n\r\nTo make the viewer work, the first one should be replaced with the following:\r\n```python\r\ndataset_module = datasets.load.dataset_module_factory(path)\r\nbuilder_cls = datasets.load.import_main_class(dataset_module.module_path)\r\nconfs = builder_cls.BUILDER_CONFIGS\r\n```\r\nAnd the second one:\r\n```python\r\ndataset_module = datasets.load.dataset_module_factory(path)\r\nbuilder_cls = datasets.load.import_main_class(dataset_module.module_path)\r\nif conf:\r\n builder_instance = builder_cls(name=conf, cache_dir=path if path_to_datasets is not None else None)\r\nelse:\r\n builder_instance = builder_cls(cache_dir=path if path_to_datasets is not None else None)\r\n```\r\n\r\nBut as @lhoestq suggested, it's better to use the `datasets-server` API nowadays to [fetch the rows](https://huggingface.co/docs/datasets-server/rows).",
"> The dataset viewer on the Hugging Face website is incredibly useful\r\n\r\n@mariosasko i think @lilyorlilypad wants to run the new dataset-viewer, not the old one",
"> wants to run the new dataset-viewer, not the old one\r\n\r\nThanks for the clarification for me. I do want to run the new dataset-viewer. ",
"It should be possible to run it locally using the HF datasets-server API (docs [here](https://huggingface.co/docs/datasets-server)) but the front end part is not open source (yet ?)\r\n\r\nThe back-end is open source though if you're interested: https://github.com/huggingface/datasets-server\r\nIt automatically converts datasets on HF to Parquet, which is the format we use to power the viewer.",
"the new frontend would probably be hard to open source, as is, as it's quite intertwined with the Hub's code.\r\n\r\nHowever, at some point it would be amazing to have a community-driven open source implementation of a frontend to datasets-server! ",
"For the frontend viewer, see https://github.com/huggingface/datasets/issues/6139.\r\n\r\nAlso mentioned in https://github.com/huggingface/datasets-server/issues/213 and https://github.com/huggingface/datasets-server/issues/441\r\n\r\nClosing as a duplicate of https://github.com/huggingface/datasets/issues/6139",
"Hi team,\r\n\r\nI'm currently researching the Dataset Viewer project and would like to understand more about the frontend technologies used. Specifically, I'm interested in knowing:\r\n\r\nWhich frontend framework is being utilized (e.g., React, Vue, etc.)?\r\nAre there any specific libraries or components being used for UI (e.g., Material-UI, Ant Design)?\r\nAny other notable frontend tools or technologies that are part of this project?\r\nYour assistance in providing these details would be greatly appreciated. Thank you for your time and effort!\r\n\r\nBest regards",
"@jacob-rodgers-max we use https://svelte.dev/",
"> @jacob-rodgers-max we use https://svelte.dev/\r\n\r\nThank you very much for your prompt and detailed response!"
] |
6,013
|
[FR] `map` should reuse unchanged columns from the previous dataset to avoid disk usage
|
### Feature request
Currently adding a new column with `map` will cause all the data in the dataset to be duplicated and stored/cached on the disk again. It should reuse unchanged columns.
### Motivation
This allows having datasets with different columns but sharing some basic columns. Currently, these datasets would become too expensive to store and one would need some kind of on-the-fly join; which also doesn't seem implemented.
### Your contribution
_
|
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"name": "good second issue",
"color": "BDE59C",
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[
"You can use the `remove_columns` parameter in `map` to avoid duplicating the columns (and save disk space) and then concatenate the original dataset with the map result:\r\n```python\r\nfrom datasets import concatenate_datasets\r\n# dummy example\r\nds_new = ds.map(lambda x: {\"new_col\": x[\"col\"] + 2}, remove_columns=ds.column_names)\r\nds_combined = concatenate_datasets([ds, ds_new], axis=1)\r\n```\r\n\r\nDoing this automatically is hard to implement efficiently unless we know ahead of time which existing columns will be modified by a `map` transform. We have this info when `input_columns` are specified, so I think this is the only case we can optimize."
] |
6,012
|
[FR] Transform Chaining, Lazy Mapping
|
### Feature request
Currently using a `map` call processes and duplicates the whole dataset, which takes both time and disk space.
The solution is to allow lazy mapping, which is essentially a saved chain of transforms that are applied on the fly whenever a slice of the dataset is requested.
The API should look like `map`, as `set_transform` changes the current dataset while `map` returns another dataset.
### Motivation
Lazy processing allows lower disk usage and faster experimentation.
### Your contribution
_
|
[
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"node_id": "MDU6TGFiZWwxOTM1ODkyODcx",
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[
"You can use `with_transform` to get a new dataset object.\r\n\r\nSupport for lazy `map` has already been discussed [here](https://github.com/huggingface/datasets/issues/3385) a little bit. Personally, I'm not a fan, as this would make `map` even more complex. ",
"> You can use `with_transform` to get a new dataset object.\r\n> \r\n> Support for lazy `map` has already been discussed [here](https://github.com/huggingface/datasets/issues/3385) a little bit. Personally, I'm not a fan, as this would make `map` even more complex.\r\n\r\nI read about IterableDataset, and it seems to have lazy mapping. But I can't figure out how to convert an IterableDataset into a normal one when needed.\r\n\r\n`with_transform` still does not chain AFAIU.",
"> I read about IterableDataset, and it seems to have lazy mapping. But I can't figure out how to convert an IterableDataset into a normal one when needed.\r\n\r\nYou must cache an `IterableDataset` to disk to load it as a `Dataset`. One way to do this is with `Dataset.from_generator`:\r\n```python\r\nfrom functools import partial\r\nfrom datasets import Dataset\r\n\r\ndef gen_from_iterable_dataset(iterable_ds)\r\n yield from iterable_ds\r\n\r\nds = Dataset.from_generator(partial(gen_from_iterable_dataset, iterable_ds), features=iterable_ds.features})\r\n```\r\n\r\n> with_transform still does not chain AFAIU.\r\n\r\nYes, not supported yet - the solution is to combine the transforms into a single one.",
"I wonder if it would be beneficial to have a dedicated method to do that ? Maybe a `.save_to_disk()` so that the user can reload the resulting dataset later ?",
"> ```python\r\n> from functools import partial\r\n> from datasets import Dataset\r\n> \r\n> def gen_from_iterable_dataset(iterable_ds)\r\n> yield from iterable_ds\r\n> \r\n> ds = Dataset.from_generator(partial(gen_from_iterable_dataset, iterable_ds), features=iterable_ds.features})\r\n> ```\r\n\r\n@mariosasko With these complex mapping functions, what hash will be used to cache this dataset?\r\n",
"The params passed to `Dataset.from_generator` will be used to compute the hash (`partial` encapsulates the `iterable_ds` value, so changing it will also change the hash)",
"Hi, I think this feature would be very useful. I want to concatenate large datasets with heterogeneous columns. I dislike `map` since I don't want multiple copy of that datasets locally. I tried to use \"set_transform\" on each dataset to convert it to a standard features format, but `datasets.concatenate_datasets` ignores the updated format of the datasets. A work around is to use `torch.utils.data.ConcatDataset`. Is there a neat way to do it using HF datasets?"
] |
6,011
|
Documentation: wiki_dpr Dataset has no metric_type for Faiss Index
|
### Describe the bug
After loading `wiki_dpr` using:
```py
ds = load_dataset(path='wiki_dpr', name='psgs_w100.multiset.compressed', split='train')
print(ds.get_index("embeddings").metric_type) # prints nothing because the value is None
```
the index does not have a defined `metric_type`. This is an issue because I do not know how the `scores` are being computed for `get_nearest_examples()`.
### Steps to reproduce the bug
System: Python 3.9.16, Transformers 4.30.2, WSL
After loading `wiki_dpr` using:
```py
ds = load_dataset(path='wiki_dpr', name='psgs_w100.multiset.compressed', split='train')
print(ds.get_index("embeddings").metric_type) # prints nothing because the value is None
```
the index does not have a defined `metric_type`. This is an issue because I do not know how the `scores` are being computed for `get_nearest_examples()`.
```py
from transformers import DPRQuestionEncoder, DPRContextEncoder, DPRQuestionEncoderTokenizer, DPRContextEncoderTokenizer
tokenizer = DPRQuestionEncoderTokenizer.from_pretrained("facebook/dpr-question_encoder-multiset-base")
encoder = DPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-multiset-base")
def encode_question(query, tokenizer=tokenizer, encoder=encoder):
inputs = tokenizer(query, return_tensors='pt')
question_embedding = encoder(**inputs)[0].detach().numpy()
return question_embedding
def get_knn(query, k=5, tokenizer=tokenizer, encoder=encoder, verbose=False):
enc_question = encode_question(query, tokenizer, encoder)
topk_results = ds.get_nearest_examples(index_name='embeddings',
query=enc_question,
k=k)
a = torch.tensor(enc_question[0]).reshape(768)
b = torch.tensor(topk_results.examples['embeddings'][0])
print(a.shape, b.shape)
print(torch.dot(a, b))
print((a-b).pow(2).sum())
return topk_results
```
The [FAISS documentation](https://github.com/facebookresearch/faiss/wiki/MetricType-and-distances) suggests the metric is usually L2 distance (without the square root) or the inner product. I compute both for the sample query:
```py
query = """ it catapulted into popular culture along with a line of action figures and other toys by Bandai.[2] By 2001, the media franchise had generated over $6 billion in toy sales.
Despite initial criticism that its action violence targeted child audiences, the franchise has been commercially successful."""
get_knn(query,k=5)
```
Here, I get dot product of 80.6020 and L2 distance of 77.6616 and
```py
NearestExamplesResults(scores=array([76.20431 , 75.312416, 74.945404, 74.866394, 74.68506 ],
dtype=float32), examples={'id': ['3081096', '2004811', '8908258', '9594124', '286575'], 'text': ['actors, resulting in the "Power Rangers" franchise which has continued since then into sequel TV series (with "Power Rangers Beast Morphers" set to premiere in 2019), comic books, video games, and three feature films, with a further cinematic universe planned. Following from the success of "Power Rangers", Saban acquired the rights to more of Toei\'s library, creating "VR Troopers" and "Big Bad Beetleborgs" from several Metal Hero Series shows and "Masked Rider" from Kamen Rider Series footage. DIC Entertainment joined this boom by acquiring the rights to "Gridman the Hyper Agent" and turning it into "Superhuman Samurai Syber-Squad". In 2002,',
```
Doing `k=1` indicates the higher the outputted number, the better the match, so the metric should not be L2 distance. However, my manually computed inner product (80.6) has a discrepancy with the reported (76.2). Perhaps, this has to do with me using the `compressed` embeddings?
### Expected behavior
```py
ds = load_dataset(path='wiki_dpr', name='psgs_w100.multiset.compressed', split='train')
print(ds.get_index("embeddings").metric_type) # METRIC_INNER_PRODUCT
```
### Environment info
- `datasets` version: 2.12.0
- Platform: Linux-4.18.0-477.13.1.el8_8.x86_64-x86_64-with-glibc2.28
- Python version: 3.9.16
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.1
|
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|
[
"Hi! You can do `ds.get_index(\"embeddings\").faiss_index.metric_type` to get the metric type and then match the result with the FAISS metric [enum](https://github.com/facebookresearch/faiss/blob/43d86e30736ede853c384b24667fc3ab897d6ba9/faiss/MetricType.h#L22-L36) (should be L2).",
"Ah! Thank you for pointing this out. FYI: the enum indicates it's using the inner product. Using `torch.inner` or `torch.dot` still produces a discrepancy compared to the built-in score. I think this is because of the compression/quantization that occurs with the FAISS index."
] |
6,010
|
Improve `Dataset`'s string representation
|
Currently, `Dataset.__repr__` outputs a dataset's column names and the number of rows. We could improve it by printing its features and the first few rows.
We should also implement `_repr_html_` to have a rich HTML representation in notebooks/Streamlit.
|
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|
[
"I want to take a shot at this if possible ",
"Yes, feel free to work on this.\r\n\r\nYou can check the PyArrow Table `__repr__` and Polars DataFrame `__repr__`/`_repr_html_` implementations for some pointers/ideas.",
"@mariosasko are there any other similar issues that I could work on? I see this has been already solved. "
] |
6,009
|
Fix cast for dictionaries with no keys
|
Fix #5677
|
[] |
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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) | 0.006961 / 0.011353 (-0.004392) | 0.004390 / 0.011008 (-0.006618) | 0.103249 / 0.038508 (0.064741) | 0.048084 / 0.023109 (0.024975) | 0.351213 / 0.275898 (0.075315) | 0.416918 / 0.323480 (0.093439) | 0.005539 / 0.007986 (-0.002446) | 0.003555 / 0.004328 (-0.000774) | 0.079306 / 0.004250 (0.075055) | 0.066937 / 0.037052 (0.029884) | 0.382601 / 0.258489 (0.124112) | 0.406125 / 0.293841 (0.112284) | 0.032269 / 0.128546 (-0.096277) | 0.009133 / 0.075646 (-0.066514) | 0.354449 / 0.419271 (-0.064822) | 0.068978 / 0.043533 (0.025445) | 0.352314 / 0.255139 (0.097175) | 0.390398 / 0.283200 (0.107199) | 0.025640 / 0.141683 (-0.116043) | 1.553865 / 1.452155 (0.101710) | 1.601292 / 1.492716 (0.108576) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208310 / 0.018006 (0.190303) | 0.440076 / 0.000490 (0.439586) | 0.000363 / 0.000200 (0.000163) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029173 / 0.037411 (-0.008238) | 0.111323 / 0.014526 (0.096797) | 0.123001 / 0.176557 (-0.053556) | 0.180180 / 0.737135 (-0.556955) | 0.125804 / 0.296338 (-0.170534) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419919 / 0.215209 (0.204710) | 4.194515 / 2.077655 (2.116860) | 1.881234 / 1.504120 (0.377114) | 1.672914 / 1.541195 (0.131720) | 1.723102 / 1.468490 (0.254612) | 0.543584 / 4.584777 (-4.041193) | 3.822477 / 3.745712 (0.076765) | 1.837946 / 5.269862 (-3.431915) | 1.094975 / 4.565676 (-3.470701) | 0.066788 / 0.424275 (-0.357487) | 0.011689 / 0.007607 (0.004082) | 0.520983 / 0.226044 (0.294938) | 5.209245 / 2.268929 (2.940316) | 2.392916 / 55.444624 (-53.051708) | 2.060042 / 6.876477 (-4.816434) | 2.162291 / 2.142072 (0.020219) | 0.668472 / 4.805227 (-4.136755) | 0.144373 / 6.500664 (-6.356291) | 0.066152 / 0.075469 (-0.009318) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.251256 / 1.841788 (-0.590532) | 15.161338 / 8.074308 (7.087030) | 14.416133 / 10.191392 (4.224741) | 0.166145 / 0.680424 (-0.514279) | 0.018168 / 0.534201 (-0.516033) | 0.433364 / 0.579283 (-0.145919) | 0.417484 / 0.434364 (-0.016880) | 0.502543 / 0.540337 (-0.037794) | 0.602904 / 1.386936 (-0.784032) |\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) | 0.006946 / 0.011353 (-0.004407) | 0.004248 / 0.011008 (-0.006761) | 0.079707 / 0.038508 (0.041199) | 0.046226 / 0.023109 (0.023117) | 0.375864 / 0.275898 (0.099966) | 0.430740 / 0.323480 (0.107260) | 0.006222 / 0.007986 (-0.001764) | 0.003474 / 0.004328 (-0.000854) | 0.079622 / 0.004250 (0.075372) | 0.066666 / 0.037052 (0.029613) | 0.379487 / 0.258489 (0.120998) | 0.423002 / 0.293841 (0.129161) | 0.032836 / 0.128546 (-0.095710) | 0.008976 / 0.075646 (-0.066670) | 0.086578 / 0.419271 (-0.332693) | 0.055651 / 0.043533 (0.012118) | 0.360787 / 0.255139 (0.105648) | 0.384265 / 0.283200 (0.101065) | 0.025350 / 0.141683 (-0.116333) | 1.547880 / 1.452155 (0.095725) | 1.605850 / 1.492716 (0.113134) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.184227 / 0.018006 (0.166220) | 0.442071 / 0.000490 (0.441582) | 0.002887 / 0.000200 (0.002687) | 0.000088 / 0.000054 (0.000034) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031923 / 0.037411 (-0.005488) | 0.119093 / 0.014526 (0.104568) | 0.128704 / 0.176557 (-0.047853) | 0.187065 / 0.737135 (-0.550070) | 0.134135 / 0.296338 (-0.162204) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.455731 / 0.215209 (0.240522) | 4.562911 / 2.077655 (2.485256) | 2.247431 / 1.504120 (0.743311) | 2.053346 / 1.541195 (0.512151) | 2.049611 / 1.468490 (0.581121) | 0.546069 / 4.584777 (-4.038708) | 3.821852 / 3.745712 (0.076140) | 3.358497 / 5.269862 (-1.911364) | 1.667697 / 4.565676 (-2.897979) | 0.067968 / 0.424275 (-0.356307) | 0.012344 / 0.007607 (0.004737) | 0.550864 / 0.226044 (0.324820) | 5.496867 / 2.268929 (3.227939) | 2.680031 / 55.444624 (-52.764594) | 2.328673 / 6.876477 (-4.547804) | 2.436754 / 2.142072 (0.294682) | 0.681195 / 4.805227 (-4.124033) | 0.148761 / 6.500664 (-6.351904) | 0.067716 / 0.075469 (-0.007753) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.353798 / 1.841788 (-0.487990) | 15.992965 / 8.074308 (7.918657) | 14.051539 / 10.191392 (3.860147) | 0.181087 / 0.680424 (-0.499337) | 0.018653 / 0.534201 (-0.515548) | 0.433499 / 0.579283 (-0.145784) | 0.428845 / 0.434364 (-0.005519) | 0.501100 / 0.540337 (-0.039238) | 0.603666 / 1.386936 (-0.783270) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#10cfa871a2f387fe9c6360e1873ea74c6d69ff67 \"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) | 0.010983 / 0.011353 (-0.000370) | 0.005630 / 0.011008 (-0.005378) | 0.109967 / 0.038508 (0.071458) | 0.101580 / 0.023109 (0.078471) | 0.490205 / 0.275898 (0.214307) | 0.534653 / 0.323480 (0.211173) | 0.008365 / 0.007986 (0.000379) | 0.004317 / 0.004328 (-0.000012) | 0.082429 / 0.004250 (0.078179) | 0.080556 / 0.037052 (0.043504) | 0.494627 / 0.258489 (0.236138) | 0.544189 / 0.293841 (0.250348) | 0.049419 / 0.128546 (-0.079127) | 0.014033 / 0.075646 (-0.061613) | 0.370406 / 0.419271 (-0.048866) | 0.083468 / 0.043533 (0.039935) | 0.463829 / 0.255139 (0.208690) | 0.507516 / 0.283200 (0.224316) | 0.053266 / 0.141683 (-0.088417) | 1.778680 / 1.452155 (0.326525) | 1.916616 / 1.492716 (0.423900) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.267646 / 0.018006 (0.249640) | 0.617824 / 0.000490 (0.617334) | 0.007720 / 0.000200 (0.007520) | 0.000139 / 0.000054 (0.000085) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034464 / 0.037411 (-0.002948) | 0.113626 / 0.014526 (0.099100) | 0.118911 / 0.176557 (-0.057646) | 0.194701 / 0.737135 (-0.542434) | 0.123431 / 0.296338 (-0.172907) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.606073 / 0.215209 (0.390863) | 6.086393 / 2.077655 (4.008738) | 2.568712 / 1.504120 (1.064593) | 2.260801 / 1.541195 (0.719606) | 2.411798 / 1.468490 (0.943307) | 0.876433 / 4.584777 (-3.708344) | 5.521280 / 3.745712 (1.775568) | 5.969722 / 5.269862 (0.699861) | 3.671028 / 4.565676 (-0.894649) | 0.097082 / 0.424275 (-0.327193) | 0.011354 / 0.007607 (0.003747) | 0.713842 / 0.226044 (0.487798) | 7.291172 / 2.268929 (5.022244) | 3.315272 / 55.444624 (-52.129352) | 2.777487 / 6.876477 (-4.098990) | 3.025449 / 2.142072 (0.883377) | 1.014115 / 4.805227 (-3.791112) | 0.217928 / 6.500664 (-6.282736) | 0.083097 / 0.075469 (0.007627) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.640060 / 1.841788 (-0.201728) | 25.342172 / 8.074308 (17.267864) | 22.776510 / 10.191392 (12.585118) | 0.227300 / 0.680424 (-0.453124) | 0.032233 / 0.534201 (-0.501968) | 0.507547 / 0.579283 (-0.071736) | 0.647044 / 0.434364 (0.212680) | 0.607019 / 0.540337 (0.066682) | 0.823548 / 1.386936 (-0.563388) |\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) | 0.009576 / 0.011353 (-0.001777) | 0.009322 / 0.011008 (-0.001687) | 0.087184 / 0.038508 (0.048676) | 0.100795 / 0.023109 (0.077685) | 0.492138 / 0.275898 (0.216240) | 0.528386 / 0.323480 (0.204906) | 0.006689 / 0.007986 (-0.001296) | 0.004735 / 0.004328 (0.000406) | 0.085519 / 0.004250 (0.081269) | 0.072648 / 0.037052 (0.035595) | 0.496068 / 0.258489 (0.237579) | 0.549634 / 0.293841 (0.255793) | 0.049709 / 0.128546 (-0.078837) | 0.015077 / 0.075646 (-0.060569) | 0.099445 / 0.419271 (-0.319826) | 0.068080 / 0.043533 (0.024547) | 0.500426 / 0.255139 (0.245287) | 0.531437 / 0.283200 (0.248238) | 0.053176 / 0.141683 (-0.088507) | 1.827942 / 1.452155 (0.375787) | 1.914286 / 1.492716 (0.421570) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.247658 / 0.018006 (0.229652) | 0.590805 / 0.000490 (0.590315) | 0.005319 / 0.000200 (0.005119) | 0.000165 / 0.000054 (0.000110) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036993 / 0.037411 (-0.000418) | 0.112944 / 0.014526 (0.098419) | 0.118964 / 0.176557 (-0.057593) | 0.194867 / 0.737135 (-0.542269) | 0.120816 / 0.296338 (-0.175523) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.638062 / 0.215209 (0.422853) | 6.246785 / 2.077655 (4.169130) | 2.957779 / 1.504120 (1.453659) | 2.739118 / 1.541195 (1.197924) | 2.795362 / 1.468490 (1.326872) | 0.890532 / 4.584777 (-3.694245) | 5.508198 / 3.745712 (1.762486) | 5.222315 / 5.269862 (-0.047547) | 3.152731 / 4.565676 (-1.412946) | 0.098344 / 0.424275 (-0.325931) | 0.008800 / 0.007607 (0.001193) | 0.757889 / 0.226044 (0.531845) | 7.545715 / 2.268929 (5.276787) | 3.694536 / 55.444624 (-51.750088) | 3.112872 / 6.876477 (-3.763605) | 3.182358 / 2.142072 (1.040285) | 1.028171 / 4.805227 (-3.777056) | 0.215223 / 6.500664 (-6.285441) | 0.085856 / 0.075469 (0.010387) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.853138 / 1.841788 (0.011350) | 25.939672 / 8.074308 (17.865364) | 23.118029 / 10.191392 (12.926637) | 0.250599 / 0.680424 (-0.429825) | 0.029942 / 0.534201 (-0.504259) | 0.508748 / 0.579283 (-0.070535) | 0.593966 / 0.434364 (0.159602) | 0.605499 / 0.540337 (0.065162) | 0.863827 / 1.386936 (-0.523109) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5d15950d99677e9473cdcd31cfd83aa17e313e28 \"CML watermark\")\n"
] |
6,008
|
Dataset.from_generator consistently freezes at ~1000 rows
|
### Describe the bug
Whenever I try to create a dataset which contains images using `Dataset.from_generator`, it freezes around 996 rows. I suppose it has something to do with memory consumption, but there's more memory available. I
Somehow it worked a few times but mostly this makes the datasets library much more cumbersome to work with because generators are the easiest way to turn an existing dataset into a Hugging Face dataset.
I've let it run in the frozen state for way longer than it can possibly take to load the actual dataset.
Let me know if you have ideas how to resolve it!
### Steps to reproduce the bug
```python
from datasets import Dataset
import numpy as np
def gen():
for row in range(10000):
yield {"i": np.random.rand(512, 512, 3)}
Dataset.from_generator(gen)
# -> 90% of the time gets stuck around 1000 rows
```
### Expected behavior
Should continue and go through all the examples yielded by the generator, or at least throw an error or somehow communicate what's going on.
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-5.15.0-52-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 12.0.1
- Pandas version: 1.5.1
|
[] |
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| false
|
[
"By default, we write data to disk (so it can be memory-mapped) every 1000 rows/samples. You can control this with the `writer_batch_size` parameter. Also, when working with fixed-size arrays, the `ArrayXD` feature types yield better performance (e.g., in your case, `features=datasets.Features({\"i\": datasets.Array3D(shape=(512,512,3), dtype=\"float32\")})` should be faster).\r\n\r\nOur support for multi-dim arrays could be better, and we plan to improve it as part of https://github.com/huggingface/datasets/issues/5272.",
"> By default, we write data to disk (so it can be memory-mapped) every 1000 rows/samples. You can control this with the `writer_batch_size` parameter. Also, when working with fixed-size arrays, the `ArrayXD` feature types yield better performance (e.g., in your case, `features=datasets.Features({\"i\": datasets.Array3D(shape=(512,512,3), dtype=\"float32\")})` should be faster).\r\n> \r\n> Our support for multi-dim arrays could be better, and we plan to improve it as part of #5272.\r\n\r\nThanks for the explanation! The Image array was just for demonstration, I use PIL Images in practice. Does that make a difference? What's the best approach for a dataset with PIL Images as rows?",
"It's best to use the `datasets.Image()` feature type for PIL images (to save space) :)"
] |
6,007
|
Get an error "OverflowError: Python int too large to convert to C long" when loading a large dataset
|
### Describe the bug
When load a large dataset with the following code
```python
from datasets import load_dataset
dataset = load_dataset("liwu/MNBVC", 'news_peoples_daily', split='train')
```
We encountered the error: "OverflowError: Python int too large to convert to C long"
The error look something like:
```
OverflowError: Python int too large to convert to C long
During handling of the above exception, another exception occurred:
OverflowError Traceback (most recent call last)
<ipython-input-7-0ed8700e662d> in <module>
----> 1 dataset = load_dataset("liwu/MNBVC", 'news_peoples_daily', split='train', cache_dir='/sfs/MNBVC/.cache/')
/sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1749 ignore_verifications=ignore_verifications,
1750 try_from_hf_gcs=try_from_hf_gcs,
-> 1751 use_auth_token=use_auth_token,
1752 )
1753
/sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
703 if not downloaded_from_gcs:
704 self._download_and_prepare(
--> 705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
707 # Sync info
/sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
1225
1226 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1227 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
1228
1229 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable:
/sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
791 try:
792 # Prepare split will record examples associated to the split
--> 793 self._prepare_split(split_generator, **prepare_split_kwargs)
794 except OSError as e:
795 raise OSError(
/sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/builder.py in _prepare_split(self, split_generator, check_duplicate_keys)
1219 writer.write(example, key)
1220 finally:
-> 1221 num_examples, num_bytes = writer.finalize()
1222
1223 split_generator.split_info.num_examples = num_examples
/sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/arrow_writer.py in finalize(self, close_stream)
536 # Re-intializing to empty list for next batch
537 self.hkey_record = []
--> 538 self.write_examples_on_file()
539 if self.pa_writer is None:
540 if self.schema:
/sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/arrow_writer.py in write_examples_on_file(self)
407 # Since current_examples contains (example, key) tuples
408 batch_examples[col] = [row[0][col] for row in self.current_examples]
--> 409 self.write_batch(batch_examples=batch_examples)
410 self.current_examples = []
411
/sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)
506 col_try_type = try_features[col] if try_features is not None and col in try_features else None
507 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)
--> 508 arrays.append(pa.array(typed_sequence))
509 inferred_features[col] = typed_sequence.get_inferred_type()
510 schema = inferred_features.arrow_schema if self.pa_writer is None else self.schema
/sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/array.pxi in pyarrow.lib.array()
/sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol()
/sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/arrow_writer.py in __arrow_array__(self, type)
180 else:
181 trying_cast_to_python_objects = True
--> 182 out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))
183 # use smaller integer precisions if possible
184 if self.trying_int_optimization:
/sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/array.pxi in pyarrow.lib.array()
/sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/array.pxi in pyarrow.lib._sequence_to_array()
/sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
OverflowError: Python int too large to convert to C long
```
However, that dataset can be loaded in a streaming manner:
```python
from datasets import load_dataset
dataset = load_dataset("liwu/MNBVC", 'news_peoples_daily', split='train', streaming=True)
for i in dataset:
pass # it work well
```
Another issue is reported in our dataset hub:
https://huggingface.co/datasets/liwu/MNBVC/discussions/2
### Steps to reproduce the bug
from datasets import load_dataset
dataset = load_dataset("liwu/MNBVC", 'news_peoples_daily', split='train')
### Expected behavior
the dataset can be safely loaded
### Environment info
- `datasets` version: 2.4.0
- Platform: Linux-3.10.0-1160.an7.x86_64-x86_64-with-centos-7.9
- Python version: 3.6.8
- PyArrow version: 6.0.1
- Pandas version: 1.1.5
|
[
{
"id": 5705560427,
"node_id": "LA_kwDODunzps8AAAABVBPxaw",
"url": "https://api.github.com/repos/huggingface/datasets/labels/arrow",
"name": "arrow",
"color": "c2e0c6",
"default": false,
"description": "Related to Apache Arrow"
}
] |
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|
[
"This error means that one of the int32 (`Value(\"int32\")`) columns in the dataset has a value that is out of the valid (int32) range.\r\n\r\nI'll open a PR to print the name of a problematic column to make debugging such errors easier.",
"I am afraid int32 is not the reason for this error.\r\n\r\nI have submitted a commit to use int64 for all ints in the dataset:\r\nhttps://huggingface.co/datasets/liwu/MNBVC/commit/857ac00d9eab96a6708ad6a82bd9001686042a9e\r\n\r\nand I have updated my env to the latest datasets release:\r\nCopy-and-paste the text below in your GitHub issue.\r\n\r\n- `datasets` version: 2.13.1\r\n- Platform: macOS-13.2.1-arm64-arm-64bit\r\n- Python version: 3.11.2\r\n- Huggingface_hub version: 0.13.4\r\n- PyArrow version: 11.0.0\r\n- Pandas version: 1.5.3\r\n\r\nBut the error still exist\r\n\r\n```\r\nDownloading and preparing dataset mnbvc/news_peoples_daily to /Users/silver/.cache/huggingface/datasets/liwu___mnbvc/news_peoples_daily/0.0.1/ee380f6309fe9b8b0d1fb14d77118f132444f22c8c4b28bf5c1645312688e051...\r\nDownloading data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:00<00:00, 9070.40it/s]\r\nExtracting data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:00<00:00, 2697.16it/s]\r\n---------------------------------------------------------------------------\r\nOverflowError Traceback (most recent call last)\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1647, in GeneratorBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)\r\n 1646 example = self.info.features.encode_example(record) if self.info.features is not None else record\r\n-> 1647 writer.write(example, key)\r\n 1648 num_examples_progress_update += 1\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:490, in ArrowWriter.write(self, example, key, writer_batch_size)\r\n 488 self.hkey_record = []\r\n--> 490 self.write_examples_on_file()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:448, in ArrowWriter.write_examples_on_file(self)\r\n 444 batch_examples[col] = [\r\n 445 row[0][col].to_pylist()[0] if isinstance(row[0][col], (pa.Array, pa.ChunkedArray)) else row[0][col]\r\n 446 for row in self.current_examples\r\n 447 ]\r\n--> 448 self.write_batch(batch_examples=batch_examples)\r\n 449 self.current_examples = []\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:553, in ArrowWriter.write_batch(self, batch_examples, writer_batch_size)\r\n 552 typed_sequence = OptimizedTypedSequence(col_values, type=col_type, try_type=col_try_type, col=col)\r\n--> 553 arrays.append(pa.array(typed_sequence))\r\n 554 inferred_features[col] = typed_sequence.get_inferred_type()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:236, in pyarrow.lib.array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:110, in pyarrow.lib._handle_arrow_array_protocol()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:189, in TypedSequence.__arrow_array__(self, type)\r\n 188 trying_cast_to_python_objects = True\r\n--> 189 out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n 190 # use smaller integer precisions if possible\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:320, in pyarrow.lib.array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:39, in pyarrow.lib._sequence_to_array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/error.pxi:144, in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\nOverflowError: Python int too large to convert to C long\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nOverflowError Traceback (most recent call last)\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1656, in GeneratorBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)\r\n 1655 num_shards = shard_id + 1\r\n-> 1656 num_examples, num_bytes = writer.finalize()\r\n 1657 writer.close()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:584, in ArrowWriter.finalize(self, close_stream)\r\n 583 self.hkey_record = []\r\n--> 584 self.write_examples_on_file()\r\n 585 # If schema is known, infer features even if no examples were written\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:448, in ArrowWriter.write_examples_on_file(self)\r\n 444 batch_examples[col] = [\r\n 445 row[0][col].to_pylist()[0] if isinstance(row[0][col], (pa.Array, pa.ChunkedArray)) else row[0][col]\r\n 446 for row in self.current_examples\r\n 447 ]\r\n--> 448 self.write_batch(batch_examples=batch_examples)\r\n 449 self.current_examples = []\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:553, in ArrowWriter.write_batch(self, batch_examples, writer_batch_size)\r\n 552 typed_sequence = OptimizedTypedSequence(col_values, type=col_type, try_type=col_try_type, col=col)\r\n--> 553 arrays.append(pa.array(typed_sequence))\r\n 554 inferred_features[col] = typed_sequence.get_inferred_type()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:236, in pyarrow.lib.array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:110, in pyarrow.lib._handle_arrow_array_protocol()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:189, in TypedSequence.__arrow_array__(self, type)\r\n 188 trying_cast_to_python_objects = True\r\n--> 189 out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n 190 # use smaller integer precisions if possible\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:320, in pyarrow.lib.array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:39, in pyarrow.lib._sequence_to_array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/error.pxi:144, in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\nOverflowError: Python int too large to convert to C long\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nDatasetGenerationError Traceback (most recent call last)\r\nCell In[2], line 1\r\n----> 1 dataset = load_dataset(\"liwu/MNBVC\", 'news_peoples_daily', split='train')\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/load.py:1809, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)\r\n 1806 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES\r\n 1808 # Download and prepare data\r\n-> 1809 builder_instance.download_and_prepare(\r\n 1810 download_config=download_config,\r\n 1811 download_mode=download_mode,\r\n 1812 verification_mode=verification_mode,\r\n 1813 try_from_hf_gcs=try_from_hf_gcs,\r\n 1814 num_proc=num_proc,\r\n 1815 storage_options=storage_options,\r\n 1816 )\r\n 1818 # Build dataset for splits\r\n 1819 keep_in_memory = (\r\n 1820 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)\r\n 1821 )\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:909, 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)\r\n 907 if num_proc is not None:\r\n 908 prepare_split_kwargs[\"num_proc\"] = num_proc\r\n--> 909 self._download_and_prepare(\r\n 910 dl_manager=dl_manager,\r\n 911 verification_mode=verification_mode,\r\n 912 **prepare_split_kwargs,\r\n 913 **download_and_prepare_kwargs,\r\n 914 )\r\n 915 # Sync info\r\n 916 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1670, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_splits_kwargs)\r\n 1669 def _download_and_prepare(self, dl_manager, verification_mode, **prepare_splits_kwargs):\r\n-> 1670 super()._download_and_prepare(\r\n 1671 dl_manager,\r\n 1672 verification_mode,\r\n 1673 check_duplicate_keys=verification_mode == VerificationMode.BASIC_CHECKS\r\n 1674 or verification_mode == VerificationMode.ALL_CHECKS,\r\n 1675 **prepare_splits_kwargs,\r\n 1676 )\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1004, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs)\r\n 1000 split_dict.add(split_generator.split_info)\r\n 1002 try:\r\n 1003 # Prepare split will record examples associated to the split\r\n-> 1004 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n 1005 except OSError as e:\r\n 1006 raise OSError(\r\n 1007 \"Cannot find data file. \"\r\n 1008 + (self.manual_download_instructions or \"\")\r\n 1009 + \"\\nOriginal error:\\n\"\r\n 1010 + str(e)\r\n 1011 ) from None\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1508, in GeneratorBasedBuilder._prepare_split(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size)\r\n 1506 job_id = 0\r\n 1507 with pbar:\r\n-> 1508 for job_id, done, content in self._prepare_split_single(\r\n 1509 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args\r\n 1510 ):\r\n 1511 if done:\r\n 1512 result = content\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1665, in GeneratorBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)\r\n 1663 if isinstance(e, SchemaInferenceError) and e.__context__ is not None:\r\n 1664 e = e.__context__\r\n-> 1665 raise DatasetGenerationError(\"An error occurred while generating the dataset\") from e\r\n 1667 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)\r\n\r\nDatasetGenerationError: An error occurred while generating the dataset\r\n```\r\n\r\nBesides, it works fine when I am using streamed dataset.",
"`simhash` is the problematic column - it has values such as `18329103420363166823` that are out of the int64 range. You can fix this by setting the feature type to `Value(\"string\")` (it's advised to use this type for hash values in general)\r\n\r\n> Besides, it works fine when I am using streamed dataset.\r\n\r\nStreaming yields Python dictionaries from the script without converting them to the Arrow representation, as this conversion step is not that cheap performance-wise.",
"i am using uint64 for simhash\r\n\r\nuint64 ranges up to about 3.69E19.\r\n\r\n18329103420363166823 is less than this value.\r\n\r\nmoreover, our simhash algorithm use 64 bits. it should fit in uint64.\r\n\r\n\r\n\r\n",
"You are right. I overlooked the feature type.\r\n\r\nThis is a reproducer:\r\n```python\r\nimport pyarrow as pa\r\nfrom datasets.arrow_writer import TypedSequence\r\n\r\npa.array(TypedSequence([18329103420363166823], type=Value(\"uint64\")))\r\n```\r\n\r\n`pa.array([18329103420363166823])` also fails with the same error, so it seems PyArrow does not always infer the correct type as NumPy does (`uint64` in this case).\r\n\r\nI'll report this issue in the Arrow repo.\r\n\r\n`pa.array([18329103420363166823], pa.uint64)` works, so maybe we can implement a temporary fix (supporting complex input such as `[{\"image\": pil_image, \"num\": uint64_value}]` would be hard though).\r\n\r\nIn the meantime, you should be able to bypass this error by returning the `simhash` values as NumPy scalars in the script:\r\n```python\r\ndef _generate_examples(self, ...):\r\n ...\r\n yield {..., \"simhash\": np.uint64(simhash), ...}\r\n```",
"Thank you for checking this issue in detail.\r\n\r\nHowever, it seems that using `np.uint64(simhash)` does not work. The same issue still exists.\r\n\r\nhttps://huggingface.co/datasets/liwu/MNBVC/commit/1e44f1e400b7e61052647d44c99cdae3bae9c830\r\n\r\nAnyway, we decide to use string type for these simhash values. Hope pyarrow can fix their bug soon.",
"Arrow issue: https://github.com/apache/arrow/issues/36520",
"May be something read your training data line by line.\r\nThen your training data just only one line. \r\nIt is so large.\r\nI guess.\r\n"
] |
6,006
|
NotADirectoryError when loading gigawords
|
### Describe the bug
got `NotADirectoryError` whtn loading gigawords dataset
### Steps to reproduce the bug
When running
```
import datasets
datasets.load_dataset('gigaword')
```
Got the following exception:
```bash
Traceback (most recent call last): [0/1862]
File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1629, in _prepare_split_single
for key, record in generator:
File "/home/x/.cache/huggingface/modules/datasets_modules/datasets/gigaword/ea83a8b819190acac5f2dae011fad51dccf269a0604ec5dd24795b
64efb424b6/gigaword.py", line 115, in _generate_examples
with open(src_path, encoding="utf-8") as f_d, open(tgt_path, encoding="utf-8") as f_s:
File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/streaming.py", line 71, in wrapper
return function(*args, use_auth_token=use_auth_token, **kwargs)
File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/download/streaming_download_manager.py", line 493, in xope
n
return open(main_hop, mode, *args, **kwargs)
NotADirectoryError: [Errno 20] Not a directory: '/home/x/.cache/huggingface/datasets/downloads/6da52431bb5124d90cf51a0187d2dbee9046e
89780c4be7599794a4f559048ec/org_data/train.src.txt'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "gigaword.py", line 38, in <module>
main()
File "gigaword.py", line 35, in main
train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/")
File "/home/x/MICL/preprocess/fewshot_gym_dataset.py", line 199, in generate_k_shot_data
dataset = self.load_dataset()
File "gigaword.py", line 29, in load_dataset
return datasets.load_dataset('gigaword')
File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/load.py", line 1809, in load_dataset
builder_instance.download_and_prepare(
File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 909, in download_and_prepare
self._download_and_prepare(
File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1670, in _download_and_prepare
super()._download_and_prepare(
File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1004, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1508, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1665, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
```
### Expected behavior
Download and process the dataset successfully
### Environment info
- `datasets` version: 2.13.1
- Platform: Linux-5.0.0-1032-azure-x86_64-with-glibc2.10
- Python version: 3.8.0
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
|
[] |
{
"url": null,
"html_url": null,
"diff_url": null,
"patch_url": null,
"merged_at": null
}
| false
|
[
"issue due to corrupted download files. resolved after cleaning download cache. sorry for any inconvinence."
] |
6,005
|
Drop Python 3.7 support
|
`hfh` and `transformers` have dropped Python 3.7 support, so we should do the same :).
(Based on the stats, it seems less than 10% of the users use `datasets` with Python 3.7)
|
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6005",
"html_url": "https://github.com/huggingface/datasets/pull/6005",
"diff_url": "https://github.com/huggingface/datasets/pull/6005.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6005.patch",
"merged_at": "2023-07-06T15:22:43"
}
| true
|
[
"<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) | 0.006152 / 0.011353 (-0.005200) | 0.003916 / 0.011008 (-0.007092) | 0.097355 / 0.038508 (0.058847) | 0.037228 / 0.023109 (0.014119) | 0.315753 / 0.275898 (0.039855) | 0.387949 / 0.323480 (0.064470) | 0.004804 / 0.007986 (-0.003181) | 0.002975 / 0.004328 (-0.001353) | 0.076932 / 0.004250 (0.072682) | 0.053497 / 0.037052 (0.016445) | 0.331143 / 0.258489 (0.072654) | 0.388347 / 0.293841 (0.094506) | 0.027535 / 0.128546 (-0.101011) | 0.008509 / 0.075646 (-0.067137) | 0.312639 / 0.419271 (-0.106632) | 0.047212 / 0.043533 (0.003679) | 0.316875 / 0.255139 (0.061736) | 0.352191 / 0.283200 (0.068992) | 0.021380 / 0.141683 (-0.120303) | 1.541401 / 1.452155 (0.089247) | 1.519420 / 1.492716 (0.026704) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206332 / 0.018006 (0.188326) | 0.412252 / 0.000490 (0.411762) | 0.005119 / 0.000200 (0.004919) | 0.000077 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023856 / 0.037411 (-0.013556) | 0.098216 / 0.014526 (0.083691) | 0.106553 / 0.176557 (-0.070003) | 0.168767 / 0.737135 (-0.568369) | 0.109244 / 0.296338 (-0.187094) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.457580 / 0.215209 (0.242371) | 4.583246 / 2.077655 (2.505591) | 2.296356 / 1.504120 (0.792236) | 2.096216 / 1.541195 (0.555021) | 2.159086 / 1.468490 (0.690596) | 0.557905 / 4.584777 (-4.026872) | 3.345910 / 3.745712 (-0.399802) | 1.767436 / 5.269862 (-3.502426) | 1.021583 / 4.565676 (-3.544094) | 0.067265 / 0.424275 (-0.357011) | 0.011411 / 0.007607 (0.003804) | 0.559841 / 0.226044 (0.333797) | 5.586892 / 2.268929 (3.317963) | 2.735520 / 55.444624 (-52.709104) | 2.429393 / 6.876477 (-4.447084) | 2.544901 / 2.142072 (0.402829) | 0.667603 / 4.805227 (-4.137625) | 0.136244 / 6.500664 (-6.364421) | 0.066961 / 0.075469 (-0.008508) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.206529 / 1.841788 (-0.635259) | 13.988306 / 8.074308 (5.913998) | 13.481813 / 10.191392 (3.290421) | 0.161901 / 0.680424 (-0.518523) | 0.016850 / 0.534201 (-0.517351) | 0.367657 / 0.579283 (-0.211626) | 0.393343 / 0.434364 (-0.041021) | 0.465288 / 0.540337 (-0.075050) | 0.559888 / 1.386936 (-0.827048) |\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) | 0.005956 / 0.011353 (-0.005397) | 0.003734 / 0.011008 (-0.007274) | 0.077841 / 0.038508 (0.039333) | 0.036532 / 0.023109 (0.013422) | 0.438923 / 0.275898 (0.163025) | 0.490133 / 0.323480 (0.166653) | 0.004651 / 0.007986 (-0.003335) | 0.002881 / 0.004328 (-0.001448) | 0.077868 / 0.004250 (0.073618) | 0.051700 / 0.037052 (0.014647) | 0.448018 / 0.258489 (0.189529) | 0.500304 / 0.293841 (0.206464) | 0.029051 / 0.128546 (-0.099496) | 0.008498 / 0.075646 (-0.067148) | 0.082932 / 0.419271 (-0.336339) | 0.043665 / 0.043533 (0.000132) | 0.431613 / 0.255139 (0.176474) | 0.458749 / 0.283200 (0.175549) | 0.021951 / 0.141683 (-0.119731) | 1.556043 / 1.452155 (0.103888) | 1.588391 / 1.492716 (0.095675) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220674 / 0.018006 (0.202667) | 0.415408 / 0.000490 (0.414918) | 0.002613 / 0.000200 (0.002413) | 0.000075 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025548 / 0.037411 (-0.011863) | 0.103633 / 0.014526 (0.089107) | 0.115193 / 0.176557 (-0.061364) | 0.163971 / 0.737135 (-0.573164) | 0.114754 / 0.296338 (-0.181585) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.456823 / 0.215209 (0.241614) | 4.569950 / 2.077655 (2.492296) | 2.196339 / 1.504120 (0.692219) | 1.985822 / 1.541195 (0.444628) | 2.044083 / 1.468490 (0.575593) | 0.567919 / 4.584777 (-4.016858) | 3.397515 / 3.745712 (-0.348197) | 1.741087 / 5.269862 (-3.528775) | 1.041237 / 4.565676 (-3.524440) | 0.068963 / 0.424275 (-0.355313) | 0.011677 / 0.007607 (0.004070) | 0.565010 / 0.226044 (0.338966) | 5.625886 / 2.268929 (3.356957) | 2.670658 / 55.444624 (-52.773967) | 2.300279 / 6.876477 (-4.576198) | 2.392178 / 2.142072 (0.250106) | 0.680226 / 4.805227 (-4.125001) | 0.139119 / 6.500664 (-6.361545) | 0.067953 / 0.075469 (-0.007516) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.303280 / 1.841788 (-0.538507) | 14.458686 / 8.074308 (6.384378) | 14.409369 / 10.191392 (4.217977) | 0.144581 / 0.680424 (-0.535843) | 0.016634 / 0.534201 (-0.517567) | 0.364607 / 0.579283 (-0.214676) | 0.394521 / 0.434364 (-0.039843) | 0.433417 / 0.540337 (-0.106921) | 0.527127 / 1.386936 (-0.859809) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#04a36f9546484dceadb84a133c1a460281d018f8 \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006245 / 0.011353 (-0.005108) | 0.003871 / 0.011008 (-0.007138) | 0.098823 / 0.038508 (0.060315) | 0.039853 / 0.023109 (0.016744) | 0.314989 / 0.275898 (0.039091) | 0.376733 / 0.323480 (0.053254) | 0.004754 / 0.007986 (-0.003232) | 0.002971 / 0.004328 (-0.001357) | 0.078451 / 0.004250 (0.074201) | 0.053160 / 0.037052 (0.016107) | 0.324443 / 0.258489 (0.065954) | 0.361488 / 0.293841 (0.067647) | 0.027942 / 0.128546 (-0.100604) | 0.008535 / 0.075646 (-0.067111) | 0.315526 / 0.419271 (-0.103745) | 0.045706 / 0.043533 (0.002174) | 0.329614 / 0.255139 (0.074475) | 0.336339 / 0.283200 (0.053139) | 0.021278 / 0.141683 (-0.120405) | 1.529710 / 1.452155 (0.077555) | 1.566833 / 1.492716 (0.074116) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.215263 / 0.018006 (0.197257) | 0.440320 / 0.000490 (0.439830) | 0.002627 / 0.000200 (0.002427) | 0.000075 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023971 / 0.037411 (-0.013441) | 0.100549 / 0.014526 (0.086023) | 0.106995 / 0.176557 (-0.069561) | 0.169630 / 0.737135 (-0.567505) | 0.111614 / 0.296338 (-0.184724) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424911 / 0.215209 (0.209702) | 4.246920 / 2.077655 (2.169266) | 1.923321 / 1.504120 (0.419202) | 1.714795 / 1.541195 (0.173600) | 1.772906 / 1.468490 (0.304416) | 0.554676 / 4.584777 (-4.030101) | 3.478896 / 3.745712 (-0.266816) | 2.800494 / 5.269862 (-2.469368) | 1.382630 / 4.565676 (-3.183047) | 0.067271 / 0.424275 (-0.357004) | 0.010967 / 0.007607 (0.003360) | 0.526769 / 0.226044 (0.300725) | 5.288564 / 2.268929 (3.019636) | 2.337459 / 55.444624 (-53.107165) | 1.999975 / 6.876477 (-4.876502) | 2.102680 / 2.142072 (-0.039392) | 0.672181 / 4.805227 (-4.133046) | 0.135097 / 6.500664 (-6.365567) | 0.066950 / 0.075469 (-0.008519) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.264365 / 1.841788 (-0.577423) | 14.282440 / 8.074308 (6.208132) | 14.220200 / 10.191392 (4.028808) | 0.139055 / 0.680424 (-0.541369) | 0.016681 / 0.534201 (-0.517520) | 0.367936 / 0.579283 (-0.211348) | 0.393959 / 0.434364 (-0.040404) | 0.424438 / 0.540337 (-0.115900) | 0.508065 / 1.386936 (-0.878872) |\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) | 0.006514 / 0.011353 (-0.004839) | 0.003890 / 0.011008 (-0.007118) | 0.078871 / 0.038508 (0.040363) | 0.038080 / 0.023109 (0.014971) | 0.358282 / 0.275898 (0.082384) | 0.430654 / 0.323480 (0.107174) | 0.005712 / 0.007986 (-0.002273) | 0.003030 / 0.004328 (-0.001299) | 0.078636 / 0.004250 (0.074386) | 0.057771 / 0.037052 (0.020719) | 0.368814 / 0.258489 (0.110325) | 0.437047 / 0.293841 (0.143206) | 0.029470 / 0.128546 (-0.099076) | 0.008523 / 0.075646 (-0.067124) | 0.083334 / 0.419271 (-0.335938) | 0.044505 / 0.043533 (0.000972) | 0.357484 / 0.255139 (0.102345) | 0.393839 / 0.283200 (0.110639) | 0.023340 / 0.141683 (-0.118343) | 1.561033 / 1.452155 (0.108878) | 1.595560 / 1.492716 (0.102844) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.204149 / 0.018006 (0.186143) | 0.442747 / 0.000490 (0.442257) | 0.003105 / 0.000200 (0.002905) | 0.000085 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027002 / 0.037411 (-0.010409) | 0.105595 / 0.014526 (0.091070) | 0.108695 / 0.176557 (-0.067861) | 0.163182 / 0.737135 (-0.573953) | 0.114999 / 0.296338 (-0.181339) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.483713 / 0.215209 (0.268504) | 4.836063 / 2.077655 (2.758409) | 2.488072 / 1.504120 (0.983952) | 2.289556 / 1.541195 (0.748361) | 2.342912 / 1.468490 (0.874422) | 0.565937 / 4.584777 (-4.018840) | 3.479085 / 3.745712 (-0.266627) | 1.770922 / 5.269862 (-3.498940) | 1.046084 / 4.565676 (-3.519592) | 0.067857 / 0.424275 (-0.356418) | 0.011283 / 0.007607 (0.003676) | 0.592966 / 0.226044 (0.366921) | 5.932842 / 2.268929 (3.663914) | 2.956252 / 55.444624 (-52.488372) | 2.602704 / 6.876477 (-4.273772) | 2.715625 / 2.142072 (0.573552) | 0.674299 / 4.805227 (-4.130929) | 0.136039 / 6.500664 (-6.364625) | 0.067629 / 0.075469 (-0.007840) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.333734 / 1.841788 (-0.508054) | 14.561943 / 8.074308 (6.487634) | 14.455385 / 10.191392 (4.263993) | 0.132020 / 0.680424 (-0.548404) | 0.016893 / 0.534201 (-0.517308) | 0.367146 / 0.579283 (-0.212137) | 0.399623 / 0.434364 (-0.034741) | 0.432658 / 0.540337 (-0.107680) | 0.530475 / 1.386936 (-0.856461) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#18da5adb22b2b403b8d8ae673192746d2ed7e9f9 \"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) | 0.006045 / 0.011353 (-0.005308) | 0.003906 / 0.011008 (-0.007103) | 0.097558 / 0.038508 (0.059050) | 0.038827 / 0.023109 (0.015718) | 0.393564 / 0.275898 (0.117666) | 0.442459 / 0.323480 (0.118980) | 0.004792 / 0.007986 (-0.003194) | 0.002984 / 0.004328 (-0.001345) | 0.076419 / 0.004250 (0.072169) | 0.053606 / 0.037052 (0.016554) | 0.409743 / 0.258489 (0.151254) | 0.445753 / 0.293841 (0.151912) | 0.027753 / 0.128546 (-0.100793) | 0.008428 / 0.075646 (-0.067219) | 0.310267 / 0.419271 (-0.109004) | 0.057582 / 0.043533 (0.014049) | 0.396624 / 0.255139 (0.141485) | 0.416288 / 0.283200 (0.133089) | 0.029048 / 0.141683 (-0.112635) | 1.495362 / 1.452155 (0.043207) | 1.546331 / 1.492716 (0.053615) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203832 / 0.018006 (0.185826) | 0.423649 / 0.000490 (0.423160) | 0.004533 / 0.000200 (0.004333) | 0.000076 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023084 / 0.037411 (-0.014328) | 0.100503 / 0.014526 (0.085977) | 0.105058 / 0.176557 (-0.071499) | 0.168506 / 0.737135 (-0.568629) | 0.112019 / 0.296338 (-0.184320) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425877 / 0.215209 (0.210668) | 4.251278 / 2.077655 (2.173624) | 1.931339 / 1.504120 (0.427219) | 1.730578 / 1.541195 (0.189383) | 1.750637 / 1.468490 (0.282147) | 0.559307 / 4.584777 (-4.025470) | 3.461665 / 3.745712 (-0.284047) | 2.826959 / 5.269862 (-2.442903) | 1.418448 / 4.565676 (-3.147229) | 0.067881 / 0.424275 (-0.356394) | 0.011394 / 0.007607 (0.003787) | 0.533226 / 0.226044 (0.307181) | 5.341849 / 2.268929 (3.072921) | 2.367832 / 55.444624 (-53.076792) | 2.027240 / 6.876477 (-4.849236) | 2.095852 / 2.142072 (-0.046220) | 0.673790 / 4.805227 (-4.131437) | 0.136044 / 6.500664 (-6.364620) | 0.066350 / 0.075469 (-0.009119) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.203740 / 1.841788 (-0.638048) | 13.720879 / 8.074308 (5.646571) | 13.405939 / 10.191392 (3.214547) | 0.146792 / 0.680424 (-0.533632) | 0.016844 / 0.534201 (-0.517357) | 0.373455 / 0.579283 (-0.205828) | 0.394596 / 0.434364 (-0.039768) | 0.464715 / 0.540337 (-0.075623) | 0.558931 / 1.386936 (-0.828005) |\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) | 0.006118 / 0.011353 (-0.005235) | 0.003817 / 0.011008 (-0.007191) | 0.077494 / 0.038508 (0.038985) | 0.037507 / 0.023109 (0.014398) | 0.387030 / 0.275898 (0.111132) | 0.437352 / 0.323480 (0.113872) | 0.004810 / 0.007986 (-0.003176) | 0.002935 / 0.004328 (-0.001394) | 0.077143 / 0.004250 (0.072892) | 0.053986 / 0.037052 (0.016933) | 0.393164 / 0.258489 (0.134675) | 0.449603 / 0.293841 (0.155762) | 0.029303 / 0.128546 (-0.099244) | 0.008481 / 0.075646 (-0.067165) | 0.083363 / 0.419271 (-0.335908) | 0.043877 / 0.043533 (0.000344) | 0.378175 / 0.255139 (0.123036) | 0.403996 / 0.283200 (0.120797) | 0.021688 / 0.141683 (-0.119995) | 1.541606 / 1.452155 (0.089452) | 1.552996 / 1.492716 (0.060280) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236759 / 0.018006 (0.218752) | 0.416221 / 0.000490 (0.415732) | 0.000862 / 0.000200 (0.000662) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025543 / 0.037411 (-0.011868) | 0.101731 / 0.014526 (0.087206) | 0.108482 / 0.176557 (-0.068075) | 0.160290 / 0.737135 (-0.576845) | 0.111392 / 0.296338 (-0.184946) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.457767 / 0.215209 (0.242558) | 4.565976 / 2.077655 (2.488321) | 2.245413 / 1.504120 (0.741294) | 2.031458 / 1.541195 (0.490264) | 2.073193 / 1.468490 (0.604702) | 0.560461 / 4.584777 (-4.024316) | 3.422536 / 3.745712 (-0.323176) | 2.977017 / 5.269862 (-2.292845) | 1.377021 / 4.565676 (-3.188655) | 0.068444 / 0.424275 (-0.355831) | 0.011036 / 0.007607 (0.003429) | 0.571501 / 0.226044 (0.345456) | 5.702652 / 2.268929 (3.433723) | 2.727132 / 55.444624 (-52.717492) | 2.399269 / 6.876477 (-4.477208) | 2.574281 / 2.142072 (0.432208) | 0.682600 / 4.805227 (-4.122627) | 0.136943 / 6.500664 (-6.363722) | 0.067126 / 0.075469 (-0.008343) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.322196 / 1.841788 (-0.519592) | 14.239509 / 8.074308 (6.165201) | 14.235779 / 10.191392 (4.044387) | 0.148262 / 0.680424 (-0.532162) | 0.016566 / 0.534201 (-0.517635) | 0.364034 / 0.579283 (-0.215249) | 0.399157 / 0.434364 (-0.035207) | 0.426348 / 0.540337 (-0.113990) | 0.520804 / 1.386936 (-0.866132) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8f57aae06bd325d76cb70cb774450f3a66f169cf \"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) | 0.007808 / 0.011353 (-0.003545) | 0.004706 / 0.011008 (-0.006303) | 0.100530 / 0.038508 (0.062022) | 0.052052 / 0.023109 (0.028943) | 0.419300 / 0.275898 (0.143402) | 0.488451 / 0.323480 (0.164971) | 0.006350 / 0.007986 (-0.001636) | 0.003875 / 0.004328 (-0.000453) | 0.076489 / 0.004250 (0.072238) | 0.077554 / 0.037052 (0.040502) | 0.435863 / 0.258489 (0.177373) | 0.483241 / 0.293841 (0.189400) | 0.037518 / 0.128546 (-0.091028) | 0.009857 / 0.075646 (-0.065789) | 0.340933 / 0.419271 (-0.078339) | 0.087046 / 0.043533 (0.043514) | 0.410721 / 0.255139 (0.155582) | 0.428995 / 0.283200 (0.145795) | 0.041701 / 0.141683 (-0.099982) | 1.821017 / 1.452155 (0.368862) | 1.837021 / 1.492716 (0.344305) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228444 / 0.018006 (0.210438) | 0.480446 / 0.000490 (0.479956) | 0.004963 / 0.000200 (0.004763) | 0.000101 / 0.000054 (0.000046) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032485 / 0.037411 (-0.004926) | 0.096500 / 0.014526 (0.081974) | 0.111547 / 0.176557 (-0.065010) | 0.178842 / 0.737135 (-0.558294) | 0.111099 / 0.296338 (-0.185240) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.467159 / 0.215209 (0.251950) | 4.701676 / 2.077655 (2.624021) | 2.390560 / 1.504120 (0.886440) | 2.197722 / 1.541195 (0.656528) | 2.264705 / 1.468490 (0.796215) | 0.568667 / 4.584777 (-4.016110) | 4.200724 / 3.745712 (0.455012) | 3.777625 / 5.269862 (-1.492236) | 2.372451 / 4.565676 (-2.193225) | 0.067562 / 0.424275 (-0.356714) | 0.008947 / 0.007607 (0.001340) | 0.556910 / 0.226044 (0.330865) | 5.528927 / 2.268929 (3.259998) | 2.902780 / 55.444624 (-52.541844) | 2.507933 / 6.876477 (-4.368544) | 2.734627 / 2.142072 (0.592554) | 0.683305 / 4.805227 (-4.121922) | 0.158288 / 6.500664 (-6.342376) | 0.071252 / 0.075469 (-0.004217) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.487502 / 1.841788 (-0.354286) | 22.193341 / 8.074308 (14.119033) | 15.922607 / 10.191392 (5.731215) | 0.172189 / 0.680424 (-0.508235) | 0.021502 / 0.534201 (-0.512699) | 0.471198 / 0.579283 (-0.108085) | 0.475979 / 0.434364 (0.041615) | 0.544675 / 0.540337 (0.004338) | 0.756102 / 1.386936 (-0.630834) |\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) | 0.007635 / 0.011353 (-0.003717) | 0.004614 / 0.011008 (-0.006394) | 0.075852 / 0.038508 (0.037344) | 0.049700 / 0.023109 (0.026591) | 0.425957 / 0.275898 (0.150059) | 0.512590 / 0.323480 (0.189110) | 0.006921 / 0.007986 (-0.001065) | 0.003714 / 0.004328 (-0.000615) | 0.075536 / 0.004250 (0.071286) | 0.070206 / 0.037052 (0.033153) | 0.455706 / 0.258489 (0.197217) | 0.512231 / 0.293841 (0.218390) | 0.036685 / 0.128546 (-0.091861) | 0.009793 / 0.075646 (-0.065853) | 0.084208 / 0.419271 (-0.335064) | 0.065262 / 0.043533 (0.021729) | 0.423761 / 0.255139 (0.168622) | 0.456791 / 0.283200 (0.173591) | 0.044539 / 0.141683 (-0.097144) | 1.797029 / 1.452155 (0.344874) | 1.864124 / 1.492716 (0.371408) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.366840 / 0.018006 (0.348834) | 0.479254 / 0.000490 (0.478765) | 0.070383 / 0.000200 (0.070183) | 0.000762 / 0.000054 (0.000707) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034233 / 0.037411 (-0.003178) | 0.103140 / 0.014526 (0.088614) | 0.117099 / 0.176557 (-0.059457) | 0.178532 / 0.737135 (-0.558603) | 0.120092 / 0.296338 (-0.176247) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.492993 / 0.215209 (0.277784) | 4.878776 / 2.077655 (2.801121) | 2.566666 / 1.504120 (1.062547) | 2.356383 / 1.541195 (0.815188) | 2.454723 / 1.468490 (0.986233) | 0.571432 / 4.584777 (-4.013345) | 4.240554 / 3.745712 (0.494842) | 7.509259 / 5.269862 (2.239398) | 4.040294 / 4.565676 (-0.525382) | 0.067409 / 0.424275 (-0.356866) | 0.008657 / 0.007607 (0.001050) | 0.585751 / 0.226044 (0.359707) | 5.967668 / 2.268929 (3.698739) | 3.195573 / 55.444624 (-52.249052) | 2.839772 / 6.876477 (-4.036704) | 2.806319 / 2.142072 (0.664246) | 0.681502 / 4.805227 (-4.123725) | 0.158673 / 6.500664 (-6.341991) | 0.073224 / 0.075469 (-0.002245) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.623335 / 1.841788 (-0.218453) | 22.490806 / 8.074308 (14.416498) | 16.762435 / 10.191392 (6.571043) | 0.180961 / 0.680424 (-0.499463) | 0.022716 / 0.534201 (-0.511485) | 0.472910 / 0.579283 (-0.106373) | 0.471616 / 0.434364 (0.037252) | 0.548192 / 0.540337 (0.007854) | 0.734357 / 1.386936 (-0.652579) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c0498b47a00153d4730352b6595fc51ab054fb95 \"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) | 0.005858 / 0.011353 (-0.005495) | 0.003512 / 0.011008 (-0.007497) | 0.079739 / 0.038508 (0.041231) | 0.057736 / 0.023109 (0.034627) | 0.317640 / 0.275898 (0.041742) | 0.354157 / 0.323480 (0.030677) | 0.004772 / 0.007986 (-0.003214) | 0.002824 / 0.004328 (-0.001504) | 0.063288 / 0.004250 (0.059037) | 0.049542 / 0.037052 (0.012489) | 0.323974 / 0.258489 (0.065485) | 0.372149 / 0.293841 (0.078308) | 0.026841 / 0.128546 (-0.101705) | 0.007846 / 0.075646 (-0.067800) | 0.262546 / 0.419271 (-0.156725) | 0.051952 / 0.043533 (0.008420) | 0.319439 / 0.255139 (0.064300) | 0.343862 / 0.283200 (0.060663) | 0.027021 / 0.141683 (-0.114662) | 1.445211 / 1.452155 (-0.006944) | 1.485006 / 1.492716 (-0.007711) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.183174 / 0.018006 (0.165167) | 0.422794 / 0.000490 (0.422304) | 0.004148 / 0.000200 (0.003948) | 0.000067 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023037 / 0.037411 (-0.014374) | 0.071300 / 0.014526 (0.056775) | 0.083022 / 0.176557 (-0.093535) | 0.146215 / 0.737135 (-0.590920) | 0.082549 / 0.296338 (-0.213789) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422846 / 0.215209 (0.207637) | 4.215280 / 2.077655 (2.137626) | 2.256802 / 1.504120 (0.752682) | 2.056867 / 1.541195 (0.515673) | 2.102478 / 1.468490 (0.633988) | 0.497552 / 4.584777 (-4.087225) | 3.049716 / 3.745712 (-0.695996) | 4.209227 / 5.269862 (-1.060635) | 2.599947 / 4.565676 (-1.965730) | 0.059131 / 0.424275 (-0.365144) | 0.006459 / 0.007607 (-0.001148) | 0.495047 / 0.226044 (0.269003) | 4.952332 / 2.268929 (2.683404) | 2.675260 / 55.444624 (-52.769365) | 2.333223 / 6.876477 (-4.543254) | 2.449573 / 2.142072 (0.307500) | 0.583420 / 4.805227 (-4.221807) | 0.125140 / 6.500664 (-6.375524) | 0.060209 / 0.075469 (-0.015260) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.215033 / 1.841788 (-0.626755) | 18.101107 / 8.074308 (10.026799) | 13.489222 / 10.191392 (3.297830) | 0.147122 / 0.680424 (-0.533302) | 0.016567 / 0.534201 (-0.517634) | 0.329909 / 0.579283 (-0.249374) | 0.340952 / 0.434364 (-0.093412) | 0.379166 / 0.540337 (-0.161172) | 0.510767 / 1.386936 (-0.876169) |\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) | 0.005942 / 0.011353 (-0.005411) | 0.003628 / 0.011008 (-0.007380) | 0.061975 / 0.038508 (0.023467) | 0.058331 / 0.023109 (0.035221) | 0.393277 / 0.275898 (0.117379) | 0.410740 / 0.323480 (0.087261) | 0.004546 / 0.007986 (-0.003440) | 0.002826 / 0.004328 (-0.001503) | 0.062216 / 0.004250 (0.057966) | 0.049801 / 0.037052 (0.012748) | 0.394070 / 0.258489 (0.135581) | 0.414407 / 0.293841 (0.120566) | 0.027161 / 0.128546 (-0.101385) | 0.007901 / 0.075646 (-0.067746) | 0.066778 / 0.419271 (-0.352493) | 0.041354 / 0.043533 (-0.002179) | 0.379432 / 0.255139 (0.124293) | 0.402966 / 0.283200 (0.119766) | 0.020279 / 0.141683 (-0.121404) | 1.416986 / 1.452155 (-0.035169) | 1.474335 / 1.492716 (-0.018382) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226147 / 0.018006 (0.208140) | 0.404361 / 0.000490 (0.403871) | 0.000358 / 0.000200 (0.000158) | 0.000054 / 0.000054 (-0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025105 / 0.037411 (-0.012306) | 0.075849 / 0.014526 (0.061323) | 0.084781 / 0.176557 (-0.091775) | 0.137415 / 0.737135 (-0.599720) | 0.086288 / 0.296338 (-0.210051) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.445925 / 0.215209 (0.230716) | 4.453478 / 2.077655 (2.375823) | 2.419048 / 1.504120 (0.914928) | 2.246363 / 1.541195 (0.705168) | 2.304022 / 1.468490 (0.835532) | 0.499132 / 4.584777 (-4.085645) | 3.001336 / 3.745712 (-0.744376) | 2.902593 / 5.269862 (-2.367269) | 1.819843 / 4.565676 (-2.745834) | 0.057210 / 0.424275 (-0.367065) | 0.006338 / 0.007607 (-0.001269) | 0.523280 / 0.226044 (0.297236) | 5.235969 / 2.268929 (2.967040) | 2.897585 / 55.444624 (-52.547039) | 2.541586 / 6.876477 (-4.334891) | 2.564233 / 2.142072 (0.422160) | 0.584714 / 4.805227 (-4.220513) | 0.124611 / 6.500664 (-6.376053) | 0.061774 / 0.075469 (-0.013695) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.349799 / 1.841788 (-0.491988) | 18.225076 / 8.074308 (10.150768) | 13.781518 / 10.191392 (3.590126) | 0.130562 / 0.680424 (-0.549862) | 0.016434 / 0.534201 (-0.517767) | 0.331607 / 0.579283 (-0.247676) | 0.343456 / 0.434364 (-0.090908) | 0.380437 / 0.540337 (-0.159900) | 0.522793 / 1.386936 (-0.864143) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f0a3dbbd2e7ace162346d95ec27db674e80c1e23 \"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) | 0.013721 / 0.011353 (0.002368) | 0.005715 / 0.011008 (-0.005293) | 0.090116 / 0.038508 (0.051608) | 0.087185 / 0.023109 (0.064075) | 0.427813 / 0.275898 (0.151915) | 0.390614 / 0.323480 (0.067135) | 0.006976 / 0.007986 (-0.001009) | 0.004231 / 0.004328 (-0.000098) | 0.078320 / 0.004250 (0.074070) | 0.066235 / 0.037052 (0.029183) | 0.439904 / 0.258489 (0.181415) | 0.424119 / 0.293841 (0.130278) | 0.050362 / 0.128546 (-0.078184) | 0.014992 / 0.075646 (-0.060654) | 0.293519 / 0.419271 (-0.125753) | 0.066906 / 0.043533 (0.023373) | 0.449657 / 0.255139 (0.194518) | 0.393800 / 0.283200 (0.110600) | 0.032258 / 0.141683 (-0.109425) | 1.539534 / 1.452155 (0.087379) | 1.675292 / 1.492716 (0.182576) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210515 / 0.018006 (0.192508) | 0.506817 / 0.000490 (0.506327) | 0.001938 / 0.000200 (0.001738) | 0.000118 / 0.000054 (0.000064) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026019 / 0.037411 (-0.011393) | 0.080635 / 0.014526 (0.066109) | 0.103050 / 0.176557 (-0.073507) | 0.160597 / 0.737135 (-0.576538) | 0.095844 / 0.296338 (-0.200495) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.506359 / 0.215209 (0.291150) | 5.041586 / 2.077655 (2.963931) | 2.198288 / 1.504120 (0.694168) | 1.987544 / 1.541195 (0.446349) | 1.866790 / 1.468490 (0.398300) | 0.681642 / 4.584777 (-3.903135) | 4.719306 / 3.745712 (0.973593) | 7.669869 / 5.269862 (2.400008) | 4.466082 / 4.565676 (-0.099595) | 0.092974 / 0.424275 (-0.331301) | 0.008196 / 0.007607 (0.000589) | 0.707656 / 0.226044 (0.481612) | 6.974507 / 2.268929 (4.705579) | 3.254206 / 55.444624 (-52.190418) | 2.499019 / 6.876477 (-4.377457) | 2.509089 / 2.142072 (0.367017) | 0.915952 / 4.805227 (-3.889276) | 0.192119 / 6.500664 (-6.308545) | 0.065473 / 0.075469 (-0.009996) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.309078 / 1.841788 (-0.532710) | 19.660348 / 8.074308 (11.586040) | 16.659582 / 10.191392 (6.468190) | 0.194315 / 0.680424 (-0.486109) | 0.027773 / 0.534201 (-0.506428) | 0.401241 / 0.579283 (-0.178042) | 0.515799 / 0.434364 (0.081435) | 0.488772 / 0.540337 (-0.051566) | 0.604790 / 1.386936 (-0.782146) |\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) | 0.006823 / 0.011353 (-0.004530) | 0.003940 / 0.011008 (-0.007068) | 0.061533 / 0.038508 (0.023025) | 0.065241 / 0.023109 (0.042132) | 0.411790 / 0.275898 (0.135892) | 0.475720 / 0.323480 (0.152241) | 0.005376 / 0.007986 (-0.002609) | 0.003433 / 0.004328 (-0.000895) | 0.065703 / 0.004250 (0.061452) | 0.050736 / 0.037052 (0.013683) | 0.435890 / 0.258489 (0.177401) | 0.436698 / 0.293841 (0.142857) | 0.040357 / 0.128546 (-0.088189) | 0.011578 / 0.075646 (-0.064069) | 0.072831 / 0.419271 (-0.346440) | 0.055698 / 0.043533 (0.012165) | 0.408225 / 0.255139 (0.153086) | 0.439551 / 0.283200 (0.156352) | 0.030469 / 0.141683 (-0.111214) | 1.443866 / 1.452155 (-0.008289) | 1.502022 / 1.492716 (0.009306) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.290338 / 0.018006 (0.272332) | 0.540726 / 0.000490 (0.540236) | 0.003244 / 0.000200 (0.003044) | 0.000170 / 0.000054 (0.000116) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030865 / 0.037411 (-0.006547) | 0.090866 / 0.014526 (0.076340) | 0.106224 / 0.176557 (-0.070332) | 0.166583 / 0.737135 (-0.570553) | 0.104448 / 0.296338 (-0.191891) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.518025 / 0.215209 (0.302816) | 6.027065 / 2.077655 (3.949410) | 2.671840 / 1.504120 (1.167720) | 2.273949 / 1.541195 (0.732754) | 2.414892 / 1.468490 (0.946402) | 0.774318 / 4.584777 (-3.810459) | 5.020364 / 3.745712 (1.274652) | 4.146927 / 5.269862 (-1.122934) | 2.584598 / 4.565676 (-1.981078) | 0.089519 / 0.424275 (-0.334756) | 0.009181 / 0.007607 (0.001574) | 0.654467 / 0.226044 (0.428423) | 6.421595 / 2.268929 (4.152666) | 3.091589 / 55.444624 (-52.353036) | 2.554798 / 6.876477 (-4.321679) | 2.441354 / 2.142072 (0.299282) | 0.943386 / 4.805227 (-3.861841) | 0.173641 / 6.500664 (-6.327023) | 0.072209 / 0.075469 (-0.003260) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.557147 / 1.841788 (-0.284641) | 19.980747 / 8.074308 (11.906439) | 17.816813 / 10.191392 (7.625421) | 0.212078 / 0.680424 (-0.468346) | 0.025435 / 0.534201 (-0.508766) | 0.396200 / 0.579283 (-0.183084) | 0.546249 / 0.434364 (0.111885) | 0.459632 / 0.540337 (-0.080705) | 0.616548 / 1.386936 (-0.770388) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#535e972a70a3d4f8490a7e1a77ac43d5a4ab2655 \"CML watermark\")\n"
] |
6,004
|
Misc improvements
|
Contains the following improvements:
* fixes a "share dataset" link in README and modifies the "hosting" part in the disclaimer section
* updates `Makefile` to also run the style checks on `utils` and `setup.py`
* deletes a test for GH-hosted datasets (no longer supported)
* deletes `convert_dataset.sh` (outdated)
* aligns `utils/release.py` with `transformers` (the current version is outdated)
|
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6004",
"html_url": "https://github.com/huggingface/datasets/pull/6004",
"diff_url": "https://github.com/huggingface/datasets/pull/6004.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6004.patch",
"merged_at": "2023-07-06T16:55:25"
}
| true
|
[
"<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) | 0.006897 / 0.011353 (-0.004456) | 0.004207 / 0.011008 (-0.006802) | 0.104828 / 0.038508 (0.066320) | 0.048054 / 0.023109 (0.024945) | 0.373991 / 0.275898 (0.098093) | 0.426740 / 0.323480 (0.103260) | 0.005540 / 0.007986 (-0.002446) | 0.003531 / 0.004328 (-0.000797) | 0.079304 / 0.004250 (0.075053) | 0.066996 / 0.037052 (0.029944) | 0.370675 / 0.258489 (0.112186) | 0.414154 / 0.293841 (0.120313) | 0.031567 / 0.128546 (-0.096979) | 0.008843 / 0.075646 (-0.066803) | 0.357426 / 0.419271 (-0.061845) | 0.067040 / 0.043533 (0.023508) | 0.362384 / 0.255139 (0.107245) | 0.376056 / 0.283200 (0.092856) | 0.032985 / 0.141683 (-0.108697) | 1.560603 / 1.452155 (0.108448) | 1.619024 / 1.492716 (0.126308) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229059 / 0.018006 (0.211053) | 0.440513 / 0.000490 (0.440023) | 0.004647 / 0.000200 (0.004447) | 0.000085 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029517 / 0.037411 (-0.007894) | 0.120974 / 0.014526 (0.106448) | 0.125070 / 0.176557 (-0.051486) | 0.184695 / 0.737135 (-0.552441) | 0.130244 / 0.296338 (-0.166095) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436930 / 0.215209 (0.221721) | 4.356118 / 2.077655 (2.278463) | 2.049169 / 1.504120 (0.545049) | 1.842898 / 1.541195 (0.301703) | 1.918948 / 1.468490 (0.450458) | 0.553573 / 4.584777 (-4.031204) | 3.883195 / 3.745712 (0.137483) | 3.209780 / 5.269862 (-2.060081) | 1.551707 / 4.565676 (-3.013970) | 0.068181 / 0.424275 (-0.356094) | 0.012370 / 0.007607 (0.004762) | 0.539899 / 0.226044 (0.313854) | 5.380008 / 2.268929 (3.111079) | 2.518178 / 55.444624 (-52.926446) | 2.174190 / 6.876477 (-4.702286) | 2.317812 / 2.142072 (0.175740) | 0.674154 / 4.805227 (-4.131073) | 0.149313 / 6.500664 (-6.351351) | 0.068297 / 0.075469 (-0.007172) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.261426 / 1.841788 (-0.580362) | 15.316378 / 8.074308 (7.242070) | 13.573512 / 10.191392 (3.382120) | 0.190022 / 0.680424 (-0.490401) | 0.018697 / 0.534201 (-0.515504) | 0.448122 / 0.579283 (-0.131161) | 0.435044 / 0.434364 (0.000681) | 0.550065 / 0.540337 (0.009728) | 0.653547 / 1.386936 (-0.733389) |\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) | 0.007116 / 0.011353 (-0.004237) | 0.004375 / 0.011008 (-0.006633) | 0.081793 / 0.038508 (0.043285) | 0.047980 / 0.023109 (0.024871) | 0.392185 / 0.275898 (0.116287) | 0.462263 / 0.323480 (0.138783) | 0.005574 / 0.007986 (-0.002412) | 0.003552 / 0.004328 (-0.000776) | 0.080413 / 0.004250 (0.076162) | 0.065539 / 0.037052 (0.028487) | 0.413137 / 0.258489 (0.154648) | 0.467377 / 0.293841 (0.173536) | 0.034386 / 0.128546 (-0.094160) | 0.009183 / 0.075646 (-0.066464) | 0.087542 / 0.419271 (-0.331730) | 0.053954 / 0.043533 (0.010421) | 0.385096 / 0.255139 (0.129957) | 0.404900 / 0.283200 (0.121701) | 0.025908 / 0.141683 (-0.115775) | 1.550159 / 1.452155 (0.098005) | 1.598794 / 1.492716 (0.106078) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246222 / 0.018006 (0.228216) | 0.441095 / 0.000490 (0.440605) | 0.006863 / 0.000200 (0.006663) | 0.000109 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032179 / 0.037411 (-0.005233) | 0.120112 / 0.014526 (0.105586) | 0.129326 / 0.176557 (-0.047230) | 0.184542 / 0.737135 (-0.552593) | 0.135038 / 0.296338 (-0.161300) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.459002 / 0.215209 (0.243793) | 4.580258 / 2.077655 (2.502604) | 2.296689 / 1.504120 (0.792569) | 2.104338 / 1.541195 (0.563143) | 2.182896 / 1.468490 (0.714406) | 0.546447 / 4.584777 (-4.038330) | 3.854047 / 3.745712 (0.108335) | 1.873829 / 5.269862 (-3.396032) | 1.116484 / 4.565676 (-3.449193) | 0.067158 / 0.424275 (-0.357117) | 0.012035 / 0.007607 (0.004428) | 0.556642 / 0.226044 (0.330597) | 5.574436 / 2.268929 (3.305508) | 2.828223 / 55.444624 (-52.616402) | 2.519851 / 6.876477 (-4.356626) | 2.668594 / 2.142072 (0.526521) | 0.675989 / 4.805227 (-4.129238) | 0.146075 / 6.500664 (-6.354589) | 0.067788 / 0.075469 (-0.007681) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.345958 / 1.841788 (-0.495830) | 15.672748 / 8.074308 (7.598440) | 14.937583 / 10.191392 (4.746191) | 0.163479 / 0.680424 (-0.516945) | 0.018364 / 0.534201 (-0.515837) | 0.433296 / 0.579283 (-0.145987) | 0.432463 / 0.434364 (-0.001901) | 0.512000 / 0.540337 (-0.028338) | 0.619397 / 1.386936 (-0.767539) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0832d48a07ed00b406271f4b4439e6d54ae38ebf \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010097 / 0.011353 (-0.001256) | 0.005070 / 0.011008 (-0.005939) | 0.118638 / 0.038508 (0.080130) | 0.043651 / 0.023109 (0.020542) | 0.356074 / 0.275898 (0.080176) | 0.414578 / 0.323480 (0.091098) | 0.005939 / 0.007986 (-0.002046) | 0.004927 / 0.004328 (0.000598) | 0.089545 / 0.004250 (0.085294) | 0.067533 / 0.037052 (0.030481) | 0.371550 / 0.258489 (0.113061) | 0.417808 / 0.293841 (0.123967) | 0.045186 / 0.128546 (-0.083361) | 0.015763 / 0.075646 (-0.059883) | 0.393304 / 0.419271 (-0.025967) | 0.065123 / 0.043533 (0.021591) | 0.345057 / 0.255139 (0.089918) | 0.378809 / 0.283200 (0.095610) | 0.033243 / 0.141683 (-0.108440) | 1.679956 / 1.452155 (0.227802) | 1.775456 / 1.492716 (0.282739) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229723 / 0.018006 (0.211717) | 0.554630 / 0.000490 (0.554140) | 0.008729 / 0.000200 (0.008529) | 0.000183 / 0.000054 (0.000129) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027284 / 0.037411 (-0.010128) | 0.114741 / 0.014526 (0.100215) | 0.129188 / 0.176557 (-0.047369) | 0.189270 / 0.737135 (-0.547866) | 0.126000 / 0.296338 (-0.170339) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.580417 / 0.215209 (0.365208) | 5.829337 / 2.077655 (3.751683) | 2.421191 / 1.504120 (0.917071) | 2.063673 / 1.541195 (0.522479) | 2.133427 / 1.468490 (0.664937) | 0.830964 / 4.584777 (-3.753813) | 5.107139 / 3.745712 (1.361427) | 4.599451 / 5.269862 (-0.670410) | 2.406502 / 4.565676 (-2.159175) | 0.100422 / 0.424275 (-0.323853) | 0.011850 / 0.007607 (0.004243) | 0.741881 / 0.226044 (0.515836) | 7.425689 / 2.268929 (5.156760) | 3.068948 / 55.444624 (-52.375676) | 2.496292 / 6.876477 (-4.380184) | 2.566420 / 2.142072 (0.424348) | 1.093084 / 4.805227 (-3.712144) | 0.224106 / 6.500664 (-6.276558) | 0.084549 / 0.075469 (0.009080) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.416315 / 1.841788 (-0.425473) | 16.306901 / 8.074308 (8.232593) | 19.792419 / 10.191392 (9.601027) | 0.224223 / 0.680424 (-0.456201) | 0.026385 / 0.534201 (-0.507816) | 0.463460 / 0.579283 (-0.115823) | 0.598385 / 0.434364 (0.164021) | 0.543981 / 0.540337 (0.003644) | 0.647454 / 1.386936 (-0.739482) |\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) | 0.009470 / 0.011353 (-0.001883) | 0.004800 / 0.011008 (-0.006208) | 0.094276 / 0.038508 (0.055768) | 0.045157 / 0.023109 (0.022048) | 0.397302 / 0.275898 (0.121404) | 0.474213 / 0.323480 (0.150733) | 0.005826 / 0.007986 (-0.002160) | 0.003724 / 0.004328 (-0.000605) | 0.090060 / 0.004250 (0.085809) | 0.066671 / 0.037052 (0.029618) | 0.439560 / 0.258489 (0.181071) | 0.468598 / 0.293841 (0.174757) | 0.044549 / 0.128546 (-0.083997) | 0.014000 / 0.075646 (-0.061646) | 0.110457 / 0.419271 (-0.308815) | 0.065898 / 0.043533 (0.022365) | 0.408101 / 0.255139 (0.152962) | 0.433473 / 0.283200 (0.150273) | 0.038438 / 0.141683 (-0.103245) | 1.767781 / 1.452155 (0.315626) | 1.791575 / 1.492716 (0.298859) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230257 / 0.018006 (0.212251) | 0.492280 / 0.000490 (0.491790) | 0.005110 / 0.000200 (0.004910) | 0.000119 / 0.000054 (0.000065) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028854 / 0.037411 (-0.008557) | 0.111702 / 0.014526 (0.097176) | 0.122040 / 0.176557 (-0.054517) | 0.179103 / 0.737135 (-0.558032) | 0.128869 / 0.296338 (-0.167470) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.634795 / 0.215209 (0.419586) | 6.204760 / 2.077655 (4.127105) | 2.692479 / 1.504120 (1.188359) | 2.324260 / 1.541195 (0.783066) | 2.380640 / 1.468490 (0.912149) | 0.887827 / 4.584777 (-3.696950) | 5.251648 / 3.745712 (1.505935) | 2.632767 / 5.269862 (-2.637095) | 1.745721 / 4.565676 (-2.819955) | 0.108364 / 0.424275 (-0.315911) | 0.013409 / 0.007607 (0.005802) | 0.783427 / 0.226044 (0.557383) | 7.765144 / 2.268929 (5.496216) | 3.340686 / 55.444624 (-52.103938) | 2.715340 / 6.876477 (-4.161137) | 2.768604 / 2.142072 (0.626531) | 1.119746 / 4.805227 (-3.685481) | 0.210804 / 6.500664 (-6.289860) | 0.072600 / 0.075469 (-0.002869) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.517334 / 1.841788 (-0.324454) | 17.046837 / 8.074308 (8.972529) | 19.371090 / 10.191392 (9.179698) | 0.194275 / 0.680424 (-0.486148) | 0.026712 / 0.534201 (-0.507488) | 0.462731 / 0.579283 (-0.116552) | 0.568958 / 0.434364 (0.134595) | 0.555707 / 0.540337 (0.015370) | 0.663654 / 1.386936 (-0.723283) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5d20476b1d4c8e11e0ffafc1570cbf4bd19011cf \"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) | 0.006423 / 0.011353 (-0.004930) | 0.003882 / 0.011008 (-0.007126) | 0.082976 / 0.038508 (0.044468) | 0.071281 / 0.023109 (0.048171) | 0.311367 / 0.275898 (0.035469) | 0.348228 / 0.323480 (0.024748) | 0.005315 / 0.007986 (-0.002671) | 0.003326 / 0.004328 (-0.001003) | 0.064641 / 0.004250 (0.060391) | 0.056134 / 0.037052 (0.019081) | 0.314071 / 0.258489 (0.055582) | 0.360534 / 0.293841 (0.066693) | 0.030642 / 0.128546 (-0.097904) | 0.008301 / 0.075646 (-0.067345) | 0.285820 / 0.419271 (-0.133451) | 0.069241 / 0.043533 (0.025708) | 0.313995 / 0.255139 (0.058856) | 0.336656 / 0.283200 (0.053457) | 0.031686 / 0.141683 (-0.109997) | 1.467627 / 1.452155 (0.015472) | 1.536493 / 1.492716 (0.043777) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.196518 / 0.018006 (0.178512) | 0.458235 / 0.000490 (0.457745) | 0.005599 / 0.000200 (0.005399) | 0.000088 / 0.000054 (0.000034) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027371 / 0.037411 (-0.010040) | 0.080986 / 0.014526 (0.066460) | 0.093296 / 0.176557 (-0.083260) | 0.150592 / 0.737135 (-0.586543) | 0.094150 / 0.296338 (-0.202188) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.379412 / 0.215209 (0.164202) | 3.797927 / 2.077655 (1.720272) | 1.830654 / 1.504120 (0.326534) | 1.669569 / 1.541195 (0.128374) | 1.746738 / 1.468490 (0.278248) | 0.479536 / 4.584777 (-4.105241) | 3.592867 / 3.745712 (-0.152845) | 5.468098 / 5.269862 (0.198237) | 3.268013 / 4.565676 (-1.297663) | 0.056635 / 0.424275 (-0.367640) | 0.007224 / 0.007607 (-0.000383) | 0.456681 / 0.226044 (0.230636) | 4.566736 / 2.268929 (2.297807) | 2.362831 / 55.444624 (-53.081793) | 1.965141 / 6.876477 (-4.911336) | 2.156905 / 2.142072 (0.014833) | 0.572543 / 4.805227 (-4.232684) | 0.132203 / 6.500664 (-6.368461) | 0.059254 / 0.075469 (-0.016215) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.256134 / 1.841788 (-0.585654) | 19.905438 / 8.074308 (11.831130) | 14.179556 / 10.191392 (3.988164) | 0.168043 / 0.680424 (-0.512381) | 0.018215 / 0.534201 (-0.515986) | 0.392740 / 0.579283 (-0.186543) | 0.398397 / 0.434364 (-0.035967) | 0.463806 / 0.540337 (-0.076531) | 0.616248 / 1.386936 (-0.770688) |\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) | 0.006564 / 0.011353 (-0.004789) | 0.003923 / 0.011008 (-0.007085) | 0.063929 / 0.038508 (0.025421) | 0.073780 / 0.023109 (0.050671) | 0.360242 / 0.275898 (0.084344) | 0.395078 / 0.323480 (0.071598) | 0.005265 / 0.007986 (-0.002720) | 0.003229 / 0.004328 (-0.001100) | 0.064094 / 0.004250 (0.059843) | 0.057468 / 0.037052 (0.020416) | 0.369530 / 0.258489 (0.111041) | 0.411159 / 0.293841 (0.117318) | 0.031278 / 0.128546 (-0.097268) | 0.008424 / 0.075646 (-0.067222) | 0.070411 / 0.419271 (-0.348860) | 0.048714 / 0.043533 (0.005181) | 0.361280 / 0.255139 (0.106141) | 0.382468 / 0.283200 (0.099269) | 0.023059 / 0.141683 (-0.118624) | 1.452369 / 1.452155 (0.000215) | 1.519192 / 1.492716 (0.026475) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223745 / 0.018006 (0.205739) | 0.442086 / 0.000490 (0.441596) | 0.000379 / 0.000200 (0.000179) | 0.000055 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030919 / 0.037411 (-0.006493) | 0.088483 / 0.014526 (0.073958) | 0.101165 / 0.176557 (-0.075391) | 0.154332 / 0.737135 (-0.582804) | 0.103030 / 0.296338 (-0.193309) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.414520 / 0.215209 (0.199311) | 4.126754 / 2.077655 (2.049099) | 2.142677 / 1.504120 (0.638557) | 1.995300 / 1.541195 (0.454106) | 2.101678 / 1.468490 (0.633188) | 0.481099 / 4.584777 (-4.103678) | 3.562813 / 3.745712 (-0.182900) | 3.392463 / 5.269862 (-1.877399) | 1.983943 / 4.565676 (-2.581734) | 0.056594 / 0.424275 (-0.367681) | 0.007216 / 0.007607 (-0.000391) | 0.495085 / 0.226044 (0.269041) | 4.955640 / 2.268929 (2.686712) | 2.629434 / 55.444624 (-52.815191) | 2.269577 / 6.876477 (-4.606900) | 2.357708 / 2.142072 (0.215635) | 0.612370 / 4.805227 (-4.192857) | 0.131169 / 6.500664 (-6.369495) | 0.061029 / 0.075469 (-0.014440) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.339438 / 1.841788 (-0.502350) | 19.757611 / 8.074308 (11.683303) | 14.246254 / 10.191392 (4.054862) | 0.170750 / 0.680424 (-0.509674) | 0.018192 / 0.534201 (-0.516009) | 0.395693 / 0.579283 (-0.183590) | 0.411003 / 0.434364 (-0.023361) | 0.478531 / 0.540337 (-0.061806) | 0.650291 / 1.386936 (-0.736645) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3e34d06d746688dd5d26e4c85517b7e1a2f361ca \"CML watermark\")\n"
] |
6,003
|
interleave_datasets & DataCollatorForLanguageModeling having a conflict ?
|
### Describe the bug
Hi everyone :)
I have two local & custom datasets (1 "sentence" per line) which I split along the 95/5 lines for pre-training a Bert model. I use a modified version of `run_mlm.py` in order to be able to make use of `interleave_dataset`:
- `tokenize()` runs fine
- `group_text()` runs fine
Everytime, on step 19, I get
```pytb
File "env/lib/python3.9/site-packages/transformers/data/data_collator.py", line 779, in torch_mask_tokens
inputs[indices_random] = random_words[indices_random]
RuntimeError: Index put requires the source and destination dtypes match, got Float for the destination and Long for the source.
```
I tried:
- training without interleave on dataset 1, it runs
- training without interleave on dataset 2, it runs
- training without `.to_iterable_dataset()`, it hangs then crash
- training without group_text() and padding to max_length seemed to fix the issue, but who knows if this was just because it was an issue that would come much later in terms of steps.
I might have coded something wrong, but I don't get what
### Steps to reproduce the bug
I have this function:
```py
def build_dataset(path: str, percent: str):
dataset = load_dataset(
"text",
data_files={"train": [path]},
split=f"train[{percent}]"
)
dataset = dataset.map(
lambda examples: tokenize(examples["text"]),
batched=True,
num_proc=num_proc,
)
dataset = dataset.map(
group_texts,
batched=True,
num_proc=num_proc,
desc=f"Grouping texts in chunks of {tokenizer.max_seq_length}",
remove_columns=["text"]
)
print(len(dataset))
return dataset.to_iterable_dataset()
```
I hardcoded group_text:
```py
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, and if the total_length < max_seq_length we exclude this batch and return an empty dict.
# We could add padding if the model supported it instead of this drop, you can customize this part to your needs.
total_length = (total_length // 512) * 512
# Split by chunks of max_len.
result = {
k: [t[i: i + 512] for i in range(0, total_length, 512)]
for k, t in concatenated_examples.items()
}
# result = {k: [el for el in elements if el] for k, elements in result.items()}
return result
```
And then I build datasets using the following code:
```py
train1 = build_dataset("d1.txt", ":95%")
train2 = build_dataset("d2.txt", ":95%")
dev1 = build_dataset("d1.txt", "95%:")
dev2 = build_dataset("d2.txt", "95%:")
```
and finally I run
```py
train_dataset = interleave_datasets(
[train1, train2],
probabilities=[0.8, 0.2],
seed=42
)
eval_dataset = interleave_datasets(
[dev1, dev2],
probabilities=[0.8, 0.2],
seed=42
)
```
Then I run the training part which remains mostly untouched:
> CUDA_VISIBLE_DEVICES=1 python custom_dataset.py --model_type bert --per_device_train_batch_size 32 --do_train --output_dir /var/mlm/training-bert/model --max_seq_length 512 --save_steps 10000 --save_total_limit 3 --auto_find_batch_size --logging_dir ./logs-bert --learning_rate 0.0001 --do_train --num_train_epochs 25 --warmup_steps 10000 --max_step 45000 --fp16
### Expected behavior
The model should then train normally, but fails every time at the same step (19).
printing the variables at `inputs[indices_random] = random_words[indices_random]` shows a magnificient empty tensor (, 32) [if I remember well]
### Environment info
transformers[torch] 4.30.2
Ubuntu
A100 0 CUDA 12
Driver Version: 525.116.04
|
[] |
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| false
|
[] |
6,002
|
Add KLUE-MRC metrics
|
## Metrics for KLUE-MRC (Korean Language Understanding Evaluation — Machine Reading Comprehension)
Adding metrics for [KLUE-MRC](https://huggingface.co/datasets/klue).
KLUE-MRC is very similar to SQuAD 2.0 but has a slightly different format which is why I added metrics for KLUE-MRC.
Specifically, in the case of [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness), it leverages the scoring script of SQuAD to evaluate SQuAD 2.0 and KorQuAD. But the script isn't suitable for KLUE-MRC because KLUE-MRC is a bit different from SQuAD 2.0. And this is why I added the scoring script for KLUE-MRC.
- [x] All tests passed
- [x] Added a metric card (referred the metric card of SQuAD 2.0)
- [x] Compatibility test with [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) passed
### References
- [KLUE: Korean Language Understanding Evaluation](https://datasets-benchmarks-proceedings.neurips.cc/paper_files/paper/2021/file/98dce83da57b0395e163467c9dae521b-Paper-round2.pdf)
- [KLUE on Hugging Face Datasets](https://huggingface.co/datasets/klue)
- #2416
|
[] |
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"url": "https://api.github.com/repos/huggingface/datasets/pulls/6002",
"html_url": "https://github.com/huggingface/datasets/pull/6002",
"diff_url": "https://github.com/huggingface/datasets/pull/6002.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6002.patch",
"merged_at": null
}
| true
|
[
"The metrics API in `datasets` is deprecated as of version 2.0, and `evaulate` is our new library for metrics. You can add a new metric to it by following [these steps](https://huggingface.co/docs/evaluate/creating_and_sharing)."
] |
6,001
|
Align `column_names` type check with type hint in `sort`
|
Fix #5998
|
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6001",
"html_url": "https://github.com/huggingface/datasets/pull/6001",
"diff_url": "https://github.com/huggingface/datasets/pull/6001.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6001.patch",
"merged_at": "2023-06-30T14:11:24"
}
| true
|
[
"_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) | 0.006038 / 0.011353 (-0.005315) | 0.003797 / 0.011008 (-0.007211) | 0.097686 / 0.038508 (0.059178) | 0.035235 / 0.023109 (0.012126) | 0.317294 / 0.275898 (0.041396) | 0.377682 / 0.323480 (0.054202) | 0.003485 / 0.007986 (-0.004501) | 0.003603 / 0.004328 (-0.000725) | 0.077268 / 0.004250 (0.073017) | 0.054649 / 0.037052 (0.017597) | 0.322293 / 0.258489 (0.063804) | 0.372277 / 0.293841 (0.078436) | 0.027927 / 0.128546 (-0.100619) | 0.008495 / 0.075646 (-0.067151) | 0.313078 / 0.419271 (-0.106193) | 0.046974 / 0.043533 (0.003441) | 0.313848 / 0.255139 (0.058709) | 0.338454 / 0.283200 (0.055255) | 0.020462 / 0.141683 (-0.121221) | 1.473027 / 1.452155 (0.020873) | 1.539468 / 1.492716 (0.046752) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221429 / 0.018006 (0.203423) | 0.412044 / 0.000490 (0.411555) | 0.005866 / 0.000200 (0.005666) | 0.000075 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022870 / 0.037411 (-0.014541) | 0.099129 / 0.014526 (0.084603) | 0.103463 / 0.176557 (-0.073094) | 0.164969 / 0.737135 (-0.572166) | 0.110000 / 0.296338 (-0.186339) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431311 / 0.215209 (0.216102) | 4.293562 / 2.077655 (2.215907) | 1.961209 / 1.504120 (0.457089) | 1.733680 / 1.541195 (0.192485) | 1.793171 / 1.468490 (0.324681) | 0.568566 / 4.584777 (-4.016211) | 3.401794 / 3.745712 (-0.343918) | 1.827949 / 5.269862 (-3.441913) | 1.055963 / 4.565676 (-3.509714) | 0.068459 / 0.424275 (-0.355816) | 0.011586 / 0.007607 (0.003979) | 0.533936 / 0.226044 (0.307891) | 5.347637 / 2.268929 (3.078708) | 2.378056 / 55.444624 (-53.066569) | 2.032159 / 6.876477 (-4.844318) | 2.159064 / 2.142072 (0.016991) | 0.674528 / 4.805227 (-4.130699) | 0.136859 / 6.500664 (-6.363805) | 0.066629 / 0.075469 (-0.008840) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.218084 / 1.841788 (-0.623704) | 14.141710 / 8.074308 (6.067402) | 13.588415 / 10.191392 (3.397023) | 0.155104 / 0.680424 (-0.525320) | 0.017160 / 0.534201 (-0.517041) | 0.375558 / 0.579283 (-0.203725) | 0.386293 / 0.434364 (-0.048071) | 0.459476 / 0.540337 (-0.080862) | 0.548561 / 1.386936 (-0.838375) |\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) | 0.005878 / 0.011353 (-0.005475) | 0.003750 / 0.011008 (-0.007259) | 0.077720 / 0.038508 (0.039212) | 0.034955 / 0.023109 (0.011846) | 0.357480 / 0.275898 (0.081582) | 0.418210 / 0.323480 (0.094730) | 0.004566 / 0.007986 (-0.003419) | 0.002918 / 0.004328 (-0.001410) | 0.076517 / 0.004250 (0.072266) | 0.050202 / 0.037052 (0.013150) | 0.368166 / 0.258489 (0.109677) | 0.415681 / 0.293841 (0.121840) | 0.029496 / 0.128546 (-0.099050) | 0.008547 / 0.075646 (-0.067099) | 0.083037 / 0.419271 (-0.336234) | 0.045001 / 0.043533 (0.001468) | 0.356503 / 0.255139 (0.101364) | 0.383747 / 0.283200 (0.100547) | 0.025071 / 0.141683 (-0.116612) | 1.541985 / 1.452155 (0.089830) | 1.594710 / 1.492716 (0.101994) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.204491 / 0.018006 (0.186484) | 0.408686 / 0.000490 (0.408196) | 0.002505 / 0.000200 (0.002305) | 0.000082 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024446 / 0.037411 (-0.012965) | 0.101432 / 0.014526 (0.086906) | 0.108105 / 0.176557 (-0.068452) | 0.161195 / 0.737135 (-0.575940) | 0.112671 / 0.296338 (-0.183667) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.459697 / 0.215209 (0.244488) | 4.570071 / 2.077655 (2.492416) | 2.211547 / 1.504120 (0.707427) | 1.996651 / 1.541195 (0.455457) | 2.015621 / 1.468490 (0.547131) | 0.567423 / 4.584777 (-4.017354) | 3.408027 / 3.745712 (-0.337685) | 2.913824 / 5.269862 (-2.356038) | 1.423223 / 4.565676 (-3.142453) | 0.068740 / 0.424275 (-0.355535) | 0.010997 / 0.007607 (0.003390) | 0.567340 / 0.226044 (0.341296) | 5.666280 / 2.268929 (3.397351) | 2.804934 / 55.444624 (-52.639690) | 2.430761 / 6.876477 (-4.445716) | 2.451820 / 2.142072 (0.309748) | 0.681926 / 4.805227 (-4.123301) | 0.137761 / 6.500664 (-6.362903) | 0.067173 / 0.075469 (-0.008296) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.329853 / 1.841788 (-0.511934) | 14.436232 / 8.074308 (6.361924) | 14.398645 / 10.191392 (4.207253) | 0.147421 / 0.680424 (-0.533002) | 0.016743 / 0.534201 (-0.517458) | 0.364964 / 0.579283 (-0.214319) | 0.387072 / 0.434364 (-0.047292) | 0.423892 / 0.540337 (-0.116445) | 0.521304 / 1.386936 (-0.865632) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a62b6ce65f718e9ff4189da86d160ae4bb197fc2 \"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) | 0.006463 / 0.011353 (-0.004889) | 0.003923 / 0.011008 (-0.007086) | 0.102096 / 0.038508 (0.063588) | 0.040230 / 0.023109 (0.017121) | 0.384688 / 0.275898 (0.108789) | 0.445574 / 0.323480 (0.122094) | 0.003590 / 0.007986 (-0.004395) | 0.004023 / 0.004328 (-0.000306) | 0.080125 / 0.004250 (0.075875) | 0.057406 / 0.037052 (0.020354) | 0.395049 / 0.258489 (0.136560) | 0.438065 / 0.293841 (0.144224) | 0.028963 / 0.128546 (-0.099583) | 0.008693 / 0.075646 (-0.066954) | 0.317158 / 0.419271 (-0.102114) | 0.047930 / 0.043533 (0.004397) | 0.382442 / 0.255139 (0.127303) | 0.410665 / 0.283200 (0.127466) | 0.020127 / 0.141683 (-0.121555) | 1.558554 / 1.452155 (0.106400) | 1.590959 / 1.492716 (0.098242) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208826 / 0.018006 (0.190820) | 0.432037 / 0.000490 (0.431547) | 0.006509 / 0.000200 (0.006309) | 0.000285 / 0.000054 (0.000230) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023460 / 0.037411 (-0.013951) | 0.099070 / 0.014526 (0.084545) | 0.105771 / 0.176557 (-0.070785) | 0.166683 / 0.737135 (-0.570452) | 0.108755 / 0.296338 (-0.187583) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424324 / 0.215209 (0.209115) | 4.225696 / 2.077655 (2.148042) | 1.910955 / 1.504120 (0.406835) | 1.704493 / 1.541195 (0.163298) | 1.782784 / 1.468490 (0.314293) | 0.562927 / 4.584777 (-4.021850) | 3.380163 / 3.745712 (-0.365550) | 1.779641 / 5.269862 (-3.490221) | 1.029134 / 4.565676 (-3.536543) | 0.068325 / 0.424275 (-0.355950) | 0.011528 / 0.007607 (0.003921) | 0.530141 / 0.226044 (0.304097) | 5.323443 / 2.268929 (3.054514) | 2.346956 / 55.444624 (-53.097668) | 2.013335 / 6.876477 (-4.863142) | 2.118531 / 2.142072 (-0.023541) | 0.675206 / 4.805227 (-4.130021) | 0.135473 / 6.500664 (-6.365191) | 0.064804 / 0.075469 (-0.010665) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.240179 / 1.841788 (-0.601608) | 14.692449 / 8.074308 (6.618141) | 13.672223 / 10.191392 (3.480831) | 0.147748 / 0.680424 (-0.532676) | 0.017119 / 0.534201 (-0.517082) | 0.369481 / 0.579283 (-0.209802) | 0.390133 / 0.434364 (-0.044231) | 0.458768 / 0.540337 (-0.081569) | 0.548989 / 1.386936 (-0.837947) |\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) | 0.006319 / 0.011353 (-0.005034) | 0.003975 / 0.011008 (-0.007033) | 0.077886 / 0.038508 (0.039378) | 0.038322 / 0.023109 (0.015213) | 0.379851 / 0.275898 (0.103953) | 0.456749 / 0.323480 (0.133269) | 0.005320 / 0.007986 (-0.002665) | 0.003135 / 0.004328 (-0.001194) | 0.078272 / 0.004250 (0.074022) | 0.059919 / 0.037052 (0.022866) | 0.430062 / 0.258489 (0.171573) | 0.477432 / 0.293841 (0.183591) | 0.029713 / 0.128546 (-0.098833) | 0.008704 / 0.075646 (-0.066942) | 0.082488 / 0.419271 (-0.336784) | 0.044667 / 0.043533 (0.001134) | 0.354910 / 0.255139 (0.099771) | 0.434637 / 0.283200 (0.151438) | 0.026402 / 0.141683 (-0.115281) | 1.528825 / 1.452155 (0.076671) | 1.548209 / 1.492716 (0.055493) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237988 / 0.018006 (0.219982) | 0.420402 / 0.000490 (0.419913) | 0.003098 / 0.000200 (0.002898) | 0.000077 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026253 / 0.037411 (-0.011159) | 0.106137 / 0.014526 (0.091611) | 0.110273 / 0.176557 (-0.066284) | 0.165316 / 0.737135 (-0.571819) | 0.115720 / 0.296338 (-0.180619) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.454244 / 0.215209 (0.239035) | 4.526018 / 2.077655 (2.448364) | 2.395985 / 1.504120 (0.891865) | 2.234822 / 1.541195 (0.693627) | 2.370235 / 1.468490 (0.901745) | 0.567607 / 4.584777 (-4.017169) | 3.650156 / 3.745712 (-0.095556) | 3.360094 / 5.269862 (-1.909768) | 1.415252 / 4.565676 (-3.150424) | 0.068012 / 0.424275 (-0.356263) | 0.011135 / 0.007607 (0.003528) | 0.561967 / 0.226044 (0.335923) | 5.621819 / 2.268929 (3.352890) | 2.676912 / 55.444624 (-52.767712) | 2.338306 / 6.876477 (-4.538171) | 2.430888 / 2.142072 (0.288815) | 0.684576 / 4.805227 (-4.120651) | 0.138923 / 6.500664 (-6.361741) | 0.069933 / 0.075469 (-0.005536) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.313383 / 1.841788 (-0.528405) | 15.125088 / 8.074308 (7.050780) | 14.801501 / 10.191392 (4.610109) | 0.134235 / 0.680424 (-0.546189) | 0.017058 / 0.534201 (-0.517143) | 0.365166 / 0.579283 (-0.214117) | 0.395415 / 0.434364 (-0.038949) | 0.419355 / 0.540337 (-0.120983) | 0.513411 / 1.386936 (-0.873525) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8b9649b3cfb49342e44873ce7e29e0c75eaf3efa \"CML watermark\")\n"
] |
6,000
|
Pin `joblib` to avoid `joblibspark` test failures
|
`joblibspark` doesn't support the latest `joblib` release.
See https://github.com/huggingface/datasets/actions/runs/5401870932/jobs/9812337078 for the errors
|
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6000",
"html_url": "https://github.com/huggingface/datasets/pull/6000",
"diff_url": "https://github.com/huggingface/datasets/pull/6000.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6000.patch",
"merged_at": "2023-06-30T13:08:27"
}
| true
|
[
"<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) | 0.006722 / 0.011353 (-0.004631) | 0.004425 / 0.011008 (-0.006583) | 0.100850 / 0.038508 (0.062341) | 0.040816 / 0.023109 (0.017707) | 0.348823 / 0.275898 (0.072925) | 0.446285 / 0.323480 (0.122805) | 0.005738 / 0.007986 (-0.002247) | 0.003517 / 0.004328 (-0.000811) | 0.078824 / 0.004250 (0.074574) | 0.064695 / 0.037052 (0.027643) | 0.389894 / 0.258489 (0.131405) | 0.416107 / 0.293841 (0.122266) | 0.028850 / 0.128546 (-0.099696) | 0.009011 / 0.075646 (-0.066635) | 0.323117 / 0.419271 (-0.096154) | 0.049162 / 0.043533 (0.005629) | 0.340144 / 0.255139 (0.085005) | 0.382072 / 0.283200 (0.098872) | 0.023160 / 0.141683 (-0.118523) | 1.549218 / 1.452155 (0.097063) | 1.581266 / 1.492716 (0.088550) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.293360 / 0.018006 (0.275353) | 0.602189 / 0.000490 (0.601700) | 0.004608 / 0.000200 (0.004408) | 0.000082 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028144 / 0.037411 (-0.009267) | 0.107088 / 0.014526 (0.092562) | 0.112188 / 0.176557 (-0.064369) | 0.174669 / 0.737135 (-0.562466) | 0.116359 / 0.296338 (-0.179980) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422911 / 0.215209 (0.207702) | 4.231524 / 2.077655 (2.153869) | 1.906711 / 1.504120 (0.402591) | 1.706841 / 1.541195 (0.165646) | 1.792066 / 1.468490 (0.323576) | 0.559221 / 4.584777 (-4.025556) | 3.434280 / 3.745712 (-0.311433) | 1.918714 / 5.269862 (-3.351148) | 1.073070 / 4.565676 (-3.492606) | 0.067891 / 0.424275 (-0.356384) | 0.011927 / 0.007607 (0.004320) | 0.530843 / 0.226044 (0.304799) | 5.309213 / 2.268929 (3.040285) | 2.439246 / 55.444624 (-53.005378) | 2.101245 / 6.876477 (-4.775231) | 2.177436 / 2.142072 (0.035363) | 0.672150 / 4.805227 (-4.133077) | 0.137571 / 6.500664 (-6.363093) | 0.068343 / 0.075469 (-0.007126) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.265262 / 1.841788 (-0.576525) | 14.988021 / 8.074308 (6.913713) | 13.611677 / 10.191392 (3.420285) | 0.171389 / 0.680424 (-0.509035) | 0.017681 / 0.534201 (-0.516520) | 0.377542 / 0.579283 (-0.201741) | 0.399475 / 0.434364 (-0.034889) | 0.469553 / 0.540337 (-0.070785) | 0.561888 / 1.386936 (-0.825048) |\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) | 0.006782 / 0.011353 (-0.004571) | 0.004412 / 0.011008 (-0.006597) | 0.078594 / 0.038508 (0.040086) | 0.039930 / 0.023109 (0.016820) | 0.371879 / 0.275898 (0.095981) | 0.444910 / 0.323480 (0.121430) | 0.005707 / 0.007986 (-0.002279) | 0.003901 / 0.004328 (-0.000427) | 0.080125 / 0.004250 (0.075875) | 0.063977 / 0.037052 (0.026925) | 0.382781 / 0.258489 (0.124292) | 0.441791 / 0.293841 (0.147950) | 0.030428 / 0.128546 (-0.098118) | 0.009008 / 0.075646 (-0.066638) | 0.084447 / 0.419271 (-0.334824) | 0.044432 / 0.043533 (0.000899) | 0.365686 / 0.255139 (0.110547) | 0.394312 / 0.283200 (0.111113) | 0.024508 / 0.141683 (-0.117175) | 1.577020 / 1.452155 (0.124865) | 1.630259 / 1.492716 (0.137543) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.307960 / 0.018006 (0.289953) | 0.591473 / 0.000490 (0.590983) | 0.008098 / 0.000200 (0.007898) | 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) | 0.029567 / 0.037411 (-0.007845) | 0.112773 / 0.014526 (0.098247) | 0.117362 / 0.176557 (-0.059194) | 0.174293 / 0.737135 (-0.562843) | 0.123156 / 0.296338 (-0.173182) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.457475 / 0.215209 (0.242266) | 4.599067 / 2.077655 (2.521412) | 2.262638 / 1.504120 (0.758518) | 2.124943 / 1.541195 (0.583748) | 2.339912 / 1.468490 (0.871422) | 0.566264 / 4.584777 (-4.018513) | 3.489261 / 3.745712 (-0.256451) | 1.925151 / 5.269862 (-3.344711) | 1.099389 / 4.565676 (-3.466287) | 0.068232 / 0.424275 (-0.356043) | 0.011660 / 0.007607 (0.004052) | 0.571227 / 0.226044 (0.345183) | 5.702059 / 2.268929 (3.433130) | 2.837701 / 55.444624 (-52.606924) | 2.605468 / 6.876477 (-4.271008) | 2.818396 / 2.142072 (0.676323) | 0.681856 / 4.805227 (-4.123371) | 0.141401 / 6.500664 (-6.359263) | 0.069728 / 0.075469 (-0.005741) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.354935 / 1.841788 (-0.486853) | 15.437404 / 8.074308 (7.363095) | 15.415193 / 10.191392 (5.223801) | 0.153459 / 0.680424 (-0.526964) | 0.017190 / 0.534201 (-0.517011) | 0.367256 / 0.579283 (-0.212027) | 0.392709 / 0.434364 (-0.041655) | 0.426125 / 0.540337 (-0.114213) | 0.522612 / 1.386936 (-0.864324) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#25ac13d8ab23e7d99252ce083a45e8333b6bbcdc \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009183 / 0.011353 (-0.002170) | 0.005232 / 0.011008 (-0.005776) | 0.120349 / 0.038508 (0.081841) | 0.044715 / 0.023109 (0.021606) | 0.361519 / 0.275898 (0.085621) | 0.463702 / 0.323480 (0.140223) | 0.005842 / 0.007986 (-0.002144) | 0.004041 / 0.004328 (-0.000288) | 0.096953 / 0.004250 (0.092703) | 0.070593 / 0.037052 (0.033540) | 0.409790 / 0.258489 (0.151301) | 0.477452 / 0.293841 (0.183611) | 0.045827 / 0.128546 (-0.082719) | 0.014038 / 0.075646 (-0.061608) | 0.421317 / 0.419271 (0.002045) | 0.065276 / 0.043533 (0.021743) | 0.360074 / 0.255139 (0.104935) | 0.409147 / 0.283200 (0.125947) | 0.032444 / 0.141683 (-0.109238) | 1.739257 / 1.452155 (0.287102) | 1.831408 / 1.492716 (0.338692) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.274852 / 0.018006 (0.256846) | 0.596320 / 0.000490 (0.595830) | 0.006399 / 0.000200 (0.006199) | 0.000133 / 0.000054 (0.000079) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031400 / 0.037411 (-0.006012) | 0.127052 / 0.014526 (0.112526) | 0.134269 / 0.176557 (-0.042288) | 0.225998 / 0.737135 (-0.511137) | 0.150019 / 0.296338 (-0.146319) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.654202 / 0.215209 (0.438993) | 6.216735 / 2.077655 (4.139081) | 2.440214 / 1.504120 (0.936094) | 2.150575 / 1.541195 (0.609380) | 2.124790 / 1.468490 (0.656300) | 0.923514 / 4.584777 (-3.661263) | 5.556924 / 3.745712 (1.811212) | 2.843886 / 5.269862 (-2.425975) | 1.834232 / 4.565676 (-2.731444) | 0.111735 / 0.424275 (-0.312540) | 0.014823 / 0.007607 (0.007216) | 0.820503 / 0.226044 (0.594459) | 7.887737 / 2.268929 (5.618809) | 3.120307 / 55.444624 (-52.324317) | 2.405856 / 6.876477 (-4.470621) | 2.411239 / 2.142072 (0.269167) | 1.071283 / 4.805227 (-3.733944) | 0.227738 / 6.500664 (-6.272926) | 0.073516 / 0.075469 (-0.001953) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.531806 / 1.841788 (-0.309982) | 18.547661 / 8.074308 (10.473353) | 21.083922 / 10.191392 (10.892530) | 0.241706 / 0.680424 (-0.438718) | 0.034169 / 0.534201 (-0.500032) | 0.497514 / 0.579283 (-0.081769) | 0.599801 / 0.434364 (0.165437) | 0.576465 / 0.540337 (0.036127) | 0.673509 / 1.386936 (-0.713427) |\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) | 0.007558 / 0.011353 (-0.003795) | 0.005001 / 0.011008 (-0.006008) | 0.093809 / 0.038508 (0.055301) | 0.039792 / 0.023109 (0.016683) | 0.456869 / 0.275898 (0.180971) | 0.493370 / 0.323480 (0.169891) | 0.005561 / 0.007986 (-0.002424) | 0.003982 / 0.004328 (-0.000346) | 0.085421 / 0.004250 (0.081170) | 0.059817 / 0.037052 (0.022765) | 0.468040 / 0.258489 (0.209550) | 0.514853 / 0.293841 (0.221012) | 0.044267 / 0.128546 (-0.084279) | 0.012674 / 0.075646 (-0.062972) | 0.098324 / 0.419271 (-0.320948) | 0.056604 / 0.043533 (0.013071) | 0.432200 / 0.255139 (0.177061) | 0.459812 / 0.283200 (0.176612) | 0.033872 / 0.141683 (-0.107811) | 1.618576 / 1.452155 (0.166421) | 1.676562 / 1.492716 (0.183846) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230625 / 0.018006 (0.212619) | 0.600558 / 0.000490 (0.600068) | 0.003419 / 0.000200 (0.003219) | 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) | 0.026916 / 0.037411 (-0.010496) | 0.103003 / 0.014526 (0.088478) | 0.117078 / 0.176557 (-0.059478) | 0.169359 / 0.737135 (-0.567776) | 0.120305 / 0.296338 (-0.176034) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.616877 / 0.215209 (0.401668) | 6.157232 / 2.077655 (4.079577) | 2.869219 / 1.504120 (1.365099) | 2.381410 / 1.541195 (0.840216) | 2.417357 / 1.468490 (0.948867) | 0.914947 / 4.584777 (-3.669830) | 5.718526 / 3.745712 (1.972814) | 2.757253 / 5.269862 (-2.512609) | 1.794122 / 4.565676 (-2.771554) | 0.108423 / 0.424275 (-0.315852) | 0.013378 / 0.007607 (0.005771) | 0.831067 / 0.226044 (0.605023) | 8.478946 / 2.268929 (6.210018) | 3.685937 / 55.444624 (-51.758687) | 2.867472 / 6.876477 (-4.009005) | 2.895975 / 2.142072 (0.753903) | 1.137547 / 4.805227 (-3.667681) | 0.213891 / 6.500664 (-6.286773) | 0.075825 / 0.075469 (0.000356) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.621193 / 1.841788 (-0.220594) | 17.322110 / 8.074308 (9.247802) | 21.804016 / 10.191392 (11.612624) | 0.243692 / 0.680424 (-0.436732) | 0.030331 / 0.534201 (-0.503870) | 0.492186 / 0.579283 (-0.087097) | 0.632583 / 0.434364 (0.198219) | 0.576265 / 0.540337 (0.035927) | 0.713165 / 1.386936 (-0.673771) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a293ceb5aa41c4ae265c0e2aa9ada2d544466121 \"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) | 0.008916 / 0.011353 (-0.002437) | 0.004737 / 0.011008 (-0.006271) | 0.134271 / 0.038508 (0.095763) | 0.054472 / 0.023109 (0.031363) | 0.380942 / 0.275898 (0.105044) | 0.474138 / 0.323480 (0.150658) | 0.007917 / 0.007986 (-0.000068) | 0.003748 / 0.004328 (-0.000580) | 0.092765 / 0.004250 (0.088515) | 0.077873 / 0.037052 (0.040821) | 0.397533 / 0.258489 (0.139043) | 0.454737 / 0.293841 (0.160896) | 0.039901 / 0.128546 (-0.088645) | 0.010188 / 0.075646 (-0.065458) | 0.447312 / 0.419271 (0.028040) | 0.068684 / 0.043533 (0.025151) | 0.371554 / 0.255139 (0.116415) | 0.459655 / 0.283200 (0.176455) | 0.027157 / 0.141683 (-0.114526) | 1.874643 / 1.452155 (0.422488) | 2.014800 / 1.492716 (0.522083) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227079 / 0.018006 (0.209073) | 0.483241 / 0.000490 (0.482751) | 0.012404 / 0.000200 (0.012204) | 0.000409 / 0.000054 (0.000354) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033135 / 0.037411 (-0.004277) | 0.137782 / 0.014526 (0.123257) | 0.142951 / 0.176557 (-0.033605) | 0.209825 / 0.737135 (-0.527311) | 0.152438 / 0.296338 (-0.143900) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.513066 / 0.215209 (0.297857) | 5.122776 / 2.077655 (3.045121) | 2.399270 / 1.504120 (0.895150) | 2.180143 / 1.541195 (0.638949) | 2.286395 / 1.468490 (0.817905) | 0.641866 / 4.584777 (-3.942911) | 4.694922 / 3.745712 (0.949210) | 2.543390 / 5.269862 (-2.726472) | 1.398592 / 4.565676 (-3.167084) | 0.088662 / 0.424275 (-0.335613) | 0.015854 / 0.007607 (0.008247) | 0.688891 / 0.226044 (0.462847) | 6.370148 / 2.268929 (4.101220) | 2.949974 / 55.444624 (-52.494650) | 2.538049 / 6.876477 (-4.338428) | 2.699380 / 2.142072 (0.557308) | 0.792670 / 4.805227 (-4.012557) | 0.169126 / 6.500664 (-6.331538) | 0.078511 / 0.075469 (0.003042) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.609119 / 1.841788 (-0.232669) | 18.785069 / 8.074308 (10.710761) | 16.670783 / 10.191392 (6.479391) | 0.213081 / 0.680424 (-0.467343) | 0.023904 / 0.534201 (-0.510296) | 0.567720 / 0.579283 (-0.011564) | 0.505806 / 0.434364 (0.071442) | 0.649466 / 0.540337 (0.109129) | 0.773174 / 1.386936 (-0.613762) |\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) | 0.008036 / 0.011353 (-0.003317) | 0.004808 / 0.011008 (-0.006201) | 0.094316 / 0.038508 (0.055808) | 0.056174 / 0.023109 (0.033065) | 0.481618 / 0.275898 (0.205720) | 0.565300 / 0.323480 (0.241820) | 0.006339 / 0.007986 (-0.001646) | 0.003950 / 0.004328 (-0.000379) | 0.093389 / 0.004250 (0.089139) | 0.076163 / 0.037052 (0.039111) | 0.489013 / 0.258489 (0.230524) | 0.565451 / 0.293841 (0.271611) | 0.039392 / 0.128546 (-0.089155) | 0.010553 / 0.075646 (-0.065093) | 0.101406 / 0.419271 (-0.317865) | 0.062355 / 0.043533 (0.018822) | 0.470461 / 0.255139 (0.215322) | 0.502574 / 0.283200 (0.219375) | 0.030196 / 0.141683 (-0.111486) | 1.893926 / 1.452155 (0.441771) | 1.958902 / 1.492716 (0.466185) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198074 / 0.018006 (0.180068) | 0.476828 / 0.000490 (0.476338) | 0.003457 / 0.000200 (0.003257) | 0.000105 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037576 / 0.037411 (0.000165) | 0.146663 / 0.014526 (0.132138) | 0.152969 / 0.176557 (-0.023588) | 0.218683 / 0.737135 (-0.518452) | 0.161552 / 0.296338 (-0.134786) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.525988 / 0.215209 (0.310779) | 5.234673 / 2.077655 (3.157018) | 2.571668 / 1.504120 (1.067548) | 2.339760 / 1.541195 (0.798565) | 2.422886 / 1.468490 (0.954395) | 0.651537 / 4.584777 (-3.933240) | 4.811148 / 3.745712 (1.065436) | 4.451165 / 5.269862 (-0.818697) | 2.016283 / 4.565676 (-2.549394) | 0.096393 / 0.424275 (-0.327882) | 0.015222 / 0.007607 (0.007615) | 0.739132 / 0.226044 (0.513087) | 6.813327 / 2.268929 (4.544399) | 3.169018 / 55.444624 (-52.275606) | 2.783120 / 6.876477 (-4.093356) | 2.918979 / 2.142072 (0.776907) | 0.797476 / 4.805227 (-4.007751) | 0.171038 / 6.500664 (-6.329626) | 0.079878 / 0.075469 (0.004409) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.595082 / 1.841788 (-0.246705) | 19.685844 / 8.074308 (11.611536) | 17.518989 / 10.191392 (7.327597) | 0.220015 / 0.680424 (-0.460409) | 0.026351 / 0.534201 (-0.507850) | 0.578977 / 0.579283 (-0.000306) | 0.549564 / 0.434364 (0.115200) | 0.667564 / 0.540337 (0.127227) | 0.802121 / 1.386936 (-0.584815) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e9aee64766aaddfda60a735cfc93345aed64bdcf \"CML watermark\")\n"
] |
5,999
|
Getting a 409 error while loading xglue dataset
|
### Describe the bug
Unable to load xglue dataset
### Steps to reproduce the bug
```python
import datasets
dataset = datasets.load_dataset("xglue", "ntg")
```
> ConnectionError: Couldn't reach https://xglue.blob.core.windows.net/xglue/xglue_full_dataset.tar.gz (error 409)
### Expected behavior
Expected the dataset to load
### Environment info
- `datasets` version: 2.13.1
- Platform: Linux-5.15.107+-x86_64-with-glibc2.31
- Python version: 3.10.12
- Huggingface_hub version: 0.15.1
- PyArrow version: 9.0.0
- Pandas version: 1.5.3
|
[] |
{
"url": null,
"html_url": null,
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"merged_at": null
}
| false
|
[
"Thanks for reporting, @Praful932.\r\n\r\nLet's continue the conversation on the Hub: https://huggingface.co/datasets/xglue/discussions/5"
] |
5,998
|
The current implementation has a potential bug in the sort method
|
### Describe the bug
In the sort method，here's a piece of code
```python
# column_names: Union[str, Sequence_[str]]
# Check proper format of and for duplicates in column_names
if not isinstance(column_names, list):
column_names = [column_names]
```
I get an error when I pass in a tuple based on the column_names type annotation, it will raise an errror.As in the example below, while the type annotation implies that a tuple can be passed.
```python
from datasets import load_dataset
dataset = load_dataset('glue', 'ax')['test']
dataset.sort(column_names=('premise', 'hypothesis'))
# Raise ValueError: Column '('premise', 'hypothesis')' not found in the dataset.
```
Of course, after I modified the tuple into a list, everything worked fine
Change the code to the following so there will be no problem
```python
# Check proper format of and for duplicates in column_names
if not isinstance(column_names, list):
if isinstance(column_names, str):
column_names = [column_names]
else:
column_names = list(column_names)
```
### Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('glue', 'ax')['test']
dataset.sort(column_names=('premise', 'hypothesis'))
# Raise ValueError: Column '('premise', 'hypothesis')' not found in the dataset.
```
### Expected behavior
Passing tuple into column_names should be equivalent to passing list
### Environment info
- `datasets` version: 2.13.0
- Platform: macOS-13.1-arm64-arm-64bit
- Python version: 3.10.11
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.1
- Pandas version: 2.0.2
|
[] |
{
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"html_url": null,
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}
| false
|
[
"Thanks for reporting, @wangyuxinwhy. "
] |
5,997
|
extend the map function so it can wrap around long text that does not fit in the context window
|
### Feature request
I understand `dataset` provides a [`map`](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_dataset.py#L2849) function. This function in turn takes in a callable that is used to tokenize the text on which a model is trained. Frequently this text will not fit within a models's context window. In this case it would be useful to wrap around the text into multiple rows with each row fitting the model's context window. I tried to do it using this code as example which in turn I have borrowed from [here](https://stackoverflow.com/a/76343993/147530):
```
data = data.map(lambda samples: tokenizer(samples["text"], max_length=tokenizer.model_max_length, truncation=True, stride=4, return_overflowing_tokens=True), batched=True)
```
but running the code gives me this error:
```
File "/llm/fine-tune.py", line 117, in <module>
data = data.map(lambda samples: tokenizer(samples["text"], max_length=tokenizer.model_max_length, truncation=True, stride=4, return_overflowing_tokens=True), batched=True)
File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 580, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 545, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3087, in map
for rank, done, content in Dataset._map_single(**dataset_kwargs):
File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3480, in _map_single
writer.write_batch(batch)
File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_writer.py", line 556, in write_batch
pa_table = pa.Table.from_arrays(arrays, schema=schema)
File "pyarrow/table.pxi", line 3798, in pyarrow.lib.Table.from_arrays
File "pyarrow/table.pxi", line 2962, in pyarrow.lib.Table.validate
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Column 1 named input_ids expected length 394 but got length 447
```
The lambda function I have provided is correctly chopping up long text so it wraps around (and because of this 394 samples become 447 after wrap around) but the dataset `map` function does not like it.
### Motivation
please see above
### Your contribution
I'm afraid I don't have much knowledge to help
|
[
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"id": 1935892871,
"node_id": "MDU6TGFiZWwxOTM1ODkyODcx",
"url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement",
"name": "enhancement",
"color": "a2eeef",
"default": true,
"description": "New feature or request"
}
] |
{
"url": null,
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| false
|
[
"I just noticed the [docs](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_dataset.py#L2881C11-L2881C200) say:\r\n\r\n>If batched is `True` and `batch_size` is `n > 1`, then the function takes a batch of `n` examples as input and can return a batch with `n` examples, or with an arbitrary number of examples.\r\n\r\nso maybe this is a bug then.",
"All the values in a batch must be of the same length. So one solution is dropping all the input columns:\r\n```python\r\ndata = data.map(lambda samples: tokenizer(samples[\"text\"], max_length=tokenizer.model_max_length, truncation=True, stride=4, return_overflowing_tokens=True), batched=True, remove_columns=data.column_names)\r\n```\r\n\r\nAnother is padding/transforming the input columns to the tokenizer output's length (447). "
] |
5,996
|
Deprecate `use_auth_token` in favor of `token`
|
... to be consistent with `transformers` and `huggingface_hub`.
|
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5996",
"html_url": "https://github.com/huggingface/datasets/pull/5996",
"diff_url": "https://github.com/huggingface/datasets/pull/5996.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5996.patch",
"merged_at": "2023-07-03T16:03:33"
}
| true
|
[
"_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) | 0.006134 / 0.011353 (-0.005219) | 0.003816 / 0.011008 (-0.007193) | 0.098226 / 0.038508 (0.059718) | 0.036830 / 0.023109 (0.013721) | 0.314551 / 0.275898 (0.038653) | 0.372251 / 0.323480 (0.048771) | 0.004762 / 0.007986 (-0.003224) | 0.003041 / 0.004328 (-0.001287) | 0.077651 / 0.004250 (0.073401) | 0.052445 / 0.037052 (0.015393) | 0.324632 / 0.258489 (0.066143) | 0.365724 / 0.293841 (0.071883) | 0.028069 / 0.128546 (-0.100477) | 0.008444 / 0.075646 (-0.067203) | 0.312767 / 0.419271 (-0.106505) | 0.047773 / 0.043533 (0.004240) | 0.305317 / 0.255139 (0.050178) | 0.332007 / 0.283200 (0.048807) | 0.018985 / 0.141683 (-0.122698) | 1.538022 / 1.452155 (0.085868) | 1.575898 / 1.492716 (0.083182) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.204780 / 0.018006 (0.186774) | 0.428125 / 0.000490 (0.427635) | 0.003454 / 0.000200 (0.003254) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025064 / 0.037411 (-0.012348) | 0.099419 / 0.014526 (0.084893) | 0.111068 / 0.176557 (-0.065489) | 0.169775 / 0.737135 (-0.567361) | 0.112067 / 0.296338 (-0.184271) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429642 / 0.215209 (0.214433) | 4.275556 / 2.077655 (2.197901) | 1.914658 / 1.504120 (0.410539) | 1.706556 / 1.541195 (0.165361) | 1.754228 / 1.468490 (0.285738) | 0.563669 / 4.584777 (-4.021108) | 3.391501 / 3.745712 (-0.354211) | 1.791517 / 5.269862 (-3.478345) | 1.030704 / 4.565676 (-3.534973) | 0.070882 / 0.424275 (-0.353393) | 0.011351 / 0.007607 (0.003744) | 0.529438 / 0.226044 (0.303394) | 5.294316 / 2.268929 (3.025387) | 2.344653 / 55.444624 (-53.099972) | 1.997468 / 6.876477 (-4.879009) | 2.108932 / 2.142072 (-0.033140) | 0.676794 / 4.805227 (-4.128433) | 0.135058 / 6.500664 (-6.365607) | 0.065857 / 0.075469 (-0.009612) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.231864 / 1.841788 (-0.609924) | 13.986694 / 8.074308 (5.912386) | 13.306600 / 10.191392 (3.115208) | 0.145520 / 0.680424 (-0.534904) | 0.016717 / 0.534201 (-0.517484) | 0.366303 / 0.579283 (-0.212980) | 0.391637 / 0.434364 (-0.042727) | 0.425445 / 0.540337 (-0.114892) | 0.507719 / 1.386936 (-0.879217) |\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) | 0.006236 / 0.011353 (-0.005116) | 0.003766 / 0.011008 (-0.007242) | 0.076794 / 0.038508 (0.038286) | 0.037210 / 0.023109 (0.014101) | 0.378387 / 0.275898 (0.102489) | 0.425456 / 0.323480 (0.101977) | 0.004694 / 0.007986 (-0.003291) | 0.002921 / 0.004328 (-0.001407) | 0.076985 / 0.004250 (0.072735) | 0.052188 / 0.037052 (0.015136) | 0.394385 / 0.258489 (0.135896) | 0.432527 / 0.293841 (0.138686) | 0.029091 / 0.128546 (-0.099455) | 0.008364 / 0.075646 (-0.067282) | 0.082583 / 0.419271 (-0.336689) | 0.042928 / 0.043533 (-0.000605) | 0.375321 / 0.255139 (0.120182) | 0.391719 / 0.283200 (0.108519) | 0.019388 / 0.141683 (-0.122295) | 1.550644 / 1.452155 (0.098489) | 1.604882 / 1.492716 (0.112166) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236859 / 0.018006 (0.218853) | 0.418528 / 0.000490 (0.418039) | 0.000388 / 0.000200 (0.000188) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025548 / 0.037411 (-0.011863) | 0.100644 / 0.014526 (0.086118) | 0.109102 / 0.176557 (-0.067455) | 0.161694 / 0.737135 (-0.575441) | 0.112088 / 0.296338 (-0.184250) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.484128 / 0.215209 (0.268919) | 4.849952 / 2.077655 (2.772297) | 2.512769 / 1.504120 (1.008649) | 2.303295 / 1.541195 (0.762100) | 2.356699 / 1.468490 (0.888209) | 0.564181 / 4.584777 (-4.020596) | 3.421393 / 3.745712 (-0.324319) | 2.570875 / 5.269862 (-2.698987) | 1.474307 / 4.565676 (-3.091370) | 0.068035 / 0.424275 (-0.356240) | 0.011300 / 0.007607 (0.003693) | 0.587867 / 0.226044 (0.361823) | 5.862447 / 2.268929 (3.593519) | 3.004017 / 55.444624 (-52.440607) | 2.664989 / 6.876477 (-4.211488) | 2.740020 / 2.142072 (0.597948) | 0.680840 / 4.805227 (-4.124387) | 0.137001 / 6.500664 (-6.363663) | 0.068098 / 0.075469 (-0.007371) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.297362 / 1.841788 (-0.544426) | 14.207891 / 8.074308 (6.133583) | 14.087562 / 10.191392 (3.896170) | 0.149514 / 0.680424 (-0.530910) | 0.016566 / 0.534201 (-0.517635) | 0.367602 / 0.579283 (-0.211681) | 0.400692 / 0.434364 (-0.033671) | 0.432907 / 0.540337 (-0.107431) | 0.525924 / 1.386936 (-0.861012) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1ec069feaaf6c28d4e4df76d344693b591a74c3f \"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) | 0.006223 / 0.011353 (-0.005130) | 0.003672 / 0.011008 (-0.007336) | 0.097451 / 0.038508 (0.058943) | 0.036243 / 0.023109 (0.013133) | 0.375650 / 0.275898 (0.099752) | 0.431652 / 0.323480 (0.108172) | 0.004758 / 0.007986 (-0.003227) | 0.002941 / 0.004328 (-0.001387) | 0.077383 / 0.004250 (0.073132) | 0.055342 / 0.037052 (0.018289) | 0.390335 / 0.258489 (0.131846) | 0.427867 / 0.293841 (0.134026) | 0.027619 / 0.128546 (-0.100927) | 0.008244 / 0.075646 (-0.067402) | 0.313499 / 0.419271 (-0.105773) | 0.054987 / 0.043533 (0.011454) | 0.394044 / 0.255139 (0.138905) | 0.398784 / 0.283200 (0.115584) | 0.026499 / 0.141683 (-0.115184) | 1.496907 / 1.452155 (0.044753) | 1.554465 / 1.492716 (0.061749) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.241197 / 0.018006 (0.223190) | 0.427856 / 0.000490 (0.427366) | 0.006264 / 0.000200 (0.006065) | 0.000218 / 0.000054 (0.000164) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025550 / 0.037411 (-0.011862) | 0.104426 / 0.014526 (0.089901) | 0.110310 / 0.176557 (-0.066246) | 0.173813 / 0.737135 (-0.563322) | 0.112129 / 0.296338 (-0.184209) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.458806 / 0.215209 (0.243597) | 4.576351 / 2.077655 (2.498697) | 2.265670 / 1.504120 (0.761550) | 2.073230 / 1.541195 (0.532035) | 2.135283 / 1.468490 (0.666793) | 0.562506 / 4.584777 (-4.022271) | 3.375101 / 3.745712 (-0.370611) | 1.734393 / 5.269862 (-3.535469) | 1.026622 / 4.565676 (-3.539054) | 0.068144 / 0.424275 (-0.356131) | 0.011092 / 0.007607 (0.003485) | 0.562779 / 0.226044 (0.336734) | 5.608256 / 2.268929 (3.339328) | 2.706468 / 55.444624 (-52.738157) | 2.381607 / 6.876477 (-4.494869) | 2.451027 / 2.142072 (0.308954) | 0.671590 / 4.805227 (-4.133637) | 0.135749 / 6.500664 (-6.364915) | 0.065389 / 0.075469 (-0.010080) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.244806 / 1.841788 (-0.596981) | 14.042150 / 8.074308 (5.967841) | 14.246612 / 10.191392 (4.055220) | 0.134309 / 0.680424 (-0.546114) | 0.017082 / 0.534201 (-0.517119) | 0.366043 / 0.579283 (-0.213240) | 0.400748 / 0.434364 (-0.033616) | 0.425695 / 0.540337 (-0.114643) | 0.509355 / 1.386936 (-0.877581) |\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) | 0.006134 / 0.011353 (-0.005219) | 0.003980 / 0.011008 (-0.007028) | 0.078353 / 0.038508 (0.039845) | 0.038011 / 0.023109 (0.014902) | 0.375784 / 0.275898 (0.099886) | 0.433619 / 0.323480 (0.110139) | 0.004897 / 0.007986 (-0.003088) | 0.002981 / 0.004328 (-0.001347) | 0.077362 / 0.004250 (0.073112) | 0.056108 / 0.037052 (0.019056) | 0.395984 / 0.258489 (0.137495) | 0.427397 / 0.293841 (0.133556) | 0.029325 / 0.128546 (-0.099221) | 0.008498 / 0.075646 (-0.067148) | 0.082478 / 0.419271 (-0.336794) | 0.044085 / 0.043533 (0.000552) | 0.389923 / 0.255139 (0.134784) | 0.391180 / 0.283200 (0.107980) | 0.022452 / 0.141683 (-0.119231) | 1.507758 / 1.452155 (0.055603) | 1.530459 / 1.492716 (0.037743) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230928 / 0.018006 (0.212922) | 0.408484 / 0.000490 (0.407995) | 0.000806 / 0.000200 (0.000606) | 0.000067 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025183 / 0.037411 (-0.012228) | 0.102292 / 0.014526 (0.087766) | 0.108142 / 0.176557 (-0.068415) | 0.161172 / 0.737135 (-0.575963) | 0.114476 / 0.296338 (-0.181862) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.482978 / 0.215209 (0.267769) | 4.816103 / 2.077655 (2.738448) | 2.505567 / 1.504120 (1.001447) | 2.302598 / 1.541195 (0.761404) | 2.371238 / 1.468490 (0.902748) | 0.567467 / 4.584777 (-4.017310) | 3.363407 / 3.745712 (-0.382306) | 1.746213 / 5.269862 (-3.523649) | 1.035468 / 4.565676 (-3.530208) | 0.068431 / 0.424275 (-0.355844) | 0.011069 / 0.007607 (0.003462) | 0.598241 / 0.226044 (0.372196) | 5.953927 / 2.268929 (3.684999) | 3.007493 / 55.444624 (-52.437132) | 2.629399 / 6.876477 (-4.247078) | 2.737201 / 2.142072 (0.595129) | 0.682456 / 4.805227 (-4.122771) | 0.137613 / 6.500664 (-6.363051) | 0.067941 / 0.075469 (-0.007528) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.306015 / 1.841788 (-0.535772) | 14.359240 / 8.074308 (6.284932) | 14.187601 / 10.191392 (3.996209) | 0.138612 / 0.680424 (-0.541812) | 0.016708 / 0.534201 (-0.517493) | 0.366365 / 0.579283 (-0.212918) | 0.396982 / 0.434364 (-0.037382) | 0.426939 / 0.540337 (-0.113398) | 0.520064 / 1.386936 (-0.866872) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#21d0fd041a5eca02d3ee787396216ac613c662ac \"CML watermark\")\n",
"They use `token` and emit a deprecation warning if `use_auth_token` is passed instead (see https://github.com/huggingface/transformers/blob/78a2b19fc84ed55c65f4bf20a901edb7ceb73c5f/src/transformers/modeling_utils.py#L1933). \r\n\r\nI think we can update the `examples` scripts after merging this PR.",
"> I think we can update the examples scripts after merging this PR.\r\n\r\nWe should do a release before updated in the examples scripts no ? That's why it's an option to not have a deprecation warning until transformers and co are updated with the `token` arg",
"> We should do a release before updated in the examples scripts no ? That's why it's an option to not have a deprecation warning until transformers and co are updated with the token arg\r\n\r\nThis would avoid the warning only for the latest `datasets` release. TBH, I don't think this is worth the hassle, considering how simple it is to remove it.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007644 / 0.011353 (-0.003709) | 0.004667 / 0.011008 (-0.006341) | 0.117347 / 0.038508 (0.078839) | 0.050620 / 0.023109 (0.027510) | 0.415402 / 0.275898 (0.139504) | 0.485898 / 0.323480 (0.162418) | 0.005848 / 0.007986 (-0.002138) | 0.003736 / 0.004328 (-0.000592) | 0.089798 / 0.004250 (0.085547) | 0.069344 / 0.037052 (0.032292) | 0.441684 / 0.258489 (0.183195) | 0.468972 / 0.293841 (0.175131) | 0.036637 / 0.128546 (-0.091909) | 0.010219 / 0.075646 (-0.065427) | 0.394293 / 0.419271 (-0.024978) | 0.061462 / 0.043533 (0.017929) | 0.409448 / 0.255139 (0.154309) | 0.431557 / 0.283200 (0.148358) | 0.027795 / 0.141683 (-0.113888) | 1.837844 / 1.452155 (0.385690) | 1.862683 / 1.492716 (0.369967) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230500 / 0.018006 (0.212494) | 0.483139 / 0.000490 (0.482649) | 0.006517 / 0.000200 (0.006317) | 0.000143 / 0.000054 (0.000088) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033152 / 0.037411 (-0.004259) | 0.133673 / 0.014526 (0.119147) | 0.143853 / 0.176557 (-0.032704) | 0.215254 / 0.737135 (-0.521882) | 0.150676 / 0.296338 (-0.145662) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.503796 / 0.215209 (0.288587) | 5.049981 / 2.077655 (2.972326) | 2.399427 / 1.504120 (0.895307) | 2.167635 / 1.541195 (0.626441) | 2.257448 / 1.468490 (0.788958) | 0.641298 / 4.584777 (-3.943479) | 4.828676 / 3.745712 (1.082964) | 4.346069 / 5.269862 (-0.923793) | 2.103890 / 4.565676 (-2.461786) | 0.079115 / 0.424275 (-0.345160) | 0.013377 / 0.007607 (0.005770) | 0.621207 / 0.226044 (0.395162) | 6.190939 / 2.268929 (3.922011) | 2.920129 / 55.444624 (-52.524495) | 2.549225 / 6.876477 (-4.327252) | 2.719221 / 2.142072 (0.577149) | 0.790949 / 4.805227 (-4.014278) | 0.172032 / 6.500664 (-6.328632) | 0.077779 / 0.075469 (0.002310) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.432572 / 1.841788 (-0.409216) | 21.000031 / 8.074308 (12.925723) | 17.555093 / 10.191392 (7.363701) | 0.166646 / 0.680424 (-0.513778) | 0.020451 / 0.534201 (-0.513750) | 0.488767 / 0.579283 (-0.090516) | 0.737036 / 0.434364 (0.302672) | 0.621694 / 0.540337 (0.081356) | 0.732074 / 1.386936 (-0.654862) |\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) | 0.008198 / 0.011353 (-0.003155) | 0.004987 / 0.011008 (-0.006021) | 0.090714 / 0.038508 (0.052206) | 0.053379 / 0.023109 (0.030270) | 0.425199 / 0.275898 (0.149301) | 0.514036 / 0.323480 (0.190556) | 0.006043 / 0.007986 (-0.001943) | 0.003888 / 0.004328 (-0.000441) | 0.088294 / 0.004250 (0.084043) | 0.073024 / 0.037052 (0.035971) | 0.435983 / 0.258489 (0.177494) | 0.514293 / 0.293841 (0.220452) | 0.039451 / 0.128546 (-0.089095) | 0.010439 / 0.075646 (-0.065207) | 0.096885 / 0.419271 (-0.322387) | 0.060165 / 0.043533 (0.016632) | 0.421053 / 0.255139 (0.165914) | 0.455545 / 0.283200 (0.172345) | 0.027234 / 0.141683 (-0.114449) | 1.768975 / 1.452155 (0.316820) | 1.842853 / 1.492716 (0.350137) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.278940 / 0.018006 (0.260933) | 0.480709 / 0.000490 (0.480219) | 0.000436 / 0.000200 (0.000236) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034900 / 0.037411 (-0.002511) | 0.144893 / 0.014526 (0.130368) | 0.149567 / 0.176557 (-0.026989) | 0.213200 / 0.737135 (-0.523935) | 0.156735 / 0.296338 (-0.139604) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.535897 / 0.215209 (0.320687) | 5.336998 / 2.077655 (3.259343) | 2.685854 / 1.504120 (1.181734) | 2.470177 / 1.541195 (0.928983) | 2.547495 / 1.468490 (1.079004) | 0.642830 / 4.584777 (-3.941947) | 4.595866 / 3.745712 (0.850154) | 2.186696 / 5.269862 (-3.083165) | 1.317969 / 4.565676 (-3.247708) | 0.079268 / 0.424275 (-0.345007) | 0.013792 / 0.007607 (0.006185) | 0.662236 / 0.226044 (0.436192) | 6.604775 / 2.268929 (4.335847) | 3.355888 / 55.444624 (-52.088736) | 2.968911 / 6.876477 (-3.907565) | 3.121862 / 2.142072 (0.979790) | 0.794752 / 4.805227 (-4.010475) | 0.170800 / 6.500664 (-6.329864) | 0.078393 / 0.075469 (0.002924) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.601605 / 1.841788 (-0.240183) | 20.743553 / 8.074308 (12.669245) | 17.543968 / 10.191392 (7.352576) | 0.221884 / 0.680424 (-0.458540) | 0.020779 / 0.534201 (-0.513422) | 0.479677 / 0.579283 (-0.099606) | 0.516207 / 0.434364 (0.081843) | 0.564046 / 0.540337 (0.023709) | 0.711336 / 1.386936 (-0.675600) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#819bb4346434912eb405ce3f3e9f21dc25a2fe85 \"CML watermark\")\n",
"Yes, sounds great! Thanks",
"yup"
] |
5,995
|
Support returning dataframe in map transform
|
Allow returning Pandas DataFrames in `map` transforms.
(Plus, raise an error in the non-batched mode if a returned PyArrow table/Pandas DataFrame has more than one row)
|
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5995",
"html_url": "https://github.com/huggingface/datasets/pull/5995",
"diff_url": "https://github.com/huggingface/datasets/pull/5995.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5995.patch",
"merged_at": "2023-06-28T13:46:33"
}
| true
|
[
"_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) | 0.009725 / 0.011353 (-0.001628) | 0.006014 / 0.011008 (-0.004994) | 0.136039 / 0.038508 (0.097531) | 0.049685 / 0.023109 (0.026576) | 0.492967 / 0.275898 (0.217068) | 0.553775 / 0.323480 (0.230295) | 0.007421 / 0.007986 (-0.000564) | 0.004686 / 0.004328 (0.000357) | 0.106639 / 0.004250 (0.102389) | 0.073483 / 0.037052 (0.036431) | 0.507194 / 0.258489 (0.248705) | 0.535760 / 0.293841 (0.241919) | 0.049666 / 0.128546 (-0.078880) | 0.014139 / 0.075646 (-0.061507) | 0.435459 / 0.419271 (0.016188) | 0.076026 / 0.043533 (0.032493) | 0.454542 / 0.255139 (0.199403) | 0.512724 / 0.283200 (0.229524) | 0.034969 / 0.141683 (-0.106713) | 1.881048 / 1.452155 (0.428893) | 1.959915 / 1.492716 (0.467199) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.265322 / 0.018006 (0.247316) | 0.573963 / 0.000490 (0.573474) | 0.017493 / 0.000200 (0.017293) | 0.000637 / 0.000054 (0.000582) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028712 / 0.037411 (-0.008699) | 0.149554 / 0.014526 (0.135029) | 0.130013 / 0.176557 (-0.046544) | 0.203408 / 0.737135 (-0.533727) | 0.144778 / 0.296338 (-0.151561) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.664198 / 0.215209 (0.448989) | 6.418054 / 2.077655 (4.340399) | 2.602338 / 1.504120 (1.098219) | 2.212992 / 1.541195 (0.671797) | 2.214309 / 1.468490 (0.745819) | 0.914772 / 4.584777 (-3.670005) | 5.824831 / 3.745712 (2.079119) | 2.865381 / 5.269862 (-2.404481) | 1.906020 / 4.565676 (-2.659657) | 0.106947 / 0.424275 (-0.317328) | 0.013467 / 0.007607 (0.005860) | 0.834556 / 0.226044 (0.608512) | 8.237078 / 2.268929 (5.968150) | 3.380919 / 55.444624 (-52.063705) | 2.656713 / 6.876477 (-4.219764) | 2.834941 / 2.142072 (0.692869) | 1.151241 / 4.805227 (-3.653986) | 0.220860 / 6.500664 (-6.279804) | 0.080781 / 0.075469 (0.005312) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.655128 / 1.841788 (-0.186660) | 18.696108 / 8.074308 (10.621800) | 22.882108 / 10.191392 (12.690716) | 0.236041 / 0.680424 (-0.444383) | 0.031073 / 0.534201 (-0.503128) | 0.525263 / 0.579283 (-0.054021) | 0.632933 / 0.434364 (0.198569) | 0.707228 / 0.540337 (0.166890) | 0.753508 / 1.386936 (-0.633428) |\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) | 0.009875 / 0.011353 (-0.001478) | 0.005135 / 0.011008 (-0.005873) | 0.101307 / 0.038508 (0.062799) | 0.044895 / 0.023109 (0.021786) | 0.497824 / 0.275898 (0.221926) | 0.573098 / 0.323480 (0.249618) | 0.006669 / 0.007986 (-0.001317) | 0.004289 / 0.004328 (-0.000039) | 0.105824 / 0.004250 (0.101573) | 0.061002 / 0.037052 (0.023950) | 0.510127 / 0.258489 (0.251638) | 0.581387 / 0.293841 (0.287546) | 0.052843 / 0.128546 (-0.075703) | 0.015506 / 0.075646 (-0.060140) | 0.116057 / 0.419271 (-0.303215) | 0.063444 / 0.043533 (0.019912) | 0.479366 / 0.255139 (0.224227) | 0.518419 / 0.283200 (0.235220) | 0.034876 / 0.141683 (-0.106806) | 2.018446 / 1.452155 (0.566292) | 1.960755 / 1.492716 (0.468039) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.269077 / 0.018006 (0.251070) | 0.606059 / 0.000490 (0.605569) | 0.000488 / 0.000200 (0.000288) | 0.000093 / 0.000054 (0.000038) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032465 / 0.037411 (-0.004946) | 0.136517 / 0.014526 (0.121991) | 0.147740 / 0.176557 (-0.028816) | 0.193802 / 0.737135 (-0.543334) | 0.151876 / 0.296338 (-0.144462) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.709866 / 0.215209 (0.494657) | 6.848193 / 2.077655 (4.770538) | 3.310853 / 1.504120 (1.806733) | 2.940813 / 1.541195 (1.399619) | 2.934934 / 1.468490 (1.466444) | 0.927104 / 4.584777 (-3.657673) | 5.921607 / 3.745712 (2.175895) | 4.926558 / 5.269862 (-0.343303) | 2.853269 / 4.565676 (-1.712407) | 0.120278 / 0.424275 (-0.303998) | 0.015468 / 0.007607 (0.007861) | 0.820509 / 0.226044 (0.594464) | 8.263136 / 2.268929 (5.994208) | 3.780214 / 55.444624 (-51.664410) | 3.108482 / 6.876477 (-3.767995) | 3.101544 / 2.142072 (0.959471) | 1.165539 / 4.805227 (-3.639688) | 0.229215 / 6.500664 (-6.271449) | 0.079862 / 0.075469 (0.004393) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.775071 / 1.841788 (-0.066717) | 19.327621 / 8.074308 (11.253313) | 23.057537 / 10.191392 (12.866145) | 0.250649 / 0.680424 (-0.429775) | 0.029767 / 0.534201 (-0.504434) | 0.554774 / 0.579283 (-0.024509) | 0.651919 / 0.434364 (0.217555) | 0.651641 / 0.540337 (0.111304) | 0.762386 / 1.386936 (-0.624550) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fdc3ce7060366f480621e8640903c9ab476164e7 \"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) | 0.005997 / 0.011353 (-0.005356) | 0.003892 / 0.011008 (-0.007116) | 0.098020 / 0.038508 (0.059512) | 0.042584 / 0.023109 (0.019475) | 0.317909 / 0.275898 (0.042011) | 0.395042 / 0.323480 (0.071563) | 0.005358 / 0.007986 (-0.002628) | 0.003266 / 0.004328 (-0.001062) | 0.076698 / 0.004250 (0.072447) | 0.062331 / 0.037052 (0.025279) | 0.334900 / 0.258489 (0.076411) | 0.379355 / 0.293841 (0.085514) | 0.030815 / 0.128546 (-0.097731) | 0.008596 / 0.075646 (-0.067050) | 0.327739 / 0.419271 (-0.091533) | 0.054061 / 0.043533 (0.010528) | 0.311044 / 0.255139 (0.055905) | 0.336705 / 0.283200 (0.053506) | 0.022785 / 0.141683 (-0.118898) | 1.516793 / 1.452155 (0.064639) | 1.590435 / 1.492716 (0.097719) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.289157 / 0.018006 (0.271151) | 0.531074 / 0.000490 (0.530585) | 0.004672 / 0.000200 (0.004472) | 0.000095 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026173 / 0.037411 (-0.011238) | 0.105723 / 0.014526 (0.091197) | 0.118010 / 0.176557 (-0.058547) | 0.178062 / 0.737135 (-0.559073) | 0.120059 / 0.296338 (-0.176279) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.410870 / 0.215209 (0.195661) | 4.042183 / 2.077655 (1.964528) | 1.830059 / 1.504120 (0.325939) | 1.638996 / 1.541195 (0.097802) | 1.701368 / 1.468490 (0.232878) | 0.529915 / 4.584777 (-4.054861) | 3.693308 / 3.745712 (-0.052404) | 1.827875 / 5.269862 (-3.441986) | 1.063237 / 4.565676 (-3.502440) | 0.065368 / 0.424275 (-0.358907) | 0.010986 / 0.007607 (0.003379) | 0.509399 / 0.226044 (0.283354) | 5.092739 / 2.268929 (2.823810) | 2.293490 / 55.444624 (-53.151135) | 1.958742 / 6.876477 (-4.917735) | 2.024985 / 2.142072 (-0.117088) | 0.646978 / 4.805227 (-4.158249) | 0.138616 / 6.500664 (-6.362048) | 0.062101 / 0.075469 (-0.013368) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.202016 / 1.841788 (-0.639772) | 14.493204 / 8.074308 (6.418896) | 12.992160 / 10.191392 (2.800768) | 0.188922 / 0.680424 (-0.491502) | 0.017594 / 0.534201 (-0.516606) | 0.399917 / 0.579283 (-0.179367) | 0.429760 / 0.434364 (-0.004604) | 0.497906 / 0.540337 (-0.042431) | 0.608745 / 1.386936 (-0.778191) |\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) | 0.006164 / 0.011353 (-0.005189) | 0.003980 / 0.011008 (-0.007028) | 0.074676 / 0.038508 (0.036168) | 0.041337 / 0.023109 (0.018228) | 0.400981 / 0.275898 (0.125083) | 0.448791 / 0.323480 (0.125312) | 0.004063 / 0.007986 (-0.003923) | 0.004443 / 0.004328 (0.000114) | 0.075011 / 0.004250 (0.070760) | 0.056494 / 0.037052 (0.019441) | 0.402054 / 0.258489 (0.143565) | 0.446122 / 0.293841 (0.152281) | 0.031752 / 0.128546 (-0.096794) | 0.008835 / 0.075646 (-0.066811) | 0.081226 / 0.419271 (-0.338046) | 0.051501 / 0.043533 (0.007969) | 0.383674 / 0.255139 (0.128535) | 0.405524 / 0.283200 (0.122325) | 0.025929 / 0.141683 (-0.115754) | 1.492985 / 1.452155 (0.040830) | 1.541601 / 1.492716 (0.048885) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.305149 / 0.018006 (0.287142) | 0.497259 / 0.000490 (0.496770) | 0.000420 / 0.000200 (0.000220) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027933 / 0.037411 (-0.009479) | 0.111900 / 0.014526 (0.097374) | 0.124879 / 0.176557 (-0.051678) | 0.178952 / 0.737135 (-0.558184) | 0.127698 / 0.296338 (-0.168640) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.448525 / 0.215209 (0.233316) | 4.486791 / 2.077655 (2.409137) | 2.256687 / 1.504120 (0.752567) | 2.061078 / 1.541195 (0.519884) | 2.078924 / 1.468490 (0.610434) | 0.534412 / 4.584777 (-4.050365) | 3.721098 / 3.745712 (-0.024614) | 1.818735 / 5.269862 (-3.451127) | 1.104198 / 4.565676 (-3.461479) | 0.066277 / 0.424275 (-0.357998) | 0.011441 / 0.007607 (0.003834) | 0.550140 / 0.226044 (0.324095) | 5.498079 / 2.268929 (3.229150) | 2.717398 / 55.444624 (-52.727227) | 2.410194 / 6.876477 (-4.466283) | 2.405304 / 2.142072 (0.263231) | 0.665432 / 4.805227 (-4.139796) | 0.141488 / 6.500664 (-6.359177) | 0.064051 / 0.075469 (-0.011419) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.272334 / 1.841788 (-0.569454) | 14.901608 / 8.074308 (6.827300) | 14.287857 / 10.191392 (4.096465) | 0.165337 / 0.680424 (-0.515086) | 0.017402 / 0.534201 (-0.516799) | 0.398120 / 0.579283 (-0.181163) | 0.416539 / 0.434364 (-0.017825) | 0.463890 / 0.540337 (-0.076447) | 0.567909 / 1.386936 (-0.819027) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#504ec0f2e00ee38e0993ed1e4f1e10f1eefaea0d \"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) | 0.009434 / 0.011353 (-0.001919) | 0.005567 / 0.011008 (-0.005441) | 0.122652 / 0.038508 (0.084144) | 0.050177 / 0.023109 (0.027067) | 0.384292 / 0.275898 (0.108394) | 0.446608 / 0.323480 (0.123128) | 0.006502 / 0.007986 (-0.001484) | 0.004523 / 0.004328 (0.000194) | 0.100581 / 0.004250 (0.096331) | 0.073615 / 0.037052 (0.036563) | 0.420179 / 0.258489 (0.161690) | 0.474631 / 0.293841 (0.180790) | 0.047942 / 0.128546 (-0.080604) | 0.013864 / 0.075646 (-0.061783) | 0.419384 / 0.419271 (0.000112) | 0.088317 / 0.043533 (0.044784) | 0.379620 / 0.255139 (0.124481) | 0.412639 / 0.283200 (0.129440) | 0.048947 / 0.141683 (-0.092736) | 1.823498 / 1.452155 (0.371343) | 1.966629 / 1.492716 (0.473913) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.300669 / 0.018006 (0.282663) | 0.593499 / 0.000490 (0.593009) | 0.007247 / 0.000200 (0.007047) | 0.000114 / 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) | 0.030556 / 0.037411 (-0.006856) | 0.119252 / 0.014526 (0.104726) | 0.131403 / 0.176557 (-0.045153) | 0.201845 / 0.737135 (-0.535291) | 0.139350 / 0.296338 (-0.156989) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.652400 / 0.215209 (0.437191) | 6.536540 / 2.077655 (4.458886) | 2.644565 / 1.504120 (1.140445) | 2.245181 / 1.541195 (0.703986) | 2.316030 / 1.468490 (0.847540) | 0.922535 / 4.584777 (-3.662242) | 5.469065 / 3.745712 (1.723353) | 2.800489 / 5.269862 (-2.469373) | 1.749042 / 4.565676 (-2.816635) | 0.108444 / 0.424275 (-0.315831) | 0.015651 / 0.007607 (0.008044) | 0.846085 / 0.226044 (0.620041) | 8.018460 / 2.268929 (5.749531) | 3.338710 / 55.444624 (-52.105914) | 2.675998 / 6.876477 (-4.200479) | 2.918550 / 2.142072 (0.776478) | 1.135145 / 4.805227 (-3.670082) | 0.215165 / 6.500664 (-6.285499) | 0.082066 / 0.075469 (0.006597) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.561661 / 1.841788 (-0.280127) | 18.519035 / 8.074308 (10.444727) | 19.046300 / 10.191392 (8.854908) | 0.236890 / 0.680424 (-0.443534) | 0.027681 / 0.534201 (-0.506520) | 0.511998 / 0.579283 (-0.067285) | 0.591627 / 0.434364 (0.157264) | 0.562021 / 0.540337 (0.021683) | 0.679354 / 1.386936 (-0.707582) |\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) | 0.009643 / 0.011353 (-0.001710) | 0.005768 / 0.011008 (-0.005241) | 0.104430 / 0.038508 (0.065922) | 0.050044 / 0.023109 (0.026935) | 0.464117 / 0.275898 (0.188219) | 0.518439 / 0.323480 (0.194959) | 0.006935 / 0.007986 (-0.001051) | 0.004316 / 0.004328 (-0.000013) | 0.094330 / 0.004250 (0.090080) | 0.071451 / 0.037052 (0.034399) | 0.492248 / 0.258489 (0.233759) | 0.555740 / 0.293841 (0.261899) | 0.047836 / 0.128546 (-0.080711) | 0.014788 / 0.075646 (-0.060859) | 0.107590 / 0.419271 (-0.311682) | 0.064396 / 0.043533 (0.020863) | 0.451529 / 0.255139 (0.196390) | 0.475025 / 0.283200 (0.191826) | 0.040006 / 0.141683 (-0.101677) | 1.797107 / 1.452155 (0.344953) | 1.879261 / 1.492716 (0.386545) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.298458 / 0.018006 (0.280451) | 0.613022 / 0.000490 (0.612532) | 0.003582 / 0.000200 (0.003382) | 0.000106 / 0.000054 (0.000052) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030179 / 0.037411 (-0.007232) | 0.123286 / 0.014526 (0.108760) | 0.132070 / 0.176557 (-0.044486) | 0.190883 / 0.737135 (-0.546252) | 0.138526 / 0.296338 (-0.157812) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.666908 / 0.215209 (0.451699) | 6.489035 / 2.077655 (4.411381) | 2.897027 / 1.504120 (1.392907) | 2.565150 / 1.541195 (1.023956) | 2.504827 / 1.468490 (1.036336) | 0.916112 / 4.584777 (-3.668665) | 5.651751 / 3.745712 (1.906039) | 2.743382 / 5.269862 (-2.526479) | 1.773338 / 4.565676 (-2.792338) | 0.128764 / 0.424275 (-0.295511) | 0.013140 / 0.007607 (0.005533) | 0.803281 / 0.226044 (0.577236) | 8.258874 / 2.268929 (5.989945) | 3.633260 / 55.444624 (-51.811364) | 2.878827 / 6.876477 (-3.997649) | 2.977178 / 2.142072 (0.835106) | 1.130467 / 4.805227 (-3.674760) | 0.226381 / 6.500664 (-6.274283) | 0.081550 / 0.075469 (0.006081) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.842927 / 1.841788 (0.001139) | 18.411520 / 8.074308 (10.337212) | 21.118228 / 10.191392 (10.926836) | 0.231526 / 0.680424 (-0.448898) | 0.029300 / 0.534201 (-0.504901) | 0.527450 / 0.579283 (-0.051834) | 0.618873 / 0.434364 (0.184509) | 0.593314 / 0.540337 (0.052976) | 0.734430 / 1.386936 (-0.652506) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0d2b8854c265b4dc202e480427890f472b34ea15 \"CML watermark\")\n"
] |
5,994
|
Fix select_columns columns order
|
Fix the order of the columns in dataset.features when the order changes with `dataset.select_columns()`.
I also fixed the same issue for `dataset.flatten()`
Close https://github.com/huggingface/datasets/issues/5993
|
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5994",
"html_url": "https://github.com/huggingface/datasets/pull/5994",
"diff_url": "https://github.com/huggingface/datasets/pull/5994.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5994.patch",
"merged_at": "2023-06-27T15:32:43"
}
| true
|
[
"<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) | 0.005969 / 0.011353 (-0.005384) | 0.003687 / 0.011008 (-0.007321) | 0.100843 / 0.038508 (0.062335) | 0.036912 / 0.023109 (0.013803) | 0.312389 / 0.275898 (0.036491) | 0.370335 / 0.323480 (0.046855) | 0.003434 / 0.007986 (-0.004552) | 0.003710 / 0.004328 (-0.000619) | 0.076899 / 0.004250 (0.072648) | 0.053647 / 0.037052 (0.016594) | 0.324825 / 0.258489 (0.066336) | 0.367711 / 0.293841 (0.073870) | 0.028079 / 0.128546 (-0.100467) | 0.008326 / 0.075646 (-0.067320) | 0.312342 / 0.419271 (-0.106930) | 0.047423 / 0.043533 (0.003890) | 0.321063 / 0.255139 (0.065924) | 0.336508 / 0.283200 (0.053308) | 0.019973 / 0.141683 (-0.121710) | 1.529334 / 1.452155 (0.077179) | 1.573746 / 1.492716 (0.081030) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210849 / 0.018006 (0.192843) | 0.418798 / 0.000490 (0.418309) | 0.007347 / 0.000200 (0.007147) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022718 / 0.037411 (-0.014694) | 0.098400 / 0.014526 (0.083874) | 0.106590 / 0.176557 (-0.069967) | 0.168460 / 0.737135 (-0.568675) | 0.108401 / 0.296338 (-0.187938) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.443066 / 0.215209 (0.227857) | 4.416658 / 2.077655 (2.339003) | 2.088844 / 1.504120 (0.584724) | 1.879564 / 1.541195 (0.338369) | 1.933815 / 1.468490 (0.465325) | 0.565085 / 4.584777 (-4.019692) | 3.412440 / 3.745712 (-0.333273) | 1.754686 / 5.269862 (-3.515175) | 1.024576 / 4.565676 (-3.541100) | 0.067909 / 0.424275 (-0.356366) | 0.011054 / 0.007607 (0.003447) | 0.534748 / 0.226044 (0.308703) | 5.351457 / 2.268929 (3.082529) | 2.517368 / 55.444624 (-52.927256) | 2.182762 / 6.876477 (-4.693715) | 2.238205 / 2.142072 (0.096133) | 0.672962 / 4.805227 (-4.132265) | 0.136098 / 6.500664 (-6.364566) | 0.066534 / 0.075469 (-0.008935) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.281241 / 1.841788 (-0.560547) | 13.872881 / 8.074308 (5.798573) | 13.161023 / 10.191392 (2.969631) | 0.130011 / 0.680424 (-0.550412) | 0.016759 / 0.534201 (-0.517442) | 0.359802 / 0.579283 (-0.219481) | 0.392577 / 0.434364 (-0.041787) | 0.427742 / 0.540337 (-0.112595) | 0.522241 / 1.386936 (-0.864695) |\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) | 0.005985 / 0.011353 (-0.005368) | 0.003705 / 0.011008 (-0.007304) | 0.077699 / 0.038508 (0.039191) | 0.035686 / 0.023109 (0.012577) | 0.420356 / 0.275898 (0.144458) | 0.476753 / 0.323480 (0.153273) | 0.003510 / 0.007986 (-0.004475) | 0.002807 / 0.004328 (-0.001521) | 0.077151 / 0.004250 (0.072901) | 0.046420 / 0.037052 (0.009368) | 0.391781 / 0.258489 (0.133292) | 0.461128 / 0.293841 (0.167287) | 0.027847 / 0.128546 (-0.100699) | 0.008322 / 0.075646 (-0.067324) | 0.082768 / 0.419271 (-0.336503) | 0.042629 / 0.043533 (-0.000904) | 0.405745 / 0.255139 (0.150606) | 0.430797 / 0.283200 (0.147598) | 0.019832 / 0.141683 (-0.121851) | 1.556208 / 1.452155 (0.104054) | 1.612166 / 1.492716 (0.119450) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230633 / 0.018006 (0.212626) | 0.401667 / 0.000490 (0.401178) | 0.000776 / 0.000200 (0.000576) | 0.000069 / 0.000054 (0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024959 / 0.037411 (-0.012452) | 0.100560 / 0.014526 (0.086034) | 0.109175 / 0.176557 (-0.067382) | 0.159919 / 0.737135 (-0.577217) | 0.112810 / 0.296338 (-0.183528) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.460601 / 0.215209 (0.245392) | 4.620039 / 2.077655 (2.542385) | 2.257900 / 1.504120 (0.753780) | 2.039192 / 1.541195 (0.497997) | 2.064451 / 1.468490 (0.595961) | 0.557887 / 4.584777 (-4.026890) | 3.356100 / 3.745712 (-0.389612) | 1.703578 / 5.269862 (-3.566284) | 1.024984 / 4.565676 (-3.540693) | 0.067602 / 0.424275 (-0.356673) | 0.011450 / 0.007607 (0.003842) | 0.563230 / 0.226044 (0.337186) | 5.632150 / 2.268929 (3.363221) | 2.698701 / 55.444624 (-52.745924) | 2.363218 / 6.876477 (-4.513259) | 2.363997 / 2.142072 (0.221925) | 0.671260 / 4.805227 (-4.133967) | 0.136166 / 6.500664 (-6.364499) | 0.067094 / 0.075469 (-0.008375) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.303030 / 1.841788 (-0.538757) | 14.137277 / 8.074308 (6.062969) | 13.937631 / 10.191392 (3.746239) | 0.162626 / 0.680424 (-0.517798) | 0.016687 / 0.534201 (-0.517514) | 0.363657 / 0.579283 (-0.215626) | 0.392021 / 0.434364 (-0.042343) | 0.427275 / 0.540337 (-0.113062) | 0.512192 / 1.386936 (-0.874744) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#42603528d9bd8c3ab287ed0eadc7fa3d1ef4cfd8 \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005974 / 0.011353 (-0.005378) | 0.003947 / 0.011008 (-0.007061) | 0.098604 / 0.038508 (0.060096) | 0.036947 / 0.023109 (0.013838) | 0.311844 / 0.275898 (0.035946) | 0.375243 / 0.323480 (0.051763) | 0.003453 / 0.007986 (-0.004533) | 0.003834 / 0.004328 (-0.000495) | 0.077943 / 0.004250 (0.073692) | 0.052956 / 0.037052 (0.015904) | 0.320812 / 0.258489 (0.062323) | 0.373963 / 0.293841 (0.080122) | 0.028382 / 0.128546 (-0.100164) | 0.008525 / 0.075646 (-0.067121) | 0.311306 / 0.419271 (-0.107965) | 0.047029 / 0.043533 (0.003496) | 0.309933 / 0.255139 (0.054794) | 0.335114 / 0.283200 (0.051915) | 0.019629 / 0.141683 (-0.122054) | 1.569771 / 1.452155 (0.117617) | 1.585899 / 1.492716 (0.093182) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216565 / 0.018006 (0.198559) | 0.426717 / 0.000490 (0.426228) | 0.003609 / 0.000200 (0.003409) | 0.000077 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023079 / 0.037411 (-0.014332) | 0.096954 / 0.014526 (0.082428) | 0.105398 / 0.176557 (-0.071158) | 0.165433 / 0.737135 (-0.571703) | 0.109703 / 0.296338 (-0.186636) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.456227 / 0.215209 (0.241018) | 4.529857 / 2.077655 (2.452202) | 2.214054 / 1.504120 (0.709934) | 2.029716 / 1.541195 (0.488521) | 2.081175 / 1.468490 (0.612685) | 0.563642 / 4.584777 (-4.021135) | 3.355393 / 3.745712 (-0.390320) | 1.765938 / 5.269862 (-3.503924) | 1.039062 / 4.565676 (-3.526615) | 0.067952 / 0.424275 (-0.356323) | 0.011044 / 0.007607 (0.003437) | 0.556935 / 0.226044 (0.330890) | 5.588167 / 2.268929 (3.319239) | 2.667217 / 55.444624 (-52.777407) | 2.337383 / 6.876477 (-4.539094) | 2.429590 / 2.142072 (0.287517) | 0.676972 / 4.805227 (-4.128256) | 0.135782 / 6.500664 (-6.364882) | 0.066323 / 0.075469 (-0.009146) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.237358 / 1.841788 (-0.604429) | 13.910492 / 8.074308 (5.836184) | 13.227275 / 10.191392 (3.035883) | 0.146857 / 0.680424 (-0.533567) | 0.016991 / 0.534201 (-0.517210) | 0.363637 / 0.579283 (-0.215646) | 0.392462 / 0.434364 (-0.041902) | 0.450009 / 0.540337 (-0.090329) | 0.536077 / 1.386936 (-0.850859) |\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) | 0.006067 / 0.011353 (-0.005286) | 0.003851 / 0.011008 (-0.007158) | 0.078462 / 0.038508 (0.039954) | 0.036221 / 0.023109 (0.013112) | 0.389195 / 0.275898 (0.113297) | 0.428710 / 0.323480 (0.105230) | 0.004645 / 0.007986 (-0.003341) | 0.002973 / 0.004328 (-0.001355) | 0.078299 / 0.004250 (0.074048) | 0.047076 / 0.037052 (0.010024) | 0.375673 / 0.258489 (0.117184) | 0.432352 / 0.293841 (0.138511) | 0.028212 / 0.128546 (-0.100334) | 0.008475 / 0.075646 (-0.067172) | 0.083902 / 0.419271 (-0.335369) | 0.046699 / 0.043533 (0.003166) | 0.364502 / 0.255139 (0.109363) | 0.389792 / 0.283200 (0.106592) | 0.025266 / 0.141683 (-0.116417) | 1.517458 / 1.452155 (0.065303) | 1.543634 / 1.492716 (0.050918) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236479 / 0.018006 (0.218472) | 0.411528 / 0.000490 (0.411038) | 0.005213 / 0.000200 (0.005013) | 0.000091 / 0.000054 (0.000036) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025764 / 0.037411 (-0.011647) | 0.103174 / 0.014526 (0.088648) | 0.110609 / 0.176557 (-0.065948) | 0.164630 / 0.737135 (-0.572506) | 0.114863 / 0.296338 (-0.181475) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.457155 / 0.215209 (0.241946) | 4.550675 / 2.077655 (2.473021) | 2.350473 / 1.504120 (0.846353) | 2.204919 / 1.541195 (0.663724) | 2.076724 / 1.468490 (0.608234) | 0.563107 / 4.584777 (-4.021670) | 3.390669 / 3.745712 (-0.355043) | 1.741111 / 5.269862 (-3.528751) | 1.033268 / 4.565676 (-3.532408) | 0.068400 / 0.424275 (-0.355875) | 0.011607 / 0.007607 (0.004000) | 0.561944 / 0.226044 (0.335900) | 5.620224 / 2.268929 (3.351296) | 2.705241 / 55.444624 (-52.739384) | 2.344520 / 6.876477 (-4.531957) | 2.386119 / 2.142072 (0.244046) | 0.681583 / 4.805227 (-4.123644) | 0.137272 / 6.500664 (-6.363392) | 0.069217 / 0.075469 (-0.006252) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.322690 / 1.841788 (-0.519098) | 14.464953 / 8.074308 (6.390645) | 14.269350 / 10.191392 (4.077958) | 0.158879 / 0.680424 (-0.521545) | 0.016722 / 0.534201 (-0.517479) | 0.360299 / 0.579283 (-0.218984) | 0.391609 / 0.434364 (-0.042755) | 0.420507 / 0.540337 (-0.119831) | 0.512822 / 1.386936 (-0.874114) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ca68191900d97b29abb3c2c4ba0502fe30d137d1 \"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) | 0.007106 / 0.011353 (-0.004247) | 0.005224 / 0.011008 (-0.005784) | 0.127563 / 0.038508 (0.089055) | 0.055067 / 0.023109 (0.031958) | 0.418660 / 0.275898 (0.142761) | 0.487891 / 0.323480 (0.164411) | 0.005712 / 0.007986 (-0.002274) | 0.004585 / 0.004328 (0.000256) | 0.090994 / 0.004250 (0.086743) | 0.071837 / 0.037052 (0.034784) | 0.446957 / 0.258489 (0.188468) | 0.475966 / 0.293841 (0.182125) | 0.038062 / 0.128546 (-0.090484) | 0.010056 / 0.075646 (-0.065590) | 0.406796 / 0.419271 (-0.012475) | 0.066542 / 0.043533 (0.023009) | 0.413676 / 0.255139 (0.158537) | 0.448624 / 0.283200 (0.165424) | 0.030332 / 0.141683 (-0.111351) | 1.895307 / 1.452155 (0.443152) | 1.904411 / 1.492716 (0.411694) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221246 / 0.018006 (0.203240) | 0.461288 / 0.000490 (0.460799) | 0.005957 / 0.000200 (0.005757) | 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) | 0.029255 / 0.037411 (-0.008156) | 0.131299 / 0.014526 (0.116773) | 0.135814 / 0.176557 (-0.040742) | 0.201342 / 0.737135 (-0.535793) | 0.141748 / 0.296338 (-0.154591) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.463936 / 0.215209 (0.248727) | 4.709621 / 2.077655 (2.631966) | 2.093844 / 1.504120 (0.589724) | 1.897963 / 1.541195 (0.356768) | 1.927865 / 1.468490 (0.459375) | 0.610879 / 4.584777 (-3.973898) | 4.481370 / 3.745712 (0.735658) | 2.112235 / 5.269862 (-3.157627) | 1.203349 / 4.565676 (-3.362327) | 0.074828 / 0.424275 (-0.349447) | 0.013121 / 0.007607 (0.005514) | 0.580894 / 0.226044 (0.354849) | 5.801872 / 2.268929 (3.532943) | 2.579950 / 55.444624 (-52.864674) | 2.251569 / 6.876477 (-4.624908) | 2.421305 / 2.142072 (0.279232) | 0.760938 / 4.805227 (-4.044289) | 0.169554 / 6.500664 (-6.331110) | 0.077499 / 0.075469 (0.002030) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.410419 / 1.841788 (-0.431368) | 17.442331 / 8.074308 (9.368023) | 15.782183 / 10.191392 (5.590791) | 0.180649 / 0.680424 (-0.499775) | 0.021790 / 0.534201 (-0.512411) | 0.511040 / 0.579283 (-0.068243) | 0.510472 / 0.434364 (0.076108) | 0.607141 / 0.540337 (0.066804) | 0.724794 / 1.386936 (-0.662142) |\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) | 0.007280 / 0.011353 (-0.004073) | 0.004712 / 0.011008 (-0.006296) | 0.089225 / 0.038508 (0.050717) | 0.053157 / 0.023109 (0.030048) | 0.431949 / 0.275898 (0.156051) | 0.478128 / 0.323480 (0.154648) | 0.006181 / 0.007986 (-0.001804) | 0.003387 / 0.004328 (-0.000941) | 0.083741 / 0.004250 (0.079490) | 0.071610 / 0.037052 (0.034557) | 0.414698 / 0.258489 (0.156209) | 0.484422 / 0.293841 (0.190581) | 0.034988 / 0.128546 (-0.093558) | 0.009831 / 0.075646 (-0.065816) | 0.089644 / 0.419271 (-0.329628) | 0.057053 / 0.043533 (0.013520) | 0.413144 / 0.255139 (0.158005) | 0.445464 / 0.283200 (0.162264) | 0.026109 / 0.141683 (-0.115574) | 1.842899 / 1.452155 (0.390745) | 1.923774 / 1.492716 (0.431057) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.245051 / 0.018006 (0.227045) | 0.460444 / 0.000490 (0.459954) | 0.000444 / 0.000200 (0.000244) | 0.000067 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034835 / 0.037411 (-0.002577) | 0.130078 / 0.014526 (0.115553) | 0.147012 / 0.176557 (-0.029544) | 0.203097 / 0.737135 (-0.534038) | 0.149636 / 0.296338 (-0.146702) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.521664 / 0.215209 (0.306455) | 5.283865 / 2.077655 (3.206210) | 2.456701 / 1.504120 (0.952581) | 2.266059 / 1.541195 (0.724864) | 2.295387 / 1.468490 (0.826897) | 0.613200 / 4.584777 (-3.971577) | 4.526107 / 3.745712 (0.780394) | 2.047327 / 5.269862 (-3.222535) | 1.261063 / 4.565676 (-3.304614) | 0.070402 / 0.424275 (-0.353873) | 0.014128 / 0.007607 (0.006521) | 0.620929 / 0.226044 (0.394884) | 6.109127 / 2.268929 (3.840198) | 3.081406 / 55.444624 (-52.363218) | 2.658224 / 6.876477 (-4.218253) | 2.671974 / 2.142072 (0.529902) | 0.744081 / 4.805227 (-4.061146) | 0.161498 / 6.500664 (-6.339166) | 0.075148 / 0.075469 (-0.000321) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.585640 / 1.841788 (-0.256148) | 17.884321 / 8.074308 (9.810013) | 15.938937 / 10.191392 (5.747545) | 0.220818 / 0.680424 (-0.459605) | 0.021452 / 0.534201 (-0.512749) | 0.499747 / 0.579283 (-0.079536) | 0.512318 / 0.434364 (0.077954) | 0.562853 / 0.540337 (0.022515) | 0.678512 / 1.386936 (-0.708424) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aa50937d82256827aee3dbd749c7a23555e05e38 \"CML watermark\")\n"
] |
5,993
|
ValueError: Table schema does not match schema used to create file
|
### Describe the bug
Saving a dataset as parquet fails with a `ValueError: Table schema does not match schema used to create file` if the dataset was obtained out of a `.select_columns()` call with columns selected out of order.
### Steps to reproduce the bug
```python
import datasets
dataset = datasets.Dataset.from_dict(
{
"x1": [1, 2, 3],
"x2": [10, 11, 12],
}
)
ds = dataset.select_columns(["x2", "x1"])
ds.to_parquet("demo.parquet")
```
```shell
>>>
ValueError: Table schema does not match schema used to create file:
table:
x2: int64
x1: int64
-- schema metadata --
huggingface: '{"info": {"features": {"x2": {"dtype": "int64", "_type": "V' + 53 vs.
file:
x1: int64
x2: int64
-- schema metadata --
huggingface: '{"info": {"features": {"x1": {"dtype": "int64", "_type": "V' + 53
```
---
I think this is because after the `.select_columns()` call with out of order columns, the output dataset features' schema ends up being out of sync with the schema of the arrow table backing it.
```python
ds.features.arrow_schema
>>>
x1: int64
x2: int64
-- schema metadata --
huggingface: '{"info": {"features": {"x1": {"dtype": "int64", "_type": "V' + 53
ds.data.schema
>>>
x2: int64
x1: int64
-- schema metadata --
huggingface: '{"info": {"features": {"x2": {"dtype": "int64", "_type": "V' + 53
```
So when we call `.to_parquet()`, the call behind the scenes to `datasets.io.parquet.ParquetDatasetWriter(...).write()` which initialises the backend `pyarrow.parquet.ParquetWriter` with `schema = self.dataset.features.arrow_schema` triggers `pyarrow` on write when [it checks](https://github.com/apache/arrow/blob/11b140a734a516e436adaddaeb35d23f30dcce44/python/pyarrow/parquet/core.py#L1086-L1090) that the `ParquetWriter` schema matches the schema of the table being written 🙌
https://github.com/huggingface/datasets/blob/6ed837325cb539a5deb99129e5ad181d0269e050/src/datasets/io/parquet.py#L139-L141
### Expected behavior
The dataset gets successfully saved as parquet.
*In the same way as it does if saving it as csv:
```python
import datasets
dataset = datasets.Dataset.from_dict(
{
"x1": [1, 2, 3],
"x2": [10, 11, 12],
}
)
ds = dataset.select_columns(["x2", "x1"])
ds.to_csv("demo.csv")
```
### Environment info
`python==3.11`
`datasets==2.13.1`
|
[] |
{
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}
| false
|
[
"We'll do a new release of `datasets` soon to make the fix available :)\r\n\r\nIn the meantime you can use `datasets` from source (main)",
"Thank you very much @lhoestq ! 🚀 "
] |
5,992
|
speedup
| null |
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5992",
"html_url": "https://github.com/huggingface/datasets/pull/5992",
"diff_url": "https://github.com/huggingface/datasets/pull/5992.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5992.patch",
"merged_at": null
}
| true
|
[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5992). All of your documentation changes will be reflected on that endpoint."
] |
5,991
|
`map` with any joblib backend
|
We recently enabled the (experimental) parallel backend switch for data download and extraction but not for `map` yet.
Right now we're using our `iflatmap_unordered` implementation for multiprocessing that uses a shared Queue to gather progress updates from the subprocesses and show a progress bar in the main process.
If a Queue implementation that would work on any joblib backend by leveraging the filesystem that is shared among workers, we can have `iflatmap_unordered` for joblib and therefore a `map` with any joblib backend with a progress bar !
Note that the Queue doesn't need to be that optimized though since we can choose a small frequency for progress updates (like 1 update per second).
|
[
{
"id": 1935892871,
"node_id": "MDU6TGFiZWwxOTM1ODkyODcx",
"url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement",
"name": "enhancement",
"color": "a2eeef",
"default": true,
"description": "New feature or request"
}
] |
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| false
|
[] |
5,989
|
Set a rule on the config and split names
|
> should we actually allow characters like spaces? maybe it's better to add validation for whitespace symbols and directly in datasets and raise
https://github.com/huggingface/datasets-server/issues/853
|
[] |
{
"url": null,
"html_url": null,
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| false
|
[
"in this case we need to decide what to do with the existing datasets with white space characters (there shouldn't be a lot of them I think)",
"I imagine that we should stop supporting them, and help the user fix them?",
"See a report where the datasets server fails: https://huggingface.co/datasets/poloclub/diffusiondb/discussions/2#6374ff55b93cbdf65675f564\r\n\r\nThe config name is `random_10k [2m]`!"
] |
5,988
|
ConnectionError: Couldn't reach dataset_infos.json
|
### Describe the bug
I'm trying to load codeparrot/codeparrot-clean-train, but get the following error:
ConnectionError: Couldn't reach https://huggingface.co/datasets/codeparrot/codeparrot-clean-train/resolve/main/dataset_infos.json (ConnectionError(ProtocolError('Connection aborted.', ConnectionResetError(104, 'Connection reset by peer'))))
### Steps to reproduce the bug
train_data = load_dataset('codeparrot/codeparrot-clean-train', split='train')
### Expected behavior
download the dataset
### Environment info
centos7
|
[] |
{
"url": null,
"html_url": null,
"diff_url": null,
"patch_url": null,
"merged_at": null
}
| false
|
[
"Unfortunately, I can't reproduce the error. What does the following code return for you?\r\n```python\r\nimport requests\r\nfrom huggingface_hub import hf_hub_url\r\nr = requests.get(hf_hub_url(\"codeparrot/codeparrot-clean-train\", \"dataset_infos.json\", repo_type=\"dataset\"))\r\n```\r\n\r\nAlso, can you provide more info about your network (region, proxies, etc.)?"
] |
5,987
|
Why max_shard_size is not supported in load_dataset and passed to download_and_prepare
|
### Describe the bug
https://github.com/huggingface/datasets/blob/a8a797cc92e860c8d0df71e0aa826f4d2690713e/src/datasets/load.py#L1809
What I can to is break the `load_dataset` and use `load_datset_builder` + `download_and_prepare` instead.
### Steps to reproduce the bug
https://github.com/huggingface/datasets/blob/a8a797cc92e860c8d0df71e0aa826f4d2690713e/src/datasets/load.py#L1809
### Expected behavior
Users can define the max shard size.
### Environment info
datasets==2.13.1
|
[] |
{
"url": null,
"html_url": null,
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| false
|
[
"Can you explain your use case for `max_shard_size`? \r\n\r\nOn some systems, there is a limit to the size of a memory-mapped file, so we could consider exposing this parameter in `load_dataset`.",
"In my use case, users may choose a proper size to balance the cost and benefit of using large shard size. (On azure blob or hdfs which may automatically download the shard from background)",
"But `load_dataset` doesn't support caching (and reading) Arrow datasets from remote storage. \r\n\r\n`load_datset_builder` + `download_and_prepare` is not equal to `load_dataset`. The latter has one more step, `builder.as_dataset`, that memory-maps Arrow files, which only works for local files.",
"Thanks. So if I want to use `IterableDataset` and control the size of single arrow file, how should I organize the data loader? Maybe `load_dataset_build` + `download_and_prepare` + `builder.as_dataset` + `dataset.to_iterable_dataset`?",
"Yes, this should work.\r\n\r\nI think we can expose `max_shard_size` in `load_dataset`, so feel free to open a PR."
] |
5,986
|
Make IterableDataset.from_spark more efficient
|
Moved the code from using collect() to using toLocalIterator, which allows for prefetching partitions that will be selected next, thus allowing for better performance when iterating.
|
[] |
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"patch_url": "https://github.com/huggingface/datasets/pull/5986.patch",
"merged_at": "2023-07-07T09:56:09"
}
| true
|
[
"@lhoestq would you be able to review this please and also approve the workflow?",
"Sounds good to me :) feel free to run `make style` to apply code formatting",
"_The documentation is not available anymore as the PR was closed or merged._",
"cool ! I think we can merge once all comments have been addressed",
"@lhoestq I just addressed the comments and I think we can move ahead with this! \r\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007734 / 0.011353 (-0.003619) | 0.004608 / 0.011008 (-0.006400) | 0.094466 / 0.038508 (0.055958) | 0.086477 / 0.023109 (0.063368) | 0.410311 / 0.275898 (0.134413) | 0.455560 / 0.323480 (0.132080) | 0.006112 / 0.007986 (-0.001874) | 0.003845 / 0.004328 (-0.000483) | 0.072506 / 0.004250 (0.068256) | 0.066721 / 0.037052 (0.029669) | 0.409967 / 0.258489 (0.151478) | 0.460480 / 0.293841 (0.166639) | 0.036700 / 0.128546 (-0.091847) | 0.009854 / 0.075646 (-0.065792) | 0.320936 / 0.419271 (-0.098335) | 0.061002 / 0.043533 (0.017469) | 0.413963 / 0.255139 (0.158824) | 0.426787 / 0.283200 (0.143588) | 0.029182 / 0.141683 (-0.112501) | 1.685136 / 1.452155 (0.232981) | 1.754590 / 1.492716 (0.261873) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222698 / 0.018006 (0.204692) | 0.505929 / 0.000490 (0.505440) | 0.005291 / 0.000200 (0.005091) | 0.000097 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032527 / 0.037411 (-0.004884) | 0.094842 / 0.014526 (0.080317) | 0.110138 / 0.176557 (-0.066418) | 0.193786 / 0.737135 (-0.543349) | 0.112593 / 0.296338 (-0.183745) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441671 / 0.215209 (0.226461) | 4.392961 / 2.077655 (2.315306) | 2.161111 / 1.504120 (0.656991) | 1.967080 / 1.541195 (0.425885) | 2.065411 / 1.468490 (0.596920) | 0.561080 / 4.584777 (-4.023697) | 4.159612 / 3.745712 (0.413900) | 6.435248 / 5.269862 (1.165386) | 3.732338 / 4.565676 (-0.833339) | 0.066156 / 0.424275 (-0.358119) | 0.008030 / 0.007607 (0.000423) | 0.532182 / 0.226044 (0.306137) | 5.315142 / 2.268929 (3.046213) | 2.680157 / 55.444624 (-52.764467) | 2.303799 / 6.876477 (-4.572677) | 2.530911 / 2.142072 (0.388838) | 0.669504 / 4.805227 (-4.135723) | 0.151940 / 6.500664 (-6.348724) | 0.066999 / 0.075469 (-0.008470) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.424275 / 1.841788 (-0.417513) | 21.550742 / 8.074308 (13.476434) | 16.031414 / 10.191392 (5.840022) | 0.194681 / 0.680424 (-0.485743) | 0.020389 / 0.534201 (-0.513812) | 0.429808 / 0.579283 (-0.149475) | 0.457503 / 0.434364 (0.023139) | 0.511522 / 0.540337 (-0.028816) | 0.682621 / 1.386936 (-0.704315) |\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) | 0.007519 / 0.011353 (-0.003834) | 0.004445 / 0.011008 (-0.006563) | 0.071946 / 0.038508 (0.033438) | 0.082982 / 0.023109 (0.059873) | 0.459938 / 0.275898 (0.184040) | 0.504875 / 0.323480 (0.181395) | 0.005805 / 0.007986 (-0.002181) | 0.003740 / 0.004328 (-0.000589) | 0.071998 / 0.004250 (0.067747) | 0.062580 / 0.037052 (0.025527) | 0.462263 / 0.258489 (0.203774) | 0.506355 / 0.293841 (0.212514) | 0.036321 / 0.128546 (-0.092225) | 0.009830 / 0.075646 (-0.065816) | 0.079810 / 0.419271 (-0.339461) | 0.055291 / 0.043533 (0.011758) | 0.464093 / 0.255139 (0.208954) | 0.481109 / 0.283200 (0.197910) | 0.026909 / 0.141683 (-0.114774) | 1.652538 / 1.452155 (0.200383) | 1.750713 / 1.492716 (0.257997) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.267552 / 0.018006 (0.249546) | 0.502021 / 0.000490 (0.501531) | 0.001635 / 0.000200 (0.001435) | 0.000099 / 0.000054 (0.000044) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033747 / 0.037411 (-0.003665) | 0.104242 / 0.014526 (0.089716) | 0.113829 / 0.176557 (-0.062728) | 0.176242 / 0.737135 (-0.560893) | 0.117002 / 0.296338 (-0.179336) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.476731 / 0.215209 (0.261522) | 4.727054 / 2.077655 (2.649399) | 2.589396 / 1.504120 (1.085276) | 2.511180 / 1.541195 (0.969985) | 2.634122 / 1.468490 (1.165632) | 0.563840 / 4.584777 (-4.020937) | 4.140212 / 3.745712 (0.394500) | 6.188789 / 5.269862 (0.918928) | 3.716897 / 4.565676 (-0.848780) | 0.065823 / 0.424275 (-0.358452) | 0.007705 / 0.007607 (0.000098) | 0.566580 / 0.226044 (0.340535) | 5.653306 / 2.268929 (3.384377) | 3.028756 / 55.444624 (-52.415868) | 2.592319 / 6.876477 (-4.284158) | 2.614250 / 2.142072 (0.472178) | 0.667135 / 4.805227 (-4.138093) | 0.153455 / 6.500664 (-6.347209) | 0.069321 / 0.075469 (-0.006148) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.541978 / 1.841788 (-0.299810) | 21.747360 / 8.074308 (13.673052) | 15.963657 / 10.191392 (5.772265) | 0.192843 / 0.680424 (-0.487581) | 0.020702 / 0.534201 (-0.513499) | 0.433620 / 0.579283 (-0.145663) | 0.467327 / 0.434364 (0.032963) | 0.507398 / 0.540337 (-0.032940) | 0.692797 / 1.386936 (-0.694140) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#396cf9419d12e3150e2051793b10f2c813780a90 \"CML watermark\")\n"
] |
5,985
|
Cannot reuse tokenizer object for dataset map
|
### Describe the bug
Related to https://github.com/huggingface/transformers/issues/24441. Not sure if this is a tokenizer issue or caching issue, so filing in both.
Passing the tokenizer to the dataset map function causes the tokenizer to be fingerprinted weirdly. After calling the tokenizer with arguments like padding and truncation the tokenizer object changes interanally, even though the hash remains the same.
But dumps is able to detect that internal change which causes the tokenizer object's fingerprint to change.
### Steps to reproduce the bug
```python
from transformers import AutoTokenizer
from datasets.utils.py_utils import dumps # Huggingface datasets
t = AutoTokenizer.from_pretrained('bert-base-uncased')
t.save_pretrained("tok1")
th1 = hash(dumps(t))
text = "This is an example text"
ttext = t(text, max_length=512, padding="max_length", truncation=True)
t.save_pretrained("tok2")
th2 = hash(dumps(t))
assert th1 == th2 # Assertion Error
```
But if you use just the hash of the object without dumps, the hashes don't change
```python
from transformers import AutoTokenizer
from datasets.utils.py_utils import dumps # Huggingface datasets
t = AutoTokenizer.from_pretrained('bert-base-uncased')
th1 = hash(t) # Just hash no dumps
text = "This is an example text"
ttext = t(text, max_length=512, padding="max_length", truncation=True)
th2 = hash(t) # Just hash no dumps
assert th1 == th2 # This is OK
```
This causes situations such as the following
1. Create a text file like this `yes "This is an example text" | head -n 10000 > lines.txt`
```python
from transformers import AutoTokenizer
import datasets
class TokenizeMapper(object):
"""Mapper for tokenizer.
This is needed because the caching mechanism of HuggingFace does not work on
lambdas. Each time a new lambda will be created by a new process which will
lead to a different hash.
This way we can have a universal mapper object in init and reuse it with the same
hash for each process.
"""
def __init__(self, tokenizer):
"""Initialize the tokenizer."""
self.tokenizer = tokenizer
def __call__(self, examples, **kwargs):
"""Run the mapper."""
texts = examples["text"]
tt = self.tokenizer(texts, max_length=256, padding="max_length", truncation=True)
batch_outputs = {
"input_ids": tt.input_ids,
"attention_mask": tt.attention_mask,
}
return batch_outputs
t = AutoTokenizer.from_pretrained('bert-base-uncased')
mapper = TokenizeMapper(t)
ds = datasets.load_dataset("text", data_files="lines.txt")
mds1 = ds.map(
mapper,
batched=False,
remove_columns=["text"],
).with_format("torch")
mds2 = ds.map(
mapper,
batched=False,
remove_columns=["text"],
).with_format("torch")
```
The second call to map should reuse the cached processed dataset from mds1, but it instead it redoes the tokenization because of the behavior of dumps.
### Expected behavior
We should be able to initialize a tokenizer. And reusing it should let us reuse the same map computation for the same dataset.
The second call to map should reuse the cached processed dataset from mds1, but it instead it redoes the tokenization because of the behavior of dumps.
### Environment info
- `datasets` version: 2.13.0
- Platform: Linux-6.1.31_1-x86_64-with-glibc2.36
- Python version: 3.9.16
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.1
- Pandas version: 2.0.2
|
[
{
"id": 1935892865,
"node_id": "MDU6TGFiZWwxOTM1ODkyODY1",
"url": "https://api.github.com/repos/huggingface/datasets/labels/duplicate",
"name": "duplicate",
"color": "cfd3d7",
"default": true,
"description": "This issue or pull request already exists"
}
] |
{
"url": null,
"html_url": null,
"diff_url": null,
"patch_url": null,
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}
| false
|
[
"This is a known issue: https://github.com/huggingface/datasets/issues/3847.\r\n\r\nFixing this requires significant work - rewriting the `tokenizers` lib to make them immutable.\r\n\r\nThe current solution is to pass `cache_file_name` to `map` to use that file for caching or calling a tokenizer before `map` (with the same set of parameters as the ones in the map transform)",
"Closing since this is a duplicate"
] |
5,984
|
AutoSharding IterableDataset's when num_workers > 1
|
### Feature request
Minimal Example
```
import torch
from datasets import IterableDataset
d = IterableDataset.from_file(<file_name>)
dl = torch.utils.data.dataloader.DataLoader(d,num_workers=3)
for sample in dl:
print(sample)
```
Warning:
Too many dataloader workers: 2 (max is dataset.n_shards=1). Stopping 1 dataloader workers.
To parallelize data loading, we give each process some shards (or data sources) to process. Therefore it's unnecessary to have a number of workers greater than dataset.n_shards=1. To enable more parallelism, please split the dataset in more files than 1.
Expected Behavior:
Dataset is sharded each cpu uses subset (contiguously - so you can do checkpoint loading/saving)
### Motivation
I have a lot of unused cpu's and would like to be able to shard iterable datasets with pytorch's dataloader when num_workers > 1. This is for a very large single file. I am aware that we can use the `split_dataset_by_node` to ensure that each node (for distributed) gets different shards, but we should extend it so that this also continues for multiple workers.
### Your contribution
If someone points me to what needs to change, I can create a PR.
|
[
{
"id": 1935892871,
"node_id": "MDU6TGFiZWwxOTM1ODkyODcx",
"url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement",
"name": "enhancement",
"color": "a2eeef",
"default": true,
"description": "New feature or request"
}
] |
{
"url": null,
"html_url": null,
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}
| false
|
[
"For this to be possible, we would have to switch from the \"Streaming\" Arrow format to the \"Random Access\" (IPC/Feather) format, which allows reading arbitrary record batches (explained [here](https://arrow.apache.org/docs/python/ipc.html)). We could then use these batches to construct shards.\r\n\r\n@lhoestq @albertvillanova Do you think this use case is worth the switch? Also, we currently shard files, not inner row groups/chunks. Should we also support sharding row groups (e.g. if the number of input files is 1)?\r\n\r\nPS: I don't expect significant speed-up for local, uncompressed Arrow files.",
"Alternatively we could support multiprocessing map for iterable datasets and let the user do the CPU intensive task there ?\r\n\r\nThis way it would work on arrow data but also on any iterable dataset",
"> For this to be possible, we would have to switch from the \"Streaming\" Arrow format to the \"Random Access\" (IPC/Feather) format, which allows reading arbitrary record batches (explained [here](https://arrow.apache.org/docs/python/ipc.html)). We could then use these batches to construct shards.\r\n> \r\n> @lhoestq @albertvillanova Do you think this use case is worth the switch? Also, we currently shard files, not inner row groups/chunks. Should we also support sharding row groups (e.g. if the number of input files is 1)?\r\n> \r\n> PS: I don't expect significant speed-up for local, uncompressed Arrow files.\r\n\r\nCould you explain why you'd need to change the arrow format?\r\n\r\nWhen we use streaming datasets we simply determine the number of worker shards and then add some modulo logic at the appropriate place. Worst case scenario, you'd skip streaming entries according to the number of shards.\r\n\r\nFor PyTorch, I'd be happy to provide an implementation or a sketch thereof, if you point me toward what the testing requirements would be for such a PR.",
"> Could you explain why you'd need to change the arrow format?\r\n\r\nThis way workers have random access to the location of the file where its dataset subset starts. Currently we're using the Arrow streaming format which doesn't include the metadata of the record batches offsets. This is needed here to efficiently split a dataset made of one single file.",
"> > Could you explain why you'd need to change the arrow format?\r\n> \r\n> This way workers have random access to the location of the file where its dataset subset starts. Currently we're using the Arrow streaming format which doesn't include the metadata of the record batches offsets. This is needed here to efficiently split a dataset made of one single file.\r\n\r\nI guess I don't understand why you'd need to subset the dataset in the first place. \r\nIt seems sufficient to figure out how to offset or skip rows.\r\n\r\nFor instance, using pyArrow, you could use RecordBatchStreamReader to zero-copy iterate over records with read_next_batch and then only initiate the next step for records modulo worker shard.\r\nThat's one way to do it, where of course you'd need to account for gpu sharding as well.\r\n\r\n\r\nOtherwise, how did you implement worker/node/GPU sharding for iterable/streaming data where you do not have index information or prior splits (e.g. files)?",
"> For instance, using pyArrow, you could use RecordBatchStreamReader to zero-copy iterate over records with read_next_batch and then only initiate the next step for records modulo worker shard.\r\n\r\nThat works indeed ! And what we meant is that you can make it even faster to instantiate. Indeed using RecordBatchStreamReader you need to get the list of all the record batches in each worker, whereas you could just get the list of record batches per worker if you use the record batches locations in the Arrow IPC file footer. This would be especially appreciated to have a fast instantiation in case you have tens of thousands of Arrow files for example.",
"Any recent updates on this ? ",
"I would also appreciate this feature"
] |
5,983
|
replaced PathLike as a variable for save_to_disk for dataset_path wit…
|
…h str like that of load_from_disk
|
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5983",
"html_url": "https://github.com/huggingface/datasets/pull/5983",
"diff_url": "https://github.com/huggingface/datasets/pull/5983.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5983.patch",
"merged_at": null
}
| true
|
[] |
5,982
|
404 on Datasets Documentation Page
|
### Describe the bug
Getting a 404 from the Hugging Face Datasets docs page:
https://huggingface.co/docs/datasets/index
### Steps to reproduce the bug
1. Go to URL https://huggingface.co/docs/datasets/index
2. Notice 404 not found
### Expected behavior
URL should either show docs or redirect to new location
### Environment info
hugginface.co
|
[] |
{
"url": null,
"html_url": null,
"diff_url": null,
"patch_url": null,
"merged_at": null
}
| false
|
[
"This wasn’t working for me a bit earlier, but it looks to be back up now",
"We had a minor issue updating the docs after the latest release. It should work now :)."
] |
5,981
|
Only two cores are getting used in sagemaker with pytorch 3.10 kernel
|
### Describe the bug
When using the newer pytorch 3.10 kernel, only 2 cores are being used by huggingface filter and map functions. The Pytorch 3.9 kernel would use as many cores as specified in the num_proc field.
We have solved this in our own code by placing the following snippet in the code that is called inside subprocesses:
```os.sched_setaffinity(0, {i for i in range(1000)})```
The problem, as near as we can tell, us that once upon a time, cpu affinity was set using a bitmask ("0xfffff" and the like), and affinity recently changed to a list of processors rather than to using the mask. As such, only processors 1 and 17 are shown to be working in htop.
When running functions via `map`, the above resetting of affinity works to spread across the cores. When using `filter`, however, only two cores are active.
### Steps to reproduce the bug
Repro steps:
1. Create an aws sagemaker instance
2. use the pytorch 3_10 kernel
3. Load a dataset
4. run a filter operation
5. watch as only 2 cores are used when num_proc > 2
6. run a map operation
7. watch as only 2 cores are used when num_proc > 2
8. run a map operation with processor affinity reset inside the function called via map
9. Watch as all cores run
### Expected behavior
All specified cores are used via the num_proc argument.
### Environment info
AWS sagemaker with the following init script run in the terminal after instance creation:
conda init bash
bash
conda activate pytorch_p310
pip install Wand PyPDF pytesseract datasets seqeval pdfplumber transformers pymupdf sentencepiece timm donut-python accelerate optimum xgboost
python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'
sudo yum -y install htop
sudo yum -y update
sudo yum -y install wget libstdc++ autoconf automake libtool autoconf-archive pkg-config gcc gcc-c++ make libjpeg-devel libpng-devel libtiff-devel zlib-devel
|
[] |
{
"url": null,
"html_url": null,
"diff_url": null,
"patch_url": null,
"merged_at": null
}
| false
|
[
"I think it's more likely that this issue is related to PyTorch than Datasets, as PyTorch (on import) registers functions to execute when forking a process. Maybe this is the culprit: https://github.com/pytorch/pytorch/issues/99625",
"From reading that ticket, it may be down in mkl? Is it worth hotfixing in the meantime, with the express intention of turning it off? I know that's a horribly crufty solution, but it's also deeply frustrating to be limited to 2 cores for operations as simple as filtration.",
"This is too specific and unrelated to `datasets`, so this shouldn't be fixed here.",
"@mariosasko @mmr-crexi I had the exact same problem on my kubernetes cluster. the datasets subprocess only user 1 and 17 core"
] |
5,980
|
Viewing dataset card returns “502 Bad Gateway”
|
The url is: https://huggingface.co/datasets/Confirm-Labs/pile_ngrams_trigrams
I am able to successfully view the “Files and versions” tab: [Confirm-Labs/pile_ngrams_trigrams at main](https://huggingface.co/datasets/Confirm-Labs/pile_ngrams_trigrams/tree/main)
Any help would be appreciated! Thanks! I hope this is the right place to report an issue like this.
|
[] |
{
"url": null,
"html_url": null,
"diff_url": null,
"patch_url": null,
"merged_at": null
}
| false
|
[
"Can you try again? Maybe there was a minor outage.",
"Yes, it seems to be working now. In case it's helpful, the outage lasted several days. It was failing as late as yesterday morning. ",
"we fixed something on the server side, glad it's fixed now"
] |
5,979
|
set dev version
| null |
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5979",
"html_url": "https://github.com/huggingface/datasets/pull/5979",
"diff_url": "https://github.com/huggingface/datasets/pull/5979.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5979.patch",
"merged_at": "2023-06-22T18:32:22"
}
| true
|
[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5979). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008087 / 0.011353 (-0.003266) | 0.004691 / 0.011008 (-0.006317) | 0.121545 / 0.038508 (0.083037) | 0.057436 / 0.023109 (0.034326) | 0.368864 / 0.275898 (0.092966) | 0.457199 / 0.323480 (0.133719) | 0.006745 / 0.007986 (-0.001241) | 0.003689 / 0.004328 (-0.000640) | 0.090480 / 0.004250 (0.086229) | 0.071368 / 0.037052 (0.034316) | 0.372788 / 0.258489 (0.114299) | 0.429894 / 0.293841 (0.136053) | 0.037544 / 0.128546 (-0.091002) | 0.010142 / 0.075646 (-0.065505) | 0.420467 / 0.419271 (0.001196) | 0.064359 / 0.043533 (0.020826) | 0.370345 / 0.255139 (0.115206) | 0.405220 / 0.283200 (0.122020) | 0.028410 / 0.141683 (-0.113273) | 1.824845 / 1.452155 (0.372690) | 1.888109 / 1.492716 (0.395392) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.234585 / 0.018006 (0.216578) | 0.499965 / 0.000490 (0.499476) | 0.000461 / 0.000200 (0.000261) | 0.000064 / 0.000054 (0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032294 / 0.037411 (-0.005117) | 0.131769 / 0.014526 (0.117243) | 0.146472 / 0.176557 (-0.030085) | 0.210035 / 0.737135 (-0.527100) | 0.145600 / 0.296338 (-0.150739) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.507455 / 0.215209 (0.292246) | 5.080090 / 2.077655 (3.002435) | 2.506104 / 1.504120 (1.001984) | 2.297655 / 1.541195 (0.756460) | 2.324920 / 1.468490 (0.856430) | 0.645003 / 4.584777 (-3.939774) | 4.677856 / 3.745712 (0.932144) | 2.254179 / 5.269862 (-3.015683) | 1.280663 / 4.565676 (-3.285013) | 0.078809 / 0.424275 (-0.345466) | 0.014059 / 0.007607 (0.006452) | 0.628053 / 0.226044 (0.402009) | 6.327289 / 2.268929 (4.058360) | 2.957918 / 55.444624 (-52.486706) | 2.571568 / 6.876477 (-4.304909) | 2.708766 / 2.142072 (0.566694) | 0.772868 / 4.805227 (-4.032360) | 0.164835 / 6.500664 (-6.335829) | 0.075334 / 0.075469 (-0.000135) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.471930 / 1.841788 (-0.369858) | 17.917340 / 8.074308 (9.843032) | 15.719327 / 10.191392 (5.527935) | 0.191999 / 0.680424 (-0.488424) | 0.022464 / 0.534201 (-0.511737) | 0.511038 / 0.579283 (-0.068245) | 0.512050 / 0.434364 (0.077686) | 0.608711 / 0.540337 (0.068373) | 0.749660 / 1.386936 (-0.637276) |\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) | 0.008028 / 0.011353 (-0.003325) | 0.004908 / 0.011008 (-0.006100) | 0.092294 / 0.038508 (0.053786) | 0.053051 / 0.023109 (0.029942) | 0.453862 / 0.275898 (0.177964) | 0.512548 / 0.323480 (0.189068) | 0.004817 / 0.007986 (-0.003168) | 0.005330 / 0.004328 (0.001002) | 0.095600 / 0.004250 (0.091350) | 0.068763 / 0.037052 (0.031710) | 0.453654 / 0.258489 (0.195165) | 0.504995 / 0.293841 (0.211154) | 0.038123 / 0.128546 (-0.090423) | 0.010650 / 0.075646 (-0.064996) | 0.102854 / 0.419271 (-0.316417) | 0.062973 / 0.043533 (0.019440) | 0.430420 / 0.255139 (0.175281) | 0.465448 / 0.283200 (0.182248) | 0.029736 / 0.141683 (-0.111947) | 1.844225 / 1.452155 (0.392070) | 1.934685 / 1.492716 (0.441968) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227797 / 0.018006 (0.209791) | 0.467868 / 0.000490 (0.467378) | 0.004531 / 0.000200 (0.004331) | 0.000105 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035632 / 0.037411 (-0.001780) | 0.145943 / 0.014526 (0.131417) | 0.151944 / 0.176557 (-0.024613) | 0.220519 / 0.737135 (-0.516616) | 0.159732 / 0.296338 (-0.136606) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.520641 / 0.215209 (0.305432) | 5.184740 / 2.077655 (3.107086) | 2.538751 / 1.504120 (1.034631) | 2.316571 / 1.541195 (0.775377) | 2.387898 / 1.468490 (0.919408) | 0.614515 / 4.584777 (-3.970262) | 4.573142 / 3.745712 (0.827430) | 4.657052 / 5.269862 (-0.612809) | 2.159664 / 4.565676 (-2.406013) | 0.079713 / 0.424275 (-0.344562) | 0.014462 / 0.007607 (0.006855) | 0.656611 / 0.226044 (0.430566) | 6.481630 / 2.268929 (4.212702) | 3.135047 / 55.444624 (-52.309577) | 2.757502 / 6.876477 (-4.118975) | 2.851488 / 2.142072 (0.709415) | 0.790795 / 4.805227 (-4.014432) | 0.172358 / 6.500664 (-6.328306) | 0.080255 / 0.075469 (0.004786) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.571391 / 1.841788 (-0.270396) | 19.025224 / 8.074308 (10.950916) | 17.079230 / 10.191392 (6.887838) | 0.172823 / 0.680424 (-0.507601) | 0.021845 / 0.534201 (-0.512356) | 0.522286 / 0.579283 (-0.056998) | 0.510406 / 0.434364 (0.076042) | 0.604830 / 0.540337 (0.064493) | 0.735466 / 1.386936 (-0.651471) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4084609bdc40d173d1daa74ad2fe98f3ead72f8e \"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) | 0.010025 / 0.011353 (-0.001328) | 0.005699 / 0.011008 (-0.005310) | 0.134194 / 0.038508 (0.095686) | 0.056154 / 0.023109 (0.033045) | 0.470091 / 0.275898 (0.194193) | 0.539225 / 0.323480 (0.215745) | 0.006659 / 0.007986 (-0.001326) | 0.004468 / 0.004328 (0.000140) | 0.110040 / 0.004250 (0.105790) | 0.074172 / 0.037052 (0.037119) | 0.497450 / 0.258489 (0.238961) | 0.535048 / 0.293841 (0.241207) | 0.051195 / 0.128546 (-0.077352) | 0.014926 / 0.075646 (-0.060721) | 0.461334 / 0.419271 (0.042062) | 0.073773 / 0.043533 (0.030240) | 0.450741 / 0.255139 (0.195602) | 0.474853 / 0.283200 (0.191653) | 0.036372 / 0.141683 (-0.105311) | 1.982873 / 1.452155 (0.530719) | 1.989912 / 1.492716 (0.497196) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.287817 / 0.018006 (0.269811) | 0.613415 / 0.000490 (0.612926) | 0.007082 / 0.000200 (0.006882) | 0.000100 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031119 / 0.037411 (-0.006292) | 0.129886 / 0.014526 (0.115361) | 0.143492 / 0.176557 (-0.033065) | 0.208536 / 0.737135 (-0.528600) | 0.147081 / 0.296338 (-0.149257) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.668312 / 0.215209 (0.453103) | 6.568609 / 2.077655 (4.490955) | 2.708788 / 1.504120 (1.204668) | 2.366737 / 1.541195 (0.825542) | 2.392598 / 1.468490 (0.924108) | 0.967582 / 4.584777 (-3.617195) | 5.582743 / 3.745712 (1.837031) | 3.021607 / 5.269862 (-2.248255) | 1.866402 / 4.565676 (-2.699275) | 0.115998 / 0.424275 (-0.308277) | 0.015571 / 0.007607 (0.007964) | 0.820069 / 0.226044 (0.594025) | 8.229725 / 2.268929 (5.960797) | 3.437068 / 55.444624 (-52.007557) | 2.902312 / 6.876477 (-3.974164) | 3.025874 / 2.142072 (0.883802) | 1.230359 / 4.805227 (-3.574868) | 0.237341 / 6.500664 (-6.263323) | 0.089923 / 0.075469 (0.014453) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.670970 / 1.841788 (-0.170818) | 19.667167 / 8.074308 (11.592859) | 21.624423 / 10.191392 (11.433031) | 0.231683 / 0.680424 (-0.448741) | 0.029145 / 0.534201 (-0.505056) | 0.543441 / 0.579283 (-0.035842) | 0.617510 / 0.434364 (0.183146) | 0.612662 / 0.540337 (0.072324) | 0.790589 / 1.386936 (-0.596347) |\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) | 0.010324 / 0.011353 (-0.001029) | 0.005339 / 0.011008 (-0.005669) | 0.104762 / 0.038508 (0.066254) | 0.052631 / 0.023109 (0.029522) | 0.485864 / 0.275898 (0.209966) | 0.595768 / 0.323480 (0.272288) | 0.007417 / 0.007986 (-0.000569) | 0.005229 / 0.004328 (0.000900) | 0.100775 / 0.004250 (0.096524) | 0.067144 / 0.037052 (0.030092) | 0.522269 / 0.258489 (0.263780) | 0.592597 / 0.293841 (0.298756) | 0.051101 / 0.128546 (-0.077446) | 0.015277 / 0.075646 (-0.060369) | 0.115530 / 0.419271 (-0.303741) | 0.071922 / 0.043533 (0.028390) | 0.490208 / 0.255139 (0.235069) | 0.578936 / 0.283200 (0.295736) | 0.040382 / 0.141683 (-0.101301) | 1.986059 / 1.452155 (0.533904) | 2.040600 / 1.492716 (0.547883) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.300399 / 0.018006 (0.282393) | 0.624702 / 0.000490 (0.624212) | 0.004908 / 0.000200 (0.004708) | 0.000155 / 0.000054 (0.000100) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038031 / 0.037411 (0.000619) | 0.140353 / 0.014526 (0.125828) | 0.152600 / 0.176557 (-0.023956) | 0.219165 / 0.737135 (-0.517970) | 0.154232 / 0.296338 (-0.142106) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.698855 / 0.215209 (0.483646) | 7.125543 / 2.077655 (5.047889) | 3.251222 / 1.504120 (1.747102) | 2.953404 / 1.541195 (1.412209) | 3.051108 / 1.468490 (1.582618) | 0.962068 / 4.584777 (-3.622709) | 5.789579 / 3.745712 (2.043867) | 5.193271 / 5.269862 (-0.076591) | 2.757886 / 4.565676 (-1.807790) | 0.111865 / 0.424275 (-0.312410) | 0.014684 / 0.007607 (0.007077) | 0.875967 / 0.226044 (0.649923) | 8.818359 / 2.268929 (6.549430) | 4.165216 / 55.444624 (-51.279408) | 3.372059 / 6.876477 (-3.504418) | 3.486886 / 2.142072 (1.344813) | 1.232276 / 4.805227 (-3.572951) | 0.238967 / 6.500664 (-6.261697) | 0.091584 / 0.075469 (0.016115) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.850755 / 1.841788 (0.008968) | 20.058756 / 8.074308 (11.984448) | 23.761271 / 10.191392 (13.569879) | 0.231826 / 0.680424 (-0.448598) | 0.030119 / 0.534201 (-0.504082) | 0.532614 / 0.579283 (-0.046669) | 0.628968 / 0.434364 (0.194604) | 0.628403 / 0.540337 (0.088066) | 0.745648 / 1.386936 (-0.641288) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a8a797cc92e860c8d0df71e0aa826f4d2690713e \"CML watermark\")\n"
] |
5,978
|
Release: 2.13.1
| null |
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5978",
"html_url": "https://github.com/huggingface/datasets/pull/5978",
"diff_url": "https://github.com/huggingface/datasets/pull/5978.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5978.patch",
"merged_at": "2023-06-22T18:30:16"
}
| true
|
[
"_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) | 0.006173 / 0.011353 (-0.005180) | 0.003773 / 0.011008 (-0.007235) | 0.099499 / 0.038508 (0.060991) | 0.037918 / 0.023109 (0.014809) | 0.321329 / 0.275898 (0.045431) | 0.379739 / 0.323480 (0.056259) | 0.004664 / 0.007986 (-0.003322) | 0.002943 / 0.004328 (-0.001385) | 0.077759 / 0.004250 (0.073509) | 0.055271 / 0.037052 (0.018219) | 0.329428 / 0.258489 (0.070939) | 0.378731 / 0.293841 (0.084890) | 0.027737 / 0.128546 (-0.100810) | 0.008566 / 0.075646 (-0.067081) | 0.313220 / 0.419271 (-0.106052) | 0.047101 / 0.043533 (0.003568) | 0.316211 / 0.255139 (0.061072) | 0.341826 / 0.283200 (0.058626) | 0.020838 / 0.141683 (-0.120845) | 1.550064 / 1.452155 (0.097909) | 1.706518 / 1.492716 (0.213801) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203093 / 0.018006 (0.185087) | 0.425345 / 0.000490 (0.424856) | 0.004800 / 0.000200 (0.004600) | 0.000077 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024590 / 0.037411 (-0.012821) | 0.098115 / 0.014526 (0.083589) | 0.108274 / 0.176557 (-0.068282) | 0.170804 / 0.737135 (-0.566332) | 0.110560 / 0.296338 (-0.185778) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425251 / 0.215209 (0.210042) | 4.239075 / 2.077655 (2.161421) | 1.955601 / 1.504120 (0.451481) | 1.774796 / 1.541195 (0.233602) | 1.826641 / 1.468490 (0.358150) | 0.558777 / 4.584777 (-4.026000) | 3.361697 / 3.745712 (-0.384015) | 1.764468 / 5.269862 (-3.505394) | 1.032280 / 4.565676 (-3.533396) | 0.067872 / 0.424275 (-0.356403) | 0.010998 / 0.007607 (0.003391) | 0.525682 / 0.226044 (0.299637) | 5.254356 / 2.268929 (2.985427) | 2.384332 / 55.444624 (-53.060292) | 2.045578 / 6.876477 (-4.830898) | 2.170914 / 2.142072 (0.028841) | 0.674782 / 4.805227 (-4.130445) | 0.135351 / 6.500664 (-6.365314) | 0.066591 / 0.075469 (-0.008878) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.209181 / 1.841788 (-0.632606) | 14.044518 / 8.074308 (5.970210) | 13.184705 / 10.191392 (2.993313) | 0.130836 / 0.680424 (-0.549588) | 0.016582 / 0.534201 (-0.517619) | 0.360005 / 0.579283 (-0.219279) | 0.379519 / 0.434364 (-0.054845) | 0.422174 / 0.540337 (-0.118164) | 0.515546 / 1.386936 (-0.871390) |\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) | 0.006293 / 0.011353 (-0.005060) | 0.003784 / 0.011008 (-0.007224) | 0.079248 / 0.038508 (0.040739) | 0.038452 / 0.023109 (0.015343) | 0.444727 / 0.275898 (0.168829) | 0.500535 / 0.323480 (0.177055) | 0.003455 / 0.007986 (-0.004531) | 0.002873 / 0.004328 (-0.001455) | 0.077439 / 0.004250 (0.073189) | 0.047855 / 0.037052 (0.010803) | 0.448049 / 0.258489 (0.189560) | 0.509517 / 0.293841 (0.215676) | 0.028359 / 0.128546 (-0.100188) | 0.008503 / 0.075646 (-0.067143) | 0.084961 / 0.419271 (-0.334310) | 0.042880 / 0.043533 (-0.000653) | 0.436628 / 0.255139 (0.181489) | 0.456574 / 0.283200 (0.173375) | 0.019539 / 0.141683 (-0.122144) | 1.561273 / 1.452155 (0.109118) | 1.572018 / 1.492716 (0.079301) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230250 / 0.018006 (0.212244) | 0.415189 / 0.000490 (0.414700) | 0.003213 / 0.000200 (0.003013) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025541 / 0.037411 (-0.011871) | 0.102326 / 0.014526 (0.087800) | 0.110258 / 0.176557 (-0.066298) | 0.162488 / 0.737135 (-0.574647) | 0.112782 / 0.296338 (-0.183556) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.457936 / 0.215209 (0.242727) | 4.581503 / 2.077655 (2.503848) | 2.237659 / 1.504120 (0.733540) | 2.029960 / 1.541195 (0.488765) | 2.082911 / 1.468490 (0.614421) | 0.556485 / 4.584777 (-4.028292) | 3.384418 / 3.745712 (-0.361295) | 1.748809 / 5.269862 (-3.521053) | 1.034759 / 4.565676 (-3.530917) | 0.067500 / 0.424275 (-0.356776) | 0.011425 / 0.007607 (0.003818) | 0.561340 / 0.226044 (0.335295) | 5.623629 / 2.268929 (3.354701) | 2.733587 / 55.444624 (-52.711038) | 2.401578 / 6.876477 (-4.474899) | 2.524569 / 2.142072 (0.382496) | 0.673170 / 4.805227 (-4.132057) | 0.136681 / 6.500664 (-6.363983) | 0.068060 / 0.075469 (-0.007409) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.318651 / 1.841788 (-0.523137) | 14.362123 / 8.074308 (6.287815) | 14.385964 / 10.191392 (4.194572) | 0.149914 / 0.680424 (-0.530510) | 0.016877 / 0.534201 (-0.517324) | 0.358406 / 0.579283 (-0.220877) | 0.394349 / 0.434364 (-0.040015) | 0.422471 / 0.540337 (-0.117866) | 0.513807 / 1.386936 (-0.873129) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1b9ce11d1b94e6178df663ff5fcad029849d10fb \"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) | 0.006272 / 0.011353 (-0.005080) | 0.003903 / 0.011008 (-0.007105) | 0.100180 / 0.038508 (0.061672) | 0.037799 / 0.023109 (0.014690) | 0.385627 / 0.275898 (0.109729) | 0.446518 / 0.323480 (0.123038) | 0.004811 / 0.007986 (-0.003175) | 0.003032 / 0.004328 (-0.001296) | 0.077063 / 0.004250 (0.072812) | 0.055564 / 0.037052 (0.018512) | 0.397346 / 0.258489 (0.138857) | 0.443242 / 0.293841 (0.149401) | 0.027904 / 0.128546 (-0.100642) | 0.008386 / 0.075646 (-0.067260) | 0.315013 / 0.419271 (-0.104259) | 0.047943 / 0.043533 (0.004410) | 0.378443 / 0.255139 (0.123304) | 0.411472 / 0.283200 (0.128272) | 0.020465 / 0.141683 (-0.121218) | 1.526594 / 1.452155 (0.074439) | 1.547018 / 1.492716 (0.054301) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.219377 / 0.018006 (0.201370) | 0.430254 / 0.000490 (0.429764) | 0.003218 / 0.000200 (0.003018) | 0.000072 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023667 / 0.037411 (-0.013744) | 0.099143 / 0.014526 (0.084617) | 0.106044 / 0.176557 (-0.070513) | 0.166186 / 0.737135 (-0.570949) | 0.108736 / 0.296338 (-0.187603) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437971 / 0.215209 (0.222762) | 4.363675 / 2.077655 (2.286021) | 2.011993 / 1.504120 (0.507873) | 1.845189 / 1.541195 (0.303994) | 1.831848 / 1.468490 (0.363358) | 0.562402 / 4.584777 (-4.022375) | 3.365259 / 3.745712 (-0.380453) | 1.781491 / 5.269862 (-3.488371) | 1.023454 / 4.565676 (-3.542223) | 0.067857 / 0.424275 (-0.356418) | 0.011076 / 0.007607 (0.003469) | 0.532267 / 0.226044 (0.306223) | 5.340344 / 2.268929 (3.071415) | 2.388649 / 55.444624 (-53.055976) | 2.055373 / 6.876477 (-4.821104) | 2.205047 / 2.142072 (0.062975) | 0.672909 / 4.805227 (-4.132318) | 0.135244 / 6.500664 (-6.365420) | 0.066184 / 0.075469 (-0.009285) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.206838 / 1.841788 (-0.634950) | 13.967075 / 8.074308 (5.892767) | 13.143971 / 10.191392 (2.952579) | 0.143991 / 0.680424 (-0.536433) | 0.016673 / 0.534201 (-0.517527) | 0.376180 / 0.579283 (-0.203103) | 0.386550 / 0.434364 (-0.047814) | 0.440590 / 0.540337 (-0.099747) | 0.529974 / 1.386936 (-0.856962) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006299 / 0.011353 (-0.005054) | 0.003784 / 0.011008 (-0.007224) | 0.077875 / 0.038508 (0.039367) | 0.038689 / 0.023109 (0.015580) | 0.421684 / 0.275898 (0.145786) | 0.472649 / 0.323480 (0.149169) | 0.003570 / 0.007986 (-0.004415) | 0.004448 / 0.004328 (0.000120) | 0.077867 / 0.004250 (0.073616) | 0.049514 / 0.037052 (0.012462) | 0.375983 / 0.258489 (0.117494) | 0.470632 / 0.293841 (0.176791) | 0.028238 / 0.128546 (-0.100308) | 0.008462 / 0.075646 (-0.067185) | 0.082452 / 0.419271 (-0.336819) | 0.043617 / 0.043533 (0.000084) | 0.400874 / 0.255139 (0.145735) | 0.426191 / 0.283200 (0.142992) | 0.020602 / 0.141683 (-0.121081) | 1.567658 / 1.452155 (0.115504) | 1.572610 / 1.492716 (0.079893) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246144 / 0.018006 (0.228138) | 0.419402 / 0.000490 (0.418913) | 0.001691 / 0.000200 (0.001491) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026105 / 0.037411 (-0.011306) | 0.104734 / 0.014526 (0.090208) | 0.110257 / 0.176557 (-0.066300) | 0.161429 / 0.737135 (-0.575706) | 0.114367 / 0.296338 (-0.181972) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.453352 / 0.215209 (0.238143) | 4.537924 / 2.077655 (2.460269) | 2.196193 / 1.504120 (0.692073) | 2.002087 / 1.541195 (0.460892) | 2.041722 / 1.468490 (0.573231) | 0.561643 / 4.584777 (-4.023134) | 3.449108 / 3.745712 (-0.296605) | 2.862800 / 5.269862 (-2.407062) | 1.387895 / 4.565676 (-3.177782) | 0.068076 / 0.424275 (-0.356199) | 0.011568 / 0.007607 (0.003961) | 0.559279 / 0.226044 (0.333235) | 5.598738 / 2.268929 (3.329809) | 2.676649 / 55.444624 (-52.767975) | 2.334588 / 6.876477 (-4.541889) | 2.376215 / 2.142072 (0.234142) | 0.673109 / 4.805227 (-4.132118) | 0.137587 / 6.500664 (-6.363077) | 0.069131 / 0.075469 (-0.006338) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.307332 / 1.841788 (-0.534456) | 14.536036 / 8.074308 (6.461728) | 14.173734 / 10.191392 (3.982342) | 0.145143 / 0.680424 (-0.535281) | 0.016662 / 0.534201 (-0.517539) | 0.366901 / 0.579283 (-0.212383) | 0.394498 / 0.434364 (-0.039866) | 0.430546 / 0.540337 (-0.109792) | 0.518950 / 1.386936 (-0.867986) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#682d21e94ab1e64c11b583de39dc4c93f0101c5a \"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) | 0.008122 / 0.011353 (-0.003231) | 0.005585 / 0.011008 (-0.005424) | 0.121219 / 0.038508 (0.082711) | 0.047616 / 0.023109 (0.024507) | 0.440576 / 0.275898 (0.164678) | 0.491053 / 0.323480 (0.167573) | 0.004774 / 0.007986 (-0.003211) | 0.006758 / 0.004328 (0.002430) | 0.103852 / 0.004250 (0.099602) | 0.071560 / 0.037052 (0.034508) | 0.463107 / 0.258489 (0.204618) | 0.516904 / 0.293841 (0.223063) | 0.048052 / 0.128546 (-0.080494) | 0.013679 / 0.075646 (-0.061968) | 0.428383 / 0.419271 (0.009112) | 0.069468 / 0.043533 (0.025936) | 0.432593 / 0.255139 (0.177454) | 0.471810 / 0.283200 (0.188611) | 0.037541 / 0.141683 (-0.104142) | 1.823490 / 1.452155 (0.371335) | 1.922558 / 1.492716 (0.429842) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.252315 / 0.018006 (0.234309) | 0.541757 / 0.000490 (0.541267) | 0.000373 / 0.000200 (0.000173) | 0.000083 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030361 / 0.037411 (-0.007050) | 0.125928 / 0.014526 (0.111402) | 0.145102 / 0.176557 (-0.031455) | 0.209798 / 0.737135 (-0.527337) | 0.147349 / 0.296338 (-0.148990) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.627554 / 0.215209 (0.412345) | 5.917422 / 2.077655 (3.839767) | 2.491083 / 1.504120 (0.986963) | 2.147078 / 1.541195 (0.605883) | 2.167511 / 1.468490 (0.699021) | 0.903061 / 4.584777 (-3.681716) | 5.518537 / 3.745712 (1.772825) | 2.654348 / 5.269862 (-2.615514) | 1.645121 / 4.565676 (-2.920556) | 0.103782 / 0.424275 (-0.320493) | 0.013048 / 0.007607 (0.005441) | 0.756732 / 0.226044 (0.530687) | 7.622873 / 2.268929 (5.353945) | 3.122689 / 55.444624 (-52.321936) | 2.537735 / 6.876477 (-4.338742) | 2.640090 / 2.142072 (0.498018) | 1.128635 / 4.805227 (-3.676593) | 0.228089 / 6.500664 (-6.272575) | 0.086207 / 0.075469 (0.010738) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.561591 / 1.841788 (-0.280197) | 18.110299 / 8.074308 (10.035991) | 20.718017 / 10.191392 (10.526625) | 0.225741 / 0.680424 (-0.454682) | 0.031738 / 0.534201 (-0.502463) | 0.530789 / 0.579283 (-0.048495) | 0.607364 / 0.434364 (0.173000) | 0.581593 / 0.540337 (0.041256) | 0.726033 / 1.386936 (-0.660903) |\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) | 0.009323 / 0.011353 (-0.002030) | 0.005360 / 0.011008 (-0.005649) | 0.103608 / 0.038508 (0.065100) | 0.050158 / 0.023109 (0.027049) | 0.499906 / 0.275898 (0.224008) | 0.561005 / 0.323480 (0.237525) | 0.005093 / 0.007986 (-0.002892) | 0.008285 / 0.004328 (0.003956) | 0.103446 / 0.004250 (0.099196) | 0.061478 / 0.037052 (0.024426) | 0.494016 / 0.258489 (0.235527) | 0.537550 / 0.293841 (0.243709) | 0.048829 / 0.128546 (-0.079717) | 0.017032 / 0.075646 (-0.058614) | 0.107748 / 0.419271 (-0.311524) | 0.065607 / 0.043533 (0.022074) | 0.488709 / 0.255139 (0.233570) | 0.512023 / 0.283200 (0.228823) | 0.032067 / 0.141683 (-0.109616) | 1.907585 / 1.452155 (0.455431) | 1.960994 / 1.492716 (0.468278) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.278378 / 0.018006 (0.260371) | 0.551474 / 0.000490 (0.550985) | 0.006886 / 0.000200 (0.006686) | 0.000106 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030674 / 0.037411 (-0.006737) | 0.135179 / 0.014526 (0.120654) | 0.133703 / 0.176557 (-0.042853) | 0.198923 / 0.737135 (-0.538212) | 0.155108 / 0.296338 (-0.141231) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.690566 / 0.215209 (0.475357) | 6.789594 / 2.077655 (4.711940) | 2.940668 / 1.504120 (1.436549) | 2.562431 / 1.541195 (1.021236) | 2.554232 / 1.468490 (1.085742) | 0.888470 / 4.584777 (-3.696307) | 5.672318 / 3.745712 (1.926606) | 2.741626 / 5.269862 (-2.528236) | 1.818336 / 4.565676 (-2.747340) | 0.110434 / 0.424275 (-0.313841) | 0.014114 / 0.007607 (0.006507) | 0.830632 / 0.226044 (0.604588) | 8.270787 / 2.268929 (6.001859) | 3.723486 / 55.444624 (-51.721139) | 2.993671 / 6.876477 (-3.882806) | 2.918273 / 2.142072 (0.776201) | 1.105337 / 4.805227 (-3.699891) | 0.222976 / 6.500664 (-6.277688) | 0.085290 / 0.075469 (0.009820) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.816027 / 1.841788 (-0.025760) | 18.496850 / 8.074308 (10.422541) | 20.457032 / 10.191392 (10.265640) | 0.243533 / 0.680424 (-0.436891) | 0.027044 / 0.534201 (-0.507157) | 0.500752 / 0.579283 (-0.078531) | 0.620963 / 0.434364 (0.186599) | 0.607995 / 0.540337 (0.067658) | 0.722915 / 1.386936 (-0.664021) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#682d21e94ab1e64c11b583de39dc4c93f0101c5a \"CML watermark\")\n"
] |
5,976
|
Avoid stuck map operation when subprocesses crashes
|
I've been using Dataset.map() with `num_proc=os.cpu_count()` to leverage multicore processing for my datasets, but from time to time I get stuck processes waiting forever. Apparently, when one of the subprocesses is abruptly killed (OOM killer, segfault, SIGKILL, etc), the main process keeps waiting for the async task sent to that child process to finish.
It seems to be easy to reproduce the issue with the following script:
```
import os
from datasets import Dataset, Features, Value
def do_stuck(item):
os.kill(os.getpid(), 9)
data = {
"col1": list(range(5)),
"col2": list(range(5)),
}
ds = Dataset.from_dict(
data,
features=Features({
"col1": Value("int64"),
"col2": Value("int64"),
}),
)
print(ds.map(do_stuck, num_proc=4))
```
This is an old behavior in Python, which apparently was fixed a few years ago in `concurrent.futures.ProcessPoolExecutor` ([ref](https://bugs.python.org/issue9205)), but not in `multiprocessing.pool.Pool` / `multiprocess.pool.Pool`, which is used by `Dataset.map` ([ref](https://bugs.python.org/issue22393)).
This PR is an idea to try to detect when a child process gets killed, and raises a `RuntimeError` warning the dataset.map() caller.
EDIT: Related proposal for future improvement: https://github.com/huggingface/datasets/discussions/5977
|
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5976",
"html_url": "https://github.com/huggingface/datasets/pull/5976",
"diff_url": "https://github.com/huggingface/datasets/pull/5976.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5976.patch",
"merged_at": "2023-07-10T09:50:07"
}
| true
|
[
"Hi ! Do you think this can be fixed at the Pool level ? Ideally it should be the Pool responsibility to handle this, not the `map` code. We could even subclass Pool if needed (at least the one from `multiprocess`)",
"@lhoestq it makes sense to me. Just pushed a refactoring creating a `class ProcessPool(multiprocess.pool.Pool)` to keep track of the PID changes.",
"_The documentation is not available anymore as the PR was closed or merged._",
"I managed to raise an error without subclassing Pool with two additions to `iflatmap_unordered`:\r\n\r\n1. at the beggining\r\n```python\r\noriginal_pool = list(pool._pool)\r\n```\r\n\r\n2. in the loop\r\n```python\r\nif any(async_result._pool != original_pool for async_result in async_results) and queue.empty():\r\n raise RuntimeError(\r\n \"One of the subprocesses has abruptly died during map operation.\"\r\n \"To debug the error, disable multiprocessing.\"\r\n )\r\n```\r\n\r\nIt's still a fix that only works for `iflatmap_unordered` (so not for map, imap etc) but is maybe simpler that subclassing. It also works for both multiprocessing.Pool and multiprocess.Pool",
"@lhoestq sorry for the delay. Busy weeks here. \r\n\r\nI just pushed the change you requested. It looks closer to the original proposal, actually.\r\n\r\nIt seems that `map` actually uses `iflatmap_unordered` ([here](https://github.com/huggingface/datasets/blob/819bb4346434912eb405ce3f3e9f21dc25a2fe85/src/datasets/arrow_dataset.py#L1509)). I think this solution works fine for the `map` method (which is the one being tested by the new `tests/test_arrow_dataset.py::BaseDatasetTest::test_map_crash_subprocess`, right?).",
"Yes fixing iflatmap_unordered does fix Dataset.map, but it won't fix any Pool.map that we may use elsewhere so we'll have to keep this in mind.",
"It looks all good to me, feel free to fix code formatting by running `make style` and we can merge :)",
"> Yes fixing iflatmap_unordered does fix Dataset.map, but it won't fix any Pool.map that we may use elsewhere so we'll have to keep this in mind.\r\n\r\nRight, I agree. The best way moving forward is probably not using the buggy `multiprocess.Pool` anymore, and replace it with `concurrent.futures.ProcessPoolExecutor` as much as possible.\r\n\r\nAnyway, I've run `make style` now. Thanks for the support!",
"It looks like checking the async_result._pool doesn't always work - sorry about that. We might just go back to your original solution then. Would also be cool to open an issue in `multiprocess` to ask if they have a solution or if they plan to fix this.",
"@lhoestq no problem! Reverted to the previous version.\r\n\r\nTBH, given the discussions [in this python issue](https://github.com/python/cpython/issues/66587), I don't think the error in `multiprocess` will be merged upstream any time soon...",
"<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) | 0.006060 / 0.011353 (-0.005293) | 0.003695 / 0.011008 (-0.007313) | 0.080484 / 0.038508 (0.041976) | 0.061894 / 0.023109 (0.038785) | 0.312510 / 0.275898 (0.036612) | 0.352398 / 0.323480 (0.028918) | 0.004638 / 0.007986 (-0.003348) | 0.002918 / 0.004328 (-0.001410) | 0.062932 / 0.004250 (0.058681) | 0.050859 / 0.037052 (0.013807) | 0.316812 / 0.258489 (0.058323) | 0.357684 / 0.293841 (0.063843) | 0.027622 / 0.128546 (-0.100924) | 0.008012 / 0.075646 (-0.067634) | 0.260970 / 0.419271 (-0.158302) | 0.045807 / 0.043533 (0.002275) | 0.321235 / 0.255139 (0.066096) | 0.343162 / 0.283200 (0.059962) | 0.021136 / 0.141683 (-0.120547) | 1.465886 / 1.452155 (0.013731) | 1.500216 / 1.492716 (0.007500) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.187286 / 0.018006 (0.169279) | 0.428724 / 0.000490 (0.428235) | 0.003029 / 0.000200 (0.002829) | 0.000063 / 0.000054 (0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022703 / 0.037411 (-0.014708) | 0.072740 / 0.014526 (0.058215) | 0.083436 / 0.176557 (-0.093120) | 0.144559 / 0.737135 (-0.592577) | 0.083958 / 0.296338 (-0.212380) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.435729 / 0.215209 (0.220520) | 4.351146 / 2.077655 (2.273491) | 2.316627 / 1.504120 (0.812508) | 2.144587 / 1.541195 (0.603393) | 2.209182 / 1.468490 (0.740692) | 0.501131 / 4.584777 (-4.083646) | 3.077085 / 3.745712 (-0.668627) | 4.353706 / 5.269862 (-0.916156) | 2.621523 / 4.565676 (-1.944154) | 0.058976 / 0.424275 (-0.365299) | 0.006467 / 0.007607 (-0.001141) | 0.506690 / 0.226044 (0.280646) | 5.085787 / 2.268929 (2.816858) | 2.731336 / 55.444624 (-52.713289) | 2.419451 / 6.876477 (-4.457025) | 2.583649 / 2.142072 (0.441577) | 0.589869 / 4.805227 (-4.215359) | 0.131040 / 6.500664 (-6.369624) | 0.061332 / 0.075469 (-0.014137) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.220542 / 1.841788 (-0.621245) | 18.169643 / 8.074308 (10.095335) | 13.251704 / 10.191392 (3.060312) | 0.142952 / 0.680424 (-0.537472) | 0.016639 / 0.534201 (-0.517562) | 0.334851 / 0.579283 (-0.244432) | 0.361865 / 0.434364 (-0.072499) | 0.380933 / 0.540337 (-0.159404) | 0.527374 / 1.386936 (-0.859562) |\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) | 0.006319 / 0.011353 (-0.005034) | 0.003778 / 0.011008 (-0.007231) | 0.062388 / 0.038508 (0.023880) | 0.062228 / 0.023109 (0.039119) | 0.373727 / 0.275898 (0.097829) | 0.399442 / 0.323480 (0.075962) | 0.005434 / 0.007986 (-0.002551) | 0.003020 / 0.004328 (-0.001308) | 0.062774 / 0.004250 (0.058524) | 0.052784 / 0.037052 (0.015732) | 0.376428 / 0.258489 (0.117939) | 0.405039 / 0.293841 (0.111198) | 0.027884 / 0.128546 (-0.100662) | 0.008086 / 0.075646 (-0.067561) | 0.067078 / 0.419271 (-0.352194) | 0.042927 / 0.043533 (-0.000606) | 0.372142 / 0.255139 (0.117003) | 0.389604 / 0.283200 (0.106405) | 0.021582 / 0.141683 (-0.120101) | 1.473332 / 1.452155 (0.021177) | 1.536018 / 1.492716 (0.043302) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.184729 / 0.018006 (0.166723) | 0.421065 / 0.000490 (0.420575) | 0.002681 / 0.000200 (0.002481) | 0.000070 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026067 / 0.037411 (-0.011344) | 0.077138 / 0.014526 (0.062612) | 0.085178 / 0.176557 (-0.091379) | 0.139681 / 0.737135 (-0.597454) | 0.087528 / 0.296338 (-0.208810) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.444899 / 0.215209 (0.229690) | 4.459168 / 2.077655 (2.381513) | 2.408792 / 1.504120 (0.904672) | 2.237243 / 1.541195 (0.696048) | 2.296298 / 1.468490 (0.827808) | 0.498508 / 4.584777 (-4.086269) | 3.067064 / 3.745712 (-0.678648) | 4.470577 / 5.269862 (-0.799284) | 2.701972 / 4.565676 (-1.863705) | 0.057711 / 0.424275 (-0.366564) | 0.006443 / 0.007607 (-0.001164) | 0.524046 / 0.226044 (0.298002) | 5.229928 / 2.268929 (2.961000) | 2.862101 / 55.444624 (-52.582523) | 2.545972 / 6.876477 (-4.330504) | 2.606459 / 2.142072 (0.464387) | 0.593285 / 4.805227 (-4.211942) | 0.124913 / 6.500664 (-6.375751) | 0.061942 / 0.075469 (-0.013527) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.322162 / 1.841788 (-0.519625) | 18.745796 / 8.074308 (10.671488) | 13.955443 / 10.191392 (3.764051) | 0.145610 / 0.680424 (-0.534814) | 0.016817 / 0.534201 (-0.517384) | 0.331180 / 0.579283 (-0.248103) | 0.343019 / 0.434364 (-0.091345) | 0.379459 / 0.540337 (-0.160878) | 0.526403 / 1.386936 (-0.860533) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aca4cdcc79f16ec5157a2a3a665fdef0e3aa176d \"CML watermark\")\n"
] |
5,975
|
Streaming Dataset behind Proxy - FileNotFoundError
|
### Describe the bug
When trying to stream a dataset i get the following error after a few minutes of waiting.
```
FileNotFoundError: https://huggingface.co/datasets/facebook/voxpopuli/resolve/main/data/n_files.json
If the repo is private or gated, make sure to log in with `huggingface-cli login`.
```
I have already set the proxy environment variables. Downloading a Dataset without streaming works as expected.
Still i suspect that this is connected to being behind a proxy.
Is there a way to set the proxy for streaming datasets? Possibly a keyword argument that gets passed to ffspec?
### Steps to reproduce the bug
This is the code i use.
```
import os
os.environ['http_proxy'] = "http://example.com:xxxx"
os.environ['https_proxy'] = "http://example.com:xxxx"
from datasets import load_dataset
ds = load_dataset("facebook/voxpopuli", name="de", streaming=True)
```
### Expected behavior
I would expect the streaming functionality to use the set proxy settings.
### Environment info
- `datasets` version: 2.13.0
- Platform: Linux-5.15.0-73-generic-x86_64-with-glibc2.35
- Python version: 3.10.11
- Huggingface_hub version: 0.15.1
- PyArrow version: 11.0.0
- Pandas version: 2.0.2
|
[] |
{
"url": null,
"html_url": null,
"diff_url": null,
"patch_url": null,
"merged_at": null
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| false
|
[
"Duplicate of #",
"Hi ! can you try to set the upper case environment variables `HTTP_PROXY` and `HTTPS_PROXY` ?\r\n\r\nWe use `aiohttp` for streaming and it uses case sensitive environment variables",
"Hi, thanks for the quick reply.\r\n\r\nI set the uppercase env variables with\r\n\r\n`\r\nos.environ['HTTP_PROXY'] = \"http://example.com:xxxx\" \r\nos.environ['HTTPS_PROXY'] = \"http://example.com:xxxx\" \r\n`\r\n\r\nHowever, I still get the same error.\r\n\r\nOne thing that could be helpfull: When downloading a dataset without streaming i get the following message:\r\n_HF google storage unreachable. Downloading and preparing it from source_.\r\nThe download does however work as expected.\r\n",
"Are you able to use `aiohttp` to get the file at `https://huggingface.co/datasets/facebook/voxpopuli/resolve/main/data/n_files.json` using your proxy ?",
"It only works when passing trust_env=True when creating the ClientSession, as well as setting ssl=False.\r\n\r\nWorking Example:\r\n\r\n```\r\nimport os\r\n\r\nos.environ['HTTP_PROXY'] = \"xyz\"\r\nos.environ['HTTPS_PROXY'] = \"xyz\"\r\n\r\nimport asyncio\r\nimport aiohttp\r\n\r\nasync def download_pep(url):\r\n async with aiohttp.ClientSession(trust_env=True) as session:\r\n print(\"1\")\r\n async with session.get(url, ssl=False) as resp:\r\n print(\"2\")\r\n content = await resp.text()\r\n print(content)\r\n return content\r\n\r\nasyncio.run(download_pep(\"https://huggingface.co/datasets/facebook/voxpopuli/resolve/main/data/n_files.json\"))\r\n```\r\n\r\n\r\n\r\nSSL Verification has been a problem with other packages as well. Usually I circumvent the problem by setting\r\n```\r\nimport ssl\r\nssl._create_default_https_context = ssl._create_unverified_context\r\n```\r\n(probably not the best idea for security), although here aiohttp does not seem to use this default context.",
"We do pass `trust_env` as well. Could you share the full stack trace you get when streaming using `datasets` ? That could help locate where we might have forgotten to pass `trust_env`",
"Is there a way to disable ssl verification when streaming a dataset. I suspect this might be the isssue with my proxy.\r\n\r\n\r\nHere you go:\r\n\r\n```\r\nFileNotFoundError Traceback (most recent call last)\r\nCell In[8], line 3\r\n 1 from datasets import load_dataset\r\n----> 3 ds = load_dataset(\"facebook/voxpopuli\", name=\"de\", streaming=True)\r\n 5 sample = next(iter(ds))\r\n\r\nFile [~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/load.py:1790](https://vscode-remote+ssh-002dremote-002bml-002er-002dsoftware-002eat.vscode-resource.vscode-cdn.net/home/wrsbri/projects/audio_course/~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/load.py:1790), in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)\r\n 1788 # Return iterable dataset in case of streaming\r\n 1789 if streaming:\r\n-> 1790 return builder_instance.as_streaming_dataset(split=split)\r\n 1792 # Some datasets are already processed on the HF google storage\r\n 1793 # Don't try downloading from Google storage for the packaged datasets as text, json, csv or pandas\r\n 1794 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES\r\n\r\nFile [~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/builder.py:1281](https://vscode-remote+ssh-002dremote-002bml-002er-002dsoftware-002eat.vscode-resource.vscode-cdn.net/home/wrsbri/projects/audio_course/~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/builder.py:1281), in DatasetBuilder.as_streaming_dataset(self, split, base_path)\r\n 1274 dl_manager = StreamingDownloadManager(\r\n 1275 base_path=base_path or self.base_path,\r\n 1276 download_config=DownloadConfig(use_auth_token=self.use_auth_token, storage_options=self.storage_options),\r\n 1277 dataset_name=self.name,\r\n 1278 data_dir=self.config.data_dir,\r\n 1279 )\r\n 1280 self._check_manual_download(dl_manager)\r\n-> 1281 splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}\r\n 1282 # By default, return all splits\r\n 1283 if split is None:\r\n\r\nFile [~/.cache/huggingface/modules/datasets_modules/datasets/facebook--voxpopuli/b5ff837284f0778eefe0f642734e142d8c3f574eba8c9c8a4b13602297f73604/voxpopuli.py:120](https://vscode-remote+ssh-002dremote-002bml-002er-002dsoftware-002eat.vscode-resource.vscode-cdn.net/home/wrsbri/projects/audio_course/~/.cache/huggingface/modules/datasets_modules/datasets/facebook--voxpopuli/b5ff837284f0778eefe0f642734e142d8c3f574eba8c9c8a4b13602297f73604/voxpopuli.py:120), in Voxpopuli._split_generators(self, dl_manager)\r\n 118 def _split_generators(self, dl_manager):\r\n 119 n_shards_path = dl_manager.download_and_extract(_N_SHARDS_FILE)\r\n--> 120 with open(n_shards_path) as f:\r\n 121 n_shards = json.load(f)\r\n 123 if self.config.name == \"en_accented\":\r\n\r\nFile [~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/streaming.py:71](https://vscode-remote+ssh-002dremote-002bml-002er-002dsoftware-002eat.vscode-resource.vscode-cdn.net/home/wrsbri/projects/audio_course/~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/streaming.py:71), in extend_module_for_streaming..wrap_auth..wrapper(*args, **kwargs)\r\n 69 @wraps(function)\r\n 70 def wrapper(*args, **kwargs):\r\n---> 71 return function(*args, use_auth_token=use_auth_token, **kwargs)\r\n\r\nFile [~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py:517](https://vscode-remote+ssh-002dremote-002bml-002er-002dsoftware-002eat.vscode-resource.vscode-cdn.net/home/wrsbri/projects/audio_course/~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py:517), in xopen(file, mode, use_auth_token, *args, **kwargs)\r\n 515 except FileNotFoundError:\r\n 516 if file.startswith(config.HF_ENDPOINT):\r\n--> 517 raise FileNotFoundError(\r\n 518 file + \"\\nIf the repo is private or gated, make sure to log in with `huggingface-cli login`.\"\r\n 519 ) from None\r\n 520 else:\r\n 521 raise\r\n\r\nFileNotFoundError: https://huggingface.co/datasets/facebook/voxpopuli/resolve/main/data/n_files.json\r\nIf the repo is private or gated, make sure to log in with `huggingface-cli login`.\r\n```",
"> Is there a way to disable ssl verification when streaming a dataset.\r\n\r\nI don't think so.\r\n\r\nWe use `fsspec` HTTPFileSystem implementation that is based on `aiohttp`. If you register a subclass of HTTPFileSystem that has SSL disabled by default it could work, but I wouldn't recommended it because it can raise security issues.",
"Okay thanks for your help! I guess I have to figure out how to improve the proxy environment / see if I can make it work with ssl connections."
] |
5,974
|
Deprecate `errors` param in favor of `encoding_errors` in text builder
|
For consistency with the JSON builder and Pandas
|
[] |
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"html_url": "https://github.com/huggingface/datasets/pull/5974",
"diff_url": "https://github.com/huggingface/datasets/pull/5974.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5974.patch",
"merged_at": "2023-06-26T10:27:40"
}
| true
|
[
"_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) | 0.006518 / 0.011353 (-0.004835) | 0.004121 / 0.011008 (-0.006887) | 0.103350 / 0.038508 (0.064842) | 0.045030 / 0.023109 (0.021920) | 0.351670 / 0.275898 (0.075772) | 0.408110 / 0.323480 (0.084630) | 0.003883 / 0.007986 (-0.004102) | 0.003352 / 0.004328 (-0.000977) | 0.078786 / 0.004250 (0.074535) | 0.063977 / 0.037052 (0.026925) | 0.369759 / 0.258489 (0.111270) | 0.415103 / 0.293841 (0.121262) | 0.033069 / 0.128546 (-0.095477) | 0.008863 / 0.075646 (-0.066783) | 0.353660 / 0.419271 (-0.065611) | 0.055714 / 0.043533 (0.012181) | 0.350458 / 0.255139 (0.095319) | 0.369505 / 0.283200 (0.086305) | 0.022822 / 0.141683 (-0.118861) | 1.537588 / 1.452155 (0.085433) | 1.590569 / 1.492716 (0.097853) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206826 / 0.018006 (0.188819) | 0.471625 / 0.000490 (0.471135) | 0.005188 / 0.000200 (0.004988) | 0.000316 / 0.000054 (0.000261) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028148 / 0.037411 (-0.009263) | 0.111941 / 0.014526 (0.097415) | 0.122106 / 0.176557 (-0.054451) | 0.181127 / 0.737135 (-0.556009) | 0.127534 / 0.296338 (-0.168805) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.409520 / 0.215209 (0.194311) | 4.098455 / 2.077655 (2.020800) | 1.852447 / 1.504120 (0.348327) | 1.657036 / 1.541195 (0.115842) | 1.709624 / 1.468490 (0.241134) | 0.542806 / 4.584777 (-4.041970) | 3.809352 / 3.745712 (0.063640) | 1.855412 / 5.269862 (-3.414449) | 1.109180 / 4.565676 (-3.456497) | 0.066801 / 0.424275 (-0.357474) | 0.011832 / 0.007607 (0.004225) | 0.518338 / 0.226044 (0.292293) | 5.190108 / 2.268929 (2.921179) | 2.320602 / 55.444624 (-53.124023) | 1.991416 / 6.876477 (-4.885060) | 2.106989 / 2.142072 (-0.035084) | 0.668914 / 4.805227 (-4.136313) | 0.145325 / 6.500664 (-6.355340) | 0.065145 / 0.075469 (-0.010324) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254706 / 1.841788 (-0.587082) | 14.707264 / 8.074308 (6.632956) | 14.615423 / 10.191392 (4.424031) | 0.170764 / 0.680424 (-0.509659) | 0.017905 / 0.534201 (-0.516296) | 0.435606 / 0.579283 (-0.143677) | 0.434648 / 0.434364 (0.000284) | 0.520813 / 0.540337 (-0.019524) | 0.633902 / 1.386936 (-0.753034) |\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) | 0.007212 / 0.011353 (-0.004141) | 0.004301 / 0.011008 (-0.006707) | 0.080767 / 0.038508 (0.042258) | 0.051949 / 0.023109 (0.028840) | 0.398473 / 0.275898 (0.122575) | 0.465038 / 0.323480 (0.141558) | 0.005580 / 0.007986 (-0.002406) | 0.003556 / 0.004328 (-0.000773) | 0.080682 / 0.004250 (0.076431) | 0.059517 / 0.037052 (0.022464) | 0.421171 / 0.258489 (0.162682) | 0.459752 / 0.293841 (0.165911) | 0.032960 / 0.128546 (-0.095586) | 0.009107 / 0.075646 (-0.066539) | 0.086382 / 0.419271 (-0.332889) | 0.056053 / 0.043533 (0.012520) | 0.393357 / 0.255139 (0.138218) | 0.412972 / 0.283200 (0.129772) | 0.031115 / 0.141683 (-0.110568) | 1.576961 / 1.452155 (0.124806) | 1.627249 / 1.492716 (0.134533) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227618 / 0.018006 (0.209612) | 0.444640 / 0.000490 (0.444150) | 0.004376 / 0.000200 (0.004176) | 0.000092 / 0.000054 (0.000038) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030826 / 0.037411 (-0.006586) | 0.117587 / 0.014526 (0.103062) | 0.127467 / 0.176557 (-0.049089) | 0.184440 / 0.737135 (-0.552695) | 0.133664 / 0.296338 (-0.162675) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.443183 / 0.215209 (0.227974) | 4.408312 / 2.077655 (2.330658) | 2.132487 / 1.504120 (0.628367) | 1.923632 / 1.541195 (0.382438) | 1.967882 / 1.468490 (0.499392) | 0.552954 / 4.584777 (-4.031823) | 3.777701 / 3.745712 (0.031989) | 1.857686 / 5.269862 (-3.412176) | 1.104847 / 4.565676 (-3.460829) | 0.068350 / 0.424275 (-0.355925) | 0.012437 / 0.007607 (0.004830) | 0.559258 / 0.226044 (0.333214) | 5.593258 / 2.268929 (3.324330) | 2.648059 / 55.444624 (-52.796565) | 2.277428 / 6.876477 (-4.599049) | 2.351685 / 2.142072 (0.209612) | 0.678750 / 4.805227 (-4.126477) | 0.145550 / 6.500664 (-6.355114) | 0.066556 / 0.075469 (-0.008913) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.327128 / 1.841788 (-0.514659) | 15.649079 / 8.074308 (7.574771) | 14.478659 / 10.191392 (4.287267) | 0.147633 / 0.680424 (-0.532791) | 0.018502 / 0.534201 (-0.515699) | 0.438556 / 0.579283 (-0.140727) | 0.433381 / 0.434364 (-0.000983) | 0.514367 / 0.540337 (-0.025970) | 0.618347 / 1.386936 (-0.768589) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#16aa1c886c5b499641a4bb3d8ce4a4f7de8244b7 \"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) | 0.006078 / 0.011353 (-0.005275) | 0.003914 / 0.011008 (-0.007095) | 0.102039 / 0.038508 (0.063531) | 0.037660 / 0.023109 (0.014551) | 0.348963 / 0.275898 (0.073065) | 0.407284 / 0.323480 (0.083804) | 0.004661 / 0.007986 (-0.003324) | 0.003253 / 0.004328 (-0.001076) | 0.078276 / 0.004250 (0.074025) | 0.054144 / 0.037052 (0.017091) | 0.376715 / 0.258489 (0.118225) | 0.418499 / 0.293841 (0.124658) | 0.027627 / 0.128546 (-0.100919) | 0.008494 / 0.075646 (-0.067152) | 0.316894 / 0.419271 (-0.102377) | 0.046560 / 0.043533 (0.003027) | 0.339835 / 0.255139 (0.084696) | 0.374628 / 0.283200 (0.091428) | 0.020729 / 0.141683 (-0.120954) | 1.502769 / 1.452155 (0.050615) | 1.548756 / 1.492716 (0.056040) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229192 / 0.018006 (0.211186) | 0.426245 / 0.000490 (0.425756) | 0.005190 / 0.000200 (0.004990) | 0.000081 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024271 / 0.037411 (-0.013140) | 0.098869 / 0.014526 (0.084343) | 0.105079 / 0.176557 (-0.071477) | 0.164707 / 0.737135 (-0.572428) | 0.110337 / 0.296338 (-0.186002) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426593 / 0.215209 (0.211383) | 4.293977 / 2.077655 (2.216323) | 1.928502 / 1.504120 (0.424382) | 1.728623 / 1.541195 (0.187428) | 1.792084 / 1.468490 (0.323594) | 0.568737 / 4.584777 (-4.016040) | 3.438534 / 3.745712 (-0.307178) | 1.797798 / 5.269862 (-3.472063) | 1.054078 / 4.565676 (-3.511598) | 0.068711 / 0.424275 (-0.355564) | 0.011250 / 0.007607 (0.003643) | 0.529299 / 0.226044 (0.303255) | 5.283965 / 2.268929 (3.015037) | 2.358274 / 55.444624 (-53.086350) | 2.012818 / 6.876477 (-4.863659) | 2.109923 / 2.142072 (-0.032149) | 0.679556 / 4.805227 (-4.125671) | 0.138346 / 6.500664 (-6.362318) | 0.066349 / 0.075469 (-0.009120) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.193994 / 1.841788 (-0.647794) | 14.073158 / 8.074308 (5.998850) | 13.488525 / 10.191392 (3.297133) | 0.144536 / 0.680424 (-0.535888) | 0.016748 / 0.534201 (-0.517453) | 0.362703 / 0.579283 (-0.216580) | 0.389511 / 0.434364 (-0.044853) | 0.427296 / 0.540337 (-0.113041) | 0.513227 / 1.386936 (-0.873709) |\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) | 0.006215 / 0.011353 (-0.005138) | 0.003834 / 0.011008 (-0.007174) | 0.078001 / 0.038508 (0.039493) | 0.036537 / 0.023109 (0.013428) | 0.369724 / 0.275898 (0.093826) | 0.426761 / 0.323480 (0.103281) | 0.003602 / 0.007986 (-0.004383) | 0.003001 / 0.004328 (-0.001327) | 0.075989 / 0.004250 (0.071739) | 0.048618 / 0.037052 (0.011566) | 0.374296 / 0.258489 (0.115807) | 0.430330 / 0.293841 (0.136489) | 0.028299 / 0.128546 (-0.100247) | 0.008537 / 0.075646 (-0.067109) | 0.083275 / 0.419271 (-0.335997) | 0.043136 / 0.043533 (-0.000397) | 0.359072 / 0.255139 (0.103933) | 0.387391 / 0.283200 (0.104192) | 0.021202 / 0.141683 (-0.120481) | 1.520832 / 1.452155 (0.068677) | 1.567030 / 1.492716 (0.074313) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230944 / 0.018006 (0.212938) | 0.422159 / 0.000490 (0.421669) | 0.003447 / 0.000200 (0.003247) | 0.000125 / 0.000054 (0.000071) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025442 / 0.037411 (-0.011969) | 0.103944 / 0.014526 (0.089418) | 0.110577 / 0.176557 (-0.065979) | 0.161393 / 0.737135 (-0.575743) | 0.113482 / 0.296338 (-0.182857) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.485765 / 0.215209 (0.270556) | 4.845737 / 2.077655 (2.768083) | 2.556732 / 1.504120 (1.052612) | 2.348638 / 1.541195 (0.807443) | 2.379289 / 1.468490 (0.910799) | 0.561261 / 4.584777 (-4.023516) | 3.482468 / 3.745712 (-0.263244) | 3.061319 / 5.269862 (-2.208543) | 1.483938 / 4.565676 (-3.081738) | 0.067584 / 0.424275 (-0.356691) | 0.011333 / 0.007607 (0.003726) | 0.594342 / 0.226044 (0.368297) | 5.935477 / 2.268929 (3.666548) | 3.025029 / 55.444624 (-52.419595) | 2.687032 / 6.876477 (-4.189445) | 2.752470 / 2.142072 (0.610398) | 0.674470 / 4.805227 (-4.130757) | 0.136777 / 6.500664 (-6.363887) | 0.068335 / 0.075469 (-0.007134) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.336456 / 1.841788 (-0.505332) | 14.376007 / 8.074308 (6.301699) | 14.171375 / 10.191392 (3.979983) | 0.159620 / 0.680424 (-0.520804) | 0.016685 / 0.534201 (-0.517516) | 0.364344 / 0.579283 (-0.214939) | 0.395358 / 0.434364 (-0.039006) | 0.424876 / 0.540337 (-0.115461) | 0.513267 / 1.386936 (-0.873669) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6ed837325cb539a5deb99129e5ad181d0269e050 \"CML watermark\")\n"
] |
5,972
|
Filter unsupported extensions
|
I used a regex to filter the data files based on their extension for packaged builders.
I tried and a regex is 10x faster that using `in` to check if the extension is in the list of supported extensions.
Supersedes https://github.com/huggingface/datasets/pull/5850
Close https://github.com/huggingface/datasets/issues/5849
I also did a small change to favor the parquet module in case of a draw in the extension counter.
|
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5972",
"html_url": "https://github.com/huggingface/datasets/pull/5972",
"diff_url": "https://github.com/huggingface/datasets/pull/5972.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5972.patch",
"merged_at": "2023-06-22T14:16:26"
}
| true
|
[
"<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) | 0.006983 / 0.011353 (-0.004369) | 0.004473 / 0.011008 (-0.006535) | 0.105158 / 0.038508 (0.066650) | 0.048973 / 0.023109 (0.025864) | 0.358771 / 0.275898 (0.082873) | 0.432389 / 0.323480 (0.108909) | 0.005689 / 0.007986 (-0.002297) | 0.003584 / 0.004328 (-0.000744) | 0.080852 / 0.004250 (0.076601) | 0.066133 / 0.037052 (0.029081) | 0.370981 / 0.258489 (0.112492) | 0.406942 / 0.293841 (0.113101) | 0.032123 / 0.128546 (-0.096424) | 0.009313 / 0.075646 (-0.066333) | 0.355220 / 0.419271 (-0.064051) | 0.055768 / 0.043533 (0.012235) | 0.370545 / 0.255139 (0.115406) | 0.375619 / 0.283200 (0.092419) | 0.024258 / 0.141683 (-0.117425) | 1.559073 / 1.452155 (0.106918) | 1.616520 / 1.492716 (0.123804) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.277893 / 0.018006 (0.259887) | 0.535447 / 0.000490 (0.534957) | 0.004877 / 0.000200 (0.004677) | 0.000092 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029444 / 0.037411 (-0.007968) | 0.114366 / 0.014526 (0.099841) | 0.130957 / 0.176557 (-0.045599) | 0.189604 / 0.737135 (-0.547531) | 0.131682 / 0.296338 (-0.164656) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.412315 / 0.215209 (0.197106) | 4.093879 / 2.077655 (2.016225) | 1.856169 / 1.504120 (0.352050) | 1.655358 / 1.541195 (0.114164) | 1.758190 / 1.468490 (0.289699) | 0.545829 / 4.584777 (-4.038948) | 3.871436 / 3.745712 (0.125724) | 1.938244 / 5.269862 (-3.331618) | 1.122727 / 4.565676 (-3.442950) | 0.067107 / 0.424275 (-0.357168) | 0.012012 / 0.007607 (0.004405) | 0.518868 / 0.226044 (0.292824) | 5.235081 / 2.268929 (2.966153) | 2.335115 / 55.444624 (-53.109509) | 2.013074 / 6.876477 (-4.863402) | 2.219808 / 2.142072 (0.077735) | 0.674602 / 4.805227 (-4.130626) | 0.147051 / 6.500664 (-6.353613) | 0.068444 / 0.075469 (-0.007025) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.245600 / 1.841788 (-0.596188) | 15.537727 / 8.074308 (7.463419) | 15.074300 / 10.191392 (4.882908) | 0.194217 / 0.680424 (-0.486207) | 0.018536 / 0.534201 (-0.515665) | 0.437085 / 0.579283 (-0.142198) | 0.441123 / 0.434364 (0.006759) | 0.530681 / 0.540337 (-0.009657) | 0.649154 / 1.386936 (-0.737782) |\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) | 0.007243 / 0.011353 (-0.004110) | 0.004688 / 0.011008 (-0.006320) | 0.079809 / 0.038508 (0.041301) | 0.046915 / 0.023109 (0.023805) | 0.415144 / 0.275898 (0.139246) | 0.474867 / 0.323480 (0.151388) | 0.004550 / 0.007986 (-0.003435) | 0.004585 / 0.004328 (0.000257) | 0.080837 / 0.004250 (0.076587) | 0.061667 / 0.037052 (0.024614) | 0.411321 / 0.258489 (0.152832) | 0.464195 / 0.293841 (0.170354) | 0.032510 / 0.128546 (-0.096037) | 0.009306 / 0.075646 (-0.066340) | 0.086637 / 0.419271 (-0.332635) | 0.053335 / 0.043533 (0.009802) | 0.402302 / 0.255139 (0.147163) | 0.424864 / 0.283200 (0.141664) | 0.026573 / 0.141683 (-0.115110) | 1.566793 / 1.452155 (0.114639) | 1.628118 / 1.492716 (0.135401) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.317802 / 0.018006 (0.299796) | 0.544593 / 0.000490 (0.544103) | 0.005690 / 0.000200 (0.005490) | 0.000107 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033015 / 0.037411 (-0.004397) | 0.121940 / 0.014526 (0.107414) | 0.132920 / 0.176557 (-0.043637) | 0.191481 / 0.737135 (-0.545655) | 0.139139 / 0.296338 (-0.157199) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.460382 / 0.215209 (0.245173) | 4.610046 / 2.077655 (2.532392) | 2.296573 / 1.504120 (0.792453) | 2.099735 / 1.541195 (0.558540) | 2.213913 / 1.468490 (0.745423) | 0.544871 / 4.584777 (-4.039906) | 3.814174 / 3.745712 (0.068462) | 3.246397 / 5.269862 (-2.023464) | 1.480236 / 4.565676 (-3.085440) | 0.068464 / 0.424275 (-0.355811) | 0.012651 / 0.007607 (0.005043) | 0.564989 / 0.226044 (0.338944) | 5.639188 / 2.268929 (3.370259) | 2.827601 / 55.444624 (-52.617023) | 2.473743 / 6.876477 (-4.402734) | 2.567413 / 2.142072 (0.425340) | 0.674351 / 4.805227 (-4.130876) | 0.146248 / 6.500664 (-6.354416) | 0.067553 / 0.075469 (-0.007916) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.346703 / 1.841788 (-0.495085) | 16.494787 / 8.074308 (8.420479) | 15.179487 / 10.191392 (4.988095) | 0.181864 / 0.680424 (-0.498560) | 0.018857 / 0.534201 (-0.515344) | 0.437787 / 0.579283 (-0.141496) | 0.431770 / 0.434364 (-0.002594) | 0.507116 / 0.540337 (-0.033221) | 0.608899 / 1.386936 (-0.778037) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0fd5b7412f907675e76b183a6e39ef6d176fdcc0 \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005963 / 0.011353 (-0.005390) | 0.003743 / 0.011008 (-0.007265) | 0.098519 / 0.038508 (0.060011) | 0.037392 / 0.023109 (0.014283) | 0.322706 / 0.275898 (0.046808) | 0.380032 / 0.323480 (0.056552) | 0.004694 / 0.007986 (-0.003292) | 0.002897 / 0.004328 (-0.001432) | 0.078664 / 0.004250 (0.074414) | 0.052646 / 0.037052 (0.015594) | 0.335523 / 0.258489 (0.077034) | 0.375464 / 0.293841 (0.081623) | 0.027537 / 0.128546 (-0.101010) | 0.008452 / 0.075646 (-0.067194) | 0.313844 / 0.419271 (-0.105427) | 0.047368 / 0.043533 (0.003835) | 0.313833 / 0.255139 (0.058694) | 0.342284 / 0.283200 (0.059085) | 0.021136 / 0.141683 (-0.120547) | 1.544764 / 1.452155 (0.092610) | 1.563850 / 1.492716 (0.071134) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.188609 / 0.018006 (0.170603) | 0.421686 / 0.000490 (0.421196) | 0.003336 / 0.000200 (0.003136) | 0.000077 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023678 / 0.037411 (-0.013733) | 0.099191 / 0.014526 (0.084665) | 0.105819 / 0.176557 (-0.070738) | 0.169654 / 0.737135 (-0.567481) | 0.110240 / 0.296338 (-0.186099) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425497 / 0.215209 (0.210288) | 4.237165 / 2.077655 (2.159510) | 1.902953 / 1.504120 (0.398833) | 1.699012 / 1.541195 (0.157818) | 1.751107 / 1.468490 (0.282617) | 0.563326 / 4.584777 (-4.021451) | 3.394189 / 3.745712 (-0.351523) | 2.706129 / 5.269862 (-2.563732) | 1.361522 / 4.565676 (-3.204155) | 0.067776 / 0.424275 (-0.356499) | 0.010959 / 0.007607 (0.003352) | 0.530905 / 0.226044 (0.304860) | 5.322467 / 2.268929 (3.053538) | 2.384356 / 55.444624 (-53.060269) | 2.044196 / 6.876477 (-4.832281) | 2.119837 / 2.142072 (-0.022235) | 0.682236 / 4.805227 (-4.122991) | 0.136921 / 6.500664 (-6.363743) | 0.066784 / 0.075469 (-0.008685) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.210642 / 1.841788 (-0.631146) | 13.804572 / 8.074308 (5.730264) | 13.309229 / 10.191392 (3.117837) | 0.154356 / 0.680424 (-0.526068) | 0.016833 / 0.534201 (-0.517368) | 0.366503 / 0.579283 (-0.212780) | 0.385201 / 0.434364 (-0.049163) | 0.426713 / 0.540337 (-0.113624) | 0.516795 / 1.386936 (-0.870141) |\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) | 0.006144 / 0.011353 (-0.005209) | 0.003723 / 0.011008 (-0.007285) | 0.077427 / 0.038508 (0.038919) | 0.037636 / 0.023109 (0.014527) | 0.375048 / 0.275898 (0.099150) | 0.442254 / 0.323480 (0.118774) | 0.003506 / 0.007986 (-0.004480) | 0.003751 / 0.004328 (-0.000577) | 0.076771 / 0.004250 (0.072521) | 0.047915 / 0.037052 (0.010862) | 0.378918 / 0.258489 (0.120429) | 0.435300 / 0.293841 (0.141459) | 0.028317 / 0.128546 (-0.100230) | 0.008413 / 0.075646 (-0.067233) | 0.082774 / 0.419271 (-0.336497) | 0.043211 / 0.043533 (-0.000321) | 0.362022 / 0.255139 (0.106883) | 0.404928 / 0.283200 (0.121728) | 0.020692 / 0.141683 (-0.120991) | 1.527303 / 1.452155 (0.075148) | 1.596091 / 1.492716 (0.103375) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225537 / 0.018006 (0.207530) | 0.399901 / 0.000490 (0.399412) | 0.000424 / 0.000200 (0.000224) | 0.000058 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026483 / 0.037411 (-0.010928) | 0.104373 / 0.014526 (0.089847) | 0.111271 / 0.176557 (-0.065286) | 0.163872 / 0.737135 (-0.573264) | 0.113991 / 0.296338 (-0.182347) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.456484 / 0.215209 (0.241275) | 4.572652 / 2.077655 (2.494998) | 2.374908 / 1.504120 (0.870788) | 2.207855 / 1.541195 (0.666661) | 2.260009 / 1.468490 (0.791519) | 0.562678 / 4.584777 (-4.022099) | 3.441778 / 3.745712 (-0.303934) | 1.729006 / 5.269862 (-3.540855) | 1.024937 / 4.565676 (-3.540739) | 0.068707 / 0.424275 (-0.355568) | 0.011334 / 0.007607 (0.003727) | 0.564293 / 0.226044 (0.338248) | 5.638367 / 2.268929 (3.369438) | 2.665654 / 55.444624 (-52.778970) | 2.320033 / 6.876477 (-4.556444) | 2.328706 / 2.142072 (0.186634) | 0.677433 / 4.805227 (-4.127794) | 0.137190 / 6.500664 (-6.363474) | 0.068585 / 0.075469 (-0.006885) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.312476 / 1.841788 (-0.529312) | 14.206685 / 8.074308 (6.132377) | 14.217928 / 10.191392 (4.026536) | 0.143416 / 0.680424 (-0.537007) | 0.016647 / 0.534201 (-0.517554) | 0.361228 / 0.579283 (-0.218055) | 0.396185 / 0.434364 (-0.038178) | 0.423275 / 0.540337 (-0.117063) | 0.512966 / 1.386936 (-0.873970) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b424648fd68bd0b5279eb916cec4836d1220e268 \"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) | 0.008913 / 0.011353 (-0.002440) | 0.005142 / 0.011008 (-0.005866) | 0.133958 / 0.038508 (0.095449) | 0.049180 / 0.023109 (0.026071) | 0.389169 / 0.275898 (0.113270) | 0.481513 / 0.323480 (0.158033) | 0.006555 / 0.007986 (-0.001430) | 0.003806 / 0.004328 (-0.000522) | 0.102056 / 0.004250 (0.097806) | 0.083259 / 0.037052 (0.046207) | 0.392536 / 0.258489 (0.134047) | 0.447503 / 0.293841 (0.153662) | 0.047472 / 0.128546 (-0.081074) | 0.014748 / 0.075646 (-0.060899) | 0.475619 / 0.419271 (0.056348) | 0.107306 / 0.043533 (0.063773) | 0.421942 / 0.255139 (0.166803) | 0.419736 / 0.283200 (0.136536) | 0.044195 / 0.141683 (-0.097488) | 1.793840 / 1.452155 (0.341686) | 1.960204 / 1.492716 (0.467488) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.252046 / 0.018006 (0.234040) | 0.627725 / 0.000490 (0.627236) | 0.007435 / 0.000200 (0.007235) | 0.000526 / 0.000054 (0.000472) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034656 / 0.037411 (-0.002755) | 0.114534 / 0.014526 (0.100008) | 0.135804 / 0.176557 (-0.040753) | 0.209309 / 0.737135 (-0.527826) | 0.140369 / 0.296338 (-0.155969) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.636736 / 0.215209 (0.421527) | 6.039985 / 2.077655 (3.962330) | 2.640141 / 1.504120 (1.136021) | 2.284492 / 1.541195 (0.743297) | 2.324956 / 1.468490 (0.856466) | 0.934499 / 4.584777 (-3.650278) | 5.673415 / 3.745712 (1.927703) | 5.184584 / 5.269862 (-0.085278) | 2.661911 / 4.565676 (-1.903766) | 0.150420 / 0.424275 (-0.273855) | 0.015655 / 0.007607 (0.008048) | 0.748290 / 0.226044 (0.522246) | 7.579755 / 2.268929 (5.310827) | 3.346732 / 55.444624 (-52.097892) | 2.708212 / 6.876477 (-4.168264) | 2.682423 / 2.142072 (0.540351) | 1.170389 / 4.805227 (-3.634838) | 0.215775 / 6.500664 (-6.284889) | 0.076360 / 0.075469 (0.000891) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.516794 / 1.841788 (-0.324993) | 18.709117 / 8.074308 (10.634809) | 22.492542 / 10.191392 (12.301150) | 0.237978 / 0.680424 (-0.442446) | 0.027828 / 0.534201 (-0.506373) | 0.499968 / 0.579283 (-0.079315) | 0.645899 / 0.434364 (0.211535) | 0.548599 / 0.540337 (0.008262) | 0.675428 / 1.386936 (-0.711508) |\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) | 0.008469 / 0.011353 (-0.002884) | 0.005420 / 0.011008 (-0.005589) | 0.093340 / 0.038508 (0.054832) | 0.045896 / 0.023109 (0.022786) | 0.533267 / 0.275898 (0.257369) | 0.596034 / 0.323480 (0.272555) | 0.004816 / 0.007986 (-0.003170) | 0.004379 / 0.004328 (0.000051) | 0.096356 / 0.004250 (0.092106) | 0.058339 / 0.037052 (0.021287) | 0.574464 / 0.258489 (0.315975) | 0.649301 / 0.293841 (0.355461) | 0.047599 / 0.128546 (-0.080947) | 0.013759 / 0.075646 (-0.061887) | 0.104672 / 0.419271 (-0.314599) | 0.061658 / 0.043533 (0.018125) | 0.560956 / 0.255139 (0.305817) | 0.585328 / 0.283200 (0.302128) | 0.034137 / 0.141683 (-0.107546) | 1.844528 / 1.452155 (0.392373) | 1.971398 / 1.492716 (0.478682) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.278666 / 0.018006 (0.260660) | 0.577342 / 0.000490 (0.576853) | 0.005496 / 0.000200 (0.005296) | 0.000131 / 0.000054 (0.000076) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029863 / 0.037411 (-0.007549) | 0.161703 / 0.014526 (0.147177) | 0.132279 / 0.176557 (-0.044277) | 0.227345 / 0.737135 (-0.509791) | 0.138047 / 0.296338 (-0.158291) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.651535 / 0.215209 (0.436326) | 7.077949 / 2.077655 (5.000295) | 2.926990 / 1.504120 (1.422871) | 2.598872 / 1.541195 (1.057678) | 2.614192 / 1.468490 (1.145702) | 0.913845 / 4.584777 (-3.670932) | 5.704301 / 3.745712 (1.958589) | 2.796914 / 5.269862 (-2.472948) | 1.836096 / 4.565676 (-2.729580) | 0.106294 / 0.424275 (-0.317981) | 0.012705 / 0.007607 (0.005098) | 0.836336 / 0.226044 (0.610291) | 8.234079 / 2.268929 (5.965150) | 3.836410 / 55.444624 (-51.608215) | 3.116752 / 6.876477 (-3.759724) | 3.154258 / 2.142072 (1.012186) | 1.195794 / 4.805227 (-3.609434) | 0.240491 / 6.500664 (-6.260173) | 0.087913 / 0.075469 (0.012444) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.724723 / 1.841788 (-0.117064) | 19.492194 / 8.074308 (11.417885) | 21.443341 / 10.191392 (11.251949) | 0.245819 / 0.680424 (-0.434605) | 0.027024 / 0.534201 (-0.507177) | 0.481071 / 0.579283 (-0.098212) | 0.596359 / 0.434364 (0.161995) | 0.646462 / 0.540337 (0.106124) | 0.706380 / 1.386936 (-0.680556) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#67ca664e6d5ef137127b238aae1d0aff54e22db2 \"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) | 0.006634 / 0.011353 (-0.004719) | 0.004003 / 0.011008 (-0.007005) | 0.097874 / 0.038508 (0.059365) | 0.043528 / 0.023109 (0.020419) | 0.302293 / 0.275898 (0.026395) | 0.357041 / 0.323480 (0.033561) | 0.003761 / 0.007986 (-0.004225) | 0.004312 / 0.004328 (-0.000016) | 0.076253 / 0.004250 (0.072003) | 0.062807 / 0.037052 (0.025755) | 0.316737 / 0.258489 (0.058248) | 0.356722 / 0.293841 (0.062881) | 0.030816 / 0.128546 (-0.097730) | 0.008691 / 0.075646 (-0.066955) | 0.328366 / 0.419271 (-0.090906) | 0.062299 / 0.043533 (0.018766) | 0.293877 / 0.255139 (0.038738) | 0.319832 / 0.283200 (0.036632) | 0.024996 / 0.141683 (-0.116687) | 1.473912 / 1.452155 (0.021758) | 1.565439 / 1.492716 (0.072723) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208428 / 0.018006 (0.190422) | 0.435618 / 0.000490 (0.435128) | 0.000695 / 0.000200 (0.000495) | 0.000056 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026253 / 0.037411 (-0.011158) | 0.106908 / 0.014526 (0.092382) | 0.117075 / 0.176557 (-0.059482) | 0.177969 / 0.737135 (-0.559166) | 0.123400 / 0.296338 (-0.172938) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424970 / 0.215209 (0.209761) | 4.203233 / 2.077655 (2.125578) | 2.009679 / 1.504120 (0.505559) | 1.825691 / 1.541195 (0.284496) | 1.870639 / 1.468490 (0.402149) | 0.530758 / 4.584777 (-4.054019) | 3.718791 / 3.745712 (-0.026921) | 1.800206 / 5.269862 (-3.469656) | 1.071651 / 4.565676 (-3.494025) | 0.065126 / 0.424275 (-0.359149) | 0.011312 / 0.007607 (0.003704) | 0.532503 / 0.226044 (0.306458) | 5.353950 / 2.268929 (3.085021) | 2.463548 / 55.444624 (-52.981076) | 2.139832 / 6.876477 (-4.736645) | 2.238722 / 2.142072 (0.096650) | 0.655736 / 4.805227 (-4.149492) | 0.141689 / 6.500664 (-6.358975) | 0.063282 / 0.075469 (-0.012187) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.183523 / 1.841788 (-0.658265) | 14.146428 / 8.074308 (6.072120) | 14.312883 / 10.191392 (4.121491) | 0.169286 / 0.680424 (-0.511138) | 0.017343 / 0.534201 (-0.516858) | 0.397934 / 0.579283 (-0.181349) | 0.417791 / 0.434364 (-0.016573) | 0.463639 / 0.540337 (-0.076698) | 0.562787 / 1.386936 (-0.824149) |\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) | 0.006594 / 0.011353 (-0.004759) | 0.004086 / 0.011008 (-0.006922) | 0.075122 / 0.038508 (0.036614) | 0.041849 / 0.023109 (0.018740) | 0.362645 / 0.275898 (0.086747) | 0.464350 / 0.323480 (0.140870) | 0.003760 / 0.007986 (-0.004226) | 0.003327 / 0.004328 (-0.001001) | 0.076154 / 0.004250 (0.071904) | 0.053232 / 0.037052 (0.016180) | 0.407863 / 0.258489 (0.149374) | 0.460787 / 0.293841 (0.166946) | 0.031917 / 0.128546 (-0.096630) | 0.008770 / 0.075646 (-0.066876) | 0.082612 / 0.419271 (-0.336660) | 0.051311 / 0.043533 (0.007779) | 0.354508 / 0.255139 (0.099369) | 0.419533 / 0.283200 (0.136334) | 0.023980 / 0.141683 (-0.117703) | 1.491255 / 1.452155 (0.039100) | 1.536101 / 1.492716 (0.043384) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.178261 / 0.018006 (0.160255) | 0.444680 / 0.000490 (0.444190) | 0.013761 / 0.000200 (0.013561) | 0.000117 / 0.000054 (0.000063) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027875 / 0.037411 (-0.009536) | 0.111269 / 0.014526 (0.096744) | 0.121096 / 0.176557 (-0.055461) | 0.174387 / 0.737135 (-0.562749) | 0.124714 / 0.296338 (-0.171624) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.445422 / 0.215209 (0.230213) | 4.435877 / 2.077655 (2.358222) | 2.221895 / 1.504120 (0.717775) | 2.030571 / 1.541195 (0.489376) | 2.074863 / 1.468490 (0.606373) | 0.543331 / 4.584777 (-4.041446) | 3.753615 / 3.745712 (0.007903) | 3.317074 / 5.269862 (-1.952787) | 1.630390 / 4.565676 (-2.935286) | 0.066726 / 0.424275 (-0.357549) | 0.011556 / 0.007607 (0.003949) | 0.546985 / 0.226044 (0.320941) | 5.460634 / 2.268929 (3.191705) | 2.705945 / 55.444624 (-52.738679) | 2.373425 / 6.876477 (-4.503052) | 2.401472 / 2.142072 (0.259399) | 0.663225 / 4.805227 (-4.142002) | 0.143694 / 6.500664 (-6.356970) | 0.065283 / 0.075469 (-0.010186) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.264804 / 1.841788 (-0.576983) | 14.803228 / 8.074308 (6.728919) | 14.178514 / 10.191392 (3.987122) | 0.162651 / 0.680424 (-0.517772) | 0.017586 / 0.534201 (-0.516615) | 0.398740 / 0.579283 (-0.180543) | 0.414478 / 0.434364 (-0.019886) | 0.465442 / 0.540337 (-0.074895) | 0.563450 / 1.386936 (-0.823486) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#76f75a9a3b2aaad05ea0ea5ab77e01fd2ca66760 \"CML watermark\")\n"
] |
5,971
|
Docs: make "repository structure" easier to find
|
The page https://huggingface.co/docs/datasets/repository_structure explains how to create a simple repository structure without a dataset script.
It's the simplest way to create a dataset and should be easier to find, particularly on the docs' first pages.
|
[
{
"id": 1935892861,
"node_id": "MDU6TGFiZWwxOTM1ODkyODYx",
"url": "https://api.github.com/repos/huggingface/datasets/labels/documentation",
"name": "documentation",
"color": "0075ca",
"default": true,
"description": "Improvements or additions to documentation"
}
] |
{
"url": null,
"html_url": null,
"diff_url": null,
"patch_url": null,
"merged_at": null
}
| false
|
[
"Loading a local dataset also works the same way when `data_files` are not specified, so I agree we should make this info easier to discover \r\n\r\ncc @stevhliu ",
"Is this issue open? If so, I will self assign. ",
"@benjaminbrown038 Yes, it is. Maybe @stevhliu can give some pointers on improving this doc page's discoverability.",
"I think we can add a version of the [Main use-case](https://huggingface.co/docs/datasets/repository_structure#main-usecase) section to the [Share a dataset to the Hub](https://huggingface.co/docs/datasets/upload_dataset) tutorial. \r\n\r\nCurrently, it doesn't tell you *how* to structure the repository; it only tells you how to create it. So adding the \"main use-case\" will help bridge the gap and make it easier to find. We should also add a link to the [Structure your repository](https://huggingface.co/docs/datasets/repository_structure) guide for users who want to learn about the other options.",
"#self-assign"
] |
5,970
|
description disappearing from Info when Uploading a Dataset Created with `from_dict`
|
### Describe the bug
When uploading a dataset created locally using `from_dict` with a specified `description` field. It appears before upload, but is missing after upload and re-download.
### Steps to reproduce the bug
I think the most relevant pattern in the code might be the following lines:
```
description_json_str = json.dumps(
{
"dataset_id": dataset.spec.dataset_id,
"env_name": dataset.spec.env_spec.id,
"action_space": serialize_space(dataset.spec.action_space),
"observation_space": serialize_space(dataset.spec.observation_space),
}
)
hugging_face_dataset = Dataset.from_dict(
episodes_dict, info=DatasetInfo(description=description_json_str)
)
```
Which comes from this function https://github.com/balisujohn/minarai/blob/8e023727f0a8488c4451651d9f7a79b981412c40/minari/integrations/hugging_face.py#L39
To replicate,
clone this branch of my Minari fork https://github.com/balisujohn/minarai/tree/dev-huggingface then run
```
python3.8 -m venv env
source env/bin/activate
python3 -m pip install -e .
python3 -m pip install pytest
```
The change the hugging face repo path in the test called `test_hugging_face_push_and_pull_dataset` in `tests/integrations/test_hugging_face.py` to one you have permissions to write to.
Then run:
```
pytest tests/integrations/test_hugging_face.py::test_hugging_face_push_and_pull_dataset
```
### Expected behavior
DATASET INFO BEFORE UPLOADING
DatasetInfo(description='{"dataset_id": "dummy-combo-test-v0", "env_name": "DummyComboEnv-v0", "action_space": "{\\"type\\": \\"Tuple\\", \\"subspaces\\": [{\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [2.0], \\"high\\": [3.0]}, {\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [4.0], \\"high\\": [5.0]}]}", "observation_space": "{\\"type\\": \\"Tuple\\", \\"subspaces\\": [{\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [2.0], \\"high\\": [3.0]}, {\\"type\\": \\"Tuple\\", \\"subspaces\\": [{\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [2.0], \\"high\\": [3.0]}, {\\"type\\": \\"Dict\\", \\"subspaces\\": {\\"component_1\\": {\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [-1.0], \\"high\\": [1.0]}, \\"component_2\\": {\\"type\\": \\"Dict\\", \\"subspaces\\": {\\"subcomponent_1\\": {\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [2.0], \\"high\\": [3.0]}, \\"subcomponent_2\\": {\\"type\\": \\"Tuple\\", \\"subspaces\\": [{\\"type\\": \\"Box\\", \\"dtype\\": \\"float32\\", \\"shape\\": [1], \\"low\\": [4.0], \\"high\\": [5.0]}, {\\"type\\": \\"Discrete\\", \\"dtype\\": \\"int64\\", \\"start\\": 0, \\"n\\": 10}]}}}}}]}]}"}', citation='', homepage='', license='', features={'observations': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': {'component_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'component_2': {'subcomponent_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'subcomponent_2': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': Value(dtype='int64', id=None)}}}}}, 'actions': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None)}, 'rewards': Value(dtype='int64', id=None), 'truncations': Value(dtype='bool', id=None), 'terminations': Value(dtype='bool', id=None), 'episode_ids': Value(dtype='int64', id=None)}, post_processed=None, supervised_keys=None, task_templates=None, builder_name=None, config_name=None, version=None, splits=None, download_checksums=None, download_size=None, post_processing_size=None, dataset_size=None, size_in_bytes=None)
...
DATASET INFO AFTER UPLOADING AND DOWNLOADING
DatasetInfo(description='', citation='', homepage='', license='', features={'observations': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': {'component_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'component_2': {'subcomponent_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), 'subcomponent_2': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': Value(dtype='int64', id=None)}}}}}, 'actions': {'_index_0': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None), '_index_1': Sequence(feature=Value(dtype='float32', id=None), length=-1, id=None)}, 'rewards': Value(dtype='int64', id=None), 'truncations': Value(dtype='bool', id=None), 'terminations': Value(dtype='bool', id=None), 'episode_ids': Value(dtype='int64', id=None)}, post_processed=None, supervised_keys=None, task_templates=None, builder_name=None, config_name=None, version=None, splits={'train': SplitInfo(name='train', num_bytes=4846, num_examples=60, shard_lengths=None, dataset_name='parquet')}, download_checksums={'https://huggingface.co/datasets/balisujohn/minari_test/resolve/8217b614ff9ba5edc1a30c7df430e92a46f65363/data/train-00000-of-00001-7c5900b93b35745e.parquet': {'num_bytes': 9052, 'checksum': None}}, download_size=9052, post_processing_size=None, dataset_size=4846, size_in_bytes=13898)
...
### Environment info
- `datasets` version: 2.13.0
- Platform: Linux-5.15.0-75-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.1
- Pandas version: 2.0.2
|
[] |
{
"url": null,
"html_url": null,
"diff_url": null,
"patch_url": null,
"merged_at": null
}
| false
|
[
"Here's a minimal way to reproduce the bug, for the sake of convenience.\r\n````\r\nfrom datasets import Dataset, DatasetInfo, load_dataset\r\n\r\n\r\nepisodes_dict = {\"test\":[1,2,3],\"test2\": [1,2,4]}\r\n\r\nhugging_face_dataset = Dataset.from_dict(\r\n episodes_dict, info=DatasetInfo(description=\"test_str\")\r\n)\r\nprint(hugging_face_dataset.info)\r\n\r\nhugging_face_dataset.push_to_hub(\"balisujohn/minari_test\", private=True)\r\n\r\nredownloaded_dataset= load_dataset(\"balisujohn/minari_test\")[\"train\"]\r\n\r\n\r\nprint(redownloaded_dataset.info)\r\n````\r\n",
"Thanks for reporting !\r\n\r\nFor now I would recommend uploading a separate JSON file for your metadata.\r\n\r\nAlternatively you can upload a second configuration of the dataset containing your metadata but this feature is not released yet (though you can already use it from [here](https://github.com/huggingface/datasets/pull/5331), it will be released soon)"
] |
5,969
|
Add `encoding` and `errors` params to JSON loader
|
"Requested" in https://discuss.huggingface.co/t/utf-16-for-datasets/43828/3.
`pd.read_json` also has these parameters, so it makes sense to be consistent.
|
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5969",
"html_url": "https://github.com/huggingface/datasets/pull/5969",
"diff_url": "https://github.com/huggingface/datasets/pull/5969.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5969.patch",
"merged_at": "2023-06-21T13:32:22"
}
| true
|
[
"_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) | 0.006770 / 0.011353 (-0.004583) | 0.004143 / 0.011008 (-0.006865) | 0.098928 / 0.038508 (0.060420) | 0.044893 / 0.023109 (0.021783) | 0.302630 / 0.275898 (0.026732) | 0.368173 / 0.323480 (0.044693) | 0.005631 / 0.007986 (-0.002354) | 0.003397 / 0.004328 (-0.000931) | 0.075748 / 0.004250 (0.071497) | 0.062582 / 0.037052 (0.025530) | 0.329586 / 0.258489 (0.071097) | 0.362625 / 0.293841 (0.068784) | 0.033250 / 0.128546 (-0.095296) | 0.008880 / 0.075646 (-0.066766) | 0.329683 / 0.419271 (-0.089588) | 0.054426 / 0.043533 (0.010893) | 0.297940 / 0.255139 (0.042801) | 0.319796 / 0.283200 (0.036597) | 0.023296 / 0.141683 (-0.118387) | 1.462142 / 1.452155 (0.009987) | 1.495796 / 1.492716 (0.003079) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201771 / 0.018006 (0.183765) | 0.454514 / 0.000490 (0.454024) | 0.003333 / 0.000200 (0.003133) | 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) | 0.028084 / 0.037411 (-0.009327) | 0.109452 / 0.014526 (0.094926) | 0.119200 / 0.176557 (-0.057357) | 0.180302 / 0.737135 (-0.556834) | 0.125653 / 0.296338 (-0.170686) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.409819 / 0.215209 (0.194610) | 4.055117 / 2.077655 (1.977462) | 1.855279 / 1.504120 (0.351159) | 1.655281 / 1.541195 (0.114086) | 1.687938 / 1.468490 (0.219448) | 0.528352 / 4.584777 (-4.056425) | 3.750250 / 3.745712 (0.004538) | 3.386741 / 5.269862 (-1.883121) | 1.572036 / 4.565676 (-2.993640) | 0.065125 / 0.424275 (-0.359150) | 0.011259 / 0.007607 (0.003652) | 0.513449 / 0.226044 (0.287405) | 5.139421 / 2.268929 (2.870492) | 2.316973 / 55.444624 (-53.127651) | 1.984109 / 6.876477 (-4.892368) | 2.127915 / 2.142072 (-0.014158) | 0.653238 / 4.805227 (-4.151989) | 0.142686 / 6.500664 (-6.357978) | 0.063666 / 0.075469 (-0.011803) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.185174 / 1.841788 (-0.656614) | 14.790282 / 8.074308 (6.715974) | 13.089222 / 10.191392 (2.897830) | 0.146055 / 0.680424 (-0.534369) | 0.017835 / 0.534201 (-0.516366) | 0.399598 / 0.579283 (-0.179685) | 0.425296 / 0.434364 (-0.009068) | 0.478552 / 0.540337 (-0.061786) | 0.579702 / 1.386936 (-0.807234) |\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) | 0.006750 / 0.011353 (-0.004603) | 0.004156 / 0.011008 (-0.006853) | 0.074948 / 0.038508 (0.036440) | 0.043368 / 0.023109 (0.020259) | 0.355389 / 0.275898 (0.079491) | 0.429167 / 0.323480 (0.105687) | 0.003911 / 0.007986 (-0.004075) | 0.004340 / 0.004328 (0.000012) | 0.075940 / 0.004250 (0.071689) | 0.054293 / 0.037052 (0.017241) | 0.400317 / 0.258489 (0.141827) | 0.432001 / 0.293841 (0.138160) | 0.032340 / 0.128546 (-0.096206) | 0.008876 / 0.075646 (-0.066770) | 0.082284 / 0.419271 (-0.336987) | 0.050819 / 0.043533 (0.007286) | 0.351994 / 0.255139 (0.096855) | 0.375917 / 0.283200 (0.092717) | 0.022466 / 0.141683 (-0.119217) | 1.538824 / 1.452155 (0.086669) | 1.563995 / 1.492716 (0.071279) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227330 / 0.018006 (0.209323) | 0.446380 / 0.000490 (0.445890) | 0.000408 / 0.000200 (0.000208) | 0.000058 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028534 / 0.037411 (-0.008878) | 0.113467 / 0.014526 (0.098941) | 0.123590 / 0.176557 (-0.052966) | 0.174309 / 0.737135 (-0.562827) | 0.130631 / 0.296338 (-0.165707) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441020 / 0.215209 (0.225811) | 4.386564 / 2.077655 (2.308909) | 2.100704 / 1.504120 (0.596584) | 1.901484 / 1.541195 (0.360289) | 1.963494 / 1.468490 (0.495004) | 0.536838 / 4.584777 (-4.047939) | 3.739071 / 3.745712 (-0.006642) | 3.278981 / 5.269862 (-1.990881) | 1.515476 / 4.565676 (-3.050201) | 0.066388 / 0.424275 (-0.357887) | 0.011857 / 0.007607 (0.004250) | 0.545507 / 0.226044 (0.319463) | 5.441479 / 2.268929 (3.172550) | 2.602144 / 55.444624 (-52.842480) | 2.235583 / 6.876477 (-4.640894) | 2.293458 / 2.142072 (0.151385) | 0.658535 / 4.805227 (-4.146692) | 0.141327 / 6.500664 (-6.359337) | 0.063726 / 0.075469 (-0.011743) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.247819 / 1.841788 (-0.593968) | 15.234524 / 8.074308 (7.160216) | 14.592700 / 10.191392 (4.401308) | 0.141952 / 0.680424 (-0.538472) | 0.017747 / 0.534201 (-0.516454) | 0.396819 / 0.579283 (-0.182465) | 0.415902 / 0.434364 (-0.018462) | 0.464619 / 0.540337 (-0.075718) | 0.560866 / 1.386936 (-0.826070) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4b7f6c59deb868e21f295917548fa2df10dd0158 \"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) | 0.008278 / 0.011353 (-0.003075) | 0.005044 / 0.011008 (-0.005964) | 0.123382 / 0.038508 (0.084874) | 0.054039 / 0.023109 (0.030929) | 0.382338 / 0.275898 (0.106440) | 0.453287 / 0.323480 (0.129807) | 0.006342 / 0.007986 (-0.001644) | 0.003930 / 0.004328 (-0.000398) | 0.094039 / 0.004250 (0.089789) | 0.076525 / 0.037052 (0.039472) | 0.394066 / 0.258489 (0.135577) | 0.445600 / 0.293841 (0.151759) | 0.039348 / 0.128546 (-0.089199) | 0.010485 / 0.075646 (-0.065161) | 0.433730 / 0.419271 (0.014459) | 0.082671 / 0.043533 (0.039138) | 0.375250 / 0.255139 (0.120111) | 0.416269 / 0.283200 (0.133070) | 0.038397 / 0.141683 (-0.103286) | 1.864834 / 1.452155 (0.412680) | 2.010453 / 1.492716 (0.517737) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.240008 / 0.018006 (0.222002) | 0.470975 / 0.000490 (0.470485) | 0.004001 / 0.000200 (0.003801) | 0.000097 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031107 / 0.037411 (-0.006304) | 0.129371 / 0.014526 (0.114846) | 0.141559 / 0.176557 (-0.034997) | 0.205571 / 0.737135 (-0.531564) | 0.144611 / 0.296338 (-0.151728) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.506972 / 0.215209 (0.291763) | 5.055951 / 2.077655 (2.978296) | 2.397438 / 1.504120 (0.893318) | 2.170435 / 1.541195 (0.629240) | 2.240296 / 1.468490 (0.771806) | 0.641559 / 4.584777 (-3.943218) | 4.644772 / 3.745712 (0.899060) | 4.064200 / 5.269862 (-1.205662) | 1.946991 / 4.565676 (-2.618685) | 0.086413 / 0.424275 (-0.337862) | 0.015082 / 0.007607 (0.007475) | 0.670413 / 0.226044 (0.444369) | 6.331346 / 2.268929 (4.062418) | 2.965813 / 55.444624 (-52.478812) | 2.547952 / 6.876477 (-4.328524) | 2.718390 / 2.142072 (0.576318) | 0.796657 / 4.805227 (-4.008571) | 0.173229 / 6.500664 (-6.327435) | 0.079606 / 0.075469 (0.004137) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.568761 / 1.841788 (-0.273026) | 18.485432 / 8.074308 (10.411124) | 15.758513 / 10.191392 (5.567121) | 0.170427 / 0.680424 (-0.509997) | 0.021421 / 0.534201 (-0.512780) | 0.518623 / 0.579283 (-0.060660) | 0.525887 / 0.434364 (0.091523) | 0.640331 / 0.540337 (0.099993) | 0.766748 / 1.386936 (-0.620188) |\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) | 0.007680 / 0.011353 (-0.003673) | 0.005289 / 0.011008 (-0.005719) | 0.093773 / 0.038508 (0.055265) | 0.054997 / 0.023109 (0.031888) | 0.456277 / 0.275898 (0.180379) | 0.500642 / 0.323480 (0.177162) | 0.005935 / 0.007986 (-0.002050) | 0.004375 / 0.004328 (0.000047) | 0.094131 / 0.004250 (0.089881) | 0.063399 / 0.037052 (0.026347) | 0.470546 / 0.258489 (0.212057) | 0.504989 / 0.293841 (0.211148) | 0.038541 / 0.128546 (-0.090006) | 0.010403 / 0.075646 (-0.065244) | 0.102469 / 0.419271 (-0.316802) | 0.063105 / 0.043533 (0.019572) | 0.466005 / 0.255139 (0.210866) | 0.458677 / 0.283200 (0.175477) | 0.028407 / 0.141683 (-0.113276) | 1.893829 / 1.452155 (0.441675) | 1.917954 / 1.492716 (0.425238) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.272760 / 0.018006 (0.254754) | 0.476159 / 0.000490 (0.475669) | 0.008467 / 0.000200 (0.008267) | 0.000146 / 0.000054 (0.000091) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035755 / 0.037411 (-0.001656) | 0.145038 / 0.014526 (0.130512) | 0.148322 / 0.176557 (-0.028235) | 0.210193 / 0.737135 (-0.526943) | 0.156547 / 0.296338 (-0.139792) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.541204 / 0.215209 (0.325995) | 5.382746 / 2.077655 (3.305091) | 2.704229 / 1.504120 (1.200109) | 2.468422 / 1.541195 (0.927227) | 2.522672 / 1.468490 (1.054182) | 0.644899 / 4.584777 (-3.939878) | 4.654401 / 3.745712 (0.908689) | 2.159223 / 5.269862 (-3.110638) | 1.280098 / 4.565676 (-3.285578) | 0.080053 / 0.424275 (-0.344222) | 0.014383 / 0.007607 (0.006776) | 0.662770 / 0.226044 (0.436725) | 6.617651 / 2.268929 (4.348722) | 3.234347 / 55.444624 (-52.210277) | 2.861417 / 6.876477 (-4.015059) | 2.888928 / 2.142072 (0.746856) | 0.792854 / 4.805227 (-4.012374) | 0.172553 / 6.500664 (-6.328111) | 0.078402 / 0.075469 (0.002933) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.565351 / 1.841788 (-0.276436) | 18.681916 / 8.074308 (10.607608) | 17.264473 / 10.191392 (7.073081) | 0.168461 / 0.680424 (-0.511963) | 0.021353 / 0.534201 (-0.512848) | 0.517843 / 0.579283 (-0.061440) | 0.519907 / 0.434364 (0.085543) | 0.623687 / 0.540337 (0.083350) | 0.761796 / 1.386936 (-0.625140) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bbf58747f734a46e75937bdbcbc05b06ade0224a \"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) | 0.006750 / 0.011353 (-0.004603) | 0.004268 / 0.011008 (-0.006741) | 0.098644 / 0.038508 (0.060136) | 0.044643 / 0.023109 (0.021534) | 0.309420 / 0.275898 (0.033522) | 0.379294 / 0.323480 (0.055815) | 0.005729 / 0.007986 (-0.002256) | 0.003615 / 0.004328 (-0.000714) | 0.076086 / 0.004250 (0.071835) | 0.068994 / 0.037052 (0.031942) | 0.325653 / 0.258489 (0.067164) | 0.375187 / 0.293841 (0.081347) | 0.032546 / 0.128546 (-0.096000) | 0.009089 / 0.075646 (-0.066557) | 0.329905 / 0.419271 (-0.089366) | 0.066832 / 0.043533 (0.023300) | 0.299247 / 0.255139 (0.044108) | 0.323460 / 0.283200 (0.040260) | 0.034226 / 0.141683 (-0.107457) | 1.475659 / 1.452155 (0.023505) | 1.556234 / 1.492716 (0.063518) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.292305 / 0.018006 (0.274299) | 0.542584 / 0.000490 (0.542094) | 0.003047 / 0.000200 (0.002847) | 0.000082 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030096 / 0.037411 (-0.007315) | 0.112341 / 0.014526 (0.097815) | 0.124965 / 0.176557 (-0.051591) | 0.183159 / 0.737135 (-0.553976) | 0.131885 / 0.296338 (-0.164453) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426437 / 0.215209 (0.211228) | 4.260984 / 2.077655 (2.183330) | 2.078358 / 1.504120 (0.574238) | 1.877644 / 1.541195 (0.336449) | 2.044036 / 1.468490 (0.575546) | 0.532980 / 4.584777 (-4.051797) | 3.749573 / 3.745712 (0.003860) | 1.944155 / 5.269862 (-3.325706) | 1.090307 / 4.565676 (-3.475370) | 0.065445 / 0.424275 (-0.358830) | 0.011237 / 0.007607 (0.003630) | 0.521448 / 0.226044 (0.295403) | 5.213118 / 2.268929 (2.944189) | 2.507829 / 55.444624 (-52.936795) | 2.177179 / 6.876477 (-4.699297) | 2.351161 / 2.142072 (0.209088) | 0.656775 / 4.805227 (-4.148452) | 0.141207 / 6.500664 (-6.359457) | 0.063286 / 0.075469 (-0.012183) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.190281 / 1.841788 (-0.651506) | 15.327424 / 8.074308 (7.253116) | 13.300695 / 10.191392 (3.109303) | 0.190484 / 0.680424 (-0.489939) | 0.017984 / 0.534201 (-0.516217) | 0.405714 / 0.579283 (-0.173569) | 0.435915 / 0.434364 (0.001551) | 0.494083 / 0.540337 (-0.046254) | 0.600616 / 1.386936 (-0.786320) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006740 / 0.011353 (-0.004613) | 0.004289 / 0.011008 (-0.006719) | 0.076532 / 0.038508 (0.038024) | 0.043305 / 0.023109 (0.020196) | 0.356111 / 0.275898 (0.080213) | 0.434121 / 0.323480 (0.110641) | 0.005599 / 0.007986 (-0.002387) | 0.003461 / 0.004328 (-0.000868) | 0.077097 / 0.004250 (0.072847) | 0.055369 / 0.037052 (0.018317) | 0.367093 / 0.258489 (0.108604) | 0.418801 / 0.293841 (0.124960) | 0.032057 / 0.128546 (-0.096489) | 0.009048 / 0.075646 (-0.066599) | 0.082897 / 0.419271 (-0.336374) | 0.050287 / 0.043533 (0.006754) | 0.352060 / 0.255139 (0.096921) | 0.376278 / 0.283200 (0.093078) | 0.023924 / 0.141683 (-0.117759) | 1.522780 / 1.452155 (0.070626) | 1.578938 / 1.492716 (0.086222) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.287317 / 0.018006 (0.269311) | 0.508490 / 0.000490 (0.508000) | 0.000431 / 0.000200 (0.000231) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031139 / 0.037411 (-0.006272) | 0.113927 / 0.014526 (0.099401) | 0.128147 / 0.176557 (-0.048409) | 0.179712 / 0.737135 (-0.557424) | 0.134364 / 0.296338 (-0.161975) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.452834 / 0.215209 (0.237625) | 4.507944 / 2.077655 (2.430289) | 2.287758 / 1.504120 (0.783638) | 2.091145 / 1.541195 (0.549951) | 2.196228 / 1.468490 (0.727738) | 0.539306 / 4.584777 (-4.045471) | 3.838941 / 3.745712 (0.093228) | 1.908801 / 5.269862 (-3.361060) | 1.139235 / 4.565676 (-3.426442) | 0.066677 / 0.424275 (-0.357599) | 0.011422 / 0.007607 (0.003815) | 0.562966 / 0.226044 (0.336921) | 5.633712 / 2.268929 (3.364784) | 2.788622 / 55.444624 (-52.656002) | 2.438465 / 6.876477 (-4.438012) | 2.523479 / 2.142072 (0.381407) | 0.668730 / 4.805227 (-4.136498) | 0.143977 / 6.500664 (-6.356687) | 0.064661 / 0.075469 (-0.010808) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.291708 / 1.841788 (-0.550080) | 15.573316 / 8.074308 (7.499008) | 14.435099 / 10.191392 (4.243707) | 0.147745 / 0.680424 (-0.532679) | 0.017602 / 0.534201 (-0.516599) | 0.401560 / 0.579283 (-0.177723) | 0.429861 / 0.434364 (-0.004502) | 0.469800 / 0.540337 (-0.070538) | 0.567515 / 1.386936 (-0.819421) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79c340f5dcfd06340f180f6c6ea2d5ef81f49d98 \"CML watermark\")\n"
] |
5,968
|
Common Voice datasets still need `use_auth_token=True`
|
### Describe the bug
We don't need to pass `use_auth_token=True` anymore to download gated datasets or models, so the following should work if correctly logged in.
```py
from datasets import load_dataset
load_dataset("mozilla-foundation/common_voice_6_1", "tr", split="train+validation")
```
However it throws an error - probably because something weird is hardcoded into the dataset loading script.
### Steps to reproduce the bug
1.)
```
huggingface-cli login
```
2.) Make sure that you have accepted the license here:
https://huggingface.co/datasets/mozilla-foundation/common_voice_6_1
3.) Run:
```py
from datasets import load_dataset
load_dataset("mozilla-foundation/common_voice_6_1", "tr", split="train+validation")
```
4.) You'll get:
```
File ~/hf/lib/python3.10/site-packages/datasets/builder.py:963, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs)
961 split_dict = SplitDict(dataset_name=self.name)
962 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 963 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
965 # Checksums verification
966 if verification_mode == VerificationMode.ALL_CHECKS and dl_manager.record_checksums:
File ~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_6_1/f4d7854c466f5bd4908988dbd39044ec4fc634d89e0515ab0c51715c0127ffe3/common_voice_6_1.py:150, in CommonVoice._split_generators(self, dl_manager)
148 hf_auth_token = dl_manager.download_config.use_auth_token
149 if hf_auth_token is None:
--> 150 raise ConnectionError(
151 "Please set use_auth_token=True or use_auth_token='<TOKEN>' to download this dataset"
152 )
154 bundle_url_template = STATS["bundleURLTemplate"]
155 bundle_version = bundle_url_template.split("/")[0]
ConnectionError: Please set use_auth_token=True or use_auth_token='<TOKEN>' to download this dataset
```
### Expected behavior
One should not have to pass `use_auth_token=True`. Also see discussion here: https://github.com/huggingface/blog/pull/1243#discussion_r1235131150
### Environment info
```
- `datasets` version: 2.13.0
- Platform: Linux-6.2.0-76060200-generic-x86_64-with-glibc2.35
- Python version: 3.10.6
- Huggingface_hub version: 0.16.0.dev0
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
```
|
[] |
{
"url": null,
"html_url": null,
"diff_url": null,
"patch_url": null,
"merged_at": null
}
| false
|
[
"cc @pcuenca as well. \r\n\r\nNot super urgent btw",
"The issue commes from the dataset itself and is not related to the `datasets` lib\r\n\r\nsee https://huggingface.co/datasets/mozilla-foundation/common_voice_6_1/blob/2c475b3b88e0f2e5828f830a4b91618a25ff20b7/common_voice_6_1.py#L148-L152",
"Let's remove these lines in the dataset no? cc @anton-l @Vaibhavs10 ",
"Addressed in:\r\n\r\n* `mozilla-foundation/common_voice_1_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_1_0/discussions/4)\r\n* `mozilla-foundation/common_voice_2_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_2_0/discussions/3)\r\n* `mozilla-foundation/common_voice_3_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_3_0/discussions/3)\r\n* `mozilla-foundation/common_voice_4_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_4_0/discussions/3)\r\n* `mozilla-foundation/common_voice_5_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_5_0/discussions/3)\r\n* `mozilla-foundation/common_voice_5_1` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_5_1/discussions/3)\r\n* `mozilla-foundation/common_voice_6_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_6_0/discussions/3)\r\n* `mozilla-foundation/common_voice_6_1` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_6_1/discussions/3)\r\n* `mozilla-foundation/common_voice_7_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0/discussions/3)\r\n* `mozilla-foundation/common_voice_8_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0/discussions/7)\r\n* `mozilla-foundation/common_voice_9_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0/discussions/8)\r\n* `mozilla-foundation/common_voice_10_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_10_0/discussions/7)"
] |
5,967
|
Config name / split name lost after map with multiproc
|
### Describe the bug
Performing a `.map` method on a dataset loses it's config name / split name only if run with multiproc
### Steps to reproduce the bug
```python
from datasets import Audio, load_dataset
from transformers import AutoFeatureExtractor
import numpy as np
# load dummy dataset
libri = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean")
# make train / test splits
libri = libri["validation"].train_test_split(seed=42, shuffle=True, test_size=0.1)
# example feature extractor
model_id = "ntu-spml/distilhubert"
feature_extractor = AutoFeatureExtractor.from_pretrained(model_id, do_normalize=True, return_attention_mask=True)
sampling_rate = feature_extractor.sampling_rate
libri = libri.cast_column("audio", Audio(sampling_rate=sampling_rate))
max_duration = 30.0
def preprocess_function(examples):
audio_arrays = [x["array"] for x in examples["audio"]]
inputs = feature_extractor(
audio_arrays,
sampling_rate=feature_extractor.sampling_rate,
max_length=int(feature_extractor.sampling_rate * max_duration),
truncation=True,
return_attention_mask=True,
)
return inputs
# single proc map
libri_encoded = libri.map(
preprocess_function, remove_columns=["audio", "file"], batched=True, num_proc=1
)
print(10 * "=" ,"Single processing", 10 * "=")
print("Config name before: ", libri["train"].config_name, " Split name before: ", libri["train"].split)
print("Config name after: ", libri_encoded["train"].config_name, " Split name after: ", libri_encoded["train"].split)
# multi proc map
libri_encoded = libri.map(
preprocess_function, remove_columns=["audio", "file"], batched=True, num_proc=2
)
print(10 * "=" ,"Multi processing", 10 * "=")
print("Config name before: ", libri["train"].config_name, " Split name before: ", libri["train"].split)
print("Config name after: ", libri_encoded["train"].config_name, " Split name after: ", libri_encoded["train"].split)
```
**Print Output:**
```
========== Single processing ==========
Config name before: clean Split name before: validation
Config name after: clean Split name after: validation
========== Multi processing ==========
Config name before: clean Split name before: validation
Config name after: None Split name after: None
```
=> we can see that the config/split names are lost in the multiprocessing setting
### Expected behavior
Should retain both config / split names in the multiproc setting
### Environment info
- `datasets` version: 2.13.1.dev0
- Platform: Linux-5.15.0-67-generic-x86_64-with-glibc2.35
- Python version: 3.10.6
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.2
|
[] |
{
"url": null,
"html_url": null,
"diff_url": null,
"patch_url": null,
"merged_at": null
}
| false
|
[
"This must be due to DatasetInfo.from_merge which drops them and is used in `concatenate_datasets`.\r\n\r\nAnd you're experiencing this issue because multiprocessing does concatenate the resulting datasets from each process.\r\n\r\nMaybe they should be kept if all the subdatasets share the same values for config_name and split",
"That sounds like a clean workaround!"
] |
5,966
|
Fix JSON generation in benchmarks CI
|
Related to changes made in https://github.com/iterative/dvc/pull/9475
|
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5966",
"html_url": "https://github.com/huggingface/datasets/pull/5966",
"diff_url": "https://github.com/huggingface/datasets/pull/5966.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5966.patch",
"merged_at": "2023-06-19T17:22:10"
}
| true
|
[
"<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) | 0.006186 / 0.011353 (-0.005167) | 0.003744 / 0.011008 (-0.007264) | 0.097295 / 0.038508 (0.058787) | 0.037106 / 0.023109 (0.013997) | 0.424154 / 0.275898 (0.148256) | 0.474536 / 0.323480 (0.151057) | 0.003454 / 0.007986 (-0.004532) | 0.003865 / 0.004328 (-0.000463) | 0.077348 / 0.004250 (0.073097) | 0.051728 / 0.037052 (0.014675) | 0.437120 / 0.258489 (0.178631) | 0.478379 / 0.293841 (0.184538) | 0.028939 / 0.128546 (-0.099608) | 0.008376 / 0.075646 (-0.067270) | 0.312002 / 0.419271 (-0.107270) | 0.053723 / 0.043533 (0.010190) | 0.424815 / 0.255139 (0.169676) | 0.446203 / 0.283200 (0.163004) | 0.026553 / 0.141683 (-0.115130) | 1.479983 / 1.452155 (0.027828) | 1.530613 / 1.492716 (0.037896) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.196627 / 0.018006 (0.178620) | 0.422361 / 0.000490 (0.421871) | 0.003442 / 0.000200 (0.003242) | 0.000077 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022913 / 0.037411 (-0.014499) | 0.096011 / 0.014526 (0.081485) | 0.104091 / 0.176557 (-0.072466) | 0.163273 / 0.737135 (-0.573862) | 0.109142 / 0.296338 (-0.187197) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431032 / 0.215209 (0.215823) | 4.314391 / 2.077655 (2.236737) | 2.003812 / 1.504120 (0.499692) | 1.799538 / 1.541195 (0.258344) | 1.830026 / 1.468490 (0.361536) | 0.560131 / 4.584777 (-4.024646) | 3.368997 / 3.745712 (-0.376715) | 1.703032 / 5.269862 (-3.566830) | 1.026949 / 4.565676 (-3.538727) | 0.067507 / 0.424275 (-0.356768) | 0.010910 / 0.007607 (0.003303) | 0.532606 / 0.226044 (0.306562) | 5.345179 / 2.268929 (3.076250) | 2.368077 / 55.444624 (-53.076548) | 2.028913 / 6.876477 (-4.847564) | 2.147621 / 2.142072 (0.005549) | 0.675696 / 4.805227 (-4.129531) | 0.134902 / 6.500664 (-6.365762) | 0.065004 / 0.075469 (-0.010465) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.233412 / 1.841788 (-0.608376) | 13.767465 / 8.074308 (5.693157) | 13.933653 / 10.191392 (3.742261) | 0.129010 / 0.680424 (-0.551414) | 0.016708 / 0.534201 (-0.517493) | 0.362341 / 0.579283 (-0.216942) | 0.390902 / 0.434364 (-0.043462) | 0.429156 / 0.540337 (-0.111182) | 0.521166 / 1.386936 (-0.865770) |\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) | 0.006169 / 0.011353 (-0.005184) | 0.003839 / 0.011008 (-0.007169) | 0.078784 / 0.038508 (0.040276) | 0.040218 / 0.023109 (0.017109) | 0.360439 / 0.275898 (0.084541) | 0.423957 / 0.323480 (0.100477) | 0.003456 / 0.007986 (-0.004529) | 0.002900 / 0.004328 (-0.001428) | 0.078820 / 0.004250 (0.074569) | 0.047240 / 0.037052 (0.010187) | 0.372081 / 0.258489 (0.113592) | 0.424263 / 0.293841 (0.130422) | 0.027977 / 0.128546 (-0.100569) | 0.008400 / 0.075646 (-0.067246) | 0.084399 / 0.419271 (-0.334872) | 0.043303 / 0.043533 (-0.000230) | 0.361583 / 0.255139 (0.106444) | 0.394987 / 0.283200 (0.111787) | 0.020006 / 0.141683 (-0.121677) | 1.520208 / 1.452155 (0.068053) | 1.587335 / 1.492716 (0.094619) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223847 / 0.018006 (0.205840) | 0.402194 / 0.000490 (0.401704) | 0.000384 / 0.000200 (0.000184) | 0.000057 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024902 / 0.037411 (-0.012509) | 0.099076 / 0.014526 (0.084550) | 0.108041 / 0.176557 (-0.068516) | 0.159385 / 0.737135 (-0.577750) | 0.111442 / 0.296338 (-0.184896) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.446232 / 0.215209 (0.231023) | 4.464927 / 2.077655 (2.387272) | 2.155234 / 1.504120 (0.651114) | 1.953645 / 1.541195 (0.412450) | 1.965991 / 1.468490 (0.497501) | 0.553473 / 4.584777 (-4.031304) | 3.321397 / 3.745712 (-0.424315) | 1.693761 / 5.269862 (-3.576101) | 1.006299 / 4.565676 (-3.559378) | 0.067013 / 0.424275 (-0.357262) | 0.011116 / 0.007607 (0.003509) | 0.555014 / 0.226044 (0.328970) | 5.535694 / 2.268929 (3.266765) | 2.598339 / 55.444624 (-52.846285) | 2.249298 / 6.876477 (-4.627179) | 2.243419 / 2.142072 (0.101347) | 0.667603 / 4.805227 (-4.137624) | 0.133322 / 6.500664 (-6.367343) | 0.065473 / 0.075469 (-0.009996) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.293051 / 1.841788 (-0.548737) | 14.103731 / 8.074308 (6.029423) | 14.215204 / 10.191392 (4.023812) | 0.143990 / 0.680424 (-0.536434) | 0.016805 / 0.534201 (-0.517396) | 0.363264 / 0.579283 (-0.216019) | 0.392769 / 0.434364 (-0.041594) | 0.425291 / 0.540337 (-0.115046) | 0.515479 / 1.386936 (-0.871457) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e03a58f3f5d7e6f07279fb833e62d859a0babaad \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006346 / 0.011353 (-0.005006) | 0.004130 / 0.011008 (-0.006878) | 0.096898 / 0.038508 (0.058390) | 0.042564 / 0.023109 (0.019455) | 0.343748 / 0.275898 (0.067850) | 0.412515 / 0.323480 (0.089035) | 0.006153 / 0.007986 (-0.001833) | 0.003345 / 0.004328 (-0.000984) | 0.075314 / 0.004250 (0.071064) | 0.061478 / 0.037052 (0.024426) | 0.362948 / 0.258489 (0.104459) | 0.401533 / 0.293841 (0.107692) | 0.032363 / 0.128546 (-0.096184) | 0.008780 / 0.075646 (-0.066867) | 0.328691 / 0.419271 (-0.090580) | 0.054253 / 0.043533 (0.010721) | 0.340783 / 0.255139 (0.085644) | 0.360705 / 0.283200 (0.077505) | 0.023183 / 0.141683 (-0.118500) | 1.484078 / 1.452155 (0.031924) | 1.528581 / 1.492716 (0.035865) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208732 / 0.018006 (0.190726) | 0.452572 / 0.000490 (0.452082) | 0.002936 / 0.000200 (0.002737) | 0.000082 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024616 / 0.037411 (-0.012795) | 0.107547 / 0.014526 (0.093021) | 0.114492 / 0.176557 (-0.062065) | 0.171770 / 0.737135 (-0.565365) | 0.122538 / 0.296338 (-0.173800) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.406140 / 0.215209 (0.190930) | 4.062391 / 2.077655 (1.984736) | 1.865962 / 1.504120 (0.361842) | 1.682236 / 1.541195 (0.141041) | 1.738119 / 1.468490 (0.269629) | 0.532244 / 4.584777 (-4.052533) | 3.816421 / 3.745712 (0.070709) | 2.981205 / 5.269862 (-2.288656) | 1.519497 / 4.565676 (-3.046179) | 0.065904 / 0.424275 (-0.358371) | 0.011277 / 0.007607 (0.003670) | 0.512789 / 0.226044 (0.286745) | 5.107618 / 2.268929 (2.838690) | 2.419399 / 55.444624 (-53.025226) | 2.079262 / 6.876477 (-4.797214) | 2.150447 / 2.142072 (0.008375) | 0.696737 / 4.805227 (-4.108490) | 0.142497 / 6.500664 (-6.358167) | 0.063521 / 0.075469 (-0.011949) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.180692 / 1.841788 (-0.661095) | 14.343084 / 8.074308 (6.268776) | 13.303719 / 10.191392 (3.112327) | 0.164234 / 0.680424 (-0.516190) | 0.017439 / 0.534201 (-0.516762) | 0.399712 / 0.579283 (-0.179571) | 0.428248 / 0.434364 (-0.006115) | 0.471909 / 0.540337 (-0.068428) | 0.573853 / 1.386936 (-0.813083) |\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) | 0.006210 / 0.011353 (-0.005143) | 0.004104 / 0.011008 (-0.006905) | 0.075140 / 0.038508 (0.036632) | 0.044647 / 0.023109 (0.021538) | 0.370120 / 0.275898 (0.094222) | 0.452936 / 0.323480 (0.129457) | 0.003943 / 0.007986 (-0.004042) | 0.003285 / 0.004328 (-0.001043) | 0.075267 / 0.004250 (0.071017) | 0.055517 / 0.037052 (0.018465) | 0.396385 / 0.258489 (0.137896) | 0.447870 / 0.293841 (0.154029) | 0.031342 / 0.128546 (-0.097204) | 0.008720 / 0.075646 (-0.066926) | 0.082702 / 0.419271 (-0.336570) | 0.051010 / 0.043533 (0.007477) | 0.350546 / 0.255139 (0.095407) | 0.425395 / 0.283200 (0.142195) | 0.024483 / 0.141683 (-0.117200) | 1.467341 / 1.452155 (0.015186) | 1.537187 / 1.492716 (0.044471) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218067 / 0.018006 (0.200061) | 0.441603 / 0.000490 (0.441114) | 0.003711 / 0.000200 (0.003512) | 0.000092 / 0.000054 (0.000038) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028669 / 0.037411 (-0.008742) | 0.112941 / 0.014526 (0.098415) | 0.122584 / 0.176557 (-0.053972) | 0.176494 / 0.737135 (-0.560641) | 0.129369 / 0.296338 (-0.166970) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434543 / 0.215209 (0.219334) | 4.344056 / 2.077655 (2.266401) | 2.079286 / 1.504120 (0.575166) | 1.887264 / 1.541195 (0.346069) | 1.910386 / 1.468490 (0.441896) | 0.538824 / 4.584777 (-4.045953) | 3.844786 / 3.745712 (0.099074) | 2.902091 / 5.269862 (-2.367770) | 1.270852 / 4.565676 (-3.294824) | 0.066324 / 0.424275 (-0.357951) | 0.011346 / 0.007607 (0.003739) | 0.537122 / 0.226044 (0.311078) | 5.367354 / 2.268929 (3.098426) | 2.533672 / 55.444624 (-52.910952) | 2.203260 / 6.876477 (-4.673217) | 2.224310 / 2.142072 (0.082237) | 0.663806 / 4.805227 (-4.141422) | 0.142758 / 6.500664 (-6.357906) | 0.063870 / 0.075469 (-0.011599) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.260487 / 1.841788 (-0.581301) | 14.800106 / 8.074308 (6.725798) | 13.993488 / 10.191392 (3.802096) | 0.165829 / 0.680424 (-0.514595) | 0.017347 / 0.534201 (-0.516854) | 0.401819 / 0.579283 (-0.177464) | 0.424577 / 0.434364 (-0.009787) | 0.475161 / 0.540337 (-0.065176) | 0.574659 / 1.386936 (-0.812277) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#02e1e9ab6df4720f57b2d08c0b800cecac79a7c8 \"CML watermark\")\n"
] |
5,965
|
"Couldn't cast array of type" in complex datasets
|
### Describe the bug
When doing a map of a dataset with complex types, sometimes `datasets` is unable to interpret the valid schema of a returned datasets.map() function. This often comes from conflicting types, like when both empty lists and filled lists are competing for the same field value.
This is prone to happen in batch mapping, when the mapper returns a sequence of null/empty values and other batches are non-null. A workaround is to manually cast the new batch to a pyarrow table (like implemented in this [workaround](https://github.com/piercefreeman/lassen/pull/3)) but it feels like this ideally should be solved at the core library level.
Note that the reproduction case only throws this error if the first datapoint has the empty list. If it is processed later, datasets already detects its representation as list-type and therefore allows the empty list to be provided.
### Steps to reproduce the bug
A trivial reproduction case:
```python
from typing import Iterator, Any
import pandas as pd
from datasets import Dataset
def batch_to_examples(batch: dict[str, list[Any]]) -> Iterator[dict[str, Any]]:
for i in range(next(iter(lengths))):
yield {feature: values[i] for feature, values in batch.items()}
def examples_to_batch(examples) -> dict[str, list[Any]]:
batch = {}
for example in examples:
for feature, value in example.items():
if feature not in batch:
batch[feature] = []
batch[feature].append(value)
return batch
def batch_process(examples, explicit_schema: bool):
new_examples = []
for example in batch_to_examples(examples):
new_examples.append(dict(texts=example["raw_text"].split()))
return examples_to_batch(new_examples)
df = pd.DataFrame(
[
{"raw_text": ""},
{"raw_text": "This is a test"},
{"raw_text": "This is another test"},
]
)
dataset = Dataset.from_pandas(df)
# datasets won't be able to typehint a dataset that starts with an empty example.
with pytest.raises(TypeError, match="Couldn't cast array of type"):
dataset = dataset.map(
batch_process,
batched=True,
batch_size=1,
num_proc=1,
remove_columns=dataset.column_names,
)
```
This results in crashes like:
```bash
File "/Users/piercefreeman/Library/Caches/pypoetry/virtualenvs/example-9kBqeSPy-py3.11/lib/python3.11/site-packages/datasets/table.py", line 1819, in wrapper
return func(array, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/piercefreeman/Library/Caches/pypoetry/virtualenvs/example-9kBqeSPy-py3.11/lib/python3.11/site-packages/datasets/table.py", line 2109, in cast_array_to_feature
return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/piercefreeman/Library/Caches/pypoetry/virtualenvs/example-9kBqeSPy-py3.11/lib/python3.11/site-packages/datasets/table.py", line 1819, in wrapper
return func(array, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/piercefreeman/Library/Caches/pypoetry/virtualenvs/example-9kBqeSPy-py3.11/lib/python3.11/site-packages/datasets/table.py", line 1998, in array_cast
raise TypeError(f"Couldn't cast array of type {array.type} to {pa_type}")
TypeError: Couldn't cast array of type string to null
```
### Expected behavior
The code should successfully map and create a new dataset without error.
### Environment info
Mac OSX, Linux
|
[] |
{
"url": null,
"html_url": null,
"diff_url": null,
"patch_url": null,
"merged_at": null
}
| false
|
[
"Thanks for reporting! \r\n\r\nSpecifying the target features explicitly should avoid this error:\r\n```python\r\ndataset = dataset.map(\r\n batch_process,\r\n batched=True,\r\n batch_size=1,\r\n num_proc=1,\r\n remove_columns=dataset.column_names,\r\n features=datasets.Features({\"texts\": datasets.Sequence(datasets.Value(\"string\"))})\r\n)\r\n```\r\n\r\nThis error stems from our type promotion not handling the nested case. But this promotion/casting allocates memory in most scenarios, which can be problematic for large datasets, so explicitly passing the features is the optimal solution.",
"Hi @mariosasko thanks for the context, this is helpful to know. Would it be worth having some logic to generate this explicit feature specification automatically if a type annotation for a .map returns a dataclass that can be inferred?\r\n\r\nFeels like something that would be easy to implement and could save memory / deal with this case in a standardized way.",
"> . Would it be worth having some logic to generate this explicit feature specification automatically if a type annotation for a .map returns a dataclass that can be inferred?\r\n\r\nInteresting proposal! Yes, we could consider doing this if the (return) type hint is `TypedDict`, and raise an error that type hints are incorrect if the cast using the inferred types fails.",
"@mariosasko Put up an initial PR to implement this proposal. Let me know your thoughts on direction and what else should be in-scope here."
] |
5,964
|
Always return list in `list_datasets`
|
Fix #5925
Plus, deprecate `list_datasets`/`inspect_dataset` in favor of `huggingface_hub.list_datasets`/"git clone workflow" (downloads data files)
|
[] |
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5964",
"html_url": "https://github.com/huggingface/datasets/pull/5964",
"diff_url": "https://github.com/huggingface/datasets/pull/5964.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/5964.patch",
"merged_at": "2023-06-19T17:22:41"
}
| true
|
[
"_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) | 0.006795 / 0.011353 (-0.004558) | 0.004170 / 0.011008 (-0.006838) | 0.098698 / 0.038508 (0.060190) | 0.045393 / 0.023109 (0.022284) | 0.309205 / 0.275898 (0.033307) | 0.361333 / 0.323480 (0.037853) | 0.006009 / 0.007986 (-0.001977) | 0.003334 / 0.004328 (-0.000995) | 0.075071 / 0.004250 (0.070821) | 0.062587 / 0.037052 (0.025535) | 0.322395 / 0.258489 (0.063906) | 0.360499 / 0.293841 (0.066659) | 0.032243 / 0.128546 (-0.096303) | 0.008768 / 0.075646 (-0.066878) | 0.329799 / 0.419271 (-0.089472) | 0.062261 / 0.043533 (0.018728) | 0.298112 / 0.255139 (0.042973) | 0.322815 / 0.283200 (0.039615) | 0.032348 / 0.141683 (-0.109335) | 1.445807 / 1.452155 (-0.006347) | 1.528768 / 1.492716 (0.036051) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.195701 / 0.018006 (0.177695) | 0.437042 / 0.000490 (0.436552) | 0.003867 / 0.000200 (0.003667) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026713 / 0.037411 (-0.010698) | 0.109548 / 0.014526 (0.095022) | 0.119216 / 0.176557 (-0.057341) | 0.178947 / 0.737135 (-0.558188) | 0.125224 / 0.296338 (-0.171114) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400885 / 0.215209 (0.185676) | 3.991223 / 2.077655 (1.913568) | 1.818449 / 1.504120 (0.314329) | 1.609285 / 1.541195 (0.068090) | 1.666675 / 1.468490 (0.198184) | 0.531486 / 4.584777 (-4.053291) | 3.770142 / 3.745712 (0.024430) | 3.057189 / 5.269862 (-2.212673) | 1.517491 / 4.565676 (-3.048186) | 0.065782 / 0.424275 (-0.358493) | 0.011251 / 0.007607 (0.003644) | 0.504277 / 0.226044 (0.278233) | 5.038979 / 2.268929 (2.770050) | 2.254717 / 55.444624 (-53.189908) | 1.929743 / 6.876477 (-4.946734) | 2.080051 / 2.142072 (-0.062022) | 0.656831 / 4.805227 (-4.148396) | 0.142860 / 6.500664 (-6.357804) | 0.063057 / 0.075469 (-0.012412) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.208819 / 1.841788 (-0.632969) | 14.456966 / 8.074308 (6.382658) | 12.839799 / 10.191392 (2.648407) | 0.164361 / 0.680424 (-0.516063) | 0.017330 / 0.534201 (-0.516871) | 0.397384 / 0.579283 (-0.181899) | 0.422704 / 0.434364 (-0.011660) | 0.472065 / 0.540337 (-0.068273) | 0.576960 / 1.386936 (-0.809976) |\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) | 0.006950 / 0.011353 (-0.004403) | 0.004012 / 0.011008 (-0.006997) | 0.076050 / 0.038508 (0.037542) | 0.046646 / 0.023109 (0.023537) | 0.353813 / 0.275898 (0.077915) | 0.417111 / 0.323480 (0.093631) | 0.005422 / 0.007986 (-0.002564) | 0.003356 / 0.004328 (-0.000972) | 0.076662 / 0.004250 (0.072411) | 0.055018 / 0.037052 (0.017966) | 0.371561 / 0.258489 (0.113072) | 0.410471 / 0.293841 (0.116630) | 0.031860 / 0.128546 (-0.096686) | 0.008754 / 0.075646 (-0.066893) | 0.083192 / 0.419271 (-0.336079) | 0.050479 / 0.043533 (0.006946) | 0.351725 / 0.255139 (0.096586) | 0.371596 / 0.283200 (0.088396) | 0.023042 / 0.141683 (-0.118641) | 1.480533 / 1.452155 (0.028379) | 1.545970 / 1.492716 (0.053254) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220095 / 0.018006 (0.202089) | 0.441550 / 0.000490 (0.441061) | 0.000375 / 0.000200 (0.000175) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029527 / 0.037411 (-0.007884) | 0.111645 / 0.014526 (0.097119) | 0.125732 / 0.176557 (-0.050825) | 0.177322 / 0.737135 (-0.559813) | 0.128620 / 0.296338 (-0.167718) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.432415 / 0.215209 (0.217206) | 4.314381 / 2.077655 (2.236726) | 2.079450 / 1.504120 (0.575331) | 1.893139 / 1.541195 (0.351944) | 1.951363 / 1.468490 (0.482873) | 0.531466 / 4.584777 (-4.053311) | 3.716860 / 3.745712 (-0.028852) | 1.850111 / 5.269862 (-3.419750) | 1.100676 / 4.565676 (-3.465000) | 0.066247 / 0.424275 (-0.358028) | 0.011503 / 0.007607 (0.003896) | 0.537208 / 0.226044 (0.311164) | 5.367560 / 2.268929 (3.098631) | 2.543697 / 55.444624 (-52.900927) | 2.221670 / 6.876477 (-4.654806) | 2.252009 / 2.142072 (0.109937) | 0.658509 / 4.805227 (-4.146718) | 0.142345 / 6.500664 (-6.358319) | 0.064701 / 0.075469 (-0.010768) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.266442 / 1.841788 (-0.575346) | 15.105953 / 8.074308 (7.031645) | 14.288229 / 10.191392 (4.096837) | 0.161182 / 0.680424 (-0.519242) | 0.017074 / 0.534201 (-0.517127) | 0.399464 / 0.579283 (-0.179819) | 0.419459 / 0.434364 (-0.014905) | 0.467553 / 0.540337 (-0.072784) | 0.566337 / 1.386936 (-0.820599) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53ac2d9662f9e5923ae7c52199eaa620d82f0043 \"CML watermark\")\n"
] |
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