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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) | 0.006334 / 0.011353 (-0.005018) | 0.004447 / 0.011008 (-0.006561) | 0.099287 / 0.038508 (0.060779) | 0.027426 / 0.023109 (0.004317) | 0.322638 / 0.275898 (0.046740) | 0.370501 / 0.323480 (0.047021) | 0.004775 / 0.007986 (-0.003210) | 0.003289 / 0.004328 (-0.001040) | 0.076531 / 0.004250 (0.072280) | 0.037485 / 0.037052 (0.000432) | 0.335634 / 0.258489 (0.077145) | 0.384031 / 0.293841 (0.090190) | 0.031258 / 0.128546 (-0.097288) | 0.011619 / 0.075646 (-0.064027) | 0.326309 / 0.419271 (-0.092963) | 0.042513 / 0.043533 (-0.001020) | 0.340817 / 0.255139 (0.085678) | 0.369846 / 0.283200 (0.086646) | 0.084904 / 0.141683 (-0.056779) | 1.481739 / 1.452155 (0.029584) | 1.566593 / 1.492716 (0.073877) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.186424 / 0.018006 (0.168418) | 0.400879 / 0.000490 (0.400389) | 0.003520 / 0.000200 (0.003320) | 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) | 0.023287 / 0.037411 (-0.014124) | 0.097767 / 0.014526 (0.083241) | 0.103271 / 0.176557 (-0.073286) | 0.165414 / 0.737135 (-0.571722) | 0.106437 / 0.296338 (-0.189901) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422711 / 0.215209 (0.207502) | 4.221382 / 2.077655 (2.143727) | 1.906807 / 1.504120 (0.402687) | 1.709595 / 1.541195 (0.168400) | 1.720452 / 1.468490 (0.251962) | 0.699477 / 4.584777 (-3.885300) | 3.415840 / 3.745712 (-0.329873) | 2.835669 / 5.269862 (-2.434192) | 1.501775 / 4.565676 (-3.063901) | 0.082896 / 0.424275 (-0.341379) | 0.012855 / 0.007607 (0.005248) | 0.514373 / 0.226044 (0.288329) | 5.190000 / 2.268929 (2.921071) | 2.302539 / 55.444624 (-53.142086) | 1.963410 / 6.876477 (-4.913067) | 2.020944 / 2.142072 (-0.121128) | 0.805919 / 4.805227 (-3.999308) | 0.150604 / 6.500664 (-6.350060) | 0.065977 / 0.075469 (-0.009492) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.206487 / 1.841788 (-0.635300) | 13.631513 / 8.074308 (5.557205) | 13.800258 / 10.191392 (3.608866) | 0.146914 / 0.680424 (-0.533509) | 0.016454 / 0.534201 (-0.517747) | 0.377752 / 0.579283 (-0.201532) | 0.384312 / 0.434364 (-0.050052) | 0.434912 / 0.540337 (-0.105425) | 0.522507 / 1.386936 (-0.864429) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006328 / 0.011353 (-0.005025) | 0.004406 / 0.011008 (-0.006602) | 0.077951 / 0.038508 (0.039443) | 0.026716 / 0.023109 (0.003607) | 0.337303 / 0.275898 (0.061405) | 0.372036 / 0.323480 (0.048556) | 0.004800 / 0.007986 (-0.003185) | 0.003153 / 0.004328 (-0.001175) | 0.076823 / 0.004250 (0.072573) | 0.035873 / 0.037052 (-0.001179) | 0.340243 / 0.258489 (0.081754) | 0.380183 / 0.293841 (0.086342) | 0.032185 / 0.128546 (-0.096361) | 0.011545 / 0.075646 (-0.064101) | 0.086887 / 0.419271 (-0.332384) | 0.041560 / 0.043533 (-0.001973) | 0.338716 / 0.255139 (0.083577) | 0.363080 / 0.283200 (0.079881) | 0.088375 / 0.141683 (-0.053308) | 1.499004 / 1.452155 (0.046850) | 1.585904 / 1.492716 (0.093188) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211645 / 0.018006 (0.193639) | 0.403707 / 0.000490 (0.403218) | 0.000415 / 0.000200 (0.000215) | 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) | 0.024972 / 0.037411 (-0.012440) | 0.097996 / 0.014526 (0.083470) | 0.105941 / 0.176557 (-0.070616) | 0.155521 / 0.737135 (-0.581615) | 0.108246 / 0.296338 (-0.188092) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.442316 / 0.215209 (0.227107) | 4.417977 / 2.077655 (2.340322) | 2.078324 / 1.504120 (0.574205) | 1.863678 / 1.541195 (0.322483) | 1.917149 / 1.468490 (0.448659) | 0.697628 / 4.584777 (-3.887149) | 3.412810 / 3.745712 (-0.332902) | 1.866473 / 5.269862 (-3.403389) | 1.155923 / 4.565676 (-3.409754) | 0.082831 / 0.424275 (-0.341444) | 0.012367 / 0.007607 (0.004760) | 0.540018 / 0.226044 (0.313974) | 5.420472 / 2.268929 (3.151544) | 2.508540 / 55.444624 (-52.936084) | 2.166397 / 6.876477 (-4.710080) | 2.153486 / 2.142072 (0.011414) | 0.804860 / 4.805227 (-4.000367) | 0.151178 / 6.500664 (-6.349486) | 0.067870 / 0.075469 (-0.007599) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.310387 / 1.841788 (-0.531400) | 13.908916 / 8.074308 (5.834608) | 14.136895 / 10.191392 (3.945503) | 0.139389 / 0.680424 (-0.541035) | 0.016687 / 0.534201 (-0.517514) | 0.379624 / 0.579283 (-0.199659) | 0.382634 / 0.434364 (-0.051730) | 0.439632 / 0.540337 (-0.100706) | 0.524913 / 1.386936 (-0.862023) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f8f2143b4ed39b58ed415029e7838d767662da91 \"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) | 0.006365 / 0.011353 (-0.004988) | 0.004457 / 0.011008 (-0.006551) | 0.097989 / 0.038508 (0.059481) | 0.027686 / 0.023109 (0.004577) | 0.357412 / 0.275898 (0.081514) | 0.368573 / 0.323480 (0.045093) | 0.004859 / 0.007986 (-0.003127) | 0.003262 / 0.004328 (-0.001066) | 0.076487 / 0.004250 (0.072237) | 0.035526 / 0.037052 (-0.001527) | 0.332862 / 0.258489 (0.074373) | 0.369334 / 0.293841 (0.075493) | 0.030750 / 0.128546 (-0.097796) | 0.011503 / 0.075646 (-0.064143) | 0.323289 / 0.419271 (-0.095982) | 0.042302 / 0.043533 (-0.001231) | 0.334009 / 0.255139 (0.078870) | 0.354150 / 0.283200 (0.070951) | 0.082895 / 0.141683 (-0.058788) | 1.499727 / 1.452155 (0.047572) | 1.574123 / 1.492716 (0.081407) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.192583 / 0.018006 (0.174577) | 0.408136 / 0.000490 (0.407646) | 0.001272 / 0.000200 (0.001072) | 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) | 0.022883 / 0.037411 (-0.014528) | 0.095710 / 0.014526 (0.081185) | 0.106545 / 0.176557 (-0.070011) | 0.165784 / 0.737135 (-0.571352) | 0.108594 / 0.296338 (-0.187744) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429483 / 0.215209 (0.214274) | 4.292338 / 2.077655 (2.214683) | 1.917759 / 1.504120 (0.413639) | 1.711489 / 1.541195 (0.170294) | 1.735668 / 1.468490 (0.267178) | 0.707602 / 4.584777 (-3.877175) | 3.369643 / 3.745712 (-0.376070) | 1.874517 / 5.269862 (-3.395344) | 1.248560 / 4.565676 (-3.317117) | 0.083247 / 0.424275 (-0.341028) | 0.012606 / 0.007607 (0.004999) | 0.519342 / 0.226044 (0.293297) | 5.225462 / 2.268929 (2.956533) | 2.433230 / 55.444624 (-53.011394) | 2.006005 / 6.876477 (-4.870471) | 2.093156 / 2.142072 (-0.048916) | 0.809372 / 4.805227 (-3.995855) | 0.151691 / 6.500664 (-6.348973) | 0.066680 / 0.075469 (-0.008789) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.226283 / 1.841788 (-0.615505) | 13.604338 / 8.074308 (5.530030) | 13.953245 / 10.191392 (3.761853) | 0.132904 / 0.680424 (-0.547520) | 0.016420 / 0.534201 (-0.517781) | 0.395316 / 0.579283 (-0.183967) | 0.385003 / 0.434364 (-0.049361) | 0.483303 / 0.540337 (-0.057034) | 0.578459 / 1.386936 (-0.808477) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006218 / 0.011353 (-0.005135) | 0.004451 / 0.011008 (-0.006557) | 0.076892 / 0.038508 (0.038384) | 0.027017 / 0.023109 (0.003908) | 0.356976 / 0.275898 (0.081078) | 0.396083 / 0.323480 (0.072603) | 0.005510 / 0.007986 (-0.002476) | 0.003265 / 0.004328 (-0.001063) | 0.075771 / 0.004250 (0.071521) | 0.037117 / 0.037052 (0.000064) | 0.362181 / 0.258489 (0.103692) | 0.401771 / 0.293841 (0.107931) | 0.032062 / 0.128546 (-0.096484) | 0.011453 / 0.075646 (-0.064194) | 0.085773 / 0.419271 (-0.333498) | 0.041679 / 0.043533 (-0.001854) | 0.355120 / 0.255139 (0.099981) | 0.390170 / 0.283200 (0.106970) | 0.088210 / 0.141683 (-0.053473) | 1.526434 / 1.452155 (0.074279) | 1.586019 / 1.492716 (0.093302) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.196836 / 0.018006 (0.178830) | 0.401161 / 0.000490 (0.400671) | 0.002880 / 0.000200 (0.002680) | 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) | 0.024445 / 0.037411 (-0.012966) | 0.100187 / 0.014526 (0.085661) | 0.106391 / 0.176557 (-0.070165) | 0.159764 / 0.737135 (-0.577372) | 0.109828 / 0.296338 (-0.186511) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.444228 / 0.215209 (0.229018) | 4.420769 / 2.077655 (2.343114) | 2.069437 / 1.504120 (0.565318) | 1.862587 / 1.541195 (0.321392) | 1.934627 / 1.468490 (0.466137) | 0.699681 / 4.584777 (-3.885095) | 3.352540 / 3.745712 (-0.393172) | 2.613172 / 5.269862 (-2.656689) | 1.445116 / 4.565676 (-3.120561) | 0.083086 / 0.424275 (-0.341189) | 0.012715 / 0.007607 (0.005108) | 0.537450 / 0.226044 (0.311405) | 5.403052 / 2.268929 (3.134123) | 2.506703 / 55.444624 (-52.937921) | 2.170198 / 6.876477 (-4.706279) | 2.201909 / 2.142072 (0.059837) | 0.799555 / 4.805227 (-4.005672) | 0.150825 / 6.500664 (-6.349839) | 0.067234 / 0.075469 (-0.008235) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.293097 / 1.841788 (-0.548691) | 13.817133 / 8.074308 (5.742825) | 14.247231 / 10.191392 (4.055839) | 0.128422 / 0.680424 (-0.552002) | 0.016541 / 0.534201 (-0.517660) | 0.382466 / 0.579283 (-0.196817) | 0.380560 / 0.434364 (-0.053804) | 0.439061 / 0.540337 (-0.101276) | 0.521865 / 1.386936 (-0.865071) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#69e60be438c334919f590512fd664436bd6b3667 \"CML watermark\")\n",
"I also took the liberty of removing `_hf_hub_fixes.py` completely :)\r\n\r\n> Do you think this is really necessary and convenient? I would naively say that 5% of the users is not a negligible number...\r\n\r\nI think it's ok. Most of them are using old versions of `datasets` anyway.\r\n\r\n",
"merging, but lmk if you have other concerns",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006810 / 0.011353 (-0.004543) | 0.004683 / 0.011008 (-0.006325) | 0.100889 / 0.038508 (0.062381) | 0.030135 / 0.023109 (0.007026) | 0.356407 / 0.275898 (0.080509) | 0.389175 / 0.323480 (0.065695) | 0.005358 / 0.007986 (-0.002627) | 0.004760 / 0.004328 (0.000432) | 0.075904 / 0.004250 (0.071654) | 0.040341 / 0.037052 (0.003288) | 0.357363 / 0.258489 (0.098874) | 0.394185 / 0.293841 (0.100344) | 0.031322 / 0.128546 (-0.097224) | 0.011636 / 0.075646 (-0.064010) | 0.327327 / 0.419271 (-0.091944) | 0.042494 / 0.043533 (-0.001039) | 0.338079 / 0.255139 (0.082940) | 0.363388 / 0.283200 (0.080189) | 0.087102 / 0.141683 (-0.054581) | 1.505686 / 1.452155 (0.053531) | 1.562112 / 1.492716 (0.069396) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203630 / 0.018006 (0.185624) | 0.425986 / 0.000490 (0.425496) | 0.003786 / 0.000200 (0.003586) | 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) | 0.024138 / 0.037411 (-0.013274) | 0.101752 / 0.014526 (0.087226) | 0.105436 / 0.176557 (-0.071121) | 0.165385 / 0.737135 (-0.571750) | 0.114510 / 0.296338 (-0.181828) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.447561 / 0.215209 (0.232352) | 4.449212 / 2.077655 (2.371557) | 2.169472 / 1.504120 (0.665352) | 1.989025 / 1.541195 (0.447831) | 2.036267 / 1.468490 (0.567776) | 0.698647 / 4.584777 (-3.886130) | 3.483281 / 3.745712 (-0.262431) | 1.949306 / 5.269862 (-3.320555) | 1.290313 / 4.565676 (-3.275363) | 0.083079 / 0.424275 (-0.341196) | 0.012759 / 0.007607 (0.005152) | 0.540944 / 0.226044 (0.314899) | 5.473391 / 2.268929 (3.204463) | 2.632037 / 55.444624 (-52.812587) | 2.327396 / 6.876477 (-4.549081) | 2.428880 / 2.142072 (0.286808) | 0.808918 / 4.805227 (-3.996309) | 0.153283 / 6.500664 (-6.347381) | 0.068325 / 0.075469 (-0.007145) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.212527 / 1.841788 (-0.629260) | 14.306444 / 8.074308 (6.232136) | 14.904980 / 10.191392 (4.713588) | 0.142796 / 0.680424 (-0.537628) | 0.016829 / 0.534201 (-0.517372) | 0.384806 / 0.579283 (-0.194477) | 0.390505 / 0.434364 (-0.043859) | 0.441734 / 0.540337 (-0.098603) | 0.526159 / 1.386936 (-0.860777) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated 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) | 0.004647 / 0.011008 (-0.006362) | 0.078925 / 0.038508 (0.040417) | 0.028081 / 0.023109 (0.004971) | 0.343420 / 0.275898 (0.067522) | 0.380567 / 0.323480 (0.057087) | 0.005286 / 0.007986 (-0.002700) | 0.004816 / 0.004328 (0.000487) | 0.077332 / 0.004250 (0.073081) | 0.042131 / 0.037052 (0.005078) | 0.345371 / 0.258489 (0.086882) | 0.390232 / 0.293841 (0.096392) | 0.032395 / 0.128546 (-0.096152) | 0.011669 / 0.075646 (-0.063978) | 0.087649 / 0.419271 (-0.331622) | 0.042465 / 0.043533 (-0.001068) | 0.342863 / 0.255139 (0.087724) | 0.368947 / 0.283200 (0.085748) | 0.091725 / 0.141683 (-0.049958) | 1.477435 / 1.452155 (0.025280) | 1.563449 / 1.492716 (0.070733) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208016 / 0.018006 (0.190010) | 0.428387 / 0.000490 (0.427898) | 0.000443 / 0.000200 (0.000243) | 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) | 0.026963 / 0.037411 (-0.010449) | 0.103854 / 0.014526 (0.089328) | 0.109068 / 0.176557 (-0.067488) | 0.160107 / 0.737135 (-0.577028) | 0.112843 / 0.296338 (-0.183496) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437161 / 0.215209 (0.221952) | 4.396178 / 2.077655 (2.318523) | 2.067597 / 1.504120 (0.563477) | 1.875247 / 1.541195 (0.334053) | 1.962451 / 1.468490 (0.493961) | 0.701427 / 4.584777 (-3.883350) | 3.459564 / 3.745712 (-0.286148) | 1.959482 / 5.269862 (-3.310380) | 1.191866 / 4.565676 (-3.373810) | 0.083243 / 0.424275 (-0.341032) | 0.012740 / 0.007607 (0.005133) | 0.535236 / 0.226044 (0.309191) | 5.351715 / 2.268929 (3.082786) | 2.490868 / 55.444624 (-52.953756) | 2.195680 / 6.876477 (-4.680797) | 2.233854 / 2.142072 (0.091781) | 0.809041 / 4.805227 (-3.996187) | 0.151498 / 6.500664 (-6.349166) | 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) | 1.303596 / 1.841788 (-0.538192) | 14.712746 / 8.074308 (6.638438) | 14.778412 / 10.191392 (4.587020) | 0.147093 / 0.680424 (-0.533331) | 0.017105 / 0.534201 (-0.517096) | 0.381687 / 0.579283 (-0.197596) | 0.402435 / 0.434364 (-0.031929) | 0.453538 / 0.540337 (-0.086800) | 0.538866 / 1.386936 (-0.848070) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#10f637c3a598c8042865b31f779e315a3da5337e \"CML watermark\")\n"
] | "2023-03-15T18:26:07Z" | "2023-03-20T12:34:09Z" | "2023-03-20T12:26:58Z" | MEMBER | null | 0 | {
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"merged_at": "2023-03-20T12:26:58Z",
"patch_url": "https://github.com/huggingface/datasets/pull/5642.patch",
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5642"
} | to fix errors like
```
requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://hub-ci.huggingface.co/api/datasets/__DUMMY_TRANSFORMERS_USER__/...
```
(e.g. from this [failing CI](https://github.com/huggingface/datasets/actions/runs/4428956210/jobs/7769160997))
0.11.0 is the current minimum version in `transformers`
around 5% of users are currently using versions `<0.11.0` | {
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https://api.github.com/repos/huggingface/datasets/issues/5641 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5641/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5641/comments | https://api.github.com/repos/huggingface/datasets/issues/5641/events | https://github.com/huggingface/datasets/issues/5641 | 1,625,942,730 | I_kwDODunzps5g6erK | 5,641 | Features cannot be named "self" | {
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} | [] | closed | false | null | [] | null | [] | "2023-03-15T17:16:40Z" | "2023-03-16T17:14:51Z" | "2023-03-16T17:14:51Z" | NONE | null | null | null | ### Describe the bug
Hi,
I noticed that we cannot create a HuggingFace dataset from Pandas DataFrame with a column named `self`.
The error seems to be coming from arguments validation in the `Features.from_dict` function.
### Steps to reproduce the bug
```python
import datasets
dummy_pandas = pd.DataFrame([0,1,2,3], columns = ["self"])
datasets.arrow_dataset.Dataset.from_pandas(dummy_pandas)
```
### Expected behavior
No error thrown
### Environment info
- `datasets` version: 2.8.0
- Python version: 3.9.5
- PyArrow version: 6.0.1
- Pandas version: 1.4.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/5640 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5640/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5640/comments | https://api.github.com/repos/huggingface/datasets/issues/5640/events | https://github.com/huggingface/datasets/pull/5640 | 1,625,896,057 | PR_kwDODunzps5MID3I | 5,640 | Less zip false positives | {
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} | [] | closed | false | null | [] | null | [
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006998 / 0.011353 (-0.004355) | 0.005093 / 0.011008 (-0.005916) | 0.100490 / 0.038508 (0.061982) | 0.032736 / 0.023109 (0.009627) | 0.297738 / 0.275898 (0.021840) | 0.322255 / 0.323480 (-0.001225) | 0.005583 / 0.007986 (-0.002402) | 0.004007 / 0.004328 (-0.000321) | 0.075863 / 0.004250 (0.071613) | 0.044212 / 0.037052 (0.007159) | 0.300033 / 0.258489 (0.041544) | 0.341997 / 0.293841 (0.048156) | 0.036172 / 0.128546 (-0.092374) | 0.012176 / 0.075646 (-0.063471) | 0.356052 / 0.419271 (-0.063220) | 0.050438 / 0.043533 (0.006905) | 0.294677 / 0.255139 (0.039538) | 0.318050 / 0.283200 (0.034850) | 0.104733 / 0.141683 (-0.036950) | 1.435681 / 1.452155 (-0.016474) | 1.534793 / 1.492716 (0.042076) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242815 / 0.018006 (0.224809) | 0.565983 / 0.000490 (0.565494) | 0.006800 / 0.000200 (0.006600) | 0.000124 / 0.000054 (0.000070) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026548 / 0.037411 (-0.010863) | 0.104816 / 0.014526 (0.090290) | 0.116222 / 0.176557 (-0.060335) | 0.172143 / 0.737135 (-0.564992) | 0.121631 / 0.296338 (-0.174707) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400126 / 0.215209 (0.184917) | 4.004538 / 2.077655 (1.926883) | 1.798822 / 1.504120 (0.294702) | 1.595191 / 1.541195 (0.053996) | 1.645777 / 1.468490 (0.177287) | 0.705643 / 4.584777 (-3.879134) | 3.750887 / 3.745712 (0.005175) | 2.136547 / 5.269862 (-3.133315) | 1.475881 / 4.565676 (-3.089795) | 0.086921 / 0.424275 (-0.337354) | 0.012379 / 0.007607 (0.004771) | 0.505824 / 0.226044 (0.279779) | 5.052364 / 2.268929 (2.783435) | 2.279983 / 55.444624 (-53.164641) | 1.932253 / 6.876477 (-4.944224) | 2.051359 / 2.142072 (-0.090714) | 0.851906 / 4.805227 (-3.953321) | 0.169566 / 6.500664 (-6.331098) | 0.064600 / 0.075469 (-0.010869) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.165859 / 1.841788 (-0.675929) | 15.049950 / 8.074308 (6.975642) | 14.095981 / 10.191392 (3.904589) | 0.151779 / 0.680424 (-0.528645) | 0.017537 / 0.534201 (-0.516664) | 0.420164 / 0.579283 (-0.159119) | 0.418932 / 0.434364 (-0.015432) | 0.488749 / 0.540337 (-0.051588) | 0.582359 / 1.386936 (-0.804577) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007426 / 0.011353 (-0.003927) | 0.005248 / 0.011008 (-0.005761) | 0.074118 / 0.038508 (0.035610) | 0.034223 / 0.023109 (0.011114) | 0.337780 / 0.275898 (0.061882) | 0.376300 / 0.323480 (0.052820) | 0.006142 / 0.007986 (-0.001843) | 0.004246 / 0.004328 (-0.000083) | 0.074177 / 0.004250 (0.069926) | 0.052698 / 0.037052 (0.015646) | 0.340229 / 0.258489 (0.081740) | 0.396172 / 0.293841 (0.102331) | 0.037293 / 0.128546 (-0.091253) | 0.012514 / 0.075646 (-0.063132) | 0.087144 / 0.419271 (-0.332128) | 0.051922 / 0.043533 (0.008390) | 0.333188 / 0.255139 (0.078049) | 0.355420 / 0.283200 (0.072220) | 0.110273 / 0.141683 (-0.031410) | 1.447826 / 1.452155 (-0.004329) | 1.561135 / 1.492716 (0.068419) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.269203 / 0.018006 (0.251197) | 0.551997 / 0.000490 (0.551508) | 0.001558 / 0.000200 (0.001359) | 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) | 0.029511 / 0.037411 (-0.007900) | 0.108614 / 0.014526 (0.094089) | 0.123438 / 0.176557 (-0.053118) | 0.171596 / 0.737135 (-0.565539) | 0.126828 / 0.296338 (-0.169511) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420520 / 0.215209 (0.205310) | 4.175672 / 2.077655 (2.098017) | 1.982220 / 1.504120 (0.478101) | 1.788575 / 1.541195 (0.247381) | 1.860840 / 1.468490 (0.392349) | 0.706730 / 4.584777 (-3.878047) | 3.858718 / 3.745712 (0.113005) | 3.069389 / 5.269862 (-2.200472) | 1.827603 / 4.565676 (-2.738073) | 0.087893 / 0.424275 (-0.336382) | 0.012613 / 0.007607 (0.005006) | 0.524177 / 0.226044 (0.298132) | 5.177077 / 2.268929 (2.908148) | 2.494397 / 55.444624 (-52.950227) | 2.189484 / 6.876477 (-4.686992) | 2.217626 / 2.142072 (0.075554) | 0.846326 / 4.805227 (-3.958901) | 0.176558 / 6.500664 (-6.324106) | 0.065018 / 0.075469 (-0.010451) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.268618 / 1.841788 (-0.573170) | 15.132711 / 8.074308 (7.058403) | 14.585530 / 10.191392 (4.394138) | 0.163454 / 0.680424 (-0.516970) | 0.017442 / 0.534201 (-0.516759) | 0.421746 / 0.579283 (-0.157537) | 0.425412 / 0.434364 (-0.008952) | 0.499178 / 0.540337 (-0.041159) | 0.595458 / 1.386936 (-0.791478) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ab77e58cd32413f4ef4828134a2470ebd53bb542 \"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) | 0.007980 / 0.011353 (-0.003373) | 0.005414 / 0.011008 (-0.005594) | 0.099226 / 0.038508 (0.060718) | 0.035442 / 0.023109 (0.012332) | 0.304851 / 0.275898 (0.028952) | 0.337144 / 0.323480 (0.013664) | 0.006162 / 0.007986 (-0.001823) | 0.004151 / 0.004328 (-0.000177) | 0.074708 / 0.004250 (0.070458) | 0.049690 / 0.037052 (0.012638) | 0.307658 / 0.258489 (0.049168) | 0.358472 / 0.293841 (0.064631) | 0.037181 / 0.128546 (-0.091365) | 0.012259 / 0.075646 (-0.063387) | 0.335426 / 0.419271 (-0.083846) | 0.050790 / 0.043533 (0.007257) | 0.301715 / 0.255139 (0.046576) | 0.320834 / 0.283200 (0.037634) | 0.102357 / 0.141683 (-0.039326) | 1.454750 / 1.452155 (0.002596) | 1.571994 / 1.492716 (0.079278) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218708 / 0.018006 (0.200702) | 0.444391 / 0.000490 (0.443901) | 0.005717 / 0.000200 (0.005517) | 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) | 0.028017 / 0.037411 (-0.009395) | 0.112753 / 0.014526 (0.098227) | 0.121003 / 0.176557 (-0.055554) | 0.181085 / 0.737135 (-0.556050) | 0.127211 / 0.296338 (-0.169127) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400803 / 0.215209 (0.185594) | 4.007315 / 2.077655 (1.929660) | 1.826911 / 1.504120 (0.322791) | 1.637799 / 1.541195 (0.096605) | 1.699754 / 1.468490 (0.231264) | 0.709413 / 4.584777 (-3.875364) | 4.008904 / 3.745712 (0.263192) | 3.916540 / 5.269862 (-1.353322) | 1.902102 / 4.565676 (-2.663575) | 0.089048 / 0.424275 (-0.335227) | 0.012763 / 0.007607 (0.005155) | 0.498957 / 0.226044 (0.272913) | 4.979865 / 2.268929 (2.710937) | 2.301987 / 55.444624 (-53.142637) | 1.929404 / 6.876477 (-4.947073) | 2.107839 / 2.142072 (-0.034233) | 0.857253 / 4.805227 (-3.947974) | 0.171935 / 6.500664 (-6.328729) | 0.066753 / 0.075469 (-0.008716) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.186811 / 1.841788 (-0.654977) | 15.866319 / 8.074308 (7.792011) | 14.738555 / 10.191392 (4.547163) | 0.142879 / 0.680424 (-0.537544) | 0.017679 / 0.534201 (-0.516522) | 0.422840 / 0.579283 (-0.156443) | 0.450307 / 0.434364 (0.015943) | 0.491802 / 0.540337 (-0.048536) | 0.588837 / 1.386936 (-0.798099) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007659 / 0.011353 (-0.003694) | 0.005331 / 0.011008 (-0.005678) | 0.075360 / 0.038508 (0.036852) | 0.034011 / 0.023109 (0.010902) | 0.354488 / 0.275898 (0.078590) | 0.401781 / 0.323480 (0.078301) | 0.005806 / 0.007986 (-0.002179) | 0.004029 / 0.004328 (-0.000300) | 0.073822 / 0.004250 (0.069572) | 0.049067 / 0.037052 (0.012015) | 0.364483 / 0.258489 (0.105994) | 0.405637 / 0.293841 (0.111796) | 0.037166 / 0.128546 (-0.091380) | 0.012397 / 0.075646 (-0.063249) | 0.087346 / 0.419271 (-0.331926) | 0.050888 / 0.043533 (0.007355) | 0.334796 / 0.255139 (0.079657) | 0.387681 / 0.283200 (0.104481) | 0.105056 / 0.141683 (-0.036627) | 1.471630 / 1.452155 (0.019475) | 1.554764 / 1.492716 (0.062047) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231825 / 0.018006 (0.213819) | 0.449746 / 0.000490 (0.449256) | 0.000888 / 0.000200 (0.000688) | 0.000078 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030363 / 0.037411 (-0.007049) | 0.115234 / 0.014526 (0.100708) | 0.123005 / 0.176557 (-0.053551) | 0.172772 / 0.737135 (-0.564363) | 0.127818 / 0.296338 (-0.168520) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425761 / 0.215209 (0.210552) | 4.237950 / 2.077655 (2.160295) | 1.992045 / 1.504120 (0.487925) | 1.801622 / 1.541195 (0.260427) | 1.918477 / 1.468490 (0.449987) | 0.722730 / 4.584777 (-3.862047) | 4.015968 / 3.745712 (0.270256) | 3.720412 / 5.269862 (-1.549450) | 1.763111 / 4.565676 (-2.802566) | 0.089041 / 0.424275 (-0.335234) | 0.012608 / 0.007607 (0.005001) | 0.522645 / 0.226044 (0.296601) | 5.227108 / 2.268929 (2.958180) | 2.444714 / 55.444624 (-52.999910) | 2.109745 / 6.876477 (-4.766732) | 2.194042 / 2.142072 (0.051969) | 0.871781 / 4.805227 (-3.933447) | 0.173149 / 6.500664 (-6.327515) | 0.066192 / 0.075469 (-0.009277) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.312051 / 1.841788 (-0.529737) | 16.024315 / 8.074308 (7.950007) | 15.123823 / 10.191392 (4.932431) | 0.163997 / 0.680424 (-0.516427) | 0.017595 / 0.534201 (-0.516606) | 0.426379 / 0.579283 (-0.152904) | 0.467709 / 0.434364 (0.033345) | 0.498308 / 0.540337 (-0.042030) | 0.591426 / 1.386936 (-0.795510) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#13488cc110b67090289794f48d5c84a4fd0c063a \"CML watermark\")\n",
"CI is failing due to unrelated issues, hopefully https://github.com/huggingface/datasets/pull/5642 fixes 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) | 0.006478 / 0.011353 (-0.004875) | 0.004347 / 0.011008 (-0.006661) | 0.097103 / 0.038508 (0.058595) | 0.027650 / 0.023109 (0.004541) | 0.372355 / 0.275898 (0.096457) | 0.408794 / 0.323480 (0.085314) | 0.005034 / 0.007986 (-0.002952) | 0.003252 / 0.004328 (-0.001076) | 0.074068 / 0.004250 (0.069818) | 0.035542 / 0.037052 (-0.001510) | 0.367392 / 0.258489 (0.108903) | 0.409644 / 0.293841 (0.115803) | 0.031745 / 0.128546 (-0.096801) | 0.011501 / 0.075646 (-0.064145) | 0.323355 / 0.419271 (-0.095917) | 0.043065 / 0.043533 (-0.000467) | 0.377313 / 0.255139 (0.122174) | 0.395326 / 0.283200 (0.112127) | 0.087101 / 0.141683 (-0.054582) | 1.461228 / 1.452155 (0.009073) | 1.529413 / 1.492716 (0.036696) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.199245 / 0.018006 (0.181239) | 0.409978 / 0.000490 (0.409488) | 0.002655 / 0.000200 (0.002455) | 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) | 0.023903 / 0.037411 (-0.013508) | 0.097855 / 0.014526 (0.083330) | 0.106405 / 0.176557 (-0.070152) | 0.166889 / 0.737135 (-0.570247) | 0.110256 / 0.296338 (-0.186082) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440351 / 0.215209 (0.225142) | 4.382848 / 2.077655 (2.305194) | 2.049602 / 1.504120 (0.545482) | 1.824638 / 1.541195 (0.283443) | 1.850519 / 1.468490 (0.382029) | 0.702652 / 4.584777 (-3.882125) | 3.394571 / 3.745712 (-0.351141) | 1.940608 / 5.269862 (-3.329254) | 1.263961 / 4.565676 (-3.301716) | 0.083985 / 0.424275 (-0.340290) | 0.013046 / 0.007607 (0.005439) | 0.538272 / 0.226044 (0.312228) | 5.407563 / 2.268929 (3.138634) | 2.519207 / 55.444624 (-52.925418) | 2.153379 / 6.876477 (-4.723098) | 2.394512 / 2.142072 (0.252439) | 0.812840 / 4.805227 (-3.992387) | 0.152868 / 6.500664 (-6.347796) | 0.067823 / 0.075469 (-0.007646) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.220031 / 1.841788 (-0.621757) | 13.781237 / 8.074308 (5.706929) | 14.203975 / 10.191392 (4.012583) | 0.141077 / 0.680424 (-0.539347) | 0.016518 / 0.534201 (-0.517682) | 0.379079 / 0.579283 (-0.200204) | 0.378916 / 0.434364 (-0.055448) | 0.434589 / 0.540337 (-0.105749) | 0.521129 / 1.386936 (-0.865807) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006997 / 0.011353 (-0.004356) | 0.004599 / 0.011008 (-0.006410) | 0.078700 / 0.038508 (0.040192) | 0.027902 / 0.023109 (0.004793) | 0.344406 / 0.275898 (0.068508) | 0.392918 / 0.323480 (0.069438) | 0.005175 / 0.007986 (-0.002811) | 0.004755 / 0.004328 (0.000427) | 0.077707 / 0.004250 (0.073457) | 0.039409 / 0.037052 (0.002357) | 0.343250 / 0.258489 (0.084761) | 0.405544 / 0.293841 (0.111703) | 0.032286 / 0.128546 (-0.096260) | 0.011674 / 0.075646 (-0.063972) | 0.087633 / 0.419271 (-0.331639) | 0.043346 / 0.043533 (-0.000186) | 0.355076 / 0.255139 (0.099937) | 0.382155 / 0.283200 (0.098955) | 0.090914 / 0.141683 (-0.050769) | 1.518369 / 1.452155 (0.066215) | 1.583530 / 1.492716 (0.090813) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.160369 / 0.018006 (0.142362) | 0.406844 / 0.000490 (0.406354) | 0.002651 / 0.000200 (0.002451) | 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) | 0.025295 / 0.037411 (-0.012116) | 0.101490 / 0.014526 (0.086964) | 0.108825 / 0.176557 (-0.067732) | 0.161673 / 0.737135 (-0.575462) | 0.113610 / 0.296338 (-0.182729) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.443514 / 0.215209 (0.228305) | 4.436722 / 2.077655 (2.359067) | 2.144008 / 1.504120 (0.639888) | 2.005324 / 1.541195 (0.464129) | 2.123356 / 1.468490 (0.654866) | 0.697217 / 4.584777 (-3.887560) | 3.401105 / 3.745712 (-0.344607) | 1.874621 / 5.269862 (-3.395240) | 1.165069 / 4.565676 (-3.400608) | 0.082799 / 0.424275 (-0.341476) | 0.012806 / 0.007607 (0.005199) | 0.542688 / 0.226044 (0.316644) | 5.420963 / 2.268929 (3.152034) | 2.579034 / 55.444624 (-52.865590) | 2.240201 / 6.876477 (-4.636276) | 2.261309 / 2.142072 (0.119237) | 0.800246 / 4.805227 (-4.004981) | 0.150380 / 6.500664 (-6.350285) | 0.066880 / 0.075469 (-0.008589) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.281721 / 1.841788 (-0.560067) | 13.906361 / 8.074308 (5.832053) | 14.135336 / 10.191392 (3.943944) | 0.128865 / 0.680424 (-0.551559) | 0.016452 / 0.534201 (-0.517749) | 0.373563 / 0.579283 (-0.205720) | 0.385321 / 0.434364 (-0.049043) | 0.437198 / 0.540337 (-0.103139) | 0.530720 / 1.386936 (-0.856216) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e2f8e17f3c8f8d0cb77a4c566a78e31fab47108c \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008099 / 0.011353 (-0.003254) | 0.005093 / 0.011008 (-0.005916) | 0.106258 / 0.038508 (0.067750) | 0.037051 / 0.023109 (0.013942) | 0.347960 / 0.275898 (0.072062) | 0.370849 / 0.323480 (0.047369) | 0.006122 / 0.007986 (-0.001863) | 0.004094 / 0.004328 (-0.000235) | 0.079549 / 0.004250 (0.075299) | 0.046563 / 0.037052 (0.009510) | 0.332735 / 0.258489 (0.074246) | 0.417061 / 0.293841 (0.123220) | 0.038105 / 0.128546 (-0.090441) | 0.011886 / 0.075646 (-0.063760) | 0.342103 / 0.419271 (-0.077169) | 0.053233 / 0.043533 (0.009700) | 0.344754 / 0.255139 (0.089615) | 0.355354 / 0.283200 (0.072155) | 0.101059 / 0.141683 (-0.040624) | 1.518561 / 1.452155 (0.066406) | 1.558652 / 1.492716 (0.065935) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225919 / 0.018006 (0.207913) | 0.518539 / 0.000490 (0.518049) | 0.006230 / 0.000200 (0.006030) | 0.000124 / 0.000054 (0.000070) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026782 / 0.037411 (-0.010629) | 0.108457 / 0.014526 (0.093931) | 0.125203 / 0.176557 (-0.051353) | 0.175726 / 0.737135 (-0.561409) | 0.127051 / 0.296338 (-0.169287) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.416427 / 0.215209 (0.201217) | 4.168851 / 2.077655 (2.091196) | 1.962238 / 1.504120 (0.458118) | 1.825224 / 1.541195 (0.284029) | 1.831200 / 1.468490 (0.362710) | 0.765526 / 4.584777 (-3.819250) | 4.303957 / 3.745712 (0.558245) | 2.193467 / 5.269862 (-3.076395) | 1.654605 / 4.565676 (-2.911071) | 0.096709 / 0.424275 (-0.327566) | 0.013792 / 0.007607 (0.006185) | 0.537862 / 0.226044 (0.311818) | 5.152230 / 2.268929 (2.883302) | 2.520938 / 55.444624 (-52.923686) | 2.108422 / 6.876477 (-4.768054) | 2.214220 / 2.142072 (0.072147) | 0.834320 / 4.805227 (-3.970907) | 0.170635 / 6.500664 (-6.330029) | 0.063131 / 0.075469 (-0.012338) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.215767 / 1.841788 (-0.626020) | 15.254781 / 8.074308 (7.180473) | 14.360764 / 10.191392 (4.169372) | 0.172511 / 0.680424 (-0.507913) | 0.020161 / 0.534201 (-0.514040) | 0.426936 / 0.579283 (-0.152347) | 0.438771 / 0.434364 (0.004407) | 0.486973 / 0.540337 (-0.053364) | 0.584238 / 1.386936 (-0.802698) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006777 / 0.011353 (-0.004576) | 0.005304 / 0.011008 (-0.005704) | 0.073717 / 0.038508 (0.035209) | 0.033604 / 0.023109 (0.010494) | 0.340448 / 0.275898 (0.064550) | 0.351861 / 0.323480 (0.028381) | 0.005786 / 0.007986 (-0.002199) | 0.005013 / 0.004328 (0.000685) | 0.071263 / 0.004250 (0.067012) | 0.048189 / 0.037052 (0.011137) | 0.339457 / 0.258489 (0.080968) | 0.384383 / 0.293841 (0.090542) | 0.035563 / 0.128546 (-0.092983) | 0.011509 / 0.075646 (-0.064137) | 0.083722 / 0.419271 (-0.335550) | 0.048886 / 0.043533 (0.005353) | 0.350184 / 0.255139 (0.095045) | 0.361037 / 0.283200 (0.077837) | 0.105191 / 0.141683 (-0.036492) | 1.503247 / 1.452155 (0.051093) | 1.582298 / 1.492716 (0.089581) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221687 / 0.018006 (0.203681) | 0.466489 / 0.000490 (0.465999) | 0.000484 / 0.000200 (0.000284) | 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) | 0.027978 / 0.037411 (-0.009434) | 0.119572 / 0.014526 (0.105047) | 0.133530 / 0.176557 (-0.043026) | 0.177892 / 0.737135 (-0.559243) | 0.127045 / 0.296338 (-0.169294) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430198 / 0.215209 (0.214989) | 4.435512 / 2.077655 (2.357858) | 2.007183 / 1.504120 (0.503063) | 1.799230 / 1.541195 (0.258036) | 1.884750 / 1.468490 (0.416260) | 0.745232 / 4.584777 (-3.839545) | 4.088069 / 3.745712 (0.342357) | 4.114669 / 5.269862 (-1.155193) | 2.374086 / 4.565676 (-2.191590) | 0.089154 / 0.424275 (-0.335121) | 0.012938 / 0.007607 (0.005331) | 0.505954 / 0.226044 (0.279909) | 5.194226 / 2.268929 (2.925298) | 2.487230 / 55.444624 (-52.957394) | 2.163353 / 6.876477 (-4.713124) | 2.177879 / 2.142072 (0.035807) | 0.828728 / 4.805227 (-3.976499) | 0.171157 / 6.500664 (-6.329507) | 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) | 1.275906 / 1.841788 (-0.565882) | 15.235484 / 8.074308 (7.161176) | 14.467396 / 10.191392 (4.276004) | 0.198994 / 0.680424 (-0.481430) | 0.020203 / 0.534201 (-0.513998) | 0.447904 / 0.579283 (-0.131380) | 0.454210 / 0.434364 (0.019846) | 0.528062 / 0.540337 (-0.012275) | 0.619311 / 1.386936 (-0.767625) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#11cd0f73acbce1d16174f2555e56fda511d5a08b \"CML watermark\")\n"
] | "2023-03-15T16:48:59Z" | "2023-03-16T13:47:37Z" | "2023-03-16T13:40:12Z" | MEMBER | null | 0 | {
"diff_url": "https://github.com/huggingface/datasets/pull/5640.diff",
"html_url": "https://github.com/huggingface/datasets/pull/5640",
"merged_at": "2023-03-16T13:40:12Z",
"patch_url": "https://github.com/huggingface/datasets/pull/5640.patch",
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5640"
} | `zipfile.is_zipfile` return false positives for some Parquet files. It causes errors when loading certain parquet datasets, where some files are considered ZIP files by `zipfile.is_zipfile`
This is a known issue: https://github.com/python/cpython/issues/72680
At first I wanted to rely only on magic numbers, but then I found that someone contributed a [fix to is_zipfile](https://github.com/python/cpython/pull/5053) - do you think we should use it @albertvillanova or not ?
IMO it's ok to rely on magic numbers only for now, since in streaming mode we've had no issue checking only the magic number so far.
Close https://github.com/huggingface/datasets/issues/5639 | {
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https://api.github.com/repos/huggingface/datasets/issues/5639 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5639/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5639/comments | https://api.github.com/repos/huggingface/datasets/issues/5639/events | https://github.com/huggingface/datasets/issues/5639 | 1,625,737,098 | I_kwDODunzps5g5seK | 5,639 | Parquet file wrongly recognized as zip prevents loading a dataset | {
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} | [] | closed | false | null | [] | null | [] | "2023-03-15T15:20:45Z" | "2023-03-16T13:40:14Z" | "2023-03-16T13:40:14Z" | CONTRIBUTOR | null | null | null | ### Describe the bug
When trying to `load_dataset_builder` for `HuggingFaceGECLM/StackExchange_Mar2023`, extraction fails, because parquet file [devops-00000-of-00001-22fe902fd8702892.parquet](https://huggingface.co/datasets/HuggingFaceGECLM/StackExchange_Mar2023/resolve/1f8c9a2ab6f7d0f9ae904b8b922e4384592ae1a5/data/devops-00000-of-00001-22fe902fd8702892.parquet) is wrongly identified by python as being a zip not a parquet.
(Full thread on [Slack](https://huggingface.slack.com/archives/C02V51Q3800/p1678890880803599))
### Steps to reproduce the bug
```python
from datasets import load_dataset_builder
ds = load_dataset_builder("HuggingFaceGECLM/StackExchange_Mar2023")
```
### Expected behavior
Loading the file normally.
### Environment info
- `datasets` version: 2.3.2
- Platform: Linux-5.14.0-1058-oem-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 8.0.0
- Pandas version: 1.4.3 | {
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" I think https://github.com/fsspec/universal_pathlib is the project you are looking for.\r\n\r\n`xPath` has the methods often used in dataset scripts, and `mkdir` is not one of them (`dl_manager`'s role is to \"interact\" with the file system, so using `mkdir` is discouraged).",
"Right is there a difference between UPath and xPath? Typically is xPath less well implemented compared to Upath, ie missing some implementations of some methods? Or are there methods in xPath that are not implemented with UPath?",
"`xPath` is an internal component (it doesn't have a leading underscore in the name, but it should) not meant to be used outside of `datasets`, and it's only tested on HTTP URLs, not S3.\r\n\r\n",
"Okay I understand that xPath won't support my usecase. What I was perhaps getting to is why not use UPath in `datasets` instead of `xPath` if UPath seems to have strictly more robust implementations.",
"It seems like `universal_pathlib` does not support `fsspec` URL chaining (`::` is the chaining symbol) and \"compression\" filesystems (e.g., `zip`), but this is what we need to access and stream files from within an archive (e.g., we want to stream URLs such as this one: `zip://data.parquet::https://www.dummyurl.com/archive.zip`)"
] | "2023-03-15T13:47:11Z" | "2023-03-17T13:21:12Z" | "2023-03-17T13:21:12Z" | CONTRIBUTOR | null | null | null | ### Feature request
Current xPath implementation is a great extension of Path in order to work with remote objects. However some methods such as `mkdir` are not implemented correctly. It should instead rely on `fsspec` methods, instead of defaulting do `Path` methods which only work locally.
### Motivation
I'm using xPath to interact with remote objects.
### Your contribution
I could try to make a PR. I'm a bit unfamiliar with chaining right now. | {
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"Hi! Yes, only `torch` is currently supported. Unlike `Dataset`, `IterableDataset` is not PyArrow-backed, so we cannot simply call `to_numpy` on the underlying subtables to format them numerically. Instead, we must manually convert examples to (numeric) arrays while preserving consistency with `Dataset`, which is not trivial, so this is still a to-do.",
"Any plans to support it in the future? Or would streaming dataset be left without support for jax and tensorflow?"
] | "2023-03-15T11:04:12Z" | "2023-03-16T18:30:59Z" | null | NONE | null | null | null | ### Describe the bug
As seen here: https://huggingface.co/docs/datasets/use_with_jax dataset.with_format() supports the keyword 'device', to put data on a specific device when loaded as jax. However, when called on an IterableDataset, I got the error `TypeError: with_format() got an unexpected keyword argument 'device'`
Looking over the code, it seems IterableDataset support only pytorch and no support for jax device keyword?
https://github.com/huggingface/datasets/blob/fc5c84f36684343bff3e424cb0fd1ac5ecdd66da/src/datasets/iterable_dataset.py#L1029
### Steps to reproduce the bug
1. Load an IterableDataset (tested in streaming mode)
2. Call with_format('jax',device=device)
### Expected behavior
I expect to call `with_format('jax', device=device)` as per [documentation](https://huggingface.co/docs/datasets/use_with_jax) without error
### Environment info
Tested with installing newest (dev) and also pip release (2.10.1).
- `datasets` version: 2.10.2.dev0
- Platform: Linux-5.15.89+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- Huggingface_hub version: 0.12.1
- PyArrow version: 11.0.0
- Pandas version: 1.3.5
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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) | 0.006529 / 0.011353 (-0.004824) | 0.004527 / 0.011008 (-0.006481) | 0.098051 / 0.038508 (0.059543) | 0.028058 / 0.023109 (0.004949) | 0.368543 / 0.275898 (0.092645) | 0.397126 / 0.323480 (0.073646) | 0.005072 / 0.007986 (-0.002913) | 0.003377 / 0.004328 (-0.000952) | 0.076867 / 0.004250 (0.072617) | 0.040121 / 0.037052 (0.003069) | 0.373422 / 0.258489 (0.114933) | 0.403969 / 0.293841 (0.110128) | 0.031485 / 0.128546 (-0.097061) | 0.011673 / 0.075646 (-0.063973) | 0.321837 / 0.419271 (-0.097434) | 0.042828 / 0.043533 (-0.000704) | 0.370391 / 0.255139 (0.115252) | 0.391737 / 0.283200 (0.108538) | 0.084764 / 0.141683 (-0.056919) | 1.463114 / 1.452155 (0.010959) | 1.527042 / 1.492716 (0.034325) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.200964 / 0.018006 (0.182958) | 0.403967 / 0.000490 (0.403477) | 0.002439 / 0.000200 (0.002239) | 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) | 0.023531 / 0.037411 (-0.013880) | 0.097424 / 0.014526 (0.082899) | 0.104854 / 0.176557 (-0.071703) | 0.165682 / 0.737135 (-0.571453) | 0.109416 / 0.296338 (-0.186922) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431041 / 0.215209 (0.215832) | 4.326039 / 2.077655 (2.248384) | 2.085123 / 1.504120 (0.581003) | 1.922720 / 1.541195 (0.381525) | 2.006608 / 1.468490 (0.538118) | 0.703348 / 4.584777 (-3.881428) | 3.441516 / 3.745712 (-0.304196) | 1.875244 / 5.269862 (-3.394618) | 1.181341 / 4.565676 (-3.384336) | 0.083442 / 0.424275 (-0.340833) | 0.012966 / 0.007607 (0.005359) | 0.536047 / 0.226044 (0.310002) | 5.354856 / 2.268929 (3.085927) | 2.451064 / 55.444624 (-52.993560) | 2.076110 / 6.876477 (-4.800367) | 2.196507 / 2.142072 (0.054435) | 0.811196 / 4.805227 (-3.994032) | 0.152547 / 6.500664 (-6.348118) | 0.067978 / 0.075469 (-0.007491) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.196169 / 1.841788 (-0.645618) | 13.697234 / 8.074308 (5.622926) | 13.966652 / 10.191392 (3.775260) | 0.143735 / 0.680424 (-0.536688) | 0.016484 / 0.534201 (-0.517717) | 0.382349 / 0.579283 (-0.196934) | 0.401507 / 0.434364 (-0.032857) | 0.447297 / 0.540337 (-0.093041) | 0.529779 / 1.386936 (-0.857157) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006698 / 0.011353 (-0.004655) | 0.004608 / 0.011008 (-0.006400) | 0.076220 / 0.038508 (0.037712) | 0.027340 / 0.023109 (0.004231) | 0.344095 / 0.275898 (0.068197) | 0.374715 / 0.323480 (0.051235) | 0.004883 / 0.007986 (-0.003102) | 0.004658 / 0.004328 (0.000330) | 0.075381 / 0.004250 (0.071130) | 0.036099 / 0.037052 (-0.000953) | 0.340382 / 0.258489 (0.081893) | 0.383488 / 0.293841 (0.089647) | 0.031534 / 0.128546 (-0.097012) | 0.011735 / 0.075646 (-0.063912) | 0.085895 / 0.419271 (-0.333377) | 0.042226 / 0.043533 (-0.001306) | 0.340301 / 0.255139 (0.085162) | 0.366079 / 0.283200 (0.082879) | 0.088828 / 0.141683 (-0.052854) | 1.487880 / 1.452155 (0.035725) | 1.561318 / 1.492716 (0.068601) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226366 / 0.018006 (0.208360) | 0.408934 / 0.000490 (0.408444) | 0.000396 / 0.000200 (0.000196) | 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) | 0.024521 / 0.037411 (-0.012891) | 0.100167 / 0.014526 (0.085641) | 0.106480 / 0.176557 (-0.070077) | 0.156377 / 0.737135 (-0.580758) | 0.111709 / 0.296338 (-0.184630) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436138 / 0.215209 (0.220928) | 4.370919 / 2.077655 (2.293265) | 2.066402 / 1.504120 (0.562282) | 1.862157 / 1.541195 (0.320962) | 1.920701 / 1.468490 (0.452211) | 0.695517 / 4.584777 (-3.889260) | 3.435558 / 3.745712 (-0.310154) | 1.864000 / 5.269862 (-3.405861) | 1.164134 / 4.565676 (-3.401543) | 0.083006 / 0.424275 (-0.341269) | 0.012751 / 0.007607 (0.005144) | 0.535405 / 0.226044 (0.309360) | 5.368530 / 2.268929 (3.099602) | 2.494197 / 55.444624 (-52.950427) | 2.161370 / 6.876477 (-4.715107) | 2.180345 / 2.142072 (0.038272) | 0.808076 / 4.805227 (-3.997151) | 0.151891 / 6.500664 (-6.348773) | 0.067643 / 0.075469 (-0.007826) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.334245 / 1.841788 (-0.507543) | 14.112805 / 8.074308 (6.038497) | 14.152303 / 10.191392 (3.960911) | 0.153492 / 0.680424 (-0.526932) | 0.016542 / 0.534201 (-0.517659) | 0.376013 / 0.579283 (-0.203270) | 0.386528 / 0.434364 (-0.047836) | 0.436461 / 0.540337 (-0.103876) | 0.519278 / 1.386936 (-0.867658) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ce1d1076fc55ac49277398304e551f0b56c3c9e2 \"CML watermark\")\n"
] | "2023-03-14T15:29:11Z" | "2023-03-14T16:37:06Z" | "2023-03-14T16:29:52Z" | CONTRIBUTOR | null | 0 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5635). All of your documentation changes will be reflected on that endpoint.",
"I'm not a big fan of this new param - I find assigning metadata files to splits via the `data_files` param cleaner. Also, assuming that the metadata filename is `metadata.json`/`metadata.csv` (I don't think we should allow other names), a user can do `load_dataset(\"imagefolder\", data_dir=\"data\")` to load a dataset with that structure.",
"@mariosasko I don't really like this change in it's current state either but passing specific files with `data_files` also looks not quite user-friendly to me. The idea of providing specific parameter for metadata filename seems natural to me but I don't see a way for implementing it without some ugly changes in `load.py` (passing the param to factories and creating metadata patterns on the fly). Why don't you like this parameter?\r\n\r\nFor context: this PR emerged from the case where users wanted to use different metadata files with the same large set of images without copying directories on disk and it's not possible with `data_files` approach.\r\n\r\nedit: ah no, it's possible if one puts metadata files in different subdirs (so that the filenames can be left the same)",
">For context: this PR emerged from the case where users wanted to use different metadata files with the same large set of images without copying directories on disk and it's not possible with data_files approach.\r\n>\r\n>edit: ah no, it's possible if one puts metadata files in different subdirs (so that the filenames can be left the same)\r\n\r\nSeems low prio, but one way to address this would be by allowing to pass \"exclude patterns\" to `data_files`"
] | "2023-03-14T15:08:16Z" | "2023-03-22T17:50:31Z" | null | CONTRIBUTOR | null | 1 | {
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} | This is a quick fix.
Now it requires to pass data via `data_files` parameters and include a required metadata file there and pass its filename as `metadata_filename` parameter.
For example, with the structure like:
```
data
images_dir/
im1.jpg
im2.jpg
...
metadata_dir/
meta_file1.jsonl
meta_file2.jsonl
...
```
to load data with `metadata_file1.jsonl` do:
```python
ds = load_dataset("imagefolder", data_files=["data/images_dir/**", "data/metadata_dir/meta_file1.jsonl"], metadata_filename="meta_file1.jsonl")
```
Note that if you have multiple splits, metadata file should be specified in each of them in `data_files`, smth like:
```python
data_files={
"train": ["data/train/**", "data/metadata_dir/meta_file1.jsonl"],
"test": ["data/train/**", "data/metadata_dir/meta_file1.jsonl"]
}
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/5634 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5634/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5634/comments | https://api.github.com/repos/huggingface/datasets/issues/5634/events | https://github.com/huggingface/datasets/issues/5634 | 1,622,424,174 | I_kwDODunzps5gtDpu | 5,634 | Not all progress bars are showing up when they should for downloading dataset | {
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"Hi! \r\n\r\nBy default, tqdm has `leave=True` to \"keep all traces of the progress bar upon the termination of iteration\". However, we use `leave=False` in some places (as of recently), which removes the bar once the iteration is over.\r\n\r\nI feel like our TQDM bars are noisy, so I think we should always set `leave=False` and also use the `delay` parameter to display progress bars only for tasks that take time (e.g., more than 3s). What do you think about this? Do you find these bars useful (after the dataset generation is over)?\r\n",
"Hi sorry for the late update. I think the problem still exists despite the `leave` flag\r\n\r\n<img width=\"1105\" alt=\"image\" src=\"https://user-images.githubusercontent.com/110427462/226501615-5b02fb02-fd5f-4eda-b1f7-a7ed6570892d.png\">\r\n\r\n\r\n```\r\nPackage Version\r\n------------------------ ---------\r\naiofiles 22.1.0\r\naiohttp 3.8.4\r\naiosignal 1.3.1\r\naiosqlite 0.18.0\r\nanyio 3.6.2\r\nappnope 0.1.3\r\nargon2-cffi 21.3.0\r\nargon2-cffi-bindings 21.2.0\r\narrow 1.2.3\r\nasttokens 2.2.1\r\nasync-generator 1.10\r\nasync-timeout 4.0.2\r\nattrs 22.2.0\r\nBabel 2.12.1\r\nbackcall 0.2.0\r\nbeautifulsoup4 4.11.2\r\nbleach 6.0.0\r\nbrotlipy 0.7.0\r\ncertifi 2022.12.7\r\ncffi 1.15.1\r\ncfgv 3.3.1\r\ncharset-normalizer 2.1.1\r\ncomm 0.1.2\r\nconda 22.9.0\r\nconda-package-handling 2.0.2\r\nconda_package_streaming 0.7.0\r\ncoverage 7.2.1\r\ncryptography 38.0.4\r\ndatasets 2.8.0\r\ndebugpy 1.6.6\r\ndecorator 5.1.1\r\ndefusedxml 0.7.1\r\ndill 0.3.6\r\ndistlib 0.3.6\r\ndistro 1.4.0\r\nentrypoints 0.4\r\nexceptiongroup 1.1.0\r\nexecuting 1.2.0\r\nfastjsonschema 2.16.3\r\nfilelock 3.9.0\r\nflaky 3.7.0\r\nfqdn 1.5.1\r\nfrozenlist 1.3.3\r\nfsspec 2023.3.0\r\nhuggingface-hub 0.10.1\r\nidentify 2.5.18\r\nidna 3.4\r\niniconfig 2.0.0\r\nipykernel 6.12.1\r\nipyparallel 8.4.1\r\nipython 7.32.0\r\nipython-genutils 0.2.0\r\nipywidgets 8.0.4\r\nisoduration 20.11.0\r\njedi 0.18.2\r\nJinja2 3.1.2\r\njson5 0.9.11\r\njsonpointer 2.3\r\njsonschema 4.17.3\r\njupyter_client 8.0.3\r\njupyter_core 5.2.0\r\njupyter-events 0.6.3\r\njupyter_server 2.4.0\r\njupyter_server_fileid 0.8.0\r\njupyter_server_terminals 0.4.4\r\njupyter_server_ydoc 0.6.1\r\njupyter-ydoc 0.2.2\r\njupyterlab 3.6.1\r\njupyterlab-pygments 0.2.2\r\njupyterlab_server 2.20.0\r\njupyterlab-widgets 3.0.5\r\nlibmambapy 1.1.0\r\nmamba 1.1.0\r\nMarkupSafe 2.1.2\r\nmatplotlib-inline 0.1.6\r\nmistune 2.0.5\r\nmultidict 6.0.4\r\nmultiprocess 0.70.14\r\nnbclassic 0.5.3\r\nnbclient 0.7.2\r\nnbconvert 7.2.9\r\nnbformat 5.7.3\r\nnest-asyncio 1.5.6\r\nnodeenv 1.7.0\r\nnotebook 6.5.3\r\nnotebook_shim 0.2.2\r\nnumpy 1.24.2\r\noutcome 1.2.0\r\npackaging 23.0\r\npandas 1.5.3\r\npandocfilters 1.5.0\r\nparso 0.8.3\r\npexpect 4.8.0\r\npickleshare 0.7.5\r\npip 22.3.1\r\nplatformdirs 3.0.0\r\nplotly 5.13.1\r\npluggy 1.0.0\r\npre-commit 3.1.0\r\nprometheus-client 0.16.0\r\nprompt-toolkit 3.0.38\r\npsutil 5.9.4\r\nptyprocess 0.7.0\r\npure-eval 0.2.2\r\npyarrow 11.0.0\r\npycosat 0.6.4\r\npycparser 2.21\r\nPygments 2.14.0\r\npyOpenSSL 22.1.0\r\npyrsistent 0.19.3\r\nPySocks 1.7.1\r\npytest 7.2.1\r\npytest-asyncio 0.20.3\r\npytest-cov 4.0.0\r\npytest-timeout 2.1.0\r\npython-dateutil 2.8.2\r\npython-json-logger 2.0.7\r\npytz 2022.7.1\r\nPyYAML 6.0\r\npyzmq 25.0.0\r\nrequests 2.28.1\r\nresponses 0.18.0\r\nrfc3339-validator 0.1.4\r\nrfc3986-validator 0.1.1\r\nruamel-yaml-conda 0.15.80\r\nSend2Trash 1.8.0\r\nsetuptools 65.6.3\r\nsimplegeneric 0.8.1\r\nsix 1.16.0\r\nsniffio 1.3.0\r\nsortedcontainers 2.4.0\r\nsoupsieve 2.4\r\nstack-data 0.6.2\r\ntenacity 8.2.2\r\nterminado 0.17.1\r\ntinycss2 1.2.1\r\ntomli 2.0.1\r\ntoolz 0.12.0\r\ntornado 6.2\r\ntqdm 4.65.0\r\ntraitlets 5.8.1\r\ntrio 0.22.0\r\ntyping_extensions 4.5.0\r\nuri-template 1.2.0\r\nurllib3 1.26.13\r\nvirtualenv 20.19.0\r\nwcwidth 0.2.6\r\nwebcolors 1.12\r\nwebencodings 0.5.1\r\nwebsocket-client 1.5.1\r\nwheel 0.38.4\r\nwidgetsnbextension 4.0.5\r\nxxhash 3.2.0\r\ny-py 0.5.9\r\nyarl 1.8.2\r\nypy-websocket 0.8.2\r\nzstandard 0.19.0\r\n```\r\n\r\nAny idea why this is happening? I debugged this to know the tqdm.pbar value is not being updated properly and its not the kernel not sending the comm messages to the IProgress bar"
] | "2023-03-13T23:04:18Z" | "2023-03-21T01:59:59Z" | null | NONE | null | null | null | ### Describe the bug
During downloading the rotten tomatoes dataset, not all progress bars are displayed properly. This might be related to [this ticket](https://github.com/huggingface/datasets/issues/5117) as it raised the same concern but its not clear if the fix solves this issue too.
ipywidgets
<img width="1243" alt="image" src="https://user-images.githubusercontent.com/110427462/224851138-13fee5b7-ab51-4883-b96f-1b9808782e3b.png">
tqdm
<img width="1251" alt="Screen Shot 2023-03-13 at 3 58 59 PM" src="https://user-images.githubusercontent.com/110427462/224851180-5feb7825-9250-4b1e-ad0c-f3172ac1eb78.png">
### Steps to reproduce the bug
1. Run this line
```
from datasets import load_dataset
rotten_tomatoes = load_dataset("rotten_tomatoes", split="train")
```
### Expected behavior
all progress bars for builder script, metadata, readme, training, validation, and test set
### Environment info
requirements.txt
```
aiofiles==22.1.0
aiohttp==3.8.4
aiosignal==1.3.1
aiosqlite==0.18.0
anyio==3.6.2
appnope==0.1.3
argon2-cffi==21.3.0
argon2-cffi-bindings==21.2.0
arrow==1.2.3
asttokens==2.2.1
async-generator==1.10
async-timeout==4.0.2
attrs==22.2.0
Babel==2.12.1
backcall==0.2.0
beautifulsoup4==4.11.2
bleach==6.0.0
brotlipy @ file:///Users/runner/miniforge3/conda-bld/brotlipy_1666764961872/work
certifi==2022.12.7
cffi @ file:///Users/runner/miniforge3/conda-bld/cffi_1671179414629/work
cfgv==3.3.1
charset-normalizer @ file:///home/conda/feedstock_root/build_artifacts/charset-normalizer_1661170624537/work
comm==0.1.2
conda==22.9.0
conda-package-handling @ file:///home/conda/feedstock_root/build_artifacts/conda-package-handling_1669907009957/work
conda_package_streaming @ file:///home/conda/feedstock_root/build_artifacts/conda-package-streaming_1669733752472/work
coverage==7.2.1
cryptography @ file:///Users/runner/miniforge3/conda-bld/cryptography_1669592251328/work
datasets==2.1.0
debugpy==1.6.6
decorator==5.1.1
defusedxml==0.7.1
dill==0.3.6
distlib==0.3.6
distro==1.4.0
entrypoints==0.4
exceptiongroup==1.1.0
executing==1.2.0
fastjsonschema==2.16.3
filelock==3.9.0
flaky==3.7.0
fqdn==1.5.1
frozenlist==1.3.3
fsspec==2023.3.0
huggingface-hub==0.10.1
identify==2.5.18
idna @ file:///home/conda/feedstock_root/build_artifacts/idna_1663625384323/work
iniconfig==2.0.0
ipykernel==6.12.1
ipyparallel==8.4.1
ipython==7.32.0
ipython-genutils==0.2.0
ipywidgets==8.0.4
isoduration==20.11.0
jedi==0.18.2
Jinja2==3.1.2
json5==0.9.11
jsonpointer==2.3
jsonschema==4.17.3
jupyter-events==0.6.3
jupyter-ydoc==0.2.2
jupyter_client==8.0.3
jupyter_core==5.2.0
jupyter_server==2.4.0
jupyter_server_fileid==0.8.0
jupyter_server_terminals==0.4.4
jupyter_server_ydoc==0.6.1
jupyterlab==3.6.1
jupyterlab-pygments==0.2.2
jupyterlab-widgets==3.0.5
jupyterlab_server==2.20.0
libmambapy @ file:///Users/runner/miniforge3/conda-bld/mamba-split_1671598370072/work/libmambapy
mamba @ file:///Users/runner/miniforge3/conda-bld/mamba-split_1671598370072/work/mamba
MarkupSafe==2.1.2
matplotlib-inline==0.1.6
mistune==2.0.5
multidict==6.0.4
multiprocess==0.70.14
nbclassic==0.5.3
nbclient==0.7.2
nbconvert==7.2.9
nbformat==5.7.3
nest-asyncio==1.5.6
nodeenv==1.7.0
notebook==6.5.3
notebook_shim==0.2.2
numpy==1.24.2
outcome==1.2.0
packaging==23.0
pandas==1.5.3
pandocfilters==1.5.0
parso==0.8.3
pexpect==4.8.0
pickleshare==0.7.5
platformdirs==3.0.0
plotly==5.13.1
pluggy==1.0.0
pre-commit==3.1.0
prometheus-client==0.16.0
prompt-toolkit==3.0.38
psutil==5.9.4
ptyprocess==0.7.0
pure-eval==0.2.2
pyarrow==11.0.0
pycosat @ file:///Users/runner/miniforge3/conda-bld/pycosat_1666836580084/work
pycparser @ file:///home/conda/feedstock_root/build_artifacts/pycparser_1636257122734/work
Pygments==2.14.0
pyOpenSSL @ file:///home/conda/feedstock_root/build_artifacts/pyopenssl_1665350324128/work
pyrsistent==0.19.3
PySocks @ file:///home/conda/feedstock_root/build_artifacts/pysocks_1661604839144/work
pytest==7.2.1
pytest-asyncio==0.20.3
pytest-cov==4.0.0
pytest-timeout==2.1.0
python-dateutil==2.8.2
python-json-logger==2.0.7
pytz==2022.7.1
PyYAML==6.0
pyzmq==25.0.0
requests @ file:///home/conda/feedstock_root/build_artifacts/requests_1661872987712/work
responses==0.18.0
rfc3339-validator==0.1.4
rfc3986-validator==0.1.1
ruamel-yaml-conda @ file:///Users/runner/miniforge3/conda-bld/ruamel_yaml_1666819760545/work
Send2Trash==1.8.0
simplegeneric==0.8.1
six==1.16.0
sniffio==1.3.0
sortedcontainers==2.4.0
soupsieve==2.4
stack-data==0.6.2
tenacity==8.2.2
terminado==0.17.1
tinycss2==1.2.1
tomli==2.0.1
toolz @ file:///home/conda/feedstock_root/build_artifacts/toolz_1657485559105/work
tornado==6.2
tqdm==4.64.1
traitlets==5.8.1
trio==0.22.0
typing_extensions==4.5.0
uri-template==1.2.0
urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1669259737463/work
virtualenv==20.19.0
wcwidth==0.2.6
webcolors==1.12
webencodings==0.5.1
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xxhash==3.2.0
y-py==0.5.9
yarl==1.8.2
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zstandard==0.19.0
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"Okay, the issue was likely caused by mixing `conda` and `pip` usage - I forgot that I have already used `pip` in this environment previously and that it was 'spoiled' because of it. Creating another environment and installing `datasets` by pip with other packages from the `requirements.txt` file solved the problem."
] | "2023-03-13T13:14:44Z" | "2023-03-13T17:54:19Z" | "2023-03-13T17:54:19Z" | NONE | null | null | null | ### Describe the bug
Hi,
I cannot even import the library :( I installed it by running:
```
$ conda install datasets
```
Then I realized I should maybe use the huggingface channel, because I encountered the error below, so I ran:
```
$ conda remove datasets
$ conda install -c huggingface datasets
```
Please see 'steps to reproduce the bug' for the specific error, as steps to reproduce is just importing the library
### Steps to reproduce the bug
```
$ python3
Python 3.8.15 (default, Nov 24 2022, 15:19:38)
[GCC 11.2.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import datasets
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/jack/.conda/envs/jack_zpp/lib/python3.8/site-packages/datasets/__init__.py", line 33, in <module>
from .arrow_dataset import Dataset, concatenate_datasets
File "/home/jack/.conda/envs/jack_zpp/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 59, in <module>
from .arrow_reader import ArrowReader
File "/home/jack/.conda/envs/jack_zpp/lib/python3.8/site-packages/datasets/arrow_reader.py", line 27, in <module>
import pyarrow.parquet as pq
File "/home/jack/.conda/envs/jack_zpp/lib/python3.8/site-packages/pyarrow/parquet/__init__.py", line 20, in <module>
from .core import *
File "/home/jack/.conda/envs/jack_zpp/lib/python3.8/site-packages/pyarrow/parquet/core.py", line 37, in <module>
from pyarrow._parquet import (ParquetReader, Statistics, # noqa
ImportError: cannot import name 'FileEncryptionProperties' from 'pyarrow._parquet' (/home/jack/.conda/envs/jack_zpp/lib/python3.8/site-packages/pyarrow/_parquet.cpython-38-x86_64-linux-gnu.so)
```
### Expected behavior
I would expect for the statement `import datasets` to cause no error
### Environment info
Output of `conda list`:
```
# packages in environment at /home/jack/.conda/envs/pbalawender_zpp:
#
# Name Version Build Channel
_libgcc_mutex 0.1 main
_openmp_mutex 5.1 1_gnu
abseil-cpp 20210324.2 h2531618_0
advertools 0.13.2 pypi_0 pypi
aiofiles 0.8.0 pypi_0 pypi
aiohttp 3.8.3 py38h5eee18b_0
aiosignal 1.2.0 pyhd3eb1b0_0
aiosqlite 0.17.0 pypi_0 pypi
anyio 3.6.2 pypi_0 pypi
aquirdturtle-collapsible-headings 3.1.0 pypi_0 pypi
argon2-cffi 21.3.0 pypi_0 pypi
argon2-cffi-bindings 21.2.0 pypi_0 pypi
arrow 1.2.3 pypi_0 pypi
arrow-cpp 3.0.0 py38h6b21186_4
asttokens 2.2.0 pypi_0 pypi
async-timeout 4.0.2 py38h06a4308_0
attrs 22.1.0 py38h06a4308_0
automat 22.10.0 pypi_0 pypi
aws-c-common 0.4.57 he6710b0_1
aws-c-event-stream 0.1.6 h2531618_5
aws-checksums 0.1.9 he6710b0_0
aws-sdk-cpp 1.8.185 hce553d0_0
babel 2.11.0 pypi_0 pypi
backcall 0.2.0 pyhd3eb1b0_0
beautifulsoup4 4.11.1 pypi_0 pypi
blas 1.0 mkl
bleach 5.0.1 pypi_0 pypi
boost-cpp 1.73.0 h27cfd23_11
bottleneck 1.3.5 py38h7deecbd_0
brotli 1.0.9 h5eee18b_7
brotli-bin 1.0.9 h5eee18b_7
brotlipy 0.7.0 py38h27cfd23_1003
bzip2 1.0.8 h7b6447c_0
c-ares 1.18.1 h7f8727e_0
ca-certificates 2023.01.10 h06a4308_0
certifi 2022.9.24 pypi_0 pypi
cffi 1.15.1 py38h5eee18b_3
charset-normalizer 2.1.1 pypi_0 pypi
click 8.1.3 pypi_0 pypi
constantly 15.1.0 pypi_0 pypi
contourpy 1.0.6 pypi_0 pypi
cryptography 38.0.4 pypi_0 pypi
cssselect 1.2.0 pypi_0 pypi
cudatoolkit 10.1.243 h8cb64d8_10 conda-forge
cycler 0.11.0 pypi_0 pypi
dacite 1.6.0 pypi_0 pypi
dataclasses 0.8 pyh6d0b6a4_7
datasets 1.18.4 py_0 huggingface
datetime 4.7 pypi_0 pypi
debugpy 1.6.4 pypi_0 pypi
decorator 5.1.1 pyhd3eb1b0_0
defusedxml 0.7.1 pypi_0 pypi
dill 0.3.6 py38h06a4308_0
docker-pycreds 0.4.0 pypi_0 pypi
double-conversion 3.1.5 he6710b0_1
entrypoints 0.4 py38h06a4308_0
executing 0.8.3 pyhd3eb1b0_0
filelock 3.8.0 pypi_0 pypi
flake8 6.0.0 pypi_0 pypi
flask 2.1.3 py38h06a4308_0
flit-core 3.6.0 pyhd3eb1b0_0
fonttools 4.38.0 pypi_0 pypi
fqdn 1.5.1 pypi_0 pypi
freetype 2.12.1 h4a9f257_0
frozenlist 1.3.3 py38h5eee18b_0
fsspec 2022.11.0 py38h06a4308_0
gensim 4.2.0 pypi_0 pypi
gflags 2.2.2 he6710b0_0
giflib 5.2.1 h5eee18b_3
gitdb 4.0.10 pypi_0 pypi
gitpython 3.1.30 pypi_0 pypi
glog 0.5.0 h2531618_0
grpc-cpp 1.39.0 hae934f6_5
huggingface-hub 0.11.1 pypi_0 pypi
huggingface_hub 0.13.1 py_0 huggingface
hyperlink 21.0.0 pypi_0 pypi
icu 58.2 he6710b0_3
idna 3.4 py38h06a4308_0
importlib-metadata 5.1.0 pypi_0 pypi
importlib_metadata 4.11.3 hd3eb1b0_0
importlib_resources 5.2.0 pyhd3eb1b0_1
incremental 22.10.0 pypi_0 pypi
intel-openmp 2021.4.0 h06a4308_3561
ipykernel 6.17.1 pyh210e3f2_0 conda-forge
ipython 8.7.0 pypi_0 pypi
ipython-genutils 0.2.0 pypi_0 pypi
ipywidgets 8.0.2 pyhd8ed1ab_1 conda-forge
isoduration 20.11.0 pypi_0 pypi
itemadapter 0.7.0 pypi_0 pypi
itemloaders 1.0.6 pypi_0 pypi
itsdangerous 2.0.1 pyhd3eb1b0_0
jedi 0.18.2 pypi_0 pypi
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joblib 1.2.0 pypi_0 pypi
jpeg 9b h024ee3a_2
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jsonpickle 3.0.0 pypi_0 pypi
jsonpointer 2.3 pypi_0 pypi
jsonschema 4.17.3 py38h06a4308_0
jupyter-core 5.1.0 pypi_0 pypi
jupyter-events 0.5.0 pypi_0 pypi
jupyter-server 1.23.3 pypi_0 pypi
jupyter-server-fileid 0.6.0 pypi_0 pypi
jupyter-server-ydoc 0.4.0 pypi_0 pypi
jupyter-ydoc 0.2.2 pypi_0 pypi
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jupyterlab 3.6.0a4 pypi_0 pypi
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kiwisolver 1.4.4 pypi_0 pypi
krb5 1.19.4 h568e23c_0
lcms2 2.12 h3be6417_0
ld_impl_linux-64 2.38 h1181459_1
libboost 1.73.0 h3ff78a5_11
libbrotlicommon 1.0.9 h5eee18b_7
libbrotlidec 1.0.9 h5eee18b_7
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libcurl 7.88.1 h91b91d3_0
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libev 4.33 h7f8727e_1
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libsodium 1.0.18 h7b6447c_0
libssh2 1.10.0 h8f2d780_0
libstdcxx-ng 11.2.0 h1234567_1
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libwebp 1.2.0 h89dd481_0
lz4-c 1.9.4 h6a678d5_0
markupsafe 2.1.1 py38h7f8727e_0
matplotlib 3.6.2 pypi_0 pypi
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mccabe 0.7.0 pypi_0 pypi
mistune 2.0.4 pypi_0 pypi
mkl 2021.4.0 h06a4308_640
mkl-service 2.4.0 py38h7f8727e_0
mkl_fft 1.3.1 py38hd3c417c_0
mkl_random 1.2.2 py38h51133e4_0
morfeusz2 1.99.6 pypi_0 pypi
multidict 6.0.2 py38h5eee18b_0
multiprocess 0.70.14 py38h06a4308_0
nbclassic 0.4.8 pypi_0 pypi
nbclient 0.7.2 pypi_0 pypi
nbconvert 7.2.5 pypi_0 pypi
nbformat 5.7.0 py38h06a4308_0
ncurses 6.4 h6a678d5_0
nest-asyncio 1.5.6 py38h06a4308_0
ninja 1.10.2 h06a4308_5
ninja-base 1.10.2 hd09550d_5
notebook 6.5.2 pypi_0 pypi
notebook-shim 0.2.2 pypi_0 pypi
numexpr 2.8.4 py38he184ba9_0
numpy 1.23.5 py38h14f4228_0
numpy-base 1.23.5 py38h31eccc5_0
oauthlib 3.2.2 pypi_0 pypi
opencv-python 4.6.0.66 pypi_0 pypi
openssl 1.1.1t h7f8727e_0
orc 1.6.9 ha97a36c_3
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pandas 1.5.2 pypi_0 pypi
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pip 22.2.2 py38h06a4308_0
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sentry-sdk 1.12.1 pypi_0 pypi
service-identity 21.1.0 pypi_0 pypi
setproctitle 1.3.2 pypi_0 pypi
setuptools 65.6.3 pypi_0 pypi
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six 1.16.0 pyhd3eb1b0_1
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smmap 5.0.0 pypi_0 pypi
snappy 1.1.9 h295c915_0
sniffio 1.3.0 pypi_0 pypi
soupsieve 2.3.2.post1 pypi_0 pypi
sqlite 3.40.1 h5082296_0
stack-data 0.6.2 pypi_0 pypi
stack_data 0.2.0 pyhd3eb1b0_0
terminado 0.17.0 pypi_0 pypi
threadpoolctl 3.1.0 pypi_0 pypi
tinycss2 1.2.1 pypi_0 pypi
tk 8.6.12 h1ccaba5_0
tldextract 3.4.0 pypi_0 pypi
tokenizers 0.13.2 pypi_0 pypi
tomli 2.0.1 pypi_0 pypi
torchvision 0.8.2 py38_cu101 pytorch
tornado 6.2 py38h5eee18b_0
tqdm 4.64.1 py38h06a4308_0
traitlets 5.6.0 pypi_0 pypi
transformers 4.25.1 pypi_0 pypi
tweepy 4.12.1 pypi_0 pypi
twisted 22.10.0 pypi_0 pypi
twython 3.9.1 pypi_0 pypi
typing-extensions 4.4.0 py38h06a4308_0
typing_extensions 4.4.0 py38h06a4308_0
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w3lib 2.1.0 pypi_0 pypi
wandb 0.13.7 pypi_0 pypi
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websocket-client 1.4.2 pypi_0 pypi
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xxhash 0.8.0 h7f8727e_3
xz 5.2.10 h5eee18b_1
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"Answered on the forum:\r\n\r\n> To fix the overflow error, we need to merge [support LargeListArray in pyarrow by xwwwwww · Pull Request #4800 · huggingface/datasets · GitHub](https://github.com/huggingface/datasets/pull/4800), which adds support for the large lists. However, before merging it, we need to come up with a cleaner API for large lists. I hope to find some time to address this before Datasets 3.0."
] | "2023-03-13T10:14:40Z" | "2023-03-16T15:28:57Z" | null | NONE | null | null | null | ### Describe the bug
Hello everyone!
I tried to build a new dataset with the command "dict_valid = datasets.Dataset.from_dict({'input_values': values_array})".
However, I have a very large dataset (~400Go) and it seems that dataset cannot handle this.
Indeed, I can create the dataset until a certain size of my dictionnary, and then I have the error "OverflowError: Python int too large to convert to C long".
Do you know how to solve this problem?
Unfortunately I cannot give a reproductible code because I cannot share a so large file, but you can find the code below (it's a test on only a part of the validation data ~10Go, but it's already the case).
Thank you!
### Steps to reproduce the bug
SAVE_DIR = './data/'
features = h5py.File(SAVE_DIR+'features.hdf5','r')
valid_data = features["validation"]["data/features"]
v_array_values = [np.float32(item[()]) for item in valid_data.values()]
for i in range(len(v_array_values)):
v_array_values[i] = v_array_values[i].round(decimals=5)
dict_valid = datasets.Dataset.from_dict({'input_values': v_array_values})
### Expected behavior
The code is expected to give me a Huggingface dataset.
### Environment info
python: 3.8.15
numpy: 1.22.3
datasets: 2.3.2
pyarrow: 8.0.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5631 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5631/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5631/comments | https://api.github.com/repos/huggingface/datasets/issues/5631/events | https://github.com/huggingface/datasets/issues/5631 | 1,620,442,854 | I_kwDODunzps5glf7m | 5,631 | Custom split names | {
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"id": 1935892871,
"name": "enhancement",
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] | closed | false | null | [] | null | [
"Hi!\r\n\r\nYou can also use names other than \"train\", \"validation\" and \"test\". As an example, check the [script](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/blob/e095840f23f3dffc1056c078c2f9320dad9ca74d/common_voice_11_0.py#L139) of the Common Voice 11 dataset. "
] | "2023-03-12T17:21:43Z" | "2023-03-24T14:13:00Z" | "2023-03-24T14:13:00Z" | NONE | null | null | null | ### Feature request
Hi,
I participated in multiple NLP tasks where there are more than just train, test, validation splits, there could be multiple validation sets or test sets. But it seems currently only those mentioned three splits supported. It would be nice to have the support for more splits on the hub. (currently i can have more splits when I am loading datasets from urls, but not hub)
### Motivation
Easier access to more splits
### Your contribution
No | {
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https://api.github.com/repos/huggingface/datasets/issues/5630 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5630/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5630/comments | https://api.github.com/repos/huggingface/datasets/issues/5630/events | https://github.com/huggingface/datasets/pull/5630 | 1,620,327,510 | PR_kwDODunzps5L1ahF | 5,630 | adds early exit if url is `PathLike` | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5630). All of your documentation changes will be reflected on that endpoint."
] | "2023-03-12T11:23:28Z" | "2023-03-15T11:58:38Z" | null | NONE | null | 0 | {
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} | Closes #4864
Should fix errors thrown when attempting to load `json` dataset using `pathlib.Path` in `data_files` argument. | {
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https://api.github.com/repos/huggingface/datasets/issues/5629 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5629/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5629/comments | https://api.github.com/repos/huggingface/datasets/issues/5629/events | https://github.com/huggingface/datasets/issues/5629 | 1,619,921,247 | I_kwDODunzps5gjglf | 5,629 | load_dataset gives "403" error when using Financial phrasebank | {
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} | [] | open | false | null | [] | null | [
"Hi! You seem to be using an outdated version of `datasets` that downloads the older script version. To avoid the error, you can either pass `revision=\"main\"` to `load_dataset` (this can fail if a script uses newer features of the lib) or update your installation with `pip install -U datasets` (better solution)."
] | "2023-03-11T07:46:39Z" | "2023-03-13T18:27:26Z" | null | NONE | null | null | null | When I try to load this dataset, I receive the following error:
ConnectionError: Couldn't reach https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip (error 403)
Has this been seen before? Thanks. The website loads when I try to access it manually. | {
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https://api.github.com/repos/huggingface/datasets/issues/5628 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5628/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5628/comments | https://api.github.com/repos/huggingface/datasets/issues/5628/events | https://github.com/huggingface/datasets/pull/5628 | 1,619,641,810 | PR_kwDODunzps5LzVKi | 5,628 | add kwargs to index search | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | "2023-03-10T21:24:58Z" | "2023-03-15T14:48:47Z" | "2023-03-15T14:46:04Z" | CONTRIBUTOR | null | 0 | {
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} | This PR proposes to add kwargs to index search methods.
This is particularly useful for setting the timeout of a query on elasticsearch.
A typical use case would be:
```python
dset.add_elasticsearch_index("filename", es_client=es_client)
scores, examples = dset.get_nearest_examples("filename", "my_name-train_29", request_timeout=60)
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/5627 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5627/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5627/comments | https://api.github.com/repos/huggingface/datasets/issues/5627/events | https://github.com/huggingface/datasets/issues/5627 | 1,619,336,609 | I_kwDODunzps5ghR2h | 5,627 | Unable to load AutoTrain-generated dataset from the hub | {
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"The AutoTrain format is not supported right now. I think it would require a dedicated dataset builder",
"Okay, good to know. Thanks for the reply. For now I will just have to\nmanage the split manually before training, because I can’t find any way of\npulling out file indices or file names from the autogenerated split. The\nfile names field of the image dataset (loaded directly from arrow file) is\nmissing, just fyi (for anyone else this might be relevant too).\n\nOn Fri, Mar 10, 2023 at 7:02 PM Quentin Lhoest ***@***.***>\nwrote:\n\n> The AutoTrain format is not supported right now. I think it would require\n> a dedicated dataset builder\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/5627#issuecomment-1464734308>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ACBJ4F5A353MCZ76OGRJ6CTW3PFI7ANCNFSM6AAAAAAVWXNUTE>\n> .\n> You are receiving this because you authored the thread.Message ID:\n> ***@***.***>\n>\n"
] | "2023-03-10T17:25:58Z" | "2023-03-11T15:44:42Z" | null | NONE | null | null | null | ### Describe the bug
DatasetGenerationError: An error occurred while generating the dataset -> ValueError: Couldn't cast ... because column names don't match
```
ValueError: Couldn't cast
_data_files: list<item: struct<filename: string>>
child 0, item: struct<filename: string>
child 0, filename: string
_fingerprint: string
_format_columns: list<item: string>
child 0, item: string
_format_kwargs: struct<>
_format_type: null
_indexes: struct<>
_output_all_columns: bool
_split: null
to
{'citation': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'features': {'image': {'_type': Value(dtype='string', id=None)}, 'target': {'names': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), '_type': Value(dtype='string', id=None)}}, 'homepage': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'splits': {'train': {'name': Value(dtype='string', id=None), 'num_bytes': Value(dtype='int64', id=None), 'num_examples': Value(dtype='int64', id=None), 'dataset_name': Value(dtype='null', id=None)}}}
because column names don't match
```
### Steps to reproduce the bug
Steps to reproduce:
1. `pip install datasets==2.10.1`
2. Attempt to load (private dataset). Note that I'm authenticated via ` huggingface-cli login`
```
from datasets import load_dataset
# load dataset
dataset = "ijmiller2/autotrain-data-betterbin-vision-10000"
dataset = load_dataset(dataset)
```
Here's the full traceback:
```Downloading and preparing dataset json/ijmiller2--autotrain-data-betterbin-vision-10000 to /Users/ian/.cache/huggingface/datasets/ijmiller2___json/ijmiller2--autotrain-data-betterbin-vision-10000-2eae034a9ff8a1a9/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51...
Downloading data files: 100%|███████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2383.80it/s]
Extracting data files: 100%|█████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 505.95it/s]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:1874, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1868 writer = writer_class(
1869 features=writer._features,
1870 path=fpath.replace("SSSSS", f"{shard_id:05d}").replace("JJJJJ", f"{job_id:05d}"),
1871 storage_options=self._fs.storage_options,
1872 embed_local_files=embed_local_files,
1873 )
-> 1874 writer.write_table(table)
1875 num_examples_progress_update += len(table)
File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/arrow_writer.py:568, in ArrowWriter.write_table(self, pa_table, writer_batch_size)
567 pa_table = pa_table.combine_chunks()
--> 568 pa_table = table_cast(pa_table, self._schema)
569 if self.embed_local_files:
File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/table.py:2312, in table_cast(table, schema)
2311 if table.schema != schema:
-> 2312 return cast_table_to_schema(table, schema)
2313 elif table.schema.metadata != schema.metadata:
File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/table.py:2270, in cast_table_to_schema(table, schema)
2269 if sorted(table.column_names) != sorted(features):
-> 2270 raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nbecause column names don't match")
2271 arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
ValueError: Couldn't cast
_data_files: list<item: struct<filename: string>>
child 0, item: struct<filename: string>
child 0, filename: string
_fingerprint: string
_format_columns: list<item: string>
child 0, item: string
_format_kwargs: struct<>
_format_type: null
_indexes: struct<>
_output_all_columns: bool
_split: null
to
{'citation': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'features': {'image': {'_type': Value(dtype='string', id=None)}, 'target': {'names': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), '_type': Value(dtype='string', id=None)}}, 'homepage': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'splits': {'train': {'name': Value(dtype='string', id=None), 'num_bytes': Value(dtype='int64', id=None), 'num_examples': Value(dtype='int64', id=None), 'dataset_name': Value(dtype='null', id=None)}}}
because column names don't match
The above exception was the direct cause of the following exception:
DatasetGenerationError Traceback (most recent call last)
Input In [8], in <cell line: 6>()
4 # load dataset
5 dataset = "ijmiller2/autotrain-data-betterbin-vision-10000"
----> 6 dataset = load_dataset(dataset)
File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/load.py:1782, 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, **config_kwargs)
1779 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1781 # Download and prepare data
-> 1782 builder_instance.download_and_prepare(
1783 download_config=download_config,
1784 download_mode=download_mode,
1785 verification_mode=verification_mode,
1786 try_from_hf_gcs=try_from_hf_gcs,
1787 num_proc=num_proc,
1788 )
1790 # Build dataset for splits
1791 keep_in_memory = (
1792 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1793 )
File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:872, 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)
870 if num_proc is not None:
871 prepare_split_kwargs["num_proc"] = num_proc
--> 872 self._download_and_prepare(
873 dl_manager=dl_manager,
874 verification_mode=verification_mode,
875 **prepare_split_kwargs,
876 **download_and_prepare_kwargs,
877 )
878 # Sync info
879 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:967, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs)
963 split_dict.add(split_generator.split_info)
965 try:
966 # Prepare split will record examples associated to the split
--> 967 self._prepare_split(split_generator, **prepare_split_kwargs)
968 except OSError as e:
969 raise OSError(
970 "Cannot find data file. "
971 + (self.manual_download_instructions or "")
972 + "\nOriginal error:\n"
973 + str(e)
974 ) from None
File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:1749, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, num_proc, max_shard_size)
1747 job_id = 0
1748 with pbar:
-> 1749 for job_id, done, content in self._prepare_split_single(
1750 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
1751 ):
1752 if done:
1753 result = content
File ~/anaconda3/envs/betterbin/lib/python3.8/site-packages/datasets/builder.py:1892, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1890 if isinstance(e, SchemaInferenceError) and e.__context__ is not None:
1891 e = e.__context__
-> 1892 raise DatasetGenerationError("An error occurred while generating the dataset") from e
1894 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)
DatasetGenerationError: An error occurred while generating the dataset
```
### Expected behavior
I'm ultimately trying to generate my own performance metrics on validation data (before putting an endpoint into production) and so was hoping to load all or at least the validation subset from the hub.
I'm expecting the `load_dataset()` function to work as shown in the documentation [here](https://huggingface.co/docs/datasets/loading#hugging-face-hub):
```python
dataset = load_dataset(
"lhoestq/custom_squad",
revision="main" # tag name, or branch name, or commit hash
)
```
### Environment info
- `datasets` version: 2.10.1
- Platform: macOS-13.2.1-arm64-arm-64bit
- Python version: 3.8.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.4 | {
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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) | 0.006607 / 0.011353 (-0.004746) | 0.004610 / 0.011008 (-0.006398) | 0.100673 / 0.038508 (0.062165) | 0.027739 / 0.023109 (0.004630) | 0.326290 / 0.275898 (0.050392) | 0.344296 / 0.323480 (0.020816) | 0.005021 / 0.007986 (-0.002964) | 0.003327 / 0.004328 (-0.001002) | 0.077779 / 0.004250 (0.073529) | 0.040237 / 0.037052 (0.003185) | 0.308992 / 0.258489 (0.050503) | 0.355017 / 0.293841 (0.061176) | 0.031203 / 0.128546 (-0.097343) | 0.011749 / 0.075646 (-0.063898) | 0.327431 / 0.419271 (-0.091840) | 0.043033 / 0.043533 (-0.000500) | 0.309713 / 0.255139 (0.054574) | 0.336550 / 0.283200 (0.053351) | 0.084891 / 0.141683 (-0.056792) | 1.555641 / 1.452155 (0.103487) | 1.613214 / 1.492716 (0.120497) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216269 / 0.018006 (0.198262) | 0.422066 / 0.000490 (0.421576) | 0.004055 / 0.000200 (0.003855) | 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) | 0.023759 / 0.037411 (-0.013652) | 0.096937 / 0.014526 (0.082411) | 0.105312 / 0.176557 (-0.071244) | 0.167840 / 0.737135 (-0.569295) | 0.107998 / 0.296338 (-0.188340) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.458315 / 0.215209 (0.243106) | 4.584803 / 2.077655 (2.507148) | 2.193641 / 1.504120 (0.689521) | 1.981494 / 1.541195 (0.440299) | 2.020358 / 1.468490 (0.551868) | 0.696763 / 4.584777 (-3.888014) | 3.388432 / 3.745712 (-0.357280) | 3.335038 / 5.269862 (-1.934823) | 1.648551 / 4.565676 (-2.917126) | 0.083753 / 0.424275 (-0.340522) | 0.012855 / 0.007607 (0.005248) | 0.562331 / 0.226044 (0.336286) | 5.649259 / 2.268929 (3.380330) | 2.680309 / 55.444624 (-52.764315) | 2.319297 / 6.876477 (-4.557180) | 2.444016 / 2.142072 (0.301943) | 0.809821 / 4.805227 (-3.995407) | 0.152855 / 6.500664 (-6.347809) | 0.067756 / 0.075469 (-0.007713) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.213318 / 1.841788 (-0.628470) | 13.887822 / 8.074308 (5.813514) | 14.276325 / 10.191392 (4.084933) | 0.156227 / 0.680424 (-0.524197) | 0.016377 / 0.534201 (-0.517824) | 0.377080 / 0.579283 (-0.202203) | 0.386561 / 0.434364 (-0.047803) | 0.435631 / 0.540337 (-0.104707) | 0.520863 / 1.386936 (-0.866073) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated 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) | 0.004704 / 0.011008 (-0.006304) | 0.076840 / 0.038508 (0.038331) | 0.027519 / 0.023109 (0.004409) | 0.343219 / 0.275898 (0.067321) | 0.376810 / 0.323480 (0.053330) | 0.005048 / 0.007986 (-0.002938) | 0.003356 / 0.004328 (-0.000972) | 0.077098 / 0.004250 (0.072848) | 0.038601 / 0.037052 (0.001548) | 0.345723 / 0.258489 (0.087233) | 0.388635 / 0.293841 (0.094794) | 0.033612 / 0.128546 (-0.094934) | 0.011689 / 0.075646 (-0.063957) | 0.086446 / 0.419271 (-0.332825) | 0.044390 / 0.043533 (0.000857) | 0.343763 / 0.255139 (0.088624) | 0.368591 / 0.283200 (0.085392) | 0.091605 / 0.141683 (-0.050078) | 1.478615 / 1.452155 (0.026461) | 1.580858 / 1.492716 (0.088142) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223547 / 0.018006 (0.205541) | 0.411243 / 0.000490 (0.410753) | 0.000916 / 0.000200 (0.000716) | 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) | 0.025223 / 0.037411 (-0.012189) | 0.100970 / 0.014526 (0.086445) | 0.108178 / 0.176557 (-0.068378) | 0.156827 / 0.737135 (-0.580308) | 0.111431 / 0.296338 (-0.184907) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434168 / 0.215209 (0.218959) | 4.361874 / 2.077655 (2.284219) | 2.060735 / 1.504120 (0.556615) | 1.861100 / 1.541195 (0.319906) | 1.920692 / 1.468490 (0.452202) | 0.697909 / 4.584777 (-3.886868) | 3.477036 / 3.745712 (-0.268676) | 3.002469 / 5.269862 (-2.267392) | 1.449325 / 4.565676 (-3.116351) | 0.083034 / 0.424275 (-0.341241) | 0.012805 / 0.007607 (0.005198) | 0.531391 / 0.226044 (0.305347) | 5.323015 / 2.268929 (3.054086) | 2.488605 / 55.444624 (-52.956020) | 2.158254 / 6.876477 (-4.718222) | 2.189633 / 2.142072 (0.047560) | 0.805972 / 4.805227 (-3.999256) | 0.153105 / 6.500664 (-6.347559) | 0.068909 / 0.075469 (-0.006561) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.276851 / 1.841788 (-0.564937) | 14.431510 / 8.074308 (6.357202) | 14.544788 / 10.191392 (4.353396) | 0.146589 / 0.680424 (-0.533835) | 0.016890 / 0.534201 (-0.517311) | 0.379897 / 0.579283 (-0.199387) | 0.389153 / 0.434364 (-0.045211) | 0.440097 / 0.540337 (-0.100241) | 0.524191 / 1.386936 (-0.862745) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e1af108015e43f9df8734a1faeeaeb9eafce3971 \"CML watermark\")\n"
] | "2023-03-10T16:33:39Z" | "2023-03-21T06:36:05Z" | "2023-03-21T06:28:54Z" | MEMBER | null | 0 | {
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See: https://huggingface.co/datasets/qgallouedec/gia_dataset/discussions/1 | {
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"You can use \"json\" instead. It doesn't work by extension names, but rather by dataset builder names, e.g. \"text\", \"imagefolder\", etc. I don't think the example in `transformers` is correct because of that",
"Yes, I understand the reasoning but this issue is to propose that the example in transformers (while incorrect) \"makes sense\" in terms of user expectation. So the question is whether it would be possible to add \"aliases\" for common types (like \"json\" and \"text\") based on common extensions (like jsonl and txt)?"
] | "2023-03-10T13:21:48Z" | "2023-03-11T10:35:39Z" | null | CONTRIBUTOR | null | null | null | ### Feature request
`load_dataset` currently does not accept `jsonl` as type but only `json`.
### Motivation
I was working with one of the `run_translation` scripts and used my own datasets (`.jsonl`) as train_dataset. But the default code did not work because
```
FileNotFoundError: Couldn't find a dataset script at jsonl\jsonl.py or any data file in the same directory. Couldn't find 'jsonl' on the Hugging Face Hub either: FileNotFoundError: Dataset 'jsonl' doesn't exist on the Hub. If the repo is private or gated, make sure to log in with `huggingface-cli login`.
```
The reason is because the script has these lines to extract the data type by its extension. Therefore, the derived type is `jsonl` which is not recognized by datasets as the error above shows.
https://github.com/huggingface/transformers/blob/ade26bf9912f69e2110137443e4406d7dbe253e7/examples/pytorch/translation/run_translation.py#L342-L356
I suppose you could argue that this is the script's fault (in which case I'll do a PR over at `transformers`) but it makes sense to me to add `jsonl` as an alias to `json` in `datasets`.
### Your contribution
At the moment I cannot work on this. I think it can be as "easy" as having an alias for json, namely jsonl. | {
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"Hi @lithafnium, thanks for reporting.\r\n\r\nPlease note that you can use the \"Community\" tab in the corresponding dataset page to start any discussion: https://huggingface.co/datasets/glue/discussions\r\n\r\nIndeed this issue was already raised there (https://huggingface.co/datasets/glue/discussions/5) and answered: https://huggingface.co/datasets/glue/discussions/5#63907885937867f0cb3cde31\r\n> The test labels are not public.\r\n>\r\n> Note this dataset belongs to a benchmark: people send their predictions for the test split to GLUE (https://gluebenchmark.com/) and then they get a score in their leaderboard...\r\n"
] | "2023-03-09T14:47:18Z" | "2023-03-09T16:49:29Z" | "2023-03-09T16:49:29Z" | NONE | null | null | null | ### Describe the bug
Downloading any dataset from GLUE has -1 as class labels for test split. Train and validation have regular 0/1 class labels. This is also present in the dataset card online.
### Steps to reproduce the bug
```
dataset = load_dataset("glue", "sst2")
for d in dataset:
# prints out -1
print(d["label"]
```
### Expected behavior
Expected behavior should be 0/1 instead of -1.
### Environment info
- `datasets` version: 2.4.0
- Platform: Linux-5.15.0-46-generic-x86_64-with-glibc2.17
- Python version: 3.8.16
- PyArrow version: 8.0.0
- Pandas version: 1.5.3
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008505 / 0.011353 (-0.002848) | 0.004445 / 0.011008 (-0.006563) | 0.102197 / 0.038508 (0.063689) | 0.029886 / 0.023109 (0.006776) | 0.305387 / 0.275898 (0.029489) | 0.355986 / 0.323480 (0.032507) | 0.006814 / 0.007986 (-0.001172) | 0.003298 / 0.004328 (-0.001030) | 0.079204 / 0.004250 (0.074954) | 0.035618 / 0.037052 (-0.001434) | 0.320430 / 0.258489 (0.061941) | 0.353330 / 0.293841 (0.059490) | 0.033280 / 0.128546 (-0.095266) | 0.011300 / 0.075646 (-0.064347) | 0.324627 / 0.419271 (-0.094644) | 0.040405 / 0.043533 (-0.003128) | 0.308760 / 0.255139 (0.053621) | 0.331885 / 0.283200 (0.048685) | 0.084605 / 0.141683 (-0.057077) | 1.576598 / 1.452155 (0.124443) | 1.530694 / 1.492716 (0.037977) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.191142 / 0.018006 (0.173136) | 0.404042 / 0.000490 (0.403552) | 0.001185 / 0.000200 (0.000985) | 0.000074 / 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) | 0.022889 / 0.037411 (-0.014523) | 0.095862 / 0.014526 (0.081336) | 0.104382 / 0.176557 (-0.072175) | 0.139407 / 0.737135 (-0.597728) | 0.106813 / 0.296338 (-0.189525) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419083 / 0.215209 (0.203874) | 4.188702 / 2.077655 (2.111047) | 1.897854 / 1.504120 (0.393734) | 1.689544 / 1.541195 (0.148350) | 1.714032 / 1.468490 (0.245542) | 0.695541 / 4.584777 (-3.889236) | 3.370584 / 3.745712 (-0.375128) | 3.205549 / 5.269862 (-2.064313) | 1.641202 / 4.565676 (-2.924474) | 0.081849 / 0.424275 (-0.342426) | 0.012043 / 0.007607 (0.004436) | 0.529618 / 0.226044 (0.303574) | 5.314167 / 2.268929 (3.045238) | 2.357271 / 55.444624 (-53.087353) | 1.979684 / 6.876477 (-4.896793) | 2.030057 / 2.142072 (-0.112015) | 0.813013 / 4.805227 (-3.992214) | 0.150165 / 6.500664 (-6.350499) | 0.064595 / 0.075469 (-0.010874) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.237824 / 1.841788 (-0.603964) | 13.552178 / 8.074308 (5.477870) | 14.089433 / 10.191392 (3.898041) | 0.149325 / 0.680424 (-0.531099) | 0.028543 / 0.534201 (-0.505658) | 0.396848 / 0.579283 (-0.182435) | 0.396230 / 0.434364 (-0.038134) | 0.466317 / 0.540337 (-0.074021) | 0.539579 / 1.386936 (-0.847357) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006224 / 0.011353 (-0.005128) | 0.004429 / 0.011008 (-0.006579) | 0.075740 / 0.038508 (0.037232) | 0.026717 / 0.023109 (0.003608) | 0.341685 / 0.275898 (0.065787) | 0.383671 / 0.323480 (0.060191) | 0.004682 / 0.007986 (-0.003304) | 0.004681 / 0.004328 (0.000352) | 0.076638 / 0.004250 (0.072387) | 0.034577 / 0.037052 (-0.002476) | 0.341160 / 0.258489 (0.082671) | 0.407590 / 0.293841 (0.113749) | 0.031121 / 0.128546 (-0.097425) | 0.011479 / 0.075646 (-0.064167) | 0.085299 / 0.419271 (-0.333973) | 0.042005 / 0.043533 (-0.001528) | 0.339682 / 0.255139 (0.084543) | 0.377669 / 0.283200 (0.094469) | 0.087751 / 0.141683 (-0.053932) | 1.523910 / 1.452155 (0.071756) | 1.607487 / 1.492716 (0.114771) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225605 / 0.018006 (0.207599) | 0.395851 / 0.000490 (0.395361) | 0.004404 / 0.000200 (0.004204) | 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) | 0.024489 / 0.037411 (-0.012922) | 0.099813 / 0.014526 (0.085287) | 0.107392 / 0.176557 (-0.069165) | 0.139567 / 0.737135 (-0.597568) | 0.110080 / 0.296338 (-0.186258) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.449051 / 0.215209 (0.233841) | 4.463098 / 2.077655 (2.385443) | 2.122548 / 1.504120 (0.618428) | 1.913863 / 1.541195 (0.372669) | 1.963988 / 1.468490 (0.495498) | 0.698442 / 4.584777 (-3.886335) | 3.330425 / 3.745712 (-0.415287) | 1.867843 / 5.269862 (-3.402019) | 1.163740 / 4.565676 (-3.401937) | 0.083209 / 0.424275 (-0.341066) | 0.012594 / 0.007607 (0.004987) | 0.547074 / 0.226044 (0.321030) | 5.474779 / 2.268929 (3.205851) | 2.548025 / 55.444624 (-52.896599) | 2.202435 / 6.876477 (-4.674041) | 2.220330 / 2.142072 (0.078257) | 0.810104 / 4.805227 (-3.995124) | 0.151141 / 6.500664 (-6.349523) | 0.066204 / 0.075469 (-0.009265) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.272075 / 1.841788 (-0.569712) | 13.749523 / 8.074308 (5.675215) | 14.270974 / 10.191392 (4.079582) | 0.141285 / 0.680424 (-0.539139) | 0.016526 / 0.534201 (-0.517675) | 0.393175 / 0.579283 (-0.186109) | 0.391577 / 0.434364 (-0.042787) | 0.492824 / 0.540337 (-0.047513) | 0.580069 / 1.386936 (-0.806867) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1cda14136c9f79c763c17d49b77eabfb233fbb35 \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008901 / 0.011353 (-0.002452) | 0.005017 / 0.011008 (-0.005991) | 0.099340 / 0.038508 (0.060832) | 0.034218 / 0.023109 (0.011109) | 0.295927 / 0.275898 (0.020029) | 0.330087 / 0.323480 (0.006607) | 0.008041 / 0.007986 (0.000056) | 0.005013 / 0.004328 (0.000685) | 0.074255 / 0.004250 (0.070004) | 0.049634 / 0.037052 (0.012582) | 0.299972 / 0.258489 (0.041483) | 0.349879 / 0.293841 (0.056038) | 0.038500 / 0.128546 (-0.090047) | 0.011980 / 0.075646 (-0.063666) | 0.332408 / 0.419271 (-0.086863) | 0.048385 / 0.043533 (0.004852) | 0.300393 / 0.255139 (0.045254) | 0.316972 / 0.283200 (0.033772) | 0.101674 / 0.141683 (-0.040009) | 1.424300 / 1.452155 (-0.027854) | 1.520658 / 1.492716 (0.027942) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.270084 / 0.018006 (0.252078) | 0.538612 / 0.000490 (0.538123) | 0.004439 / 0.000200 (0.004240) | 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) | 0.026841 / 0.037411 (-0.010570) | 0.106454 / 0.014526 (0.091928) | 0.118371 / 0.176557 (-0.058186) | 0.155545 / 0.737135 (-0.581590) | 0.125119 / 0.296338 (-0.171220) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.395794 / 0.215209 (0.180585) | 3.958195 / 2.077655 (1.880540) | 1.789010 / 1.504120 (0.284890) | 1.601380 / 1.541195 (0.060186) | 1.641062 / 1.468490 (0.172572) | 0.679547 / 4.584777 (-3.905230) | 3.778018 / 3.745712 (0.032306) | 2.101232 / 5.269862 (-3.168630) | 1.463932 / 4.565676 (-3.101745) | 0.083639 / 0.424275 (-0.340636) | 0.012339 / 0.007607 (0.004732) | 0.498708 / 0.226044 (0.272663) | 4.995178 / 2.268929 (2.726249) | 2.272650 / 55.444624 (-53.171975) | 1.907879 / 6.876477 (-4.968598) | 2.012666 / 2.142072 (-0.129407) | 0.829564 / 4.805227 (-3.975663) | 0.165049 / 6.500664 (-6.335615) | 0.062291 / 0.075469 (-0.013178) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.193977 / 1.841788 (-0.647811) | 14.816939 / 8.074308 (6.742631) | 14.369729 / 10.191392 (4.178337) | 0.156339 / 0.680424 (-0.524084) | 0.029151 / 0.534201 (-0.505050) | 0.449362 / 0.579283 (-0.129921) | 0.451895 / 0.434364 (0.017531) | 0.520324 / 0.540337 (-0.020013) | 0.610716 / 1.386936 (-0.776220) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007145 / 0.011353 (-0.004207) | 0.005299 / 0.011008 (-0.005710) | 0.074216 / 0.038508 (0.035708) | 0.033015 / 0.023109 (0.009906) | 0.337117 / 0.275898 (0.061219) | 0.367161 / 0.323480 (0.043682) | 0.005898 / 0.007986 (-0.002088) | 0.005283 / 0.004328 (0.000955) | 0.073795 / 0.004250 (0.069544) | 0.049253 / 0.037052 (0.012201) | 0.343327 / 0.258489 (0.084838) | 0.396417 / 0.293841 (0.102576) | 0.037162 / 0.128546 (-0.091384) | 0.012456 / 0.075646 (-0.063191) | 0.086668 / 0.419271 (-0.332604) | 0.049937 / 0.043533 (0.006404) | 0.335138 / 0.255139 (0.079999) | 0.358111 / 0.283200 (0.074912) | 0.107328 / 0.141683 (-0.034355) | 1.482290 / 1.452155 (0.030135) | 1.557872 / 1.492716 (0.065156) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.343759 / 0.018006 (0.325752) | 0.542697 / 0.000490 (0.542207) | 0.025943 / 0.000200 (0.025743) | 0.000264 / 0.000054 (0.000209) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028469 / 0.037411 (-0.008943) | 0.108620 / 0.014526 (0.094094) | 0.123667 / 0.176557 (-0.052890) | 0.168829 / 0.737135 (-0.568306) | 0.125875 / 0.296338 (-0.170464) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424640 / 0.215209 (0.209431) | 4.227611 / 2.077655 (2.149956) | 2.003605 / 1.504120 (0.499486) | 1.810696 / 1.541195 (0.269501) | 1.882700 / 1.468490 (0.414210) | 0.701361 / 4.584777 (-3.883416) | 3.808054 / 3.745712 (0.062342) | 3.234896 / 5.269862 (-2.034966) | 1.872195 / 4.565676 (-2.693482) | 0.088102 / 0.424275 (-0.336173) | 0.012810 / 0.007607 (0.005203) | 0.551855 / 0.226044 (0.325810) | 5.245654 / 2.268929 (2.976725) | 2.557123 / 55.444624 (-52.887502) | 2.238897 / 6.876477 (-4.637580) | 2.256260 / 2.142072 (0.114187) | 0.849804 / 4.805227 (-3.955424) | 0.170557 / 6.500664 (-6.330107) | 0.064718 / 0.075469 (-0.010751) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.271701 / 1.841788 (-0.570087) | 14.925010 / 8.074308 (6.850702) | 14.966948 / 10.191392 (4.775556) | 0.162966 / 0.680424 (-0.517458) | 0.017618 / 0.534201 (-0.516583) | 0.433484 / 0.579283 (-0.145799) | 0.430047 / 0.434364 (-0.004316) | 0.537356 / 0.540337 (-0.002981) | 0.639237 / 1.386936 (-0.747699) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aba888cb4d225b1a05596f52258a079bda98df70 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.012054 / 0.011353 (0.000702) | 0.005923 / 0.011008 (-0.005085) | 0.129531 / 0.038508 (0.091023) | 0.036283 / 0.023109 (0.013173) | 0.374406 / 0.275898 (0.098508) | 0.452538 / 0.323480 (0.129058) | 0.009419 / 0.007986 (0.001434) | 0.004783 / 0.004328 (0.000454) | 0.095292 / 0.004250 (0.091042) | 0.041290 / 0.037052 (0.004238) | 0.403940 / 0.258489 (0.145451) | 0.443091 / 0.293841 (0.149250) | 0.054635 / 0.128546 (-0.073911) | 0.019062 / 0.075646 (-0.056584) | 0.417053 / 0.419271 (-0.002218) | 0.060865 / 0.043533 (0.017332) | 0.378535 / 0.255139 (0.123396) | 0.401036 / 0.283200 (0.117836) | 0.122959 / 0.141683 (-0.018724) | 1.768517 / 1.452155 (0.316362) | 1.794700 / 1.492716 (0.301984) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246529 / 0.018006 (0.228523) | 0.576887 / 0.000490 (0.576397) | 0.005031 / 0.000200 (0.004831) | 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) | 0.027363 / 0.037411 (-0.010049) | 0.119037 / 0.014526 (0.104511) | 0.148109 / 0.176557 (-0.028447) | 0.179370 / 0.737135 (-0.557765) | 0.145105 / 0.296338 (-0.151234) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.588748 / 0.215209 (0.373539) | 5.934433 / 2.077655 (3.856778) | 2.549811 / 1.504120 (1.045691) | 2.234616 / 1.541195 (0.693421) | 2.268002 / 1.468490 (0.799512) | 1.154643 / 4.584777 (-3.430134) | 5.333935 / 3.745712 (1.588223) | 2.971065 / 5.269862 (-2.298796) | 2.131427 / 4.565676 (-2.434250) | 0.127737 / 0.424275 (-0.296538) | 0.014699 / 0.007607 (0.007091) | 0.735160 / 0.226044 (0.509115) | 7.403838 / 2.268929 (5.134909) | 3.298169 / 55.444624 (-52.146455) | 2.661285 / 6.876477 (-4.215192) | 2.688877 / 2.142072 (0.546805) | 1.344110 / 4.805227 (-3.461118) | 0.242016 / 6.500664 (-6.258648) | 0.077418 / 0.075469 (0.001948) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.566426 / 1.841788 (-0.275362) | 17.144308 / 8.074308 (9.070000) | 19.360598 / 10.191392 (9.169206) | 0.238554 / 0.680424 (-0.441870) | 0.044946 / 0.534201 (-0.489255) | 0.554183 / 0.579283 (-0.025100) | 0.630175 / 0.434364 (0.195811) | 0.630319 / 0.540337 (0.089982) | 0.745060 / 1.386936 (-0.641876) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009255 / 0.011353 (-0.002098) | 0.006951 / 0.011008 (-0.004057) | 0.092021 / 0.038508 (0.053513) | 0.035588 / 0.023109 (0.012479) | 0.415564 / 0.275898 (0.139666) | 0.446393 / 0.323480 (0.122913) | 0.006532 / 0.007986 (-0.001453) | 0.005099 / 0.004328 (0.000771) | 0.094801 / 0.004250 (0.090550) | 0.044926 / 0.037052 (0.007874) | 0.439125 / 0.258489 (0.180636) | 0.473004 / 0.293841 (0.179163) | 0.057025 / 0.128546 (-0.071522) | 0.018711 / 0.075646 (-0.056935) | 0.110844 / 0.419271 (-0.308427) | 0.058347 / 0.043533 (0.014814) | 0.435721 / 0.255139 (0.180583) | 0.434624 / 0.283200 (0.151424) | 0.114505 / 0.141683 (-0.027178) | 1.722379 / 1.452155 (0.270225) | 1.775836 / 1.492716 (0.283120) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.275893 / 0.018006 (0.257887) | 0.552590 / 0.000490 (0.552100) | 0.007919 / 0.000200 (0.007719) | 0.000122 / 0.000054 (0.000068) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030003 / 0.037411 (-0.007408) | 0.130145 / 0.014526 (0.115619) | 0.131878 / 0.176557 (-0.044678) | 0.194693 / 0.737135 (-0.542442) | 0.137689 / 0.296338 (-0.158650) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.619591 / 0.215209 (0.404382) | 6.324095 / 2.077655 (4.246441) | 2.756563 / 1.504120 (1.252444) | 2.384744 / 1.541195 (0.843549) | 2.450407 / 1.468490 (0.981917) | 1.235391 / 4.584777 (-3.349386) | 5.535383 / 3.745712 (1.789671) | 4.831927 / 5.269862 (-0.437934) | 2.757158 / 4.565676 (-1.808519) | 0.133980 / 0.424275 (-0.290295) | 0.014965 / 0.007607 (0.007358) | 0.731423 / 0.226044 (0.505379) | 7.401850 / 2.268929 (5.132921) | 3.346585 / 55.444624 (-52.098039) | 2.705523 / 6.876477 (-4.170953) | 2.637397 / 2.142072 (0.495324) | 1.347745 / 4.805227 (-3.457482) | 0.248658 / 6.500664 (-6.252006) | 0.077427 / 0.075469 (0.001958) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.520860 / 1.841788 (-0.320928) | 17.153000 / 8.074308 (9.078692) | 19.051393 / 10.191392 (8.860001) | 0.236840 / 0.680424 (-0.443584) | 0.026638 / 0.534201 (-0.507563) | 0.518417 / 0.579283 (-0.060866) | 0.607555 / 0.434364 (0.173191) | 0.637381 / 0.540337 (0.097044) | 0.767109 / 1.386936 (-0.619827) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5ee291f2c5e68a782c82f916e250d470a7e285e7 \"CML watermark\")\n",
"Great, I merged it. Thanks for the review :)",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006711 / 0.011353 (-0.004641) | 0.004472 / 0.011008 (-0.006536) | 0.099581 / 0.038508 (0.061073) | 0.028036 / 0.023109 (0.004927) | 0.301197 / 0.275898 (0.025298) | 0.339341 / 0.323480 (0.015861) | 0.005107 / 0.007986 (-0.002879) | 0.003312 / 0.004328 (-0.001017) | 0.075823 / 0.004250 (0.071573) | 0.040861 / 0.037052 (0.003809) | 0.303407 / 0.258489 (0.044918) | 0.350717 / 0.293841 (0.056876) | 0.031657 / 0.128546 (-0.096889) | 0.011627 / 0.075646 (-0.064020) | 0.325465 / 0.419271 (-0.093806) | 0.052671 / 0.043533 (0.009138) | 0.301953 / 0.255139 (0.046814) | 0.327164 / 0.283200 (0.043964) | 0.091264 / 0.141683 (-0.050419) | 1.508947 / 1.452155 (0.056792) | 1.605685 / 1.492716 (0.112968) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.202977 / 0.018006 (0.184971) | 0.400602 / 0.000490 (0.400112) | 0.003253 / 0.000200 (0.003053) | 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) | 0.022453 / 0.037411 (-0.014958) | 0.098633 / 0.014526 (0.084107) | 0.105996 / 0.176557 (-0.070561) | 0.162428 / 0.737135 (-0.574707) | 0.107139 / 0.296338 (-0.189199) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.453061 / 0.215209 (0.237852) | 4.530844 / 2.077655 (2.453190) | 2.286394 / 1.504120 (0.782274) | 2.076479 / 1.541195 (0.535284) | 2.143730 / 1.468490 (0.675240) | 0.702540 / 4.584777 (-3.882237) | 3.442688 / 3.745712 (-0.303024) | 1.874429 / 5.269862 (-3.395433) | 1.172331 / 4.565676 (-3.393346) | 0.083643 / 0.424275 (-0.340632) | 0.012519 / 0.007607 (0.004911) | 0.556859 / 0.226044 (0.330814) | 5.582843 / 2.268929 (3.313915) | 2.753734 / 55.444624 (-52.690890) | 2.415771 / 6.876477 (-4.460705) | 2.531428 / 2.142072 (0.389356) | 0.813005 / 4.805227 (-3.992222) | 0.153322 / 6.500664 (-6.347343) | 0.068061 / 0.075469 (-0.007408) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.180481 / 1.841788 (-0.661306) | 13.623933 / 8.074308 (5.549625) | 14.431288 / 10.191392 (4.239896) | 0.127580 / 0.680424 (-0.552844) | 0.016714 / 0.534201 (-0.517487) | 0.394236 / 0.579283 (-0.185047) | 0.381718 / 0.434364 (-0.052646) | 0.486749 / 0.540337 (-0.053589) | 0.565939 / 1.386936 (-0.820997) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006720 / 0.011353 (-0.004633) | 0.004518 / 0.011008 (-0.006491) | 0.076819 / 0.038508 (0.038311) | 0.027272 / 0.023109 (0.004163) | 0.340890 / 0.275898 (0.064992) | 0.381435 / 0.323480 (0.057955) | 0.004980 / 0.007986 (-0.003005) | 0.003382 / 0.004328 (-0.000947) | 0.076368 / 0.004250 (0.072117) | 0.037365 / 0.037052 (0.000313) | 0.341484 / 0.258489 (0.082995) | 0.388917 / 0.293841 (0.095076) | 0.032004 / 0.128546 (-0.096543) | 0.011612 / 0.075646 (-0.064034) | 0.084929 / 0.419271 (-0.334342) | 0.041861 / 0.043533 (-0.001671) | 0.350392 / 0.255139 (0.095253) | 0.369745 / 0.283200 (0.086546) | 0.088301 / 0.141683 (-0.053382) | 1.587296 / 1.452155 (0.135141) | 1.629761 / 1.492716 (0.137045) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.174825 / 0.018006 (0.156818) | 0.414371 / 0.000490 (0.413881) | 0.001595 / 0.000200 (0.001395) | 0.000078 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025403 / 0.037411 (-0.012009) | 0.099593 / 0.014526 (0.085067) | 0.108819 / 0.176557 (-0.067738) | 0.161613 / 0.737135 (-0.575523) | 0.112302 / 0.296338 (-0.184037) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.439234 / 0.215209 (0.224024) | 4.389073 / 2.077655 (2.311418) | 2.063215 / 1.504120 (0.559095) | 1.852550 / 1.541195 (0.311356) | 1.920014 / 1.468490 (0.451524) | 0.710255 / 4.584777 (-3.874522) | 3.430549 / 3.745712 (-0.315164) | 1.886072 / 5.269862 (-3.383790) | 1.177490 / 4.565676 (-3.388186) | 0.084877 / 0.424275 (-0.339398) | 0.012894 / 0.007607 (0.005287) | 0.544950 / 0.226044 (0.318906) | 5.467347 / 2.268929 (3.198419) | 2.508169 / 55.444624 (-52.936455) | 2.167756 / 6.876477 (-4.708721) | 2.212817 / 2.142072 (0.070744) | 0.824762 / 4.805227 (-3.980465) | 0.154387 / 6.500664 (-6.346277) | 0.068535 / 0.075469 (-0.006934) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.284165 / 1.841788 (-0.557623) | 14.153006 / 8.074308 (6.078697) | 14.152569 / 10.191392 (3.961177) | 0.130083 / 0.680424 (-0.550341) | 0.016556 / 0.534201 (-0.517645) | 0.383828 / 0.579283 (-0.195455) | 0.388241 / 0.434364 (-0.046123) | 0.477982 / 0.540337 (-0.062355) | 0.565583 / 1.386936 (-0.821353) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f1e7442d34a059ff377437381542cc762feab057 \"CML watermark\")\n"
] | "2023-03-09T08:46:01Z" | "2023-03-09T15:39:00Z" | "2023-03-09T15:31:59Z" | CONTRIBUTOR | null | 0 | {
"diff_url": "https://github.com/huggingface/datasets/pull/5623.diff",
"html_url": "https://github.com/huggingface/datasets/pull/5623",
"merged_at": "2023-03-09T15:31:58Z",
"patch_url": "https://github.com/huggingface/datasets/pull/5623.patch",
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5623"
} | `set_access_token` is deprecated and will be removed in `huggingface_hub>=0.14`.
This PR removes it from the tests (it was not used in `datasets` source code itself). FYI, it was not needed since `set_access_token` was just setting git credentials and `datasets` doesn't seem to use git anywhere.
In the future, use `set_git_credential` if needed. It is a git-credential-agnostic helper, i.e. you can store your git token in `git-credential-cache`, `git-credential-store`, `osxkeychain`, etc. The legacy `set_access_token` could only set in `git-credential-store` no matter the user preference.
(for context, I found out about this while working on https://github.com/huggingface/huggingface_hub/pull/1381)
---
In addition to this, I have added
```
filterwarnings =
error::FutureWarning:huggingface_hub*
```
to the `setup.cfg` config file to fail on future warnings from `huggingface_hub`. In `hfh`'s CI we trigger on FutureWarning from any package but it's less robust (any package update leads can lead to a failure). No obligation to keep it like that (I can remove it if you prefer) but I think it's a good idea in order to track future FutureWarnings.
FYI, in `huggingface_hub` tests we use `-Werror::FutureWarning --log-cli-level=INFO -sv --durations=0`
- FutureWarning are processed as error
- verbose mode / INFO logs (and above) are captured for easier debugging in github report
- track each test duration, just to see where we can improve. We have a quite long CI (~10min) so it helped improve that. | {
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"IMO this template should stay generic.\r\n\r\nAlso, we now use [the card template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md) from `hugginface_hub` as the source of truth on the Hub (you now have the option to import it into the dataset card/README.md), so I think the next step would be deleting this template rather than updating it.",
"Agreed, the PR was a mistake and meant for my own repo. My bad",
"Feel free to close the PR then."
] | "2023-03-08T12:30:23Z" | "2023-03-11T05:07:38Z" | "2023-03-11T05:07:38Z" | NONE | null | 0 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006183 / 0.011353 (-0.005170) | 0.004377 / 0.011008 (-0.006631) | 0.096898 / 0.038508 (0.058390) | 0.027729 / 0.023109 (0.004620) | 0.336582 / 0.275898 (0.060684) | 0.353792 / 0.323480 (0.030312) | 0.004541 / 0.007986 (-0.003445) | 0.004349 / 0.004328 (0.000020) | 0.074403 / 0.004250 (0.070153) | 0.033918 / 0.037052 (-0.003134) | 0.341505 / 0.258489 (0.083016) | 0.380192 / 0.293841 (0.086351) | 0.031703 / 0.128546 (-0.096843) | 0.011561 / 0.075646 (-0.064086) | 0.321848 / 0.419271 (-0.097423) | 0.043407 / 0.043533 (-0.000126) | 0.330365 / 0.255139 (0.075226) | 0.364630 / 0.283200 (0.081430) | 0.084798 / 0.141683 (-0.056885) | 1.450908 / 1.452155 (-0.001246) | 1.522235 / 1.492716 (0.029519) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198267 / 0.018006 (0.180261) | 0.409554 / 0.000490 (0.409065) | 0.002501 / 0.000200 (0.002301) | 0.000270 / 0.000054 (0.000215) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021801 / 0.037411 (-0.015610) | 0.097429 / 0.014526 (0.082904) | 0.103259 / 0.176557 (-0.073298) | 0.161483 / 0.737135 (-0.575652) | 0.107843 / 0.296338 (-0.188496) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.427057 / 0.215209 (0.211848) | 4.259477 / 2.077655 (2.181823) | 1.945819 / 1.504120 (0.441699) | 1.733013 / 1.541195 (0.191819) | 1.748486 / 1.468490 (0.279996) | 0.702231 / 4.584777 (-3.882546) | 3.387608 / 3.745712 (-0.358104) | 1.890187 / 5.269862 (-3.379675) | 1.300465 / 4.565676 (-3.265211) | 0.083702 / 0.424275 (-0.340573) | 0.012674 / 0.007607 (0.005067) | 0.527978 / 0.226044 (0.301934) | 5.259610 / 2.268929 (2.990681) | 2.366512 / 55.444624 (-53.078113) | 2.013811 / 6.876477 (-4.862666) | 2.058175 / 2.142072 (-0.083898) | 0.815042 / 4.805227 (-3.990185) | 0.153496 / 6.500664 (-6.347168) | 0.065442 / 0.075469 (-0.010027) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.227494 / 1.841788 (-0.614294) | 13.812921 / 8.074308 (5.738613) | 14.430149 / 10.191392 (4.238757) | 0.145422 / 0.680424 (-0.535002) | 0.016672 / 0.534201 (-0.517529) | 0.382126 / 0.579283 (-0.197157) | 0.388369 / 0.434364 (-0.045995) | 0.446133 / 0.540337 (-0.094204) | 0.531044 / 1.386936 (-0.855892) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006273 / 0.011353 (-0.005080) | 0.004557 / 0.011008 (-0.006452) | 0.077398 / 0.038508 (0.038890) | 0.027295 / 0.023109 (0.004185) | 0.340866 / 0.275898 (0.064968) | 0.373918 / 0.323480 (0.050438) | 0.004967 / 0.007986 (-0.003018) | 0.003337 / 0.004328 (-0.000991) | 0.076041 / 0.004250 (0.071791) | 0.036708 / 0.037052 (-0.000344) | 0.346126 / 0.258489 (0.087637) | 0.385177 / 0.293841 (0.091336) | 0.032272 / 0.128546 (-0.096275) | 0.011756 / 0.075646 (-0.063890) | 0.086512 / 0.419271 (-0.332759) | 0.049310 / 0.043533 (0.005777) | 0.339352 / 0.255139 (0.084213) | 0.372058 / 0.283200 (0.088859) | 0.089712 / 0.141683 (-0.051971) | 1.501964 / 1.452155 (0.049809) | 1.573753 / 1.492716 (0.081037) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.162075 / 0.018006 (0.144069) | 0.391462 / 0.000490 (0.390973) | 0.002868 / 0.000200 (0.002668) | 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) | 0.024176 / 0.037411 (-0.013235) | 0.099631 / 0.014526 (0.085105) | 0.107544 / 0.176557 (-0.069013) | 0.157659 / 0.737135 (-0.579477) | 0.111130 / 0.296338 (-0.185209) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.442086 / 0.215209 (0.226877) | 4.426311 / 2.077655 (2.348657) | 2.086133 / 1.504120 (0.582013) | 1.860415 / 1.541195 (0.319220) | 1.892306 / 1.468490 (0.423816) | 0.702752 / 4.584777 (-3.882025) | 3.394358 / 3.745712 (-0.351354) | 1.857396 / 5.269862 (-3.412466) | 1.167168 / 4.565676 (-3.398509) | 0.083549 / 0.424275 (-0.340726) | 0.012780 / 0.007607 (0.005173) | 0.547075 / 0.226044 (0.321031) | 5.466619 / 2.268929 (3.197691) | 2.548893 / 55.444624 (-52.895731) | 2.185574 / 6.876477 (-4.690903) | 2.188000 / 2.142072 (0.045928) | 0.810370 / 4.805227 (-3.994857) | 0.153320 / 6.500664 (-6.347344) | 0.068409 / 0.075469 (-0.007060) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.330431 / 1.841788 (-0.511356) | 14.178916 / 8.074308 (6.104608) | 14.409594 / 10.191392 (4.218202) | 0.156270 / 0.680424 (-0.524154) | 0.016452 / 0.534201 (-0.517749) | 0.379837 / 0.579283 (-0.199447) | 0.389896 / 0.434364 (-0.044468) | 0.443892 / 0.540337 (-0.096446) | 0.531392 / 1.386936 (-0.855544) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e502117cafd92fd9c25d1d6dd047cc650c691629 \"CML watermark\")\n"
] | "2023-03-08T10:22:50Z" | "2023-03-11T00:57:18Z" | "2023-03-11T00:49:56Z" | CONTRIBUTOR | null | 0 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009873 / 0.011353 (-0.001480) | 0.005180 / 0.011008 (-0.005828) | 0.099587 / 0.038508 (0.061079) | 0.035674 / 0.023109 (0.012565) | 0.299156 / 0.275898 (0.023258) | 0.361253 / 0.323480 (0.037773) | 0.008159 / 0.007986 (0.000173) | 0.004245 / 0.004328 (-0.000084) | 0.076809 / 0.004250 (0.072559) | 0.045251 / 0.037052 (0.008199) | 0.306002 / 0.258489 (0.047513) | 0.345758 / 0.293841 (0.051917) | 0.037826 / 0.128546 (-0.090721) | 0.011887 / 0.075646 (-0.063759) | 0.333804 / 0.419271 (-0.085467) | 0.047859 / 0.043533 (0.004326) | 0.291866 / 0.255139 (0.036727) | 0.319356 / 0.283200 (0.036157) | 0.104241 / 0.141683 (-0.037442) | 1.443816 / 1.452155 (-0.008338) | 1.514654 / 1.492716 (0.021938) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.009846 / 0.018006 (-0.008160) | 0.439488 / 0.000490 (0.438999) | 0.003227 / 0.000200 (0.003028) | 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) | 0.027553 / 0.037411 (-0.009858) | 0.105337 / 0.014526 (0.090811) | 0.116203 / 0.176557 (-0.060354) | 0.161140 / 0.737135 (-0.575995) | 0.123002 / 0.296338 (-0.173336) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400102 / 0.215209 (0.184893) | 3.976748 / 2.077655 (1.899094) | 1.794763 / 1.504120 (0.290643) | 1.602477 / 1.541195 (0.061282) | 1.703689 / 1.468490 (0.235199) | 0.696751 / 4.584777 (-3.888026) | 3.713832 / 3.745712 (-0.031880) | 2.124536 / 5.269862 (-3.145326) | 1.313005 / 4.565676 (-3.252671) | 0.086130 / 0.424275 (-0.338146) | 0.012085 / 0.007607 (0.004477) | 0.512976 / 0.226044 (0.286932) | 5.135313 / 2.268929 (2.866384) | 2.318173 / 55.444624 (-53.126451) | 1.996360 / 6.876477 (-4.880117) | 2.060150 / 2.142072 (-0.081922) | 0.853534 / 4.805227 (-3.951693) | 0.165586 / 6.500664 (-6.335078) | 0.062365 / 0.075469 (-0.013104) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.178843 / 1.841788 (-0.662945) | 14.541639 / 8.074308 (6.467331) | 14.090782 / 10.191392 (3.899390) | 0.158717 / 0.680424 (-0.521707) | 0.028825 / 0.534201 (-0.505376) | 0.441427 / 0.579283 (-0.137856) | 0.439856 / 0.434364 (0.005492) | 0.530610 / 0.540337 (-0.009727) | 0.634044 / 1.386936 (-0.752892) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007502 / 0.011353 (-0.003851) | 0.005208 / 0.011008 (-0.005801) | 0.075020 / 0.038508 (0.036512) | 0.033297 / 0.023109 (0.010188) | 0.342218 / 0.275898 (0.066320) | 0.376716 / 0.323480 (0.053236) | 0.005906 / 0.007986 (-0.002080) | 0.005320 / 0.004328 (0.000992) | 0.073531 / 0.004250 (0.069281) | 0.049091 / 0.037052 (0.012039) | 0.344202 / 0.258489 (0.085713) | 0.380556 / 0.293841 (0.086715) | 0.037500 / 0.128546 (-0.091047) | 0.012404 / 0.075646 (-0.063242) | 0.087254 / 0.419271 (-0.332017) | 0.055145 / 0.043533 (0.011612) | 0.344112 / 0.255139 (0.088973) | 0.359052 / 0.283200 (0.075852) | 0.108337 / 0.141683 (-0.033345) | 1.450332 / 1.452155 (-0.001822) | 1.553607 / 1.492716 (0.060891) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216335 / 0.018006 (0.198329) | 0.436813 / 0.000490 (0.436323) | 0.005055 / 0.000200 (0.004855) | 0.000088 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030037 / 0.037411 (-0.007374) | 0.110854 / 0.014526 (0.096329) | 0.121967 / 0.176557 (-0.054589) | 0.174029 / 0.737135 (-0.563107) | 0.128340 / 0.296338 (-0.167998) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424463 / 0.215209 (0.209254) | 4.201822 / 2.077655 (2.124167) | 2.043075 / 1.504120 (0.538956) | 1.851841 / 1.541195 (0.310647) | 1.947790 / 1.468490 (0.479300) | 0.684110 / 4.584777 (-3.900667) | 3.763536 / 3.745712 (0.017824) | 3.106988 / 5.269862 (-2.162873) | 1.498305 / 4.565676 (-3.067372) | 0.085079 / 0.424275 (-0.339196) | 0.012241 / 0.007607 (0.004634) | 0.520877 / 0.226044 (0.294832) | 5.181455 / 2.268929 (2.912527) | 2.443038 / 55.444624 (-53.001586) | 2.130823 / 6.876477 (-4.745654) | 2.217901 / 2.142072 (0.075829) | 0.837116 / 4.805227 (-3.968111) | 0.166581 / 6.500664 (-6.334083) | 0.065510 / 0.075469 (-0.009959) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.289317 / 1.841788 (-0.552471) | 15.122019 / 8.074308 (7.047710) | 13.919670 / 10.191392 (3.728278) | 0.150047 / 0.680424 (-0.530377) | 0.017612 / 0.534201 (-0.516589) | 0.426239 / 0.579283 (-0.153044) | 0.425686 / 0.434364 (-0.008678) | 0.521436 / 0.540337 (-0.018901) | 0.618217 / 1.386936 (-0.768719) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#879fc6d5186ce593fe819f1e9e67897a1873766b \"CML watermark\")\n",
"We haven't updated the minimal version requirement for PyArrow in a while, so it's ok to make a bigger leap IMO, e.g., PyArrow 8.0 (Colab installs 9.0). With this change, we should also remove the PyArrow version check in `folder_based_builder.py`, and the ones in `table.py`/`arrow_dataset.py` regarding the `to_reader` API if we decide to bump PyArrow to version 8.0.",
"I think it's a good opportunity to bump the version to 8.0 which offers higher performance anyway, I wouldn't bother trying to support 6.0.1 anymore. Only 1% of users based on 6.0.1 use the latest `datasets` version 2.10.1\r\n\r\nBumping to 8.0 if it sounds good to you",
"Sure, it is OK for those other reasons. I would just not stress that the increase of the minimum version is to support pandas 2.0 though...",
"If requiring min 8.0, do you know the percentage of people using 7.0 and latest datasets version?",
"Around 10% of users have 7.0.0, and 25% among them use the latest datasets version",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006744 / 0.011353 (-0.004609) | 0.004585 / 0.011008 (-0.006423) | 0.097828 / 0.038508 (0.059320) | 0.028230 / 0.023109 (0.005121) | 0.302190 / 0.275898 (0.026292) | 0.335022 / 0.323480 (0.011542) | 0.005107 / 0.007986 (-0.002878) | 0.004648 / 0.004328 (0.000320) | 0.076842 / 0.004250 (0.072592) | 0.038291 / 0.037052 (0.001239) | 0.313286 / 0.258489 (0.054797) | 0.342534 / 0.293841 (0.048693) | 0.031325 / 0.128546 (-0.097221) | 0.011632 / 0.075646 (-0.064014) | 0.321879 / 0.419271 (-0.097392) | 0.042204 / 0.043533 (-0.001329) | 0.304442 / 0.255139 (0.049303) | 0.330912 / 0.283200 (0.047712) | 0.085446 / 0.141683 (-0.056237) | 1.469990 / 1.452155 (0.017835) | 1.551147 / 1.492716 (0.058431) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.185961 / 0.018006 (0.167955) | 0.404675 / 0.000490 (0.404186) | 0.003212 / 0.000200 (0.003012) | 0.000074 / 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) | 0.023876 / 0.037411 (-0.013535) | 0.097820 / 0.014526 (0.083295) | 0.107382 / 0.176557 (-0.069174) | 0.167598 / 0.737135 (-0.569537) | 0.108789 / 0.296338 (-0.187550) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.455004 / 0.215209 (0.239795) | 4.529104 / 2.077655 (2.451449) | 2.180068 / 1.504120 (0.675948) | 1.982109 / 1.541195 (0.440914) | 2.041856 / 1.468490 (0.573366) | 0.702029 / 4.584777 (-3.882747) | 3.368613 / 3.745712 (-0.377099) | 1.932303 / 5.269862 (-3.337559) | 1.278340 / 4.565676 (-3.287336) | 0.082836 / 0.424275 (-0.341439) | 0.012349 / 0.007607 (0.004742) | 0.548197 / 0.226044 (0.322153) | 5.509982 / 2.268929 (3.241053) | 2.612889 / 55.444624 (-52.831736) | 2.278157 / 6.876477 (-4.598320) | 2.386923 / 2.142072 (0.244851) | 0.803332 / 4.805227 (-4.001896) | 0.151222 / 6.500664 (-6.349442) | 0.066673 / 0.075469 (-0.008796) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.209453 / 1.841788 (-0.632335) | 13.649733 / 8.074308 (5.575424) | 14.065917 / 10.191392 (3.874525) | 0.128872 / 0.680424 (-0.551551) | 0.016773 / 0.534201 (-0.517428) | 0.385475 / 0.579283 (-0.193809) | 0.386208 / 0.434364 (-0.048156) | 0.475144 / 0.540337 (-0.065194) | 0.564183 / 1.386936 (-0.822753) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated 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) | 0.004433 / 0.011008 (-0.006575) | 0.076008 / 0.038508 (0.037500) | 0.027471 / 0.023109 (0.004362) | 0.339837 / 0.275898 (0.063939) | 0.376857 / 0.323480 (0.053377) | 0.004930 / 0.007986 (-0.003055) | 0.003312 / 0.004328 (-0.001016) | 0.075070 / 0.004250 (0.070820) | 0.035897 / 0.037052 (-0.001156) | 0.342398 / 0.258489 (0.083909) | 0.380202 / 0.293841 (0.086361) | 0.031781 / 0.128546 (-0.096766) | 0.011697 / 0.075646 (-0.063950) | 0.085926 / 0.419271 (-0.333345) | 0.041599 / 0.043533 (-0.001934) | 0.343098 / 0.255139 (0.087959) | 0.371275 / 0.283200 (0.088076) | 0.090489 / 0.141683 (-0.051194) | 1.483738 / 1.452155 (0.031584) | 1.554973 / 1.492716 (0.062256) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.183703 / 0.018006 (0.165697) | 0.395105 / 0.000490 (0.394616) | 0.002162 / 0.000200 (0.001963) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025432 / 0.037411 (-0.011979) | 0.101322 / 0.014526 (0.086796) | 0.107839 / 0.176557 (-0.068718) | 0.160328 / 0.737135 (-0.576807) | 0.109899 / 0.296338 (-0.186440) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.448001 / 0.215209 (0.232792) | 4.485321 / 2.077655 (2.407666) | 2.157064 / 1.504120 (0.652944) | 1.966141 / 1.541195 (0.424947) | 2.032808 / 1.468490 (0.564318) | 0.705684 / 4.584777 (-3.879093) | 3.359802 / 3.745712 (-0.385910) | 2.694952 / 5.269862 (-2.574910) | 1.471309 / 4.565676 (-3.094368) | 0.084185 / 0.424275 (-0.340090) | 0.012330 / 0.007607 (0.004723) | 0.554083 / 0.226044 (0.328038) | 5.569137 / 2.268929 (3.300208) | 2.586009 / 55.444624 (-52.858615) | 2.234920 / 6.876477 (-4.641557) | 2.285128 / 2.142072 (0.143056) | 0.818825 / 4.805227 (-3.986402) | 0.152604 / 6.500664 (-6.348060) | 0.067722 / 0.075469 (-0.007747) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.305571 / 1.841788 (-0.536217) | 13.687471 / 8.074308 (5.613163) | 13.305401 / 10.191392 (3.114009) | 0.140477 / 0.680424 (-0.539947) | 0.018138 / 0.534201 (-0.516063) | 0.377255 / 0.579283 (-0.202028) | 0.379522 / 0.434364 (-0.054842) | 0.458489 / 0.540337 (-0.081849) | 0.543767 / 1.386936 (-0.843169) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#02570894db6ecc46bf25b7fa1cb1bcdc1dede853 \"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) | 0.009606 / 0.011353 (-0.001747) | 0.006795 / 0.011008 (-0.004213) | 0.133738 / 0.038508 (0.095230) | 0.043379 / 0.023109 (0.020270) | 0.412917 / 0.275898 (0.137019) | 0.418790 / 0.323480 (0.095310) | 0.007290 / 0.007986 (-0.000696) | 0.004960 / 0.004328 (0.000632) | 0.095496 / 0.004250 (0.091246) | 0.057607 / 0.037052 (0.020555) | 0.402638 / 0.258489 (0.144149) | 0.436206 / 0.293841 (0.142365) | 0.056023 / 0.128546 (-0.072523) | 0.019909 / 0.075646 (-0.055737) | 0.463958 / 0.419271 (0.044687) | 0.064073 / 0.043533 (0.020541) | 0.398337 / 0.255139 (0.143198) | 0.421786 / 0.283200 (0.138586) | 0.131563 / 0.141683 (-0.010120) | 1.840217 / 1.452155 (0.388063) | 1.912013 / 1.492716 (0.419296) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230519 / 0.018006 (0.212513) | 0.550506 / 0.000490 (0.550017) | 0.003649 / 0.000200 (0.003449) | 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) | 0.029713 / 0.037411 (-0.007698) | 0.129913 / 0.014526 (0.115387) | 0.131543 / 0.176557 (-0.045013) | 0.203571 / 0.737135 (-0.533565) | 0.141483 / 0.296338 (-0.154856) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.626383 / 0.215209 (0.411174) | 6.193043 / 2.077655 (4.115388) | 2.442728 / 1.504120 (0.938608) | 2.079049 / 1.541195 (0.537855) | 2.117761 / 1.468490 (0.649271) | 1.315296 / 4.584777 (-3.269481) | 5.643709 / 3.745712 (1.897997) | 5.245789 / 5.269862 (-0.024073) | 2.757442 / 4.565676 (-1.808235) | 0.151655 / 0.424275 (-0.272620) | 0.014686 / 0.007607 (0.007079) | 0.779937 / 0.226044 (0.553893) | 7.796685 / 2.268929 (5.527756) | 3.349580 / 55.444624 (-52.095045) | 2.493750 / 6.876477 (-4.382727) | 2.506200 / 2.142072 (0.364128) | 1.534964 / 4.805227 (-3.270263) | 0.260001 / 6.500664 (-6.240663) | 0.080543 / 0.075469 (0.005074) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.541940 / 1.841788 (-0.299848) | 17.851935 / 8.074308 (9.777627) | 22.418859 / 10.191392 (12.227467) | 0.258602 / 0.680424 (-0.421822) | 0.027679 / 0.534201 (-0.506522) | 0.548379 / 0.579283 (-0.030904) | 0.625505 / 0.434364 (0.191141) | 0.664074 / 0.540337 (0.123737) | 0.797418 / 1.386936 (-0.589518) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009800 / 0.011353 (-0.001553) | 0.006178 / 0.011008 (-0.004830) | 0.105667 / 0.038508 (0.067159) | 0.039380 / 0.023109 (0.016271) | 0.419528 / 0.275898 (0.143630) | 0.469857 / 0.323480 (0.146377) | 0.006672 / 0.007986 (-0.001314) | 0.004745 / 0.004328 (0.000417) | 0.101647 / 0.004250 (0.097397) | 0.048531 / 0.037052 (0.011478) | 0.433364 / 0.258489 (0.174875) | 0.459719 / 0.293841 (0.165878) | 0.054291 / 0.128546 (-0.074256) | 0.020406 / 0.075646 (-0.055240) | 0.122321 / 0.419271 (-0.296951) | 0.059719 / 0.043533 (0.016186) | 0.416083 / 0.255139 (0.160944) | 0.455277 / 0.283200 (0.172077) | 0.119342 / 0.141683 (-0.022341) | 1.862544 / 1.452155 (0.410390) | 2.001428 / 1.492716 (0.508712) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.240951 / 0.018006 (0.222945) | 0.516958 / 0.000490 (0.516468) | 0.000449 / 0.000200 (0.000249) | 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) | 0.032725 / 0.037411 (-0.004686) | 0.130291 / 0.014526 (0.115765) | 0.139834 / 0.176557 (-0.036723) | 0.214995 / 0.737135 (-0.522140) | 0.150925 / 0.296338 (-0.145414) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.652062 / 0.215209 (0.436853) | 6.584447 / 2.077655 (4.506793) | 2.654838 / 1.504120 (1.150718) | 2.297209 / 1.541195 (0.756015) | 2.420394 / 1.468490 (0.951904) | 1.299285 / 4.584777 (-3.285492) | 5.605849 / 3.745712 (1.860137) | 3.166103 / 5.269862 (-2.103759) | 2.138123 / 4.565676 (-2.427554) | 0.152562 / 0.424275 (-0.271713) | 0.015499 / 0.007607 (0.007892) | 0.816300 / 0.226044 (0.590256) | 8.308746 / 2.268929 (6.039817) | 3.482982 / 55.444624 (-51.961642) | 2.689247 / 6.876477 (-4.187229) | 2.792728 / 2.142072 (0.650656) | 1.566320 / 4.805227 (-3.238907) | 0.264110 / 6.500664 (-6.236554) | 0.083652 / 0.075469 (0.008183) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.643027 / 1.841788 (-0.198760) | 18.612349 / 8.074308 (10.538041) | 19.460644 / 10.191392 (9.269252) | 0.260795 / 0.680424 (-0.419629) | 0.026050 / 0.534201 (-0.508151) | 0.539750 / 0.579283 (-0.039533) | 0.620791 / 0.434364 (0.186428) | 0.645023 / 0.540337 (0.104686) | 0.765604 / 1.386936 (-0.621332) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e6dcf4c50e14ee6dbc6d763ed1b7ce3501460863 \"CML watermark\")\n",
"ready for re-review :)",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006388 / 0.011353 (-0.004965) | 0.004469 / 0.011008 (-0.006540) | 0.097082 / 0.038508 (0.058573) | 0.028005 / 0.023109 (0.004895) | 0.364797 / 0.275898 (0.088899) | 0.399671 / 0.323480 (0.076191) | 0.005062 / 0.007986 (-0.002923) | 0.004580 / 0.004328 (0.000252) | 0.075670 / 0.004250 (0.071420) | 0.038328 / 0.037052 (0.001276) | 0.365948 / 0.258489 (0.107459) | 0.402631 / 0.293841 (0.108790) | 0.031378 / 0.128546 (-0.097168) | 0.011443 / 0.075646 (-0.064203) | 0.321590 / 0.419271 (-0.097682) | 0.042263 / 0.043533 (-0.001270) | 0.368238 / 0.255139 (0.113099) | 0.389928 / 0.283200 (0.106728) | 0.085203 / 0.141683 (-0.056480) | 1.462820 / 1.452155 (0.010665) | 1.529207 / 1.492716 (0.036490) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.197194 / 0.018006 (0.179188) | 0.410897 / 0.000490 (0.410407) | 0.003394 / 0.000200 (0.003194) | 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) | 0.022911 / 0.037411 (-0.014500) | 0.097012 / 0.014526 (0.082486) | 0.102247 / 0.176557 (-0.074309) | 0.163363 / 0.737135 (-0.573772) | 0.106897 / 0.296338 (-0.189441) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.416303 / 0.215209 (0.201094) | 4.159325 / 2.077655 (2.081671) | 1.844893 / 1.504120 (0.340773) | 1.646131 / 1.541195 (0.104936) | 1.706763 / 1.468490 (0.238273) | 0.699607 / 4.584777 (-3.885170) | 3.462048 / 3.745712 (-0.283664) | 1.939076 / 5.269862 (-3.330786) | 1.324744 / 4.565676 (-3.240932) | 0.082949 / 0.424275 (-0.341326) | 0.012327 / 0.007607 (0.004720) | 0.513812 / 0.226044 (0.287768) | 5.171021 / 2.268929 (2.902093) | 2.288039 / 55.444624 (-53.156585) | 1.957403 / 6.876477 (-4.919074) | 1.990060 / 2.142072 (-0.152013) | 0.805571 / 4.805227 (-3.999656) | 0.152641 / 6.500664 (-6.348023) | 0.068169 / 0.075469 (-0.007300) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.200624 / 1.841788 (-0.641164) | 13.836334 / 8.074308 (5.762026) | 14.065340 / 10.191392 (3.873948) | 0.143406 / 0.680424 (-0.537018) | 0.016709 / 0.534201 (-0.517492) | 0.380080 / 0.579283 (-0.199204) | 0.398414 / 0.434364 (-0.035950) | 0.479192 / 0.540337 (-0.061145) | 0.572508 / 1.386936 (-0.814428) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006622 / 0.011353 (-0.004731) | 0.004511 / 0.011008 (-0.006497) | 0.076454 / 0.038508 (0.037946) | 0.027431 / 0.023109 (0.004322) | 0.339041 / 0.275898 (0.063143) | 0.375691 / 0.323480 (0.052211) | 0.004854 / 0.007986 (-0.003131) | 0.004654 / 0.004328 (0.000325) | 0.075300 / 0.004250 (0.071049) | 0.036469 / 0.037052 (-0.000583) | 0.341357 / 0.258489 (0.082868) | 0.381561 / 0.293841 (0.087720) | 0.031754 / 0.128546 (-0.096792) | 0.011544 / 0.075646 (-0.064102) | 0.085956 / 0.419271 (-0.333315) | 0.041704 / 0.043533 (-0.001828) | 0.340088 / 0.255139 (0.084950) | 0.364037 / 0.283200 (0.080838) | 0.091016 / 0.141683 (-0.050667) | 1.483515 / 1.452155 (0.031360) | 1.562878 / 1.492716 (0.070162) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228019 / 0.018006 (0.210013) | 0.404809 / 0.000490 (0.404320) | 0.000384 / 0.000200 (0.000184) | 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) | 0.025230 / 0.037411 (-0.012181) | 0.099790 / 0.014526 (0.085264) | 0.107923 / 0.176557 (-0.068634) | 0.157651 / 0.737135 (-0.579484) | 0.112525 / 0.296338 (-0.183813) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440360 / 0.215209 (0.225151) | 4.387749 / 2.077655 (2.310094) | 2.077592 / 1.504120 (0.573472) | 1.872532 / 1.541195 (0.331337) | 1.941607 / 1.468490 (0.473117) | 0.699394 / 4.584777 (-3.885383) | 3.411210 / 3.745712 (-0.334502) | 1.901816 / 5.269862 (-3.368046) | 1.177042 / 4.565676 (-3.388634) | 0.083536 / 0.424275 (-0.340739) | 0.012418 / 0.007607 (0.004811) | 0.548463 / 0.226044 (0.322419) | 5.487107 / 2.268929 (3.218178) | 2.548076 / 55.444624 (-52.896548) | 2.215012 / 6.876477 (-4.661465) | 2.253472 / 2.142072 (0.111400) | 0.812925 / 4.805227 (-3.992302) | 0.152935 / 6.500664 (-6.347729) | 0.068144 / 0.075469 (-0.007325) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.267914 / 1.841788 (-0.573873) | 14.015185 / 8.074308 (5.940877) | 13.153967 / 10.191392 (2.962575) | 0.140666 / 0.680424 (-0.539758) | 0.016718 / 0.534201 (-0.517483) | 0.383411 / 0.579283 (-0.195872) | 0.395424 / 0.434364 (-0.038940) | 0.466069 / 0.540337 (-0.074269) | 0.553825 / 1.386936 (-0.833111) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#14568bf072b38e3b295f29774c874c8e78b9fe37 \"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) | 0.007463 / 0.011353 (-0.003890) | 0.005017 / 0.011008 (-0.005991) | 0.098777 / 0.038508 (0.060269) | 0.033859 / 0.023109 (0.010750) | 0.298569 / 0.275898 (0.022670) | 0.343717 / 0.323480 (0.020237) | 0.005806 / 0.007986 (-0.002180) | 0.005403 / 0.004328 (0.001074) | 0.075840 / 0.004250 (0.071590) | 0.046539 / 0.037052 (0.009487) | 0.300058 / 0.258489 (0.041569) | 0.345036 / 0.293841 (0.051195) | 0.036258 / 0.128546 (-0.092288) | 0.011992 / 0.075646 (-0.063654) | 0.334986 / 0.419271 (-0.084286) | 0.050427 / 0.043533 (0.006894) | 0.295319 / 0.255139 (0.040180) | 0.318980 / 0.283200 (0.035780) | 0.098407 / 0.141683 (-0.043276) | 1.437626 / 1.452155 (-0.014529) | 1.562548 / 1.492716 (0.069832) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231502 / 0.018006 (0.213496) | 0.441550 / 0.000490 (0.441060) | 0.005863 / 0.000200 (0.005663) | 0.000724 / 0.000054 (0.000670) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027501 / 0.037411 (-0.009911) | 0.111490 / 0.014526 (0.096964) | 0.117503 / 0.176557 (-0.059054) | 0.173849 / 0.737135 (-0.563286) | 0.124521 / 0.296338 (-0.171818) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419266 / 0.215209 (0.204057) | 4.170337 / 2.077655 (2.092683) | 2.015883 / 1.504120 (0.511763) | 1.832683 / 1.541195 (0.291488) | 1.950195 / 1.468490 (0.481705) | 0.698150 / 4.584777 (-3.886627) | 3.775601 / 3.745712 (0.029889) | 2.094581 / 5.269862 (-3.175281) | 1.325437 / 4.565676 (-3.240240) | 0.085382 / 0.424275 (-0.338894) | 0.012151 / 0.007607 (0.004544) | 0.526441 / 0.226044 (0.300397) | 5.256124 / 2.268929 (2.987196) | 2.488408 / 55.444624 (-52.956216) | 2.157228 / 6.876477 (-4.719249) | 2.228991 / 2.142072 (0.086919) | 0.837002 / 4.805227 (-3.968225) | 0.167520 / 6.500664 (-6.333144) | 0.066435 / 0.075469 (-0.009035) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.174544 / 1.841788 (-0.667243) | 14.684207 / 8.074308 (6.609899) | 14.494676 / 10.191392 (4.303284) | 0.143423 / 0.680424 (-0.537001) | 0.017289 / 0.534201 (-0.516912) | 0.424727 / 0.579283 (-0.154556) | 0.417077 / 0.434364 (-0.017287) | 0.498955 / 0.540337 (-0.041383) | 0.584838 / 1.386936 (-0.802098) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007666 / 0.011353 (-0.003687) | 0.005269 / 0.011008 (-0.005739) | 0.073548 / 0.038508 (0.035040) | 0.033683 / 0.023109 (0.010573) | 0.342646 / 0.275898 (0.066747) | 0.380948 / 0.323480 (0.057468) | 0.005737 / 0.007986 (-0.002248) | 0.005366 / 0.004328 (0.001038) | 0.073228 / 0.004250 (0.068978) | 0.050065 / 0.037052 (0.013013) | 0.348593 / 0.258489 (0.090104) | 0.393930 / 0.293841 (0.100089) | 0.037411 / 0.128546 (-0.091135) | 0.012476 / 0.075646 (-0.063170) | 0.084884 / 0.419271 (-0.334387) | 0.049368 / 0.043533 (0.005835) | 0.343142 / 0.255139 (0.088003) | 0.362828 / 0.283200 (0.079628) | 0.102962 / 0.141683 (-0.038721) | 1.505703 / 1.452155 (0.053549) | 1.580695 / 1.492716 (0.087979) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.207621 / 0.018006 (0.189615) | 0.437678 / 0.000490 (0.437188) | 0.003931 / 0.000200 (0.003731) | 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) | 0.029079 / 0.037411 (-0.008332) | 0.108600 / 0.014526 (0.094074) | 0.124787 / 0.176557 (-0.051770) | 0.173354 / 0.737135 (-0.563781) | 0.126124 / 0.296338 (-0.170214) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.427911 / 0.215209 (0.212702) | 4.254227 / 2.077655 (2.176572) | 2.052142 / 1.504120 (0.548022) | 1.857042 / 1.541195 (0.315848) | 1.965244 / 1.468490 (0.496754) | 0.707994 / 4.584777 (-3.876783) | 3.807593 / 3.745712 (0.061880) | 3.387588 / 5.269862 (-1.882274) | 1.844853 / 4.565676 (-2.720824) | 0.088548 / 0.424275 (-0.335727) | 0.012398 / 0.007607 (0.004791) | 0.565896 / 0.226044 (0.339851) | 5.228024 / 2.268929 (2.959095) | 2.467220 / 55.444624 (-52.977405) | 2.144413 / 6.876477 (-4.732064) | 2.214049 / 2.142072 (0.071977) | 0.869381 / 4.805227 (-3.935846) | 0.170991 / 6.500664 (-6.329673) | 0.064932 / 0.075469 (-0.010537) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.246661 / 1.841788 (-0.595127) | 14.902743 / 8.074308 (6.828435) | 13.264294 / 10.191392 (3.072902) | 0.165328 / 0.680424 (-0.515095) | 0.017567 / 0.534201 (-0.516634) | 0.425491 / 0.579283 (-0.153792) | 0.427327 / 0.434364 (-0.007037) | 0.526475 / 0.540337 (-0.013862) | 0.627309 / 1.386936 (-0.759627) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dd31bce76b554447bccb2b1447440e1f8ddba035 \"CML watermark\")\n"
] | "2023-03-07T13:31:53Z" | "2023-03-08T14:01:27Z" | "2023-03-08T13:54:22Z" | MEMBER | null | 0 | {
"diff_url": "https://github.com/huggingface/datasets/pull/5620.diff",
"html_url": "https://github.com/huggingface/datasets/pull/5620",
"merged_at": "2023-03-08T13:54:21Z",
"patch_url": "https://github.com/huggingface/datasets/pull/5620.patch",
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5620"
} | Fix those for Pandas 2.0 (tested [here](https://github.com/huggingface/datasets/actions/runs/4346221280/jobs/7592010397) with pandas==2.0.0.rc0):
```python
=========================== short test summary info ============================
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_to_parquet_in_memory - ImportError: Unable to find a usable engine; tried using: 'pyarrow', 'fastparquet'.
A suitable version of pyarrow or fastparquet is required for parquet support.
Trying to import the above resulted in these errors:
- Pandas requires version '7.0.0' or newer of 'pyarrow' (version '6.0.1' currently installed).
- Missing optional dependency 'fastparquet'. fastparquet is required for parquet support. Use pip or conda to install fastparquet.
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_to_parquet_on_disk - ImportError: Unable to find a usable engine; tried using: 'pyarrow', 'fastparquet'.
A suitable version of pyarrow or fastparquet is required for parquet support.
Trying to import the above resulted in these errors:
- Pandas requires version '7.0.0' or newer of 'pyarrow' (version '6.0.1' currently installed).
- Missing optional dependency 'fastparquet'. fastparquet is required for parquet support. Use pip or conda to install fastparquet.
===== 2 failed, 2137 passed, 18 skipped, 32 warnings in 212.76s (0:03:32) ======
```
EDIT: also for performance - with 8.0 we can use `.to_reader()` | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009954 / 0.011353 (-0.001398) | 0.005468 / 0.011008 (-0.005541) | 0.101228 / 0.038508 (0.062720) | 0.037878 / 0.023109 (0.014769) | 0.305635 / 0.275898 (0.029737) | 0.391672 / 0.323480 (0.068192) | 0.008893 / 0.007986 (0.000908) | 0.005861 / 0.004328 (0.001533) | 0.076940 / 0.004250 (0.072689) | 0.046242 / 0.037052 (0.009190) | 0.324033 / 0.258489 (0.065544) | 0.383306 / 0.293841 (0.089465) | 0.039298 / 0.128546 (-0.089249) | 0.012187 / 0.075646 (-0.063459) | 0.336774 / 0.419271 (-0.082498) | 0.053493 / 0.043533 (0.009960) | 0.303381 / 0.255139 (0.048242) | 0.323494 / 0.283200 (0.040295) | 0.118613 / 0.141683 (-0.023070) | 1.463430 / 1.452155 (0.011275) | 1.549856 / 1.492716 (0.057139) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.289264 / 0.018006 (0.271258) | 0.520348 / 0.000490 (0.519858) | 0.004543 / 0.000200 (0.004343) | 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) | 0.028183 / 0.037411 (-0.009229) | 0.107869 / 0.014526 (0.093343) | 0.124019 / 0.176557 (-0.052537) | 0.167769 / 0.737135 (-0.569367) | 0.130304 / 0.296338 (-0.166034) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.402296 / 0.215209 (0.187087) | 4.018884 / 2.077655 (1.941229) | 1.834050 / 1.504120 (0.329930) | 1.649974 / 1.541195 (0.108779) | 1.741697 / 1.468490 (0.273207) | 0.684354 / 4.584777 (-3.900423) | 3.778213 / 3.745712 (0.032501) | 2.158086 / 5.269862 (-3.111775) | 1.472671 / 4.565676 (-3.093006) | 0.083912 / 0.424275 (-0.340363) | 0.012285 / 0.007607 (0.004678) | 0.501689 / 0.226044 (0.275645) | 5.014722 / 2.268929 (2.745794) | 2.310722 / 55.444624 (-53.133902) | 1.983214 / 6.876477 (-4.893262) | 2.154518 / 2.142072 (0.012446) | 0.821277 / 4.805227 (-3.983950) | 0.164434 / 6.500664 (-6.336231) | 0.062568 / 0.075469 (-0.012901) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.224338 / 1.841788 (-0.617450) | 14.981623 / 8.074308 (6.907315) | 14.296356 / 10.191392 (4.104964) | 0.193554 / 0.680424 (-0.486870) | 0.028511 / 0.534201 (-0.505690) | 0.437649 / 0.579283 (-0.141634) | 0.448934 / 0.434364 (0.014570) | 0.552624 / 0.540337 (0.012287) | 0.654268 / 1.386936 (-0.732668) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007772 / 0.011353 (-0.003581) | 0.005534 / 0.011008 (-0.005474) | 0.074347 / 0.038508 (0.035839) | 0.034486 / 0.023109 (0.011376) | 0.343430 / 0.275898 (0.067532) | 0.385778 / 0.323480 (0.062298) | 0.006424 / 0.007986 (-0.001562) | 0.004241 / 0.004328 (-0.000087) | 0.072839 / 0.004250 (0.068589) | 0.055523 / 0.037052 (0.018471) | 0.342778 / 0.258489 (0.084289) | 0.389961 / 0.293841 (0.096120) | 0.037238 / 0.128546 (-0.091308) | 0.012450 / 0.075646 (-0.063197) | 0.085282 / 0.419271 (-0.333990) | 0.049678 / 0.043533 (0.006146) | 0.345300 / 0.255139 (0.090161) | 0.365220 / 0.283200 (0.082020) | 0.109257 / 0.141683 (-0.032426) | 1.480284 / 1.452155 (0.028129) | 1.627881 / 1.492716 (0.135165) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.323330 / 0.018006 (0.305324) | 0.530824 / 0.000490 (0.530334) | 0.000463 / 0.000200 (0.000263) | 0.000063 / 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) | 0.032398 / 0.037411 (-0.005013) | 0.115889 / 0.014526 (0.101363) | 0.131093 / 0.176557 (-0.045464) | 0.180757 / 0.737135 (-0.556379) | 0.134395 / 0.296338 (-0.161943) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.423931 / 0.215209 (0.208722) | 4.238207 / 2.077655 (2.160553) | 2.075721 / 1.504120 (0.571602) | 1.887752 / 1.541195 (0.346557) | 2.055054 / 1.468490 (0.586564) | 0.703145 / 4.584777 (-3.881632) | 3.937120 / 3.745712 (0.191408) | 3.748550 / 5.269862 (-1.521311) | 1.562849 / 4.565676 (-3.002827) | 0.087695 / 0.424275 (-0.336580) | 0.012614 / 0.007607 (0.005007) | 0.523901 / 0.226044 (0.297856) | 5.230210 / 2.268929 (2.961282) | 2.592667 / 55.444624 (-52.851958) | 2.345662 / 6.876477 (-4.530815) | 2.475388 / 2.142072 (0.333316) | 0.836443 / 4.805227 (-3.968784) | 0.170304 / 6.500664 (-6.330360) | 0.067741 / 0.075469 (-0.007729) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.255171 / 1.841788 (-0.586617) | 16.312856 / 8.074308 (8.238548) | 13.184770 / 10.191392 (2.993378) | 0.145557 / 0.680424 (-0.534867) | 0.017723 / 0.534201 (-0.516478) | 0.423447 / 0.579283 (-0.155836) | 0.423063 / 0.434364 (-0.011301) | 0.494159 / 0.540337 (-0.046179) | 0.589590 / 1.386936 (-0.797346) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4ea6f1db3f80eb3bb7ac6f252c2cd5bd97537c01 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.012068 / 0.011353 (0.000715) | 0.006127 / 0.011008 (-0.004881) | 0.112550 / 0.038508 (0.074042) | 0.043201 / 0.023109 (0.020092) | 0.346666 / 0.275898 (0.070768) | 0.413852 / 0.323480 (0.090372) | 0.009342 / 0.007986 (0.001356) | 0.006302 / 0.004328 (0.001974) | 0.086901 / 0.004250 (0.082650) | 0.053992 / 0.037052 (0.016940) | 0.362192 / 0.258489 (0.103703) | 0.409867 / 0.293841 (0.116026) | 0.046124 / 0.128546 (-0.082422) | 0.014139 / 0.075646 (-0.061507) | 0.386386 / 0.419271 (-0.032886) | 0.058465 / 0.043533 (0.014932) | 0.344832 / 0.255139 (0.089693) | 0.370684 / 0.283200 (0.087485) | 0.122886 / 0.141683 (-0.018796) | 1.724013 / 1.452155 (0.271858) | 1.775756 / 1.492716 (0.283039) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220289 / 0.018006 (0.202283) | 0.493585 / 0.000490 (0.493096) | 0.001970 / 0.000200 (0.001770) | 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) | 0.030763 / 0.037411 (-0.006649) | 0.128237 / 0.014526 (0.113711) | 0.138364 / 0.176557 (-0.038192) | 0.188115 / 0.737135 (-0.549021) | 0.145367 / 0.296338 (-0.150972) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.452487 / 0.215209 (0.237277) | 4.592728 / 2.077655 (2.515074) | 2.075712 / 1.504120 (0.571592) | 1.845424 / 1.541195 (0.304229) | 1.956400 / 1.468490 (0.487910) | 0.808387 / 4.584777 (-3.776390) | 4.483678 / 3.745712 (0.737966) | 3.870287 / 5.269862 (-1.399574) | 2.151205 / 4.565676 (-2.414471) | 0.098123 / 0.424275 (-0.326152) | 0.014139 / 0.007607 (0.006531) | 0.577775 / 0.226044 (0.351730) | 5.785545 / 2.268929 (3.516616) | 2.614418 / 55.444624 (-52.830206) | 2.312136 / 6.876477 (-4.564341) | 2.364189 / 2.142072 (0.222117) | 0.970028 / 4.805227 (-3.835199) | 0.189592 / 6.500664 (-6.311072) | 0.072883 / 0.075469 (-0.002586) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.414252 / 1.841788 (-0.427535) | 17.518307 / 8.074308 (9.443999) | 16.053748 / 10.191392 (5.862356) | 0.215297 / 0.680424 (-0.465127) | 0.033947 / 0.534201 (-0.500253) | 0.525794 / 0.579283 (-0.053489) | 0.514676 / 0.434364 (0.080312) | 0.595066 / 0.540337 (0.054728) | 0.689404 / 1.386936 (-0.697532) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008185 / 0.011353 (-0.003168) | 0.005776 / 0.011008 (-0.005232) | 0.084919 / 0.038508 (0.046411) | 0.037575 / 0.023109 (0.014466) | 0.401192 / 0.275898 (0.125294) | 0.443920 / 0.323480 (0.120440) | 0.006446 / 0.007986 (-0.001540) | 0.004428 / 0.004328 (0.000099) | 0.084013 / 0.004250 (0.079763) | 0.052013 / 0.037052 (0.014961) | 0.398429 / 0.258489 (0.139940) | 0.455676 / 0.293841 (0.161836) | 0.041568 / 0.128546 (-0.086978) | 0.013631 / 0.075646 (-0.062015) | 0.098709 / 0.419271 (-0.320563) | 0.055889 / 0.043533 (0.012356) | 0.402002 / 0.255139 (0.146863) | 0.424248 / 0.283200 (0.141049) | 0.113288 / 0.141683 (-0.028395) | 1.672214 / 1.452155 (0.220059) | 1.792940 / 1.492716 (0.300223) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211847 / 0.018006 (0.193841) | 0.486711 / 0.000490 (0.486221) | 0.002907 / 0.000200 (0.002707) | 0.000118 / 0.000054 (0.000063) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032931 / 0.037411 (-0.004480) | 0.142073 / 0.014526 (0.127547) | 0.142872 / 0.176557 (-0.033685) | 0.202612 / 0.737135 (-0.534523) | 0.154390 / 0.296338 (-0.141949) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.488682 / 0.215209 (0.273473) | 4.755805 / 2.077655 (2.678150) | 2.348778 / 1.504120 (0.844658) | 2.144992 / 1.541195 (0.603797) | 2.245654 / 1.468490 (0.777164) | 0.792690 / 4.584777 (-3.792087) | 4.569190 / 3.745712 (0.823478) | 3.919317 / 5.269862 (-1.350545) | 2.140302 / 4.565676 (-2.425374) | 0.096430 / 0.424275 (-0.327845) | 0.014551 / 0.007607 (0.006944) | 0.605138 / 0.226044 (0.379094) | 5.989470 / 2.268929 (3.720542) | 2.915525 / 55.444624 (-52.529099) | 2.516243 / 6.876477 (-4.360234) | 2.673114 / 2.142072 (0.531041) | 0.932330 / 4.805227 (-3.872897) | 0.191456 / 6.500664 (-6.309209) | 0.073887 / 0.075469 (-0.001582) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.455552 / 1.841788 (-0.386236) | 17.824864 / 8.074308 (9.750556) | 15.764150 / 10.191392 (5.572758) | 0.184935 / 0.680424 (-0.495489) | 0.020552 / 0.534201 (-0.513649) | 0.486816 / 0.579283 (-0.092467) | 0.489006 / 0.434364 (0.054642) | 0.609826 / 0.540337 (0.069488) | 0.721313 / 1.386936 (-0.665623) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a0a35c5fa84a8a7df656c1f5b0a7266126fa9b75 \"CML watermark\")\n"
] | "2023-03-07T13:22:41Z" | "2023-03-07T13:47:01Z" | "2023-03-07T13:39:02Z" | MEMBER | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5618 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5618/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5618/comments | https://api.github.com/repos/huggingface/datasets/issues/5618/events | https://github.com/huggingface/datasets/issues/5618 | 1,612,977,934 | I_kwDODunzps5gJBcO | 5,618 | Unpin fsspec < 2023.3.0 once issue fixed | {
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See:
- #5614 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008771 / 0.011353 (-0.002582) | 0.004665 / 0.011008 (-0.006343) | 0.101645 / 0.038508 (0.063137) | 0.030190 / 0.023109 (0.007081) | 0.298581 / 0.275898 (0.022683) | 0.371206 / 0.323480 (0.047727) | 0.007272 / 0.007986 (-0.000714) | 0.003432 / 0.004328 (-0.000896) | 0.078645 / 0.004250 (0.074395) | 0.037640 / 0.037052 (0.000588) | 0.314014 / 0.258489 (0.055525) | 0.345682 / 0.293841 (0.051841) | 0.033675 / 0.128546 (-0.094871) | 0.011513 / 0.075646 (-0.064134) | 0.320683 / 0.419271 (-0.098589) | 0.041633 / 0.043533 (-0.001900) | 0.302697 / 0.255139 (0.047558) | 0.323560 / 0.283200 (0.040361) | 0.089309 / 0.141683 (-0.052374) | 1.477570 / 1.452155 (0.025415) | 1.528004 / 1.492716 (0.035287) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.184710 / 0.018006 (0.166704) | 0.412794 / 0.000490 (0.412305) | 0.001421 / 0.000200 (0.001221) | 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) | 0.023133 / 0.037411 (-0.014278) | 0.099492 / 0.014526 (0.084967) | 0.104806 / 0.176557 (-0.071751) | 0.150765 / 0.737135 (-0.586370) | 0.110127 / 0.296338 (-0.186211) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.438642 / 0.215209 (0.223433) | 4.349753 / 2.077655 (2.272098) | 2.178754 / 1.504120 (0.674634) | 1.952839 / 1.541195 (0.411645) | 1.840574 / 1.468490 (0.372084) | 0.694016 / 4.584777 (-3.890761) | 3.375186 / 3.745712 (-0.370526) | 1.892391 / 5.269862 (-3.377470) | 1.177643 / 4.565676 (-3.388033) | 0.082328 / 0.424275 (-0.341947) | 0.012280 / 0.007607 (0.004673) | 0.534478 / 0.226044 (0.308434) | 5.377043 / 2.268929 (3.108114) | 2.645273 / 55.444624 (-52.799351) | 2.336391 / 6.876477 (-4.540086) | 2.387917 / 2.142072 (0.245845) | 0.814399 / 4.805227 (-3.990828) | 0.149226 / 6.500664 (-6.351438) | 0.066614 / 0.075469 (-0.008855) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.205467 / 1.841788 (-0.636321) | 13.857481 / 8.074308 (5.783173) | 14.269958 / 10.191392 (4.078566) | 0.152199 / 0.680424 (-0.528225) | 0.029083 / 0.534201 (-0.505118) | 0.397590 / 0.579283 (-0.181693) | 0.410587 / 0.434364 (-0.023777) | 0.480479 / 0.540337 (-0.059858) | 0.576014 / 1.386936 (-0.810922) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006956 / 0.011353 (-0.004397) | 0.004914 / 0.011008 (-0.006094) | 0.077571 / 0.038508 (0.039063) | 0.028309 / 0.023109 (0.005200) | 0.344523 / 0.275898 (0.068625) | 0.383039 / 0.323480 (0.059560) | 0.005202 / 0.007986 (-0.002783) | 0.003513 / 0.004328 (-0.000816) | 0.076393 / 0.004250 (0.072142) | 0.042035 / 0.037052 (0.004982) | 0.342950 / 0.258489 (0.084461) | 0.387432 / 0.293841 (0.093591) | 0.032267 / 0.128546 (-0.096280) | 0.011914 / 0.075646 (-0.063732) | 0.087140 / 0.419271 (-0.332131) | 0.042624 / 0.043533 (-0.000909) | 0.342391 / 0.255139 (0.087253) | 0.367016 / 0.283200 (0.083817) | 0.091757 / 0.141683 (-0.049926) | 1.515845 / 1.452155 (0.063690) | 1.607929 / 1.492716 (0.115213) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.234461 / 0.018006 (0.216455) | 0.420430 / 0.000490 (0.419941) | 0.000403 / 0.000200 (0.000203) | 0.000059 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026639 / 0.037411 (-0.010772) | 0.101860 / 0.014526 (0.087334) | 0.109696 / 0.176557 (-0.066860) | 0.160902 / 0.737135 (-0.576233) | 0.112431 / 0.296338 (-0.183907) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.438444 / 0.215209 (0.223235) | 4.378881 / 2.077655 (2.301226) | 2.063975 / 1.504120 (0.559855) | 1.863069 / 1.541195 (0.321874) | 1.955684 / 1.468490 (0.487193) | 0.694106 / 4.584777 (-3.890671) | 3.467683 / 3.745712 (-0.278029) | 2.882441 / 5.269862 (-2.387421) | 1.484533 / 4.565676 (-3.081143) | 0.082682 / 0.424275 (-0.341593) | 0.012597 / 0.007607 (0.004990) | 0.539219 / 0.226044 (0.313174) | 5.384838 / 2.268929 (3.115909) | 2.528273 / 55.444624 (-52.916351) | 2.190332 / 6.876477 (-4.686145) | 2.252573 / 2.142072 (0.110500) | 0.801047 / 4.805227 (-4.004180) | 0.151082 / 6.500664 (-6.349582) | 0.067564 / 0.075469 (-0.007905) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.306469 / 1.841788 (-0.535319) | 14.220154 / 8.074308 (6.145846) | 13.300979 / 10.191392 (3.109586) | 0.153827 / 0.680424 (-0.526597) | 0.016818 / 0.534201 (-0.517383) | 0.383528 / 0.579283 (-0.195755) | 0.393970 / 0.434364 (-0.040394) | 0.468395 / 0.540337 (-0.071943) | 0.558748 / 1.386936 (-0.828188) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#824860ca204a3bd84a7d63f71df5df4c56c2432f \"CML watermark\")\n"
] | "2023-03-07T08:18:20Z" | "2023-03-07T08:44:55Z" | "2023-03-07T08:37:28Z" | MEMBER | null | 0 | {
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} | As a hotfix for our CI, temporarily pin `fsspec`:
Fix #5616.
Until root cause is fixed, see:
- #5614 | {
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] | closed | false | null | [] | null | [] | "2023-03-07T08:06:39Z" | "2023-03-07T08:37:29Z" | "2023-03-07T08:37:29Z" | MEMBER | null | null | null | As reported by @lhoestq, our CI is broken after `fsspec` 2023.3.0 release:
```
FAILED tests/test_filesystem.py::test_compression_filesystems[Bz2FileSystem] - AssertionError: assert [{'created': ...: False, ...}] == ['file.txt']
At index 0 diff: {'name': 'file.txt', 'size': 70, 'type': 'file', 'created': 1678175677.1887748, 'islink': False, 'mode': 33188, 'uid': 1001, 'gid': 123, 'mtime': 1678175677.1887748, 'ino': 286957, 'nlink': 1} != 'file.txt'
Full diff:
[
- 'file.txt',
+ {'created': 1678175677.1887748,
+ 'gid': 123,
+ 'ino': 286957,
+ 'islink': False,
+ 'mode': 33188,
+ 'mtime': 1678175677.1887748,
+ 'name': 'file.txt',
+ 'nlink': 1,
+ 'size': 70,
+ 'type': 'file',
+ 'uid': 1001},
]
```
Also:
```
FAILED tests/test_filesystem.py::test_compression_filesystems[GzipFileSystem] - AssertionError: assert [{'created': ...: False, ...}] == ['file.txt']
FAILED tests/test_filesystem.py::test_compression_filesystems[Lz4FileSystem] - AssertionError: assert [{'created': ...: False, ...}] == ['file.txt']
FAILED tests/test_filesystem.py::test_compression_filesystems[XzFileSystem] - AssertionError: assert [{'created': ...: False, ...}] == ['file.txt']
FAILED tests/test_filesystem.py::test_compression_filesystems[ZstdFileSystem] - AssertionError: assert [{'created': ...: False, ...}] == ['file.txt']
===== 5 failed, 2134 passed, 18 skipped, 38 warnings in 157.21s (0:02:37) ======
```
See:
- fsspec/filesystem_spec#1205 | {
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] | closed | false | null | [] | null | [
"Hi! You can use `concatenate_datasets([ids1, ids2], axis=1)` to do this."
] | "2023-03-07T01:52:00Z" | "2023-03-09T15:24:05Z" | "2023-03-09T15:23:54Z" | NONE | null | null | null | ### Describe the bug
`IterableDataset.add_column` occurs an exception when passing another `IterableDataset` as a parameter.
The method seems to accept only eager evaluated values.
https://github.com/huggingface/datasets/blob/35b789e8f6826b6b5a6b48fcc2416c890a1f326a/src/datasets/iterable_dataset.py#L1388-L1391
I wrote codes below to make it.
```py
def add_column(dataset: IterableDataset, name: str, add_dataset: IterableDataset, key: str) -> IterableDataset:
iter_add_dataset = iter(add_dataset)
def add_column_fn(example):
if name in example:
raise ValueError(f"Error when adding {name}: column {name} is already in the dataset.")
return {name: next(iter_add_dataset)[key]}
return dataset.map(add_column_fn)
```
Is there other way to do it? Or is it intended?
### Steps to reproduce the bug
Thie codes below occurs `NotImplementedError`
```py
from datasets import IterableDataset
def gen(num):
yield {f"col{num}": 1}
yield {f"col{num}": 2}
yield {f"col{num}": 3}
ids1 = IterableDataset.from_generator(gen, gen_kwargs={"num": 1})
ids2 = IterableDataset.from_generator(gen, gen_kwargs={"num": 2})
new_ids = ids1.add_column("new_col", ids1)
for row in new_ids:
print(row)
```
### Expected behavior
`IterableDataset.add_column` is able to task `IterableDataset` and lazy evaluated values as a parameter since IterableDataset is lazy evalued.
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-3.10.0-1160.36.2.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.9.7
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
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https://api.github.com/repos/huggingface/datasets/issues/5614 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5614/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5614/comments | https://api.github.com/repos/huggingface/datasets/issues/5614/events | https://github.com/huggingface/datasets/pull/5614 | 1,611,896,357 | PR_kwDODunzps5LZOTd | 5,614 | Fix archive fs test | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008664 / 0.011353 (-0.002689) | 0.004622 / 0.011008 (-0.006387) | 0.101716 / 0.038508 (0.063208) | 0.030044 / 0.023109 (0.006935) | 0.298476 / 0.275898 (0.022578) | 0.360873 / 0.323480 (0.037393) | 0.007012 / 0.007986 (-0.000974) | 0.003409 / 0.004328 (-0.000919) | 0.077731 / 0.004250 (0.073480) | 0.035493 / 0.037052 (-0.001560) | 0.311474 / 0.258489 (0.052985) | 0.357276 / 0.293841 (0.063435) | 0.033909 / 0.128546 (-0.094638) | 0.011315 / 0.075646 (-0.064332) | 0.323149 / 0.419271 (-0.096122) | 0.040678 / 0.043533 (-0.002855) | 0.298487 / 0.255139 (0.043348) | 0.323107 / 0.283200 (0.039907) | 0.086641 / 0.141683 (-0.055042) | 1.452905 / 1.452155 (0.000750) | 1.510953 / 1.492716 (0.018237) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.190607 / 0.018006 (0.172601) | 0.409786 / 0.000490 (0.409297) | 0.000818 / 0.000200 (0.000618) | 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) | 0.023267 / 0.037411 (-0.014144) | 0.095390 / 0.014526 (0.080864) | 0.104381 / 0.176557 (-0.072175) | 0.150735 / 0.737135 (-0.586401) | 0.106876 / 0.296338 (-0.189462) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434259 / 0.215209 (0.219050) | 4.326978 / 2.077655 (2.249323) | 2.036690 / 1.504120 (0.532570) | 1.836459 / 1.541195 (0.295264) | 1.904003 / 1.468490 (0.435513) | 0.697265 / 4.584777 (-3.887512) | 3.435911 / 3.745712 (-0.309802) | 3.240918 / 5.269862 (-2.028944) | 1.629220 / 4.565676 (-2.936456) | 0.083158 / 0.424275 (-0.341117) | 0.012604 / 0.007607 (0.004997) | 0.539818 / 0.226044 (0.313773) | 5.397860 / 2.268929 (3.128932) | 2.483890 / 55.444624 (-52.960735) | 2.132404 / 6.876477 (-4.744072) | 2.162583 / 2.142072 (0.020510) | 0.817773 / 4.805227 (-3.987454) | 0.151677 / 6.500664 (-6.348987) | 0.066569 / 0.075469 (-0.008900) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.243449 / 1.841788 (-0.598339) | 13.699854 / 8.074308 (5.625546) | 13.930979 / 10.191392 (3.739587) | 0.165344 / 0.680424 (-0.515079) | 0.028910 / 0.534201 (-0.505291) | 0.396201 / 0.579283 (-0.183082) | 0.404448 / 0.434364 (-0.029916) | 0.482031 / 0.540337 (-0.058306) | 0.570023 / 1.386936 (-0.816913) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006785 / 0.011353 (-0.004568) | 0.004643 / 0.011008 (-0.006365) | 0.076755 / 0.038508 (0.038247) | 0.027893 / 0.023109 (0.004783) | 0.342539 / 0.275898 (0.066641) | 0.379103 / 0.323480 (0.055623) | 0.005107 / 0.007986 (-0.002879) | 0.003413 / 0.004328 (-0.000915) | 0.075779 / 0.004250 (0.071528) | 0.039251 / 0.037052 (0.002199) | 0.343425 / 0.258489 (0.084935) | 0.385292 / 0.293841 (0.091451) | 0.032229 / 0.128546 (-0.096317) | 0.011666 / 0.075646 (-0.063980) | 0.086452 / 0.419271 (-0.332819) | 0.042918 / 0.043533 (-0.000615) | 0.343145 / 0.255139 (0.088006) | 0.367916 / 0.283200 (0.084717) | 0.090810 / 0.141683 (-0.050873) | 1.471679 / 1.452155 (0.019524) | 1.566683 / 1.492716 (0.073966) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220343 / 0.018006 (0.202336) | 0.396155 / 0.000490 (0.395665) | 0.003831 / 0.000200 (0.003631) | 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) | 0.024990 / 0.037411 (-0.012421) | 0.101270 / 0.014526 (0.086744) | 0.110115 / 0.176557 (-0.066442) | 0.161770 / 0.737135 (-0.575365) | 0.112187 / 0.296338 (-0.184151) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436199 / 0.215209 (0.220989) | 4.329084 / 2.077655 (2.251429) | 2.043335 / 1.504120 (0.539215) | 1.836799 / 1.541195 (0.295604) | 1.908362 / 1.468490 (0.439872) | 0.700518 / 4.584777 (-3.884259) | 3.418003 / 3.745712 (-0.327710) | 1.860621 / 5.269862 (-3.409241) | 1.171343 / 4.565676 (-3.394334) | 0.083150 / 0.424275 (-0.341125) | 0.012543 / 0.007607 (0.004936) | 0.533528 / 0.226044 (0.307483) | 5.339660 / 2.268929 (3.070732) | 2.499494 / 55.444624 (-52.945131) | 2.154773 / 6.876477 (-4.721704) | 2.198734 / 2.142072 (0.056661) | 0.803383 / 4.805227 (-4.001844) | 0.150980 / 6.500664 (-6.349684) | 0.068050 / 0.075469 (-0.007419) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.309487 / 1.841788 (-0.532301) | 14.177068 / 8.074308 (6.102760) | 13.218912 / 10.191392 (3.027520) | 0.156857 / 0.680424 (-0.523567) | 0.016534 / 0.534201 (-0.517667) | 0.383986 / 0.579283 (-0.195297) | 0.395264 / 0.434364 (-0.039100) | 0.442310 / 0.540337 (-0.098027) | 0.535535 / 1.386936 (-0.851401) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#64e24bca88be711f4fdcb9c18edaddc1db0bbe2e \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009446 / 0.011353 (-0.001907) | 0.005061 / 0.011008 (-0.005948) | 0.099783 / 0.038508 (0.061275) | 0.036379 / 0.023109 (0.013270) | 0.296769 / 0.275898 (0.020871) | 0.368990 / 0.323480 (0.045510) | 0.007891 / 0.007986 (-0.000094) | 0.003940 / 0.004328 (-0.000389) | 0.076284 / 0.004250 (0.072034) | 0.044390 / 0.037052 (0.007337) | 0.313373 / 0.258489 (0.054884) | 0.361118 / 0.293841 (0.067277) | 0.039058 / 0.128546 (-0.089488) | 0.012016 / 0.075646 (-0.063631) | 0.334239 / 0.419271 (-0.085033) | 0.047028 / 0.043533 (0.003495) | 0.297766 / 0.255139 (0.042627) | 0.312853 / 0.283200 (0.029653) | 0.099117 / 0.141683 (-0.042566) | 1.475487 / 1.452155 (0.023332) | 1.557487 / 1.492716 (0.064771) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206243 / 0.018006 (0.188237) | 0.443920 / 0.000490 (0.443430) | 0.001404 / 0.000200 (0.001205) | 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) | 0.026347 / 0.037411 (-0.011065) | 0.105880 / 0.014526 (0.091354) | 0.116227 / 0.176557 (-0.060330) | 0.157404 / 0.737135 (-0.579732) | 0.121668 / 0.296338 (-0.174671) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.398614 / 0.215209 (0.183405) | 3.970657 / 2.077655 (1.893002) | 1.778899 / 1.504120 (0.274779) | 1.591806 / 1.541195 (0.050611) | 1.687717 / 1.468490 (0.219227) | 0.695399 / 4.584777 (-3.889378) | 3.829281 / 3.745712 (0.083569) | 2.140856 / 5.269862 (-3.129006) | 1.355027 / 4.565676 (-3.210650) | 0.085714 / 0.424275 (-0.338561) | 0.012130 / 0.007607 (0.004523) | 0.505807 / 0.226044 (0.279762) | 5.053098 / 2.268929 (2.784170) | 2.321694 / 55.444624 (-53.122931) | 2.015909 / 6.876477 (-4.860568) | 2.100862 / 2.142072 (-0.041210) | 0.855689 / 4.805227 (-3.949539) | 0.167192 / 6.500664 (-6.333472) | 0.062376 / 0.075469 (-0.013093) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.196647 / 1.841788 (-0.645141) | 14.971356 / 8.074308 (6.897048) | 13.897184 / 10.191392 (3.705792) | 0.193267 / 0.680424 (-0.487157) | 0.029252 / 0.534201 (-0.504949) | 0.444885 / 0.579283 (-0.134398) | 0.452792 / 0.434364 (0.018429) | 0.550157 / 0.540337 (0.009819) | 0.658524 / 1.386936 (-0.728412) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007774 / 0.011353 (-0.003579) | 0.005304 / 0.011008 (-0.005704) | 0.075530 / 0.038508 (0.037022) | 0.034930 / 0.023109 (0.011821) | 0.343879 / 0.275898 (0.067981) | 0.386487 / 0.323480 (0.063008) | 0.005998 / 0.007986 (-0.001987) | 0.005619 / 0.004328 (0.001291) | 0.075865 / 0.004250 (0.071614) | 0.050499 / 0.037052 (0.013446) | 0.345503 / 0.258489 (0.087014) | 0.392081 / 0.293841 (0.098240) | 0.037118 / 0.128546 (-0.091429) | 0.012540 / 0.075646 (-0.063107) | 0.086202 / 0.419271 (-0.333069) | 0.050672 / 0.043533 (0.007139) | 0.343622 / 0.255139 (0.088483) | 0.353853 / 0.283200 (0.070653) | 0.105408 / 0.141683 (-0.036274) | 1.460695 / 1.452155 (0.008540) | 1.524270 / 1.492716 (0.031554) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.219356 / 0.018006 (0.201350) | 0.440740 / 0.000490 (0.440251) | 0.014313 / 0.000200 (0.014114) | 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) | 0.030297 / 0.037411 (-0.007115) | 0.108723 / 0.014526 (0.094197) | 0.125085 / 0.176557 (-0.051471) | 0.176664 / 0.737135 (-0.560471) | 0.126659 / 0.296338 (-0.169680) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.445790 / 0.215209 (0.230581) | 4.241046 / 2.077655 (2.163391) | 2.027381 / 1.504120 (0.523261) | 1.821070 / 1.541195 (0.279876) | 1.934417 / 1.468490 (0.465927) | 0.710897 / 4.584777 (-3.873880) | 3.840397 / 3.745712 (0.094685) | 3.959196 / 5.269862 (-1.310666) | 1.646069 / 4.565676 (-2.919608) | 0.088615 / 0.424275 (-0.335660) | 0.012321 / 0.007607 (0.004714) | 0.523463 / 0.226044 (0.297418) | 5.240147 / 2.268929 (2.971218) | 2.521639 / 55.444624 (-52.922986) | 2.246535 / 6.876477 (-4.629942) | 2.365913 / 2.142072 (0.223841) | 0.851288 / 4.805227 (-3.953939) | 0.170179 / 6.500664 (-6.330485) | 0.064732 / 0.075469 (-0.010737) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.255505 / 1.841788 (-0.586283) | 15.305457 / 8.074308 (7.231148) | 13.214186 / 10.191392 (3.022794) | 0.188971 / 0.680424 (-0.491453) | 0.018972 / 0.534201 (-0.515229) | 0.429621 / 0.579283 (-0.149662) | 0.428738 / 0.434364 (-0.005626) | 0.536241 / 0.540337 (-0.004096) | 0.632998 / 1.386936 (-0.753938) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b64fae9509f6e9da9cabf0ce677966598fc61e38 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008435 / 0.011353 (-0.002918) | 0.004454 / 0.011008 (-0.006554) | 0.099091 / 0.038508 (0.060583) | 0.028890 / 0.023109 (0.005781) | 0.297450 / 0.275898 (0.021551) | 0.329025 / 0.323480 (0.005545) | 0.006584 / 0.007986 (-0.001401) | 0.004669 / 0.004328 (0.000340) | 0.077387 / 0.004250 (0.073137) | 0.033701 / 0.037052 (-0.003352) | 0.301272 / 0.258489 (0.042783) | 0.345401 / 0.293841 (0.051560) | 0.033473 / 0.128546 (-0.095073) | 0.011244 / 0.075646 (-0.064402) | 0.321941 / 0.419271 (-0.097330) | 0.040646 / 0.043533 (-0.002887) | 0.306686 / 0.255139 (0.051547) | 0.321868 / 0.283200 (0.038668) | 0.084281 / 0.141683 (-0.057401) | 1.491414 / 1.452155 (0.039259) | 1.542799 / 1.492716 (0.050083) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.188368 / 0.018006 (0.170362) | 0.398595 / 0.000490 (0.398105) | 0.000805 / 0.000200 (0.000605) | 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) | 0.022690 / 0.037411 (-0.014721) | 0.096795 / 0.014526 (0.082269) | 0.104037 / 0.176557 (-0.072520) | 0.149409 / 0.737135 (-0.587727) | 0.108022 / 0.296338 (-0.188317) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419316 / 0.215209 (0.204107) | 4.186850 / 2.077655 (2.109196) | 1.920182 / 1.504120 (0.416062) | 1.715493 / 1.541195 (0.174298) | 1.757767 / 1.468490 (0.289277) | 0.692296 / 4.584777 (-3.892480) | 3.342330 / 3.745712 (-0.403382) | 1.842063 / 5.269862 (-3.427798) | 1.150190 / 4.565676 (-3.415487) | 0.082792 / 0.424275 (-0.341483) | 0.012540 / 0.007607 (0.004933) | 0.528867 / 0.226044 (0.302822) | 5.297818 / 2.268929 (3.028890) | 2.313173 / 55.444624 (-53.131451) | 1.941723 / 6.876477 (-4.934754) | 1.982948 / 2.142072 (-0.159125) | 0.808951 / 4.805227 (-3.996276) | 0.149338 / 6.500664 (-6.351326) | 0.064838 / 0.075469 (-0.010631) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.187865 / 1.841788 (-0.653923) | 13.381918 / 8.074308 (5.307610) | 13.730627 / 10.191392 (3.539234) | 0.149976 / 0.680424 (-0.530447) | 0.028249 / 0.534201 (-0.505952) | 0.392591 / 0.579283 (-0.186692) | 0.403451 / 0.434364 (-0.030912) | 0.467484 / 0.540337 (-0.072853) | 0.560296 / 1.386936 (-0.826640) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006440 / 0.011353 (-0.004913) | 0.004488 / 0.011008 (-0.006521) | 0.077875 / 0.038508 (0.039367) | 0.027284 / 0.023109 (0.004174) | 0.341625 / 0.275898 (0.065727) | 0.374960 / 0.323480 (0.051480) | 0.005581 / 0.007986 (-0.002405) | 0.003326 / 0.004328 (-0.001003) | 0.076928 / 0.004250 (0.072677) | 0.038205 / 0.037052 (0.001153) | 0.345933 / 0.258489 (0.087444) | 0.383675 / 0.293841 (0.089834) | 0.031908 / 0.128546 (-0.096638) | 0.011724 / 0.075646 (-0.063922) | 0.086974 / 0.419271 (-0.332298) | 0.043084 / 0.043533 (-0.000449) | 0.339663 / 0.255139 (0.084524) | 0.363782 / 0.283200 (0.080582) | 0.090934 / 0.141683 (-0.050749) | 1.459718 / 1.452155 (0.007563) | 1.541104 / 1.492716 (0.048388) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224005 / 0.018006 (0.205998) | 0.400727 / 0.000490 (0.400238) | 0.000427 / 0.000200 (0.000227) | 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) | 0.024604 / 0.037411 (-0.012807) | 0.099813 / 0.014526 (0.085287) | 0.104034 / 0.176557 (-0.072523) | 0.156245 / 0.737135 (-0.580890) | 0.108739 / 0.296338 (-0.187600) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440500 / 0.215209 (0.225291) | 4.379934 / 2.077655 (2.302279) | 2.075826 / 1.504120 (0.571706) | 1.867635 / 1.541195 (0.326441) | 1.919035 / 1.468490 (0.450545) | 0.696613 / 4.584777 (-3.888164) | 3.334993 / 3.745712 (-0.410720) | 1.857139 / 5.269862 (-3.412723) | 1.160598 / 4.565676 (-3.405079) | 0.083120 / 0.424275 (-0.341155) | 0.012475 / 0.007607 (0.004868) | 0.544607 / 0.226044 (0.318563) | 5.436808 / 2.268929 (3.167879) | 2.518562 / 55.444624 (-52.926063) | 2.158434 / 6.876477 (-4.718042) | 2.170691 / 2.142072 (0.028618) | 0.811297 / 4.805227 (-3.993930) | 0.150675 / 6.500664 (-6.349990) | 0.065655 / 0.075469 (-0.009814) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.277627 / 1.841788 (-0.564160) | 13.833501 / 8.074308 (5.759193) | 13.038718 / 10.191392 (2.847325) | 0.148837 / 0.680424 (-0.531587) | 0.016440 / 0.534201 (-0.517761) | 0.379147 / 0.579283 (-0.200136) | 0.379753 / 0.434364 (-0.054611) | 0.460197 / 0.540337 (-0.080141) | 0.544152 / 1.386936 (-0.842784) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6e2a235cbab1c91dc5eca0cb123f9c9d9f743461 \"CML watermark\")\n"
] | "2023-03-06T17:28:09Z" | "2023-03-07T13:27:50Z" | "2023-03-07T13:20:57Z" | MEMBER | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5613 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5613/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5613/comments | https://api.github.com/repos/huggingface/datasets/issues/5613/events | https://github.com/huggingface/datasets/issues/5613 | 1,611,875,473 | I_kwDODunzps5gE0SR | 5,613 | Version mismatch with multiprocess and dill on Python 3.10 | {
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"Sorry, I just found https://github.com/apache/beam/issues/24458. It seems this issue is being worked on. ",
"Reopening, since I think the docs should inform the user of this problem. For example, [this page](https://huggingface.co/docs/datasets/installation) says \r\n> Datasets is tested on Python 3.7+.\r\n\r\nbut it should probably say that Beam Datasets do not work with Python 3.10 (or link to a known issues page). ",
"Same problem on Colab using a vanilla setup running :\r\nPython 3.10.11 \r\napache-beam 2.47.0\r\ndatasets 2.12.0",
"Same problem, \r\npy 3.10.11\r\napache-beam==2.47.0\r\ndatasets==2.12.0"
] | "2023-03-06T17:14:41Z" | "2023-05-28T01:03:55Z" | null | NONE | null | null | null | ### Describe the bug
Grabbing the latest version of `datasets` and `apache-beam` with `poetry` using Python 3.10 gives a crash at runtime. The crash is
```
File "/Users/adpauls/sc/git/DSI-transformers/data/NQ/create_NQ_train_vali.py", line 1, in <module>
import datasets
File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/__init__.py", line 43, in <module>
from .arrow_dataset import Dataset
File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 65, in <module>
from .arrow_reader import ArrowReader
File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/arrow_reader.py", line 30, in <module>
from .download.download_config import DownloadConfig
File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/download/__init__.py", line 9, in <module>
from .download_manager import DownloadManager, DownloadMode
File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/download/download_manager.py", line 35, in <module>
from ..utils.py_utils import NestedDataStructure, map_nested, size_str
File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 40, in <module>
import multiprocess.pool
File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/multiprocess/pool.py", line 609, in <module>
class ThreadPool(Pool):
File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/multiprocess/pool.py", line 611, in ThreadPool
from .dummy import Process
File "/Users/adpauls/Library/Caches/pypoetry/virtualenvs/yyy-oPbZ7mKM-py3.10/lib/python3.10/site-packages/multiprocess/dummy/__init__.py", line 87, in <module>
class Condition(threading._Condition):
AttributeError: module 'threading' has no attribute '_Condition'. Did you mean: 'Condition'?
```
I think this is a bad interaction of versions from `dill`, `multiprocess`, `apache-beam`, and `threading` from the Python (3.10) standard lib. Upgrading `multiprocess` to a version that does not crash like this is not possible because `apache-beam` pins `dill` to and old version:
```
Because multiprocess (0.70.10) depends on dill (>=0.3.2)
and apache-beam (2.45.0) depends on dill (>=0.3.1.1,<0.3.2), multiprocess (0.70.10) is incompatible with apache-beam (2.45.0).
And because no versions of apache-beam match >2.45.0,<3.0.0, multiprocess (0.70.10) is incompatible with apache-beam (>=2.45.0,<3.0.0).
So, because yyy depends on both apache-beam (^2.45.0) and multiprocess (0.70.10), version solving failed.
```
Perhaps it is not right to file a bug here, but I'm not totally sure whose fault it is. And in any case, this is an immediate blocker to using `datasets` out of the box.
Possibly related to https://github.com/huggingface/datasets/issues/5232.
### Steps to reproduce the bug
Steps to reproduce:
1. Make a poetry project with this configuration
```
[tool.poetry]
name = "yyy"
version = "0.1.0"
description = ""
authors = ["Adam Pauls <adpauls@gmail.com>"]
readme = "README.md"
packages = [{ include = "xxx" }]
[tool.poetry.dependencies]
python = ">=3.10,<3.11"
datasets = "^2.10.1"
apache-beam = "^2.45.0"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
```
2. `poetry install`.
3. `poetry run python -c "import datasets"`.
### Expected behavior
Script runs.
### Environment info
Python 3.10. Here are the versions installed by `poetry`:
```
•• Installing frozenlist (1.3.3)
• Installing idna (3.4)
• Installing multidict (6.0.4)
• Installing aiosignal (1.3.1)
• Installing async-timeout (4.0.2)
• Installing attrs (22.2.0)
• Installing certifi (2022.12.7)
• Installing charset-normalizer (3.1.0)
• Installing six (1.16.0)
• Installing urllib3 (1.26.14)
• Installing yarl (1.8.2)
• Installing aiohttp (3.8.4)
• Installing dill (0.3.1.1)
• Installing docopt (0.6.2)
• Installing filelock (3.9.0)
• Installing numpy (1.22.4)
• Installing pyparsing (3.0.9)
• Installing protobuf (3.19.4)
• Installing packaging (23.0)
• Installing python-dateutil (2.8.2)
• Installing pytz (2022.7.1)
• Installing pyyaml (6.0)
• Installing requests (2.28.2)
• Installing tqdm (4.65.0)
• Installing typing-extensions (4.5.0)
• Installing cloudpickle (2.2.1)
• Installing crcmod (1.7)
• Installing fastavro (1.7.2)
• Installing fasteners (0.18)
• Installing fsspec (2023.3.0)
• Installing grpcio (1.51.3)
• Installing hdfs (2.7.0)
• Installing httplib2 (0.20.4)
• Installing huggingface-hub (0.12.1)
• Installing multiprocess (0.70.9)
• Installing objsize (0.6.1)
• Installing orjson (3.8.7)
• Installing pandas (1.5.3)
• Installing proto-plus (1.22.2)
• Installing pyarrow (9.0.0)
• Installing pydot (1.4.2)
• Installing pymongo (3.13.0)
• Installing regex (2022.10.31)
• Installing responses (0.18.0)
• Installing xxhash (3.2.0)
• Installing zstandard (0.20.0)
• Installing apache-beam (2.45.0)
• Installing datasets (2.10.1)
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/5612 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5612/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5612/comments | https://api.github.com/repos/huggingface/datasets/issues/5612/events | https://github.com/huggingface/datasets/issues/5612 | 1,611,262,510 | I_kwDODunzps5gCeou | 5,612 | Arrow map type in parquet files unsupported | {
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"I'm attaching a minimal reproducible example:\r\n```python\r\nfrom datasets import load_dataset\r\nimport pyarrow as pa\r\nimport pyarrow.parquet as pq\r\n\r\ntable_with_map = pa.Table.from_pydict(\r\n {\"a\": [1, 2], \"b\": [[(\"a\", 2)], [(\"b\", 4)]]},\r\n schema=pa.schema({\"a\": pa.int32(), \"b\": pa.map_(pa.string(), pa.int32())})\r\n)\r\npq.write_table(table_with_map, \"parquet_with_map.parquet\")\r\ndset = load_dataset(\"parquet\", data_files=\"parquet_with_map.parquet\", split=\"train\") # error unless streaming=True\r\n``` \r\n\r\nFor a dataset generated with the packaged loaders (CSV, JSON, Parquet), `streaming=True` sets the dataset's features to `None` (unless explicitly provided in `load_dataset`), hence no error will be thrown as long as the features stay \"unresolved\" (resolving the features with `_resolve_features` will lead to an error)."
] | "2023-03-06T12:03:24Z" | "2023-03-14T17:20:25Z" | null | CONTRIBUTOR | null | null | null | ### Describe the bug
When I try to load parquet files that were processed with Spark, I get the following issue:
`ValueError: Arrow type map<string, string ('warc_headers')> does not have a datasets dtype equivalent.`
Strangely, loading the dataset with `streaming=True` solves the issue.
### Steps to reproduce the bug
The dataset is private, but this can be reproduced with any dataset that has Arrow maps.
### Expected behavior
Loading the dataset no matter whether streaming is True or not.
### Environment info
- `datasets` version: 2.10.1
- Platform: Linux-5.15.0-1029-gcp-x86_64-with-glibc2.31
- Python version: 3.10.7
- PyArrow version: 8.0.0
- Pandas version: 1.4.2 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Hi, thanks for working on this! `Table.to_pylist` requires PyArrow 7.0+, and our minimal version requirement is 6.0, so we need to bump the version requirement to avoid CI failure. I'll do this in a separate PR.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006857 / 0.011353 (-0.004496) | 0.004711 / 0.011008 (-0.006297) | 0.098332 / 0.038508 (0.059824) | 0.028547 / 0.023109 (0.005438) | 0.307647 / 0.275898 (0.031749) | 0.334891 / 0.323480 (0.011411) | 0.005252 / 0.007986 (-0.002734) | 0.003495 / 0.004328 (-0.000833) | 0.075529 / 0.004250 (0.071279) | 0.042167 / 0.037052 (0.005114) | 0.308509 / 0.258489 (0.050020) | 0.348294 / 0.293841 (0.054453) | 0.032042 / 0.128546 (-0.096504) | 0.011684 / 0.075646 (-0.063962) | 0.321740 / 0.419271 (-0.097531) | 0.057725 / 0.043533 (0.014193) | 0.309431 / 0.255139 (0.054292) | 0.326818 / 0.283200 (0.043618) | 0.093261 / 0.141683 (-0.048422) | 1.475344 / 1.452155 (0.023190) | 1.563952 / 1.492716 (0.071236) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.205056 / 0.018006 (0.187050) | 0.421656 / 0.000490 (0.421166) | 0.004167 / 0.000200 (0.003967) | 0.000075 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023935 / 0.037411 (-0.013476) | 0.097220 / 0.014526 (0.082695) | 0.104942 / 0.176557 (-0.071615) | 0.170339 / 0.737135 (-0.566796) | 0.107556 / 0.296338 (-0.188782) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424509 / 0.215209 (0.209300) | 4.223637 / 2.077655 (2.145982) | 2.090700 / 1.504120 (0.586580) | 1.902537 / 1.541195 (0.361343) | 1.981192 / 1.468490 (0.512701) | 0.695272 / 4.584777 (-3.889505) | 3.570169 / 3.745712 (-0.175544) | 1.885007 / 5.269862 (-3.384854) | 1.162828 / 4.565676 (-3.402848) | 0.084956 / 0.424275 (-0.339319) | 0.012818 / 0.007607 (0.005210) | 0.534395 / 0.226044 (0.308351) | 5.354318 / 2.268929 (3.085389) | 2.436875 / 55.444624 (-53.007749) | 2.111365 / 6.876477 (-4.765112) | 2.232874 / 2.142072 (0.090802) | 0.804703 / 4.805227 (-4.000524) | 0.152406 / 6.500664 (-6.348258) | 0.066926 / 0.075469 (-0.008543) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.198621 / 1.841788 (-0.643166) | 13.907491 / 8.074308 (5.833183) | 14.356286 / 10.191392 (4.164894) | 0.140714 / 0.680424 (-0.539710) | 0.016440 / 0.534201 (-0.517761) | 0.380868 / 0.579283 (-0.198415) | 0.396004 / 0.434364 (-0.038360) | 0.448275 / 0.540337 (-0.092062) | 0.537818 / 1.386936 (-0.849118) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006789 / 0.011353 (-0.004564) | 0.004652 / 0.011008 (-0.006356) | 0.076449 / 0.038508 (0.037941) | 0.028389 / 0.023109 (0.005280) | 0.378644 / 0.275898 (0.102746) | 0.423870 / 0.323480 (0.100391) | 0.005824 / 0.007986 (-0.002162) | 0.003398 / 0.004328 (-0.000931) | 0.075575 / 0.004250 (0.071324) | 0.039656 / 0.037052 (0.002604) | 0.370072 / 0.258489 (0.111583) | 0.441812 / 0.293841 (0.147971) | 0.031817 / 0.128546 (-0.096729) | 0.011701 / 0.075646 (-0.063946) | 0.085759 / 0.419271 (-0.333513) | 0.042328 / 0.043533 (-0.001205) | 0.364103 / 0.255139 (0.108964) | 0.413910 / 0.283200 (0.130711) | 0.090871 / 0.141683 (-0.050812) | 1.505749 / 1.452155 (0.053594) | 1.608555 / 1.492716 (0.115839) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212533 / 0.018006 (0.194527) | 0.404519 / 0.000490 (0.404030) | 0.000373 / 0.000200 (0.000174) | 0.000059 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024849 / 0.037411 (-0.012562) | 0.100769 / 0.014526 (0.086243) | 0.110450 / 0.176557 (-0.066107) | 0.161715 / 0.737135 (-0.575420) | 0.113599 / 0.296338 (-0.182739) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436780 / 0.215209 (0.221571) | 4.387103 / 2.077655 (2.309448) | 2.081942 / 1.504120 (0.577822) | 1.873661 / 1.541195 (0.332466) | 1.947718 / 1.468490 (0.479228) | 0.696434 / 4.584777 (-3.888343) | 3.405300 / 3.745712 (-0.340412) | 1.897388 / 5.269862 (-3.372474) | 1.169969 / 4.565676 (-3.395707) | 0.083085 / 0.424275 (-0.341190) | 0.012480 / 0.007607 (0.004873) | 0.535635 / 0.226044 (0.309591) | 5.364462 / 2.268929 (3.095533) | 2.531168 / 55.444624 (-52.913457) | 2.184324 / 6.876477 (-4.692153) | 2.228613 / 2.142072 (0.086541) | 0.807127 / 4.805227 (-3.998100) | 0.151971 / 6.500664 (-6.348693) | 0.068430 / 0.075469 (-0.007039) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.306401 / 1.841788 (-0.535387) | 14.479552 / 8.074308 (6.405244) | 14.428398 / 10.191392 (4.237006) | 0.159505 / 0.680424 (-0.520919) | 0.016856 / 0.534201 (-0.517344) | 0.375197 / 0.579283 (-0.204086) | 0.384328 / 0.434364 (-0.050036) | 0.440688 / 0.540337 (-0.099650) | 0.524998 / 1.386936 (-0.861938) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#50b887b840cf3cab86b0394b41050b579c4b79ba \"CML watermark\")\n"
] | "2023-03-06T11:21:57Z" | "2023-03-27T13:34:19Z" | "2023-03-27T13:26:38Z" | CONTRIBUTOR | null | 0 | {
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} | close https://github.com/huggingface/datasets/issues/5606
This PR is for adding the `Dataset.to_list` method.
Thank you in advance.
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https://api.github.com/repos/huggingface/datasets/issues/5610 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5610/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5610/comments | https://api.github.com/repos/huggingface/datasets/issues/5610/events | https://github.com/huggingface/datasets/issues/5610 | 1,610,698,006 | I_kwDODunzps5gAU0W | 5,610 | use datasets streaming mode in trainer ddp mode cause memory leak | {
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"Same problem, \r\ntransformers 4.28.1\r\ndatasets 2.12.0\r\n\r\nleak around 100Mb per 10 seconds when use dataloader_num_werker > 0 in training argumennts for transformer train, possile bug in transformers repo, but still not found solution :(\r\n",
"found an article described a problem, may be helpful for somebody:\r\nhttps://ppwwyyxx.com/blog/2022/Demystify-RAM-Usage-in-Multiprocess-DataLoader/\r\nI confirm, it`s not memory leak, after some time memory growing has stopped"
] | "2023-03-06T05:26:49Z" | "2023-05-07T15:15:32Z" | null | NONE | null | null | null | ### Describe the bug
use datasets streaming mode in trainer ddp mode cause memory leak
### Steps to reproduce the bug
import os
import time
import datetime
import sys
import numpy as np
import random
import torch
from torch.utils.data import Dataset, DataLoader, random_split, RandomSampler, SequentialSampler,DistributedSampler,BatchSampler
torch.manual_seed(42)
from transformers import GPT2LMHeadModel, GPT2Tokenizer, GPT2Config, GPT2Model,DataCollatorForLanguageModeling,AutoModelForCausalLM
from transformers import AdamW, get_linear_schedule_with_warmup
hf_model_path ='./Wenzhong-GPT2-110M'
tokenizer = GPT2Tokenizer.from_pretrained(hf_model_path)
tokenizer.add_special_tokens({'pad_token': '<|pad|>'})
from datasets import load_dataset
gpus=8
max_len = 576
batch_size_node = 17
save_step = 5000
gradient_accumulation = 2
dataloader_num = 4
max_step = 351000*1000//batch_size_node//gradient_accumulation//gpus
#max_step = -1
print("total_step:%d"%(max_step))
import datasets
datasets.version
dataset = load_dataset("text", data_files="./gpt_data_v1/*",split='train',cache_dir='./dataset_cache',streaming=True)
print('load over')
shuffled_dataset = dataset.shuffle(seed=42)
print('shuffle over')
def dataset_tokener(example,max_lenth=max_len):
example['text'] = list(map(lambda x : x.strip()+'<|endoftext|>',example['text'] ))
return tokenizer(example['text'], truncation=True, max_length=max_lenth, padding="longest")
new_new_dataset = shuffled_dataset.map(dataset_tokener, batched=True, remove_columns=["text"])
print('map over')
configuration = GPT2Config.from_pretrained(hf_model_path, output_hidden_states=False)
model = AutoModelForCausalLM.from_pretrained(hf_model_path)
model.resize_token_embeddings(len(tokenizer))
seed_val = 42
random.seed(seed_val)
np.random.seed(seed_val)
torch.manual_seed(seed_val)
torch.cuda.manual_seed_all(seed_val)
from transformers import Trainer,TrainingArguments
import os
print("strat train")
training_args = TrainingArguments(output_dir="./test_trainer",
num_train_epochs=1.0,
report_to="none",
do_train=True,
dataloader_num_workers=dataloader_num,
local_rank=int(os.environ.get('LOCAL_RANK', -1)),
overwrite_output_dir=True,
logging_strategy='steps',
logging_first_step=True,
logging_dir="./logs",
log_on_each_node=False,
per_device_train_batch_size=batch_size_node,
warmup_ratio=0.03,
save_steps=save_step,
save_total_limit=5,
gradient_accumulation_steps=gradient_accumulation,
max_steps=max_step,
disable_tqdm=False,
data_seed=42
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=new_new_dataset,
eval_dataset=None,
tokenizer=tokenizer,
data_collator=DataCollatorForLanguageModeling(tokenizer,mlm=False),
#compute_metrics=compute_metrics if training_args.do_eval and not is_torch_tpu_available() else None,
#preprocess_logits_for_metrics=preprocess_logits_for_metrics
#if training_args.do_eval and not is_torch_tpu_available()
#else None,
)
trainer.train(resume_from_checkpoint=True)
### Expected behavior
use the train code uppper
my dataset ./gpt_data_v1 have 1000 files, each file size is 120mb
start cmd is : python -m torch.distributed.launch --nproc_per_node=8 my_train.py
here is result:
![image](https://user-images.githubusercontent.com/15223544/223026042-1a81489f-897a-43e4-8339-65a202fd5dc7.png)
here is memory usage monitor in 12 hours
![image](https://user-images.githubusercontent.com/15223544/223027076-14e32e8b-9608-4282-9a80-f15d0277026d.png)
every dataloader work allocate over 24gb cpu memory
according to memory usage monitor in 12 hours,sometime small memory releases, but total memory usage is increase.
i think datasets streaming mode should not used so much memery,so maybe somewhere has memory leak.
### Environment info
pytorch 1.11.0
py 3.8
cuda 11.3
transformers 4.26.1
datasets 2.9.0
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https://api.github.com/repos/huggingface/datasets/issues/5609 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5609/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5609/comments | https://api.github.com/repos/huggingface/datasets/issues/5609/events | https://github.com/huggingface/datasets/issues/5609 | 1,610,062,862 | I_kwDODunzps5f95wO | 5,609 | `load_from_disk` vs `load_dataset` performance. | {
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"Hi! We've recently made some improvements to `save_to_disk`/`list_to_disk` (100x faster in some scenarios), so it would help if you could install `datasets` directly from `main` (`pip install git+https://github.com/huggingface/datasets.git`) and re-run the \"benchmark\".",
"Great to hear! I'll give it a try when I've got a moment.",
"@mariosasko is that fix released to pip in the meantime? Asking cause im facing still the same issue (regarding loading images from local paths):\r\n```\r\ndataset = load_dataset(\"csv\", cache_dir=\"cache\", data_files=[\"/STORAGE/DATA/mijam/vit/code/list_filtered.csv\"], num_proc=16, split=\"train\").cast_column(\"image\", Image())\r\ndataset = dataset.class_encode_column(\"label\")\r\n```\r\nquite fast. \r\n\r\nThen I do `save_to_disk()` and some time later:\r\n```\r\ndataset = load_from_disk('/STORAGE/DATA/mijam/accel/saved_arrow_big')\r\n```\r\nreally slow. In theory it should be quicked since it only loads arrow files, no conversions and so on.\r\n",
"@mjamroz I assume your CSV file stores image file paths. This means `save_to_disk` needs to embed the image bytes resulting in a much bigger Arrow file (than the initial one). Maybe specifying `num_shards` to make the Arrow files smaller can help (large Arrow files on some systems take a long time to load)."
] | "2023-03-05T05:27:15Z" | "2023-07-13T18:48:05Z" | null | NONE | null | null | null | ### Describe the bug
I have downloaded `openwebtext` (~12GB) and filtered out a small amount of junk (it's still huge). Now, I would like to use this filtered version for future work. It seems I have two choices:
1. Use `load_dataset` each time, relying on the cache mechanism, and re-run my filtering.
2. `save_to_disk` and then use `load_from_disk` to load the filtered version.
The performance of these two approaches is wildly different:
* Using `load_dataset` takes about 20 seconds to load the dataset, and a few seconds to re-filter (thanks to the brilliant filter/map caching)
* Using `load_from_disk` takes 14 minutes! And the second time I tried, the session just crashed (on a machine with 32GB of RAM)
I don't know if you'd call this a bug, but it seems like there shouldn't need to be two methods to load from disk, or that they should not take such wildly different amounts of time, or that one should not crash. Or maybe that the docs could offer some guidance about when to pick which method and why two methods exist, or just how do most people do it?
Something I couldn't work out from reading the docs was this: can I modify a dataset from the hub, save it (locally) and use `load_dataset` to load it? This [post seemed to suggest that the answer is no](https://discuss.huggingface.co/t/save-and-load-datasets/9260).
### Steps to reproduce the bug
See above
### Expected behavior
Load times should be about the same.
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31
- Python version: 3.10.8
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | {
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"Hi!\r\n\r\n> naming convention of mp3 files\r\n\r\nYes, this could be the problem. MP3 files should end with `.mp3`/`.MP3` to be recognized as audio files.\r\n\r\nIf the file names are not the culprit, can you paste the audio folder's directory structure to help us reproduce the error (e.g., by running the `tree \"x\"` command)?",
"Hi! I'm sorry, I don't want to reveal my entire dataset, but here's a snippet (all of the mp3 files below are some of the ones not being recognized by audiofolder. Also, for another dataset, audiofolder loaded zero mp3 files because \"train\" was in the name of one of the mp3 files. \r\nmy_dataset\r\n├── data\r\n│ ├── VHA_Innovation_Stories_-_Day_2-123.mp3\r\n│ ├── VHA_Innovation_Stories_-_Day_2-124.mp3\r\n│ ├── ASSOCIATION_OF_GENERAL_PRACTITIONERS_OF_JAMAICA_NEPHROLOGY_CONFERENCE_-_JULY_3,_2022-93.mp3\r\n│ ├── ASSOCIATION_OF_GENERAL_PRACTITIONERS_OF_JAMAICA_NEPHROLOGY_CONFERENCE_-_JULY_3,_2022-94.mp3\r\n│ ├── ASSOCIATION_OF_GENERAL_PRACTITIONERS_OF_JAMAICA_NEPHROLOGY_CONFERENCE_-_JULY_3,_2022-95.mp3\r\n│ ├── Your_Impact\\357\\274\\232_Neurosurgery_equipment-5.mp3\r\n│ └── Your_Impact\\357\\274\\232_Neurosurgery_equipment-6.mp3\r\n└── metadata.csv\r\n\r\nHere's a few of the 13 files recognized by the dataset:\r\nBritish_Heart_Foundation_-_Your_guide_to_a_Coronary_Angiogram,_a_test_for_heart_disease-1.mp3\r\nBritish_Heart_Foundation_-_Your_guide_to_a_Coronary_Angiogram,_a_test_for_heart_disease-2.mp3\r\nBritish_Heart_Foundation_-_Your_guide_to_a_Coronary_Angiogram,_a_test_for_heart_disease-3.mp3\r\nIVP_⧸_IVU_test_Procedure_for_Kidneys_intravenous_pyelogram_-_medical_radiology_X-ray_ivp-1.mp3\r\nIVP_⧸_IVU_test_Procedure_for_Kidneys_intravenous_pyelogram_-_medical_radiology_X-ray_ivp-2.mp3"
] | "2023-03-05T00:14:45Z" | "2023-03-12T00:02:57Z" | "2023-03-12T00:02:57Z" | NONE | null | null | null | ### Describe the bug
x = load_dataset("audiofolder", data_dir="x")
When running this, x is a dataset of 13 rows (files) when it should be 20,000 rows (files) as the data_dir "x" has 20,000 mp3 files. Does anyone know what could possibly cause this (naming convention of mp3 files, etc.)
### Steps to reproduce the bug
x = load_dataset("audiofolder", data_dir="x")
### Expected behavior
x = load_dataset("audiofolder", data_dir="x") should create a dataset of 20,000 rows (files).
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-3.10.0-1160.80.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.9.16
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006142 / 0.011353 (-0.005211) | 0.004506 / 0.011008 (-0.006502) | 0.100224 / 0.038508 (0.061715) | 0.026988 / 0.023109 (0.003879) | 0.301625 / 0.275898 (0.025727) | 0.346337 / 0.323480 (0.022857) | 0.004642 / 0.007986 (-0.003343) | 0.003481 / 0.004328 (-0.000847) | 0.075847 / 0.004250 (0.071597) | 0.036959 / 0.037052 (-0.000094) | 0.302697 / 0.258489 (0.044208) | 0.351917 / 0.293841 (0.058076) | 0.030719 / 0.128546 (-0.097828) | 0.011591 / 0.075646 (-0.064056) | 0.319709 / 0.419271 (-0.099563) | 0.042000 / 0.043533 (-0.001532) | 0.306854 / 0.255139 (0.051715) | 0.326903 / 0.283200 (0.043703) | 0.082711 / 0.141683 (-0.058972) | 1.486616 / 1.452155 (0.034461) | 1.603229 / 1.492716 (0.110513) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198990 / 0.018006 (0.180983) | 0.427733 / 0.000490 (0.427243) | 0.003612 / 0.000200 (0.003412) | 0.000071 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022932 / 0.037411 (-0.014480) | 0.096969 / 0.014526 (0.082443) | 0.105749 / 0.176557 (-0.070807) | 0.166101 / 0.737135 (-0.571034) | 0.108646 / 0.296338 (-0.187692) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428174 / 0.215209 (0.212965) | 4.271452 / 2.077655 (2.193797) | 1.907588 / 1.504120 (0.403468) | 1.680870 / 1.541195 (0.139675) | 1.761336 / 1.468490 (0.292846) | 0.700380 / 4.584777 (-3.884396) | 3.415168 / 3.745712 (-0.330544) | 1.886122 / 5.269862 (-3.383740) | 1.276814 / 4.565676 (-3.288863) | 0.083429 / 0.424275 (-0.340846) | 0.012988 / 0.007607 (0.005381) | 0.518821 / 0.226044 (0.292776) | 5.188284 / 2.268929 (2.919356) | 2.433084 / 55.444624 (-53.011540) | 1.988034 / 6.876477 (-4.888443) | 2.100275 / 2.142072 (-0.041797) | 0.808252 / 4.805227 (-3.996976) | 0.158102 / 6.500664 (-6.342562) | 0.067686 / 0.075469 (-0.007783) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.204171 / 1.841788 (-0.637616) | 13.548756 / 8.074308 (5.474448) | 14.339805 / 10.191392 (4.148413) | 0.142853 / 0.680424 (-0.537571) | 0.016529 / 0.534201 (-0.517672) | 0.383800 / 0.579283 (-0.195483) | 0.380362 / 0.434364 (-0.054002) | 0.437716 / 0.540337 (-0.102621) | 0.524306 / 1.386936 (-0.862630) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006730 / 0.011353 (-0.004623) | 0.004652 / 0.011008 (-0.006356) | 0.077476 / 0.038508 (0.038968) | 0.027584 / 0.023109 (0.004475) | 0.340907 / 0.275898 (0.065009) | 0.377950 / 0.323480 (0.054470) | 0.005946 / 0.007986 (-0.002040) | 0.003548 / 0.004328 (-0.000780) | 0.076270 / 0.004250 (0.072019) | 0.037483 / 0.037052 (0.000431) | 0.346390 / 0.258489 (0.087901) | 0.384739 / 0.293841 (0.090898) | 0.031744 / 0.128546 (-0.096802) | 0.011598 / 0.075646 (-0.064049) | 0.085651 / 0.419271 (-0.333620) | 0.047308 / 0.043533 (0.003775) | 0.344704 / 0.255139 (0.089565) | 0.363410 / 0.283200 (0.080211) | 0.095009 / 0.141683 (-0.046674) | 1.478307 / 1.452155 (0.026152) | 1.576808 / 1.492716 (0.084092) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.197545 / 0.018006 (0.179539) | 0.431984 / 0.000490 (0.431494) | 0.001529 / 0.000200 (0.001329) | 0.000079 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025452 / 0.037411 (-0.011959) | 0.100176 / 0.014526 (0.085651) | 0.108222 / 0.176557 (-0.068335) | 0.160556 / 0.737135 (-0.576580) | 0.112748 / 0.296338 (-0.183591) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436326 / 0.215209 (0.221117) | 4.378443 / 2.077655 (2.300788) | 2.056001 / 1.504120 (0.551881) | 1.853406 / 1.541195 (0.312211) | 1.931645 / 1.468490 (0.463155) | 0.698340 / 4.584777 (-3.886437) | 3.368961 / 3.745712 (-0.376751) | 2.583622 / 5.269862 (-2.686239) | 1.501274 / 4.565676 (-3.064402) | 0.083034 / 0.424275 (-0.341241) | 0.012725 / 0.007607 (0.005117) | 0.539991 / 0.226044 (0.313947) | 5.418413 / 2.268929 (3.149485) | 2.517205 / 55.444624 (-52.927420) | 2.179332 / 6.876477 (-4.697144) | 2.215376 / 2.142072 (0.073304) | 0.806133 / 4.805227 (-3.999094) | 0.151499 / 6.500664 (-6.349165) | 0.067270 / 0.075469 (-0.008199) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.308324 / 1.841788 (-0.533464) | 14.357361 / 8.074308 (6.283053) | 14.684768 / 10.191392 (4.493376) | 0.139575 / 0.680424 (-0.540849) | 0.016409 / 0.534201 (-0.517792) | 0.374087 / 0.579283 (-0.205196) | 0.390628 / 0.434364 (-0.043735) | 0.443102 / 0.540337 (-0.097235) | 0.536089 / 1.386936 (-0.850847) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#778d4e1c13ece980e706f8c7cb06e8473fd61315 \"CML watermark\")\n"
] | "2023-03-03T19:50:29Z" | "2023-03-09T17:34:13Z" | "2023-03-09T17:27:07Z" | CONTRIBUTOR | null | 0 | {
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} | ~I think it makes sense not to save `dataset_info.json` file to a dataset cache directory when loading dataset with `verification_mode="no_checks"` because otherwise when next time the dataset is loaded **without** `verification_mode="no_checks"`, it will be loaded successfully, despite some values in info might not correspond to the ones in the repo which was the reason for using `verification_mode="no_checks"` first.~
Updated values of `verification_mode` to the current ones in some places ("none" -> "no_checks", "all" -> "all_checks") | {
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"Hello, I have an interest in this issue.\r\nIs the `Dataset.to_dict` you are describing correct in the code here?\r\n\r\nhttps://github.com/huggingface/datasets/blob/35b789e8f6826b6b5a6b48fcc2416c890a1f326a/src/datasets/arrow_dataset.py#L4633-L4667",
"Yes, this is where `Dataset.to_dict` is defined.",
"#self-assign"
] | "2023-03-03T16:17:10Z" | "2023-03-27T13:26:40Z" | "2023-03-27T13:26:40Z" | CONTRIBUTOR | null | null | null | Since there is `Dataset.from_list` in the API, we should also add `Dataset.to_list` to be consistent.
Regarding the implementation, we can re-use `Dataset.to_dict`'s code and replace the `to_pydict` calls with `to_pylist`. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Are you sure it's safe to remove? https://github.com/huggingface/datasets/pull/3866",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009520 / 0.011353 (-0.001833) | 0.005319 / 0.011008 (-0.005690) | 0.099372 / 0.038508 (0.060863) | 0.036173 / 0.023109 (0.013064) | 0.295752 / 0.275898 (0.019853) | 0.362882 / 0.323480 (0.039402) | 0.008442 / 0.007986 (0.000456) | 0.004225 / 0.004328 (-0.000103) | 0.076645 / 0.004250 (0.072394) | 0.044198 / 0.037052 (0.007146) | 0.311948 / 0.258489 (0.053459) | 0.342963 / 0.293841 (0.049122) | 0.038613 / 0.128546 (-0.089933) | 0.012127 / 0.075646 (-0.063519) | 0.334427 / 0.419271 (-0.084844) | 0.048309 / 0.043533 (0.004776) | 0.297046 / 0.255139 (0.041907) | 0.314562 / 0.283200 (0.031363) | 0.105797 / 0.141683 (-0.035886) | 1.460967 / 1.452155 (0.008812) | 1.500907 / 1.492716 (0.008190) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216185 / 0.018006 (0.198179) | 0.438924 / 0.000490 (0.438435) | 0.001210 / 0.000200 (0.001011) | 0.000081 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026193 / 0.037411 (-0.011219) | 0.105888 / 0.014526 (0.091363) | 0.115812 / 0.176557 (-0.060744) | 0.158748 / 0.737135 (-0.578387) | 0.121514 / 0.296338 (-0.174824) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.399837 / 0.215209 (0.184628) | 3.996992 / 2.077655 (1.919338) | 1.784964 / 1.504120 (0.280844) | 1.591078 / 1.541195 (0.049883) | 1.666424 / 1.468490 (0.197934) | 0.711450 / 4.584777 (-3.873327) | 3.787814 / 3.745712 (0.042102) | 2.056776 / 5.269862 (-3.213085) | 1.332163 / 4.565676 (-3.233514) | 0.085755 / 0.424275 (-0.338520) | 0.012033 / 0.007607 (0.004426) | 0.511500 / 0.226044 (0.285455) | 5.098999 / 2.268929 (2.830071) | 2.288261 / 55.444624 (-53.156364) | 1.947483 / 6.876477 (-4.928994) | 1.987838 / 2.142072 (-0.154234) | 0.852241 / 4.805227 (-3.952986) | 0.164781 / 6.500664 (-6.335883) | 0.061825 / 0.075469 (-0.013644) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.202253 / 1.841788 (-0.639534) | 14.632608 / 8.074308 (6.558300) | 13.331320 / 10.191392 (3.139928) | 0.157944 / 0.680424 (-0.522480) | 0.029284 / 0.534201 (-0.504917) | 0.446636 / 0.579283 (-0.132647) | 0.437009 / 0.434364 (0.002645) | 0.521883 / 0.540337 (-0.018455) | 0.606687 / 1.386936 (-0.780249) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007528 / 0.011353 (-0.003825) | 0.005274 / 0.011008 (-0.005734) | 0.073524 / 0.038508 (0.035016) | 0.033893 / 0.023109 (0.010784) | 0.335432 / 0.275898 (0.059534) | 0.379981 / 0.323480 (0.056501) | 0.005954 / 0.007986 (-0.002031) | 0.004126 / 0.004328 (-0.000203) | 0.072891 / 0.004250 (0.068641) | 0.046517 / 0.037052 (0.009465) | 0.337241 / 0.258489 (0.078752) | 0.385562 / 0.293841 (0.091721) | 0.036410 / 0.128546 (-0.092136) | 0.012246 / 0.075646 (-0.063401) | 0.085974 / 0.419271 (-0.333298) | 0.049665 / 0.043533 (0.006133) | 0.330919 / 0.255139 (0.075780) | 0.352041 / 0.283200 (0.068841) | 0.103751 / 0.141683 (-0.037931) | 1.468851 / 1.452155 (0.016696) | 1.565380 / 1.492716 (0.072663) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.260431 / 0.018006 (0.242425) | 0.444554 / 0.000490 (0.444064) | 0.016055 / 0.000200 (0.015855) | 0.000283 / 0.000054 (0.000228) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029130 / 0.037411 (-0.008281) | 0.112002 / 0.014526 (0.097476) | 0.120769 / 0.176557 (-0.055788) | 0.169345 / 0.737135 (-0.567790) | 0.129609 / 0.296338 (-0.166730) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.432211 / 0.215209 (0.217002) | 4.293008 / 2.077655 (2.215353) | 2.071291 / 1.504120 (0.567171) | 1.859322 / 1.541195 (0.318127) | 1.971434 / 1.468490 (0.502943) | 0.704042 / 4.584777 (-3.880735) | 3.791696 / 3.745712 (0.045983) | 3.142632 / 5.269862 (-2.127230) | 1.735151 / 4.565676 (-2.830525) | 0.086203 / 0.424275 (-0.338072) | 0.012542 / 0.007607 (0.004935) | 0.534870 / 0.226044 (0.308826) | 5.326042 / 2.268929 (3.057113) | 2.547960 / 55.444624 (-52.896664) | 2.212730 / 6.876477 (-4.663747) | 2.296177 / 2.142072 (0.154105) | 0.840311 / 4.805227 (-3.964917) | 0.168353 / 6.500664 (-6.332311) | 0.065949 / 0.075469 (-0.009520) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.255589 / 1.841788 (-0.586199) | 14.947344 / 8.074308 (6.873036) | 13.253721 / 10.191392 (3.062329) | 0.162349 / 0.680424 (-0.518075) | 0.017579 / 0.534201 (-0.516622) | 0.420758 / 0.579283 (-0.158525) | 0.430030 / 0.434364 (-0.004334) | 0.524669 / 0.540337 (-0.015669) | 0.623920 / 1.386936 (-0.763016) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#35b789e8f6826b6b5a6b48fcc2416c890a1f326a \"CML watermark\")\n"
] | "2023-03-03T15:46:31Z" | "2023-03-03T21:57:18Z" | "2023-03-03T21:50:17Z" | CONTRIBUTOR | null | 0 | {
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"Hi! \r\n\r\n\r\nYou can specify `download_config=DownloadConfig(resume_download=True))` in `load_dataset` to resume the download when re-running the code after the timeout error:\r\n```python\r\nfrom datasets import load_dataset, DownloadConfig\r\ndataset = load_dataset('the_pile', split='train', cache_dir='F:\\datasets', download_config=DownloadConfig(resume_download=True))\r\n```\r\n\r\n",
"@mariosasko , I used your suggestion but its not saving anything , just stops and runs from the same point .\r\nbelow is the script to download and save on disk .\r\n\r\n```\r\nfrom datasets import load_dataset, DownloadConfig\r\n\r\n\r\n#load the Pile dataset from Hugging Face Datasets\r\n#dataset = load_dataset('the_pile')\r\ndataset = load_dataset('the_pile', split='train', cache_dir='datasets', download_config=DownloadConfig(resume_download=True))\r\n\r\n\r\n# save each file in the dataset to disk\r\nfor i, example in enumerate(dataset['train']):\r\n filename = f'pile_file_{i}.json'\r\n with open(filename, 'w') as f:\r\n f.write(str(example))\r\n\r\nprint(\"Finished saving Pile dataset files to disk.\")\r\n```\r\n",
"@mariosasko , it shows nothing in dataset folder\r\n\r\n```\r\n du -sh /mnt/nlp/hugging_face/*\r\n20K /mnt/nlp/hugging_face/datasets\r\n4.0K /mnt/nlp/hugging_face/download_pile.py\r\n```\r\n",
"@mariosasko \r\n\r\n```\r\nroot@d20f0ab8f4f8:/mnt/hugging_face# python3 download_pile.py\r\nNo config specified, defaulting to: the_pile/all\r\nDownloading and preparing dataset the_pile/all to /mnt/hugging_face/datasets/the_pile/all/0.0.0/6fadc480ecb32470826cbf5900a9558b791ce55d5e9a0fdc8ad653e7b64bb349...\r\nDownloading data files: 0%| | 0/3 [00:00<?, ?it/s]\r\n\r\n\r\n\r\n\r\n\r\nDownloading data: 70%|████████████████████████████████████████████████████████████████████▊ | 10.7G/15.2G [12:09<11:53, 6.36MB/s]\r\nDownloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 15.2G/15.2G [22:15<00:00, 7.25MB/s]\r\nDownloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 15.2G/15.2G [46:17<00:00, 5.48MB/s]\r\nDownloading data: 40%|██████████████████████████████████████▏ | 6.07G/15.3G [50:49<1:17:02, 1.99MB/s]\r\nTraceback (most recent call last):██████████████████████████▊ | 6.07G/15.3G [50:49<25:35:23, 99.9kB/s]\r\n File \"/usr/local/lib/python3.8/dist-packages/urllib3/response.py\", line 444, in _error_catcher\r\n yield\r\n File \"/usr/local/lib/python3.8/dist-packages/urllib3/response.py\", line 567, in read\r\n data = self._fp_read(amt) if not fp_closed else b\"\"\r\n File \"/usr/local/lib/python3.8/dist-packages/urllib3/response.py\", line 525, in _fp_read\r\n data = self._fp.read(chunk_amt)\r\n File \"/usr/lib/python3.8/http/client.py\", line 459, in read\r\n n = self.readinto(b)\r\n File \"/usr/lib/python3.8/http/client.py\", line 503, in readinto\r\n n = self.fp.readinto(b)\r\n File \"/usr/lib/python3.8/socket.py\", line 669, in readinto\r\n return self._sock.recv_into(b)\r\n File \"/usr/lib/python3.8/ssl.py\", line 1241, in recv_into\r\n return self.read(nbytes, buffer)\r\n File \"/usr/lib/python3.8/ssl.py\", line 1099, in read\r\n return self._sslobj.read(len, buffer)\r\nConnectionResetError: [Errno 104] Connection reset by peer\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/usr/local/lib/python3.8/dist-packages/requests/models.py\", line 816, in generate\r\n yield from self.raw.stream(chunk_size, decode_content=True)\r\n File \"/usr/local/lib/python3.8/dist-packages/urllib3/response.py\", line 628, in stream\r\n data = self.read(amt=amt, decode_content=decode_content)\r\n File \"/usr/local/lib/python3.8/dist-packages/urllib3/response.py\", line 593, in read\r\n raise IncompleteRead(self._fp_bytes_read, self.length_remaining)\r\n File \"/usr/lib/python3.8/contextlib.py\", line 131, in __exit__\r\n self.gen.throw(type, value, traceback)\r\n File \"/usr/local/lib/python3.8/dist-packages/urllib3/response.py\", line 461, in _error_catcher\r\n raise ProtocolError(\"Connection broken: %r\" % e, e)\r\nurllib3.exceptions.ProtocolError: (\"Connection broken: ConnectionResetError(104, 'Connection reset by peer')\", ConnectionResetError(104, 'Connection reset by peer'))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"download_pile.py\", line 6, in <module>\r\n dataset = load_dataset('the_pile', split='train', cache_dir='datasets', download_config=DownloadConfig(resume_download=True))\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/load.py\", line 1782, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/builder.py\", line 872, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/builder.py\", line 1649, in _download_and_prepare\r\n super()._download_and_prepare(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/builder.py\", line 945, in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n File \"/root/.cache/huggingface/modules/datasets_modules/datasets/the_pile/6fadc480ecb32470826cbf5900a9558b791ce55d5e9a0fdc8ad653e7b64bb349/the_pile.py\", line 192, in _split_generators\r\n data_dir = dl_manager.download(_DATA_URLS[self.config.name])\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/download/download_manager.py\", line 427, in download\r\n downloaded_path_or_paths = map_nested(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py\", line 443, in map_nested\r\n mapped = [\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py\", line 444, in <listcomp>\r\n _single_map_nested((function, obj, types, None, True, None))\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py\", line 363, in _single_map_nested\r\n mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py\", line 363, in <listcomp>\r\n mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py\", line 346, in _single_map_nested\r\n return function(data_struct)\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/download/download_manager.py\", line 453, in _download\r\n return cached_path(url_or_filename, download_config=download_config)\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py\", line 182, in cached_path\r\n output_path = get_from_cache(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py\", line 575, in get_from_cache\r\n http_get(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py\", line 379, in http_get\r\n for chunk in response.iter_content(chunk_size=1024):\r\n File \"/usr/local/lib/python3.8/dist-packages/requests/models.py\", line 818, in generate\r\n raise ChunkedEncodingError(e)\r\nrequests.exceptions.ChunkedEncodingError: (\"Connection broken: ConnectionResetError(104, 'Connection reset by peer')\", ConnectionResetError(104, 'Connection reset by peer'))\r\n```\r\n",
"Users with slow internet speed are doomed (4MB/s). The dataset downloads fine at minimum speed 10MB/s.\n\nAlso, when the train splits were generated and then I removed the downloads folder to save up disk space, it started redownloading the whole dataset. Is there any way to use the already generated splits instead?",
"@sentialx @mariosasko , anytime on my above script , am I downloading and saving dataset correctly . Please suggest :)"
] | "2023-03-03T09:52:08Z" | "2023-03-29T01:44:05Z" | "2023-03-24T12:44:25Z" | NONE | null | null | null | ### Describe the bug
The downloads in the screenshot seem to be interrupted after some time and the last download throws a "Read timed out" error.
![image](https://user-images.githubusercontent.com/11065386/222687870-ec5fcb65-84e8-467d-9593-4ad7bdac4d50.png)
Here are the downloaded files:
![image](https://user-images.githubusercontent.com/11065386/222688200-454c2288-49e5-4682-96e6-1eb69aca0852.png)
They should be all 14GB like here (https://the-eye.eu/public/AI/pile/train/).
Alternatively, can I somehow download the files by myself and use the datasets preparing script?
### Steps to reproduce the bug
dataset = load_dataset('the_pile', split='train', cache_dir='F:\datasets')
### Expected behavior
The files should be downloaded correctly.
### Environment info
- `datasets` version: 2.10.1
- Platform: Windows-10-10.0.22623-SP0
- Python version: 3.10.5
- PyArrow version: 9.0.0
- Pandas version: 1.4.2 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008550 / 0.011353 (-0.002803) | 0.004476 / 0.011008 (-0.006532) | 0.100902 / 0.038508 (0.062394) | 0.029684 / 0.023109 (0.006575) | 0.308081 / 0.275898 (0.032183) | 0.363435 / 0.323480 (0.039955) | 0.006987 / 0.007986 (-0.000999) | 0.003401 / 0.004328 (-0.000927) | 0.078218 / 0.004250 (0.073967) | 0.036657 / 0.037052 (-0.000395) | 0.319670 / 0.258489 (0.061181) | 0.349952 / 0.293841 (0.056111) | 0.033416 / 0.128546 (-0.095130) | 0.011511 / 0.075646 (-0.064135) | 0.323888 / 0.419271 (-0.095384) | 0.042429 / 0.043533 (-0.001104) | 0.307310 / 0.255139 (0.052171) | 0.329459 / 0.283200 (0.046259) | 0.085209 / 0.141683 (-0.056474) | 1.475893 / 1.452155 (0.023739) | 1.502782 / 1.492716 (0.010065) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.200137 / 0.018006 (0.182131) | 0.411269 / 0.000490 (0.410780) | 0.000415 / 0.000200 (0.000215) | 0.000061 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022626 / 0.037411 (-0.014785) | 0.097045 / 0.014526 (0.082519) | 0.102955 / 0.176557 (-0.073602) | 0.148411 / 0.737135 (-0.588725) | 0.107238 / 0.296338 (-0.189100) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.421683 / 0.215209 (0.206474) | 4.203031 / 2.077655 (2.125376) | 1.908232 / 1.504120 (0.404112) | 1.698867 / 1.541195 (0.157672) | 1.743561 / 1.468490 (0.275071) | 0.693199 / 4.584777 (-3.891578) | 3.361022 / 3.745712 (-0.384690) | 2.989610 / 5.269862 (-2.280251) | 1.533036 / 4.565676 (-3.032641) | 0.082675 / 0.424275 (-0.341601) | 0.012419 / 0.007607 (0.004812) | 0.531543 / 0.226044 (0.305499) | 5.330595 / 2.268929 (3.061666) | 2.347519 / 55.444624 (-53.097105) | 1.975672 / 6.876477 (-4.900804) | 2.039541 / 2.142072 (-0.102532) | 0.810281 / 4.805227 (-3.994946) | 0.148917 / 6.500664 (-6.351747) | 0.065441 / 0.075469 (-0.010028) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.266213 / 1.841788 (-0.575574) | 13.628106 / 8.074308 (5.553798) | 13.852191 / 10.191392 (3.660799) | 0.149004 / 0.680424 (-0.531420) | 0.028549 / 0.534201 (-0.505652) | 0.399824 / 0.579283 (-0.179459) | 0.401231 / 0.434364 (-0.033133) | 0.473251 / 0.540337 (-0.067086) | 0.561094 / 1.386936 (-0.825842) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006669 / 0.011353 (-0.004684) | 0.004477 / 0.011008 (-0.006532) | 0.077514 / 0.038508 (0.039006) | 0.027489 / 0.023109 (0.004380) | 0.341935 / 0.275898 (0.066037) | 0.377392 / 0.323480 (0.053912) | 0.004947 / 0.007986 (-0.003039) | 0.004600 / 0.004328 (0.000271) | 0.075938 / 0.004250 (0.071687) | 0.039586 / 0.037052 (0.002534) | 0.344966 / 0.258489 (0.086477) | 0.392181 / 0.293841 (0.098340) | 0.031838 / 0.128546 (-0.096708) | 0.011572 / 0.075646 (-0.064075) | 0.085811 / 0.419271 (-0.333461) | 0.042250 / 0.043533 (-0.001283) | 0.345605 / 0.255139 (0.090466) | 0.367814 / 0.283200 (0.084615) | 0.090683 / 0.141683 (-0.051000) | 1.483168 / 1.452155 (0.031014) | 1.559724 / 1.492716 (0.067008) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235655 / 0.018006 (0.217649) | 0.399016 / 0.000490 (0.398527) | 0.003096 / 0.000200 (0.002896) | 0.000077 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024454 / 0.037411 (-0.012957) | 0.100710 / 0.014526 (0.086185) | 0.107950 / 0.176557 (-0.068606) | 0.161560 / 0.737135 (-0.575576) | 0.111840 / 0.296338 (-0.184498) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441362 / 0.215209 (0.226153) | 4.428105 / 2.077655 (2.350450) | 2.074501 / 1.504120 (0.570381) | 1.866672 / 1.541195 (0.325477) | 1.928266 / 1.468490 (0.459776) | 0.703561 / 4.584777 (-3.881216) | 3.396537 / 3.745712 (-0.349175) | 3.047369 / 5.269862 (-2.222492) | 1.595133 / 4.565676 (-2.970543) | 0.084028 / 0.424275 (-0.340247) | 0.012349 / 0.007607 (0.004741) | 0.539354 / 0.226044 (0.313310) | 5.401535 / 2.268929 (3.132606) | 2.499874 / 55.444624 (-52.944750) | 2.161406 / 6.876477 (-4.715071) | 2.197385 / 2.142072 (0.055313) | 0.810864 / 4.805227 (-3.994363) | 0.152277 / 6.500664 (-6.348387) | 0.067266 / 0.075469 (-0.008203) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.280900 / 1.841788 (-0.560887) | 13.815731 / 8.074308 (5.741423) | 13.007438 / 10.191392 (2.816046) | 0.129711 / 0.680424 (-0.550713) | 0.016852 / 0.534201 (-0.517349) | 0.380775 / 0.579283 (-0.198508) | 0.384143 / 0.434364 (-0.050221) | 0.459954 / 0.540337 (-0.080383) | 0.549335 / 1.386936 (-0.837601) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8805d67bd81ce48f481d5c1e56b84e6ebcaa2b2b \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009570 / 0.011353 (-0.001783) | 0.005219 / 0.011008 (-0.005789) | 0.098472 / 0.038508 (0.059964) | 0.035429 / 0.023109 (0.012320) | 0.303086 / 0.275898 (0.027188) | 0.365926 / 0.323480 (0.042446) | 0.008797 / 0.007986 (0.000811) | 0.004220 / 0.004328 (-0.000108) | 0.076670 / 0.004250 (0.072419) | 0.045596 / 0.037052 (0.008543) | 0.309476 / 0.258489 (0.050987) | 0.343958 / 0.293841 (0.050117) | 0.038741 / 0.128546 (-0.089805) | 0.011990 / 0.075646 (-0.063657) | 0.332326 / 0.419271 (-0.086945) | 0.048897 / 0.043533 (0.005364) | 0.296002 / 0.255139 (0.040863) | 0.322048 / 0.283200 (0.038849) | 0.104403 / 0.141683 (-0.037280) | 1.461777 / 1.452155 (0.009622) | 1.516362 / 1.492716 (0.023645) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201565 / 0.018006 (0.183559) | 0.435781 / 0.000490 (0.435291) | 0.004215 / 0.000200 (0.004015) | 0.000282 / 0.000054 (0.000227) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027272 / 0.037411 (-0.010139) | 0.106157 / 0.014526 (0.091631) | 0.116948 / 0.176557 (-0.059609) | 0.160404 / 0.737135 (-0.576731) | 0.122518 / 0.296338 (-0.173820) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.397721 / 0.215209 (0.182512) | 3.966433 / 2.077655 (1.888778) | 1.755410 / 1.504120 (0.251290) | 1.566480 / 1.541195 (0.025285) | 1.623684 / 1.468490 (0.155194) | 0.696820 / 4.584777 (-3.887957) | 3.750437 / 3.745712 (0.004725) | 2.105875 / 5.269862 (-3.163986) | 1.442026 / 4.565676 (-3.123650) | 0.085026 / 0.424275 (-0.339249) | 0.012239 / 0.007607 (0.004632) | 0.502613 / 0.226044 (0.276569) | 5.049016 / 2.268929 (2.780087) | 2.314499 / 55.444624 (-53.130126) | 1.967943 / 6.876477 (-4.908534) | 2.033507 / 2.142072 (-0.108565) | 0.861908 / 4.805227 (-3.943319) | 0.167784 / 6.500664 (-6.332880) | 0.063022 / 0.075469 (-0.012447) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.210434 / 1.841788 (-0.631353) | 14.979319 / 8.074308 (6.905011) | 14.095263 / 10.191392 (3.903871) | 0.174203 / 0.680424 (-0.506221) | 0.028547 / 0.534201 (-0.505654) | 0.442509 / 0.579283 (-0.136774) | 0.445811 / 0.434364 (0.011447) | 0.531313 / 0.540337 (-0.009024) | 0.636541 / 1.386936 (-0.750395) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007341 / 0.011353 (-0.004012) | 0.005197 / 0.011008 (-0.005811) | 0.075413 / 0.038508 (0.036905) | 0.033261 / 0.023109 (0.010152) | 0.339596 / 0.275898 (0.063698) | 0.376051 / 0.323480 (0.052571) | 0.005827 / 0.007986 (-0.002159) | 0.005473 / 0.004328 (0.001144) | 0.074851 / 0.004250 (0.070600) | 0.049059 / 0.037052 (0.012007) | 0.357182 / 0.258489 (0.098693) | 0.384589 / 0.293841 (0.090748) | 0.037122 / 0.128546 (-0.091424) | 0.012298 / 0.075646 (-0.063348) | 0.088191 / 0.419271 (-0.331081) | 0.052002 / 0.043533 (0.008469) | 0.343216 / 0.255139 (0.088077) | 0.364534 / 0.283200 (0.081334) | 0.105462 / 0.141683 (-0.036221) | 1.486717 / 1.452155 (0.034562) | 1.584725 / 1.492716 (0.092009) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.199210 / 0.018006 (0.181203) | 0.439069 / 0.000490 (0.438580) | 0.000436 / 0.000200 (0.000236) | 0.000059 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029931 / 0.037411 (-0.007480) | 0.109564 / 0.014526 (0.095038) | 0.122284 / 0.176557 (-0.054273) | 0.170819 / 0.737135 (-0.566317) | 0.125886 / 0.296338 (-0.170452) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422724 / 0.215209 (0.207515) | 4.210304 / 2.077655 (2.132650) | 2.001481 / 1.504120 (0.497361) | 1.810818 / 1.541195 (0.269623) | 1.901367 / 1.468490 (0.432877) | 0.686004 / 4.584777 (-3.898773) | 3.768850 / 3.745712 (0.023138) | 2.079501 / 5.269862 (-3.190360) | 1.326970 / 4.565676 (-3.238706) | 0.085991 / 0.424275 (-0.338284) | 0.012298 / 0.007607 (0.004690) | 0.526878 / 0.226044 (0.300833) | 5.267241 / 2.268929 (2.998312) | 2.451781 / 55.444624 (-52.992843) | 2.109143 / 6.876477 (-4.767333) | 2.185426 / 2.142072 (0.043353) | 0.830165 / 4.805227 (-3.975063) | 0.166167 / 6.500664 (-6.334497) | 0.064077 / 0.075469 (-0.011392) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.270430 / 1.841788 (-0.571358) | 14.844852 / 8.074308 (6.770544) | 13.196672 / 10.191392 (3.005280) | 0.162853 / 0.680424 (-0.517571) | 0.017727 / 0.534201 (-0.516474) | 0.424803 / 0.579283 (-0.154480) | 0.439970 / 0.434364 (0.005606) | 0.530691 / 0.540337 (-0.009647) | 0.630474 / 1.386936 (-0.756462) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#24fb01b720ef4203d4ae6225f43cba912b1f6d55 \"CML watermark\")\n"
] | "2023-03-02T16:42:39Z" | "2023-03-03T15:45:32Z" | "2023-03-03T15:38:28Z" | MEMBER | null | 0 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5602). All of your documentation changes will be reflected on that endpoint.",
"This is a great PR! Thinking about the UX though, maybe we could do it without the extra argument? Before this PR, the logic in `to_tf_dataset` was that if the user passed a single column name in either `columns` or `label_cols`, we converted it to a length-1 list. Then, later in the code, we convert output dicts with only one key to naked Tensors.\r\n\r\nWould it be easier if we removed the argument, but instead treated the cases differently? Passing a column name as a string could yield a single naked Tensor in the output as before, but passing a list of length 1 would yield a full dict? That way if you wanted dict output with a single key you could just say `columns=[col_name]`.\r\n\r\n(I'm not totally convinced this is a good idea yet, it just seems like it might be more intuitive)",
"@Rocketknight1 Happy to implement it that way - it's certainly cleaner to not have another arg. In this case, am I right in saying we'd effectively set `return_dict` [here](https://github.com/huggingface/datasets/blob/6569014a9948eab7d031a3587405e64ba92d6c59/src/datasets/arrow_dataset.py#L410) - where columns are made into a list if they were a string? \r\n\r\nThere only concern I have is this changes the default behaviour, which might break things for people who were happily using `columns=[\"my_col_str\"]` before. \r\n\r\n\r\n",
"@amyeroberts That's correct! Probably the simplest way to implement it would be to just add the flag there.\r\n\r\nAnd yeah, I'm aware this might be a slightly breaking change, but we've mostly tried to move users to `prepare_tf_dataset` in `transformers` at this point, so hopefully as long as that method doesn't break then most users won't be negatively affected by the change.",
"@lhoestq @Rocketknight1 - I've remove the `return_dict` argument and implemented @Rocketknight1 's suggestion. LMK what you think :) ",
"@lhoestq Of course :) I've opened a draft PR here for the updates needed in transformers examples and docs to keep the returned data structure consistent: https://github.com/huggingface/transformers/pull/21935. Note: even with the different structure, `model.fit` can still successfully be called. \r\n\r\nFor the [link you shared](https://github.com/huggingface/datasets/pull/url) - for me it returns a 404 error. Is there another link I could follow to see how to run the transformers CI with this branch? \r\n\r\nCurrently looking into the failing tests 😭 ",
"Oh sorry - I fixed the URL: https://github.com/huggingface/transformers/commit/4eb55bbd593adf2e49362613ee32a11ddc4a854d",
"The error shows `There appear to be 80 leaked shared_memory objects to clean up at shutdown`. IIRC to_tf_dataset does some shared memory stuff for multiprocessing - maybe @Rocketknight1 you know what's going on ?",
"@lhoestq That warning appears anytime you interrupt a process using Python `SharedMemory` objects - it's only a problem if you still get the error when the process finishes normally! Our implementation of `to_tf_dataset` should clean things up properly.",
"Ok, not sure why it fails then :/",
"Hmm, will investigate! Sorry, I misread - I thought that warning was coming up in the context of another error",
"IMO outputing different types based on nuances in the input could confuse users.\r\n\r\nAlso, in the ideal scenario,`to_tf_function` should return a `tf.data.Dataset` that iterates over the underlying Arrow data and yields (unprocessed) dicts of TF tensors, and all the model-specific code should live in Transformers (e.g., in `prepare_tf_dataset`). So the goal would be to make `to_tf_dataset` more user-friendly, not more complex :).",
"I think we agree @mariosasko :) \r\n\r\n> Also, in the ideal scenario,to_tf_function should return a tf.data.Dataset that iterates over the underlying Arrow data and yields (unprocessed) dicts of TF tensors\r\n\r\nThis I'll leave for another PR as it's outside the scope of this one and @Rocketknight1 will have far more knowledge and ideas about what is possible\r\n\r\n> all the model-specific code should live in Transformers (e.g., in prepare_tf_dataset\r\n\r\nAgreed! This PR isn't really a model specific change - although it was highlighted when trying to train a model. We definitely want to move model specific things out of datasets as much as possible. \r\n\r\n> IMO outputing different types based on nuances in the input could confuse users.\r\n> So the goal would be to make to_tf_dataset more user-friendly, not more complex :).\r\n\r\nThe aim was to move more towards being able to return the dict of TF tensors you suggest, whilst maintaining backwards compatibility. Personally, I found it surprising to be returned a tuple structure when I was using `to_tf_dataset`. The aim was to make `to_tf_dataset` more user friendly, but I agree that it has the potential to be confusing. \r\n\r\nFor context, the thought process behind this design was to: \r\n* Not add even more arguments to `to_tf_dataset`. \r\n* Have a feature selection -> return type logic in keeping with `datasets` e.g. `dataset['train'][:10]['feat1']` returns a list of values, whereas `dataset['train'][:10]['feat1', 'feat2']` returns a dictionary. \r\n\r\nVery happy to add any suggestions or changes you might have about how to make this design better! :) \r\n",
"Hi ! Anything blocking here ? I'b be happy to help",
"Hi @lhoestq - sorry this hasn't been very active for the past ~1.5 weeks. There's nothing specific blocking, other than not being able to replicate without running on CI, and still need to test a bit more to narrow down the issue. I should have time tomorrow to pick it up again :) ",
"@lhoestq @Rocketknight1 Friendly ping for a review :) ",
"Awesome ! What about showing a warning that this change is about to happen in the next version of `datasets`, and then apply this change in a subsequent major release ? This way folks at twitter won't hate us: https://github.com/twitter/the-algorithm/blob/138bb519975407d4ea0dc1478d897d451ef05dab/trust_and_safety_models/toxicity/data/mb_generator.py#L142-L148",
"@lhoestq Sounds good! How would you like this warning to happen? I could open a PR to add a warning message within `to_tf_dataset`?",
"Yup sounds good :)"
] | "2023-03-02T15:51:12Z" | "2023-04-12T15:54:53Z" | null | CONTRIBUTOR | null | 0 | {
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} | This PR introduces new logic to `to_tf_dataset` affecting the returned data structure, enabling a dictionary structure to be returned, even if only one feature column is selected.
If the passed in `columns` or `label_cols` to `to_tf_dataset` are a list, they are returned as a dictionary, respectively. If they are a string, the tensor is returned.
An outline of the behaviour:
```
dataset,to_tf_dataset(columns=["col_1"], label_cols="col_2")
# ({'col_1': col_1}, col_2}
dataset,to_tf_dataset(columns="col1", label_cols="col_2")
# (col1, col2)
dataset,to_tf_dataset(columns="col1")
# col1
dataset,to_tf_dataset(columns=["col_1"], labels=["col_2"])
# ({'col1': tensor}, {'col2': tensor}}
dataset,to_tf_dataset(columns="col_1", labels=["col_2"])
# (col1, {'col2': tensor}}
```
## Motivation
Currently, when calling `to_tf_dataset`, the returned dataset will always return a tuple structure if a single feature column is used. This can cause issues when calling `model.fit` on models which train without labels e.g. [TFVitMAEForPreTraining](https://github.com/huggingface/transformers/blob/b6f47b539377ac1fd845c7adb4ccaa5eb514e126/src/transformers/models/vit_mae/modeling_vit_mae.py#L849). Specifically, [this line](https://github.com/huggingface/transformers/blob/d9e28d91a8b2d09b51a33155d3a03ad9fcfcbd1f/src/transformers/modeling_tf_utils.py#L1521) where it's assumed the input `x` is a dictionary if there is no label.
## Example
Previous behaviour
```python
In [1]: import tensorflow as tf
...: from datasets import load_dataset
...:
...:
...: def transform(batch):
...: def _transform_img(img):
...: img = img.convert("RGB")
...: img = tf.keras.utils.img_to_array(img)
...: img = tf.image.resize(img, (224, 224))
...: img /= 255.0
...: img = tf.transpose(img, perm=[2, 0, 1])
...: return img
...: batch['pixel_values'] = [_transform_img(pil_img) for pil_img in batch['img']]
...: return batch
...:
...:
...: def collate_fn(examples):
...: pixel_values = tf.stack([example["pixel_values"] for example in examples])
...: return {"pixel_values": pixel_values}
...:
...:
...: dataset = load_dataset('cifar10')['train']
...: dataset = dataset.with_transform(transform)
...: dataset.to_tf_dataset(batch_size=8, columns=['pixel_values'], collate_fn=collate_fn)
Out[1]: <PrefetchDataset element_spec=TensorSpec(shape=(None, 3, 224, 224), dtype=tf.float32, name=None)>
```
New behaviour
```python
In [1]: import tensorflow as tf
...: from datasets import load_dataset
...:
...:
...: def transform(batch):
...: def _transform_img(img):
...: img = img.convert("RGB")
...: img = tf.keras.utils.img_to_array(img)
...: img = tf.image.resize(img, (224, 224))
...: img /= 255.0
...: img = tf.transpose(img, perm=[2, 0, 1])
...: return img
...: batch['pixel_values'] = [_transform_img(pil_img) for pil_img in batch['img']]
...: return batch
...:
...:
...: def collate_fn(examples):
...: pixel_values = tf.stack([example["pixel_values"] for example in examples])
...: return {"pixel_values": pixel_values}
...:
...:
...: dataset = load_dataset('cifar10')['train']
...: dataset = dataset.with_transform(transform)
...: dataset.to_tf_dataset(batch_size=8, columns=['pixel_values'], collate_fn=collate_fn)
Out[1]: <PrefetchDataset element_spec={'pixel_values': TensorSpec(shape=(None, 3, 224, 224), dtype=tf.float32, name=None)}>
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/5601 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5601/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5601/comments | https://api.github.com/repos/huggingface/datasets/issues/5601/events | https://github.com/huggingface/datasets/issues/5601 | 1,606,685,976 | I_kwDODunzps5fxBUY | 5,601 | Authorization error | {
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"Hi! \r\n\r\nIt's better to report this kind of issue in the `huggingface_hub` repo, so if you still haven't resolved it, I suggest you open an issue there.",
"Yeah, I solved it. Problem was in osxkeychain. When I do `hugginface-cli login` it's add token with default account (username)`hg_user` but my repo contain other username. When I changed username in keychain - it works now."
] | "2023-03-02T12:08:39Z" | "2023-03-14T16:55:35Z" | "2023-03-14T16:55:34Z" | NONE | null | null | null | ### Describe the bug
Get `Authorization error` when try to push data into hugginface datasets hub.
### Steps to reproduce the bug
I did all steps in the [tutorial](https://huggingface.co/docs/datasets/share),
1. `huggingface-cli login` with WRITE token
2. `git lfs install`
3. `git clone https://huggingface.co/datasets/namespace/your_dataset_name`
4.
```
cp /somewhere/data/*.json .
git lfs track *.json
git add .gitattributes
git add *.json
git commit -m "add json files"
```
but when I execute `git push` I got the error:
```
Uploading LFS objects: 0% (0/1), 0 B | 0 B/s, done.
batch response: Authorization error.
error: failed to push some refs to 'https://huggingface.co/datasets/zeusfsx/ukrainian-news'
```
Size of data ~100Gb. I have five json files - different parts.
### Expected behavior
All my data pushed into hub
### Environment info
- `datasets` version: 2.10.1
- Platform: macOS-13.2.1-arm64-arm-64bit
- Python version: 3.10.10
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/5600 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5600/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5600/comments | https://api.github.com/repos/huggingface/datasets/issues/5600/events | https://github.com/huggingface/datasets/issues/5600 | 1,606,585,596 | I_kwDODunzps5fwoz8 | 5,600 | Dataloader getitem not working for DreamboothDatasets | {
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"Hi! \r\n\r\n> (see example of DreamboothDatasets)\r\n\r\n\r\nCould you please provide a link to it? If you are referring to the example in the `diffusers` repo, your issue is unrelated to `datasets` as that example uses `Dataset` from PyTorch to load data."
] | "2023-03-02T11:00:27Z" | "2023-03-13T17:59:35Z" | "2023-03-13T17:59:35Z" | NONE | null | null | null | ### Describe the bug
Dataloader getitem is not working as before (see example of [DreamboothDatasets](https://github.com/huggingface/peft/blob/main/examples/lora_dreambooth/train_dreambooth.py#L451C14-L529))
moving Datasets to 2.8.0 solved the issue.
### Steps to reproduce the bug
1- using DreamBoothDataset to load some images
2- error after loading when trying to visualise the images
### Expected behavior
I was expecting a numpy array of the image
### Environment info
- Platform: Linux-5.10.147+-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 9.0.0
- Pandas version: 1.3.5 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008823 / 0.011353 (-0.002529) | 0.004738 / 0.011008 (-0.006270) | 0.102338 / 0.038508 (0.063830) | 0.030603 / 0.023109 (0.007494) | 0.302995 / 0.275898 (0.027097) | 0.362080 / 0.323480 (0.038600) | 0.007096 / 0.007986 (-0.000889) | 0.003493 / 0.004328 (-0.000835) | 0.079129 / 0.004250 (0.074878) | 0.037966 / 0.037052 (0.000914) | 0.310412 / 0.258489 (0.051923) | 0.346740 / 0.293841 (0.052899) | 0.033795 / 0.128546 (-0.094751) | 0.011595 / 0.075646 (-0.064051) | 0.325189 / 0.419271 (-0.094083) | 0.041679 / 0.043533 (-0.001854) | 0.302339 / 0.255139 (0.047200) | 0.322519 / 0.283200 (0.039319) | 0.089058 / 0.141683 (-0.052625) | 1.496223 / 1.452155 (0.044068) | 1.512562 / 1.492716 (0.019845) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.009298 / 0.018006 (-0.008709) | 0.406726 / 0.000490 (0.406236) | 0.003753 / 0.000200 (0.003553) | 0.000082 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023327 / 0.037411 (-0.014084) | 0.098175 / 0.014526 (0.083649) | 0.106040 / 0.176557 (-0.070516) | 0.151934 / 0.737135 (-0.585201) | 0.108465 / 0.296338 (-0.187873) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419073 / 0.215209 (0.203864) | 4.188012 / 2.077655 (2.110358) | 1.857667 / 1.504120 (0.353547) | 1.664124 / 1.541195 (0.122929) | 1.704341 / 1.468490 (0.235851) | 0.699671 / 4.584777 (-3.885106) | 3.391110 / 3.745712 (-0.354602) | 1.871136 / 5.269862 (-3.398725) | 1.176794 / 4.565676 (-3.388882) | 0.083322 / 0.424275 (-0.340953) | 0.012450 / 0.007607 (0.004843) | 0.525058 / 0.226044 (0.299014) | 5.265425 / 2.268929 (2.996497) | 2.320672 / 55.444624 (-53.123952) | 1.964806 / 6.876477 (-4.911671) | 2.027055 / 2.142072 (-0.115017) | 0.819768 / 4.805227 (-3.985459) | 0.149638 / 6.500664 (-6.351026) | 0.064774 / 0.075469 (-0.010695) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.204575 / 1.841788 (-0.637212) | 13.651878 / 8.074308 (5.577570) | 13.751973 / 10.191392 (3.560581) | 0.154781 / 0.680424 (-0.525643) | 0.028887 / 0.534201 (-0.505314) | 0.404905 / 0.579283 (-0.174379) | 0.411320 / 0.434364 (-0.023043) | 0.485026 / 0.540337 (-0.055311) | 0.579690 / 1.386936 (-0.807246) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006615 / 0.011353 (-0.004737) | 0.004606 / 0.011008 (-0.006402) | 0.076099 / 0.038508 (0.037591) | 0.027247 / 0.023109 (0.004137) | 0.360731 / 0.275898 (0.084833) | 0.393688 / 0.323480 (0.070208) | 0.005079 / 0.007986 (-0.002906) | 0.003345 / 0.004328 (-0.000984) | 0.077184 / 0.004250 (0.072934) | 0.037850 / 0.037052 (0.000797) | 0.379738 / 0.258489 (0.121249) | 0.400474 / 0.293841 (0.106633) | 0.031581 / 0.128546 (-0.096966) | 0.011508 / 0.075646 (-0.064138) | 0.084966 / 0.419271 (-0.334306) | 0.041740 / 0.043533 (-0.001793) | 0.349887 / 0.255139 (0.094748) | 0.384405 / 0.283200 (0.101205) | 0.089022 / 0.141683 (-0.052661) | 1.503448 / 1.452155 (0.051293) | 1.564870 / 1.492716 (0.072154) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.233581 / 0.018006 (0.215574) | 0.413819 / 0.000490 (0.413330) | 0.000398 / 0.000200 (0.000198) | 0.000060 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024805 / 0.037411 (-0.012607) | 0.101348 / 0.014526 (0.086822) | 0.108701 / 0.176557 (-0.067856) | 0.160011 / 0.737135 (-0.577124) | 0.111696 / 0.296338 (-0.184642) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436303 / 0.215209 (0.221094) | 4.368684 / 2.077655 (2.291029) | 2.082366 / 1.504120 (0.578247) | 1.888108 / 1.541195 (0.346913) | 1.958295 / 1.468490 (0.489804) | 0.700858 / 4.584777 (-3.883919) | 3.408321 / 3.745712 (-0.337391) | 1.872960 / 5.269862 (-3.396902) | 1.165116 / 4.565676 (-3.400560) | 0.083556 / 0.424275 (-0.340719) | 0.012348 / 0.007607 (0.004741) | 0.536551 / 0.226044 (0.310506) | 5.359974 / 2.268929 (3.091045) | 2.539043 / 55.444624 (-52.905581) | 2.200314 / 6.876477 (-4.676162) | 2.222051 / 2.142072 (0.079979) | 0.808567 / 4.805227 (-3.996661) | 0.151222 / 6.500664 (-6.349442) | 0.066351 / 0.075469 (-0.009118) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.265502 / 1.841788 (-0.576286) | 13.692066 / 8.074308 (5.617758) | 13.124507 / 10.191392 (2.933115) | 0.129545 / 0.680424 (-0.550879) | 0.016827 / 0.534201 (-0.517374) | 0.380326 / 0.579283 (-0.198957) | 0.387268 / 0.434364 (-0.047096) | 0.463722 / 0.540337 (-0.076616) | 0.553681 / 1.386936 (-0.833255) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6569014a9948eab7d031a3587405e64ba92d6c59 \"CML watermark\")\n"
] | "2023-03-01T13:54:06Z" | "2023-03-02T13:47:13Z" | "2023-03-02T13:40:17Z" | MEMBER | null | 0 | {
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} | As reported in https://github.com/vijaydwivedi75/lrgb/issues/10, `push_to_hub` fails if the remote repository already exists and has a README.md without `dataset_info` in the YAML tags
cc @clefourrier | {
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"We won't support in-place modifications since `datasets` is based on the Apache Arrow format which doesn't support in-place modifications.\r\n\r\nIn your case the old dataset is garbage collected pretty quickly so you won't have memory issues.\r\n\r\nNote that datasets loaded from disk (memory mapped) are not loaded in memory, and therefore the new dataset actually use the same buffers as the old one.",
"Thank you for your detailed reply.\r\n\r\n> In your case the old dataset is garbage collected pretty quickly so you won't have memory issues.\r\n\r\nI understand this, but it still copies the old dataset to create the new one, is this correct? So maybe it is not memory-consuming, but time-consuming?",
"Indeed, and because of that it is more efficient to add multiple rows at once instead of one by one, using `concatenate_datasets` for example."
] | "2023-03-01T12:58:18Z" | "2023-03-02T13:30:41Z" | "2023-03-02T03:47:00Z" | NONE | null | null | null | ### Motivation
For the circumstance that I creat an empty `Dataset` and keep appending new rows into it, I found that it leads to creating a new dataset at each call. It looks quite memory-consuming. I just wonder if there is any more efficient way to do this.
```python
from datasets import Dataset
ds = Dataset.from_list([])
ds.add_item({'a': [1, 2, 3], 'b': 4})
print(ds)
>>> Dataset({
>>> features: [],
>>> num_rows: 0
>>> })
ds = ds.add_item({'a': [1, 2, 3], 'b': 4})
print(ds)
>>> Dataset({
>>> features: ['a', 'b'],
>>> num_rows: 1
>>> })
```
### Feature request
Call for in-place dataset update functions, that update the existing `Dataset` in place without creating a new copy. The interface is supposed to keep the same style as PyTorch, such as the in-place version of a `function` is named `function_`. For example, the in-pace version of `add_item`, i.e., `add_item_`, immediately updates the `Dataset`.
```python
from datasets import Dataset
ds = Dataset.from_list([])
ds.add_item({'a': [1, 2, 3], 'b': 4})
print(ds)
>>> Dataset({
>>> features: [],
>>> num_rows: 0
>>> })
ds.add_item_({'a': [1, 2, 3], 'b': 4})
print(ds)
>>> Dataset({
>>> features: ['a', 'b'],
>>> num_rows: 1
>>> })
```
### Related Functions
* `.map`
* `.filter`
* `.add_item` | {
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"Apparently some JSON objects have a `\"labels\"` field. Since this field is not present in every object, you must specify all the fields types in the README.md\r\n\r\nEDIT: actually specifying the feature types doesn’t solve the issue, it raises an error because “labels” is missing in the data",
"We've updated the dataset to remove the extra `labels` field from some files, closing this issue. Thanks!",
"A similar error occurs in the Pile dataset (EleutherAI/the_pile)\r\n\r\nLoading the dataset produces the following error.\r\n\r\n```\r\nTypeError: Couldn't cast array of type\r\nstruct<file: string, id: string>\r\nto\r\n{'id': Value(dtype='string', id=None)}\r\n```\r\n",
"I think this was fixed in https://huggingface.co/datasets/EleutherAI/the_pile/discussions/11"
] | "2023-03-01T12:53:08Z" | "2023-04-19T10:19:37Z" | "2023-03-02T11:12:11Z" | NONE | null | null | null | ### Describe the bug
I'm trying to load this [dataset](https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues) which consists of jsonl files and I get the following error:
```
casted_values = _c(array.values, feature[0])
File "/opt/conda/lib/python3.7/site-packages/datasets/table.py", line 1839, in wrapper
return func(array, *args, **kwargs)
File "/opt/conda/lib/python3.7/site-packages/datasets/table.py", line 2132, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
TypeError: Couldn't cast array of type
struct<type: string, action: string, datetime: timestamp[s], author: string, title: string, description: string, comment_id: int64, comment: string, labels: list<item: string>>
to
{'type': Value(dtype='string', id=None), 'action': Value(dtype='string', id=None), 'datetime': Value(dtype='timestamp[s]', id=None), 'author': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'description': Value(dtype='string', id=None), 'comment_id': Value(dtype='int64', id=None), 'comment': Value(dtype='string', id=None)}
```
But I can succesfully load a subset of the dataset, for example this works:
```python
ds = load_dataset('bigcode-data/the-stack-gh-issues', split="train", data_files=[f"data/data-{x}.jsonl" for x in range(10)])
```
and `ds.features` returns:
```
{'repo': Value(dtype='string', id=None),
'org': Value(dtype='string', id=None),
'issue_id': Value(dtype='int64', id=None),
'issue_number': Value(dtype='int64', id=None),
'pull_request': {'user_login': Value(dtype='string', id=None),
'repo': Value(dtype='string', id=None),
'number': Value(dtype='int64', id=None)},
'events': [{'type': Value(dtype='string', id=None),
'action': Value(dtype='string', id=None),
'datetime': Value(dtype='timestamp[s]', id=None),
'author': Value(dtype='string', id=None),
'title': Value(dtype='string', id=None),
'description': Value(dtype='string', id=None),
'comment_id': Value(dtype='int64', id=None),
'comment': Value(dtype='string', id=None)}]}
```
So I'm not sure if there's an issue with just some of the files. Grateful if you have any suggestions to fix the issue.
Side note:
I saw this related [issue](https://github.com/huggingface/datasets/issues/3637) and tried to write a loading script to have `events` as a `Sequence` and not `list` [here](https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues/blob/main/loading.py) (the script was renamed). It worked with a subset locally but doesn't for the remote dataset it can't find https://huggingface.co/datasets/bigcode-data/the-stack-gh-issues/resolve/main/data.
### Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset('bigcode-data/the-stack-gh-issues', split="train")
```
### Expected behavior
Load the entire dataset succesfully.
### Environment info
- `datasets` version: 2.10.1
- Platform: Linux-4.19.0-23-cloud-amd64-x86_64-with-debian-10.13
- Python version: 3.7.12
- PyArrow version: 9.0.0
- Pandas version: 1.3.4 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5595). All of your documentation changes will be reflected on that endpoint.",
"It looks like this issue hasn't been fixed yet, so let's wait a bit more.",
"@lazarust thanks for your work, but unfortunately we cannot merge it.\r\n\r\nSee my comment in: https://github.com/huggingface/datasets/issues/5477#issuecomment-1495512688\r\n\r\nThe fix was released yesterday (2023-04-03) only in `pandas-2.0.0`:\r\n- https://github.com/pandas-dev/pandas/releases/tag/v2.0.0\r\n\r\nbut it will not be back-ported to `pandas-1`:\r\n- https://github.com/pandas-dev/pandas/pull/48576#issuecomment-1466467159\r\n\r\nAlso note that `pandas-2.0.0` dropped support for Python 3.7:\r\n- https://github.com/pandas-dev/pandas/issues/41678\r\n- https://github.com/pandas-dev/pandas/pull/41989\r\n\r\nTherefore, we cannot unpin `sqlalchemy` until we drop support for Python 3.7 (these Python users cannot use `pandas-2`). See our latest CI checks below:\r\n- \"CI / test\" fails because it runs on Python 3.7\r\n- \"CI / test_py310\" succeeds because it runs on Python 3.10 "
] | "2023-03-01T01:33:45Z" | "2023-04-04T08:20:19Z" | "2023-04-04T08:19:14Z" | NONE | null | 0 | {
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"Hi! I cannot reproduce this error on my machine.\r\n\r\nThe raised error could mean that one of the downloaded files is corrupted. To verify this is not the case, you can run `load_dataset` as follows:\r\n```python\r\ntrain_dataset = load_dataset('xtreme', 'udpos.English', split=\"train\", cache_dir=args.cache_dir, download_mode=\"force_redownload\", verification_mode=\"all_checks\")\r\n```",
"Hi! Apologies for the delayed response! I tried the above and it doesn't solve the issue. Actually, the dataset gets downloaded most times, but sometimes this error occurs (at random afaik). Is it possible that there is a server issue for this particular dataset? I am able to download other datasets using the same code on the same machine with no issues :( I get this error now : \r\n```\r\nDownloading data: 16%|███████████████▌ | 55.9M/355M [04:45<25:25, 196kB/s]\r\nTraceback (most recent call last):\r\n File \"/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py\", line 1107, in <module>\r\n main()\r\n File \"/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py\", line 439, in main\r\n en_dataset = load_dataset(\"xtreme\", \"udpos.English\", split=\"train\", download_mode=\"force_redownload\", verification_mode=\"all_checks\")\r\n File \"/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py\", line 1782, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py\", line 872, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py\", line 1649, in _download_and_prepare\r\n super()._download_and_prepare(\r\n File \"/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py\", line 949, in _download_and_prepare\r\n verify_checksums(\r\n File \"/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/utils/info_utils.py\", line 62, in verify_checksums\r\n raise NonMatchingChecksumError(\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-3105/ud-treebanks-v2.5.tgz']\r\nSet `verification_mode='no_checks'` to skip checksums verification and ignore this error\r\n```",
"If this happens randomly, then this means the data file from the error message is not always downloaded correctly. \r\n\r\nThe only solution in this scenario is to download the dataset again by passing `download_mode=\"force_redownload\"` to the `load_dataset` call."
] | "2023-02-28T23:40:53Z" | "2023-07-24T14:22:18Z" | "2023-07-24T14:22:18Z" | NONE | null | null | null | ### Describe the bug
Hi,
I am facing an error while downloading the xtreme udpos dataset using load_dataset. I have datasets 2.10.1 installed
```Downloading and preparing dataset xtreme/udpos.Arabic to /compute/tir-1-18/skhanuja/multilingual_ft/cache/data/xtreme/udpos.Arabic/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4...
Downloading data: 16%|██████████████▏ | 56.9M/355M [03:11<16:43, 297kB/s]
Generating train split: 0%| | 0/6075 [00:00<?, ? examples/s]Traceback (most recent call last):
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1608, in _prepare_split_single
for key, record in generator:
File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 732, in _generate_examples
yield from UdposParser.generate_examples(config=self.config, filepath=filepath, **kwargs)
File "/home/skhanuja/.cache/huggingface/modules/datasets_modules/datasets/xtreme/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/xtreme.py", line 921, in generate_examples
for path, file in filepath:
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 158, in __iter__
yield from self.generator(*self.args, **self.kwargs)
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 211, in _iter_from_path
yield from cls._iter_tar(f)
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/download/download_manager.py", line 167, in _iter_tar
for tarinfo in stream:
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2475, in __iter__
tarinfo = self.next()
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/tarfile.py", line 2344, in next
raise ReadError("unexpected end of data")
tarfile.ReadError: unexpected end of data
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 855, in <module>
main()
File "/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py", line 487, in main
train_dataset = load_dataset(dataset_name, source_language, split="train", cache_dir=args.cache_dir, download_mode="force_redownload")
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset
builder_instance.download_and_prepare(
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 872, in download_and_prepare
self._download_and_prepare(
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1649, in _download_and_prepare
super()._download_and_prepare(
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 967, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1488, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py", line 1644, 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
```
### Steps to reproduce the bug
```
train_dataset = load_dataset('xtreme', 'udpos.English', split="train", cache_dir=args.cache_dir, download_mode="force_redownload")
```
### Expected behavior
Download the udpos dataset
### Environment info
- `datasets` version: 2.10.1
- Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.10.8
- PyArrow version: 10.0.1
- Pandas version: 1.5.2 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009526 / 0.011353 (-0.001827) | 0.005132 / 0.011008 (-0.005876) | 0.101312 / 0.038508 (0.062804) | 0.035703 / 0.023109 (0.012594) | 0.301788 / 0.275898 (0.025890) | 0.368411 / 0.323480 (0.044932) | 0.008163 / 0.007986 (0.000177) | 0.005462 / 0.004328 (0.001134) | 0.077282 / 0.004250 (0.073031) | 0.044139 / 0.037052 (0.007086) | 0.312280 / 0.258489 (0.053791) | 0.351870 / 0.293841 (0.058029) | 0.038266 / 0.128546 (-0.090281) | 0.012051 / 0.075646 (-0.063595) | 0.335109 / 0.419271 (-0.084163) | 0.047596 / 0.043533 (0.004064) | 0.300931 / 0.255139 (0.045792) | 0.325705 / 0.283200 (0.042505) | 0.100472 / 0.141683 (-0.041211) | 1.475037 / 1.452155 (0.022882) | 1.520059 / 1.492716 (0.027343) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211096 / 0.018006 (0.193089) | 0.442988 / 0.000490 (0.442498) | 0.003644 / 0.000200 (0.003444) | 0.000090 / 0.000054 (0.000036) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027492 / 0.037411 (-0.009919) | 0.108981 / 0.014526 (0.094455) | 0.117836 / 0.176557 (-0.058720) | 0.161220 / 0.737135 (-0.575915) | 0.124765 / 0.296338 (-0.171574) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.413480 / 0.215209 (0.198271) | 4.111355 / 2.077655 (2.033700) | 1.933024 / 1.504120 (0.428904) | 1.727467 / 1.541195 (0.186272) | 1.827106 / 1.468490 (0.358616) | 0.688209 / 4.584777 (-3.896568) | 3.759672 / 3.745712 (0.013960) | 2.163806 / 5.269862 (-3.106056) | 1.473521 / 4.565676 (-3.092155) | 0.082859 / 0.424275 (-0.341416) | 0.012320 / 0.007607 (0.004713) | 0.515321 / 0.226044 (0.289277) | 5.158651 / 2.268929 (2.889722) | 2.489123 / 55.444624 (-52.955501) | 2.218910 / 6.876477 (-4.657566) | 2.257306 / 2.142072 (0.115233) | 0.861477 / 4.805227 (-3.943750) | 0.165857 / 6.500664 (-6.334807) | 0.063723 / 0.075469 (-0.011746) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.195163 / 1.841788 (-0.646625) | 14.954518 / 8.074308 (6.880210) | 14.272289 / 10.191392 (4.080897) | 0.167420 / 0.680424 (-0.513004) | 0.028907 / 0.534201 (-0.505294) | 0.450117 / 0.579283 (-0.129166) | 0.448532 / 0.434364 (0.014168) | 0.534406 / 0.540337 (-0.005931) | 0.633468 / 1.386936 (-0.753468) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007658 / 0.011353 (-0.003694) | 0.005266 / 0.011008 (-0.005742) | 0.075293 / 0.038508 (0.036785) | 0.034442 / 0.023109 (0.011333) | 0.346558 / 0.275898 (0.070660) | 0.391496 / 0.323480 (0.068017) | 0.005852 / 0.007986 (-0.002133) | 0.004121 / 0.004328 (-0.000207) | 0.074254 / 0.004250 (0.070004) | 0.048361 / 0.037052 (0.011309) | 0.344613 / 0.258489 (0.086124) | 0.401497 / 0.293841 (0.107656) | 0.037243 / 0.128546 (-0.091303) | 0.012505 / 0.075646 (-0.063142) | 0.087188 / 0.419271 (-0.332084) | 0.050114 / 0.043533 (0.006581) | 0.340454 / 0.255139 (0.085315) | 0.361087 / 0.283200 (0.077887) | 0.104692 / 0.141683 (-0.036991) | 1.419432 / 1.452155 (-0.032722) | 1.524709 / 1.492716 (0.031993) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231820 / 0.018006 (0.213814) | 0.445791 / 0.000490 (0.445301) | 0.000442 / 0.000200 (0.000242) | 0.000061 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030445 / 0.037411 (-0.006967) | 0.111183 / 0.014526 (0.096657) | 0.123494 / 0.176557 (-0.053063) | 0.173121 / 0.737135 (-0.564014) | 0.124968 / 0.296338 (-0.171371) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428854 / 0.215209 (0.213645) | 4.270262 / 2.077655 (2.192608) | 2.012075 / 1.504120 (0.507955) | 1.826564 / 1.541195 (0.285370) | 1.931699 / 1.468490 (0.463209) | 0.728762 / 4.584777 (-3.856015) | 3.879640 / 3.745712 (0.133928) | 3.325715 / 5.269862 (-1.944147) | 1.818573 / 4.565676 (-2.747104) | 0.087879 / 0.424275 (-0.336396) | 0.012530 / 0.007607 (0.004923) | 0.530249 / 0.226044 (0.304204) | 5.286110 / 2.268929 (3.017181) | 2.566649 / 55.444624 (-52.877975) | 2.210162 / 6.876477 (-4.666315) | 2.297562 / 2.142072 (0.155490) | 0.906161 / 4.805227 (-3.899066) | 0.171914 / 6.500664 (-6.328750) | 0.064182 / 0.075469 (-0.011287) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.285781 / 1.841788 (-0.556006) | 16.159072 / 8.074308 (8.084763) | 14.087492 / 10.191392 (3.896100) | 0.148789 / 0.680424 (-0.531635) | 0.018078 / 0.534201 (-0.516123) | 0.427748 / 0.579283 (-0.151535) | 0.447079 / 0.434364 (0.012715) | 0.535917 / 0.540337 (-0.004421) | 0.627491 / 1.386936 (-0.759445) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#88fa043d08c12923709c0492e037130c99c029fb \"CML watermark\")\n"
] | "2023-02-28T18:42:37Z" | "2023-02-28T19:26:33Z" | "2023-02-28T19:19:15Z" | MEMBER | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5591 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5591/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5591/comments | https://api.github.com/repos/huggingface/datasets/issues/5591/events | https://github.com/huggingface/datasets/pull/5591 | 1,603,571,407 | PR_kwDODunzps5K9S79 | 5,591 | set dev version | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5591). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008826 / 0.011353 (-0.002527) | 0.004595 / 0.011008 (-0.006413) | 0.103387 / 0.038508 (0.064879) | 0.030241 / 0.023109 (0.007132) | 0.351202 / 0.275898 (0.075303) | 0.417601 / 0.323480 (0.094121) | 0.007121 / 0.007986 (-0.000865) | 0.003497 / 0.004328 (-0.000831) | 0.079256 / 0.004250 (0.075006) | 0.037617 / 0.037052 (0.000564) | 0.380542 / 0.258489 (0.122053) | 0.397863 / 0.293841 (0.104022) | 0.034291 / 0.128546 (-0.094255) | 0.011767 / 0.075646 (-0.063879) | 0.323737 / 0.419271 (-0.095534) | 0.041502 / 0.043533 (-0.002031) | 0.352982 / 0.255139 (0.097843) | 0.378618 / 0.283200 (0.095418) | 0.091671 / 0.141683 (-0.050012) | 1.499278 / 1.452155 (0.047123) | 1.517489 / 1.492716 (0.024773) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.190108 / 0.018006 (0.172102) | 0.414404 / 0.000490 (0.413915) | 0.001064 / 0.000200 (0.000864) | 0.000066 / 0.000054 (0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023214 / 0.037411 (-0.014198) | 0.099351 / 0.014526 (0.084825) | 0.105227 / 0.176557 (-0.071330) | 0.150620 / 0.737135 (-0.586516) | 0.109323 / 0.296338 (-0.187015) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.412463 / 0.215209 (0.197254) | 4.138123 / 2.077655 (2.060469) | 1.845163 / 1.504120 (0.341043) | 1.641108 / 1.541195 (0.099913) | 1.715471 / 1.468490 (0.246981) | 0.697397 / 4.584777 (-3.887380) | 3.449829 / 3.745712 (-0.295883) | 1.959309 / 5.269862 (-3.310553) | 1.285754 / 4.565676 (-3.279923) | 0.082746 / 0.424275 (-0.341529) | 0.012523 / 0.007607 (0.004916) | 0.524745 / 0.226044 (0.298700) | 5.257085 / 2.268929 (2.988156) | 2.293163 / 55.444624 (-53.151461) | 1.958309 / 6.876477 (-4.918168) | 2.016106 / 2.142072 (-0.125966) | 0.814359 / 4.805227 (-3.990869) | 0.149443 / 6.500664 (-6.351221) | 0.066013 / 0.075469 (-0.009456) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.248495 / 1.841788 (-0.593292) | 14.303301 / 8.074308 (6.228993) | 14.238533 / 10.191392 (4.047141) | 0.161421 / 0.680424 (-0.519003) | 0.028779 / 0.534201 (-0.505422) | 0.396511 / 0.579283 (-0.182772) | 0.412784 / 0.434364 (-0.021580) | 0.473984 / 0.540337 (-0.066353) | 0.569610 / 1.386936 (-0.817327) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007003 / 0.011353 (-0.004350) | 0.004621 / 0.011008 (-0.006387) | 0.079418 / 0.038508 (0.040910) | 0.028659 / 0.023109 (0.005550) | 0.340594 / 0.275898 (0.064696) | 0.377972 / 0.323480 (0.054492) | 0.005421 / 0.007986 (-0.002565) | 0.004852 / 0.004328 (0.000523) | 0.077579 / 0.004250 (0.073329) | 0.042662 / 0.037052 (0.005610) | 0.342264 / 0.258489 (0.083775) | 0.387255 / 0.293841 (0.093414) | 0.032574 / 0.128546 (-0.095972) | 0.011820 / 0.075646 (-0.063826) | 0.087960 / 0.419271 (-0.331312) | 0.045199 / 0.043533 (0.001667) | 0.341785 / 0.255139 (0.086646) | 0.365014 / 0.283200 (0.081814) | 0.096129 / 0.141683 (-0.045554) | 1.498962 / 1.452155 (0.046807) | 1.557331 / 1.492716 (0.064615) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236216 / 0.018006 (0.218210) | 0.440189 / 0.000490 (0.439699) | 0.000399 / 0.000200 (0.000199) | 0.000060 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026357 / 0.037411 (-0.011055) | 0.104485 / 0.014526 (0.089959) | 0.109616 / 0.176557 (-0.066941) | 0.163005 / 0.737135 (-0.574130) | 0.113859 / 0.296338 (-0.182479) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437452 / 0.215209 (0.222243) | 4.371854 / 2.077655 (2.294199) | 2.056845 / 1.504120 (0.552725) | 1.856071 / 1.541195 (0.314876) | 1.957978 / 1.468490 (0.489488) | 0.703171 / 4.584777 (-3.881606) | 3.433889 / 3.745712 (-0.311823) | 1.968321 / 5.269862 (-3.301541) | 1.204947 / 4.565676 (-3.360729) | 0.084499 / 0.424275 (-0.339777) | 0.012729 / 0.007607 (0.005122) | 0.537534 / 0.226044 (0.311490) | 5.383346 / 2.268929 (3.114417) | 2.522136 / 55.444624 (-52.922488) | 2.192715 / 6.876477 (-4.683762) | 2.243579 / 2.142072 (0.101507) | 0.811136 / 4.805227 (-3.994091) | 0.154015 / 6.500664 (-6.346649) | 0.069324 / 0.075469 (-0.006145) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.294232 / 1.841788 (-0.547556) | 14.809448 / 8.074308 (6.735140) | 13.510074 / 10.191392 (3.318682) | 0.158033 / 0.680424 (-0.522391) | 0.016703 / 0.534201 (-0.517498) | 0.393976 / 0.579283 (-0.185307) | 0.385983 / 0.434364 (-0.048381) | 0.476691 / 0.540337 (-0.063646) | 0.565694 / 1.386936 (-0.821242) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b0dd3126196e8fcd9ba81a6602b46623b4e77e6e \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009155 / 0.011353 (-0.002198) | 0.005227 / 0.011008 (-0.005781) | 0.099767 / 0.038508 (0.061259) | 0.035338 / 0.023109 (0.012229) | 0.293913 / 0.275898 (0.018015) | 0.366976 / 0.323480 (0.043496) | 0.007802 / 0.007986 (-0.000184) | 0.005286 / 0.004328 (0.000958) | 0.075117 / 0.004250 (0.070867) | 0.042336 / 0.037052 (0.005284) | 0.304690 / 0.258489 (0.046201) | 0.343496 / 0.293841 (0.049655) | 0.038745 / 0.128546 (-0.089802) | 0.012275 / 0.075646 (-0.063371) | 0.334455 / 0.419271 (-0.084817) | 0.052611 / 0.043533 (0.009078) | 0.293229 / 0.255139 (0.038090) | 0.314340 / 0.283200 (0.031140) | 0.108676 / 0.141683 (-0.033007) | 1.444495 / 1.452155 (-0.007659) | 1.492244 / 1.492716 (-0.000472) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.204852 / 0.018006 (0.186846) | 0.438202 / 0.000490 (0.437712) | 0.005043 / 0.000200 (0.004843) | 0.000282 / 0.000054 (0.000228) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027268 / 0.037411 (-0.010143) | 0.109497 / 0.014526 (0.094972) | 0.117187 / 0.176557 (-0.059369) | 0.162551 / 0.737135 (-0.574584) | 0.124175 / 0.296338 (-0.172164) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.401667 / 0.215209 (0.186458) | 4.010274 / 2.077655 (1.932619) | 1.882617 / 1.504120 (0.378497) | 1.721960 / 1.541195 (0.180765) | 1.806874 / 1.468490 (0.338384) | 0.711253 / 4.584777 (-3.873524) | 3.806585 / 3.745712 (0.060873) | 3.713011 / 5.269862 (-1.556851) | 1.896558 / 4.565676 (-2.669119) | 0.086092 / 0.424275 (-0.338184) | 0.012129 / 0.007607 (0.004522) | 0.504905 / 0.226044 (0.278861) | 5.050794 / 2.268929 (2.781865) | 2.324331 / 55.444624 (-53.120293) | 2.020170 / 6.876477 (-4.856307) | 2.079685 / 2.142072 (-0.062388) | 0.854782 / 4.805227 (-3.950445) | 0.166754 / 6.500664 (-6.333910) | 0.062434 / 0.075469 (-0.013035) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.187897 / 1.841788 (-0.653891) | 14.618517 / 8.074308 (6.544209) | 13.205760 / 10.191392 (3.014368) | 0.154322 / 0.680424 (-0.526102) | 0.029243 / 0.534201 (-0.504958) | 0.442390 / 0.579283 (-0.136893) | 0.434651 / 0.434364 (0.000287) | 0.523082 / 0.540337 (-0.017256) | 0.602675 / 1.386936 (-0.784261) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007214 / 0.011353 (-0.004139) | 0.005225 / 0.011008 (-0.005783) | 0.076497 / 0.038508 (0.037989) | 0.032761 / 0.023109 (0.009652) | 0.336005 / 0.275898 (0.060107) | 0.373547 / 0.323480 (0.050067) | 0.005460 / 0.007986 (-0.002526) | 0.003933 / 0.004328 (-0.000395) | 0.074540 / 0.004250 (0.070289) | 0.047785 / 0.037052 (0.010733) | 0.341917 / 0.258489 (0.083428) | 0.396978 / 0.293841 (0.103137) | 0.036763 / 0.128546 (-0.091783) | 0.012043 / 0.075646 (-0.063603) | 0.087632 / 0.419271 (-0.331640) | 0.049376 / 0.043533 (0.005843) | 0.335169 / 0.255139 (0.080030) | 0.354852 / 0.283200 (0.071652) | 0.100180 / 0.141683 (-0.041503) | 1.443422 / 1.452155 (-0.008733) | 1.518618 / 1.492716 (0.025901) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209593 / 0.018006 (0.191587) | 0.444028 / 0.000490 (0.443538) | 0.004545 / 0.000200 (0.004345) | 0.000100 / 0.000054 (0.000046) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029676 / 0.037411 (-0.007735) | 0.115444 / 0.014526 (0.100918) | 0.121765 / 0.176557 (-0.054791) | 0.171037 / 0.737135 (-0.566098) | 0.128592 / 0.296338 (-0.167746) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428556 / 0.215209 (0.213347) | 4.228531 / 2.077655 (2.150877) | 2.039190 / 1.504120 (0.535070) | 1.836518 / 1.541195 (0.295324) | 1.897040 / 1.468490 (0.428550) | 0.698893 / 4.584777 (-3.885884) | 3.753998 / 3.745712 (0.008286) | 2.097731 / 5.269862 (-3.172131) | 1.338315 / 4.565676 (-3.227361) | 0.087119 / 0.424275 (-0.337156) | 0.012149 / 0.007607 (0.004542) | 0.520774 / 0.226044 (0.294730) | 5.227420 / 2.268929 (2.958492) | 2.522235 / 55.444624 (-52.922389) | 2.194213 / 6.876477 (-4.682264) | 2.241406 / 2.142072 (0.099333) | 0.843119 / 4.805227 (-3.962109) | 0.169128 / 6.500664 (-6.331536) | 0.065071 / 0.075469 (-0.010398) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254490 / 1.841788 (-0.587298) | 15.037137 / 8.074308 (6.962829) | 13.115333 / 10.191392 (2.923941) | 0.181743 / 0.680424 (-0.498681) | 0.017748 / 0.534201 (-0.516453) | 0.425758 / 0.579283 (-0.153525) | 0.429926 / 0.434364 (-0.004438) | 0.524386 / 0.540337 (-0.015951) | 0.643044 / 1.386936 (-0.743892) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#09e820e79a3b879855b514e2a62d84b738013940 \"CML watermark\")\n"
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008717 / 0.011353 (-0.002636) | 0.004570 / 0.011008 (-0.006439) | 0.100228 / 0.038508 (0.061720) | 0.030076 / 0.023109 (0.006967) | 0.317919 / 0.275898 (0.042021) | 0.366360 / 0.323480 (0.042880) | 0.007008 / 0.007986 (-0.000978) | 0.003498 / 0.004328 (-0.000831) | 0.077607 / 0.004250 (0.073356) | 0.036106 / 0.037052 (-0.000946) | 0.314128 / 0.258489 (0.055639) | 0.351450 / 0.293841 (0.057609) | 0.033697 / 0.128546 (-0.094849) | 0.011424 / 0.075646 (-0.064222) | 0.323867 / 0.419271 (-0.095404) | 0.042073 / 0.043533 (-0.001460) | 0.304564 / 0.255139 (0.049425) | 0.334865 / 0.283200 (0.051665) | 0.087791 / 0.141683 (-0.053892) | 1.488075 / 1.452155 (0.035920) | 1.513676 / 1.492716 (0.020959) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.010936 / 0.018006 (-0.007070) | 0.409610 / 0.000490 (0.409121) | 0.004820 / 0.000200 (0.004620) | 0.000079 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023931 / 0.037411 (-0.013481) | 0.096826 / 0.014526 (0.082300) | 0.105764 / 0.176557 (-0.070792) | 0.153241 / 0.737135 (-0.583895) | 0.108976 / 0.296338 (-0.187363) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.412833 / 0.215209 (0.197624) | 4.129735 / 2.077655 (2.052081) | 1.819049 / 1.504120 (0.314929) | 1.617411 / 1.541195 (0.076216) | 1.682353 / 1.468490 (0.213863) | 0.688987 / 4.584777 (-3.895790) | 3.388276 / 3.745712 (-0.357436) | 1.857452 / 5.269862 (-3.412410) | 1.158020 / 4.565676 (-3.407657) | 0.082161 / 0.424275 (-0.342114) | 0.012319 / 0.007607 (0.004712) | 0.523052 / 0.226044 (0.297008) | 5.237726 / 2.268929 (2.968797) | 2.275605 / 55.444624 (-53.169020) | 1.931664 / 6.876477 (-4.944813) | 1.970026 / 2.142072 (-0.172046) | 0.805240 / 4.805227 (-3.999988) | 0.148431 / 6.500664 (-6.352233) | 0.064707 / 0.075469 (-0.010762) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.196456 / 1.841788 (-0.645332) | 13.750113 / 8.074308 (5.675805) | 13.853543 / 10.191392 (3.662151) | 0.137892 / 0.680424 (-0.542532) | 0.028304 / 0.534201 (-0.505897) | 0.400128 / 0.579283 (-0.179155) | 0.410409 / 0.434364 (-0.023955) | 0.479165 / 0.540337 (-0.061172) | 0.575002 / 1.386936 (-0.811934) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006587 / 0.011353 (-0.004766) | 0.004526 / 0.011008 (-0.006482) | 0.075673 / 0.038508 (0.037165) | 0.027429 / 0.023109 (0.004320) | 0.341808 / 0.275898 (0.065910) | 0.379520 / 0.323480 (0.056040) | 0.004972 / 0.007986 (-0.003014) | 0.003354 / 0.004328 (-0.000975) | 0.075373 / 0.004250 (0.071123) | 0.038347 / 0.037052 (0.001294) | 0.343671 / 0.258489 (0.085181) | 0.389632 / 0.293841 (0.095791) | 0.031694 / 0.128546 (-0.096853) | 0.011458 / 0.075646 (-0.064188) | 0.084210 / 0.419271 (-0.335062) | 0.042662 / 0.043533 (-0.000871) | 0.339436 / 0.255139 (0.084297) | 0.367493 / 0.283200 (0.084294) | 0.091604 / 0.141683 (-0.050079) | 1.526762 / 1.452155 (0.074607) | 1.569110 / 1.492716 (0.076394) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211496 / 0.018006 (0.193489) | 0.404868 / 0.000490 (0.404379) | 0.004267 / 0.000200 (0.004067) | 0.000083 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025189 / 0.037411 (-0.012222) | 0.099139 / 0.014526 (0.084613) | 0.105898 / 0.176557 (-0.070659) | 0.160997 / 0.737135 (-0.576138) | 0.110158 / 0.296338 (-0.186180) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.444286 / 0.215209 (0.229077) | 4.445479 / 2.077655 (2.367824) | 2.118920 / 1.504120 (0.614800) | 1.908296 / 1.541195 (0.367102) | 1.947211 / 1.468490 (0.478721) | 0.704850 / 4.584777 (-3.879927) | 3.395990 / 3.745712 (-0.349723) | 1.892529 / 5.269862 (-3.377332) | 1.172190 / 4.565676 (-3.393486) | 0.084235 / 0.424275 (-0.340040) | 0.012588 / 0.007607 (0.004981) | 0.546962 / 0.226044 (0.320918) | 5.475842 / 2.268929 (3.206913) | 2.575280 / 55.444624 (-52.869344) | 2.245658 / 6.876477 (-4.630818) | 2.274767 / 2.142072 (0.132695) | 0.813755 / 4.805227 (-3.991473) | 0.151927 / 6.500664 (-6.348737) | 0.067167 / 0.075469 (-0.008302) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.267666 / 1.841788 (-0.574122) | 13.658905 / 8.074308 (5.584597) | 13.207249 / 10.191392 (3.015857) | 0.128590 / 0.680424 (-0.551833) | 0.016531 / 0.534201 (-0.517670) | 0.385050 / 0.579283 (-0.194233) | 0.388945 / 0.434364 (-0.045419) | 0.472378 / 0.540337 (-0.067959) | 0.568929 / 1.386936 (-0.818007) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#87cd5f7f7fda60d0f91f50424bcc3f327fe0d059 \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009339 / 0.011353 (-0.002014) | 0.005197 / 0.011008 (-0.005811) | 0.100698 / 0.038508 (0.062190) | 0.035484 / 0.023109 (0.012375) | 0.299030 / 0.275898 (0.023132) | 0.366603 / 0.323480 (0.043124) | 0.007909 / 0.007986 (-0.000077) | 0.005683 / 0.004328 (0.001355) | 0.077719 / 0.004250 (0.073469) | 0.042147 / 0.037052 (0.005094) | 0.310174 / 0.258489 (0.051685) | 0.342720 / 0.293841 (0.048879) | 0.039679 / 0.128546 (-0.088867) | 0.012042 / 0.075646 (-0.063605) | 0.335663 / 0.419271 (-0.083609) | 0.051137 / 0.043533 (0.007604) | 0.298218 / 0.255139 (0.043079) | 0.316398 / 0.283200 (0.033198) | 0.108906 / 0.141683 (-0.032776) | 1.422823 / 1.452155 (-0.029331) | 1.472955 / 1.492716 (-0.019761) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.205845 / 0.018006 (0.187839) | 0.445942 / 0.000490 (0.445453) | 0.003553 / 0.000200 (0.003353) | 0.000083 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025506 / 0.037411 (-0.011906) | 0.107494 / 0.014526 (0.092969) | 0.116226 / 0.176557 (-0.060331) | 0.157313 / 0.737135 (-0.579822) | 0.123822 / 0.296338 (-0.172516) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400908 / 0.215209 (0.185699) | 3.980232 / 2.077655 (1.902578) | 1.805410 / 1.504120 (0.301290) | 1.615698 / 1.541195 (0.074503) | 1.677213 / 1.468490 (0.208723) | 0.697882 / 4.584777 (-3.886895) | 3.752781 / 3.745712 (0.007069) | 2.076062 / 5.269862 (-3.193800) | 1.446909 / 4.565676 (-3.118768) | 0.084572 / 0.424275 (-0.339703) | 0.011917 / 0.007607 (0.004310) | 0.511815 / 0.226044 (0.285771) | 5.121487 / 2.268929 (2.852558) | 2.277642 / 55.444624 (-53.166982) | 1.930393 / 6.876477 (-4.946084) | 1.965855 / 2.142072 (-0.176218) | 0.843391 / 4.805227 (-3.961837) | 0.163581 / 6.500664 (-6.337083) | 0.062547 / 0.075469 (-0.012922) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.223930 / 1.841788 (-0.617858) | 14.354466 / 8.074308 (6.280158) | 14.015159 / 10.191392 (3.823767) | 0.148658 / 0.680424 (-0.531766) | 0.028469 / 0.534201 (-0.505732) | 0.437614 / 0.579283 (-0.141669) | 0.435452 / 0.434364 (0.001089) | 0.523623 / 0.540337 (-0.016715) | 0.625109 / 1.386936 (-0.761827) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006917 / 0.011353 (-0.004436) | 0.005080 / 0.011008 (-0.005928) | 0.075806 / 0.038508 (0.037298) | 0.032402 / 0.023109 (0.009293) | 0.331105 / 0.275898 (0.055207) | 0.361226 / 0.323480 (0.037746) | 0.005694 / 0.007986 (-0.002292) | 0.003810 / 0.004328 (-0.000518) | 0.076886 / 0.004250 (0.072635) | 0.046158 / 0.037052 (0.009106) | 0.338791 / 0.258489 (0.080302) | 0.385733 / 0.293841 (0.091892) | 0.035590 / 0.128546 (-0.092956) | 0.011997 / 0.075646 (-0.063649) | 0.087854 / 0.419271 (-0.331417) | 0.048985 / 0.043533 (0.005452) | 0.331248 / 0.255139 (0.076109) | 0.354633 / 0.283200 (0.071434) | 0.101609 / 0.141683 (-0.040074) | 1.496899 / 1.452155 (0.044745) | 1.570469 / 1.492716 (0.077753) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.180871 / 0.018006 (0.162865) | 0.449417 / 0.000490 (0.448928) | 0.004300 / 0.000200 (0.004100) | 0.000102 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029054 / 0.037411 (-0.008358) | 0.110888 / 0.014526 (0.096362) | 0.121736 / 0.176557 (-0.054821) | 0.172563 / 0.737135 (-0.564572) | 0.126565 / 0.296338 (-0.169773) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419545 / 0.215209 (0.204336) | 4.193685 / 2.077655 (2.116031) | 2.049967 / 1.504120 (0.545847) | 1.855038 / 1.541195 (0.313843) | 1.899822 / 1.468490 (0.431332) | 0.709123 / 4.584777 (-3.875654) | 3.795939 / 3.745712 (0.050227) | 2.076055 / 5.269862 (-3.193807) | 1.335864 / 4.565676 (-3.229812) | 0.085555 / 0.424275 (-0.338720) | 0.012197 / 0.007607 (0.004590) | 0.516164 / 0.226044 (0.290119) | 5.158983 / 2.268929 (2.890054) | 2.445581 / 55.444624 (-52.999044) | 2.122256 / 6.876477 (-4.754221) | 2.160011 / 2.142072 (0.017939) | 0.840251 / 4.805227 (-3.964976) | 0.165924 / 6.500664 (-6.334740) | 0.064080 / 0.075469 (-0.011389) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.285292 / 1.841788 (-0.556495) | 14.561084 / 8.074308 (6.486776) | 12.899269 / 10.191392 (2.707877) | 0.185657 / 0.680424 (-0.494767) | 0.017866 / 0.534201 (-0.516335) | 0.425365 / 0.579283 (-0.153918) | 0.427183 / 0.434364 (-0.007181) | 0.529773 / 0.540337 (-0.010564) | 0.642061 / 1.386936 (-0.744875) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0628013d009dd5150e8a1c1a4ac9d93887b88a76 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008995 / 0.011353 (-0.002357) | 0.004540 / 0.011008 (-0.006469) | 0.099675 / 0.038508 (0.061167) | 0.030338 / 0.023109 (0.007229) | 0.307167 / 0.275898 (0.031269) | 0.338789 / 0.323480 (0.015309) | 0.007293 / 0.007986 (-0.000692) | 0.004681 / 0.004328 (0.000352) | 0.077475 / 0.004250 (0.073225) | 0.036399 / 0.037052 (-0.000654) | 0.304615 / 0.258489 (0.046126) | 0.351611 / 0.293841 (0.057770) | 0.034449 / 0.128546 (-0.094097) | 0.011565 / 0.075646 (-0.064082) | 0.322765 / 0.419271 (-0.096506) | 0.041971 / 0.043533 (-0.001562) | 0.307492 / 0.255139 (0.052354) | 0.327240 / 0.283200 (0.044040) | 0.087110 / 0.141683 (-0.054573) | 1.484600 / 1.452155 (0.032445) | 1.536651 / 1.492716 (0.043934) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.185876 / 0.018006 (0.167869) | 0.404276 / 0.000490 (0.403787) | 0.001592 / 0.000200 (0.001392) | 0.000072 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023272 / 0.037411 (-0.014139) | 0.096273 / 0.014526 (0.081747) | 0.105400 / 0.176557 (-0.071157) | 0.149720 / 0.737135 (-0.587416) | 0.107807 / 0.296338 (-0.188532) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420072 / 0.215209 (0.204863) | 4.184108 / 2.077655 (2.106454) | 1.880690 / 1.504120 (0.376570) | 1.673103 / 1.541195 (0.131909) | 1.715792 / 1.468490 (0.247302) | 0.695771 / 4.584777 (-3.889006) | 3.450224 / 3.745712 (-0.295488) | 2.999218 / 5.269862 (-2.270644) | 1.585571 / 4.565676 (-2.980106) | 0.082105 / 0.424275 (-0.342170) | 0.012453 / 0.007607 (0.004846) | 0.528538 / 0.226044 (0.302494) | 5.287951 / 2.268929 (3.019023) | 2.289127 / 55.444624 (-53.155497) | 1.956503 / 6.876477 (-4.919974) | 2.004498 / 2.142072 (-0.137575) | 0.813547 / 4.805227 (-3.991681) | 0.151574 / 6.500664 (-6.349090) | 0.063763 / 0.075469 (-0.011706) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.239125 / 1.841788 (-0.602662) | 13.627676 / 8.074308 (5.553368) | 13.747815 / 10.191392 (3.556423) | 0.157745 / 0.680424 (-0.522679) | 0.028590 / 0.534201 (-0.505611) | 0.397472 / 0.579283 (-0.181811) | 0.405925 / 0.434364 (-0.028439) | 0.477942 / 0.540337 (-0.062396) | 0.572379 / 1.386936 (-0.814557) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006637 / 0.011353 (-0.004716) | 0.004657 / 0.011008 (-0.006351) | 0.082056 / 0.038508 (0.043548) | 0.027974 / 0.023109 (0.004865) | 0.342887 / 0.275898 (0.066989) | 0.375938 / 0.323480 (0.052458) | 0.004958 / 0.007986 (-0.003028) | 0.004738 / 0.004328 (0.000409) | 0.080449 / 0.004250 (0.076198) | 0.038138 / 0.037052 (0.001085) | 0.345636 / 0.258489 (0.087147) | 0.385992 / 0.293841 (0.092151) | 0.033265 / 0.128546 (-0.095281) | 0.011965 / 0.075646 (-0.063681) | 0.091441 / 0.419271 (-0.327830) | 0.051407 / 0.043533 (0.007874) | 0.353758 / 0.255139 (0.098619) | 0.372118 / 0.283200 (0.088919) | 0.093947 / 0.141683 (-0.047735) | 1.468197 / 1.452155 (0.016042) | 1.554677 / 1.492716 (0.061960) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222034 / 0.018006 (0.204027) | 0.403658 / 0.000490 (0.403169) | 0.003242 / 0.000200 (0.003042) | 0.000082 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025335 / 0.037411 (-0.012076) | 0.100404 / 0.014526 (0.085878) | 0.107858 / 0.176557 (-0.068698) | 0.156115 / 0.737135 (-0.581021) | 0.113967 / 0.296338 (-0.182372) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437567 / 0.215209 (0.222358) | 4.362486 / 2.077655 (2.284832) | 2.067315 / 1.504120 (0.563195) | 1.857669 / 1.541195 (0.316475) | 1.926380 / 1.468490 (0.457890) | 0.703905 / 4.584777 (-3.880872) | 3.437139 / 3.745712 (-0.308573) | 3.051931 / 5.269862 (-2.217930) | 1.356494 / 4.565676 (-3.209182) | 0.083679 / 0.424275 (-0.340596) | 0.012507 / 0.007607 (0.004900) | 0.539572 / 0.226044 (0.313528) | 5.405790 / 2.268929 (3.136861) | 2.532769 / 55.444624 (-52.911855) | 2.181950 / 6.876477 (-4.694527) | 2.212627 / 2.142072 (0.070554) | 0.807468 / 4.805227 (-3.997759) | 0.152146 / 6.500664 (-6.348518) | 0.068891 / 0.075469 (-0.006578) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.286972 / 1.841788 (-0.554816) | 13.987186 / 8.074308 (5.912878) | 13.115065 / 10.191392 (2.923673) | 0.162143 / 0.680424 (-0.518281) | 0.016767 / 0.534201 (-0.517434) | 0.384766 / 0.579283 (-0.194517) | 0.397438 / 0.434364 (-0.036926) | 0.470850 / 0.540337 (-0.069487) | 0.562216 / 1.386936 (-0.824720) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2843fceabc428932754ba497f643d6e94173b91e \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010877 / 0.011353 (-0.000476) | 0.005739 / 0.011008 (-0.005269) | 0.118542 / 0.038508 (0.080034) | 0.042266 / 0.023109 (0.019157) | 0.359317 / 0.275898 (0.083419) | 0.412995 / 0.323480 (0.089515) | 0.009158 / 0.007986 (0.001173) | 0.006343 / 0.004328 (0.002014) | 0.089587 / 0.004250 (0.085336) | 0.047899 / 0.037052 (0.010847) | 0.358745 / 0.258489 (0.100256) | 0.421316 / 0.293841 (0.127476) | 0.044540 / 0.128546 (-0.084006) | 0.013872 / 0.075646 (-0.061774) | 0.399856 / 0.419271 (-0.019415) | 0.056484 / 0.043533 (0.012951) | 0.356922 / 0.255139 (0.101783) | 0.385598 / 0.283200 (0.102398) | 0.116039 / 0.141683 (-0.025644) | 1.726095 / 1.452155 (0.273940) | 1.888643 / 1.492716 (0.395927) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.269517 / 0.018006 (0.251511) | 0.511204 / 0.000490 (0.510714) | 0.001906 / 0.000200 (0.001706) | 0.000103 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031133 / 0.037411 (-0.006278) | 0.128513 / 0.014526 (0.113987) | 0.139639 / 0.176557 (-0.036918) | 0.189778 / 0.737135 (-0.547358) | 0.145219 / 0.296338 (-0.151120) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.486693 / 0.215209 (0.271484) | 4.851999 / 2.077655 (2.774344) | 2.255334 / 1.504120 (0.751214) | 2.052271 / 1.541195 (0.511077) | 2.143262 / 1.468490 (0.674772) | 0.835765 / 4.584777 (-3.749012) | 4.451280 / 3.745712 (0.705568) | 2.534392 / 5.269862 (-2.735469) | 1.747817 / 4.565676 (-2.817859) | 0.101186 / 0.424275 (-0.323089) | 0.014281 / 0.007607 (0.006674) | 0.616164 / 0.226044 (0.390120) | 6.161789 / 2.268929 (3.892860) | 2.815347 / 55.444624 (-52.629277) | 2.408305 / 6.876477 (-4.468172) | 2.508240 / 2.142072 (0.366167) | 1.017709 / 4.805227 (-3.787519) | 0.198272 / 6.500664 (-6.302392) | 0.075663 / 0.075469 (0.000194) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.435501 / 1.841788 (-0.406287) | 18.149581 / 8.074308 (10.075273) | 16.619011 / 10.191392 (6.427619) | 0.205080 / 0.680424 (-0.475344) | 0.033780 / 0.534201 (-0.500421) | 0.515768 / 0.579283 (-0.063515) | 0.542628 / 0.434364 (0.108264) | 0.634067 / 0.540337 (0.093730) | 0.757841 / 1.386936 (-0.629095) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008541 / 0.011353 (-0.002812) | 0.005733 / 0.011008 (-0.005275) | 0.089859 / 0.038508 (0.051351) | 0.039379 / 0.023109 (0.016270) | 0.402037 / 0.275898 (0.126139) | 0.454046 / 0.323480 (0.130566) | 0.006652 / 0.007986 (-0.001334) | 0.004555 / 0.004328 (0.000227) | 0.087651 / 0.004250 (0.083401) | 0.054934 / 0.037052 (0.017881) | 0.404468 / 0.258489 (0.145979) | 0.467127 / 0.293841 (0.173286) | 0.042034 / 0.128546 (-0.086512) | 0.014225 / 0.075646 (-0.061421) | 0.103281 / 0.419271 (-0.315990) | 0.057767 / 0.043533 (0.014234) | 0.396391 / 0.255139 (0.141252) | 0.429364 / 0.283200 (0.146165) | 0.120193 / 0.141683 (-0.021489) | 1.794029 / 1.452155 (0.341875) | 1.875431 / 1.492716 (0.382714) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.325707 / 0.018006 (0.307701) | 0.503841 / 0.000490 (0.503351) | 0.010224 / 0.000200 (0.010024) | 0.000137 / 0.000054 (0.000082) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035289 / 0.037411 (-0.002123) | 0.139018 / 0.014526 (0.124492) | 0.145112 / 0.176557 (-0.031445) | 0.202616 / 0.737135 (-0.534519) | 0.152975 / 0.296338 (-0.143363) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.493110 / 0.215209 (0.277901) | 4.885713 / 2.077655 (2.808058) | 2.344417 / 1.504120 (0.840297) | 2.135734 / 1.541195 (0.594540) | 2.254118 / 1.468490 (0.785628) | 0.811516 / 4.584777 (-3.773261) | 4.484454 / 3.745712 (0.738742) | 2.459913 / 5.269862 (-2.809948) | 1.553106 / 4.565676 (-3.012570) | 0.100943 / 0.424275 (-0.323332) | 0.014848 / 0.007607 (0.007241) | 0.626214 / 0.226044 (0.400170) | 6.206925 / 2.268929 (3.937997) | 2.986549 / 55.444624 (-52.458076) | 2.521895 / 6.876477 (-4.354582) | 2.610917 / 2.142072 (0.468845) | 0.998496 / 4.805227 (-3.806731) | 0.199405 / 6.500664 (-6.301260) | 0.077355 / 0.075469 (0.001886) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.525135 / 1.841788 (-0.316653) | 18.708407 / 8.074308 (10.634099) | 16.049482 / 10.191392 (5.858090) | 0.170986 / 0.680424 (-0.509437) | 0.021090 / 0.534201 (-0.513111) | 0.511734 / 0.579283 (-0.067549) | 0.495507 / 0.434364 (0.061143) | 0.628578 / 0.540337 (0.088241) | 0.749546 / 1.386936 (-0.637390) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2843fceabc428932754ba497f643d6e94173b91e \"CML watermark\")\n"
] | "2023-02-28T17:58:11Z" | "2023-02-28T18:16:27Z" | "2023-02-28T18:06:08Z" | MEMBER | null | 0 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008442 / 0.011353 (-0.002911) | 0.004567 / 0.011008 (-0.006441) | 0.100688 / 0.038508 (0.062180) | 0.029568 / 0.023109 (0.006459) | 0.306993 / 0.275898 (0.031095) | 0.362626 / 0.323480 (0.039146) | 0.006983 / 0.007986 (-0.001002) | 0.003424 / 0.004328 (-0.000905) | 0.079050 / 0.004250 (0.074799) | 0.036087 / 0.037052 (-0.000966) | 0.318205 / 0.258489 (0.059716) | 0.353882 / 0.293841 (0.060041) | 0.033091 / 0.128546 (-0.095455) | 0.011468 / 0.075646 (-0.064178) | 0.321125 / 0.419271 (-0.098146) | 0.040645 / 0.043533 (-0.002888) | 0.309827 / 0.255139 (0.054688) | 0.344848 / 0.283200 (0.061648) | 0.087100 / 0.141683 (-0.054583) | 1.465123 / 1.452155 (0.012968) | 1.499457 / 1.492716 (0.006741) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.171619 / 0.018006 (0.153613) | 0.410198 / 0.000490 (0.409709) | 0.002391 / 0.000200 (0.002191) | 0.000075 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022913 / 0.037411 (-0.014499) | 0.097275 / 0.014526 (0.082749) | 0.103902 / 0.176557 (-0.072655) | 0.148855 / 0.737135 (-0.588281) | 0.107247 / 0.296338 (-0.189092) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.413139 / 0.215209 (0.197930) | 4.131760 / 2.077655 (2.054105) | 1.854491 / 1.504120 (0.350371) | 1.625524 / 1.541195 (0.084329) | 1.666665 / 1.468490 (0.198175) | 0.687105 / 4.584777 (-3.897672) | 3.327124 / 3.745712 (-0.418588) | 1.830820 / 5.269862 (-3.439042) | 1.147930 / 4.565676 (-3.417746) | 0.081586 / 0.424275 (-0.342689) | 0.012422 / 0.007607 (0.004815) | 0.523723 / 0.226044 (0.297678) | 5.246977 / 2.268929 (2.978049) | 2.288350 / 55.444624 (-53.156275) | 1.933740 / 6.876477 (-4.942737) | 1.954356 / 2.142072 (-0.187716) | 0.804434 / 4.805227 (-4.000793) | 0.147621 / 6.500664 (-6.353043) | 0.064835 / 0.075469 (-0.010634) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.244841 / 1.841788 (-0.596947) | 13.758465 / 8.074308 (5.684157) | 13.984576 / 10.191392 (3.793184) | 0.144860 / 0.680424 (-0.535564) | 0.028616 / 0.534201 (-0.505584) | 0.401928 / 0.579283 (-0.177355) | 0.415294 / 0.434364 (-0.019069) | 0.476483 / 0.540337 (-0.063854) | 0.569257 / 1.386936 (-0.817679) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006556 / 0.011353 (-0.004797) | 0.004502 / 0.011008 (-0.006507) | 0.074828 / 0.038508 (0.036319) | 0.027537 / 0.023109 (0.004427) | 0.339961 / 0.275898 (0.064063) | 0.372491 / 0.323480 (0.049011) | 0.005010 / 0.007986 (-0.002976) | 0.004624 / 0.004328 (0.000295) | 0.074459 / 0.004250 (0.070208) | 0.037539 / 0.037052 (0.000486) | 0.341031 / 0.258489 (0.082542) | 0.383397 / 0.293841 (0.089556) | 0.031706 / 0.128546 (-0.096840) | 0.011542 / 0.075646 (-0.064104) | 0.084882 / 0.419271 (-0.334389) | 0.041860 / 0.043533 (-0.001673) | 0.338699 / 0.255139 (0.083560) | 0.365666 / 0.283200 (0.082467) | 0.088966 / 0.141683 (-0.052717) | 1.502493 / 1.452155 (0.050339) | 1.570746 / 1.492716 (0.078030) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.217547 / 0.018006 (0.199541) | 0.392407 / 0.000490 (0.391918) | 0.000388 / 0.000200 (0.000188) | 0.000058 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024571 / 0.037411 (-0.012840) | 0.099259 / 0.014526 (0.084734) | 0.107850 / 0.176557 (-0.068707) | 0.157686 / 0.737135 (-0.579449) | 0.109761 / 0.296338 (-0.186578) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434791 / 0.215209 (0.219582) | 4.323099 / 2.077655 (2.245444) | 2.063610 / 1.504120 (0.559490) | 1.866136 / 1.541195 (0.324941) | 1.910185 / 1.468490 (0.441695) | 0.696584 / 4.584777 (-3.888193) | 3.398017 / 3.745712 (-0.347695) | 1.848473 / 5.269862 (-3.421388) | 1.168238 / 4.565676 (-3.397438) | 0.083222 / 0.424275 (-0.341053) | 0.012332 / 0.007607 (0.004725) | 0.538953 / 0.226044 (0.312909) | 5.421273 / 2.268929 (3.152344) | 2.499877 / 55.444624 (-52.944747) | 2.161853 / 6.876477 (-4.714624) | 2.183941 / 2.142072 (0.041868) | 0.803916 / 4.805227 (-4.001311) | 0.150266 / 6.500664 (-6.350398) | 0.067399 / 0.075469 (-0.008070) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.280479 / 1.841788 (-0.561309) | 13.728074 / 8.074308 (5.653766) | 12.946098 / 10.191392 (2.754706) | 0.128459 / 0.680424 (-0.551965) | 0.016567 / 0.534201 (-0.517634) | 0.374461 / 0.579283 (-0.204822) | 0.386973 / 0.434364 (-0.047391) | 0.459754 / 0.540337 (-0.080583) | 0.543870 / 1.386936 (-0.843066) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#595b3d47e1fc579f5db1cbc376f756edf32904dd \"CML watermark\")\n",
"Instead of reverting the change, maybe we can use the same conversion in `to_iterable_dataset` as in `ArrowBasedBuilder._as_streaming_dataset` to avoid decoding images twice?",
"True, let me take a look",
"Closing in favor of https://github.com/huggingface/datasets/pull/5655"
] | "2023-02-28T17:52:04Z" | "2023-03-21T14:21:45Z" | "2023-03-21T14:18:18Z" | MEMBER | null | 0 | {
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} | This reverts commit b91070b9c09673e2e148eec458036ab6a62ac042 (temporarily)
It hurts iterable dataset performance a lot (e.g. x4 slower because it encodes+decodes images unnecessarily). I think we need to fix this before re-adding it
cc @mariosasko @Hubert-Bonisseur | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009866 / 0.011353 (-0.001487) | 0.005334 / 0.011008 (-0.005675) | 0.101771 / 0.038508 (0.063263) | 0.037722 / 0.023109 (0.014613) | 0.301026 / 0.275898 (0.025128) | 0.336618 / 0.323480 (0.013138) | 0.008679 / 0.007986 (0.000693) | 0.005640 / 0.004328 (0.001312) | 0.077076 / 0.004250 (0.072825) | 0.045068 / 0.037052 (0.008016) | 0.302570 / 0.258489 (0.044081) | 0.359093 / 0.293841 (0.065252) | 0.038865 / 0.128546 (-0.089681) | 0.012318 / 0.075646 (-0.063328) | 0.334819 / 0.419271 (-0.084452) | 0.047980 / 0.043533 (0.004447) | 0.296999 / 0.255139 (0.041860) | 0.318855 / 0.283200 (0.035656) | 0.110633 / 0.141683 (-0.031050) | 1.464326 / 1.452155 (0.012172) | 1.537386 / 1.492716 (0.044670) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.282906 / 0.018006 (0.264900) | 0.498418 / 0.000490 (0.497928) | 0.001507 / 0.000200 (0.001307) | 0.000087 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029948 / 0.037411 (-0.007463) | 0.114385 / 0.014526 (0.099859) | 0.125783 / 0.176557 (-0.050774) | 0.193458 / 0.737135 (-0.543678) | 0.129725 / 0.296338 (-0.166614) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.403822 / 0.215209 (0.188613) | 4.034180 / 2.077655 (1.956525) | 1.768206 / 1.504120 (0.264086) | 1.579267 / 1.541195 (0.038072) | 1.725077 / 1.468490 (0.256587) | 0.698743 / 4.584777 (-3.886034) | 3.723481 / 3.745712 (-0.022231) | 2.302374 / 5.269862 (-2.967488) | 1.497954 / 4.565676 (-3.067723) | 0.087360 / 0.424275 (-0.336915) | 0.012453 / 0.007607 (0.004846) | 0.523374 / 0.226044 (0.297329) | 5.244962 / 2.268929 (2.976033) | 2.272874 / 55.444624 (-53.171750) | 1.935570 / 6.876477 (-4.940907) | 2.043151 / 2.142072 (-0.098921) | 0.866298 / 4.805227 (-3.938929) | 0.169376 / 6.500664 (-6.331288) | 0.064578 / 0.075469 (-0.010892) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.217372 / 1.841788 (-0.624416) | 15.896050 / 8.074308 (7.821742) | 15.165190 / 10.191392 (4.973798) | 0.171168 / 0.680424 (-0.509256) | 0.029770 / 0.534201 (-0.504431) | 0.449030 / 0.579283 (-0.130253) | 0.454704 / 0.434364 (0.020340) | 0.550689 / 0.540337 (0.010351) | 0.651182 / 1.386936 (-0.735754) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008072 / 0.011353 (-0.003281) | 0.005533 / 0.011008 (-0.005475) | 0.076343 / 0.038508 (0.037835) | 0.037997 / 0.023109 (0.014888) | 0.350465 / 0.275898 (0.074567) | 0.391168 / 0.323480 (0.067688) | 0.006475 / 0.007986 (-0.001511) | 0.004299 / 0.004328 (-0.000029) | 0.074867 / 0.004250 (0.070617) | 0.055256 / 0.037052 (0.018204) | 0.363919 / 0.258489 (0.105430) | 0.396521 / 0.293841 (0.102680) | 0.037746 / 0.128546 (-0.090801) | 0.012556 / 0.075646 (-0.063091) | 0.087974 / 0.419271 (-0.331297) | 0.050850 / 0.043533 (0.007317) | 0.345857 / 0.255139 (0.090718) | 0.361019 / 0.283200 (0.077820) | 0.111007 / 0.141683 (-0.030676) | 1.444014 / 1.452155 (-0.008140) | 1.533154 / 1.492716 (0.040438) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.332114 / 0.018006 (0.314108) | 0.517232 / 0.000490 (0.516742) | 0.004459 / 0.000200 (0.004259) | 0.000102 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033147 / 0.037411 (-0.004264) | 0.119983 / 0.014526 (0.105457) | 0.125970 / 0.176557 (-0.050586) | 0.196375 / 0.737135 (-0.540760) | 0.133849 / 0.296338 (-0.162489) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429477 / 0.215209 (0.214267) | 4.263750 / 2.077655 (2.186096) | 2.079409 / 1.504120 (0.575289) | 1.899831 / 1.541195 (0.358636) | 2.048472 / 1.468490 (0.579982) | 0.720945 / 4.584777 (-3.863832) | 3.813195 / 3.745712 (0.067483) | 2.250353 / 5.269862 (-3.019508) | 1.401496 / 4.565676 (-3.164181) | 0.090052 / 0.424275 (-0.334223) | 0.012552 / 0.007607 (0.004945) | 0.536839 / 0.226044 (0.310794) | 5.361089 / 2.268929 (3.092161) | 2.559710 / 55.444624 (-52.884914) | 2.226963 / 6.876477 (-4.649513) | 2.341898 / 2.142072 (0.199825) | 0.872115 / 4.805227 (-3.933112) | 0.173776 / 6.500664 (-6.326888) | 0.068567 / 0.075469 (-0.006902) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.294583 / 1.841788 (-0.547205) | 16.624099 / 8.074308 (8.549791) | 13.698509 / 10.191392 (3.507117) | 0.161917 / 0.680424 (-0.518506) | 0.017744 / 0.534201 (-0.516457) | 0.428547 / 0.579283 (-0.150736) | 0.424687 / 0.434364 (-0.009677) | 0.525812 / 0.540337 (-0.014525) | 0.629075 / 1.386936 (-0.757861) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33e4d6af919db17bf9a1eac544a0501b5972393b \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008667 / 0.011353 (-0.002686) | 0.004921 / 0.011008 (-0.006087) | 0.098352 / 0.038508 (0.059844) | 0.033983 / 0.023109 (0.010873) | 0.291640 / 0.275898 (0.015742) | 0.323388 / 0.323480 (-0.000092) | 0.007943 / 0.007986 (-0.000043) | 0.003922 / 0.004328 (-0.000407) | 0.075861 / 0.004250 (0.071610) | 0.042606 / 0.037052 (0.005554) | 0.298571 / 0.258489 (0.040081) | 0.345496 / 0.293841 (0.051655) | 0.037443 / 0.128546 (-0.091103) | 0.012114 / 0.075646 (-0.063532) | 0.333269 / 0.419271 (-0.086003) | 0.047762 / 0.043533 (0.004229) | 0.295452 / 0.255139 (0.040313) | 0.319641 / 0.283200 (0.036441) | 0.101083 / 0.141683 (-0.040600) | 1.432179 / 1.452155 (-0.019976) | 1.523976 / 1.492716 (0.031260) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.241327 / 0.018006 (0.223321) | 0.538315 / 0.000490 (0.537825) | 0.003479 / 0.000200 (0.003279) | 0.000082 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025857 / 0.037411 (-0.011554) | 0.104833 / 0.014526 (0.090307) | 0.116826 / 0.176557 (-0.059730) | 0.183460 / 0.737135 (-0.553675) | 0.119595 / 0.296338 (-0.176743) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.397533 / 0.215209 (0.182324) | 3.968664 / 2.077655 (1.891010) | 1.774025 / 1.504120 (0.269905) | 1.577424 / 1.541195 (0.036229) | 1.623049 / 1.468490 (0.154559) | 0.701008 / 4.584777 (-3.883769) | 3.753278 / 3.745712 (0.007565) | 2.078313 / 5.269862 (-3.191549) | 1.335639 / 4.565676 (-3.230037) | 0.085216 / 0.424275 (-0.339059) | 0.012087 / 0.007607 (0.004480) | 0.513219 / 0.226044 (0.287174) | 5.097693 / 2.268929 (2.828765) | 2.275030 / 55.444624 (-53.169594) | 1.928037 / 6.876477 (-4.948439) | 1.941216 / 2.142072 (-0.200856) | 0.856720 / 4.805227 (-3.948507) | 0.166723 / 6.500664 (-6.333941) | 0.062263 / 0.075469 (-0.013206) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.196054 / 1.841788 (-0.645734) | 14.190526 / 8.074308 (6.116218) | 14.053768 / 10.191392 (3.862376) | 0.179982 / 0.680424 (-0.500442) | 0.029024 / 0.534201 (-0.505177) | 0.440391 / 0.579283 (-0.138892) | 0.445627 / 0.434364 (0.011264) | 0.543098 / 0.540337 (0.002761) | 0.640577 / 1.386936 (-0.746359) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007008 / 0.011353 (-0.004345) | 0.005015 / 0.011008 (-0.005993) | 0.073783 / 0.038508 (0.035274) | 0.032401 / 0.023109 (0.009292) | 0.343382 / 0.275898 (0.067484) | 0.358317 / 0.323480 (0.034837) | 0.005548 / 0.007986 (-0.002437) | 0.005188 / 0.004328 (0.000859) | 0.072867 / 0.004250 (0.068617) | 0.048555 / 0.037052 (0.011502) | 0.334516 / 0.258489 (0.076027) | 0.390263 / 0.293841 (0.096422) | 0.036343 / 0.128546 (-0.092203) | 0.012243 / 0.075646 (-0.063404) | 0.087067 / 0.419271 (-0.332205) | 0.049025 / 0.043533 (0.005492) | 0.333977 / 0.255139 (0.078838) | 0.354427 / 0.283200 (0.071227) | 0.104771 / 0.141683 (-0.036912) | 1.434588 / 1.452155 (-0.017567) | 1.519788 / 1.492716 (0.027072) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.264002 / 0.018006 (0.245996) | 0.547902 / 0.000490 (0.547412) | 0.000461 / 0.000200 (0.000261) | 0.000062 / 0.000054 (0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028916 / 0.037411 (-0.008496) | 0.110267 / 0.014526 (0.095741) | 0.119190 / 0.176557 (-0.057367) | 0.188599 / 0.737135 (-0.548537) | 0.126948 / 0.296338 (-0.169391) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422777 / 0.215209 (0.207568) | 4.209813 / 2.077655 (2.132158) | 2.001360 / 1.504120 (0.497240) | 1.802651 / 1.541195 (0.261456) | 1.860357 / 1.468490 (0.391867) | 0.695006 / 4.584777 (-3.889771) | 3.741917 / 3.745712 (-0.003795) | 3.313071 / 5.269862 (-1.956791) | 1.726366 / 4.565676 (-2.839311) | 0.086185 / 0.424275 (-0.338090) | 0.012256 / 0.007607 (0.004649) | 0.536874 / 0.226044 (0.310830) | 5.253008 / 2.268929 (2.984079) | 2.457189 / 55.444624 (-52.987436) | 2.112199 / 6.876477 (-4.764278) | 2.117867 / 2.142072 (-0.024205) | 0.831914 / 4.805227 (-3.973314) | 0.168238 / 6.500664 (-6.332426) | 0.065075 / 0.075469 (-0.010394) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.280795 / 1.841788 (-0.560993) | 14.606608 / 8.074308 (6.532299) | 13.317597 / 10.191392 (3.126205) | 0.166590 / 0.680424 (-0.513834) | 0.017520 / 0.534201 (-0.516681) | 0.420978 / 0.579283 (-0.158305) | 0.415708 / 0.434364 (-0.018656) | 0.523619 / 0.540337 (-0.016718) | 0.625299 / 1.386936 (-0.761637) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a2a83a8ea4b3a87a925ef44b787e87b59bf68225 \"CML watermark\")\n"
] | "2023-02-28T15:37:46Z" | "2023-02-28T17:28:35Z" | "2023-02-28T17:21:17Z" | CONTRIBUTOR | null | 0 | {
"diff_url": "https://github.com/huggingface/datasets/pull/5588.diff",
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"merged_at": "2023-02-28T17:21:17Z",
"patch_url": "https://github.com/huggingface/datasets/pull/5588.patch",
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5588"
} | Flatten a dataset on the fly in `save_to_disk` instead of doing it with `flatten_indices` to avoid creating an additional cache file.
(this is one of the sub-tasks in https://github.com/huggingface/datasets/issues/5507) | {
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https://api.github.com/repos/huggingface/datasets/issues/5587 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5587/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5587/comments | https://api.github.com/repos/huggingface/datasets/issues/5587/events | https://github.com/huggingface/datasets/pull/5587 | 1,603,139,420 | PR_kwDODunzps5K70pp | 5,587 | Fix `sort` with indices mapping | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008740 / 0.011353 (-0.002613) | 0.004501 / 0.011008 (-0.006507) | 0.100045 / 0.038508 (0.061537) | 0.029999 / 0.023109 (0.006890) | 0.303556 / 0.275898 (0.027658) | 0.335342 / 0.323480 (0.011863) | 0.006996 / 0.007986 (-0.000989) | 0.004183 / 0.004328 (-0.000145) | 0.076434 / 0.004250 (0.072183) | 0.033899 / 0.037052 (-0.003153) | 0.301312 / 0.258489 (0.042823) | 0.343136 / 0.293841 (0.049295) | 0.034062 / 0.128546 (-0.094484) | 0.011465 / 0.075646 (-0.064181) | 0.323134 / 0.419271 (-0.096137) | 0.040820 / 0.043533 (-0.002713) | 0.301708 / 0.255139 (0.046569) | 0.329528 / 0.283200 (0.046328) | 0.088393 / 0.141683 (-0.053290) | 1.460996 / 1.452155 (0.008842) | 1.531145 / 1.492716 (0.038429) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.191918 / 0.018006 (0.173912) | 0.414099 / 0.000490 (0.413610) | 0.000411 / 0.000200 (0.000211) | 0.000060 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022707 / 0.037411 (-0.014704) | 0.096991 / 0.014526 (0.082465) | 0.106070 / 0.176557 (-0.070487) | 0.151275 / 0.737135 (-0.585860) | 0.108909 / 0.296338 (-0.187430) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422499 / 0.215209 (0.207289) | 4.205551 / 2.077655 (2.127896) | 1.918960 / 1.504120 (0.414841) | 1.715421 / 1.541195 (0.174227) | 1.768969 / 1.468490 (0.300479) | 0.692243 / 4.584777 (-3.892534) | 3.382452 / 3.745712 (-0.363260) | 1.943695 / 5.269862 (-3.326166) | 1.250482 / 4.565676 (-3.315195) | 0.082084 / 0.424275 (-0.342191) | 0.012446 / 0.007607 (0.004839) | 0.525584 / 0.226044 (0.299539) | 5.275530 / 2.268929 (3.006602) | 2.386207 / 55.444624 (-53.058418) | 2.043920 / 6.876477 (-4.832557) | 2.030932 / 2.142072 (-0.111140) | 0.810233 / 4.805227 (-3.994994) | 0.148139 / 6.500664 (-6.352525) | 0.064617 / 0.075469 (-0.010852) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.227352 / 1.841788 (-0.614436) | 13.527623 / 8.074308 (5.453315) | 14.018551 / 10.191392 (3.827159) | 0.140333 / 0.680424 (-0.540091) | 0.028349 / 0.534201 (-0.505852) | 0.394904 / 0.579283 (-0.184379) | 0.406532 / 0.434364 (-0.027831) | 0.471714 / 0.540337 (-0.068624) | 0.568517 / 1.386936 (-0.818419) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006623 / 0.011353 (-0.004730) | 0.004464 / 0.011008 (-0.006544) | 0.076342 / 0.038508 (0.037834) | 0.027451 / 0.023109 (0.004341) | 0.343851 / 0.275898 (0.067953) | 0.385723 / 0.323480 (0.062243) | 0.005624 / 0.007986 (-0.002362) | 0.004685 / 0.004328 (0.000356) | 0.075669 / 0.004250 (0.071419) | 0.037297 / 0.037052 (0.000244) | 0.343363 / 0.258489 (0.084874) | 0.396115 / 0.293841 (0.102274) | 0.031577 / 0.128546 (-0.096970) | 0.011557 / 0.075646 (-0.064090) | 0.085626 / 0.419271 (-0.333645) | 0.041699 / 0.043533 (-0.001834) | 0.340826 / 0.255139 (0.085687) | 0.377167 / 0.283200 (0.093967) | 0.088632 / 0.141683 (-0.053051) | 1.464500 / 1.452155 (0.012345) | 1.556686 / 1.492716 (0.063969) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231136 / 0.018006 (0.213130) | 0.402687 / 0.000490 (0.402197) | 0.000590 / 0.000200 (0.000390) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024926 / 0.037411 (-0.012485) | 0.101062 / 0.014526 (0.086536) | 0.106481 / 0.176557 (-0.070075) | 0.159167 / 0.737135 (-0.577968) | 0.110948 / 0.296338 (-0.185390) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441813 / 0.215209 (0.226603) | 4.416332 / 2.077655 (2.338677) | 2.080621 / 1.504120 (0.576501) | 1.877832 / 1.541195 (0.336637) | 1.944778 / 1.468490 (0.476288) | 0.704634 / 4.584777 (-3.880143) | 3.433955 / 3.745712 (-0.311758) | 1.863493 / 5.269862 (-3.406368) | 1.168869 / 4.565676 (-3.396807) | 0.084095 / 0.424275 (-0.340180) | 0.012440 / 0.007607 (0.004833) | 0.545122 / 0.226044 (0.319077) | 5.472214 / 2.268929 (3.203285) | 2.514580 / 55.444624 (-52.930044) | 2.164570 / 6.876477 (-4.711907) | 2.193467 / 2.142072 (0.051395) | 0.809056 / 4.805227 (-3.996171) | 0.152343 / 6.500664 (-6.348321) | 0.067610 / 0.075469 (-0.007859) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.280968 / 1.841788 (-0.560820) | 13.887674 / 8.074308 (5.813366) | 13.160405 / 10.191392 (2.969013) | 0.128601 / 0.680424 (-0.551823) | 0.016420 / 0.534201 (-0.517780) | 0.382810 / 0.579283 (-0.196473) | 0.394386 / 0.434364 (-0.039978) | 0.470254 / 0.540337 (-0.070083) | 0.566907 / 1.386936 (-0.820029) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8cc6950322337ea8873939541c53858b10c0f3b9 \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008673 / 0.011353 (-0.002679) | 0.004475 / 0.011008 (-0.006533) | 0.102060 / 0.038508 (0.063552) | 0.029438 / 0.023109 (0.006329) | 0.351785 / 0.275898 (0.075887) | 0.388199 / 0.323480 (0.064719) | 0.007011 / 0.007986 (-0.000974) | 0.003317 / 0.004328 (-0.001012) | 0.080931 / 0.004250 (0.076681) | 0.033449 / 0.037052 (-0.003603) | 0.360329 / 0.258489 (0.101840) | 0.400069 / 0.293841 (0.106228) | 0.033628 / 0.128546 (-0.094918) | 0.011462 / 0.075646 (-0.064184) | 0.323781 / 0.419271 (-0.095490) | 0.040686 / 0.043533 (-0.002847) | 0.332715 / 0.255139 (0.077576) | 0.370339 / 0.283200 (0.087139) | 0.084633 / 0.141683 (-0.057050) | 1.459452 / 1.452155 (0.007297) | 1.547719 / 1.492716 (0.055003) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.187051 / 0.018006 (0.169045) | 0.402625 / 0.000490 (0.402135) | 0.002218 / 0.000200 (0.002018) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025240 / 0.037411 (-0.012171) | 0.102201 / 0.014526 (0.087675) | 0.108629 / 0.176557 (-0.067927) | 0.156686 / 0.737135 (-0.580449) | 0.111383 / 0.296338 (-0.184955) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418099 / 0.215209 (0.202890) | 4.163345 / 2.077655 (2.085690) | 1.868419 / 1.504120 (0.364300) | 1.662066 / 1.541195 (0.120871) | 1.705912 / 1.468490 (0.237422) | 0.696391 / 4.584777 (-3.888386) | 3.338307 / 3.745712 (-0.407405) | 1.923255 / 5.269862 (-3.346607) | 1.249220 / 4.565676 (-3.316457) | 0.082037 / 0.424275 (-0.342238) | 0.012232 / 0.007607 (0.004624) | 0.523913 / 0.226044 (0.297869) | 5.290036 / 2.268929 (3.021107) | 2.319729 / 55.444624 (-53.124896) | 1.987345 / 6.876477 (-4.889132) | 2.044516 / 2.142072 (-0.097556) | 0.812098 / 4.805227 (-3.993129) | 0.147327 / 6.500664 (-6.353337) | 0.063838 / 0.075469 (-0.011631) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.219652 / 1.841788 (-0.622136) | 13.271513 / 8.074308 (5.197205) | 13.799982 / 10.191392 (3.608590) | 0.150055 / 0.680424 (-0.530369) | 0.028804 / 0.534201 (-0.505397) | 0.395452 / 0.579283 (-0.183831) | 0.398758 / 0.434364 (-0.035606) | 0.468575 / 0.540337 (-0.071763) | 0.553324 / 1.386936 (-0.833612) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006498 / 0.011353 (-0.004855) | 0.004439 / 0.011008 (-0.006569) | 0.076525 / 0.038508 (0.038017) | 0.027184 / 0.023109 (0.004074) | 0.364705 / 0.275898 (0.088807) | 0.409481 / 0.323480 (0.086001) | 0.004831 / 0.007986 (-0.003154) | 0.004524 / 0.004328 (0.000196) | 0.075403 / 0.004250 (0.071153) | 0.039013 / 0.037052 (0.001960) | 0.364042 / 0.258489 (0.105553) | 0.413090 / 0.293841 (0.119249) | 0.032052 / 0.128546 (-0.096495) | 0.011514 / 0.075646 (-0.064132) | 0.085219 / 0.419271 (-0.334053) | 0.041448 / 0.043533 (-0.002085) | 0.350371 / 0.255139 (0.095232) | 0.386670 / 0.283200 (0.103470) | 0.089824 / 0.141683 (-0.051859) | 1.487392 / 1.452155 (0.035238) | 1.537201 / 1.492716 (0.044485) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231555 / 0.018006 (0.213549) | 0.407505 / 0.000490 (0.407016) | 0.000382 / 0.000200 (0.000182) | 0.000060 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026665 / 0.037411 (-0.010747) | 0.105852 / 0.014526 (0.091326) | 0.108228 / 0.176557 (-0.068328) | 0.164164 / 0.737135 (-0.572972) | 0.114284 / 0.296338 (-0.182054) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.448957 / 0.215209 (0.233748) | 4.500058 / 2.077655 (2.422403) | 2.331660 / 1.504120 (0.827541) | 2.119904 / 1.541195 (0.578710) | 2.101489 / 1.468490 (0.632999) | 0.696580 / 4.584777 (-3.888197) | 3.364206 / 3.745712 (-0.381506) | 2.550157 / 5.269862 (-2.719704) | 1.496455 / 4.565676 (-3.069222) | 0.083289 / 0.424275 (-0.340986) | 0.012283 / 0.007607 (0.004676) | 0.555581 / 0.226044 (0.329537) | 5.556284 / 2.268929 (3.287355) | 2.595261 / 55.444624 (-52.849363) | 2.234793 / 6.876477 (-4.641683) | 2.280150 / 2.142072 (0.138078) | 0.817885 / 4.805227 (-3.987343) | 0.151481 / 6.500664 (-6.349183) | 0.066764 / 0.075469 (-0.008705) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.318875 / 1.841788 (-0.522913) | 14.220380 / 8.074308 (6.146072) | 13.922773 / 10.191392 (3.731381) | 0.154608 / 0.680424 (-0.525816) | 0.016343 / 0.534201 (-0.517858) | 0.380758 / 0.579283 (-0.198525) | 0.392595 / 0.434364 (-0.041769) | 0.468844 / 0.540337 (-0.071493) | 0.561047 / 1.386936 (-0.825889) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d57fdcf2c8110b4b599289695fa065d1fc4936d4 \"CML watermark\")\n"
] | "2023-02-28T14:05:08Z" | "2023-02-28T17:28:57Z" | "2023-02-28T17:21:58Z" | CONTRIBUTOR | null | 0 | {
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} | Fixes the `key` range in the `query_table` call in `sort` to account for an indices mapping
Fix #5586 | {
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https://api.github.com/repos/huggingface/datasets/issues/5586 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5586/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5586/comments | https://api.github.com/repos/huggingface/datasets/issues/5586/events | https://github.com/huggingface/datasets/issues/5586 | 1,602,961,544 | I_kwDODunzps5fi0CI | 5,586 | .sort() is broken when used after .filter(), only in 2.10.0 | {
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"Thanks for reporting and thanks @mariosasko for fixing ! We just did a patch release `2.10.1` with the fix"
] | "2023-02-28T12:18:09Z" | "2023-02-28T18:17:26Z" | "2023-02-28T17:21:59Z" | NONE | null | null | null | ### Describe the bug
Hi, thank you for your support!
It seems like the addition of multiple key sort (#5502) in 2.10.0 broke the `.sort()` method.
After filtering a dataset with `.filter()`, the `.sort()` seems to refer to the query_table index of the previous unfiltered dataset, resulting in an IndexError.
This only happens with the 2.10.0 release.
### Steps to reproduce the bug
```Python
from datasets import load_dataset
# dataset with length of 1104
ds = load_dataset('glue', 'ax')['test']
ds = ds.filter(lambda x: x['idx'] > 1100)
ds.sort('premise')
print('Done')
```
File "/home/dongkeun/datasets_test/test.py", line 5, in <module>
ds.sort('premise')
File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 528, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/fingerprint.py", line 511, in wrapper
out = func(dataset, *args, **kwargs)
File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3959, in sort
sort_table = query_table(
File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 588, in query_table
_check_valid_index_key(key, size)
File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 537, in _check_valid_index_key
_check_valid_index_key(max(key), size=size)
File "/home/dongkeun/miniconda3/envs/datasets_test/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 531, in _check_valid_index_key
raise IndexError(f"Invalid key: {key} is out of bounds for size {size}")
IndexError: Invalid key: 1103 is out of bounds for size 3
### Expected behavior
It should sort the dataset and print "Done". Which it does on 2.9.0.
### Environment info
- `datasets` version: 2.10.0
- Platform: Linux-5.15.0-41-generic-x86_64-with-glibc2.31
- Python version: 3.9.16
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | {
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"Hi ! No the cache is not transportable in general. It will work on a shared filesystem if you use the same python environment, but not across machines/os/environments.\r\n\r\nIn particular, reloading cached datasets does work, but reloading cached processed datasets (e.g. from `map`) may not work. This is because some hashes used by caching are based on pickle dumps of the function you pass to `map`.\r\n\r\nFinally you may copy the cache to another machine, but all the `cached-*.arrow` files are unlikely to be reloaded.",
"OK good to know. Thanks @lhoestq !"
] | "2023-02-28T00:53:06Z" | "2023-02-28T21:26:52Z" | "2023-02-28T21:26:52Z" | NONE | null | null | null | ### Describe the bug
I would like to share cache between two machines (a Windows host machine and a WSL instance).
I run most my code in WSL. I have just run out of space in the virtual drive. Rather than expand the drive size, I plan to move to cache to the host Windows machine, thereby sharing the downloads.
I'm hoping that I can just copy/paste the cache files, but I notice that a lot of the file names start with the path name, e.g. `_home_davidg_.cache_huggingface_datasets_conll2003_default-451...98.lock` where `home/davidg` is where the cache is in WSL.
This seems to suggest that the cache is not portable/cannot be centralised or shared. Is this the case, or are the files that start with path names not integral to the caching mechanism? Because copying the cache files _seems_ to work, but I'm not filled with confidence that something isn't going to break.
A related issue, when trying to load a dataset that should come from cache (running in WSL, pointing to cache on the Windows host) it seemed to work fine, but it still uses a WSL directory for `.cache\huggingface\modules\datasets_modules`. I see nothing in the docs about this, or how to point it to a different place.
I have asked a related question on the forum: https://discuss.huggingface.co/t/is-datasets-cache-operating-system-agnostic/32656
### Steps to reproduce the bug
View the cache directory in WSL/Windows.
### Expected behavior
Cache can be shared between (virtual) machines and be transportable.
It would be nice to have a simple way to say "Dear Hugging Face packages, please put ALL your cache in `blah/de/blah`" and have all the Hugging Face packages respect that single location.
### Environment info
```
- `datasets` version: 2.9.0
- Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31
- Python version: 3.10.8
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
- ``` | {
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"Hi @manuaero \r\n\r\nThank you for your interest in the COYO dataset.\r\n\r\nOur dataset provides the img-url and alt-text in the form of a parquet, so to utilize the coyo dataset you will need to download it directly.\r\n\r\nWe provide a [guide](https://github.com/kakaobrain/coyo-dataset/blob/main/download/README.md) to download, so check it out.\r\n\r\nThank you."
] | "2023-02-27T19:35:03Z" | "2023-02-28T07:27:59Z" | "2023-02-28T07:27:58Z" | NONE | null | null | null | ### Describe the bug
Seeing this error when downloading https://huggingface.co/datasets/kakaobrain/coyo-700m:
```ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.```
Full stack trace
```Downloading and preparing dataset parquet/kakaobrain--coyo-700m to /root/.cache/huggingface/datasets/kakaobrain___parquet/kakaobrain--coyo-700m-ae729692ae3e0073/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...
Downloading data files: 100%
1/1 [00:00<00:00, 63.35it/s]
Extracting data files: 100%
1/1 [00:00<00:00, 5.00it/s]
---------------------------------------------------------------------------
ArrowInvalid Traceback (most recent call last)
[/usr/local/lib/python3.8/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1859 _time = time.time()
-> 1860 for _, table in generator:
1861 if max_shard_size is not None and writer._num_bytes > max_shard_size:
9 frames
ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
The above exception was the direct cause of the following exception:
DatasetGenerationError Traceback (most recent call last)
[/usr/local/lib/python3.8/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1890 if isinstance(e, SchemaInferenceError) and e.__context__ is not None:
1891 e = e.__context__
-> 1892 raise DatasetGenerationError("An error occurred while generating the dataset") from e
1893
1894 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)
DatasetGenerationError: An error occurred while generating the dataset```
### Steps to reproduce the bug
```
from datasets import load_dataset
hf_dataset = load_dataset("kakaobrain/coyo-700m")
```
### Expected behavior
The above commands load the dataset successfully. Or handles exception and continue loading the remainder.
### Environment info
colab. any | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009044 / 0.011353 (-0.002309) | 0.004244 / 0.011008 (-0.006765) | 0.106705 / 0.038508 (0.068197) | 0.029779 / 0.023109 (0.006670) | 0.289684 / 0.275898 (0.013786) | 0.347100 / 0.323480 (0.023620) | 0.007071 / 0.007986 (-0.000915) | 0.003734 / 0.004328 (-0.000595) | 0.077971 / 0.004250 (0.073720) | 0.035323 / 0.037052 (-0.001730) | 0.334520 / 0.258489 (0.076031) | 0.375804 / 0.293841 (0.081964) | 0.049211 / 0.128546 (-0.079335) | 0.016992 / 0.075646 (-0.058654) | 0.337208 / 0.419271 (-0.082064) | 0.053700 / 0.043533 (0.010167) | 0.295750 / 0.255139 (0.040611) | 0.330157 / 0.283200 (0.046958) | 0.097017 / 0.141683 (-0.044666) | 1.379353 / 1.452155 (-0.072802) | 1.402670 / 1.492716 (-0.090047) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.012685 / 0.018006 (-0.005321) | 0.474541 / 0.000490 (0.474051) | 0.006752 / 0.000200 (0.006552) | 0.000097 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025735 / 0.037411 (-0.011676) | 0.092507 / 0.014526 (0.077982) | 0.100275 / 0.176557 (-0.076281) | 0.180359 / 0.737135 (-0.556777) | 0.104312 / 0.296338 (-0.192026) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.456558 / 0.215209 (0.241349) | 4.786667 / 2.077655 (2.709012) | 1.873169 / 1.504120 (0.369050) | 1.640935 / 1.541195 (0.099741) | 1.614543 / 1.468490 (0.146053) | 0.936144 / 4.584777 (-3.648633) | 4.699886 / 3.745712 (0.954174) | 2.398545 / 5.269862 (-2.871317) | 1.642808 / 4.565676 (-2.922868) | 0.124803 / 0.424275 (-0.299472) | 0.011848 / 0.007607 (0.004241) | 0.631684 / 0.226044 (0.405639) | 6.096052 / 2.268929 (3.827124) | 2.463052 / 55.444624 (-52.981572) | 1.928551 / 6.876477 (-4.947926) | 1.927790 / 2.142072 (-0.214283) | 1.098912 / 4.805227 (-3.706315) | 0.196343 / 6.500664 (-6.304321) | 0.063296 / 0.075469 (-0.012173) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.255032 / 1.841788 (-0.586755) | 13.853623 / 8.074308 (5.779315) | 16.303280 / 10.191392 (6.111888) | 0.227287 / 0.680424 (-0.453137) | 0.037527 / 0.534201 (-0.496674) | 0.449345 / 0.579283 (-0.129938) | 0.522054 / 0.434364 (0.087690) | 0.552848 / 0.540337 (0.012511) | 0.642994 / 1.386936 (-0.743942) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008470 / 0.011353 (-0.002883) | 0.005167 / 0.011008 (-0.005841) | 0.077794 / 0.038508 (0.039286) | 0.029228 / 0.023109 (0.006119) | 0.340828 / 0.275898 (0.064930) | 0.400170 / 0.323480 (0.076691) | 0.005485 / 0.007986 (-0.002500) | 0.003854 / 0.004328 (-0.000475) | 0.077597 / 0.004250 (0.073346) | 0.036519 / 0.037052 (-0.000533) | 0.335522 / 0.258489 (0.077033) | 0.412622 / 0.293841 (0.118781) | 0.044587 / 0.128546 (-0.083959) | 0.016024 / 0.075646 (-0.059623) | 0.092312 / 0.419271 (-0.326960) | 0.055660 / 0.043533 (0.012127) | 0.343140 / 0.255139 (0.088001) | 0.386403 / 0.283200 (0.103203) | 0.098634 / 0.141683 (-0.043049) | 1.326126 / 1.452155 (-0.126029) | 1.430316 / 1.492716 (-0.062400) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222807 / 0.018006 (0.204801) | 0.473622 / 0.000490 (0.473132) | 0.000376 / 0.000200 (0.000176) | 0.000066 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024599 / 0.037411 (-0.012813) | 0.100743 / 0.014526 (0.086217) | 0.112086 / 0.176557 (-0.064471) | 0.198294 / 0.737135 (-0.538842) | 0.111210 / 0.296338 (-0.185129) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.494120 / 0.215209 (0.278911) | 5.117958 / 2.077655 (3.040303) | 2.305131 / 1.504120 (0.801011) | 2.015591 / 1.541195 (0.474396) | 2.027284 / 1.468490 (0.558794) | 1.014241 / 4.584777 (-3.570536) | 4.738836 / 3.745712 (0.993124) | 2.519718 / 5.269862 (-2.750143) | 1.706379 / 4.565676 (-2.859298) | 0.122452 / 0.424275 (-0.301824) | 0.011500 / 0.007607 (0.003893) | 0.632864 / 0.226044 (0.406820) | 6.295457 / 2.268929 (4.026529) | 2.824897 / 55.444624 (-52.619727) | 2.324359 / 6.876477 (-4.552117) | 2.281046 / 2.142072 (0.138974) | 1.173570 / 4.805227 (-3.631657) | 0.197195 / 6.500664 (-6.303469) | 0.064845 / 0.075469 (-0.010624) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.273224 / 1.841788 (-0.568563) | 14.531155 / 8.074308 (6.456847) | 15.892176 / 10.191392 (5.700784) | 0.208051 / 0.680424 (-0.472373) | 0.023119 / 0.534201 (-0.511082) | 0.422317 / 0.579283 (-0.156966) | 0.519946 / 0.434364 (0.085582) | 0.544517 / 0.540337 (0.004179) | 0.605955 / 1.386936 (-0.780981) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#337a4a91d0268c68f26760321c9b45bb4a98832a \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010806 / 0.011353 (-0.000547) | 0.005631 / 0.011008 (-0.005378) | 0.113166 / 0.038508 (0.074657) | 0.042980 / 0.023109 (0.019871) | 0.344856 / 0.275898 (0.068958) | 0.404417 / 0.323480 (0.080938) | 0.012222 / 0.007986 (0.004236) | 0.004470 / 0.004328 (0.000141) | 0.088072 / 0.004250 (0.083822) | 0.049815 / 0.037052 (0.012763) | 0.366532 / 0.258489 (0.108043) | 0.392558 / 0.293841 (0.098717) | 0.045411 / 0.128546 (-0.083135) | 0.014118 / 0.075646 (-0.061529) | 0.392894 / 0.419271 (-0.026378) | 0.067713 / 0.043533 (0.024181) | 0.353013 / 0.255139 (0.097874) | 0.378375 / 0.283200 (0.095175) | 0.123686 / 0.141683 (-0.017996) | 1.665272 / 1.452155 (0.213118) | 1.748383 / 1.492716 (0.255667) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.011672 / 0.018006 (-0.006335) | 0.481667 / 0.000490 (0.481178) | 0.003644 / 0.000200 (0.003444) | 0.000092 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030436 / 0.037411 (-0.006976) | 0.122577 / 0.014526 (0.108052) | 0.135409 / 0.176557 (-0.041148) | 0.220385 / 0.737135 (-0.516750) | 0.143140 / 0.296338 (-0.153199) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.471146 / 0.215209 (0.255937) | 4.645023 / 2.077655 (2.567368) | 2.126783 / 1.504120 (0.622663) | 1.907905 / 1.541195 (0.366710) | 1.969561 / 1.468490 (0.501071) | 0.798670 / 4.584777 (-3.786107) | 4.394787 / 3.745712 (0.649075) | 2.353535 / 5.269862 (-2.916327) | 1.501013 / 4.565676 (-3.064664) | 0.097472 / 0.424275 (-0.326803) | 0.014015 / 0.007607 (0.006408) | 0.589365 / 0.226044 (0.363320) | 5.897331 / 2.268929 (3.628402) | 2.656198 / 55.444624 (-52.788427) | 2.256082 / 6.876477 (-4.620395) | 2.271122 / 2.142072 (0.129050) | 0.961566 / 4.805227 (-3.843661) | 0.188303 / 6.500664 (-6.312361) | 0.073258 / 0.075469 (-0.002211) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.445266 / 1.841788 (-0.396522) | 16.876710 / 8.074308 (8.802402) | 16.004287 / 10.191392 (5.812895) | 0.212252 / 0.680424 (-0.468172) | 0.033186 / 0.534201 (-0.501015) | 0.520564 / 0.579283 (-0.058719) | 0.516865 / 0.434364 (0.082501) | 0.638482 / 0.540337 (0.098144) | 0.761959 / 1.386936 (-0.624977) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008101 / 0.011353 (-0.003252) | 0.005512 / 0.011008 (-0.005497) | 0.086138 / 0.038508 (0.047630) | 0.038605 / 0.023109 (0.015496) | 0.413082 / 0.275898 (0.137184) | 0.444016 / 0.323480 (0.120536) | 0.006196 / 0.007986 (-0.001790) | 0.005736 / 0.004328 (0.001408) | 0.086938 / 0.004250 (0.082688) | 0.052307 / 0.037052 (0.015255) | 0.415206 / 0.258489 (0.156717) | 0.481510 / 0.293841 (0.187669) | 0.041469 / 0.128546 (-0.087077) | 0.013481 / 0.075646 (-0.062165) | 0.101528 / 0.419271 (-0.317744) | 0.056507 / 0.043533 (0.012974) | 0.418166 / 0.255139 (0.163027) | 0.443834 / 0.283200 (0.160634) | 0.116434 / 0.141683 (-0.025249) | 1.651223 / 1.452155 (0.199068) | 1.746429 / 1.492716 (0.253713) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242381 / 0.018006 (0.224375) | 0.478826 / 0.000490 (0.478337) | 0.000463 / 0.000200 (0.000264) | 0.000067 / 0.000054 (0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031743 / 0.037411 (-0.005668) | 0.126141 / 0.014526 (0.111616) | 0.134539 / 0.176557 (-0.042018) | 0.216546 / 0.737135 (-0.520590) | 0.143513 / 0.296338 (-0.152825) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.486915 / 0.215209 (0.271706) | 4.833812 / 2.077655 (2.756158) | 2.317785 / 1.504120 (0.813666) | 2.114181 / 1.541195 (0.572986) | 2.153896 / 1.468490 (0.685406) | 0.797490 / 4.584777 (-3.787287) | 4.369950 / 3.745712 (0.624238) | 2.305492 / 5.269862 (-2.964370) | 1.488860 / 4.565676 (-3.076816) | 0.098071 / 0.424275 (-0.326204) | 0.014129 / 0.007607 (0.006522) | 0.611311 / 0.226044 (0.385266) | 6.087482 / 2.268929 (3.818554) | 2.837676 / 55.444624 (-52.606948) | 2.451819 / 6.876477 (-4.424657) | 2.456763 / 2.142072 (0.314690) | 0.957637 / 4.805227 (-3.847590) | 0.190974 / 6.500664 (-6.309690) | 0.074497 / 0.075469 (-0.000972) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.466214 / 1.841788 (-0.375574) | 17.063925 / 8.074308 (8.989617) | 14.630326 / 10.191392 (4.438934) | 0.170570 / 0.680424 (-0.509854) | 0.023794 / 0.534201 (-0.510407) | 0.509175 / 0.579283 (-0.070108) | 0.506485 / 0.434364 (0.072121) | 0.616965 / 0.540337 (0.076628) | 0.718176 / 1.386936 (-0.668760) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c4f14de325e26910d026f377756dd8a231150398 \"CML watermark\")\n"
] | "2023-02-27T17:04:46Z" | "2023-02-28T13:52:15Z" | "2023-02-28T13:44:04Z" | CONTRIBUTOR | null | 0 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006362 / 0.011353 (-0.004991) | 0.004546 / 0.011008 (-0.006462) | 0.097003 / 0.038508 (0.058495) | 0.028007 / 0.023109 (0.004898) | 0.315097 / 0.275898 (0.039199) | 0.365128 / 0.323480 (0.041649) | 0.004819 / 0.007986 (-0.003167) | 0.003335 / 0.004328 (-0.000994) | 0.076665 / 0.004250 (0.072415) | 0.038285 / 0.037052 (0.001233) | 0.322100 / 0.258489 (0.063611) | 0.407466 / 0.293841 (0.113625) | 0.031580 / 0.128546 (-0.096966) | 0.011645 / 0.075646 (-0.064001) | 0.321789 / 0.419271 (-0.097483) | 0.051015 / 0.043533 (0.007483) | 0.331762 / 0.255139 (0.076623) | 0.369727 / 0.283200 (0.086527) | 0.090144 / 0.141683 (-0.051539) | 1.485480 / 1.452155 (0.033326) | 1.562032 / 1.492716 (0.069316) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201192 / 0.018006 (0.183186) | 0.409760 / 0.000490 (0.409270) | 0.002220 / 0.000200 (0.002020) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022361 / 0.037411 (-0.015050) | 0.096375 / 0.014526 (0.081849) | 0.101369 / 0.176557 (-0.075188) | 0.161568 / 0.737135 (-0.575568) | 0.105094 / 0.296338 (-0.191245) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426251 / 0.215209 (0.211042) | 4.261374 / 2.077655 (2.183720) | 2.015688 / 1.504120 (0.511569) | 1.833708 / 1.541195 (0.292513) | 1.908994 / 1.468490 (0.440504) | 0.703108 / 4.584777 (-3.881669) | 3.420767 / 3.745712 (-0.324945) | 1.844776 / 5.269862 (-3.425086) | 1.158470 / 4.565676 (-3.407207) | 0.083324 / 0.424275 (-0.340951) | 0.013054 / 0.007607 (0.005447) | 0.521473 / 0.226044 (0.295429) | 5.245505 / 2.268929 (2.976576) | 2.349110 / 55.444624 (-53.095515) | 2.011119 / 6.876477 (-4.865358) | 2.217807 / 2.142072 (0.075734) | 0.808584 / 4.805227 (-3.996643) | 0.151337 / 6.500664 (-6.349327) | 0.065815 / 0.075469 (-0.009654) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.221839 / 1.841788 (-0.619949) | 13.634161 / 8.074308 (5.559853) | 13.915360 / 10.191392 (3.723968) | 0.126448 / 0.680424 (-0.553976) | 0.016614 / 0.534201 (-0.517587) | 0.379150 / 0.579283 (-0.200133) | 0.382134 / 0.434364 (-0.052230) | 0.442845 / 0.540337 (-0.097493) | 0.519578 / 1.386936 (-0.867358) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006238 / 0.011353 (-0.005115) | 0.004591 / 0.011008 (-0.006418) | 0.076652 / 0.038508 (0.038144) | 0.026882 / 0.023109 (0.003773) | 0.341948 / 0.275898 (0.066050) | 0.375244 / 0.323480 (0.051764) | 0.004770 / 0.007986 (-0.003215) | 0.004703 / 0.004328 (0.000374) | 0.075797 / 0.004250 (0.071547) | 0.035001 / 0.037052 (-0.002051) | 0.341670 / 0.258489 (0.083181) | 0.383028 / 0.293841 (0.089187) | 0.031756 / 0.128546 (-0.096791) | 0.011714 / 0.075646 (-0.063933) | 0.085552 / 0.419271 (-0.333720) | 0.047697 / 0.043533 (0.004164) | 0.340805 / 0.255139 (0.085666) | 0.365478 / 0.283200 (0.082278) | 0.093146 / 0.141683 (-0.048537) | 1.465100 / 1.452155 (0.012945) | 1.552708 / 1.492716 (0.059992) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209117 / 0.018006 (0.191111) | 0.402622 / 0.000490 (0.402132) | 0.003940 / 0.000200 (0.003740) | 0.000078 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026027 / 0.037411 (-0.011385) | 0.098346 / 0.014526 (0.083820) | 0.107349 / 0.176557 (-0.069207) | 0.157846 / 0.737135 (-0.579289) | 0.109566 / 0.296338 (-0.186772) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.445088 / 0.215209 (0.229879) | 4.450727 / 2.077655 (2.373072) | 2.237798 / 1.504120 (0.733678) | 2.026060 / 1.541195 (0.484866) | 2.020464 / 1.468490 (0.551974) | 0.700155 / 4.584777 (-3.884622) | 3.435497 / 3.745712 (-0.310215) | 2.851970 / 5.269862 (-2.417891) | 1.512689 / 4.565676 (-3.052988) | 0.083717 / 0.424275 (-0.340558) | 0.012466 / 0.007607 (0.004859) | 0.545130 / 0.226044 (0.319085) | 5.478228 / 2.268929 (3.209300) | 2.554169 / 55.444624 (-52.890456) | 2.214703 / 6.876477 (-4.661774) | 2.229997 / 2.142072 (0.087925) | 0.809851 / 4.805227 (-3.995376) | 0.151019 / 6.500664 (-6.349645) | 0.066354 / 0.075469 (-0.009115) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.281016 / 1.841788 (-0.560772) | 14.071312 / 8.074308 (5.997004) | 14.682465 / 10.191392 (4.491073) | 0.144197 / 0.680424 (-0.536227) | 0.017088 / 0.534201 (-0.517113) | 0.379049 / 0.579283 (-0.200234) | 0.390713 / 0.434364 (-0.043650) | 0.435804 / 0.540337 (-0.104534) | 0.518895 / 1.386936 (-0.868041) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fc5c84f36684343bff3e424cb0fd1ac5ecdd66da \"CML watermark\")\n"
] | "2023-02-27T10:50:07Z" | "2023-03-13T19:10:22Z" | "2023-03-13T19:03:32Z" | CONTRIBUTOR | null | 0 | {
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} | This PR closes #5383
* Add column_names property to IterableDataset
* Add multiple tests for this new property | {
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"Thanks for reporting!"
] | "2023-02-27T08:03:09Z" | "2023-02-28T19:19:17Z" | "2023-02-28T19:19:17Z" | CONTRIBUTOR | null | null | null | ### Describe the bug
https://huggingface.co/docs/datasets/v2.10.0/en/package_reference/main_classes#datasets.Dataset.set_format
<img width="700" alt="image" src="https://user-images.githubusercontent.com/36224762/221506973-ae2e3991-60a7-4d4e-99f8-965c6eb61e59.png">
While actually running it will result in:
<img width="1094" alt="image" src="https://user-images.githubusercontent.com/36224762/221507032-007dab82-8781-4319-b21a-e6e4d40d97b3.png">
### Steps to reproduce the bug
_
### Expected behavior
_
### Environment info
- `datasets` version: 2.10.0
- Platform: Linux-5.10.147+-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 9.0.0
- Pandas version: 1.3.5 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"> Regarding the tests I think it should be possible to use the mockfs fixture, it allows to play with a dummy fsspec FileSystem with the \"mock://\" protocol.\r\n\r\n> However it requires some storage_options to be passed. Maybe it can be added to DownloadConfig which is passed to cached_path, so that fsspec_get and fsspec_head can use the user's storage_options ?\r\n\r\n@lhoestq I went ahead and tested this with a patch so that I could assign the mockfs as a return value. Let me know if I'm missing something though and we need to pass storage_options down",
"> Instead of patching think it would be better to have a new filesystem TmpDirFileSystem (tmpfs) that doesn't need storage_options for the tests, and that is based on a temporary directory created just for the fixture. Maybe something like this ?\r\n\r\nThanks for the recommendation, this works great.",
"Feel free to merge `main` into your PR to fix the CI :)",
"Should be good to go. Thanks!",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006183 / 0.011353 (-0.005170) | 0.004180 / 0.011008 (-0.006829) | 0.095965 / 0.038508 (0.057457) | 0.026754 / 0.023109 (0.003645) | 0.339724 / 0.275898 (0.063826) | 0.381628 / 0.323480 (0.058149) | 0.004615 / 0.007986 (-0.003371) | 0.004469 / 0.004328 (0.000140) | 0.074035 / 0.004250 (0.069784) | 0.035089 / 0.037052 (-0.001963) | 0.352253 / 0.258489 (0.093764) | 0.389598 / 0.293841 (0.095757) | 0.032262 / 0.128546 (-0.096285) | 0.011392 / 0.075646 (-0.064254) | 0.323884 / 0.419271 (-0.095388) | 0.042658 / 0.043533 (-0.000874) | 0.331533 / 0.255139 (0.076394) | 0.364723 / 0.283200 (0.081523) | 0.086349 / 0.141683 (-0.055334) | 1.465687 / 1.452155 (0.013533) | 1.559782 / 1.492716 (0.067066) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198562 / 0.018006 (0.180556) | 0.457170 / 0.000490 (0.456680) | 0.000409 / 0.000200 (0.000209) | 0.000061 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022439 / 0.037411 (-0.014973) | 0.096551 / 0.014526 (0.082025) | 0.102230 / 0.176557 (-0.074326) | 0.160878 / 0.737135 (-0.576257) | 0.109348 / 0.296338 (-0.186990) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.456635 / 0.215209 (0.241426) | 4.563571 / 2.077655 (2.485916) | 2.313048 / 1.504120 (0.808928) | 2.117433 / 1.541195 (0.576239) | 2.127478 / 1.468490 (0.658988) | 0.699478 / 4.584777 (-3.885299) | 3.358955 / 3.745712 (-0.386757) | 1.821437 / 5.269862 (-3.448424) | 1.158239 / 4.565676 (-3.407438) | 0.083207 / 0.424275 (-0.341068) | 0.012925 / 0.007607 (0.005318) | 0.556526 / 0.226044 (0.330482) | 5.552364 / 2.268929 (3.283435) | 2.744696 / 55.444624 (-52.699928) | 2.374455 / 6.876477 (-4.502022) | 2.442021 / 2.142072 (0.299949) | 0.809393 / 4.805227 (-3.995834) | 0.152305 / 6.500664 (-6.348359) | 0.066164 / 0.075469 (-0.009305) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.258268 / 1.841788 (-0.583520) | 13.402391 / 8.074308 (5.328083) | 13.816927 / 10.191392 (3.625535) | 0.148466 / 0.680424 (-0.531958) | 0.016487 / 0.534201 (-0.517714) | 0.385888 / 0.579283 (-0.193395) | 0.378840 / 0.434364 (-0.055524) | 0.444527 / 0.540337 (-0.095810) | 0.531011 / 1.386936 (-0.855925) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006230 / 0.011353 (-0.005123) | 0.004488 / 0.011008 (-0.006520) | 0.077539 / 0.038508 (0.039031) | 0.026611 / 0.023109 (0.003502) | 0.342093 / 0.275898 (0.066195) | 0.371555 / 0.323480 (0.048075) | 0.004665 / 0.007986 (-0.003321) | 0.003289 / 0.004328 (-0.001039) | 0.078378 / 0.004250 (0.074128) | 0.035223 / 0.037052 (-0.001829) | 0.339972 / 0.258489 (0.081483) | 0.378755 / 0.293841 (0.084914) | 0.031331 / 0.128546 (-0.097215) | 0.011406 / 0.075646 (-0.064241) | 0.086891 / 0.419271 (-0.332381) | 0.047713 / 0.043533 (0.004180) | 0.342678 / 0.255139 (0.087539) | 0.364536 / 0.283200 (0.081337) | 0.092132 / 0.141683 (-0.049551) | 1.537050 / 1.452155 (0.084895) | 1.639927 / 1.492716 (0.147211) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.219933 / 0.018006 (0.201927) | 0.391627 / 0.000490 (0.391137) | 0.002238 / 0.000200 (0.002038) | 0.000072 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024890 / 0.037411 (-0.012521) | 0.098989 / 0.014526 (0.084464) | 0.104505 / 0.176557 (-0.072052) | 0.156252 / 0.737135 (-0.580884) | 0.108027 / 0.296338 (-0.188312) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.443957 / 0.215209 (0.228748) | 4.450850 / 2.077655 (2.373196) | 2.076043 / 1.504120 (0.571923) | 1.866396 / 1.541195 (0.325202) | 1.902692 / 1.468490 (0.434202) | 0.703160 / 4.584777 (-3.881617) | 3.373761 / 3.745712 (-0.371951) | 2.615649 / 5.269862 (-2.654213) | 1.340612 / 4.565676 (-3.225065) | 0.083836 / 0.424275 (-0.340439) | 0.012619 / 0.007607 (0.005012) | 0.553410 / 0.226044 (0.327365) | 5.526500 / 2.268929 (3.257571) | 2.513213 / 55.444624 (-52.931411) | 2.152701 / 6.876477 (-4.723776) | 2.165092 / 2.142072 (0.023019) | 0.818381 / 4.805227 (-3.986846) | 0.152118 / 6.500664 (-6.348546) | 0.066950 / 0.075469 (-0.008519) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.291468 / 1.841788 (-0.550320) | 13.694828 / 8.074308 (5.620520) | 13.821019 / 10.191392 (3.629627) | 0.126077 / 0.680424 (-0.554347) | 0.016543 / 0.534201 (-0.517658) | 0.381399 / 0.579283 (-0.197884) | 0.377326 / 0.434364 (-0.057038) | 0.439275 / 0.540337 (-0.101063) | 0.524021 / 1.386936 (-0.862915) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3e6269979fc80ae8939294d26298897f0db5b84d \"CML watermark\")\n"
] | "2023-02-27T04:06:05Z" | "2023-03-11T01:02:49Z" | "2023-03-11T00:55:40Z" | CONTRIBUTOR | null | 0 | {
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} | Closes https://github.com/huggingface/datasets/issues/5281
This PR uses fsspec to support datasets on cloud storage (tested manually with GCS). ETags are currently unsupported for cloud storage. In general, a much larger refactor could be done to just use fsspec for all schemes (ftp, http/s, s3, gcs) to unify the interfaces here, but I ultimately opted to leave that out of this PR
I didn't create a GCS filesystem class in `datasets.filesystems` since the S3 one appears to be a wrapper around `s3fs.S3FileSystem` and mainly used to generate docs. | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5579). All of your documentation changes will be reflected on that endpoint.",
"I'm not sure we need this part as we provide a link to the notebook that shows how to train an object detection model, and this notebook instantiates a `DataLoader` before training the model. I'd like to hear what @stevhliu thinks.\r\n\r\nPS: Your `collate_fn` calls `torch.stack` on the `bbox` tensors, which don't have the same shape, so this will fail.",
"I agree with @mariosasko; we also have a [Use with PyTorch](https://huggingface.co/docs/datasets/use_with_pytorch) guide that shows how you can create a `DataLoader`. "
] | "2023-02-25T14:53:17Z" | "2023-03-23T19:24:59Z" | "2023-03-23T19:24:50Z" | CONTRIBUTOR | null | 0 | {
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} | The following adds instructions on how to create a `DataLoader` from the guide on how to use object detection with augmentations (#4710). I am open to hearing any suggestions for improvement ! | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008124 / 0.011353 (-0.003229) | 0.004594 / 0.011008 (-0.006414) | 0.101575 / 0.038508 (0.063066) | 0.029074 / 0.023109 (0.005965) | 0.314641 / 0.275898 (0.038743) | 0.372006 / 0.323480 (0.048526) | 0.006882 / 0.007986 (-0.001103) | 0.003371 / 0.004328 (-0.000958) | 0.078800 / 0.004250 (0.074550) | 0.034030 / 0.037052 (-0.003023) | 0.326917 / 0.258489 (0.068428) | 0.357628 / 0.293841 (0.063788) | 0.033076 / 0.128546 (-0.095470) | 0.011552 / 0.075646 (-0.064094) | 0.321715 / 0.419271 (-0.097557) | 0.040426 / 0.043533 (-0.003107) | 0.315091 / 0.255139 (0.059952) | 0.339291 / 0.283200 (0.056091) | 0.087280 / 0.141683 (-0.054403) | 1.443445 / 1.452155 (-0.008710) | 1.489233 / 1.492716 (-0.003483) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.182643 / 0.018006 (0.164637) | 0.390205 / 0.000490 (0.389716) | 0.001361 / 0.000200 (0.001161) | 0.000072 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022767 / 0.037411 (-0.014644) | 0.095744 / 0.014526 (0.081219) | 0.102763 / 0.176557 (-0.073794) | 0.166760 / 0.737135 (-0.570375) | 0.106393 / 0.296338 (-0.189945) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424649 / 0.215209 (0.209440) | 4.257982 / 2.077655 (2.180327) | 2.135847 / 1.504120 (0.631727) | 1.924810 / 1.541195 (0.383615) | 1.813797 / 1.468490 (0.345307) | 0.695467 / 4.584777 (-3.889310) | 3.330164 / 3.745712 (-0.415548) | 2.665606 / 5.269862 (-2.604255) | 1.458619 / 4.565676 (-3.107058) | 0.082408 / 0.424275 (-0.341867) | 0.012259 / 0.007607 (0.004652) | 0.527737 / 0.226044 (0.301693) | 5.271119 / 2.268929 (3.002191) | 2.618655 / 55.444624 (-52.825970) | 2.312321 / 6.876477 (-4.564155) | 2.270096 / 2.142072 (0.128023) | 0.811563 / 4.805227 (-3.993664) | 0.148512 / 6.500664 (-6.352152) | 0.064562 / 0.075469 (-0.010907) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.212483 / 1.841788 (-0.629304) | 13.471679 / 8.074308 (5.397371) | 13.691054 / 10.191392 (3.499662) | 0.137399 / 0.680424 (-0.543025) | 0.028489 / 0.534201 (-0.505711) | 0.398879 / 0.579283 (-0.180404) | 0.396712 / 0.434364 (-0.037652) | 0.458879 / 0.540337 (-0.081458) | 0.537143 / 1.386936 (-0.849793) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006911 / 0.011353 (-0.004442) | 0.004941 / 0.011008 (-0.006067) | 0.078606 / 0.038508 (0.040098) | 0.028411 / 0.023109 (0.005302) | 0.352172 / 0.275898 (0.076274) | 0.401155 / 0.323480 (0.077675) | 0.005433 / 0.007986 (-0.002552) | 0.003704 / 0.004328 (-0.000625) | 0.076615 / 0.004250 (0.072365) | 0.043814 / 0.037052 (0.006761) | 0.346928 / 0.258489 (0.088439) | 0.405587 / 0.293841 (0.111746) | 0.032176 / 0.128546 (-0.096370) | 0.011863 / 0.075646 (-0.063783) | 0.087209 / 0.419271 (-0.332063) | 0.042977 / 0.043533 (-0.000556) | 0.345366 / 0.255139 (0.090227) | 0.419664 / 0.283200 (0.136464) | 0.093862 / 0.141683 (-0.047821) | 1.490968 / 1.452155 (0.038813) | 1.566644 / 1.492716 (0.073927) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216703 / 0.018006 (0.198697) | 0.472411 / 0.000490 (0.471921) | 0.002234 / 0.000200 (0.002034) | 0.000085 / 0.000054 (0.000031) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027672 / 0.037411 (-0.009740) | 0.109793 / 0.014526 (0.095267) | 0.110720 / 0.176557 (-0.065837) | 0.182342 / 0.737135 (-0.554793) | 0.116150 / 0.296338 (-0.180188) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.438165 / 0.215209 (0.222956) | 4.366213 / 2.077655 (2.288558) | 2.065036 / 1.504120 (0.560917) | 1.860105 / 1.541195 (0.318911) | 1.966885 / 1.468490 (0.498395) | 0.705194 / 4.584777 (-3.879583) | 3.389408 / 3.745712 (-0.356304) | 2.632155 / 5.269862 (-2.637707) | 1.471090 / 4.565676 (-3.094587) | 0.083579 / 0.424275 (-0.340697) | 0.012643 / 0.007607 (0.005036) | 0.542230 / 0.226044 (0.316186) | 5.416293 / 2.268929 (3.147365) | 2.517391 / 55.444624 (-52.927233) | 2.160159 / 6.876477 (-4.716317) | 2.167104 / 2.142072 (0.025031) | 0.807142 / 4.805227 (-3.998085) | 0.152249 / 6.500664 (-6.348415) | 0.067559 / 0.075469 (-0.007910) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.399516 / 1.841788 (-0.442272) | 15.289898 / 8.074308 (7.215590) | 14.188758 / 10.191392 (3.997366) | 0.161277 / 0.680424 (-0.519147) | 0.016854 / 0.534201 (-0.517347) | 0.382091 / 0.579283 (-0.197192) | 0.396639 / 0.434364 (-0.037725) | 0.467932 / 0.540337 (-0.072405) | 0.552017 / 1.386936 (-0.834919) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2e050273ec3d2a7e53d817544318b23fb51430d0 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011038 / 0.011353 (-0.000315) | 0.005878 / 0.011008 (-0.005130) | 0.118247 / 0.038508 (0.079739) | 0.043988 / 0.023109 (0.020879) | 0.350823 / 0.275898 (0.074925) | 0.430350 / 0.323480 (0.106870) | 0.009259 / 0.007986 (0.001274) | 0.004614 / 0.004328 (0.000286) | 0.089366 / 0.004250 (0.085116) | 0.049993 / 0.037052 (0.012941) | 0.367620 / 0.258489 (0.109131) | 0.404809 / 0.293841 (0.110968) | 0.044078 / 0.128546 (-0.084468) | 0.014226 / 0.075646 (-0.061421) | 0.397707 / 0.419271 (-0.021565) | 0.056631 / 0.043533 (0.013098) | 0.355942 / 0.255139 (0.100803) | 0.375537 / 0.283200 (0.092338) | 0.121956 / 0.141683 (-0.019727) | 1.757958 / 1.452155 (0.305803) | 1.822183 / 1.492716 (0.329466) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.024505 / 0.018006 (0.006499) | 0.488754 / 0.000490 (0.488265) | 0.011032 / 0.000200 (0.010832) | 0.000540 / 0.000054 (0.000486) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032895 / 0.037411 (-0.004516) | 0.132496 / 0.014526 (0.117970) | 0.140620 / 0.176557 (-0.035937) | 0.220628 / 0.737135 (-0.516507) | 0.147622 / 0.296338 (-0.148717) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.471335 / 0.215209 (0.256126) | 4.699792 / 2.077655 (2.622137) | 2.119782 / 1.504120 (0.615662) | 1.894784 / 1.541195 (0.353590) | 2.002694 / 1.468490 (0.534204) | 0.822610 / 4.584777 (-3.762167) | 4.511510 / 3.745712 (0.765797) | 2.467017 / 5.269862 (-2.802845) | 1.568500 / 4.565676 (-2.997177) | 0.101488 / 0.424275 (-0.322787) | 0.014567 / 0.007607 (0.006960) | 0.603033 / 0.226044 (0.376989) | 6.041397 / 2.268929 (3.772468) | 2.759140 / 55.444624 (-52.685484) | 2.397192 / 6.876477 (-4.479285) | 2.491986 / 2.142072 (0.349914) | 1.021198 / 4.805227 (-3.784029) | 0.196415 / 6.500664 (-6.304249) | 0.076409 / 0.075469 (0.000939) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.406816 / 1.841788 (-0.434972) | 17.740263 / 8.074308 (9.665954) | 16.926489 / 10.191392 (6.735097) | 0.235302 / 0.680424 (-0.445122) | 0.036829 / 0.534201 (-0.497372) | 0.525326 / 0.579283 (-0.053957) | 0.530905 / 0.434364 (0.096541) | 0.650357 / 0.540337 (0.110019) | 0.770641 / 1.386936 (-0.616295) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008728 / 0.011353 (-0.002625) | 0.006023 / 0.011008 (-0.004985) | 0.088694 / 0.038508 (0.050186) | 0.040345 / 0.023109 (0.017236) | 0.408126 / 0.275898 (0.132228) | 0.461178 / 0.323480 (0.137698) | 0.007456 / 0.007986 (-0.000529) | 0.004722 / 0.004328 (0.000394) | 0.087340 / 0.004250 (0.083090) | 0.055826 / 0.037052 (0.018774) | 0.422432 / 0.258489 (0.163942) | 0.466308 / 0.293841 (0.172467) | 0.043637 / 0.128546 (-0.084909) | 0.014602 / 0.075646 (-0.061044) | 0.103610 / 0.419271 (-0.315662) | 0.069999 / 0.043533 (0.026466) | 0.410676 / 0.255139 (0.155537) | 0.434551 / 0.283200 (0.151351) | 0.127699 / 0.141683 (-0.013984) | 1.699858 / 1.452155 (0.247703) | 1.830331 / 1.492716 (0.337615) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235217 / 0.018006 (0.217211) | 0.494814 / 0.000490 (0.494325) | 0.004942 / 0.000200 (0.004742) | 0.000099 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035996 / 0.037411 (-0.001416) | 0.139419 / 0.014526 (0.124893) | 0.146859 / 0.176557 (-0.029698) | 0.234793 / 0.737135 (-0.502343) | 0.152495 / 0.296338 (-0.143843) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.509812 / 0.215209 (0.294603) | 5.067227 / 2.077655 (2.989572) | 2.455505 / 1.504120 (0.951385) | 2.223516 / 1.541195 (0.682321) | 2.367783 / 1.468490 (0.899293) | 0.852550 / 4.584777 (-3.732227) | 4.517284 / 3.745712 (0.771572) | 4.860399 / 5.269862 (-0.409462) | 2.175290 / 4.565676 (-2.390386) | 0.106155 / 0.424275 (-0.318120) | 0.015023 / 0.007607 (0.007416) | 0.633753 / 0.226044 (0.407708) | 6.316214 / 2.268929 (4.047285) | 3.021118 / 55.444624 (-52.423506) | 2.601317 / 6.876477 (-4.275160) | 2.807988 / 2.142072 (0.665916) | 1.028695 / 4.805227 (-3.776532) | 0.204387 / 6.500664 (-6.296277) | 0.077368 / 0.075469 (0.001899) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.540299 / 1.841788 (-0.301489) | 18.311957 / 8.074308 (10.237649) | 16.139892 / 10.191392 (5.948500) | 0.217231 / 0.680424 (-0.463193) | 0.020544 / 0.534201 (-0.513657) | 0.505589 / 0.579283 (-0.073694) | 0.506694 / 0.434364 (0.072330) | 0.622162 / 0.540337 (0.081824) | 0.739537 / 1.386936 (-0.647399) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0f595fc2aa4786720f7a21da56069a1c46b4552a \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009465 / 0.011353 (-0.001887) | 0.005307 / 0.011008 (-0.005701) | 0.104111 / 0.038508 (0.065603) | 0.036083 / 0.023109 (0.012974) | 0.296608 / 0.275898 (0.020710) | 0.351365 / 0.323480 (0.027885) | 0.008309 / 0.007986 (0.000323) | 0.004383 / 0.004328 (0.000055) | 0.078297 / 0.004250 (0.074047) | 0.044062 / 0.037052 (0.007009) | 0.295592 / 0.258489 (0.037103) | 0.354442 / 0.293841 (0.060602) | 0.038651 / 0.128546 (-0.089896) | 0.012311 / 0.075646 (-0.063335) | 0.337933 / 0.419271 (-0.081338) | 0.048179 / 0.043533 (0.004646) | 0.308320 / 0.255139 (0.053181) | 0.335028 / 0.283200 (0.051829) | 0.105394 / 0.141683 (-0.036289) | 1.444104 / 1.452155 (-0.008050) | 1.573953 / 1.492716 (0.081237) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236548 / 0.018006 (0.218542) | 0.552862 / 0.000490 (0.552372) | 0.003925 / 0.000200 (0.003726) | 0.000107 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026386 / 0.037411 (-0.011025) | 0.108002 / 0.014526 (0.093476) | 0.118327 / 0.176557 (-0.058230) | 0.182861 / 0.737135 (-0.554274) | 0.126032 / 0.296338 (-0.170307) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.397037 / 0.215209 (0.181827) | 3.960978 / 2.077655 (1.883323) | 1.771955 / 1.504120 (0.267835) | 1.575033 / 1.541195 (0.033839) | 1.696552 / 1.468490 (0.228062) | 0.679013 / 4.584777 (-3.905764) | 3.770136 / 3.745712 (0.024424) | 2.068323 / 5.269862 (-3.201539) | 1.310823 / 4.565676 (-3.254853) | 0.083752 / 0.424275 (-0.340523) | 0.012366 / 0.007607 (0.004759) | 0.512679 / 0.226044 (0.286635) | 5.127036 / 2.268929 (2.858108) | 2.313200 / 55.444624 (-53.131424) | 1.931007 / 6.876477 (-4.945470) | 2.018336 / 2.142072 (-0.123737) | 0.833033 / 4.805227 (-3.972194) | 0.163778 / 6.500664 (-6.336886) | 0.064053 / 0.075469 (-0.011417) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.234102 / 1.841788 (-0.607685) | 15.227921 / 8.074308 (7.153613) | 14.587146 / 10.191392 (4.395754) | 0.176236 / 0.680424 (-0.504187) | 0.028905 / 0.534201 (-0.505295) | 0.439758 / 0.579283 (-0.139525) | 0.439211 / 0.434364 (0.004848) | 0.544325 / 0.540337 (0.003988) | 0.633804 / 1.386936 (-0.753132) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007933 / 0.011353 (-0.003420) | 0.005446 / 0.011008 (-0.005563) | 0.077846 / 0.038508 (0.039338) | 0.036017 / 0.023109 (0.012907) | 0.358925 / 0.275898 (0.083027) | 0.402757 / 0.323480 (0.079277) | 0.006478 / 0.007986 (-0.001508) | 0.005708 / 0.004328 (0.001380) | 0.074833 / 0.004250 (0.070583) | 0.053412 / 0.037052 (0.016360) | 0.358587 / 0.258489 (0.100098) | 0.430904 / 0.293841 (0.137063) | 0.037778 / 0.128546 (-0.090768) | 0.012698 / 0.075646 (-0.062948) | 0.087615 / 0.419271 (-0.331657) | 0.050236 / 0.043533 (0.006703) | 0.344160 / 0.255139 (0.089021) | 0.390870 / 0.283200 (0.107670) | 0.111035 / 0.141683 (-0.030648) | 1.446963 / 1.452155 (-0.005192) | 1.566158 / 1.492716 (0.073442) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.302380 / 0.018006 (0.284373) | 0.554005 / 0.000490 (0.553515) | 0.007244 / 0.000200 (0.007044) | 0.000115 / 0.000054 (0.000061) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032291 / 0.037411 (-0.005120) | 0.117117 / 0.014526 (0.102591) | 0.127513 / 0.176557 (-0.049044) | 0.204208 / 0.737135 (-0.532927) | 0.133730 / 0.296338 (-0.162608) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424597 / 0.215209 (0.209388) | 4.233852 / 2.077655 (2.156198) | 2.029731 / 1.504120 (0.525611) | 1.830075 / 1.541195 (0.288880) | 1.966198 / 1.468490 (0.497707) | 0.697881 / 4.584777 (-3.886896) | 3.758012 / 3.745712 (0.012299) | 3.405319 / 5.269862 (-1.864542) | 1.870816 / 4.565676 (-2.694860) | 0.086892 / 0.424275 (-0.337383) | 0.012438 / 0.007607 (0.004831) | 0.524252 / 0.226044 (0.298207) | 5.209534 / 2.268929 (2.940606) | 2.478608 / 55.444624 (-52.966017) | 2.151535 / 6.876477 (-4.724942) | 2.249260 / 2.142072 (0.107187) | 0.831955 / 4.805227 (-3.973273) | 0.165955 / 6.500664 (-6.334710) | 0.064663 / 0.075469 (-0.010806) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.253327 / 1.841788 (-0.588460) | 15.904393 / 8.074308 (7.830085) | 13.253464 / 10.191392 (3.062072) | 0.162148 / 0.680424 (-0.518276) | 0.017643 / 0.534201 (-0.516558) | 0.425028 / 0.579283 (-0.154255) | 0.425615 / 0.434364 (-0.008749) | 0.521503 / 0.540337 (-0.018835) | 0.629473 / 1.386936 (-0.757463) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#939b2332115c7ec3dd56f58169800ed81cc4a982 \"CML watermark\")\n"
] | "2023-02-24T15:37:43Z" | "2023-02-27T17:28:25Z" | "2023-02-27T17:21:09Z" | CONTRIBUTOR | null | 0 | {
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"Hi! I've merged a PR to use `int32` instead of `int8` for `reddit_scores`, so it should work now.\r\n\r\n"
] | "2023-02-24T13:01:48Z" | "2023-02-24T14:01:09Z" | "2023-02-24T14:01:09Z" | NONE | null | null | null | ### Describe the bug
I met the same bug mentioned in #3053 which is never fixed. Because several `reddit_scores` are larger than `int8` even `int16`. https://huggingface.co/datasets/the_pile_openwebtext2/blob/main/the_pile_openwebtext2.py#L62
### Steps to reproduce the bug
```python3
from datasets import load_dataset
dataset = load_dataset("the_pile_openwebtext2")
```
### Expected behavior
load as normal.
### Environment info
- `datasets` version: 2.10.0
- Platform: Linux-5.4.143.bsk.7-amd64-x86_64-with-glibc2.31
- Python version: 3.9.2
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | {
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"Duplicated issue."
] | "2023-02-24T12:57:49Z" | "2023-02-24T12:58:31Z" | "2023-02-24T12:58:18Z" | NONE | null | null | null | I was getting a similar error `pyarrow.lib.ArrowInvalid: Integer value 528 not in range: -128 to 127` - AFAICT, this is because the type specified for `reddit_scores` is `datasets.Sequence(datasets.Value("int8"))`, but the actual values can be well outside the max range for 8-bit integers.
I worked around this by downloading the `the_pile_openwebtext2.py` and editing it to use local files and drop reddit scores as a column (not needed for my purposes).
_Originally posted by @tc-wolf in https://github.com/huggingface/datasets/issues/3053#issuecomment-1281392422_
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} | [
"Hi! Indeed it would be useful to support this. PyArrow natively supports schema-level and column-level metadata, so implementing this should be straightforward. The API I have in mind would work as follows:\r\n```python\r\ncol_feature = Value(\"string\", metadata=\"Some column-level metadata\")\r\n\r\nfeatures = Features({\"col\": col_feature}, metadata=\"Some schema-level metadata\")\r\n```\r\n\r\nWDYT?",
"Sorry for the late reply, \r\nYes, I think this is the most straight-forward approach with the things that we already have.\r\n\r\n",
"@mariosasko Let me know how I can help."
] | "2023-02-24T10:53:44Z" | "2023-03-10T17:04:04Z" | null | NONE | null | null | null | ### Feature request
Being able to put some metadata for each column as a string or any other type.
### Motivation
I will bring the motivation by an example, lets say we are experimenting with embedding produced by some image encoder network, and we want to iterate through a couple of preprocessing and see which one works better in our downstream task, here as workaround right now what I do is the compute the hash of the preprocessing that the images went through as part of the new columns name, it would be nice to attach some kinda meta data in these scenarios to the each columns. metadata
### Your contribution
Maybe we could map another relational like database as the metadata? | {
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"Also encountering this issue for every dataset I try to stream! Installed datasets from main:\r\n```\r\n- `datasets` version: 2.10.1.dev0\r\n- Platform: macOS-13.1-arm64-arm-64bit\r\n- Python version: 3.9.13\r\n- PyArrow version: 10.0.1\r\n- Pandas version: 1.5.2\r\n```\r\n\r\nRepro:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nspigi = load_dataset(\"kensho/spgispeech\", \"dev\", split=\"validation\", streaming=True, use_auth_token=True)\r\nsample = next(iter(spigi))\r\n```\r\n\r\n<details>\r\n<summary> Traceback </summary>\r\n\r\n```python\r\n---------------------------------------------------------------------------\r\nClientResponseError Traceback (most recent call last)\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/implementations/http.py:407, in HTTPFileSystem._info(self, url, **kwargs)\r\n 405 try:\r\n 406 info.update(\r\n--> 407 await _file_info(\r\n 408 self.encode_url(url),\r\n 409 size_policy=policy,\r\n 410 session=session,\r\n 411 **self.kwargs,\r\n 412 **kwargs,\r\n 413 )\r\n 414 )\r\n 415 if info.get(\"size\") is not None:\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/implementations/http.py:792, in _file_info(url, session, size_policy, **kwargs)\r\n 791 async with r:\r\n--> 792 r.raise_for_status()\r\n 794 # TODO:\r\n 795 # recognise lack of 'Accept-Ranges',\r\n 796 # or 'Accept-Ranges': 'none' (not 'bytes')\r\n 797 # to mean streaming only, no random access => return None\r\n\r\nFile ~/venv/lib/python3.9/site-packages/aiohttp/client_reqrep.py:1005, in ClientResponse.raise_for_status(self)\r\n 1004 self.release()\r\n-> 1005 raise ClientResponseError(\r\n 1006 self.request_info,\r\n 1007 self.history,\r\n 1008 status=self.status,\r\n 1009 message=self.reason,\r\n 1010 headers=self.headers,\r\n 1011 )\r\n\r\nClientResponseError: 403, message='Forbidden', url=URL('[https://cdn-lfs.huggingface.co/repos/e2/89/e28905247d6f48bb4edad5baf9b1bb4158e897a13fdf18bf3b8ee89ff8387ab8/46eca7431a7b6bad344bf451800e5b10cea1dd168f26d1027a6d9eb374b7fac3?response-content-disposition=attachment%3B+filename*%3DUTF-8''dev.csv%3B+filename%3D%22dev.csv%22%3B&response-content-type=text/csv&Expires=1677494732&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL3JlcG9zL2UyLzg5L2UyODkwNTI0N2Q2ZjQ4YmI0ZWRhZDViYWY5YjFiYjQxNThlODk3YTEzZmRmMThiZjNiOGVlODlmZjgzODdhYjgvNDZlY2E3NDMxYTdiNmJhZDM0NGJmNDUxODAwZTViMTBjZWExZGQxNjhmMjZkMTAyN2E2ZDllYjM3NGI3ZmFjMz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPXRleHQlMkZjc3YiLCJDb25kaXRpb24iOnsiRGF0ZUxlc3NUaGFuIjp7IkFXUzpFcG9jaFRpbWUiOjE2Nzc0OTQ3MzJ9fX1dfQ__&Signature=EzQB9f7xPckvqfFB6LzcyR-wzTnQCqtPDdWtQUzZ3QJ-gY-IHG5mxQITJgMr1nVTbJZrPmGAaDngMcPFUfSQa8RmCqYH~dZl-UGE8CO4neKNUT1DvA2WEvLDS4WaAJ3SN-9rX0uFb03~c1QS78cIgIRboYvf6ugKiJz86Bd7Vs~tcp201JFR0A6jIMseqApOnkb9d8dHMP3Ny~F6gO3Qf2QpEWM-QsDIyw2Kz2QV55nq8TsDpRYZCZo50~WwD~73Hej0PoDhEA1K37d19pa0CQhkaN-gjCrbT9xLabbvhJWa~ZkWcMdD0teCgjYqv1wKyvFXDAxukxLGEc7OBXVbYw__&Key-Pair-Id=KVTP0A1DKRTAX](https://cdn-lfs.huggingface.co/repos/e2/89/e28905247d6f48bb4edad5baf9b1bb4158e897a13fdf18bf3b8ee89ff8387ab8/46eca7431a7b6bad344bf451800e5b10cea1dd168f26d1027a6d9eb374b7fac3?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27dev.csv%3B+filename%3D%22dev.csv%22%3B&response-content-type=text/csv&Expires=1677494732&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL3JlcG9zL2UyLzg5L2UyODkwNTI0N2Q2ZjQ4YmI0ZWRhZDViYWY5YjFiYjQxNThlODk3YTEzZmRmMThiZjNiOGVlODlmZjgzODdhYjgvNDZlY2E3NDMxYTdiNmJhZDM0NGJmNDUxODAwZTViMTBjZWExZGQxNjhmMjZkMTAyN2E2ZDllYjM3NGI3ZmFjMz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPXRleHQlMkZjc3YiLCJDb25kaXRpb24iOnsiRGF0ZUxlc3NUaGFuIjp7IkFXUzpFcG9jaFRpbWUiOjE2Nzc0OTQ3MzJ9fX1dfQ__&Signature=EzQB9f7xPckvqfFB6LzcyR-wzTnQCqtPDdWtQUzZ3QJ-gY-IHG5mxQITJgMr1nVTbJZrPmGAaDngMcPFUfSQa8RmCqYH~dZl-UGE8CO4neKNUT1DvA2WEvLDS4WaAJ3SN-9rX0uFb03~c1QS78cIgIRboYvf6ugKiJz86Bd7Vs~tcp201JFR0A6jIMseqApOnkb9d8dHMP3Ny~F6gO3Qf2QpEWM-QsDIyw2Kz2QV55nq8TsDpRYZCZo50~WwD~73Hej0PoDhEA1K37d19pa0CQhkaN-gjCrbT9xLabbvhJWa~ZkWcMdD0teCgjYqv1wKyvFXDAxukxLGEc7OBXVbYw__&Key-Pair-Id=KVTP0A1DKRTAX)')\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nFileNotFoundError Traceback (most recent call last)\r\nCell In[5], line 4\r\n 1 from datasets import load_dataset\r\n 3 spigi = load_dataset(\"kensho/spgispeech\", \"dev\", split=\"validation\", streaming=True)\r\n----> 4 sample = next(iter(spigi))\r\n\r\nFile ~/datasets/src/datasets/iterable_dataset.py:937, in IterableDataset.__iter__(self)\r\n 934 yield from self._iter_pytorch(ex_iterable)\r\n 935 return\r\n--> 937 for key, example in ex_iterable:\r\n 938 if self.features:\r\n 939 # `IterableDataset` automatically fills missing columns with None.\r\n 940 # This is done with `_apply_feature_types_on_example`.\r\n 941 yield _apply_feature_types_on_example(\r\n 942 example, self.features, token_per_repo_id=self._token_per_repo_id\r\n 943 )\r\n\r\nFile ~/datasets/src/datasets/iterable_dataset.py:113, in ExamplesIterable.__iter__(self)\r\n 112 def __iter__(self):\r\n--> 113 yield from self.generate_examples_fn(**self.kwargs)\r\n\r\nFile ~/.cache/huggingface/modules/datasets_modules/datasets/kensho--spgispeech/5fbf75dd9ef795a9b5a673457d2cbaf0b8fa0de8fb62acbd1da338d83a41e2f0/spgispeech.py:186, in Spgispeech._generate_examples(self, local_extracted_archive_paths, archives, meta_path)\r\n 183 dict_keys = [\"wav_filename\", \"wav_filesize\", \"transcript\"]\r\n 185 logging.info(\"Reading metadata...\")\r\n--> 186 with open(meta_path, encoding=\"utf-8\") as f:\r\n 187 csvreader = csv.DictReader(f, delimiter=\"|\")\r\n 188 metadata = {x[\"wav_filename\"]: dict((k, x[k]) for k in dict_keys) for x in csvreader}\r\n\r\nFile ~/datasets/src/datasets/streaming.py:70, in extend_module_for_streaming.<locals>.wrap_auth.<locals>.wrapper(*args, **kwargs)\r\n 68 @wraps(function)\r\n 69 def wrapper(*args, **kwargs):\r\n---> 70 return function(*args, use_auth_token=use_auth_token, **kwargs)\r\n\r\nFile ~/datasets/src/datasets/download/streaming_download_manager.py:495, in xopen(file, mode, use_auth_token, *args, **kwargs)\r\n 493 kwargs = {**kwargs, **new_kwargs}\r\n 494 try:\r\n--> 495 file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()\r\n 496 except ValueError as e:\r\n 497 if str(e) == \"Cannot seek streaming HTTP file\":\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/core.py:135, in OpenFile.open(self)\r\n 128 def open(self):\r\n 129 \"\"\"Materialise this as a real open file without context\r\n 130 \r\n 131 The OpenFile object should be explicitly closed to avoid enclosed file\r\n 132 instances persisting. You must, therefore, keep a reference to the OpenFile\r\n 133 during the life of the file-like it generates.\r\n 134 \"\"\"\r\n--> 135 return self.__enter__()\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/core.py:103, in OpenFile.__enter__(self)\r\n 100 def __enter__(self):\r\n 101 mode = self.mode.replace(\"t\", \"\").replace(\"b\", \"\") + \"b\"\r\n--> 103 f = self.fs.open(self.path, mode=mode)\r\n 105 self.fobjects = [f]\r\n 107 if self.compression is not None:\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/spec.py:1106, in AbstractFileSystem.open(self, path, mode, block_size, cache_options, compression, **kwargs)\r\n 1104 else:\r\n 1105 ac = kwargs.pop(\"autocommit\", not self._intrans)\r\n-> 1106 f = self._open(\r\n 1107 path,\r\n 1108 mode=mode,\r\n 1109 block_size=block_size,\r\n 1110 autocommit=ac,\r\n 1111 cache_options=cache_options,\r\n 1112 **kwargs,\r\n 1113 )\r\n 1114 if compression is not None:\r\n 1115 from fsspec.compression import compr\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/implementations/http.py:346, in HTTPFileSystem._open(self, path, mode, block_size, autocommit, cache_type, cache_options, size, **kwargs)\r\n 344 kw[\"asynchronous\"] = self.asynchronous\r\n 345 kw.update(kwargs)\r\n--> 346 size = size or self.info(path, **kwargs)[\"size\"]\r\n 347 session = sync(self.loop, self.set_session)\r\n 348 if block_size and size:\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/asyn.py:113, in sync_wrapper.<locals>.wrapper(*args, **kwargs)\r\n 110 @functools.wraps(func)\r\n 111 def wrapper(*args, **kwargs):\r\n 112 self = obj or args[0]\r\n--> 113 return sync(self.loop, func, *args, **kwargs)\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/asyn.py:98, in sync(loop, func, timeout, *args, **kwargs)\r\n 96 raise FSTimeoutError from return_result\r\n 97 elif isinstance(return_result, BaseException):\r\n---> 98 raise return_result\r\n 99 else:\r\n 100 return return_result\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/asyn.py:53, in _runner(event, coro, result, timeout)\r\n 51 coro = asyncio.wait_for(coro, timeout=timeout)\r\n 52 try:\r\n---> 53 result[0] = await coro\r\n 54 except Exception as ex:\r\n 55 result[0] = ex\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/implementations/http.py:420, in HTTPFileSystem._info(self, url, **kwargs)\r\n 417 except Exception as exc:\r\n 418 if policy == \"get\":\r\n 419 # If get failed, then raise a FileNotFoundError\r\n--> 420 raise FileNotFoundError(url) from exc\r\n 421 logger.debug(str(exc))\r\n 423 return {\"name\": url, \"size\": None, **info, \"type\": \"file\"}\r\n\r\nFileNotFoundError: https://huggingface.co/datasets/kensho/spgispeech/resolve/main/data/meta/dev.csv\r\n```\r\n</details>",
"Hi ! We're investigating this issue, sorry for the inconvenience",
"This has been resolved ! Thanks for reporting",
"Wow, thanks for the very quick fix!",
"This problem now appears again, this time with an underlying HTTP 502 status code:\r\n\r\n```\r\naiohttp.client_exceptions.ClientResponseError: 502, message='Bad Gateway', url=URL('https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-validation.00002-of-00008.json.gz')\r\n```",
"Re-executing a minute later, the underlying cause is an HTTP 403 status code, as reported yesterday:\r\n\r\n```\r\naiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://cdn-lfs.huggingface.co/datasets/allenai/c4/4bf6b248b0f910dcde2cdf2118d6369d8208c8f9515ec29ab73e531f380b18e2?response-content-disposition=attachment%3B+filename*%3DUTF-8''c4-validation.00002-of-00008.json.gz%3B+filename%3D%22c4-validation.00002-of-00008.json.gz%22%3B&response-content-type=application/gzip&Expires=1677571273&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL2RhdGFzZXRzL2FsbGVuYWkvYzQvNGJmNmIyNDhiMGY5MTBkY2RlMmNkZjIxMThkNjM2OWQ4MjA4YzhmOTUxNWVjMjlhYjczZTUzMWYzODBiMThlMj9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPWFwcGxpY2F0aW9uJTJGZ3ppcCIsIkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY3NzU3MTI3M319fV19&Signature=WW42NOKkLuX~xVB1QfbkqzdvGo2AOXpgbF3PjTXy6iKd~ffilr1N9ScPXfvTXqy5yvdhJg1G0xJy1zYtUjGAL8GEx3Av-0vIhpWMGYTM8XKEU5gYA9qt30oVtNph6TkTYSABrsYTaj-hzQL9WCgyapmjvG69ETMh4wj44r2rcbk4T3j0l6l4u76Gh~lyRSll3aK4qycdUwcyL7FECDu~0W1mJIJwKkCrWHhSpHJSshb-0ElwG71pq4eyQ5g2uxHdK6JbRF7loxUpRQQJ1vlk0EHXdw0wTMaQ9tqHy6xcrQd8Ep0Yvx3tUD8MR0vWOcbQKnL6LwPQByc8tkChlpjnig__&Key-Pair-Id=KVTP0A1DKRTAX')\r\n```",
"I'm facing the same problem. Interestingly using `wget` I can download the file. ",
"It's been resolved again ;)",
"> It's been resolved again ;)\r\n\r\nI'm experiencing the same issue when trying to load this dataset, `FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/realnewslike/c4-train.00000-of-00512.json.gz`"
] | "2023-02-24T07:57:32Z" | "2023-03-06T13:14:11Z" | "2023-02-27T04:03:38Z" | NONE | null | null | null | ### Describe the bug
Loading the `c4` dataset in streaming mode with `load_dataset("c4", "en", split="validation", streaming=True)` and then using it fails with a `FileNotFoundException`.
### Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("c4", "en", split="train", streaming=True)
next(iter(dataset))
```
causes a
```
FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-train.00000-of-01024.json.gz
```
I can download this file manually though e.g. by entering this URL in a browser.
There is an underlying HTTP 403 status code:
```
aiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://cdn-lfs.huggingface.co/datasets/allenai/c4/8ef8d75b0e045dec4aa5123a671b4564466b0707086a7ed1ba8721626dfffbc9?response-content-disposition=attachment%3B+filename*%3DUTF-8''c4-train.00000-of-01024.json.gz%3B+filename%3D%22c4-train.00000-of-01024.json.gz%22%3B&response-content-type=application/gzip&Expires=1677483770&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL2RhdGFzZXRzL2FsbGVuYWkvYzQvOGVmOGQ3NWIwZTA0NWRlYzRhYTUxMjNhNjcxYjQ1NjQ0NjZiMDcwNzA4NmE3ZWQxYmE4NzIxNjI2ZGZmZmJjOT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPWFwcGxpY2F0aW9uJTJGZ3ppcCIsIkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY3NzQ4Mzc3MH19fV19&Signature=yjL3UeY72cf2xpnvPvD68eAYOEe2qtaUJV55sB-jnPskBJEMwpMJcBZvg2~GqXZdM3O-GWV-Z3CI~d4u5VCb4YZ-HlmOjr3VBYkvox2EKiXnBIhjMecf2UVUPtxhTa9kBVlWjqu4qKzB9gKXZF2Cwpp5ctLzapEaT2nnqF84RAL-rsqMA3I~M8vWWfivQsbBK63hMfgZqqKMgdWM0iKMaItveDl0ufQ29azMFmsR7qd8V7sU2Z-F1fAeohS8HpN9OOnClW34yi~YJ2AbgZJJBXA~qsylfVA0Qp7Q~yX~q4P8JF1vmJ2BjkiSbGrj3bAXOGugpOVU5msI52DT88yMdA__&Key-Pair-Id=KVTP0A1DKRTAX')
```
### Expected behavior
This should retrieve the first example from the C4 validation set. This worked a few days ago but stopped working now.
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.31
- Python version: 3.9.16
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
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https://api.github.com/repos/huggingface/datasets/issues/5573 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5573/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5573/comments | https://api.github.com/repos/huggingface/datasets/issues/5573/events | https://github.com/huggingface/datasets/pull/5573 | 1,597,400,836 | PR_kwDODunzps5Kop7n | 5,573 | Use soundfile for mp3 decoding instead of torchaudio | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"@mariosasko thank you for the review! do you have any idea why `test_hash_torch_tensor` fails on \"ubuntu-latest deps-minimum\"? I removed the `torchaudio<0.12.0` test dependency so it uses the latest `torch` now, might it be connected?",
"@polinaeterna The failure is due to `torch.from_numpy` not being picklable in newer versions of PyTorch. You can replace the current definition of `_save_tensor` in `utils/py_utils.py` with the following one to fix it: \r\n\r\n```python\r\n@pklregister(obj_type)\r\ndef _save_tensor(pickler, obj):\r\n # `torch.from_numpy` is not picklable in `torch>=1.11.0`\r\n def _create_tensor(np_array):\r\n return torch.from_numpy(np_array)\r\n\r\n dill_log(pickler, f\"To: {obj}\")\r\n args = (obj.detach().cpu().numpy(),)\r\n pickler.save_reduce(_create_tensor, args, obj=obj)\r\n dill_log(pickler, \"# To\")\r\n return\r\n```",
"(doing a patch release now - please wait before merging ^^)",
"@mariosasko génial, merci!! i've integrated all your changes, can you pls take a look one more time?",
"Patch release is done (I did it from another branch than `main` anyway)",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010927 / 0.011353 (-0.000426) | 0.006232 / 0.011008 (-0.004776) | 0.119815 / 0.038508 (0.081307) | 0.034138 / 0.023109 (0.011029) | 0.349945 / 0.275898 (0.074047) | 0.404967 / 0.323480 (0.081487) | 0.008672 / 0.007986 (0.000687) | 0.005010 / 0.004328 (0.000681) | 0.091931 / 0.004250 (0.087680) | 0.042534 / 0.037052 (0.005482) | 0.374701 / 0.258489 (0.116212) | 0.401027 / 0.293841 (0.107186) | 0.053523 / 0.128546 (-0.075024) | 0.019704 / 0.075646 (-0.055942) | 0.384207 / 0.419271 (-0.035064) | 0.065350 / 0.043533 (0.021817) | 0.375074 / 0.255139 (0.119935) | 0.390458 / 0.283200 (0.107259) | 0.110549 / 0.141683 (-0.031134) | 1.719812 / 1.452155 (0.267657) | 1.748906 / 1.492716 (0.256190) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210051 / 0.018006 (0.192045) | 0.546503 / 0.000490 (0.546013) | 0.004078 / 0.000200 (0.003878) | 0.000111 / 0.000054 (0.000056) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030212 / 0.037411 (-0.007199) | 0.121845 / 0.014526 (0.107319) | 0.136309 / 0.176557 (-0.040247) | 0.204667 / 0.737135 (-0.532468) | 0.157327 / 0.296338 (-0.139012) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.672548 / 0.215209 (0.457339) | 6.239409 / 2.077655 (4.161754) | 2.462441 / 1.504120 (0.958322) | 2.063985 / 1.541195 (0.522791) | 2.098858 / 1.468490 (0.630368) | 1.262600 / 4.584777 (-3.322177) | 5.478462 / 3.745712 (1.732750) | 5.454672 / 5.269862 (0.184810) | 2.991866 / 4.565676 (-1.573810) | 0.153415 / 0.424275 (-0.270861) | 0.015061 / 0.007607 (0.007454) | 0.796115 / 0.226044 (0.570071) | 8.206858 / 2.268929 (5.937930) | 3.226395 / 55.444624 (-52.218229) | 2.503522 / 6.876477 (-4.372955) | 2.547489 / 2.142072 (0.405417) | 1.504776 / 4.805227 (-3.300451) | 0.256536 / 6.500664 (-6.244128) | 0.078543 / 0.075469 (0.003073) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.591109 / 1.841788 (-0.250678) | 18.153317 / 8.074308 (10.079008) | 20.465684 / 10.191392 (10.274292) | 0.229808 / 0.680424 (-0.450616) | 0.045263 / 0.534201 (-0.488938) | 0.556760 / 0.579283 (-0.022524) | 0.614985 / 0.434364 (0.180622) | 0.635675 / 0.540337 (0.095337) | 0.729817 / 1.386936 (-0.657119) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011247 / 0.011353 (-0.000106) | 0.006823 / 0.011008 (-0.004185) | 0.101989 / 0.038508 (0.063481) | 0.036077 / 0.023109 (0.012968) | 0.413469 / 0.275898 (0.137571) | 0.505560 / 0.323480 (0.182080) | 0.007506 / 0.007986 (-0.000480) | 0.006369 / 0.004328 (0.002040) | 0.099597 / 0.004250 (0.095346) | 0.058115 / 0.037052 (0.021063) | 0.414735 / 0.258489 (0.156246) | 0.466801 / 0.293841 (0.172960) | 0.064771 / 0.128546 (-0.063775) | 0.021100 / 0.075646 (-0.054546) | 0.135407 / 0.419271 (-0.283864) | 0.068784 / 0.043533 (0.025251) | 0.410467 / 0.255139 (0.155328) | 0.465993 / 0.283200 (0.182794) | 0.119404 / 0.141683 (-0.022279) | 1.767107 / 1.452155 (0.314952) | 1.938342 / 1.492716 (0.445626) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227038 / 0.018006 (0.209032) | 0.511389 / 0.000490 (0.510899) | 0.006723 / 0.000200 (0.006523) | 0.000118 / 0.000054 (0.000064) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033078 / 0.037411 (-0.004333) | 0.133159 / 0.014526 (0.118633) | 0.147928 / 0.176557 (-0.028629) | 0.214005 / 0.737135 (-0.523130) | 0.151655 / 0.296338 (-0.144683) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.634829 / 0.215209 (0.419620) | 6.578640 / 2.077655 (4.500985) | 2.673598 / 1.504120 (1.169478) | 2.338671 / 1.541195 (0.797476) | 2.389104 / 1.468490 (0.920614) | 1.274938 / 4.584777 (-3.309839) | 5.746524 / 3.745712 (2.000812) | 5.992084 / 5.269862 (0.722222) | 3.092090 / 4.565676 (-1.473587) | 0.150375 / 0.424275 (-0.273900) | 0.015470 / 0.007607 (0.007863) | 0.792962 / 0.226044 (0.566918) | 8.057491 / 2.268929 (5.788563) | 3.483966 / 55.444624 (-51.960659) | 2.715038 / 6.876477 (-4.161438) | 2.747186 / 2.142072 (0.605114) | 1.532951 / 4.805227 (-3.272276) | 0.262214 / 6.500664 (-6.238450) | 0.081308 / 0.075469 (0.005839) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.698448 / 1.841788 (-0.143340) | 18.590002 / 8.074308 (10.515694) | 20.584508 / 10.191392 (10.393116) | 0.227237 / 0.680424 (-0.453187) | 0.028445 / 0.534201 (-0.505756) | 0.527874 / 0.579283 (-0.051409) | 0.602844 / 0.434364 (0.168480) | 0.672948 / 0.540337 (0.132611) | 0.788103 / 1.386936 (-0.598833) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f96547708a889c09ca8a02ed7aadd8c5690503c5 \"CML watermark\")\n"
] | "2023-02-23T19:19:44Z" | "2023-02-28T20:25:14Z" | "2023-02-28T20:16:02Z" | CONTRIBUTOR | null | 0 | {
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} | I've removed `torchaudio` completely and switched to use `soundfile` for everything. With the new version of `soundfile` package this should work smoothly because the `libsndfile` C library is bundled, in Linux wheels too.
Let me know if you think it's too harsh and we should continue to support `torchaudio` decoding.
I decided that we can drop it completely because:
1. it's always something wrong with `torchaudio` (for example recently https://github.com/huggingface/datasets/issues/5488 )
2. the results of mp3 decoding are different depending on `torchaudio` version
3. `soundfile` is slightly faster then the latest `torchaudio`
4. anyway users can pass any custom decoding function with any library they want if needed (worth putting a snippet in the docs).
cc @sanchit-gandhi @vaibhavad | {
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} | [] | closed | false | null | [] | null | [] | "2023-02-23T17:28:11Z" | "2023-02-23T18:03:55Z" | "2023-02-23T18:03:55Z" | NONE | null | null | null | ### Describe the bug
download_mode="reuse_dataset_if_exists" will always consider that a dataset doesn't exist.
Specifically, upon losing an internet connection trying to load a dataset for a second time in ten seconds, a connection error results, showing a breakpoint of:
```
File ~/jupyterlab/.direnv/python-3.9.6/lib/python3.9/site-packages/datasets/load.py:1174, in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1165 except Exception as e: # noqa: catch any exception of hf_hub and consider that the dataset doesn't exist
1166 if isinstance(
1167 e,
1168 (
(...)
1172 ),
1173 ):
-> 1174 raise ConnectionError(f"Couldn't reach '{path}' on the Hub ({type(e).__name__})")
1175 elif "404" in str(e):
1176 msg = f"Dataset '{path}' doesn't exist on the Hub"
ConnectionError: Couldn't reach 'lsb/tenk' on the Hub (ConnectionError)
```
This has been around since at least v2.0.
### Steps to reproduce the bug
```
from datasets import load_dataset
import numpy as np
tenk = load_dataset("lsb/tenk") # ten thousand integers
print(np.average(tenk['train']['a'])) # prints 4999.5
### now disconnect your internet
tenk_too = load_dataset("lsb/tenk", download_mode="reuse_dataset_if_exists")
# Raises ConnectionError: Couldn't reach 'lsb/tenk' on the Hub (ConnectionError)
```
### Expected behavior
I expected that I would be able to reuse the dataset I just downloaded.
### Environment info
- `datasets` version: 2.10.0
- Platform: macOS-13.1-arm64-arm-64bit
- Python version: 3.9.6
- PyArrow version: 7.0.0
- Pandas version: 1.5.2 | {
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"Hi! \r\n\r\nYou need to pass an input json file explicitly as `data_files` to `load_dataset` to avoid this error:\r\n```python\r\n ds = load_dataset(\"json\", data_files=args.input_json)\r\n```\r\n\r\n",
"Thanks it worked!"
] | "2023-02-23T16:50:11Z" | "2023-02-24T13:21:47Z" | "2023-02-24T13:21:47Z" | NONE | null | null | null | ### Describe the bug
Steps:
1. Created a dataset in a Linux VM and created a small sample using dataset.to_json() method.
2. Downloaded the JSON file to my local Windows machine for working and saved in say - r"C:\Users\name\file.json"
3. I am reading the file in my local PyCharm - the location of python file is different than the location of the JSON.
4. When I read using load_dataset("json",args.input_json), it throws and error from builder.py.
raise InvalidConfigName(
f"Bad characters from black list '{invalid_windows_characters}' found in '{self.name}'. "
f"They could create issues when creating a directory for this config on Windows filesystem."
6. When I bring the data to the current directory, it works fine.
### Steps to reproduce the bug
Steps:
1. Created a dataset in a Linux VM and created a small sample using dataset.to_json() method.
2. Downloaded the JSON file to my local Windows machine for working and saved in say - r"C:\Users\name\file.json"
3. I am reading the file in my local PyCharm - the location of python file is different than the location of the JSON.
4. When I read using load_dataset("json",args.input_json), it throws and error from builder.py.
raise InvalidConfigName(
f"Bad characters from black list '{invalid_windows_characters}' found in '{self.name}'. "
f"They could create issues when creating a directory for this config on Windows filesystem."
6. When I bring the data to the current directory, it works fine.
### Expected behavior
Should be able to read from a path different than current directory in Windows machine.
### Environment info
datasets version: 2.3.1
python version: 3.8
Windows OS | {
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"Hi, thanks for the feedback! Would it help to add a tip or note saying the dataset is gated and you need to accept the license before downloading it?",
"The error is now more informative:\r\n```\r\nFileNotFoundError: Couldn't find a dataset script at /content/imagenet-1k/imagenet-1k.py or any data file in the same directory. Couldn't find 'imagenet-1k' on the Hugging Face Hub either: FileNotFoundError: Dataset 'imagenet-1k' doesn't exist on the Hub. If the repo is private or gated, make sure to log in with `huggingface-cli login`.\r\n```\r\n\r\n"
] | "2023-02-23T16:44:32Z" | "2023-07-24T15:18:50Z" | "2023-07-24T15:18:50Z" | NONE | null | null | null | ### Describe the bug
When calling ```load_dataset('imagenet-1k')``` FileNotFoundError is raised, if not logged in and if logged in with huggingface-cli but not having accepted the licence on the hub. There is no error once accepting.
### Steps to reproduce the bug
```
from datasets import load_dataset
imagenet = load_dataset("imagenet-1k", split="train", streaming=True)
FileNotFoundError: Couldn't find a dataset script at /content/imagenet-1k/imagenet-1k.py or any data file in the same directory. Couldn't find 'imagenet-1k' on the Hugging Face Hub either: FileNotFoundError: Dataset 'imagenet-1k' doesn't exist on the Hub
```
tested on a colab notebook.
### Expected behavior
I would expect a specific error indicating that I have to login then accept the dataset licence.
I find this bug very relevant as this code is on a guide on the [Huggingface documentation for Datasets](https://huggingface.co/docs/datasets/about_mapstyle_vs_iterable)
### Environment info
google colab cpu-only instance | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008753 / 0.011353 (-0.002600) | 0.004877 / 0.011008 (-0.006131) | 0.098320 / 0.038508 (0.059812) | 0.034123 / 0.023109 (0.011014) | 0.289539 / 0.275898 (0.013641) | 0.323584 / 0.323480 (0.000104) | 0.007455 / 0.007986 (-0.000531) | 0.004763 / 0.004328 (0.000434) | 0.074350 / 0.004250 (0.070100) | 0.039018 / 0.037052 (0.001966) | 0.294319 / 0.258489 (0.035830) | 0.348686 / 0.293841 (0.054845) | 0.037814 / 0.128546 (-0.090732) | 0.011808 / 0.075646 (-0.063838) | 0.333808 / 0.419271 (-0.085464) | 0.047758 / 0.043533 (0.004225) | 0.298533 / 0.255139 (0.043394) | 0.320790 / 0.283200 (0.037590) | 0.095909 / 0.141683 (-0.045774) | 1.434422 / 1.452155 (-0.017732) | 1.509703 / 1.492716 (0.016987) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201728 / 0.018006 (0.183722) | 0.432243 / 0.000490 (0.431753) | 0.002760 / 0.000200 (0.002560) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026090 / 0.037411 (-0.011321) | 0.105914 / 0.014526 (0.091388) | 0.115869 / 0.176557 (-0.060688) | 0.178291 / 0.737135 (-0.558844) | 0.121435 / 0.296338 (-0.174904) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.402304 / 0.215209 (0.187095) | 3.995183 / 2.077655 (1.917529) | 1.794548 / 1.504120 (0.290428) | 1.603034 / 1.541195 (0.061839) | 1.643836 / 1.468490 (0.175346) | 0.694934 / 4.584777 (-3.889843) | 3.695128 / 3.745712 (-0.050584) | 2.018582 / 5.269862 (-3.251279) | 1.294315 / 4.565676 (-3.271362) | 0.085346 / 0.424275 (-0.338929) | 0.012201 / 0.007607 (0.004594) | 0.510057 / 0.226044 (0.284012) | 5.123404 / 2.268929 (2.854476) | 2.319089 / 55.444624 (-53.125535) | 1.930935 / 6.876477 (-4.945542) | 1.939700 / 2.142072 (-0.202372) | 0.848282 / 4.805227 (-3.956945) | 0.165561 / 6.500664 (-6.335103) | 0.062028 / 0.075469 (-0.013441) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.220576 / 1.841788 (-0.621212) | 14.413853 / 8.074308 (6.339544) | 14.027156 / 10.191392 (3.835764) | 0.170109 / 0.680424 (-0.510315) | 0.029412 / 0.534201 (-0.504789) | 0.443898 / 0.579283 (-0.135386) | 0.433059 / 0.434364 (-0.001305) | 0.533465 / 0.540337 (-0.006872) | 0.626562 / 1.386936 (-0.760374) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007148 / 0.011353 (-0.004205) | 0.005019 / 0.011008 (-0.005989) | 0.073132 / 0.038508 (0.034624) | 0.032763 / 0.023109 (0.009654) | 0.329309 / 0.275898 (0.053411) | 0.361658 / 0.323480 (0.038178) | 0.005683 / 0.007986 (-0.002302) | 0.003793 / 0.004328 (-0.000536) | 0.071858 / 0.004250 (0.067608) | 0.045160 / 0.037052 (0.008107) | 0.335852 / 0.258489 (0.077363) | 0.384274 / 0.293841 (0.090433) | 0.036647 / 0.128546 (-0.091899) | 0.012217 / 0.075646 (-0.063430) | 0.086265 / 0.419271 (-0.333007) | 0.049223 / 0.043533 (0.005690) | 0.331460 / 0.255139 (0.076321) | 0.353175 / 0.283200 (0.069975) | 0.102214 / 0.141683 (-0.039469) | 1.491451 / 1.452155 (0.039296) | 1.553702 / 1.492716 (0.060985) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222972 / 0.018006 (0.204966) | 0.432862 / 0.000490 (0.432372) | 0.000421 / 0.000200 (0.000221) | 0.000059 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028401 / 0.037411 (-0.009010) | 0.109331 / 0.014526 (0.094805) | 0.119246 / 0.176557 (-0.057311) | 0.187997 / 0.737135 (-0.549138) | 0.124212 / 0.296338 (-0.172127) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.427240 / 0.215209 (0.212031) | 4.271619 / 2.077655 (2.193964) | 2.104948 / 1.504120 (0.600828) | 1.910624 / 1.541195 (0.369430) | 1.943812 / 1.468490 (0.475322) | 0.711466 / 4.584777 (-3.873311) | 3.778987 / 3.745712 (0.033275) | 2.976258 / 5.269862 (-2.293604) | 1.807591 / 4.565676 (-2.758086) | 0.088286 / 0.424275 (-0.335989) | 0.012461 / 0.007607 (0.004854) | 0.527554 / 0.226044 (0.301509) | 5.279461 / 2.268929 (3.010532) | 2.517911 / 55.444624 (-52.926713) | 2.176557 / 6.876477 (-4.699920) | 2.205322 / 2.142072 (0.063249) | 0.855012 / 4.805227 (-3.950215) | 0.170007 / 6.500664 (-6.330658) | 0.065273 / 0.075469 (-0.010196) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.282785 / 1.841788 (-0.559003) | 14.819500 / 8.074308 (6.745192) | 13.282211 / 10.191392 (3.090819) | 0.161804 / 0.680424 (-0.518620) | 0.017615 / 0.534201 (-0.516586) | 0.420159 / 0.579283 (-0.159124) | 0.441304 / 0.434364 (0.006940) | 0.531704 / 0.540337 (-0.008634) | 0.627477 / 1.386936 (-0.759459) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b91070b9c09673e2e148eec458036ab6a62ac042 \"CML watermark\")\n",
"Hmm I think we need to add more tests. Not sure what would happen with :\r\n- decodable features that may end up decoded twice \r\n- formatted datasets \r\n\r\nI'd be in favor of reverting this until we checked those"
] | "2023-02-23T16:06:04Z" | "2023-02-24T14:06:37Z" | "2023-02-23T18:15:16Z" | CONTRIBUTOR | null | 0 | {
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"Hi ! Oh good catch. I think the features should be passed to `IterableDataset.from_generator()` in `to_iterable_dataset()` indeed.\r\n\r\nSetting this as a good first issue if someone would like to contribute, otherwise we can take care of it :)",
"#self-assign",
"seems like the feature parameter is missing from `return IterableDataset.from_generator(Dataset._iter_shards, gen_kwargs={\"shards\": shards})` hence it defaults to None."
] | "2023-02-23T13:45:33Z" | "2023-02-24T13:22:36Z" | "2023-02-24T13:22:36Z" | CONTRIBUTOR | null | null | null | ### Describe the bug
Hello,
I like the new `to_iterable_dataset` feature but I noticed something that seems to be missing.
When using `to_iterable_dataset` to transform your map style dataset into iterable dataset, you lose valuable metadata like the features.
These metadata are useful if you want to interleave iterable datasets, cast columns etc.
### Steps to reproduce the bug
```python
dataset = load_dataset("lhoestq/demo1")["train"]
print(dataset.features)
# {'id': Value(dtype='string', id=None), 'package_name': Value(dtype='string', id=None), 'review': Value(dtype='string', id=None), 'date': Value(dtype='string', id=None), 'star': Value(dtype='int64', id=None), 'version_id': Value(dtype='int64', id=None)}
dataset = dataset.to_iterable_dataset()
print(dataset.features)
# None
```
### Expected behavior
Keep the relevant information
### Environment info
datasets==2.10.0 | {
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"Hi ! I think is in the scope of this other issue: to https://github.com/huggingface/datasets/issues/5281 "
] | "2023-02-22T22:13:40Z" | "2023-02-23T11:03:29Z" | null | NONE | null | null | null | ### Feature request
Right now, we have to read the get the parquet file to the local storage. So having ability to read given the bucket directly address would be benificial
### Motivation
In a production set up, this feature can help us a lot. So we do not need move training datafiles in between storage.
### Your contribution
I am willing to help if there's anyway. | {
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https://api.github.com/repos/huggingface/datasets/issues/5565 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5565/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5565/comments | https://api.github.com/repos/huggingface/datasets/issues/5565/events | https://github.com/huggingface/datasets/pull/5565 | 1,595,281,752 | PR_kwDODunzps5KhfTH | 5,565 | Add writer_batch_size for ArrowBasedBuilder | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008745 / 0.011353 (-0.002608) | 0.004651 / 0.011008 (-0.006357) | 0.099678 / 0.038508 (0.061170) | 0.029441 / 0.023109 (0.006332) | 0.300314 / 0.275898 (0.024416) | 0.342022 / 0.323480 (0.018542) | 0.006965 / 0.007986 (-0.001021) | 0.003382 / 0.004328 (-0.000946) | 0.078195 / 0.004250 (0.073945) | 0.033308 / 0.037052 (-0.003744) | 0.300857 / 0.258489 (0.042368) | 0.356763 / 0.293841 (0.062922) | 0.033919 / 0.128546 (-0.094627) | 0.011436 / 0.075646 (-0.064210) | 0.319581 / 0.419271 (-0.099691) | 0.041303 / 0.043533 (-0.002229) | 0.299387 / 0.255139 (0.044248) | 0.327783 / 0.283200 (0.044583) | 0.087210 / 0.141683 (-0.054473) | 1.498757 / 1.452155 (0.046603) | 1.560417 / 1.492716 (0.067701) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.191806 / 0.018006 (0.173800) | 0.407044 / 0.000490 (0.406554) | 0.005116 / 0.000200 (0.004916) | 0.000073 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023760 / 0.037411 (-0.013652) | 0.096844 / 0.014526 (0.082318) | 0.104710 / 0.176557 (-0.071847) | 0.168161 / 0.737135 (-0.568974) | 0.107808 / 0.296338 (-0.188531) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417707 / 0.215209 (0.202498) | 4.155952 / 2.077655 (2.078297) | 1.864934 / 1.504120 (0.360814) | 1.654925 / 1.541195 (0.113730) | 1.731341 / 1.468490 (0.262851) | 0.692014 / 4.584777 (-3.892763) | 3.407318 / 3.745712 (-0.338394) | 3.394791 / 5.269862 (-1.875071) | 1.650429 / 4.565676 (-2.915247) | 0.082177 / 0.424275 (-0.342098) | 0.012463 / 0.007607 (0.004856) | 0.523681 / 0.226044 (0.297637) | 5.249426 / 2.268929 (2.980498) | 2.327443 / 55.444624 (-53.117181) | 1.982160 / 6.876477 (-4.894317) | 2.019822 / 2.142072 (-0.122250) | 0.804820 / 4.805227 (-4.000408) | 0.148423 / 6.500664 (-6.352241) | 0.064938 / 0.075469 (-0.010531) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.225722 / 1.841788 (-0.616066) | 13.774257 / 8.074308 (5.699949) | 14.090298 / 10.191392 (3.898906) | 0.152489 / 0.680424 (-0.527935) | 0.028595 / 0.534201 (-0.505606) | 0.399011 / 0.579283 (-0.180272) | 0.399546 / 0.434364 (-0.034818) | 0.485513 / 0.540337 (-0.054824) | 0.564055 / 1.386936 (-0.822881) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006891 / 0.011353 (-0.004462) | 0.004557 / 0.011008 (-0.006451) | 0.077868 / 0.038508 (0.039360) | 0.028767 / 0.023109 (0.005657) | 0.344127 / 0.275898 (0.068229) | 0.377097 / 0.323480 (0.053617) | 0.005119 / 0.007986 (-0.002866) | 0.003547 / 0.004328 (-0.000782) | 0.077047 / 0.004250 (0.072796) | 0.043037 / 0.037052 (0.005984) | 0.341900 / 0.258489 (0.083410) | 0.384570 / 0.293841 (0.090729) | 0.032606 / 0.128546 (-0.095940) | 0.011752 / 0.075646 (-0.063894) | 0.086731 / 0.419271 (-0.332540) | 0.045459 / 0.043533 (0.001926) | 0.339308 / 0.255139 (0.084169) | 0.370498 / 0.283200 (0.087298) | 0.096237 / 0.141683 (-0.045446) | 1.499253 / 1.452155 (0.047098) | 1.583871 / 1.492716 (0.091154) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.245471 / 0.018006 (0.227465) | 0.408750 / 0.000490 (0.408260) | 0.008992 / 0.000200 (0.008792) | 0.000249 / 0.000054 (0.000194) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025508 / 0.037411 (-0.011903) | 0.102103 / 0.014526 (0.087578) | 0.109247 / 0.176557 (-0.067310) | 0.176369 / 0.737135 (-0.560766) | 0.111241 / 0.296338 (-0.185097) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437209 / 0.215209 (0.222000) | 4.354386 / 2.077655 (2.276731) | 2.064008 / 1.504120 (0.559888) | 1.855518 / 1.541195 (0.314323) | 1.931647 / 1.468490 (0.463157) | 0.704913 / 4.584777 (-3.879864) | 3.397913 / 3.745712 (-0.347800) | 1.871524 / 5.269862 (-3.398338) | 1.176492 / 4.565676 (-3.389185) | 0.083976 / 0.424275 (-0.340299) | 0.012806 / 0.007607 (0.005199) | 0.539138 / 0.226044 (0.313094) | 5.401493 / 2.268929 (3.132564) | 2.539185 / 55.444624 (-52.905440) | 2.186445 / 6.876477 (-4.690031) | 2.222170 / 2.142072 (0.080097) | 0.815641 / 4.805227 (-3.989586) | 0.153033 / 6.500664 (-6.347631) | 0.069168 / 0.075469 (-0.006301) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.283530 / 1.841788 (-0.558258) | 14.075831 / 8.074308 (6.001523) | 13.649137 / 10.191392 (3.457745) | 0.127517 / 0.680424 (-0.552907) | 0.016619 / 0.534201 (-0.517582) | 0.377400 / 0.579283 (-0.201883) | 0.410796 / 0.434364 (-0.023568) | 0.463996 / 0.540337 (-0.076342) | 0.551867 / 1.386936 (-0.835069) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1135285d80ff9cd65fc51905f08343b4d7c2fa9c \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009161 / 0.011353 (-0.002192) | 0.004987 / 0.011008 (-0.006022) | 0.098553 / 0.038508 (0.060045) | 0.034326 / 0.023109 (0.011216) | 0.295325 / 0.275898 (0.019427) | 0.326361 / 0.323480 (0.002881) | 0.007827 / 0.007986 (-0.000159) | 0.004933 / 0.004328 (0.000604) | 0.074236 / 0.004250 (0.069986) | 0.040410 / 0.037052 (0.003357) | 0.295644 / 0.258489 (0.037155) | 0.355050 / 0.293841 (0.061209) | 0.038384 / 0.128546 (-0.090162) | 0.011845 / 0.075646 (-0.063801) | 0.340678 / 0.419271 (-0.078594) | 0.047615 / 0.043533 (0.004082) | 0.292429 / 0.255139 (0.037290) | 0.312610 / 0.283200 (0.029410) | 0.100106 / 0.141683 (-0.041577) | 1.446186 / 1.452155 (-0.005969) | 1.534763 / 1.492716 (0.042046) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.213667 / 0.018006 (0.195661) | 0.447310 / 0.000490 (0.446820) | 0.000402 / 0.000200 (0.000202) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027604 / 0.037411 (-0.009807) | 0.112785 / 0.014526 (0.098259) | 0.119450 / 0.176557 (-0.057106) | 0.185728 / 0.737135 (-0.551407) | 0.122860 / 0.296338 (-0.173478) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.399162 / 0.215209 (0.183953) | 3.992701 / 2.077655 (1.915046) | 1.773881 / 1.504120 (0.269761) | 1.589842 / 1.541195 (0.048647) | 1.670065 / 1.468490 (0.201575) | 0.707669 / 4.584777 (-3.877107) | 3.719657 / 3.745712 (-0.026055) | 2.139629 / 5.269862 (-3.130232) | 1.467224 / 4.565676 (-3.098453) | 0.086033 / 0.424275 (-0.338242) | 0.012151 / 0.007607 (0.004544) | 0.519700 / 0.226044 (0.293656) | 5.150254 / 2.268929 (2.881325) | 2.305076 / 55.444624 (-53.139548) | 1.927914 / 6.876477 (-4.948563) | 1.999461 / 2.142072 (-0.142612) | 0.851819 / 4.805227 (-3.953408) | 0.165513 / 6.500664 (-6.335151) | 0.061898 / 0.075469 (-0.013571) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.226251 / 1.841788 (-0.615536) | 14.990253 / 8.074308 (6.915945) | 14.658720 / 10.191392 (4.467328) | 0.191665 / 0.680424 (-0.488759) | 0.028768 / 0.534201 (-0.505433) | 0.443907 / 0.579283 (-0.135376) | 0.455183 / 0.434364 (0.020819) | 0.552760 / 0.540337 (0.012422) | 0.653927 / 1.386936 (-0.733009) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007677 / 0.011353 (-0.003675) | 0.005340 / 0.011008 (-0.005668) | 0.075644 / 0.038508 (0.037136) | 0.035046 / 0.023109 (0.011937) | 0.341437 / 0.275898 (0.065538) | 0.377782 / 0.323480 (0.054302) | 0.006091 / 0.007986 (-0.001895) | 0.004170 / 0.004328 (-0.000158) | 0.074294 / 0.004250 (0.070044) | 0.049851 / 0.037052 (0.012798) | 0.351691 / 0.258489 (0.093202) | 0.386020 / 0.293841 (0.092179) | 0.036884 / 0.128546 (-0.091662) | 0.012475 / 0.075646 (-0.063172) | 0.087267 / 0.419271 (-0.332005) | 0.058623 / 0.043533 (0.015090) | 0.347186 / 0.255139 (0.092047) | 0.355869 / 0.283200 (0.072669) | 0.112022 / 0.141683 (-0.029661) | 1.451798 / 1.452155 (-0.000357) | 1.553262 / 1.492716 (0.060546) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.233451 / 0.018006 (0.215445) | 0.444384 / 0.000490 (0.443895) | 0.003695 / 0.000200 (0.003495) | 0.000088 / 0.000054 (0.000034) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029686 / 0.037411 (-0.007725) | 0.113736 / 0.014526 (0.099210) | 0.123998 / 0.176557 (-0.052559) | 0.197847 / 0.737135 (-0.539288) | 0.129936 / 0.296338 (-0.166403) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.421904 / 0.215209 (0.206695) | 4.203533 / 2.077655 (2.125878) | 2.038199 / 1.504120 (0.534079) | 1.832402 / 1.541195 (0.291208) | 1.930765 / 1.468490 (0.462274) | 0.709775 / 4.584777 (-3.875002) | 3.760893 / 3.745712 (0.015181) | 2.091185 / 5.269862 (-3.178677) | 1.342248 / 4.565676 (-3.223428) | 0.087770 / 0.424275 (-0.336505) | 0.012357 / 0.007607 (0.004750) | 0.519605 / 0.226044 (0.293560) | 5.215883 / 2.268929 (2.946954) | 2.510200 / 55.444624 (-52.934425) | 2.192482 / 6.876477 (-4.683995) | 2.290214 / 2.142072 (0.148141) | 0.872067 / 4.805227 (-3.933160) | 0.168491 / 6.500664 (-6.332173) | 0.064707 / 0.075469 (-0.010762) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.291956 / 1.841788 (-0.549832) | 15.244530 / 8.074308 (7.170222) | 13.594895 / 10.191392 (3.403503) | 0.172669 / 0.680424 (-0.507755) | 0.017765 / 0.534201 (-0.516436) | 0.426946 / 0.579283 (-0.152337) | 0.442843 / 0.434364 (0.008479) | 0.549683 / 0.540337 (0.009346) | 0.653433 / 1.386936 (-0.733503) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b54a6d21795cf6cc50a13ff870648241a60fd2e0 \"CML watermark\")\n",
"Can you review this @mariosasko ? since Albert is off",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008396 / 0.011353 (-0.002957) | 0.004556 / 0.011008 (-0.006452) | 0.101343 / 0.038508 (0.062835) | 0.029137 / 0.023109 (0.006027) | 0.298553 / 0.275898 (0.022655) | 0.334050 / 0.323480 (0.010570) | 0.006746 / 0.007986 (-0.001239) | 0.005050 / 0.004328 (0.000721) | 0.076055 / 0.004250 (0.071804) | 0.031988 / 0.037052 (-0.005064) | 0.301324 / 0.258489 (0.042835) | 0.340121 / 0.293841 (0.046280) | 0.033827 / 0.128546 (-0.094720) | 0.011447 / 0.075646 (-0.064200) | 0.321827 / 0.419271 (-0.097445) | 0.040846 / 0.043533 (-0.002687) | 0.296957 / 0.255139 (0.041818) | 0.324178 / 0.283200 (0.040979) | 0.083852 / 0.141683 (-0.057831) | 1.456123 / 1.452155 (0.003968) | 1.538311 / 1.492716 (0.045595) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208897 / 0.018006 (0.190891) | 0.430560 / 0.000490 (0.430070) | 0.002917 / 0.000200 (0.002717) | 0.000077 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024332 / 0.037411 (-0.013080) | 0.101659 / 0.014526 (0.087133) | 0.107636 / 0.176557 (-0.068920) | 0.168805 / 0.737135 (-0.568330) | 0.111404 / 0.296338 (-0.184934) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.412704 / 0.215209 (0.197495) | 4.124852 / 2.077655 (2.047197) | 1.843555 / 1.504120 (0.339435) | 1.641636 / 1.541195 (0.100441) | 1.755783 / 1.468490 (0.287293) | 0.693212 / 4.584777 (-3.891565) | 3.391803 / 3.745712 (-0.353909) | 1.954473 / 5.269862 (-3.315389) | 1.274395 / 4.565676 (-3.291282) | 0.082536 / 0.424275 (-0.341739) | 0.012335 / 0.007607 (0.004728) | 0.523720 / 0.226044 (0.297676) | 5.268339 / 2.268929 (2.999411) | 2.318163 / 55.444624 (-53.126461) | 1.978503 / 6.876477 (-4.897974) | 2.046689 / 2.142072 (-0.095384) | 0.806735 / 4.805227 (-3.998492) | 0.148010 / 6.500664 (-6.352654) | 0.065305 / 0.075469 (-0.010164) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.266950 / 1.841788 (-0.574838) | 13.870803 / 8.074308 (5.796495) | 14.272556 / 10.191392 (4.081164) | 0.151703 / 0.680424 (-0.528720) | 0.028991 / 0.534201 (-0.505210) | 0.400831 / 0.579283 (-0.178452) | 0.400891 / 0.434364 (-0.033473) | 0.476225 / 0.540337 (-0.064113) | 0.564925 / 1.386936 (-0.822011) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006810 / 0.011353 (-0.004543) | 0.004544 / 0.011008 (-0.006464) | 0.076516 / 0.038508 (0.038008) | 0.027705 / 0.023109 (0.004596) | 0.343215 / 0.275898 (0.067317) | 0.379136 / 0.323480 (0.055656) | 0.005227 / 0.007986 (-0.002758) | 0.003527 / 0.004328 (-0.000801) | 0.074775 / 0.004250 (0.070524) | 0.041700 / 0.037052 (0.004648) | 0.343612 / 0.258489 (0.085123) | 0.385657 / 0.293841 (0.091817) | 0.032082 / 0.128546 (-0.096464) | 0.011567 / 0.075646 (-0.064079) | 0.083814 / 0.419271 (-0.335458) | 0.042173 / 0.043533 (-0.001360) | 0.340261 / 0.255139 (0.085122) | 0.364778 / 0.283200 (0.081578) | 0.093401 / 0.141683 (-0.048282) | 1.513475 / 1.452155 (0.061320) | 1.599393 / 1.492716 (0.106677) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237117 / 0.018006 (0.219111) | 0.424241 / 0.000490 (0.423751) | 0.002900 / 0.000200 (0.002700) | 0.000076 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031122 / 0.037411 (-0.006289) | 0.107530 / 0.014526 (0.093004) | 0.117777 / 0.176557 (-0.058780) | 0.188300 / 0.737135 (-0.548836) | 0.119989 / 0.296338 (-0.176349) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.438563 / 0.215209 (0.223354) | 4.404969 / 2.077655 (2.327315) | 2.260182 / 1.504120 (0.756062) | 2.035472 / 1.541195 (0.494277) | 2.045685 / 1.468490 (0.577195) | 0.706758 / 4.584777 (-3.878019) | 3.434843 / 3.745712 (-0.310869) | 1.909533 / 5.269862 (-3.360328) | 1.175374 / 4.565676 (-3.390303) | 0.084831 / 0.424275 (-0.339444) | 0.012441 / 0.007607 (0.004833) | 0.551818 / 0.226044 (0.325774) | 5.509005 / 2.268929 (3.240077) | 2.576545 / 55.444624 (-52.868080) | 2.226204 / 6.876477 (-4.650272) | 2.276544 / 2.142072 (0.134471) | 0.818069 / 4.805227 (-3.987158) | 0.152797 / 6.500664 (-6.347867) | 0.067896 / 0.075469 (-0.007573) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.276859 / 1.841788 (-0.564929) | 14.312914 / 8.074308 (6.238606) | 13.406602 / 10.191392 (3.215210) | 0.157466 / 0.680424 (-0.522958) | 0.016709 / 0.534201 (-0.517492) | 0.390951 / 0.579283 (-0.188333) | 0.395525 / 0.434364 (-0.038839) | 0.484486 / 0.540337 (-0.055852) | 0.576125 / 1.386936 (-0.810811) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b951e1b6cdd927604599f1aa5dadfb8ee8e62e05 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007316 / 0.011353 (-0.004037) | 0.005041 / 0.011008 (-0.005968) | 0.100477 / 0.038508 (0.061969) | 0.034068 / 0.023109 (0.010959) | 0.351156 / 0.275898 (0.075258) | 0.373892 / 0.323480 (0.050412) | 0.005748 / 0.007986 (-0.002237) | 0.003959 / 0.004328 (-0.000370) | 0.075540 / 0.004250 (0.071290) | 0.045282 / 0.037052 (0.008230) | 0.362364 / 0.258489 (0.103874) | 0.376461 / 0.293841 (0.082620) | 0.036724 / 0.128546 (-0.091822) | 0.012008 / 0.075646 (-0.063638) | 0.333802 / 0.419271 (-0.085470) | 0.050107 / 0.043533 (0.006574) | 0.348003 / 0.255139 (0.092864) | 0.367187 / 0.283200 (0.083988) | 0.103171 / 0.141683 (-0.038511) | 1.448281 / 1.452155 (-0.003874) | 1.516231 / 1.492716 (0.023514) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203651 / 0.018006 (0.185645) | 0.438103 / 0.000490 (0.437613) | 0.004165 / 0.000200 (0.003966) | 0.000085 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027068 / 0.037411 (-0.010343) | 0.111728 / 0.014526 (0.097202) | 0.116963 / 0.176557 (-0.059594) | 0.172652 / 0.737135 (-0.564483) | 0.124257 / 0.296338 (-0.172082) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.407937 / 0.215209 (0.192728) | 4.066008 / 2.077655 (1.988353) | 1.895000 / 1.504120 (0.390880) | 1.698422 / 1.541195 (0.157227) | 1.872446 / 1.468490 (0.403956) | 0.688888 / 4.584777 (-3.895889) | 3.743635 / 3.745712 (-0.002077) | 2.161507 / 5.269862 (-3.108354) | 1.485218 / 4.565676 (-3.080458) | 0.085959 / 0.424275 (-0.338316) | 0.012554 / 0.007607 (0.004947) | 0.510953 / 0.226044 (0.284909) | 5.103241 / 2.268929 (2.834312) | 2.439670 / 55.444624 (-53.004955) | 2.057089 / 6.876477 (-4.819387) | 2.240137 / 2.142072 (0.098065) | 0.847750 / 4.805227 (-3.957477) | 0.172952 / 6.500664 (-6.327712) | 0.066023 / 0.075469 (-0.009446) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.190677 / 1.841788 (-0.651110) | 14.593162 / 8.074308 (6.518854) | 14.254983 / 10.191392 (4.063591) | 0.155811 / 0.680424 (-0.524613) | 0.017698 / 0.534201 (-0.516503) | 0.420455 / 0.579283 (-0.158828) | 0.412146 / 0.434364 (-0.022218) | 0.493113 / 0.540337 (-0.047225) | 0.582097 / 1.386936 (-0.804839) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007319 / 0.011353 (-0.004033) | 0.005102 / 0.011008 (-0.005906) | 0.073760 / 0.038508 (0.035252) | 0.033496 / 0.023109 (0.010387) | 0.338778 / 0.275898 (0.062880) | 0.371870 / 0.323480 (0.048391) | 0.005804 / 0.007986 (-0.002182) | 0.004142 / 0.004328 (-0.000186) | 0.073203 / 0.004250 (0.068953) | 0.046568 / 0.037052 (0.009516) | 0.343544 / 0.258489 (0.085055) | 0.381188 / 0.293841 (0.087347) | 0.036391 / 0.128546 (-0.092155) | 0.012046 / 0.075646 (-0.063600) | 0.086007 / 0.419271 (-0.333265) | 0.048706 / 0.043533 (0.005173) | 0.330836 / 0.255139 (0.075697) | 0.355328 / 0.283200 (0.072128) | 0.100104 / 0.141683 (-0.041579) | 1.434237 / 1.452155 (-0.017917) | 1.549380 / 1.492716 (0.056663) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231099 / 0.018006 (0.213093) | 0.450650 / 0.000490 (0.450160) | 0.000404 / 0.000200 (0.000204) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030534 / 0.037411 (-0.006877) | 0.119005 / 0.014526 (0.104479) | 0.125362 / 0.176557 (-0.051195) | 0.176823 / 0.737135 (-0.560313) | 0.132044 / 0.296338 (-0.164295) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431004 / 0.215209 (0.215795) | 4.318969 / 2.077655 (2.241315) | 1.994941 / 1.504120 (0.490821) | 1.791870 / 1.541195 (0.250675) | 1.904134 / 1.468490 (0.435644) | 0.723493 / 4.584777 (-3.861284) | 3.823670 / 3.745712 (0.077958) | 2.118892 / 5.269862 (-3.150969) | 1.375088 / 4.565676 (-3.190588) | 0.088875 / 0.424275 (-0.335400) | 0.013137 / 0.007607 (0.005530) | 0.530523 / 0.226044 (0.304479) | 5.341438 / 2.268929 (3.072509) | 2.459044 / 55.444624 (-52.985580) | 2.150119 / 6.876477 (-4.726357) | 2.228567 / 2.142072 (0.086494) | 0.877549 / 4.805227 (-3.927678) | 0.175040 / 6.500664 (-6.325625) | 0.068188 / 0.075469 (-0.007281) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.273780 / 1.841788 (-0.568008) | 15.206331 / 8.074308 (7.132023) | 14.963058 / 10.191392 (4.771666) | 0.184543 / 0.680424 (-0.495881) | 0.017612 / 0.534201 (-0.516589) | 0.426248 / 0.579283 (-0.153035) | 0.437889 / 0.434364 (0.003525) | 0.508979 / 0.540337 (-0.031359) | 0.602040 / 1.386936 (-0.784896) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c5ca1d86949ec3a5fdaec03b80500fb822bcfab4 \"CML watermark\")\n"
] | "2023-02-22T15:09:30Z" | "2023-03-10T13:53:03Z" | "2023-03-10T13:45:43Z" | MEMBER | null | 0 | {
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} | This way we can control the size of the record_batches/row_groups of arrow/parquet files.
This can be useful for `datasets-server` to keep control of the row groups size which can affect random access performance for audio/image/video datasets
Right now having 1,000 examples per row group cause some image datasets to be pretty slow for random access (e.g. 4seconds for `beans` to get 20 rows) | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5564). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008810 / 0.011353 (-0.002543) | 0.004583 / 0.011008 (-0.006425) | 0.100787 / 0.038508 (0.062279) | 0.030170 / 0.023109 (0.007061) | 0.301749 / 0.275898 (0.025851) | 0.386958 / 0.323480 (0.063478) | 0.007211 / 0.007986 (-0.000775) | 0.004939 / 0.004328 (0.000611) | 0.078046 / 0.004250 (0.073796) | 0.035672 / 0.037052 (-0.001380) | 0.314403 / 0.258489 (0.055914) | 0.348547 / 0.293841 (0.054706) | 0.034242 / 0.128546 (-0.094304) | 0.011599 / 0.075646 (-0.064047) | 0.321936 / 0.419271 (-0.097336) | 0.043214 / 0.043533 (-0.000319) | 0.298782 / 0.255139 (0.043643) | 0.334513 / 0.283200 (0.051313) | 0.091630 / 0.141683 (-0.050053) | 1.518194 / 1.452155 (0.066039) | 1.553665 / 1.492716 (0.060949) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.196322 / 0.018006 (0.178316) | 0.427280 / 0.000490 (0.426790) | 0.001933 / 0.000200 (0.001733) | 0.000069 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023190 / 0.037411 (-0.014221) | 0.097387 / 0.014526 (0.082862) | 0.104532 / 0.176557 (-0.072024) | 0.166670 / 0.737135 (-0.570465) | 0.108787 / 0.296338 (-0.187552) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.415776 / 0.215209 (0.200567) | 4.135899 / 2.077655 (2.058244) | 1.857600 / 1.504120 (0.353480) | 1.654099 / 1.541195 (0.112904) | 1.729102 / 1.468490 (0.260612) | 0.695946 / 4.584777 (-3.888831) | 3.352776 / 3.745712 (-0.392936) | 2.754443 / 5.269862 (-2.515418) | 1.517181 / 4.565676 (-3.048495) | 0.082782 / 0.424275 (-0.341493) | 0.012431 / 0.007607 (0.004824) | 0.526593 / 0.226044 (0.300548) | 5.263051 / 2.268929 (2.994123) | 2.290713 / 55.444624 (-53.153911) | 1.953017 / 6.876477 (-4.923460) | 1.998419 / 2.142072 (-0.143653) | 0.817055 / 4.805227 (-3.988173) | 0.148213 / 6.500664 (-6.352451) | 0.065527 / 0.075469 (-0.009942) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254275 / 1.841788 (-0.587513) | 13.618962 / 8.074308 (5.544654) | 14.057134 / 10.191392 (3.865742) | 0.137180 / 0.680424 (-0.543244) | 0.028460 / 0.534201 (-0.505741) | 0.393836 / 0.579283 (-0.185447) | 0.406665 / 0.434364 (-0.027699) | 0.476812 / 0.540337 (-0.063526) | 0.561047 / 1.386936 (-0.825889) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006483 / 0.011353 (-0.004870) | 0.004525 / 0.011008 (-0.006483) | 0.075696 / 0.038508 (0.037188) | 0.027306 / 0.023109 (0.004197) | 0.359141 / 0.275898 (0.083243) | 0.394595 / 0.323480 (0.071115) | 0.004907 / 0.007986 (-0.003079) | 0.003403 / 0.004328 (-0.000925) | 0.074473 / 0.004250 (0.070223) | 0.037801 / 0.037052 (0.000749) | 0.359350 / 0.258489 (0.100861) | 0.411902 / 0.293841 (0.118061) | 0.032280 / 0.128546 (-0.096267) | 0.011728 / 0.075646 (-0.063918) | 0.085692 / 0.419271 (-0.333580) | 0.047779 / 0.043533 (0.004246) | 0.348820 / 0.255139 (0.093681) | 0.389396 / 0.283200 (0.106197) | 0.094923 / 0.141683 (-0.046760) | 1.507137 / 1.452155 (0.054982) | 1.556873 / 1.492716 (0.064157) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.197510 / 0.018006 (0.179504) | 0.413885 / 0.000490 (0.413395) | 0.002527 / 0.000200 (0.002327) | 0.000073 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024571 / 0.037411 (-0.012840) | 0.099845 / 0.014526 (0.085319) | 0.108130 / 0.176557 (-0.068426) | 0.176153 / 0.737135 (-0.560982) | 0.111907 / 0.296338 (-0.184432) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436393 / 0.215209 (0.221184) | 4.343296 / 2.077655 (2.265642) | 2.056062 / 1.504120 (0.551942) | 1.855372 / 1.541195 (0.314177) | 1.946429 / 1.468490 (0.477939) | 0.701862 / 4.584777 (-3.882915) | 3.337115 / 3.745712 (-0.408597) | 2.755416 / 5.269862 (-2.514446) | 1.335596 / 4.565676 (-3.230081) | 0.083938 / 0.424275 (-0.340337) | 0.012914 / 0.007607 (0.005307) | 0.530272 / 0.226044 (0.304228) | 5.307739 / 2.268929 (3.038810) | 2.506435 / 55.444624 (-52.938189) | 2.170830 / 6.876477 (-4.705646) | 2.224641 / 2.142072 (0.082568) | 0.804416 / 4.805227 (-4.000811) | 0.151594 / 6.500664 (-6.349070) | 0.067221 / 0.075469 (-0.008248) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.257063 / 1.841788 (-0.584725) | 14.054346 / 8.074308 (5.980038) | 13.490649 / 10.191392 (3.299257) | 0.139320 / 0.680424 (-0.541104) | 0.016501 / 0.534201 (-0.517700) | 0.382655 / 0.579283 (-0.196629) | 0.383305 / 0.434364 (-0.051059) | 0.465091 / 0.540337 (-0.075247) | 0.552552 / 1.386936 (-0.834384) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c480083958126c40bb7bdba8e1eeb3945a8fe6ea \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011278 / 0.011353 (-0.000075) | 0.007351 / 0.011008 (-0.003657) | 0.131145 / 0.038508 (0.092637) | 0.041585 / 0.023109 (0.018476) | 0.410230 / 0.275898 (0.134332) | 0.464069 / 0.323480 (0.140589) | 0.010228 / 0.007986 (0.002242) | 0.005324 / 0.004328 (0.000996) | 0.102680 / 0.004250 (0.098430) | 0.041644 / 0.037052 (0.004592) | 0.439127 / 0.258489 (0.180638) | 0.467828 / 0.293841 (0.173987) | 0.054373 / 0.128546 (-0.074173) | 0.019495 / 0.075646 (-0.056152) | 0.432425 / 0.419271 (0.013153) | 0.056863 / 0.043533 (0.013331) | 0.405883 / 0.255139 (0.150744) | 0.452786 / 0.283200 (0.169586) | 0.109888 / 0.141683 (-0.031795) | 1.797015 / 1.452155 (0.344860) | 1.985937 / 1.492716 (0.493221) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.275121 / 0.018006 (0.257115) | 0.587585 / 0.000490 (0.587095) | 0.005557 / 0.000200 (0.005357) | 0.000118 / 0.000054 (0.000064) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032968 / 0.037411 (-0.004443) | 0.135886 / 0.014526 (0.121360) | 0.154000 / 0.176557 (-0.022557) | 0.233345 / 0.737135 (-0.503790) | 0.144125 / 0.296338 (-0.152214) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.613056 / 0.215209 (0.397847) | 6.206135 / 2.077655 (4.128480) | 2.686989 / 1.504120 (1.182869) | 2.389946 / 1.541195 (0.848751) | 2.437506 / 1.468490 (0.969016) | 1.255900 / 4.584777 (-3.328877) | 5.654803 / 3.745712 (1.909091) | 5.467693 / 5.269862 (0.197832) | 2.872397 / 4.565676 (-1.693279) | 0.145658 / 0.424275 (-0.278617) | 0.016883 / 0.007607 (0.009276) | 0.793820 / 0.226044 (0.567775) | 7.961881 / 2.268929 (5.692952) | 3.617422 / 55.444624 (-51.827203) | 2.795185 / 6.876477 (-4.081292) | 2.881726 / 2.142072 (0.739653) | 1.434543 / 4.805227 (-3.370685) | 0.252206 / 6.500664 (-6.248458) | 0.094694 / 0.075469 (0.019225) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.552401 / 1.841788 (-0.289386) | 18.436068 / 8.074308 (10.361760) | 22.539049 / 10.191392 (12.347657) | 0.269471 / 0.680424 (-0.410953) | 0.053242 / 0.534201 (-0.480959) | 0.568325 / 0.579283 (-0.010958) | 0.660339 / 0.434364 (0.225975) | 0.689507 / 0.540337 (0.149169) | 0.836785 / 1.386936 (-0.550151) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009853 / 0.011353 (-0.001500) | 0.009752 / 0.011008 (-0.001256) | 0.095422 / 0.038508 (0.056914) | 0.037760 / 0.023109 (0.014651) | 0.450898 / 0.275898 (0.175000) | 0.501671 / 0.323480 (0.178191) | 0.006748 / 0.007986 (-0.001237) | 0.005054 / 0.004328 (0.000725) | 0.099382 / 0.004250 (0.095131) | 0.058078 / 0.037052 (0.021026) | 0.447606 / 0.258489 (0.189116) | 0.503887 / 0.293841 (0.210046) | 0.054579 / 0.128546 (-0.073967) | 0.026150 / 0.075646 (-0.049496) | 0.113042 / 0.419271 (-0.306230) | 0.061049 / 0.043533 (0.017516) | 0.437831 / 0.255139 (0.182692) | 0.480830 / 0.283200 (0.197630) | 0.121199 / 0.141683 (-0.020484) | 1.795409 / 1.452155 (0.343254) | 1.911207 / 1.492716 (0.418491) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.311774 / 0.018006 (0.293768) | 0.602027 / 0.000490 (0.601537) | 0.000651 / 0.000200 (0.000451) | 0.000136 / 0.000054 (0.000081) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035185 / 0.037411 (-0.002227) | 0.149574 / 0.014526 (0.135048) | 0.153672 / 0.176557 (-0.022884) | 0.241720 / 0.737135 (-0.495416) | 0.153543 / 0.296338 (-0.142795) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.678508 / 0.215209 (0.463299) | 6.535313 / 2.077655 (4.457658) | 2.840175 / 1.504120 (1.336055) | 2.458141 / 1.541195 (0.916947) | 2.551369 / 1.468490 (1.082879) | 1.339117 / 4.584777 (-3.245660) | 5.844429 / 3.745712 (2.098717) | 3.221100 / 5.269862 (-2.048762) | 2.114844 / 4.565676 (-2.450833) | 0.149263 / 0.424275 (-0.275012) | 0.016101 / 0.007607 (0.008494) | 0.830650 / 0.226044 (0.604605) | 8.096655 / 2.268929 (5.827727) | 3.445947 / 55.444624 (-51.998677) | 2.826874 / 6.876477 (-4.049603) | 2.812765 / 2.142072 (0.670693) | 1.453789 / 4.805227 (-3.351438) | 0.263911 / 6.500664 (-6.236753) | 0.082609 / 0.075469 (0.007139) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.651624 / 1.841788 (-0.190163) | 18.703020 / 8.074308 (10.628712) | 21.360445 / 10.191392 (11.169053) | 0.249718 / 0.680424 (-0.430706) | 0.028373 / 0.534201 (-0.505828) | 0.576237 / 0.579283 (-0.003046) | 0.620574 / 0.434364 (0.186210) | 0.684155 / 0.540337 (0.143817) | 0.758950 / 1.386936 (-0.627986) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f51ef325602bb297a18a75680575cbe9b940b1d9 \"CML watermark\")\n"
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009437 / 0.011353 (-0.001916) | 0.004999 / 0.011008 (-0.006010) | 0.098839 / 0.038508 (0.060331) | 0.035496 / 0.023109 (0.012386) | 0.300726 / 0.275898 (0.024828) | 0.359793 / 0.323480 (0.036313) | 0.007694 / 0.007986 (-0.000292) | 0.003980 / 0.004328 (-0.000348) | 0.075240 / 0.004250 (0.070989) | 0.041149 / 0.037052 (0.004097) | 0.313185 / 0.258489 (0.054696) | 0.344111 / 0.293841 (0.050270) | 0.037775 / 0.128546 (-0.090772) | 0.011901 / 0.075646 (-0.063745) | 0.332631 / 0.419271 (-0.086641) | 0.047194 / 0.043533 (0.003661) | 0.306902 / 0.255139 (0.051763) | 0.321725 / 0.283200 (0.038525) | 0.101031 / 0.141683 (-0.040652) | 1.458778 / 1.452155 (0.006623) | 1.530196 / 1.492716 (0.037480) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203241 / 0.018006 (0.185235) | 0.447147 / 0.000490 (0.446657) | 0.004159 / 0.000200 (0.003959) | 0.000131 / 0.000054 (0.000076) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025845 / 0.037411 (-0.011566) | 0.106966 / 0.014526 (0.092440) | 0.115876 / 0.176557 (-0.060681) | 0.179052 / 0.737135 (-0.558084) | 0.123012 / 0.296338 (-0.173327) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.408766 / 0.215209 (0.193557) | 4.080400 / 2.077655 (2.002745) | 1.893747 / 1.504120 (0.389627) | 1.709389 / 1.541195 (0.168194) | 1.768071 / 1.468490 (0.299581) | 0.689717 / 4.584777 (-3.895059) | 3.760897 / 3.745712 (0.015185) | 2.017050 / 5.269862 (-3.252811) | 1.333027 / 4.565676 (-3.232650) | 0.083559 / 0.424275 (-0.340716) | 0.011951 / 0.007607 (0.004344) | 0.512313 / 0.226044 (0.286268) | 5.162696 / 2.268929 (2.893767) | 2.418559 / 55.444624 (-53.026065) | 2.110178 / 6.876477 (-4.766299) | 2.113635 / 2.142072 (-0.028437) | 0.835171 / 4.805227 (-3.970056) | 0.164222 / 6.500664 (-6.336442) | 0.061955 / 0.075469 (-0.013515) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.198336 / 1.841788 (-0.643452) | 14.531468 / 8.074308 (6.457160) | 13.882133 / 10.191392 (3.690741) | 0.154524 / 0.680424 (-0.525900) | 0.028782 / 0.534201 (-0.505419) | 0.441808 / 0.579283 (-0.137475) | 0.433096 / 0.434364 (-0.001268) | 0.518229 / 0.540337 (-0.022108) | 0.603201 / 1.386936 (-0.783735) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007385 / 0.011353 (-0.003967) | 0.005193 / 0.011008 (-0.005815) | 0.075517 / 0.038508 (0.037009) | 0.033192 / 0.023109 (0.010083) | 0.332299 / 0.275898 (0.056401) | 0.363043 / 0.323480 (0.039563) | 0.006368 / 0.007986 (-0.001617) | 0.004003 / 0.004328 (-0.000326) | 0.073710 / 0.004250 (0.069460) | 0.046916 / 0.037052 (0.009863) | 0.336307 / 0.258489 (0.077818) | 0.384910 / 0.293841 (0.091069) | 0.038132 / 0.128546 (-0.090414) | 0.012283 / 0.075646 (-0.063364) | 0.088036 / 0.419271 (-0.331235) | 0.049699 / 0.043533 (0.006166) | 0.333953 / 0.255139 (0.078814) | 0.352961 / 0.283200 (0.069762) | 0.101905 / 0.141683 (-0.039778) | 1.470480 / 1.452155 (0.018325) | 1.498212 / 1.492716 (0.005496) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.275067 / 0.018006 (0.257061) | 0.452589 / 0.000490 (0.452099) | 0.047067 / 0.000200 (0.046867) | 0.000983 / 0.000054 (0.000929) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028649 / 0.037411 (-0.008762) | 0.108385 / 0.014526 (0.093859) | 0.121213 / 0.176557 (-0.055343) | 0.192236 / 0.737135 (-0.544899) | 0.124620 / 0.296338 (-0.171719) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428742 / 0.215209 (0.213533) | 4.264893 / 2.077655 (2.187238) | 2.061650 / 1.504120 (0.557530) | 1.873267 / 1.541195 (0.332072) | 1.961012 / 1.468490 (0.492522) | 0.708904 / 4.584777 (-3.875873) | 3.821289 / 3.745712 (0.075577) | 3.287231 / 5.269862 (-1.982631) | 1.903539 / 4.565676 (-2.662137) | 0.086474 / 0.424275 (-0.337801) | 0.012101 / 0.007607 (0.004494) | 0.531411 / 0.226044 (0.305367) | 5.216785 / 2.268929 (2.947857) | 2.575209 / 55.444624 (-52.869416) | 2.264902 / 6.876477 (-4.611574) | 2.291225 / 2.142072 (0.149153) | 0.853486 / 4.805227 (-3.951741) | 0.168550 / 6.500664 (-6.332114) | 0.064158 / 0.075469 (-0.011311) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.295830 / 1.841788 (-0.545958) | 14.419524 / 8.074308 (6.345216) | 13.397985 / 10.191392 (3.206593) | 0.181367 / 0.680424 (-0.499057) | 0.017666 / 0.534201 (-0.516535) | 0.420645 / 0.579283 (-0.158638) | 0.421025 / 0.434364 (-0.013339) | 0.527369 / 0.540337 (-0.012969) | 0.627175 / 1.386936 (-0.759761) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#589b49dfaffa729bc9997a38d4cedafb107ea2e4 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008717 / 0.011353 (-0.002635) | 0.004573 / 0.011008 (-0.006435) | 0.103660 / 0.038508 (0.065151) | 0.035274 / 0.023109 (0.012165) | 0.298563 / 0.275898 (0.022665) | 0.384397 / 0.323480 (0.060917) | 0.006932 / 0.007986 (-0.001053) | 0.003422 / 0.004328 (-0.000907) | 0.080193 / 0.004250 (0.075943) | 0.039767 / 0.037052 (0.002714) | 0.310296 / 0.258489 (0.051807) | 0.351361 / 0.293841 (0.057520) | 0.033532 / 0.128546 (-0.095014) | 0.011543 / 0.075646 (-0.064104) | 0.374816 / 0.419271 (-0.044456) | 0.046046 / 0.043533 (0.002513) | 0.306918 / 0.255139 (0.051779) | 0.382242 / 0.283200 (0.099042) | 0.098945 / 0.141683 (-0.042738) | 1.456929 / 1.452155 (0.004775) | 1.535763 / 1.492716 (0.043046) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.011759 / 0.018006 (-0.006247) | 0.405345 / 0.000490 (0.404855) | 0.002667 / 0.000200 (0.002467) | 0.000075 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023924 / 0.037411 (-0.013487) | 0.095537 / 0.014526 (0.081011) | 0.106959 / 0.176557 (-0.069598) | 0.170782 / 0.737135 (-0.566353) | 0.109169 / 0.296338 (-0.187170) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437521 / 0.215209 (0.222312) | 4.383556 / 2.077655 (2.305902) | 2.092055 / 1.504120 (0.587935) | 1.889316 / 1.541195 (0.348121) | 1.937436 / 1.468490 (0.468946) | 0.700175 / 4.584777 (-3.884602) | 3.358107 / 3.745712 (-0.387605) | 3.243226 / 5.269862 (-2.026636) | 1.620497 / 4.565676 (-2.945180) | 0.083063 / 0.424275 (-0.341212) | 0.012970 / 0.007607 (0.005363) | 0.544226 / 0.226044 (0.318181) | 5.483315 / 2.268929 (3.214386) | 2.555183 / 55.444624 (-52.889441) | 2.204230 / 6.876477 (-4.672247) | 2.230551 / 2.142072 (0.088478) | 0.816121 / 4.805227 (-3.989106) | 0.151356 / 6.500664 (-6.349308) | 0.068564 / 0.075469 (-0.006905) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.208420 / 1.841788 (-0.633367) | 13.652597 / 8.074308 (5.578289) | 14.096318 / 10.191392 (3.904926) | 0.154473 / 0.680424 (-0.525951) | 0.028436 / 0.534201 (-0.505765) | 0.399949 / 0.579283 (-0.179334) | 0.398961 / 0.434364 (-0.035403) | 0.488703 / 0.540337 (-0.051634) | 0.572640 / 1.386936 (-0.814296) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006373 / 0.011353 (-0.004979) | 0.004368 / 0.011008 (-0.006640) | 0.076410 / 0.038508 (0.037902) | 0.027055 / 0.023109 (0.003945) | 0.336969 / 0.275898 (0.061071) | 0.374533 / 0.323480 (0.051053) | 0.004781 / 0.007986 (-0.003204) | 0.003317 / 0.004328 (-0.001011) | 0.076099 / 0.004250 (0.071849) | 0.038414 / 0.037052 (0.001361) | 0.339578 / 0.258489 (0.081089) | 0.384138 / 0.293841 (0.090297) | 0.031581 / 0.128546 (-0.096965) | 0.011666 / 0.075646 (-0.063981) | 0.085690 / 0.419271 (-0.333582) | 0.042277 / 0.043533 (-0.001256) | 0.337931 / 0.255139 (0.082792) | 0.365827 / 0.283200 (0.082628) | 0.088713 / 0.141683 (-0.052970) | 1.519789 / 1.452155 (0.067635) | 1.583097 / 1.492716 (0.090381) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223472 / 0.018006 (0.205466) | 0.392474 / 0.000490 (0.391984) | 0.002739 / 0.000200 (0.002539) | 0.000076 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024373 / 0.037411 (-0.013038) | 0.099822 / 0.014526 (0.085296) | 0.106128 / 0.176557 (-0.070428) | 0.174688 / 0.737135 (-0.562447) | 0.112660 / 0.296338 (-0.183678) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436317 / 0.215209 (0.221108) | 4.358277 / 2.077655 (2.280622) | 2.089746 / 1.504120 (0.585626) | 1.881040 / 1.541195 (0.339845) | 1.923653 / 1.468490 (0.455163) | 0.698176 / 4.584777 (-3.886601) | 3.346460 / 3.745712 (-0.399252) | 3.301429 / 5.269862 (-1.968433) | 1.391042 / 4.565676 (-3.174634) | 0.083025 / 0.424275 (-0.341250) | 0.012459 / 0.007607 (0.004851) | 0.533011 / 0.226044 (0.306967) | 5.334984 / 2.268929 (3.066056) | 2.534105 / 55.444624 (-52.910520) | 2.206295 / 6.876477 (-4.670181) | 2.231752 / 2.142072 (0.089680) | 0.798650 / 4.805227 (-4.006577) | 0.150070 / 6.500664 (-6.350594) | 0.066898 / 0.075469 (-0.008571) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.310527 / 1.841788 (-0.531261) | 13.920492 / 8.074308 (5.846184) | 13.359382 / 10.191392 (3.167990) | 0.154561 / 0.680424 (-0.525863) | 0.016387 / 0.534201 (-0.517814) | 0.379892 / 0.579283 (-0.199391) | 0.376746 / 0.434364 (-0.057618) | 0.462606 / 0.540337 (-0.077732) | 0.550895 / 1.386936 (-0.836041) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cac733fdaef84cfee92856bd259ce024ec157c91 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009373 / 0.011353 (-0.001980) | 0.005212 / 0.011008 (-0.005797) | 0.099287 / 0.038508 (0.060779) | 0.035175 / 0.023109 (0.012066) | 0.307012 / 0.275898 (0.031114) | 0.335105 / 0.323480 (0.011625) | 0.008006 / 0.007986 (0.000020) | 0.004017 / 0.004328 (-0.000311) | 0.075519 / 0.004250 (0.071269) | 0.040276 / 0.037052 (0.003223) | 0.302615 / 0.258489 (0.044126) | 0.361742 / 0.293841 (0.067901) | 0.038773 / 0.128546 (-0.089773) | 0.011892 / 0.075646 (-0.063754) | 0.334199 / 0.419271 (-0.085073) | 0.048035 / 0.043533 (0.004503) | 0.301361 / 0.255139 (0.046222) | 0.321996 / 0.283200 (0.038796) | 0.101818 / 0.141683 (-0.039865) | 1.442601 / 1.452155 (-0.009554) | 1.530669 / 1.492716 (0.037953) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201470 / 0.018006 (0.183464) | 0.496305 / 0.000490 (0.495815) | 0.003794 / 0.000200 (0.003594) | 0.000149 / 0.000054 (0.000094) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028401 / 0.037411 (-0.009010) | 0.107924 / 0.014526 (0.093398) | 0.121716 / 0.176557 (-0.054840) | 0.187407 / 0.737135 (-0.549728) | 0.124755 / 0.296338 (-0.171583) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.395667 / 0.215209 (0.180457) | 3.939079 / 2.077655 (1.861424) | 1.776308 / 1.504120 (0.272188) | 1.583487 / 1.541195 (0.042292) | 1.682957 / 1.468490 (0.214467) | 0.677322 / 4.584777 (-3.907455) | 3.796987 / 3.745712 (0.051275) | 3.406199 / 5.269862 (-1.863663) | 1.905467 / 4.565676 (-2.660210) | 0.083189 / 0.424275 (-0.341086) | 0.012156 / 0.007607 (0.004549) | 0.507078 / 0.226044 (0.281033) | 5.031293 / 2.268929 (2.762365) | 2.228403 / 55.444624 (-53.216221) | 1.885760 / 6.876477 (-4.990717) | 1.962340 / 2.142072 (-0.179732) | 0.824979 / 4.805227 (-3.980248) | 0.162107 / 6.500664 (-6.338557) | 0.062324 / 0.075469 (-0.013145) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.205104 / 1.841788 (-0.636683) | 15.368896 / 8.074308 (7.294588) | 14.757540 / 10.191392 (4.566148) | 0.177544 / 0.680424 (-0.502880) | 0.029097 / 0.534201 (-0.505104) | 0.445252 / 0.579283 (-0.134031) | 0.456521 / 0.434364 (0.022157) | 0.544166 / 0.540337 (0.003829) | 0.640675 / 1.386936 (-0.746261) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007438 / 0.011353 (-0.003914) | 0.005236 / 0.011008 (-0.005772) | 0.075379 / 0.038508 (0.036871) | 0.033274 / 0.023109 (0.010165) | 0.344584 / 0.275898 (0.068686) | 0.372161 / 0.323480 (0.048681) | 0.005914 / 0.007986 (-0.002071) | 0.004176 / 0.004328 (-0.000152) | 0.073311 / 0.004250 (0.069061) | 0.050845 / 0.037052 (0.013793) | 0.338978 / 0.258489 (0.080489) | 0.391563 / 0.293841 (0.097722) | 0.037559 / 0.128546 (-0.090987) | 0.012455 / 0.075646 (-0.063192) | 0.086224 / 0.419271 (-0.333047) | 0.052956 / 0.043533 (0.009423) | 0.338529 / 0.255139 (0.083390) | 0.356752 / 0.283200 (0.073553) | 0.105864 / 0.141683 (-0.035819) | 1.467727 / 1.452155 (0.015572) | 1.588727 / 1.492716 (0.096010) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.215959 / 0.018006 (0.197953) | 0.440619 / 0.000490 (0.440129) | 0.000397 / 0.000200 (0.000197) | 0.000058 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028855 / 0.037411 (-0.008556) | 0.114239 / 0.014526 (0.099713) | 0.121726 / 0.176557 (-0.054830) | 0.190377 / 0.737135 (-0.546759) | 0.127858 / 0.296338 (-0.168480) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.415399 / 0.215209 (0.200190) | 4.159012 / 2.077655 (2.081357) | 1.987593 / 1.504120 (0.483474) | 1.794785 / 1.541195 (0.253591) | 1.924819 / 1.468490 (0.456329) | 0.696082 / 4.584777 (-3.888694) | 3.820461 / 3.745712 (0.074749) | 2.139236 / 5.269862 (-3.130626) | 1.348593 / 4.565676 (-3.217084) | 0.086536 / 0.424275 (-0.337739) | 0.012510 / 0.007607 (0.004902) | 0.518804 / 0.226044 (0.292760) | 5.188659 / 2.268929 (2.919730) | 2.501303 / 55.444624 (-52.943322) | 2.138831 / 6.876477 (-4.737646) | 2.220451 / 2.142072 (0.078378) | 0.836277 / 4.805227 (-3.968950) | 0.170940 / 6.500664 (-6.329724) | 0.067326 / 0.075469 (-0.008143) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.307848 / 1.841788 (-0.533940) | 15.995785 / 8.074308 (7.921477) | 13.646285 / 10.191392 (3.454893) | 0.181120 / 0.680424 (-0.499304) | 0.017500 / 0.534201 (-0.516701) | 0.426697 / 0.579283 (-0.152586) | 0.436702 / 0.434364 (0.002338) | 0.518060 / 0.540337 (-0.022278) | 0.632577 / 1.386936 (-0.754359) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cac733fdaef84cfee92856bd259ce024ec157c91 \"CML watermark\")\n"
] | "2023-02-22T12:48:52Z" | "2023-02-22T13:05:55Z" | "2023-02-22T12:56:48Z" | MEMBER | null | 0 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Removed it :)",
"Changed it :)",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008358 / 0.011353 (-0.002995) | 0.004555 / 0.011008 (-0.006453) | 0.100935 / 0.038508 (0.062427) | 0.029473 / 0.023109 (0.006364) | 0.336165 / 0.275898 (0.060266) | 0.420397 / 0.323480 (0.096917) | 0.006609 / 0.007986 (-0.001376) | 0.003338 / 0.004328 (-0.000991) | 0.078639 / 0.004250 (0.074388) | 0.034051 / 0.037052 (-0.003001) | 0.342820 / 0.258489 (0.084331) | 0.399392 / 0.293841 (0.105551) | 0.033935 / 0.128546 (-0.094611) | 0.011555 / 0.075646 (-0.064092) | 0.323467 / 0.419271 (-0.095804) | 0.040675 / 0.043533 (-0.002858) | 0.321247 / 0.255139 (0.066108) | 0.370967 / 0.283200 (0.087767) | 0.085766 / 0.141683 (-0.055917) | 1.461158 / 1.452155 (0.009003) | 1.504641 / 1.492716 (0.011925) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.180060 / 0.018006 (0.162053) | 0.403623 / 0.000490 (0.403134) | 0.002253 / 0.000200 (0.002053) | 0.000072 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022793 / 0.037411 (-0.014618) | 0.098869 / 0.014526 (0.084343) | 0.104512 / 0.176557 (-0.072045) | 0.167721 / 0.737135 (-0.569414) | 0.107969 / 0.296338 (-0.188370) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.411179 / 0.215209 (0.195969) | 4.095345 / 2.077655 (2.017690) | 1.825992 / 1.504120 (0.321872) | 1.624386 / 1.541195 (0.083192) | 1.654903 / 1.468490 (0.186413) | 0.695041 / 4.584777 (-3.889736) | 3.319087 / 3.745712 (-0.426625) | 1.881945 / 5.269862 (-3.387917) | 1.250360 / 4.565676 (-3.315316) | 0.082405 / 0.424275 (-0.341870) | 0.012499 / 0.007607 (0.004892) | 0.522846 / 0.226044 (0.296801) | 5.241103 / 2.268929 (2.972175) | 2.293100 / 55.444624 (-53.151524) | 1.942937 / 6.876477 (-4.933540) | 1.957434 / 2.142072 (-0.184638) | 0.809782 / 4.805227 (-3.995445) | 0.148290 / 6.500664 (-6.352374) | 0.064157 / 0.075469 (-0.011312) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.185616 / 1.841788 (-0.656172) | 13.616791 / 8.074308 (5.542483) | 13.741806 / 10.191392 (3.550414) | 0.137396 / 0.680424 (-0.543028) | 0.028751 / 0.534201 (-0.505450) | 0.397636 / 0.579283 (-0.181647) | 0.403594 / 0.434364 (-0.030770) | 0.484039 / 0.540337 (-0.056299) | 0.568398 / 1.386936 (-0.818538) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006712 / 0.011353 (-0.004640) | 0.004511 / 0.011008 (-0.006497) | 0.076946 / 0.038508 (0.038438) | 0.027219 / 0.023109 (0.004110) | 0.350769 / 0.275898 (0.074871) | 0.408539 / 0.323480 (0.085059) | 0.005014 / 0.007986 (-0.002971) | 0.003361 / 0.004328 (-0.000968) | 0.077106 / 0.004250 (0.072856) | 0.040105 / 0.037052 (0.003053) | 0.342041 / 0.258489 (0.083552) | 0.426355 / 0.293841 (0.132514) | 0.031684 / 0.128546 (-0.096863) | 0.011575 / 0.075646 (-0.064072) | 0.085797 / 0.419271 (-0.333474) | 0.041575 / 0.043533 (-0.001958) | 0.340837 / 0.255139 (0.085698) | 0.390461 / 0.283200 (0.107262) | 0.089531 / 0.141683 (-0.052152) | 1.504600 / 1.452155 (0.052445) | 1.538712 / 1.492716 (0.045996) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236679 / 0.018006 (0.218673) | 0.396258 / 0.000490 (0.395768) | 0.006479 / 0.000200 (0.006279) | 0.000081 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024682 / 0.037411 (-0.012729) | 0.100167 / 0.014526 (0.085641) | 0.106627 / 0.176557 (-0.069929) | 0.174592 / 0.737135 (-0.562543) | 0.109499 / 0.296338 (-0.186839) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.444702 / 0.215209 (0.229493) | 4.462779 / 2.077655 (2.385125) | 2.087711 / 1.504120 (0.583591) | 1.874900 / 1.541195 (0.333705) | 1.918609 / 1.468490 (0.450119) | 0.705867 / 4.584777 (-3.878910) | 3.355483 / 3.745712 (-0.390229) | 2.808348 / 5.269862 (-2.461514) | 1.253319 / 4.565676 (-3.312358) | 0.083747 / 0.424275 (-0.340528) | 0.012491 / 0.007607 (0.004884) | 0.542885 / 0.226044 (0.316841) | 5.453921 / 2.268929 (3.184993) | 2.545688 / 55.444624 (-52.898937) | 2.185022 / 6.876477 (-4.691455) | 2.215351 / 2.142072 (0.073279) | 0.808201 / 4.805227 (-3.997027) | 0.151754 / 6.500664 (-6.348910) | 0.066886 / 0.075469 (-0.008583) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.298583 / 1.841788 (-0.543205) | 14.014276 / 8.074308 (5.939968) | 13.505338 / 10.191392 (3.313946) | 0.142033 / 0.680424 (-0.538391) | 0.016863 / 0.534201 (-0.517338) | 0.381195 / 0.579283 (-0.198088) | 0.384455 / 0.434364 (-0.049909) | 0.465765 / 0.540337 (-0.074572) | 0.552571 / 1.386936 (-0.834366) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a29cca79ce64a5c64ad7047e57845b22154d7b8d \"CML watermark\")\n"
] | "2023-02-22T07:56:10Z" | "2023-02-23T11:07:49Z" | "2023-02-23T11:00:58Z" | CONTRIBUTOR | null | 0 | {
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} | Removed mangle_dup_cols=True from BuilderConfig.
It triggered following deprecation warning:
/usr/local/lib/python3.8/dist-packages/datasets/download/streaming_download_manager.py:776: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'
return pd.read_csv(xopen(filepath_or_buffer, "rb", use_auth_token=use_auth_token), **kwargs)
Further documentation of pandas: https://pandas.pydata.org/docs/whatsnew/v1.4.0.html#mangle-dupe-cols-in-read-csv-no-longer-renames-unique-columns-conflicting-with-target-names
At first sight it seems like this flag is resolved internally, it might need some more research. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Better yet have someone enable pre-commit CI https://pre-commit.ci/ and it will apply the pre-commit fixes to the PR automatically as an additional commit.",
"@Skylion007 hi! I agree with @nateraw here, I'd better not force to use pre-commit so I'm not setting it up in the CI for now. And regarding end-of-file - currently it's being done by `black`. \r\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008704 / 0.011353 (-0.002649) | 0.004448 / 0.011008 (-0.006560) | 0.099530 / 0.038508 (0.061022) | 0.029739 / 0.023109 (0.006629) | 0.329267 / 0.275898 (0.053369) | 0.368805 / 0.323480 (0.045325) | 0.006852 / 0.007986 (-0.001133) | 0.004575 / 0.004328 (0.000246) | 0.076838 / 0.004250 (0.072588) | 0.033885 / 0.037052 (-0.003167) | 0.336340 / 0.258489 (0.077851) | 0.384880 / 0.293841 (0.091039) | 0.034051 / 0.128546 (-0.094495) | 0.011638 / 0.075646 (-0.064009) | 0.321650 / 0.419271 (-0.097622) | 0.041202 / 0.043533 (-0.002330) | 0.330841 / 0.255139 (0.075702) | 0.361329 / 0.283200 (0.078130) | 0.084864 / 0.141683 (-0.056819) | 1.454005 / 1.452155 (0.001850) | 1.542167 / 1.492716 (0.049451) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.196207 / 0.018006 (0.178200) | 0.400675 / 0.000490 (0.400185) | 0.000403 / 0.000200 (0.000203) | 0.000059 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022694 / 0.037411 (-0.014717) | 0.095139 / 0.014526 (0.080613) | 0.104129 / 0.176557 (-0.072427) | 0.168688 / 0.737135 (-0.568447) | 0.109243 / 0.296338 (-0.187096) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.427520 / 0.215209 (0.212311) | 4.237726 / 2.077655 (2.160071) | 2.191887 / 1.504120 (0.687767) | 1.987750 / 1.541195 (0.446555) | 1.996540 / 1.468490 (0.528050) | 0.696416 / 4.584777 (-3.888361) | 3.454536 / 3.745712 (-0.291176) | 2.023600 / 5.269862 (-3.246261) | 1.336394 / 4.565676 (-3.229282) | 0.082933 / 0.424275 (-0.341342) | 0.012572 / 0.007607 (0.004965) | 0.534330 / 0.226044 (0.308285) | 5.347588 / 2.268929 (3.078659) | 2.640397 / 55.444624 (-52.804228) | 2.338266 / 6.876477 (-4.538211) | 2.431969 / 2.142072 (0.289897) | 0.821335 / 4.805227 (-3.983893) | 0.151905 / 6.500664 (-6.348759) | 0.067983 / 0.075469 (-0.007486) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.228841 / 1.841788 (-0.612947) | 13.660437 / 8.074308 (5.586128) | 13.729442 / 10.191392 (3.538050) | 0.165835 / 0.680424 (-0.514589) | 0.028753 / 0.534201 (-0.505448) | 0.400143 / 0.579283 (-0.179140) | 0.403714 / 0.434364 (-0.030650) | 0.492168 / 0.540337 (-0.048170) | 0.581151 / 1.386936 (-0.805785) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006289 / 0.011353 (-0.005064) | 0.004419 / 0.011008 (-0.006589) | 0.077220 / 0.038508 (0.038712) | 0.027170 / 0.023109 (0.004060) | 0.344988 / 0.275898 (0.069090) | 0.374150 / 0.323480 (0.050670) | 0.004842 / 0.007986 (-0.003144) | 0.003289 / 0.004328 (-0.001039) | 0.076200 / 0.004250 (0.071950) | 0.036287 / 0.037052 (-0.000766) | 0.345764 / 0.258489 (0.087275) | 0.387439 / 0.293841 (0.093599) | 0.031547 / 0.128546 (-0.096999) | 0.011586 / 0.075646 (-0.064060) | 0.086599 / 0.419271 (-0.332672) | 0.042338 / 0.043533 (-0.001195) | 0.355384 / 0.255139 (0.100246) | 0.369474 / 0.283200 (0.086275) | 0.090945 / 0.141683 (-0.050738) | 1.488632 / 1.452155 (0.036477) | 1.554606 / 1.492716 (0.061890) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212962 / 0.018006 (0.194956) | 0.399647 / 0.000490 (0.399157) | 0.003055 / 0.000200 (0.002856) | 0.000083 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024349 / 0.037411 (-0.013062) | 0.100342 / 0.014526 (0.085817) | 0.105657 / 0.176557 (-0.070899) | 0.175139 / 0.737135 (-0.561997) | 0.110014 / 0.296338 (-0.186324) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434785 / 0.215209 (0.219575) | 4.346950 / 2.077655 (2.269295) | 2.045411 / 1.504120 (0.541291) | 1.844258 / 1.541195 (0.303064) | 1.889503 / 1.468490 (0.421013) | 0.704530 / 4.584777 (-3.880247) | 3.362435 / 3.745712 (-0.383277) | 2.797205 / 5.269862 (-2.472656) | 1.504431 / 4.565676 (-3.061245) | 0.083331 / 0.424275 (-0.340945) | 0.012274 / 0.007607 (0.004666) | 0.531123 / 0.226044 (0.305078) | 5.322588 / 2.268929 (3.053660) | 2.483875 / 55.444624 (-52.960750) | 2.147218 / 6.876477 (-4.729258) | 2.164024 / 2.142072 (0.021952) | 0.807191 / 4.805227 (-3.998036) | 0.151189 / 6.500664 (-6.349475) | 0.068027 / 0.075469 (-0.007442) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.316001 / 1.841788 (-0.525787) | 13.892785 / 8.074308 (5.818477) | 13.485982 / 10.191392 (3.294590) | 0.138904 / 0.680424 (-0.541520) | 0.016748 / 0.534201 (-0.517453) | 0.379840 / 0.579283 (-0.199443) | 0.384854 / 0.434364 (-0.049510) | 0.464275 / 0.540337 (-0.076063) | 0.553622 / 1.386936 (-0.833314) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a940972a9a38543b2066129dc6e7987e08dca082 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009179 / 0.011353 (-0.002174) | 0.005080 / 0.011008 (-0.005929) | 0.099061 / 0.038508 (0.060553) | 0.035252 / 0.023109 (0.012143) | 0.293496 / 0.275898 (0.017598) | 0.360365 / 0.323480 (0.036886) | 0.007757 / 0.007986 (-0.000229) | 0.003985 / 0.004328 (-0.000343) | 0.076021 / 0.004250 (0.071771) | 0.042286 / 0.037052 (0.005233) | 0.316542 / 0.258489 (0.058053) | 0.341711 / 0.293841 (0.047870) | 0.037970 / 0.128546 (-0.090576) | 0.011977 / 0.075646 (-0.063670) | 0.333341 / 0.419271 (-0.085931) | 0.049211 / 0.043533 (0.005678) | 0.297401 / 0.255139 (0.042262) | 0.313424 / 0.283200 (0.030224) | 0.105719 / 0.141683 (-0.035964) | 1.487879 / 1.452155 (0.035724) | 1.529785 / 1.492716 (0.037068) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201062 / 0.018006 (0.183056) | 0.438024 / 0.000490 (0.437534) | 0.002129 / 0.000200 (0.001929) | 0.000083 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026422 / 0.037411 (-0.010989) | 0.104863 / 0.014526 (0.090337) | 0.114934 / 0.176557 (-0.061623) | 0.179173 / 0.737135 (-0.557962) | 0.119734 / 0.296338 (-0.176604) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.397195 / 0.215209 (0.181986) | 3.959945 / 2.077655 (1.882290) | 1.794059 / 1.504120 (0.289939) | 1.606814 / 1.541195 (0.065619) | 1.674681 / 1.468490 (0.206191) | 0.680130 / 4.584777 (-3.904646) | 3.742730 / 3.745712 (-0.002982) | 2.021793 / 5.269862 (-3.248069) | 1.322726 / 4.565676 (-3.242950) | 0.084519 / 0.424275 (-0.339756) | 0.012012 / 0.007607 (0.004405) | 0.510076 / 0.226044 (0.284032) | 5.084163 / 2.268929 (2.815234) | 2.241032 / 55.444624 (-53.203592) | 1.911936 / 6.876477 (-4.964540) | 1.947992 / 2.142072 (-0.194080) | 0.838779 / 4.805227 (-3.966448) | 0.165103 / 6.500664 (-6.335561) | 0.060722 / 0.075469 (-0.014747) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.180274 / 1.841788 (-0.661514) | 14.285364 / 8.074308 (6.211056) | 12.941205 / 10.191392 (2.749813) | 0.153815 / 0.680424 (-0.526609) | 0.028554 / 0.534201 (-0.505647) | 0.441551 / 0.579283 (-0.137732) | 0.434906 / 0.434364 (0.000542) | 0.516120 / 0.540337 (-0.024217) | 0.603062 / 1.386936 (-0.783874) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007287 / 0.011353 (-0.004066) | 0.004998 / 0.011008 (-0.006010) | 0.074997 / 0.038508 (0.036489) | 0.033209 / 0.023109 (0.010100) | 0.336836 / 0.275898 (0.060938) | 0.365562 / 0.323480 (0.042082) | 0.005739 / 0.007986 (-0.002246) | 0.003942 / 0.004328 (-0.000387) | 0.074681 / 0.004250 (0.070430) | 0.049530 / 0.037052 (0.012478) | 0.335642 / 0.258489 (0.077153) | 0.388874 / 0.293841 (0.095033) | 0.037198 / 0.128546 (-0.091349) | 0.011983 / 0.075646 (-0.063664) | 0.087601 / 0.419271 (-0.331671) | 0.053761 / 0.043533 (0.010228) | 0.334142 / 0.255139 (0.079003) | 0.351348 / 0.283200 (0.068148) | 0.107462 / 0.141683 (-0.034221) | 1.497015 / 1.452155 (0.044860) | 1.608287 / 1.492716 (0.115571) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.255395 / 0.018006 (0.237389) | 0.439141 / 0.000490 (0.438651) | 0.021391 / 0.000200 (0.021191) | 0.000230 / 0.000054 (0.000176) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028331 / 0.037411 (-0.009080) | 0.108744 / 0.014526 (0.094218) | 0.118201 / 0.176557 (-0.058355) | 0.189556 / 0.737135 (-0.547579) | 0.123112 / 0.296338 (-0.173226) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431394 / 0.215209 (0.216185) | 4.296121 / 2.077655 (2.218466) | 2.126371 / 1.504120 (0.622251) | 1.978178 / 1.541195 (0.436983) | 2.082674 / 1.468490 (0.614184) | 0.701789 / 4.584777 (-3.882988) | 3.791495 / 3.745712 (0.045783) | 2.115267 / 5.269862 (-3.154594) | 1.342159 / 4.565676 (-3.223517) | 0.088132 / 0.424275 (-0.336143) | 0.011903 / 0.007607 (0.004295) | 0.528398 / 0.226044 (0.302354) | 5.270077 / 2.268929 (3.001148) | 2.498860 / 55.444624 (-52.945765) | 2.155515 / 6.876477 (-4.720962) | 2.192866 / 2.142072 (0.050793) | 0.859596 / 4.805227 (-3.945631) | 0.170544 / 6.500664 (-6.330120) | 0.063883 / 0.075469 (-0.011587) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.240679 / 1.841788 (-0.601109) | 14.497379 / 8.074308 (6.423071) | 12.881417 / 10.191392 (2.690025) | 0.147295 / 0.680424 (-0.533129) | 0.017465 / 0.534201 (-0.516736) | 0.424695 / 0.579283 (-0.154588) | 0.414929 / 0.434364 (-0.019435) | 0.536079 / 0.540337 (-0.004259) | 0.638245 / 1.386936 (-0.748691) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a940972a9a38543b2066129dc6e7987e08dca082 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008806 / 0.011353 (-0.002547) | 0.004712 / 0.011008 (-0.006297) | 0.102383 / 0.038508 (0.063875) | 0.030260 / 0.023109 (0.007151) | 0.330175 / 0.275898 (0.054277) | 0.376816 / 0.323480 (0.053337) | 0.008065 / 0.007986 (0.000079) | 0.003534 / 0.004328 (-0.000794) | 0.078824 / 0.004250 (0.074573) | 0.036704 / 0.037052 (-0.000349) | 0.331848 / 0.258489 (0.073359) | 0.351031 / 0.293841 (0.057190) | 0.033406 / 0.128546 (-0.095140) | 0.011543 / 0.075646 (-0.064103) | 0.322114 / 0.419271 (-0.097157) | 0.041249 / 0.043533 (-0.002284) | 0.309413 / 0.255139 (0.054274) | 0.329156 / 0.283200 (0.045956) | 0.088636 / 0.141683 (-0.053047) | 1.508226 / 1.452155 (0.056071) | 1.557203 / 1.492716 (0.064487) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.196696 / 0.018006 (0.178690) | 0.426360 / 0.000490 (0.425870) | 0.001263 / 0.000200 (0.001064) | 0.000079 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023747 / 0.037411 (-0.013664) | 0.100756 / 0.014526 (0.086230) | 0.105817 / 0.176557 (-0.070739) | 0.172573 / 0.737135 (-0.564562) | 0.110705 / 0.296338 (-0.185634) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436913 / 0.215209 (0.221704) | 4.365753 / 2.077655 (2.288099) | 2.201346 / 1.504120 (0.697226) | 1.978800 / 1.541195 (0.437605) | 1.951585 / 1.468490 (0.483094) | 0.699208 / 4.584777 (-3.885569) | 3.381492 / 3.745712 (-0.364220) | 2.966174 / 5.269862 (-2.303687) | 1.487521 / 4.565676 (-3.078156) | 0.082673 / 0.424275 (-0.341602) | 0.012436 / 0.007607 (0.004829) | 0.553276 / 0.226044 (0.327232) | 5.554081 / 2.268929 (3.285153) | 2.653286 / 55.444624 (-52.791339) | 2.404788 / 6.876477 (-4.471689) | 2.484610 / 2.142072 (0.342537) | 0.817073 / 4.805227 (-3.988154) | 0.151619 / 6.500664 (-6.349045) | 0.068259 / 0.075469 (-0.007210) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.273481 / 1.841788 (-0.568306) | 13.908825 / 8.074308 (5.834517) | 13.106695 / 10.191392 (2.915303) | 0.139609 / 0.680424 (-0.540815) | 0.028425 / 0.534201 (-0.505776) | 0.395626 / 0.579283 (-0.183657) | 0.405526 / 0.434364 (-0.028838) | 0.465628 / 0.540337 (-0.074709) | 0.542824 / 1.386936 (-0.844112) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006821 / 0.011353 (-0.004532) | 0.004570 / 0.011008 (-0.006438) | 0.076568 / 0.038508 (0.038060) | 0.028109 / 0.023109 (0.004999) | 0.342768 / 0.275898 (0.066870) | 0.390680 / 0.323480 (0.067200) | 0.005056 / 0.007986 (-0.002930) | 0.003359 / 0.004328 (-0.000970) | 0.075835 / 0.004250 (0.071584) | 0.038888 / 0.037052 (0.001836) | 0.343489 / 0.258489 (0.085000) | 0.400766 / 0.293841 (0.106925) | 0.031816 / 0.128546 (-0.096730) | 0.011637 / 0.075646 (-0.064009) | 0.085474 / 0.419271 (-0.333797) | 0.041740 / 0.043533 (-0.001793) | 0.342501 / 0.255139 (0.087362) | 0.377467 / 0.283200 (0.094267) | 0.091532 / 0.141683 (-0.050151) | 1.457368 / 1.452155 (0.005213) | 1.537187 / 1.492716 (0.044471) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.187507 / 0.018006 (0.169501) | 0.415706 / 0.000490 (0.415217) | 0.001816 / 0.000200 (0.001616) | 0.000072 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026251 / 0.037411 (-0.011161) | 0.106609 / 0.014526 (0.092083) | 0.109822 / 0.176557 (-0.066735) | 0.180462 / 0.737135 (-0.556674) | 0.114647 / 0.296338 (-0.181691) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.438804 / 0.215209 (0.223595) | 4.387960 / 2.077655 (2.310306) | 2.056804 / 1.504120 (0.552684) | 1.848584 / 1.541195 (0.307389) | 1.939470 / 1.468490 (0.470980) | 0.702539 / 4.584777 (-3.882238) | 3.419535 / 3.745712 (-0.326177) | 1.933889 / 5.269862 (-3.335973) | 1.189631 / 4.565676 (-3.376045) | 0.084105 / 0.424275 (-0.340170) | 0.012520 / 0.007607 (0.004913) | 0.538125 / 0.226044 (0.312081) | 5.370000 / 2.268929 (3.101072) | 2.497487 / 55.444624 (-52.947137) | 2.156054 / 6.876477 (-4.720423) | 2.225909 / 2.142072 (0.083837) | 0.811456 / 4.805227 (-3.993771) | 0.151461 / 6.500664 (-6.349203) | 0.066940 / 0.075469 (-0.008530) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.301246 / 1.841788 (-0.540542) | 14.459755 / 8.074308 (6.385447) | 13.147151 / 10.191392 (2.955759) | 0.129236 / 0.680424 (-0.551188) | 0.016427 / 0.534201 (-0.517774) | 0.380047 / 0.579283 (-0.199236) | 0.392217 / 0.434364 (-0.042147) | 0.470338 / 0.540337 (-0.069999) | 0.559800 / 1.386936 (-0.827136) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a940972a9a38543b2066129dc6e7987e08dca082 \"CML watermark\")\n"
] | "2023-02-21T17:35:07Z" | "2023-02-28T15:37:22Z" | "2023-02-23T18:23:29Z" | CONTRIBUTOR | null | 0 | {
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} | @huggingface/datasets do you think it would be useful? Motivation - sometimes PRs are like 30% "fix: style" commits :)
If so - I need to double check the config but for me locally it works as expected. | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011060 / 0.011353 (-0.000293) | 0.005752 / 0.011008 (-0.005256) | 0.120349 / 0.038508 (0.081841) | 0.045303 / 0.023109 (0.022194) | 0.359196 / 0.275898 (0.083298) | 0.406351 / 0.323480 (0.082871) | 0.009474 / 0.007986 (0.001489) | 0.004524 / 0.004328 (0.000195) | 0.091990 / 0.004250 (0.087739) | 0.050034 / 0.037052 (0.012982) | 0.372479 / 0.258489 (0.113990) | 0.418907 / 0.293841 (0.125067) | 0.044300 / 0.128546 (-0.084247) | 0.013989 / 0.075646 (-0.061657) | 0.397406 / 0.419271 (-0.021866) | 0.056070 / 0.043533 (0.012537) | 0.357597 / 0.255139 (0.102458) | 0.382938 / 0.283200 (0.099738) | 0.117060 / 0.141683 (-0.024623) | 1.670869 / 1.452155 (0.218714) | 1.780944 / 1.492716 (0.288227) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229578 / 0.018006 (0.211572) | 0.493711 / 0.000490 (0.493222) | 0.008413 / 0.000200 (0.008213) | 0.000118 / 0.000054 (0.000063) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033364 / 0.037411 (-0.004047) | 0.135953 / 0.014526 (0.121427) | 0.141942 / 0.176557 (-0.034614) | 0.225891 / 0.737135 (-0.511244) | 0.151010 / 0.296338 (-0.145328) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.470937 / 0.215209 (0.255728) | 4.710258 / 2.077655 (2.632603) | 2.132025 / 1.504120 (0.627905) | 1.913134 / 1.541195 (0.371939) | 2.025993 / 1.468490 (0.557503) | 0.835993 / 4.584777 (-3.748784) | 4.446678 / 3.745712 (0.700965) | 4.260014 / 5.269862 (-1.009847) | 2.193078 / 4.565676 (-2.372598) | 0.100132 / 0.424275 (-0.324143) | 0.014163 / 0.007607 (0.006556) | 0.599252 / 0.226044 (0.373208) | 5.976377 / 2.268929 (3.707448) | 2.678116 / 55.444624 (-52.766508) | 2.309311 / 6.876477 (-4.567166) | 2.410284 / 2.142072 (0.268212) | 1.002415 / 4.805227 (-3.802813) | 0.194588 / 6.500664 (-6.306076) | 0.074921 / 0.075469 (-0.000548) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.432389 / 1.841788 (-0.409399) | 17.915288 / 8.074308 (9.840980) | 17.190906 / 10.191392 (6.999514) | 0.238469 / 0.680424 (-0.441955) | 0.036270 / 0.534201 (-0.497931) | 0.537320 / 0.579283 (-0.041963) | 0.512876 / 0.434364 (0.078512) | 0.629022 / 0.540337 (0.088685) | 0.750109 / 1.386936 (-0.636827) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008544 / 0.011353 (-0.002809) | 0.005933 / 0.011008 (-0.005075) | 0.088879 / 0.038508 (0.050371) | 0.040387 / 0.023109 (0.017278) | 0.406392 / 0.275898 (0.130494) | 0.449572 / 0.323480 (0.126092) | 0.006623 / 0.007986 (-0.001362) | 0.004727 / 0.004328 (0.000398) | 0.086745 / 0.004250 (0.082495) | 0.054335 / 0.037052 (0.017283) | 0.405652 / 0.258489 (0.147163) | 0.473934 / 0.293841 (0.180093) | 0.042157 / 0.128546 (-0.086390) | 0.014249 / 0.075646 (-0.061397) | 0.102130 / 0.419271 (-0.317141) | 0.056815 / 0.043533 (0.013282) | 0.407945 / 0.255139 (0.152806) | 0.431720 / 0.283200 (0.148521) | 0.119901 / 0.141683 (-0.021781) | 1.738381 / 1.452155 (0.286227) | 1.838981 / 1.492716 (0.346265) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.251926 / 0.018006 (0.233919) | 0.498117 / 0.000490 (0.497627) | 0.000439 / 0.000200 (0.000239) | 0.000065 / 0.000054 (0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034526 / 0.037411 (-0.002886) | 0.133038 / 0.014526 (0.118512) | 0.147494 / 0.176557 (-0.029063) | 0.234392 / 0.737135 (-0.502743) | 0.152361 / 0.296338 (-0.143978) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.495144 / 0.215209 (0.279935) | 4.936646 / 2.077655 (2.858991) | 2.385549 / 1.504120 (0.881429) | 2.173817 / 1.541195 (0.632622) | 2.327508 / 1.468490 (0.859018) | 0.851899 / 4.584777 (-3.732878) | 4.820388 / 3.745712 (1.074676) | 2.500304 / 5.269862 (-2.769558) | 1.621246 / 4.565676 (-2.944430) | 0.102858 / 0.424275 (-0.321417) | 0.014719 / 0.007607 (0.007112) | 0.611880 / 0.226044 (0.385836) | 6.100737 / 2.268929 (3.831808) | 2.955681 / 55.444624 (-52.488943) | 2.563533 / 6.876477 (-4.312943) | 2.659030 / 2.142072 (0.516958) | 1.004737 / 4.805227 (-3.800490) | 0.198379 / 6.500664 (-6.302285) | 0.078705 / 0.075469 (0.003236) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.501155 / 1.841788 (-0.340633) | 18.381513 / 8.074308 (10.307205) | 16.173893 / 10.191392 (5.982501) | 0.209497 / 0.680424 (-0.470927) | 0.021640 / 0.534201 (-0.512561) | 0.505905 / 0.579283 (-0.073378) | 0.513446 / 0.434364 (0.079082) | 0.652704 / 0.540337 (0.112366) | 0.761038 / 1.386936 (-0.625898) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b8235c92b46b6a63286fcee1a56adae4c0a751d3 \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009085 / 0.011353 (-0.002268) | 0.004589 / 0.011008 (-0.006419) | 0.100820 / 0.038508 (0.062312) | 0.030677 / 0.023109 (0.007568) | 0.306702 / 0.275898 (0.030804) | 0.360623 / 0.323480 (0.037144) | 0.007377 / 0.007986 (-0.000608) | 0.003480 / 0.004328 (-0.000848) | 0.077813 / 0.004250 (0.073562) | 0.037293 / 0.037052 (0.000241) | 0.314137 / 0.258489 (0.055648) | 0.343394 / 0.293841 (0.049554) | 0.034202 / 0.128546 (-0.094344) | 0.011417 / 0.075646 (-0.064230) | 0.322584 / 0.419271 (-0.096687) | 0.041524 / 0.043533 (-0.002009) | 0.308116 / 0.255139 (0.052977) | 0.324527 / 0.283200 (0.041327) | 0.090973 / 0.141683 (-0.050710) | 1.515941 / 1.452155 (0.063787) | 1.548975 / 1.492716 (0.056259) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.185901 / 0.018006 (0.167895) | 0.420742 / 0.000490 (0.420252) | 0.002958 / 0.000200 (0.002758) | 0.000086 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024242 / 0.037411 (-0.013170) | 0.098827 / 0.014526 (0.084302) | 0.107609 / 0.176557 (-0.068947) | 0.172228 / 0.737135 (-0.564908) | 0.110042 / 0.296338 (-0.186296) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429647 / 0.215209 (0.214438) | 4.265406 / 2.077655 (2.187751) | 1.924514 / 1.504120 (0.420394) | 1.709881 / 1.541195 (0.168686) | 1.764872 / 1.468490 (0.296382) | 0.698089 / 4.584777 (-3.886688) | 3.439154 / 3.745712 (-0.306558) | 1.925058 / 5.269862 (-3.344804) | 1.267506 / 4.565676 (-3.298171) | 0.082167 / 0.424275 (-0.342108) | 0.012450 / 0.007607 (0.004843) | 0.523077 / 0.226044 (0.297033) | 5.240422 / 2.268929 (2.971494) | 2.363666 / 55.444624 (-53.080959) | 2.021903 / 6.876477 (-4.854574) | 2.136430 / 2.142072 (-0.005643) | 0.816377 / 4.805227 (-3.988850) | 0.151516 / 6.500664 (-6.349148) | 0.066590 / 0.075469 (-0.008879) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.216477 / 1.841788 (-0.625310) | 13.685044 / 8.074308 (5.610736) | 14.082620 / 10.191392 (3.891228) | 0.148399 / 0.680424 (-0.532025) | 0.028337 / 0.534201 (-0.505864) | 0.405379 / 0.579283 (-0.173904) | 0.405650 / 0.434364 (-0.028714) | 0.492658 / 0.540337 (-0.047679) | 0.578836 / 1.386936 (-0.808100) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006863 / 0.011353 (-0.004490) | 0.004746 / 0.011008 (-0.006262) | 0.075802 / 0.038508 (0.037294) | 0.027950 / 0.023109 (0.004840) | 0.347613 / 0.275898 (0.071715) | 0.401201 / 0.323480 (0.077721) | 0.005765 / 0.007986 (-0.002221) | 0.003567 / 0.004328 (-0.000762) | 0.074188 / 0.004250 (0.069937) | 0.041209 / 0.037052 (0.004157) | 0.346541 / 0.258489 (0.088052) | 0.425729 / 0.293841 (0.131888) | 0.032430 / 0.128546 (-0.096116) | 0.011708 / 0.075646 (-0.063938) | 0.084667 / 0.419271 (-0.334604) | 0.042155 / 0.043533 (-0.001378) | 0.341210 / 0.255139 (0.086071) | 0.389759 / 0.283200 (0.106559) | 0.092640 / 0.141683 (-0.049042) | 1.526093 / 1.452155 (0.073938) | 1.556277 / 1.492716 (0.063561) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.232383 / 0.018006 (0.214377) | 0.412353 / 0.000490 (0.411863) | 0.004009 / 0.000200 (0.003809) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025854 / 0.037411 (-0.011557) | 0.102660 / 0.014526 (0.088134) | 0.108420 / 0.176557 (-0.068137) | 0.175834 / 0.737135 (-0.561301) | 0.113472 / 0.296338 (-0.182867) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.443595 / 0.215209 (0.228386) | 4.420959 / 2.077655 (2.343305) | 2.112790 / 1.504120 (0.608670) | 1.908836 / 1.541195 (0.367641) | 1.998340 / 1.468490 (0.529850) | 0.706096 / 4.584777 (-3.878681) | 3.400871 / 3.745712 (-0.344841) | 2.803315 / 5.269862 (-2.466547) | 1.539392 / 4.565676 (-3.026284) | 0.083523 / 0.424275 (-0.340752) | 0.012541 / 0.007607 (0.004934) | 0.543428 / 0.226044 (0.317383) | 5.467416 / 2.268929 (3.198488) | 2.551970 / 55.444624 (-52.892654) | 2.212708 / 6.876477 (-4.663768) | 2.266169 / 2.142072 (0.124096) | 0.809943 / 4.805227 (-3.995284) | 0.152300 / 6.500664 (-6.348364) | 0.068591 / 0.075469 (-0.006878) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.330141 / 1.841788 (-0.511646) | 14.292734 / 8.074308 (6.218426) | 13.556157 / 10.191392 (3.364765) | 0.155949 / 0.680424 (-0.524475) | 0.016464 / 0.534201 (-0.517737) | 0.377906 / 0.579283 (-0.201377) | 0.390385 / 0.434364 (-0.043979) | 0.471867 / 0.540337 (-0.068471) | 0.557794 / 1.386936 (-0.829142) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ba50512b76ef315f73bf821b0487296cdb373850 \"CML watermark\")\n",
"I just tried on colab and it didn't finish the progress bar for some reason.\r\n\r\nMaybe we need to call `pbar.close()` before `return`\r\n\r\n<img width=\"729\" alt=\"image\" src=\"https://user-images.githubusercontent.com/42851186/220417517-919438a4-5462-4e87-8f84-e9399a9be27c.png\">\r\n",
"(just added .close() - let me try quickly if it works now)",
"it worked ! :)\r\n\r\n<img width=\"575\" alt=\"image\" src=\"https://user-images.githubusercontent.com/42851186/220419220-8108f225-13cb-4968-acff-fe4543d5a324.png\">\r\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008465 / 0.011353 (-0.002888) | 0.004622 / 0.011008 (-0.006387) | 0.100365 / 0.038508 (0.061857) | 0.029453 / 0.023109 (0.006344) | 0.358041 / 0.275898 (0.082143) | 0.424777 / 0.323480 (0.101298) | 0.006930 / 0.007986 (-0.001055) | 0.004756 / 0.004328 (0.000428) | 0.077128 / 0.004250 (0.072878) | 0.036338 / 0.037052 (-0.000715) | 0.367613 / 0.258489 (0.109124) | 0.397798 / 0.293841 (0.103957) | 0.033500 / 0.128546 (-0.095047) | 0.011427 / 0.075646 (-0.064219) | 0.321617 / 0.419271 (-0.097654) | 0.040937 / 0.043533 (-0.002596) | 0.345358 / 0.255139 (0.090219) | 0.366932 / 0.283200 (0.083733) | 0.086506 / 0.141683 (-0.055177) | 1.482434 / 1.452155 (0.030280) | 1.522773 / 1.492716 (0.030057) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.188815 / 0.018006 (0.170809) | 0.404689 / 0.000490 (0.404200) | 0.000390 / 0.000200 (0.000190) | 0.000059 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023165 / 0.037411 (-0.014246) | 0.095934 / 0.014526 (0.081408) | 0.105788 / 0.176557 (-0.070769) | 0.169908 / 0.737135 (-0.567227) | 0.107871 / 0.296338 (-0.188467) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.457543 / 0.215209 (0.242334) | 4.563209 / 2.077655 (2.485554) | 2.172272 / 1.504120 (0.668152) | 1.965064 / 1.541195 (0.423870) | 2.020811 / 1.468490 (0.552321) | 0.705138 / 4.584777 (-3.879638) | 3.353430 / 3.745712 (-0.392283) | 1.861970 / 5.269862 (-3.407892) | 1.159201 / 4.565676 (-3.406476) | 0.083187 / 0.424275 (-0.341088) | 0.012750 / 0.007607 (0.005143) | 0.566377 / 0.226044 (0.340333) | 5.662645 / 2.268929 (3.393717) | 2.609565 / 55.444624 (-52.835059) | 2.244519 / 6.876477 (-4.631957) | 2.284111 / 2.142072 (0.142038) | 0.821974 / 4.805227 (-3.983253) | 0.151080 / 6.500664 (-6.349584) | 0.065373 / 0.075469 (-0.010096) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.230960 / 1.841788 (-0.610828) | 13.930408 / 8.074308 (5.856100) | 13.989082 / 10.191392 (3.797690) | 0.151961 / 0.680424 (-0.528462) | 0.028770 / 0.534201 (-0.505431) | 0.392269 / 0.579283 (-0.187015) | 0.400490 / 0.434364 (-0.033874) | 0.459770 / 0.540337 (-0.080568) | 0.534174 / 1.386936 (-0.852762) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006740 / 0.011353 (-0.004613) | 0.004496 / 0.011008 (-0.006512) | 0.076886 / 0.038508 (0.038377) | 0.027593 / 0.023109 (0.004484) | 0.339570 / 0.275898 (0.063672) | 0.379915 / 0.323480 (0.056435) | 0.004999 / 0.007986 (-0.002987) | 0.004253 / 0.004328 (-0.000076) | 0.074973 / 0.004250 (0.070722) | 0.037321 / 0.037052 (0.000269) | 0.344720 / 0.258489 (0.086230) | 0.398919 / 0.293841 (0.105078) | 0.032146 / 0.128546 (-0.096400) | 0.011694 / 0.075646 (-0.063952) | 0.085134 / 0.419271 (-0.334138) | 0.042328 / 0.043533 (-0.001205) | 0.339384 / 0.255139 (0.084245) | 0.368031 / 0.283200 (0.084831) | 0.092088 / 0.141683 (-0.049595) | 1.492313 / 1.452155 (0.040158) | 1.538406 / 1.492716 (0.045690) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.265619 / 0.018006 (0.247613) | 0.415478 / 0.000490 (0.414988) | 0.030221 / 0.000200 (0.030021) | 0.000277 / 0.000054 (0.000223) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024489 / 0.037411 (-0.012922) | 0.099920 / 0.014526 (0.085395) | 0.108301 / 0.176557 (-0.068256) | 0.179525 / 0.737135 (-0.557610) | 0.111492 / 0.296338 (-0.184847) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440759 / 0.215209 (0.225550) | 4.382754 / 2.077655 (2.305100) | 2.088686 / 1.504120 (0.584566) | 1.890557 / 1.541195 (0.349363) | 1.947461 / 1.468490 (0.478971) | 0.701751 / 4.584777 (-3.883025) | 3.368896 / 3.745712 (-0.376816) | 1.867238 / 5.269862 (-3.402624) | 1.166787 / 4.565676 (-3.398890) | 0.083427 / 0.424275 (-0.340848) | 0.012406 / 0.007607 (0.004799) | 0.539467 / 0.226044 (0.313423) | 5.376083 / 2.268929 (3.107154) | 2.516566 / 55.444624 (-52.928058) | 2.177991 / 6.876477 (-4.698486) | 2.207438 / 2.142072 (0.065366) | 0.803316 / 4.805227 (-4.001911) | 0.150900 / 6.500664 (-6.349764) | 0.066328 / 0.075469 (-0.009141) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.295308 / 1.841788 (-0.546480) | 14.081343 / 8.074308 (6.007035) | 13.516853 / 10.191392 (3.325461) | 0.160530 / 0.680424 (-0.519894) | 0.016516 / 0.534201 (-0.517685) | 0.380160 / 0.579283 (-0.199123) | 0.443484 / 0.434364 (0.009120) | 0.466645 / 0.540337 (-0.073692) | 0.555339 / 1.386936 (-0.831597) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e8a12313cd728e37b4dc4ce67864621ffc79fedb \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011321 / 0.011353 (-0.000031) | 0.006365 / 0.011008 (-0.004643) | 0.125613 / 0.038508 (0.087105) | 0.035327 / 0.023109 (0.012218) | 0.391998 / 0.275898 (0.116100) | 0.475402 / 0.323480 (0.151923) | 0.009579 / 0.007986 (0.001593) | 0.005621 / 0.004328 (0.001293) | 0.106097 / 0.004250 (0.101846) | 0.042774 / 0.037052 (0.005722) | 0.420850 / 0.258489 (0.162361) | 0.454501 / 0.293841 (0.160660) | 0.056885 / 0.128546 (-0.071661) | 0.021718 / 0.075646 (-0.053928) | 0.419422 / 0.419271 (0.000150) | 0.056690 / 0.043533 (0.013157) | 0.405375 / 0.255139 (0.150236) | 0.444404 / 0.283200 (0.161204) | 0.136912 / 0.141683 (-0.004771) | 1.846363 / 1.452155 (0.394208) | 1.747433 / 1.492716 (0.254717) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.282260 / 0.018006 (0.264254) | 0.615813 / 0.000490 (0.615323) | 0.000515 / 0.000200 (0.000315) | 0.000106 / 0.000054 (0.000052) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029913 / 0.037411 (-0.007499) | 0.135568 / 0.014526 (0.121042) | 0.134476 / 0.176557 (-0.042081) | 0.206974 / 0.737135 (-0.530161) | 0.136976 / 0.296338 (-0.159362) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.605241 / 0.215209 (0.390032) | 6.125097 / 2.077655 (4.047442) | 2.390102 / 1.504120 (0.885982) | 2.082196 / 1.541195 (0.541001) | 2.226527 / 1.468490 (0.758037) | 1.244807 / 4.584777 (-3.339970) | 5.476437 / 3.745712 (1.730725) | 3.014970 / 5.269862 (-2.254891) | 1.963428 / 4.565676 (-2.602249) | 0.137813 / 0.424275 (-0.286462) | 0.013794 / 0.007607 (0.006187) | 0.766149 / 0.226044 (0.540104) | 7.566103 / 2.268929 (5.297175) | 3.048958 / 55.444624 (-52.395666) | 2.394819 / 6.876477 (-4.481658) | 2.416021 / 2.142072 (0.273949) | 1.369896 / 4.805227 (-3.435331) | 0.245159 / 6.500664 (-6.255506) | 0.076848 / 0.075469 (0.001379) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.530448 / 1.841788 (-0.311340) | 18.580227 / 8.074308 (10.505919) | 20.108470 / 10.191392 (9.917078) | 0.227124 / 0.680424 (-0.453300) | 0.052050 / 0.534201 (-0.482151) | 0.604565 / 0.579283 (0.025282) | 0.686475 / 0.434364 (0.252111) | 0.672298 / 0.540337 (0.131960) | 0.770552 / 1.386936 (-0.616384) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010043 / 0.011353 (-0.001310) | 0.006445 / 0.011008 (-0.004563) | 0.099486 / 0.038508 (0.060978) | 0.037720 / 0.023109 (0.014610) | 0.425571 / 0.275898 (0.149673) | 0.467031 / 0.323480 (0.143551) | 0.007394 / 0.007986 (-0.000591) | 0.005008 / 0.004328 (0.000679) | 0.096176 / 0.004250 (0.091926) | 0.053694 / 0.037052 (0.016641) | 0.418653 / 0.258489 (0.160164) | 0.492441 / 0.293841 (0.198600) | 0.054593 / 0.128546 (-0.073953) | 0.023410 / 0.075646 (-0.052236) | 0.113825 / 0.419271 (-0.305446) | 0.066000 / 0.043533 (0.022467) | 0.418127 / 0.255139 (0.162988) | 0.457416 / 0.283200 (0.174217) | 0.119911 / 0.141683 (-0.021771) | 1.733805 / 1.452155 (0.281651) | 1.961252 / 1.492716 (0.468536) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.296126 / 0.018006 (0.278120) | 0.602169 / 0.000490 (0.601680) | 0.000454 / 0.000200 (0.000254) | 0.000075 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032970 / 0.037411 (-0.004442) | 0.124071 / 0.014526 (0.109545) | 0.143800 / 0.176557 (-0.032757) | 0.227168 / 0.737135 (-0.509967) | 0.142817 / 0.296338 (-0.153521) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.626239 / 0.215209 (0.411030) | 6.438629 / 2.077655 (4.360974) | 2.760747 / 1.504120 (1.256627) | 2.355419 / 1.541195 (0.814224) | 2.384924 / 1.468490 (0.916434) | 1.210543 / 4.584777 (-3.374234) | 5.440389 / 3.745712 (1.694677) | 5.047939 / 5.269862 (-0.221922) | 2.759618 / 4.565676 (-1.806059) | 0.132757 / 0.424275 (-0.291518) | 0.013163 / 0.007607 (0.005556) | 0.745721 / 0.226044 (0.519677) | 7.660327 / 2.268929 (5.391398) | 3.559385 / 55.444624 (-51.885240) | 2.764344 / 6.876477 (-4.112133) | 2.975274 / 2.142072 (0.833202) | 1.460346 / 4.805227 (-3.344881) | 0.257222 / 6.500664 (-6.243443) | 0.081106 / 0.075469 (0.005637) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.698245 / 1.841788 (-0.143543) | 18.754129 / 8.074308 (10.679821) | 19.065596 / 10.191392 (8.874204) | 0.228237 / 0.680424 (-0.452187) | 0.030688 / 0.534201 (-0.503513) | 0.532561 / 0.579283 (-0.046722) | 0.601133 / 0.434364 (0.166769) | 0.620218 / 0.540337 (0.079881) | 0.751392 / 1.386936 (-0.635545) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5f293ff23853fea210388bbef11d1621e54f22e7 \"CML watermark\")\n",
"(the BadZipFile error is unrelated to the changes)",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009368 / 0.011353 (-0.001984) | 0.005143 / 0.011008 (-0.005865) | 0.100675 / 0.038508 (0.062167) | 0.036033 / 0.023109 (0.012924) | 0.297391 / 0.275898 (0.021493) | 0.362230 / 0.323480 (0.038750) | 0.008041 / 0.007986 (0.000055) | 0.004041 / 0.004328 (-0.000287) | 0.075395 / 0.004250 (0.071144) | 0.043020 / 0.037052 (0.005968) | 0.308936 / 0.258489 (0.050447) | 0.343723 / 0.293841 (0.049883) | 0.038416 / 0.128546 (-0.090131) | 0.012086 / 0.075646 (-0.063560) | 0.335102 / 0.419271 (-0.084170) | 0.047718 / 0.043533 (0.004185) | 0.297856 / 0.255139 (0.042717) | 0.317326 / 0.283200 (0.034126) | 0.101462 / 0.141683 (-0.040221) | 1.459965 / 1.452155 (0.007810) | 1.491194 / 1.492716 (-0.001522) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211311 / 0.018006 (0.193305) | 0.443663 / 0.000490 (0.443174) | 0.003654 / 0.000200 (0.003454) | 0.000076 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027316 / 0.037411 (-0.010095) | 0.109929 / 0.014526 (0.095403) | 0.117170 / 0.176557 (-0.059387) | 0.182494 / 0.737135 (-0.554641) | 0.124693 / 0.296338 (-0.171646) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.395904 / 0.215209 (0.180695) | 3.950906 / 2.077655 (1.873251) | 1.768807 / 1.504120 (0.264687) | 1.578979 / 1.541195 (0.037784) | 1.689976 / 1.468490 (0.221486) | 0.696458 / 4.584777 (-3.888319) | 3.750491 / 3.745712 (0.004778) | 2.117863 / 5.269862 (-3.151998) | 1.340403 / 4.565676 (-3.225274) | 0.085752 / 0.424275 (-0.338523) | 0.012206 / 0.007607 (0.004599) | 0.505561 / 0.226044 (0.279517) | 5.048721 / 2.268929 (2.779792) | 2.256623 / 55.444624 (-53.188001) | 1.905912 / 6.876477 (-4.970565) | 1.988400 / 2.142072 (-0.153672) | 0.843066 / 4.805227 (-3.962161) | 0.165717 / 6.500664 (-6.334947) | 0.062910 / 0.075469 (-0.012559) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.225668 / 1.841788 (-0.616120) | 14.660082 / 8.074308 (6.585773) | 14.295369 / 10.191392 (4.103977) | 0.171075 / 0.680424 (-0.509348) | 0.029279 / 0.534201 (-0.504922) | 0.441559 / 0.579283 (-0.137724) | 0.445382 / 0.434364 (0.011018) | 0.525350 / 0.540337 (-0.014987) | 0.608493 / 1.386936 (-0.778443) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007288 / 0.011353 (-0.004065) | 0.004999 / 0.011008 (-0.006009) | 0.074656 / 0.038508 (0.036147) | 0.033897 / 0.023109 (0.010788) | 0.345826 / 0.275898 (0.069928) | 0.390891 / 0.323480 (0.067411) | 0.005811 / 0.007986 (-0.002174) | 0.003976 / 0.004328 (-0.000353) | 0.073546 / 0.004250 (0.069295) | 0.047245 / 0.037052 (0.010193) | 0.351851 / 0.258489 (0.093362) | 0.403217 / 0.293841 (0.109376) | 0.036771 / 0.128546 (-0.091775) | 0.012240 / 0.075646 (-0.063407) | 0.086720 / 0.419271 (-0.332552) | 0.049440 / 0.043533 (0.005907) | 0.339520 / 0.255139 (0.084381) | 0.372160 / 0.283200 (0.088961) | 0.100813 / 0.141683 (-0.040870) | 1.436436 / 1.452155 (-0.015718) | 1.514723 / 1.492716 (0.022007) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231394 / 0.018006 (0.213388) | 0.440825 / 0.000490 (0.440336) | 0.000994 / 0.000200 (0.000794) | 0.000077 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028999 / 0.037411 (-0.008412) | 0.111391 / 0.014526 (0.096865) | 0.123058 / 0.176557 (-0.053498) | 0.194348 / 0.737135 (-0.542787) | 0.125730 / 0.296338 (-0.170609) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431950 / 0.215209 (0.216741) | 4.298724 / 2.077655 (2.221069) | 2.064116 / 1.504120 (0.559996) | 1.892062 / 1.541195 (0.350867) | 1.985441 / 1.468490 (0.516951) | 0.707028 / 4.584777 (-3.877749) | 3.812976 / 3.745712 (0.067264) | 3.078704 / 5.269862 (-2.191158) | 1.832737 / 4.565676 (-2.732939) | 0.086182 / 0.424275 (-0.338093) | 0.012289 / 0.007607 (0.004681) | 0.530265 / 0.226044 (0.304220) | 5.283122 / 2.268929 (3.014194) | 2.558491 / 55.444624 (-52.886134) | 2.237046 / 6.876477 (-4.639431) | 2.354548 / 2.142072 (0.212475) | 0.848947 / 4.805227 (-3.956280) | 0.167907 / 6.500664 (-6.332757) | 0.064998 / 0.075469 (-0.010471) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.248287 / 1.841788 (-0.593500) | 14.976327 / 8.074308 (6.902019) | 13.596143 / 10.191392 (3.404751) | 0.145730 / 0.680424 (-0.534694) | 0.017340 / 0.534201 (-0.516861) | 0.430111 / 0.579283 (-0.149172) | 0.433462 / 0.434364 (-0.000902) | 0.540365 / 0.540337 (0.000028) | 0.650586 / 1.386936 (-0.736350) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1875c8a4c928aeaccc826f13ffdbf7543112024d \"CML watermark\")\n"
] | "2023-02-21T16:56:17Z" | "2023-02-21T18:26:23Z" | "2023-02-21T18:19:09Z" | CONTRIBUTOR | null | 0 | {
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} | This PR modifies `map` to:
* ensure the TQDM bar gets the last progress update
* when a map function fails, avoid throwing a chained exception in the single-proc mode | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011596 / 0.011353 (0.000244) | 0.005845 / 0.011008 (-0.005164) | 0.121302 / 0.038508 (0.082794) | 0.034306 / 0.023109 (0.011196) | 0.355973 / 0.275898 (0.080075) | 0.419903 / 0.323480 (0.096423) | 0.009049 / 0.007986 (0.001064) | 0.004245 / 0.004328 (-0.000084) | 0.092004 / 0.004250 (0.087753) | 0.042782 / 0.037052 (0.005730) | 0.355805 / 0.258489 (0.097316) | 0.407298 / 0.293841 (0.113457) | 0.052481 / 0.128546 (-0.076066) | 0.020880 / 0.075646 (-0.054766) | 0.379948 / 0.419271 (-0.039324) | 0.061337 / 0.043533 (0.017804) | 0.359829 / 0.255139 (0.104690) | 0.379244 / 0.283200 (0.096044) | 0.116692 / 0.141683 (-0.024990) | 1.733717 / 1.452155 (0.281562) | 1.700246 / 1.492716 (0.207530) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.014622 / 0.018006 (-0.003384) | 0.518777 / 0.000490 (0.518288) | 0.004086 / 0.000200 (0.003886) | 0.000136 / 0.000054 (0.000082) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031208 / 0.037411 (-0.006204) | 0.143003 / 0.014526 (0.128477) | 0.132625 / 0.176557 (-0.043932) | 0.187681 / 0.737135 (-0.549455) | 0.136576 / 0.296338 (-0.159763) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.626516 / 0.215209 (0.411307) | 6.282558 / 2.077655 (4.204904) | 2.702686 / 1.504120 (1.198566) | 2.287445 / 1.541195 (0.746250) | 2.333014 / 1.468490 (0.864524) | 1.227815 / 4.584777 (-3.356962) | 5.545640 / 3.745712 (1.799928) | 4.953226 / 5.269862 (-0.316635) | 2.774549 / 4.565676 (-1.791128) | 0.145257 / 0.424275 (-0.279018) | 0.014887 / 0.007607 (0.007280) | 0.812226 / 0.226044 (0.586182) | 8.002727 / 2.268929 (5.733798) | 3.314852 / 55.444624 (-52.129773) | 2.602348 / 6.876477 (-4.274128) | 2.593511 / 2.142072 (0.451438) | 1.440498 / 4.805227 (-3.364730) | 0.254849 / 6.500664 (-6.245815) | 0.077020 / 0.075469 (0.001551) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.487633 / 1.841788 (-0.354155) | 17.385773 / 8.074308 (9.311465) | 21.775511 / 10.191392 (11.584118) | 0.273514 / 0.680424 (-0.406910) | 0.059644 / 0.534201 (-0.474557) | 0.578710 / 0.579283 (-0.000573) | 0.630221 / 0.434364 (0.195857) | 0.632089 / 0.540337 (0.091752) | 0.762367 / 1.386936 (-0.624569) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009513 / 0.011353 (-0.001840) | 0.006009 / 0.011008 (-0.004999) | 0.087589 / 0.038508 (0.049081) | 0.037487 / 0.023109 (0.014378) | 0.397660 / 0.275898 (0.121762) | 0.474438 / 0.323480 (0.150958) | 0.007373 / 0.007986 (-0.000613) | 0.005839 / 0.004328 (0.001511) | 0.092759 / 0.004250 (0.088509) | 0.052128 / 0.037052 (0.015075) | 0.382378 / 0.258489 (0.123889) | 0.458244 / 0.293841 (0.164403) | 0.057232 / 0.128546 (-0.071314) | 0.020662 / 0.075646 (-0.054984) | 0.110314 / 0.419271 (-0.308957) | 0.063014 / 0.043533 (0.019481) | 0.386020 / 0.255139 (0.130881) | 0.476169 / 0.283200 (0.192970) | 0.118081 / 0.141683 (-0.023602) | 1.724158 / 1.452155 (0.272003) | 1.862257 / 1.492716 (0.369541) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224288 / 0.018006 (0.206281) | 0.523631 / 0.000490 (0.523141) | 0.004420 / 0.000200 (0.004220) | 0.000127 / 0.000054 (0.000073) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032359 / 0.037411 (-0.005052) | 0.140045 / 0.014526 (0.125519) | 0.138164 / 0.176557 (-0.038393) | 0.181068 / 0.737135 (-0.556067) | 0.143965 / 0.296338 (-0.152374) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.573809 / 0.215209 (0.358600) | 6.083247 / 2.077655 (4.005592) | 2.671258 / 1.504120 (1.167138) | 2.277062 / 1.541195 (0.735868) | 2.299544 / 1.468490 (0.831054) | 1.267351 / 4.584777 (-3.317425) | 5.494461 / 3.745712 (1.748749) | 5.083169 / 5.269862 (-0.186692) | 2.531738 / 4.565676 (-2.033938) | 0.151834 / 0.424275 (-0.272441) | 0.014123 / 0.007607 (0.006516) | 0.800222 / 0.226044 (0.574177) | 7.637624 / 2.268929 (5.368695) | 3.325574 / 55.444624 (-52.119050) | 2.563008 / 6.876477 (-4.313468) | 2.596259 / 2.142072 (0.454187) | 1.459206 / 4.805227 (-3.346021) | 0.237771 / 6.500664 (-6.262893) | 0.071854 / 0.075469 (-0.003615) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.605504 / 1.841788 (-0.236284) | 17.593594 / 8.074308 (9.519285) | 20.618005 / 10.191392 (10.426612) | 0.270938 / 0.680424 (-0.409486) | 0.026205 / 0.534201 (-0.507996) | 0.562223 / 0.579283 (-0.017060) | 0.617571 / 0.434364 (0.183207) | 0.616398 / 0.540337 (0.076060) | 0.715293 / 1.386936 (-0.671643) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#673dc0dd7d063b2313f7adcc9e0be53d4718f5cf \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.013213 / 0.011353 (0.001860) | 0.006253 / 0.011008 (-0.004756) | 0.125175 / 0.038508 (0.086667) | 0.037491 / 0.023109 (0.014382) | 0.401379 / 0.275898 (0.125481) | 0.395826 / 0.323480 (0.072346) | 0.009224 / 0.007986 (0.001238) | 0.005163 / 0.004328 (0.000835) | 0.096490 / 0.004250 (0.092239) | 0.042473 / 0.037052 (0.005420) | 0.383713 / 0.258489 (0.125224) | 0.429234 / 0.293841 (0.135393) | 0.063261 / 0.128546 (-0.065285) | 0.020114 / 0.075646 (-0.055532) | 0.401687 / 0.419271 (-0.017585) | 0.062831 / 0.043533 (0.019298) | 0.405211 / 0.255139 (0.150072) | 0.380810 / 0.283200 (0.097610) | 0.109166 / 0.141683 (-0.032517) | 1.869580 / 1.452155 (0.417426) | 1.949947 / 1.492716 (0.457231) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.207481 / 0.018006 (0.189475) | 0.504161 / 0.000490 (0.503671) | 0.008429 / 0.000200 (0.008229) | 0.000101 / 0.000054 (0.000047) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029182 / 0.037411 (-0.008229) | 0.126284 / 0.014526 (0.111758) | 0.140381 / 0.176557 (-0.036175) | 0.175878 / 0.737135 (-0.561257) | 0.138824 / 0.296338 (-0.157514) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.643658 / 0.215209 (0.428449) | 6.396224 / 2.077655 (4.318569) | 2.600702 / 1.504120 (1.096582) | 2.176721 / 1.541195 (0.635526) | 2.216116 / 1.468490 (0.747626) | 1.235069 / 4.584777 (-3.349708) | 5.457228 / 3.745712 (1.711516) | 3.060455 / 5.269862 (-2.209407) | 2.028123 / 4.565676 (-2.537554) | 0.141617 / 0.424275 (-0.282658) | 0.016596 / 0.007607 (0.008989) | 0.804915 / 0.226044 (0.578870) | 7.968821 / 2.268929 (5.699893) | 3.340650 / 55.444624 (-52.103974) | 2.533620 / 6.876477 (-4.342856) | 2.457388 / 2.142072 (0.315315) | 1.486527 / 4.805227 (-3.318700) | 0.253767 / 6.500664 (-6.246897) | 0.082192 / 0.075469 (0.006723) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.470896 / 1.841788 (-0.370892) | 17.566637 / 8.074308 (9.492329) | 23.144148 / 10.191392 (12.952756) | 0.235510 / 0.680424 (-0.444913) | 0.046051 / 0.534201 (-0.488150) | 0.559954 / 0.579283 (-0.019329) | 0.645390 / 0.434364 (0.211026) | 0.690983 / 0.540337 (0.150646) | 0.776252 / 1.386936 (-0.610684) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010564 / 0.011353 (-0.000789) | 0.006150 / 0.011008 (-0.004858) | 0.100030 / 0.038508 (0.061522) | 0.036873 / 0.023109 (0.013764) | 0.448508 / 0.275898 (0.172610) | 0.492593 / 0.323480 (0.169113) | 0.007337 / 0.007986 (-0.000648) | 0.004804 / 0.004328 (0.000475) | 0.099218 / 0.004250 (0.094967) | 0.055513 / 0.037052 (0.018461) | 0.462147 / 0.258489 (0.203658) | 0.510229 / 0.293841 (0.216388) | 0.055307 / 0.128546 (-0.073239) | 0.021989 / 0.075646 (-0.053657) | 0.118487 / 0.419271 (-0.300785) | 0.071752 / 0.043533 (0.028219) | 0.456572 / 0.255139 (0.201433) | 0.475160 / 0.283200 (0.191961) | 0.117472 / 0.141683 (-0.024211) | 1.813212 / 1.452155 (0.361058) | 1.908413 / 1.492716 (0.415696) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.352929 / 0.018006 (0.334923) | 0.543874 / 0.000490 (0.543384) | 0.078529 / 0.000200 (0.078329) | 0.000669 / 0.000054 (0.000614) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033157 / 0.037411 (-0.004254) | 0.162503 / 0.014526 (0.147977) | 0.146424 / 0.176557 (-0.030132) | 0.201781 / 0.737135 (-0.535354) | 0.168110 / 0.296338 (-0.128229) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.644205 / 0.215209 (0.428996) | 6.327519 / 2.077655 (4.249865) | 2.728102 / 1.504120 (1.223982) | 2.306426 / 1.541195 (0.765232) | 2.373125 / 1.468490 (0.904635) | 1.350649 / 4.584777 (-3.234128) | 5.652714 / 3.745712 (1.907002) | 3.175335 / 5.269862 (-2.094526) | 2.222902 / 4.565676 (-2.342775) | 0.160609 / 0.424275 (-0.263666) | 0.015596 / 0.007607 (0.007989) | 0.790357 / 0.226044 (0.564313) | 8.289758 / 2.268929 (6.020830) | 3.479215 / 55.444624 (-51.965410) | 2.860063 / 6.876477 (-4.016413) | 2.806720 / 2.142072 (0.664648) | 1.639046 / 4.805227 (-3.166181) | 0.267017 / 6.500664 (-6.233648) | 0.083990 / 0.075469 (0.008521) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.632262 / 1.841788 (-0.209525) | 17.794357 / 8.074308 (9.720049) | 21.203547 / 10.191392 (11.012155) | 0.250899 / 0.680424 (-0.429525) | 0.024502 / 0.534201 (-0.509699) | 0.519960 / 0.579283 (-0.059323) | 0.615412 / 0.434364 (0.181048) | 0.641914 / 0.540337 (0.101577) | 0.772355 / 1.386936 (-0.614581) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#32cc4d10243b0feb69650f007d010971fd861dc1 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009501 / 0.011353 (-0.001852) | 0.005262 / 0.011008 (-0.005747) | 0.100809 / 0.038508 (0.062301) | 0.036601 / 0.023109 (0.013492) | 0.299612 / 0.275898 (0.023714) | 0.366970 / 0.323480 (0.043490) | 0.007879 / 0.007986 (-0.000107) | 0.004216 / 0.004328 (-0.000113) | 0.076749 / 0.004250 (0.072498) | 0.042081 / 0.037052 (0.005029) | 0.299572 / 0.258489 (0.041083) | 0.339687 / 0.293841 (0.045846) | 0.038706 / 0.128546 (-0.089840) | 0.012295 / 0.075646 (-0.063352) | 0.336172 / 0.419271 (-0.083100) | 0.047524 / 0.043533 (0.003992) | 0.296800 / 0.255139 (0.041661) | 0.331592 / 0.283200 (0.048393) | 0.101191 / 0.141683 (-0.040491) | 1.486200 / 1.452155 (0.034046) | 1.509955 / 1.492716 (0.017239) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.204735 / 0.018006 (0.186728) | 0.446381 / 0.000490 (0.445891) | 0.005177 / 0.000200 (0.004977) | 0.000099 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028655 / 0.037411 (-0.008756) | 0.116559 / 0.014526 (0.102033) | 0.122551 / 0.176557 (-0.054006) | 0.189764 / 0.737135 (-0.547372) | 0.126446 / 0.296338 (-0.169892) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400104 / 0.215209 (0.184895) | 4.001524 / 2.077655 (1.923869) | 1.779267 / 1.504120 (0.275147) | 1.580168 / 1.541195 (0.038974) | 1.684100 / 1.468490 (0.215610) | 0.703354 / 4.584777 (-3.881423) | 3.828131 / 3.745712 (0.082419) | 2.098500 / 5.269862 (-3.171362) | 1.331161 / 4.565676 (-3.234516) | 0.085417 / 0.424275 (-0.338858) | 0.012380 / 0.007607 (0.004772) | 0.504189 / 0.226044 (0.278144) | 5.094672 / 2.268929 (2.825743) | 2.264352 / 55.444624 (-53.180272) | 1.909573 / 6.876477 (-4.966904) | 2.005425 / 2.142072 (-0.136648) | 0.840893 / 4.805227 (-3.964335) | 0.164689 / 6.500664 (-6.335975) | 0.062754 / 0.075469 (-0.012715) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.250001 / 1.841788 (-0.591786) | 14.993313 / 8.074308 (6.919005) | 14.880601 / 10.191392 (4.689209) | 0.175141 / 0.680424 (-0.505283) | 0.028952 / 0.534201 (-0.505249) | 0.447073 / 0.579283 (-0.132210) | 0.445993 / 0.434364 (0.011629) | 0.525527 / 0.540337 (-0.014811) | 0.613156 / 1.386936 (-0.773780) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007796 / 0.011353 (-0.003557) | 0.005399 / 0.011008 (-0.005609) | 0.078240 / 0.038508 (0.039732) | 0.035303 / 0.023109 (0.012193) | 0.364603 / 0.275898 (0.088705) | 0.400794 / 0.323480 (0.077314) | 0.006152 / 0.007986 (-0.001834) | 0.004324 / 0.004328 (-0.000004) | 0.074949 / 0.004250 (0.070698) | 0.051939 / 0.037052 (0.014887) | 0.377079 / 0.258489 (0.118590) | 0.413630 / 0.293841 (0.119789) | 0.037567 / 0.128546 (-0.090979) | 0.012793 / 0.075646 (-0.062854) | 0.089013 / 0.419271 (-0.330258) | 0.050748 / 0.043533 (0.007215) | 0.370100 / 0.255139 (0.114961) | 0.384838 / 0.283200 (0.101638) | 0.105840 / 0.141683 (-0.035843) | 1.476490 / 1.452155 (0.024335) | 1.544688 / 1.492716 (0.051972) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220987 / 0.018006 (0.202981) | 0.443801 / 0.000490 (0.443311) | 0.005747 / 0.000200 (0.005547) | 0.000106 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030187 / 0.037411 (-0.007225) | 0.118230 / 0.014526 (0.103704) | 0.126810 / 0.176557 (-0.049746) | 0.200482 / 0.737135 (-0.536654) | 0.130831 / 0.296338 (-0.165507) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.423231 / 0.215209 (0.208022) | 4.196576 / 2.077655 (2.118921) | 1.992919 / 1.504120 (0.488799) | 1.809172 / 1.541195 (0.267977) | 1.932706 / 1.468490 (0.464216) | 0.727319 / 4.584777 (-3.857458) | 3.833295 / 3.745712 (0.087583) | 3.527005 / 5.269862 (-1.742857) | 1.937348 / 4.565676 (-2.628329) | 0.088713 / 0.424275 (-0.335562) | 0.012711 / 0.007607 (0.005104) | 0.531385 / 0.226044 (0.305341) | 5.308051 / 2.268929 (3.039123) | 2.493494 / 55.444624 (-52.951131) | 2.168359 / 6.876477 (-4.708118) | 2.258160 / 2.142072 (0.116088) | 0.865629 / 4.805227 (-3.939598) | 0.171281 / 6.500664 (-6.329383) | 0.065746 / 0.075469 (-0.009723) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.290378 / 1.841788 (-0.551409) | 15.900804 / 8.074308 (7.826496) | 14.809614 / 10.191392 (4.618222) | 0.177287 / 0.680424 (-0.503137) | 0.017875 / 0.534201 (-0.516326) | 0.429646 / 0.579283 (-0.149637) | 0.451646 / 0.434364 (0.017282) | 0.545669 / 0.540337 (0.005332) | 0.633215 / 1.386936 (-0.753721) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2c67b5f4bc9cea088e977a135644d38da8c144ff \"CML watermark\")\n"
] | "2023-02-21T15:26:26Z" | "2023-02-21T17:21:37Z" | "2023-02-21T17:14:29Z" | MEMBER | null | 0 | {
"diff_url": "https://github.com/huggingface/datasets/pull/5559.diff",
"html_url": "https://github.com/huggingface/datasets/pull/5559",
"merged_at": "2023-02-21T17:14:29Z",
"patch_url": "https://github.com/huggingface/datasets/pull/5559.patch",
"url": "https://api.github.com/repos/huggingface/datasets/pulls/5559"
} | #5455 introduced a small bug that lead `map` to ignore the `suffix_template` argument and not put suffixes to cached files in multiprocessing.
I fixed this and also improved a few things:
- regarding logging: "Loading cached processed dataset" is now logged only once even in multiprocessing (it used to be logged `num_proc` times)
- regarding new_fingerprint: I made sure that the returned dataset satisfies `ds._fingerprint==new_fingerprint` if `new_fingerprint` is passed to `map` | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.014525 / 0.011353 (0.003172) | 0.006871 / 0.011008 (-0.004137) | 0.135577 / 0.038508 (0.097069) | 0.039620 / 0.023109 (0.016511) | 0.499829 / 0.275898 (0.223931) | 0.571000 / 0.323480 (0.247520) | 0.009726 / 0.007986 (0.001740) | 0.005654 / 0.004328 (0.001325) | 0.104732 / 0.004250 (0.100482) | 0.046849 / 0.037052 (0.009796) | 0.486667 / 0.258489 (0.228178) | 0.543611 / 0.293841 (0.249770) | 0.056414 / 0.128546 (-0.072133) | 0.019974 / 0.075646 (-0.055672) | 0.484878 / 0.419271 (0.065606) | 0.059244 / 0.043533 (0.015711) | 0.490046 / 0.255139 (0.234907) | 0.517427 / 0.283200 (0.234227) | 0.114692 / 0.141683 (-0.026991) | 1.935935 / 1.452155 (0.483780) | 1.990253 / 1.492716 (0.497537) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.271008 / 0.018006 (0.253002) | 0.610964 / 0.000490 (0.610474) | 0.013423 / 0.000200 (0.013223) | 0.000523 / 0.000054 (0.000468) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031940 / 0.037411 (-0.005472) | 0.130755 / 0.014526 (0.116229) | 0.146616 / 0.176557 (-0.029941) | 0.239386 / 0.737135 (-0.497749) | 0.146612 / 0.296338 (-0.149726) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.675383 / 0.215209 (0.460174) | 6.656828 / 2.077655 (4.579174) | 2.741231 / 1.504120 (1.237111) | 2.232921 / 1.541195 (0.691726) | 2.172116 / 1.468490 (0.703626) | 1.221623 / 4.584777 (-3.363154) | 5.683653 / 3.745712 (1.937941) | 5.344137 / 5.269862 (0.074275) | 2.969670 / 4.565676 (-1.596006) | 0.142107 / 0.424275 (-0.282168) | 0.015808 / 0.007607 (0.008201) | 0.767366 / 0.226044 (0.541321) | 8.059605 / 2.268929 (5.790676) | 3.333535 / 55.444624 (-52.111089) | 2.669619 / 6.876477 (-4.206857) | 2.652989 / 2.142072 (0.510917) | 1.526397 / 4.805227 (-3.278830) | 0.265609 / 6.500664 (-6.235055) | 0.082759 / 0.075469 (0.007290) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.631086 / 1.841788 (-0.210701) | 18.701351 / 8.074308 (10.627043) | 22.843802 / 10.191392 (12.652410) | 0.240134 / 0.680424 (-0.440290) | 0.046683 / 0.534201 (-0.487518) | 0.576488 / 0.579283 (-0.002795) | 0.650123 / 0.434364 (0.215759) | 0.661190 / 0.540337 (0.120853) | 0.759563 / 1.386936 (-0.627373) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009883 / 0.011353 (-0.001470) | 0.006692 / 0.011008 (-0.004316) | 0.098550 / 0.038508 (0.060042) | 0.035188 / 0.023109 (0.012078) | 0.463535 / 0.275898 (0.187637) | 0.472762 / 0.323480 (0.149282) | 0.007199 / 0.007986 (-0.000787) | 0.007961 / 0.004328 (0.003632) | 0.093140 / 0.004250 (0.088890) | 0.051752 / 0.037052 (0.014700) | 0.453412 / 0.258489 (0.194922) | 0.502741 / 0.293841 (0.208900) | 0.056006 / 0.128546 (-0.072540) | 0.020164 / 0.075646 (-0.055482) | 0.116828 / 0.419271 (-0.302444) | 0.067205 / 0.043533 (0.023672) | 0.442715 / 0.255139 (0.187576) | 0.472525 / 0.283200 (0.189326) | 0.122767 / 0.141683 (-0.018915) | 1.881366 / 1.452155 (0.429212) | 1.978786 / 1.492716 (0.486069) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.284180 / 0.018006 (0.266174) | 0.601556 / 0.000490 (0.601067) | 0.008455 / 0.000200 (0.008255) | 0.000112 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033515 / 0.037411 (-0.003896) | 0.136407 / 0.014526 (0.121881) | 0.143341 / 0.176557 (-0.033215) | 0.225394 / 0.737135 (-0.511741) | 0.153343 / 0.296338 (-0.142995) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.688202 / 0.215209 (0.472993) | 6.576502 / 2.077655 (4.498847) | 2.839175 / 1.504120 (1.335055) | 2.481152 / 1.541195 (0.939957) | 2.617227 / 1.468490 (1.148736) | 1.314854 / 4.584777 (-3.269922) | 5.805950 / 3.745712 (2.060238) | 3.188930 / 5.269862 (-2.080932) | 2.141719 / 4.565676 (-2.423957) | 0.145069 / 0.424275 (-0.279206) | 0.014567 / 0.007607 (0.006960) | 0.780000 / 0.226044 (0.553955) | 7.898016 / 2.268929 (5.629088) | 3.549060 / 55.444624 (-51.895564) | 2.856569 / 6.876477 (-4.019907) | 3.117719 / 2.142072 (0.975647) | 1.512560 / 4.805227 (-3.292668) | 0.262689 / 6.500664 (-6.237975) | 0.085979 / 0.075469 (0.010509) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.623550 / 1.841788 (-0.218238) | 19.597063 / 8.074308 (11.522755) | 21.293369 / 10.191392 (11.101977) | 0.263780 / 0.680424 (-0.416643) | 0.027289 / 0.534201 (-0.506912) | 0.560361 / 0.579283 (-0.018922) | 0.646288 / 0.434364 (0.211924) | 0.712699 / 0.540337 (0.172361) | 0.818332 / 1.386936 (-0.568604) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b304de5dde30c945ec1397d3b4fe86f3b323ca8b \"CML watermark\")\n"
] | "2023-02-21T15:13:36Z" | "2023-03-01T13:46:04Z" | "2023-02-23T13:50:27Z" | CONTRIBUTOR | null | 0 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008477 / 0.011353 (-0.002875) | 0.004565 / 0.011008 (-0.006443) | 0.101640 / 0.038508 (0.063132) | 0.029581 / 0.023109 (0.006472) | 0.296524 / 0.275898 (0.020625) | 0.363175 / 0.323480 (0.039695) | 0.006961 / 0.007986 (-0.001024) | 0.003365 / 0.004328 (-0.000963) | 0.079689 / 0.004250 (0.075439) | 0.034881 / 0.037052 (-0.002171) | 0.310979 / 0.258489 (0.052489) | 0.348663 / 0.293841 (0.054822) | 0.034549 / 0.128546 (-0.093997) | 0.011463 / 0.075646 (-0.064184) | 0.326218 / 0.419271 (-0.093053) | 0.041393 / 0.043533 (-0.002140) | 0.297604 / 0.255139 (0.042465) | 0.335751 / 0.283200 (0.052551) | 0.086521 / 0.141683 (-0.055162) | 1.478906 / 1.452155 (0.026752) | 1.512777 / 1.492716 (0.020060) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.008767 / 0.018006 (-0.009239) | 0.397386 / 0.000490 (0.396897) | 0.003136 / 0.000200 (0.002936) | 0.000096 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022804 / 0.037411 (-0.014608) | 0.097591 / 0.014526 (0.083066) | 0.103189 / 0.176557 (-0.073368) | 0.138165 / 0.737135 (-0.598970) | 0.107464 / 0.296338 (-0.188874) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428956 / 0.215209 (0.213747) | 4.269656 / 2.077655 (2.192001) | 2.154418 / 1.504120 (0.650298) | 1.914176 / 1.541195 (0.372982) | 1.818452 / 1.468490 (0.349962) | 0.701381 / 4.584777 (-3.883396) | 3.425190 / 3.745712 (-0.320522) | 1.862545 / 5.269862 (-3.407316) | 1.166271 / 4.565676 (-3.399405) | 0.083678 / 0.424275 (-0.340597) | 0.012254 / 0.007607 (0.004647) | 0.535710 / 0.226044 (0.309665) | 5.342528 / 2.268929 (3.073600) | 2.627135 / 55.444624 (-52.817489) | 2.308313 / 6.876477 (-4.568164) | 2.325568 / 2.142072 (0.183496) | 0.818318 / 4.805227 (-3.986909) | 0.149812 / 6.500664 (-6.350853) | 0.064559 / 0.075469 (-0.010910) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.253611 / 1.841788 (-0.588176) | 13.646763 / 8.074308 (5.572455) | 14.387630 / 10.191392 (4.196238) | 0.159937 / 0.680424 (-0.520487) | 0.029123 / 0.534201 (-0.505078) | 0.400909 / 0.579283 (-0.178374) | 0.422830 / 0.434364 (-0.011534) | 0.488205 / 0.540337 (-0.052133) | 0.577982 / 1.386936 (-0.808954) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006430 / 0.011353 (-0.004923) | 0.004433 / 0.011008 (-0.006576) | 0.077459 / 0.038508 (0.038951) | 0.026949 / 0.023109 (0.003840) | 0.350276 / 0.275898 (0.074378) | 0.376189 / 0.323480 (0.052709) | 0.004945 / 0.007986 (-0.003041) | 0.003280 / 0.004328 (-0.001048) | 0.076465 / 0.004250 (0.072215) | 0.037510 / 0.037052 (0.000457) | 0.350410 / 0.258489 (0.091921) | 0.386778 / 0.293841 (0.092937) | 0.031933 / 0.128546 (-0.096613) | 0.011691 / 0.075646 (-0.063956) | 0.086519 / 0.419271 (-0.332753) | 0.042490 / 0.043533 (-0.001043) | 0.355930 / 0.255139 (0.100791) | 0.366500 / 0.283200 (0.083301) | 0.089542 / 0.141683 (-0.052141) | 1.492859 / 1.452155 (0.040704) | 1.548626 / 1.492716 (0.055910) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220123 / 0.018006 (0.202117) | 0.396970 / 0.000490 (0.396480) | 0.000398 / 0.000200 (0.000198) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024831 / 0.037411 (-0.012580) | 0.099681 / 0.014526 (0.085156) | 0.108922 / 0.176557 (-0.067635) | 0.143004 / 0.737135 (-0.594131) | 0.109671 / 0.296338 (-0.186667) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.444237 / 0.215209 (0.229028) | 4.430330 / 2.077655 (2.352675) | 2.235003 / 1.504120 (0.730883) | 2.010499 / 1.541195 (0.469305) | 2.030585 / 1.468490 (0.562095) | 0.701938 / 4.584777 (-3.882839) | 3.334569 / 3.745712 (-0.411144) | 1.861680 / 5.269862 (-3.408181) | 1.166072 / 4.565676 (-3.399604) | 0.083870 / 0.424275 (-0.340405) | 0.012615 / 0.007607 (0.005008) | 0.548789 / 0.226044 (0.322744) | 5.488064 / 2.268929 (3.219136) | 2.614926 / 55.444624 (-52.829698) | 2.246455 / 6.876477 (-4.630022) | 2.277439 / 2.142072 (0.135367) | 0.808449 / 4.805227 (-3.996778) | 0.152434 / 6.500664 (-6.348230) | 0.066709 / 0.075469 (-0.008760) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.316880 / 1.841788 (-0.524908) | 13.965269 / 8.074308 (5.890961) | 13.660187 / 10.191392 (3.468795) | 0.157801 / 0.680424 (-0.522623) | 0.016580 / 0.534201 (-0.517621) | 0.382834 / 0.579283 (-0.196449) | 0.394717 / 0.434364 (-0.039647) | 0.465138 / 0.540337 (-0.075200) | 0.552399 / 1.386936 (-0.834537) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fa06927a62e2983e2f0e8b7ba8262070c1543d78 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009341 / 0.011353 (-0.002012) | 0.005303 / 0.011008 (-0.005705) | 0.099287 / 0.038508 (0.060779) | 0.035587 / 0.023109 (0.012478) | 0.295146 / 0.275898 (0.019248) | 0.370470 / 0.323480 (0.046990) | 0.008910 / 0.007986 (0.000925) | 0.004358 / 0.004328 (0.000029) | 0.076298 / 0.004250 (0.072047) | 0.047187 / 0.037052 (0.010135) | 0.309025 / 0.258489 (0.050536) | 0.346659 / 0.293841 (0.052818) | 0.038378 / 0.128546 (-0.090168) | 0.012475 / 0.075646 (-0.063172) | 0.334370 / 0.419271 (-0.084901) | 0.048391 / 0.043533 (0.004858) | 0.298613 / 0.255139 (0.043474) | 0.317329 / 0.283200 (0.034130) | 0.108748 / 0.141683 (-0.032934) | 1.450454 / 1.452155 (-0.001701) | 1.519883 / 1.492716 (0.027167) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.011513 / 0.018006 (-0.006494) | 0.498941 / 0.000490 (0.498451) | 0.005098 / 0.000200 (0.004898) | 0.000096 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030523 / 0.037411 (-0.006888) | 0.105478 / 0.014526 (0.090952) | 0.121101 / 0.176557 (-0.055456) | 0.159951 / 0.737135 (-0.577184) | 0.126766 / 0.296338 (-0.169572) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.399101 / 0.215209 (0.183892) | 3.997069 / 2.077655 (1.919414) | 1.851592 / 1.504120 (0.347472) | 1.695708 / 1.541195 (0.154513) | 1.759504 / 1.468490 (0.291014) | 0.708241 / 4.584777 (-3.876536) | 3.786724 / 3.745712 (0.041012) | 3.523731 / 5.269862 (-1.746131) | 1.899474 / 4.565676 (-2.666203) | 0.086680 / 0.424275 (-0.337595) | 0.012232 / 0.007607 (0.004625) | 0.508507 / 0.226044 (0.282462) | 5.086320 / 2.268929 (2.817391) | 2.234906 / 55.444624 (-53.209718) | 1.911090 / 6.876477 (-4.965386) | 1.989232 / 2.142072 (-0.152841) | 0.863660 / 4.805227 (-3.941567) | 0.169334 / 6.500664 (-6.331330) | 0.063273 / 0.075469 (-0.012196) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.237590 / 1.841788 (-0.604198) | 15.417631 / 8.074308 (7.343323) | 15.235308 / 10.191392 (5.043916) | 0.209431 / 0.680424 (-0.470993) | 0.029214 / 0.534201 (-0.504987) | 0.444767 / 0.579283 (-0.134516) | 0.447776 / 0.434364 (0.013413) | 0.538440 / 0.540337 (-0.001897) | 0.635760 / 1.386936 (-0.751176) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007758 / 0.011353 (-0.003594) | 0.005539 / 0.011008 (-0.005469) | 0.077011 / 0.038508 (0.038503) | 0.034305 / 0.023109 (0.011196) | 0.363352 / 0.275898 (0.087454) | 0.411882 / 0.323480 (0.088403) | 0.006286 / 0.007986 (-0.001700) | 0.004378 / 0.004328 (0.000050) | 0.075504 / 0.004250 (0.071253) | 0.052728 / 0.037052 (0.015675) | 0.370122 / 0.258489 (0.111633) | 0.421910 / 0.293841 (0.128069) | 0.038444 / 0.128546 (-0.090102) | 0.012602 / 0.075646 (-0.063045) | 0.088540 / 0.419271 (-0.330731) | 0.060321 / 0.043533 (0.016788) | 0.350502 / 0.255139 (0.095363) | 0.393211 / 0.283200 (0.110011) | 0.113057 / 0.141683 (-0.028626) | 1.453275 / 1.452155 (0.001120) | 1.541033 / 1.492716 (0.048317) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.333603 / 0.018006 (0.315597) | 0.510548 / 0.000490 (0.510058) | 0.003573 / 0.000200 (0.003373) | 0.000116 / 0.000054 (0.000061) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032783 / 0.037411 (-0.004628) | 0.111943 / 0.014526 (0.097418) | 0.127154 / 0.176557 (-0.049403) | 0.171716 / 0.737135 (-0.565420) | 0.132441 / 0.296338 (-0.163898) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.439110 / 0.215209 (0.223901) | 4.440874 / 2.077655 (2.363220) | 2.145850 / 1.504120 (0.641730) | 1.909566 / 1.541195 (0.368371) | 2.032199 / 1.468490 (0.563709) | 0.711295 / 4.584777 (-3.873482) | 3.845729 / 3.745712 (0.100017) | 3.583555 / 5.269862 (-1.686307) | 1.836856 / 4.565676 (-2.728820) | 0.085966 / 0.424275 (-0.338309) | 0.012479 / 0.007607 (0.004872) | 0.545379 / 0.226044 (0.319334) | 5.425724 / 2.268929 (3.156796) | 2.648304 / 55.444624 (-52.796321) | 2.286369 / 6.876477 (-4.590108) | 2.367714 / 2.142072 (0.225642) | 0.831035 / 4.805227 (-3.974192) | 0.167603 / 6.500664 (-6.333061) | 0.064721 / 0.075469 (-0.010748) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.244495 / 1.841788 (-0.597292) | 15.304267 / 8.074308 (7.229958) | 13.912185 / 10.191392 (3.720793) | 0.156459 / 0.680424 (-0.523965) | 0.019181 / 0.534201 (-0.515019) | 0.425940 / 0.579283 (-0.153343) | 0.427956 / 0.434364 (-0.006408) | 0.529126 / 0.540337 (-0.011212) | 0.628360 / 1.386936 (-0.758576) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#da31f6ee02af29d92ee5541e4a3fc388c3d9abfc \"CML watermark\")\n"
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008730 / 0.011353 (-0.002623) | 0.004551 / 0.011008 (-0.006457) | 0.100206 / 0.038508 (0.061698) | 0.030264 / 0.023109 (0.007154) | 0.303310 / 0.275898 (0.027412) | 0.339040 / 0.323480 (0.015560) | 0.006923 / 0.007986 (-0.001063) | 0.004707 / 0.004328 (0.000379) | 0.077822 / 0.004250 (0.073571) | 0.034368 / 0.037052 (-0.002684) | 0.303125 / 0.258489 (0.044636) | 0.348322 / 0.293841 (0.054481) | 0.033831 / 0.128546 (-0.094715) | 0.011459 / 0.075646 (-0.064187) | 0.322092 / 0.419271 (-0.097180) | 0.047720 / 0.043533 (0.004187) | 0.304849 / 0.255139 (0.049710) | 0.330767 / 0.283200 (0.047567) | 0.087362 / 0.141683 (-0.054321) | 1.536095 / 1.452155 (0.083941) | 1.599979 / 1.492716 (0.107263) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.188985 / 0.018006 (0.170979) | 0.410775 / 0.000490 (0.410286) | 0.004215 / 0.000200 (0.004015) | 0.000086 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023124 / 0.037411 (-0.014287) | 0.096962 / 0.014526 (0.082436) | 0.104070 / 0.176557 (-0.072486) | 0.141248 / 0.737135 (-0.595887) | 0.108534 / 0.296338 (-0.187804) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417118 / 0.215209 (0.201909) | 4.167808 / 2.077655 (2.090154) | 2.016540 / 1.504120 (0.512420) | 1.847812 / 1.541195 (0.306617) | 1.967023 / 1.468490 (0.498532) | 0.689262 / 4.584777 (-3.895515) | 3.378747 / 3.745712 (-0.366965) | 1.854126 / 5.269862 (-3.415735) | 1.152102 / 4.565676 (-3.413575) | 0.081839 / 0.424275 (-0.342437) | 0.012426 / 0.007607 (0.004819) | 0.521334 / 0.226044 (0.295289) | 5.230593 / 2.268929 (2.961664) | 2.269386 / 55.444624 (-53.175238) | 1.965631 / 6.876477 (-4.910846) | 2.028994 / 2.142072 (-0.113079) | 0.802142 / 4.805227 (-4.003085) | 0.147954 / 6.500664 (-6.352710) | 0.065031 / 0.075469 (-0.010438) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.235289 / 1.841788 (-0.606499) | 13.723507 / 8.074308 (5.649199) | 14.197923 / 10.191392 (4.006531) | 0.147950 / 0.680424 (-0.532473) | 0.028332 / 0.534201 (-0.505869) | 0.400180 / 0.579283 (-0.179103) | 0.418970 / 0.434364 (-0.015393) | 0.478381 / 0.540337 (-0.061957) | 0.576138 / 1.386936 (-0.810798) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006548 / 0.011353 (-0.004805) | 0.004567 / 0.011008 (-0.006441) | 0.075658 / 0.038508 (0.037150) | 0.027190 / 0.023109 (0.004080) | 0.363417 / 0.275898 (0.087518) | 0.399575 / 0.323480 (0.076095) | 0.004982 / 0.007986 (-0.003004) | 0.003364 / 0.004328 (-0.000964) | 0.074392 / 0.004250 (0.070142) | 0.038839 / 0.037052 (0.001787) | 0.361133 / 0.258489 (0.102644) | 0.408557 / 0.293841 (0.114717) | 0.031468 / 0.128546 (-0.097078) | 0.011645 / 0.075646 (-0.064001) | 0.085145 / 0.419271 (-0.334126) | 0.041775 / 0.043533 (-0.001758) | 0.348624 / 0.255139 (0.093485) | 0.389610 / 0.283200 (0.106410) | 0.088576 / 0.141683 (-0.053107) | 1.511208 / 1.452155 (0.059054) | 1.560568 / 1.492716 (0.067852) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.185017 / 0.018006 (0.167011) | 0.407543 / 0.000490 (0.407053) | 0.002486 / 0.000200 (0.002286) | 0.000076 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025181 / 0.037411 (-0.012231) | 0.099056 / 0.014526 (0.084530) | 0.108597 / 0.176557 (-0.067959) | 0.144664 / 0.737135 (-0.592471) | 0.110417 / 0.296338 (-0.185922) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434302 / 0.215209 (0.219093) | 4.327840 / 2.077655 (2.250185) | 2.059939 / 1.504120 (0.555819) | 1.853267 / 1.541195 (0.312072) | 1.906616 / 1.468490 (0.438126) | 0.700165 / 4.584777 (-3.884611) | 3.439216 / 3.745712 (-0.306496) | 2.792034 / 5.269862 (-2.477827) | 1.424852 / 4.565676 (-3.140824) | 0.083926 / 0.424275 (-0.340349) | 0.013943 / 0.007607 (0.006336) | 0.535964 / 0.226044 (0.309920) | 5.368671 / 2.268929 (3.099743) | 2.497027 / 55.444624 (-52.947597) | 2.166222 / 6.876477 (-4.710254) | 2.183766 / 2.142072 (0.041693) | 0.805886 / 4.805227 (-3.999341) | 0.152474 / 6.500664 (-6.348190) | 0.067354 / 0.075469 (-0.008115) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.284052 / 1.841788 (-0.557736) | 13.714066 / 8.074308 (5.639758) | 14.195212 / 10.191392 (4.003820) | 0.151815 / 0.680424 (-0.528609) | 0.016847 / 0.534201 (-0.517354) | 0.391174 / 0.579283 (-0.188109) | 0.409784 / 0.434364 (-0.024580) | 0.473880 / 0.540337 (-0.066458) | 0.561016 / 1.386936 (-0.825920) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#47ab08d9f06abd5bc23bddaa4875b93e926dd3b1 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010284 / 0.011353 (-0.001068) | 0.005654 / 0.011008 (-0.005355) | 0.100522 / 0.038508 (0.062014) | 0.039201 / 0.023109 (0.016092) | 0.320831 / 0.275898 (0.044933) | 0.365351 / 0.323480 (0.041871) | 0.009066 / 0.007986 (0.001080) | 0.005805 / 0.004328 (0.001476) | 0.076969 / 0.004250 (0.072719) | 0.045813 / 0.037052 (0.008760) | 0.327115 / 0.258489 (0.068626) | 0.362823 / 0.293841 (0.068982) | 0.040521 / 0.128546 (-0.088025) | 0.013166 / 0.075646 (-0.062481) | 0.358579 / 0.419271 (-0.060692) | 0.051753 / 0.043533 (0.008220) | 0.323741 / 0.255139 (0.068602) | 0.360211 / 0.283200 (0.077011) | 0.111534 / 0.141683 (-0.030149) | 1.594887 / 1.452155 (0.142732) | 1.651516 / 1.492716 (0.158799) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.012051 / 0.018006 (-0.005956) | 0.475316 / 0.000490 (0.474826) | 0.004804 / 0.000200 (0.004604) | 0.000100 / 0.000054 (0.000046) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027480 / 0.037411 (-0.009931) | 0.112022 / 0.014526 (0.097496) | 0.121539 / 0.176557 (-0.055017) | 0.166327 / 0.737135 (-0.570809) | 0.132575 / 0.296338 (-0.163763) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418322 / 0.215209 (0.203113) | 4.149463 / 2.077655 (2.071808) | 1.890901 / 1.504120 (0.386781) | 1.682521 / 1.541195 (0.141327) | 1.716331 / 1.468490 (0.247841) | 0.729176 / 4.584777 (-3.855601) | 4.173303 / 3.745712 (0.427591) | 2.166249 / 5.269862 (-3.103612) | 1.384623 / 4.565676 (-3.181053) | 0.095486 / 0.424275 (-0.328789) | 0.013800 / 0.007607 (0.006193) | 0.573917 / 0.226044 (0.347872) | 5.348843 / 2.268929 (3.079914) | 2.421716 / 55.444624 (-53.022909) | 2.002048 / 6.876477 (-4.874428) | 2.079493 / 2.142072 (-0.062579) | 0.882818 / 4.805227 (-3.922409) | 0.172936 / 6.500664 (-6.327728) | 0.068384 / 0.075469 (-0.007085) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.285704 / 1.841788 (-0.556084) | 16.036346 / 8.074308 (7.962038) | 15.181557 / 10.191392 (4.990165) | 0.194044 / 0.680424 (-0.486380) | 0.033128 / 0.534201 (-0.501073) | 0.480290 / 0.579283 (-0.098993) | 0.497525 / 0.434364 (0.063161) | 0.602304 / 0.540337 (0.061966) | 0.754273 / 1.386936 (-0.632663) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007263 / 0.011353 (-0.004090) | 0.005164 / 0.011008 (-0.005845) | 0.079833 / 0.038508 (0.041325) | 0.033974 / 0.023109 (0.010865) | 0.382057 / 0.275898 (0.106159) | 0.402924 / 0.323480 (0.079444) | 0.007273 / 0.007986 (-0.000712) | 0.004378 / 0.004328 (0.000049) | 0.080556 / 0.004250 (0.076305) | 0.047376 / 0.037052 (0.010324) | 0.379044 / 0.258489 (0.120555) | 0.422135 / 0.293841 (0.128294) | 0.038294 / 0.128546 (-0.090252) | 0.013974 / 0.075646 (-0.061672) | 0.094936 / 0.419271 (-0.324335) | 0.051033 / 0.043533 (0.007501) | 0.368197 / 0.255139 (0.113058) | 0.409627 / 0.283200 (0.126427) | 0.107365 / 0.141683 (-0.034318) | 1.537501 / 1.452155 (0.085346) | 1.618021 / 1.492716 (0.125305) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227187 / 0.018006 (0.209181) | 0.473226 / 0.000490 (0.472736) | 0.006532 / 0.000200 (0.006332) | 0.000121 / 0.000054 (0.000066) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029814 / 0.037411 (-0.007597) | 0.121113 / 0.014526 (0.106587) | 0.125107 / 0.176557 (-0.051450) | 0.167008 / 0.737135 (-0.570127) | 0.128720 / 0.296338 (-0.167619) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.452158 / 0.215209 (0.236949) | 4.507087 / 2.077655 (2.429433) | 2.193910 / 1.504120 (0.689790) | 1.991234 / 1.541195 (0.450039) | 2.120256 / 1.468490 (0.651766) | 0.726664 / 4.584777 (-3.858113) | 4.213148 / 3.745712 (0.467436) | 4.082857 / 5.269862 (-1.187005) | 1.741018 / 4.565676 (-2.824658) | 0.090176 / 0.424275 (-0.334099) | 0.013221 / 0.007607 (0.005614) | 0.558868 / 0.226044 (0.332824) | 5.617242 / 2.268929 (3.348313) | 2.985430 / 55.444624 (-52.459194) | 2.623136 / 6.876477 (-4.253341) | 2.383177 / 2.142072 (0.241105) | 0.917237 / 4.805227 (-3.887990) | 0.178774 / 6.500664 (-6.321890) | 0.064707 / 0.075469 (-0.010762) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.365402 / 1.841788 (-0.476385) | 16.035773 / 8.074308 (7.961465) | 13.917612 / 10.191392 (3.726220) | 0.152191 / 0.680424 (-0.528233) | 0.020734 / 0.534201 (-0.513467) | 0.442055 / 0.579283 (-0.137228) | 0.470588 / 0.434364 (0.036224) | 0.563433 / 0.540337 (0.023096) | 0.651161 / 1.386936 (-0.735775) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6ab909a44b723fe0a8a586beafc8c5cbf9c91c21 \"CML watermark\")\n",
"If it's good for you @polinaeterna I'd like to merge it and then run the `transformers` CI to see if it changes anything",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008829 / 0.011353 (-0.002524) | 0.004652 / 0.011008 (-0.006356) | 0.102505 / 0.038508 (0.063997) | 0.030164 / 0.023109 (0.007054) | 0.306551 / 0.275898 (0.030653) | 0.368920 / 0.323480 (0.045440) | 0.007084 / 0.007986 (-0.000902) | 0.003545 / 0.004328 (-0.000783) | 0.079402 / 0.004250 (0.075152) | 0.035296 / 0.037052 (-0.001756) | 0.312010 / 0.258489 (0.053520) | 0.348773 / 0.293841 (0.054932) | 0.034622 / 0.128546 (-0.093924) | 0.011727 / 0.075646 (-0.063920) | 0.326911 / 0.419271 (-0.092361) | 0.043832 / 0.043533 (0.000300) | 0.306357 / 0.255139 (0.051218) | 0.328744 / 0.283200 (0.045544) | 0.091954 / 0.141683 (-0.049729) | 1.563989 / 1.452155 (0.111834) | 1.591901 / 1.492716 (0.099185) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.194955 / 0.018006 (0.176948) | 0.412864 / 0.000490 (0.412374) | 0.003710 / 0.000200 (0.003510) | 0.000081 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023132 / 0.037411 (-0.014279) | 0.099586 / 0.014526 (0.085060) | 0.105031 / 0.176557 (-0.071525) | 0.141206 / 0.737135 (-0.595929) | 0.111978 / 0.296338 (-0.184360) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.413729 / 0.215209 (0.198520) | 4.161713 / 2.077655 (2.084058) | 1.887442 / 1.504120 (0.383322) | 1.711847 / 1.541195 (0.170653) | 1.756833 / 1.468490 (0.288343) | 0.699239 / 4.584777 (-3.885538) | 3.346213 / 3.745712 (-0.399499) | 2.822289 / 5.269862 (-2.447573) | 1.475650 / 4.565676 (-3.090027) | 0.082800 / 0.424275 (-0.341475) | 0.012302 / 0.007607 (0.004695) | 0.523068 / 0.226044 (0.297024) | 5.242833 / 2.268929 (2.973904) | 2.310768 / 55.444624 (-53.133856) | 1.954629 / 6.876477 (-4.921847) | 2.015563 / 2.142072 (-0.126510) | 0.812613 / 4.805227 (-3.992614) | 0.149512 / 6.500664 (-6.351152) | 0.065162 / 0.075469 (-0.010307) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.270177 / 1.841788 (-0.571610) | 13.664765 / 8.074308 (5.590457) | 14.317968 / 10.191392 (4.126576) | 0.138135 / 0.680424 (-0.542289) | 0.028503 / 0.534201 (-0.505698) | 0.402921 / 0.579283 (-0.176362) | 0.400999 / 0.434364 (-0.033365) | 0.470983 / 0.540337 (-0.069355) | 0.544319 / 1.386936 (-0.842617) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006841 / 0.011353 (-0.004512) | 0.004570 / 0.011008 (-0.006439) | 0.076398 / 0.038508 (0.037890) | 0.028136 / 0.023109 (0.005027) | 0.339864 / 0.275898 (0.063966) | 0.375496 / 0.323480 (0.052016) | 0.004967 / 0.007986 (-0.003019) | 0.003411 / 0.004328 (-0.000917) | 0.075727 / 0.004250 (0.071476) | 0.040025 / 0.037052 (0.002973) | 0.340473 / 0.258489 (0.081984) | 0.384396 / 0.293841 (0.090555) | 0.031683 / 0.128546 (-0.096863) | 0.011752 / 0.075646 (-0.063894) | 0.085635 / 0.419271 (-0.333636) | 0.042764 / 0.043533 (-0.000769) | 0.339417 / 0.255139 (0.084278) | 0.364190 / 0.283200 (0.080991) | 0.093842 / 0.141683 (-0.047841) | 1.480999 / 1.452155 (0.028844) | 1.549752 / 1.492716 (0.057036) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.174146 / 0.018006 (0.156140) | 0.415459 / 0.000490 (0.414970) | 0.002854 / 0.000200 (0.002654) | 0.000077 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024671 / 0.037411 (-0.012740) | 0.101229 / 0.014526 (0.086703) | 0.108841 / 0.176557 (-0.067716) | 0.144530 / 0.737135 (-0.592606) | 0.112509 / 0.296338 (-0.183829) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.460561 / 0.215209 (0.245352) | 4.591139 / 2.077655 (2.513484) | 2.275535 / 1.504120 (0.771415) | 2.070976 / 1.541195 (0.529781) | 2.028766 / 1.468490 (0.560276) | 0.706166 / 4.584777 (-3.878611) | 3.408498 / 3.745712 (-0.337215) | 3.034665 / 5.269862 (-2.235197) | 1.586805 / 4.565676 (-2.978872) | 0.083355 / 0.424275 (-0.340920) | 0.012460 / 0.007607 (0.004853) | 0.565256 / 0.226044 (0.339212) | 5.662643 / 2.268929 (3.393715) | 2.697019 / 55.444624 (-52.747605) | 2.302044 / 6.876477 (-4.574433) | 2.373081 / 2.142072 (0.231009) | 0.809804 / 4.805227 (-3.995423) | 0.151481 / 6.500664 (-6.349183) | 0.066870 / 0.075469 (-0.008599) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.257293 / 1.841788 (-0.584495) | 14.059454 / 8.074308 (5.985146) | 13.783251 / 10.191392 (3.591859) | 0.140007 / 0.680424 (-0.540417) | 0.016624 / 0.534201 (-0.517577) | 0.381703 / 0.579283 (-0.197580) | 0.389032 / 0.434364 (-0.045332) | 0.466127 / 0.540337 (-0.074211) | 0.551052 / 1.386936 (-0.835884) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4a767f7a3dffdf45886690b81c6e624146ae14da \"CML watermark\")\n"
] | "2023-02-21T10:45:50Z" | "2023-02-21T12:49:50Z" | "2023-02-21T12:42:52Z" | MEMBER | null | 0 | {
"diff_url": "https://github.com/huggingface/datasets/pull/5556.diff",
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"merged_at": "2023-02-21T12:42:52Z",
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"url": "https://api.github.com/repos/huggingface/datasets/pulls/5556"
} | ...instead of relying on the optional librosa dependency `resampy`.
It was only used for `_decode_non_mp3_file_like` anyway and not for the other ones - removing it fixes consistency between decoding methods (except torchaudio decoding)
Therefore I think it is a better solution than adding `resampy` as a dependency in https://github.com/huggingface/datasets/pull/5554
cc @polinaeterna | {
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} | [] | open | false | null | [] | null | [
"Hi ! The indices mapping is written in the same cachedirectory as your dataset.\r\n\r\nCan you run this to show your current cache directory ?\r\n```python\r\nprint(train_dataset.cache_files)\r\n```",
"```\r\n[{'filename': '.../train/dataset.arrow'}, {'filename': '.../train/dataset.arrow'}]\r\n```\r\n\r\nThese are the actual paths where `.hf` files are stored. ",
"I'm not aware of any `.hf` file ? What are you referring to ?\r\n\r\nAlso the error says \"Protocol unknown: parent\". Is there a chance you may have ended up with a path that contains this string `parent://` ?",
"I figured out why the issue was occuring but don't know the long-term fix.\r\nThe dataset I was trying to shuffle was loaded from a saved file which had `::` delimiter in filename. When I try with the exact same file without `::` in filename, it works as expected.\r\nQuick fix is to not use colons in filename. But if this is expected behaviour, this should be clearly stated in the documentation.\r\nThanks for help @lhoestq "
] | "2023-02-20T21:33:45Z" | "2023-02-27T09:23:34Z" | null | NONE | null | null | null | ### Describe the bug
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In [16], line 1
----> 1 train_dataset = train_dataset.shuffle()
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:551, in transmit_format.<locals>.wrapper(*args, **kwargs)
544 self_format = {
545 "type": self._format_type,
546 "format_kwargs": self._format_kwargs,
547 "columns": self._format_columns,
548 "output_all_columns": self._output_all_columns,
549 }
550 # apply actual function
--> 551 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
552 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
553 # re-apply format to the output
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/fingerprint.py:480, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs)
476 validate_fingerprint(kwargs[fingerprint_name])
478 # Call actual function
--> 480 out = func(self, *args, **kwargs)
482 # Update fingerprint of in-place transforms + update in-place history of transforms
484 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:3616, in Dataset.shuffle(self, seed, generator, keep_in_memory, load_from_cache_file, indices_cache_file_name, writer_batch_size, new_fingerprint)
3610 return self._new_dataset_with_indices(
3611 fingerprint=new_fingerprint, indices_cache_file_name=indices_cache_file_name
3612 )
3614 permutation = generator.permutation(len(self))
-> 3616 return self.select(
3617 indices=permutation,
3618 keep_in_memory=keep_in_memory,
3619 indices_cache_file_name=indices_cache_file_name if not keep_in_memory else None,
3620 writer_batch_size=writer_batch_size,
3621 new_fingerprint=new_fingerprint,
3622 )
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:551, in transmit_format.<locals>.wrapper(*args, **kwargs)
544 self_format = {
545 "type": self._format_type,
546 "format_kwargs": self._format_kwargs,
547 "columns": self._format_columns,
548 "output_all_columns": self._output_all_columns,
549 }
550 # apply actual function
--> 551 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
552 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
553 # re-apply format to the output
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/fingerprint.py:480, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs)
476 validate_fingerprint(kwargs[fingerprint_name])
478 # Call actual function
--> 480 out = func(self, *args, **kwargs)
482 # Update fingerprint of in-place transforms + update in-place history of transforms
484 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:3266, in Dataset.select(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint)
3263 return self._select_contiguous(start, length, new_fingerprint=new_fingerprint)
3265 # If not contiguous, we need to create a new indices mapping
-> 3266 return self._select_with_indices_mapping(
3267 indices,
3268 keep_in_memory=keep_in_memory,
3269 indices_cache_file_name=indices_cache_file_name,
3270 writer_batch_size=writer_batch_size,
3271 new_fingerprint=new_fingerprint,
3272 )
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:551, in transmit_format.<locals>.wrapper(*args, **kwargs)
544 self_format = {
545 "type": self._format_type,
546 "format_kwargs": self._format_kwargs,
547 "columns": self._format_columns,
548 "output_all_columns": self._output_all_columns,
549 }
550 # apply actual function
--> 551 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
552 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
553 # re-apply format to the output
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/fingerprint.py:480, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs)
476 validate_fingerprint(kwargs[fingerprint_name])
478 # Call actual function
--> 480 out = func(self, *args, **kwargs)
482 # Update fingerprint of in-place transforms + update in-place history of transforms
484 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:3389, in Dataset._select_with_indices_mapping(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint)
3387 logger.info(f"Caching indices mapping at {indices_cache_file_name}")
3388 tmp_file = tempfile.NamedTemporaryFile("wb", dir=os.path.dirname(indices_cache_file_name), delete=False)
-> 3389 writer = ArrowWriter(
3390 path=tmp_file.name, writer_batch_size=writer_batch_size, fingerprint=new_fingerprint, unit="indices"
3391 )
3393 indices = indices if isinstance(indices, list) else list(indices)
3395 size = len(self)
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_writer.py:315, in ArrowWriter.__init__(self, schema, features, path, stream, fingerprint, writer_batch_size, hash_salt, check_duplicates, disable_nullable, update_features, with_metadata, unit, embed_local_files, storage_options)
312 self._disable_nullable = disable_nullable
314 if stream is None:
--> 315 fs_token_paths = fsspec.get_fs_token_paths(path, storage_options=storage_options)
316 self._fs: fsspec.AbstractFileSystem = fs_token_paths[0]
317 self._path = (
318 fs_token_paths[2][0]
319 if not is_remote_filesystem(self._fs)
320 else self._fs.unstrip_protocol(fs_token_paths[2][0])
321 )
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/fsspec/core.py:593, in get_fs_token_paths(urlpath, mode, num, name_function, storage_options, protocol, expand)
591 else:
592 urlpath = stringify_path(urlpath)
--> 593 chain = _un_chain(urlpath, storage_options or {})
594 if len(chain) > 1:
595 inkwargs = {}
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/fsspec/core.py:330, in _un_chain(path, kwargs)
328 for bit in reversed(bits):
329 protocol = split_protocol(bit)[0] or "file"
--> 330 cls = get_filesystem_class(protocol)
331 extra_kwargs = cls._get_kwargs_from_urls(bit)
332 kws = kwargs.get(protocol, {})
File /opt/conda/envs/pytorch/lib/python3.9/site-packages/fsspec/registry.py:240, in get_filesystem_class(protocol)
238 if protocol not in registry:
239 if protocol not in known_implementations:
--> 240 raise ValueError("Protocol not known: %s" % protocol)
241 bit = known_implementations[protocol]
242 try:
ValueError: Protocol not known: parent
```
This is what the `train_dataset` object looks like
```
Dataset({
features: ['label', 'input_ids', 'attention_mask'],
num_rows: 364166
})
```
### Steps to reproduce the bug
The `train_dataset` obj is created by concatenating two datasets
And then shuffle is called, but it throws the mentioned error.
### Expected behavior
Should shuffle the dataset properly.
### Environment info
- `datasets` version: 2.6.1
- Platform: Linux-5.15.0-1022-aws-x86_64-with-glibc2.31
- Python version: 3.9.13
- PyArrow version: 10.0.0
- Pandas version: 1.4.4
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https://api.github.com/repos/huggingface/datasets/issues/5554 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5554/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5554/comments | https://api.github.com/repos/huggingface/datasets/issues/5554/events | https://github.com/huggingface/datasets/pull/5554 | 1,592,285,062 | PR_kwDODunzps5KXhZh | 5,554 | Add resampy dep | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008735 / 0.011353 (-0.002618) | 0.004514 / 0.011008 (-0.006494) | 0.099348 / 0.038508 (0.060840) | 0.030060 / 0.023109 (0.006951) | 0.302189 / 0.275898 (0.026291) | 0.339535 / 0.323480 (0.016055) | 0.007053 / 0.007986 (-0.000933) | 0.003420 / 0.004328 (-0.000909) | 0.076967 / 0.004250 (0.072717) | 0.034484 / 0.037052 (-0.002568) | 0.304349 / 0.258489 (0.045860) | 0.354032 / 0.293841 (0.060191) | 0.033552 / 0.128546 (-0.094995) | 0.011405 / 0.075646 (-0.064241) | 0.324773 / 0.419271 (-0.094498) | 0.041103 / 0.043533 (-0.002429) | 0.313559 / 0.255139 (0.058420) | 0.333251 / 0.283200 (0.050052) | 0.087580 / 0.141683 (-0.054103) | 1.460324 / 1.452155 (0.008169) | 1.552239 / 1.492716 (0.059523) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.183759 / 0.018006 (0.165753) | 0.413274 / 0.000490 (0.412784) | 0.001684 / 0.000200 (0.001484) | 0.000076 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023341 / 0.037411 (-0.014071) | 0.098368 / 0.014526 (0.083842) | 0.105522 / 0.176557 (-0.071034) | 0.151581 / 0.737135 (-0.585554) | 0.108980 / 0.296338 (-0.187358) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417856 / 0.215209 (0.202647) | 4.167570 / 2.077655 (2.089915) | 1.843669 / 1.504120 (0.339549) | 1.643130 / 1.541195 (0.101936) | 1.717587 / 1.468490 (0.249097) | 0.696392 / 4.584777 (-3.888384) | 3.427617 / 3.745712 (-0.318096) | 2.816486 / 5.269862 (-2.453376) | 1.539519 / 4.565676 (-3.026157) | 0.082112 / 0.424275 (-0.342163) | 0.012425 / 0.007607 (0.004818) | 0.525325 / 0.226044 (0.299281) | 5.251710 / 2.268929 (2.982781) | 2.273641 / 55.444624 (-53.170983) | 1.931002 / 6.876477 (-4.945474) | 1.977253 / 2.142072 (-0.164819) | 0.804794 / 4.805227 (-4.000434) | 0.147324 / 6.500664 (-6.353340) | 0.064966 / 0.075469 (-0.010503) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.193173 / 1.841788 (-0.648615) | 13.705127 / 8.074308 (5.630819) | 14.348408 / 10.191392 (4.157016) | 0.165374 / 0.680424 (-0.515050) | 0.028288 / 0.534201 (-0.505913) | 0.402546 / 0.579283 (-0.176737) | 0.413503 / 0.434364 (-0.020861) | 0.473298 / 0.540337 (-0.067039) | 0.567571 / 1.386936 (-0.819365) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006735 / 0.011353 (-0.004618) | 0.004601 / 0.011008 (-0.006407) | 0.077414 / 0.038508 (0.038906) | 0.027402 / 0.023109 (0.004293) | 0.353469 / 0.275898 (0.077571) | 0.381697 / 0.323480 (0.058218) | 0.005076 / 0.007986 (-0.002910) | 0.004665 / 0.004328 (0.000336) | 0.076210 / 0.004250 (0.071960) | 0.039114 / 0.037052 (0.002061) | 0.354980 / 0.258489 (0.096491) | 0.389648 / 0.293841 (0.095807) | 0.031674 / 0.128546 (-0.096872) | 0.011752 / 0.075646 (-0.063894) | 0.086330 / 0.419271 (-0.332942) | 0.041530 / 0.043533 (-0.002003) | 0.343002 / 0.255139 (0.087863) | 0.365959 / 0.283200 (0.082760) | 0.091848 / 0.141683 (-0.049835) | 1.519427 / 1.452155 (0.067272) | 1.591529 / 1.492716 (0.098813) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216458 / 0.018006 (0.198452) | 0.403326 / 0.000490 (0.402836) | 0.000432 / 0.000200 (0.000232) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025106 / 0.037411 (-0.012305) | 0.101113 / 0.014526 (0.086588) | 0.108104 / 0.176557 (-0.068453) | 0.142342 / 0.737135 (-0.594794) | 0.112012 / 0.296338 (-0.184326) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.443128 / 0.215209 (0.227919) | 4.434707 / 2.077655 (2.357052) | 2.115434 / 1.504120 (0.611315) | 1.902865 / 1.541195 (0.361670) | 1.996981 / 1.468490 (0.528491) | 0.702485 / 4.584777 (-3.882292) | 3.419151 / 3.745712 (-0.326561) | 1.911977 / 5.269862 (-3.357884) | 1.178195 / 4.565676 (-3.387481) | 0.082985 / 0.424275 (-0.341290) | 0.012415 / 0.007607 (0.004808) | 0.546188 / 0.226044 (0.320144) | 5.463592 / 2.268929 (3.194664) | 2.574911 / 55.444624 (-52.869713) | 2.232883 / 6.876477 (-4.643594) | 2.284391 / 2.142072 (0.142319) | 0.807389 / 4.805227 (-3.997839) | 0.151461 / 6.500664 (-6.349203) | 0.067831 / 0.075469 (-0.007638) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.286605 / 1.841788 (-0.555183) | 14.230328 / 8.074308 (6.156020) | 13.944645 / 10.191392 (3.753253) | 0.153725 / 0.680424 (-0.526699) | 0.016876 / 0.534201 (-0.517325) | 0.386109 / 0.579283 (-0.193174) | 0.401798 / 0.434364 (-0.032566) | 0.467883 / 0.540337 (-0.072454) | 0.557788 / 1.386936 (-0.829148) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c07f5c9268ce55d0e2022b018d5f44cfcedf1e43 \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009305 / 0.011353 (-0.002048) | 0.004978 / 0.011008 (-0.006031) | 0.101687 / 0.038508 (0.063179) | 0.035339 / 0.023109 (0.012230) | 0.294770 / 0.275898 (0.018872) | 0.355491 / 0.323480 (0.032011) | 0.008183 / 0.007986 (0.000197) | 0.004076 / 0.004328 (-0.000253) | 0.077552 / 0.004250 (0.073302) | 0.042891 / 0.037052 (0.005838) | 0.305727 / 0.258489 (0.047238) | 0.336508 / 0.293841 (0.042667) | 0.038525 / 0.128546 (-0.090022) | 0.011878 / 0.075646 (-0.063768) | 0.334136 / 0.419271 (-0.085136) | 0.047548 / 0.043533 (0.004015) | 0.301749 / 0.255139 (0.046610) | 0.318221 / 0.283200 (0.035022) | 0.099172 / 0.141683 (-0.042511) | 1.440638 / 1.452155 (-0.011516) | 1.503505 / 1.492716 (0.010789) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.202748 / 0.018006 (0.184742) | 0.433670 / 0.000490 (0.433181) | 0.003139 / 0.000200 (0.002939) | 0.000083 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025555 / 0.037411 (-0.011856) | 0.107156 / 0.014526 (0.092631) | 0.116706 / 0.176557 (-0.059851) | 0.153165 / 0.737135 (-0.583970) | 0.122614 / 0.296338 (-0.173724) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.398912 / 0.215209 (0.183703) | 3.965048 / 2.077655 (1.887394) | 1.894678 / 1.504120 (0.390558) | 1.706925 / 1.541195 (0.165730) | 1.745264 / 1.468490 (0.276774) | 0.691174 / 4.584777 (-3.893603) | 3.824583 / 3.745712 (0.078871) | 3.876806 / 5.269862 (-1.393055) | 1.898991 / 4.565676 (-2.666685) | 0.083687 / 0.424275 (-0.340588) | 0.012122 / 0.007607 (0.004514) | 0.510870 / 0.226044 (0.284825) | 5.094523 / 2.268929 (2.825594) | 2.265557 / 55.444624 (-53.179067) | 1.930882 / 6.876477 (-4.945594) | 2.016090 / 2.142072 (-0.125983) | 0.833108 / 4.805227 (-3.972119) | 0.164804 / 6.500664 (-6.335860) | 0.062864 / 0.075469 (-0.012605) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.192673 / 1.841788 (-0.649115) | 14.730393 / 8.074308 (6.656085) | 14.550736 / 10.191392 (4.359344) | 0.154451 / 0.680424 (-0.525973) | 0.029222 / 0.534201 (-0.504979) | 0.440939 / 0.579283 (-0.138345) | 0.442772 / 0.434364 (0.008409) | 0.543948 / 0.540337 (0.003610) | 0.638113 / 1.386936 (-0.748824) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007589 / 0.011353 (-0.003764) | 0.005208 / 0.011008 (-0.005800) | 0.073797 / 0.038508 (0.035289) | 0.034021 / 0.023109 (0.010912) | 0.366120 / 0.275898 (0.090222) | 0.397105 / 0.323480 (0.073625) | 0.005837 / 0.007986 (-0.002148) | 0.004028 / 0.004328 (-0.000301) | 0.073502 / 0.004250 (0.069252) | 0.051233 / 0.037052 (0.014181) | 0.359849 / 0.258489 (0.101360) | 0.397476 / 0.293841 (0.103635) | 0.036727 / 0.128546 (-0.091819) | 0.012249 / 0.075646 (-0.063397) | 0.086600 / 0.419271 (-0.332671) | 0.051156 / 0.043533 (0.007623) | 0.343441 / 0.255139 (0.088302) | 0.389672 / 0.283200 (0.106472) | 0.105180 / 0.141683 (-0.036503) | 1.439719 / 1.452155 (-0.012435) | 1.537779 / 1.492716 (0.045062) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.199429 / 0.018006 (0.181422) | 0.440837 / 0.000490 (0.440347) | 0.005333 / 0.000200 (0.005133) | 0.000099 / 0.000054 (0.000044) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029581 / 0.037411 (-0.007830) | 0.113789 / 0.014526 (0.099263) | 0.123799 / 0.176557 (-0.052758) | 0.163772 / 0.737135 (-0.573363) | 0.127156 / 0.296338 (-0.169183) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422803 / 0.215209 (0.207594) | 4.192400 / 2.077655 (2.114745) | 1.994561 / 1.504120 (0.490441) | 1.807085 / 1.541195 (0.265890) | 1.927539 / 1.468490 (0.459049) | 0.708804 / 4.584777 (-3.875973) | 3.790662 / 3.745712 (0.044950) | 3.667207 / 5.269862 (-1.602655) | 1.985107 / 4.565676 (-2.580570) | 0.086609 / 0.424275 (-0.337666) | 0.012613 / 0.007607 (0.005006) | 0.520167 / 0.226044 (0.294122) | 5.208657 / 2.268929 (2.939729) | 2.500383 / 55.444624 (-52.944241) | 2.129817 / 6.876477 (-4.746660) | 2.181205 / 2.142072 (0.039133) | 0.847925 / 4.805227 (-3.957303) | 0.168293 / 6.500664 (-6.332372) | 0.065066 / 0.075469 (-0.010403) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.261053 / 1.841788 (-0.580735) | 15.091644 / 8.074308 (7.017336) | 14.126139 / 10.191392 (3.934747) | 0.184956 / 0.680424 (-0.495468) | 0.017909 / 0.534201 (-0.516292) | 0.428918 / 0.579283 (-0.150365) | 0.429637 / 0.434364 (-0.004727) | 0.530900 / 0.540337 (-0.009437) | 0.627966 / 1.386936 (-0.758970) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a72fd153d3499a5c5eda783673073c9f557f11e0 \"CML watermark\")\n",
"I think we should also suggest installing `resampy` in the error message thrown by the Audio feature when `librosa` is not installed.",
"exploring a better solution at https://github.com/huggingface/datasets/pull/5556"
] | "2023-02-20T18:15:43Z" | "2023-02-21T12:46:10Z" | "2023-02-21T12:43:38Z" | MEMBER | null | 0 | {
"diff_url": "https://github.com/huggingface/datasets/pull/5554.diff",
"html_url": "https://github.com/huggingface/datasets/pull/5554",
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} | In librosa 0.10 they removed the `resmpy` dependency and set it to optional.
However it is necessary for resampling. I added it to the "audio" extra dependencies. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.014953 / 0.011353 (0.003600) | 0.006936 / 0.011008 (-0.004072) | 0.144039 / 0.038508 (0.105531) | 0.046719 / 0.023109 (0.023610) | 0.408832 / 0.275898 (0.132934) | 0.501419 / 0.323480 (0.177939) | 0.010190 / 0.007986 (0.002204) | 0.007618 / 0.004328 (0.003290) | 0.108553 / 0.004250 (0.104303) | 0.048484 / 0.037052 (0.011432) | 0.451586 / 0.258489 (0.193097) | 0.469864 / 0.293841 (0.176023) | 0.062159 / 0.128546 (-0.066387) | 0.019937 / 0.075646 (-0.055710) | 0.473718 / 0.419271 (0.054446) | 0.064777 / 0.043533 (0.021244) | 0.428675 / 0.255139 (0.173536) | 0.467665 / 0.283200 (0.184465) | 0.133528 / 0.141683 (-0.008155) | 1.978084 / 1.452155 (0.525930) | 1.965878 / 1.492716 (0.473162) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.290112 / 0.018006 (0.272106) | 0.629481 / 0.000490 (0.628992) | 0.003600 / 0.000200 (0.003400) | 0.000144 / 0.000054 (0.000089) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030806 / 0.037411 (-0.006605) | 0.142376 / 0.014526 (0.127850) | 0.150020 / 0.176557 (-0.026537) | 0.193679 / 0.737135 (-0.543457) | 0.151329 / 0.296338 (-0.145009) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.629725 / 0.215209 (0.414516) | 6.656313 / 2.077655 (4.578659) | 2.712160 / 1.504120 (1.208041) | 2.328461 / 1.541195 (0.787266) | 2.452502 / 1.468490 (0.984012) | 1.353183 / 4.584777 (-3.231594) | 5.981521 / 3.745712 (2.235809) | 3.707186 / 5.269862 (-1.562676) | 2.460583 / 4.565676 (-2.105094) | 0.178300 / 0.424275 (-0.245975) | 0.020357 / 0.007607 (0.012750) | 0.813564 / 0.226044 (0.587520) | 8.465600 / 2.268929 (6.196671) | 3.491507 / 55.444624 (-51.953117) | 2.810781 / 6.876477 (-4.065695) | 3.100182 / 2.142072 (0.958110) | 1.539321 / 4.805227 (-3.265906) | 0.257735 / 6.500664 (-6.242929) | 0.082785 / 0.075469 (0.007316) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.766586 / 1.841788 (-0.075201) | 20.534638 / 8.074308 (12.460330) | 24.066176 / 10.191392 (13.874784) | 0.272419 / 0.680424 (-0.408005) | 0.048940 / 0.534201 (-0.485261) | 0.606004 / 0.579283 (0.026721) | 0.669684 / 0.434364 (0.235320) | 0.716858 / 0.540337 (0.176521) | 0.949394 / 1.386936 (-0.437542) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010865 / 0.011353 (-0.000488) | 0.009855 / 0.011008 (-0.001153) | 0.105973 / 0.038508 (0.067465) | 0.039818 / 0.023109 (0.016709) | 0.544505 / 0.275898 (0.268607) | 0.511253 / 0.323480 (0.187773) | 0.007350 / 0.007986 (-0.000635) | 0.006950 / 0.004328 (0.002622) | 0.106548 / 0.004250 (0.102298) | 0.062740 / 0.037052 (0.025688) | 0.465881 / 0.258489 (0.207392) | 0.524426 / 0.293841 (0.230585) | 0.056052 / 0.128546 (-0.072495) | 0.020906 / 0.075646 (-0.054741) | 0.125337 / 0.419271 (-0.293935) | 0.064689 / 0.043533 (0.021156) | 0.483055 / 0.255139 (0.227916) | 0.518878 / 0.283200 (0.235678) | 0.127288 / 0.141683 (-0.014394) | 1.936246 / 1.452155 (0.484092) | 2.162532 / 1.492716 (0.669816) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.253691 / 0.018006 (0.235685) | 0.606244 / 0.000490 (0.605754) | 0.004251 / 0.000200 (0.004051) | 0.000126 / 0.000054 (0.000071) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038356 / 0.037411 (0.000944) | 0.146690 / 0.014526 (0.132164) | 0.146545 / 0.176557 (-0.030012) | 0.218452 / 0.737135 (-0.518684) | 0.165314 / 0.296338 (-0.131025) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.645768 / 0.215209 (0.430559) | 7.229186 / 2.077655 (5.151531) | 3.484778 / 1.504120 (1.980658) | 2.585310 / 1.541195 (1.044116) | 2.727670 / 1.468490 (1.259180) | 1.393416 / 4.584777 (-3.191361) | 6.448707 / 3.745712 (2.702995) | 3.433652 / 5.269862 (-1.836209) | 2.106450 / 4.565676 (-2.459226) | 0.143899 / 0.424275 (-0.280376) | 0.015097 / 0.007607 (0.007490) | 0.860960 / 0.226044 (0.634916) | 9.509725 / 2.268929 (7.240797) | 3.881601 / 55.444624 (-51.563024) | 3.156018 / 6.876477 (-3.720459) | 3.556330 / 2.142072 (1.414257) | 1.525940 / 4.805227 (-3.279287) | 0.264588 / 6.500664 (-6.236076) | 0.090327 / 0.075469 (0.014858) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.829761 / 1.841788 (-0.012027) | 21.037774 / 8.074308 (12.963466) | 24.464737 / 10.191392 (14.273345) | 0.394165 / 0.680424 (-0.286259) | 0.039286 / 0.534201 (-0.494915) | 0.546412 / 0.579283 (-0.032871) | 0.741760 / 0.434364 (0.307396) | 0.683969 / 0.540337 (0.143632) | 0.831392 / 1.386936 (-0.555544) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e453eeac5239d0ff3e98adcba59a6724ee68b46b \"CML watermark\")\n"
] | "2023-02-20T17:29:14Z" | "2023-02-21T13:08:25Z" | "2023-02-21T12:58:12Z" | CONTRIBUTOR | null | 0 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011635 / 0.011353 (0.000282) | 0.005446 / 0.011008 (-0.005562) | 0.111044 / 0.038508 (0.072536) | 0.034243 / 0.023109 (0.011134) | 0.357560 / 0.275898 (0.081662) | 0.403940 / 0.323480 (0.080460) | 0.008532 / 0.007986 (0.000546) | 0.004327 / 0.004328 (-0.000002) | 0.084659 / 0.004250 (0.080408) | 0.040914 / 0.037052 (0.003861) | 0.367142 / 0.258489 (0.108653) | 0.381651 / 0.293841 (0.087810) | 0.053865 / 0.128546 (-0.074681) | 0.019060 / 0.075646 (-0.056587) | 0.371994 / 0.419271 (-0.047277) | 0.058417 / 0.043533 (0.014884) | 0.357740 / 0.255139 (0.102601) | 0.367423 / 0.283200 (0.084224) | 0.104336 / 0.141683 (-0.037347) | 1.632128 / 1.452155 (0.179974) | 1.676216 / 1.492716 (0.183499) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.199649 / 0.018006 (0.181642) | 0.490945 / 0.000490 (0.490455) | 0.001598 / 0.000200 (0.001398) | 0.000094 / 0.000054 (0.000039) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024541 / 0.037411 (-0.012871) | 0.104713 / 0.014526 (0.090187) | 0.119438 / 0.176557 (-0.057118) | 0.160854 / 0.737135 (-0.576281) | 0.127323 / 0.296338 (-0.169016) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.586483 / 0.215209 (0.371274) | 5.771689 / 2.077655 (3.694034) | 2.378962 / 1.504120 (0.874842) | 1.998787 / 1.541195 (0.457592) | 1.993016 / 1.468490 (0.524526) | 1.199169 / 4.584777 (-3.385608) | 5.281648 / 3.745712 (1.535936) | 5.589235 / 5.269862 (0.319373) | 2.715162 / 4.565676 (-1.850514) | 0.153312 / 0.424275 (-0.270963) | 0.014302 / 0.007607 (0.006695) | 0.761185 / 0.226044 (0.535140) | 7.602517 / 2.268929 (5.333589) | 3.095271 / 55.444624 (-52.349354) | 2.407394 / 6.876477 (-4.469083) | 2.519074 / 2.142072 (0.377002) | 1.459270 / 4.805227 (-3.345957) | 0.259578 / 6.500664 (-6.241086) | 0.077356 / 0.075469 (0.001887) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.502123 / 1.841788 (-0.339665) | 16.254010 / 8.074308 (8.179702) | 19.971713 / 10.191392 (9.780321) | 0.221491 / 0.680424 (-0.458933) | 0.043959 / 0.534201 (-0.490242) | 0.512566 / 0.579283 (-0.066717) | 0.594724 / 0.434364 (0.160360) | 0.573855 / 0.540337 (0.033518) | 0.680503 / 1.386936 (-0.706433) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008543 / 0.011353 (-0.002810) | 0.005828 / 0.011008 (-0.005180) | 0.083696 / 0.038508 (0.045188) | 0.036186 / 0.023109 (0.013077) | 0.379777 / 0.275898 (0.103879) | 0.437361 / 0.323480 (0.113881) | 0.006788 / 0.007986 (-0.001197) | 0.005110 / 0.004328 (0.000782) | 0.106075 / 0.004250 (0.101824) | 0.048770 / 0.037052 (0.011718) | 0.390770 / 0.258489 (0.132281) | 0.420813 / 0.293841 (0.126972) | 0.050622 / 0.128546 (-0.077924) | 0.019939 / 0.075646 (-0.055707) | 0.106890 / 0.419271 (-0.312382) | 0.070800 / 0.043533 (0.027267) | 0.406094 / 0.255139 (0.150955) | 0.419796 / 0.283200 (0.136597) | 0.107237 / 0.141683 (-0.034446) | 1.687894 / 1.452155 (0.235739) | 1.735680 / 1.492716 (0.242963) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216403 / 0.018006 (0.198397) | 0.495002 / 0.000490 (0.494512) | 0.004841 / 0.000200 (0.004641) | 0.000117 / 0.000054 (0.000063) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.043774 / 0.037411 (0.006363) | 0.119144 / 0.014526 (0.104618) | 0.143694 / 0.176557 (-0.032862) | 0.195548 / 0.737135 (-0.541587) | 0.151426 / 0.296338 (-0.144912) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.617694 / 0.215209 (0.402485) | 6.216237 / 2.077655 (4.138582) | 2.578341 / 1.504120 (1.074221) | 2.184868 / 1.541195 (0.643673) | 2.244954 / 1.468490 (0.776464) | 1.236072 / 4.584777 (-3.348705) | 5.257919 / 3.745712 (1.512207) | 4.634682 / 5.269862 (-0.635180) | 2.722579 / 4.565676 (-1.843097) | 0.131433 / 0.424275 (-0.292843) | 0.012928 / 0.007607 (0.005321) | 0.768315 / 0.226044 (0.542270) | 7.625277 / 2.268929 (5.356349) | 3.146364 / 55.444624 (-52.298260) | 2.577886 / 6.876477 (-4.298590) | 2.572626 / 2.142072 (0.430554) | 1.468160 / 4.805227 (-3.337067) | 0.252524 / 6.500664 (-6.248140) | 0.083264 / 0.075469 (0.007794) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.452614 / 1.841788 (-0.389174) | 15.906162 / 8.074308 (7.831853) | 17.803630 / 10.191392 (7.612238) | 0.210769 / 0.680424 (-0.469655) | 0.024672 / 0.534201 (-0.509529) | 0.486486 / 0.579283 (-0.092797) | 0.545256 / 0.434364 (0.110892) | 0.598736 / 0.540337 (0.058399) | 0.689083 / 1.386936 (-0.697853) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#189a870b4f0964d77b43c2f4e79c4ca7b799f690 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008806 / 0.011353 (-0.002547) | 0.004947 / 0.011008 (-0.006061) | 0.098559 / 0.038508 (0.060051) | 0.034293 / 0.023109 (0.011183) | 0.311924 / 0.275898 (0.036026) | 0.377501 / 0.323480 (0.054021) | 0.007916 / 0.007986 (-0.000069) | 0.004131 / 0.004328 (-0.000197) | 0.074934 / 0.004250 (0.070684) | 0.043396 / 0.037052 (0.006344) | 0.344788 / 0.258489 (0.086299) | 0.369943 / 0.293841 (0.076102) | 0.036846 / 0.128546 (-0.091700) | 0.011803 / 0.075646 (-0.063843) | 0.331306 / 0.419271 (-0.087965) | 0.047015 / 0.043533 (0.003483) | 0.305890 / 0.255139 (0.050751) | 0.332658 / 0.283200 (0.049459) | 0.101134 / 0.141683 (-0.040549) | 1.485615 / 1.452155 (0.033461) | 1.510230 / 1.492716 (0.017514) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.274272 / 0.018006 (0.256266) | 0.514739 / 0.000490 (0.514250) | 0.003433 / 0.000200 (0.003234) | 0.000078 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027054 / 0.037411 (-0.010357) | 0.106416 / 0.014526 (0.091890) | 0.118761 / 0.176557 (-0.057796) | 0.156115 / 0.737135 (-0.581021) | 0.123801 / 0.296338 (-0.172537) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.403121 / 0.215209 (0.187912) | 4.008806 / 2.077655 (1.931151) | 1.891253 / 1.504120 (0.387133) | 1.698523 / 1.541195 (0.157328) | 1.778533 / 1.468490 (0.310043) | 0.688207 / 4.584777 (-3.896570) | 3.674350 / 3.745712 (-0.071362) | 1.848438 / 5.269862 (-3.421423) | 1.202380 / 4.565676 (-3.363297) | 0.073490 / 0.424275 (-0.350785) | 0.010655 / 0.007607 (0.003048) | 0.446939 / 0.226044 (0.220894) | 4.478489 / 2.268929 (2.209560) | 1.992281 / 55.444624 (-53.452343) | 1.684077 / 6.876477 (-5.192400) | 1.715435 / 2.142072 (-0.426638) | 0.731454 / 4.805227 (-4.073773) | 0.143679 / 6.500664 (-6.356985) | 0.053415 / 0.075469 (-0.022054) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.060583 / 1.841788 (-0.781205) | 13.730462 / 8.074308 (5.656153) | 13.038976 / 10.191392 (2.847583) | 0.144168 / 0.680424 (-0.536256) | 0.025788 / 0.534201 (-0.508413) | 0.393332 / 0.579283 (-0.185951) | 0.409495 / 0.434364 (-0.024869) | 0.523745 / 0.540337 (-0.016592) | 0.601595 / 1.386936 (-0.785341) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006369 / 0.011353 (-0.004983) | 0.005019 / 0.011008 (-0.005990) | 0.065226 / 0.038508 (0.026718) | 0.029634 / 0.023109 (0.006524) | 0.302871 / 0.275898 (0.026972) | 0.331055 / 0.323480 (0.007575) | 0.005470 / 0.007986 (-0.002516) | 0.005372 / 0.004328 (0.001043) | 0.064930 / 0.004250 (0.060680) | 0.046979 / 0.037052 (0.009927) | 0.305633 / 0.258489 (0.047144) | 0.345305 / 0.293841 (0.051464) | 0.032951 / 0.128546 (-0.095596) | 0.011447 / 0.075646 (-0.064199) | 0.077054 / 0.419271 (-0.342218) | 0.045744 / 0.043533 (0.002211) | 0.303446 / 0.255139 (0.048307) | 0.319837 / 0.283200 (0.036637) | 0.098631 / 0.141683 (-0.043052) | 1.266593 / 1.452155 (-0.185562) | 1.355388 / 1.492716 (-0.137328) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.291301 / 0.018006 (0.273295) | 0.537848 / 0.000490 (0.537359) | 0.006697 / 0.000200 (0.006497) | 0.000110 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027677 / 0.037411 (-0.009734) | 0.099633 / 0.014526 (0.085107) | 0.110626 / 0.176557 (-0.065931) | 0.144724 / 0.737135 (-0.592412) | 0.114955 / 0.296338 (-0.181383) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.375344 / 0.215209 (0.160135) | 3.717490 / 2.077655 (1.639835) | 1.845886 / 1.504120 (0.341766) | 1.713274 / 1.541195 (0.172079) | 1.761286 / 1.468490 (0.292796) | 0.627924 / 4.584777 (-3.956853) | 3.628154 / 3.745712 (-0.117558) | 3.261851 / 5.269862 (-2.008011) | 1.701008 / 4.565676 (-2.864669) | 0.076703 / 0.424275 (-0.347572) | 0.010839 / 0.007607 (0.003231) | 0.459193 / 0.226044 (0.233148) | 4.589066 / 2.268929 (2.320137) | 2.193972 / 55.444624 (-53.250653) | 1.892115 / 6.876477 (-4.984362) | 1.892453 / 2.142072 (-0.249619) | 0.745727 / 4.805227 (-4.059500) | 0.150232 / 6.500664 (-6.350432) | 0.057245 / 0.075469 (-0.018224) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.114657 / 1.841788 (-0.727131) | 13.595215 / 8.074308 (5.520907) | 12.267177 / 10.191392 (2.075785) | 0.151362 / 0.680424 (-0.529061) | 0.015609 / 0.534201 (-0.518591) | 0.379151 / 0.579283 (-0.200132) | 0.386125 / 0.434364 (-0.048238) | 0.470037 / 0.540337 (-0.070301) | 0.562340 / 1.386936 (-0.824596) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#526578cd473a266fa86643d15905181bf346ecac \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009847 / 0.011353 (-0.001505) | 0.005609 / 0.011008 (-0.005399) | 0.101951 / 0.038508 (0.063443) | 0.038082 / 0.023109 (0.014972) | 0.299933 / 0.275898 (0.024035) | 0.377081 / 0.323480 (0.053601) | 0.008900 / 0.007986 (0.000915) | 0.004608 / 0.004328 (0.000279) | 0.077723 / 0.004250 (0.073473) | 0.048592 / 0.037052 (0.011540) | 0.310789 / 0.258489 (0.052300) | 0.345627 / 0.293841 (0.051787) | 0.038716 / 0.128546 (-0.089830) | 0.012653 / 0.075646 (-0.062993) | 0.336885 / 0.419271 (-0.082387) | 0.048715 / 0.043533 (0.005182) | 0.295336 / 0.255139 (0.040197) | 0.316735 / 0.283200 (0.033536) | 0.115142 / 0.141683 (-0.026541) | 1.480332 / 1.452155 (0.028177) | 1.604972 / 1.492716 (0.112256) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.299516 / 0.018006 (0.281510) | 0.525892 / 0.000490 (0.525402) | 0.002246 / 0.000200 (0.002046) | 0.000095 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031547 / 0.037411 (-0.005864) | 0.120611 / 0.014526 (0.106085) | 0.124516 / 0.176557 (-0.052041) | 0.166036 / 0.737135 (-0.571100) | 0.131689 / 0.296338 (-0.164650) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400728 / 0.215209 (0.185519) | 4.007027 / 2.077655 (1.929372) | 1.793922 / 1.504120 (0.289803) | 1.596709 / 1.541195 (0.055514) | 1.752130 / 1.468490 (0.283640) | 0.717464 / 4.584777 (-3.867313) | 3.798844 / 3.745712 (0.053132) | 3.685088 / 5.269862 (-1.584774) | 1.914041 / 4.565676 (-2.651636) | 0.086181 / 0.424275 (-0.338094) | 0.012753 / 0.007607 (0.005146) | 0.507984 / 0.226044 (0.281940) | 5.086255 / 2.268929 (2.817326) | 2.280650 / 55.444624 (-53.163974) | 1.929294 / 6.876477 (-4.947183) | 2.057884 / 2.142072 (-0.084188) | 0.852863 / 4.805227 (-3.952364) | 0.165497 / 6.500664 (-6.335168) | 0.063356 / 0.075469 (-0.012113) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.212593 / 1.841788 (-0.629194) | 16.270507 / 8.074308 (8.196199) | 15.708406 / 10.191392 (5.517014) | 0.162346 / 0.680424 (-0.518078) | 0.029702 / 0.534201 (-0.504499) | 0.447685 / 0.579283 (-0.131598) | 0.449361 / 0.434364 (0.014997) | 0.530536 / 0.540337 (-0.009801) | 0.613439 / 1.386936 (-0.773497) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007741 / 0.011353 (-0.003612) | 0.005752 / 0.011008 (-0.005256) | 0.076600 / 0.038508 (0.038092) | 0.034841 / 0.023109 (0.011732) | 0.345106 / 0.275898 (0.069208) | 0.385685 / 0.323480 (0.062205) | 0.006466 / 0.007986 (-0.001519) | 0.005806 / 0.004328 (0.001478) | 0.075110 / 0.004250 (0.070860) | 0.052936 / 0.037052 (0.015883) | 0.343576 / 0.258489 (0.085087) | 0.408749 / 0.293841 (0.114908) | 0.037345 / 0.128546 (-0.091201) | 0.012807 / 0.075646 (-0.062839) | 0.087732 / 0.419271 (-0.331540) | 0.050218 / 0.043533 (0.006685) | 0.338963 / 0.255139 (0.083824) | 0.361629 / 0.283200 (0.078429) | 0.107488 / 0.141683 (-0.034195) | 1.465284 / 1.452155 (0.013130) | 1.562218 / 1.492716 (0.069502) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.322496 / 0.018006 (0.304489) | 0.522782 / 0.000490 (0.522292) | 0.006680 / 0.000200 (0.006480) | 0.000144 / 0.000054 (0.000090) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031801 / 0.037411 (-0.005611) | 0.116839 / 0.014526 (0.102313) | 0.127552 / 0.176557 (-0.049005) | 0.167670 / 0.737135 (-0.569465) | 0.134170 / 0.296338 (-0.162168) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425449 / 0.215209 (0.210240) | 4.229367 / 2.077655 (2.151713) | 2.014663 / 1.504120 (0.510543) | 1.812981 / 1.541195 (0.271787) | 1.964039 / 1.468490 (0.495549) | 0.703454 / 4.584777 (-3.881323) | 3.786985 / 3.745712 (0.041273) | 2.262377 / 5.269862 (-3.007485) | 1.404868 / 4.565676 (-3.160808) | 0.086234 / 0.424275 (-0.338041) | 0.012616 / 0.007607 (0.005009) | 0.525784 / 0.226044 (0.299739) | 5.268295 / 2.268929 (2.999366) | 2.496674 / 55.444624 (-52.947950) | 2.177773 / 6.876477 (-4.698704) | 2.313677 / 2.142072 (0.171605) | 0.846202 / 4.805227 (-3.959026) | 0.170152 / 6.500664 (-6.330513) | 0.066772 / 0.075469 (-0.008698) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254719 / 1.841788 (-0.587069) | 16.017627 / 8.074308 (7.943319) | 14.560583 / 10.191392 (4.369191) | 0.168275 / 0.680424 (-0.512149) | 0.017935 / 0.534201 (-0.516266) | 0.430806 / 0.579283 (-0.148477) | 0.428737 / 0.434364 (-0.005626) | 0.532001 / 0.540337 (-0.008336) | 0.633680 / 1.386936 (-0.753256) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c2c75dff81c3f060cc4731be3416fd962cc6383e \"CML watermark\")\n"
] | "2023-02-20T16:50:09Z" | "2023-02-21T13:20:42Z" | "2023-02-21T13:13:05Z" | CONTRIBUTOR | null | 0 | {
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} | Fix for https://discord.com/channels/879548962464493619/1075729627546406912/1075729627546406912
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"good catch!",
"_The documentation is not available anymore as the PR was closed or merged._",
"The test fail is unrelated to this PR and fixed on `main` - merging :)",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008942 / 0.011353 (-0.002411) | 0.004617 / 0.011008 (-0.006391) | 0.101310 / 0.038508 (0.062802) | 0.030997 / 0.023109 (0.007888) | 0.306292 / 0.275898 (0.030394) | 0.370533 / 0.323480 (0.047053) | 0.007318 / 0.007986 (-0.000667) | 0.003473 / 0.004328 (-0.000856) | 0.078557 / 0.004250 (0.074307) | 0.036312 / 0.037052 (-0.000740) | 0.308993 / 0.258489 (0.050504) | 0.344411 / 0.293841 (0.050570) | 0.034384 / 0.128546 (-0.094162) | 0.011631 / 0.075646 (-0.064016) | 0.323948 / 0.419271 (-0.095324) | 0.041176 / 0.043533 (-0.002357) | 0.302512 / 0.255139 (0.047373) | 0.322439 / 0.283200 (0.039239) | 0.088955 / 0.141683 (-0.052728) | 1.534918 / 1.452155 (0.082763) | 1.555803 / 1.492716 (0.063087) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.195639 / 0.018006 (0.177633) | 0.423068 / 0.000490 (0.422579) | 0.004101 / 0.000200 (0.003901) | 0.000079 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023691 / 0.037411 (-0.013721) | 0.100536 / 0.014526 (0.086011) | 0.108399 / 0.176557 (-0.068157) | 0.143515 / 0.737135 (-0.593620) | 0.111886 / 0.296338 (-0.184452) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417519 / 0.215209 (0.202310) | 4.180463 / 2.077655 (2.102808) | 1.862511 / 1.504120 (0.358391) | 1.658724 / 1.541195 (0.117529) | 1.735847 / 1.468490 (0.267357) | 0.688257 / 4.584777 (-3.896520) | 3.447976 / 3.745712 (-0.297737) | 1.877939 / 5.269862 (-3.391922) | 1.157385 / 4.565676 (-3.408292) | 0.081418 / 0.424275 (-0.342857) | 0.012395 / 0.007607 (0.004788) | 0.518935 / 0.226044 (0.292891) | 5.220355 / 2.268929 (2.951427) | 2.308355 / 55.444624 (-53.136269) | 1.960026 / 6.876477 (-4.916450) | 2.013179 / 2.142072 (-0.128893) | 0.802850 / 4.805227 (-4.002377) | 0.146941 / 6.500664 (-6.353723) | 0.064080 / 0.075469 (-0.011389) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.284443 / 1.841788 (-0.557344) | 13.903755 / 8.074308 (5.829447) | 14.467101 / 10.191392 (4.275709) | 0.156813 / 0.680424 (-0.523611) | 0.028583 / 0.534201 (-0.505618) | 0.406349 / 0.579283 (-0.172934) | 0.413178 / 0.434364 (-0.021186) | 0.491283 / 0.540337 (-0.049055) | 0.571171 / 1.386936 (-0.815765) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006868 / 0.011353 (-0.004484) | 0.004593 / 0.011008 (-0.006416) | 0.077574 / 0.038508 (0.039066) | 0.027703 / 0.023109 (0.004593) | 0.342096 / 0.275898 (0.066198) | 0.378500 / 0.323480 (0.055020) | 0.005785 / 0.007986 (-0.002201) | 0.003342 / 0.004328 (-0.000986) | 0.076105 / 0.004250 (0.071855) | 0.040369 / 0.037052 (0.003317) | 0.343611 / 0.258489 (0.085122) | 0.391859 / 0.293841 (0.098018) | 0.032675 / 0.128546 (-0.095871) | 0.011623 / 0.075646 (-0.064023) | 0.086623 / 0.419271 (-0.332648) | 0.051955 / 0.043533 (0.008423) | 0.343425 / 0.255139 (0.088286) | 0.368887 / 0.283200 (0.085688) | 0.097117 / 0.141683 (-0.044566) | 1.499546 / 1.452155 (0.047391) | 1.593100 / 1.492716 (0.100383) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.193568 / 0.018006 (0.175562) | 0.409211 / 0.000490 (0.408722) | 0.003797 / 0.000200 (0.003597) | 0.000083 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024982 / 0.037411 (-0.012430) | 0.101367 / 0.014526 (0.086841) | 0.108546 / 0.176557 (-0.068010) | 0.144402 / 0.737135 (-0.592733) | 0.112233 / 0.296338 (-0.184105) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.432820 / 0.215209 (0.217611) | 4.341045 / 2.077655 (2.263391) | 2.058326 / 1.504120 (0.554207) | 1.853913 / 1.541195 (0.312718) | 1.942436 / 1.468490 (0.473946) | 0.699130 / 4.584777 (-3.885647) | 3.392879 / 3.745712 (-0.352833) | 1.908277 / 5.269862 (-3.361585) | 1.177998 / 4.565676 (-3.387678) | 0.082700 / 0.424275 (-0.341576) | 0.012505 / 0.007607 (0.004898) | 0.526286 / 0.226044 (0.300242) | 5.279599 / 2.268929 (3.010670) | 2.505771 / 55.444624 (-52.938854) | 2.158460 / 6.876477 (-4.718016) | 2.211437 / 2.142072 (0.069365) | 0.802065 / 4.805227 (-4.003163) | 0.150766 / 6.500664 (-6.349898) | 0.067639 / 0.075469 (-0.007830) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.286595 / 1.841788 (-0.555192) | 13.961894 / 8.074308 (5.887586) | 14.021865 / 10.191392 (3.830473) | 0.164590 / 0.680424 (-0.515834) | 0.016909 / 0.534201 (-0.517292) | 0.392215 / 0.579283 (-0.187069) | 0.408080 / 0.434364 (-0.026284) | 0.488247 / 0.540337 (-0.052090) | 0.575524 / 1.386936 (-0.811412) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#699b0293876015457bfce40f7245d346c34c7717 \"CML watermark\")\n"
] | "2023-02-20T16:16:57Z" | "2023-02-21T13:27:57Z" | "2023-02-21T13:21:07Z" | CONTRIBUTOR | null | 0 | {
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} | This is kinda unimportant fix but, the suggested `pip install sklearn` does not work.
The current error message if sklearn is not installed:
```
ImportError: To be able to use [dataset name], you need to install the following dependency: sklearn.
Please install it using 'pip install sklearn' for instance.
```
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"_The documentation is not available anymore as the PR was closed or merged._",
"See the resolved changes [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5550/en/package_reference/main_classes#datasets.Dataset.class_encode_column), [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5550/en/package_reference/main_classes#datasets.Dataset.unique) and [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5550/en/package_reference/main_classes#datasets.DatasetDict.class_encode_column), respectively",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008256 / 0.011353 (-0.003097) | 0.004400 / 0.011008 (-0.006608) | 0.098676 / 0.038508 (0.060168) | 0.028937 / 0.023109 (0.005828) | 0.302578 / 0.275898 (0.026680) | 0.334170 / 0.323480 (0.010690) | 0.006657 / 0.007986 (-0.001329) | 0.004581 / 0.004328 (0.000253) | 0.076874 / 0.004250 (0.072624) | 0.034401 / 0.037052 (-0.002652) | 0.303928 / 0.258489 (0.045439) | 0.348421 / 0.293841 (0.054580) | 0.033303 / 0.128546 (-0.095243) | 0.011445 / 0.075646 (-0.064202) | 0.322137 / 0.419271 (-0.097135) | 0.041072 / 0.043533 (-0.002461) | 0.306007 / 0.255139 (0.050868) | 0.325945 / 0.283200 (0.042745) | 0.086685 / 0.141683 (-0.054998) | 1.454956 / 1.452155 (0.002801) | 1.545525 / 1.492716 (0.052809) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.175536 / 0.018006 (0.157530) | 0.400203 / 0.000490 (0.399713) | 0.002103 / 0.000200 (0.001903) | 0.000072 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022750 / 0.037411 (-0.014661) | 0.095163 / 0.014526 (0.080637) | 0.103995 / 0.176557 (-0.072561) | 0.138806 / 0.737135 (-0.598330) | 0.105711 / 0.296338 (-0.190628) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.427860 / 0.215209 (0.212651) | 4.259594 / 2.077655 (2.181940) | 2.157986 / 1.504120 (0.653866) | 1.913814 / 1.541195 (0.372619) | 1.793455 / 1.468490 (0.324965) | 0.702341 / 4.584777 (-3.882436) | 3.353086 / 3.745712 (-0.392626) | 1.856952 / 5.269862 (-3.412909) | 1.149963 / 4.565676 (-3.415713) | 0.082926 / 0.424275 (-0.341349) | 0.012307 / 0.007607 (0.004700) | 0.524531 / 0.226044 (0.298487) | 5.254766 / 2.268929 (2.985838) | 2.590157 / 55.444624 (-52.854468) | 2.272613 / 6.876477 (-4.603864) | 2.304367 / 2.142072 (0.162294) | 0.819298 / 4.805227 (-3.985929) | 0.152170 / 6.500664 (-6.348494) | 0.066563 / 0.075469 (-0.008906) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.205054 / 1.841788 (-0.636733) | 13.729073 / 8.074308 (5.654765) | 14.061037 / 10.191392 (3.869645) | 0.138020 / 0.680424 (-0.542404) | 0.028042 / 0.534201 (-0.506159) | 0.392260 / 0.579283 (-0.187024) | 0.405632 / 0.434364 (-0.028732) | 0.469583 / 0.540337 (-0.070755) | 0.563110 / 1.386936 (-0.823826) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006513 / 0.011353 (-0.004839) | 0.004402 / 0.011008 (-0.006606) | 0.076339 / 0.038508 (0.037831) | 0.027222 / 0.023109 (0.004112) | 0.338968 / 0.275898 (0.063070) | 0.378475 / 0.323480 (0.054995) | 0.005443 / 0.007986 (-0.002542) | 0.003312 / 0.004328 (-0.001016) | 0.075352 / 0.004250 (0.071102) | 0.034951 / 0.037052 (-0.002102) | 0.342268 / 0.258489 (0.083779) | 0.381024 / 0.293841 (0.087183) | 0.031568 / 0.128546 (-0.096979) | 0.011558 / 0.075646 (-0.064088) | 0.085267 / 0.419271 (-0.334005) | 0.041248 / 0.043533 (-0.002284) | 0.340422 / 0.255139 (0.085283) | 0.365497 / 0.283200 (0.082297) | 0.088278 / 0.141683 (-0.053405) | 1.479838 / 1.452155 (0.027683) | 1.554440 / 1.492716 (0.061724) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223240 / 0.018006 (0.205234) | 0.394771 / 0.000490 (0.394282) | 0.003022 / 0.000200 (0.002822) | 0.000071 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024842 / 0.037411 (-0.012570) | 0.099167 / 0.014526 (0.084641) | 0.106376 / 0.176557 (-0.070180) | 0.141397 / 0.737135 (-0.595738) | 0.110355 / 0.296338 (-0.185983) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437598 / 0.215209 (0.222389) | 4.394964 / 2.077655 (2.317310) | 2.082660 / 1.504120 (0.578540) | 1.868690 / 1.541195 (0.327496) | 1.915190 / 1.468490 (0.446700) | 0.701035 / 4.584777 (-3.883742) | 3.306594 / 3.745712 (-0.439118) | 1.842681 / 5.269862 (-3.427181) | 1.155022 / 4.565676 (-3.410654) | 0.083310 / 0.424275 (-0.340965) | 0.012413 / 0.007607 (0.004806) | 0.543179 / 0.226044 (0.317135) | 5.445605 / 2.268929 (3.176676) | 2.545080 / 55.444624 (-52.899544) | 2.188741 / 6.876477 (-4.687736) | 2.205561 / 2.142072 (0.063489) | 0.804967 / 4.805227 (-4.000261) | 0.151024 / 6.500664 (-6.349640) | 0.066448 / 0.075469 (-0.009021) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.304671 / 1.841788 (-0.537117) | 13.996631 / 8.074308 (5.922323) | 13.617626 / 10.191392 (3.426234) | 0.141512 / 0.680424 (-0.538912) | 0.016527 / 0.534201 (-0.517674) | 0.384981 / 0.579283 (-0.194302) | 0.385198 / 0.434364 (-0.049166) | 0.469033 / 0.540337 (-0.071305) | 0.554738 / 1.386936 (-0.832198) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d09dc897e153fed7c7f459a122fb03faa46688ed \"CML watermark\")\n"
] | "2023-02-20T08:52:11Z" | "2023-02-20T15:16:13Z" | "2023-02-20T15:09:13Z" | CONTRIBUTOR | null | 0 | {
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## Pull Request overview
* Resolve 4 broken references in the docs
## The problems
Two broken references [here](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.class_encode_column):
![image](https://user-images.githubusercontent.com/37621491/220056232-366b64dc-33c9-461b-8f82-1ac4aa570280.png)
---
One broken reference [here](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.unique):
![image](https://user-images.githubusercontent.com/37621491/220057135-2f249d60-c01d-48b5-82bb-5085a7635198.png)
---
One missing reference [here](https://huggingface.co/docs/datasets/v2.9.0/en/package_reference/main_classes#datasets.DatasetDict.class_encode_column):
![image](https://user-images.githubusercontent.com/37621491/220057025-4a8e5556-5041-4ec7-b8d8-ed4fdc266495.png)
- Tom Aarsen | {
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https://api.github.com/repos/huggingface/datasets/issues/5549 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5549/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5549/comments | https://api.github.com/repos/huggingface/datasets/issues/5549/events | https://github.com/huggingface/datasets/pull/5549 | 1,590,836,848 | PR_kwDODunzps5KSsi3 | 5,549 | Apply ruff flake8-comprehension checks | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009598 / 0.011353 (-0.001755) | 0.005115 / 0.011008 (-0.005893) | 0.100100 / 0.038508 (0.061592) | 0.036193 / 0.023109 (0.013083) | 0.296478 / 0.275898 (0.020580) | 0.355997 / 0.323480 (0.032517) | 0.007846 / 0.007986 (-0.000140) | 0.004082 / 0.004328 (-0.000247) | 0.076949 / 0.004250 (0.072699) | 0.044304 / 0.037052 (0.007252) | 0.310775 / 0.258489 (0.052286) | 0.333914 / 0.293841 (0.040073) | 0.037783 / 0.128546 (-0.090763) | 0.012023 / 0.075646 (-0.063623) | 0.333311 / 0.419271 (-0.085961) | 0.047568 / 0.043533 (0.004035) | 0.295567 / 0.255139 (0.040428) | 0.315707 / 0.283200 (0.032507) | 0.102675 / 0.141683 (-0.039008) | 1.471546 / 1.452155 (0.019391) | 1.507991 / 1.492716 (0.015274) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208658 / 0.018006 (0.190651) | 0.445026 / 0.000490 (0.444536) | 0.002593 / 0.000200 (0.002393) | 0.000084 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026968 / 0.037411 (-0.010444) | 0.108188 / 0.014526 (0.093662) | 0.117965 / 0.176557 (-0.058592) | 0.182769 / 0.737135 (-0.554366) | 0.121671 / 0.296338 (-0.174667) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400677 / 0.215209 (0.185468) | 4.012577 / 2.077655 (1.934922) | 1.821324 / 1.504120 (0.317204) | 1.624438 / 1.541195 (0.083244) | 1.731886 / 1.468490 (0.263396) | 0.698089 / 4.584777 (-3.886688) | 3.786165 / 3.745712 (0.040453) | 2.079742 / 5.269862 (-3.190119) | 1.325032 / 4.565676 (-3.240644) | 0.085229 / 0.424275 (-0.339046) | 0.012017 / 0.007607 (0.004410) | 0.511779 / 0.226044 (0.285734) | 5.114358 / 2.268929 (2.845430) | 2.324763 / 55.444624 (-53.119861) | 2.011864 / 6.876477 (-4.864612) | 2.075875 / 2.142072 (-0.066198) | 0.853475 / 4.805227 (-3.951752) | 0.166949 / 6.500664 (-6.333715) | 0.064669 / 0.075469 (-0.010800) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.230212 / 1.841788 (-0.611576) | 14.942371 / 8.074308 (6.868063) | 14.075795 / 10.191392 (3.884403) | 0.156920 / 0.680424 (-0.523504) | 0.029002 / 0.534201 (-0.505199) | 0.442213 / 0.579283 (-0.137070) | 0.436888 / 0.434364 (0.002524) | 0.519725 / 0.540337 (-0.020613) | 0.604634 / 1.386936 (-0.782303) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007649 / 0.011353 (-0.003704) | 0.005298 / 0.011008 (-0.005710) | 0.076559 / 0.038508 (0.038050) | 0.033723 / 0.023109 (0.010614) | 0.334946 / 0.275898 (0.059048) | 0.372785 / 0.323480 (0.049305) | 0.006032 / 0.007986 (-0.001953) | 0.004125 / 0.004328 (-0.000204) | 0.075366 / 0.004250 (0.071116) | 0.049061 / 0.037052 (0.012009) | 0.338188 / 0.258489 (0.079699) | 0.389693 / 0.293841 (0.095852) | 0.037246 / 0.128546 (-0.091301) | 0.012530 / 0.075646 (-0.063116) | 0.088053 / 0.419271 (-0.331219) | 0.049844 / 0.043533 (0.006311) | 0.338476 / 0.255139 (0.083337) | 0.361672 / 0.283200 (0.078473) | 0.101982 / 0.141683 (-0.039701) | 1.479550 / 1.452155 (0.027396) | 1.541031 / 1.492716 (0.048315) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226162 / 0.018006 (0.208156) | 0.439108 / 0.000490 (0.438618) | 0.001102 / 0.000200 (0.000902) | 0.000076 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030240 / 0.037411 (-0.007171) | 0.113754 / 0.014526 (0.099229) | 0.122839 / 0.176557 (-0.053717) | 0.192531 / 0.737135 (-0.544604) | 0.129455 / 0.296338 (-0.166884) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424701 / 0.215209 (0.209492) | 4.208161 / 2.077655 (2.130507) | 2.045733 / 1.504120 (0.541613) | 1.892369 / 1.541195 (0.351174) | 1.997024 / 1.468490 (0.528534) | 0.739883 / 4.584777 (-3.844894) | 3.760939 / 3.745712 (0.015227) | 3.195748 / 5.269862 (-2.074113) | 1.731480 / 4.565676 (-2.834197) | 0.087013 / 0.424275 (-0.337262) | 0.012550 / 0.007607 (0.004943) | 0.540829 / 0.226044 (0.314785) | 5.329933 / 2.268929 (3.061005) | 2.507572 / 55.444624 (-52.937052) | 2.167761 / 6.876477 (-4.708716) | 2.250298 / 2.142072 (0.108226) | 0.868718 / 4.805227 (-3.936510) | 0.181643 / 6.500664 (-6.319021) | 0.064817 / 0.075469 (-0.010653) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.295001 / 1.841788 (-0.546787) | 15.236413 / 8.074308 (7.162105) | 13.692212 / 10.191392 (3.500820) | 0.186330 / 0.680424 (-0.494094) | 0.017492 / 0.534201 (-0.516709) | 0.427365 / 0.579283 (-0.151919) | 0.427781 / 0.434364 (-0.006583) | 0.533763 / 0.540337 (-0.006575) | 0.636011 / 1.386936 (-0.750925) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#94b16b674111ca5e1a03ddcb71dc0b53acc2f934 \"CML watermark\")\n"
] | "2023-02-19T20:09:28Z" | "2023-02-23T14:06:39Z" | "2023-02-23T13:59:39Z" | CONTRIBUTOR | null | 0 | {
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Apply ruff's flake8-comprehension checks for better performance, and more readable code. | {
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] | closed | false | null | [] | null | [] | "2023-02-19T20:05:38Z" | "2023-02-23T13:59:41Z" | "2023-02-23T13:59:41Z" | CONTRIBUTOR | null | null | null | ### Feature request
Apply ruff flake8 comprehension checks to codebase.
### Motivation
This should strictly improve the performance / readability of the codebase by removing unnecessary iteration, function calls, etc. This should generate better Python bytecode which should strictly improve performance.
I already applied this fixes to PyTorch and Sympy with little issue and have opened PRs to diffusers and transformers todo this as well.
### Your contribution
Making a PR. | {
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"The code below was throwing a warning:\r\n\r\n```python\r\nclass JaxFormatter(Formatter[Mapping, \"jax.Array\", Mapping]):\r\n def __init__(self, features=None, device=None, **jnp_array_kwargs):\r\n super().__init__(features=features)\r\n import jax\r\n from jaxlib.xla_extension import Device\r\n \r\n self.device = (\r\n device if isinstance(device, Device) else jax.devices()[0]\r\n )\r\n self.jnp_array_kwargs = jnp_array_kwargs\r\n\r\n ...\r\n\r\n def _tensorize(self, value):\r\n ...\r\n\r\n with jax.default_device(self.device):\r\n # calling jnp.array on a np.ndarray does copy the data\r\n # see https://github.com/google/jax/issues/4486\r\n return jnp.array(value, **{**default_dtype, **self.jnp_array_kwargs})\r\n```\r\n\r\nWhen providing `device` via param:\r\n\r\n```python\r\nfrom datasets import Dataset\r\nimport jax\r\n\r\nds = Dataset.from_dict({\"a\": [1, 2, 3], \"b\": [4, 5, 6]})\r\nds = ds.with_format(\"jax\", device=jax.devices()[0])\r\nprint(ds[0])\r\n```\r\n\r\nProducing the following warning:\r\n\r\n```\r\nWARNING:datasets.fingerprint:Parameter 'device'=TFRT_CPU_0 of the transform datasets.arrow_dataset.Dataset.set_format couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n```\r\n\r\nThat's why I decided to map all the available devices, and assign their string representation e.g. `TFRT_CPU_0` to `self.device` instead of `jaxlib.xla_extension.Device`, so that the value of the param `device` is washable. So on, the code that remains at the end is:\r\n\r\n```python\r\nclass JaxFormatter(Formatter[Mapping, \"jax.Array\", Mapping]):\r\n def __init__(self, features=None, device=None, **jnp_array_kwargs):\r\n super().__init__(features=features)\r\n import jax\r\n from jaxlib.xla_client import Device\r\n\r\n self.device_mapping = self._map_devices_to_str()\r\n self.device = (\r\n device if isinstance(device, str) else str(device) if isinstance(device, Device) else str(jax.devices()[0])\r\n )\r\n self.jnp_array_kwargs = jnp_array_kwargs\r\n\r\n def _map_devices_to_str(self) -> Mapping[str, \"jaxlib.xla_extension.Device\"]:\r\n import jax\r\n\r\n return {str(device): device for device in jax.devices()}\r\n\r\n ...\r\n\r\n def _tensorize(self, value):\r\n ...\r\n\r\n with jax.default_device(self.device_mapping[self.device]):\r\n # calling jnp.array on a np.ndarray does copy the data\r\n # see https://github.com/google/jax/issues/4486\r\n return jnp.array(value, **{**default_dtype, **self.jnp_array_kwargs})\r\n```\r\n\r\nBut note that the latter also throws a warning if the provided `device` is not a string but a `jaxlib.xla_extension.Device`, so that's why it needs to be converted to string.",
"_The documentation is not available anymore as the PR was closed or merged._",
"After some investigation, it seems that when using `device=jaxlib.xla_extension.Device` instead of `device=string` it shows the warning so that later formats fail as that cannot be unpickled.\r\n\r\nSo I think we can either add that specifically in `use_with_jax.mdx` documentation entry I'm creating at #5535 so that the users know that they need to surroung the `jaxlib.xla_extension.Device` with `str()`, or find a workaround to override default `deepcopy` behavior with `def __deepcopy__(self)` so that the device param is converted to string if provided as a `jaxlib.xla_extension.Device`, but not sure if the latter works 😕 \r\n\r\nDo you think there's any other possible solution to this issue? Thanks, @lhoestq ",
"Cool ! Specifying the device is indeed super important.\r\n\r\n\r\nI think we can just require `device` to always be a string for now, and add an example in the doc on how to get the string that corresponds to a `jaxlib.xla_extension.Device` ? This way we never deal with objects that are not picklable",
"> Cool ! Specifying the device is indeed super important.\r\n> \r\n> I think we can just require `device` to always be a string for now, and add an example in the doc on how to get the string that corresponds to a `jaxlib.xla_extension.Device` ? This way we never deal with objects that are not picklable\r\n\r\nSure, then I'll restrict it to string for now! Also regarding the documentation update, should we wait until #5535 is merged so that I add this on top of that?",
"CI is failing due to missing `resampy` in `librosa` already being fixed by @lhoestq in https://github.com/huggingface/datasets/pull/5554",
"@lhoestq already moved to a global variable, I can confirm that the following now works:\r\n\r\n```python\r\nimport copy\r\nimport pickle\r\n\r\nimport jax\r\nimport pyarrow as pa\r\n\r\nfrom datasets.formatting import JaxFormatter\r\n\r\n\r\n_COL_A = [0, 1, 2]\r\n_COL_B = [\"foo\", \"bar\", \"foobar\"]\r\n_COL_C = [[[1.0, 0.0, 0.0]] * 2, [[0.0, 1.0, 0.0]] * 2, [[0.0, 0.0, 1.0]] * 2]\r\npa_table = pa.Table.from_pydict({\"a\": _COL_A, \"b\": _COL_B, \"c\": _COL_C})\r\n\r\ndevice = jax.devices()[0]\r\nformatter = JaxFormatter(device=str(device))\r\n\r\npickle.dumps(formatter)\r\ncopy.deepcopy(formatter)\r\n```",
"> Looks all good now thank you !\r\n> \r\n> Is there anything else you wanted to add ? Otherwise I think it's ready for merge\r\n\r\nNothing else to add, I've already applied your suggestions, so ready to merge! Thanks for your input/feedback @lhoestq :hugs:",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009815 / 0.011353 (-0.001538) | 0.005443 / 0.011008 (-0.005565) | 0.101244 / 0.038508 (0.062736) | 0.036573 / 0.023109 (0.013464) | 0.304761 / 0.275898 (0.028863) | 0.365527 / 0.323480 (0.042047) | 0.008244 / 0.007986 (0.000258) | 0.004200 / 0.004328 (-0.000128) | 0.077471 / 0.004250 (0.073221) | 0.045266 / 0.037052 (0.008214) | 0.310213 / 0.258489 (0.051724) | 0.344247 / 0.293841 (0.050406) | 0.039530 / 0.128546 (-0.089016) | 0.012254 / 0.075646 (-0.063393) | 0.335039 / 0.419271 (-0.084233) | 0.049525 / 0.043533 (0.005992) | 0.298350 / 0.255139 (0.043211) | 0.312031 / 0.283200 (0.028832) | 0.108581 / 0.141683 (-0.033102) | 1.481178 / 1.452155 (0.029023) | 1.497662 / 1.492716 (0.004946) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.014762 / 0.018006 (-0.003244) | 0.447099 / 0.000490 (0.446609) | 0.009074 / 0.000200 (0.008874) | 0.000688 / 0.000054 (0.000633) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027466 / 0.037411 (-0.009945) | 0.109715 / 0.014526 (0.095189) | 0.119062 / 0.176557 (-0.057495) | 0.188964 / 0.737135 (-0.548171) | 0.127057 / 0.296338 (-0.169282) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.395092 / 0.215209 (0.179883) | 3.948091 / 2.077655 (1.870436) | 1.795160 / 1.504120 (0.291040) | 1.603704 / 1.541195 (0.062509) | 1.714491 / 1.468490 (0.246001) | 0.700489 / 4.584777 (-3.884288) | 3.767493 / 3.745712 (0.021781) | 3.288374 / 5.269862 (-1.981488) | 1.783711 / 4.565676 (-2.781965) | 0.085119 / 0.424275 (-0.339156) | 0.012349 / 0.007607 (0.004742) | 0.502135 / 0.226044 (0.276091) | 5.019321 / 2.268929 (2.750392) | 2.236469 / 55.444624 (-53.208155) | 1.914376 / 6.876477 (-4.962101) | 1.998579 / 2.142072 (-0.143494) | 0.847841 / 4.805227 (-3.957386) | 0.166035 / 6.500664 (-6.334629) | 0.062469 / 0.075469 (-0.013000) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.245380 / 1.841788 (-0.596408) | 14.757872 / 8.074308 (6.683564) | 14.460373 / 10.191392 (4.268981) | 0.152981 / 0.680424 (-0.527443) | 0.029001 / 0.534201 (-0.505200) | 0.439597 / 0.579283 (-0.139686) | 0.437232 / 0.434364 (0.002868) | 0.532464 / 0.540337 (-0.007873) | 0.629225 / 1.386936 (-0.757711) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007165 / 0.011353 (-0.004188) | 0.005220 / 0.011008 (-0.005789) | 0.075849 / 0.038508 (0.037341) | 0.032717 / 0.023109 (0.009608) | 0.331205 / 0.275898 (0.055307) | 0.364955 / 0.323480 (0.041475) | 0.005518 / 0.007986 (-0.002468) | 0.004069 / 0.004328 (-0.000259) | 0.073900 / 0.004250 (0.069650) | 0.046346 / 0.037052 (0.009294) | 0.337473 / 0.258489 (0.078984) | 0.393062 / 0.293841 (0.099222) | 0.037533 / 0.128546 (-0.091013) | 0.012577 / 0.075646 (-0.063070) | 0.087975 / 0.419271 (-0.331297) | 0.049508 / 0.043533 (0.005975) | 0.333423 / 0.255139 (0.078284) | 0.354345 / 0.283200 (0.071145) | 0.099879 / 0.141683 (-0.041804) | 1.413304 / 1.452155 (-0.038851) | 1.494222 / 1.492716 (0.001506) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206835 / 0.018006 (0.188828) | 0.438246 / 0.000490 (0.437757) | 0.000410 / 0.000200 (0.000210) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028186 / 0.037411 (-0.009225) | 0.109322 / 0.014526 (0.094797) | 0.119581 / 0.176557 (-0.056975) | 0.191784 / 0.737135 (-0.545351) | 0.125100 / 0.296338 (-0.171238) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419418 / 0.215209 (0.204209) | 4.167374 / 2.077655 (2.089720) | 1.995812 / 1.504120 (0.491693) | 1.804602 / 1.541195 (0.263407) | 1.869131 / 1.468490 (0.400641) | 0.709486 / 4.584777 (-3.875291) | 3.838019 / 3.745712 (0.092307) | 2.086206 / 5.269862 (-3.183656) | 1.323970 / 4.565676 (-3.241707) | 0.089477 / 0.424275 (-0.334798) | 0.012402 / 0.007607 (0.004795) | 0.519291 / 0.226044 (0.293246) | 5.194091 / 2.268929 (2.925162) | 2.487055 / 55.444624 (-52.957570) | 2.122495 / 6.876477 (-4.753982) | 2.194910 / 2.142072 (0.052837) | 0.842837 / 4.805227 (-3.962390) | 0.167229 / 6.500664 (-6.333435) | 0.064690 / 0.075469 (-0.010779) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.275931 / 1.841788 (-0.565857) | 14.577000 / 8.074308 (6.502692) | 13.633235 / 10.191392 (3.441843) | 0.184511 / 0.680424 (-0.495913) | 0.017439 / 0.534201 (-0.516762) | 0.424374 / 0.579283 (-0.154909) | 0.427803 / 0.434364 (-0.006561) | 0.527790 / 0.540337 (-0.012548) | 0.627301 / 1.386936 (-0.759635) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#21c86d570faad32c3abbed4305bfd3698daa7fd0 \"CML watermark\")\n"
] | "2023-02-18T20:57:40Z" | "2023-02-21T16:10:55Z" | "2023-02-21T16:04:03Z" | CONTRIBUTOR | null | 0 | {
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} | ## What's in this PR?
After exploring for a while the JAX integration in 🤗`datasets`, I found out that, even though JAX prioritizes the TPU and GPU as the default device when available, the `JaxFormatter` doesn't let you specify the device where you want to place the `jax.Array`s in case you don't want to rely on JAX's default array placement.
So on, I've included the `device` param in `JaxFormatter` but there are some things to take into consideration:
* A formatted `Dataset` is copied with `copy.deepcopy` which means that if one adds the param `device` in `JaxFormatter` as a `jaxlib.xla_extension.Device`, it "fails" because that object cannot be serialized (instead of serializing the param adds a random hash instead). That's the reason why I added a function `_map_devices_to_str` to basically create a mapping of strings to `jaxlib.xla_extension.Device`s so that `self.device` is a string and not a `jaxlib.xla_extension.Device`.
* To create a `jax.Array` in a device you need to either create it in the default device and then move it to the desired device with `jax.device_put` or directly create it in the device you want with `jax.default_device()` context manager.
* JAX will create an array by default in `jax.devices()[0]`
More information on JAX device management is available at https://jax.readthedocs.io/en/latest/faq.html#controlling-data-and-computation-placement-on-devices
## What's missing in this PR?
I've tested it both locally in CPU (Mac M2 and Mac M1, as no GPU support for Mac yet), and in GPU and TPU in Google Colab, let me know if you want me to provide you the Notebook for the latter.
But I did not implement any integration test as I wanted to get your feedback first. | {
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https://api.github.com/repos/huggingface/datasets/issues/5546 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5546/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5546/comments | https://api.github.com/repos/huggingface/datasets/issues/5546/events | https://github.com/huggingface/datasets/issues/5546 | 1,590,346,349 | I_kwDODunzps5eysJt | 5,546 | Downloaded datasets do not cache at $HF_HOME | {
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"Hi ! Can you make sure you set `HF_HOME` before importing `datasets` ?\r\n\r\nThen you can print\r\n```python\r\nprint(datasets.config.HF_CACHE_HOME)\r\nprint(datasets.config.HF_DATASETS_CACHE)\r\n```"
] | "2023-02-18T13:30:35Z" | "2023-07-24T14:22:43Z" | "2023-07-24T14:22:43Z" | NONE | null | null | null | ### Describe the bug
In the huggingface course (https://huggingface.co/course/chapter3/2?fw=pt) it said that if we set HF_HOME, downloaded datasets would be cached at specified address but it does not. downloaded models from checkpoint names are downloaded and cached at HF_HOME but this is not the case for datasets, they are still cached at ~/.cache/huggingface/datasets.
### Steps to reproduce the bug
Run the following code
```
from datasets import load_dataset
raw_datasets = load_dataset("glue", "mrpc")
raw_datasets
```
it downloads and store dataset at ~/.cache/huggingface/datasets
### Expected behavior
to cache dataset at HF_HOME.
### Environment info
python 3.10.6
Kubuntu 22.04
HF_HOME located on a separate partition | {
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https://api.github.com/repos/huggingface/datasets/issues/5545 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5545/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5545/comments | https://api.github.com/repos/huggingface/datasets/issues/5545/events | https://github.com/huggingface/datasets/pull/5545 | 1,590,315,972 | PR_kwDODunzps5KRKct | 5,545 | Added return methods for URL-references to the pushed dataset | {
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"Hi ! Maybe we'd need to align with `transformers` and other libraries that implement `push_to_hub` to agree on what it should return.\r\n\r\ne.g. in `transformers` the typing says it returns a string, but in practice it returns a `CommitInfo`.\r\n\r\nTherefore I'd not add an output to `push_to_hub` here unless we had a chance to discuss more broadly.\r\n\r\nAnyway in my opinion it should no just return the URL of the repository, but ideally the URL at the revision where the data were pushed",
"Perhaps a mixin or something similar could be defined on the `hfh` side to ensure the `push_to_hub` API is aligned across our projects. \r\n\r\nPS: this would also mean that the PRs such as https://github.com/huggingface/datasets/pull/5528 would no longer be our responsibility\r\n\r\ncc @Wauplin ",
"I agree, with universability and the idea is more about returning at least something that references where to find the uploaded file/model or otherwise. \r\n\r\nIdeally, the referenced PR would work.",
"imo this would be a good use case to just use `huggingface_hub` and align to what we do there :)",
"@mariosasko, can you give me some pointers to where I might help implementing this for the `huggingface-hub`?"
] | "2023-02-18T11:26:25Z" | "2023-08-11T09:46:28Z" | null | NONE | null | 0 | {
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} | Hi,
I was missing the ability to easily open the pushed dataset and it seemed like a quick fix.
Maybe we also want to log this info somewhere, but let me know if I need to add that too.
Cheers,
David | {
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https://api.github.com/repos/huggingface/datasets/issues/5543 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5543/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5543/comments | https://api.github.com/repos/huggingface/datasets/issues/5543/events | https://github.com/huggingface/datasets/issues/5543 | 1,588,951,379 | I_kwDODunzps5etXlT | 5,543 | the pile datasets url seems to change back | {
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"Thanks for reporting, @wjfwzzc.\r\n\r\nI am transferring this issue to the corresponding dataset on the Hub: https://huggingface.co/datasets/bookcorpusopen/discussions/1",
"Thank you. All fixes are done:\r\n- [x] https://huggingface.co/datasets/bookcorpusopen/discussions/2\r\n- [x] https://huggingface.co/datasets/the_pile/discussions/1\r\n- [x] https://huggingface.co/datasets/the_pile_books3/discussions/1\r\n- [x] https://huggingface.co/datasets/the_pile_openwebtext2/discussions/2\r\n- [x] https://huggingface.co/datasets/the_pile_stack_exchange/discussions/2"
] | "2023-02-17T08:40:11Z" | "2023-02-21T06:37:00Z" | "2023-02-20T08:41:33Z" | NONE | null | null | null | ### Describe the bug
in #3627, the host url of the pile dataset became `https://mystic.the-eye.eu`. Now the new url is broken, but `https://the-eye.eu` seems to work again.
### Steps to reproduce the bug
```python3
from datasets import load_dataset
dataset = load_dataset("bookcorpusopen")
```
shows
```python3
ConnectionError: Couldn't reach https://mystic.the-eye.eu/public/AI/pile_preliminary_components/books1.tar.gz (ProxyError(MaxRetryError("HTTPSConnectionPool(host='mystic.the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_pr
eliminary_components/books1.tar.gz (Caused by ProxyError('Cannot connect to proxy.', OSError('Tunnel connection failed: 504 Gateway Timeout')))")))
```
### Expected behavior
Downloading as normal.
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-5.4.143.bsk.7-amd64-x86_64-with-glibc2.31
- Python version: 3.9.2
- PyArrow version: 6.0.1
- Pandas version: 1.5.3 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008452 / 0.011353 (-0.002901) | 0.004500 / 0.011008 (-0.006508) | 0.100103 / 0.038508 (0.061595) | 0.029395 / 0.023109 (0.006286) | 0.297740 / 0.275898 (0.021842) | 0.359132 / 0.323480 (0.035652) | 0.007045 / 0.007986 (-0.000941) | 0.003415 / 0.004328 (-0.000913) | 0.076389 / 0.004250 (0.072138) | 0.036612 / 0.037052 (-0.000440) | 0.308773 / 0.258489 (0.050284) | 0.345701 / 0.293841 (0.051860) | 0.033230 / 0.128546 (-0.095317) | 0.011463 / 0.075646 (-0.064183) | 0.322382 / 0.419271 (-0.096890) | 0.041194 / 0.043533 (-0.002339) | 0.300685 / 0.255139 (0.045546) | 0.323076 / 0.283200 (0.039876) | 0.087330 / 0.141683 (-0.054353) | 1.508661 / 1.452155 (0.056506) | 1.531776 / 1.492716 (0.039059) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.188391 / 0.018006 (0.170385) | 0.400102 / 0.000490 (0.399612) | 0.002006 / 0.000200 (0.001806) | 0.000075 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023232 / 0.037411 (-0.014179) | 0.097313 / 0.014526 (0.082787) | 0.106244 / 0.176557 (-0.070313) | 0.141180 / 0.737135 (-0.595955) | 0.107871 / 0.296338 (-0.188468) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418610 / 0.215209 (0.203400) | 4.162243 / 2.077655 (2.084588) | 1.884300 / 1.504120 (0.380180) | 1.694197 / 1.541195 (0.153002) | 1.727740 / 1.468490 (0.259250) | 0.692129 / 4.584777 (-3.892648) | 3.364230 / 3.745712 (-0.381482) | 1.871507 / 5.269862 (-3.398355) | 1.261520 / 4.565676 (-3.304156) | 0.083258 / 0.424275 (-0.341017) | 0.012479 / 0.007607 (0.004872) | 0.528802 / 0.226044 (0.302757) | 5.281029 / 2.268929 (3.012100) | 2.402222 / 55.444624 (-53.042403) | 2.064954 / 6.876477 (-4.811522) | 2.027044 / 2.142072 (-0.115029) | 0.813124 / 4.805227 (-3.992103) | 0.149397 / 6.500664 (-6.351267) | 0.065032 / 0.075469 (-0.010437) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.239192 / 1.841788 (-0.602595) | 13.529913 / 8.074308 (5.455605) | 14.253251 / 10.191392 (4.061859) | 0.165145 / 0.680424 (-0.515278) | 0.028367 / 0.534201 (-0.505834) | 0.395121 / 0.579283 (-0.184162) | 0.405372 / 0.434364 (-0.028992) | 0.472201 / 0.540337 (-0.068137) | 0.560620 / 1.386936 (-0.826316) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006368 / 0.011353 (-0.004985) | 0.004542 / 0.011008 (-0.006466) | 0.076361 / 0.038508 (0.037853) | 0.026893 / 0.023109 (0.003784) | 0.341210 / 0.275898 (0.065312) | 0.378377 / 0.323480 (0.054898) | 0.004833 / 0.007986 (-0.003153) | 0.003358 / 0.004328 (-0.000970) | 0.075516 / 0.004250 (0.071265) | 0.038841 / 0.037052 (0.001788) | 0.342230 / 0.258489 (0.083741) | 0.384317 / 0.293841 (0.090476) | 0.031874 / 0.128546 (-0.096672) | 0.011651 / 0.075646 (-0.063995) | 0.085816 / 0.419271 (-0.333455) | 0.042389 / 0.043533 (-0.001144) | 0.340678 / 0.255139 (0.085539) | 0.367441 / 0.283200 (0.084241) | 0.089748 / 0.141683 (-0.051935) | 1.487358 / 1.452155 (0.035203) | 1.615049 / 1.492716 (0.122333) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220933 / 0.018006 (0.202926) | 0.397162 / 0.000490 (0.396673) | 0.002336 / 0.000200 (0.002136) | 0.000069 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025004 / 0.037411 (-0.012407) | 0.100877 / 0.014526 (0.086351) | 0.110624 / 0.176557 (-0.065932) | 0.152042 / 0.737135 (-0.585094) | 0.112951 / 0.296338 (-0.183388) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441071 / 0.215209 (0.225862) | 4.419471 / 2.077655 (2.341817) | 2.082976 / 1.504120 (0.578856) | 1.884023 / 1.541195 (0.342828) | 1.950590 / 1.468490 (0.482100) | 0.706104 / 4.584777 (-3.878673) | 3.329825 / 3.745712 (-0.415887) | 1.868850 / 5.269862 (-3.401011) | 1.178785 / 4.565676 (-3.386892) | 0.083910 / 0.424275 (-0.340365) | 0.012296 / 0.007607 (0.004689) | 0.542998 / 0.226044 (0.316953) | 5.429944 / 2.268929 (3.161015) | 2.502285 / 55.444624 (-52.942339) | 2.150507 / 6.876477 (-4.725970) | 2.170492 / 2.142072 (0.028420) | 0.813410 / 4.805227 (-3.991817) | 0.152310 / 6.500664 (-6.348354) | 0.066999 / 0.075469 (-0.008470) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.290839 / 1.841788 (-0.550949) | 14.089491 / 8.074308 (6.015183) | 13.704922 / 10.191392 (3.513530) | 0.130089 / 0.680424 (-0.550335) | 0.017000 / 0.534201 (-0.517201) | 0.381173 / 0.579283 (-0.198110) | 0.389271 / 0.434364 (-0.045093) | 0.461700 / 0.540337 (-0.078637) | 0.556428 / 1.386936 (-0.830508) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2cfa9be08f17519ff3deeae63cb998f4be7616e0 \"CML watermark\")\n"
] | "2023-02-17T01:52:38Z" | "2023-02-17T19:20:49Z" | "2023-02-17T11:12:32Z" | CONTRIBUTOR | null | 0 | {
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"Running the script above on the branch https://github.com/huggingface/datasets/pull/5542 results in the expected behaviour:\r\n```\r\nNum chunks for original ds: 1\r\nOriginal ds save/load\r\nsave_to_disk -- RAM memory used: 0.671875 MB -- Total time: 0.255265 s\r\nload_from_disk -- RAM memory used: 42.796875 MB -- Total time: 0.014899 s\r\nNum chunks for original ds after reloading: 5000\r\n\r\nNum chunks for selected ds: 1\r\nflatten_indices -- RAM memory used: 42.546875 MB -- Total time: 23.735089 s\r\nNum chunks for selected ds after flattening: 5000\r\n\r\nSelected ds save/load\r\nsave_to_disk -- RAM memory used: 0.0 MB -- Total time: 0.287112 s\r\nload_from_disk -- RAM memory used: 38.84375 MB -- Total time: 0.014772 s\r\nNum chunks for selected ds after reloading: 5000\r\n```",
"Wouahouh super cool @marioga thanks a lot!",
"We just released `datasets==2.10.0` with this big improvement, thanks again @marioga "
] | "2023-02-17T01:52:24Z" | "2023-02-22T13:15:20Z" | "2023-02-17T11:12:33Z" | CONTRIBUTOR | null | null | null | ### Describe the bug
If we perform a `select` (or `shuffle`, `train_test_split`, etc.) operation on a dataset , we end up with a dataset with an `indices_table`. Currently, flattening such dataset consumes a lot of memory and the resulting flat dataset contains ChunkedArrays with as many chunks as there are rows. This is extremely inefficient and slows down the operations on the flat dataset, e.g., saving/loading the dataset to disk becomes really slow.
Perhaps more importantly, loading the dataset back from disk basically loads the whole table into RAM, as it cannot take advantage of memory mapping.
### Steps to reproduce the bug
The following script reproduces the issue:
```python
import gc
import os
import psutil
import tempfile
import time
from datasets import Dataset
DATASET_SIZE = 5000000
def profile(func):
def wrapper(*args, **kwargs):
mem_before = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)
start = time.time()
# Run function here
out = func(*args, **kwargs)
end = time.time()
mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)
print(f"{func.__name__} -- RAM memory used: {mem_after - mem_before} MB -- Total time: {end - start:.6f} s")
return out
return wrapper
def main():
ds = Dataset.from_list([{'col': i} for i in range(DATASET_SIZE)])
print(f"Num chunks for original ds: {ds.data['col'].num_chunks}")
with tempfile.TemporaryDirectory() as tmpdir:
path1 = os.path.join(tmpdir, 'ds1')
print("Original ds save/load")
profile(ds.save_to_disk)(path1)
ds_loaded = profile(Dataset.load_from_disk)(path1)
print(f"Num chunks for original ds after reloading: {ds_loaded.data['col'].num_chunks}")
print("")
ds_select = ds.select(reversed(range(len(ds))))
print(f"Num chunks for selected ds: {ds_select.data['col'].num_chunks}")
del ds
del ds_loaded
gc.collect()
# This would happen anyway when we call save_to_disk
ds_select = profile(ds_select.flatten_indices)()
print(f"Num chunks for selected ds after flattening: {ds_select.data['col'].num_chunks}")
print("")
path2 = os.path.join(tmpdir, 'ds2')
print("Selected ds save/load")
profile(ds_select.save_to_disk)(path2)
del ds_select
gc.collect()
ds_select_loaded = profile(Dataset.load_from_disk)(path2)
print(f"Num chunks for selected ds after reloading: {ds_select_loaded.data['col'].num_chunks}")
if __name__ == '__main__':
main()
```
Sample result:
```
Num chunks for original ds: 1
Original ds save/load
save_to_disk -- RAM memory used: 0.515625 MB -- Total time: 0.253888 s
load_from_disk -- RAM memory used: 42.765625 MB -- Total time: 0.015176 s
Num chunks for original ds after reloading: 5000
Num chunks for selected ds: 1
flatten_indices -- RAM memory used: 4852.609375 MB -- Total time: 46.116774 s
Num chunks for selected ds after flattening: 5000000
Selected ds save/load
save_to_disk -- RAM memory used: 1326.65625 MB -- Total time: 42.309825 s
load_from_disk -- RAM memory used: 2085.953125 MB -- Total time: 11.659137 s
Num chunks for selected ds after reloading: 5000000
```
### Expected behavior
Saving/loading the dataset should be much faster and consume almost no extra memory thanks to pyarrow memory mapping.
### Environment info
- `datasets` version: 2.9.1.dev0
- Platform: macOS-13.1-arm64-arm-64bit
- Python version: 3.10.8
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.012018 / 0.011353 (0.000665) | 0.006204 / 0.011008 (-0.004804) | 0.134119 / 0.038508 (0.095611) | 0.038436 / 0.023109 (0.015327) | 0.381397 / 0.275898 (0.105499) | 0.456362 / 0.323480 (0.132882) | 0.009826 / 0.007986 (0.001840) | 0.004746 / 0.004328 (0.000417) | 0.103755 / 0.004250 (0.099505) | 0.043867 / 0.037052 (0.006815) | 0.395322 / 0.258489 (0.136833) | 0.475812 / 0.293841 (0.181971) | 0.057865 / 0.128546 (-0.070682) | 0.019919 / 0.075646 (-0.055727) | 0.465343 / 0.419271 (0.046072) | 0.061574 / 0.043533 (0.018041) | 0.371668 / 0.255139 (0.116529) | 0.400375 / 0.283200 (0.117176) | 0.106539 / 0.141683 (-0.035144) | 1.822931 / 1.452155 (0.370776) | 1.875535 / 1.492716 (0.382819) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.013583 / 0.018006 (-0.004423) | 0.535515 / 0.000490 (0.535025) | 0.007920 / 0.000200 (0.007720) | 0.000305 / 0.000054 (0.000250) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030204 / 0.037411 (-0.007207) | 0.131671 / 0.014526 (0.117145) | 0.143977 / 0.176557 (-0.032579) | 0.175498 / 0.737135 (-0.561637) | 0.166134 / 0.296338 (-0.130204) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.630995 / 0.215209 (0.415786) | 6.152275 / 2.077655 (4.074620) | 2.519887 / 1.504120 (1.015767) | 2.110926 / 1.541195 (0.569732) | 2.207555 / 1.468490 (0.739064) | 1.296197 / 4.584777 (-3.288580) | 5.510619 / 3.745712 (1.764906) | 3.167468 / 5.269862 (-2.102394) | 2.043924 / 4.565676 (-2.521753) | 0.144772 / 0.424275 (-0.279503) | 0.014456 / 0.007607 (0.006848) | 0.783629 / 0.226044 (0.557585) | 7.836962 / 2.268929 (5.568033) | 3.248593 / 55.444624 (-52.196032) | 2.577092 / 6.876477 (-4.299385) | 2.671918 / 2.142072 (0.529846) | 1.471586 / 4.805227 (-3.333641) | 0.251391 / 6.500664 (-6.249273) | 0.091947 / 0.075469 (0.016478) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.594839 / 1.841788 (-0.246949) | 18.250630 / 8.074308 (10.176322) | 23.948781 / 10.191392 (13.757389) | 0.275505 / 0.680424 (-0.404919) | 0.045202 / 0.534201 (-0.488999) | 0.545552 / 0.579283 (-0.033731) | 0.639352 / 0.434364 (0.204989) | 0.666345 / 0.540337 (0.126008) | 0.795614 / 1.386936 (-0.591322) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011234 / 0.011353 (-0.000119) | 0.005983 / 0.011008 (-0.005025) | 0.109144 / 0.038508 (0.070636) | 0.036070 / 0.023109 (0.012961) | 0.429313 / 0.275898 (0.153415) | 0.490615 / 0.323480 (0.167135) | 0.007448 / 0.007986 (-0.000538) | 0.004424 / 0.004328 (0.000095) | 0.097100 / 0.004250 (0.092850) | 0.049719 / 0.037052 (0.012667) | 0.412719 / 0.258489 (0.154230) | 0.485717 / 0.293841 (0.191876) | 0.061168 / 0.128546 (-0.067378) | 0.021510 / 0.075646 (-0.054136) | 0.116598 / 0.419271 (-0.302673) | 0.066116 / 0.043533 (0.022583) | 0.426212 / 0.255139 (0.171073) | 0.448368 / 0.283200 (0.165168) | 0.116003 / 0.141683 (-0.025680) | 1.799329 / 1.452155 (0.347175) | 1.967256 / 1.492716 (0.474540) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.214893 / 0.018006 (0.196887) | 0.497843 / 0.000490 (0.497354) | 0.000464 / 0.000200 (0.000264) | 0.000094 / 0.000054 (0.000039) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031758 / 0.037411 (-0.005653) | 0.131182 / 0.014526 (0.116656) | 0.141251 / 0.176557 (-0.035305) | 0.186526 / 0.737135 (-0.550609) | 0.142975 / 0.296338 (-0.153363) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.662094 / 0.215209 (0.446885) | 6.664841 / 2.077655 (4.587186) | 2.690613 / 1.504120 (1.186493) | 2.305399 / 1.541195 (0.764205) | 2.383697 / 1.468490 (0.915207) | 1.280692 / 4.584777 (-3.304085) | 5.629215 / 3.745712 (1.883503) | 5.007083 / 5.269862 (-0.262778) | 2.482163 / 4.565676 (-2.083513) | 0.147662 / 0.424275 (-0.276613) | 0.017770 / 0.007607 (0.010163) | 0.818380 / 0.226044 (0.592335) | 8.006521 / 2.268929 (5.737592) | 3.472262 / 55.444624 (-51.972363) | 2.709550 / 6.876477 (-4.166926) | 2.775138 / 2.142072 (0.633066) | 1.570545 / 4.805227 (-3.234683) | 0.266323 / 6.500664 (-6.234341) | 0.090591 / 0.075469 (0.015122) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.657927 / 1.841788 (-0.183861) | 18.448981 / 8.074308 (10.374673) | 20.336909 / 10.191392 (10.145517) | 0.230322 / 0.680424 (-0.450102) | 0.025972 / 0.534201 (-0.508229) | 0.561361 / 0.579283 (-0.017922) | 0.623758 / 0.434364 (0.189394) | 0.664120 / 0.540337 (0.123783) | 0.763144 / 1.386936 (-0.623792) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#29de6179766418c937fb33b0cc8803ec24a39e9e \"CML watermark\")\n"
] | "2023-02-16T22:09:35Z" | "2023-02-17T18:50:46Z" | "2023-02-17T18:41:28Z" | MEMBER | null | 0 | {
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} | A tutorial for creating datasets based on the folder-based builders and `from_dict` and `from_generator` methods. I've also mentioned loading scripts as a next step, but I think we should keep the tutorial focused on the low-code methods. Let me know what you think! 🙂 | {
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https://api.github.com/repos/huggingface/datasets/issues/5539 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5539/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5539/comments | https://api.github.com/repos/huggingface/datasets/issues/5539/events | https://github.com/huggingface/datasets/issues/5539 | 1,587,970,083 | I_kwDODunzps5epoAj | 5,539 | IndexError: invalid index of a 0-dim tensor. Use `tensor.item()` in Python or `tensor.item<T>()` in C++ to convert a 0-dim tensor to a number | {
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"Hi! The `set_transform` does not apply a custom formatting transform on a single example but the entire batch, so the fixed version of your transform would look as follows:\r\n```python\r\nfrom datasets import load_dataset\r\nimport torch\r\n\r\ndataset = load_dataset(\"lambdalabs/pokemon-blip-captions\", split='train')\r\ndef t(batch):\r\n return {\"test\": torch.tensor([1] * len(batch[next(iter(batch))]))}\r\n \r\ndataset.set_transform(t)\r\nd_0 = dataset[0]\r\n```\r\n\r\nStill, the formatter's error message should mention that a dict of **sequences** is expected as the returned value (not just a dict) to make debugging easier.",
"I can take this",
"Fixed in #5553 ",
"> Hi! The `set_transform` does not apply a custom formatting transform on a single example but the entire batch, so the fixed version of your transform would look as follows:\r\n> \r\n> ```python\r\n> from datasets import load_dataset\r\n> import torch\r\n> \r\n> dataset = load_dataset(\"lambdalabs/pokemon-blip-captions\", split='train')\r\n> def t(batch):\r\n> return {\"test\": torch.tensor([1] * len(batch[next(iter(batch))]))}\r\n> \r\n> dataset.set_transform(t)\r\n> d_0 = dataset[0]\r\n> ```\r\n> \r\n> Still, the formatter's error message should mention that a dict of **sequences** is expected as the returned value (not just a dict) to make debugging easier.\r\n\r\nok, will change it according to suggestion. Thanks for the reply!"
] | "2023-02-16T16:08:51Z" | "2023-02-22T10:30:30Z" | "2023-02-21T13:03:57Z" | NONE | null | null | null | ### Describe the bug
When dataset contains a 0-dim tensor, formatting.py raises a following error and fails.
```bash
Traceback (most recent call last):
File "<path>/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 501, in format_row
return _unnest(formatted_batch)
File "<path>/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 137, in _unnest
return {key: array[0] for key, array in py_dict.items()}
File "<path>/lib/python3.8/site-packages/datasets/formatting/formatting.py", line 137, in <dictcomp>
return {key: array[0] for key, array in py_dict.items()}
IndexError: invalid index of a 0-dim tensor. Use `tensor.item()` in Python or `tensor.item<T>()` in C++ to convert a 0-dim tensor to a number
```
### Steps to reproduce the bug
Load whichever dataset and add transform method to add 0-dim tensor. Or create/find a dataset containing 0-dim tensor. E.g.
```python
from datasets import load_dataset
import torch
dataset = load_dataset("lambdalabs/pokemon-blip-captions", split='train')
def t(batch):
return {"test": torch.tensor(1)}
dataset.set_transform(t)
d_0 = dataset[0]
```
### Expected behavior
Extractor will correctly get a row from the dataset, even if it contains 0-dim tensor.
### Environment info
`datasets==2.8.0`, but it looks like it is also applicable to main branch version (as of 16th February) | {
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