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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) | 0.009729 / 0.011353 (-0.001624) | 0.005342 / 0.011008 (-0.005666) | 0.100194 / 0.038508 (0.061686) | 0.036391 / 0.023109 (0.013282) | 0.294163 / 0.275898 (0.018264) | 0.364117 / 0.323480 (0.040637) | 0.008231 / 0.007986 (0.000246) | 0.005954 / 0.004328 (0.001626) | 0.076484 / 0.004250 (0.072234) | 0.045028 / 0.037052 (0.007976) | 0.308163 / 0.258489 (0.049674) | 0.339473 / 0.293841 (0.045632) | 0.039268 / 0.128546 (-0.089279) | 0.012357 / 0.075646 (-0.063289) | 0.334176 / 0.419271 (-0.085096) | 0.049502 / 0.043533 (0.005969) | 0.294134 / 0.255139 (0.038995) | 0.319370 / 0.283200 (0.036170) | 0.113040 / 0.141683 (-0.028643) | 1.450750 / 1.452155 (-0.001405) | 1.490265 / 1.492716 (-0.002452) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.252860 / 0.018006 (0.234854) | 0.554299 / 0.000490 (0.553810) | 0.002105 / 0.000200 (0.001905) | 0.000091 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026557 / 0.037411 (-0.010854) | 0.104464 / 0.014526 (0.089938) | 0.116724 / 0.176557 (-0.059833) | 0.154736 / 0.737135 (-0.582399) | 0.122017 / 0.296338 (-0.174322) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.398170 / 0.215209 (0.182961) | 3.979309 / 2.077655 (1.901654) | 1.773051 / 1.504120 (0.268931) | 1.587247 / 1.541195 (0.046053) | 1.620446 / 1.468490 (0.151956) | 0.692152 / 4.584777 (-3.892625) | 3.724821 / 3.745712 (-0.020891) | 2.133122 / 5.269862 (-3.136739) | 1.455612 / 4.565676 (-3.110065) | 0.084721 / 0.424275 (-0.339554) | 0.012461 / 0.007607 (0.004854) | 0.498909 / 0.226044 (0.272865) | 4.983837 / 2.268929 (2.714908) | 2.258489 / 55.444624 (-53.186135) | 1.891690 / 6.876477 (-4.984786) | 1.976944 / 2.142072 (-0.165128) | 0.836950 / 4.805227 (-3.968277) | 0.165401 / 6.500664 (-6.335263) | 0.061623 / 0.075469 (-0.013846) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.205945 / 1.841788 (-0.635842) | 15.101603 / 8.074308 (7.027295) | 14.393739 / 10.191392 (4.202347) | 0.176313 / 0.680424 (-0.504110) | 0.029102 / 0.534201 (-0.505099) | 0.439785 / 0.579283 (-0.139498) | 0.437360 / 0.434364 (0.002996) | 0.539668 / 0.540337 (-0.000669) | 0.641452 / 1.386936 (-0.745484) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007184 / 0.011353 (-0.004169) | 0.005215 / 0.011008 (-0.005793) | 0.074617 / 0.038508 (0.036109) | 0.033209 / 0.023109 (0.010100) | 0.334304 / 0.275898 (0.058406) | 0.370270 / 0.323480 (0.046790) | 0.005851 / 0.007986 (-0.002135) | 0.004106 / 0.004328 (-0.000222) | 0.075487 / 0.004250 (0.071237) | 0.051133 / 0.037052 (0.014080) | 0.335401 / 0.258489 (0.076912) | 0.391457 / 0.293841 (0.097616) | 0.036525 / 0.128546 (-0.092021) | 0.012423 / 0.075646 (-0.063223) | 0.086446 / 0.419271 (-0.332825) | 0.050707 / 0.043533 (0.007174) | 0.336186 / 0.255139 (0.081047) | 0.353273 / 0.283200 (0.070074) | 0.105625 / 0.141683 (-0.036057) | 1.486118 / 1.452155 (0.033963) | 1.584931 / 1.492716 (0.092214) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237589 / 0.018006 (0.219583) | 0.552030 / 0.000490 (0.551540) | 0.002863 / 0.000200 (0.002663) | 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) | 0.028078 / 0.037411 (-0.009333) | 0.112516 / 0.014526 (0.097990) | 0.121119 / 0.176557 (-0.055438) | 0.158874 / 0.737135 (-0.578262) | 0.129501 / 0.296338 (-0.166837) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419479 / 0.215209 (0.204270) | 4.192216 / 2.077655 (2.114561) | 1.990513 / 1.504120 (0.486393) | 1.792892 / 1.541195 (0.251697) | 1.853904 / 1.468490 (0.385413) | 0.712702 / 4.584777 (-3.872074) | 3.820682 / 3.745712 (0.074970) | 2.143695 / 5.269862 (-3.126166) | 1.369621 / 4.565676 (-3.196055) | 0.087451 / 0.424275 (-0.336824) | 0.012622 / 0.007607 (0.005014) | 0.521056 / 0.226044 (0.295011) | 5.204873 / 2.268929 (2.935944) | 2.481169 / 55.444624 (-52.963455) | 2.112134 / 6.876477 (-4.764342) | 2.200681 / 2.142072 (0.058609) | 0.860323 / 4.805227 (-3.944904) | 0.171452 / 6.500664 (-6.329212) | 0.065235 / 0.075469 (-0.010234) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.241047 / 1.841788 (-0.600741) | 14.977890 / 8.074308 (6.903582) | 13.584265 / 10.191392 (3.392873) | 0.180050 / 0.680424 (-0.500374) | 0.018247 / 0.534201 (-0.515954) | 0.429585 / 0.579283 (-0.149698) | 0.429448 / 0.434364 (-0.004916) | 0.542663 / 0.540337 (0.002326) | 0.649525 / 1.386936 (-0.737411) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#26cf1d2548eb313a06565d36bd400436e350bc86 \"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) | 0.011289 / 0.011353 (-0.000064) | 0.005841 / 0.011008 (-0.005167) | 0.120994 / 0.038508 (0.082486) | 0.043627 / 0.023109 (0.020517) | 0.353254 / 0.275898 (0.077356) | 0.394685 / 0.323480 (0.071205) | 0.009520 / 0.007986 (0.001535) | 0.004770 / 0.004328 (0.000442) | 0.088857 / 0.004250 (0.084607) | 0.048426 / 0.037052 (0.011373) | 0.353815 / 0.258489 (0.095326) | 0.404109 / 0.293841 (0.110268) | 0.060079 / 0.128546 (-0.068467) | 0.013840 / 0.075646 (-0.061806) | 0.403133 / 0.419271 (-0.016139) | 0.072227 / 0.043533 (0.028694) | 0.354585 / 0.255139 (0.099446) | 0.377937 / 0.283200 (0.094737) | 0.139080 / 0.141683 (-0.002602) | 1.733266 / 1.452155 (0.281112) | 1.828402 / 1.492716 (0.335686) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.215095 / 0.018006 (0.197088) | 0.486669 / 0.000490 (0.486179) | 0.001425 / 0.000200 (0.001225) | 0.000097 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032832 / 0.037411 (-0.004579) | 0.136335 / 0.014526 (0.121809) | 0.141827 / 0.176557 (-0.034730) | 0.185917 / 0.737135 (-0.551218) | 0.149046 / 0.296338 (-0.147293) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.474587 / 0.215209 (0.259378) | 4.753686 / 2.077655 (2.676031) | 2.152147 / 1.504120 (0.648027) | 1.941762 / 1.541195 (0.400567) | 2.077493 / 1.468490 (0.609003) | 0.822432 / 4.584777 (-3.762345) | 4.860151 / 3.745712 (1.114439) | 2.527292 / 5.269862 (-2.742569) | 1.580442 / 4.565676 (-2.985234) | 0.102104 / 0.424275 (-0.322171) | 0.015060 / 0.007607 (0.007453) | 0.598780 / 0.226044 (0.372736) | 5.998318 / 2.268929 (3.729390) | 2.754115 / 55.444624 (-52.690509) | 2.317509 / 6.876477 (-4.558967) | 2.409942 / 2.142072 (0.267870) | 1.008830 / 4.805227 (-3.796397) | 0.196203 / 6.500664 (-6.304461) | 0.075378 / 0.075469 (-0.000091) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.430676 / 1.841788 (-0.411112) | 19.597628 / 8.074308 (11.523320) | 17.364673 / 10.191392 (7.173281) | 0.216621 / 0.680424 (-0.463803) | 0.039505 / 0.534201 (-0.494696) | 0.529027 / 0.579283 (-0.050256) | 0.572014 / 0.434364 (0.137650) | 0.702898 / 0.540337 (0.162560) | 0.785748 / 1.386936 (-0.601188) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009150 / 0.011353 (-0.002203) | 0.006088 / 0.011008 (-0.004920) | 0.090629 / 0.038508 (0.052121) | 0.044284 / 0.023109 (0.021174) | 0.411363 / 0.275898 (0.135465) | 0.445499 / 0.323480 (0.122020) | 0.007129 / 0.007986 (-0.000856) | 0.004843 / 0.004328 (0.000515) | 0.087919 / 0.004250 (0.083668) | 0.060329 / 0.037052 (0.023277) | 0.405802 / 0.258489 (0.147313) | 0.468301 / 0.293841 (0.174460) | 0.044271 / 0.128546 (-0.084275) | 0.014895 / 0.075646 (-0.060751) | 0.103728 / 0.419271 (-0.315544) | 0.084190 / 0.043533 (0.040657) | 0.407210 / 0.255139 (0.152071) | 0.432585 / 0.283200 (0.149386) | 0.137132 / 0.141683 (-0.004550) | 1.720261 / 1.452155 (0.268107) | 1.858575 / 1.492716 (0.365858) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.331395 / 0.018006 (0.313389) | 0.494757 / 0.000490 (0.494267) | 0.043426 / 0.000200 (0.043226) | 0.000470 / 0.000054 (0.000415) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035288 / 0.037411 (-0.002123) | 0.140856 / 0.014526 (0.126330) | 0.146597 / 0.176557 (-0.029959) | 0.192775 / 0.737135 (-0.544360) | 0.155307 / 0.296338 (-0.141032) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.504000 / 0.215209 (0.288791) | 5.011081 / 2.077655 (2.933427) | 2.380420 / 1.504120 (0.876300) | 2.154819 / 1.541195 (0.613624) | 2.293883 / 1.468490 (0.825393) | 0.864429 / 4.584777 (-3.720348) | 5.134475 / 3.745712 (1.388763) | 4.984024 / 5.269862 (-0.285837) | 2.333754 / 4.565676 (-2.231923) | 0.105854 / 0.424275 (-0.318422) | 0.015833 / 0.007607 (0.008226) | 0.633614 / 0.226044 (0.407569) | 6.330974 / 2.268929 (4.062046) | 3.020498 / 55.444624 (-52.424126) | 2.578234 / 6.876477 (-4.298243) | 2.654429 / 2.142072 (0.512357) | 1.022041 / 4.805227 (-3.783186) | 0.205085 / 6.500664 (-6.295579) | 0.081122 / 0.075469 (0.005653) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.538929 / 1.841788 (-0.302859) | 19.907799 / 8.074308 (11.833490) | 17.174568 / 10.191392 (6.983176) | 0.228165 / 0.680424 (-0.452258) | 0.024688 / 0.534201 (-0.509513) | 0.508958 / 0.579283 (-0.070326) | 0.544469 / 0.434364 (0.110105) | 0.590805 / 0.540337 (0.050468) | 0.705947 / 1.386936 (-0.680989) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2573861afb170fd575dbe67270294a4e88ab4be6 \"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) | 0.008377 / 0.011353 (-0.002975) | 0.004445 / 0.011008 (-0.006563) | 0.100671 / 0.038508 (0.062163) | 0.029216 / 0.023109 (0.006107) | 0.300311 / 0.275898 (0.024413) | 0.356907 / 0.323480 (0.033427) | 0.006921 / 0.007986 (-0.001065) | 0.003384 / 0.004328 (-0.000944) | 0.078529 / 0.004250 (0.074278) | 0.034689 / 0.037052 (-0.002364) | 0.304647 / 0.258489 (0.046158) | 0.343584 / 0.293841 (0.049743) | 0.032700 / 0.128546 (-0.095846) | 0.011403 / 0.075646 (-0.064244) | 0.321540 / 0.419271 (-0.097732) | 0.040770 / 0.043533 (-0.002762) | 0.306900 / 0.255139 (0.051761) | 0.322482 / 0.283200 (0.039282) | 0.085396 / 0.141683 (-0.056287) | 1.450735 / 1.452155 (-0.001419) | 1.491829 / 1.492716 (-0.000888) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.009439 / 0.018006 (-0.008567) | 0.406805 / 0.000490 (0.406315) | 0.002993 / 0.000200 (0.002793) | 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) | 0.025034 / 0.037411 (-0.012378) | 0.100567 / 0.014526 (0.086042) | 0.107267 / 0.176557 (-0.069290) | 0.149945 / 0.737135 (-0.587190) | 0.111150 / 0.296338 (-0.185189) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418387 / 0.215209 (0.203178) | 4.177979 / 2.077655 (2.100324) | 1.886650 / 1.504120 (0.382530) | 1.685692 / 1.541195 (0.144497) | 1.728270 / 1.468490 (0.259780) | 0.700904 / 4.584777 (-3.883873) | 3.379998 / 3.745712 (-0.365714) | 1.874779 / 5.269862 (-3.395083) | 1.170366 / 4.565676 (-3.395310) | 0.083190 / 0.424275 (-0.341085) | 0.012506 / 0.007607 (0.004899) | 0.528633 / 0.226044 (0.302589) | 5.301793 / 2.268929 (3.032865) | 2.334050 / 55.444624 (-53.110574) | 1.986988 / 6.876477 (-4.889488) | 2.020508 / 2.142072 (-0.121565) | 0.817227 / 4.805227 (-3.988000) | 0.150284 / 6.500664 (-6.350380) | 0.065489 / 0.075469 (-0.009980) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.224216 / 1.841788 (-0.617572) | 13.729808 / 8.074308 (5.655500) | 14.283402 / 10.191392 (4.092010) | 0.159434 / 0.680424 (-0.520990) | 0.028471 / 0.534201 (-0.505730) | 0.395102 / 0.579283 (-0.184181) | 0.402733 / 0.434364 (-0.031631) | 0.470852 / 0.540337 (-0.069485) | 0.568530 / 1.386936 (-0.818406) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006750 / 0.011353 (-0.004603) | 0.004479 / 0.011008 (-0.006529) | 0.074926 / 0.038508 (0.036418) | 0.027619 / 0.023109 (0.004510) | 0.342070 / 0.275898 (0.066172) | 0.372452 / 0.323480 (0.048972) | 0.005094 / 0.007986 (-0.002892) | 0.003494 / 0.004328 (-0.000834) | 0.074963 / 0.004250 (0.070713) | 0.038457 / 0.037052 (0.001405) | 0.340587 / 0.258489 (0.082098) | 0.381212 / 0.293841 (0.087371) | 0.031597 / 0.128546 (-0.096950) | 0.011631 / 0.075646 (-0.064015) | 0.084646 / 0.419271 (-0.334626) | 0.042072 / 0.043533 (-0.001461) | 0.340977 / 0.255139 (0.085838) | 0.366502 / 0.283200 (0.083302) | 0.091181 / 0.141683 (-0.050502) | 1.435119 / 1.452155 (-0.017035) | 1.520426 / 1.492716 (0.027710) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211320 / 0.018006 (0.193313) | 0.466154 / 0.000490 (0.465664) | 0.002901 / 0.000200 (0.002701) | 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) | 0.025122 / 0.037411 (-0.012289) | 0.098929 / 0.014526 (0.084403) | 0.106551 / 0.176557 (-0.070005) | 0.142820 / 0.737135 (-0.594316) | 0.110701 / 0.296338 (-0.185637) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.445187 / 0.215209 (0.229978) | 4.457524 / 2.077655 (2.379870) | 2.088323 / 1.504120 (0.584203) | 1.888076 / 1.541195 (0.346881) | 1.923340 / 1.468490 (0.454850) | 0.723354 / 4.584777 (-3.861423) | 3.428479 / 3.745712 (-0.317233) | 1.914580 / 5.269862 (-3.355281) | 1.191810 / 4.565676 (-3.373866) | 0.087008 / 0.424275 (-0.337267) | 0.013431 / 0.007607 (0.005824) | 0.545089 / 0.226044 (0.319044) | 5.465887 / 2.268929 (3.196958) | 2.527431 / 55.444624 (-52.917194) | 2.240622 / 6.876477 (-4.635854) | 2.232472 / 2.142072 (0.090399) | 0.815968 / 4.805227 (-3.989259) | 0.152842 / 6.500664 (-6.347822) | 0.067152 / 0.075469 (-0.008317) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.328360 / 1.841788 (-0.513427) | 14.163349 / 8.074308 (6.089040) | 13.814255 / 10.191392 (3.622863) | 0.131684 / 0.680424 (-0.548740) | 0.016980 / 0.534201 (-0.517221) | 0.396045 / 0.579283 (-0.183238) | 0.395078 / 0.434364 (-0.039286) | 0.471728 / 0.540337 (-0.068609) | 0.567830 / 1.386936 (-0.819106) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#82331b032891671c334afe30c5f3cc21245b2d72 \"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) | 0.012630 / 0.011353 (0.001277) | 0.007038 / 0.011008 (-0.003970) | 0.158816 / 0.038508 (0.120308) | 0.044142 / 0.023109 (0.021032) | 0.389393 / 0.275898 (0.113495) | 0.479745 / 0.323480 (0.156265) | 0.009335 / 0.007986 (0.001349) | 0.005434 / 0.004328 (0.001105) | 0.107747 / 0.004250 (0.103497) | 0.048382 / 0.037052 (0.011330) | 0.398144 / 0.258489 (0.139655) | 0.446373 / 0.293841 (0.152532) | 0.066285 / 0.128546 (-0.062261) | 0.021174 / 0.075646 (-0.054472) | 0.449176 / 0.419271 (0.029905) | 0.063044 / 0.043533 (0.019511) | 0.390523 / 0.255139 (0.135384) | 0.451435 / 0.283200 (0.168236) | 0.116369 / 0.141683 (-0.025314) | 1.881269 / 1.452155 (0.429114) | 1.944527 / 1.492716 (0.451811) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227989 / 0.018006 (0.209983) | 0.538514 / 0.000490 (0.538024) | 0.009404 / 0.000200 (0.009204) | 0.000510 / 0.000054 (0.000455) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029826 / 0.037411 (-0.007585) | 0.129623 / 0.014526 (0.115098) | 0.142067 / 0.176557 (-0.034489) | 0.218586 / 0.737135 (-0.518549) | 0.160524 / 0.296338 (-0.135814) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.667195 / 0.215209 (0.451986) | 6.694192 / 2.077655 (4.616537) | 2.542493 / 1.504120 (1.038373) | 2.124042 / 1.541195 (0.582847) | 2.024854 / 1.468490 (0.556364) | 1.306222 / 4.584777 (-3.278555) | 5.631557 / 3.745712 (1.885845) | 3.405978 / 5.269862 (-1.863884) | 2.471399 / 4.565676 (-2.094278) | 0.165187 / 0.424275 (-0.259088) | 0.014880 / 0.007607 (0.007273) | 0.842718 / 0.226044 (0.616673) | 8.584358 / 2.268929 (6.315430) | 3.377228 / 55.444624 (-52.067396) | 2.667265 / 6.876477 (-4.209212) | 2.699462 / 2.142072 (0.557389) | 1.623115 / 4.805227 (-3.182112) | 0.253929 / 6.500664 (-6.246735) | 0.077189 / 0.075469 (0.001720) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.778962 / 1.841788 (-0.062825) | 18.997636 / 8.074308 (10.923328) | 24.255222 / 10.191392 (14.063830) | 0.304754 / 0.680424 (-0.375670) | 0.049656 / 0.534201 (-0.484545) | 0.590871 / 0.579283 (0.011588) | 0.649292 / 0.434364 (0.214928) | 0.751281 / 0.540337 (0.210943) | 0.872193 / 1.386936 (-0.514743) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010660 / 0.011353 (-0.000693) | 0.006492 / 0.011008 (-0.004516) | 0.112190 / 0.038508 (0.073682) | 0.045391 / 0.023109 (0.022281) | 0.439852 / 0.275898 (0.163954) | 0.486489 / 0.323480 (0.163009) | 0.007155 / 0.007986 (-0.000830) | 0.006323 / 0.004328 (0.001995) | 0.099775 / 0.004250 (0.095525) | 0.055762 / 0.037052 (0.018709) | 0.439457 / 0.258489 (0.180968) | 0.505322 / 0.293841 (0.211481) | 0.057019 / 0.128546 (-0.071527) | 0.031382 / 0.075646 (-0.044264) | 0.121211 / 0.419271 (-0.298061) | 0.066091 / 0.043533 (0.022558) | 0.499760 / 0.255139 (0.244622) | 0.508312 / 0.283200 (0.225113) | 0.146975 / 0.141683 (0.005292) | 1.916347 / 1.452155 (0.464193) | 2.065860 / 1.492716 (0.573144) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.247176 / 0.018006 (0.229170) | 0.565141 / 0.000490 (0.564652) | 0.004841 / 0.000200 (0.004641) | 0.000141 / 0.000054 (0.000087) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036378 / 0.037411 (-0.001033) | 0.143470 / 0.014526 (0.128944) | 0.148096 / 0.176557 (-0.028461) | 0.225877 / 0.737135 (-0.511258) | 0.147072 / 0.296338 (-0.149266) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.723119 / 0.215209 (0.507910) | 6.824981 / 2.077655 (4.747326) | 2.883840 / 1.504120 (1.379720) | 2.468707 / 1.541195 (0.927513) | 2.525549 / 1.468490 (1.057059) | 1.426640 / 4.584777 (-3.158137) | 5.816045 / 3.745712 (2.070333) | 5.727037 / 5.269862 (0.457175) | 2.650307 / 4.565676 (-1.915369) | 0.160306 / 0.424275 (-0.263970) | 0.015371 / 0.007607 (0.007764) | 0.835778 / 0.226044 (0.609733) | 8.622836 / 2.268929 (6.353907) | 3.616338 / 55.444624 (-51.828287) | 2.974243 / 6.876477 (-3.902234) | 2.884557 / 2.142072 (0.742485) | 1.734874 / 4.805227 (-3.070353) | 0.277474 / 6.500664 (-6.223190) | 0.094189 / 0.075469 (0.018720) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.785728 / 1.841788 (-0.056059) | 19.376490 / 8.074308 (11.302182) | 24.560403 / 10.191392 (14.369011) | 0.250686 / 0.680424 (-0.429738) | 0.034333 / 0.534201 (-0.499868) | 0.557331 / 0.579283 (-0.021952) | 0.641007 / 0.434364 (0.206643) | 0.657138 / 0.540337 (0.116800) | 0.759023 / 1.386936 (-0.627913) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#06ae3f678651bfbb3ca7dd3274ee2f38e0e0237e \"CML watermark\")\n", "I am editing the title and description of this PR:\r\n- It should be \"Lint code\" instead of \"Format code\": formatting was still done with `black`\r\n- This PR uses `ruff` instead of `isort` and `flake8` (not `black`): note that `black` was still used for formatting" ]

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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) | 0.008283 / 0.011353 (-0.003070) | 0.004450 / 0.011008 (-0.006558) | 0.099773 / 0.038508 (0.061265) | 0.029068 / 0.023109 (0.005959) | 0.296799 / 0.275898 (0.020901) | 0.350946 / 0.323480 (0.027466) | 0.007331 / 0.007986 (-0.000655) | 0.004550 / 0.004328 (0.000222) | 0.077603 / 0.004250 (0.073352) | 0.034307 / 0.037052 (-0.002746) | 0.313174 / 0.258489 (0.054685) | 0.342270 / 0.293841 (0.048429) | 0.033463 / 0.128546 (-0.095083) | 0.011421 / 0.075646 (-0.064225) | 0.317188 / 0.419271 (-0.102083) | 0.040985 / 0.043533 (-0.002548) | 0.300800 / 0.255139 (0.045661) | 0.360171 / 0.283200 (0.076972) | 0.086702 / 0.141683 (-0.054981) | 1.474679 / 1.452155 (0.022525) | 1.518319 / 1.492716 (0.025603) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198059 / 0.018006 (0.180052) | 0.403502 / 0.000490 (0.403012) | 0.002663 / 0.000200 (0.002463) | 0.000218 / 0.000054 (0.000164) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022946 / 0.037411 (-0.014465) | 0.096466 / 0.014526 (0.081940) | 0.104092 / 0.176557 (-0.072465) | 0.138499 / 0.737135 (-0.598636) | 0.106941 / 0.296338 (-0.189397) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.416000 / 0.215209 (0.200791) | 4.153120 / 2.077655 (2.075465) | 1.843957 / 1.504120 (0.339837) | 1.650391 / 1.541195 (0.109197) | 1.684765 / 1.468490 (0.216275) | 0.688917 / 4.584777 (-3.895860) | 3.442797 / 3.745712 (-0.302916) | 1.834685 / 5.269862 (-3.435176) | 1.148046 / 4.565676 (-3.417631) | 0.082299 / 0.424275 (-0.341976) | 0.012399 / 0.007607 (0.004792) | 0.521099 / 0.226044 (0.295054) | 5.223695 / 2.268929 (2.954767) | 2.270970 / 55.444624 (-53.173654) | 1.921321 / 6.876477 (-4.955156) | 1.954675 / 2.142072 (-0.187398) | 0.809383 / 4.805227 (-3.995845) | 0.148562 / 6.500664 (-6.352102) | 0.064764 / 0.075469 (-0.010705) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.212687 / 1.841788 (-0.629101) | 13.491641 / 8.074308 (5.417333) | 12.972926 / 10.191392 (2.781534) | 0.137036 / 0.680424 (-0.543388) | 0.028591 / 0.534201 (-0.505610) | 0.391980 / 0.579283 (-0.187303) | 0.394474 / 0.434364 (-0.039889) | 0.456582 / 0.540337 (-0.083755) | 0.535984 / 1.386936 (-0.850952) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006419 / 0.011353 (-0.004934) | 0.004295 / 0.011008 (-0.006713) | 0.077702 / 0.038508 (0.039194) | 0.027368 / 0.023109 (0.004259) | 0.336713 / 0.275898 (0.060815) | 0.370074 / 0.323480 (0.046594) | 0.004657 / 0.007986 (-0.003328) | 0.003308 / 0.004328 (-0.001021) | 0.075747 / 0.004250 (0.071496) | 0.037323 / 0.037052 (0.000271) | 0.342382 / 0.258489 (0.083893) | 0.381109 / 0.293841 (0.087269) | 0.031804 / 0.128546 (-0.096742) | 0.011761 / 0.075646 (-0.063885) | 0.086818 / 0.419271 (-0.332454) | 0.042058 / 0.043533 (-0.001475) | 0.346295 / 0.255139 (0.091156) | 0.366857 / 0.283200 (0.083658) | 0.088666 / 0.141683 (-0.053016) | 1.533711 / 1.452155 (0.081556) | 1.537422 / 1.492716 (0.044705) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220416 / 0.018006 (0.202410) | 0.387393 / 0.000490 (0.386903) | 0.003739 / 0.000200 (0.003539) | 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) | 0.024083 / 0.037411 (-0.013329) | 0.098036 / 0.014526 (0.083510) | 0.102908 / 0.176557 (-0.073648) | 0.139512 / 0.737135 (-0.597623) | 0.107703 / 0.296338 (-0.188635) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437615 / 0.215209 (0.222406) | 4.373140 / 2.077655 (2.295486) | 2.065063 / 1.504120 (0.560943) | 1.863938 / 1.541195 (0.322743) | 1.907955 / 1.468490 (0.439465) | 0.695830 / 4.584777 (-3.888947) | 3.394248 / 3.745712 (-0.351464) | 1.842794 / 5.269862 (-3.427068) | 1.156928 / 4.565676 (-3.408748) | 0.082505 / 0.424275 (-0.341771) | 0.012405 / 0.007607 (0.004798) | 0.538041 / 0.226044 (0.311997) | 5.363508 / 2.268929 (3.094579) | 2.509383 / 55.444624 (-52.935241) | 2.160416 / 6.876477 (-4.716061) | 2.162054 / 2.142072 (0.019982) | 0.802419 / 4.805227 (-4.002809) | 0.150529 / 6.500664 (-6.350135) | 0.066418 / 0.075469 (-0.009051) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.257221 / 1.841788 (-0.584567) | 13.748839 / 8.074308 (5.674531) | 13.310555 / 10.191392 (3.119163) | 0.152997 / 0.680424 (-0.527427) | 0.016618 / 0.534201 (-0.517583) | 0.375443 / 0.579283 (-0.203840) | 0.374942 / 0.434364 (-0.059422) | 0.466704 / 0.540337 (-0.073633) | 0.553563 / 1.386936 (-0.833373) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1ac8343af4e2dc6fe0771d0be70eaf8a6e5a8fbc \"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) | 0.009260 / 0.011353 (-0.002092) | 0.005213 / 0.011008 (-0.005795) | 0.102151 / 0.038508 (0.063643) | 0.035619 / 0.023109 (0.012510) | 0.296266 / 0.275898 (0.020368) | 0.359884 / 0.323480 (0.036404) | 0.008176 / 0.007986 (0.000190) | 0.005031 / 0.004328 (0.000703) | 0.077178 / 0.004250 (0.072927) | 0.041898 / 0.037052 (0.004846) | 0.305640 / 0.258489 (0.047151) | 0.346275 / 0.293841 (0.052434) | 0.037684 / 0.128546 (-0.090863) | 0.011816 / 0.075646 (-0.063831) | 0.334853 / 0.419271 (-0.084419) | 0.046535 / 0.043533 (0.003002) | 0.291544 / 0.255139 (0.036405) | 0.317194 / 0.283200 (0.033994) | 0.103212 / 0.141683 (-0.038471) | 1.424994 / 1.452155 (-0.027161) | 1.486216 / 1.492716 (-0.006501) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.011816 / 0.018006 (-0.006190) | 0.442092 / 0.000490 (0.441602) | 0.001297 / 0.000200 (0.001097) | 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) | 0.028277 / 0.037411 (-0.009134) | 0.110431 / 0.014526 (0.095905) | 0.118456 / 0.176557 (-0.058100) | 0.156778 / 0.737135 (-0.580357) | 0.123036 / 0.296338 (-0.173302) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.399006 / 0.215209 (0.183797) | 3.990367 / 2.077655 (1.912712) | 1.798739 / 1.504120 (0.294620) | 1.607133 / 1.541195 (0.065938) | 1.748897 / 1.468490 (0.280407) | 0.690666 / 4.584777 (-3.894111) | 3.795892 / 3.745712 (0.050180) | 3.479317 / 5.269862 (-1.790545) | 1.861268 / 4.565676 (-2.704409) | 0.085235 / 0.424275 (-0.339040) | 0.012997 / 0.007607 (0.005390) | 0.512489 / 0.226044 (0.286445) | 5.039515 / 2.268929 (2.770587) | 2.258079 / 55.444624 (-53.186545) | 1.907178 / 6.876477 (-4.969299) | 1.985953 / 2.142072 (-0.156119) | 0.843595 / 4.805227 (-3.961633) | 0.165286 / 6.500664 (-6.335378) | 0.063026 / 0.075469 (-0.012443) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.186680 / 1.841788 (-0.655108) | 14.976016 / 8.074308 (6.901708) | 14.436941 / 10.191392 (4.245549) | 0.172620 / 0.680424 (-0.507804) | 0.028760 / 0.534201 (-0.505441) | 0.443505 / 0.579283 (-0.135778) | 0.435665 / 0.434364 (0.001301) | 0.520164 / 0.540337 (-0.020174) | 0.608348 / 1.386936 (-0.778588) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007510 / 0.011353 (-0.003842) | 0.005012 / 0.011008 (-0.005996) | 0.077865 / 0.038508 (0.039357) | 0.033610 / 0.023109 (0.010500) | 0.365996 / 0.275898 (0.090098) | 0.416393 / 0.323480 (0.092913) | 0.005672 / 0.007986 (-0.002314) | 0.005334 / 0.004328 (0.001006) | 0.074948 / 0.004250 (0.070698) | 0.045962 / 0.037052 (0.008909) | 0.362209 / 0.258489 (0.103719) | 0.410522 / 0.293841 (0.116681) | 0.036247 / 0.128546 (-0.092299) | 0.012432 / 0.075646 (-0.063214) | 0.088754 / 0.419271 (-0.330517) | 0.048848 / 0.043533 (0.005315) | 0.370994 / 0.255139 (0.115855) | 0.382476 / 0.283200 (0.099277) | 0.103443 / 0.141683 (-0.038240) | 1.483127 / 1.452155 (0.030972) | 1.573366 / 1.492716 (0.080650) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224163 / 0.018006 (0.206157) | 0.475136 / 0.000490 (0.474646) | 0.000394 / 0.000200 (0.000194) | 0.000057 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030612 / 0.037411 (-0.006799) | 0.113983 / 0.014526 (0.099457) | 0.121835 / 0.176557 (-0.054722) | 0.160092 / 0.737135 (-0.577043) | 0.127431 / 0.296338 (-0.168908) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.421389 / 0.215209 (0.206179) | 4.207638 / 2.077655 (2.129984) | 2.040265 / 1.504120 (0.536145) | 1.868617 / 1.541195 (0.327422) | 1.979016 / 1.468490 (0.510526) | 0.712499 / 4.584777 (-3.872278) | 3.783091 / 3.745712 (0.037379) | 2.124293 / 5.269862 (-3.145569) | 1.382028 / 4.565676 (-3.183649) | 0.087133 / 0.424275 (-0.337142) | 0.012634 / 0.007607 (0.005027) | 0.518965 / 0.226044 (0.292920) | 5.188330 / 2.268929 (2.919401) | 2.556593 / 55.444624 (-52.888031) | 2.243081 / 6.876477 (-4.633396) | 2.340420 / 2.142072 (0.198347) | 0.858010 / 4.805227 (-3.947218) | 0.169165 / 6.500664 (-6.331499) | 0.065177 / 0.075469 (-0.010292) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.297350 / 1.841788 (-0.544438) | 15.404241 / 8.074308 (7.329933) | 13.806039 / 10.191392 (3.614647) | 0.182055 / 0.680424 (-0.498369) | 0.017789 / 0.534201 (-0.516412) | 0.422828 / 0.579283 (-0.156455) | 0.418269 / 0.434364 (-0.016095) | 0.521561 / 0.540337 (-0.018777) | 0.642526 / 1.386936 (-0.744410) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0009eea6819c32a888f65b0fdb5889b6d311c436 \"CML watermark\")\n" ]

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[ "Hi! This behavior stems from these lines:\r\n\r\nhttps://github.com/huggingface/datasets/blob/b065547654efa0ec633cf373ac1512884c68b2e1/src/datasets/formatting/np_formatter.py#L45-L46\r\n\r\nI agree we should preserve the original type whenever possible and downcast explicitly with a warning.\r\n\r\n@lhoestq Do you remember why we need this \"default dtype\" logic in our formatters?", "I was also wondering why the default type logic is needed. Me just deleting it is probably too naive of a solution.", "Hmm I think the idea was to end up with the usual default precision for deep learning models - no matter how the data was stored or where it comes from.\r\n\r\nFor example in NLP we store tokens using an optimized low precision to save disk space, but when we set the format to `torch` we actually need to get `int64`. Although the need for a default for integers also comes from numpy not returning the same integer precision depending on your machine. Finally I guess we added a default for floats as well for consistency.\r\n\r\nI'm a bit embarrassed by this though, as a user I'd have expected to get the same precision indeed as well and get a zero copy view.", "Will you fix this or should I open a PR?", "Unfortunately removing it for integers is a breaking change for most `transformers` + `datasets` users for NLP (which is a common case). Removing it for floats is a breaking change for `transformers` + `datasets` for ASR as well. And it also is a breaking change for the other users relying on this behavior.\r\n\r\nTherefore I think that the only short term solution is for the user to provide `dtype=` manually and document better this behavior. We could also extend `dtype` to accept a value that means \"return the same dtype as the underlying storage\" and make it easier to do zero copy.", "@lhoestq It should be fine to remove this conversion in Datasets 3.0, no? For now, we can warn the user (with a log message) about the future change when the default type is changed.", "Let's see with the transformers team if it sounds reasonable ? We'd have to fix multiple example scripts though.\r\n\r\nIf it's not ok we can also explore keeping this behavior only for tokens and audio data.", "IMO being coupled with Transformers can lead to unexpected behavior when one tries to use our lib without pairing it with Transformers, so I think it's still important to \"fix\" this, even if it means we will need to update Transformers' example scripts afterward.\r\n", "Ideally let's update the `transformers` example scripts before the change :P", "For others that run into the same issue: A temporary workaround for me is this:\r\n```python\r\ndef numpy_transform(batch):\r\n return {key: np.asarray(val) for key, val in batch.items()}\r\n\r\ndataset = dataset.with_transform(numpy_transform)\r\n```", "This behavior (silent upcast from `int32` to `int64`) is also unexpected for the user in https://discuss.huggingface.co/t/standard-getitem-returns-wrong-data-type-for-arrays/62470/2", "Hi, I stumbled on a variation that upcasts uint8 to int64. I would expect the dtype to be the same as it was when I generated the dataset.\r\n\r\n```\r\nimport numpy as np\r\nimport datasets as ds\r\n\r\nfoo = np.random.randint(0, 256, size=(5, 10, 10), dtype=np.uint8)\r\n\r\nfeatures = ds.Features({\"foo\": ds.Array2D((10, 10), \"uint8\")})\r\ndataset = ds.Dataset.from_dict({\"foo\": foo}, features=features)\r\ndataset.set_format(\"torch\")\r\nprint(\"feature dtype:\", dataset.features[\"foo\"].dtype)\r\nprint(\"array dtype:\", dataset[\"foo\"].dtype)\r\n\r\n# feature dtype: uint8\r\n# array dtype: torch.int64\r\n```\r\n", "workaround to remove torch upcasting\r\n\r\n```\r\nimport datasets as ds\r\nimport torch\r\n\r\nclass FixedTorchFormatter(ds.formatting.TorchFormatter):\r\n def _tensorize(self, value):\r\n return torch.from_numpy(value)\r\n\r\n\r\nds.formatting._register_formatter(FixedTorchFormatter, \"torch\")\r\n```" ]

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[ "Thanks a lot for your help @lhoestq. I've simplified what turned out to be a simple fix and added the unit test.\r\n\r\nDoes this look ready to be merged or is there anything I'm still missing?", "Cool ! I think you just need to remove the unused import in `io/parquet.py`\r\n```\r\nsrc/datasets/io/parquet.py:4:1: F401 'pyarrow as pa' imported but unused\r\n```\r\nand we're good to merge :)", "_The documentation is not available anymore as the PR was closed or merged._", "> Cool ! I think you just need to remove the unused import in `io/parquet.py`\r\n> \r\n> ```\r\n> src/datasets/io/parquet.py:4:1: F401 'pyarrow as pa' imported but unused\r\n> ```\r\n> \r\n> and we're good to merge :)\r\n\r\nDone! Thanks a lot, this was fun :)" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "The commit also includes the changes to the `DatasetDict` methods or am I missing something?", "Oh, indeed. Feel free to mark the PR as \"Ready for review\" then.", "<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) | 0.010149 / 0.011353 (-0.001204) | 0.005606 / 0.011008 (-0.005402) | 0.103455 / 0.038508 (0.064947) | 0.042934 / 0.023109 (0.019825) | 0.308365 / 0.275898 (0.032467) | 0.394188 / 0.323480 (0.070708) | 0.008760 / 0.007986 (0.000774) | 0.004567 / 0.004328 (0.000239) | 0.077959 / 0.004250 (0.073708) | 0.050115 / 0.037052 (0.013063) | 0.318009 / 0.258489 (0.059520) | 0.358578 / 0.293841 (0.064737) | 0.039231 / 0.128546 (-0.089315) | 0.012381 / 0.075646 (-0.063265) | 0.340046 / 0.419271 (-0.079226) | 0.048366 / 0.043533 (0.004834) | 0.307643 / 0.255139 (0.052504) | 0.342886 / 0.283200 (0.059687) | 0.109628 / 0.141683 (-0.032055) | 1.457297 / 1.452155 (0.005142) | 1.518067 / 1.492716 (0.025351) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.295590 / 0.018006 (0.277584) | 0.531515 / 0.000490 (0.531026) | 0.005677 / 0.000200 (0.005477) | 0.000095 / 0.000054 (0.000041) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030901 / 0.037411 (-0.006511) | 0.118312 / 0.014526 (0.103786) | 0.123146 / 0.176557 (-0.053410) | 0.163608 / 0.737135 (-0.573527) | 0.128604 / 0.296338 (-0.167734) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.404143 / 0.215209 (0.188934) | 4.000118 / 2.077655 (1.922464) | 1.804502 / 1.504120 (0.300382) | 1.597287 / 1.541195 (0.056093) | 1.738512 / 1.468490 (0.270022) | 0.704658 / 4.584777 (-3.880119) | 3.830101 / 3.745712 (0.084389) | 2.186598 / 5.269862 (-3.083263) | 1.367873 / 4.565676 (-3.197804) | 0.085550 / 0.424275 (-0.338725) | 0.012226 / 0.007607 (0.004619) | 0.505760 / 0.226044 (0.279716) | 5.054583 / 2.268929 (2.785655) | 2.284942 / 55.444624 (-53.159682) | 1.961413 / 6.876477 (-4.915064) | 2.059449 / 2.142072 (-0.082623) | 0.845009 / 4.805227 (-3.960218) | 0.167204 / 6.500664 (-6.333460) | 0.065998 / 0.075469 (-0.009471) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.221861 / 1.841788 (-0.619927) | 15.925213 / 8.074308 (7.850905) | 15.359308 / 10.191392 (5.167916) | 0.171776 / 0.680424 (-0.508648) | 0.029234 / 0.534201 (-0.504967) | 0.446349 / 0.579283 (-0.132934) | 0.447873 / 0.434364 (0.013509) | 0.527400 / 0.540337 (-0.012937) | 0.610208 / 1.386936 (-0.776728) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008030 / 0.011353 (-0.003323) | 0.005686 / 0.011008 (-0.005322) | 0.076204 / 0.038508 (0.037696) | 0.037131 / 0.023109 (0.014022) | 0.341461 / 0.275898 (0.065563) | 0.378734 / 0.323480 (0.055255) | 0.006580 / 0.007986 (-0.001406) | 0.004379 / 0.004328 (0.000050) | 0.073983 / 0.004250 (0.069732) | 0.055895 / 0.037052 (0.018842) | 0.342667 / 0.258489 (0.084178) | 0.401464 / 0.293841 (0.107623) | 0.037710 / 0.128546 (-0.090837) | 0.012604 / 0.075646 (-0.063042) | 0.087563 / 0.419271 (-0.331709) | 0.050887 / 0.043533 (0.007354) | 0.333491 / 0.255139 (0.078352) | 0.357437 / 0.283200 (0.074237) | 0.109566 / 0.141683 (-0.032117) | 1.423372 / 1.452155 (-0.028783) | 1.569423 / 1.492716 (0.076706) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.340986 / 0.018006 (0.322980) | 0.530885 / 0.000490 (0.530395) | 0.004172 / 0.000200 (0.003972) | 0.000115 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030424 / 0.037411 (-0.006987) | 0.121191 / 0.014526 (0.106666) | 0.129066 / 0.176557 (-0.047491) | 0.166938 / 0.737135 (-0.570198) | 0.132000 / 0.296338 (-0.164338) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418718 / 0.215209 (0.203509) | 4.163973 / 2.077655 (2.086318) | 1.982665 / 1.504120 (0.478545) | 1.798866 / 1.541195 (0.257671) | 1.918867 / 1.468490 (0.450377) | 0.724634 / 4.584777 (-3.860143) | 3.864549 / 3.745712 (0.118837) | 3.697768 / 5.269862 (-1.572093) | 1.983942 / 4.565676 (-2.581735) | 0.086818 / 0.424275 (-0.337457) | 0.012336 / 0.007607 (0.004728) | 0.522314 / 0.226044 (0.296269) | 5.216813 / 2.268929 (2.947884) | 2.516187 / 55.444624 (-52.928437) | 2.172057 / 6.876477 (-4.704420) | 2.342773 / 2.142072 (0.200701) | 0.851805 / 4.805227 (-3.953422) | 0.170139 / 6.500664 (-6.330525) | 0.068494 / 0.075469 (-0.006975) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.307370 / 1.841788 (-0.534418) | 16.737937 / 8.074308 (8.663629) | 14.483384 / 10.191392 (4.291992) | 0.172418 / 0.680424 (-0.508006) | 0.018241 / 0.534201 (-0.515960) | 0.432049 / 0.579283 (-0.147234) | 0.447590 / 0.434364 (0.013227) | 0.550332 / 0.540337 (0.009994) | 0.646756 / 1.386936 (-0.740180) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#819bc6e9f88459f363e6fb6948e9cbe5c231500d \"CML watermark\")\n" ]

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[ "Hi, thanks for noticing this! We can't just remove the cache control as this allows us to control where the arrow files generated by the ops are written (cached on disk if enabled or a temporary directory if disabled). The right way to address this inconsistency would be by having `load_from_cache_file=None` by default everywhere.", "Hi! Yes, this seems more plausible. I can implement that. One last thing is the type annotation `load_from_cache_file: bool = None`. Which I then would change to `load_from_cache_file: Optional[bool] = None`.", "PR #5515 ", "Yes, `Optional[bool]` is the correct type annotation and thanks for the PR." ]

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[ "Hi! Let's not do this - renaming it would be a breaking change, and going through the deprecation cycle is only worth it if it improves user experience.", "Hi @mariosasko, ok it makes sense. Anyway, don't you think it's worth it at some point to start a deprecation cycle e.g. `fs` in `load_from_disk`? It doesn't affect user experience but it's for sure a bad practice IMO, but's up to you 😄 Feel free to close this issue otherwise!", "I don't think deprecating a param name in this particular instance is worth the hassle, so I'm closing the issue 🙂.", "Sure, makes sense @mariosasko thanks!" ]

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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) | 0.008882 / 0.011353 (-0.002471) | 0.004562 / 0.011008 (-0.006446) | 0.100035 / 0.038508 (0.061527) | 0.030654 / 0.023109 (0.007545) | 0.298745 / 0.275898 (0.022847) | 0.356869 / 0.323480 (0.033389) | 0.007170 / 0.007986 (-0.000815) | 0.003471 / 0.004328 (-0.000858) | 0.077975 / 0.004250 (0.073725) | 0.037861 / 0.037052 (0.000809) | 0.311643 / 0.258489 (0.053154) | 0.343504 / 0.293841 (0.049663) | 0.033768 / 0.128546 (-0.094778) | 0.011342 / 0.075646 (-0.064304) | 0.323953 / 0.419271 (-0.095319) | 0.040818 / 0.043533 (-0.002715) | 0.298492 / 0.255139 (0.043353) | 0.327292 / 0.283200 (0.044092) | 0.088423 / 0.141683 (-0.053260) | 1.489520 / 1.452155 (0.037366) | 1.532962 / 1.492716 (0.040245) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223654 / 0.018006 (0.205647) | 0.415134 / 0.000490 (0.414644) | 0.007394 / 0.000200 (0.007194) | 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) | 0.023616 / 0.037411 (-0.013795) | 0.096652 / 0.014526 (0.082126) | 0.105239 / 0.176557 (-0.071318) | 0.148637 / 0.737135 (-0.588498) | 0.107937 / 0.296338 (-0.188402) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426816 / 0.215209 (0.211607) | 4.241533 / 2.077655 (2.163878) | 1.946493 / 1.504120 (0.442373) | 1.735765 / 1.541195 (0.194570) | 1.781424 / 1.468490 (0.312934) | 0.688082 / 4.584777 (-3.896694) | 3.396444 / 3.745712 (-0.349268) | 1.920333 / 5.269862 (-3.349528) | 1.293833 / 4.565676 (-3.271843) | 0.081967 / 0.424275 (-0.342308) | 0.012911 / 0.007607 (0.005304) | 0.536928 / 0.226044 (0.310884) | 5.452327 / 2.268929 (3.183399) | 2.505785 / 55.444624 (-52.938840) | 2.173627 / 6.876477 (-4.702850) | 2.119978 / 2.142072 (-0.022095) | 0.809012 / 4.805227 (-3.996215) | 0.149124 / 6.500664 (-6.351540) | 0.066008 / 0.075469 (-0.009461) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.215702 / 1.841788 (-0.626085) | 13.757525 / 8.074308 (5.683217) | 13.999208 / 10.191392 (3.807816) | 0.164875 / 0.680424 (-0.515549) | 0.028517 / 0.534201 (-0.505684) | 0.394829 / 0.579283 (-0.184454) | 0.404962 / 0.434364 (-0.029401) | 0.484455 / 0.540337 (-0.055882) | 0.575008 / 1.386936 (-0.811928) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006754 / 0.011353 (-0.004598) | 0.004579 / 0.011008 (-0.006430) | 0.076617 / 0.038508 (0.038109) | 0.027902 / 0.023109 (0.004793) | 0.346278 / 0.275898 (0.070380) | 0.398060 / 0.323480 (0.074580) | 0.004938 / 0.007986 (-0.003047) | 0.004681 / 0.004328 (0.000353) | 0.076336 / 0.004250 (0.072086) | 0.038018 / 0.037052 (0.000966) | 0.358701 / 0.258489 (0.100212) | 0.408413 / 0.293841 (0.114572) | 0.031772 / 0.128546 (-0.096774) | 0.011604 / 0.075646 (-0.064042) | 0.085964 / 0.419271 (-0.333308) | 0.042030 / 0.043533 (-0.001502) | 0.343568 / 0.255139 (0.088429) | 0.381805 / 0.283200 (0.098605) | 0.090759 / 0.141683 (-0.050924) | 1.504553 / 1.452155 (0.052398) | 1.594006 / 1.492716 (0.101289) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227395 / 0.018006 (0.209389) | 0.403097 / 0.000490 (0.402608) | 0.000413 / 0.000200 (0.000213) | 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) | 0.024693 / 0.037411 (-0.012718) | 0.100470 / 0.014526 (0.085944) | 0.108481 / 0.176557 (-0.068076) | 0.142791 / 0.737135 (-0.594345) | 0.109949 / 0.296338 (-0.186389) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.443674 / 0.215209 (0.228465) | 4.412207 / 2.077655 (2.334553) | 2.073752 / 1.504120 (0.569632) | 1.863153 / 1.541195 (0.321958) | 1.940063 / 1.468490 (0.471573) | 0.696456 / 4.584777 (-3.888321) | 3.422120 / 3.745712 (-0.323592) | 1.902579 / 5.269862 (-3.367282) | 1.184948 / 4.565676 (-3.380729) | 0.083079 / 0.424275 (-0.341196) | 0.012649 / 0.007607 (0.005042) | 0.542035 / 0.226044 (0.315991) | 5.421826 / 2.268929 (3.152897) | 2.525092 / 55.444624 (-52.919532) | 2.177144 / 6.876477 (-4.699332) | 2.225224 / 2.142072 (0.083151) | 0.804739 / 4.805227 (-4.000488) | 0.151000 / 6.500664 (-6.349664) | 0.066987 / 0.075469 (-0.008482) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.277199 / 1.841788 (-0.564589) | 14.184146 / 8.074308 (6.109838) | 13.413348 / 10.191392 (3.221956) | 0.128551 / 0.680424 (-0.551872) | 0.016461 / 0.534201 (-0.517740) | 0.379963 / 0.579283 (-0.199320) | 0.381350 / 0.434364 (-0.053014) | 0.439044 / 0.540337 (-0.101293) | 0.521559 / 1.386936 (-0.865377) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4f3c152c1c35df250d2fbeb25d5823a65714f2d8 \"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) | 0.008876 / 0.011353 (-0.002477) | 0.004629 / 0.011008 (-0.006379) | 0.101697 / 0.038508 (0.063189) | 0.030373 / 0.023109 (0.007264) | 0.302206 / 0.275898 (0.026308) | 0.365835 / 0.323480 (0.042355) | 0.007877 / 0.007986 (-0.000109) | 0.004473 / 0.004328 (0.000144) | 0.077334 / 0.004250 (0.073084) | 0.038066 / 0.037052 (0.001014) | 0.308064 / 0.258489 (0.049575) | 0.347329 / 0.293841 (0.053488) | 0.034478 / 0.128546 (-0.094068) | 0.011651 / 0.075646 (-0.063995) | 0.323481 / 0.419271 (-0.095791) | 0.043515 / 0.043533 (-0.000018) | 0.299885 / 0.255139 (0.044746) | 0.328959 / 0.283200 (0.045760) | 0.095308 / 0.141683 (-0.046375) | 1.474058 / 1.452155 (0.021903) | 1.535335 / 1.492716 (0.042619) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.197416 / 0.018006 (0.179410) | 0.421935 / 0.000490 (0.421446) | 0.003490 / 0.000200 (0.003290) | 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) | 0.024519 / 0.037411 (-0.012892) | 0.100710 / 0.014526 (0.086185) | 0.104520 / 0.176557 (-0.072036) | 0.142048 / 0.737135 (-0.595087) | 0.109274 / 0.296338 (-0.187064) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled 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) | 4.101720 / 2.077655 (2.024065) | 1.812375 / 1.504120 (0.308256) | 1.605819 / 1.541195 (0.064624) | 1.688923 / 1.468490 (0.220433) | 0.691198 / 4.584777 (-3.893579) | 3.422137 / 3.745712 (-0.323575) | 1.921318 / 5.269862 (-3.348544) | 1.168770 / 4.565676 (-3.396906) | 0.082840 / 0.424275 (-0.341435) | 0.012740 / 0.007607 (0.005133) | 0.524333 / 0.226044 (0.298289) | 5.258077 / 2.268929 (2.989149) | 2.273177 / 55.444624 (-53.171447) | 1.931919 / 6.876477 (-4.944558) | 1.988415 / 2.142072 (-0.153658) | 0.812227 / 4.805227 (-3.993000) | 0.150043 / 6.500664 (-6.350622) | 0.066422 / 0.075469 (-0.009047) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.188069 / 1.841788 (-0.653718) | 13.942681 / 8.074308 (5.868373) | 14.104658 / 10.191392 (3.913266) | 0.151966 / 0.680424 (-0.528458) | 0.028833 / 0.534201 (-0.505368) | 0.395125 / 0.579283 (-0.184158) | 0.408512 / 0.434364 (-0.025852) | 0.487587 / 0.540337 (-0.052751) | 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) | 0.006860 / 0.011353 (-0.004493) | 0.004582 / 0.011008 (-0.006426) | 0.079902 / 0.038508 (0.041394) | 0.027565 / 0.023109 (0.004456) | 0.341393 / 0.275898 (0.065495) | 0.378911 / 0.323480 (0.055431) | 0.005847 / 0.007986 (-0.002138) | 0.004681 / 0.004328 (0.000353) | 0.079422 / 0.004250 (0.075171) | 0.039135 / 0.037052 (0.002083) | 0.342026 / 0.258489 (0.083537) | 0.387510 / 0.293841 (0.093669) | 0.031999 / 0.128546 (-0.096547) | 0.011782 / 0.075646 (-0.063865) | 0.088563 / 0.419271 (-0.330709) | 0.042435 / 0.043533 (-0.001098) | 0.343055 / 0.255139 (0.087916) | 0.367437 / 0.283200 (0.084237) | 0.091578 / 0.141683 (-0.050104) | 1.506828 / 1.452155 (0.054673) | 1.599590 / 1.492716 (0.106874) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.217939 / 0.018006 (0.199932) | 0.408352 / 0.000490 (0.407863) | 0.000394 / 0.000200 (0.000194) | 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) | 0.026344 / 0.037411 (-0.011067) | 0.102968 / 0.014526 (0.088442) | 0.110340 / 0.176557 (-0.066217) | 0.145696 / 0.737135 (-0.591439) | 0.111632 / 0.296338 (-0.184707) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440764 / 0.215209 (0.225555) | 4.423179 / 2.077655 (2.345524) | 2.057016 / 1.504120 (0.552896) | 1.848741 / 1.541195 (0.307546) | 1.939827 / 1.468490 (0.471337) | 0.699370 / 4.584777 (-3.885407) | 3.472521 / 3.745712 (-0.273191) | 3.232557 / 5.269862 (-2.037305) | 1.755534 / 4.565676 (-2.810143) | 0.083469 / 0.424275 (-0.340807) | 0.012980 / 0.007607 (0.005373) | 0.557662 / 0.226044 (0.331618) | 5.435657 / 2.268929 (3.166729) | 2.545106 / 55.444624 (-52.899519) | 2.168047 / 6.876477 (-4.708430) | 2.234070 / 2.142072 (0.091997) | 0.804662 / 4.805227 (-4.000565) | 0.152832 / 6.500664 (-6.347833) | 0.069372 / 0.075469 (-0.006097) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.299189 / 1.841788 (-0.542598) | 14.752880 / 8.074308 (6.678572) | 13.607676 / 10.191392 (3.416284) | 0.150773 / 0.680424 (-0.529650) | 0.016701 / 0.534201 (-0.517500) | 0.379507 / 0.579283 (-0.199776) | 0.389401 / 0.434364 (-0.044963) | 0.444199 / 0.540337 (-0.096139) | 0.524264 / 1.386936 (-0.862672) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#12be850b36c0b9d4841af86c75e08c0a726ffb5c \"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) | 0.008694 / 0.011353 (-0.002659) | 0.004549 / 0.011008 (-0.006459) | 0.101164 / 0.038508 (0.062656) | 0.029644 / 0.023109 (0.006535) | 0.294849 / 0.275898 (0.018950) | 0.366755 / 0.323480 (0.043275) | 0.007205 / 0.007986 (-0.000780) | 0.004255 / 0.004328 (-0.000074) | 0.077433 / 0.004250 (0.073183) | 0.038024 / 0.037052 (0.000972) | 0.310380 / 0.258489 (0.051891) | 0.347093 / 0.293841 (0.053252) | 0.033232 / 0.128546 (-0.095314) | 0.011404 / 0.075646 (-0.064242) | 0.323341 / 0.419271 (-0.095930) | 0.040586 / 0.043533 (-0.002946) | 0.296083 / 0.255139 (0.040944) | 0.321870 / 0.283200 (0.038671) | 0.087377 / 0.141683 (-0.054306) | 1.466869 / 1.452155 (0.014715) | 1.514763 / 1.492716 (0.022046) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.010272 / 0.018006 (-0.007734) | 0.414645 / 0.000490 (0.414155) | 0.003730 / 0.000200 (0.003530) | 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) | 0.024093 / 0.037411 (-0.013318) | 0.098718 / 0.014526 (0.084192) | 0.105526 / 0.176557 (-0.071030) | 0.141578 / 0.737135 (-0.595557) | 0.109679 / 0.296338 (-0.186660) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.412907 / 0.215209 (0.197698) | 4.134934 / 2.077655 (2.057280) | 1.881180 / 1.504120 (0.377060) | 1.693207 / 1.541195 (0.152012) | 1.753725 / 1.468490 (0.285235) | 0.693077 / 4.584777 (-3.891700) | 3.367409 / 3.745712 (-0.378303) | 2.749035 / 5.269862 (-2.520827) | 1.565015 / 4.565676 (-3.000662) | 0.082609 / 0.424275 (-0.341666) | 0.012500 / 0.007607 (0.004892) | 0.523619 / 0.226044 (0.297575) | 5.250188 / 2.268929 (2.981259) | 2.314255 / 55.444624 (-53.130369) | 1.962357 / 6.876477 (-4.914120) | 2.020632 / 2.142072 (-0.121441) | 0.812504 / 4.805227 (-3.992724) | 0.149921 / 6.500664 (-6.350743) | 0.065816 / 0.075469 (-0.009653) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.230811 / 1.841788 (-0.610977) | 14.008566 / 8.074308 (5.934258) | 14.371285 / 10.191392 (4.179893) | 0.166323 / 0.680424 (-0.514101) | 0.029702 / 0.534201 (-0.504499) | 0.408629 / 0.579283 (-0.170654) | 0.410529 / 0.434364 (-0.023835) | 0.484482 / 0.540337 (-0.055855) | 0.572360 / 1.386936 (-0.814576) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006873 / 0.011353 (-0.004480) | 0.004609 / 0.011008 (-0.006400) | 0.075492 / 0.038508 (0.036984) | 0.028560 / 0.023109 (0.005450) | 0.340321 / 0.275898 (0.064423) | 0.376758 / 0.323480 (0.053278) | 0.005271 / 0.007986 (-0.002715) | 0.004786 / 0.004328 (0.000457) | 0.074843 / 0.004250 (0.070592) | 0.041072 / 0.037052 (0.004019) | 0.339952 / 0.258489 (0.081463) | 0.384375 / 0.293841 (0.090534) | 0.031771 / 0.128546 (-0.096775) | 0.011607 / 0.075646 (-0.064039) | 0.084338 / 0.419271 (-0.334933) | 0.042251 / 0.043533 (-0.001282) | 0.338904 / 0.255139 (0.083765) | 0.365360 / 0.283200 (0.082160) | 0.093151 / 0.141683 (-0.048532) | 1.449833 / 1.452155 (-0.002322) | 1.601946 / 1.492716 (0.109229) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225149 / 0.018006 (0.207142) | 0.409855 / 0.000490 (0.409365) | 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) | 0.025914 / 0.037411 (-0.011497) | 0.100443 / 0.014526 (0.085917) | 0.108557 / 0.176557 (-0.067999) | 0.150338 / 0.737135 (-0.586798) | 0.111472 / 0.296338 (-0.184866) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440221 / 0.215209 (0.225012) | 4.409268 / 2.077655 (2.331613) | 2.096008 / 1.504120 (0.591888) | 1.849443 / 1.541195 (0.308248) | 1.934901 / 1.468490 (0.466410) | 0.704072 / 4.584777 (-3.880705) | 3.371370 / 3.745712 (-0.374343) | 3.185478 / 5.269862 (-2.084384) | 1.514541 / 4.565676 (-3.051135) | 0.083724 / 0.424275 (-0.340551) | 0.012674 / 0.007607 (0.005067) | 0.542155 / 0.226044 (0.316111) | 5.413456 / 2.268929 (3.144528) | 2.508567 / 55.444624 (-52.936057) | 2.163235 / 6.876477 (-4.713242) | 2.193914 / 2.142072 (0.051842) | 0.810955 / 4.805227 (-3.994272) | 0.152769 / 6.500664 (-6.347895) | 0.068009 / 0.075469 (-0.007460) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.272511 / 1.841788 (-0.569276) | 14.334861 / 8.074308 (6.260553) | 13.555445 / 10.191392 (3.364053) | 0.160520 / 0.680424 (-0.519904) | 0.018363 / 0.534201 (-0.515838) | 0.384937 / 0.579283 (-0.194346) | 0.409138 / 0.434364 (-0.025225) | 0.484037 / 0.540337 (-0.056300) | 0.565595 / 1.386936 (-0.821341) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#23f076ef0187a4009d3c62b14a02e146baf0e35f \"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) | 0.010077 / 0.011353 (-0.001276) | 0.005650 / 0.011008 (-0.005359) | 0.101285 / 0.038508 (0.062777) | 0.039571 / 0.023109 (0.016462) | 0.291855 / 0.275898 (0.015957) | 0.363582 / 0.323480 (0.040102) | 0.008513 / 0.007986 (0.000527) | 0.004472 / 0.004328 (0.000144) | 0.077314 / 0.004250 (0.073064) | 0.050707 / 0.037052 (0.013654) | 0.317282 / 0.258489 (0.058792) | 0.342348 / 0.293841 (0.048507) | 0.042951 / 0.128546 (-0.085595) | 0.012295 / 0.075646 (-0.063351) | 0.337269 / 0.419271 (-0.082003) | 0.048953 / 0.043533 (0.005420) | 0.292547 / 0.255139 (0.037408) | 0.325436 / 0.283200 (0.042236) | 0.111859 / 0.141683 (-0.029824) | 1.501958 / 1.452155 (0.049804) | 1.522281 / 1.492716 (0.029565) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.011775 / 0.018006 (-0.006231) | 0.513283 / 0.000490 (0.512793) | 0.002941 / 0.000200 (0.002741) | 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) | 0.028702 / 0.037411 (-0.008710) | 0.108465 / 0.014526 (0.093940) | 0.121806 / 0.176557 (-0.054750) | 0.158424 / 0.737135 (-0.578712) | 0.128077 / 0.296338 (-0.168262) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.395392 / 0.215209 (0.180183) | 3.944138 / 2.077655 (1.866483) | 1.773698 / 1.504120 (0.269578) | 1.588907 / 1.541195 (0.047712) | 1.697794 / 1.468490 (0.229304) | 0.690281 / 4.584777 (-3.894496) | 3.819661 / 3.745712 (0.073948) | 3.228006 / 5.269862 (-2.041856) | 1.755625 / 4.565676 (-2.810052) | 0.083169 / 0.424275 (-0.341106) | 0.012337 / 0.007607 (0.004730) | 0.504730 / 0.226044 (0.278686) | 5.016916 / 2.268929 (2.747988) | 2.245484 / 55.444624 (-53.199141) | 1.911682 / 6.876477 (-4.964795) | 1.957659 / 2.142072 (-0.184413) | 0.818361 / 4.805227 (-3.986866) | 0.162386 / 6.500664 (-6.338279) | 0.062461 / 0.075469 (-0.013008) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.197654 / 1.841788 (-0.644134) | 15.465611 / 8.074308 (7.391303) | 14.409126 / 10.191392 (4.217734) | 0.171776 / 0.680424 (-0.508647) | 0.028749 / 0.534201 (-0.505452) | 0.439666 / 0.579283 (-0.139618) | 0.445159 / 0.434364 (0.010795) | 0.543992 / 0.540337 (0.003655) | 0.643911 / 1.386936 (-0.743025) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007036 / 0.011353 (-0.004317) | 0.005273 / 0.011008 (-0.005735) | 0.075314 / 0.038508 (0.036806) | 0.033075 / 0.023109 (0.009966) | 0.350133 / 0.275898 (0.074235) | 0.399366 / 0.323480 (0.075886) | 0.005945 / 0.007986 (-0.002041) | 0.004276 / 0.004328 (-0.000052) | 0.074975 / 0.004250 (0.070725) | 0.051758 / 0.037052 (0.014706) | 0.355077 / 0.258489 (0.096588) | 0.430296 / 0.293841 (0.136455) | 0.036257 / 0.128546 (-0.092290) | 0.012376 / 0.075646 (-0.063270) | 0.087441 / 0.419271 (-0.331830) | 0.049066 / 0.043533 (0.005534) | 0.339867 / 0.255139 (0.084728) | 0.384379 / 0.283200 (0.101179) | 0.104843 / 0.141683 (-0.036840) | 1.498897 / 1.452155 (0.046742) | 1.551400 / 1.492716 (0.058684) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.334504 / 0.018006 (0.316498) | 0.516551 / 0.000490 (0.516061) | 0.000450 / 0.000200 (0.000250) | 0.000057 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029313 / 0.037411 (-0.008099) | 0.110667 / 0.014526 (0.096141) | 0.124001 / 0.176557 (-0.052556) | 0.159154 / 0.737135 (-0.577981) | 0.129503 / 0.296338 (-0.166836) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.416749 / 0.215209 (0.201540) | 4.171163 / 2.077655 (2.093508) | 1.981071 / 1.504120 (0.476951) | 1.788303 / 1.541195 (0.247108) | 1.912118 / 1.468490 (0.443628) | 0.708764 / 4.584777 (-3.876013) | 3.815222 / 3.745712 (0.069510) | 2.121633 / 5.269862 (-3.148229) | 1.347866 / 4.565676 (-3.217811) | 0.086340 / 0.424275 (-0.337935) | 0.012646 / 0.007607 (0.005039) | 0.525286 / 0.226044 (0.299241) | 5.254922 / 2.268929 (2.985994) | 2.488743 / 55.444624 (-52.955881) | 2.128069 / 6.876477 (-4.748408) | 2.180358 / 2.142072 (0.038286) | 0.841011 / 4.805227 (-3.964216) | 0.168732 / 6.500664 (-6.331932) | 0.065559 / 0.075469 (-0.009910) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.270518 / 1.841788 (-0.571270) | 15.557563 / 8.074308 (7.483255) | 13.660757 / 10.191392 (3.469365) | 0.185636 / 0.680424 (-0.494788) | 0.018152 / 0.534201 (-0.516049) | 0.423553 / 0.579283 (-0.155730) | 0.412718 / 0.434364 (-0.021646) | 0.528455 / 0.540337 (-0.011882) | 0.635274 / 1.386936 (-0.751662) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d40f05ef827c52344a2c6e83f7c8d13bb6b660d3 \"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) | 0.011194 / 0.011353 (-0.000159) | 0.006344 / 0.011008 (-0.004664) | 0.122013 / 0.038508 (0.083505) | 0.044323 / 0.023109 (0.021214) | 0.356665 / 0.275898 (0.080767) | 0.439871 / 0.323480 (0.116391) | 0.010694 / 0.007986 (0.002709) | 0.004648 / 0.004328 (0.000320) | 0.091140 / 0.004250 (0.086890) | 0.052457 / 0.037052 (0.015404) | 0.369282 / 0.258489 (0.110793) | 0.403279 / 0.293841 (0.109438) | 0.054075 / 0.128546 (-0.074472) | 0.014484 / 0.075646 (-0.061162) | 0.407932 / 0.419271 (-0.011340) | 0.060681 / 0.043533 (0.017148) | 0.350889 / 0.255139 (0.095750) | 0.392041 / 0.283200 (0.108841) | 0.121252 / 0.141683 (-0.020431) | 1.809527 / 1.452155 (0.357373) | 1.835141 / 1.492716 (0.342425) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227372 / 0.018006 (0.209366) | 0.481908 / 0.000490 (0.481418) | 0.007262 / 0.000200 (0.007062) | 0.000148 / 0.000054 (0.000093) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031039 / 0.037411 (-0.006372) | 0.133947 / 0.014526 (0.119421) | 0.141935 / 0.176557 (-0.034622) | 0.197854 / 0.737135 (-0.539281) | 0.152393 / 0.296338 (-0.143945) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.517400 / 0.215209 (0.302191) | 4.899972 / 2.077655 (2.822317) | 2.171023 / 1.504120 (0.666903) | 2.008706 / 1.541195 (0.467511) | 1.988777 / 1.468490 (0.520287) | 0.859872 / 4.584777 (-3.724905) | 4.673923 / 3.745712 (0.928211) | 2.703189 / 5.269862 (-2.566672) | 1.891680 / 4.565676 (-2.673997) | 0.109601 / 0.424275 (-0.314674) | 0.014622 / 0.007607 (0.007015) | 0.618990 / 0.226044 (0.392946) | 6.255608 / 2.268929 (3.986679) | 2.822199 / 55.444624 (-52.622425) | 2.457684 / 6.876477 (-4.418793) | 2.500041 / 2.142072 (0.357968) | 1.054529 / 4.805227 (-3.750698) | 0.209501 / 6.500664 (-6.291163) | 0.074929 / 0.075469 (-0.000540) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.532780 / 1.841788 (-0.309008) | 19.159455 / 8.074308 (11.085147) | 17.817063 / 10.191392 (7.625671) | 0.194078 / 0.680424 (-0.486346) | 0.038211 / 0.534201 (-0.495990) | 0.537366 / 0.579283 (-0.041917) | 0.538995 / 0.434364 (0.104631) | 0.679431 / 0.540337 (0.139094) | 0.801960 / 1.386936 (-0.584976) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008729 / 0.011353 (-0.002624) | 0.005711 / 0.011008 (-0.005297) | 0.091570 / 0.038508 (0.053062) | 0.039805 / 0.023109 (0.016696) | 0.413507 / 0.275898 (0.137609) | 0.456342 / 0.323480 (0.132862) | 0.006201 / 0.007986 (-0.001785) | 0.009700 / 0.004328 (0.005372) | 0.089146 / 0.004250 (0.084896) | 0.057543 / 0.037052 (0.020490) | 0.420806 / 0.258489 (0.162317) | 0.471962 / 0.293841 (0.178121) | 0.043940 / 0.128546 (-0.084606) | 0.014457 / 0.075646 (-0.061190) | 0.106674 / 0.419271 (-0.312598) | 0.058930 / 0.043533 (0.015397) | 0.419111 / 0.255139 (0.163972) | 0.452974 / 0.283200 (0.169774) | 0.124573 / 0.141683 (-0.017110) | 1.864753 / 1.452155 (0.412599) | 1.935387 / 1.492716 (0.442670) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.275657 / 0.018006 (0.257651) | 0.498096 / 0.000490 (0.497606) | 0.000480 / 0.000200 (0.000280) | 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) | 0.034377 / 0.037411 (-0.003035) | 0.138050 / 0.014526 (0.123524) | 0.153718 / 0.176557 (-0.022838) | 0.201445 / 0.737135 (-0.535690) | 0.160346 / 0.296338 (-0.135992) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.540670 / 0.215209 (0.325461) | 5.376291 / 2.077655 (3.298636) | 2.581799 / 1.504120 (1.077679) | 2.328858 / 1.541195 (0.787663) | 2.446458 / 1.468490 (0.977968) | 0.923005 / 4.584777 (-3.661772) | 4.815977 / 3.745712 (1.070265) | 4.205725 / 5.269862 (-1.064137) | 2.400466 / 4.565676 (-2.165211) | 0.107207 / 0.424275 (-0.317068) | 0.015427 / 0.007607 (0.007819) | 0.657267 / 0.226044 (0.431222) | 6.491256 / 2.268929 (4.222327) | 3.179099 / 55.444624 (-52.265525) | 2.722434 / 6.876477 (-4.154042) | 2.788202 / 2.142072 (0.646129) | 1.060016 / 4.805227 (-3.745211) | 0.206899 / 6.500664 (-6.293766) | 0.077868 / 0.075469 (0.002399) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.567894 / 1.841788 (-0.273893) | 19.314330 / 8.074308 (11.240022) | 17.597614 / 10.191392 (7.406222) | 0.195777 / 0.680424 (-0.484647) | 0.022160 / 0.534201 (-0.512041) | 0.530592 / 0.579283 (-0.048691) | 0.508591 / 0.434364 (0.074227) | 0.619794 / 0.540337 (0.079457) | 0.749773 / 1.386936 (-0.637163) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8637141a67639c510294620306c9bb25d31d34ef \"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) | 0.012431 / 0.011353 (0.001078) | 0.006526 / 0.011008 (-0.004482) | 0.132266 / 0.038508 (0.093757) | 0.043199 / 0.023109 (0.020089) | 0.405230 / 0.275898 (0.129332) | 0.494643 / 0.323480 (0.171163) | 0.009927 / 0.007986 (0.001941) | 0.005227 / 0.004328 (0.000899) | 0.110914 / 0.004250 (0.106664) | 0.047815 / 0.037052 (0.010763) | 0.419099 / 0.258489 (0.160610) | 0.463405 / 0.293841 (0.169564) | 0.057858 / 0.128546 (-0.070688) | 0.018918 / 0.075646 (-0.056728) | 0.450584 / 0.419271 (0.031313) | 0.060457 / 0.043533 (0.016924) | 0.408234 / 0.255139 (0.153095) | 0.433722 / 0.283200 (0.150523) | 0.119403 / 0.141683 (-0.022280) | 1.966742 / 1.452155 (0.514587) | 1.980685 / 1.492716 (0.487969) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.292853 / 0.018006 (0.274847) | 0.619697 / 0.000490 (0.619207) | 0.002135 / 0.000200 (0.001935) | 0.000117 / 0.000054 (0.000062) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031283 / 0.037411 (-0.006129) | 0.128649 / 0.014526 (0.114123) | 0.150116 / 0.176557 (-0.026441) | 0.187605 / 0.737135 (-0.549530) | 0.153334 / 0.296338 (-0.143005) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.659660 / 0.215209 (0.444451) | 6.459749 / 2.077655 (4.382094) | 2.764566 / 1.504120 (1.260446) | 2.362630 / 1.541195 (0.821435) | 2.426421 / 1.468490 (0.957931) | 1.282407 / 4.584777 (-3.302370) | 5.668865 / 3.745712 (1.923153) | 3.236255 / 5.269862 (-2.033606) | 2.248836 / 4.565676 (-2.316841) | 0.145861 / 0.424275 (-0.278414) | 0.015707 / 0.007607 (0.008100) | 0.805218 / 0.226044 (0.579174) | 8.146831 / 2.268929 (5.877903) | 3.506283 / 55.444624 (-51.938341) | 2.736682 / 6.876477 (-4.139795) | 2.959039 / 2.142072 (0.816967) | 1.528428 / 4.805227 (-3.276799) | 0.270980 / 6.500664 (-6.229684) | 0.086824 / 0.075469 (0.011355) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.682506 / 1.841788 (-0.159282) | 18.844103 / 8.074308 (10.769795) | 21.008471 / 10.191392 (10.817079) | 0.258372 / 0.680424 (-0.422052) | 0.046505 / 0.534201 (-0.487696) | 0.574760 / 0.579283 (-0.004523) | 0.663745 / 0.434364 (0.229381) | 0.702411 / 0.540337 (0.162074) | 0.824024 / 1.386936 (-0.562912) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010016 / 0.011353 (-0.001337) | 0.007459 / 0.011008 (-0.003549) | 0.103954 / 0.038508 (0.065446) | 0.036363 / 0.023109 (0.013254) | 0.464079 / 0.275898 (0.188181) | 0.504730 / 0.323480 (0.181250) | 0.007865 / 0.007986 (-0.000121) | 0.005210 / 0.004328 (0.000882) | 0.105018 / 0.004250 (0.100767) | 0.062191 / 0.037052 (0.025139) | 0.483304 / 0.258489 (0.224815) | 0.547030 / 0.293841 (0.253189) | 0.055436 / 0.128546 (-0.073110) | 0.021073 / 0.075646 (-0.054573) | 0.120952 / 0.419271 (-0.298319) | 0.075593 / 0.043533 (0.032060) | 0.459930 / 0.255139 (0.204791) | 0.486924 / 0.283200 (0.203724) | 0.129465 / 0.141683 (-0.012218) | 1.902322 / 1.452155 (0.450167) | 1.980809 / 1.492716 (0.488092) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.259263 / 0.018006 (0.241257) | 0.596703 / 0.000490 (0.596213) | 0.004520 / 0.000200 (0.004320) | 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) | 0.032802 / 0.037411 (-0.004609) | 0.138751 / 0.014526 (0.124225) | 0.147106 / 0.176557 (-0.029451) | 0.194791 / 0.737135 (-0.542345) | 0.152643 / 0.296338 (-0.143696) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.678455 / 0.215209 (0.463246) | 6.673643 / 2.077655 (4.595989) | 2.943368 / 1.504120 (1.439248) | 2.591223 / 1.541195 (1.050029) | 2.741097 / 1.468490 (1.272607) | 1.261178 / 4.584777 (-3.323599) | 5.773853 / 3.745712 (2.028141) | 3.171559 / 5.269862 (-2.098303) | 2.124898 / 4.565676 (-2.440779) | 0.161849 / 0.424275 (-0.262426) | 0.015498 / 0.007607 (0.007891) | 0.857984 / 0.226044 (0.631940) | 8.456946 / 2.268929 (6.188018) | 3.818787 / 55.444624 (-51.625837) | 3.009953 / 6.876477 (-3.866523) | 3.113006 / 2.142072 (0.970934) | 1.477299 / 4.805227 (-3.327929) | 0.267207 / 6.500664 (-6.233457) | 0.087590 / 0.075469 (0.012121) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.757389 / 1.841788 (-0.084398) | 19.287690 / 8.074308 (11.213381) | 21.601991 / 10.191392 (11.410599) | 0.260464 / 0.680424 (-0.419960) | 0.028552 / 0.534201 (-0.505649) | 0.558934 / 0.579283 (-0.020349) | 0.673651 / 0.434364 (0.239287) | 0.714448 / 0.540337 (0.174111) | 0.857608 / 1.386936 (-0.529328) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2d3bd0134de444ffd10c4a39873dbf9aa3732c08 \"CML watermark\")\n", "Ready for review @mariosasko, LMKWYT :)\r\n\r\nSorry it tooks me a few tries to fix the CI - I ended up not trying to use the latest `torch` version in the CI.", "<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) | 0.009474 / 0.011353 (-0.001878) | 0.005507 / 0.011008 (-0.005501) | 0.101219 / 0.038508 (0.062711) | 0.035591 / 0.023109 (0.012481) | 0.305841 / 0.275898 (0.029943) | 0.339135 / 0.323480 (0.015656) | 0.007920 / 0.007986 (-0.000066) | 0.004252 / 0.004328 (-0.000077) | 0.076912 / 0.004250 (0.072662) | 0.041923 / 0.037052 (0.004871) | 0.301405 / 0.258489 (0.042916) | 0.356488 / 0.293841 (0.062647) | 0.039342 / 0.128546 (-0.089204) | 0.012711 / 0.075646 (-0.062935) | 0.334193 / 0.419271 (-0.085079) | 0.049112 / 0.043533 (0.005579) | 0.301484 / 0.255139 (0.046345) | 0.315306 / 0.283200 (0.032106) | 0.102959 / 0.141683 (-0.038724) | 1.420677 / 1.452155 (-0.031478) | 1.549493 / 1.492716 (0.056777) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.284639 / 0.018006 (0.266633) | 0.501226 / 0.000490 (0.500736) | 0.004328 / 0.000200 (0.004128) | 0.000091 / 0.000054 (0.000036) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027034 / 0.037411 (-0.010377) | 0.108066 / 0.014526 (0.093540) | 0.122106 / 0.176557 (-0.054451) | 0.162908 / 0.737135 (-0.574227) | 0.127233 / 0.296338 (-0.169105) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.394023 / 0.215209 (0.178813) | 3.932729 / 2.077655 (1.855075) | 1.771195 / 1.504120 (0.267075) | 1.582788 / 1.541195 (0.041594) | 1.703219 / 1.468490 (0.234728) | 0.702629 / 4.584777 (-3.882148) | 3.780187 / 3.745712 (0.034475) | 2.180433 / 5.269862 (-3.089428) | 1.504806 / 4.565676 (-3.060871) | 0.085289 / 0.424275 (-0.338986) | 0.012580 / 0.007607 (0.004973) | 0.515408 / 0.226044 (0.289363) | 5.010613 / 2.268929 (2.741685) | 2.256648 / 55.444624 (-53.187976) | 1.914971 / 6.876477 (-4.961505) | 2.038436 / 2.142072 (-0.103636) | 0.846240 / 4.805227 (-3.958987) | 0.164920 / 6.500664 (-6.335744) | 0.063899 / 0.075469 (-0.011570) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.224160 / 1.841788 (-0.617627) | 15.089995 / 8.074308 (7.015687) | 14.777003 / 10.191392 (4.585611) | 0.169873 / 0.680424 (-0.510551) | 0.029233 / 0.534201 (-0.504968) | 0.445424 / 0.579283 (-0.133859) | 0.439194 / 0.434364 (0.004830) | 0.536370 / 0.540337 (-0.003968) | 0.636694 / 1.386936 (-0.750242) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008230 / 0.011353 (-0.003122) | 0.005499 / 0.011008 (-0.005509) | 0.076108 / 0.038508 (0.037600) | 0.037444 / 0.023109 (0.014335) | 0.364420 / 0.275898 (0.088522) | 0.412308 / 0.323480 (0.088828) | 0.006704 / 0.007986 (-0.001282) | 0.004359 / 0.004328 (0.000031) | 0.075080 / 0.004250 (0.070830) | 0.057698 / 0.037052 (0.020646) | 0.366088 / 0.258489 (0.107599) | 0.409583 / 0.293841 (0.115742) | 0.037882 / 0.128546 (-0.090664) | 0.012421 / 0.075646 (-0.063225) | 0.087701 / 0.419271 (-0.331571) | 0.050669 / 0.043533 (0.007136) | 0.351139 / 0.255139 (0.096000) | 0.384340 / 0.283200 (0.101140) | 0.108097 / 0.141683 (-0.033586) | 1.445010 / 1.452155 (-0.007145) | 1.559570 / 1.492716 (0.066853) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.324114 / 0.018006 (0.306108) | 0.549134 / 0.000490 (0.548644) | 0.003544 / 0.000200 (0.003344) | 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) | 0.030646 / 0.037411 (-0.006765) | 0.108573 / 0.014526 (0.094047) | 0.125291 / 0.176557 (-0.051266) | 0.174798 / 0.737135 (-0.562338) | 0.128000 / 0.296338 (-0.168338) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428881 / 0.215209 (0.213672) | 4.282320 / 2.077655 (2.204665) | 2.061462 / 1.504120 (0.557342) | 1.858477 / 1.541195 (0.317283) | 1.971646 / 1.468490 (0.503156) | 0.723631 / 4.584777 (-3.861146) | 3.822376 / 3.745712 (0.076664) | 2.174427 / 5.269862 (-3.095434) | 1.386066 / 4.565676 (-3.179611) | 0.088391 / 0.424275 (-0.335884) | 0.012948 / 0.007607 (0.005341) | 0.524423 / 0.226044 (0.298378) | 5.249389 / 2.268929 (2.980460) | 2.528662 / 55.444624 (-52.915962) | 2.245329 / 6.876477 (-4.631147) | 2.402733 / 2.142072 (0.260660) | 0.868864 / 4.805227 (-3.936364) | 0.174066 / 6.500664 (-6.326598) | 0.066165 / 0.075469 (-0.009304) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.296922 / 1.841788 (-0.544865) | 15.814109 / 8.074308 (7.739801) | 14.086059 / 10.191392 (3.894667) | 0.190952 / 0.680424 (-0.489472) | 0.017679 / 0.534201 (-0.516522) | 0.428872 / 0.579283 (-0.150411) | 0.435399 / 0.434364 (0.001035) | 0.540856 / 0.540337 (0.000519) | 0.648904 / 1.386936 (-0.738032) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f401758c5019ede4404994d5d59220125984874d \"CML watermark\")\n" ]

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[ "Update `datasets` or downgrade `huggingface-hub` ;)\r\n\r\nThe `huggingface-hub` lib did a breaking change a few months ago, and you're using an old version of `datasets` that does't support it", "Awesome thanks a lot! Everything works just fine with `datasets==2.9.0` :-) ", "Getting same error with latest versions.\r\n\r\n\r\n```shell\r\n---------------------------------------------------------------------------\r\nTypeError Traceback (most recent call last)\r\nCell In[99], line 1\r\n----> 1 dataset.push_to_hub(\"mirfan899/kids_phoneme_asr\")\r\n\r\nFile /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:3538, in Dataset.push_to_hub(self, repo_id, split, private, token, branch, shard_size, embed_external_files)\r\n 3493 def push_to_hub(\r\n 3494 self,\r\n 3495 repo_id: str,\r\n (...)\r\n 3501 embed_external_files: bool = True,\r\n 3502 ):\r\n 3503 \"\"\"Pushes the dataset to the hub.\r\n 3504 The dataset is pushed using HTTP requests and does not need to have neither git or git-lfs installed.\r\n 3505 \r\n (...)\r\n 3536 ```\r\n 3537 \"\"\"\r\n-> 3538 repo_id, split, uploaded_size, dataset_nbytes = self._push_parquet_shards_to_hub(\r\n 3539 repo_id=repo_id,\r\n 3540 split=split,\r\n 3541 private=private,\r\n 3542 token=token,\r\n 3543 branch=branch,\r\n 3544 shard_size=shard_size,\r\n 3545 embed_external_files=embed_external_files,\r\n 3546 )\r\n 3547 organization, dataset_name = repo_id.split(\"/\")\r\n 3548 info_to_dump = self.info.copy()\r\n\r\nFile /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:3474, in Dataset._push_parquet_shards_to_hub(self, repo_id, split, private, token, branch, shard_size, embed_external_files)\r\n 3472 shard.to_parquet(buffer)\r\n 3473 uploaded_size += buffer.tell()\r\n-> 3474 _retry(\r\n 3475 api.upload_file,\r\n 3476 func_kwargs=dict(\r\n 3477 path_or_fileobj=buffer.getvalue(),\r\n 3478 path_in_repo=path_in_repo(index),\r\n 3479 repo_id=repo_id,\r\n 3480 token=token,\r\n 3481 repo_type=\"dataset\",\r\n 3482 revision=branch,\r\n 3483 identical_ok=True,\r\n 3484 ),\r\n 3485 exceptions=HTTPError,\r\n 3486 status_codes=[504],\r\n 3487 base_wait_time=2.0,\r\n 3488 max_retries=5,\r\n 3489 max_wait_time=20.0,\r\n 3490 )\r\n 3491 return repo_id, split, uploaded_size, dataset_nbytes\r\n\r\nFile /opt/conda/lib/python3.10/site-packages/datasets/utils/file_utils.py:330, in _retry(func, func_args, func_kwargs, exceptions, status_codes, max_retries, base_wait_time, max_wait_time)\r\n 328 while True:\r\n 329 try:\r\n--> 330 return func(*func_args, **func_kwargs)\r\n 331 except exceptions as err:\r\n 332 if retry >= max_retries or (status_codes and err.response.status_code not in status_codes):\r\n\r\nFile /opt/conda/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:120, in validate_hf_hub_args.<locals>._inner_fn(*args, **kwargs)\r\n 117 if check_use_auth_token:\r\n 118 kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.__name__, has_token=has_token, kwargs=kwargs)\r\n--> 120 return fn(*args, **kwargs)\r\n\r\nTypeError: HfApi.upload_file() got an unexpected keyword argument 'identical_ok'\r\n```", "Feel free to update `datasets` and `huggingface-hub`, it should fix it :)", "I went ahead and upgraded both datasets and hub and still getting the same error\r\n", "Which version do you have ? It's been a while since it has been fixed", "huggingface 0.0.1\r\nhuggingface-hub 0.17.1\r\ndatasets 2.14.5\r\n\r\nstill has the issue!!", "I face the same issue even after upgrading :/" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5510). All of your documentation changes will be reflected on that endpoint.", "To the maintainer, sorry about the repeated run requests for formatting. Missed the `make style` outlined in contributing guidelines. ", "Anything I can do to get the workflow to run? @lhoestq ", "cc @mariosasko \r\n\r\n> Anything I can do to get the workflow to run?\r\n\r\nYou can merge `main` into your branch to fix code formatting (we switched from isort+flake8 to ruff this week), and then run `make style`", "I believe that should be good. @mariosasko" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5509). All of your documentation changes will be reflected on that endpoint.", "Hi! I've commented on the original issue to provide some context. Feel free to share your opinion there." ]

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[ "Hey, I'm a research engineer working on language modelling wanting to contribute to open source. I was wondering if I could give it a shot?", "Hi! This issue was fixed in https://github.com/huggingface/datasets/pull/4972, so please install `datasets>=2.5.0` to avoid it." ]

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[ "Hi ! `datasets` doesn't do batching - the PyTorch DataLoader does and is created by the `Trainer`. Do you pass other arguments to training_args with respect to data loading ?\r\n\r\nAlso we recently released `.to_iterable_dataset` that does pretty much what you implemented, but using contiguous shards to get a better speed:\r\n```python\r\nif use_iterable_dataset:\r\n num_shards = 100\r\n dataset = dataset.to_iterable_dataset(num_shards=num_shards)\r\n```", "This is the full set of training args passed. No training args were changed when switching dataset types.\r\n\r\n```python\r\ntraining_args = TrainingArguments(\r\n output_dir=\"./checkpoints\",\r\n overwrite_output_dir=True,\r\n num_train_epochs=1,\r\n per_device_train_batch_size=256,\r\n save_steps=2000,\r\n save_total_limit=4,\r\n prediction_loss_only=True,\r\n report_to='none',\r\n gradient_accumulation_steps=6,\r\n fp16=True,\r\n max_steps=60000,\r\n lr_scheduler_type='linear',\r\n warmup_ratio=0.1,\r\n logging_steps=100,\r\n weight_decay=0.01,\r\n adam_beta1=0.9,\r\n adam_beta2=0.98,\r\n adam_epsilon=1e-6,\r\n learning_rate=1e-4\r\n)\r\n```", "I think the issue comes from `transformers`: https://github.com/huggingface/transformers/issues/21444", "Makes sense. Given that it's a `transformers` issue and already being tracked, I'll close this out." ]

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[ "This change seems to come from a few months ago in the PyTorch side. That's good news and it means we may not need to pass a batch_sampler as soon as we add `Dataset.__getitems__` to get the optimal speed :)\r\n\r\nThanks for reporting ! Would you like to open a PR to add `__getitems__` and remove this outdated documentation ?", "Yeah I figured this was the sort of thing that probably once worked. I can confirm that you no longer need the batch sampler, just `batch_size=n` in the `DataLoader`.\r\n\r\nI'll pass on the PR, I'm flat out right now, sorry." ]

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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) | 0.008606 / 0.011353 (-0.002747) | 0.004659 / 0.011008 (-0.006349) | 0.101311 / 0.038508 (0.062802) | 0.029664 / 0.023109 (0.006555) | 0.321850 / 0.275898 (0.045952) | 0.380497 / 0.323480 (0.057017) | 0.007003 / 0.007986 (-0.000982) | 0.003393 / 0.004328 (-0.000936) | 0.078704 / 0.004250 (0.074453) | 0.035810 / 0.037052 (-0.001242) | 0.327271 / 0.258489 (0.068782) | 0.369302 / 0.293841 (0.075461) | 0.033625 / 0.128546 (-0.094921) | 0.011563 / 0.075646 (-0.064084) | 0.323950 / 0.419271 (-0.095322) | 0.040660 / 0.043533 (-0.002872) | 0.327211 / 0.255139 (0.072072) | 0.350325 / 0.283200 (0.067125) | 0.085427 / 0.141683 (-0.056256) | 1.464370 / 1.452155 (0.012216) | 1.490355 / 1.492716 (-0.002362) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.202879 / 0.018006 (0.184873) | 0.419836 / 0.000490 (0.419346) | 0.000303 / 0.000200 (0.000103) | 0.000063 / 0.000054 (0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023336 / 0.037411 (-0.014075) | 0.096817 / 0.014526 (0.082291) | 0.103990 / 0.176557 (-0.072567) | 0.137749 / 0.737135 (-0.599386) | 0.108236 / 0.296338 (-0.188102) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420801 / 0.215209 (0.205592) | 4.205308 / 2.077655 (2.127653) | 2.050363 / 1.504120 (0.546243) | 1.877390 / 1.541195 (0.336195) | 2.031060 / 1.468490 (0.562570) | 0.687950 / 4.584777 (-3.896827) | 3.363202 / 3.745712 (-0.382510) | 1.869482 / 5.269862 (-3.400379) | 1.159131 / 4.565676 (-3.406545) | 0.082374 / 0.424275 (-0.341901) | 0.012425 / 0.007607 (0.004818) | 0.519775 / 0.226044 (0.293731) | 5.244612 / 2.268929 (2.975684) | 2.371314 / 55.444624 (-53.073311) | 2.052713 / 6.876477 (-4.823764) | 2.190015 / 2.142072 (0.047942) | 0.803806 / 4.805227 (-4.001421) | 0.148110 / 6.500664 (-6.352554) | 0.064174 / 0.075469 (-0.011295) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.250424 / 1.841788 (-0.591364) | 13.487870 / 8.074308 (5.413561) | 13.080736 / 10.191392 (2.889344) | 0.147715 / 0.680424 (-0.532709) | 0.028409 / 0.534201 (-0.505792) | 0.397531 / 0.579283 (-0.181752) | 0.399458 / 0.434364 (-0.034905) | 0.461467 / 0.540337 (-0.078871) | 0.541639 / 1.386936 (-0.845297) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006753 / 0.011353 (-0.004600) | 0.004573 / 0.011008 (-0.006435) | 0.076122 / 0.038508 (0.037614) | 0.027529 / 0.023109 (0.004419) | 0.341291 / 0.275898 (0.065393) | 0.376889 / 0.323480 (0.053409) | 0.005032 / 0.007986 (-0.002953) | 0.003447 / 0.004328 (-0.000882) | 0.075186 / 0.004250 (0.070936) | 0.038516 / 0.037052 (0.001463) | 0.340927 / 0.258489 (0.082438) | 0.386626 / 0.293841 (0.092785) | 0.031929 / 0.128546 (-0.096617) | 0.011759 / 0.075646 (-0.063888) | 0.085616 / 0.419271 (-0.333656) | 0.042858 / 0.043533 (-0.000674) | 0.341881 / 0.255139 (0.086742) | 0.367502 / 0.283200 (0.084303) | 0.090788 / 0.141683 (-0.050895) | 1.472871 / 1.452155 (0.020716) | 1.577825 / 1.492716 (0.085109) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.233137 / 0.018006 (0.215131) | 0.415016 / 0.000490 (0.414526) | 0.000379 / 0.000200 (0.000179) | 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) | 0.024966 / 0.037411 (-0.012445) | 0.102794 / 0.014526 (0.088268) | 0.107543 / 0.176557 (-0.069014) | 0.143133 / 0.737135 (-0.594002) | 0.111494 / 0.296338 (-0.184845) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.438354 / 0.215209 (0.223145) | 4.382244 / 2.077655 (2.304589) | 2.056340 / 1.504120 (0.552220) | 1.851524 / 1.541195 (0.310330) | 1.933147 / 1.468490 (0.464657) | 0.701446 / 4.584777 (-3.883331) | 3.396893 / 3.745712 (-0.348819) | 2.837516 / 5.269862 (-2.432346) | 1.538298 / 4.565676 (-3.027379) | 0.083449 / 0.424275 (-0.340826) | 0.012793 / 0.007607 (0.005186) | 0.539661 / 0.226044 (0.313616) | 5.428415 / 2.268929 (3.159487) | 2.527582 / 55.444624 (-52.917042) | 2.172795 / 6.876477 (-4.703682) | 2.220011 / 2.142072 (0.077938) | 0.814338 / 4.805227 (-3.990889) | 0.153468 / 6.500664 (-6.347196) | 0.069056 / 0.075469 (-0.006413) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.278434 / 1.841788 (-0.563354) | 14.284924 / 8.074308 (6.210616) | 13.486596 / 10.191392 (3.295203) | 0.138457 / 0.680424 (-0.541967) | 0.016609 / 0.534201 (-0.517592) | 0.382828 / 0.579283 (-0.196455) | 0.387604 / 0.434364 (-0.046760) | 0.478801 / 0.540337 (-0.061536) | 0.565352 / 1.386936 (-0.821584) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c39ba501daab763b9972f44f229c66d900d20bee \"CML watermark\")\n", "> Thanks! I modified the test a bit to make it more consistent with the rest of the \"extractor\" tests.\r\n\r\nAppreciate the assist on the tests! 🚀 " ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "> Thanks! I've left some comments.\r\n> \r\n> We should also add some tests, mainly to make sure `reverse` behaves as expected. Let me know if you need help with that.\r\n\r\nThanks for the offer! I couldn't find any guidelines on how huggingface goes about testing, so it would indeed be great to get a few pointers on that. I assume I should expand on the `test_sort` function in `test_arrow_dataset.py` but since I am not very familiar with the `datasets` package, it isn't immediately for which cases I should test (i.e., expand on).", "@MichlF \r\n\r\nResolving a comment means that the comment has been addressed with the code change, so since this is not the case here, can you please \"unresolve\" the comments and address them adequately? \r\n\r\n> I assume I should expand on the `test_sort` function in `test_arrow_dataset.py`\r\n\r\nYes, that's correct. I think one test to check sorting on multiple keys and another one to check if an error is raised when `len(reverse)!=len(column_names)` should be enough.\r\n", "> Yes, that's correct. I think one test to check sorting on multiple keys and another one to check if an error is raised when `len(reverse)!=len(column_names)` should be enough.\r\n\r\nI have added the tests in https://github.com/huggingface/datasets/pull/5502/commits/0efa259732e822e94d67b96a70031a3daccedfc1 by keeping them in the same format of the tests of the old `sort` function. Let me know if they can be improved.\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) | 0.010170 / 0.011353 (-0.001183) | 0.005891 / 0.011008 (-0.005117) | 0.100416 / 0.038508 (0.061908) | 0.041309 / 0.023109 (0.018200) | 0.300813 / 0.275898 (0.024915) | 0.376679 / 0.323480 (0.053199) | 0.008806 / 0.007986 (0.000821) | 0.005964 / 0.004328 (0.001636) | 0.075862 / 0.004250 (0.071611) | 0.050370 / 0.037052 (0.013318) | 0.313365 / 0.258489 (0.054876) | 0.351184 / 0.293841 (0.057343) | 0.039556 / 0.128546 (-0.088991) | 0.012462 / 0.075646 (-0.063185) | 0.337141 / 0.419271 (-0.082130) | 0.049678 / 0.043533 (0.006145) | 0.298547 / 0.255139 (0.043408) | 0.317547 / 0.283200 (0.034347) | 0.113595 / 0.141683 (-0.028088) | 1.448467 / 1.452155 (-0.003688) | 1.501303 / 1.492716 (0.008587) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.011005 / 0.018006 (-0.007002) | 0.527430 / 0.000490 (0.526940) | 0.005073 / 0.000200 (0.004873) | 0.000100 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030377 / 0.037411 (-0.007034) | 0.116932 / 0.014526 (0.102406) | 0.124047 / 0.176557 (-0.052509) | 0.192358 / 0.737135 (-0.544777) | 0.130528 / 0.296338 (-0.165811) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.401158 / 0.215209 (0.185949) | 4.005854 / 2.077655 (1.928200) | 1.810365 / 1.504120 (0.306245) | 1.626490 / 1.541195 (0.085295) | 1.752591 / 1.468490 (0.284101) | 0.709065 / 4.584777 (-3.875712) | 3.893356 / 3.745712 (0.147643) | 3.655180 / 5.269862 (-1.614682) | 1.873660 / 4.565676 (-2.692017) | 0.085860 / 0.424275 (-0.338415) | 0.012671 / 0.007607 (0.005063) | 0.512804 / 0.226044 (0.286759) | 5.103426 / 2.268929 (2.834497) | 2.336148 / 55.444624 (-53.108477) | 2.000140 / 6.876477 (-4.876336) | 2.095155 / 2.142072 (-0.046918) | 0.848612 / 4.805227 (-3.956615) | 0.171840 / 6.500664 (-6.328824) | 0.064144 / 0.075469 (-0.011325) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.222106 / 1.841788 (-0.619682) | 15.828559 / 8.074308 (7.754251) | 14.995298 / 10.191392 (4.803906) | 0.172783 / 0.680424 (-0.507641) | 0.029296 / 0.534201 (-0.504905) | 0.447469 / 0.579283 (-0.131814) | 0.658615 / 0.434364 (0.224251) | 1.527607 / 0.540337 (0.987270) | 1.830018 / 1.386936 (0.443082) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007922 / 0.011353 (-0.003431) | 0.005369 / 0.011008 (-0.005639) | 0.076580 / 0.038508 (0.038071) | 0.038770 / 0.023109 (0.015661) | 0.338995 / 0.275898 (0.063097) | 0.380865 / 0.323480 (0.057385) | 0.006489 / 0.007986 (-0.001497) | 0.004421 / 0.004328 (0.000093) | 0.074143 / 0.004250 (0.069893) | 0.054224 / 0.037052 (0.017171) | 0.348887 / 0.258489 (0.090397) | 0.395044 / 0.293841 (0.101203) | 0.037040 / 0.128546 (-0.091507) | 0.012547 / 0.075646 (-0.063099) | 0.087521 / 0.419271 (-0.331751) | 0.049918 / 0.043533 (0.006385) | 0.342428 / 0.255139 (0.087289) | 0.362216 / 0.283200 (0.079016) | 0.107204 / 0.141683 (-0.034479) | 1.509206 / 1.452155 (0.057052) | 1.596010 / 1.492716 (0.103293) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246795 / 0.018006 (0.228788) | 0.505998 / 0.000490 (0.505509) | 0.000446 / 0.000200 (0.000246) | 0.000064 / 0.000054 (0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031591 / 0.037411 (-0.005821) | 0.117595 / 0.014526 (0.103069) | 0.132500 / 0.176557 (-0.044056) | 0.202244 / 0.737135 (-0.534891) | 0.136624 / 0.296338 (-0.159715) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428235 / 0.215209 (0.213026) | 4.262691 / 2.077655 (2.185036) | 2.057348 / 1.504120 (0.553228) | 1.928559 / 1.541195 (0.387364) | 2.120838 / 1.468490 (0.652347) | 0.706300 / 4.584777 (-3.878477) | 3.951828 / 3.745712 (0.206115) | 2.144218 / 5.269862 (-3.125644) | 1.359500 / 4.565676 (-3.206177) | 0.085404 / 0.424275 (-0.338872) | 0.012363 / 0.007607 (0.004756) | 0.529985 / 0.226044 (0.303941) | 5.295831 / 2.268929 (3.026903) | 2.522602 / 55.444624 (-52.922022) | 2.182850 / 6.876477 (-4.693627) | 2.270187 / 2.142072 (0.128114) | 0.841676 / 4.805227 (-3.963551) | 0.168366 / 6.500664 (-6.332298) | 0.065371 / 0.075469 (-0.010098) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.261464 / 1.841788 (-0.580324) | 17.010125 / 8.074308 (8.935817) | 14.304453 / 10.191392 (4.113061) | 0.177782 / 0.680424 (-0.502642) | 0.017762 / 0.534201 (-0.516439) | 0.427283 / 0.579283 (-0.152000) | 0.455176 / 0.434364 (0.020812) | 0.525962 / 0.540337 (-0.014375) | 0.625583 / 1.386936 (-0.761353) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3b2aba6637dc61f145acda40e4e7b028c3947d72 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5501). 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) | 0.008407 / 0.011353 (-0.002946) | 0.004651 / 0.011008 (-0.006357) | 0.100367 / 0.038508 (0.061859) | 0.029107 / 0.023109 (0.005998) | 0.302798 / 0.275898 (0.026900) | 0.354379 / 0.323480 (0.030899) | 0.006985 / 0.007986 (-0.001001) | 0.003365 / 0.004328 (-0.000963) | 0.078312 / 0.004250 (0.074062) | 0.034205 / 0.037052 (-0.002847) | 0.310431 / 0.258489 (0.051941) | 0.346239 / 0.293841 (0.052398) | 0.033800 / 0.128546 (-0.094747) | 0.011515 / 0.075646 (-0.064131) | 0.323588 / 0.419271 (-0.095684) | 0.040766 / 0.043533 (-0.002767) | 0.300914 / 0.255139 (0.045775) | 0.332983 / 0.283200 (0.049784) | 0.087500 / 0.141683 (-0.054182) | 1.469505 / 1.452155 (0.017350) | 1.505119 / 1.492716 (0.012403) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.187319 / 0.018006 (0.169313) | 0.405498 / 0.000490 (0.405008) | 0.001000 / 0.000200 (0.000800) | 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) | 0.022583 / 0.037411 (-0.014828) | 0.098096 / 0.014526 (0.083570) | 0.104272 / 0.176557 (-0.072284) | 0.142801 / 0.737135 (-0.594335) | 0.109749 / 0.296338 (-0.186590) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.423343 / 0.215209 (0.208134) | 4.215116 / 2.077655 (2.137461) | 1.899714 / 1.504120 (0.395594) | 1.689579 / 1.541195 (0.148384) | 1.710292 / 1.468490 (0.241801) | 0.690976 / 4.584777 (-3.893801) | 3.432501 / 3.745712 (-0.313212) | 1.899600 / 5.269862 (-3.370261) | 1.279801 / 4.565676 (-3.285876) | 0.082763 / 0.424275 (-0.341512) | 0.012545 / 0.007607 (0.004938) | 0.531381 / 0.226044 (0.305336) | 5.320077 / 2.268929 (3.051148) | 2.370705 / 55.444624 (-53.073919) | 2.007089 / 6.876477 (-4.869388) | 2.062412 / 2.142072 (-0.079661) | 0.814998 / 4.805227 (-3.990229) | 0.149822 / 6.500664 (-6.350842) | 0.064399 / 0.075469 (-0.011070) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.226196 / 1.841788 (-0.615591) | 13.823443 / 8.074308 (5.749134) | 13.813667 / 10.191392 (3.622275) | 0.161289 / 0.680424 (-0.519135) | 0.028569 / 0.534201 (-0.505632) | 0.390360 / 0.579283 (-0.188923) | 0.396217 / 0.434364 (-0.038147) | 0.483120 / 0.540337 (-0.057217) | 0.570041 / 1.386936 (-0.816895) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006422 / 0.011353 (-0.004931) | 0.004528 / 0.011008 (-0.006481) | 0.076043 / 0.038508 (0.037535) | 0.027631 / 0.023109 (0.004522) | 0.340622 / 0.275898 (0.064724) | 0.376694 / 0.323480 (0.053214) | 0.004993 / 0.007986 (-0.002992) | 0.003403 / 0.004328 (-0.000926) | 0.074521 / 0.004250 (0.070270) | 0.037568 / 0.037052 (0.000516) | 0.343423 / 0.258489 (0.084934) | 0.387729 / 0.293841 (0.093888) | 0.031790 / 0.128546 (-0.096757) | 0.011767 / 0.075646 (-0.063879) | 0.085182 / 0.419271 (-0.334090) | 0.042867 / 0.043533 (-0.000666) | 0.341269 / 0.255139 (0.086130) | 0.368460 / 0.283200 (0.085261) | 0.090153 / 0.141683 (-0.051530) | 1.536490 / 1.452155 (0.084335) | 1.596403 / 1.492716 (0.103686) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222373 / 0.018006 (0.204367) | 0.396145 / 0.000490 (0.395655) | 0.000384 / 0.000200 (0.000184) | 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) | 0.024801 / 0.037411 (-0.012610) | 0.099711 / 0.014526 (0.085185) | 0.106094 / 0.176557 (-0.070463) | 0.147819 / 0.737135 (-0.589316) | 0.110065 / 0.296338 (-0.186274) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.442863 / 0.215209 (0.227654) | 4.420043 / 2.077655 (2.342388) | 2.070136 / 1.504120 (0.566016) | 1.862363 / 1.541195 (0.321168) | 1.910890 / 1.468490 (0.442400) | 0.702570 / 4.584777 (-3.882207) | 3.435855 / 3.745712 (-0.309857) | 1.871290 / 5.269862 (-3.398572) | 1.169321 / 4.565676 (-3.396355) | 0.083674 / 0.424275 (-0.340601) | 0.012823 / 0.007607 (0.005216) | 0.539330 / 0.226044 (0.313285) | 5.403317 / 2.268929 (3.134389) | 2.536508 / 55.444624 (-52.908117) | 2.179629 / 6.876477 (-4.696847) | 2.207586 / 2.142072 (0.065514) | 0.812256 / 4.805227 (-3.992972) | 0.152915 / 6.500664 (-6.347749) | 0.068431 / 0.075469 (-0.007038) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.294982 / 1.841788 (-0.546806) | 13.912811 / 8.074308 (5.838503) | 13.415658 / 10.191392 (3.224266) | 0.149531 / 0.680424 (-0.530893) | 0.016785 / 0.534201 (-0.517416) | 0.381055 / 0.579283 (-0.198228) | 0.392084 / 0.434364 (-0.042280) | 0.472614 / 0.540337 (-0.067724) | 0.559799 / 1.386936 (-0.827137) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6ef20f9b71acbb387caab2d297d8c22ba3db3633 \"CML watermark\")\n", "We simply do GET requests to hf.co to download files from the Hub right now. We may switch to hfh when we update how we do caching \r\n\r\nYou can try on any dataset hosted on the hub like `imagenet-1k`", "<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) | 0.010931 / 0.011353 (-0.000422) | 0.005730 / 0.011008 (-0.005278) | 0.116653 / 0.038508 (0.078145) | 0.041439 / 0.023109 (0.018330) | 0.359559 / 0.275898 (0.083661) | 0.408398 / 0.323480 (0.084918) | 0.009193 / 0.007986 (0.001208) | 0.006024 / 0.004328 (0.001695) | 0.087743 / 0.004250 (0.083492) | 0.048636 / 0.037052 (0.011584) | 0.363133 / 0.258489 (0.104643) | 0.407144 / 0.293841 (0.113303) | 0.044610 / 0.128546 (-0.083936) | 0.014075 / 0.075646 (-0.061571) | 0.396506 / 0.419271 (-0.022766) | 0.057014 / 0.043533 (0.013482) | 0.358254 / 0.255139 (0.103115) | 0.399887 / 0.283200 (0.116687) | 0.115337 / 0.141683 (-0.026346) | 1.731655 / 1.452155 (0.279500) | 1.813276 / 1.492716 (0.320560) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210197 / 0.018006 (0.192191) | 0.475887 / 0.000490 (0.475397) | 0.003323 / 0.000200 (0.003123) | 0.000100 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031686 / 0.037411 (-0.005725) | 0.131167 / 0.014526 (0.116641) | 0.137919 / 0.176557 (-0.038637) | 0.184843 / 0.737135 (-0.552293) | 0.144998 / 0.296338 (-0.151340) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.471371 / 0.215209 (0.256162) | 4.693739 / 2.077655 (2.616084) | 2.251567 / 1.504120 (0.747447) | 1.993653 / 1.541195 (0.452458) | 2.053236 / 1.468490 (0.584746) | 0.809226 / 4.584777 (-3.775551) | 4.494120 / 3.745712 (0.748408) | 2.436921 / 5.269862 (-2.832940) | 1.541973 / 4.565676 (-3.023704) | 0.098401 / 0.424275 (-0.325874) | 0.014329 / 0.007607 (0.006722) | 0.597813 / 0.226044 (0.371769) | 5.964035 / 2.268929 (3.695107) | 2.709283 / 55.444624 (-52.735341) | 2.323537 / 6.876477 (-4.552940) | 2.401707 / 2.142072 (0.259635) | 0.976379 / 4.805227 (-3.828848) | 0.194638 / 6.500664 (-6.306026) | 0.076904 / 0.075469 (0.001435) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.516877 / 1.841788 (-0.324911) | 18.228010 / 8.074308 (10.153702) | 16.631750 / 10.191392 (6.440358) | 0.176030 / 0.680424 (-0.504394) | 0.033769 / 0.534201 (-0.500432) | 0.520511 / 0.579283 (-0.058773) | 0.531764 / 0.434364 (0.097400) | 0.648658 / 0.540337 (0.108321) | 0.779124 / 1.386936 (-0.607812) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008635 / 0.011353 (-0.002718) | 0.005785 / 0.011008 (-0.005223) | 0.087042 / 0.038508 (0.048534) | 0.039632 / 0.023109 (0.016523) | 0.419719 / 0.275898 (0.143821) | 0.463860 / 0.323480 (0.140380) | 0.006621 / 0.007986 (-0.001364) | 0.004655 / 0.004328 (0.000327) | 0.087003 / 0.004250 (0.082753) | 0.057122 / 0.037052 (0.020069) | 0.417820 / 0.258489 (0.159331) | 0.485981 / 0.293841 (0.192140) | 0.042606 / 0.128546 (-0.085940) | 0.014369 / 0.075646 (-0.061278) | 0.101939 / 0.419271 (-0.317333) | 0.058303 / 0.043533 (0.014770) | 0.415053 / 0.255139 (0.159914) | 0.439914 / 0.283200 (0.156714) | 0.134628 / 0.141683 (-0.007055) | 1.765464 / 1.452155 (0.313309) | 1.843963 / 1.492716 (0.351247) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.307156 / 0.018006 (0.289150) | 0.476657 / 0.000490 (0.476167) | 0.019718 / 0.000200 (0.019518) | 0.000160 / 0.000054 (0.000105) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035286 / 0.037411 (-0.002125) | 0.138094 / 0.014526 (0.123568) | 0.144768 / 0.176557 (-0.031789) | 0.191386 / 0.737135 (-0.545750) | 0.151988 / 0.296338 (-0.144350) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.504733 / 0.215209 (0.289523) | 5.027048 / 2.077655 (2.949394) | 2.441571 / 1.504120 (0.937451) | 2.198242 / 1.541195 (0.657047) | 2.298473 / 1.468490 (0.829983) | 0.848048 / 4.584777 (-3.736729) | 4.613102 / 3.745712 (0.867390) | 2.522824 / 5.269862 (-2.747037) | 1.610159 / 4.565676 (-2.955517) | 0.105197 / 0.424275 (-0.319078) | 0.015195 / 0.007607 (0.007588) | 0.626976 / 0.226044 (0.400932) | 6.268459 / 2.268929 (3.999530) | 3.014387 / 55.444624 (-52.430237) | 2.554102 / 6.876477 (-4.322375) | 2.656051 / 2.142072 (0.513979) | 1.027978 / 4.805227 (-3.777249) | 0.200686 / 6.500664 (-6.299978) | 0.077104 / 0.075469 (0.001635) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.485228 / 1.841788 (-0.356560) | 18.319949 / 8.074308 (10.245641) | 15.855739 / 10.191392 (5.664347) | 0.204365 / 0.680424 (-0.476059) | 0.023824 / 0.534201 (-0.510377) | 0.505000 / 0.579283 (-0.074283) | 0.502866 / 0.434364 (0.068502) | 0.629574 / 0.540337 (0.089237) | 0.746602 / 1.386936 (-0.640334) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#900d429d3601657f766737b8670f855033078d57 \"CML watermark\")\n" ]

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[ "I update the `datatsets` version and it works." ]

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[ "Hi ! To skip the verification step that checks if newer data exist, you can enable offline mode with `HF_DATASETS_OFFLINE=1`.\r\n\r\nAlthough I agree this step should be much faster for datasets hosted on the HF Hub - we could just compare the commit hash from the local data and the remote git repository. We're not been leveraging the git commit hashes, since the library was built before we even had git repositories for each dataset on HF.", "Thanks @lhoestq, for memory when I recorded those times I had `HF_DATASETS_OFFLINE` set." ]

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[ "Hi! Instead of a single boolean, your filter function should return an iterable (of booleans) in the batched mode like so:\r\n```python\r\ntrain_dataset = train_dataset.filter(\r\n function=lambda batch: [image is not None for image in batch[\"image\"]], \r\n batched=True,\r\n batch_size=10)\r\n```\r\n\r\nPS: You can make this operation much faster by operating directly on the arrow data to skip the decoding part:\r\n```python\r\ntrain_dataset = train_dataset.with_format(\"arrow\")\r\ntrain_dataset = train_dataset.filter(\r\n function=lambda table: table[\"image\"].is_valid().to_pylist(), \r\n batched=True,\r\n batch_size=100)\r\ntrain_dataset = train_dataset.with_format(None)\r\n```", "Thank a lot!", "I hit the same issue and the error message isn't really clear on what's going wrong. It might be helpful to update the docs with a batched example." ]

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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) | 0.009491 / 0.011353 (-0.001862) | 0.004690 / 0.011008 (-0.006319) | 0.111904 / 0.038508 (0.073396) | 0.030781 / 0.023109 (0.007671) | 0.309442 / 0.275898 (0.033544) | 0.389511 / 0.323480 (0.066031) | 0.007277 / 0.007986 (-0.000709) | 0.004364 / 0.004328 (0.000036) | 0.074501 / 0.004250 (0.070250) | 0.036799 / 0.037052 (-0.000254) | 0.320279 / 0.258489 (0.061790) | 0.353887 / 0.293841 (0.060046) | 0.047969 / 0.128546 (-0.080577) | 0.017281 / 0.075646 (-0.058366) | 0.339655 / 0.419271 (-0.079617) | 0.049317 / 0.043533 (0.005784) | 0.321221 / 0.255139 (0.066082) | 0.354743 / 0.283200 (0.071544) | 0.098634 / 0.141683 (-0.043049) | 1.408640 / 1.452155 (-0.043515) | 1.488361 / 1.492716 (-0.004356) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.233677 / 0.018006 (0.215671) | 0.604424 / 0.000490 (0.603934) | 0.003834 / 0.000200 (0.003634) | 0.000103 / 0.000054 (0.000049) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022682 / 0.037411 (-0.014729) | 0.103800 / 0.014526 (0.089274) | 0.113868 / 0.176557 (-0.062689) | 0.155111 / 0.737135 (-0.582025) | 0.111862 / 0.296338 (-0.184476) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.474992 / 0.215209 (0.259783) | 4.755325 / 2.077655 (2.677670) | 1.889754 / 1.504120 (0.385634) | 1.597009 / 1.541195 (0.055814) | 1.639570 / 1.468490 (0.171080) | 0.970681 / 4.584777 (-3.614096) | 4.782567 / 3.745712 (1.036855) | 4.350465 / 5.269862 (-0.919397) | 2.413533 / 4.565676 (-2.152144) | 0.115510 / 0.424275 (-0.308765) | 0.011663 / 0.007607 (0.004055) | 0.626450 / 0.226044 (0.400406) | 6.238147 / 2.268929 (3.969218) | 2.603070 / 55.444624 (-52.841555) | 2.030378 / 6.876477 (-4.846099) | 1.996883 / 2.142072 (-0.145190) | 1.206436 / 4.805227 (-3.598792) | 0.203018 / 6.500664 (-6.297646) | 0.060550 / 0.075469 (-0.014919) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.259850 / 1.841788 (-0.581937) | 14.079936 / 8.074308 (6.005628) | 16.036329 / 10.191392 (5.844937) | 0.221546 / 0.680424 (-0.458878) | 0.042416 / 0.534201 (-0.491785) | 0.438851 / 0.579283 (-0.140432) | 0.507053 / 0.434364 (0.072689) | 0.518672 / 0.540337 (-0.021665) | 0.585278 / 1.386936 (-0.801659) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010718 / 0.011353 (-0.000635) | 0.005469 / 0.011008 (-0.005539) | 0.075624 / 0.038508 (0.037116) | 0.029103 / 0.023109 (0.005994) | 0.353294 / 0.275898 (0.077395) | 0.353674 / 0.323480 (0.030194) | 0.005678 / 0.007986 (-0.002308) | 0.004610 / 0.004328 (0.000282) | 0.075213 / 0.004250 (0.070963) | 0.040032 / 0.037052 (0.002980) | 0.344363 / 0.258489 (0.085874) | 0.376861 / 0.293841 (0.083020) | 0.043718 / 0.128546 (-0.084828) | 0.016057 / 0.075646 (-0.059589) | 0.087746 / 0.419271 (-0.331526) | 0.051380 / 0.043533 (0.007848) | 0.336904 / 0.255139 (0.081765) | 0.357636 / 0.283200 (0.074436) | 0.089425 / 0.141683 (-0.052258) | 1.377462 / 1.452155 (-0.074692) | 1.448844 / 1.492716 (-0.043872) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.259038 / 0.018006 (0.241031) | 0.512284 / 0.000490 (0.511794) | 0.005666 / 0.000200 (0.005466) | 0.000123 / 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) | 0.023669 / 0.037411 (-0.013742) | 0.097979 / 0.014526 (0.083453) | 0.117947 / 0.176557 (-0.058610) | 0.140764 / 0.737135 (-0.596372) | 0.114700 / 0.296338 (-0.181638) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.528844 / 0.215209 (0.313635) | 5.073828 / 2.077655 (2.996173) | 2.088738 / 1.504120 (0.584618) | 1.855820 / 1.541195 (0.314626) | 1.838639 / 1.468490 (0.370149) | 0.968228 / 4.584777 (-3.616549) | 4.589792 / 3.745712 (0.844079) | 2.586149 / 5.269862 (-2.683712) | 1.714241 / 4.565676 (-2.851435) | 0.124502 / 0.424275 (-0.299774) | 0.012115 / 0.007607 (0.004507) | 0.679539 / 0.226044 (0.453494) | 6.541335 / 2.268929 (4.272407) | 2.749153 / 55.444624 (-52.695471) | 2.124164 / 6.876477 (-4.752313) | 2.181249 / 2.142072 (0.039177) | 1.196846 / 4.805227 (-3.608381) | 0.213352 / 6.500664 (-6.287312) | 0.075021 / 0.075469 (-0.000448) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254301 / 1.841788 (-0.587487) | 14.494254 / 8.074308 (6.419946) | 16.619679 / 10.191392 (6.428287) | 0.205158 / 0.680424 (-0.475266) | 0.022181 / 0.534201 (-0.512019) | 0.422928 / 0.579283 (-0.156355) | 0.539825 / 0.434364 (0.105461) | 0.523165 / 0.540337 (-0.017173) | 0.615014 / 1.386936 (-0.771922) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e4d8a3d43569d61e73f7ab12ff3a6b48466afa8d \"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) | 0.011522 / 0.011353 (0.000169) | 0.006906 / 0.011008 (-0.004102) | 0.114692 / 0.038508 (0.076184) | 0.037686 / 0.023109 (0.014577) | 0.393662 / 0.275898 (0.117764) | 0.377730 / 0.323480 (0.054250) | 0.008212 / 0.007986 (0.000226) | 0.005470 / 0.004328 (0.001142) | 0.086962 / 0.004250 (0.082712) | 0.039085 / 0.037052 (0.002033) | 0.357565 / 0.258489 (0.099076) | 0.404384 / 0.293841 (0.110543) | 0.055523 / 0.128546 (-0.073023) | 0.018277 / 0.075646 (-0.057369) | 0.389812 / 0.419271 (-0.029459) | 0.058706 / 0.043533 (0.015173) | 0.344735 / 0.255139 (0.089597) | 0.395734 / 0.283200 (0.112535) | 0.096098 / 0.141683 (-0.045584) | 1.546654 / 1.452155 (0.094499) | 1.665314 / 1.492716 (0.172597) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.255893 / 0.018006 (0.237887) | 0.589563 / 0.000490 (0.589074) | 0.005890 / 0.000200 (0.005690) | 0.000123 / 0.000054 (0.000069) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029167 / 0.037411 (-0.008245) | 0.113561 / 0.014526 (0.099036) | 0.125361 / 0.176557 (-0.051195) | 0.182225 / 0.737135 (-0.554910) | 0.125147 / 0.296338 (-0.171192) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.596859 / 0.215209 (0.381650) | 5.797725 / 2.077655 (3.720071) | 2.238420 / 1.504120 (0.734300) | 1.933177 / 1.541195 (0.391982) | 2.030750 / 1.468490 (0.562260) | 1.122655 / 4.584777 (-3.462122) | 5.247913 / 3.745712 (1.502201) | 2.792742 / 5.269862 (-2.477120) | 1.861487 / 4.565676 (-2.704190) | 0.133009 / 0.424275 (-0.291266) | 0.013219 / 0.007607 (0.005612) | 0.696905 / 0.226044 (0.470861) | 6.961298 / 2.268929 (4.692369) | 2.895352 / 55.444624 (-52.549273) | 2.353677 / 6.876477 (-4.522799) | 2.458804 / 2.142072 (0.316731) | 1.271905 / 4.805227 (-3.533322) | 0.224850 / 6.500664 (-6.275814) | 0.083773 / 0.075469 (0.008304) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.502425 / 1.841788 (-0.339363) | 16.959241 / 8.074308 (8.884933) | 19.865569 / 10.191392 (9.674177) | 0.228608 / 0.680424 (-0.451816) | 0.044035 / 0.534201 (-0.490166) | 0.545172 / 0.579283 (-0.034112) | 0.677193 / 0.434364 (0.242829) | 0.608988 / 0.540337 (0.068650) | 0.719210 / 1.386936 (-0.667726) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008297 / 0.011353 (-0.003056) | 0.005729 / 0.011008 (-0.005280) | 0.084762 / 0.038508 (0.046254) | 0.030622 / 0.023109 (0.007512) | 0.408017 / 0.275898 (0.132119) | 0.432114 / 0.323480 (0.108634) | 0.006965 / 0.007986 (-0.001021) | 0.004830 / 0.004328 (0.000502) | 0.087375 / 0.004250 (0.083124) | 0.048110 / 0.037052 (0.011058) | 0.414978 / 0.258489 (0.156489) | 0.446136 / 0.293841 (0.152295) | 0.064351 / 0.128546 (-0.064195) | 0.018273 / 0.075646 (-0.057374) | 0.114853 / 0.419271 (-0.304418) | 0.056962 / 0.043533 (0.013429) | 0.427791 / 0.255139 (0.172652) | 0.428829 / 0.283200 (0.145629) | 0.108004 / 0.141683 (-0.033679) | 1.639285 / 1.452155 (0.187130) | 1.652106 / 1.492716 (0.159390) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.359744 / 0.018006 (0.341738) | 0.596060 / 0.000490 (0.595570) | 0.025448 / 0.000200 (0.025248) | 0.000158 / 0.000054 (0.000104) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026348 / 0.037411 (-0.011064) | 0.119153 / 0.014526 (0.104628) | 0.129304 / 0.176557 (-0.047253) | 0.195670 / 0.737135 (-0.541465) | 0.135559 / 0.296338 (-0.160780) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.588963 / 0.215209 (0.373754) | 5.682957 / 2.077655 (3.605302) | 2.380178 / 1.504120 (0.876059) | 2.131299 / 1.541195 (0.590104) | 2.167839 / 1.468490 (0.699349) | 1.126418 / 4.584777 (-3.458359) | 5.289104 / 3.745712 (1.543392) | 2.952128 / 5.269862 (-2.317734) | 1.922974 / 4.565676 (-2.642702) | 0.143874 / 0.424275 (-0.280401) | 0.015399 / 0.007607 (0.007792) | 0.815675 / 0.226044 (0.589631) | 7.320146 / 2.268929 (5.051217) | 3.453670 / 55.444624 (-51.990954) | 2.579133 / 6.876477 (-4.297344) | 2.532331 / 2.142072 (0.390258) | 1.345881 / 4.805227 (-3.459347) | 0.242448 / 6.500664 (-6.258216) | 0.070007 / 0.075469 (-0.005462) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.433173 / 1.841788 (-0.408614) | 17.127287 / 8.074308 (9.052979) | 17.953878 / 10.191392 (7.762486) | 0.220035 / 0.680424 (-0.460389) | 0.028660 / 0.534201 (-0.505541) | 0.496233 / 0.579283 (-0.083050) | 0.591587 / 0.434364 (0.157223) | 0.635204 / 0.540337 (0.094867) | 0.702143 / 1.386936 (-0.684793) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7cfac43b980ab9e4a69c2328f085770996323005 \"CML watermark\")\n" ]

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[]

[ "Hi! Sure, feel free to open a PR, so we can see the API you have in mind.", "I would like to give it a go! #self-assign", "Closing as `Dataset.map` can be used instead (see https://github.com/huggingface/datasets/pull/5533#issuecomment-1440571658 and https://github.com/huggingface/datasets/pull/5533#issuecomment-1446403263)" ]

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[ "Hi! This is indeed a bug in our zero-copy logic.\r\n\r\nTo fix it, instead of the line:\r\nhttps://github.com/huggingface/datasets/blob/7cfac43b980ab9e4a69c2328f085770996323005/src/datasets/features/features.py#L702\r\n\r\nwe should have:\r\n```python\r\nreturn pa.types.is_primitive(pa_type) and not (pa.types.is_boolean(pa_type) or pa.types.is_temporal(pa_type))\r\n```", "@mariosasko submitted a small PR [here](https://github.com/huggingface/datasets/pull/5504)" ]

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[ "Totally agree, the docs should be in sync with our code.\r\n\r\nIndeed to avoid confusing users, I think we should have updated the docs at the same time as this PR:\r\n- #5167", "@albertvillanova yeah sure I should have, but I forgot back then, sorry for that 😶", "No, @polinaeterna, nothing to be sorry about.\r\n\r\nMy comment was for all of us datasets team, as a reminder: when making a PR, but also when reviewing some other's PR, we should not forget to update the corresponding docstring and doc pages. It is something we can improve if we help each other in reminding about it... :hugs: ", "@polinaeterna I think we can close this issue now as we no longer use `torchaudio` for decoding." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5493). 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) | 0.008956 / 0.011353 (-0.002397) | 0.004590 / 0.011008 (-0.006418) | 0.101305 / 0.038508 (0.062797) | 0.030347 / 0.023109 (0.007237) | 0.302492 / 0.275898 (0.026594) | 0.335986 / 0.323480 (0.012506) | 0.007272 / 0.007986 (-0.000714) | 0.004303 / 0.004328 (-0.000025) | 0.078592 / 0.004250 (0.074341) | 0.035545 / 0.037052 (-0.001507) | 0.316052 / 0.258489 (0.057563) | 0.342523 / 0.293841 (0.048682) | 0.034128 / 0.128546 (-0.094419) | 0.011475 / 0.075646 (-0.064171) | 0.325272 / 0.419271 (-0.093999) | 0.041815 / 0.043533 (-0.001717) | 0.303093 / 0.255139 (0.047955) | 0.331987 / 0.283200 (0.048788) | 0.087264 / 0.141683 (-0.054419) | 1.476284 / 1.452155 (0.024129) | 1.562034 / 1.492716 (0.069318) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206502 / 0.018006 (0.188496) | 0.409893 / 0.000490 (0.409404) | 0.002479 / 0.000200 (0.002279) | 0.000073 / 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) | 0.022891 / 0.037411 (-0.014520) | 0.100209 / 0.014526 (0.085683) | 0.105576 / 0.176557 (-0.070981) | 0.141035 / 0.737135 (-0.596100) | 0.109733 / 0.296338 (-0.186606) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.413791 / 0.215209 (0.198582) | 4.125890 / 2.077655 (2.048235) | 1.833023 / 1.504120 (0.328903) | 1.631325 / 1.541195 (0.090130) | 1.708406 / 1.468490 (0.239916) | 0.690100 / 4.584777 (-3.894677) | 3.379058 / 3.745712 (-0.366654) | 2.019044 / 5.269862 (-3.250818) | 1.323332 / 4.565676 (-3.242344) | 0.082709 / 0.424275 (-0.341566) | 0.012434 / 0.007607 (0.004827) | 0.527139 / 0.226044 (0.301095) | 5.271529 / 2.268929 (3.002601) | 2.297311 / 55.444624 (-53.147314) | 1.949021 / 6.876477 (-4.927456) | 2.001098 / 2.142072 (-0.140975) | 0.811591 / 4.805227 (-3.993636) | 0.149028 / 6.500664 (-6.351637) | 0.066233 / 0.075469 (-0.009236) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254276 / 1.841788 (-0.587512) | 13.638485 / 8.074308 (5.564177) | 13.943274 / 10.191392 (3.751882) | 0.147426 / 0.680424 (-0.532997) | 0.028602 / 0.534201 (-0.505599) | 0.398080 / 0.579283 (-0.181203) | 0.402178 / 0.434364 (-0.032186) | 0.477045 / 0.540337 (-0.063292) | 0.567731 / 1.386936 (-0.819205) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006936 / 0.011353 (-0.004417) | 0.004614 / 0.011008 (-0.006394) | 0.079779 / 0.038508 (0.041271) | 0.027941 / 0.023109 (0.004832) | 0.347224 / 0.275898 (0.071326) | 0.378183 / 0.323480 (0.054703) | 0.005249 / 0.007986 (-0.002737) | 0.004907 / 0.004328 (0.000579) | 0.078678 / 0.004250 (0.074428) | 0.041912 / 0.037052 (0.004860) | 0.347838 / 0.258489 (0.089349) | 0.386760 / 0.293841 (0.092919) | 0.032680 / 0.128546 (-0.095867) | 0.014321 / 0.075646 (-0.061325) | 0.087924 / 0.419271 (-0.331347) | 0.045060 / 0.043533 (0.001527) | 0.340986 / 0.255139 (0.085847) | 0.368689 / 0.283200 (0.085489) | 0.093274 / 0.141683 (-0.048409) | 1.474435 / 1.452155 (0.022281) | 1.569753 / 1.492716 (0.077037) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206789 / 0.018006 (0.188783) | 0.416518 / 0.000490 (0.416028) | 0.000404 / 0.000200 (0.000204) | 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) | 0.026207 / 0.037411 (-0.011205) | 0.101914 / 0.014526 (0.087388) | 0.108585 / 0.176557 (-0.067972) | 0.150438 / 0.737135 (-0.586697) | 0.110744 / 0.296338 (-0.185594) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.443571 / 0.215209 (0.228362) | 4.433139 / 2.077655 (2.355485) | 2.109525 / 1.504120 (0.605405) | 1.901484 / 1.541195 (0.360290) | 1.968812 / 1.468490 (0.500322) | 0.704334 / 4.584777 (-3.880443) | 3.392028 / 3.745712 (-0.353684) | 3.072693 / 5.269862 (-2.197168) | 1.552227 / 4.565676 (-3.013449) | 0.083741 / 0.424275 (-0.340534) | 0.012627 / 0.007607 (0.005020) | 0.544706 / 0.226044 (0.318662) | 5.462743 / 2.268929 (3.193815) | 2.551265 / 55.444624 (-52.893360) | 2.208075 / 6.876477 (-4.668401) | 2.259092 / 2.142072 (0.117020) | 0.810687 / 4.805227 (-3.994540) | 0.152347 / 6.500664 (-6.348317) | 0.068346 / 0.075469 (-0.007123) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.269716 / 1.841788 (-0.572072) | 14.215698 / 8.074308 (6.141390) | 13.691773 / 10.191392 (3.500381) | 0.152620 / 0.680424 (-0.527804) | 0.017219 / 0.534201 (-0.516982) | 0.382533 / 0.579283 (-0.196750) | 0.388994 / 0.434364 (-0.045370) | 0.479400 / 0.540337 (-0.060938) | 0.572699 / 1.386936 (-0.814237) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f2d90f14cd6e756abeb27045940a6756104cc2d6 \"CML watermark\")\n" ]

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[ "Assigned to myself and will get to it in the next week, but if someone finds this issue annoying and wants to submit a PR before I do, just ping me here and I'll reassign :). ", "I would like to be assigned to this issue, @nateraw . #self-assign" ]

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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) | 0.008726 / 0.011353 (-0.002627) | 0.004589 / 0.011008 (-0.006419) | 0.101078 / 0.038508 (0.062570) | 0.029732 / 0.023109 (0.006622) | 0.298309 / 0.275898 (0.022411) | 0.367800 / 0.323480 (0.044320) | 0.007025 / 0.007986 (-0.000961) | 0.003513 / 0.004328 (-0.000815) | 0.079531 / 0.004250 (0.075281) | 0.035588 / 0.037052 (-0.001465) | 0.307850 / 0.258489 (0.049361) | 0.351603 / 0.293841 (0.057762) | 0.033593 / 0.128546 (-0.094954) | 0.011669 / 0.075646 (-0.063977) | 0.323025 / 0.419271 (-0.096246) | 0.042047 / 0.043533 (-0.001486) | 0.300565 / 0.255139 (0.045426) | 0.329362 / 0.283200 (0.046163) | 0.089001 / 0.141683 (-0.052682) | 1.472799 / 1.452155 (0.020644) | 1.488902 / 1.492716 (-0.003814) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.012491 / 0.018006 (-0.005515) | 0.408245 / 0.000490 (0.407755) | 0.003878 / 0.000200 (0.003678) | 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) | 0.023698 / 0.037411 (-0.013713) | 0.100442 / 0.014526 (0.085916) | 0.108233 / 0.176557 (-0.068323) | 0.145308 / 0.737135 (-0.591827) | 0.113121 / 0.296338 (-0.183218) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420490 / 0.215209 (0.205281) | 4.179838 / 2.077655 (2.102183) | 2.156007 / 1.504120 (0.651887) | 1.911358 / 1.541195 (0.370163) | 1.867961 / 1.468490 (0.399471) | 0.685254 / 4.584777 (-3.899523) | 3.382386 / 3.745712 (-0.363326) | 3.285657 / 5.269862 (-1.984205) | 1.693878 / 4.565676 (-2.871798) | 0.081680 / 0.424275 (-0.342595) | 0.012182 / 0.007607 (0.004575) | 0.526021 / 0.226044 (0.299977) | 5.276217 / 2.268929 (3.007289) | 2.541518 / 55.444624 (-52.903106) | 2.313452 / 6.876477 (-4.563025) | 2.340000 / 2.142072 (0.197928) | 0.807099 / 4.805227 (-3.998128) | 0.147587 / 6.500664 (-6.353077) | 0.064280 / 0.075469 (-0.011189) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.223466 / 1.841788 (-0.618321) | 13.911365 / 8.074308 (5.837057) | 14.261550 / 10.191392 (4.070158) | 0.135922 / 0.680424 (-0.544502) | 0.028832 / 0.534201 (-0.505368) | 0.393142 / 0.579283 (-0.186141) | 0.400507 / 0.434364 (-0.033857) | 0.471792 / 0.540337 (-0.068546) | 0.558278 / 1.386936 (-0.828658) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006644 / 0.011353 (-0.004709) | 0.004531 / 0.011008 (-0.006478) | 0.076285 / 0.038508 (0.037777) | 0.027249 / 0.023109 (0.004140) | 0.343137 / 0.275898 (0.067239) | 0.378498 / 0.323480 (0.055018) | 0.004950 / 0.007986 (-0.003036) | 0.003422 / 0.004328 (-0.000907) | 0.075662 / 0.004250 (0.071412) | 0.039692 / 0.037052 (0.002640) | 0.343402 / 0.258489 (0.084913) | 0.385067 / 0.293841 (0.091226) | 0.032382 / 0.128546 (-0.096164) | 0.011577 / 0.075646 (-0.064069) | 0.085534 / 0.419271 (-0.333738) | 0.052139 / 0.043533 (0.008606) | 0.342176 / 0.255139 (0.087037) | 0.367298 / 0.283200 (0.084098) | 0.096088 / 0.141683 (-0.045595) | 1.470770 / 1.452155 (0.018615) | 1.567316 / 1.492716 (0.074600) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.217664 / 0.018006 (0.199657) | 0.397807 / 0.000490 (0.397317) | 0.006864 / 0.000200 (0.006664) | 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) | 0.025064 / 0.037411 (-0.012348) | 0.100906 / 0.014526 (0.086380) | 0.107444 / 0.176557 (-0.069113) | 0.143679 / 0.737135 (-0.593457) | 0.112460 / 0.296338 (-0.183879) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.442634 / 0.215209 (0.227425) | 4.410687 / 2.077655 (2.333032) | 2.067445 / 1.504120 (0.563325) | 1.860569 / 1.541195 (0.319374) | 1.943523 / 1.468490 (0.475033) | 0.694585 / 4.584777 (-3.890192) | 3.375906 / 3.745712 (-0.369806) | 3.483334 / 5.269862 (-1.786528) | 1.437700 / 4.565676 (-3.127977) | 0.083138 / 0.424275 (-0.341137) | 0.012979 / 0.007607 (0.005372) | 0.536414 / 0.226044 (0.310370) | 5.379872 / 2.268929 (3.110943) | 2.517907 / 55.444624 (-52.926717) | 2.164772 / 6.876477 (-4.711705) | 2.212839 / 2.142072 (0.070767) | 0.799675 / 4.805227 (-4.005553) | 0.150253 / 6.500664 (-6.350411) | 0.067033 / 0.075469 (-0.008436) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.295592 / 1.841788 (-0.546196) | 14.372932 / 8.074308 (6.298623) | 13.618423 / 10.191392 (3.427031) | 0.141212 / 0.680424 (-0.539212) | 0.016933 / 0.534201 (-0.517268) | 0.385664 / 0.579283 (-0.193619) | 0.386919 / 0.434364 (-0.047445) | 0.477022 / 0.540337 (-0.063315) | 0.565158 / 1.386936 (-0.821778) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#38c715cc787a81d0fd894205b4b24aca2f45f84b \"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) | 0.008581 / 0.011353 (-0.002772) | 0.004519 / 0.011008 (-0.006490) | 0.099721 / 0.038508 (0.061213) | 0.029217 / 0.023109 (0.006107) | 0.298229 / 0.275898 (0.022331) | 0.332605 / 0.323480 (0.009125) | 0.006880 / 0.007986 (-0.001106) | 0.003324 / 0.004328 (-0.001005) | 0.078143 / 0.004250 (0.073892) | 0.034262 / 0.037052 (-0.002790) | 0.304162 / 0.258489 (0.045673) | 0.342351 / 0.293841 (0.048510) | 0.033387 / 0.128546 (-0.095159) | 0.011397 / 0.075646 (-0.064249) | 0.321527 / 0.419271 (-0.097744) | 0.040886 / 0.043533 (-0.002647) | 0.299968 / 0.255139 (0.044829) | 0.322484 / 0.283200 (0.039285) | 0.083832 / 0.141683 (-0.057851) | 1.482241 / 1.452155 (0.030086) | 1.548438 / 1.492716 (0.055721) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.191002 / 0.018006 (0.172996) | 0.403423 / 0.000490 (0.402933) | 0.002493 / 0.000200 (0.002293) | 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) | 0.023720 / 0.037411 (-0.013691) | 0.100806 / 0.014526 (0.086281) | 0.105314 / 0.176557 (-0.071242) | 0.141490 / 0.737135 (-0.595645) | 0.108695 / 0.296338 (-0.187644) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.412250 / 0.215209 (0.197041) | 4.124830 / 2.077655 (2.047175) | 1.851948 / 1.504120 (0.347828) | 1.651597 / 1.541195 (0.110403) | 1.712486 / 1.468490 (0.243996) | 0.696634 / 4.584777 (-3.888143) | 3.304220 / 3.745712 (-0.441492) | 1.862776 / 5.269862 (-3.407086) | 1.159452 / 4.565676 (-3.406224) | 0.082930 / 0.424275 (-0.341345) | 0.012586 / 0.007607 (0.004979) | 0.524499 / 0.226044 (0.298455) | 5.249235 / 2.268929 (2.980307) | 2.293187 / 55.444624 (-53.151437) | 1.950101 / 6.876477 (-4.926376) | 2.008274 / 2.142072 (-0.133799) | 0.811641 / 4.805227 (-3.993586) | 0.148785 / 6.500664 (-6.351879) | 0.064461 / 0.075469 (-0.011008) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.232227 / 1.841788 (-0.609561) | 13.235896 / 8.074308 (5.161588) | 13.837420 / 10.191392 (3.646028) | 0.135586 / 0.680424 (-0.544838) | 0.028935 / 0.534201 (-0.505266) | 0.397064 / 0.579283 (-0.182220) | 0.393814 / 0.434364 (-0.040549) | 0.480450 / 0.540337 (-0.059887) | 0.561159 / 1.386936 (-0.825777) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006696 / 0.011353 (-0.004657) | 0.004528 / 0.011008 (-0.006480) | 0.077335 / 0.038508 (0.038827) | 0.027181 / 0.023109 (0.004072) | 0.345379 / 0.275898 (0.069481) | 0.372544 / 0.323480 (0.049064) | 0.006808 / 0.007986 (-0.001178) | 0.003284 / 0.004328 (-0.001045) | 0.077379 / 0.004250 (0.073129) | 0.039954 / 0.037052 (0.002901) | 0.348094 / 0.258489 (0.089605) | 0.382315 / 0.293841 (0.088474) | 0.031694 / 0.128546 (-0.096852) | 0.011714 / 0.075646 (-0.063933) | 0.086425 / 0.419271 (-0.332846) | 0.041778 / 0.043533 (-0.001754) | 0.342161 / 0.255139 (0.087022) | 0.363798 / 0.283200 (0.080599) | 0.091315 / 0.141683 (-0.050368) | 1.462066 / 1.452155 (0.009912) | 1.541417 / 1.492716 (0.048700) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235840 / 0.018006 (0.217834) | 0.397096 / 0.000490 (0.396606) | 0.004597 / 0.000200 (0.004397) | 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) | 0.024296 / 0.037411 (-0.013115) | 0.099167 / 0.014526 (0.084641) | 0.108257 / 0.176557 (-0.068299) | 0.143434 / 0.737135 (-0.593701) | 0.111933 / 0.296338 (-0.184406) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440306 / 0.215209 (0.225096) | 4.374065 / 2.077655 (2.296410) | 2.072653 / 1.504120 (0.568533) | 1.864829 / 1.541195 (0.323635) | 1.927970 / 1.468490 (0.459479) | 0.710118 / 4.584777 (-3.874659) | 3.391216 / 3.745712 (-0.354496) | 1.888847 / 5.269862 (-3.381015) | 1.178740 / 4.565676 (-3.386936) | 0.083950 / 0.424275 (-0.340325) | 0.012567 / 0.007607 (0.004960) | 0.540557 / 0.226044 (0.314513) | 5.437621 / 2.268929 (3.168692) | 2.531165 / 55.444624 (-52.913460) | 2.181450 / 6.876477 (-4.695027) | 2.209108 / 2.142072 (0.067035) | 0.814236 / 4.805227 (-3.990991) | 0.153000 / 6.500664 (-6.347664) | 0.066769 / 0.075469 (-0.008700) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.301057 / 1.841788 (-0.540731) | 14.066786 / 8.074308 (5.992478) | 13.641455 / 10.191392 (3.450063) | 0.138838 / 0.680424 (-0.541586) | 0.016733 / 0.534201 (-0.517468) | 0.391823 / 0.579283 (-0.187460) | 0.390817 / 0.434364 (-0.043547) | 0.487682 / 0.540337 (-0.052656) | 0.581134 / 1.386936 (-0.805802) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b065547654efa0ec633cf373ac1512884c68b2e1 \"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) | 0.008798 / 0.011353 (-0.002554) | 0.005313 / 0.011008 (-0.005695) | 0.099234 / 0.038508 (0.060726) | 0.033935 / 0.023109 (0.010826) | 0.306610 / 0.275898 (0.030712) | 0.373151 / 0.323480 (0.049671) | 0.008305 / 0.007986 (0.000320) | 0.004647 / 0.004328 (0.000319) | 0.079984 / 0.004250 (0.075733) | 0.042546 / 0.037052 (0.005493) | 0.355105 / 0.258489 (0.096616) | 0.332769 / 0.293841 (0.038928) | 0.037708 / 0.128546 (-0.090839) | 0.012141 / 0.075646 (-0.063505) | 0.365338 / 0.419271 (-0.053933) | 0.048875 / 0.043533 (0.005343) | 0.301771 / 0.255139 (0.046632) | 0.323301 / 0.283200 (0.040101) | 0.099116 / 0.141683 (-0.042566) | 1.463948 / 1.452155 (0.011793) | 1.563006 / 1.492716 (0.070290) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.219799 / 0.018006 (0.201793) | 0.524126 / 0.000490 (0.523636) | 0.003899 / 0.000200 (0.003699) | 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) | 0.028361 / 0.037411 (-0.009050) | 0.111386 / 0.014526 (0.096860) | 0.125749 / 0.176557 (-0.050807) | 0.167026 / 0.737135 (-0.570109) | 0.132082 / 0.296338 (-0.164257) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.385046 / 0.215209 (0.169837) | 3.933129 / 2.077655 (1.855475) | 1.823395 / 1.504120 (0.319276) | 1.646468 / 1.541195 (0.105273) | 1.658835 / 1.468490 (0.190344) | 0.708300 / 4.584777 (-3.876477) | 4.001478 / 3.745712 (0.255766) | 2.221773 / 5.269862 (-3.048089) | 1.597925 / 4.565676 (-2.967751) | 0.088699 / 0.424275 (-0.335577) | 0.013575 / 0.007607 (0.005968) | 0.520577 / 0.226044 (0.294533) | 5.044313 / 2.268929 (2.775385) | 2.239862 / 55.444624 (-53.204763) | 2.060394 / 6.876477 (-4.816083) | 2.060684 / 2.142072 (-0.081389) | 0.844862 / 4.805227 (-3.960365) | 0.190321 / 6.500664 (-6.310343) | 0.071595 / 0.075469 (-0.003875) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.400048 / 1.841788 (-0.441740) | 15.684159 / 8.074308 (7.609851) | 14.369298 / 10.191392 (4.177906) | 0.164874 / 0.680424 (-0.515550) | 0.033219 / 0.534201 (-0.500982) | 0.449176 / 0.579283 (-0.130107) | 0.456560 / 0.434364 (0.022196) | 0.517978 / 0.540337 (-0.022359) | 0.635467 / 1.386936 (-0.751469) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated 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 (-0.004089) | 0.005451 / 0.011008 (-0.005558) | 0.078785 / 0.038508 (0.040277) | 0.032656 / 0.023109 (0.009546) | 0.346384 / 0.275898 (0.070486) | 0.390778 / 0.323480 (0.067299) | 0.005848 / 0.007986 (-0.002137) | 0.004565 / 0.004328 (0.000236) | 0.077903 / 0.004250 (0.073652) | 0.048659 / 0.037052 (0.011606) | 0.368629 / 0.258489 (0.110140) | 0.401632 / 0.293841 (0.107791) | 0.038516 / 0.128546 (-0.090030) | 0.011895 / 0.075646 (-0.063752) | 0.089185 / 0.419271 (-0.330086) | 0.049875 / 0.043533 (0.006342) | 0.344771 / 0.255139 (0.089632) | 0.378237 / 0.283200 (0.095038) | 0.099184 / 0.141683 (-0.042498) | 1.505058 / 1.452155 (0.052903) | 1.555330 / 1.492716 (0.062614) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209132 / 0.018006 (0.191126) | 0.479928 / 0.000490 (0.479438) | 0.005923 / 0.000200 (0.005723) | 0.000113 / 0.000054 (0.000058) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029187 / 0.037411 (-0.008224) | 0.117026 / 0.014526 (0.102500) | 0.131834 / 0.176557 (-0.044722) | 0.172797 / 0.737135 (-0.564339) | 0.129098 / 0.296338 (-0.167240) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.450214 / 0.215209 (0.235005) | 4.323950 / 2.077655 (2.246295) | 2.210100 / 1.504120 (0.705980) | 2.058733 / 1.541195 (0.517538) | 1.968191 / 1.468490 (0.499701) | 0.694918 / 4.584777 (-3.889859) | 4.176559 / 3.745712 (0.430846) | 2.118211 / 5.269862 (-3.151651) | 1.410652 / 4.565676 (-3.155024) | 0.093606 / 0.424275 (-0.330669) | 0.013729 / 0.007607 (0.006122) | 0.528463 / 0.226044 (0.302418) | 5.311766 / 2.268929 (3.042837) | 2.522981 / 55.444624 (-52.921644) | 2.177191 / 6.876477 (-4.699285) | 2.211448 / 2.142072 (0.069375) | 0.824334 / 4.805227 (-3.980893) | 0.166642 / 6.500664 (-6.334022) | 0.062774 / 0.075469 (-0.012695) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.367573 / 1.841788 (-0.474215) | 15.913637 / 8.074308 (7.839328) | 13.397411 / 10.191392 (3.206019) | 0.162599 / 0.680424 (-0.517825) | 0.020325 / 0.534201 (-0.513876) | 0.438745 / 0.579283 (-0.140538) | 0.449892 / 0.434364 (0.015528) | 0.556226 / 0.540337 (0.015888) | 0.672661 / 1.386936 (-0.714275) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5f810b7011a8a4ab077a1847c024d2d9e267b065 \"CML watermark\")\n" ]

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[ "Hi @kradonneoh, thanks for reporting.\r\n\r\nPlease note that to work with audio datasets (and specifically with MP3 files) we have detailed installation instructions in our docs: https://huggingface.co/docs/datasets/installation#audio\r\n- one of the requirements is torchaudio<0.12.0\r\n\r\nLet us know if the problem persists after having followed them.", "I saw that and have followed it (hence the Expected Behavior section of the bug report). \r\n\r\nIs there no intention of updating to the latest version? It does limit the version of `torch` I can use, which isn’t ideal.", "@kradonneoh hey! actually with `ffmpeg4` loading of mp3 files should work, so this is a not expected behavior and we need to investigate it. It works on my side with `torchaudio==0.13` and `ffmpeg==4.2.7`. Which `torchaudio` version do you use?\r\n\r\n`datasets` should support decoding of mp3 files with `torchaudio` when its version is `>0.12` but as you noted it requires `ffmpeg>4`, we need to fix this in the documentation, thank you for pointing to this! \r\n\r\nBut according to your traceback it seems that it tries to use [`libsndfile`](https://github.com/libsndfile/libsndfile) backend for mp3 decoding. And `libsndfile` library supports mp3 decoding starting from version 1.1.0 which on Linux has to be compiled from source for now afaik. \r\n\r\nfyi - we are aiming at getting rid of `torchaudio` dependency at all by the next major library release in favor of `libsndfile` too.", "We now decode MP3 with `soundfile`, so I'm closing this issue" ]

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[ "Hi! The correct path is still `dill._dill.XXXX` in the latest release. What do you get when you run `python -c \"import dill; print(dill.__version__)\"` in your environment?", "`0.3.6` I feel like that's bad news, because it's probably not the issue.\r\n\r\nMy mistake, about the wrong path guess. I think I didn't notice that the first `dill` in the path isn't supposed to be included in the path specification in python.\r\n<img width=\"146\" alt=\"Screen Shot 2023-01-31 at 12 58 32 PM\" src=\"https://user-images.githubusercontent.com/35349273/215844209-74af6a8f-9bff-4c75-9495-44c658c8e9f7.png\">\r\n", "Hi, @avivbrokman, this issue you report appeared only with old versions of dill. See:\r\n- #288\r\n\r\nAre you sure you are in the right Python environment?\r\n- Please note that Jupyter (where I guess you get the error) may have multiple execution backends (IPython kernels) that might be different from the Python environment your are using to get the dill version\r\n - Have you run `import dill; print(dill.__version__)` in the same Jupyter/IPython that you were using when you got the error while executing `import datasets`?", "I'm using spyder, and I am still getting `0.3.6` for `dill`, so unfortunately #288 isn't applicable, I think. However, I found something odd that I believe is a clue: \r\n\r\n```\r\nimport inspect\r\nimport dill\r\n\r\ninspect.getfile(dill)\r\n>>> '/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/dill/__init__.py'\r\n```\r\n\r\nI checked out the directory, and there is no `dill` subdirectory within '/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/dill`, as there should be. Rather, `_dill.py` is in '/Users/avivbrokman/opt/anaconda3/lib/python3.9/site-packages/dill` itself. \r\n\r\n If I run `pip install dill` or `pip install --upgrade dill`, I get the message `Requirement already satisfied: dill in ./opt/anaconda3/lib/python3.9/site-packages (0.3.6)`. If I run `conda upgrade dill`, I get the message `Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.` a couple of times, followed by\r\n\r\n```\r\nSolving environment: failed\r\nSolving environment: / \r\nFound conflicts! Looking for incompatible packages.\r\n```\r\n\r\nAnd then terminal proceeds to list conflicts between different packages I have.\r\n\r\nThis is all very strange to me because I recently uninstalled and reinstalled `anaconda`.\r\n", "As I said above, I guess this is not a problem with `datasets`. I think you have different Python environments: one with the new dill version (the one you get while using pip) and other with the old dill version (the one where you get the AttributeError).\r\n\r\nYou should update `dill` in the Python environment you are using within spyder.\r\n\r\nPlease note that the `_dill` module is present in the `dill` package since their 2.8.0 version." ]

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[ "Hi @omar-araboghli, thanks for your proposal.\r\n\r\nHave you tried to use \"csv\" loader instead of \"text\"? That already has a `sep` argument.", "Hi @albertvillanova, thanks for the quick response!\r\n\r\nIndeed, I have been trying to use `csv` instead of `text`. However I am still not able to define range of rows as one sequence, that is achievable with passing `sample_by='paragraph'` to the `TextConfig`\r\n\r\nFor instance, the below code\r\n\r\n```python\r\nimport datasets\r\n\r\ndataset = datasets.load_dataset(\r\n path='csv',\r\n data_files={'train': TRAINING_SET_PATH},\r\n sep='\\t',\r\n header=None,\r\n column_names=['tokens', 'pos_tags', 'chunk_tags', 'ner_tags']\r\n)\r\n```\r\n\r\nleads to \r\n\r\n```python\r\ndataset\r\n>>> DatasetDict({\r\n train: Dataset({\r\n features: ['tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\r\n num_rows: 62543\r\n })\r\n})\r\n\r\ndataset['train'][0]\r\n>>> {'tokens': 'Distribution',\r\n 'pos_tags': 'NN',\r\n 'chunk_tags': 'O',\r\n 'ner_tags': 'O'\r\n}\r\n```\r\nIs there a way to deal with multiple csv rows as one dataset instance, where each column is a sequence of those rows ?" ]

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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) | 0.008492 / 0.011353 (-0.002861) | 0.004717 / 0.011008 (-0.006292) | 0.101111 / 0.038508 (0.062602) | 0.029129 / 0.023109 (0.006019) | 0.307564 / 0.275898 (0.031666) | 0.367038 / 0.323480 (0.043558) | 0.007105 / 0.007986 (-0.000881) | 0.003622 / 0.004328 (-0.000706) | 0.078370 / 0.004250 (0.074120) | 0.036960 / 0.037052 (-0.000093) | 0.315612 / 0.258489 (0.057123) | 0.353601 / 0.293841 (0.059760) | 0.032900 / 0.128546 (-0.095647) | 0.011405 / 0.075646 (-0.064241) | 0.322331 / 0.419271 (-0.096940) | 0.040823 / 0.043533 (-0.002710) | 0.306734 / 0.255139 (0.051595) | 0.328155 / 0.283200 (0.044955) | 0.087169 / 0.141683 (-0.054514) | 1.460543 / 1.452155 (0.008389) | 1.498094 / 1.492716 (0.005378) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.011863 / 0.018006 (-0.006143) | 0.416315 / 0.000490 (0.415826) | 0.003463 / 0.000200 (0.003263) | 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) | 0.023219 / 0.037411 (-0.014192) | 0.096469 / 0.014526 (0.081943) | 0.105960 / 0.176557 (-0.070596) | 0.148993 / 0.737135 (-0.588142) | 0.108112 / 0.296338 (-0.188226) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.415662 / 0.215209 (0.200453) | 4.155111 / 2.077655 (2.077456) | 1.834943 / 1.504120 (0.330823) | 1.622752 / 1.541195 (0.081557) | 1.701630 / 1.468490 (0.233140) | 0.690596 / 4.584777 (-3.894181) | 3.399385 / 3.745712 (-0.346327) | 3.140521 / 5.269862 (-2.129341) | 1.609152 / 4.565676 (-2.956524) | 0.082132 / 0.424275 (-0.342143) | 0.012343 / 0.007607 (0.004735) | 0.532715 / 0.226044 (0.306670) | 5.323032 / 2.268929 (3.054104) | 2.326625 / 55.444624 (-53.118000) | 1.944263 / 6.876477 (-4.932213) | 1.994015 / 2.142072 (-0.148058) | 0.813805 / 4.805227 (-3.991422) | 0.149233 / 6.500664 (-6.351431) | 0.065318 / 0.075469 (-0.010151) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.212441 / 1.841788 (-0.629347) | 13.979069 / 8.074308 (5.904761) | 14.003998 / 10.191392 (3.812606) | 0.146956 / 0.680424 (-0.533468) | 0.028564 / 0.534201 (-0.505637) | 0.392370 / 0.579283 (-0.186913) | 0.399695 / 0.434364 (-0.034669) | 0.473481 / 0.540337 (-0.066856) | 0.562625 / 1.386936 (-0.824311) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated 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) | 0.076217 / 0.038508 (0.037709) | 0.028888 / 0.023109 (0.005779) | 0.345431 / 0.275898 (0.069533) | 0.389246 / 0.323480 (0.065766) | 0.005939 / 0.007986 (-0.002046) | 0.003356 / 0.004328 (-0.000973) | 0.075880 / 0.004250 (0.071629) | 0.041427 / 0.037052 (0.004374) | 0.344481 / 0.258489 (0.085992) | 0.398508 / 0.293841 (0.104667) | 0.031801 / 0.128546 (-0.096745) | 0.011763 / 0.075646 (-0.063884) | 0.085600 / 0.419271 (-0.333672) | 0.042656 / 0.043533 (-0.000876) | 0.345893 / 0.255139 (0.090754) | 0.376910 / 0.283200 (0.093711) | 0.092451 / 0.141683 (-0.049232) | 1.461222 / 1.452155 (0.009068) | 1.555822 / 1.492716 (0.063106) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235781 / 0.018006 (0.217774) | 0.418485 / 0.000490 (0.417995) | 0.005560 / 0.000200 (0.005360) | 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) | 0.025410 / 0.037411 (-0.012001) | 0.103780 / 0.014526 (0.089254) | 0.110183 / 0.176557 (-0.066374) | 0.151097 / 0.737135 (-0.586039) | 0.112539 / 0.296338 (-0.183799) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436686 / 0.215209 (0.221477) | 4.341594 / 2.077655 (2.263940) | 2.062309 / 1.504120 (0.558190) | 1.857461 / 1.541195 (0.316267) | 1.947204 / 1.468490 (0.478713) | 0.699641 / 4.584777 (-3.885136) | 3.406983 / 3.745712 (-0.338729) | 3.294705 / 5.269862 (-1.975157) | 1.360582 / 4.565676 (-3.205095) | 0.083025 / 0.424275 (-0.341250) | 0.012461 / 0.007607 (0.004854) | 0.537767 / 0.226044 (0.311722) | 5.393316 / 2.268929 (3.124387) | 2.516692 / 55.444624 (-52.927932) | 2.163987 / 6.876477 (-4.712490) | 2.220480 / 2.142072 (0.078408) | 0.810648 / 4.805227 (-3.994579) | 0.151820 / 6.500664 (-6.348844) | 0.068080 / 0.075469 (-0.007389) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.279382 / 1.841788 (-0.562405) | 13.989947 / 8.074308 (5.915638) | 14.039229 / 10.191392 (3.847836) | 0.141071 / 0.680424 (-0.539352) | 0.017118 / 0.534201 (-0.517083) | 0.381558 / 0.579283 (-0.197725) | 0.390407 / 0.434364 (-0.043957) | 0.440920 / 0.540337 (-0.099418) | 0.525478 / 1.386936 (-0.861458) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#eeedb5167d150888a640cd70ca63d6d72bbe1043 \"CML watermark\")\n" ]

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[ "I think I need to create another PR on https://huggingface.co/datasets/huggingface/documentation-images/tree/main/datasets for hosting the images there?", "_The documentation is not available anymore as the PR was closed or merged._", "Thanks for the update @awsaf49 !", "> Thanks a lot for the updates!\r\n> \r\n> Just some minor things remain and the we should be good to ship this 🚀\r\n\r\n@sayakpaul I have updated the minor things. Please approve the workflows", "I think this PR is good to go..\r\n@sayakpaul @lhoestq ", "<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) | 0.009064 / 0.011353 (-0.002289) | 0.005262 / 0.011008 (-0.005746) | 0.099608 / 0.038508 (0.061100) | 0.035015 / 0.023109 (0.011906) | 0.296501 / 0.275898 (0.020602) | 0.353619 / 0.323480 (0.030139) | 0.007903 / 0.007986 (-0.000083) | 0.004093 / 0.004328 (-0.000235) | 0.075260 / 0.004250 (0.071009) | 0.043142 / 0.037052 (0.006089) | 0.307755 / 0.258489 (0.049266) | 0.336340 / 0.293841 (0.042499) | 0.038596 / 0.128546 (-0.089950) | 0.011861 / 0.075646 (-0.063786) | 0.334226 / 0.419271 (-0.085045) | 0.051472 / 0.043533 (0.007940) | 0.298539 / 0.255139 (0.043400) | 0.316856 / 0.283200 (0.033656) | 0.108620 / 0.141683 (-0.033063) | 1.434901 / 1.452155 (-0.017254) | 1.468368 / 1.492716 (-0.024348) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208402 / 0.018006 (0.190395) | 0.445799 / 0.000490 (0.445309) | 0.003704 / 0.000200 (0.003504) | 0.000084 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025435 / 0.037411 (-0.011976) | 0.105874 / 0.014526 (0.091348) | 0.115652 / 0.176557 (-0.060905) | 0.150872 / 0.737135 (-0.586263) | 0.121705 / 0.296338 (-0.174633) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.397816 / 0.215209 (0.182607) | 3.977766 / 2.077655 (1.900111) | 1.850848 / 1.504120 (0.346728) | 1.686062 / 1.541195 (0.144867) | 1.786277 / 1.468490 (0.317787) | 0.696250 / 4.584777 (-3.888527) | 3.785255 / 3.745712 (0.039543) | 3.355013 / 5.269862 (-1.914849) | 1.818232 / 4.565676 (-2.747444) | 0.085408 / 0.424275 (-0.338867) | 0.012567 / 0.007607 (0.004960) | 0.524185 / 0.226044 (0.298140) | 5.061975 / 2.268929 (2.793047) | 2.299866 / 55.444624 (-53.144758) | 1.966709 / 6.876477 (-4.909768) | 2.018760 / 2.142072 (-0.123313) | 0.841341 / 4.805227 (-3.963886) | 0.166374 / 6.500664 (-6.334290) | 0.061854 / 0.075469 (-0.013615) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.221666 / 1.841788 (-0.620122) | 14.373194 / 8.074308 (6.298886) | 14.253614 / 10.191392 (4.062222) | 0.172979 / 0.680424 (-0.507445) | 0.029176 / 0.534201 (-0.505025) | 0.447399 / 0.579283 (-0.131884) | 0.443663 / 0.434364 (0.009299) | 0.537071 / 0.540337 (-0.003267) | 0.640539 / 1.386936 (-0.746397) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007019 / 0.011353 (-0.004334) | 0.005091 / 0.011008 (-0.005917) | 0.074588 / 0.038508 (0.036080) | 0.032391 / 0.023109 (0.009282) | 0.340548 / 0.275898 (0.064650) | 0.367159 / 0.323480 (0.043679) | 0.005594 / 0.007986 (-0.002392) | 0.004003 / 0.004328 (-0.000325) | 0.073946 / 0.004250 (0.069695) | 0.045921 / 0.037052 (0.008868) | 0.340245 / 0.258489 (0.081756) | 0.397958 / 0.293841 (0.104117) | 0.036539 / 0.128546 (-0.092007) | 0.012258 / 0.075646 (-0.063388) | 0.087406 / 0.419271 (-0.331865) | 0.049276 / 0.043533 (0.005743) | 0.345235 / 0.255139 (0.090096) | 0.361250 / 0.283200 (0.078050) | 0.100757 / 0.141683 (-0.040926) | 1.464644 / 1.452155 (0.012489) | 1.545852 / 1.492716 (0.053136) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222952 / 0.018006 (0.204945) | 0.434607 / 0.000490 (0.434117) | 0.000438 / 0.000200 (0.000238) | 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) | 0.028834 / 0.037411 (-0.008577) | 0.107523 / 0.014526 (0.092997) | 0.122077 / 0.176557 (-0.054479) | 0.156574 / 0.737135 (-0.580561) | 0.122917 / 0.296338 (-0.173421) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417292 / 0.215209 (0.202083) | 4.165980 / 2.077655 (2.088325) | 1.996731 / 1.504120 (0.492611) | 1.802946 / 1.541195 (0.261751) | 1.878456 / 1.468490 (0.409966) | 0.711035 / 4.584777 (-3.873742) | 3.847357 / 3.745712 (0.101644) | 2.088354 / 5.269862 (-3.181508) | 1.344763 / 4.565676 (-3.220913) | 0.086356 / 0.424275 (-0.337919) | 0.012530 / 0.007607 (0.004923) | 0.511693 / 0.226044 (0.285648) | 5.126093 / 2.268929 (2.857165) | 2.490023 / 55.444624 (-52.954602) | 2.180274 / 6.876477 (-4.696202) | 2.221511 / 2.142072 (0.079438) | 0.836348 / 4.805227 (-3.968879) | 0.169554 / 6.500664 (-6.331110) | 0.064555 / 0.075469 (-0.010914) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.293466 / 1.841788 (-0.548321) | 14.785700 / 8.074308 (6.711392) | 13.858493 / 10.191392 (3.667101) | 0.161777 / 0.680424 (-0.518646) | 0.017794 / 0.534201 (-0.516407) | 0.426286 / 0.579283 (-0.152997) | 0.422517 / 0.434364 (-0.011847) | 0.530777 / 0.540337 (-0.009560) | 0.634822 / 1.386936 (-0.752114) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c6e08fcfc3a04e53430c26fa7c07da4cb18d977d \"CML watermark\")\n" ]

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[ "Seems to work now, perhaps it was something internal with our university's network." ]

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[ "I'd be happy to have a look, if nobody else has started working on this yet @lhoestq. \r\n\r\nIt seems to me that for the `arrow` format features are currently attached as metadata [in `datasets.arrow_writer`](https://github.com/huggingface/datasets/blob/5f810b7011a8a4ab077a1847c024d2d9e267b065/src/datasets/arrow_writer.py#L412) and retrieved from the metadata at `load_dataset` time using [`datasets.features.features.from_arrow_schema`](https://github.com/huggingface/datasets/blob/5f810b7011a8a4ab077a1847c024d2d9e267b065/src/datasets/features/features.py#L1602). \r\n\r\nThis will need to be replicated for `parquet` via calls to [this api](https://arrow.apache.org/docs/python/generated/pyarrow.parquet.write_metadata.html) from `io.parquet.ParquetWriter` and `io.parquet.ParquetReader` [respectively](https://github.com/huggingface/datasets/blob/5f810b7011a8a4ab077a1847c024d2d9e267b065/src/datasets/io/parquet.py#L104).\r\n\r\nAny other important considerations?\r\n", "Thanks @MFreidank ! That's correct :)\r\n\r\nReading the metadata to infer the features can be ideally done in the `parquet.py` file in `packaged_builder` when a parquet file is read. You can cast the arrow table to the schema you get from the features.arrow_schema", "#self-assign" ]

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[]

[ "Can I work on this issue? I am pretty new to this.", "Hi ! Sure :) you can comment `#self-assign` to assign yourself to this issue.\r\n\r\nI can give you some pointers to get started:\r\n\r\n`load_dataset` works roughly this way:\r\n1. it instantiate a dataset builder using `load_dataset_builder()`\r\n2. the builder download and prepare the dataset as Arrow files in the cache using `download_and_prepare()`\r\n3. the builder returns a Dataset object with `as_dataset()`\r\n\r\nOne way to approach this would be to implement `as_iterable_dataset()` in `builder.py`.\r\n\r\nAnd similarly to `as_dataset()`, you can use the `ArrowReader`. It has a `get_file_instructions()` method that can be helpful. It gives you the files to read as list of dictionaries with those keys: `filename`, `skip` and `take`.\r\n\r\nThe `skip` and `take` arguments are used in case the user wants to load a subset of the dataset, e.g.\r\n```python\r\nload_dataset(..., split=\"train[:10]\")\r\n```\r\n\r\nLet me know if you have questions or if I can help :)", "This use-case is a bit specific, and `load_dataset` already has enough parameters (plus, `streaming=True` also returns an iterable dataset, so we would have to explain the difference), so I think it would be better to add `IterableDataset.from_file` to the API (more flexible and aligned with the goal from https://github.com/huggingface/datasets/issues/3444) instead.", "> This use-case is a bit specific\r\n\r\nThis allows to use `datasets` for large scale training where map-style datasets are too slow and use too much memory in PyTorch. So I would still consider adding it.\r\n\r\nAlternatively we could add this feature one level bellow:\r\n```python\r\nbuilder = load_dataset_builder(...)\r\nbuilder.download_and_prepare()\r\nids = builder.as_iterable_dataset()\r\n```", "Yes, I see how this can be useful. Still, I think `Dataset.to_iterable` + `IterableDataset.from_file` would be much cleaner in terms of the API design (and more flexible since `load_dataset` can only access the \"initial\" (unprocessed) version of a dataset).\r\n\r\nAnd since it can be tricky to manually find the \"initial\" version of a dataset in the cache, maybe `load_dataset` could return an iterable dataset streamed from the cache if `streaming=True` and the cache is up-to-date. ", "> This allows to use datasets for large scale training where map-style datasets are too slow and use too much memory in PyTorch.\r\n\r\nI second that. e.g. In my last experiment Oscar-en uses 16GB RSS RAM per process and when using multiple processes the host quickly runs out cpu memory. ", ">And since it can be tricky to manually find the \"initial\" version of a dataset in the cache, maybe load_dataset could return an iterable dataset streamed from the cache if streaming=True and the cache is up-to-date.\r\n\r\nThis is exactly the need on JeanZay (HPC) - I have the dataset cache ready, but the compute node is offline, so making streaming work off a local cache would address that need.\r\n\r\nIf you will have a working POC I can be the tester. ", "> Yes, I see how this can be useful. Still, I think Dataset.to_iterable + IterableDataset.from_file would be much cleaner in terms of the API design (and more flexible since load_dataset can only access the \"initial\" (unprocessed) version of a dataset).\r\n\r\nI like `IterableDataset.from_file` as well. On the other hand `Dataset.to_iterable` first requires to load a Dataset object, which can take time depending on your hardware and your dataset size (sometimes 1h+).\r\n\r\n> And since it can be tricky to manually find the \"initial\" version of a dataset in the cache, maybe load_dataset could return an iterable dataset streamed from the cache if streaming=True and the cache is up-to-date.\r\n\r\nThat would definitely do the job. I was suggesting a different parameter just to make explicit the difference between\r\n- streaming from the raw data\r\n- streaming from the local cache\r\n\r\nBut I'd be fine with streaming from cache is the cache is up-to-date since it's always faster. We could log a message as usual to make it explicit that the cache is used", "> I was suggesting a different parameter just to make explicit the difference between\r\n\r\nMosaicML's `streaming` library does the same (tries to stream from the local cache if possible), so logging a message should be explicit enough :).", "Ok ! Sounds good then :)", "Hi Both! It has been a while since my first issue so I am gonna go for this one ! #self-assign", "#self-assign", "I like idea of `IterableDataset.from_file`. ", "https://github.com/huggingface/datasets/pull/5821 should be helpful to implement `IterableDataset.from_file`, since it defines a new ArrowExamplesIterable that takes an Arrow tables generator function (e.g. from a file) and can be used in an IterableDataset", "@lhoestq I have just started working on this issue. ", "@lhoestq Thank you for taking over.", "So what's recommanded usage of `IterableDataset.from_file` and `load_dataset`? How about I have multiple arrow files and `load_dataset` is often convenient to handle that.", "If you have multiple Arrow files you can load them using\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndata_files = {\"train\": [\"path/to/0.arrow\", \"path/to/1.arrow\", ..., \"path/to/n.arrow\"]}\r\n\r\nds = load_dataset(\"arrow\", data_files=data_files, streaming=True)\r\n```\r\n\r\nThis is equivalent to calling `IterableDataset.from_file` and `concatenate_datasets`." ]

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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) | 0.009963 / 0.011353 (-0.001390) | 0.005512 / 0.011008 (-0.005496) | 0.100495 / 0.038508 (0.061987) | 0.039929 / 0.023109 (0.016820) | 0.299749 / 0.275898 (0.023850) | 0.372330 / 0.323480 (0.048850) | 0.008689 / 0.007986 (0.000703) | 0.004334 / 0.004328 (0.000006) | 0.076469 / 0.004250 (0.072218) | 0.048091 / 0.037052 (0.011039) | 0.303884 / 0.258489 (0.045395) | 0.352747 / 0.293841 (0.058906) | 0.038941 / 0.128546 (-0.089605) | 0.012541 / 0.075646 (-0.063105) | 0.334227 / 0.419271 (-0.085044) | 0.048802 / 0.043533 (0.005269) | 0.295800 / 0.255139 (0.040661) | 0.316222 / 0.283200 (0.033022) | 0.108246 / 0.141683 (-0.033437) | 1.452735 / 1.452155 (0.000580) | 1.466293 / 1.492716 (-0.026423) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.010497 / 0.018006 (-0.007510) | 0.507427 / 0.000490 (0.506937) | 0.003054 / 0.000200 (0.002854) | 0.000084 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029529 / 0.037411 (-0.007883) | 0.114151 / 0.014526 (0.099625) | 0.120599 / 0.176557 (-0.055957) | 0.161881 / 0.737135 (-0.575255) | 0.127669 / 0.296338 (-0.168669) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.399631 / 0.215209 (0.184421) | 3.992997 / 2.077655 (1.915343) | 1.803770 / 1.504120 (0.299650) | 1.612301 / 1.541195 (0.071106) | 1.717846 / 1.468490 (0.249356) | 0.706753 / 4.584777 (-3.878024) | 3.798224 / 3.745712 (0.052512) | 2.169733 / 5.269862 (-3.100128) | 1.358264 / 4.565676 (-3.207413) | 0.086828 / 0.424275 (-0.337447) | 0.012606 / 0.007607 (0.004999) | 0.512085 / 0.226044 (0.286041) | 5.101491 / 2.268929 (2.832563) | 2.285688 / 55.444624 (-53.158936) | 1.955160 / 6.876477 (-4.921317) | 2.045887 / 2.142072 (-0.096186) | 0.878836 / 4.805227 (-3.926392) | 0.166483 / 6.500664 (-6.334181) | 0.062656 / 0.075469 (-0.012814) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.215152 / 1.841788 (-0.626636) | 15.436187 / 8.074308 (7.361879) | 14.489951 / 10.191392 (4.298559) | 0.199019 / 0.680424 (-0.481404) | 0.029148 / 0.534201 (-0.505053) | 0.440309 / 0.579283 (-0.138974) | 0.452041 / 0.434364 (0.017677) | 0.527102 / 0.540337 (-0.013236) | 0.634302 / 1.386936 (-0.752634) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007814 / 0.011353 (-0.003539) | 0.005582 / 0.011008 (-0.005427) | 0.075466 / 0.038508 (0.036958) | 0.034421 / 0.023109 (0.011312) | 0.342345 / 0.275898 (0.066447) | 0.389943 / 0.323480 (0.066463) | 0.006346 / 0.007986 (-0.001639) | 0.004442 / 0.004328 (0.000113) | 0.074440 / 0.004250 (0.070190) | 0.056383 / 0.037052 (0.019331) | 0.340293 / 0.258489 (0.081804) | 0.394416 / 0.293841 (0.100575) | 0.037217 / 0.128546 (-0.091330) | 0.012597 / 0.075646 (-0.063050) | 0.087005 / 0.419271 (-0.332267) | 0.051626 / 0.043533 (0.008094) | 0.336690 / 0.255139 (0.081551) | 0.369143 / 0.283200 (0.085943) | 0.110764 / 0.141683 (-0.030919) | 1.459003 / 1.452155 (0.006849) | 1.557333 / 1.492716 (0.064617) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.319596 / 0.018006 (0.301590) | 0.514697 / 0.000490 (0.514207) | 0.005286 / 0.000200 (0.005086) | 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) | 0.032579 / 0.037411 (-0.004832) | 0.111094 / 0.014526 (0.096568) | 0.127827 / 0.176557 (-0.048730) | 0.169967 / 0.737135 (-0.567168) | 0.133149 / 0.296338 (-0.163189) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424637 / 0.215209 (0.209428) | 4.217889 / 2.077655 (2.140235) | 2.044844 / 1.504120 (0.540724) | 1.863513 / 1.541195 (0.322319) | 1.975674 / 1.468490 (0.507184) | 0.695493 / 4.584777 (-3.889284) | 3.815562 / 3.745712 (0.069850) | 3.534427 / 5.269862 (-1.735435) | 1.684874 / 4.565676 (-2.880802) | 0.085560 / 0.424275 (-0.338715) | 0.012439 / 0.007607 (0.004832) | 0.541231 / 0.226044 (0.315187) | 5.287166 / 2.268929 (3.018237) | 2.596622 / 55.444624 (-52.848002) | 2.315913 / 6.876477 (-4.560564) | 2.418454 / 2.142072 (0.276381) | 0.838947 / 4.805227 (-3.966281) | 0.168149 / 6.500664 (-6.332515) | 0.066439 / 0.075469 (-0.009030) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.264814 / 1.841788 (-0.576974) | 15.861324 / 8.074308 (7.787016) | 14.352515 / 10.191392 (4.161123) | 0.167032 / 0.680424 (-0.513391) | 0.017766 / 0.534201 (-0.516435) | 0.421821 / 0.579283 (-0.157462) | 0.426657 / 0.434364 (-0.007707) | 0.526742 / 0.540337 (-0.013595) | 0.623851 / 1.386936 (-0.763085) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#69b19755e9e37b746ef56780a62d21ef20c574d5 \"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) | 0.008167 / 0.011353 (-0.003186) | 0.004404 / 0.011008 (-0.006605) | 0.100462 / 0.038508 (0.061954) | 0.028835 / 0.023109 (0.005726) | 0.326759 / 0.275898 (0.050861) | 0.355150 / 0.323480 (0.031670) | 0.007200 / 0.007986 (-0.000786) | 0.003293 / 0.004328 (-0.001035) | 0.078006 / 0.004250 (0.073756) | 0.033298 / 0.037052 (-0.003754) | 0.307119 / 0.258489 (0.048630) | 0.337689 / 0.293841 (0.043848) | 0.033016 / 0.128546 (-0.095530) | 0.011383 / 0.075646 (-0.064263) | 0.321989 / 0.419271 (-0.097283) | 0.039793 / 0.043533 (-0.003740) | 0.295388 / 0.255139 (0.040249) | 0.322694 / 0.283200 (0.039494) | 0.082989 / 0.141683 (-0.058694) | 1.496701 / 1.452155 (0.044546) | 1.548861 / 1.492716 (0.056145) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.176587 / 0.018006 (0.158580) | 0.397660 / 0.000490 (0.397170) | 0.001063 / 0.000200 (0.000863) | 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) | 0.022386 / 0.037411 (-0.015025) | 0.096380 / 0.014526 (0.081854) | 0.103032 / 0.176557 (-0.073525) | 0.135050 / 0.737135 (-0.602086) | 0.105941 / 0.296338 (-0.190397) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430989 / 0.215209 (0.215780) | 4.310309 / 2.077655 (2.232654) | 2.142596 / 1.504120 (0.638477) | 1.952043 / 1.541195 (0.410848) | 1.817803 / 1.468490 (0.349312) | 0.690026 / 4.584777 (-3.894751) | 3.315413 / 3.745712 (-0.430299) | 3.370336 / 5.269862 (-1.899525) | 1.668707 / 4.565676 (-2.896970) | 0.081860 / 0.424275 (-0.342415) | 0.012493 / 0.007607 (0.004886) | 0.527779 / 0.226044 (0.301735) | 5.318732 / 2.268929 (3.049804) | 2.467029 / 55.444624 (-52.977596) | 2.247171 / 6.876477 (-4.629306) | 2.270825 / 2.142072 (0.128752) | 0.802288 / 4.805227 (-4.002939) | 0.148895 / 6.500664 (-6.351770) | 0.064967 / 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) | 1.259304 / 1.841788 (-0.582484) | 13.662441 / 8.074308 (5.588133) | 14.074662 / 10.191392 (3.883270) | 0.152907 / 0.680424 (-0.527516) | 0.028340 / 0.534201 (-0.505861) | 0.397356 / 0.579283 (-0.181927) | 0.392600 / 0.434364 (-0.041764) | 0.467935 / 0.540337 (-0.072402) | 0.539890 / 1.386936 (-0.847046) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006156 / 0.011353 (-0.005197) | 0.004371 / 0.011008 (-0.006637) | 0.076391 / 0.038508 (0.037883) | 0.026455 / 0.023109 (0.003346) | 0.339816 / 0.275898 (0.063917) | 0.370032 / 0.323480 (0.046552) | 0.004614 / 0.007986 (-0.003372) | 0.003200 / 0.004328 (-0.001129) | 0.075408 / 0.004250 (0.071157) | 0.034100 / 0.037052 (-0.002953) | 0.341232 / 0.258489 (0.082743) | 0.380290 / 0.293841 (0.086449) | 0.031021 / 0.128546 (-0.097525) | 0.011562 / 0.075646 (-0.064084) | 0.085564 / 0.419271 (-0.333708) | 0.041431 / 0.043533 (-0.002102) | 0.359570 / 0.255139 (0.104431) | 0.366919 / 0.283200 (0.083719) | 0.088242 / 0.141683 (-0.053441) | 1.460703 / 1.452155 (0.008548) | 1.534351 / 1.492716 (0.041635) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225703 / 0.018006 (0.207697) | 0.395014 / 0.000490 (0.394524) | 0.000385 / 0.000200 (0.000185) | 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) | 0.023975 / 0.037411 (-0.013436) | 0.098658 / 0.014526 (0.084132) | 0.105043 / 0.176557 (-0.071513) | 0.139988 / 0.737135 (-0.597148) | 0.106854 / 0.296338 (-0.189484) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.442454 / 0.215209 (0.227245) | 4.430860 / 2.077655 (2.353205) | 2.084823 / 1.504120 (0.580704) | 1.870421 / 1.541195 (0.329226) | 1.901618 / 1.468490 (0.433128) | 0.699214 / 4.584777 (-3.885563) | 3.336911 / 3.745712 (-0.408801) | 1.856479 / 5.269862 (-3.413383) | 1.166496 / 4.565676 (-3.399180) | 0.083189 / 0.424275 (-0.341086) | 0.012293 / 0.007607 (0.004686) | 0.543147 / 0.226044 (0.317102) | 5.452030 / 2.268929 (3.183101) | 2.506689 / 55.444624 (-52.937936) | 2.168186 / 6.876477 (-4.708291) | 2.172277 / 2.142072 (0.030205) | 0.813554 / 4.805227 (-3.991673) | 0.152074 / 6.500664 (-6.348590) | 0.066891 / 0.075469 (-0.008579) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.278635 / 1.841788 (-0.563153) | 13.690232 / 8.074308 (5.615924) | 13.403201 / 10.191392 (3.211809) | 0.128171 / 0.680424 (-0.552253) | 0.016687 / 0.534201 (-0.517514) | 0.378645 / 0.579283 (-0.200638) | 0.382922 / 0.434364 (-0.051442) | 0.467483 / 0.540337 (-0.072854) | 0.559026 / 1.386936 (-0.827910) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b262d411ec0e252615a140c4e3e60e7dbd38eef1 \"CML watermark\")\n" ]

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[ "@albertvillanova It looks like that issue has been fixed so I made a PR to unpin sqlalchemy! ", "The source issue:\r\n- https://github.com/pandas-dev/pandas/issues/40686\r\n\r\nhas been fixed:\r\n- https://github.com/pandas-dev/pandas/pull/48576\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`)." ]

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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) | 0.012442 / 0.011353 (0.001089) | 0.006274 / 0.011008 (-0.004734) | 0.128249 / 0.038508 (0.089741) | 0.040117 / 0.023109 (0.017008) | 0.383725 / 0.275898 (0.107827) | 0.510494 / 0.323480 (0.187014) | 0.009037 / 0.007986 (0.001051) | 0.008256 / 0.004328 (0.003927) | 0.105329 / 0.004250 (0.101079) | 0.046909 / 0.037052 (0.009857) | 0.401980 / 0.258489 (0.143491) | 0.461332 / 0.293841 (0.167491) | 0.065629 / 0.128546 (-0.062917) | 0.020043 / 0.075646 (-0.055604) | 0.453773 / 0.419271 (0.034501) | 0.063456 / 0.043533 (0.019923) | 0.384458 / 0.255139 (0.129319) | 0.449699 / 0.283200 (0.166499) | 0.118197 / 0.141683 (-0.023486) | 1.915080 / 1.452155 (0.462925) | 1.957132 / 1.492716 (0.464416) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209657 / 0.018006 (0.191651) | 0.592478 / 0.000490 (0.591988) | 0.004137 / 0.000200 (0.003937) | 0.000124 / 0.000054 (0.000069) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029607 / 0.037411 (-0.007804) | 0.129559 / 0.014526 (0.115033) | 0.148326 / 0.176557 (-0.028231) | 0.190506 / 0.737135 (-0.546629) | 0.143177 / 0.296338 (-0.153162) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.626166 / 0.215209 (0.410957) | 6.612680 / 2.077655 (4.535026) | 2.432354 / 1.504120 (0.928234) | 2.051482 / 1.541195 (0.510287) | 2.055822 / 1.468490 (0.587332) | 1.210099 / 4.584777 (-3.374678) | 5.498117 / 3.745712 (1.752405) | 3.054838 / 5.269862 (-2.215024) | 2.182875 / 4.565676 (-2.382802) | 0.144518 / 0.424275 (-0.279757) | 0.014132 / 0.007607 (0.006525) | 0.801805 / 0.226044 (0.575761) | 7.911235 / 2.268929 (5.642307) | 3.372762 / 55.444624 (-52.071862) | 2.517266 / 6.876477 (-4.359210) | 2.515329 / 2.142072 (0.373256) | 1.501731 / 4.805227 (-3.303497) | 0.252569 / 6.500664 (-6.248096) | 0.080987 / 0.075469 (0.005518) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.709880 / 1.841788 (-0.131907) | 18.640340 / 8.074308 (10.566032) | 23.560908 / 10.191392 (13.369516) | 0.265680 / 0.680424 (-0.414744) | 0.046438 / 0.534201 (-0.487763) | 0.571973 / 0.579283 (-0.007310) | 0.642425 / 0.434364 (0.208061) | 0.698167 / 0.540337 (0.157830) | 0.842132 / 1.386936 (-0.544804) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009268 / 0.011353 (-0.002085) | 0.006052 / 0.011008 (-0.004956) | 0.133448 / 0.038508 (0.094939) | 0.034417 / 0.023109 (0.011308) | 0.435573 / 0.275898 (0.159675) | 0.479642 / 0.323480 (0.156162) | 0.008016 / 0.007986 (0.000030) | 0.006616 / 0.004328 (0.002288) | 0.106256 / 0.004250 (0.102005) | 0.048995 / 0.037052 (0.011942) | 0.450056 / 0.258489 (0.191567) | 0.511027 / 0.293841 (0.217187) | 0.052928 / 0.128546 (-0.075618) | 0.020824 / 0.075646 (-0.054822) | 0.450105 / 0.419271 (0.030834) | 0.062729 / 0.043533 (0.019196) | 0.438887 / 0.255139 (0.183748) | 0.468732 / 0.283200 (0.185532) | 0.116101 / 0.141683 (-0.025582) | 1.909689 / 1.452155 (0.457534) | 2.042007 / 1.492716 (0.549291) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198265 / 0.018006 (0.180259) | 0.541799 / 0.000490 (0.541309) | 0.003938 / 0.000200 (0.003738) | 0.000116 / 0.000054 (0.000062) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035933 / 0.037411 (-0.001478) | 0.130754 / 0.014526 (0.116229) | 0.146143 / 0.176557 (-0.030414) | 0.202042 / 0.737135 (-0.535094) | 0.155648 / 0.296338 (-0.140691) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.691123 / 0.215209 (0.475914) | 6.708370 / 2.077655 (4.630715) | 2.957120 / 1.504120 (1.453000) | 2.558350 / 1.541195 (1.017155) | 2.611271 / 1.468490 (1.142781) | 1.327355 / 4.584777 (-3.257422) | 5.755975 / 3.745712 (2.010263) | 3.295556 / 5.269862 (-1.974305) | 2.159831 / 4.565676 (-2.405845) | 0.161409 / 0.424275 (-0.262866) | 0.015470 / 0.007607 (0.007863) | 0.840611 / 0.226044 (0.614567) | 8.550064 / 2.268929 (6.281136) | 3.832013 / 55.444624 (-51.612612) | 3.032909 / 6.876477 (-3.843568) | 3.155651 / 2.142072 (1.013578) | 1.612486 / 4.805227 (-3.192741) | 0.273789 / 6.500664 (-6.226875) | 0.085618 / 0.075469 (0.010149) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.808376 / 1.841788 (-0.033412) | 18.267614 / 8.074308 (10.193306) | 21.047679 / 10.191392 (10.856286) | 0.259089 / 0.680424 (-0.421335) | 0.029211 / 0.534201 (-0.504990) | 0.556303 / 0.579283 (-0.022980) | 0.625264 / 0.434364 (0.190900) | 0.680814 / 0.540337 (0.140476) | 0.810146 / 1.386936 (-0.576790) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#20ea76c80e07acad78cf67198a4046a982feda21 \"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) | 0.008779 / 0.011353 (-0.002574) | 0.004644 / 0.011008 (-0.006364) | 0.099814 / 0.038508 (0.061306) | 0.029830 / 0.023109 (0.006721) | 0.299159 / 0.275898 (0.023261) | 0.354815 / 0.323480 (0.031335) | 0.006968 / 0.007986 (-0.001018) | 0.003521 / 0.004328 (-0.000808) | 0.077687 / 0.004250 (0.073437) | 0.035019 / 0.037052 (-0.002034) | 0.309548 / 0.258489 (0.051059) | 0.345228 / 0.293841 (0.051387) | 0.033644 / 0.128546 (-0.094902) | 0.011564 / 0.075646 (-0.064083) | 0.321835 / 0.419271 (-0.097437) | 0.041798 / 0.043533 (-0.001735) | 0.298190 / 0.255139 (0.043051) | 0.328874 / 0.283200 (0.045674) | 0.088175 / 0.141683 (-0.053508) | 1.481755 / 1.452155 (0.029600) | 1.503085 / 1.492716 (0.010369) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.170930 / 0.018006 (0.152924) | 0.422155 / 0.000490 (0.421666) | 0.001708 / 0.000200 (0.001509) | 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) | 0.022588 / 0.037411 (-0.014824) | 0.095775 / 0.014526 (0.081249) | 0.103939 / 0.176557 (-0.072618) | 0.138441 / 0.737135 (-0.598694) | 0.107896 / 0.296338 (-0.188442) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418243 / 0.215209 (0.203034) | 4.171432 / 2.077655 (2.093777) | 1.906029 / 1.504120 (0.401909) | 1.698174 / 1.541195 (0.156979) | 1.748339 / 1.468490 (0.279849) | 0.691026 / 4.584777 (-3.893751) | 3.393354 / 3.745712 (-0.352358) | 2.722412 / 5.269862 (-2.547450) | 1.462439 / 4.565676 (-3.103238) | 0.084713 / 0.424275 (-0.339562) | 0.012131 / 0.007607 (0.004524) | 0.522153 / 0.226044 (0.296109) | 5.197916 / 2.268929 (2.928988) | 2.314270 / 55.444624 (-53.130354) | 1.986599 / 6.876477 (-4.889878) | 2.012757 / 2.142072 (-0.129315) | 0.802540 / 4.805227 (-4.002687) | 0.148673 / 6.500664 (-6.351991) | 0.065924 / 0.075469 (-0.009545) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.263790 / 1.841788 (-0.577998) | 13.874784 / 8.074308 (5.800476) | 13.842276 / 10.191392 (3.650884) | 0.149002 / 0.680424 (-0.531422) | 0.028550 / 0.534201 (-0.505651) | 0.396913 / 0.579283 (-0.182370) | 0.401543 / 0.434364 (-0.032821) | 0.473754 / 0.540337 (-0.066583) | 0.560455 / 1.386936 (-0.826481) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006724 / 0.011353 (-0.004629) | 0.004507 / 0.011008 (-0.006502) | 0.098447 / 0.038508 (0.059939) | 0.027888 / 0.023109 (0.004779) | 0.428956 / 0.275898 (0.153058) | 0.451557 / 0.323480 (0.128077) | 0.005056 / 0.007986 (-0.002929) | 0.003363 / 0.004328 (-0.000965) | 0.075990 / 0.004250 (0.071740) | 0.038688 / 0.037052 (0.001635) | 0.421550 / 0.258489 (0.163061) | 0.459480 / 0.293841 (0.165639) | 0.031408 / 0.128546 (-0.097138) | 0.011559 / 0.075646 (-0.064088) | 0.320054 / 0.419271 (-0.099217) | 0.041917 / 0.043533 (-0.001616) | 0.420878 / 0.255139 (0.165739) | 0.444813 / 0.283200 (0.161613) | 0.090409 / 0.141683 (-0.051274) | 1.490058 / 1.452155 (0.037904) | 1.645206 / 1.492716 (0.152489) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221105 / 0.018006 (0.203099) | 0.407537 / 0.000490 (0.407047) | 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) | 0.024658 / 0.037411 (-0.012754) | 0.099230 / 0.014526 (0.084705) | 0.107788 / 0.176557 (-0.068769) | 0.143040 / 0.737135 (-0.594096) | 0.109440 / 0.296338 (-0.186899) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.453303 / 0.215209 (0.238094) | 4.520376 / 2.077655 (2.442722) | 2.133909 / 1.504120 (0.629789) | 1.926996 / 1.541195 (0.385801) | 2.019870 / 1.468490 (0.551380) | 0.707423 / 4.584777 (-3.877354) | 3.391903 / 3.745712 (-0.353809) | 1.860661 / 5.269862 (-3.409201) | 1.159940 / 4.565676 (-3.405736) | 0.083773 / 0.424275 (-0.340502) | 0.012228 / 0.007607 (0.004621) | 0.554666 / 0.226044 (0.328622) | 5.567564 / 2.268929 (3.298636) | 2.636718 / 55.444624 (-52.807907) | 2.240215 / 6.876477 (-4.636262) | 2.218951 / 2.142072 (0.076879) | 0.817167 / 4.805227 (-3.988060) | 0.151633 / 6.500664 (-6.349032) | 0.066515 / 0.075469 (-0.008954) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.296665 / 1.841788 (-0.545123) | 13.997898 / 8.074308 (5.923590) | 13.286607 / 10.191392 (3.095215) | 0.148906 / 0.680424 (-0.531518) | 0.016600 / 0.534201 (-0.517601) | 0.377459 / 0.579283 (-0.201824) | 0.379938 / 0.434364 (-0.054426) | 0.461628 / 0.540337 (-0.078709) | 0.550592 / 1.386936 (-0.836344) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#053f51a3e2adb762236eb29dd02791307f45f02f \"CML watermark\")\n" ]

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[ "Hi ! In your code you only iterate on the Arrow buffers - you don't actually load the data as python objects. For a fair comparison, you can modify your code using:\r\n```diff\r\n- for _ in range(0, len(table), bsz):\r\n- _ = {k:table[k][_ : _ + bsz] for k in cols}\r\n+ for _ in range(0, len(table), bsz):\r\n+ _ = {k:table[k][_ : _ + bsz].to_pylist() for k in cols}\r\n```\r\n\r\nI re-ran your code and got a speed ratio of 1.00x and 1.02x", "Ah I see, datasets is implicitly making this conversion. Thanks for pointing that out!\r\n\r\nIf it's not too much, I would also suggest updating some of your docs with the same `.to_pylist()` conversion in the code snippet that follows [here](https://huggingface.co/course/chapter5/4?fw=pt#:~:text=let%E2%80%99s%20run%20a%20little%20speed%20test%20by%20iterating%20over%20all%20the%20elements%20in%20the%20PubMed%20Abstracts%20dataset%3A).", "This code snippet shows `datasets` code that reads the Arrow data as python objects already, there is no need to add to_pylist. Or were you thinking about something else ?" ]

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[ "Hi ! This would be a nice addition indeed :) This sounds like a duplicate of https://github.com/huggingface/datasets/issues/5468\r\n\r\n> Not sure. Some of my PRs are still open and some do not have any discussions.\r\n\r\nSorry to hear that, feel free to ping me on those PRs" ]

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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) | 0.008959 / 0.011353 (-0.002394) | 0.004549 / 0.011008 (-0.006460) | 0.102012 / 0.038508 (0.063504) | 0.030122 / 0.023109 (0.007013) | 0.303731 / 0.275898 (0.027833) | 0.344418 / 0.323480 (0.020938) | 0.007199 / 0.007986 (-0.000787) | 0.003415 / 0.004328 (-0.000913) | 0.079784 / 0.004250 (0.075534) | 0.034894 / 0.037052 (-0.002158) | 0.304739 / 0.258489 (0.046250) | 0.359457 / 0.293841 (0.065616) | 0.034194 / 0.128546 (-0.094352) | 0.011348 / 0.075646 (-0.064298) | 0.324340 / 0.419271 (-0.094931) | 0.041071 / 0.043533 (-0.002461) | 0.304437 / 0.255139 (0.049298) | 0.335517 / 0.283200 (0.052317) | 0.087787 / 0.141683 (-0.053895) | 1.467293 / 1.452155 (0.015138) | 1.543529 / 1.492716 (0.050813) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.187654 / 0.018006 (0.169648) | 0.426558 / 0.000490 (0.426068) | 0.003585 / 0.000200 (0.003385) | 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) | 0.023410 / 0.037411 (-0.014001) | 0.097065 / 0.014526 (0.082539) | 0.105358 / 0.176557 (-0.071198) | 0.140941 / 0.737135 (-0.596195) | 0.109484 / 0.296338 (-0.186855) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420334 / 0.215209 (0.205125) | 4.223235 / 2.077655 (2.145581) | 1.866213 / 1.504120 (0.362093) | 1.673829 / 1.541195 (0.132634) | 1.757828 / 1.468490 (0.289337) | 0.702203 / 4.584777 (-3.882574) | 3.426192 / 3.745712 (-0.319521) | 1.950392 / 5.269862 (-3.319470) | 1.286139 / 4.565676 (-3.279538) | 0.082858 / 0.424275 (-0.341417) | 0.012587 / 0.007607 (0.004980) | 0.531920 / 0.226044 (0.305876) | 5.344425 / 2.268929 (3.075497) | 2.337875 / 55.444624 (-53.106749) | 1.967713 / 6.876477 (-4.908764) | 2.022075 / 2.142072 (-0.119997) | 0.829267 / 4.805227 (-3.975961) | 0.151712 / 6.500664 (-6.348952) | 0.066617 / 0.075469 (-0.008852) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.251867 / 1.841788 (-0.589921) | 13.861756 / 8.074308 (5.787448) | 14.236309 / 10.191392 (4.044917) | 0.138215 / 0.680424 (-0.542209) | 0.028600 / 0.534201 (-0.505601) | 0.395890 / 0.579283 (-0.183393) | 0.403971 / 0.434364 (-0.030393) | 0.479033 / 0.540337 (-0.061305) | 0.564019 / 1.386936 (-0.822917) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006845 / 0.011353 (-0.004508) | 0.004544 / 0.011008 (-0.006464) | 0.098719 / 0.038508 (0.060211) | 0.029082 / 0.023109 (0.005973) | 0.426011 / 0.275898 (0.150113) | 0.447185 / 0.323480 (0.123705) | 0.005203 / 0.007986 (-0.002783) | 0.004790 / 0.004328 (0.000462) | 0.076446 / 0.004250 (0.072196) | 0.040649 / 0.037052 (0.003596) | 0.414810 / 0.258489 (0.156321) | 0.452082 / 0.293841 (0.158241) | 0.031842 / 0.128546 (-0.096704) | 0.011575 / 0.075646 (-0.064071) | 0.320710 / 0.419271 (-0.098561) | 0.044994 / 0.043533 (0.001461) | 0.415645 / 0.255139 (0.160506) | 0.435235 / 0.283200 (0.152035) | 0.091756 / 0.141683 (-0.049927) | 1.493900 / 1.452155 (0.041746) | 1.592353 / 1.492716 (0.099637) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.264710 / 0.018006 (0.246703) | 0.410553 / 0.000490 (0.410064) | 0.024497 / 0.000200 (0.024297) | 0.000232 / 0.000054 (0.000178) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024452 / 0.037411 (-0.012959) | 0.102673 / 0.014526 (0.088147) | 0.107787 / 0.176557 (-0.068770) | 0.147368 / 0.737135 (-0.589767) | 0.112127 / 0.296338 (-0.184211) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.471294 / 0.215209 (0.256085) | 4.711638 / 2.077655 (2.633983) | 2.436819 / 1.504120 (0.932699) | 2.238540 / 1.541195 (0.697345) | 2.334134 / 1.468490 (0.865644) | 0.697668 / 4.584777 (-3.887108) | 3.414332 / 3.745712 (-0.331380) | 2.783248 / 5.269862 (-2.486614) | 1.529599 / 4.565676 (-3.036078) | 0.082626 / 0.424275 (-0.341649) | 0.012385 / 0.007607 (0.004778) | 0.580486 / 0.226044 (0.354441) | 5.837914 / 2.268929 (3.568986) | 2.915129 / 55.444624 (-52.529495) | 2.606254 / 6.876477 (-4.270223) | 2.659031 / 2.142072 (0.516958) | 0.810431 / 4.805227 (-3.994796) | 0.151666 / 6.500664 (-6.348998) | 0.066873 / 0.075469 (-0.008596) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.259933 / 1.841788 (-0.581855) | 14.052388 / 8.074308 (5.978080) | 13.356141 / 10.191392 (3.164749) | 0.138416 / 0.680424 (-0.542008) | 0.016582 / 0.534201 (-0.517619) | 0.378110 / 0.579283 (-0.201173) | 0.385089 / 0.434364 (-0.049275) | 0.465299 / 0.540337 (-0.075038) | 0.559780 / 1.386936 (-0.827156) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d2859fd4d4beca33f21539a6e1df9a7f012cbd10 \"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) | 0.011945 / 0.011353 (0.000592) | 0.006128 / 0.011008 (-0.004880) | 0.128926 / 0.038508 (0.090418) | 0.037708 / 0.023109 (0.014599) | 0.373449 / 0.275898 (0.097551) | 0.423567 / 0.323480 (0.100088) | 0.009848 / 0.007986 (0.001863) | 0.006097 / 0.004328 (0.001769) | 0.098275 / 0.004250 (0.094024) | 0.043199 / 0.037052 (0.006147) | 0.376848 / 0.258489 (0.118359) | 0.441819 / 0.293841 (0.147978) | 0.055094 / 0.128546 (-0.073453) | 0.019704 / 0.075646 (-0.055942) | 0.422746 / 0.419271 (0.003474) | 0.061764 / 0.043533 (0.018231) | 0.381056 / 0.255139 (0.125917) | 0.419343 / 0.283200 (0.136144) | 0.116720 / 0.141683 (-0.024963) | 1.763913 / 1.452155 (0.311759) | 1.872306 / 1.492716 (0.379589) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198651 / 0.018006 (0.180645) | 0.560565 / 0.000490 (0.560075) | 0.004269 / 0.000200 (0.004069) | 0.000114 / 0.000054 (0.000059) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027307 / 0.037411 (-0.010104) | 0.128276 / 0.014526 (0.113750) | 0.129015 / 0.176557 (-0.047542) | 0.167269 / 0.737135 (-0.569866) | 0.143955 / 0.296338 (-0.152384) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.564954 / 0.215209 (0.349745) | 5.810570 / 2.077655 (3.732916) | 2.456382 / 1.504120 (0.952262) | 2.115809 / 1.541195 (0.574614) | 2.097363 / 1.468490 (0.628873) | 1.189712 / 4.584777 (-3.395065) | 5.318287 / 3.745712 (1.572575) | 2.965763 / 5.269862 (-2.304099) | 2.177958 / 4.565676 (-2.387719) | 0.144135 / 0.424275 (-0.280140) | 0.014348 / 0.007607 (0.006741) | 0.781715 / 0.226044 (0.555670) | 7.688349 / 2.268929 (5.419421) | 3.189260 / 55.444624 (-52.255365) | 2.552340 / 6.876477 (-4.324137) | 2.559312 / 2.142072 (0.417240) | 1.490755 / 4.805227 (-3.314473) | 0.257908 / 6.500664 (-6.242756) | 0.082016 / 0.075469 (0.006547) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.565735 / 1.841788 (-0.276053) | 17.660338 / 8.074308 (9.586030) | 19.493573 / 10.191392 (9.302181) | 0.241310 / 0.680424 (-0.439114) | 0.043485 / 0.534201 (-0.490716) | 0.557397 / 0.579283 (-0.021886) | 0.624385 / 0.434364 (0.190021) | 0.634601 / 0.540337 (0.094264) | 0.743140 / 1.386936 (-0.643796) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010134 / 0.011353 (-0.001219) | 0.005858 / 0.011008 (-0.005150) | 0.128741 / 0.038508 (0.090232) | 0.036769 / 0.023109 (0.013660) | 0.470894 / 0.275898 (0.194996) | 0.524302 / 0.323480 (0.200822) | 0.006830 / 0.007986 (-0.001156) | 0.006166 / 0.004328 (0.001838) | 0.094875 / 0.004250 (0.090625) | 0.051201 / 0.037052 (0.014148) | 0.493992 / 0.258489 (0.235503) | 0.510540 / 0.293841 (0.216699) | 0.056354 / 0.128546 (-0.072192) | 0.020512 / 0.075646 (-0.055134) | 0.417809 / 0.419271 (-0.001463) | 0.061941 / 0.043533 (0.018408) | 0.498883 / 0.255139 (0.243744) | 0.480762 / 0.283200 (0.197563) | 0.110753 / 0.141683 (-0.030930) | 1.914096 / 1.452155 (0.461941) | 1.941338 / 1.492716 (0.448622) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237955 / 0.018006 (0.219949) | 0.518136 / 0.000490 (0.517647) | 0.000475 / 0.000200 (0.000275) | 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) | 0.032947 / 0.037411 (-0.004465) | 0.127857 / 0.014526 (0.113331) | 0.133911 / 0.176557 (-0.042646) | 0.188406 / 0.737135 (-0.548729) | 0.143939 / 0.296338 (-0.152400) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.787553 / 0.215209 (0.572344) | 6.976572 / 2.077655 (4.898918) | 2.897964 / 1.504120 (1.393844) | 2.545906 / 1.541195 (1.004711) | 2.622111 / 1.468490 (1.153620) | 1.278283 / 4.584777 (-3.306494) | 5.650447 / 3.745712 (1.904734) | 4.955835 / 5.269862 (-0.314027) | 2.767946 / 4.565676 (-1.797731) | 0.149385 / 0.424275 (-0.274890) | 0.014340 / 0.007607 (0.006733) | 0.861774 / 0.226044 (0.635730) | 8.660985 / 2.268929 (6.392057) | 3.685611 / 55.444624 (-51.759014) | 2.963087 / 6.876477 (-3.913390) | 3.020746 / 2.142072 (0.878673) | 1.538908 / 4.805227 (-3.266319) | 0.285875 / 6.500664 (-6.214789) | 0.080337 / 0.075469 (0.004867) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.575155 / 1.841788 (-0.266633) | 17.548946 / 8.074308 (9.474638) | 19.954104 / 10.191392 (9.762712) | 0.242025 / 0.680424 (-0.438398) | 0.025586 / 0.534201 (-0.508615) | 0.515676 / 0.579283 (-0.063607) | 0.607035 / 0.434364 (0.172671) | 0.633597 / 0.540337 (0.093259) | 0.744577 / 1.386936 (-0.642359) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6529cada7879496bf18dd686e4d281de81d6203c \"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) | 0.008578 / 0.011353 (-0.002775) | 0.004535 / 0.011008 (-0.006473) | 0.100694 / 0.038508 (0.062186) | 0.029570 / 0.023109 (0.006460) | 0.296384 / 0.275898 (0.020486) | 0.354405 / 0.323480 (0.030925) | 0.006962 / 0.007986 (-0.001024) | 0.003405 / 0.004328 (-0.000924) | 0.077275 / 0.004250 (0.073025) | 0.036623 / 0.037052 (-0.000429) | 0.309844 / 0.258489 (0.051355) | 0.340343 / 0.293841 (0.046502) | 0.033626 / 0.128546 (-0.094920) | 0.011433 / 0.075646 (-0.064214) | 0.322659 / 0.419271 (-0.096612) | 0.040509 / 0.043533 (-0.003024) | 0.294002 / 0.255139 (0.038863) | 0.323259 / 0.283200 (0.040059) | 0.088023 / 0.141683 (-0.053660) | 1.462039 / 1.452155 (0.009885) | 1.495401 / 1.492716 (0.002684) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218614 / 0.018006 (0.200608) | 0.482359 / 0.000490 (0.481869) | 0.001216 / 0.000200 (0.001016) | 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) | 0.023167 / 0.037411 (-0.014245) | 0.098468 / 0.014526 (0.083942) | 0.108273 / 0.176557 (-0.068284) | 0.139991 / 0.737135 (-0.597144) | 0.109032 / 0.296338 (-0.187307) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.421526 / 0.215209 (0.206317) | 4.216808 / 2.077655 (2.139153) | 1.860550 / 1.504120 (0.356431) | 1.654518 / 1.541195 (0.113323) | 1.699064 / 1.468490 (0.230574) | 0.691489 / 4.584777 (-3.893287) | 3.401885 / 3.745712 (-0.343827) | 2.792860 / 5.269862 (-2.477001) | 1.516269 / 4.565676 (-3.049408) | 0.081627 / 0.424275 (-0.342648) | 0.012556 / 0.007607 (0.004949) | 0.531535 / 0.226044 (0.305491) | 5.320752 / 2.268929 (3.051823) | 2.314502 / 55.444624 (-53.130123) | 1.967118 / 6.876477 (-4.909359) | 2.008252 / 2.142072 (-0.133821) | 0.809730 / 4.805227 (-3.995497) | 0.148112 / 6.500664 (-6.352552) | 0.064821 / 0.075469 (-0.010648) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.269754 / 1.841788 (-0.572033) | 13.884200 / 8.074308 (5.809892) | 13.914390 / 10.191392 (3.722998) | 0.150176 / 0.680424 (-0.530248) | 0.028463 / 0.534201 (-0.505738) | 0.398723 / 0.579283 (-0.180561) | 0.400433 / 0.434364 (-0.033931) | 0.485169 / 0.540337 (-0.055169) | 0.565995 / 1.386936 (-0.820941) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006479 / 0.011353 (-0.004874) | 0.004504 / 0.011008 (-0.006504) | 0.097905 / 0.038508 (0.059397) | 0.027140 / 0.023109 (0.004031) | 0.408742 / 0.275898 (0.132844) | 0.448707 / 0.323480 (0.125228) | 0.004819 / 0.007986 (-0.003166) | 0.004761 / 0.004328 (0.000433) | 0.075456 / 0.004250 (0.071205) | 0.036282 / 0.037052 (-0.000771) | 0.405961 / 0.258489 (0.147472) | 0.449411 / 0.293841 (0.155570) | 0.031159 / 0.128546 (-0.097387) | 0.011693 / 0.075646 (-0.063954) | 0.321124 / 0.419271 (-0.098147) | 0.041369 / 0.043533 (-0.002164) | 0.408070 / 0.255139 (0.152931) | 0.428704 / 0.283200 (0.145504) | 0.086839 / 0.141683 (-0.054844) | 1.477772 / 1.452155 (0.025617) | 1.555913 / 1.492716 (0.063197) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.239494 / 0.018006 (0.221488) | 0.410785 / 0.000490 (0.410295) | 0.000989 / 0.000200 (0.000789) | 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) | 0.023805 / 0.037411 (-0.013607) | 0.097904 / 0.014526 (0.083378) | 0.106437 / 0.176557 (-0.070120) | 0.140555 / 0.737135 (-0.596580) | 0.107169 / 0.296338 (-0.189170) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.470233 / 0.215209 (0.255024) | 4.700451 / 2.077655 (2.622797) | 2.391712 / 1.504120 (0.887592) | 2.191125 / 1.541195 (0.649930) | 2.268924 / 1.468490 (0.800434) | 0.692421 / 4.584777 (-3.892356) | 3.387117 / 3.745712 (-0.358595) | 1.881731 / 5.269862 (-3.388130) | 1.155759 / 4.565676 (-3.409917) | 0.082040 / 0.424275 (-0.342236) | 0.012687 / 0.007607 (0.005080) | 0.567556 / 0.226044 (0.341511) | 5.701408 / 2.268929 (3.432480) | 2.864368 / 55.444624 (-52.580256) | 2.512073 / 6.876477 (-4.364404) | 2.546078 / 2.142072 (0.404005) | 0.795939 / 4.805227 (-4.009288) | 0.150078 / 6.500664 (-6.350586) | 0.067644 / 0.075469 (-0.007825) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.281681 / 1.841788 (-0.560107) | 13.967107 / 8.074308 (5.892799) | 13.293648 / 10.191392 (3.102256) | 0.128027 / 0.680424 (-0.552397) | 0.016791 / 0.534201 (-0.517410) | 0.379400 / 0.579283 (-0.199884) | 0.386847 / 0.434364 (-0.047517) | 0.469859 / 0.540337 (-0.070478) | 0.564203 / 1.386936 (-0.822733) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#90832b5e33774ea8ec35ccb92ac14649a345bdbe \"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) | 0.008701 / 0.011353 (-0.002652) | 0.004564 / 0.011008 (-0.006444) | 0.100578 / 0.038508 (0.062070) | 0.029209 / 0.023109 (0.006100) | 0.315308 / 0.275898 (0.039410) | 0.381022 / 0.323480 (0.057542) | 0.007152 / 0.007986 (-0.000834) | 0.003511 / 0.004328 (-0.000817) | 0.078361 / 0.004250 (0.074110) | 0.035394 / 0.037052 (-0.001658) | 0.331076 / 0.258489 (0.072586) | 0.366613 / 0.293841 (0.072772) | 0.033466 / 0.128546 (-0.095080) | 0.011521 / 0.075646 (-0.064126) | 0.322178 / 0.419271 (-0.097093) | 0.040891 / 0.043533 (-0.002641) | 0.320418 / 0.255139 (0.065279) | 0.345199 / 0.283200 (0.062000) | 0.087906 / 0.141683 (-0.053777) | 1.476801 / 1.452155 (0.024646) | 1.497738 / 1.492716 (0.005022) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.178094 / 0.018006 (0.160087) | 0.408317 / 0.000490 (0.407827) | 0.001825 / 0.000200 (0.001625) | 0.000067 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022402 / 0.037411 (-0.015010) | 0.097104 / 0.014526 (0.082578) | 0.105361 / 0.176557 (-0.071196) | 0.139728 / 0.737135 (-0.597407) | 0.109613 / 0.296338 (-0.186725) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418245 / 0.215209 (0.203036) | 4.155655 / 2.077655 (2.078000) | 1.865892 / 1.504120 (0.361772) | 1.659003 / 1.541195 (0.117809) | 1.725649 / 1.468490 (0.257159) | 0.688733 / 4.584777 (-3.896044) | 3.323529 / 3.745712 (-0.422184) | 1.867807 / 5.269862 (-3.402054) | 1.157740 / 4.565676 (-3.407936) | 0.081947 / 0.424275 (-0.342329) | 0.012471 / 0.007607 (0.004864) | 0.529333 / 0.226044 (0.303288) | 5.284898 / 2.268929 (3.015970) | 2.321741 / 55.444624 (-53.122883) | 1.975683 / 6.876477 (-4.900794) | 2.029691 / 2.142072 (-0.112381) | 0.810212 / 4.805227 (-3.995015) | 0.148185 / 6.500664 (-6.352479) | 0.064594 / 0.075469 (-0.010875) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.183391 / 1.841788 (-0.658396) | 13.574760 / 8.074308 (5.500452) | 14.215015 / 10.191392 (4.023623) | 0.150776 / 0.680424 (-0.529648) | 0.029058 / 0.534201 (-0.505143) | 0.404071 / 0.579283 (-0.175212) | 0.401289 / 0.434364 (-0.033075) | 0.490946 / 0.540337 (-0.049392) | 0.582292 / 1.386936 (-0.804644) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006695 / 0.011353 (-0.004658) | 0.004499 / 0.011008 (-0.006510) | 0.097633 / 0.038508 (0.059125) | 0.027606 / 0.023109 (0.004496) | 0.413191 / 0.275898 (0.137293) | 0.441896 / 0.323480 (0.118416) | 0.005703 / 0.007986 (-0.002283) | 0.004608 / 0.004328 (0.000280) | 0.074392 / 0.004250 (0.070141) | 0.037966 / 0.037052 (0.000913) | 0.410736 / 0.258489 (0.152247) | 0.448581 / 0.293841 (0.154740) | 0.031594 / 0.128546 (-0.096952) | 0.011597 / 0.075646 (-0.064049) | 0.319632 / 0.419271 (-0.099639) | 0.041189 / 0.043533 (-0.002343) | 0.407120 / 0.255139 (0.151981) | 0.433416 / 0.283200 (0.150216) | 0.089932 / 0.141683 (-0.051751) | 1.453919 / 1.452155 (0.001764) | 1.545892 / 1.492716 (0.053176) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224302 / 0.018006 (0.206296) | 0.415519 / 0.000490 (0.415029) | 0.000407 / 0.000200 (0.000207) | 0.000060 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024104 / 0.037411 (-0.013307) | 0.098202 / 0.014526 (0.083676) | 0.106416 / 0.176557 (-0.070140) | 0.141090 / 0.737135 (-0.596045) | 0.110188 / 0.296338 (-0.186150) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.478252 / 0.215209 (0.263043) | 4.739684 / 2.077655 (2.662029) | 2.419040 / 1.504120 (0.914920) | 2.217705 / 1.541195 (0.676510) | 2.303288 / 1.468490 (0.834798) | 0.696682 / 4.584777 (-3.888095) | 3.401962 / 3.745712 (-0.343750) | 1.886015 / 5.269862 (-3.383846) | 1.175084 / 4.565676 (-3.390592) | 0.083064 / 0.424275 (-0.341211) | 0.012613 / 0.007607 (0.005006) | 0.579105 / 0.226044 (0.353060) | 5.792119 / 2.268929 (3.523191) | 2.889778 / 55.444624 (-52.554846) | 2.537438 / 6.876477 (-4.339039) | 2.574814 / 2.142072 (0.432741) | 0.803438 / 4.805227 (-4.001789) | 0.151912 / 6.500664 (-6.348752) | 0.068291 / 0.075469 (-0.007178) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.286002 / 1.841788 (-0.555786) | 14.179443 / 8.074308 (6.105135) | 13.443939 / 10.191392 (3.252547) | 0.152427 / 0.680424 (-0.527996) | 0.017248 / 0.534201 (-0.516953) | 0.378734 / 0.579283 (-0.200549) | 0.382276 / 0.434364 (-0.052087) | 0.465323 / 0.540337 (-0.075014) | 0.556454 / 1.386936 (-0.830482) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b5672a956d5de864e6f5550e493527d962d6ae55 \"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) | 0.008675 / 0.011353 (-0.002678) | 0.004537 / 0.011008 (-0.006471) | 0.100179 / 0.038508 (0.061671) | 0.029307 / 0.023109 (0.006198) | 0.294687 / 0.275898 (0.018789) | 0.356868 / 0.323480 (0.033388) | 0.006992 / 0.007986 (-0.000994) | 0.003380 / 0.004328 (-0.000949) | 0.076961 / 0.004250 (0.072710) | 0.036047 / 0.037052 (-0.001005) | 0.308037 / 0.258489 (0.049548) | 0.341089 / 0.293841 (0.047248) | 0.033416 / 0.128546 (-0.095131) | 0.011534 / 0.075646 (-0.064112) | 0.322976 / 0.419271 (-0.096296) | 0.040894 / 0.043533 (-0.002639) | 0.296501 / 0.255139 (0.041362) | 0.324605 / 0.283200 (0.041405) | 0.086713 / 0.141683 (-0.054970) | 1.502784 / 1.452155 (0.050630) | 1.535013 / 1.492716 (0.042297) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.186647 / 0.018006 (0.168641) | 0.411003 / 0.000490 (0.410514) | 0.003594 / 0.000200 (0.003394) | 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) | 0.023704 / 0.037411 (-0.013707) | 0.096154 / 0.014526 (0.081629) | 0.103671 / 0.176557 (-0.072885) | 0.138878 / 0.737135 (-0.598258) | 0.106947 / 0.296338 (-0.189391) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417180 / 0.215209 (0.201970) | 4.149579 / 2.077655 (2.071925) | 1.865763 / 1.504120 (0.361643) | 1.669722 / 1.541195 (0.128527) | 1.722345 / 1.468490 (0.253855) | 0.695910 / 4.584777 (-3.888867) | 3.342266 / 3.745712 (-0.403446) | 1.884568 / 5.269862 (-3.385294) | 1.265013 / 4.565676 (-3.300664) | 0.081836 / 0.424275 (-0.342439) | 0.012371 / 0.007607 (0.004764) | 0.522997 / 0.226044 (0.296953) | 5.225434 / 2.268929 (2.956506) | 2.304701 / 55.444624 (-53.139924) | 1.949067 / 6.876477 (-4.927410) | 2.016347 / 2.142072 (-0.125725) | 0.809850 / 4.805227 (-3.995377) | 0.148396 / 6.500664 (-6.352268) | 0.063340 / 0.075469 (-0.012129) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.224621 / 1.841788 (-0.617167) | 13.814223 / 8.074308 (5.739915) | 13.879728 / 10.191392 (3.688336) | 0.149530 / 0.680424 (-0.530894) | 0.028439 / 0.534201 (-0.505762) | 0.392726 / 0.579283 (-0.186557) | 0.396894 / 0.434364 (-0.037469) | 0.474395 / 0.540337 (-0.065943) | 0.569090 / 1.386936 (-0.817847) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated 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) | 0.004527 / 0.011008 (-0.006481) | 0.098038 / 0.038508 (0.059530) | 0.027239 / 0.023109 (0.004130) | 0.441773 / 0.275898 (0.165875) | 0.471448 / 0.323480 (0.147968) | 0.005034 / 0.007986 (-0.002951) | 0.004732 / 0.004328 (0.000403) | 0.075036 / 0.004250 (0.070785) | 0.036711 / 0.037052 (-0.000341) | 0.442634 / 0.258489 (0.184145) | 0.476479 / 0.293841 (0.182638) | 0.031303 / 0.128546 (-0.097243) | 0.011642 / 0.075646 (-0.064005) | 0.320750 / 0.419271 (-0.098521) | 0.048698 / 0.043533 (0.005165) | 0.441205 / 0.255139 (0.186066) | 0.464845 / 0.283200 (0.181645) | 0.092716 / 0.141683 (-0.048967) | 1.510028 / 1.452155 (0.057874) | 1.574065 / 1.492716 (0.081349) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220756 / 0.018006 (0.202750) | 0.393971 / 0.000490 (0.393482) | 0.002506 / 0.000200 (0.002306) | 0.000073 / 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) | 0.024455 / 0.037411 (-0.012956) | 0.100164 / 0.014526 (0.085638) | 0.108053 / 0.176557 (-0.068504) | 0.142973 / 0.737135 (-0.594163) | 0.110108 / 0.296338 (-0.186231) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.473639 / 0.215209 (0.258430) | 4.737521 / 2.077655 (2.659866) | 2.466208 / 1.504120 (0.962088) | 2.272608 / 1.541195 (0.731413) | 2.349255 / 1.468490 (0.880764) | 0.699928 / 4.584777 (-3.884849) | 3.348443 / 3.745712 (-0.397269) | 2.604611 / 5.269862 (-2.665250) | 1.543080 / 4.565676 (-3.022597) | 0.082627 / 0.424275 (-0.341648) | 0.012251 / 0.007607 (0.004644) | 0.569949 / 0.226044 (0.343905) | 5.732316 / 2.268929 (3.463388) | 2.913541 / 55.444624 (-52.531084) | 2.560584 / 6.876477 (-4.315892) | 2.615192 / 2.142072 (0.473120) | 0.803822 / 4.805227 (-4.001406) | 0.150821 / 6.500664 (-6.349843) | 0.067128 / 0.075469 (-0.008341) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.272278 / 1.841788 (-0.569510) | 13.783339 / 8.074308 (5.709030) | 13.243601 / 10.191392 (3.052209) | 0.136421 / 0.680424 (-0.544003) | 0.016565 / 0.534201 (-0.517636) | 0.381102 / 0.579283 (-0.198181) | 0.386166 / 0.434364 (-0.048197) | 0.474249 / 0.540337 (-0.066089) | 0.566826 / 1.386936 (-0.820110) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b5672a956d5de864e6f5550e493527d962d6ae55 \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "I thought this issue was resolved in my parallel `to_tf_dataset` PR! I changed the default `num_test_batches` in `_get_output_signature` to 20 and used a test batch size of 1 to maximize variance to detect shorter samples. I think it's still okay to have this PR, though - but I'd use the new value of 20 as the default!", "@Rocketknight1 You're right - I didn't have the most recent changes to the default values. Updated now to 20! I still think it would be good to have it configurable from the `to_tf_dataset` call so the user has the option to either make it more robust if many samples are needed, or faster if only one is needed. That, and I selfishly want it for faster tests. ", "<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) | 0.010441 / 0.011353 (-0.000912) | 0.005605 / 0.011008 (-0.005404) | 0.115712 / 0.038508 (0.077204) | 0.040907 / 0.023109 (0.017797) | 0.357673 / 0.275898 (0.081775) | 0.415427 / 0.323480 (0.091947) | 0.008827 / 0.007986 (0.000842) | 0.006069 / 0.004328 (0.001740) | 0.088985 / 0.004250 (0.084735) | 0.048461 / 0.037052 (0.011409) | 0.362065 / 0.258489 (0.103576) | 0.393643 / 0.293841 (0.099802) | 0.043844 / 0.128546 (-0.084703) | 0.013757 / 0.075646 (-0.061889) | 0.390993 / 0.419271 (-0.028278) | 0.053612 / 0.043533 (0.010079) | 0.348688 / 0.255139 (0.093549) | 0.377818 / 0.283200 (0.094619) | 0.115762 / 0.141683 (-0.025920) | 1.751826 / 1.452155 (0.299672) | 1.773326 / 1.492716 (0.280609) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220668 / 0.018006 (0.202662) | 0.536830 / 0.000490 (0.536340) | 0.000467 / 0.000200 (0.000267) | 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) | 0.031500 / 0.037411 (-0.005911) | 0.125796 / 0.014526 (0.111270) | 0.137539 / 0.176557 (-0.039017) | 0.184651 / 0.737135 (-0.552484) | 0.145707 / 0.296338 (-0.150632) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.465876 / 0.215209 (0.250667) | 4.637711 / 2.077655 (2.560056) | 2.132335 / 1.504120 (0.628215) | 1.862593 / 1.541195 (0.321398) | 1.961701 / 1.468490 (0.493211) | 0.800551 / 4.584777 (-3.784226) | 4.453321 / 3.745712 (0.707608) | 4.291030 / 5.269862 (-0.978832) | 2.256685 / 4.565676 (-2.308991) | 0.097787 / 0.424275 (-0.326488) | 0.014116 / 0.007607 (0.006509) | 0.593395 / 0.226044 (0.367351) | 5.885774 / 2.268929 (3.616845) | 2.666224 / 55.444624 (-52.778400) | 2.276673 / 6.876477 (-4.599803) | 2.358190 / 2.142072 (0.216117) | 0.981398 / 4.805227 (-3.823829) | 0.196997 / 6.500664 (-6.303668) | 0.077020 / 0.075469 (0.001550) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.365646 / 1.841788 (-0.476142) | 17.418157 / 8.074308 (9.343849) | 15.838749 / 10.191392 (5.647357) | 0.172749 / 0.680424 (-0.507675) | 0.033711 / 0.534201 (-0.500490) | 0.513306 / 0.579283 (-0.065978) | 0.503201 / 0.434364 (0.068837) | 0.608954 / 0.540337 (0.068616) | 0.734697 / 1.386936 (-0.652239) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008749 / 0.011353 (-0.002604) | 0.005738 / 0.011008 (-0.005270) | 0.084946 / 0.038508 (0.046438) | 0.040386 / 0.023109 (0.017277) | 0.398698 / 0.275898 (0.122800) | 0.435843 / 0.323480 (0.112363) | 0.006812 / 0.007986 (-0.001174) | 0.004567 / 0.004328 (0.000239) | 0.085857 / 0.004250 (0.081607) | 0.054791 / 0.037052 (0.017738) | 0.400381 / 0.258489 (0.141892) | 0.460313 / 0.293841 (0.166472) | 0.042299 / 0.128546 (-0.086247) | 0.014128 / 0.075646 (-0.061519) | 0.100497 / 0.419271 (-0.318775) | 0.058356 / 0.043533 (0.014823) | 0.399774 / 0.255139 (0.144635) | 0.428210 / 0.283200 (0.145011) | 0.122084 / 0.141683 (-0.019598) | 1.683519 / 1.452155 (0.231365) | 1.798024 / 1.492716 (0.305307) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.255058 / 0.018006 (0.237051) | 0.488831 / 0.000490 (0.488342) | 0.008349 / 0.000200 (0.008149) | 0.000183 / 0.000054 (0.000129) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034870 / 0.037411 (-0.002541) | 0.131818 / 0.014526 (0.117292) | 0.143607 / 0.176557 (-0.032949) | 0.197413 / 0.737135 (-0.539722) | 0.148970 / 0.296338 (-0.147368) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.492831 / 0.215209 (0.277622) | 4.963085 / 2.077655 (2.885430) | 2.367803 / 1.504120 (0.863683) | 2.145535 / 1.541195 (0.604340) | 2.289452 / 1.468490 (0.820962) | 0.812691 / 4.584777 (-3.772086) | 4.554068 / 3.745712 (0.808356) | 2.377126 / 5.269862 (-2.892735) | 1.537243 / 4.565676 (-3.028433) | 0.099742 / 0.424275 (-0.324534) | 0.014757 / 0.007607 (0.007149) | 0.628714 / 0.226044 (0.402670) | 6.240197 / 2.268929 (3.971268) | 2.961929 / 55.444624 (-52.482696) | 2.533436 / 6.876477 (-4.343040) | 2.642619 / 2.142072 (0.500547) | 0.976002 / 4.805227 (-3.829225) | 0.197912 / 6.500664 (-6.302752) | 0.078767 / 0.075469 (0.003297) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.522863 / 1.841788 (-0.318925) | 18.210504 / 8.074308 (10.136196) | 15.664172 / 10.191392 (5.472780) | 0.178510 / 0.680424 (-0.501914) | 0.020852 / 0.534201 (-0.513349) | 0.501757 / 0.579283 (-0.077526) | 0.496542 / 0.434364 (0.062178) | 0.624958 / 0.540337 (0.084620) | 0.746960 / 1.386936 (-0.639976) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#da7f09ed65411c5941de45c372a8aa8d5e55b431 \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "The CI failure is unrelated to your PR - feel free to merge :)", "Haha thanks, you read my mind :)", "<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) | 0.008332 / 0.011353 (-0.003021) | 0.004556 / 0.011008 (-0.006452) | 0.102239 / 0.038508 (0.063731) | 0.029332 / 0.023109 (0.006222) | 0.296189 / 0.275898 (0.020291) | 0.355746 / 0.323480 (0.032266) | 0.007705 / 0.007986 (-0.000281) | 0.003488 / 0.004328 (-0.000840) | 0.079142 / 0.004250 (0.074891) | 0.034980 / 0.037052 (-0.002073) | 0.307460 / 0.258489 (0.048971) | 0.345944 / 0.293841 (0.052103) | 0.033815 / 0.128546 (-0.094731) | 0.011603 / 0.075646 (-0.064044) | 0.322097 / 0.419271 (-0.097175) | 0.043753 / 0.043533 (0.000220) | 0.296706 / 0.255139 (0.041567) | 0.323195 / 0.283200 (0.039996) | 0.092295 / 0.141683 (-0.049388) | 1.542556 / 1.452155 (0.090401) | 1.571896 / 1.492716 (0.079180) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.191075 / 0.018006 (0.173069) | 0.407394 / 0.000490 (0.406905) | 0.002033 / 0.000200 (0.001833) | 0.000073 / 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) | 0.023175 / 0.037411 (-0.014236) | 0.094774 / 0.014526 (0.080248) | 0.105782 / 0.176557 (-0.070775) | 0.146608 / 0.737135 (-0.590528) | 0.107519 / 0.296338 (-0.188819) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.421516 / 0.215209 (0.206306) | 4.201091 / 2.077655 (2.123436) | 1.880285 / 1.504120 (0.376165) | 1.676333 / 1.541195 (0.135139) | 1.734301 / 1.468490 (0.265811) | 0.688504 / 4.584777 (-3.896273) | 3.370289 / 3.745712 (-0.375423) | 3.127661 / 5.269862 (-2.142201) | 1.562570 / 4.565676 (-3.003106) | 0.081687 / 0.424275 (-0.342588) | 0.012334 / 0.007607 (0.004727) | 0.524125 / 0.226044 (0.298080) | 5.245595 / 2.268929 (2.976667) | 2.332622 / 55.444624 (-53.112002) | 1.973212 / 6.876477 (-4.903265) | 2.006507 / 2.142072 (-0.135565) | 0.807126 / 4.805227 (-3.998101) | 0.148254 / 6.500664 (-6.352411) | 0.064240 / 0.075469 (-0.011229) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.206880 / 1.841788 (-0.634907) | 13.854877 / 8.074308 (5.780569) | 13.806772 / 10.191392 (3.615380) | 0.144380 / 0.680424 (-0.536044) | 0.028492 / 0.534201 (-0.505709) | 0.393854 / 0.579283 (-0.185429) | 0.402210 / 0.434364 (-0.032154) | 0.462138 / 0.540337 (-0.078199) | 0.537480 / 1.386936 (-0.849456) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006692 / 0.011353 (-0.004661) | 0.004529 / 0.011008 (-0.006479) | 0.077925 / 0.038508 (0.039417) | 0.027824 / 0.023109 (0.004715) | 0.342288 / 0.275898 (0.066390) | 0.375071 / 0.323480 (0.051591) | 0.004889 / 0.007986 (-0.003097) | 0.003353 / 0.004328 (-0.000975) | 0.076198 / 0.004250 (0.071947) | 0.037797 / 0.037052 (0.000744) | 0.347834 / 0.258489 (0.089345) | 0.384200 / 0.293841 (0.090359) | 0.032184 / 0.128546 (-0.096362) | 0.011674 / 0.075646 (-0.063972) | 0.086242 / 0.419271 (-0.333029) | 0.044465 / 0.043533 (0.000932) | 0.341712 / 0.255139 (0.086573) | 0.366908 / 0.283200 (0.083709) | 0.091526 / 0.141683 (-0.050156) | 1.495798 / 1.452155 (0.043643) | 1.571700 / 1.492716 (0.078984) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221962 / 0.018006 (0.203955) | 0.393095 / 0.000490 (0.392605) | 0.000385 / 0.000200 (0.000185) | 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) | 0.024365 / 0.037411 (-0.013046) | 0.099278 / 0.014526 (0.084753) | 0.105940 / 0.176557 (-0.070617) | 0.141334 / 0.737135 (-0.595802) | 0.110898 / 0.296338 (-0.185440) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.446150 / 0.215209 (0.230941) | 4.471441 / 2.077655 (2.393786) | 2.124864 / 1.504120 (0.620744) | 1.909950 / 1.541195 (0.368755) | 1.970085 / 1.468490 (0.501595) | 0.706711 / 4.584777 (-3.878066) | 3.380336 / 3.745712 (-0.365376) | 1.866106 / 5.269862 (-3.403756) | 1.160657 / 4.565676 (-3.405019) | 0.082786 / 0.424275 (-0.341489) | 0.012470 / 0.007607 (0.004862) | 0.537620 / 0.226044 (0.311575) | 5.390588 / 2.268929 (3.121659) | 2.539137 / 55.444624 (-52.905488) | 2.191867 / 6.876477 (-4.684610) | 2.236212 / 2.142072 (0.094139) | 0.810756 / 4.805227 (-3.994471) | 0.150933 / 6.500664 (-6.349731) | 0.066141 / 0.075469 (-0.009328) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.271595 / 1.841788 (-0.570193) | 13.840013 / 8.074308 (5.765705) | 13.334443 / 10.191392 (3.143051) | 0.150096 / 0.680424 (-0.530328) | 0.016919 / 0.534201 (-0.517282) | 0.375534 / 0.579283 (-0.203749) | 0.387203 / 0.434364 (-0.047161) | 0.463500 / 0.540337 (-0.076838) | 0.553496 / 1.386936 (-0.833440) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5f2e47230c13f977bcebdc4380623f59da67a75f \"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) | 0.008272 / 0.011353 (-0.003081) | 0.004494 / 0.011008 (-0.006515) | 0.100764 / 0.038508 (0.062256) | 0.028741 / 0.023109 (0.005632) | 0.309020 / 0.275898 (0.033122) | 0.354184 / 0.323480 (0.030704) | 0.007455 / 0.007986 (-0.000531) | 0.003377 / 0.004328 (-0.000951) | 0.078472 / 0.004250 (0.074222) | 0.034719 / 0.037052 (-0.002333) | 0.312787 / 0.258489 (0.054298) | 0.342878 / 0.293841 (0.049037) | 0.033326 / 0.128546 (-0.095221) | 0.011519 / 0.075646 (-0.064127) | 0.323556 / 0.419271 (-0.095716) | 0.039929 / 0.043533 (-0.003604) | 0.304627 / 0.255139 (0.049488) | 0.322876 / 0.283200 (0.039677) | 0.086410 / 0.141683 (-0.055273) | 1.502607 / 1.452155 (0.050453) | 1.577953 / 1.492716 (0.085237) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.192861 / 0.018006 (0.174855) | 0.406008 / 0.000490 (0.405519) | 0.001075 / 0.000200 (0.000875) | 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) | 0.023351 / 0.037411 (-0.014060) | 0.096086 / 0.014526 (0.081561) | 0.104641 / 0.176557 (-0.071915) | 0.141940 / 0.737135 (-0.595195) | 0.109266 / 0.296338 (-0.187073) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.416496 / 0.215209 (0.201287) | 4.161581 / 2.077655 (2.083926) | 1.815357 / 1.504120 (0.311238) | 1.609536 / 1.541195 (0.068341) | 1.654105 / 1.468490 (0.185615) | 0.693947 / 4.584777 (-3.890830) | 3.349029 / 3.745712 (-0.396683) | 1.883968 / 5.269862 (-3.385893) | 1.287988 / 4.565676 (-3.277688) | 0.081765 / 0.424275 (-0.342511) | 0.012373 / 0.007607 (0.004766) | 0.517186 / 0.226044 (0.291142) | 5.200892 / 2.268929 (2.931964) | 2.247414 / 55.444624 (-53.197211) | 1.910601 / 6.876477 (-4.965876) | 1.965407 / 2.142072 (-0.176666) | 0.814386 / 4.805227 (-3.990841) | 0.149295 / 6.500664 (-6.351369) | 0.064667 / 0.075469 (-0.010802) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.247258 / 1.841788 (-0.594530) | 13.837355 / 8.074308 (5.763047) | 13.850454 / 10.191392 (3.659062) | 0.136078 / 0.680424 (-0.544346) | 0.028322 / 0.534201 (-0.505878) | 0.391394 / 0.579283 (-0.187889) | 0.407494 / 0.434364 (-0.026870) | 0.473784 / 0.540337 (-0.066554) | 0.562953 / 1.386936 (-0.823983) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006559 / 0.011353 (-0.004794) | 0.004546 / 0.011008 (-0.006462) | 0.099527 / 0.038508 (0.061019) | 0.027428 / 0.023109 (0.004319) | 0.344276 / 0.275898 (0.068377) | 0.377897 / 0.323480 (0.054417) | 0.004913 / 0.007986 (-0.003072) | 0.003338 / 0.004328 (-0.000990) | 0.077589 / 0.004250 (0.073339) | 0.038819 / 0.037052 (0.001766) | 0.343165 / 0.258489 (0.084676) | 0.386228 / 0.293841 (0.092387) | 0.031753 / 0.128546 (-0.096794) | 0.011756 / 0.075646 (-0.063890) | 0.322537 / 0.419271 (-0.096735) | 0.049865 / 0.043533 (0.006332) | 0.340493 / 0.255139 (0.085354) | 0.372179 / 0.283200 (0.088980) | 0.099669 / 0.141683 (-0.042013) | 1.487841 / 1.452155 (0.035686) | 1.527400 / 1.492716 (0.034683) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.180782 / 0.018006 (0.162776) | 0.393494 / 0.000490 (0.393004) | 0.003004 / 0.000200 (0.002804) | 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) | 0.024997 / 0.037411 (-0.012415) | 0.098232 / 0.014526 (0.083707) | 0.107869 / 0.176557 (-0.068688) | 0.141042 / 0.737135 (-0.596093) | 0.109551 / 0.296338 (-0.186787) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.477115 / 0.215209 (0.261906) | 4.783928 / 2.077655 (2.706273) | 2.435725 / 1.504120 (0.931605) | 2.233111 / 1.541195 (0.691916) | 2.341097 / 1.468490 (0.872607) | 0.694304 / 4.584777 (-3.890473) | 3.345687 / 3.745712 (-0.400025) | 1.886932 / 5.269862 (-3.382929) | 1.155585 / 4.565676 (-3.410092) | 0.082867 / 0.424275 (-0.341408) | 0.012420 / 0.007607 (0.004813) | 0.576575 / 0.226044 (0.350530) | 5.777691 / 2.268929 (3.508762) | 2.882219 / 55.444624 (-52.562405) | 2.543613 / 6.876477 (-4.332864) | 2.578939 / 2.142072 (0.436866) | 0.803143 / 4.805227 (-4.002084) | 0.151929 / 6.500664 (-6.348735) | 0.067777 / 0.075469 (-0.007693) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.282711 / 1.841788 (-0.559077) | 13.942771 / 8.074308 (5.868463) | 13.376206 / 10.191392 (3.184814) | 0.152916 / 0.680424 (-0.527508) | 0.016619 / 0.534201 (-0.517582) | 0.375141 / 0.579283 (-0.204142) | 0.381660 / 0.434364 (-0.052704) | 0.465090 / 0.540337 (-0.075247) | 0.555068 / 1.386936 (-0.831868) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#10a6a638e0feb955f7b607b4433ee715c30acccf \"CML watermark\")\n" ]

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[ "Hi! I agree it would be nice to have a method like that. Instead of `keep_columns`, we can name it `select_columns` to be more aligned with PyArrow's naming convention (`pa.Table.select`).", "Hi, I am a newbie to open source and would like to contribute. @mariosasko can I take up this issue ?", "Hey, I also want to work on this issue I am a newbie to open source. ", "This sounds related to https://github.com/huggingface/datasets/issues/5474\r\n\r\nI'm fine with `select_columns`, or we could also override `select` to also accept a list of columns maybe ?", "@lhoestq, I am planning to add a member function to the dataset class to perform the selection operation. Do you think its the right way to proceed? or there is a better option ?", "Unless @mariosasko thinks otherwise, I think it can go in `Dataset.select()` :)\r\nThough some parameters like keep_in_memory, indices_cache_file_name or writer_batch_size wouldn't when selecting columns, so we would need to update the docstring as well", "If someone wants to give it a shot, feel free to comment `#self-assign` and it will assign the issue to you.\r\n\r\nFeel free to ping us here if you have questions or if we can help :)", "I would rather have this functionality as a separate method. IMO it's always better to be explicit than to have an API where a single method can do different/uncorrelated things (somewhat reminds me of Pandas, and there is probably a good reason why PyArrow is more rigid in this aspect).", "In the end I also think it would be nice to have it as a separate method, this way we can also have it for `IterableDataset` (which can't have `select` for indices)" ]

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[ "ah didn't read well - it's all good", "or maybe it isn't ? `-c huggingface -c conda-forge` installs from HF or from conda-forge ?", "<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) | 0.010196 / 0.011353 (-0.001157) | 0.005531 / 0.011008 (-0.005477) | 0.104601 / 0.038508 (0.066093) | 0.041322 / 0.023109 (0.018213) | 0.302080 / 0.275898 (0.026182) | 0.396579 / 0.323480 (0.073099) | 0.008874 / 0.007986 (0.000888) | 0.004482 / 0.004328 (0.000153) | 0.077487 / 0.004250 (0.073236) | 0.051113 / 0.037052 (0.014061) | 0.321850 / 0.258489 (0.063361) | 0.354946 / 0.293841 (0.061105) | 0.039822 / 0.128546 (-0.088724) | 0.012622 / 0.075646 (-0.063024) | 0.337898 / 0.419271 (-0.081374) | 0.048372 / 0.043533 (0.004839) | 0.299646 / 0.255139 (0.044507) | 0.321113 / 0.283200 (0.037914) | 0.114780 / 0.141683 (-0.026903) | 1.475750 / 1.452155 (0.023595) | 1.496307 / 1.492716 (0.003590) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.311443 / 0.018006 (0.293437) | 0.567268 / 0.000490 (0.566778) | 0.006149 / 0.000200 (0.005950) | 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) | 0.029407 / 0.037411 (-0.008004) | 0.118611 / 0.014526 (0.104085) | 0.122247 / 0.176557 (-0.054309) | 0.164770 / 0.737135 (-0.572365) | 0.128561 / 0.296338 (-0.167778) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.399185 / 0.215209 (0.183976) | 3.972995 / 2.077655 (1.895340) | 1.764638 / 1.504120 (0.260518) | 1.574058 / 1.541195 (0.032863) | 1.741695 / 1.468490 (0.273205) | 0.705664 / 4.584777 (-3.879113) | 3.915399 / 3.745712 (0.169686) | 2.310154 / 5.269862 (-2.959707) | 1.554067 / 4.565676 (-3.011610) | 0.087133 / 0.424275 (-0.337142) | 0.012393 / 0.007607 (0.004786) | 0.510758 / 0.226044 (0.284713) | 5.114906 / 2.268929 (2.845977) | 2.304473 / 55.444624 (-53.140152) | 1.960768 / 6.876477 (-4.915709) | 2.092263 / 2.142072 (-0.049810) | 0.867973 / 4.805227 (-3.937255) | 0.170000 / 6.500664 (-6.330664) | 0.068358 / 0.075469 (-0.007111) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.211022 / 1.841788 (-0.630765) | 16.777269 / 8.074308 (8.702961) | 15.272659 / 10.191392 (5.081267) | 0.182149 / 0.680424 (-0.498274) | 0.029577 / 0.534201 (-0.504624) | 0.446590 / 0.579283 (-0.132693) | 0.454724 / 0.434364 (0.020360) | 0.541938 / 0.540337 (0.001601) | 0.640886 / 1.386936 (-0.746050) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008441 / 0.011353 (-0.002912) | 0.006105 / 0.011008 (-0.004904) | 0.100349 / 0.038508 (0.061841) | 0.040675 / 0.023109 (0.017565) | 0.381775 / 0.275898 (0.105877) | 0.425246 / 0.323480 (0.101767) | 0.007197 / 0.007986 (-0.000789) | 0.004972 / 0.004328 (0.000644) | 0.075346 / 0.004250 (0.071096) | 0.065339 / 0.037052 (0.028286) | 0.379340 / 0.258489 (0.120851) | 0.435646 / 0.293841 (0.141805) | 0.038891 / 0.128546 (-0.089656) | 0.013079 / 0.075646 (-0.062568) | 0.339273 / 0.419271 (-0.079999) | 0.057478 / 0.043533 (0.013945) | 0.373516 / 0.255139 (0.118377) | 0.402388 / 0.283200 (0.119189) | 0.123145 / 0.141683 (-0.018538) | 1.503765 / 1.452155 (0.051610) | 1.609797 / 1.492716 (0.117081) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.420354 / 0.018006 (0.402348) | 0.589272 / 0.000490 (0.588782) | 0.045861 / 0.000200 (0.045662) | 0.000527 / 0.000054 (0.000473) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033918 / 0.037411 (-0.003493) | 0.128041 / 0.014526 (0.113515) | 0.130274 / 0.176557 (-0.046283) | 0.180605 / 0.737135 (-0.556530) | 0.136377 / 0.296338 (-0.159962) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440343 / 0.215209 (0.225133) | 4.390264 / 2.077655 (2.312610) | 2.218738 / 1.504120 (0.714618) | 2.052399 / 1.541195 (0.511204) | 2.231912 / 1.468490 (0.763422) | 0.716805 / 4.584777 (-3.867972) | 3.909277 / 3.745712 (0.163565) | 2.302121 / 5.269862 (-2.967740) | 1.419454 / 4.565676 (-3.146222) | 0.088067 / 0.424275 (-0.336208) | 0.012994 / 0.007607 (0.005387) | 0.548267 / 0.226044 (0.322223) | 5.462973 / 2.268929 (3.194044) | 2.768414 / 55.444624 (-52.676210) | 2.489320 / 6.876477 (-4.387157) | 2.569546 / 2.142072 (0.427474) | 0.853135 / 4.805227 (-3.952092) | 0.170618 / 6.500664 (-6.330046) | 0.069908 / 0.075469 (-0.005562) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.304726 / 1.841788 (-0.537062) | 17.335977 / 8.074308 (9.261669) | 15.088319 / 10.191392 (4.896927) | 0.190893 / 0.680424 (-0.489531) | 0.018133 / 0.534201 (-0.516068) | 0.429324 / 0.579283 (-0.149959) | 0.439212 / 0.434364 (0.004848) | 0.545312 / 0.540337 (0.004975) | 0.663972 / 1.386936 (-0.722964) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e7505adc37498f5e0cb3dd4c13bbb06696afdda5 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Thanks !\r\n`os.path.join` will use a backslash `\\` on windows which will also fail. You can use this instead in `load_from_disk`:\r\n```python\r\nfrom .filesystems import is_remote_filesystem\r\n\r\nis_local = not is_remote_filesystem(fs)\r\npath_join = os.path.join if is_local else posixpath.join\r\n```", "Thank you ! I did a minor change to not have to define a new function and I ran the CI. If it's green we can merge :)", "_The documentation is not available anymore as the PR was closed or merged._", "> \r\n\r\n\r\n\r\n> Thank you ! I did a minor change to not have to define a new function and I ran the CI. If it's green we can merge :)\r\n\r\nlol it's a battle of +1 imports or +1 functions. LGTM, I was editing fast and swapped which branch gets os vs Path. Should be ok now 🤙", "<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) | 0.012043 / 0.011353 (0.000690) | 0.006585 / 0.011008 (-0.004423) | 0.149007 / 0.038508 (0.110499) | 0.039514 / 0.023109 (0.016405) | 0.403893 / 0.275898 (0.127995) | 0.431252 / 0.323480 (0.107772) | 0.009218 / 0.007986 (0.001233) | 0.006108 / 0.004328 (0.001779) | 0.114666 / 0.004250 (0.110416) | 0.044962 / 0.037052 (0.007910) | 0.411592 / 0.258489 (0.153103) | 0.461561 / 0.293841 (0.167721) | 0.059958 / 0.128546 (-0.068589) | 0.029047 / 0.075646 (-0.046599) | 0.456000 / 0.419271 (0.036728) | 0.060744 / 0.043533 (0.017211) | 0.415816 / 0.255139 (0.160677) | 0.430488 / 0.283200 (0.147289) | 0.122477 / 0.141683 (-0.019205) | 1.862910 / 1.452155 (0.410755) | 1.974698 / 1.492716 (0.481981) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.257230 / 0.018006 (0.239224) | 0.606854 / 0.000490 (0.606364) | 0.006175 / 0.000200 (0.005975) | 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) | 0.030533 / 0.037411 (-0.006879) | 0.130702 / 0.014526 (0.116177) | 0.143781 / 0.176557 (-0.032775) | 0.183272 / 0.737135 (-0.553863) | 0.151267 / 0.296338 (-0.145071) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.637422 / 0.215209 (0.422213) | 6.503535 / 2.077655 (4.425880) | 2.630387 / 1.504120 (1.126267) | 2.281180 / 1.541195 (0.739985) | 2.354341 / 1.468490 (0.885851) | 1.306497 / 4.584777 (-3.278280) | 5.837184 / 3.745712 (2.091472) | 3.257198 / 5.269862 (-2.012663) | 2.050681 / 4.565676 (-2.514995) | 0.146415 / 0.424275 (-0.277860) | 0.015386 / 0.007607 (0.007779) | 0.790146 / 0.226044 (0.564102) | 8.056137 / 2.268929 (5.787209) | 3.383566 / 55.444624 (-52.061059) | 2.707620 / 6.876477 (-4.168856) | 2.714857 / 2.142072 (0.572785) | 1.520847 / 4.805227 (-3.284380) | 0.266028 / 6.500664 (-6.234636) | 0.091422 / 0.075469 (0.015953) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.656148 / 1.841788 (-0.185640) | 18.833393 / 8.074308 (10.759085) | 21.360824 / 10.191392 (11.169432) | 0.227608 / 0.680424 (-0.452816) | 0.049018 / 0.534201 (-0.485183) | 0.593418 / 0.579283 (0.014135) | 0.656690 / 0.434364 (0.222326) | 0.709171 / 0.540337 (0.168833) | 0.828226 / 1.386936 (-0.558710) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010112 / 0.011353 (-0.001241) | 0.006761 / 0.011008 (-0.004247) | 0.146723 / 0.038508 (0.108215) | 0.038451 / 0.023109 (0.015342) | 0.524267 / 0.275898 (0.248369) | 0.609484 / 0.323480 (0.286004) | 0.008502 / 0.007986 (0.000516) | 0.006964 / 0.004328 (0.002635) | 0.111396 / 0.004250 (0.107146) | 0.056839 / 0.037052 (0.019787) | 0.514649 / 0.258489 (0.256160) | 0.604212 / 0.293841 (0.310372) | 0.061410 / 0.128546 (-0.067137) | 0.020396 / 0.075646 (-0.055250) | 0.505026 / 0.419271 (0.085754) | 0.067280 / 0.043533 (0.023747) | 0.522249 / 0.255139 (0.267110) | 0.559484 / 0.283200 (0.276284) | 0.120943 / 0.141683 (-0.020740) | 2.124323 / 1.452155 (0.672169) | 2.153397 / 1.492716 (0.660681) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216614 / 0.018006 (0.198608) | 0.594181 / 0.000490 (0.593692) | 0.004079 / 0.000200 (0.003879) | 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) | 0.036925 / 0.037411 (-0.000486) | 0.131322 / 0.014526 (0.116797) | 0.148542 / 0.176557 (-0.028015) | 0.196045 / 0.737135 (-0.541090) | 0.156867 / 0.296338 (-0.139472) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.669722 / 0.215209 (0.454513) | 6.858856 / 2.077655 (4.781202) | 3.093969 / 1.504120 (1.589849) | 2.667385 / 1.541195 (1.126190) | 2.797192 / 1.468490 (1.328702) | 1.334759 / 4.584777 (-3.250018) | 6.024861 / 3.745712 (2.279149) | 3.257779 / 5.269862 (-2.012083) | 2.202816 / 4.565676 (-2.362860) | 0.147617 / 0.424275 (-0.276658) | 0.015451 / 0.007607 (0.007844) | 0.887015 / 0.226044 (0.660970) | 8.371288 / 2.268929 (6.102360) | 3.807451 / 55.444624 (-51.637173) | 3.079483 / 6.876477 (-3.796994) | 3.103321 / 2.142072 (0.961249) | 1.520272 / 4.805227 (-3.284955) | 0.273079 / 6.500664 (-6.227585) | 0.088613 / 0.075469 (0.013143) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.818913 / 1.841788 (-0.022875) | 19.274269 / 8.074308 (11.199960) | 19.871784 / 10.191392 (9.680392) | 0.250388 / 0.680424 (-0.430036) | 0.030562 / 0.534201 (-0.503638) | 0.560566 / 0.579283 (-0.018717) | 0.664701 / 0.434364 (0.230337) | 0.714513 / 0.540337 (0.174176) | 0.827227 / 1.386936 (-0.559710) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f7a9bf823ea41b85313c0392388ec68b3033ef29 \"CML watermark\")\n" ]

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[ "Thanks for reporting, @sarahwie.\r\n\r\nPlease note this issue was already fixed in `datasets` 2.6.0 version:\r\n- #5040\r\n\r\nIf you update your `datasets` version, you will be able to load the dataset:\r\n```\r\npip install -U datasets\r\n```", "Oops, missed that I needed to upgrade. Thanks!" ]

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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) | 0.009559 / 0.011353 (-0.001794) | 0.006425 / 0.011008 (-0.004583) | 0.112951 / 0.038508 (0.074443) | 0.030835 / 0.023109 (0.007725) | 0.313846 / 0.275898 (0.037948) | 0.352780 / 0.323480 (0.029301) | 0.007740 / 0.007986 (-0.000246) | 0.006843 / 0.004328 (0.002515) | 0.082632 / 0.004250 (0.078382) | 0.039704 / 0.037052 (0.002652) | 0.328526 / 0.258489 (0.070037) | 0.369162 / 0.293841 (0.075321) | 0.047603 / 0.128546 (-0.080943) | 0.015834 / 0.075646 (-0.059812) | 0.385912 / 0.419271 (-0.033360) | 0.053838 / 0.043533 (0.010306) | 0.325778 / 0.255139 (0.070639) | 0.361863 / 0.283200 (0.078663) | 0.097388 / 0.141683 (-0.044295) | 1.510132 / 1.452155 (0.057978) | 1.555980 / 1.492716 (0.063264) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210792 / 0.018006 (0.192786) | 0.507270 / 0.000490 (0.506780) | 0.002383 / 0.000200 (0.002183) | 0.000095 / 0.000054 (0.000041) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023057 / 0.037411 (-0.014355) | 0.103471 / 0.014526 (0.088945) | 0.111671 / 0.176557 (-0.064885) | 0.145665 / 0.737135 (-0.591470) | 0.131447 / 0.296338 (-0.164891) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.502979 / 0.215209 (0.287770) | 5.111471 / 2.077655 (3.033816) | 2.093604 / 1.504120 (0.589484) | 1.761342 / 1.541195 (0.220148) | 1.919485 / 1.468490 (0.450995) | 1.065672 / 4.584777 (-3.519105) | 5.109746 / 3.745712 (1.364034) | 4.694027 / 5.269862 (-0.575835) | 2.438401 / 4.565676 (-2.127275) | 0.133579 / 0.424275 (-0.290696) | 0.012355 / 0.007607 (0.004748) | 0.669077 / 0.226044 (0.443033) | 6.533905 / 2.268929 (4.264976) | 2.698832 / 55.444624 (-52.745792) | 2.146377 / 6.876477 (-4.730100) | 2.220563 / 2.142072 (0.078491) | 1.287855 / 4.805227 (-3.517372) | 0.238221 / 6.500664 (-6.262443) | 0.071426 / 0.075469 (-0.004043) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.332659 / 1.841788 (-0.509129) | 15.610100 / 8.074308 (7.535791) | 16.691117 / 10.191392 (6.499725) | 0.226338 / 0.680424 (-0.454086) | 0.039964 / 0.534201 (-0.494237) | 0.462911 / 0.579283 (-0.116372) | 0.575923 / 0.434364 (0.141560) | 0.592583 / 0.540337 (0.052245) | 0.658552 / 1.386936 (-0.728384) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008388 / 0.011353 (-0.002965) | 0.005360 / 0.011008 (-0.005648) | 0.104574 / 0.038508 (0.066066) | 0.030109 / 0.023109 (0.007000) | 0.389294 / 0.275898 (0.113396) | 0.424813 / 0.323480 (0.101333) | 0.006629 / 0.007986 (-0.001356) | 0.005222 / 0.004328 (0.000893) | 0.080157 / 0.004250 (0.075907) | 0.045811 / 0.037052 (0.008759) | 0.398708 / 0.258489 (0.140219) | 0.429449 / 0.293841 (0.135608) | 0.052242 / 0.128546 (-0.076304) | 0.017439 / 0.075646 (-0.058207) | 0.362678 / 0.419271 (-0.056593) | 0.054151 / 0.043533 (0.010618) | 0.387932 / 0.255139 (0.132793) | 0.410544 / 0.283200 (0.127344) | 0.101210 / 0.141683 (-0.040473) | 1.486496 / 1.452155 (0.034341) | 1.576404 / 1.492716 (0.083687) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.259468 / 0.018006 (0.241461) | 0.521661 / 0.000490 (0.521172) | 0.000456 / 0.000200 (0.000256) | 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) | 0.027045 / 0.037411 (-0.010366) | 0.107615 / 0.014526 (0.093089) | 0.133228 / 0.176557 (-0.043329) | 0.156807 / 0.737135 (-0.580328) | 0.125226 / 0.296338 (-0.171113) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.528804 / 0.215209 (0.313595) | 5.516402 / 2.077655 (3.438748) | 2.387531 / 1.504120 (0.883412) | 2.084734 / 1.541195 (0.543539) | 2.091894 / 1.468490 (0.623404) | 1.089761 / 4.584777 (-3.495016) | 5.093067 / 3.745712 (1.347355) | 2.670349 / 5.269862 (-2.599512) | 1.784723 / 4.565676 (-2.780953) | 0.125528 / 0.424275 (-0.298747) | 0.013702 / 0.007607 (0.006095) | 0.667755 / 0.226044 (0.441710) | 6.653900 / 2.268929 (4.384972) | 3.006058 / 55.444624 (-52.438567) | 2.512919 / 6.876477 (-4.363558) | 2.546824 / 2.142072 (0.404751) | 1.269008 / 4.805227 (-3.536219) | 0.234388 / 6.500664 (-6.266276) | 0.065675 / 0.075469 (-0.009795) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.372222 / 1.841788 (-0.469566) | 15.565156 / 8.074308 (7.490848) | 16.800666 / 10.191392 (6.609274) | 0.220656 / 0.680424 (-0.459768) | 0.023690 / 0.534201 (-0.510511) | 0.450049 / 0.579283 (-0.129234) | 0.580433 / 0.434364 (0.146069) | 0.558899 / 0.540337 (0.018561) | 0.676799 / 1.386936 (-0.710137) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6cc5dcacecf41efc566385b323a3ca72ab44db36 \"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) | 0.009440 / 0.011353 (-0.001913) | 0.005159 / 0.011008 (-0.005849) | 0.099152 / 0.038508 (0.060644) | 0.035939 / 0.023109 (0.012830) | 0.300968 / 0.275898 (0.025070) | 0.365676 / 0.323480 (0.042196) | 0.008220 / 0.007986 (0.000235) | 0.004071 / 0.004328 (-0.000257) | 0.075216 / 0.004250 (0.070965) | 0.042173 / 0.037052 (0.005121) | 0.315055 / 0.258489 (0.056566) | 0.338287 / 0.293841 (0.044446) | 0.037789 / 0.128546 (-0.090758) | 0.011856 / 0.075646 (-0.063791) | 0.332975 / 0.419271 (-0.086297) | 0.047087 / 0.043533 (0.003554) | 0.295107 / 0.255139 (0.039968) | 0.315416 / 0.283200 (0.032217) | 0.102273 / 0.141683 (-0.039410) | 1.464908 / 1.452155 (0.012754) | 1.500281 / 1.492716 (0.007565) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208522 / 0.018006 (0.190516) | 0.446576 / 0.000490 (0.446086) | 0.005766 / 0.000200 (0.005566) | 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) | 0.027924 / 0.037411 (-0.009487) | 0.111296 / 0.014526 (0.096771) | 0.119055 / 0.176557 (-0.057502) | 0.157755 / 0.737135 (-0.579381) | 0.125539 / 0.296338 (-0.170799) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.395683 / 0.215209 (0.180474) | 3.962696 / 2.077655 (1.885042) | 1.789511 / 1.504120 (0.285391) | 1.591541 / 1.541195 (0.050346) | 1.661276 / 1.468490 (0.192786) | 0.693524 / 4.584777 (-3.891253) | 3.836526 / 3.745712 (0.090813) | 2.187284 / 5.269862 (-3.082578) | 1.521420 / 4.565676 (-3.044257) | 0.084370 / 0.424275 (-0.339905) | 0.012083 / 0.007607 (0.004476) | 0.498017 / 0.226044 (0.271972) | 4.982356 / 2.268929 (2.713428) | 2.235881 / 55.444624 (-53.208743) | 1.912067 / 6.876477 (-4.964410) | 2.052172 / 2.142072 (-0.089900) | 0.836232 / 4.805227 (-3.968995) | 0.165234 / 6.500664 (-6.335431) | 0.062933 / 0.075469 (-0.012536) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.197785 / 1.841788 (-0.644003) | 15.233655 / 8.074308 (7.159347) | 14.254450 / 10.191392 (4.063058) | 0.169149 / 0.680424 (-0.511274) | 0.028794 / 0.534201 (-0.505407) | 0.437214 / 0.579283 (-0.142069) | 0.434836 / 0.434364 (0.000472) | 0.531594 / 0.540337 (-0.008744) | 0.626266 / 1.386936 (-0.760670) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007394 / 0.011353 (-0.003959) | 0.005305 / 0.011008 (-0.005703) | 0.098888 / 0.038508 (0.060380) | 0.033069 / 0.023109 (0.009959) | 0.388427 / 0.275898 (0.112529) | 0.415216 / 0.323480 (0.091736) | 0.005610 / 0.007986 (-0.002375) | 0.004922 / 0.004328 (0.000593) | 0.073694 / 0.004250 (0.069443) | 0.047368 / 0.037052 (0.010315) | 0.379604 / 0.258489 (0.121115) | 0.424876 / 0.293841 (0.131035) | 0.039471 / 0.128546 (-0.089075) | 0.012219 / 0.075646 (-0.063427) | 0.345925 / 0.419271 (-0.073346) | 0.048981 / 0.043533 (0.005448) | 0.379303 / 0.255139 (0.124164) | 0.404682 / 0.283200 (0.121483) | 0.103932 / 0.141683 (-0.037751) | 1.490852 / 1.452155 (0.038697) | 1.578900 / 1.492716 (0.086183) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201393 / 0.018006 (0.183387) | 0.452484 / 0.000490 (0.451994) | 0.005627 / 0.000200 (0.005428) | 0.000129 / 0.000054 (0.000075) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029317 / 0.037411 (-0.008094) | 0.114904 / 0.014526 (0.100378) | 0.126678 / 0.176557 (-0.049878) | 0.178315 / 0.737135 (-0.558820) | 0.131603 / 0.296338 (-0.164736) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.459830 / 0.215209 (0.244621) | 4.595358 / 2.077655 (2.517703) | 2.383582 / 1.504120 (0.879462) | 2.181945 / 1.541195 (0.640750) | 2.309517 / 1.468490 (0.841027) | 0.704803 / 4.584777 (-3.879974) | 3.820411 / 3.745712 (0.074698) | 4.872173 / 5.269862 (-0.397689) | 2.266090 / 4.565676 (-2.299586) | 0.085805 / 0.424275 (-0.338470) | 0.012488 / 0.007607 (0.004881) | 0.557500 / 0.226044 (0.331456) | 5.570830 / 2.268929 (3.301901) | 2.836202 / 55.444624 (-52.608422) | 2.530534 / 6.876477 (-4.345943) | 2.599792 / 2.142072 (0.457720) | 0.843852 / 4.805227 (-3.961376) | 0.169427 / 6.500664 (-6.331237) | 0.065521 / 0.075469 (-0.009948) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.246014 / 1.841788 (-0.595774) | 15.455336 / 8.074308 (7.381028) | 13.559111 / 10.191392 (3.367719) | 0.169131 / 0.680424 (-0.511293) | 0.017812 / 0.534201 (-0.516389) | 0.421161 / 0.579283 (-0.158122) | 0.458286 / 0.434364 (0.023922) | 0.534692 / 0.540337 (-0.005645) | 0.639299 / 1.386936 (-0.747637) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2b7558953b5a071194356bbe4c596a2890a3b847 \"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) | 0.008860 / 0.011353 (-0.002493) | 0.004564 / 0.011008 (-0.006444) | 0.101556 / 0.038508 (0.063048) | 0.030000 / 0.023109 (0.006891) | 0.304404 / 0.275898 (0.028506) | 0.366247 / 0.323480 (0.042767) | 0.007182 / 0.007986 (-0.000804) | 0.003583 / 0.004328 (-0.000746) | 0.079665 / 0.004250 (0.075415) | 0.036529 / 0.037052 (-0.000523) | 0.310998 / 0.258489 (0.052509) | 0.346954 / 0.293841 (0.053113) | 0.034098 / 0.128546 (-0.094448) | 0.011576 / 0.075646 (-0.064070) | 0.320448 / 0.419271 (-0.098824) | 0.043328 / 0.043533 (-0.000205) | 0.307317 / 0.255139 (0.052178) | 0.325071 / 0.283200 (0.041871) | 0.096406 / 0.141683 (-0.045277) | 1.540331 / 1.452155 (0.088176) | 1.589533 / 1.492716 (0.096817) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.011034 / 0.018006 (-0.006972) | 0.422066 / 0.000490 (0.421577) | 0.002409 / 0.000200 (0.002209) | 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) | 0.023703 / 0.037411 (-0.013708) | 0.099935 / 0.014526 (0.085409) | 0.105966 / 0.176557 (-0.070591) | 0.142259 / 0.737135 (-0.594876) | 0.109327 / 0.296338 (-0.187011) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418381 / 0.215209 (0.203172) | 4.177564 / 2.077655 (2.099909) | 1.880196 / 1.504120 (0.376076) | 1.669169 / 1.541195 (0.127974) | 1.725989 / 1.468490 (0.257499) | 0.689384 / 4.584777 (-3.895393) | 3.380963 / 3.745712 (-0.364749) | 1.884192 / 5.269862 (-3.385670) | 1.162409 / 4.565676 (-3.403268) | 0.082045 / 0.424275 (-0.342230) | 0.012575 / 0.007607 (0.004968) | 0.525824 / 0.226044 (0.299779) | 5.272574 / 2.268929 (3.003646) | 2.283492 / 55.444624 (-53.161132) | 1.947390 / 6.876477 (-4.929087) | 2.013790 / 2.142072 (-0.128283) | 0.806280 / 4.805227 (-3.998948) | 0.149267 / 6.500664 (-6.351397) | 0.066967 / 0.075469 (-0.008502) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.216511 / 1.841788 (-0.625277) | 13.869829 / 8.074308 (5.795521) | 14.189967 / 10.191392 (3.998575) | 0.148716 / 0.680424 (-0.531708) | 0.028324 / 0.534201 (-0.505877) | 0.390856 / 0.579283 (-0.188427) | 0.404389 / 0.434364 (-0.029975) | 0.456050 / 0.540337 (-0.084287) | 0.544139 / 1.386936 (-0.842797) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006727 / 0.011353 (-0.004626) | 0.004515 / 0.011008 (-0.006494) | 0.098791 / 0.038508 (0.060283) | 0.027596 / 0.023109 (0.004487) | 0.439066 / 0.275898 (0.163168) | 0.480555 / 0.323480 (0.157076) | 0.005066 / 0.007986 (-0.002920) | 0.004669 / 0.004328 (0.000341) | 0.075334 / 0.004250 (0.071084) | 0.039779 / 0.037052 (0.002726) | 0.439860 / 0.258489 (0.181371) | 0.480787 / 0.293841 (0.186946) | 0.031550 / 0.128546 (-0.096996) | 0.011668 / 0.075646 (-0.063978) | 0.317348 / 0.419271 (-0.101923) | 0.041312 / 0.043533 (-0.002220) | 0.442934 / 0.255139 (0.187795) | 0.463677 / 0.283200 (0.180478) | 0.090066 / 0.141683 (-0.051617) | 1.544152 / 1.452155 (0.091998) | 1.584455 / 1.492716 (0.091738) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224284 / 0.018006 (0.206278) | 0.406982 / 0.000490 (0.406492) | 0.000427 / 0.000200 (0.000227) | 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) | 0.024914 / 0.037411 (-0.012497) | 0.102608 / 0.014526 (0.088082) | 0.106931 / 0.176557 (-0.069626) | 0.140828 / 0.737135 (-0.596308) | 0.112015 / 0.296338 (-0.184324) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.471078 / 0.215209 (0.255869) | 4.705742 / 2.077655 (2.628088) | 2.437442 / 1.504120 (0.933322) | 2.242768 / 1.541195 (0.701573) | 2.302158 / 1.468490 (0.833668) | 0.697314 / 4.584777 (-3.887462) | 3.357730 / 3.745712 (-0.387982) | 1.913306 / 5.269862 (-3.356556) | 1.173879 / 4.565676 (-3.391798) | 0.083257 / 0.424275 (-0.341018) | 0.012480 / 0.007607 (0.004873) | 0.573407 / 0.226044 (0.347362) | 5.728650 / 2.268929 (3.459721) | 2.868863 / 55.444624 (-52.575761) | 2.548640 / 6.876477 (-4.327837) | 2.596622 / 2.142072 (0.454549) | 0.805563 / 4.805227 (-3.999664) | 0.150860 / 6.500664 (-6.349804) | 0.068344 / 0.075469 (-0.007125) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.300368 / 1.841788 (-0.541420) | 13.920451 / 8.074308 (5.846143) | 14.222430 / 10.191392 (4.031038) | 0.152497 / 0.680424 (-0.527927) | 0.017415 / 0.534201 (-0.516786) | 0.378827 / 0.579283 (-0.200456) | 0.384165 / 0.434364 (-0.050199) | 0.439364 / 0.540337 (-0.100973) | 0.525710 / 1.386936 (-0.861226) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2cd22277fa87e02ad9970483f5b75aacdfbf9a70 \"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) | 0.008482 / 0.011353 (-0.002871) | 0.004405 / 0.011008 (-0.006604) | 0.099662 / 0.038508 (0.061154) | 0.029062 / 0.023109 (0.005953) | 0.298329 / 0.275898 (0.022431) | 0.332837 / 0.323480 (0.009357) | 0.006760 / 0.007986 (-0.001225) | 0.003290 / 0.004328 (-0.001039) | 0.077659 / 0.004250 (0.073409) | 0.034745 / 0.037052 (-0.002307) | 0.303134 / 0.258489 (0.044644) | 0.346402 / 0.293841 (0.052561) | 0.033511 / 0.128546 (-0.095035) | 0.011464 / 0.075646 (-0.064183) | 0.322932 / 0.419271 (-0.096340) | 0.040697 / 0.043533 (-0.002836) | 0.301951 / 0.255139 (0.046812) | 0.328961 / 0.283200 (0.045761) | 0.084802 / 0.141683 (-0.056881) | 1.506247 / 1.452155 (0.054092) | 1.547631 / 1.492716 (0.054915) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.190370 / 0.018006 (0.172363) | 0.405786 / 0.000490 (0.405297) | 0.002196 / 0.000200 (0.001997) | 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) | 0.022958 / 0.037411 (-0.014453) | 0.095736 / 0.014526 (0.081210) | 0.103684 / 0.176557 (-0.072872) | 0.138200 / 0.737135 (-0.598936) | 0.105618 / 0.296338 (-0.190721) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.415239 / 0.215209 (0.200030) | 4.147223 / 2.077655 (2.069569) | 1.850322 / 1.504120 (0.346202) | 1.662815 / 1.541195 (0.121620) | 1.671563 / 1.468490 (0.203073) | 0.693806 / 4.584777 (-3.890971) | 3.352938 / 3.745712 (-0.392774) | 1.849257 / 5.269862 (-3.420604) | 1.161603 / 4.565676 (-3.404074) | 0.081884 / 0.424275 (-0.342391) | 0.012726 / 0.007607 (0.005119) | 0.521105 / 0.226044 (0.295061) | 5.231910 / 2.268929 (2.962981) | 2.306073 / 55.444624 (-53.138551) | 1.950449 / 6.876477 (-4.926028) | 1.988433 / 2.142072 (-0.153640) | 0.811168 / 4.805227 (-3.994059) | 0.149960 / 6.500664 (-6.350704) | 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) | 1.221487 / 1.841788 (-0.620301) | 13.756534 / 8.074308 (5.682226) | 13.825369 / 10.191392 (3.633977) | 0.155641 / 0.680424 (-0.524783) | 0.028444 / 0.534201 (-0.505757) | 0.390364 / 0.579283 (-0.188919) | 0.397592 / 0.434364 (-0.036772) | 0.455905 / 0.540337 (-0.084433) | 0.534606 / 1.386936 (-0.852330) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006281 / 0.011353 (-0.005071) | 0.004533 / 0.011008 (-0.006475) | 0.098328 / 0.038508 (0.059820) | 0.026998 / 0.023109 (0.003889) | 0.424814 / 0.275898 (0.148915) | 0.457653 / 0.323480 (0.134173) | 0.004617 / 0.007986 (-0.003368) | 0.003320 / 0.004328 (-0.001009) | 0.075884 / 0.004250 (0.071634) | 0.035865 / 0.037052 (-0.001187) | 0.431674 / 0.258489 (0.173185) | 0.468286 / 0.293841 (0.174445) | 0.031915 / 0.128546 (-0.096631) | 0.011680 / 0.075646 (-0.063967) | 0.319575 / 0.419271 (-0.099696) | 0.047792 / 0.043533 (0.004259) | 0.428191 / 0.255139 (0.173052) | 0.445657 / 0.283200 (0.162458) | 0.090464 / 0.141683 (-0.051218) | 1.465480 / 1.452155 (0.013326) | 1.548985 / 1.492716 (0.056268) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.185671 / 0.018006 (0.167664) | 0.399274 / 0.000490 (0.398784) | 0.002822 / 0.000200 (0.002622) | 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) | 0.025934 / 0.037411 (-0.011477) | 0.099480 / 0.014526 (0.084954) | 0.110264 / 0.176557 (-0.066293) | 0.140558 / 0.737135 (-0.596577) | 0.110832 / 0.296338 (-0.185507) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.473491 / 0.215209 (0.258282) | 4.722507 / 2.077655 (2.644852) | 2.456242 / 1.504120 (0.952122) | 2.255999 / 1.541195 (0.714804) | 2.300816 / 1.468490 (0.832326) | 0.698226 / 4.584777 (-3.886551) | 3.397296 / 3.745712 (-0.348416) | 2.741674 / 5.269862 (-2.528187) | 1.462103 / 4.565676 (-3.103573) | 0.082736 / 0.424275 (-0.341539) | 0.012183 / 0.007607 (0.004576) | 0.580144 / 0.226044 (0.354099) | 5.794351 / 2.268929 (3.525422) | 2.881201 / 55.444624 (-52.563423) | 2.544384 / 6.876477 (-4.332093) | 2.555227 / 2.142072 (0.413154) | 0.805849 / 4.805227 (-3.999378) | 0.151822 / 6.500664 (-6.348842) | 0.067477 / 0.075469 (-0.007992) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.300224 / 1.841788 (-0.541564) | 13.595361 / 8.074308 (5.521053) | 13.967622 / 10.191392 (3.776230) | 0.129222 / 0.680424 (-0.551202) | 0.016939 / 0.534201 (-0.517262) | 0.375190 / 0.579283 (-0.204094) | 0.383511 / 0.434364 (-0.050853) | 0.437179 / 0.540337 (-0.103158) | 0.525674 / 1.386936 (-0.861262) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7ed52db3d67cc8d0f2adfe53b2ec8d1124a174b8 \"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) | 0.012364 / 0.011353 (0.001011) | 0.006098 / 0.011008 (-0.004911) | 0.158908 / 0.038508 (0.120400) | 0.039798 / 0.023109 (0.016689) | 0.383786 / 0.275898 (0.107888) | 0.533961 / 0.323480 (0.210481) | 0.012079 / 0.007986 (0.004094) | 0.006483 / 0.004328 (0.002155) | 0.109660 / 0.004250 (0.105410) | 0.048391 / 0.037052 (0.011339) | 0.447426 / 0.258489 (0.188937) | 0.477292 / 0.293841 (0.183451) | 0.066492 / 0.128546 (-0.062054) | 0.021155 / 0.075646 (-0.054492) | 0.474473 / 0.419271 (0.055202) | 0.063520 / 0.043533 (0.019987) | 0.444941 / 0.255139 (0.189802) | 0.450675 / 0.283200 (0.167475) | 0.129236 / 0.141683 (-0.012447) | 2.009362 / 1.452155 (0.557207) | 1.912067 / 1.492716 (0.419350) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.260384 / 0.018006 (0.242378) | 0.577654 / 0.000490 (0.577165) | 0.004977 / 0.000200 (0.004777) | 0.000110 / 0.000054 (0.000056) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028101 / 0.037411 (-0.009310) | 0.161680 / 0.014526 (0.147154) | 0.146107 / 0.176557 (-0.030450) | 0.173878 / 0.737135 (-0.563257) | 0.186149 / 0.296338 (-0.110190) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.689835 / 0.215209 (0.474626) | 6.775888 / 2.077655 (4.698234) | 2.885499 / 1.504120 (1.381379) | 2.486855 / 1.541195 (0.945660) | 2.540831 / 1.468490 (1.072341) | 1.328135 / 4.584777 (-3.256642) | 5.964983 / 3.745712 (2.219271) | 3.400713 / 5.269862 (-1.869149) | 2.423257 / 4.565676 (-2.142419) | 0.129767 / 0.424275 (-0.294508) | 0.017936 / 0.007607 (0.010328) | 0.909284 / 0.226044 (0.683239) | 8.778791 / 2.268929 (6.509863) | 3.890757 / 55.444624 (-51.553867) | 3.072116 / 6.876477 (-3.804360) | 3.085390 / 2.142072 (0.943318) | 1.571710 / 4.805227 (-3.233517) | 0.279290 / 6.500664 (-6.221374) | 0.087775 / 0.075469 (0.012306) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.751223 / 1.841788 (-0.090564) | 20.313135 / 8.074308 (12.238827) | 22.793800 / 10.191392 (12.602408) | 0.296052 / 0.680424 (-0.384372) | 0.053420 / 0.534201 (-0.480781) | 0.600626 / 0.579283 (0.021343) | 0.634505 / 0.434364 (0.200142) | 0.724000 / 0.540337 (0.183663) | 0.869283 / 1.386936 (-0.517653) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.014876 / 0.011353 (0.003523) | 0.008113 / 0.011008 (-0.002895) | 0.177038 / 0.038508 (0.138530) | 0.050825 / 0.023109 (0.027716) | 0.473989 / 0.275898 (0.198091) | 0.601058 / 0.323480 (0.277578) | 0.007536 / 0.007986 (-0.000450) | 0.006761 / 0.004328 (0.002432) | 0.105260 / 0.004250 (0.101010) | 0.073960 / 0.037052 (0.036908) | 0.447711 / 0.258489 (0.189222) | 0.609998 / 0.293841 (0.316157) | 0.061280 / 0.128546 (-0.067267) | 0.019370 / 0.075646 (-0.056276) | 0.510466 / 0.419271 (0.091194) | 0.062695 / 0.043533 (0.019162) | 0.436778 / 0.255139 (0.181639) | 0.489916 / 0.283200 (0.206717) | 0.137305 / 0.141683 (-0.004378) | 1.801554 / 1.452155 (0.349399) | 2.082409 / 1.492716 (0.589692) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.291304 / 0.018006 (0.273298) | 0.599041 / 0.000490 (0.598551) | 0.008017 / 0.000200 (0.007817) | 0.000127 / 0.000054 (0.000072) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031243 / 0.037411 (-0.006169) | 0.139689 / 0.014526 (0.125163) | 0.138678 / 0.176557 (-0.037878) | 0.180458 / 0.737135 (-0.556677) | 0.149753 / 0.296338 (-0.146585) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.699692 / 0.215209 (0.484482) | 7.273327 / 2.077655 (5.195672) | 3.222650 / 1.504120 (1.718530) | 2.679424 / 1.541195 (1.138229) | 2.842378 / 1.468490 (1.373888) | 1.394633 / 4.584777 (-3.190143) | 6.379970 / 3.745712 (2.634258) | 5.944663 / 5.269862 (0.674801) | 3.105214 / 4.565676 (-1.460462) | 0.138790 / 0.424275 (-0.285485) | 0.014211 / 0.007607 (0.006604) | 0.815275 / 0.226044 (0.589230) | 8.549334 / 2.268929 (6.280405) | 3.754795 / 55.444624 (-51.689829) | 3.125222 / 6.876477 (-3.751255) | 3.269639 / 2.142072 (1.127566) | 1.464187 / 4.805227 (-3.341040) | 0.314557 / 6.500664 (-6.186107) | 0.107354 / 0.075469 (0.031885) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.480793 / 1.841788 (-0.360995) | 16.770328 / 8.074308 (8.696019) | 18.054861 / 10.191392 (7.863469) | 0.198257 / 0.680424 (-0.482167) | 0.026493 / 0.534201 (-0.507708) | 0.489701 / 0.579283 (-0.089582) | 0.540890 / 0.434364 (0.106526) | 0.566675 / 0.540337 (0.026337) | 0.661918 / 1.386936 (-0.725018) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c4b839b50e9a81693e065f5299990026b97f6580 \"CML watermark\")\n" ]

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[ "Ccing @dwofk (the author of `fast-depth`). \r\n\r\nThanks, @awsaf49 for reporting this. I believe this is because the NYU Depth V2 shipped from `fast-depth` is already preprocessed. \r\n\r\nIf you think it might be better to have the NYU Depth V2 dataset from BTS [here](https://huggingface.co/datasets/sayakpaul/nyu_depth_v2) feel free to open a PR, I am happy to provide guidance :) ", "Good catch ! Ideally it would be nice to have the datasets in the raw form, this way users can choose whatever processing they want to apply", "> Ccing @dwofk (the author of `fast-depth`).\r\n> \r\n> Thanks, @awsaf49 for reporting this. I believe this is because the NYU Depth V2 shipped from `fast-depth` is already preprocessed.\r\n> \r\n> If you think it might be better to have the NYU Depth V2 dataset from BTS [here](https://huggingface.co/datasets/sayakpaul/nyu_depth_v2) feel free to open a PR, I am happy to provide guidance :)\r\n\r\n@sayakpaul I would love to create a PR on this. As this will be my first PR here, some guidance would be helpful.\r\n\r\nNeed a bit of advice on the dataset, there are three publicly available datasets. Which one should I consider for PR?\r\n1. [BTS](https://github.com/cleinc/bts): Containst train/test: 36K/654 data, dtype = `uint16` hence more precise\r\n2. [DenseDepth](https://github.com/ialhashim/DenseDepth) It contains train/test: 50K/654 data, dtype = `uint8` hence less precise\r\n3. [Official](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html#raw_parts): Size is big 400GB+, requires **MatLab** code for fixing **projection** and **sync**, DataType: `pgm` and `dump` hence can't be used directly.\r\n\r\ncc: @lhoestq\r\n\r\n", "I think BTS. Repositories like https://github.com/vinvino02/GLPDepth usually use BTS. Also, just for clarity, the PR will be to https://huggingface.co/datasets/sayakpaul/nyu_depth_v2. Once we have worked it out, we can update the following things:\r\n\r\n* https://github.com/huggingface/blog/pull/718\r\n* https://huggingface.co/docs/datasets/main/en/depth_estimation\r\n\r\nDon't worry about it if it seems overwhelming. We will work it out together :) \r\n\r\n@lhoestq what do you think? ", "@sayakpaul If I get this right I have to,\r\n1. Create a PR on https://huggingface.co/datasets/sayakpaul/nyu_depth_v2\r\n2. Create a PR on https://github.com/huggingface/blog\r\n3. Create a PR on https://github.com/huggingface/datasets to update https://github.com/huggingface/datasets/blob/main/docs/source/depth_estimation.mdx", "The last two are low-hanging fruits. Don't worry about them. ", "Yup opening a PR to use BTS on https://huggingface.co/datasets/sayakpaul/nyu_depth_v2 sounds good :) Thanks for the help !", "Finally, I have found the origin of the **discretized depth map**. When I first loaded the datasets from HF I noticed it was 30GB but in DenseDepth data is only 4GB with dtype=uint8. This means data from fast-depth (before loading to HF) must have high precision. So when I tried to dig deeper by directly loading depth_map from `h5py`, I found depth_map from `h5py` came with `float32`. But when the data is processed in HF with `datasets.Image()` it was directly converted to `uint8` from `float32` hence the **discretized** depth map.\r\nhttps://github.com/huggingface/datasets/blob/c78559cacbb0ca6e0bc8bfc313cc0359f8c23ead/src/datasets/features/image.py#L91-L93\r\n\r\n## Solutions:\r\n\r\n#### 1. Array2D\r\nUse `Array2D` feature with `float32` for depth_map \r\n\r\n* Code:\r\n```py\r\nFeatures({'depth_map': Array2D(shape=(480, 640), dtype='float32')})\r\n```\r\n* Pros:\r\nNo precision loss.\r\n\r\n* Cons:\r\nAs depth_map is saved as Array I think it can't be visuzlied in [hf.co/dataset](https://huggingface.co/datasets/sayakpaul/nyu_depth_v2) page like segmentation mask.\r\n\r\n#### 2. Uint16\r\nUse `uint16` as dtype for Image in `_h5_loader` for saving depth maps and accept `uint16` dtype in `datasets.Image()` feature.\r\n\r\n* Code\r\n```py\r\ndepth = np.array(h5f[\"depth\"])\r\ndepth /= 10.0 # [0, max_depth] -> [0, 1]\r\ndepth *= (2**16 -1) # transform from [0, 1] -> [0, 2^16 - 1]\r\ndepth = depth.astype('uint16')\r\n```\r\n* Pros:\r\n * We can visualize depth map in hf.co/datasets page like segmentation mask.\r\n * No need for post-processing.\r\n\r\n* Cons:\r\n * We need to make two change\r\n * Modify `_h5_loader` in https://huggingface.co/datasets/sayakpaul/nyu_depth_v2 to convert depth_map from `float32` to `uint16`.\r\n * Make sure `datasets.Image()` converts `np.ndarray` to `uint16` checking max value\r\n * Precision loss due to `float32` to `uint16`\r\n * Post-processing required for depth_map to transform from `[0, 2^16 - 1]` to `[0, max_depth]` before feeding them to model.", "Thanks so much for digging into this. \r\n\r\nSince the second solution entails changes to core datatypes in `datasets`, I think it's better to go with the first solution. \r\n\r\n@lhoestq WDYT?", "@sayakpaul Yes, Solution 1 requires minimal change and provides no precision loss. But I think support for `uint16` image would be a great addition as many datasets come with `uint16` image. For example [UW-Madison GI Tract Image Segmentation](https://www.kaggle.com/competitions/uw-madison-gi-tract-image-segmentation) dataset, here the image itself comes with `uint16` dtype rather than mask. So, saving `uint16` image with `uint8` will result in precision loss.\r\n\r\nPerhaps we can adapt solution 1 for this issue and Add support for `uint16` image separately?", "Using Array2D makes it not practical to use to train a model - in `transformers` we expect an image type.\r\n\r\nThere is a pull request to support more precision than uint8 in Image() here: https://github.com/huggingface/datasets/pull/5365/files\r\n\r\nwe can probably merge it today and do a release right away", "Fantastic, @lhoestq! \r\n\r\n@awsaf49 then let's wait for the PR to get merged and then take the next steps? ", "Sure", "The PR adds support for uint16 which is ok for BTS if I understand correctly, would it be ok for you ?", "If the main issue with the current version of NYU we have on the Hub is related to the precision loss stemming from `Image()`, I'd prefer if `Image()` supported float32 as well. ", "I also prefer `float32` as it offers more precision. But I'm not sure if we'll be able to visualize image with `float32` precision.", "We could have a separate loading for the float32 one using Array2D, but I feel like it's less convenient to use due to the amount of disk space and because it's not an Image() type. That's why I think uint16 is a better solution for users", "A bit confused here, If https://github.com/huggingface/datasets/pull/5365 gets merged won't this issue will be resolved automatically?", "Yes in theory :)", "actually float32 also seems to work in this PR (it just doesn't work for multi-channel)", "In that case, a new PR isn't necessary, right?", "Yep. I just tested from the PR and it works:\r\n```python\r\n>>> train_dataset = load_dataset(\"sayakpaul/nyu_depth_v2\", split=\"train\", streaming=True) \r\nDownloading readme: 100%|██████████████████| 8.71k/8.71k [00:00<00:00, 3.60MB/s]\r\n>>> next(iter(train_dataset))\r\n{'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=640x480 at 0x1382ED7F0>,\r\n 'depth_map': <PIL.TiffImagePlugin.TiffImageFile image mode=F size=640x480 at 0x1382EDF28>}\r\n>>> x = next(iter(train_dataset))\r\n>>> np.asarray(x[\"depth_map\"]) \r\narray([[0. , 0. , 0. , ..., 0. , 0. ,\r\n 0. ],\r\n [0. , 0. , 0. , ..., 0. , 0. ,\r\n 0. ],\r\n [0. , 0. , 0. , ..., 0. , 0. ,\r\n 0. ],\r\n ...,\r\n [0. , 2.2861192, 2.2861192, ..., 2.234162 , 2.234162 ,\r\n 0. ],\r\n [0. , 2.2861192, 2.2861192, ..., 2.234162 , 2.234162 ,\r\n 0. ],\r\n [0. , 2.2861192, 2.2861192, ..., 2.234162 , 2.234162 ,\r\n 0. ]], dtype=float32)\r\n```", "Great! the case is closed! This issue has been solved and I have to say, it was quite the thrill ride. I felt like Sherlock Holmes, solving a mystery and finding the bug🕵️‍♂️. But in all seriousness, it was a pleasure working on this issue and I'm glad we could get to the bottom of it.\r\n\r\nOn another note, should I consider closing the issue? I think we still need to make updates on https://github.com/huggingface/blog and https://github.com/huggingface/datasets/blob/main/docs/source/depth_estimation.mdx", "Haha thanks Mr Holmes :p\r\n\r\nmaybe let's close this issue when we're done updating the blog post and the documentation", "@awsaf49 thank you for your hard work! \r\n\r\nI am a little unsure why the other links need to be updated, though. They all rely on datasets internally. ", "I think depth_map still shows discretized version. It would be nice to have corrected one.\r\n<img src=\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/datasets/depth_est_target_viz.png\" width = 300>", "Also, I think we need to make some changes in the code to visualize depth_map as it is `float32` . `plot.imshow()` supports either [0, 1] + float32 or [0. 255] + uint8", "Oh yes! Do you want to start with the fixes? Please feel free to say no but I wanted to make sure your contributions are reflected properly in our doc and the blog :)", "Yes I think that would be nice :)", "I'll make the changes tomorrow. I hope it's okay..." ]

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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) | 0.011812 / 0.011353 (0.000459) | 0.006878 / 0.011008 (-0.004130) | 0.128720 / 0.038508 (0.090212) | 0.038506 / 0.023109 (0.015397) | 0.359670 / 0.275898 (0.083772) | 0.422908 / 0.323480 (0.099428) | 0.010115 / 0.007986 (0.002129) | 0.004332 / 0.004328 (0.000004) | 0.096281 / 0.004250 (0.092031) | 0.048850 / 0.037052 (0.011798) | 0.373795 / 0.258489 (0.115306) | 0.414643 / 0.293841 (0.120802) | 0.057568 / 0.128546 (-0.070978) | 0.024135 / 0.075646 (-0.051512) | 0.411764 / 0.419271 (-0.007507) | 0.060167 / 0.043533 (0.016634) | 0.367119 / 0.255139 (0.111980) | 0.391813 / 0.283200 (0.108613) | 0.112125 / 0.141683 (-0.029558) | 1.869560 / 1.452155 (0.417406) | 1.845649 / 1.492716 (0.352932) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211449 / 0.018006 (0.193443) | 0.522453 / 0.000490 (0.521963) | 0.003984 / 0.000200 (0.003784) | 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) | 0.026015 / 0.037411 (-0.011397) | 0.117747 / 0.014526 (0.103221) | 0.125037 / 0.176557 (-0.051520) | 0.168351 / 0.737135 (-0.568785) | 0.132390 / 0.296338 (-0.163949) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.605653 / 0.215209 (0.390444) | 5.883452 / 2.077655 (3.805798) | 2.367052 / 1.504120 (0.862932) | 2.137671 / 1.541195 (0.596476) | 2.042370 / 1.468490 (0.573880) | 1.168442 / 4.584777 (-3.416335) | 5.205236 / 3.745712 (1.459524) | 2.992514 / 5.269862 (-2.277348) | 2.191829 / 4.565676 (-2.373847) | 0.137702 / 0.424275 (-0.286574) | 0.015898 / 0.007607 (0.008291) | 0.783987 / 0.226044 (0.557942) | 7.768965 / 2.268929 (5.500036) | 3.249149 / 55.444624 (-52.195476) | 2.530687 / 6.876477 (-4.345790) | 2.675212 / 2.142072 (0.533140) | 1.482804 / 4.805227 (-3.322423) | 0.276845 / 6.500664 (-6.223819) | 0.080597 / 0.075469 (0.005128) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.519086 / 1.841788 (-0.322701) | 17.394093 / 8.074308 (9.319785) | 19.613554 / 10.191392 (9.422162) | 0.253291 / 0.680424 (-0.427133) | 0.047746 / 0.534201 (-0.486455) | 0.547114 / 0.579283 (-0.032170) | 0.623873 / 0.434364 (0.189509) | 0.631924 / 0.540337 (0.091586) | 0.744390 / 1.386936 (-0.642546) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009229 / 0.011353 (-0.002124) | 0.006206 / 0.011008 (-0.004802) | 0.121866 / 0.038508 (0.083357) | 0.033629 / 0.023109 (0.010519) | 0.435172 / 0.275898 (0.159274) | 0.472093 / 0.323480 (0.148613) | 0.006946 / 0.007986 (-0.001039) | 0.004848 / 0.004328 (0.000519) | 0.097289 / 0.004250 (0.093038) | 0.046982 / 0.037052 (0.009930) | 0.447365 / 0.258489 (0.188876) | 0.491213 / 0.293841 (0.197372) | 0.055486 / 0.128546 (-0.073060) | 0.019788 / 0.075646 (-0.055858) | 0.399830 / 0.419271 (-0.019441) | 0.058943 / 0.043533 (0.015411) | 0.447658 / 0.255139 (0.192519) | 0.465752 / 0.283200 (0.182552) | 0.110441 / 0.141683 (-0.031242) | 1.773155 / 1.452155 (0.321001) | 1.899370 / 1.492716 (0.406653) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.191188 / 0.018006 (0.173181) | 0.523721 / 0.000490 (0.523232) | 0.004008 / 0.000200 (0.003808) | 0.000126 / 0.000054 (0.000072) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032579 / 0.037411 (-0.004833) | 0.120870 / 0.014526 (0.106344) | 0.154991 / 0.176557 (-0.021565) | 0.175450 / 0.737135 (-0.561685) | 0.136526 / 0.296338 (-0.159813) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.627262 / 0.215209 (0.412052) | 6.457989 / 2.077655 (4.380334) | 2.935188 / 1.504120 (1.431068) | 2.558705 / 1.541195 (1.017510) | 2.669455 / 1.468490 (1.200965) | 1.228791 / 4.584777 (-3.355985) | 5.621262 / 3.745712 (1.875549) | 3.181775 / 5.269862 (-2.088086) | 2.115116 / 4.565676 (-2.450560) | 0.159348 / 0.424275 (-0.264927) | 0.013598 / 0.007607 (0.005991) | 0.834732 / 0.226044 (0.608687) | 8.051097 / 2.268929 (5.782168) | 3.761681 / 55.444624 (-51.682943) | 2.898158 / 6.876477 (-3.978319) | 2.936289 / 2.142072 (0.794217) | 1.476307 / 4.805227 (-3.328920) | 0.269845 / 6.500664 (-6.230819) | 0.087225 / 0.075469 (0.011756) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.632522 / 1.841788 (-0.209266) | 17.615297 / 8.074308 (9.540989) | 20.501172 / 10.191392 (10.309780) | 0.248845 / 0.680424 (-0.431579) | 0.024852 / 0.534201 (-0.509349) | 0.498957 / 0.579283 (-0.080326) | 0.588566 / 0.434364 (0.154202) | 0.611051 / 0.540337 (0.070714) | 0.726321 / 1.386936 (-0.660615) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#adaaf0b5ad596538c744d41bb56ce472834b6573 \"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) | 0.008920 / 0.011353 (-0.002433) | 0.004666 / 0.011008 (-0.006342) | 0.098584 / 0.038508 (0.060076) | 0.030213 / 0.023109 (0.007103) | 0.298180 / 0.275898 (0.022282) | 0.358932 / 0.323480 (0.035452) | 0.007182 / 0.007986 (-0.000804) | 0.005430 / 0.004328 (0.001102) | 0.077962 / 0.004250 (0.073712) | 0.038516 / 0.037052 (0.001463) | 0.308840 / 0.258489 (0.050351) | 0.343678 / 0.293841 (0.049837) | 0.033701 / 0.128546 (-0.094845) | 0.011460 / 0.075646 (-0.064186) | 0.319809 / 0.419271 (-0.099462) | 0.040731 / 0.043533 (-0.002802) | 0.299772 / 0.255139 (0.044633) | 0.324292 / 0.283200 (0.041092) | 0.087755 / 0.141683 (-0.053928) | 1.493077 / 1.452155 (0.040922) | 1.527462 / 1.492716 (0.034746) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.187927 / 0.018006 (0.169921) | 0.412785 / 0.000490 (0.412296) | 0.003235 / 0.000200 (0.003035) | 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) | 0.023313 / 0.037411 (-0.014098) | 0.095663 / 0.014526 (0.081137) | 0.105094 / 0.176557 (-0.071463) | 0.140389 / 0.737135 (-0.596746) | 0.108477 / 0.296338 (-0.187861) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.410680 / 0.215209 (0.195471) | 4.109287 / 2.077655 (2.031632) | 1.833214 / 1.504120 (0.329094) | 1.622837 / 1.541195 (0.081642) | 1.679899 / 1.468490 (0.211409) | 0.686920 / 4.584777 (-3.897857) | 3.463267 / 3.745712 (-0.282445) | 1.867035 / 5.269862 (-3.402826) | 1.150631 / 4.565676 (-3.415046) | 0.081209 / 0.424275 (-0.343066) | 0.012384 / 0.007607 (0.004777) | 0.521070 / 0.226044 (0.295026) | 5.208829 / 2.268929 (2.939900) | 2.289032 / 55.444624 (-53.155592) | 1.942976 / 6.876477 (-4.933501) | 1.990660 / 2.142072 (-0.151413) | 0.802976 / 4.805227 (-4.002252) | 0.148199 / 6.500664 (-6.352465) | 0.064644 / 0.075469 (-0.010825) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.277029 / 1.841788 (-0.564759) | 13.915489 / 8.074308 (5.841181) | 14.035486 / 10.191392 (3.844094) | 0.138205 / 0.680424 (-0.542219) | 0.028968 / 0.534201 (-0.505232) | 0.394275 / 0.579283 (-0.185008) | 0.399967 / 0.434364 (-0.034397) | 0.460595 / 0.540337 (-0.079742) | 0.537625 / 1.386936 (-0.849311) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006485 / 0.011353 (-0.004868) | 0.004534 / 0.011008 (-0.006474) | 0.097742 / 0.038508 (0.059234) | 0.027231 / 0.023109 (0.004122) | 0.431321 / 0.275898 (0.155423) | 0.469212 / 0.323480 (0.145732) | 0.004894 / 0.007986 (-0.003092) | 0.004147 / 0.004328 (-0.000181) | 0.073650 / 0.004250 (0.069400) | 0.037052 / 0.037052 (-0.000000) | 0.434196 / 0.258489 (0.175707) | 0.480539 / 0.293841 (0.186698) | 0.031923 / 0.128546 (-0.096623) | 0.011522 / 0.075646 (-0.064124) | 0.317062 / 0.419271 (-0.102209) | 0.041124 / 0.043533 (-0.002409) | 0.432013 / 0.255139 (0.176874) | 0.456760 / 0.283200 (0.173560) | 0.089757 / 0.141683 (-0.051925) | 1.497752 / 1.452155 (0.045597) | 1.585342 / 1.492716 (0.092626) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227784 / 0.018006 (0.209778) | 0.404570 / 0.000490 (0.404080) | 0.000556 / 0.000200 (0.000356) | 0.000065 / 0.000054 (0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025201 / 0.037411 (-0.012210) | 0.099348 / 0.014526 (0.084822) | 0.114984 / 0.176557 (-0.061573) | 0.147039 / 0.737135 (-0.590097) | 0.109727 / 0.296338 (-0.186611) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.468415 / 0.215209 (0.253206) | 4.692228 / 2.077655 (2.614573) | 2.403382 / 1.504120 (0.899262) | 2.196026 / 1.541195 (0.654832) | 2.234736 / 1.468490 (0.766246) | 0.703011 / 4.584777 (-3.881766) | 3.451513 / 3.745712 (-0.294199) | 2.596811 / 5.269862 (-2.673051) | 1.544079 / 4.565676 (-3.021598) | 0.083153 / 0.424275 (-0.341123) | 0.012605 / 0.007607 (0.004998) | 0.570265 / 0.226044 (0.344220) | 5.735996 / 2.268929 (3.467067) | 2.865336 / 55.444624 (-52.579288) | 2.508340 / 6.876477 (-4.368137) | 2.547144 / 2.142072 (0.405072) | 0.813018 / 4.805227 (-3.992210) | 0.150327 / 6.500664 (-6.350337) | 0.065837 / 0.075469 (-0.009632) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.268941 / 1.841788 (-0.572847) | 13.835698 / 8.074308 (5.761390) | 13.992726 / 10.191392 (3.801334) | 0.127751 / 0.680424 (-0.552673) | 0.016673 / 0.534201 (-0.517528) | 0.381921 / 0.579283 (-0.197362) | 0.390688 / 0.434364 (-0.043676) | 0.446234 / 0.540337 (-0.094103) | 0.532631 / 1.386936 (-0.854305) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1492df3311bfeac55aaedf34c93c014630c4403e \"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) | 0.008486 / 0.011353 (-0.002867) | 0.004573 / 0.011008 (-0.006435) | 0.100096 / 0.038508 (0.061588) | 0.029449 / 0.023109 (0.006340) | 0.298384 / 0.275898 (0.022486) | 0.361886 / 0.323480 (0.038406) | 0.006813 / 0.007986 (-0.001173) | 0.003394 / 0.004328 (-0.000935) | 0.077563 / 0.004250 (0.073312) | 0.035605 / 0.037052 (-0.001447) | 0.306864 / 0.258489 (0.048375) | 0.346438 / 0.293841 (0.052597) | 0.033156 / 0.128546 (-0.095390) | 0.011567 / 0.075646 (-0.064079) | 0.322189 / 0.419271 (-0.097083) | 0.040161 / 0.043533 (-0.003372) | 0.299329 / 0.255139 (0.044190) | 0.326375 / 0.283200 (0.043175) | 0.086572 / 0.141683 (-0.055111) | 1.502473 / 1.452155 (0.050319) | 1.528539 / 1.492716 (0.035823) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.008502 / 0.018006 (-0.009505) | 0.411045 / 0.000490 (0.410555) | 0.003179 / 0.000200 (0.002980) | 0.000073 / 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) | 0.023177 / 0.037411 (-0.014234) | 0.096948 / 0.014526 (0.082422) | 0.104068 / 0.176557 (-0.072489) | 0.138739 / 0.737135 (-0.598396) | 0.108241 / 0.296338 (-0.188097) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.411156 / 0.215209 (0.195947) | 4.092992 / 2.077655 (2.015337) | 1.841903 / 1.504120 (0.337783) | 1.637449 / 1.541195 (0.096254) | 1.670968 / 1.468490 (0.202478) | 0.697301 / 4.584777 (-3.887476) | 3.354717 / 3.745712 (-0.390995) | 1.851518 / 5.269862 (-3.418344) | 1.160367 / 4.565676 (-3.405309) | 0.082613 / 0.424275 (-0.341662) | 0.012477 / 0.007607 (0.004870) | 0.524839 / 0.226044 (0.298795) | 5.264173 / 2.268929 (2.995245) | 2.294530 / 55.444624 (-53.150094) | 1.933233 / 6.876477 (-4.943244) | 1.968959 / 2.142072 (-0.173113) | 0.817104 / 4.805227 (-3.988123) | 0.149072 / 6.500664 (-6.351592) | 0.064911 / 0.075469 (-0.010558) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.222215 / 1.841788 (-0.619573) | 13.607545 / 8.074308 (5.533237) | 13.990230 / 10.191392 (3.798838) | 0.150855 / 0.680424 (-0.529568) | 0.028844 / 0.534201 (-0.505357) | 0.396169 / 0.579283 (-0.183114) | 0.406957 / 0.434364 (-0.027407) | 0.464069 / 0.540337 (-0.076268) | 0.554027 / 1.386936 (-0.832909) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006296 / 0.011353 (-0.005057) | 0.004563 / 0.011008 (-0.006445) | 0.097719 / 0.038508 (0.059211) | 0.027106 / 0.023109 (0.003996) | 0.409333 / 0.275898 (0.133435) | 0.445397 / 0.323480 (0.121917) | 0.004906 / 0.007986 (-0.003080) | 0.003316 / 0.004328 (-0.001012) | 0.075363 / 0.004250 (0.071112) | 0.039366 / 0.037052 (0.002314) | 0.412710 / 0.258489 (0.154221) | 0.451789 / 0.293841 (0.157948) | 0.031810 / 0.128546 (-0.096736) | 0.011681 / 0.075646 (-0.063965) | 0.318484 / 0.419271 (-0.100788) | 0.046741 / 0.043533 (0.003208) | 0.411631 / 0.255139 (0.156492) | 0.435274 / 0.283200 (0.152074) | 0.092366 / 0.141683 (-0.049317) | 1.492243 / 1.452155 (0.040089) | 1.617603 / 1.492716 (0.124887) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.217376 / 0.018006 (0.199369) | 0.400940 / 0.000490 (0.400450) | 0.003700 / 0.000200 (0.003500) | 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) | 0.023733 / 0.037411 (-0.013678) | 0.098553 / 0.014526 (0.084027) | 0.105790 / 0.176557 (-0.070767) | 0.139537 / 0.737135 (-0.597598) | 0.109862 / 0.296338 (-0.186477) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.476562 / 0.215209 (0.261353) | 4.773469 / 2.077655 (2.695814) | 2.447302 / 1.504120 (0.943182) | 2.240596 / 1.541195 (0.699401) | 2.271370 / 1.468490 (0.802880) | 0.698913 / 4.584777 (-3.885864) | 3.345648 / 3.745712 (-0.400064) | 1.845008 / 5.269862 (-3.424854) | 1.163213 / 4.565676 (-3.402464) | 0.082456 / 0.424275 (-0.341819) | 0.012315 / 0.007607 (0.004708) | 0.575881 / 0.226044 (0.349836) | 5.769575 / 2.268929 (3.500647) | 2.909759 / 55.444624 (-52.534865) | 2.580259 / 6.876477 (-4.296218) | 2.590473 / 2.142072 (0.448401) | 0.802765 / 4.805227 (-4.002462) | 0.151514 / 6.500664 (-6.349150) | 0.067718 / 0.075469 (-0.007751) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.293014 / 1.841788 (-0.548773) | 13.934072 / 8.074308 (5.859763) | 13.538760 / 10.191392 (3.347368) | 0.126490 / 0.680424 (-0.553934) | 0.016653 / 0.534201 (-0.517548) | 0.381220 / 0.579283 (-0.198064) | 0.387571 / 0.434364 (-0.046793) | 0.444674 / 0.540337 (-0.095663) | 0.550802 / 1.386936 (-0.836134) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bed576f2205c96f6cb26b5c6522345cb8b06ecfc \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Comment by @lhoestq:\r\n> Do you think we need this in `datasets` if it's fixed on the moon landing side ? In the aiohttp doc they consider those symbols as \"non-safe\" ", "The lib `requests` does not perform that requote on redirect URLs.", "Indeed, the `requests` library does perform a requoting, but this does not unquote `%27`:\r\n```python\r\nIn [1]: from requests.utils import requote_uri\r\n\r\nIn [2]: url = \"https://netloc/path?param=param%27%27value\"\r\n\r\nIn [3]: url\r\nOut[3]: 'https://netloc/path?param=param%27%27value'\r\n\r\nIn [4]: requote_uri(url)\r\nOut[4]: 'https://netloc/path?param=param%27%27value'\r\n```\r\n\r\nHowever, the `aiohttp` library uses `yarl.ULR` and this does unquote `%27`:\r\n```python\r\nIn [5]: from yarl import URL\r\n\r\nIn [6]: url\r\nOut[6]: 'https://netloc/path?param=param%27%27value'\r\n\r\nIn [7]: str(URL(url))\r\nOut[7]: \"https://netloc/path?param=param''value\"\r\n```\r\n\r\nIf we pass `requote_redirect_url=False` to `aiohttp`, then it passes `encoded=True` to `yarl.ULR`: https://github.com/aio-libs/aiohttp/blob/4635161ee8e7ad321cca46e01ce5bfeb1ad8bf26/aiohttp/client.py#L578-L580\r\n```python\r\nparsed_url = URL(\r\n r_url, encoded=not self._requote_redirect_url\r\n)\r\n```\r\nwhich does not unquote `%27`:\r\n```python\r\nIn [8]: url\r\nOut[8]: 'https://netloc/path?param=param%27%27value'\r\n\r\nIn [9]: str(URL(url, encoded=True))\r\nOut[9]: 'https://netloc/path?param=param%27%27value'\r\n```", "See the issues we opened in the respective libraries:\r\n- aiohttp\r\n - aio-libs/aiohttp#7183\r\n- requests\r\n - psf/requests#6341", "<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) | 0.012399 / 0.011353 (0.001047) | 0.006388 / 0.011008 (-0.004620) | 0.134173 / 0.038508 (0.095665) | 0.037059 / 0.023109 (0.013949) | 0.420697 / 0.275898 (0.144799) | 0.473981 / 0.323480 (0.150502) | 0.009857 / 0.007986 (0.001871) | 0.004791 / 0.004328 (0.000463) | 0.106886 / 0.004250 (0.102636) | 0.044871 / 0.037052 (0.007818) | 0.429843 / 0.258489 (0.171354) | 0.461569 / 0.293841 (0.167728) | 0.057285 / 0.128546 (-0.071261) | 0.018809 / 0.075646 (-0.056837) | 0.432613 / 0.419271 (0.013342) | 0.058086 / 0.043533 (0.014553) | 0.413064 / 0.255139 (0.157925) | 0.444407 / 0.283200 (0.161207) | 0.119102 / 0.141683 (-0.022581) | 1.875954 / 1.452155 (0.423799) | 1.916392 / 1.492716 (0.423676) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.267489 / 0.018006 (0.249483) | 0.567554 / 0.000490 (0.567064) | 0.005901 / 0.000200 (0.005701) | 0.000134 / 0.000054 (0.000079) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031248 / 0.037411 (-0.006164) | 0.123014 / 0.014526 (0.108489) | 0.140001 / 0.176557 (-0.036556) | 0.191476 / 0.737135 (-0.545659) | 0.141687 / 0.296338 (-0.154652) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.637481 / 0.215209 (0.422272) | 6.255969 / 2.077655 (4.178314) | 2.559811 / 1.504120 (1.055691) | 2.118154 / 1.541195 (0.576960) | 2.079487 / 1.468490 (0.610997) | 1.201079 / 4.584777 (-3.383698) | 5.592625 / 3.745712 (1.846913) | 5.143344 / 5.269862 (-0.126517) | 2.764716 / 4.565676 (-1.800960) | 0.142539 / 0.424275 (-0.281736) | 0.015541 / 0.007607 (0.007934) | 0.771407 / 0.226044 (0.545363) | 7.631657 / 2.268929 (5.362728) | 3.279684 / 55.444624 (-52.164940) | 2.587566 / 6.876477 (-4.288911) | 2.624622 / 2.142072 (0.482549) | 1.427878 / 4.805227 (-3.377350) | 0.257759 / 6.500664 (-6.242906) | 0.078616 / 0.075469 (0.003147) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.609305 / 1.841788 (-0.232483) | 18.258792 / 8.074308 (10.184484) | 20.345242 / 10.191392 (10.153850) | 0.267366 / 0.680424 (-0.413058) | 0.047035 / 0.534201 (-0.487166) | 0.568881 / 0.579283 (-0.010402) | 0.662763 / 0.434364 (0.228399) | 0.668927 / 0.540337 (0.128590) | 0.755766 / 1.386936 (-0.631170) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010017 / 0.011353 (-0.001336) | 0.006816 / 0.011008 (-0.004192) | 0.105038 / 0.038508 (0.066529) | 0.038689 / 0.023109 (0.015580) | 0.482113 / 0.275898 (0.206215) | 0.540072 / 0.323480 (0.216592) | 0.007738 / 0.007986 (-0.000248) | 0.005134 / 0.004328 (0.000806) | 0.102203 / 0.004250 (0.097953) | 0.054080 / 0.037052 (0.017028) | 0.501057 / 0.258489 (0.242568) | 0.567186 / 0.293841 (0.273345) | 0.060330 / 0.128546 (-0.068217) | 0.020059 / 0.075646 (-0.055587) | 0.123102 / 0.419271 (-0.296170) | 0.063426 / 0.043533 (0.019893) | 0.494171 / 0.255139 (0.239032) | 0.538238 / 0.283200 (0.255039) | 0.119613 / 0.141683 (-0.022069) | 1.853728 / 1.452155 (0.401574) | 1.984621 / 1.492716 (0.491904) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.282511 / 0.018006 (0.264505) | 0.563190 / 0.000490 (0.562700) | 0.000465 / 0.000200 (0.000265) | 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) | 0.029267 / 0.037411 (-0.008144) | 0.135618 / 0.014526 (0.121093) | 0.146286 / 0.176557 (-0.030271) | 0.188570 / 0.737135 (-0.548565) | 0.155839 / 0.296338 (-0.140499) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.671660 / 0.215209 (0.456451) | 6.718775 / 2.077655 (4.641120) | 3.004601 / 1.504120 (1.500481) | 2.640504 / 1.541195 (1.099309) | 2.666788 / 1.468490 (1.198298) | 1.242655 / 4.584777 (-3.342122) | 5.780119 / 3.745712 (2.034407) | 3.247935 / 5.269862 (-2.021927) | 2.114007 / 4.565676 (-2.451669) | 0.147546 / 0.424275 (-0.276729) | 0.014408 / 0.007607 (0.006801) | 0.824407 / 0.226044 (0.598362) | 8.278185 / 2.268929 (6.009257) | 3.733463 / 55.444624 (-51.711161) | 2.976732 / 6.876477 (-3.899745) | 3.132758 / 2.142072 (0.990686) | 1.446095 / 4.805227 (-3.359132) | 0.258628 / 6.500664 (-6.242036) | 0.085513 / 0.075469 (0.010043) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.702681 / 1.841788 (-0.139106) | 18.725123 / 8.074308 (10.650815) | 19.622808 / 10.191392 (9.431416) | 0.215845 / 0.680424 (-0.464579) | 0.029246 / 0.534201 (-0.504955) | 0.554819 / 0.579283 (-0.024464) | 0.630926 / 0.434364 (0.196562) | 0.637663 / 0.540337 (0.097325) | 0.837948 / 1.386936 (-0.548988) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c4a4f96ef0a4ec4b25f0872f160fa1eb9d2e711c \"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) | 0.008540 / 0.011353 (-0.002813) | 0.004538 / 0.011008 (-0.006470) | 0.101507 / 0.038508 (0.062999) | 0.029751 / 0.023109 (0.006641) | 0.292608 / 0.275898 (0.016710) | 0.354734 / 0.323480 (0.031254) | 0.007430 / 0.007986 (-0.000556) | 0.003365 / 0.004328 (-0.000964) | 0.078703 / 0.004250 (0.074452) | 0.034858 / 0.037052 (-0.002194) | 0.303518 / 0.258489 (0.045029) | 0.336523 / 0.293841 (0.042682) | 0.033741 / 0.128546 (-0.094805) | 0.011460 / 0.075646 (-0.064186) | 0.319551 / 0.419271 (-0.099721) | 0.041102 / 0.043533 (-0.002431) | 0.295914 / 0.255139 (0.040775) | 0.322142 / 0.283200 (0.038943) | 0.084694 / 0.141683 (-0.056989) | 1.481308 / 1.452155 (0.029153) | 1.530271 / 1.492716 (0.037554) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.180516 / 0.018006 (0.162510) | 0.405741 / 0.000490 (0.405251) | 0.002806 / 0.000200 (0.002606) | 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) | 0.023359 / 0.037411 (-0.014052) | 0.096950 / 0.014526 (0.082424) | 0.103991 / 0.176557 (-0.072566) | 0.143700 / 0.737135 (-0.593435) | 0.106764 / 0.296338 (-0.189575) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.416966 / 0.215209 (0.201757) | 4.145601 / 2.077655 (2.067946) | 1.838258 / 1.504120 (0.334139) | 1.629396 / 1.541195 (0.088201) | 1.649707 / 1.468490 (0.181217) | 0.689624 / 4.584777 (-3.895153) | 3.414584 / 3.745712 (-0.331129) | 1.874295 / 5.269862 (-3.395566) | 1.251930 / 4.565676 (-3.313746) | 0.081782 / 0.424275 (-0.342493) | 0.012868 / 0.007607 (0.005261) | 0.523904 / 0.226044 (0.297859) | 5.251032 / 2.268929 (2.982104) | 2.301549 / 55.444624 (-53.143075) | 1.942110 / 6.876477 (-4.934367) | 2.023014 / 2.142072 (-0.119058) | 0.816492 / 4.805227 (-3.988736) | 0.150107 / 6.500664 (-6.350558) | 0.065118 / 0.075469 (-0.010351) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.226433 / 1.841788 (-0.615355) | 13.852569 / 8.074308 (5.778261) | 13.862779 / 10.191392 (3.671387) | 0.146361 / 0.680424 (-0.534062) | 0.028652 / 0.534201 (-0.505549) | 0.398251 / 0.579283 (-0.181032) | 0.403590 / 0.434364 (-0.030774) | 0.492184 / 0.540337 (-0.048154) | 0.581040 / 1.386936 (-0.805896) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006859 / 0.011353 (-0.004494) | 0.004632 / 0.011008 (-0.006376) | 0.076653 / 0.038508 (0.038145) | 0.027865 / 0.023109 (0.004755) | 0.354472 / 0.275898 (0.078573) | 0.385462 / 0.323480 (0.061982) | 0.005125 / 0.007986 (-0.002861) | 0.003420 / 0.004328 (-0.000909) | 0.076018 / 0.004250 (0.071768) | 0.040197 / 0.037052 (0.003144) | 0.353675 / 0.258489 (0.095186) | 0.394911 / 0.293841 (0.101070) | 0.032909 / 0.128546 (-0.095637) | 0.011713 / 0.075646 (-0.063933) | 0.085921 / 0.419271 (-0.333350) | 0.044462 / 0.043533 (0.000929) | 0.349997 / 0.255139 (0.094858) | 0.375207 / 0.283200 (0.092008) | 0.091288 / 0.141683 (-0.050394) | 1.536515 / 1.452155 (0.084361) | 1.581878 / 1.492716 (0.089162) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.273284 / 0.018006 (0.255277) | 0.424457 / 0.000490 (0.423967) | 0.044659 / 0.000200 (0.044459) | 0.000247 / 0.000054 (0.000192) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025473 / 0.037411 (-0.011938) | 0.100014 / 0.014526 (0.085488) | 0.108551 / 0.176557 (-0.068006) | 0.147913 / 0.737135 (-0.589223) | 0.112729 / 0.296338 (-0.183610) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.448162 / 0.215209 (0.232953) | 4.472701 / 2.077655 (2.395046) | 2.078384 / 1.504120 (0.574264) | 1.861292 / 1.541195 (0.320097) | 1.920482 / 1.468490 (0.451991) | 0.706968 / 4.584777 (-3.877809) | 3.433109 / 3.745712 (-0.312603) | 1.898684 / 5.269862 (-3.371178) | 1.174375 / 4.565676 (-3.391302) | 0.083666 / 0.424275 (-0.340609) | 0.012388 / 0.007607 (0.004781) | 0.546011 / 0.226044 (0.319966) | 5.487514 / 2.268929 (3.218585) | 2.534124 / 55.444624 (-52.910500) | 2.168441 / 6.876477 (-4.708036) | 2.203458 / 2.142072 (0.061386) | 0.813333 / 4.805227 (-3.991894) | 0.153169 / 6.500664 (-6.347495) | 0.067151 / 0.075469 (-0.008318) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.277815 / 1.841788 (-0.563972) | 13.920545 / 8.074308 (5.846237) | 13.473801 / 10.191392 (3.282409) | 0.129035 / 0.680424 (-0.551389) | 0.016737 / 0.534201 (-0.517464) | 0.388413 / 0.579283 (-0.190870) | 0.388785 / 0.434364 (-0.045579) | 0.481735 / 0.540337 (-0.058602) | 0.576390 / 1.386936 (-0.810546) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c4a4f96ef0a4ec4b25f0872f160fa1eb9d2e711c \"CML watermark\")\n" ]

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[ "Hi! Yes, that's correct. When `streaming` is `True`, only split names can be specified as `split`, and for slicing, you have to use `.skip`/`.take` instead.\r\n\r\nE.g. \r\n`load_dataset(\"lhoestq/demo1\",revision=None, streaming=True, split=\"train[:3]\")`\r\n\r\nrewritten with `.skip`/`.take`:\r\n`load_dataset(\"lhoestq/demo1\",revision=None, streaming=True, split=\"train\").take(3)`\r\n\r\n\r\n", "Thank you for your quick response!" ]

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[ "Hi! \r\n\r\nThis issue is due to our audio/image datasets not being self-contained. This allows us to save disk space (files are written only once) but also leads to the issues like this one. We plan to make all our datasets self-contained in Datasets 3.0.\r\n\r\nIn the meantime, you can run the following map to ensure your dataset is self-contained:\r\n```python\r\nfrom datasets.table import embed_table_storage\r\n# load_dataset ...\r\ndset = dset.with_format(\"arrow\")\r\ndset.map(embed_table_storage, batched=True)\r\ndset = dset.with_format(\"python\")\r\n```\r\n", "Understood. Thank you, Mario.\r\n\r\nPerhaps the solution could be very simple - move the extracted files into the directory of the cached dataset? Which would make it self-contained already and won't require waiting for a new major release. Unless I'm missing some back-compat nuance.\r\n\r\nBut regardless if X relies on Y - it could check if Y is still there when loading X. so not checking full consistency but just the top-level directory it relies on." ]

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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) | 0.012395 / 0.011353 (0.001042) | 0.006466 / 0.011008 (-0.004542) | 0.127605 / 0.038508 (0.089097) | 0.044929 / 0.023109 (0.021820) | 0.399856 / 0.275898 (0.123958) | 0.491341 / 0.323480 (0.167861) | 0.009193 / 0.007986 (0.001207) | 0.005419 / 0.004328 (0.001090) | 0.100577 / 0.004250 (0.096327) | 0.045338 / 0.037052 (0.008286) | 0.409970 / 0.258489 (0.151481) | 0.452941 / 0.293841 (0.159100) | 0.054350 / 0.128546 (-0.074197) | 0.019069 / 0.075646 (-0.056578) | 0.427036 / 0.419271 (0.007765) | 0.073616 / 0.043533 (0.030083) | 0.395384 / 0.255139 (0.140245) | 0.442381 / 0.283200 (0.159181) | 0.123185 / 0.141683 (-0.018498) | 1.797640 / 1.452155 (0.345485) | 1.888860 / 1.492716 (0.396143) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211041 / 0.018006 (0.193035) | 0.539350 / 0.000490 (0.538860) | 0.001683 / 0.000200 (0.001483) | 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) | 0.031699 / 0.037411 (-0.005712) | 0.132696 / 0.014526 (0.118170) | 0.133710 / 0.176557 (-0.042846) | 0.190074 / 0.737135 (-0.547061) | 0.142919 / 0.296338 (-0.153420) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.643521 / 0.215209 (0.428312) | 6.137350 / 2.077655 (4.059695) | 2.463894 / 1.504120 (0.959774) | 2.120043 / 1.541195 (0.578848) | 2.121898 / 1.468490 (0.653408) | 1.287319 / 4.584777 (-3.297458) | 5.517864 / 3.745712 (1.772151) | 5.070820 / 5.269862 (-0.199042) | 2.948967 / 4.565676 (-1.616710) | 0.175861 / 0.424275 (-0.248415) | 0.015292 / 0.007607 (0.007685) | 0.843195 / 0.226044 (0.617150) | 7.884275 / 2.268929 (5.615347) | 3.182821 / 55.444624 (-52.261803) | 2.576093 / 6.876477 (-4.300384) | 2.537160 / 2.142072 (0.395088) | 1.510029 / 4.805227 (-3.295198) | 0.249404 / 6.500664 (-6.251260) | 0.080434 / 0.075469 (0.004965) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.618695 / 1.841788 (-0.223093) | 18.879207 / 8.074308 (10.804899) | 21.075272 / 10.191392 (10.883880) | 0.260781 / 0.680424 (-0.419643) | 0.046387 / 0.534201 (-0.487813) | 0.570709 / 0.579283 (-0.008574) | 0.619050 / 0.434364 (0.184686) | 0.642295 / 0.540337 (0.101958) | 0.780070 / 1.386936 (-0.606866) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010418 / 0.011353 (-0.000935) | 0.006104 / 0.011008 (-0.004905) | 0.133609 / 0.038508 (0.095101) | 0.035101 / 0.023109 (0.011992) | 0.471931 / 0.275898 (0.196033) | 0.504498 / 0.323480 (0.181018) | 0.007388 / 0.007986 (-0.000598) | 0.004852 / 0.004328 (0.000523) | 0.094535 / 0.004250 (0.090284) | 0.056832 / 0.037052 (0.019779) | 0.470513 / 0.258489 (0.212024) | 0.531285 / 0.293841 (0.237444) | 0.058271 / 0.128546 (-0.070276) | 0.020523 / 0.075646 (-0.055123) | 0.437398 / 0.419271 (0.018126) | 0.065390 / 0.043533 (0.021857) | 0.503702 / 0.255139 (0.248563) | 0.515876 / 0.283200 (0.232677) | 0.118615 / 0.141683 (-0.023068) | 1.865380 / 1.452155 (0.413225) | 1.990316 / 1.492716 (0.497600) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246772 / 0.018006 (0.228766) | 0.560607 / 0.000490 (0.560118) | 0.005675 / 0.000200 (0.005475) | 0.000142 / 0.000054 (0.000088) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034692 / 0.037411 (-0.002719) | 0.174016 / 0.014526 (0.159490) | 0.179838 / 0.176557 (0.003282) | 0.217118 / 0.737135 (-0.520018) | 0.184811 / 0.296338 (-0.111527) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.675970 / 0.215209 (0.460760) | 6.787039 / 2.077655 (4.709384) | 2.932619 / 1.504120 (1.428499) | 2.545076 / 1.541195 (1.003882) | 2.566705 / 1.468490 (1.098215) | 1.287365 / 4.584777 (-3.297412) | 5.468441 / 3.745712 (1.722729) | 5.227726 / 5.269862 (-0.042136) | 2.868970 / 4.565676 (-1.696706) | 0.153535 / 0.424275 (-0.270740) | 0.020087 / 0.007607 (0.012480) | 0.860562 / 0.226044 (0.634518) | 8.656109 / 2.268929 (6.387180) | 3.749424 / 55.444624 (-51.695200) | 3.011337 / 6.876477 (-3.865139) | 3.119045 / 2.142072 (0.976973) | 1.562174 / 4.805227 (-3.243053) | 0.279161 / 6.500664 (-6.221504) | 0.084905 / 0.075469 (0.009436) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.638684 / 1.841788 (-0.203104) | 18.834760 / 8.074308 (10.760452) | 21.554310 / 10.191392 (11.362918) | 0.274518 / 0.680424 (-0.405906) | 0.030343 / 0.534201 (-0.503858) | 0.539094 / 0.579283 (-0.040189) | 0.627258 / 0.434364 (0.192895) | 0.624638 / 0.540337 (0.084301) | 0.742776 / 1.386936 (-0.644160) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#98c9b27be45e1f5bc8c18d8bb2414478efe68055 \"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) | 0.008372 / 0.011353 (-0.002981) | 0.004658 / 0.011008 (-0.006350) | 0.102005 / 0.038508 (0.063497) | 0.029030 / 0.023109 (0.005920) | 0.296968 / 0.275898 (0.021070) | 0.364898 / 0.323480 (0.041418) | 0.006899 / 0.007986 (-0.001087) | 0.003410 / 0.004328 (-0.000919) | 0.079705 / 0.004250 (0.075455) | 0.034265 / 0.037052 (-0.002787) | 0.305695 / 0.258489 (0.047206) | 0.343275 / 0.293841 (0.049434) | 0.033783 / 0.128546 (-0.094763) | 0.011604 / 0.075646 (-0.064042) | 0.322577 / 0.419271 (-0.096694) | 0.040540 / 0.043533 (-0.002993) | 0.299176 / 0.255139 (0.044037) | 0.333157 / 0.283200 (0.049957) | 0.087460 / 0.141683 (-0.054223) | 1.494392 / 1.452155 (0.042237) | 1.539580 / 1.492716 (0.046863) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.176206 / 0.018006 (0.158200) | 0.413702 / 0.000490 (0.413212) | 0.002625 / 0.000200 (0.002425) | 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) | 0.023886 / 0.037411 (-0.013525) | 0.099758 / 0.014526 (0.085232) | 0.104349 / 0.176557 (-0.072208) | 0.147138 / 0.737135 (-0.589998) | 0.108682 / 0.296338 (-0.187657) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.411957 / 0.215209 (0.196748) | 4.110004 / 2.077655 (2.032349) | 1.820951 / 1.504120 (0.316831) | 1.629726 / 1.541195 (0.088532) | 1.672573 / 1.468490 (0.204083) | 0.686627 / 4.584777 (-3.898150) | 3.382665 / 3.745712 (-0.363047) | 2.875908 / 5.269862 (-2.393954) | 1.475331 / 4.565676 (-3.090345) | 0.081353 / 0.424275 (-0.342922) | 0.012521 / 0.007607 (0.004914) | 0.516226 / 0.226044 (0.290182) | 5.157658 / 2.268929 (2.888729) | 2.302012 / 55.444624 (-53.142612) | 1.950831 / 6.876477 (-4.925646) | 1.962081 / 2.142072 (-0.179992) | 0.800007 / 4.805227 (-4.005221) | 0.148462 / 6.500664 (-6.352202) | 0.064448 / 0.075469 (-0.011021) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.227977 / 1.841788 (-0.613810) | 13.776087 / 8.074308 (5.701779) | 13.749825 / 10.191392 (3.558433) | 0.137034 / 0.680424 (-0.543390) | 0.028461 / 0.534201 (-0.505740) | 0.392335 / 0.579283 (-0.186948) | 0.397404 / 0.434364 (-0.036960) | 0.450831 / 0.540337 (-0.089507) | 0.533716 / 1.386936 (-0.853220) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006883 / 0.011353 (-0.004470) | 0.004625 / 0.011008 (-0.006383) | 0.099039 / 0.038508 (0.060531) | 0.028068 / 0.023109 (0.004958) | 0.419988 / 0.275898 (0.144090) | 0.449543 / 0.323480 (0.126063) | 0.005232 / 0.007986 (-0.002753) | 0.003527 / 0.004328 (-0.000801) | 0.076308 / 0.004250 (0.072057) | 0.040523 / 0.037052 (0.003471) | 0.420165 / 0.258489 (0.161676) | 0.463220 / 0.293841 (0.169379) | 0.032368 / 0.128546 (-0.096178) | 0.011784 / 0.075646 (-0.063863) | 0.320675 / 0.419271 (-0.098597) | 0.041861 / 0.043533 (-0.001672) | 0.424903 / 0.255139 (0.169764) | 0.443528 / 0.283200 (0.160328) | 0.090869 / 0.141683 (-0.050814) | 1.504757 / 1.452155 (0.052602) | 1.557824 / 1.492716 (0.065108) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224020 / 0.018006 (0.206014) | 0.404090 / 0.000490 (0.403601) | 0.000403 / 0.000200 (0.000203) | 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) | 0.024556 / 0.037411 (-0.012855) | 0.101280 / 0.014526 (0.086754) | 0.108017 / 0.176557 (-0.068540) | 0.146679 / 0.737135 (-0.590456) | 0.111468 / 0.296338 (-0.184870) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.478955 / 0.215209 (0.263746) | 4.769628 / 2.077655 (2.691973) | 2.473238 / 1.504120 (0.969118) | 2.263588 / 1.541195 (0.722393) | 2.285425 / 1.468490 (0.816935) | 0.699051 / 4.584777 (-3.885726) | 3.390495 / 3.745712 (-0.355217) | 1.858569 / 5.269862 (-3.411293) | 1.162081 / 4.565676 (-3.403596) | 0.083294 / 0.424275 (-0.340981) | 0.012410 / 0.007607 (0.004803) | 0.580786 / 0.226044 (0.354741) | 5.866868 / 2.268929 (3.597940) | 2.944358 / 55.444624 (-52.500266) | 2.596241 / 6.876477 (-4.280235) | 2.664464 / 2.142072 (0.522392) | 0.806751 / 4.805227 (-3.998476) | 0.152389 / 6.500664 (-6.348275) | 0.066945 / 0.075469 (-0.008524) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.290545 / 1.841788 (-0.551243) | 14.005727 / 8.074308 (5.931419) | 14.478951 / 10.191392 (4.287559) | 0.127488 / 0.680424 (-0.552935) | 0.016929 / 0.534201 (-0.517272) | 0.378380 / 0.579283 (-0.200904) | 0.387499 / 0.434364 (-0.046865) | 0.440816 / 0.540337 (-0.099522) | 0.525794 / 1.386936 (-0.861142) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#07549c6fcb2dced59d7614b4b8264d54ef573407 \"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) | 0.008704 / 0.011353 (-0.002649) | 0.004474 / 0.011008 (-0.006534) | 0.101720 / 0.038508 (0.063212) | 0.030426 / 0.023109 (0.007317) | 0.298944 / 0.275898 (0.023046) | 0.371491 / 0.323480 (0.048011) | 0.007042 / 0.007986 (-0.000944) | 0.003479 / 0.004328 (-0.000850) | 0.078086 / 0.004250 (0.073835) | 0.037014 / 0.037052 (-0.000038) | 0.312964 / 0.258489 (0.054475) | 0.351251 / 0.293841 (0.057410) | 0.033286 / 0.128546 (-0.095260) | 0.011468 / 0.075646 (-0.064179) | 0.321784 / 0.419271 (-0.097488) | 0.040700 / 0.043533 (-0.002832) | 0.303799 / 0.255139 (0.048660) | 0.336982 / 0.283200 (0.053782) | 0.089448 / 0.141683 (-0.052235) | 1.462430 / 1.452155 (0.010275) | 1.524448 / 1.492716 (0.031732) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.178390 / 0.018006 (0.160384) | 0.402474 / 0.000490 (0.401984) | 0.002697 / 0.000200 (0.002497) | 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) | 0.022679 / 0.037411 (-0.014733) | 0.097759 / 0.014526 (0.083234) | 0.105102 / 0.176557 (-0.071454) | 0.140720 / 0.737135 (-0.596415) | 0.109119 / 0.296338 (-0.187219) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.414153 / 0.215209 (0.198944) | 4.131799 / 2.077655 (2.054144) | 1.852325 / 1.504120 (0.348205) | 1.646955 / 1.541195 (0.105760) | 1.662880 / 1.468490 (0.194390) | 0.693823 / 4.584777 (-3.890954) | 3.378843 / 3.745712 (-0.366869) | 1.861324 / 5.269862 (-3.408538) | 1.156916 / 4.565676 (-3.408761) | 0.082385 / 0.424275 (-0.341890) | 0.012166 / 0.007607 (0.004559) | 0.528690 / 0.226044 (0.302646) | 5.286388 / 2.268929 (3.017459) | 2.319941 / 55.444624 (-53.124684) | 1.959462 / 6.876477 (-4.917014) | 1.995102 / 2.142072 (-0.146970) | 0.817158 / 4.805227 (-3.988069) | 0.149479 / 6.500664 (-6.351185) | 0.065668 / 0.075469 (-0.009801) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.240228 / 1.841788 (-0.601560) | 13.770357 / 8.074308 (5.696048) | 13.940638 / 10.191392 (3.749246) | 0.152589 / 0.680424 (-0.527835) | 0.028498 / 0.534201 (-0.505703) | 0.392579 / 0.579283 (-0.186704) | 0.402843 / 0.434364 (-0.031521) | 0.455429 / 0.540337 (-0.084909) | 0.541090 / 1.386936 (-0.845846) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006692 / 0.011353 (-0.004661) | 0.004514 / 0.011008 (-0.006495) | 0.097058 / 0.038508 (0.058550) | 0.027780 / 0.023109 (0.004671) | 0.415806 / 0.275898 (0.139908) | 0.443079 / 0.323480 (0.119599) | 0.005181 / 0.007986 (-0.002805) | 0.003408 / 0.004328 (-0.000921) | 0.075263 / 0.004250 (0.071013) | 0.038169 / 0.037052 (0.001116) | 0.417292 / 0.258489 (0.158803) | 0.461875 / 0.293841 (0.168034) | 0.032280 / 0.128546 (-0.096266) | 0.011571 / 0.075646 (-0.064075) | 0.319091 / 0.419271 (-0.100181) | 0.048295 / 0.043533 (0.004762) | 0.423619 / 0.255139 (0.168480) | 0.435064 / 0.283200 (0.151864) | 0.094869 / 0.141683 (-0.046814) | 1.523000 / 1.452155 (0.070846) | 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) | 0.214326 / 0.018006 (0.196320) | 0.391623 / 0.000490 (0.391134) | 0.004602 / 0.000200 (0.004403) | 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) | 0.024306 / 0.037411 (-0.013106) | 0.101178 / 0.014526 (0.086652) | 0.108504 / 0.176557 (-0.068053) | 0.144114 / 0.737135 (-0.593022) | 0.111088 / 0.296338 (-0.185250) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.472573 / 0.215209 (0.257364) | 4.748929 / 2.077655 (2.671274) | 2.441602 / 1.504120 (0.937482) | 2.238841 / 1.541195 (0.697647) | 2.303303 / 1.468490 (0.834813) | 0.696618 / 4.584777 (-3.888159) | 3.373867 / 3.745712 (-0.371845) | 2.809009 / 5.269862 (-2.460852) | 1.337240 / 4.565676 (-3.228437) | 0.082682 / 0.424275 (-0.341593) | 0.012834 / 0.007607 (0.005227) | 0.569686 / 0.226044 (0.343642) | 5.723407 / 2.268929 (3.454478) | 2.882944 / 55.444624 (-52.561680) | 2.543530 / 6.876477 (-4.332947) | 2.581856 / 2.142072 (0.439784) | 0.802353 / 4.805227 (-4.002874) | 0.149947 / 6.500664 (-6.350717) | 0.065865 / 0.075469 (-0.009604) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.282146 / 1.841788 (-0.559642) | 13.831344 / 8.074308 (5.757036) | 14.081550 / 10.191392 (3.890157) | 0.141735 / 0.680424 (-0.538689) | 0.016677 / 0.534201 (-0.517524) | 0.378967 / 0.579283 (-0.200316) | 0.383775 / 0.434364 (-0.050589) | 0.432892 / 0.540337 (-0.107446) | 0.518042 / 1.386936 (-0.868894) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#01b4a5a18b56fa7b648b0f131f6b5568b1fd436a \"CML watermark\")\n", "Omg I love this ! cc @TevenLeScao @thomasw21 this will save your terminals from infinite streams of progress bars", "<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) | 0.008680 / 0.011353 (-0.002673) | 0.004597 / 0.011008 (-0.006411) | 0.101154 / 0.038508 (0.062646) | 0.029831 / 0.023109 (0.006722) | 0.300619 / 0.275898 (0.024721) | 0.358259 / 0.323480 (0.034779) | 0.007284 / 0.007986 (-0.000701) | 0.003511 / 0.004328 (-0.000817) | 0.078805 / 0.004250 (0.074555) | 0.037192 / 0.037052 (0.000140) | 0.307241 / 0.258489 (0.048752) | 0.354648 / 0.293841 (0.060807) | 0.033696 / 0.128546 (-0.094851) | 0.011660 / 0.075646 (-0.063986) | 0.324266 / 0.419271 (-0.095006) | 0.043393 / 0.043533 (-0.000140) | 0.297503 / 0.255139 (0.042364) | 0.326037 / 0.283200 (0.042838) | 0.091165 / 0.141683 (-0.050517) | 1.479970 / 1.452155 (0.027816) | 1.508507 / 1.492716 (0.015791) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.179995 / 0.018006 (0.161989) | 0.464282 / 0.000490 (0.463793) | 0.003953 / 0.000200 (0.003753) | 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) | 0.022696 / 0.037411 (-0.014715) | 0.099510 / 0.014526 (0.084984) | 0.103741 / 0.176557 (-0.072816) | 0.137837 / 0.737135 (-0.599299) | 0.108776 / 0.296338 (-0.187563) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417034 / 0.215209 (0.201825) | 4.183479 / 2.077655 (2.105824) | 1.855329 / 1.504120 (0.351209) | 1.660675 / 1.541195 (0.119481) | 1.723936 / 1.468490 (0.255446) | 0.687815 / 4.584777 (-3.896962) | 3.331280 / 3.745712 (-0.414432) | 2.821430 / 5.269862 (-2.448432) | 1.542394 / 4.565676 (-3.023283) | 0.081665 / 0.424275 (-0.342610) | 0.012483 / 0.007607 (0.004875) | 0.524758 / 0.226044 (0.298713) | 5.277285 / 2.268929 (3.008357) | 2.278067 / 55.444624 (-53.166557) | 1.923232 / 6.876477 (-4.953245) | 1.978645 / 2.142072 (-0.163428) | 0.806225 / 4.805227 (-3.999002) | 0.147568 / 6.500664 (-6.353096) | 0.064206 / 0.075469 (-0.011263) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.175079 / 1.841788 (-0.666708) | 13.677443 / 8.074308 (5.603135) | 14.064103 / 10.191392 (3.872711) | 0.167462 / 0.680424 (-0.512962) | 0.028677 / 0.534201 (-0.505524) | 0.399090 / 0.579283 (-0.180193) | 0.398930 / 0.434364 (-0.035433) | 0.461604 / 0.540337 (-0.078733) | 0.540978 / 1.386936 (-0.845958) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006846 / 0.011353 (-0.004507) | 0.004452 / 0.011008 (-0.006556) | 0.076169 / 0.038508 (0.037661) | 0.028290 / 0.023109 (0.005181) | 0.341105 / 0.275898 (0.065207) | 0.381465 / 0.323480 (0.057986) | 0.005038 / 0.007986 (-0.002948) | 0.003298 / 0.004328 (-0.001031) | 0.075794 / 0.004250 (0.071544) | 0.039225 / 0.037052 (0.002173) | 0.342995 / 0.258489 (0.084506) | 0.384878 / 0.293841 (0.091037) | 0.031766 / 0.128546 (-0.096780) | 0.011597 / 0.075646 (-0.064049) | 0.084849 / 0.419271 (-0.334423) | 0.041795 / 0.043533 (-0.001737) | 0.341770 / 0.255139 (0.086631) | 0.383142 / 0.283200 (0.099942) | 0.088854 / 0.141683 (-0.052829) | 1.465116 / 1.452155 (0.012961) | 1.566888 / 1.492716 (0.074171) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225129 / 0.018006 (0.207123) | 0.394290 / 0.000490 (0.393801) | 0.000397 / 0.000200 (0.000197) | 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) | 0.025492 / 0.037411 (-0.011919) | 0.100494 / 0.014526 (0.085968) | 0.110587 / 0.176557 (-0.065969) | 0.142715 / 0.737135 (-0.594420) | 0.110962 / 0.296338 (-0.185376) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437240 / 0.215209 (0.222031) | 4.379191 / 2.077655 (2.301536) | 2.055059 / 1.504120 (0.550939) | 1.844643 / 1.541195 (0.303448) | 1.914678 / 1.468490 (0.446188) | 0.695607 / 4.584777 (-3.889170) | 3.353845 / 3.745712 (-0.391867) | 1.837403 / 5.269862 (-3.432459) | 1.155518 / 4.565676 (-3.410158) | 0.082753 / 0.424275 (-0.341523) | 0.012812 / 0.007607 (0.005205) | 0.537304 / 0.226044 (0.311260) | 5.387425 / 2.268929 (3.118497) | 2.506986 / 55.444624 (-52.937638) | 2.159031 / 6.876477 (-4.717445) | 2.187844 / 2.142072 (0.045772) | 0.796880 / 4.805227 (-4.008347) | 0.151850 / 6.500664 (-6.348815) | 0.067577 / 0.075469 (-0.007892) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.257779 / 1.841788 (-0.584009) | 13.968842 / 8.074308 (5.894534) | 13.544220 / 10.191392 (3.352828) | 0.149962 / 0.680424 (-0.530462) | 0.016875 / 0.534201 (-0.517326) | 0.394714 / 0.579283 (-0.184570) | 0.387845 / 0.434364 (-0.046519) | 0.481674 / 0.540337 (-0.058664) | 0.569820 / 1.386936 (-0.817116) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#71e50283422a93e805ea76722ce2520d1aae39c2 \"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) | 0.009745 / 0.011353 (-0.001607) | 0.005307 / 0.011008 (-0.005702) | 0.104230 / 0.038508 (0.065722) | 0.039745 / 0.023109 (0.016635) | 0.306102 / 0.275898 (0.030204) | 0.384390 / 0.323480 (0.060910) | 0.008265 / 0.007986 (0.000279) | 0.005516 / 0.004328 (0.001187) | 0.076023 / 0.004250 (0.071772) | 0.048266 / 0.037052 (0.011213) | 0.315380 / 0.258489 (0.056891) | 0.365735 / 0.293841 (0.071895) | 0.038222 / 0.128546 (-0.090324) | 0.012397 / 0.075646 (-0.063249) | 0.348964 / 0.419271 (-0.070307) | 0.047668 / 0.043533 (0.004135) | 0.301037 / 0.255139 (0.045898) | 0.322982 / 0.283200 (0.039783) | 0.109307 / 0.141683 (-0.032376) | 1.420777 / 1.452155 (-0.031378) | 1.468290 / 1.492716 (-0.024426) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.262386 / 0.018006 (0.244380) | 0.557151 / 0.000490 (0.556661) | 0.000352 / 0.000200 (0.000152) | 0.000062 / 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) | 0.029508 / 0.037411 (-0.007903) | 0.113960 / 0.014526 (0.099434) | 0.123176 / 0.176557 (-0.053381) | 0.161928 / 0.737135 (-0.575207) | 0.129196 / 0.296338 (-0.167142) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.407051 / 0.215209 (0.191842) | 4.072550 / 2.077655 (1.994895) | 1.899809 / 1.504120 (0.395689) | 1.751981 / 1.541195 (0.210786) | 1.841361 / 1.468490 (0.372871) | 0.713908 / 4.584777 (-3.870869) | 3.703339 / 3.745712 (-0.042373) | 2.091283 / 5.269862 (-3.178578) | 1.323810 / 4.565676 (-3.241866) | 0.084691 / 0.424275 (-0.339584) | 0.012685 / 0.007607 (0.005078) | 0.511301 / 0.226044 (0.285257) | 5.109741 / 2.268929 (2.840813) | 2.315073 / 55.444624 (-53.129551) | 2.012746 / 6.876477 (-4.863731) | 2.160074 / 2.142072 (0.018002) | 0.853025 / 4.805227 (-3.952202) | 0.165301 / 6.500664 (-6.335363) | 0.062244 / 0.075469 (-0.013225) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.219727 / 1.841788 (-0.622061) | 15.319675 / 8.074308 (7.245367) | 13.100883 / 10.191392 (2.909491) | 0.173451 / 0.680424 (-0.506973) | 0.029173 / 0.534201 (-0.505028) | 0.440162 / 0.579283 (-0.139122) | 0.429771 / 0.434364 (-0.004593) | 0.518689 / 0.540337 (-0.021648) | 0.608590 / 1.386936 (-0.778346) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007839 / 0.011353 (-0.003514) | 0.005409 / 0.011008 (-0.005599) | 0.076468 / 0.038508 (0.037960) | 0.036568 / 0.023109 (0.013459) | 0.337568 / 0.275898 (0.061670) | 0.379353 / 0.323480 (0.055873) | 0.006208 / 0.007986 (-0.001778) | 0.005971 / 0.004328 (0.001643) | 0.073765 / 0.004250 (0.069514) | 0.056609 / 0.037052 (0.019556) | 0.344578 / 0.258489 (0.086089) | 0.405249 / 0.293841 (0.111408) | 0.037652 / 0.128546 (-0.090894) | 0.012549 / 0.075646 (-0.063097) | 0.087086 / 0.419271 (-0.332186) | 0.056669 / 0.043533 (0.013136) | 0.334121 / 0.255139 (0.078983) | 0.354582 / 0.283200 (0.071383) | 0.113293 / 0.141683 (-0.028390) | 1.437327 / 1.452155 (-0.014828) | 1.574400 / 1.492716 (0.081684) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.325235 / 0.018006 (0.307229) | 0.535405 / 0.000490 (0.534915) | 0.014119 / 0.000200 (0.013919) | 0.000278 / 0.000054 (0.000224) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030826 / 0.037411 (-0.006585) | 0.114077 / 0.014526 (0.099552) | 0.128799 / 0.176557 (-0.047758) | 0.172164 / 0.737135 (-0.564971) | 0.133665 / 0.296338 (-0.162673) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430898 / 0.215209 (0.215689) | 4.285507 / 2.077655 (2.207853) | 2.089767 / 1.504120 (0.585647) | 1.899457 / 1.541195 (0.358262) | 2.042875 / 1.468490 (0.574385) | 0.690575 / 4.584777 (-3.894202) | 3.815905 / 3.745712 (0.070192) | 3.371085 / 5.269862 (-1.898776) | 1.865748 / 4.565676 (-2.699929) | 0.086678 / 0.424275 (-0.337597) | 0.013172 / 0.007607 (0.005565) | 0.552038 / 0.226044 (0.325994) | 5.275093 / 2.268929 (3.006165) | 2.561102 / 55.444624 (-52.883522) | 2.224235 / 6.876477 (-4.652242) | 2.330315 / 2.142072 (0.188243) | 0.845163 / 4.805227 (-3.960064) | 0.170675 / 6.500664 (-6.329989) | 0.068446 / 0.075469 (-0.007023) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.261213 / 1.841788 (-0.580575) | 15.354959 / 8.074308 (7.280651) | 15.034302 / 10.191392 (4.842910) | 0.146704 / 0.680424 (-0.533720) | 0.017986 / 0.534201 (-0.516215) | 0.425978 / 0.579283 (-0.153305) | 0.421806 / 0.434364 (-0.012558) | 0.494844 / 0.540337 (-0.045493) | 0.587870 / 1.386936 (-0.799066) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0933901bb757e9a386095aef0fb11de9f9a04085 \"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) | 0.012765 / 0.011353 (0.001412) | 0.006429 / 0.011008 (-0.004579) | 0.133669 / 0.038508 (0.095161) | 0.041420 / 0.023109 (0.018311) | 0.419990 / 0.275898 (0.144092) | 0.505218 / 0.323480 (0.181738) | 0.010189 / 0.007986 (0.002204) | 0.005134 / 0.004328 (0.000805) | 0.100890 / 0.004250 (0.096640) | 0.045639 / 0.037052 (0.008587) | 0.440593 / 0.258489 (0.182103) | 0.476966 / 0.293841 (0.183125) | 0.059270 / 0.128546 (-0.069276) | 0.018625 / 0.075646 (-0.057021) | 0.444957 / 0.419271 (0.025686) | 0.060669 / 0.043533 (0.017136) | 0.415373 / 0.255139 (0.160234) | 0.461810 / 0.283200 (0.178610) | 0.116119 / 0.141683 (-0.025564) | 1.873691 / 1.452155 (0.421536) | 1.939891 / 1.492716 (0.447175) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.259529 / 0.018006 (0.241523) | 0.587213 / 0.000490 (0.586723) | 0.003729 / 0.000200 (0.003529) | 0.000115 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032064 / 0.037411 (-0.005347) | 0.140228 / 0.014526 (0.125702) | 0.147139 / 0.176557 (-0.029417) | 0.193731 / 0.737135 (-0.543405) | 0.162126 / 0.296338 (-0.134213) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.639262 / 0.215209 (0.424053) | 6.496491 / 2.077655 (4.418836) | 2.602044 / 1.504120 (1.097924) | 2.245891 / 1.541195 (0.704696) | 2.301321 / 1.468490 (0.832831) | 1.234088 / 4.584777 (-3.350689) | 5.883315 / 3.745712 (2.137603) | 3.166902 / 5.269862 (-2.102959) | 2.258279 / 4.565676 (-2.307398) | 0.146203 / 0.424275 (-0.278072) | 0.015490 / 0.007607 (0.007883) | 0.800188 / 0.226044 (0.574144) | 8.150866 / 2.268929 (5.881938) | 3.419508 / 55.444624 (-52.025117) | 2.712174 / 6.876477 (-4.164302) | 2.805059 / 2.142072 (0.662987) | 1.421047 / 4.805227 (-3.384180) | 0.254274 / 6.500664 (-6.246390) | 0.083886 / 0.075469 (0.008417) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.651962 / 1.841788 (-0.189826) | 19.453202 / 8.074308 (11.378894) | 24.643881 / 10.191392 (14.452489) | 0.263612 / 0.680424 (-0.416812) | 0.046913 / 0.534201 (-0.487288) | 0.579861 / 0.579283 (0.000578) | 0.695137 / 0.434364 (0.260773) | 0.705479 / 0.540337 (0.165142) | 0.806073 / 1.386936 (-0.580863) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010384 / 0.011353 (-0.000969) | 0.007460 / 0.011008 (-0.003548) | 0.107830 / 0.038508 (0.069322) | 0.036792 / 0.023109 (0.013682) | 0.469585 / 0.275898 (0.193687) | 0.521278 / 0.323480 (0.197798) | 0.007472 / 0.007986 (-0.000513) | 0.007774 / 0.004328 (0.003446) | 0.105405 / 0.004250 (0.101154) | 0.053732 / 0.037052 (0.016680) | 0.486299 / 0.258489 (0.227810) | 0.537067 / 0.293841 (0.243226) | 0.053378 / 0.128546 (-0.075168) | 0.022018 / 0.075646 (-0.053628) | 0.127765 / 0.419271 (-0.291507) | 0.063844 / 0.043533 (0.020311) | 0.479724 / 0.255139 (0.224585) | 0.511243 / 0.283200 (0.228043) | 0.123223 / 0.141683 (-0.018460) | 1.934167 / 1.452155 (0.482013) | 2.003168 / 1.492716 (0.510451) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227670 / 0.018006 (0.209664) | 0.609125 / 0.000490 (0.608635) | 0.004408 / 0.000200 (0.004208) | 0.000147 / 0.000054 (0.000092) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035905 / 0.037411 (-0.001506) | 0.142207 / 0.014526 (0.127681) | 0.154749 / 0.176557 (-0.021808) | 0.216191 / 0.737135 (-0.520944) | 0.156577 / 0.296338 (-0.139761) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.665085 / 0.215209 (0.449876) | 6.510923 / 2.077655 (4.433269) | 2.902438 / 1.504120 (1.398318) | 2.561427 / 1.541195 (1.020232) | 2.669556 / 1.468490 (1.201066) | 1.190340 / 4.584777 (-3.394437) | 5.933066 / 3.745712 (2.187354) | 5.627784 / 5.269862 (0.357922) | 2.971922 / 4.565676 (-1.593755) | 0.140884 / 0.424275 (-0.283391) | 0.015382 / 0.007607 (0.007775) | 0.810441 / 0.226044 (0.584396) | 8.255538 / 2.268929 (5.986609) | 3.819014 / 55.444624 (-51.625611) | 3.222479 / 6.876477 (-3.653998) | 3.181700 / 2.142072 (1.039627) | 1.483403 / 4.805227 (-3.321824) | 0.262726 / 6.500664 (-6.237939) | 0.090252 / 0.075469 (0.014783) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.748566 / 1.841788 (-0.093222) | 19.566894 / 8.074308 (11.492586) | 24.382155 / 10.191392 (14.190763) | 0.260118 / 0.680424 (-0.420305) | 0.028725 / 0.534201 (-0.505476) | 0.564875 / 0.579283 (-0.014408) | 0.666708 / 0.434364 (0.232344) | 0.691165 / 0.540337 (0.150827) | 0.837061 / 1.386936 (-0.549875) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fe6bf908e9f12e0b69b4059c392da8264881525d \"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) | 0.010098 / 0.011353 (-0.001255) | 0.005797 / 0.011008 (-0.005211) | 0.111262 / 0.038508 (0.072754) | 0.039687 / 0.023109 (0.016578) | 0.331081 / 0.275898 (0.055183) | 0.395878 / 0.323480 (0.072398) | 0.009244 / 0.007986 (0.001259) | 0.004498 / 0.004328 (0.000170) | 0.086129 / 0.004250 (0.081879) | 0.046662 / 0.037052 (0.009610) | 0.361926 / 0.258489 (0.103437) | 0.386155 / 0.293841 (0.092314) | 0.043657 / 0.128546 (-0.084889) | 0.013545 / 0.075646 (-0.062101) | 0.383735 / 0.419271 (-0.035537) | 0.055727 / 0.043533 (0.012194) | 0.355356 / 0.255139 (0.100217) | 0.358749 / 0.283200 (0.075550) | 0.123219 / 0.141683 (-0.018463) | 1.707982 / 1.452155 (0.255828) | 1.773342 / 1.492716 (0.280626) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.238902 / 0.018006 (0.220896) | 0.495525 / 0.000490 (0.495036) | 0.001742 / 0.000200 (0.001542) | 0.000096 / 0.000054 (0.000041) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031276 / 0.037411 (-0.006135) | 0.124286 / 0.014526 (0.109760) | 0.136236 / 0.176557 (-0.040321) | 0.180257 / 0.737135 (-0.556879) | 0.141047 / 0.296338 (-0.155292) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.465075 / 0.215209 (0.249865) | 4.543997 / 2.077655 (2.466342) | 2.036632 / 1.504120 (0.532512) | 1.820356 / 1.541195 (0.279161) | 1.860692 / 1.468490 (0.392202) | 0.807549 / 4.584777 (-3.777227) | 4.400369 / 3.745712 (0.654657) | 2.423372 / 5.269862 (-2.846490) | 1.741338 / 4.565676 (-2.824339) | 0.099457 / 0.424275 (-0.324818) | 0.014464 / 0.007607 (0.006857) | 0.599442 / 0.226044 (0.373398) | 5.867798 / 2.268929 (3.598870) | 2.641859 / 55.444624 (-52.802766) | 2.294246 / 6.876477 (-4.582231) | 2.329639 / 2.142072 (0.187567) | 0.981897 / 4.805227 (-3.823331) | 0.189278 / 6.500664 (-6.311386) | 0.071868 / 0.075469 (-0.003601) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.471800 / 1.841788 (-0.369988) | 17.149150 / 8.074308 (9.074841) | 15.818942 / 10.191392 (5.627550) | 0.174760 / 0.680424 (-0.505664) | 0.033507 / 0.534201 (-0.500694) | 0.511055 / 0.579283 (-0.068228) | 0.517107 / 0.434364 (0.082743) | 0.650813 / 0.540337 (0.110476) | 0.752515 / 1.386936 (-0.634421) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008651 / 0.011353 (-0.002702) | 0.005935 / 0.011008 (-0.005073) | 0.088589 / 0.038508 (0.050081) | 0.038796 / 0.023109 (0.015687) | 0.415430 / 0.275898 (0.139532) | 0.443693 / 0.323480 (0.120213) | 0.006631 / 0.007986 (-0.001354) | 0.004638 / 0.004328 (0.000309) | 0.085779 / 0.004250 (0.081529) | 0.053994 / 0.037052 (0.016942) | 0.408349 / 0.258489 (0.149860) | 0.475441 / 0.293841 (0.181600) | 0.042792 / 0.128546 (-0.085754) | 0.013938 / 0.075646 (-0.061709) | 0.102173 / 0.419271 (-0.317098) | 0.057940 / 0.043533 (0.014407) | 0.408967 / 0.255139 (0.153828) | 0.422741 / 0.283200 (0.139541) | 0.121844 / 0.141683 (-0.019839) | 1.772779 / 1.452155 (0.320625) | 1.837706 / 1.492716 (0.344989) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228896 / 0.018006 (0.210890) | 0.497964 / 0.000490 (0.497475) | 0.004402 / 0.000200 (0.004202) | 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) | 0.035626 / 0.037411 (-0.001786) | 0.132021 / 0.014526 (0.117495) | 0.145599 / 0.176557 (-0.030957) | 0.192317 / 0.737135 (-0.544818) | 0.150165 / 0.296338 (-0.146174) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.500216 / 0.215209 (0.285007) | 5.002916 / 2.077655 (2.925262) | 2.502439 / 1.504120 (0.998319) | 2.353019 / 1.541195 (0.811825) | 2.485082 / 1.468490 (1.016592) | 0.827694 / 4.584777 (-3.757083) | 4.569319 / 3.745712 (0.823607) | 3.739820 / 5.269862 (-1.530042) | 2.097857 / 4.565676 (-2.467819) | 0.098636 / 0.424275 (-0.325639) | 0.014608 / 0.007607 (0.007001) | 0.604411 / 0.226044 (0.378366) | 6.131702 / 2.268929 (3.862774) | 3.043988 / 55.444624 (-52.400637) | 2.642427 / 6.876477 (-4.234050) | 2.687223 / 2.142072 (0.545151) | 0.968808 / 4.805227 (-3.836419) | 0.193876 / 6.500664 (-6.306788) | 0.076931 / 0.075469 (0.001462) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.511820 / 1.841788 (-0.329968) | 17.971574 / 8.074308 (9.897265) | 16.512738 / 10.191392 (6.321346) | 0.223702 / 0.680424 (-0.456722) | 0.020191 / 0.534201 (-0.514010) | 0.511045 / 0.579283 (-0.068238) | 0.499813 / 0.434364 (0.065449) | 0.642147 / 0.540337 (0.101810) | 0.756029 / 1.386936 (-0.630907) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1f6c7b9eb4bca89ec90c465623f7a2e6f5251062 \"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) | 0.008909 / 0.011353 (-0.002444) | 0.005096 / 0.011008 (-0.005912) | 0.098568 / 0.038508 (0.060060) | 0.034548 / 0.023109 (0.011438) | 0.294762 / 0.275898 (0.018864) | 0.366093 / 0.323480 (0.042613) | 0.007476 / 0.007986 (-0.000510) | 0.003982 / 0.004328 (-0.000347) | 0.075975 / 0.004250 (0.071725) | 0.040499 / 0.037052 (0.003446) | 0.315050 / 0.258489 (0.056561) | 0.351273 / 0.293841 (0.057433) | 0.038327 / 0.128546 (-0.090219) | 0.011943 / 0.075646 (-0.063703) | 0.332148 / 0.419271 (-0.087124) | 0.047648 / 0.043533 (0.004115) | 0.295817 / 0.255139 (0.040678) | 0.322704 / 0.283200 (0.039504) | 0.100830 / 0.141683 (-0.040853) | 1.422162 / 1.452155 (-0.029993) | 1.468972 / 1.492716 (-0.023744) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201164 / 0.018006 (0.183158) | 0.435425 / 0.000490 (0.434935) | 0.001576 / 0.000200 (0.001376) | 0.000218 / 0.000054 (0.000163) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026667 / 0.037411 (-0.010744) | 0.106161 / 0.014526 (0.091636) | 0.115836 / 0.176557 (-0.060720) | 0.151511 / 0.737135 (-0.585624) | 0.122248 / 0.296338 (-0.174091) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.395974 / 0.215209 (0.180765) | 3.952958 / 2.077655 (1.875303) | 1.772111 / 1.504120 (0.267991) | 1.581370 / 1.541195 (0.040175) | 1.602811 / 1.468490 (0.134321) | 0.694072 / 4.584777 (-3.890705) | 3.640238 / 3.745712 (-0.105474) | 2.028865 / 5.269862 (-3.240997) | 1.419182 / 4.565676 (-3.146495) | 0.084078 / 0.424275 (-0.340197) | 0.012248 / 0.007607 (0.004641) | 0.499768 / 0.226044 (0.273723) | 4.997449 / 2.268929 (2.728521) | 2.280711 / 55.444624 (-53.163913) | 1.971701 / 6.876477 (-4.904776) | 1.983248 / 2.142072 (-0.158824) | 0.831030 / 4.805227 (-3.974198) | 0.163008 / 6.500664 (-6.337656) | 0.061887 / 0.075469 (-0.013582) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.191744 / 1.841788 (-0.650043) | 14.424546 / 8.074308 (6.350238) | 14.530127 / 10.191392 (4.338735) | 0.165793 / 0.680424 (-0.514631) | 0.029099 / 0.534201 (-0.505102) | 0.447830 / 0.579283 (-0.131453) | 0.441036 / 0.434364 (0.006672) | 0.554697 / 0.540337 (0.014360) | 0.668854 / 1.386936 (-0.718082) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006825 / 0.011353 (-0.004528) | 0.004998 / 0.011008 (-0.006010) | 0.074197 / 0.038508 (0.035689) | 0.032381 / 0.023109 (0.009272) | 0.335745 / 0.275898 (0.059847) | 0.360474 / 0.323480 (0.036994) | 0.005420 / 0.007986 (-0.002566) | 0.005121 / 0.004328 (0.000792) | 0.074980 / 0.004250 (0.070730) | 0.046392 / 0.037052 (0.009340) | 0.338693 / 0.258489 (0.080204) | 0.383679 / 0.293841 (0.089838) | 0.035380 / 0.128546 (-0.093166) | 0.012197 / 0.075646 (-0.063449) | 0.085738 / 0.419271 (-0.333533) | 0.049990 / 0.043533 (0.006458) | 0.342640 / 0.255139 (0.087501) | 0.355139 / 0.283200 (0.071939) | 0.102992 / 0.141683 (-0.038690) | 1.451900 / 1.452155 (-0.000254) | 1.550919 / 1.492716 (0.058202) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223241 / 0.018006 (0.205235) | 0.436954 / 0.000490 (0.436464) | 0.003319 / 0.000200 (0.003120) | 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) | 0.028042 / 0.037411 (-0.009370) | 0.106079 / 0.014526 (0.091554) | 0.122713 / 0.176557 (-0.053843) | 0.156543 / 0.737135 (-0.580593) | 0.122424 / 0.296338 (-0.173914) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.439482 / 0.215209 (0.224273) | 4.283112 / 2.077655 (2.205457) | 2.139705 / 1.504120 (0.635585) | 1.940898 / 1.541195 (0.399703) | 2.003906 / 1.468490 (0.535416) | 0.703269 / 4.584777 (-3.881508) | 3.780391 / 3.745712 (0.034679) | 2.079963 / 5.269862 (-3.189898) | 1.330669 / 4.565676 (-3.235007) | 0.086582 / 0.424275 (-0.337693) | 0.012497 / 0.007607 (0.004890) | 0.519329 / 0.226044 (0.293284) | 5.218117 / 2.268929 (2.949189) | 2.635982 / 55.444624 (-52.808643) | 2.301111 / 6.876477 (-4.575366) | 2.341312 / 2.142072 (0.199239) | 0.840157 / 4.805227 (-3.965070) | 0.166174 / 6.500664 (-6.334490) | 0.062890 / 0.075469 (-0.012579) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.257672 / 1.841788 (-0.584116) | 14.983374 / 8.074308 (6.909066) | 14.284441 / 10.191392 (4.093049) | 0.176077 / 0.680424 (-0.504347) | 0.017544 / 0.534201 (-0.516657) | 0.429619 / 0.579283 (-0.149664) | 0.426371 / 0.434364 (-0.007993) | 0.534832 / 0.540337 (-0.005506) | 0.643322 / 1.386936 (-0.743614) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0409b1435876fa97b3674b0275285e84b49d83f8 \"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) | 0.010622 / 0.011353 (-0.000731) | 0.005856 / 0.011008 (-0.005152) | 0.108608 / 0.038508 (0.070100) | 0.039868 / 0.023109 (0.016759) | 0.327853 / 0.275898 (0.051955) | 0.396721 / 0.323480 (0.073241) | 0.008916 / 0.007986 (0.000930) | 0.004590 / 0.004328 (0.000261) | 0.085020 / 0.004250 (0.080770) | 0.046608 / 0.037052 (0.009555) | 0.356369 / 0.258489 (0.097880) | 0.391142 / 0.293841 (0.097301) | 0.040579 / 0.128546 (-0.087967) | 0.012249 / 0.075646 (-0.063397) | 0.387740 / 0.419271 (-0.031532) | 0.057794 / 0.043533 (0.014262) | 0.335763 / 0.255139 (0.080624) | 0.369847 / 0.283200 (0.086647) | 0.121276 / 0.141683 (-0.020407) | 1.605406 / 1.452155 (0.153251) | 1.709524 / 1.492716 (0.216808) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226688 / 0.018006 (0.208681) | 0.493320 / 0.000490 (0.492831) | 0.002825 / 0.000200 (0.002626) | 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) | 0.031874 / 0.037411 (-0.005538) | 0.117365 / 0.014526 (0.102840) | 0.127697 / 0.176557 (-0.048859) | 0.175589 / 0.737135 (-0.561546) | 0.137731 / 0.296338 (-0.158608) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.472563 / 0.215209 (0.257354) | 4.744383 / 2.077655 (2.666728) | 2.152015 / 1.504120 (0.647895) | 1.925398 / 1.541195 (0.384203) | 2.054613 / 1.468490 (0.586123) | 0.821703 / 4.584777 (-3.763074) | 4.468177 / 3.745712 (0.722465) | 4.687682 / 5.269862 (-0.582179) | 2.379674 / 4.565676 (-2.186003) | 0.101325 / 0.424275 (-0.322950) | 0.014891 / 0.007607 (0.007284) | 0.593161 / 0.226044 (0.367117) | 5.641670 / 2.268929 (3.372741) | 2.460206 / 55.444624 (-52.984419) | 2.131148 / 6.876477 (-4.745329) | 2.351067 / 2.142072 (0.208994) | 0.997634 / 4.805227 (-3.807593) | 0.195338 / 6.500664 (-6.305326) | 0.075540 / 0.075469 (0.000071) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.411585 / 1.841788 (-0.430203) | 17.055689 / 8.074308 (8.981381) | 16.544028 / 10.191392 (6.352636) | 0.180840 / 0.680424 (-0.499584) | 0.034549 / 0.534201 (-0.499652) | 0.510256 / 0.579283 (-0.069027) | 0.525632 / 0.434364 (0.091268) | 0.601206 / 0.540337 (0.060868) | 0.668468 / 1.386936 (-0.718469) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008989 / 0.011353 (-0.002364) | 0.006065 / 0.011008 (-0.004943) | 0.088294 / 0.038508 (0.049786) | 0.040404 / 0.023109 (0.017295) | 0.405622 / 0.275898 (0.129724) | 0.454519 / 0.323480 (0.131039) | 0.006919 / 0.007986 (-0.001067) | 0.004545 / 0.004328 (0.000217) | 0.087023 / 0.004250 (0.082772) | 0.055962 / 0.037052 (0.018910) | 0.400942 / 0.258489 (0.142453) | 0.490670 / 0.293841 (0.196829) | 0.044086 / 0.128546 (-0.084461) | 0.014485 / 0.075646 (-0.061162) | 0.103333 / 0.419271 (-0.315938) | 0.059663 / 0.043533 (0.016130) | 0.404944 / 0.255139 (0.149805) | 0.425763 / 0.283200 (0.142563) | 0.123989 / 0.141683 (-0.017694) | 1.777244 / 1.452155 (0.325089) | 1.879884 / 1.492716 (0.387167) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226440 / 0.018006 (0.208434) | 0.492688 / 0.000490 (0.492198) | 0.004691 / 0.000200 (0.004491) | 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) | 0.035123 / 0.037411 (-0.002288) | 0.134288 / 0.014526 (0.119762) | 0.145542 / 0.176557 (-0.031015) | 0.195372 / 0.737135 (-0.541764) | 0.152551 / 0.296338 (-0.143787) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.468615 / 0.215209 (0.253406) | 4.813363 / 2.077655 (2.735708) | 2.333606 / 1.504120 (0.829486) | 2.107344 / 1.541195 (0.566149) | 2.109109 / 1.468490 (0.640619) | 0.783779 / 4.584777 (-3.800998) | 4.521448 / 3.745712 (0.775736) | 2.290532 / 5.269862 (-2.979329) | 1.553488 / 4.565676 (-3.012189) | 0.088786 / 0.424275 (-0.335489) | 0.013091 / 0.007607 (0.005484) | 0.567165 / 0.226044 (0.341120) | 5.974315 / 2.268929 (3.705386) | 2.815018 / 55.444624 (-52.629606) | 2.488954 / 6.876477 (-4.387522) | 2.461849 / 2.142072 (0.319776) | 0.934487 / 4.805227 (-3.870740) | 0.190209 / 6.500664 (-6.310455) | 0.074811 / 0.075469 (-0.000658) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.513476 / 1.841788 (-0.328311) | 17.902599 / 8.074308 (9.828291) | 14.308027 / 10.191392 (4.116635) | 0.201992 / 0.680424 (-0.478432) | 0.018678 / 0.534201 (-0.515523) | 0.454707 / 0.579283 (-0.124576) | 0.470643 / 0.434364 (0.036279) | 0.612534 / 0.540337 (0.072197) | 0.685773 / 1.386936 (-0.701163) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c4a66da3633a811eb8ea01d23469c41dfec0ffb8 \"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) | 0.009385 / 0.011353 (-0.001968) | 0.005220 / 0.011008 (-0.005788) | 0.098722 / 0.038508 (0.060214) | 0.035382 / 0.023109 (0.012273) | 0.297114 / 0.275898 (0.021216) | 0.371443 / 0.323480 (0.047963) | 0.008070 / 0.007986 (0.000084) | 0.004204 / 0.004328 (-0.000125) | 0.075621 / 0.004250 (0.071370) | 0.046015 / 0.037052 (0.008963) | 0.304569 / 0.258489 (0.046080) | 0.345598 / 0.293841 (0.051757) | 0.037946 / 0.128546 (-0.090600) | 0.011972 / 0.075646 (-0.063674) | 0.331993 / 0.419271 (-0.087279) | 0.047250 / 0.043533 (0.003717) | 0.296588 / 0.255139 (0.041449) | 0.316070 / 0.283200 (0.032870) | 0.108211 / 0.141683 (-0.033472) | 1.447619 / 1.452155 (-0.004535) | 1.481243 / 1.492716 (-0.011473) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.274860 / 0.018006 (0.256854) | 0.503139 / 0.000490 (0.502649) | 0.003598 / 0.000200 (0.003398) | 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) | 0.026752 / 0.037411 (-0.010660) | 0.109008 / 0.014526 (0.094482) | 0.119109 / 0.176557 (-0.057448) | 0.158462 / 0.737135 (-0.578673) | 0.126171 / 0.296338 (-0.170168) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.396396 / 0.215209 (0.181187) | 3.963055 / 2.077655 (1.885400) | 1.796308 / 1.504120 (0.292188) | 1.600565 / 1.541195 (0.059370) | 1.742409 / 1.468490 (0.273919) | 0.690942 / 4.584777 (-3.893835) | 3.713343 / 3.745712 (-0.032369) | 2.066804 / 5.269862 (-3.203058) | 1.292946 / 4.565676 (-3.272730) | 0.084344 / 0.424275 (-0.339931) | 0.012473 / 0.007607 (0.004865) | 0.513109 / 0.226044 (0.287065) | 5.175141 / 2.268929 (2.906213) | 2.266559 / 55.444624 (-53.178066) | 1.935737 / 6.876477 (-4.940740) | 2.028911 / 2.142072 (-0.113161) | 0.831191 / 4.805227 (-3.974036) | 0.163155 / 6.500664 (-6.337509) | 0.063414 / 0.075469 (-0.012055) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.195429 / 1.841788 (-0.646358) | 15.257933 / 8.074308 (7.183625) | 14.358815 / 10.191392 (4.167423) | 0.152677 / 0.680424 (-0.527747) | 0.028890 / 0.534201 (-0.505311) | 0.455342 / 0.579283 (-0.123941) | 0.442602 / 0.434364 (0.008238) | 0.526833 / 0.540337 (-0.013505) | 0.618296 / 1.386936 (-0.768640) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007613 / 0.011353 (-0.003740) | 0.005515 / 0.011008 (-0.005493) | 0.073759 / 0.038508 (0.035251) | 0.033944 / 0.023109 (0.010835) | 0.347764 / 0.275898 (0.071866) | 0.371143 / 0.323480 (0.047664) | 0.005997 / 0.007986 (-0.001988) | 0.004322 / 0.004328 (-0.000006) | 0.073002 / 0.004250 (0.068751) | 0.053051 / 0.037052 (0.015999) | 0.340345 / 0.258489 (0.081856) | 0.383761 / 0.293841 (0.089920) | 0.037734 / 0.128546 (-0.090813) | 0.012815 / 0.075646 (-0.062831) | 0.086998 / 0.419271 (-0.332273) | 0.050165 / 0.043533 (0.006632) | 0.343864 / 0.255139 (0.088725) | 0.356734 / 0.283200 (0.073534) | 0.108955 / 0.141683 (-0.032728) | 1.464558 / 1.452155 (0.012403) | 1.560084 / 1.492716 (0.067368) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.327885 / 0.018006 (0.309878) | 0.515515 / 0.000490 (0.515025) | 0.000439 / 0.000200 (0.000239) | 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) | 0.030741 / 0.037411 (-0.006670) | 0.107634 / 0.014526 (0.093108) | 0.127121 / 0.176557 (-0.049436) | 0.164044 / 0.737135 (-0.573092) | 0.129097 / 0.296338 (-0.167242) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.435690 / 0.215209 (0.220481) | 4.350705 / 2.077655 (2.273050) | 2.199597 / 1.504120 (0.695477) | 2.022715 / 1.541195 (0.481521) | 2.265907 / 1.468490 (0.797417) | 0.695817 / 4.584777 (-3.888960) | 3.795207 / 3.745712 (0.049494) | 3.061587 / 5.269862 (-2.208274) | 1.872213 / 4.565676 (-2.693463) | 0.085265 / 0.424275 (-0.339010) | 0.012243 / 0.007607 (0.004636) | 0.547209 / 0.226044 (0.321164) | 5.383626 / 2.268929 (3.114698) | 2.707439 / 55.444624 (-52.737185) | 2.393773 / 6.876477 (-4.482703) | 2.481385 / 2.142072 (0.339312) | 0.826169 / 4.805227 (-3.979059) | 0.166643 / 6.500664 (-6.334021) | 0.065817 / 0.075469 (-0.009652) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.274469 / 1.841788 (-0.567318) | 15.565025 / 8.074308 (7.490717) | 14.254192 / 10.191392 (4.062800) | 0.166785 / 0.680424 (-0.513639) | 0.017830 / 0.534201 (-0.516371) | 0.430406 / 0.579283 (-0.148877) | 0.435655 / 0.434364 (0.001292) | 0.530605 / 0.540337 (-0.009732) | 0.636355 / 1.386936 (-0.750581) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#146983fdc70b9fe2cc38109368e185b6ffa7a05e \"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) | 0.008466 / 0.011353 (-0.002887) | 0.004679 / 0.011008 (-0.006329) | 0.100534 / 0.038508 (0.062025) | 0.029513 / 0.023109 (0.006403) | 0.302866 / 0.275898 (0.026968) | 0.352816 / 0.323480 (0.029336) | 0.006912 / 0.007986 (-0.001074) | 0.003513 / 0.004328 (-0.000815) | 0.078625 / 0.004250 (0.074375) | 0.036725 / 0.037052 (-0.000327) | 0.312135 / 0.258489 (0.053646) | 0.344579 / 0.293841 (0.050738) | 0.033870 / 0.128546 (-0.094677) | 0.011563 / 0.075646 (-0.064083) | 0.318982 / 0.419271 (-0.100290) | 0.043002 / 0.043533 (-0.000531) | 0.301956 / 0.255139 (0.046817) | 0.330798 / 0.283200 (0.047599) | 0.091755 / 0.141683 (-0.049927) | 1.458577 / 1.452155 (0.006422) | 1.532642 / 1.492716 (0.039926) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.194853 / 0.018006 (0.176847) | 0.396844 / 0.000490 (0.396354) | 0.004401 / 0.000200 (0.004201) | 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) | 0.022971 / 0.037411 (-0.014441) | 0.096595 / 0.014526 (0.082069) | 0.106104 / 0.176557 (-0.070452) | 0.144815 / 0.737135 (-0.592320) | 0.110036 / 0.296338 (-0.186303) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.415025 / 0.215209 (0.199816) | 4.138136 / 2.077655 (2.060481) | 1.861253 / 1.504120 (0.357133) | 1.653420 / 1.541195 (0.112226) | 1.703784 / 1.468490 (0.235294) | 0.698261 / 4.584777 (-3.886516) | 3.357240 / 3.745712 (-0.388472) | 3.025790 / 5.269862 (-2.244072) | 1.637191 / 4.565676 (-2.928485) | 0.085620 / 0.424275 (-0.338655) | 0.012454 / 0.007607 (0.004846) | 0.524708 / 0.226044 (0.298663) | 5.269234 / 2.268929 (3.000306) | 2.290612 / 55.444624 (-53.154012) | 1.936107 / 6.876477 (-4.940370) | 1.968216 / 2.142072 (-0.173856) | 0.810438 / 4.805227 (-3.994789) | 0.154133 / 6.500664 (-6.346531) | 0.064978 / 0.075469 (-0.010491) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.231782 / 1.841788 (-0.610006) | 13.545573 / 8.074308 (5.471264) | 14.558765 / 10.191392 (4.367373) | 0.140763 / 0.680424 (-0.539661) | 0.029259 / 0.534201 (-0.504942) | 0.407776 / 0.579283 (-0.171507) | 0.410244 / 0.434364 (-0.024120) | 0.477313 / 0.540337 (-0.063024) | 0.551465 / 1.386936 (-0.835471) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006272 / 0.011353 (-0.005081) | 0.004397 / 0.011008 (-0.006611) | 0.077496 / 0.038508 (0.038988) | 0.026946 / 0.023109 (0.003837) | 0.342992 / 0.275898 (0.067094) | 0.374407 / 0.323480 (0.050927) | 0.004849 / 0.007986 (-0.003136) | 0.004549 / 0.004328 (0.000220) | 0.076439 / 0.004250 (0.072189) | 0.035829 / 0.037052 (-0.001224) | 0.343483 / 0.258489 (0.084994) | 0.385581 / 0.293841 (0.091740) | 0.031745 / 0.128546 (-0.096801) | 0.011617 / 0.075646 (-0.064030) | 0.087207 / 0.419271 (-0.332064) | 0.042252 / 0.043533 (-0.001281) | 0.343223 / 0.255139 (0.088084) | 0.368707 / 0.283200 (0.085508) | 0.093259 / 0.141683 (-0.048424) | 1.506904 / 1.452155 (0.054750) | 1.567583 / 1.492716 (0.074867) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.158962 / 0.018006 (0.140955) | 0.395982 / 0.000490 (0.395492) | 0.003604 / 0.000200 (0.003404) | 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) | 0.025003 / 0.037411 (-0.012408) | 0.101176 / 0.014526 (0.086650) | 0.104494 / 0.176557 (-0.072062) | 0.140414 / 0.737135 (-0.596722) | 0.108398 / 0.296338 (-0.187941) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436849 / 0.215209 (0.221640) | 4.369428 / 2.077655 (2.291774) | 2.070613 / 1.504120 (0.566493) | 1.867511 / 1.541195 (0.326317) | 1.866589 / 1.468490 (0.398099) | 0.700036 / 4.584777 (-3.884741) | 3.407513 / 3.745712 (-0.338199) | 3.022409 / 5.269862 (-2.247453) | 1.581423 / 4.565676 (-2.984253) | 0.083425 / 0.424275 (-0.340850) | 0.012380 / 0.007607 (0.004773) | 0.535087 / 0.226044 (0.309043) | 5.374814 / 2.268929 (3.105886) | 2.504841 / 55.444624 (-52.939784) | 2.166484 / 6.876477 (-4.709993) | 2.166363 / 2.142072 (0.024291) | 0.803692 / 4.805227 (-4.001535) | 0.150873 / 6.500664 (-6.349791) | 0.066253 / 0.075469 (-0.009216) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.291256 / 1.841788 (-0.550532) | 13.827843 / 8.074308 (5.753535) | 13.839334 / 10.191392 (3.647942) | 0.153530 / 0.680424 (-0.526894) | 0.016896 / 0.534201 (-0.517305) | 0.379937 / 0.579283 (-0.199346) | 0.396241 / 0.434364 (-0.038123) | 0.461808 / 0.540337 (-0.078530) | 0.553023 / 1.386936 (-0.833913) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#779ddc5c7ebbd406b2a6c9092c3f455a2cc7f5e7 \"CML watermark\")\n" ]

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[]

[ "Something that'd be nice to have is \"manual update of state\". One of the learning from training LLMs is the ability to skip some batches whenever we notice huge spike might be handy.", "Your outline spec is very sound and clear, @lhoestq - thank you!\r\n\r\n@thomasw21, indeed that would be a wonderful extra feature. In Megatron-Deepspeed we manually drained the dataloader for the range we wanted. I wasn't very satisfied with the way we did it, since its behavior would change if you were to do multiple range skips. I think it should remember all the ranges it skipped and not just skip the last range - since otherwise the data is inconsistent (but we probably should discuss this in a separate issue not to derail this much bigger one).", "Hi there! I think this is a critical issue and have an urgent need for it, in my attempt to train on a super large-scale dataset using `datasets`. It is impossible to resume a time-consuming (like one month) experiment by iterating all seen data again, which could possibly cost several days.\r\n\r\n@stas00 @thomasw21 @lhoestq Any updates on this problem after 1 year passed?", "any update？", "No update so far, I wonder if someone implemented a resumable pytorch Sampler somwhere.\r\n\r\nThen regarding resuming a streaming dataset, we'd first like to have an efficient way to skip shards automatically but this is not implemented yet", "I opened a draft here for IterableDataset: https://github.com/huggingface/datasets/pull/6658\r\n\r\n\r\n\r\n```python\r\n\"\"\"Requires https://github.com/huggingface/datasets/pull/6658 (WIP)\"\"\"\r\nfrom datasets import load_dataset\r\nfrom torch.utils.data import DataLoader\r\n\r\nds = load_dataset(..., streaming=True)\r\n# ds = ds.map(tokenize)\r\n# ds = ds.shuffle(seed=42, buffer_size=1000)\r\n\r\n# Init the dataset state_dict, or load it from a checkpoint\r\ndataset_state_dict = ds.state_dict()\r\n\r\n# Resumable training loop\r\nds.load_state_dict(dataset_state_dict)\r\ndataloader = DataLoader(ds, batch_size=batch_size)\r\nfor step, batch in enumerate(dataloader):\r\n ...\r\n if step % save_steps == 0:\r\n dataset_state_dict = ds.state_dict()\r\n```", "Hi @lhoestq - can you provide more information and how to implement on saving and restoring vanilla DataLoader states with map-style datasets?\r\n\r\n", "For now the easiest is probably to use the vanilla DataLoader only for batching and multiprocessing, and implement the resuming logic using a `Dataset` (it has `.select()` to skip examples) and a `dataset_state_dict`:\r\n\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nfrom torch.utils.data import DataLoader\r\n\r\nds = load_dataset(...)\r\n# ds = ds.map(tokenize)\r\n# ds = ds.shuffle(seed=42)\r\n\r\n# Init the dataset state_dict, or load it from a checkpoint\r\ndataset_state_dict = {\"step\": 0} \r\n\r\n# Resumable training loop\r\nstart_step = dataset_state_dict[\"step\"]\r\ndataloader = DataLoader(ds.select(range(start_step * batch_size, len(ds))), batch_size=batch_size)\r\nfor step, batch in enumerate(dataloader, start=start_step):\r\n ...\r\n if step % save_steps == 0:\r\n dataset_state_dict = {\"step\": step}\r\n```", "Hello, I found a similar implementation online that seems to solve your problem. https://github.com/facebookresearch/vissl/blob/main/vissl/data/data_helper.py#L93\r\nit looks like we can set_start_iter in StatefulDistributedSampler to implement the stateful resume requirement we want.\r\n\r\n", "Hi y'all, @lhoestq I wanted to flag that we currently have a StatefulDataLoader in `pytorch/data/torchdata` that has state_dict/load_state_dict methods, which will call a dataset's state_dict/load_state_dict methods but also handle multiprocessing under the hood. Any chance we can collaborate on this and try to get them to work well together? Please have a look here for some basic examples: https://github.com/pytorch/data/tree/main/torchdata/stateful_dataloader#saving-and-loading-state ", "Fantastic ! This will help pushing our IterableDataset state_dict implementation at https://github.com/huggingface/datasets/pull/6658 :) I'll check if there is anything missing to maker them work together, and add tests and some docs referring to the StatefulDataLoader :)", "Ah I just saw this disclaimer in the torchdata README and it feels like people should not rely on it. Should the StatefulDataLoader live elsewhere @andrewkho ?\r\n\r\n> ⚠️ As of July 2023, we have paused active development on TorchData and have paused new releases. We have learnt a lot from building it and hearing from users, but also believe we need to re-evaluate the technical design and approach given how much the industry has changed since we began the project. During the rest of 2023 we will be re-evaluating our plans in this space. Please reach out if you suggestions or comments (please use https://github.com/pytorch/data/issues/1196 for feedback).", "@lhoestq Good find, we are in the midst of updating this disclaimer as we're re-starting development and regular releases, though our approach will be to iterate on DL V1 (ie StatefulDataLoader) instead of continuing development on datapipes+DLV2. Let's discuss on a call at some point to figure out the best path forward! ", "As a heads up, `IterableDataset` state_dict has been added in https://github.com/huggingface/datasets/pull/6658\r\n\r\n...and it works out of the box with the `torchdata` `StatefulDataLoader` :)\r\n\r\nSee the docs at https://huggingface.co/docs/datasets/main/en/use_with_pytorch#checkpoint-and-resume" ]

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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) | 0.008215 / 0.011353 (-0.003138) | 0.004510 / 0.011008 (-0.006498) | 0.099270 / 0.038508 (0.060761) | 0.028682 / 0.023109 (0.005573) | 0.332726 / 0.275898 (0.056827) | 0.371025 / 0.323480 (0.047545) | 0.006665 / 0.007986 (-0.001320) | 0.003329 / 0.004328 (-0.001000) | 0.078509 / 0.004250 (0.074259) | 0.032388 / 0.037052 (-0.004664) | 0.348540 / 0.258489 (0.090051) | 0.382212 / 0.293841 (0.088371) | 0.033307 / 0.128546 (-0.095239) | 0.011642 / 0.075646 (-0.064004) | 0.322573 / 0.419271 (-0.096699) | 0.041297 / 0.043533 (-0.002236) | 0.322710 / 0.255139 (0.067571) | 0.361593 / 0.283200 (0.078394) | 0.082276 / 0.141683 (-0.059407) | 1.481932 / 1.452155 (0.029777) | 1.531677 / 1.492716 (0.038961) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.194964 / 0.018006 (0.176958) | 0.406002 / 0.000490 (0.405512) | 0.001015 / 0.000200 (0.000815) | 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) | 0.023317 / 0.037411 (-0.014095) | 0.097231 / 0.014526 (0.082705) | 0.103898 / 0.176557 (-0.072659) | 0.139864 / 0.737135 (-0.597271) | 0.106785 / 0.296338 (-0.189554) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419036 / 0.215209 (0.203827) | 4.193985 / 2.077655 (2.116330) | 1.879069 / 1.504120 (0.374949) | 1.675384 / 1.541195 (0.134190) | 1.696225 / 1.468490 (0.227735) | 0.695257 / 4.584777 (-3.889520) | 3.437971 / 3.745712 (-0.307741) | 2.656037 / 5.269862 (-2.613824) | 1.463320 / 4.565676 (-3.102356) | 0.082575 / 0.424275 (-0.341700) | 0.012593 / 0.007607 (0.004986) | 0.526643 / 0.226044 (0.300599) | 5.278366 / 2.268929 (3.009437) | 2.288106 / 55.444624 (-53.156518) | 1.954875 / 6.876477 (-4.921602) | 1.950641 / 2.142072 (-0.191431) | 0.808289 / 4.805227 (-3.996938) | 0.148790 / 6.500664 (-6.351875) | 0.064775 / 0.075469 (-0.010694) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.215219 / 1.841788 (-0.626569) | 13.551467 / 8.074308 (5.477159) | 13.841547 / 10.191392 (3.650155) | 0.153610 / 0.680424 (-0.526814) | 0.028308 / 0.534201 (-0.505893) | 0.397087 / 0.579283 (-0.182196) | 0.401724 / 0.434364 (-0.032640) | 0.458042 / 0.540337 (-0.082296) | 0.544955 / 1.386936 (-0.841981) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006321 / 0.011353 (-0.005032) | 0.004336 / 0.011008 (-0.006673) | 0.097196 / 0.038508 (0.058688) | 0.026933 / 0.023109 (0.003824) | 0.416520 / 0.275898 (0.140622) | 0.450703 / 0.323480 (0.127223) | 0.004831 / 0.007986 (-0.003155) | 0.003252 / 0.004328 (-0.001076) | 0.074981 / 0.004250 (0.070730) | 0.036136 / 0.037052 (-0.000917) | 0.423166 / 0.258489 (0.164677) | 0.460936 / 0.293841 (0.167095) | 0.031859 / 0.128546 (-0.096687) | 0.011500 / 0.075646 (-0.064146) | 0.318197 / 0.419271 (-0.101074) | 0.041472 / 0.043533 (-0.002061) | 0.419227 / 0.255139 (0.164088) | 0.444712 / 0.283200 (0.161512) | 0.088841 / 0.141683 (-0.052841) | 1.497237 / 1.452155 (0.045083) | 1.572111 / 1.492716 (0.079395) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.239261 / 0.018006 (0.221255) | 0.400358 / 0.000490 (0.399868) | 0.003460 / 0.000200 (0.003261) | 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) | 0.024016 / 0.037411 (-0.013395) | 0.098414 / 0.014526 (0.083888) | 0.107220 / 0.176557 (-0.069337) | 0.143538 / 0.737135 (-0.593598) | 0.108607 / 0.296338 (-0.187731) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.473896 / 0.215209 (0.258687) | 4.740386 / 2.077655 (2.662731) | 2.458046 / 1.504120 (0.953926) | 2.260895 / 1.541195 (0.719700) | 2.280218 / 1.468490 (0.811728) | 0.694843 / 4.584777 (-3.889934) | 3.349795 / 3.745712 (-0.395917) | 1.846970 / 5.269862 (-3.422892) | 1.151481 / 4.565676 (-3.414195) | 0.082054 / 0.424275 (-0.342221) | 0.012664 / 0.007607 (0.005057) | 0.573400 / 0.226044 (0.347355) | 5.750648 / 2.268929 (3.481720) | 2.904257 / 55.444624 (-52.540367) | 2.555181 / 6.876477 (-4.321295) | 2.595830 / 2.142072 (0.453758) | 0.799580 / 4.805227 (-4.005647) | 0.151088 / 6.500664 (-6.349576) | 0.066639 / 0.075469 (-0.008831) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.251413 / 1.841788 (-0.590375) | 13.743368 / 8.074308 (5.669060) | 13.808729 / 10.191392 (3.617337) | 0.144765 / 0.680424 (-0.535659) | 0.016606 / 0.534201 (-0.517594) | 0.376503 / 0.579283 (-0.202780) | 0.381510 / 0.434364 (-0.052854) | 0.440295 / 0.540337 (-0.100043) | 0.524248 / 1.386936 (-0.862688) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#eea1226779993687845da5ecd264cf047e46a128 \"CML watermark\")\n", "Thanks a lot, @albertvillanova - I validated that your fix solves the original problem!" ]

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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) | 0.011848 / 0.011353 (0.000495) | 0.006988 / 0.011008 (-0.004020) | 0.138078 / 0.038508 (0.099570) | 0.040310 / 0.023109 (0.017201) | 0.411857 / 0.275898 (0.135959) | 0.509496 / 0.323480 (0.186016) | 0.010695 / 0.007986 (0.002709) | 0.005275 / 0.004328 (0.000946) | 0.107157 / 0.004250 (0.102907) | 0.050987 / 0.037052 (0.013935) | 0.432387 / 0.258489 (0.173898) | 0.495136 / 0.293841 (0.201295) | 0.055273 / 0.128546 (-0.073273) | 0.019573 / 0.075646 (-0.056074) | 0.460356 / 0.419271 (0.041084) | 0.060916 / 0.043533 (0.017383) | 0.426140 / 0.255139 (0.171002) | 0.430461 / 0.283200 (0.147261) | 0.124569 / 0.141683 (-0.017114) | 1.989404 / 1.452155 (0.537250) | 1.942052 / 1.492716 (0.449335) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.287233 / 0.018006 (0.269227) | 0.606056 / 0.000490 (0.605566) | 0.004435 / 0.000200 (0.004235) | 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) | 0.032353 / 0.037411 (-0.005058) | 0.124237 / 0.014526 (0.109711) | 0.143280 / 0.176557 (-0.033276) | 0.182081 / 0.737135 (-0.555055) | 0.148085 / 0.296338 (-0.148253) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.613550 / 0.215209 (0.398341) | 6.172421 / 2.077655 (4.094766) | 2.466018 / 1.504120 (0.961898) | 2.166433 / 1.541195 (0.625238) | 2.192511 / 1.468490 (0.724021) | 1.248777 / 4.584777 (-3.336000) | 5.746150 / 3.745712 (2.000438) | 3.097184 / 5.269862 (-2.172678) | 2.078176 / 4.565676 (-2.487501) | 0.144351 / 0.424275 (-0.279924) | 0.014830 / 0.007607 (0.007223) | 0.761699 / 0.226044 (0.535655) | 7.713201 / 2.268929 (5.444272) | 3.359647 / 55.444624 (-52.084977) | 2.652595 / 6.876477 (-4.223882) | 2.721952 / 2.142072 (0.579880) | 1.493036 / 4.805227 (-3.312192) | 0.252336 / 6.500664 (-6.248328) | 0.082906 / 0.075469 (0.007436) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.643887 / 1.841788 (-0.197901) | 18.762775 / 8.074308 (10.688466) | 22.003583 / 10.191392 (11.812191) | 0.256361 / 0.680424 (-0.424062) | 0.048048 / 0.534201 (-0.486153) | 0.601971 / 0.579283 (0.022688) | 0.712801 / 0.434364 (0.278438) | 0.684473 / 0.540337 (0.144136) | 0.802566 / 1.386936 (-0.584370) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010410 / 0.011353 (-0.000943) | 0.006719 / 0.011008 (-0.004289) | 0.132862 / 0.038508 (0.094354) | 0.036973 / 0.023109 (0.013863) | 0.470925 / 0.275898 (0.195027) | 0.502864 / 0.323480 (0.179384) | 0.007447 / 0.007986 (-0.000539) | 0.005629 / 0.004328 (0.001301) | 0.091985 / 0.004250 (0.087734) | 0.057537 / 0.037052 (0.020485) | 0.458362 / 0.258489 (0.199873) | 0.518324 / 0.293841 (0.224483) | 0.056540 / 0.128546 (-0.072007) | 0.021266 / 0.075646 (-0.054380) | 0.448289 / 0.419271 (0.029018) | 0.064211 / 0.043533 (0.020678) | 0.492596 / 0.255139 (0.237457) | 0.495030 / 0.283200 (0.211830) | 0.121858 / 0.141683 (-0.019825) | 1.823821 / 1.452155 (0.371667) | 2.012165 / 1.492716 (0.519449) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.296252 / 0.018006 (0.278245) | 0.601688 / 0.000490 (0.601198) | 0.006369 / 0.000200 (0.006169) | 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) | 0.035821 / 0.037411 (-0.001590) | 0.132722 / 0.014526 (0.118196) | 0.141819 / 0.176557 (-0.034738) | 0.205115 / 0.737135 (-0.532020) | 0.148917 / 0.296338 (-0.147422) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.678207 / 0.215209 (0.462998) | 6.969918 / 2.077655 (4.892263) | 3.077831 / 1.504120 (1.573711) | 2.689296 / 1.541195 (1.148102) | 2.706462 / 1.468490 (1.237972) | 1.249125 / 4.584777 (-3.335652) | 5.793917 / 3.745712 (2.048205) | 3.137565 / 5.269862 (-2.132297) | 2.056880 / 4.565676 (-2.508796) | 0.151918 / 0.424275 (-0.272357) | 0.015029 / 0.007607 (0.007422) | 0.833975 / 0.226044 (0.607930) | 8.575649 / 2.268929 (6.306720) | 3.812115 / 55.444624 (-51.632509) | 3.124219 / 6.876477 (-3.752258) | 3.178645 / 2.142072 (1.036572) | 1.488260 / 4.805227 (-3.316967) | 0.268239 / 6.500664 (-6.232425) | 0.089463 / 0.075469 (0.013993) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.645461 / 1.841788 (-0.196327) | 19.074412 / 8.074308 (11.000104) | 21.626726 / 10.191392 (11.435334) | 0.210525 / 0.680424 (-0.469899) | 0.032166 / 0.534201 (-0.502035) | 0.555572 / 0.579283 (-0.023711) | 0.654667 / 0.434364 (0.220303) | 0.632471 / 0.540337 (0.092133) | 0.756510 / 1.386936 (-0.630426) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6681c36bbaae9b8b1daa3dbbd4a96b35aaae271b \"CML watermark\")\n" ]

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[ "Hi ! Could you share the full stack trace ? Which dataset did you try to load ?\r\n\r\nit may be related to https://github.com/huggingface/datasets/pull/5640", "The `BadZipFile` error means the ZIP file is corrupted, so I'm closing this issue as it's not directly related to `datasets`.", "For others that find this issue following a `BadZipFile` error, I had the same problem because I had a file in a folder dataset `my-image.target` and the datasets library was incorrectly determining that the (PNG) file was a zip archive. When it tried to extract the file, this error occurred. \r\n\r\nUpdating to `datasets==2.12.0` fixed the problem for me." ]

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[ "wtf", "Couldn't find what's causing this, this will need more investigation", "A possible hint: The function it seems to be spending a lot of time in (when iterating over the original dataset) is `_get_mp` in the PIL JPEG decoder: \r\n\r\n", "If \"mp\" is multiprocessing, this might suggest some kind of negative interaction between the JPEG decoder and TF's handling of processes/threads. Note that we haven't merged the parallel `to_tf_dataset` PR yet, so it's not caused by that PR!", "Update: MP isn't multiprocessing at all, it's an internal PIL method for loading metadata from JPEG files. No idea why that would be a bottleneck, but I'll see if a Python profiler can't figure out where the time is actually being spent.", "After further profiling, the slowdown is in the C methods for JPEG decoding that are included as part of PIL. Because Python profilers can't inspect inside that, I don't have any further information on which lines exactly are responsible for the slowdown or why.\r\n\r\nIn the meantime, I'm going to suggest switching from `return_tensors=\"tf\"` to `return_tensors=\"np\"` in most of our `transformers` code - this generally works better for pre-processing. Two relevant PRs are [here](https://github.com/huggingface/transformers/pull/21266) and [here](https://github.com/huggingface/notebooks/pull/308).", "Closing this issue as we've done what we can with this one! " ]

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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) | 0.008227 / 0.011353 (-0.003126) | 0.004496 / 0.011008 (-0.006512) | 0.099319 / 0.038508 (0.060811) | 0.029929 / 0.023109 (0.006820) | 0.296686 / 0.275898 (0.020788) | 0.355372 / 0.323480 (0.031892) | 0.006864 / 0.007986 (-0.001122) | 0.003458 / 0.004328 (-0.000871) | 0.077234 / 0.004250 (0.072983) | 0.037072 / 0.037052 (0.000020) | 0.311675 / 0.258489 (0.053186) | 0.338965 / 0.293841 (0.045124) | 0.033562 / 0.128546 (-0.094985) | 0.011399 / 0.075646 (-0.064248) | 0.322406 / 0.419271 (-0.096865) | 0.043034 / 0.043533 (-0.000499) | 0.298083 / 0.255139 (0.042944) | 0.323661 / 0.283200 (0.040462) | 0.089380 / 0.141683 (-0.052303) | 1.479363 / 1.452155 (0.027208) | 1.518337 / 1.492716 (0.025620) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.177822 / 0.018006 (0.159816) | 0.400806 / 0.000490 (0.400317) | 0.002121 / 0.000200 (0.001921) | 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) | 0.021986 / 0.037411 (-0.015426) | 0.096749 / 0.014526 (0.082223) | 0.101443 / 0.176557 (-0.075113) | 0.137519 / 0.737135 (-0.599616) | 0.105558 / 0.296338 (-0.190780) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418983 / 0.215209 (0.203774) | 4.189579 / 2.077655 (2.111924) | 1.877831 / 1.504120 (0.373711) | 1.666213 / 1.541195 (0.125019) | 1.680735 / 1.468490 (0.212245) | 0.693033 / 4.584777 (-3.891744) | 3.420553 / 3.745712 (-0.325160) | 1.819647 / 5.269862 (-3.450214) | 1.144934 / 4.565676 (-3.420743) | 0.082209 / 0.424275 (-0.342066) | 0.012433 / 0.007607 (0.004826) | 0.526781 / 0.226044 (0.300737) | 5.273689 / 2.268929 (3.004760) | 2.323468 / 55.444624 (-53.121156) | 1.960508 / 6.876477 (-4.915969) | 2.035338 / 2.142072 (-0.106735) | 0.812789 / 4.805227 (-3.992438) | 0.148429 / 6.500664 (-6.352235) | 0.064727 / 0.075469 (-0.010742) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.253218 / 1.841788 (-0.588569) | 13.303426 / 8.074308 (5.229118) | 13.651074 / 10.191392 (3.459682) | 0.135178 / 0.680424 (-0.545246) | 0.028483 / 0.534201 (-0.505717) | 0.393284 / 0.579283 (-0.185999) | 0.401957 / 0.434364 (-0.032407) | 0.457136 / 0.540337 (-0.083201) | 0.535835 / 1.386936 (-0.851101) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006335 / 0.011353 (-0.005017) | 0.004454 / 0.011008 (-0.006554) | 0.097565 / 0.038508 (0.059057) | 0.026917 / 0.023109 (0.003808) | 0.350779 / 0.275898 (0.074881) | 0.391979 / 0.323480 (0.068499) | 0.004648 / 0.007986 (-0.003337) | 0.003204 / 0.004328 (-0.001124) | 0.076987 / 0.004250 (0.072737) | 0.035257 / 0.037052 (-0.001796) | 0.347193 / 0.258489 (0.088704) | 0.391462 / 0.293841 (0.097621) | 0.031244 / 0.128546 (-0.097302) | 0.011460 / 0.075646 (-0.064186) | 0.321606 / 0.419271 (-0.097665) | 0.041218 / 0.043533 (-0.002315) | 0.341884 / 0.255139 (0.086745) | 0.374920 / 0.283200 (0.091720) | 0.086383 / 0.141683 (-0.055300) | 1.501750 / 1.452155 (0.049595) | 1.565060 / 1.492716 (0.072344) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.165447 / 0.018006 (0.147441) | 0.401885 / 0.000490 (0.401395) | 0.000975 / 0.000200 (0.000775) | 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) | 0.024494 / 0.037411 (-0.012917) | 0.097334 / 0.014526 (0.082808) | 0.105324 / 0.176557 (-0.071232) | 0.142430 / 0.737135 (-0.594705) | 0.107249 / 0.296338 (-0.189089) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441632 / 0.215209 (0.226423) | 4.407729 / 2.077655 (2.330074) | 2.078167 / 1.504120 (0.574047) | 1.864210 / 1.541195 (0.323015) | 1.885948 / 1.468490 (0.417458) | 0.693974 / 4.584777 (-3.890803) | 3.386837 / 3.745712 (-0.358875) | 1.840291 / 5.269862 (-3.429571) | 1.150524 / 4.565676 (-3.415153) | 0.082240 / 0.424275 (-0.342035) | 0.012488 / 0.007607 (0.004881) | 0.537589 / 0.226044 (0.311545) | 5.404007 / 2.268929 (3.135078) | 2.537467 / 55.444624 (-52.907157) | 2.190775 / 6.876477 (-4.685702) | 2.224746 / 2.142072 (0.082674) | 0.799524 / 4.805227 (-4.005703) | 0.150639 / 6.500664 (-6.350025) | 0.066473 / 0.075469 (-0.008997) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.258559 / 1.841788 (-0.583228) | 13.773583 / 8.074308 (5.699275) | 13.964322 / 10.191392 (3.772930) | 0.156295 / 0.680424 (-0.524129) | 0.016824 / 0.534201 (-0.517377) | 0.377476 / 0.579283 (-0.201807) | 0.390163 / 0.434364 (-0.044201) | 0.442541 / 0.540337 (-0.097796) | 0.529404 / 1.386936 (-0.857532) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8f500a5c554b213aafe87293bd593920567742c3 \"CML watermark\")\n" ]

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