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train · 5k rows

id int64 953M 3.35B	number int64 2.72k 7.75k	title stringlengths 1 290	state stringclasses 2 values	created_at timestamp[s]date 2021-07-26 12:21:17 2025-08-23 00:18:43	updated_at timestamp[s]date 2021-07-26 13:27:59 2025-08-23 12:34:39	closed_at timestamp[s]date 2021-07-26 13:27:59 2025-08-20 16:35:55 ⌀	html_url stringlengths 49 51	pull_request dict	user_login stringlengths 3 26	is_pull_request bool 2 classes	comments listlengths 0 30
2,043,847,591	6,503	Fix streaming xnli	closed	2023-12-15T14:40:57	2023-12-15T14:51:06	2023-12-15T14:44:47	https://github.com/huggingface/datasets/pull/6503	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6503", "html_url": "https://github.com/huggingface/datasets/pull/6503", "diff_url": "https://github.com/huggingface/datasets/pull/6503.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6503.patch", "merged_at": "2023-12-15T14:44:46" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6503). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005003 / 0.011353 (-0.006350) \| 0.003020 / 0.011008 (-0.007988) \| 0.061370 / 0.038508 (0.022862) \| 0.050996 / 0.023109 (0.027887) \| 0.243434 / 0.275898 (-0.032464) \| 0.266317 / 0.323480 (-0.057163) \| 0.003888 / 0.007986 (-0.004098) \| 0.002607 / 0.004328 (-0.001721) \| 0.047541 / 0.004250 (0.043290) \| 0.037933 / 0.037052 (0.000881) \| 0.259695 / 0.258489 (0.001206) \| 0.279374 / 0.293841 (-0.014467) \| 0.027258 / 0.128546 (-0.101288) \| 0.010184 / 0.075646 (-0.065462) \| 0.207412 / 0.419271 (-0.211860) \| 0.034978 / 0.043533 (-0.008554) \| 0.247871 / 0.255139 (-0.007267) \| 0.265273 / 0.283200 (-0.017927) \| 0.017886 / 0.141683 (-0.123796) \| 1.090451 / 1.452155 (-0.361704) \| 1.152034 / 1.492716 (-0.340682) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094383 / 0.018006 (0.076377) \| 0.301151 / 0.000490 (0.300661) \| 0.000211 / 0.000200 (0.000011) \| 0.000049 / 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) \| 0.018927 / 0.037411 (-0.018484) \| 0.062152 / 0.014526 (0.047626) \| 0.072177 / 0.176557 (-0.104380) \| 0.119792 / 0.737135 (-0.617343) \| 0.073333 / 0.296338 (-0.223005) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282671 / 0.215209 (0.067462) \| 2.721148 / 2.077655 (0.643494) \| 1.472689 / 1.504120 (-0.031431) \| 1.355226 / 1.541195 (-0.185969) \| 1.375935 / 1.468490 (-0.092556) \| 0.562600 / 4.584777 (-4.022177) \| 2.364046 / 3.745712 (-1.381666) \| 2.714984 / 5.269862 (-2.554878) \| 1.738413 / 4.565676 (-2.827263) \| 0.062564 / 0.424275 (-0.361711) \| 0.004964 / 0.007607 (-0.002643) \| 0.341300 / 0.226044 (0.115255) \| 3.345187 / 2.268929 (1.076259) \| 1.857822 / 55.444624 (-53.586803) \| 1.581002 / 6.876477 (-5.295475) \| 1.585919 / 2.142072 (-0.556153) \| 0.640105 / 4.805227 (-4.165122) \| 0.117880 / 6.500664 (-6.382784) \| 0.042032 / 0.075469 (-0.033437) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.962701 / 1.841788 (-0.879086) \| 11.309251 / 8.074308 (3.234943) \| 10.462520 / 10.191392 (0.271128) \| 0.127399 / 0.680424 (-0.553025) \| 0.014549 / 0.534201 (-0.519652) \| 0.297017 / 0.579283 (-0.282266) \| 0.266152 / 0.434364 (-0.168212) \| 0.349252 / 0.540337 (-0.191085) \| 0.457015 / 1.386936 (-0.929921) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005341 / 0.011353 (-0.006012) \| 0.003108 / 0.011008 (-0.007900) \| 0.048862 / 0.038508 (0.010353) \| 0.053354 / 0.023109 (0.030245) \| 0.274499 / 0.275898 (-0.001399) \| 0.296698 / 0.323480 (-0.026782) \| 0.003974 / 0.007986 (-0.004012) \| 0.002631 / 0.004328 (-0.001697) \| 0.048013 / 0.004250 (0.043762) \| 0.040416 / 0.037052 (0.003363) \| 0.276581 / 0.258489 (0.018092) \| 0.301296 / 0.293841 (0.007455) \| 0.029049 / 0.128546 (-0.099497) \| 0.010253 / 0.075646 (-0.065393) \| 0.057157 / 0.419271 (-0.362114) \| 0.031830 / 0.043533 (-0.011703) \| 0.274341 / 0.255139 (0.019202) \| 0.292583 / 0.283200 (0.009383) \| 0.018449 / 0.141683 (-0.123234) \| 1.145099 / 1.452155 (-0.307055) \| 1.192958 / 1.492716 (-0.299758) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091596 / 0.018006 (0.073590) \| 0.300917 / 0.000490 (0.300427) \| 0.000225 / 0.000200 (0.000025) \| 0.000054 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021657 / 0.037411 (-0.015754) \| 0.068464 / 0.014526 (0.053938) \| 0.079869 / 0.176557 (-0.096687) \| 0.117523 / 0.737135 (-0.619613) \| 0.081257 / 0.296338 (-0.215082) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294876 / 0.215209 (0.079667) \| 2.879372 / 2.077655 (0.801718) \| 1.619887 / 1.504120 (0.115767) \| 1.482154 / 1.541195 (-0.059041) \| 1.494656 / 1.468490 (0.026166) \| 0.558914 / 4.584777 (-4.025862) \| 2.420948 / 3.745712 (-1.324765) \| 2.728992 / 5.269862 (-2.540869) \| 1.722135 / 4.565676 (-2.843542) \| 0.062182 / 0.424275 (-0.362093) \| 0.004933 / 0.007607 (-0.002674) \| 0.342759 / 0.226044 (0.116715) \| 3.424083 / 2.268929 (1.155154) \| 1.950673 / 55.444624 (-53.493951) \| 1.683126 / 6.876477 (-5.193351) \| 1.673135 / 2.142072 (-0.468937) \| 0.633711 / 4.805227 (-4.171516) \| 0.114898 / 6.500664 (-6.385766) \| 0.040332 / 0.075469 (-0.035137) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.975102 / 1.841788 (-0.866685) \| 11.975731 / 8.074308 (3.901423) \| 10.961103 / 10.191392 (0.769711) \| 0.131152 / 0.680424 (-0.549272) \| 0.016268 / 0.534201 (-0.517933) \| 0.285031 / 0.579283 (-0.294252) \| 0.279556 / 0.434364 (-0.154808) \| 0.324183 / 0.540337 (-0.216154) \| 0.571404 / 1.386936 (-0.815532) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4f67312956fc15572b6a0ca0dfcc0ceb90fbb794 \"CML watermark\")\n" ]
2,043,771,731	6,502	Pickle support for `torch.Generator` objects	closed	2023-12-15T13:55:12	2023-12-15T15:04:33	2023-12-15T14:58:22	https://github.com/huggingface/datasets/pull/6502	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6502", "html_url": "https://github.com/huggingface/datasets/pull/6502", "diff_url": "https://github.com/huggingface/datasets/pull/6502.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6502.patch", "merged_at": "2023-12-15T14:58:22" }	mariosasko	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6502). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005472 / 0.011353 (-0.005881) \| 0.003715 / 0.011008 (-0.007293) \| 0.063257 / 0.038508 (0.024749) \| 0.060683 / 0.023109 (0.037574) \| 0.250885 / 0.275898 (-0.025013) \| 0.271685 / 0.323480 (-0.051795) \| 0.003051 / 0.007986 (-0.004934) \| 0.002799 / 0.004328 (-0.001530) \| 0.049113 / 0.004250 (0.044863) \| 0.038965 / 0.037052 (0.001912) \| 0.252688 / 0.258489 (-0.005801) \| 0.282536 / 0.293841 (-0.011305) \| 0.028722 / 0.128546 (-0.099824) \| 0.010586 / 0.075646 (-0.065060) \| 0.205145 / 0.419271 (-0.214127) \| 0.036996 / 0.043533 (-0.006537) \| 0.248874 / 0.255139 (-0.006265) \| 0.266148 / 0.283200 (-0.017051) \| 0.018540 / 0.141683 (-0.123143) \| 1.120216 / 1.452155 (-0.331938) \| 1.191072 / 1.492716 (-0.301644) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095721 / 0.018006 (0.077714) \| 0.313401 / 0.000490 (0.312911) \| 0.000234 / 0.000200 (0.000034) \| 0.000053 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018604 / 0.037411 (-0.018807) \| 0.061571 / 0.014526 (0.047045) \| 0.075343 / 0.176557 (-0.101213) \| 0.121272 / 0.737135 (-0.615864) \| 0.076448 / 0.296338 (-0.219890) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286885 / 0.215209 (0.071676) \| 2.809100 / 2.077655 (0.731445) \| 1.485365 / 1.504120 (-0.018755) \| 1.367672 / 1.541195 (-0.173523) \| 1.423570 / 1.468490 (-0.044920) \| 0.571063 / 4.584777 (-4.013714) \| 2.385248 / 3.745712 (-1.360464) \| 2.855251 / 5.269862 (-2.414610) \| 1.799371 / 4.565676 (-2.766306) \| 0.063491 / 0.424275 (-0.360784) \| 0.004942 / 0.007607 (-0.002665) \| 0.346181 / 0.226044 (0.120137) \| 3.388123 / 2.268929 (1.119195) \| 1.819093 / 55.444624 (-53.625532) \| 1.552998 / 6.876477 (-5.323479) \| 1.627930 / 2.142072 (-0.514143) \| 0.653438 / 4.805227 (-4.151789) \| 0.123831 / 6.500664 (-6.376833) \| 0.043340 / 0.075469 (-0.032129) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.952167 / 1.841788 (-0.889621) \| 12.149515 / 8.074308 (4.075207) \| 10.665085 / 10.191392 (0.473693) \| 0.127768 / 0.680424 (-0.552656) \| 0.014022 / 0.534201 (-0.520179) \| 0.285959 / 0.579283 (-0.293324) \| 0.269727 / 0.434364 (-0.164637) \| 0.336646 / 0.540337 (-0.203692) \| 0.442932 / 1.386936 (-0.944005) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005351 / 0.011353 (-0.006002) \| 0.003561 / 0.011008 (-0.007448) \| 0.048890 / 0.038508 (0.010382) \| 0.054093 / 0.023109 (0.030984) \| 0.274397 / 0.275898 (-0.001501) \| 0.296980 / 0.323480 (-0.026500) \| 0.004126 / 0.007986 (-0.003860) \| 0.002751 / 0.004328 (-0.001578) \| 0.049131 / 0.004250 (0.044880) \| 0.040769 / 0.037052 (0.003716) \| 0.279147 / 0.258489 (0.020658) \| 0.302014 / 0.293841 (0.008173) \| 0.029847 / 0.128546 (-0.098699) \| 0.010710 / 0.075646 (-0.064936) \| 0.057626 / 0.419271 (-0.361645) \| 0.032801 / 0.043533 (-0.010732) \| 0.272698 / 0.255139 (0.017559) \| 0.289238 / 0.283200 (0.006039) \| 0.017876 / 0.141683 (-0.123807) \| 1.152059 / 1.452155 (-0.300096) \| 1.212289 / 1.492716 (-0.280427) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092914 / 0.018006 (0.074908) \| 0.303092 / 0.000490 (0.302603) \| 0.000214 / 0.000200 (0.000014) \| 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) \| 0.022074 / 0.037411 (-0.015337) \| 0.070109 / 0.014526 (0.055583) \| 0.083360 / 0.176557 (-0.093196) \| 0.122445 / 0.737135 (-0.614690) \| 0.083625 / 0.296338 (-0.212714) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282788 / 0.215209 (0.067579) \| 2.789229 / 2.077655 (0.711574) \| 1.571077 / 1.504120 (0.066957) \| 1.452627 / 1.541195 (-0.088567) \| 1.493176 / 1.468490 (0.024686) \| 0.556892 / 4.584777 (-4.027885) \| 2.442771 / 3.745712 (-1.302941) \| 2.826316 / 5.269862 (-2.443545) \| 1.758276 / 4.565676 (-2.807401) \| 0.063039 / 0.424275 (-0.361236) \| 0.004928 / 0.007607 (-0.002679) \| 0.338247 / 0.226044 (0.112202) \| 3.346344 / 2.268929 (1.077416) \| 1.952520 / 55.444624 (-53.492104) \| 1.664520 / 6.876477 (-5.211956) \| 1.701528 / 2.142072 (-0.440544) \| 0.634746 / 4.805227 (-4.170481) \| 0.116879 / 6.500664 (-6.383786) \| 0.040990 / 0.075469 (-0.034479) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.969521 / 1.841788 (-0.872267) \| 12.431395 / 8.074308 (4.357087) \| 10.907503 / 10.191392 (0.716111) \| 0.131028 / 0.680424 (-0.549396) \| 0.015239 / 0.534201 (-0.518962) \| 0.290793 / 0.579283 (-0.288490) \| 0.275072 / 0.434364 (-0.159292) \| 0.331036 / 0.540337 (-0.209301) \| 0.567858 / 1.386936 (-0.819078) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#092118fc00f7dd718ab3643739d7b23ff16c9eff \"CML watermark\")\n" ]
2,043,377,240	6,501	OverflowError: value too large to convert to int32_t	open	2023-12-15T10:10:21	2025-06-27T04:27:14	null	https://github.com/huggingface/datasets/issues/6501	null	zhangfan-algo	false	[ "+1\n" ]
2,043,258,633	6,500	Enable setting config as default when push_to_hub	closed	2023-12-15T09:17:41	2023-12-18T11:56:11	2023-12-18T11:50:03	https://github.com/huggingface/datasets/pull/6500	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6500", "html_url": "https://github.com/huggingface/datasets/pull/6500", "diff_url": "https://github.com/huggingface/datasets/pull/6500.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6500.patch", "merged_at": "2023-12-18T11:50:03" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6500). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "This is ready for review @huggingface/datasets. ", "Also what if the config is being overwritten and it was the default config and the user doesn't pass `set_default` ?\r\nI'd expect the config to keep being the default one but lmk what you think", "How can you unset a config as the default one? In the case you mentioned, I would expect the config not being the default one.", "Maybe by passing `set_default=False` ? (set_default can be None by default)", "I think that way we are unnecessarily complicating the logic of `push_to_hub` and as I told you, I would expect the contrary: the result of calling `push_to_hub` with a determined set of arguments should always be the same, independently of previous calls and the current state of the config on the Hub. Push to hub should be somehow stateless in that sense, and IMO the user expects that the push overwrites previous config if already present on the Hub. I find very confusing making it to partially update the config on the Hub.", "That makes sense, having it stateless is simpler and no need to do something too fancy indeed", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005329 / 0.011353 (-0.006024) \| 0.002998 / 0.011008 (-0.008010) \| 0.063756 / 0.038508 (0.025248) \| 0.051713 / 0.023109 (0.028603) \| 0.248135 / 0.275898 (-0.027763) \| 0.269136 / 0.323480 (-0.054344) \| 0.002970 / 0.007986 (-0.005015) \| 0.002566 / 0.004328 (-0.001763) \| 0.048110 / 0.004250 (0.043859) \| 0.038415 / 0.037052 (0.001363) \| 0.254012 / 0.258489 (-0.004477) \| 0.281915 / 0.293841 (-0.011926) \| 0.027503 / 0.128546 (-0.101043) \| 0.010370 / 0.075646 (-0.065276) \| 0.208965 / 0.419271 (-0.210306) \| 0.035508 / 0.043533 (-0.008024) \| 0.249116 / 0.255139 (-0.006023) \| 0.266350 / 0.283200 (-0.016850) \| 0.018440 / 0.141683 (-0.123243) \| 1.101089 / 1.452155 (-0.351066) \| 1.164870 / 1.492716 (-0.327847) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090909 / 0.018006 (0.072903) \| 0.298041 / 0.000490 (0.297551) \| 0.000211 / 0.000200 (0.000012) \| 0.000051 / 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) \| 0.018137 / 0.037411 (-0.019275) \| 0.059574 / 0.014526 (0.045048) \| 0.071754 / 0.176557 (-0.104803) \| 0.117980 / 0.737135 (-0.619155) \| 0.072903 / 0.296338 (-0.223435) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282844 / 0.215209 (0.067635) \| 2.740916 / 2.077655 (0.663261) \| 1.444546 / 1.504120 (-0.059574) \| 1.321904 / 1.541195 (-0.219291) \| 1.356957 / 1.468490 (-0.111533) \| 0.568389 / 4.584777 (-4.016388) \| 2.354042 / 3.745712 (-1.391671) \| 2.719427 / 5.269862 (-2.550435) \| 1.719616 / 4.565676 (-2.846061) \| 0.062537 / 0.424275 (-0.361738) \| 0.004915 / 0.007607 (-0.002692) \| 0.334716 / 0.226044 (0.108672) \| 3.299499 / 2.268929 (1.030571) \| 1.814629 / 55.444624 (-53.629996) \| 1.515245 / 6.876477 (-5.361232) \| 1.553085 / 2.142072 (-0.588987) \| 0.643859 / 4.805227 (-4.161368) \| 0.116650 / 6.500664 (-6.384014) \| 0.041432 / 0.075469 (-0.034037) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.948227 / 1.841788 (-0.893561) \| 11.331103 / 8.074308 (3.256795) \| 10.209658 / 10.191392 (0.018266) \| 0.126721 / 0.680424 (-0.553703) \| 0.013638 / 0.534201 (-0.520563) \| 0.282540 / 0.579283 (-0.296743) \| 0.262635 / 0.434364 (-0.171729) \| 0.335357 / 0.540337 (-0.204981) \| 0.441798 / 1.386936 (-0.945138) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005200 / 0.011353 (-0.006153) \| 0.003012 / 0.011008 (-0.007996) \| 0.047571 / 0.038508 (0.009063) \| 0.055069 / 0.023109 (0.031959) \| 0.271150 / 0.275898 (-0.004748) \| 0.294957 / 0.323480 (-0.028523) \| 0.003922 / 0.007986 (-0.004064) \| 0.002627 / 0.004328 (-0.001702) \| 0.047777 / 0.004250 (0.043527) \| 0.039507 / 0.037052 (0.002454) \| 0.276314 / 0.258489 (0.017825) \| 0.300436 / 0.293841 (0.006595) \| 0.028951 / 0.128546 (-0.099595) \| 0.010583 / 0.075646 (-0.065063) \| 0.056535 / 0.419271 (-0.362737) \| 0.032654 / 0.043533 (-0.010879) \| 0.272945 / 0.255139 (0.017806) \| 0.291909 / 0.283200 (0.008709) \| 0.017545 / 0.141683 (-0.124138) \| 1.195897 / 1.452155 (-0.256258) \| 1.171855 / 1.492716 (-0.320861) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091919 / 0.018006 (0.073913) \| 0.299297 / 0.000490 (0.298807) \| 0.000225 / 0.000200 (0.000025) \| 0.000051 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022271 / 0.037411 (-0.015140) \| 0.068903 / 0.014526 (0.054377) \| 0.083767 / 0.176557 (-0.092790) \| 0.120239 / 0.737135 (-0.616896) \| 0.083448 / 0.296338 (-0.212891) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295353 / 0.215209 (0.080144) \| 2.911452 / 2.077655 (0.833798) \| 1.577941 / 1.504120 (0.073821) \| 1.454514 / 1.541195 (-0.086681) \| 1.459575 / 1.468490 (-0.008915) \| 0.572475 / 4.584777 (-4.012302) \| 2.443634 / 3.745712 (-1.302078) \| 2.801171 / 5.269862 (-2.468691) \| 1.724214 / 4.565676 (-2.841462) \| 0.063539 / 0.424275 (-0.360736) \| 0.004939 / 0.007607 (-0.002668) \| 0.347705 / 0.226044 (0.121660) \| 3.489591 / 2.268929 (1.220663) \| 1.944952 / 55.444624 (-53.499672) \| 1.652810 / 6.876477 (-5.223667) \| 1.656361 / 2.142072 (-0.485712) \| 0.647052 / 4.805227 (-4.158176) \| 0.117286 / 6.500664 (-6.383379) \| 0.040979 / 0.075469 (-0.034490) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.971761 / 1.841788 (-0.870027) \| 11.770547 / 8.074308 (3.696239) \| 10.402502 / 10.191392 (0.211110) \| 0.128280 / 0.680424 (-0.552144) \| 0.015160 / 0.534201 (-0.519041) \| 0.286706 / 0.579283 (-0.292578) \| 0.274539 / 0.434364 (-0.159825) \| 0.324591 / 0.540337 (-0.215747) \| 0.573846 / 1.386936 (-0.813090) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3329be80b9abfe83285ef940a590a4e9f68835a3 \"CML watermark\")\n" ]
2,043,166,976	6,499	docs: add reference Git over SSH	closed	2023-12-15T08:38:31	2023-12-15T11:48:47	2023-12-15T11:42:38	https://github.com/huggingface/datasets/pull/6499	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6499", "html_url": "https://github.com/huggingface/datasets/pull/6499", "diff_url": "https://github.com/huggingface/datasets/pull/6499.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6499.patch", "merged_at": "2023-12-15T11:42:38" }	severo	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6499). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005701 / 0.011353 (-0.005652) \| 0.003546 / 0.011008 (-0.007463) \| 0.063335 / 0.038508 (0.024827) \| 0.051987 / 0.023109 (0.028878) \| 0.240429 / 0.275898 (-0.035469) \| 0.260659 / 0.323480 (-0.062820) \| 0.003866 / 0.007986 (-0.004120) \| 0.002617 / 0.004328 (-0.001712) \| 0.048653 / 0.004250 (0.044403) \| 0.038176 / 0.037052 (0.001124) \| 0.245496 / 0.258489 (-0.012993) \| 0.277141 / 0.293841 (-0.016700) \| 0.027886 / 0.128546 (-0.100660) \| 0.010738 / 0.075646 (-0.064908) \| 0.211255 / 0.419271 (-0.208016) \| 0.045205 / 0.043533 (0.001672) \| 0.243062 / 0.255139 (-0.012077) \| 0.262877 / 0.283200 (-0.020323) \| 0.023426 / 0.141683 (-0.118257) \| 1.092247 / 1.452155 (-0.359908) \| 1.161074 / 1.492716 (-0.331642) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090488 / 0.018006 (0.072482) \| 0.300993 / 0.000490 (0.300504) \| 0.000212 / 0.000200 (0.000012) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018543 / 0.037411 (-0.018868) \| 0.061418 / 0.014526 (0.046892) \| 0.073242 / 0.176557 (-0.103314) \| 0.120757 / 0.737135 (-0.616378) \| 0.073967 / 0.296338 (-0.222372) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282341 / 0.215209 (0.067132) \| 2.741106 / 2.077655 (0.663451) \| 1.416573 / 1.504120 (-0.087547) \| 1.287904 / 1.541195 (-0.253291) \| 1.309425 / 1.468490 (-0.159065) \| 0.582592 / 4.584777 (-4.002184) \| 2.404866 / 3.745712 (-1.340846) \| 2.895397 / 5.269862 (-2.374464) \| 1.799864 / 4.565676 (-2.765812) \| 0.064386 / 0.424275 (-0.359889) \| 0.004920 / 0.007607 (-0.002687) \| 0.330879 / 0.226044 (0.104835) \| 3.287064 / 2.268929 (1.018135) \| 1.765169 / 55.444624 (-53.679456) \| 1.490442 / 6.876477 (-5.386034) \| 1.530960 / 2.142072 (-0.611113) \| 0.655939 / 4.805227 (-4.149288) \| 0.118529 / 6.500664 (-6.382135) \| 0.042350 / 0.075469 (-0.033119) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.959027 / 1.841788 (-0.882761) \| 11.911284 / 8.074308 (3.836976) \| 10.576898 / 10.191392 (0.385506) \| 0.141038 / 0.680424 (-0.539386) \| 0.014184 / 0.534201 (-0.520017) \| 0.305335 / 0.579283 (-0.273948) \| 0.267531 / 0.434364 (-0.166832) \| 0.353362 / 0.540337 (-0.186975) \| 0.466258 / 1.386936 (-0.920678) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005194 / 0.011353 (-0.006159) \| 0.003561 / 0.011008 (-0.007448) \| 0.049181 / 0.038508 (0.010673) \| 0.056664 / 0.023109 (0.033555) \| 0.267142 / 0.275898 (-0.008756) \| 0.291871 / 0.323480 (-0.031609) \| 0.003996 / 0.007986 (-0.003990) \| 0.003147 / 0.004328 (-0.001181) \| 0.048527 / 0.004250 (0.044276) \| 0.040239 / 0.037052 (0.003187) \| 0.269728 / 0.258489 (0.011239) \| 0.295531 / 0.293841 (0.001690) \| 0.030316 / 0.128546 (-0.098231) \| 0.010666 / 0.075646 (-0.064981) \| 0.058176 / 0.419271 (-0.361095) \| 0.033218 / 0.043533 (-0.010315) \| 0.265383 / 0.255139 (0.010244) \| 0.285102 / 0.283200 (0.001902) \| 0.018295 / 0.141683 (-0.123388) \| 1.117830 / 1.452155 (-0.334325) \| 1.196919 / 1.492716 (-0.295798) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088547 / 0.018006 (0.070541) \| 0.293220 / 0.000490 (0.292730) \| 0.000211 / 0.000200 (0.000011) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022060 / 0.037411 (-0.015351) \| 0.071973 / 0.014526 (0.057448) \| 0.081721 / 0.176557 (-0.094836) \| 0.119990 / 0.737135 (-0.617145) \| 0.081639 / 0.296338 (-0.214700) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293712 / 0.215209 (0.078503) \| 2.872986 / 2.077655 (0.795331) \| 1.568944 / 1.504120 (0.064824) \| 1.434555 / 1.541195 (-0.106639) \| 1.457747 / 1.468490 (-0.010743) \| 0.559296 / 4.584777 (-4.025481) \| 2.471845 / 3.745712 (-1.273867) \| 2.840916 / 5.269862 (-2.428946) \| 1.754909 / 4.565676 (-2.810768) \| 0.064585 / 0.424275 (-0.359690) \| 0.004992 / 0.007607 (-0.002615) \| 0.349149 / 0.226044 (0.123104) \| 3.385906 / 2.268929 (1.116977) \| 1.940644 / 55.444624 (-53.503980) \| 1.638300 / 6.876477 (-5.238177) \| 1.649939 / 2.142072 (-0.492133) \| 0.645680 / 4.805227 (-4.159547) \| 0.118080 / 6.500664 (-6.382584) \| 0.040643 / 0.075469 (-0.034826) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.969965 / 1.841788 (-0.871822) \| 12.099766 / 8.074308 (4.025457) \| 10.550650 / 10.191392 (0.359258) \| 0.131736 / 0.680424 (-0.548688) \| 0.015483 / 0.534201 (-0.518718) \| 0.289231 / 0.579283 (-0.290052) \| 0.287505 / 0.434364 (-0.146858) \| 0.327326 / 0.540337 (-0.213011) \| 0.570364 / 1.386936 (-0.816572) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#533c38cef16111e9e8154eeb76c207f1f4936ddf \"CML watermark\")\n" ]
2,042,075,969	6,498	Fallback on dataset script if user wants to load default config	closed	2023-12-14T16:46:01	2023-12-15T13:16:56	2023-12-15T13:10:48	https://github.com/huggingface/datasets/pull/6498	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6498", "html_url": "https://github.com/huggingface/datasets/pull/6498", "diff_url": "https://github.com/huggingface/datasets/pull/6498.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6498.patch", "merged_at": "2023-12-15T13:10:48" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6498). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "> I was just thinking: what if the user does not pass a config name and the dataset has only a config with a name different from \"default\"?\r\n\r\nYou mean if there is a DEFAULT_CONFIG_NAME defined in the script but the dataset only has one configuration ? We can't easily get the number of configs without running the python code so I don't think we can support detect this case\r\n", "Most datasets with a script don't define DEFAULT_CONFIG_NAME if there is only one configuration anyway.\r\n\r\nSo there is no issue e.g. for `squad`", "> I was trying to mean the case where DEFAULT_CONFIG_NAME is None but there is only a single config in BUILDER_CONFIGS, with a name different from \"default\".\r\n\r\nIn this case we can detect if \"DEFAULT_CONFIG_NAME\" is not mentioned and use the Parquet export. If it is mentioned (and maybe it is set to None or to the single config) I consider that it may have multiple configs and fall back on using the script", "... but the user does not pass the config name.", "In this case we load the single configuration (this is how a DatasetBuilder works)", "see \r\n\r\nhttps://github.com/huggingface/datasets/blob/2feaa589de86dd85941301fc8c3fa091731a67c0/src/datasets/builder.py#L532-L532", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005122 / 0.011353 (-0.006231) \| 0.003565 / 0.011008 (-0.007443) \| 0.062706 / 0.038508 (0.024198) \| 0.049314 / 0.023109 (0.026205) \| 0.247325 / 0.275898 (-0.028573) \| 0.269788 / 0.323480 (-0.053692) \| 0.003895 / 0.007986 (-0.004090) \| 0.002788 / 0.004328 (-0.001540) \| 0.048615 / 0.004250 (0.044365) \| 0.037591 / 0.037052 (0.000539) \| 0.253495 / 0.258489 (-0.004994) \| 0.281200 / 0.293841 (-0.012641) \| 0.027712 / 0.128546 (-0.100834) \| 0.010901 / 0.075646 (-0.064745) \| 0.205577 / 0.419271 (-0.213694) \| 0.035989 / 0.043533 (-0.007544) \| 0.252978 / 0.255139 (-0.002161) \| 0.268042 / 0.283200 (-0.015157) \| 0.017857 / 0.141683 (-0.123826) \| 1.096633 / 1.452155 (-0.355521) \| 1.147026 / 1.492716 (-0.345691) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095609 / 0.018006 (0.077603) \| 0.311941 / 0.000490 (0.311451) \| 0.000211 / 0.000200 (0.000011) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019042 / 0.037411 (-0.018369) \| 0.060549 / 0.014526 (0.046023) \| 0.074761 / 0.176557 (-0.101796) \| 0.121729 / 0.737135 (-0.615406) \| 0.075661 / 0.296338 (-0.220677) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284774 / 0.215209 (0.069565) \| 2.764576 / 2.077655 (0.686921) \| 1.489926 / 1.504120 (-0.014194) \| 1.387276 / 1.541195 (-0.153919) \| 1.400931 / 1.468490 (-0.067559) \| 0.555623 / 4.584777 (-4.029154) \| 2.409488 / 3.745712 (-1.336224) \| 2.781053 / 5.269862 (-2.488808) \| 1.750472 / 4.565676 (-2.815204) \| 0.062232 / 0.424275 (-0.362043) \| 0.004974 / 0.007607 (-0.002633) \| 0.336324 / 0.226044 (0.110280) \| 3.286619 / 2.268929 (1.017691) \| 1.825070 / 55.444624 (-53.619554) \| 1.537993 / 6.876477 (-5.338484) \| 1.586520 / 2.142072 (-0.555553) \| 0.640090 / 4.805227 (-4.165138) \| 0.117637 / 6.500664 (-6.383027) \| 0.042318 / 0.075469 (-0.033151) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.964051 / 1.841788 (-0.877736) \| 11.706259 / 8.074308 (3.631951) \| 10.752311 / 10.191392 (0.560919) \| 0.128117 / 0.680424 (-0.552307) \| 0.014001 / 0.534201 (-0.520200) \| 0.286255 / 0.579283 (-0.293028) \| 0.263810 / 0.434364 (-0.170554) \| 0.329347 / 0.540337 (-0.210991) \| 0.437349 / 1.386936 (-0.949587) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005303 / 0.011353 (-0.006050) \| 0.003586 / 0.011008 (-0.007422) \| 0.049339 / 0.038508 (0.010831) \| 0.051287 / 0.023109 (0.028178) \| 0.274397 / 0.275898 (-0.001501) \| 0.292977 / 0.323480 (-0.030503) \| 0.004029 / 0.007986 (-0.003957) \| 0.002727 / 0.004328 (-0.001602) \| 0.048779 / 0.004250 (0.044528) \| 0.040075 / 0.037052 (0.003022) \| 0.277676 / 0.258489 (0.019187) \| 0.301963 / 0.293841 (0.008122) \| 0.029340 / 0.128546 (-0.099206) \| 0.010714 / 0.075646 (-0.064932) \| 0.057253 / 0.419271 (-0.362018) \| 0.033426 / 0.043533 (-0.010107) \| 0.276673 / 0.255139 (0.021534) \| 0.291053 / 0.283200 (0.007854) \| 0.017660 / 0.141683 (-0.124023) \| 1.122354 / 1.452155 (-0.329800) \| 1.180381 / 1.492716 (-0.312335) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091903 / 0.018006 (0.073897) \| 0.300720 / 0.000490 (0.300231) \| 0.000288 / 0.000200 (0.000088) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021521 / 0.037411 (-0.015890) \| 0.068233 / 0.014526 (0.053707) \| 0.081245 / 0.176557 (-0.095312) \| 0.119996 / 0.737135 (-0.617139) \| 0.082483 / 0.296338 (-0.213856) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302776 / 0.215209 (0.087567) \| 2.950776 / 2.077655 (0.873122) \| 1.631032 / 1.504120 (0.126912) \| 1.502021 / 1.541195 (-0.039174) \| 1.514213 / 1.468490 (0.045723) \| 0.578246 / 4.584777 (-4.006531) \| 2.443768 / 3.745712 (-1.301944) \| 2.827811 / 5.269862 (-2.442051) \| 1.771529 / 4.565676 (-2.794148) \| 0.064479 / 0.424275 (-0.359797) \| 0.005061 / 0.007607 (-0.002546) \| 0.350966 / 0.226044 (0.124922) \| 3.458616 / 2.268929 (1.189687) \| 1.967917 / 55.444624 (-53.476707) \| 1.704661 / 6.876477 (-5.171815) \| 1.698895 / 2.142072 (-0.443178) \| 0.663259 / 4.805227 (-4.141968) \| 0.122140 / 6.500664 (-6.378525) \| 0.041099 / 0.075469 (-0.034371) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.972080 / 1.841788 (-0.869708) \| 12.123286 / 8.074308 (4.048978) \| 10.819854 / 10.191392 (0.628462) \| 0.131486 / 0.680424 (-0.548938) \| 0.015785 / 0.534201 (-0.518416) \| 0.290048 / 0.579283 (-0.289235) \| 0.277822 / 0.434364 (-0.156542) \| 0.325949 / 0.540337 (-0.214388) \| 0.577681 / 1.386936 (-0.809255) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#30f6a2d9af183eba4501f0b8d90e9200bdca6bb1 \"CML watermark\")\n" ]
2,041,994,274	6,497	Support setting a default config name in push_to_hub	closed	2023-12-14T15:59:03	2023-12-18T11:50:04	2023-12-18T11:50:04	https://github.com/huggingface/datasets/issues/6497	null	albertvillanova	false	[]
2,041,589,386	6,496	Error when writing a dataset to HF Hub: A commit has happened since. Please refresh and try again.	open	2023-12-14T11:24:54	2023-12-14T12:22:21	null	https://github.com/huggingface/datasets/issues/6496	null	GeorgesLorre	false	[ "I transferred from datasets-server, since the issue is more about `datasets` and the integration with `huggingface_hub`." ]
2,039,684,839	6,494	Image Data loaded Twice	open	2023-12-13T13:11:42	2023-12-13T13:11:42	null	https://github.com/huggingface/datasets/issues/6494	null	ArcaneLex	false	[]
2,039,708,529	6,495	Newline characters don't behave as expected when calling dataset.info	open	2023-12-12T23:07:51	2023-12-13T13:24:22	null	https://github.com/huggingface/datasets/issues/6495	null	gerald-wrona	false	[]
2,038,221,490	6,493	Lazy data files resolution and offline cache reload	closed	2023-12-12T17:15:17	2023-12-21T15:19:20	2023-12-21T15:13:11	https://github.com/huggingface/datasets/pull/6493	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6493", "html_url": "https://github.com/huggingface/datasets/pull/6493", "diff_url": "https://github.com/huggingface/datasets/pull/6493.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6493.patch", "merged_at": "2023-12-21T15:13:11" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6493). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "> Naive question: is there any breaking change when loading?\r\n\r\nNo breaking changes except that the cache folders are different\r\n\r\ne.g. for glue sst2 (has parquet export)\r\n\r\n```\r\nThis branch (new format is config/version/commit_sha)\r\n~/.cache/huggingface/datasets/glue/sst2/1.0.0/fd8e86499fa5c264fcaad392a8f49ddf58bf4037\r\nOn main\r\n~/.cache/huggingface/datasets/glue/sst2/0.0.0/74a75637ac4acd3f\r\nOn 2.15.0\r\n~/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad\r\n```\r\n\r\ne.g. for wikimedia/wikipedia 20231101.ab (has metadata configs)\r\n\r\n\r\n```\r\nThis branch (new format is config/version/commit_sha)\r\n~/.cache/huggingface/datasets/wikimedia___wikipedia/20231101.ab/0.0.0/4cb9b0d719291f1a10f96f67d609c5d442980dc9\r\nOn main (takes ages to load)\r\n~/.cache/huggingface/datasets/wikimedia___wikipedia/20231101.ab/0.0.0/cfa627e27933df13\r\nOn 2.15.0 (takes ages to load)\r\n~/.cache/huggingface/datasets/wikimedia___wikipedia/20231101.ab/0.0.0/e92ee7a91c466564\r\n```\r\n\r\n\r\ne.g. for lhoestq/demo1 (no metadata configs)\r\n\r\n\r\n```\r\nThis branch (new format is config/version/commit_sha)\r\n~/.cache/huggingface/datasets/lhoestq___demo1/default/0.0.0/87ecf163bedca9d80598b528940a9c4f99e14c11\r\nOn main\r\n~/.cache/huggingface/datasets/lhoestq___demo1/default-8a4a0b7a240d3c5e/0.0.0/eea64c71ca8b46dd3f537ed218fc9bf495d5707789152eb2764f5c78fa66d59d\r\nOn 2.15.0\r\n~/.cache/huggingface/datasets/lhoestq___demo1/default-59d4029e0bb36ae0/0.0.0/eea64c71ca8b46dd3f537ed218fc9bf495d5707789152eb2764f5c78fa66d59d\r\n```", "There was a last bug I just fixed: if you modify a dataset and reload it from the hub it won't download the new version - I think I need to use another hash to name the cache directory\r\nedit: fixed", "I switched to using the git commit sha for the cache directory, which is now `config/version/commit_sha` :) much cleaner than before.\r\n\r\nAnd for local file it's a hash that takes into account the resolved files (and their last modified dates)", "I also ran the `transformers` CI on this branch and it's green", "FYI `huggingface_hub` will have a release on tuesday/wednesday (will speed up load_dataset data files resolution which is now needed for datasets loaded from parquet export) so we can aim on merging this around the same time and do a release on thursday", "Merging this one, and hopefully the cache backward compatibility PR soon too :)\r\n\r\nThen it will be release time", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005444 / 0.011353 (-0.005909) \| 0.003562 / 0.011008 (-0.007446) \| 0.063183 / 0.038508 (0.024675) \| 0.048885 / 0.023109 (0.025776) \| 0.248422 / 0.275898 (-0.027476) \| 0.277844 / 0.323480 (-0.045636) \| 0.003019 / 0.007986 (-0.004966) \| 0.002660 / 0.004328 (-0.001669) \| 0.048928 / 0.004250 (0.044677) \| 0.044850 / 0.037052 (0.007798) \| 0.248505 / 0.258489 (-0.009984) \| 0.282231 / 0.293841 (-0.011610) \| 0.028302 / 0.128546 (-0.100244) \| 0.010829 / 0.075646 (-0.064818) \| 0.206738 / 0.419271 (-0.212533) \| 0.035485 / 0.043533 (-0.008048) \| 0.244575 / 0.255139 (-0.010564) \| 0.281411 / 0.283200 (-0.001789) \| 0.019563 / 0.141683 (-0.122120) \| 1.113769 / 1.452155 (-0.338386) \| 1.176831 / 1.492716 (-0.315885) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004718 / 0.018006 (-0.013288) \| 0.304103 / 0.000490 (0.303614) \| 0.000214 / 0.000200 (0.000014) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019642 / 0.037411 (-0.017769) \| 0.060275 / 0.014526 (0.045749) \| 0.073072 / 0.176557 (-0.103484) \| 0.119789 / 0.737135 (-0.617346) \| 0.074535 / 0.296338 (-0.221804) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278799 / 0.215209 (0.063590) \| 2.725320 / 2.077655 (0.647665) \| 1.419048 / 1.504120 (-0.085071) \| 1.335041 / 1.541195 (-0.206154) \| 1.373029 / 1.468490 (-0.095461) \| 0.566774 / 4.584777 (-4.018003) \| 2.383796 / 3.745712 (-1.361916) \| 2.734804 / 5.269862 (-2.535057) \| 1.712277 / 4.565676 (-2.853399) \| 0.062119 / 0.424275 (-0.362156) \| 0.004949 / 0.007607 (-0.002658) \| 0.336126 / 0.226044 (0.110082) \| 3.298602 / 2.268929 (1.029674) \| 1.842815 / 55.444624 (-53.601809) \| 1.544028 / 6.876477 (-5.332449) \| 1.566717 / 2.142072 (-0.575355) \| 0.643006 / 4.805227 (-4.162221) \| 0.118241 / 6.500664 (-6.382423) \| 0.042453 / 0.075469 (-0.033016) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.949015 / 1.841788 (-0.892773) \| 11.717958 / 8.074308 (3.643649) \| 10.482448 / 10.191392 (0.291056) \| 0.128564 / 0.680424 (-0.551860) \| 0.014792 / 0.534201 (-0.519408) \| 0.288636 / 0.579283 (-0.290647) \| 0.263345 / 0.434364 (-0.171019) \| 0.325753 / 0.540337 (-0.214584) \| 0.421294 / 1.386936 (-0.965642) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005367 / 0.011353 (-0.005985) \| 0.003802 / 0.011008 (-0.007206) \| 0.049322 / 0.038508 (0.010814) \| 0.055201 / 0.023109 (0.032092) \| 0.287811 / 0.275898 (0.011913) \| 0.305141 / 0.323480 (-0.018339) \| 0.004095 / 0.007986 (-0.003890) \| 0.002733 / 0.004328 (-0.001595) \| 0.049508 / 0.004250 (0.045258) \| 0.039199 / 0.037052 (0.002147) \| 0.282719 / 0.258489 (0.024230) \| 0.311156 / 0.293841 (0.017315) \| 0.029469 / 0.128546 (-0.099077) \| 0.010709 / 0.075646 (-0.064937) \| 0.057646 / 0.419271 (-0.361626) \| 0.032696 / 0.043533 (-0.010837) \| 0.285087 / 0.255139 (0.029948) \| 0.294142 / 0.283200 (0.010942) \| 0.019779 / 0.141683 (-0.121904) \| 1.176844 / 1.452155 (-0.275310) \| 1.190925 / 1.492716 (-0.301792) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092885 / 0.018006 (0.074879) \| 0.301129 / 0.000490 (0.300640) \| 0.000232 / 0.000200 (0.000032) \| 0.000049 / 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) \| 0.023202 / 0.037411 (-0.014210) \| 0.076850 / 0.014526 (0.062325) \| 0.090058 / 0.176557 (-0.086499) \| 0.128091 / 0.737135 (-0.609045) \| 0.091098 / 0.296338 (-0.205240) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292973 / 0.215209 (0.077764) \| 2.876022 / 2.077655 (0.798367) \| 1.672115 / 1.504120 (0.167995) \| 1.555103 / 1.541195 (0.013909) \| 1.559832 / 1.468490 (0.091342) \| 0.558017 / 4.584777 (-4.026760) \| 2.428448 / 3.745712 (-1.317264) \| 2.812024 / 5.269862 (-2.457837) \| 1.738470 / 4.565676 (-2.827207) \| 0.062669 / 0.424275 (-0.361607) \| 0.005071 / 0.007607 (-0.002536) \| 0.351804 / 0.226044 (0.125759) \| 3.412207 / 2.268929 (1.143279) \| 2.023478 / 55.444624 (-53.421147) \| 1.761281 / 6.876477 (-5.115195) \| 1.770789 / 2.142072 (-0.371283) \| 0.643062 / 4.805227 (-4.162165) \| 0.116616 / 6.500664 (-6.384048) \| 0.041816 / 0.075469 (-0.033653) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.988430 / 1.841788 (-0.853357) \| 12.278636 / 8.074308 (4.204328) \| 11.066185 / 10.191392 (0.874793) \| 0.141191 / 0.680424 (-0.539233) \| 0.015547 / 0.534201 (-0.518654) \| 0.288045 / 0.579283 (-0.291238) \| 0.279651 / 0.434364 (-0.154713) \| 0.329869 / 0.540337 (-0.210469) \| 0.420391 / 1.386936 (-0.966545) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ef3b5dd3633995c95d77f35fb17f89ff44990bc4 \"CML watermark\")\n" ]
2,037,987,267	6,492	Make push_to_hub return CommitInfo	closed	2023-12-12T15:18:16	2023-12-13T14:29:01	2023-12-13T14:22:41	https://github.com/huggingface/datasets/pull/6492	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6492", "html_url": "https://github.com/huggingface/datasets/pull/6492", "diff_url": "https://github.com/huggingface/datasets/pull/6492.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6492.patch", "merged_at": "2023-12-13T14:22:41" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6492). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "This PR is ready to review @huggingface/datasets.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005093 / 0.011353 (-0.006259) \| 0.003695 / 0.011008 (-0.007313) \| 0.064648 / 0.038508 (0.026140) \| 0.054677 / 0.023109 (0.031568) \| 0.242007 / 0.275898 (-0.033891) \| 0.265216 / 0.323480 (-0.058264) \| 0.003847 / 0.007986 (-0.004138) \| 0.003773 / 0.004328 (-0.000556) \| 0.048595 / 0.004250 (0.044345) \| 0.038122 / 0.037052 (0.001070) \| 0.245698 / 0.258489 (-0.012791) \| 0.278095 / 0.293841 (-0.015746) \| 0.027488 / 0.128546 (-0.101058) \| 0.011002 / 0.075646 (-0.064644) \| 0.211443 / 0.419271 (-0.207829) \| 0.035664 / 0.043533 (-0.007869) \| 0.244754 / 0.255139 (-0.010385) \| 0.261078 / 0.283200 (-0.022121) \| 0.017768 / 0.141683 (-0.123915) \| 1.130765 / 1.452155 (-0.321390) \| 1.189825 / 1.492716 (-0.302891) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093027 / 0.018006 (0.075021) \| 0.302193 / 0.000490 (0.301703) \| 0.000207 / 0.000200 (0.000007) \| 0.000045 / 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) \| 0.018413 / 0.037411 (-0.018999) \| 0.062715 / 0.014526 (0.048190) \| 0.073287 / 0.176557 (-0.103269) \| 0.120394 / 0.737135 (-0.616741) \| 0.077573 / 0.296338 (-0.218765) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284445 / 0.215209 (0.069236) \| 2.780718 / 2.077655 (0.703063) \| 1.460988 / 1.504120 (-0.043132) \| 1.345799 / 1.541195 (-0.195395) \| 1.399892 / 1.468490 (-0.068598) \| 0.576051 / 4.584777 (-4.008726) \| 2.418792 / 3.745712 (-1.326921) \| 2.901330 / 5.269862 (-2.368532) \| 1.765083 / 4.565676 (-2.800593) \| 0.063555 / 0.424275 (-0.360720) \| 0.004991 / 0.007607 (-0.002616) \| 0.339657 / 0.226044 (0.113613) \| 3.372963 / 2.268929 (1.104034) \| 1.853667 / 55.444624 (-53.590958) \| 1.552022 / 6.876477 (-5.324454) \| 1.616452 / 2.142072 (-0.525620) \| 0.652309 / 4.805227 (-4.152919) \| 0.121125 / 6.500664 (-6.379539) \| 0.042420 / 0.075469 (-0.033049) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.954514 / 1.841788 (-0.887274) \| 11.853736 / 8.074308 (3.779428) \| 10.624571 / 10.191392 (0.433179) \| 0.134118 / 0.680424 (-0.546306) \| 0.014200 / 0.534201 (-0.520001) \| 0.290106 / 0.579283 (-0.289177) \| 0.270637 / 0.434364 (-0.163727) \| 0.336155 / 0.540337 (-0.204182) \| 0.443962 / 1.386936 (-0.942974) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005282 / 0.011353 (-0.006071) \| 0.003526 / 0.011008 (-0.007482) \| 0.048994 / 0.038508 (0.010486) \| 0.055345 / 0.023109 (0.032236) \| 0.271587 / 0.275898 (-0.004311) \| 0.294676 / 0.323480 (-0.028804) \| 0.003989 / 0.007986 (-0.003996) \| 0.002594 / 0.004328 (-0.001735) \| 0.048310 / 0.004250 (0.044059) \| 0.039945 / 0.037052 (0.002893) \| 0.277304 / 0.258489 (0.018815) \| 0.312017 / 0.293841 (0.018176) \| 0.028364 / 0.128546 (-0.100182) \| 0.010683 / 0.075646 (-0.064963) \| 0.057990 / 0.419271 (-0.361281) \| 0.032418 / 0.043533 (-0.011115) \| 0.273835 / 0.255139 (0.018697) \| 0.288585 / 0.283200 (0.005385) \| 0.018964 / 0.141683 (-0.122719) \| 1.148863 / 1.452155 (-0.303292) \| 1.195684 / 1.492716 (-0.297032) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091967 / 0.018006 (0.073960) \| 0.303236 / 0.000490 (0.302747) \| 0.000214 / 0.000200 (0.000015) \| 0.000051 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021960 / 0.037411 (-0.015452) \| 0.068744 / 0.014526 (0.054218) \| 0.081167 / 0.176557 (-0.095390) \| 0.119623 / 0.737135 (-0.617513) \| 0.084965 / 0.296338 (-0.211373) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297740 / 0.215209 (0.082531) \| 2.924856 / 2.077655 (0.847201) \| 1.602080 / 1.504120 (0.097960) \| 1.494083 / 1.541195 (-0.047112) \| 1.544662 / 1.468490 (0.076172) \| 0.581212 / 4.584777 (-4.003565) \| 2.451064 / 3.745712 (-1.294648) \| 2.875213 / 5.269862 (-2.394649) \| 1.780777 / 4.565676 (-2.784900) \| 0.063751 / 0.424275 (-0.360524) \| 0.004967 / 0.007607 (-0.002641) \| 0.350321 / 0.226044 (0.124276) \| 3.449585 / 2.268929 (1.180657) \| 1.977666 / 55.444624 (-53.466958) \| 1.685125 / 6.876477 (-5.191351) \| 1.734466 / 2.142072 (-0.407606) \| 0.657477 / 4.805227 (-4.147750) \| 0.116767 / 6.500664 (-6.383898) \| 0.041400 / 0.075469 (-0.034069) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.985751 / 1.841788 (-0.856037) \| 12.300065 / 8.074308 (4.225756) \| 10.608238 / 10.191392 (0.416846) \| 0.139907 / 0.680424 (-0.540517) \| 0.015379 / 0.534201 (-0.518822) \| 0.283528 / 0.579283 (-0.295755) \| 0.278751 / 0.434364 (-0.155613) \| 0.328811 / 0.540337 (-0.211527) \| 0.584041 / 1.386936 (-0.802895) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ef0f986518bd252c5314a7e3a419dedcbb166630 \"CML watermark\")\n" ]
2,037,690,643	6,491	Fix metrics dead link	closed	2023-12-12T12:51:49	2023-12-21T15:15:08	2023-12-21T15:08:53	https://github.com/huggingface/datasets/pull/6491	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6491", "html_url": "https://github.com/huggingface/datasets/pull/6491", "diff_url": "https://github.com/huggingface/datasets/pull/6491.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6491.patch", "merged_at": "2023-12-21T15:08:53" }	qgallouedec	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6491). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005193 / 0.011353 (-0.006160) \| 0.003246 / 0.011008 (-0.007762) \| 0.063053 / 0.038508 (0.024545) \| 0.049636 / 0.023109 (0.026527) \| 0.240990 / 0.275898 (-0.034908) \| 0.263732 / 0.323480 (-0.059747) \| 0.004062 / 0.007986 (-0.003923) \| 0.002681 / 0.004328 (-0.001648) \| 0.048527 / 0.004250 (0.044277) \| 0.044159 / 0.037052 (0.007107) \| 0.248031 / 0.258489 (-0.010458) \| 0.275705 / 0.293841 (-0.018136) \| 0.028210 / 0.128546 (-0.100336) \| 0.010314 / 0.075646 (-0.065332) \| 0.209887 / 0.419271 (-0.209384) \| 0.035649 / 0.043533 (-0.007884) \| 0.251321 / 0.255139 (-0.003818) \| 0.266672 / 0.283200 (-0.016528) \| 0.017382 / 0.141683 (-0.124301) \| 1.088937 / 1.452155 (-0.363217) \| 1.143692 / 1.492716 (-0.349024) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092558 / 0.018006 (0.074552) \| 0.301648 / 0.000490 (0.301159) \| 0.000208 / 0.000200 (0.000008) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018305 / 0.037411 (-0.019106) \| 0.059836 / 0.014526 (0.045310) \| 0.072926 / 0.176557 (-0.103631) \| 0.119826 / 0.737135 (-0.617309) \| 0.074357 / 0.296338 (-0.221982) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279051 / 0.215209 (0.063842) \| 2.711402 / 2.077655 (0.633747) \| 1.431782 / 1.504120 (-0.072338) \| 1.316592 / 1.541195 (-0.224603) \| 1.352062 / 1.468490 (-0.116428) \| 0.562553 / 4.584777 (-4.022224) \| 2.387719 / 3.745712 (-1.357993) \| 2.693330 / 5.269862 (-2.576532) \| 1.682040 / 4.565676 (-2.883636) \| 0.061832 / 0.424275 (-0.362443) \| 0.005066 / 0.007607 (-0.002541) \| 0.332730 / 0.226044 (0.106685) \| 3.315503 / 2.268929 (1.046575) \| 1.787129 / 55.444624 (-53.657496) \| 1.508955 / 6.876477 (-5.367522) \| 1.512620 / 2.142072 (-0.629453) \| 0.637120 / 4.805227 (-4.168107) \| 0.116005 / 6.500664 (-6.384660) \| 0.041973 / 0.075469 (-0.033496) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.936996 / 1.841788 (-0.904792) \| 11.485975 / 8.074308 (3.411667) \| 10.604481 / 10.191392 (0.413089) \| 0.130803 / 0.680424 (-0.549621) \| 0.014561 / 0.534201 (-0.519640) \| 0.285905 / 0.579283 (-0.293378) \| 0.271573 / 0.434364 (-0.162791) \| 0.329206 / 0.540337 (-0.211132) \| 0.411977 / 1.386936 (-0.974959) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005333 / 0.011353 (-0.006020) \| 0.003519 / 0.011008 (-0.007489) \| 0.050880 / 0.038508 (0.012372) \| 0.053681 / 0.023109 (0.030571) \| 0.269359 / 0.275898 (-0.006539) \| 0.291498 / 0.323480 (-0.031982) \| 0.004006 / 0.007986 (-0.003979) \| 0.002676 / 0.004328 (-0.001653) \| 0.049652 / 0.004250 (0.045401) \| 0.040588 / 0.037052 (0.003536) \| 0.271701 / 0.258489 (0.013212) \| 0.308384 / 0.293841 (0.014543) \| 0.028713 / 0.128546 (-0.099833) \| 0.010423 / 0.075646 (-0.065223) \| 0.058099 / 0.419271 (-0.361172) \| 0.032372 / 0.043533 (-0.011161) \| 0.269395 / 0.255139 (0.014256) \| 0.292252 / 0.283200 (0.009052) \| 0.020038 / 0.141683 (-0.121645) \| 1.124761 / 1.452155 (-0.327393) \| 1.177609 / 1.492716 (-0.315107) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092187 / 0.018006 (0.074181) \| 0.301936 / 0.000490 (0.301446) \| 0.000230 / 0.000200 (0.000030) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022932 / 0.037411 (-0.014480) \| 0.076552 / 0.014526 (0.062027) \| 0.088729 / 0.176557 (-0.087827) \| 0.127198 / 0.737135 (-0.609937) \| 0.091902 / 0.296338 (-0.204436) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.299964 / 0.215209 (0.084755) \| 2.929352 / 2.077655 (0.851697) \| 1.598715 / 1.504120 (0.094595) \| 1.462438 / 1.541195 (-0.078756) \| 1.474308 / 1.468490 (0.005818) \| 0.567120 / 4.584777 (-4.017657) \| 2.481757 / 3.745712 (-1.263955) \| 2.795375 / 5.269862 (-2.474487) \| 1.740346 / 4.565676 (-2.825331) \| 0.064048 / 0.424275 (-0.360227) \| 0.004995 / 0.007607 (-0.002612) \| 0.349084 / 0.226044 (0.123040) \| 3.417679 / 2.268929 (1.148750) \| 1.910615 / 55.444624 (-53.534009) \| 1.694120 / 6.876477 (-5.182356) \| 1.658654 / 2.142072 (-0.483419) \| 0.638158 / 4.805227 (-4.167069) \| 0.115509 / 6.500664 (-6.385156) \| 0.040650 / 0.075469 (-0.034819) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.988081 / 1.841788 (-0.853707) \| 12.210089 / 8.074308 (4.135781) \| 11.090203 / 10.191392 (0.898811) \| 0.131861 / 0.680424 (-0.548563) \| 0.015461 / 0.534201 (-0.518740) \| 0.287737 / 0.579283 (-0.291546) \| 0.284170 / 0.434364 (-0.150194) \| 0.324949 / 0.540337 (-0.215388) \| 0.414912 / 1.386936 (-0.972024) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cf71653947cecd84050daf0448dc5a73c2c071f3 \"CML watermark\")\n" ]
2,037,204,892	6,490	`load_dataset(...,save_infos=True)` not working without loading script	open	2023-12-12T08:09:18	2023-12-12T08:36:22	null	https://github.com/huggingface/datasets/issues/6490	null	morganveyret	false	[ "Also, once the README.md exists in the python environment it is used when loading another dataset in the same format (e.g. json) since it always resolves the path to the same directory.\r\nThe consequence here is any other dataset won't load because of infos mismatch.\r\nTo reproduce this aspect:\r\n1. Do a `load_datasets(...,save_infos=True)` with one dataset without a loading script\r\n2. Try to load another dataset without a loading script in the same format (e.g. json) but with a different schema " ]
2,036,743,777	6,489	load_dataset imageflder for aws s3 path	open	2023-12-12T00:08:43	2023-12-12T00:09:27	null	https://github.com/huggingface/datasets/issues/6489	null	segalinc	false	[]
2,035,899,898	6,488	429 Client Error	open	2023-12-11T15:06:01	2024-06-20T05:55:45	null	https://github.com/huggingface/datasets/issues/6488	null	sasaadi	false	[ "Transferring repos as this is a datasets issue ", "I'm getting a similar issue even though I've already downloaded the dataset 😅 \r\n\r\n```\r\nhuggingface_hub.utils._errors.HfHubHTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/api/datasets/HuggingFaceM4/WebSight\r\n```" ]
2,035,424,254	6,487	Update builder hash with info	closed	2023-12-11T11:09:16	2024-01-11T06:35:07	2023-12-11T11:41:34	https://github.com/huggingface/datasets/pull/6487	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6487", "html_url": "https://github.com/huggingface/datasets/pull/6487", "diff_url": "https://github.com/huggingface/datasets/pull/6487.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6487.patch", "merged_at": null }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6487). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Closing this one in favor of https://github.com/huggingface/datasets/pull/6458/commits/565c294fc12bc547730a023a610ed4f92313d8fb in https://github.com/huggingface/datasets/pull/6458" ]
2,035,206,206	6,486	Fix docs phrasing about supported formats when sharing a dataset	closed	2023-12-11T09:21:22	2023-12-13T14:21:29	2023-12-13T14:15:21	https://github.com/huggingface/datasets/pull/6486	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6486", "html_url": "https://github.com/huggingface/datasets/pull/6486", "diff_url": "https://github.com/huggingface/datasets/pull/6486.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6486.patch", "merged_at": "2023-12-13T14:15:21" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6486). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005042 / 0.011353 (-0.006311) \| 0.003452 / 0.011008 (-0.007557) \| 0.061845 / 0.038508 (0.023337) \| 0.052042 / 0.023109 (0.028933) \| 0.241791 / 0.275898 (-0.034107) \| 0.264639 / 0.323480 (-0.058841) \| 0.003940 / 0.007986 (-0.004045) \| 0.002768 / 0.004328 (-0.001560) \| 0.047851 / 0.004250 (0.043600) \| 0.037599 / 0.037052 (0.000547) \| 0.251462 / 0.258489 (-0.007028) \| 0.274737 / 0.293841 (-0.019104) \| 0.027723 / 0.128546 (-0.100823) \| 0.010510 / 0.075646 (-0.065137) \| 0.205581 / 0.419271 (-0.213691) \| 0.035504 / 0.043533 (-0.008029) \| 0.242380 / 0.255139 (-0.012759) \| 0.259791 / 0.283200 (-0.023409) \| 0.017752 / 0.141683 (-0.123931) \| 1.089289 / 1.452155 (-0.362865) \| 1.161958 / 1.492716 (-0.330759) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094288 / 0.018006 (0.076282) \| 0.303253 / 0.000490 (0.302763) \| 0.000216 / 0.000200 (0.000016) \| 0.000042 / 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) \| 0.018496 / 0.037411 (-0.018915) \| 0.060411 / 0.014526 (0.045885) \| 0.074294 / 0.176557 (-0.102262) \| 0.122934 / 0.737135 (-0.614201) \| 0.074710 / 0.296338 (-0.221629) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286394 / 0.215209 (0.071185) \| 2.806145 / 2.077655 (0.728490) \| 1.497071 / 1.504120 (-0.007049) \| 1.362254 / 1.541195 (-0.178940) \| 1.389642 / 1.468490 (-0.078848) \| 0.554503 / 4.584777 (-4.030274) \| 2.348029 / 3.745712 (-1.397684) \| 2.780862 / 5.269862 (-2.489000) \| 1.728058 / 4.565676 (-2.837619) \| 0.062617 / 0.424275 (-0.361658) \| 0.004901 / 0.007607 (-0.002707) \| 0.346267 / 0.226044 (0.120223) \| 3.363744 / 2.268929 (1.094815) \| 1.826994 / 55.444624 (-53.617630) \| 1.560656 / 6.876477 (-5.315820) \| 1.561083 / 2.142072 (-0.580990) \| 0.643395 / 4.805227 (-4.161832) \| 0.116206 / 6.500664 (-6.384458) \| 0.042008 / 0.075469 (-0.033461) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.953416 / 1.841788 (-0.888371) \| 11.461665 / 8.074308 (3.387357) \| 10.623865 / 10.191392 (0.432473) \| 0.128071 / 0.680424 (-0.552353) \| 0.014277 / 0.534201 (-0.519924) \| 0.288810 / 0.579283 (-0.290474) \| 0.267575 / 0.434364 (-0.166788) \| 0.327422 / 0.540337 (-0.212916) \| 0.435151 / 1.386936 (-0.951785) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005242 / 0.011353 (-0.006111) \| 0.003515 / 0.011008 (-0.007493) \| 0.048483 / 0.038508 (0.009975) \| 0.051684 / 0.023109 (0.028575) \| 0.276564 / 0.275898 (0.000666) \| 0.297582 / 0.323480 (-0.025898) \| 0.004117 / 0.007986 (-0.003869) \| 0.002610 / 0.004328 (-0.001719) \| 0.047811 / 0.004250 (0.043561) \| 0.040622 / 0.037052 (0.003569) \| 0.280265 / 0.258489 (0.021776) \| 0.311719 / 0.293841 (0.017878) \| 0.028811 / 0.128546 (-0.099735) \| 0.010600 / 0.075646 (-0.065047) \| 0.056660 / 0.419271 (-0.362611) \| 0.032638 / 0.043533 (-0.010894) \| 0.276434 / 0.255139 (0.021295) \| 0.299095 / 0.283200 (0.015896) \| 0.018483 / 0.141683 (-0.123200) \| 1.156382 / 1.452155 (-0.295773) \| 1.252205 / 1.492716 (-0.240511) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097868 / 0.018006 (0.079862) \| 0.309438 / 0.000490 (0.308948) \| 0.000229 / 0.000200 (0.000029) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021838 / 0.037411 (-0.015573) \| 0.068358 / 0.014526 (0.053832) \| 0.080432 / 0.176557 (-0.096125) \| 0.119788 / 0.737135 (-0.617348) \| 0.081742 / 0.296338 (-0.214597) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.301239 / 0.215209 (0.086030) \| 2.962242 / 2.077655 (0.884587) \| 1.693918 / 1.504120 (0.189798) \| 1.573663 / 1.541195 (0.032468) \| 1.583125 / 1.468490 (0.114635) \| 0.557267 / 4.584777 (-4.027510) \| 2.440048 / 3.745712 (-1.305664) \| 2.727572 / 5.269862 (-2.542290) \| 1.713557 / 4.565676 (-2.852120) \| 0.062526 / 0.424275 (-0.361749) \| 0.004982 / 0.007607 (-0.002625) \| 0.353850 / 0.226044 (0.127806) \| 3.530887 / 2.268929 (1.261958) \| 2.047864 / 55.444624 (-53.396761) \| 1.770776 / 6.876477 (-5.105701) \| 1.757621 / 2.142072 (-0.384451) \| 0.633847 / 4.805227 (-4.171381) \| 0.114055 / 6.500664 (-6.386609) \| 0.040078 / 0.075469 (-0.035391) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.983721 / 1.841788 (-0.858066) \| 11.896537 / 8.074308 (3.822229) \| 10.529883 / 10.191392 (0.338491) \| 0.129593 / 0.680424 (-0.550831) \| 0.016213 / 0.534201 (-0.517988) \| 0.289623 / 0.579283 (-0.289660) \| 0.280073 / 0.434364 (-0.154291) \| 0.327446 / 0.540337 (-0.212892) \| 0.574847 / 1.386936 (-0.812089) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2684a98fe38e0c87bb11e050586004108e32b79d \"CML watermark\")\n" ]
2,035,141,884	6,485	FileNotFoundError: [Errno 2] No such file or directory: 'nul'	closed	2023-12-11T08:52:13	2023-12-14T08:09:08	2023-12-14T08:09:08	https://github.com/huggingface/datasets/issues/6485	null	amanyara	false	[ "Hi! It seems like the problem is your environment. Maybe this issue can help: https://github.com/pytest-dev/pytest/issues/9519. " ]
2,032,946,981	6,483	Iterable Dataset: rename column clashes with remove column	closed	2023-12-08T16:11:30	2023-12-08T16:27:16	2023-12-08T16:27:04	https://github.com/huggingface/datasets/issues/6483	null	sanchit-gandhi	false	[ "Column \"text\" doesn't exist anymore so you can't remove it", "You can get the expected result by fixing typos in the snippet :)\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n# load LS in streaming mode\r\ndataset = load_dataset(\"librispeech_asr\", \"clean\", split=\"validation\", streaming=True)\r\n\r\n# check original features\r\ndataset_features = dataset.features.keys()\r\nprint(\"Original features: \", dataset_features)\r\n\r\n# rename \"text\" -> \"sentence\"\r\ndataset = dataset.rename_column(\"text\", \"sentence\")\r\n\r\n# remove unwanted columns\r\nCOLUMNS_TO_KEEP = {\"audio\", \"sentence\"}\r\ndataset = dataset.remove_columns(set(dataset.features) - COLUMNS_TO_KEEP)\r\n\r\n# stream first sample, should return \"audio\" and \"sentence\" columns\r\nprint(next(iter(dataset)))\r\n```", "Fixed code:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\n# load LS in streaming mode\r\ndataset = load_dataset(\"librispeech_asr\", \"clean\", split=\"validation\", streaming=True)\r\n\r\n# check original features\r\ndataset_features = dataset.features.keys()\r\nprint(\"Original features: \", dataset_features)\r\n\r\n# rename \"text\" -> \"sentence\"\r\ndataset = dataset.rename_column(\"text\", \"sentence\")\r\ndataset_features = dataset.features.keys()\r\n\r\n# remove unwanted columns\r\nCOLUMNS_TO_KEEP = {\"audio\", \"sentence\"}\r\ndataset = dataset.remove_columns(set(dataset_features - COLUMNS_TO_KEEP))\r\n\r\n# stream first sample, should return \"audio\" and \"sentence\" columns\r\nprint(next(iter(dataset)))\r\n```", "Whoops 😅 Thanks for the swift reply both! Works like a charm!" ]
2,033,333,294	6,484	[Feature Request] Dataset versioning	open	2023-12-08T16:01:35	2023-12-11T19:13:46	null	https://github.com/huggingface/datasets/issues/6484	null	kenfus	false	[ "Hello @kenfus, this is meant to be possible to do yes. Let me ping @lhoestq or @mariosasko from the `datasets` team (`huggingface_hub` is only the underlying library to download files from the Hub but here it looks more like a `datasets` problem). ", "Hi! https://github.com/huggingface/datasets/pull/6459 will fix this." ]
2,032,675,918	6,482	Fix max lock length on unix	closed	2023-12-08T13:39:30	2023-12-12T11:53:32	2023-12-12T11:47:27	https://github.com/huggingface/datasets/pull/6482	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6482", "html_url": "https://github.com/huggingface/datasets/pull/6482", "diff_url": "https://github.com/huggingface/datasets/pull/6482.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6482.patch", "merged_at": "2023-12-12T11:47:27" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6482). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "I'm getting `AttributeError: module 'os' has no attribute 'statvfs'` on windows - reverting", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005294 / 0.011353 (-0.006059) \| 0.003562 / 0.011008 (-0.007446) \| 0.062030 / 0.038508 (0.023522) \| 0.053335 / 0.023109 (0.030226) \| 0.233303 / 0.275898 (-0.042595) \| 0.252029 / 0.323480 (-0.071451) \| 0.002835 / 0.007986 (-0.005151) \| 0.002732 / 0.004328 (-0.001597) \| 0.047973 / 0.004250 (0.043723) \| 0.038380 / 0.037052 (0.001328) \| 0.235028 / 0.258489 (-0.023461) \| 0.265555 / 0.293841 (-0.028286) \| 0.027136 / 0.128546 (-0.101410) \| 0.010806 / 0.075646 (-0.064840) \| 0.205040 / 0.419271 (-0.214231) \| 0.035063 / 0.043533 (-0.008470) \| 0.236351 / 0.255139 (-0.018788) \| 0.254556 / 0.283200 (-0.028643) \| 0.019528 / 0.141683 (-0.122155) \| 1.099012 / 1.452155 (-0.353142) \| 1.156250 / 1.492716 (-0.336466) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093952 / 0.018006 (0.075946) \| 0.304181 / 0.000490 (0.303692) \| 0.000227 / 0.000200 (0.000027) \| 0.000045 / 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) \| 0.018568 / 0.037411 (-0.018844) \| 0.060323 / 0.014526 (0.045798) \| 0.073010 / 0.176557 (-0.103546) \| 0.121723 / 0.737135 (-0.615412) \| 0.075668 / 0.296338 (-0.220670) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288429 / 0.215209 (0.073220) \| 2.797834 / 2.077655 (0.720180) \| 1.480957 / 1.504120 (-0.023163) \| 1.360872 / 1.541195 (-0.180323) \| 1.406828 / 1.468490 (-0.061663) \| 0.587596 / 4.584777 (-3.997181) \| 2.533997 / 3.745712 (-1.211715) \| 2.906697 / 5.269862 (-2.363164) \| 1.801753 / 4.565676 (-2.763923) \| 0.064360 / 0.424275 (-0.359915) \| 0.005016 / 0.007607 (-0.002591) \| 0.347334 / 0.226044 (0.121290) \| 3.426344 / 2.268929 (1.157416) \| 1.856014 / 55.444624 (-53.588610) \| 1.581774 / 6.876477 (-5.294703) \| 1.640036 / 2.142072 (-0.502037) \| 0.656096 / 4.805227 (-4.149131) \| 0.120212 / 6.500664 (-6.380452) \| 0.044003 / 0.075469 (-0.031466) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.943933 / 1.841788 (-0.897855) \| 11.846572 / 8.074308 (3.772263) \| 10.330705 / 10.191392 (0.139313) \| 0.129767 / 0.680424 (-0.550657) \| 0.013508 / 0.534201 (-0.520693) \| 0.289672 / 0.579283 (-0.289611) \| 0.266427 / 0.434364 (-0.167937) \| 0.342766 / 0.540337 (-0.197571) \| 0.452068 / 1.386936 (-0.934868) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005308 / 0.011353 (-0.006045) \| 0.003712 / 0.011008 (-0.007296) \| 0.048848 / 0.038508 (0.010340) \| 0.055156 / 0.023109 (0.032047) \| 0.271942 / 0.275898 (-0.003956) \| 0.293166 / 0.323480 (-0.030314) \| 0.004056 / 0.007986 (-0.003930) \| 0.002722 / 0.004328 (-0.001606) \| 0.048418 / 0.004250 (0.044167) \| 0.039320 / 0.037052 (0.002268) \| 0.277184 / 0.258489 (0.018695) \| 0.312398 / 0.293841 (0.018557) \| 0.029392 / 0.128546 (-0.099155) \| 0.011314 / 0.075646 (-0.064332) \| 0.057883 / 0.419271 (-0.361389) \| 0.032603 / 0.043533 (-0.010930) \| 0.273025 / 0.255139 (0.017886) \| 0.289265 / 0.283200 (0.006065) \| 0.017553 / 0.141683 (-0.124129) \| 1.127725 / 1.452155 (-0.324430) \| 1.202293 / 1.492716 (-0.290423) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097179 / 0.018006 (0.079173) \| 0.309712 / 0.000490 (0.309222) \| 0.000269 / 0.000200 (0.000069) \| 0.000055 / 0.000054 (0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024742 / 0.037411 (-0.012670) \| 0.070097 / 0.014526 (0.055571) \| 0.082273 / 0.176557 (-0.094283) \| 0.121696 / 0.737135 (-0.615439) \| 0.082983 / 0.296338 (-0.213355) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292688 / 0.215209 (0.077479) \| 2.853436 / 2.077655 (0.775781) \| 1.588999 / 1.504120 (0.084879) \| 1.454547 / 1.541195 (-0.086648) \| 1.476342 / 1.468490 (0.007852) \| 0.559464 / 4.584777 (-4.025313) \| 2.564597 / 3.745712 (-1.181115) \| 2.900460 / 5.269862 (-2.369402) \| 1.782156 / 4.565676 (-2.783520) \| 0.061768 / 0.424275 (-0.362507) \| 0.005042 / 0.007607 (-0.002565) \| 0.345168 / 0.226044 (0.119124) \| 3.412273 / 2.268929 (1.143344) \| 1.953154 / 55.444624 (-53.491470) \| 1.667347 / 6.876477 (-5.209130) \| 1.685138 / 2.142072 (-0.456934) \| 0.643270 / 4.805227 (-4.161958) \| 0.115955 / 6.500664 (-6.384709) \| 0.041090 / 0.075469 (-0.034379) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.976324 / 1.841788 (-0.865464) \| 12.252294 / 8.074308 (4.177986) \| 10.598062 / 10.191392 (0.406670) \| 0.129779 / 0.680424 (-0.550644) \| 0.015697 / 0.534201 (-0.518504) \| 0.287241 / 0.579283 (-0.292042) \| 0.287331 / 0.434364 (-0.147033) \| 0.331710 / 0.540337 (-0.208628) \| 0.574571 / 1.386936 (-0.812365) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#702344140461b7a111139860c944d3dd0a2689e3 \"CML watermark\")\n" ]
2,032,650,003	6,481	using torchrun, save_to_disk suddenly shows SIGTERM	open	2023-12-08T13:22:03	2023-12-08T13:22:03	null	https://github.com/huggingface/datasets/issues/6481	null	Ariya12138	false	[]
2,031,116,653	6,480	Add IterableDataset `__repr__`	closed	2023-12-07T16:31:50	2023-12-08T13:33:06	2023-12-08T13:26:54	https://github.com/huggingface/datasets/pull/6480	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6480", "html_url": "https://github.com/huggingface/datasets/pull/6480", "diff_url": "https://github.com/huggingface/datasets/pull/6480.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6480.patch", "merged_at": "2023-12-08T13:26:54" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6480). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005392 / 0.011353 (-0.005960) \| 0.003120 / 0.011008 (-0.007888) \| 0.062017 / 0.038508 (0.023509) \| 0.048824 / 0.023109 (0.025715) \| 0.232300 / 0.275898 (-0.043598) \| 0.262045 / 0.323480 (-0.061435) \| 0.002909 / 0.007986 (-0.005077) \| 0.003916 / 0.004328 (-0.000413) \| 0.049469 / 0.004250 (0.045218) \| 0.038965 / 0.037052 (0.001913) \| 0.247841 / 0.258489 (-0.010648) \| 0.268259 / 0.293841 (-0.025582) \| 0.027588 / 0.128546 (-0.100958) \| 0.010334 / 0.075646 (-0.065312) \| 0.205811 / 0.419271 (-0.213460) \| 0.035456 / 0.043533 (-0.008077) \| 0.242774 / 0.255139 (-0.012365) \| 0.260377 / 0.283200 (-0.022823) \| 0.017469 / 0.141683 (-0.124214) \| 1.199665 / 1.452155 (-0.252489) \| 1.259316 / 1.492716 (-0.233400) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092357 / 0.018006 (0.074350) \| 0.303745 / 0.000490 (0.303255) \| 0.000212 / 0.000200 (0.000012) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018820 / 0.037411 (-0.018592) \| 0.061548 / 0.014526 (0.047022) \| 0.072527 / 0.176557 (-0.104030) \| 0.119696 / 0.737135 (-0.617440) \| 0.074153 / 0.296338 (-0.222185) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283952 / 0.215209 (0.068743) \| 2.769844 / 2.077655 (0.692189) \| 1.526100 / 1.504120 (0.021980) \| 1.417584 / 1.541195 (-0.123611) \| 1.440523 / 1.468490 (-0.027967) \| 0.556994 / 4.584777 (-4.027783) \| 2.400392 / 3.745712 (-1.345320) \| 2.727794 / 5.269862 (-2.542068) \| 1.724671 / 4.565676 (-2.841006) \| 0.062111 / 0.424275 (-0.362164) \| 0.004925 / 0.007607 (-0.002682) \| 0.342748 / 0.226044 (0.116704) \| 3.376790 / 2.268929 (1.107862) \| 1.856498 / 55.444624 (-53.588127) \| 1.574143 / 6.876477 (-5.302334) \| 1.591828 / 2.142072 (-0.550245) \| 0.644416 / 4.805227 (-4.160811) \| 0.116862 / 6.500664 (-6.383802) \| 0.041484 / 0.075469 (-0.033985) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.975704 / 1.841788 (-0.866084) \| 11.196447 / 8.074308 (3.122139) \| 10.567518 / 10.191392 (0.376126) \| 0.126786 / 0.680424 (-0.553638) \| 0.013768 / 0.534201 (-0.520433) \| 0.284531 / 0.579283 (-0.294752) \| 0.260855 / 0.434364 (-0.173509) \| 0.328888 / 0.540337 (-0.211450) \| 0.439911 / 1.386936 (-0.947025) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005108 / 0.011353 (-0.006245) \| 0.003006 / 0.011008 (-0.008003) \| 0.048673 / 0.038508 (0.010165) \| 0.051066 / 0.023109 (0.027957) \| 0.279578 / 0.275898 (0.003680) \| 0.298356 / 0.323480 (-0.025123) \| 0.003965 / 0.007986 (-0.004020) \| 0.002662 / 0.004328 (-0.001667) \| 0.049037 / 0.004250 (0.044786) \| 0.039385 / 0.037052 (0.002333) \| 0.284545 / 0.258489 (0.026055) \| 0.314240 / 0.293841 (0.020399) \| 0.028493 / 0.128546 (-0.100053) \| 0.010400 / 0.075646 (-0.065247) \| 0.057375 / 0.419271 (-0.361896) \| 0.032382 / 0.043533 (-0.011151) \| 0.283163 / 0.255139 (0.028024) \| 0.298967 / 0.283200 (0.015768) \| 0.017564 / 0.141683 (-0.124119) \| 1.172425 / 1.452155 (-0.279730) \| 1.219975 / 1.492716 (-0.272742) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090664 / 0.018006 (0.072658) \| 0.298419 / 0.000490 (0.297929) \| 0.000211 / 0.000200 (0.000011) \| 0.000045 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021739 / 0.037411 (-0.015672) \| 0.068274 / 0.014526 (0.053748) \| 0.080820 / 0.176557 (-0.095736) \| 0.119809 / 0.737135 (-0.617326) \| 0.081612 / 0.296338 (-0.214727) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.303346 / 0.215209 (0.088137) \| 2.971648 / 2.077655 (0.893993) \| 1.634828 / 1.504120 (0.130708) \| 1.510851 / 1.541195 (-0.030344) \| 1.515236 / 1.468490 (0.046745) \| 0.558487 / 4.584777 (-4.026289) \| 2.436263 / 3.745712 (-1.309449) \| 2.718525 / 5.269862 (-2.551336) \| 1.727421 / 4.565676 (-2.838255) \| 0.061396 / 0.424275 (-0.362879) \| 0.004951 / 0.007607 (-0.002656) \| 0.352950 / 0.226044 (0.126906) \| 3.473766 / 2.268929 (1.204838) \| 1.971299 / 55.444624 (-53.473325) \| 1.712173 / 6.876477 (-5.164304) \| 1.711334 / 2.142072 (-0.430738) \| 0.627291 / 4.805227 (-4.177936) \| 0.113779 / 6.500664 (-6.386885) \| 0.046561 / 0.075469 (-0.028908) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.989507 / 1.841788 (-0.852280) \| 11.777883 / 8.074308 (3.703575) \| 10.525453 / 10.191392 (0.334061) \| 0.129118 / 0.680424 (-0.551306) \| 0.014989 / 0.534201 (-0.519212) \| 0.282324 / 0.579283 (-0.296959) \| 0.280688 / 0.434364 (-0.153676) \| 0.322579 / 0.540337 (-0.217758) \| 0.554327 / 1.386936 (-0.832609) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79e94fcdf3d4378ddcdf7e130bb1ae23d99c6fce \"CML watermark\")\n" ]
2,029,040,121	6,479	More robust preupload retry mechanism	closed	2023-12-06T17:19:38	2023-12-06T19:47:29	2023-12-06T19:41:06	https://github.com/huggingface/datasets/pull/6479	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6479", "html_url": "https://github.com/huggingface/datasets/pull/6479", "diff_url": "https://github.com/huggingface/datasets/pull/6479.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6479.patch", "merged_at": "2023-12-06T19:41:06" }	mariosasko	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6479). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005669 / 0.011353 (-0.005683) \| 0.003684 / 0.011008 (-0.007324) \| 0.063477 / 0.038508 (0.024969) \| 0.068760 / 0.023109 (0.045651) \| 0.252741 / 0.275898 (-0.023157) \| 0.286499 / 0.323480 (-0.036981) \| 0.003311 / 0.007986 (-0.004674) \| 0.003487 / 0.004328 (-0.000842) \| 0.049636 / 0.004250 (0.045385) \| 0.040983 / 0.037052 (0.003931) \| 0.262230 / 0.258489 (0.003740) \| 0.292131 / 0.293841 (-0.001710) \| 0.028231 / 0.128546 (-0.100315) \| 0.010912 / 0.075646 (-0.064734) \| 0.211248 / 0.419271 (-0.208023) \| 0.036679 / 0.043533 (-0.006854) \| 0.258139 / 0.255139 (0.003000) \| 0.277568 / 0.283200 (-0.005631) \| 0.019576 / 0.141683 (-0.122107) \| 1.102588 / 1.452155 (-0.349567) \| 1.178587 / 1.492716 (-0.314130) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098968 / 0.018006 (0.080962) \| 0.298777 / 0.000490 (0.298287) \| 0.000220 / 0.000200 (0.000020) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020408 / 0.037411 (-0.017003) \| 0.062832 / 0.014526 (0.048306) \| 0.076047 / 0.176557 (-0.100509) \| 0.125209 / 0.737135 (-0.611926) \| 0.079098 / 0.296338 (-0.217240) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285603 / 0.215209 (0.070394) \| 2.811530 / 2.077655 (0.733875) \| 1.481012 / 1.504120 (-0.023108) \| 1.362740 / 1.541195 (-0.178455) \| 1.448999 / 1.468490 (-0.019491) \| 0.557740 / 4.584777 (-4.027037) \| 2.391377 / 3.745712 (-1.354335) \| 2.973181 / 5.269862 (-2.296681) \| 1.837147 / 4.565676 (-2.728530) \| 0.064445 / 0.424275 (-0.359831) \| 0.004992 / 0.007607 (-0.002615) \| 0.339207 / 0.226044 (0.113162) \| 3.378508 / 2.268929 (1.109580) \| 1.843969 / 55.444624 (-53.600655) \| 1.597794 / 6.876477 (-5.278682) \| 1.657665 / 2.142072 (-0.484407) \| 0.654267 / 4.805227 (-4.150961) \| 0.120408 / 6.500664 (-6.380256) \| 0.045298 / 0.075469 (-0.030171) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.949030 / 1.841788 (-0.892758) \| 12.922161 / 8.074308 (4.847852) \| 11.115660 / 10.191392 (0.924268) \| 0.130556 / 0.680424 (-0.549868) \| 0.016278 / 0.534201 (-0.517923) \| 0.288137 / 0.579283 (-0.291146) \| 0.265978 / 0.434364 (-0.168386) \| 0.331491 / 0.540337 (-0.208847) \| 0.437782 / 1.386936 (-0.949154) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005342 / 0.011353 (-0.006010) \| 0.003636 / 0.011008 (-0.007373) \| 0.049527 / 0.038508 (0.011019) \| 0.054856 / 0.023109 (0.031746) \| 0.271922 / 0.275898 (-0.003976) \| 0.295654 / 0.323480 (-0.027826) \| 0.004023 / 0.007986 (-0.003963) \| 0.002814 / 0.004328 (-0.001515) \| 0.048963 / 0.004250 (0.044712) \| 0.039936 / 0.037052 (0.002884) \| 0.274336 / 0.258489 (0.015847) \| 0.310100 / 0.293841 (0.016259) \| 0.030006 / 0.128546 (-0.098540) \| 0.010750 / 0.075646 (-0.064896) \| 0.057989 / 0.419271 (-0.361283) \| 0.033692 / 0.043533 (-0.009841) \| 0.274084 / 0.255139 (0.018945) \| 0.289428 / 0.283200 (0.006229) \| 0.018739 / 0.141683 (-0.122944) \| 1.126224 / 1.452155 (-0.325931) \| 1.171595 / 1.492716 (-0.321121) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093983 / 0.018006 (0.075977) \| 0.298516 / 0.000490 (0.298026) \| 0.000221 / 0.000200 (0.000022) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022498 / 0.037411 (-0.014914) \| 0.071909 / 0.014526 (0.057383) \| 0.083940 / 0.176557 (-0.092617) \| 0.121059 / 0.737135 (-0.616076) \| 0.084141 / 0.296338 (-0.212198) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.301792 / 0.215209 (0.086583) \| 2.971971 / 2.077655 (0.894317) \| 1.618718 / 1.504120 (0.114598) \| 1.495816 / 1.541195 (-0.045379) \| 1.546709 / 1.468490 (0.078219) \| 0.571448 / 4.584777 (-4.013329) \| 2.459182 / 3.745712 (-1.286531) \| 2.937584 / 5.269862 (-2.332278) \| 1.804670 / 4.565676 (-2.761007) \| 0.062264 / 0.424275 (-0.362011) \| 0.004915 / 0.007607 (-0.002692) \| 0.355054 / 0.226044 (0.129009) \| 3.490468 / 2.268929 (1.221539) \| 1.978948 / 55.444624 (-53.465677) \| 1.701020 / 6.876477 (-5.175457) \| 1.744684 / 2.142072 (-0.397388) \| 0.635880 / 4.805227 (-4.169347) \| 0.115933 / 6.500664 (-6.384732) \| 0.042646 / 0.075469 (-0.032823) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.999486 / 1.841788 (-0.842302) \| 13.373854 / 8.074308 (5.299546) \| 10.959784 / 10.191392 (0.768392) \| 0.131032 / 0.680424 (-0.549392) \| 0.015059 / 0.534201 (-0.519142) \| 0.289892 / 0.579283 (-0.289391) \| 0.279383 / 0.434364 (-0.154981) \| 0.337670 / 0.540337 (-0.202668) \| 0.597102 / 1.386936 (-0.789834) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dd9044cdaabc1f9abce02c1b71bdb48fd3525d4e \"CML watermark\")\n" ]
2,028,071,596	6,478	How to load data from lakefs	closed	2023-12-06T09:04:11	2024-07-03T19:13:57	2024-07-03T19:13:56	https://github.com/huggingface/datasets/issues/6478	null	d710055071	false	[ "You can create a `pandas` DataFrame following [this](https://lakefs.io/data-version-control/dvc-using-python/) tutorial, and then convert this DataFrame to a `Dataset` with `datasets.Dataset.from_pandas`. For larger datasets (to memory map them), you can use `Dataset.from_generator` with a generator function that reads lakeFS files with `s3fs`.", "@mariosasko hello,\r\nThis can achieve and https://huggingface.co/datasets Does the same effect apply to the dataset? For example, downloading while using", "There is a blogspot from lakes on this topic: https://lakefs.io/blog/data-version-control-hugging-face-datasets/" ]
2,028,022,374	6,477	Fix PermissionError on Windows CI	closed	2023-12-06T08:34:53	2023-12-06T09:24:11	2023-12-06T09:17:52	https://github.com/huggingface/datasets/pull/6477	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6477", "html_url": "https://github.com/huggingface/datasets/pull/6477", "diff_url": "https://github.com/huggingface/datasets/pull/6477.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6477.patch", "merged_at": "2023-12-06T09:17:52" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6477). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005383 / 0.011353 (-0.005969) \| 0.003644 / 0.011008 (-0.007364) \| 0.063375 / 0.038508 (0.024866) \| 0.055567 / 0.023109 (0.032457) \| 0.261376 / 0.275898 (-0.014522) \| 0.283731 / 0.323480 (-0.039749) \| 0.004022 / 0.007986 (-0.003964) \| 0.002780 / 0.004328 (-0.001549) \| 0.049407 / 0.004250 (0.045156) \| 0.038208 / 0.037052 (0.001156) \| 0.256275 / 0.258489 (-0.002214) \| 0.293203 / 0.293841 (-0.000638) \| 0.028411 / 0.128546 (-0.100135) \| 0.010753 / 0.075646 (-0.064894) \| 0.210420 / 0.419271 (-0.208851) \| 0.036062 / 0.043533 (-0.007471) \| 0.260455 / 0.255139 (0.005317) \| 0.294991 / 0.283200 (0.011791) \| 0.019020 / 0.141683 (-0.122662) \| 1.118334 / 1.452155 (-0.333821) \| 1.227391 / 1.492716 (-0.265325) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094700 / 0.018006 (0.076694) \| 0.302378 / 0.000490 (0.301888) \| 0.000215 / 0.000200 (0.000015) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018745 / 0.037411 (-0.018667) \| 0.061103 / 0.014526 (0.046578) \| 0.075369 / 0.176557 (-0.101188) \| 0.121573 / 0.737135 (-0.615563) \| 0.076898 / 0.296338 (-0.219440) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284143 / 0.215209 (0.068934) \| 2.774298 / 2.077655 (0.696644) \| 1.483557 / 1.504120 (-0.020563) \| 1.365091 / 1.541195 (-0.176104) \| 1.390170 / 1.468490 (-0.078320) \| 0.561179 / 4.584777 (-4.023598) \| 2.401654 / 3.745712 (-1.344058) \| 2.782628 / 5.269862 (-2.487233) \| 1.731497 / 4.565676 (-2.834179) \| 0.061798 / 0.424275 (-0.362477) \| 0.004998 / 0.007607 (-0.002609) \| 0.336920 / 0.226044 (0.110875) \| 3.371891 / 2.268929 (1.102963) \| 1.832173 / 55.444624 (-53.612452) \| 1.573515 / 6.876477 (-5.302962) \| 1.595609 / 2.142072 (-0.546463) \| 0.647652 / 4.805227 (-4.157575) \| 0.118501 / 6.500664 (-6.382164) \| 0.042521 / 0.075469 (-0.032948) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.939310 / 1.841788 (-0.902478) \| 11.459855 / 8.074308 (3.385547) \| 10.677954 / 10.191392 (0.486562) \| 0.141029 / 0.680424 (-0.539395) \| 0.014321 / 0.534201 (-0.519880) \| 0.306679 / 0.579283 (-0.272604) \| 0.262303 / 0.434364 (-0.172061) \| 0.327422 / 0.540337 (-0.212915) \| 0.436159 / 1.386936 (-0.950777) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005430 / 0.011353 (-0.005923) \| 0.003646 / 0.011008 (-0.007362) \| 0.049272 / 0.038508 (0.010764) \| 0.075367 / 0.023109 (0.052257) \| 0.275959 / 0.275898 (0.000061) \| 0.296317 / 0.323480 (-0.027163) \| 0.004129 / 0.007986 (-0.003857) \| 0.002731 / 0.004328 (-0.001597) \| 0.048475 / 0.004250 (0.044225) \| 0.041571 / 0.037052 (0.004518) \| 0.277993 / 0.258489 (0.019504) \| 0.298709 / 0.293841 (0.004868) \| 0.033117 / 0.128546 (-0.095429) \| 0.010914 / 0.075646 (-0.064732) \| 0.057599 / 0.419271 (-0.361673) \| 0.033354 / 0.043533 (-0.010179) \| 0.275669 / 0.255139 (0.020530) \| 0.288451 / 0.283200 (0.005251) \| 0.019953 / 0.141683 (-0.121729) \| 1.148608 / 1.452155 (-0.303547) \| 1.184818 / 1.492716 (-0.307898) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.099566 / 0.018006 (0.081560) \| 0.344935 / 0.000490 (0.344445) \| 0.000221 / 0.000200 (0.000021) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021925 / 0.037411 (-0.015486) \| 0.068623 / 0.014526 (0.054097) \| 0.081533 / 0.176557 (-0.095024) \| 0.120996 / 0.737135 (-0.616139) \| 0.082495 / 0.296338 (-0.213844) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294990 / 0.215209 (0.079781) \| 2.892344 / 2.077655 (0.814690) \| 1.611090 / 1.504120 (0.106970) \| 1.496072 / 1.541195 (-0.045123) \| 1.486069 / 1.468490 (0.017579) \| 0.569769 / 4.584777 (-4.015008) \| 2.477623 / 3.745712 (-1.268089) \| 2.819576 / 5.269862 (-2.450286) \| 1.745717 / 4.565676 (-2.819959) \| 0.063763 / 0.424275 (-0.360512) \| 0.004970 / 0.007607 (-0.002637) \| 0.344879 / 0.226044 (0.118834) \| 3.452795 / 2.268929 (1.183867) \| 1.964468 / 55.444624 (-53.480156) \| 1.674526 / 6.876477 (-5.201951) \| 1.679716 / 2.142072 (-0.462356) \| 0.650005 / 4.805227 (-4.155222) \| 0.117019 / 6.500664 (-6.383646) \| 0.048297 / 0.075469 (-0.027172) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.965422 / 1.841788 (-0.876366) \| 11.989414 / 8.074308 (3.915106) \| 10.938462 / 10.191392 (0.747070) \| 0.140089 / 0.680424 (-0.540334) \| 0.015533 / 0.534201 (-0.518668) \| 0.292188 / 0.579283 (-0.287095) \| 0.277903 / 0.434364 (-0.156461) \| 0.326164 / 0.540337 (-0.214173) \| 0.565674 / 1.386936 (-0.821262) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d78f07091bc42c41bea068bf1b6116e2bde46a6f \"CML watermark\")\n" ]
2,028,018,596	6,476	CI on windows is broken: PermissionError	closed	2023-12-06T08:32:53	2023-12-06T09:17:53	2023-12-06T09:17:53	https://github.com/huggingface/datasets/issues/6476	null	albertvillanova	false	[]
2,027,373,734	6,475	laion2B-en failed to load on Windows with PrefetchVirtualMemory failed	open	2023-12-06T00:07:34	2023-12-06T23:26:23	null	https://github.com/huggingface/datasets/issues/6475	null	doctorpangloss	false	[ "~~You will see this error if the cache dir filepath contains relative `..` paths. Use `os.path.realpath(_CACHE_DIR)` before passing it to the `load_dataset` function.~~", "This is a real issue and not related to paths.", "Based on the StackOverflow answer, this causes the error to go away:\r\n```diff\r\ndiff --git a/table.py b/table.py\r\n--- a/table.py\t\r\n+++ b/table.py\t(date 1701824849806)\r\n@@ -47,7 +47,7 @@\r\n \r\n \r\n def _memory_mapped_record_batch_reader_from_file(filename: str) -> pa.RecordBatchStreamReader:\r\n- memory_mapped_stream = pa.memory_map(filename)\r\n+ memory_mapped_stream = pa.memory_map(filename, \"r+\")\r\n return pa.ipc.open_stream(memory_mapped_stream)\r\n```\r\nBut now loading the dataset goes very, very slowly, which is unexpected.", "I don't really comprehend what it is that `datasets` gave me when it downloaded the laion2B-en dataset, because nothing can seemingly read these 1024 .arrow files it is retrieving. Not `polars`, not `pyarrow`, it's not an `ipc` file, it's not a `parquet` file...", "Hi! \r\n\r\nInstead of generating one (potentially large) Arrow file, we shard the generated data into 500 MB shards because memory-mapping large Arrow files can be problematic on some systems. Maybe deleting the dataset's cache and increasing the shard size (controlled with the `datasets.config.MAX_SHARD_SIZE` variable; e.g. to \"4GB\") can fix the issue for you.\r\n\r\n> I don't really comprehend what it is that `datasets` gave me when it downloaded the laion2B-en dataset, because nothing can seemingly read these 1024 .arrow files it is retrieving. Not `polars`, not `pyarrow`, it's not an `ipc` file, it's not a `parquet` file...\r\n\r\nOur `.arrow` files are in the [Arrow streaming format](https://arrow.apache.org/docs/python/ipc.html#using-streams). To load them as a `polars` DataFrame, do the following:\r\n```python\r\ndf = pl.from_arrow(Dataset.from_from(path_to_arrow_file).data.table)\r\n```\r\n\r\nWe plan to switch to the IPC version eventually.\r\n", "Hmm, I have a feeling this works fine on Linux, and is a real bug for however `datasets` is doing the sharding on Windows. I will follow up, but I think this is a real bug." ]
2,027,006,715	6,474	Deprecate Beam API and download from HF GCS bucket	closed	2023-12-05T19:51:33	2024-03-12T14:56:25	2024-03-12T14:50:12	https://github.com/huggingface/datasets/pull/6474	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6474", "html_url": "https://github.com/huggingface/datasets/pull/6474", "diff_url": "https://github.com/huggingface/datasets/pull/6474.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6474.patch", "merged_at": "2024-03-12T14:50:12" }	mariosasko	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6474). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005280 / 0.011353 (-0.006073) \| 0.003770 / 0.011008 (-0.007238) \| 0.064320 / 0.038508 (0.025812) \| 0.031250 / 0.023109 (0.008141) \| 0.245113 / 0.275898 (-0.030785) \| 0.268646 / 0.323480 (-0.054834) \| 0.003147 / 0.007986 (-0.004839) \| 0.002736 / 0.004328 (-0.001592) \| 0.050802 / 0.004250 (0.046551) \| 0.044539 / 0.037052 (0.007487) \| 0.261702 / 0.258489 (0.003213) \| 0.304696 / 0.293841 (0.010855) \| 0.028601 / 0.128546 (-0.099945) \| 0.010731 / 0.075646 (-0.064915) \| 0.208183 / 0.419271 (-0.211089) \| 0.036597 / 0.043533 (-0.006936) \| 0.245107 / 0.255139 (-0.010032) \| 0.265936 / 0.283200 (-0.017264) \| 0.019430 / 0.141683 (-0.122253) \| 1.184697 / 1.452155 (-0.267458) \| 1.206073 / 1.492716 (-0.286643) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.110242 / 0.018006 (0.092236) \| 0.304185 / 0.000490 (0.303695) \| 0.000220 / 0.000200 (0.000020) \| 0.000047 / 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) \| 0.018839 / 0.037411 (-0.018573) \| 0.062840 / 0.014526 (0.048314) \| 0.075849 / 0.176557 (-0.100708) \| 0.122983 / 0.737135 (-0.614153) \| 0.075352 / 0.296338 (-0.220987) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283617 / 0.215209 (0.068408) \| 2.801903 / 2.077655 (0.724248) \| 1.447678 / 1.504120 (-0.056442) \| 1.327561 / 1.541195 (-0.213633) \| 1.348714 / 1.468490 (-0.119776) \| 0.574283 / 4.584777 (-4.010493) \| 2.449396 / 3.745712 (-1.296316) \| 2.908659 / 5.269862 (-2.361203) \| 1.813935 / 4.565676 (-2.751742) \| 0.063141 / 0.424275 (-0.361134) \| 0.005024 / 0.007607 (-0.002583) \| 0.338854 / 0.226044 (0.112810) \| 3.346680 / 2.268929 (1.077752) \| 1.833792 / 55.444624 (-53.610832) \| 1.553680 / 6.876477 (-5.322796) \| 1.590403 / 2.142072 (-0.551670) \| 0.657442 / 4.805227 (-4.147785) \| 0.119961 / 6.500664 (-6.380703) \| 0.042602 / 0.075469 (-0.032867) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.977510 / 1.841788 (-0.864278) \| 12.105448 / 8.074308 (4.031140) \| 9.635282 / 10.191392 (-0.556110) \| 0.132453 / 0.680424 (-0.547971) \| 0.014225 / 0.534201 (-0.519976) \| 0.292847 / 0.579283 (-0.286436) \| 0.271498 / 0.434364 (-0.162866) \| 0.329785 / 0.540337 (-0.210552) \| 0.435058 / 1.386936 (-0.951879) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005656 / 0.011353 (-0.005697) \| 0.003903 / 0.011008 (-0.007106) \| 0.050670 / 0.038508 (0.012162) \| 0.032405 / 0.023109 (0.009296) \| 0.271443 / 0.275898 (-0.004455) \| 0.298989 / 0.323480 (-0.024491) \| 0.004397 / 0.007986 (-0.003589) \| 0.003027 / 0.004328 (-0.001301) \| 0.050227 / 0.004250 (0.045977) \| 0.047798 / 0.037052 (0.010745) \| 0.287610 / 0.258489 (0.029120) \| 0.314084 / 0.293841 (0.020243) \| 0.030406 / 0.128546 (-0.098140) \| 0.011159 / 0.075646 (-0.064487) \| 0.059510 / 0.419271 (-0.359762) \| 0.056842 / 0.043533 (0.013309) \| 0.274653 / 0.255139 (0.019514) \| 0.290175 / 0.283200 (0.006976) \| 0.019822 / 0.141683 (-0.121861) \| 1.168894 / 1.452155 (-0.283261) \| 1.205867 / 1.492716 (-0.286850) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094907 / 0.018006 (0.076901) \| 0.304920 / 0.000490 (0.304431) \| 0.000218 / 0.000200 (0.000018) \| 0.000048 / 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) \| 0.023015 / 0.037411 (-0.014396) \| 0.076868 / 0.014526 (0.062342) \| 0.089099 / 0.176557 (-0.087458) \| 0.128786 / 0.737135 (-0.608350) \| 0.090836 / 0.296338 (-0.205503) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284552 / 0.215209 (0.069343) \| 2.800870 / 2.077655 (0.723215) \| 1.577167 / 1.504120 (0.073047) \| 1.448788 / 1.541195 (-0.092406) \| 1.475493 / 1.468490 (0.007003) \| 0.566639 / 4.584777 (-4.018138) \| 2.503671 / 3.745712 (-1.242041) \| 2.796769 / 5.269862 (-2.473092) \| 1.829344 / 4.565676 (-2.736332) \| 0.064200 / 0.424275 (-0.360075) \| 0.005066 / 0.007607 (-0.002541) \| 0.335390 / 0.226044 (0.109346) \| 3.421265 / 2.268929 (1.152336) \| 1.942245 / 55.444624 (-53.502379) \| 1.641401 / 6.876477 (-5.235076) \| 1.824574 / 2.142072 (-0.317499) \| 0.661379 / 4.805227 (-4.143849) \| 0.117207 / 6.500664 (-6.383457) \| 0.041630 / 0.075469 (-0.033839) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.009636 / 1.841788 (-0.832152) \| 12.998379 / 8.074308 (4.924071) \| 10.609013 / 10.191392 (0.417621) \| 0.142794 / 0.680424 (-0.537630) \| 0.015648 / 0.534201 (-0.518553) \| 0.292825 / 0.579283 (-0.286458) \| 0.280790 / 0.434364 (-0.153574) \| 0.329016 / 0.540337 (-0.211322) \| 0.442440 / 1.386936 (-0.944496) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f90b65d6ccaceaa532b851a1010ba3dcc77b8b42 \"CML watermark\")\n" ]
2,026,495,084	6,473	Fix CI quality	closed	2023-12-05T15:36:23	2023-12-05T18:14:50	2023-12-05T18:08:41	https://github.com/huggingface/datasets/pull/6473	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6473", "html_url": "https://github.com/huggingface/datasets/pull/6473", "diff_url": "https://github.com/huggingface/datasets/pull/6473.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6473.patch", "merged_at": "2023-12-05T18:08:41" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6473). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005270 / 0.011353 (-0.006083) \| 0.003471 / 0.011008 (-0.007537) \| 0.061942 / 0.038508 (0.023434) \| 0.052671 / 0.023109 (0.029562) \| 0.250541 / 0.275898 (-0.025357) \| 0.270677 / 0.323480 (-0.052803) \| 0.002933 / 0.007986 (-0.005053) \| 0.003264 / 0.004328 (-0.001064) \| 0.048055 / 0.004250 (0.043804) \| 0.037459 / 0.037052 (0.000407) \| 0.254926 / 0.258489 (-0.003563) \| 0.292547 / 0.293841 (-0.001294) \| 0.027959 / 0.128546 (-0.100587) \| 0.010762 / 0.075646 (-0.064884) \| 0.204961 / 0.419271 (-0.214310) \| 0.035488 / 0.043533 (-0.008045) \| 0.254102 / 0.255139 (-0.001037) \| 0.273654 / 0.283200 (-0.009546) \| 0.018126 / 0.141683 (-0.123556) \| 1.082330 / 1.452155 (-0.369825) \| 1.147179 / 1.492716 (-0.345538) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093223 / 0.018006 (0.075217) \| 0.301912 / 0.000490 (0.301422) \| 0.000219 / 0.000200 (0.000019) \| 0.000051 / 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) \| 0.018407 / 0.037411 (-0.019004) \| 0.060412 / 0.014526 (0.045886) \| 0.074063 / 0.176557 (-0.102494) \| 0.118743 / 0.737135 (-0.618392) \| 0.076484 / 0.296338 (-0.219854) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289929 / 0.215209 (0.074720) \| 2.825096 / 2.077655 (0.747442) \| 1.511444 / 1.504120 (0.007324) \| 1.394812 / 1.541195 (-0.146383) \| 1.419751 / 1.468490 (-0.048739) \| 0.569995 / 4.584777 (-4.014782) \| 2.402586 / 3.745712 (-1.343126) \| 2.826223 / 5.269862 (-2.443639) \| 1.751554 / 4.565676 (-2.814123) \| 0.064266 / 0.424275 (-0.360009) \| 0.005047 / 0.007607 (-0.002561) \| 0.341513 / 0.226044 (0.115469) \| 3.372106 / 2.268929 (1.103177) \| 1.872693 / 55.444624 (-53.571931) \| 1.588200 / 6.876477 (-5.288276) \| 1.630800 / 2.142072 (-0.511272) \| 0.654266 / 4.805227 (-4.150961) \| 0.124292 / 6.500664 (-6.376372) \| 0.042876 / 0.075469 (-0.032593) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.948406 / 1.841788 (-0.893382) \| 11.652947 / 8.074308 (3.578639) \| 10.218195 / 10.191392 (0.026803) \| 0.128447 / 0.680424 (-0.551976) \| 0.014092 / 0.534201 (-0.520109) \| 0.287631 / 0.579283 (-0.291652) \| 0.264843 / 0.434364 (-0.169521) \| 0.329997 / 0.540337 (-0.210340) \| 0.439597 / 1.386936 (-0.947339) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005418 / 0.011353 (-0.005935) \| 0.003589 / 0.011008 (-0.007419) \| 0.050074 / 0.038508 (0.011566) \| 0.052566 / 0.023109 (0.029456) \| 0.293447 / 0.275898 (0.017549) \| 0.320518 / 0.323480 (-0.002962) \| 0.004094 / 0.007986 (-0.003892) \| 0.002690 / 0.004328 (-0.001639) \| 0.048200 / 0.004250 (0.043949) \| 0.040692 / 0.037052 (0.003640) \| 0.297086 / 0.258489 (0.038597) \| 0.323827 / 0.293841 (0.029986) \| 0.029511 / 0.128546 (-0.099035) \| 0.011079 / 0.075646 (-0.064568) \| 0.058562 / 0.419271 (-0.360709) \| 0.032897 / 0.043533 (-0.010636) \| 0.297244 / 0.255139 (0.042105) \| 0.316812 / 0.283200 (0.033612) \| 0.018468 / 0.141683 (-0.123215) \| 1.140948 / 1.452155 (-0.311207) \| 1.195453 / 1.492716 (-0.297263) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092677 / 0.018006 (0.074671) \| 0.300775 / 0.000490 (0.300285) \| 0.000225 / 0.000200 (0.000025) \| 0.000054 / 0.000054 (0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021617 / 0.037411 (-0.015794) \| 0.077135 / 0.014526 (0.062610) \| 0.079848 / 0.176557 (-0.096709) \| 0.118475 / 0.737135 (-0.618661) \| 0.081174 / 0.296338 (-0.215164) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294424 / 0.215209 (0.079215) \| 2.863989 / 2.077655 (0.786334) \| 1.590604 / 1.504120 (0.086484) \| 1.474345 / 1.541195 (-0.066849) \| 1.482120 / 1.468490 (0.013630) \| 0.567829 / 4.584777 (-4.016948) \| 2.493782 / 3.745712 (-1.251930) \| 2.823460 / 5.269862 (-2.446402) \| 1.732677 / 4.565676 (-2.833000) \| 0.065518 / 0.424275 (-0.358757) \| 0.004923 / 0.007607 (-0.002684) \| 0.349313 / 0.226044 (0.123268) \| 3.428618 / 2.268929 (1.159689) \| 1.970641 / 55.444624 (-53.473983) \| 1.655884 / 6.876477 (-5.220593) \| 1.657151 / 2.142072 (-0.484921) \| 0.661208 / 4.805227 (-4.144019) \| 0.119129 / 6.500664 (-6.381535) \| 0.040770 / 0.075469 (-0.034699) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.964865 / 1.841788 (-0.876923) \| 12.050218 / 8.074308 (3.975910) \| 10.458749 / 10.191392 (0.267357) \| 0.141856 / 0.680424 (-0.538568) \| 0.015091 / 0.534201 (-0.519109) \| 0.288897 / 0.579283 (-0.290387) \| 0.275343 / 0.434364 (-0.159021) \| 0.328363 / 0.540337 (-0.211975) \| 0.579243 / 1.386936 (-0.807693) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f7721021e284859ea0952444bae6300a0d00794f \"CML watermark\")\n" ]
2,026,493,439	6,472	CI quality is broken	closed	2023-12-05T15:35:34	2023-12-06T08:17:34	2023-12-05T18:08:43	https://github.com/huggingface/datasets/issues/6472	null	albertvillanova	false	[]
2,026,100,761	6,471	Remove delete doc CI	closed	2023-12-05T12:37:50	2023-12-05T12:44:59	2023-12-05T12:38:50	https://github.com/huggingface/datasets/pull/6471	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6471", "html_url": "https://github.com/huggingface/datasets/pull/6471", "diff_url": "https://github.com/huggingface/datasets/pull/6471.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6471.patch", "merged_at": "2023-12-05T12:38:50" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6471). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005573 / 0.011353 (-0.005780) \| 0.003449 / 0.011008 (-0.007559) \| 0.063323 / 0.038508 (0.024815) \| 0.049369 / 0.023109 (0.026260) \| 0.254280 / 0.275898 (-0.021618) \| 0.267721 / 0.323480 (-0.055759) \| 0.002894 / 0.007986 (-0.005092) \| 0.002646 / 0.004328 (-0.001683) \| 0.049284 / 0.004250 (0.045033) \| 0.037947 / 0.037052 (0.000895) \| 0.251654 / 0.258489 (-0.006836) \| 0.279729 / 0.293841 (-0.014112) \| 0.028022 / 0.128546 (-0.100525) \| 0.010653 / 0.075646 (-0.064993) \| 0.208567 / 0.419271 (-0.210704) \| 0.035863 / 0.043533 (-0.007670) \| 0.248522 / 0.255139 (-0.006617) \| 0.270274 / 0.283200 (-0.012925) \| 0.019683 / 0.141683 (-0.122000) \| 1.136342 / 1.452155 (-0.315812) \| 1.206757 / 1.492716 (-0.285960) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094682 / 0.018006 (0.076676) \| 0.304092 / 0.000490 (0.303602) \| 0.000220 / 0.000200 (0.000020) \| 0.000051 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018606 / 0.037411 (-0.018805) \| 0.060568 / 0.014526 (0.046042) \| 0.074067 / 0.176557 (-0.102490) \| 0.118979 / 0.737135 (-0.618156) \| 0.075676 / 0.296338 (-0.220663) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290452 / 0.215209 (0.075243) \| 2.848868 / 2.077655 (0.771213) \| 1.534932 / 1.504120 (0.030812) \| 1.386717 / 1.541195 (-0.154478) \| 1.416645 / 1.468490 (-0.051845) \| 0.569020 / 4.584777 (-4.015757) \| 2.421168 / 3.745712 (-1.324545) \| 2.781358 / 5.269862 (-2.488503) \| 1.758495 / 4.565676 (-2.807182) \| 0.063851 / 0.424275 (-0.360424) \| 0.004968 / 0.007607 (-0.002639) \| 0.339198 / 0.226044 (0.113154) \| 3.356392 / 2.268929 (1.087464) \| 1.858145 / 55.444624 (-53.586479) \| 1.589000 / 6.876477 (-5.287477) \| 1.569175 / 2.142072 (-0.572897) \| 0.650571 / 4.805227 (-4.154657) \| 0.120288 / 6.500664 (-6.380376) \| 0.042489 / 0.075469 (-0.032980) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.939963 / 1.841788 (-0.901824) \| 11.493612 / 8.074308 (3.419304) \| 10.353780 / 10.191392 (0.162388) \| 0.141945 / 0.680424 (-0.538479) \| 0.014397 / 0.534201 (-0.519804) \| 0.286971 / 0.579283 (-0.292312) \| 0.266787 / 0.434364 (-0.167577) \| 0.330385 / 0.540337 (-0.209952) \| 0.438542 / 1.386936 (-0.948394) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005360 / 0.011353 (-0.005993) \| 0.003720 / 0.011008 (-0.007288) \| 0.048790 / 0.038508 (0.010282) \| 0.050256 / 0.023109 (0.027147) \| 0.275445 / 0.275898 (-0.000453) \| 0.297725 / 0.323480 (-0.025755) \| 0.004077 / 0.007986 (-0.003909) \| 0.002759 / 0.004328 (-0.001569) \| 0.047653 / 0.004250 (0.043403) \| 0.040205 / 0.037052 (0.003153) \| 0.281028 / 0.258489 (0.022539) \| 0.304682 / 0.293841 (0.010841) \| 0.030158 / 0.128546 (-0.098388) \| 0.010957 / 0.075646 (-0.064689) \| 0.058193 / 0.419271 (-0.361079) \| 0.033277 / 0.043533 (-0.010256) \| 0.279501 / 0.255139 (0.024362) \| 0.295381 / 0.283200 (0.012181) \| 0.017889 / 0.141683 (-0.123794) \| 1.121354 / 1.452155 (-0.330801) \| 1.225702 / 1.492716 (-0.267014) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093385 / 0.018006 (0.075378) \| 0.304642 / 0.000490 (0.304152) \| 0.000219 / 0.000200 (0.000019) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021456 / 0.037411 (-0.015955) \| 0.068536 / 0.014526 (0.054010) \| 0.080867 / 0.176557 (-0.095689) \| 0.119093 / 0.737135 (-0.618042) \| 0.081875 / 0.296338 (-0.214464) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.304434 / 0.215209 (0.089225) \| 2.990303 / 2.077655 (0.912649) \| 1.616959 / 1.504120 (0.112839) \| 1.493256 / 1.541195 (-0.047939) \| 1.542857 / 1.468490 (0.074367) \| 0.575517 / 4.584777 (-4.009260) \| 2.455165 / 3.745712 (-1.290547) \| 2.810089 / 5.269862 (-2.459773) \| 1.756502 / 4.565676 (-2.809175) \| 0.064801 / 0.424275 (-0.359475) \| 0.004969 / 0.007607 (-0.002638) \| 0.360227 / 0.226044 (0.134183) \| 3.575029 / 2.268929 (1.306100) \| 1.989955 / 55.444624 (-53.454669) \| 1.705306 / 6.876477 (-5.171171) \| 1.688523 / 2.142072 (-0.453550) \| 0.663266 / 4.805227 (-4.141962) \| 0.121852 / 6.500664 (-6.378812) \| 0.041853 / 0.075469 (-0.033616) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.983535 / 1.841788 (-0.858252) \| 11.827656 / 8.074308 (3.753348) \| 10.663265 / 10.191392 (0.471873) \| 0.145942 / 0.680424 (-0.534482) \| 0.016004 / 0.534201 (-0.518197) \| 0.288907 / 0.579283 (-0.290376) \| 0.279100 / 0.434364 (-0.155264) \| 0.328061 / 0.540337 (-0.212276) \| 0.570253 / 1.386936 (-0.816683) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b52cbc18919869460557e15028e7f489eae8afc7 \"CML watermark\")\n" ]
2,024,724,319	6,470	If an image in a dataset is corrupted, we get unescapable error	open	2023-12-04T20:58:49	2023-12-04T20:58:49	null	https://github.com/huggingface/datasets/issues/6470	null	chigozienri	false	[]
2,023,695,839	6,469	Don't expand_info in HF glob	closed	2023-12-04T12:00:37	2023-12-15T13:18:37	2023-12-15T13:12:30	https://github.com/huggingface/datasets/pull/6469	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6469", "html_url": "https://github.com/huggingface/datasets/pull/6469", "diff_url": "https://github.com/huggingface/datasets/pull/6469.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6469.patch", "merged_at": "2023-12-15T13:12:30" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6469). All of your documentation changes will be reflected on that endpoint.", "Merging this one for now, but lmk if you had other optimizations in mind for the next version of `huggingface_hub`", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004998 / 0.011353 (-0.006355) \| 0.003523 / 0.011008 (-0.007486) \| 0.064932 / 0.038508 (0.026424) \| 0.050107 / 0.023109 (0.026998) \| 0.253715 / 0.275898 (-0.022183) \| 0.275364 / 0.323480 (-0.048116) \| 0.003902 / 0.007986 (-0.004084) \| 0.002716 / 0.004328 (-0.001612) \| 0.048458 / 0.004250 (0.044208) \| 0.037802 / 0.037052 (0.000750) \| 0.262328 / 0.258489 (0.003839) \| 0.285911 / 0.293841 (-0.007930) \| 0.027112 / 0.128546 (-0.101435) \| 0.010780 / 0.075646 (-0.064867) \| 0.206447 / 0.419271 (-0.212824) \| 0.035771 / 0.043533 (-0.007761) \| 0.255031 / 0.255139 (-0.000108) \| 0.270530 / 0.283200 (-0.012670) \| 0.017152 / 0.141683 (-0.124530) \| 1.094734 / 1.452155 (-0.357421) \| 1.163480 / 1.492716 (-0.329237) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092944 / 0.018006 (0.074938) \| 0.301042 / 0.000490 (0.300553) \| 0.000238 / 0.000200 (0.000038) \| 0.000049 / 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) \| 0.019090 / 0.037411 (-0.018321) \| 0.061046 / 0.014526 (0.046520) \| 0.073330 / 0.176557 (-0.103227) \| 0.121124 / 0.737135 (-0.616012) \| 0.080544 / 0.296338 (-0.215795) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.323866 / 0.215209 (0.108657) \| 2.797727 / 2.077655 (0.720072) \| 1.502994 / 1.504120 (-0.001126) \| 1.376177 / 1.541195 (-0.165018) \| 1.422741 / 1.468490 (-0.045749) \| 0.562990 / 4.584777 (-4.021786) \| 2.431781 / 3.745712 (-1.313931) \| 2.783226 / 5.269862 (-2.486635) \| 1.788055 / 4.565676 (-2.777621) \| 0.064206 / 0.424275 (-0.360069) \| 0.004989 / 0.007607 (-0.002618) \| 0.338282 / 0.226044 (0.112237) \| 3.356226 / 2.268929 (1.087297) \| 1.855644 / 55.444624 (-53.588980) \| 1.580876 / 6.876477 (-5.295601) \| 1.617418 / 2.142072 (-0.524655) \| 0.636816 / 4.805227 (-4.168411) \| 0.117680 / 6.500664 (-6.382985) \| 0.042560 / 0.075469 (-0.032909) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.956410 / 1.841788 (-0.885377) \| 11.764886 / 8.074308 (3.690578) \| 10.535801 / 10.191392 (0.344409) \| 0.137797 / 0.680424 (-0.542627) \| 0.014368 / 0.534201 (-0.519833) \| 0.286213 / 0.579283 (-0.293070) \| 0.267093 / 0.434364 (-0.167271) \| 0.334802 / 0.540337 (-0.205535) \| 0.441866 / 1.386936 (-0.945070) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005348 / 0.011353 (-0.006005) \| 0.003551 / 0.011008 (-0.007458) \| 0.049226 / 0.038508 (0.010718) \| 0.052072 / 0.023109 (0.028963) \| 0.268025 / 0.275898 (-0.007873) \| 0.289968 / 0.323480 (-0.033512) \| 0.004034 / 0.007986 (-0.003952) \| 0.002675 / 0.004328 (-0.001653) \| 0.048099 / 0.004250 (0.043848) \| 0.040141 / 0.037052 (0.003089) \| 0.272974 / 0.258489 (0.014485) \| 0.296097 / 0.293841 (0.002256) \| 0.028972 / 0.128546 (-0.099575) \| 0.010689 / 0.075646 (-0.064957) \| 0.057853 / 0.419271 (-0.361418) \| 0.032488 / 0.043533 (-0.011045) \| 0.272018 / 0.255139 (0.016879) \| 0.287179 / 0.283200 (0.003980) \| 0.018446 / 0.141683 (-0.123237) \| 1.140346 / 1.452155 (-0.311809) \| 1.247743 / 1.492716 (-0.244974) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091987 / 0.018006 (0.073980) \| 0.300527 / 0.000490 (0.300037) \| 0.000224 / 0.000200 (0.000024) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021390 / 0.037411 (-0.016021) \| 0.068768 / 0.014526 (0.054242) \| 0.080798 / 0.176557 (-0.095759) \| 0.119081 / 0.737135 (-0.618054) \| 0.082461 / 0.296338 (-0.213878) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286631 / 0.215209 (0.071422) \| 2.804633 / 2.077655 (0.726978) \| 1.574122 / 1.504120 (0.070002) \| 1.459994 / 1.541195 (-0.081201) \| 1.499739 / 1.468490 (0.031249) \| 0.579595 / 4.584777 (-4.005182) \| 2.426407 / 3.745712 (-1.319306) \| 2.917994 / 5.269862 (-2.351868) \| 1.846439 / 4.565676 (-2.719238) \| 0.063274 / 0.424275 (-0.361001) \| 0.005028 / 0.007607 (-0.002579) \| 0.341114 / 0.226044 (0.115070) \| 3.402677 / 2.268929 (1.133748) \| 1.940980 / 55.444624 (-53.503645) \| 1.651902 / 6.876477 (-5.224575) \| 1.677037 / 2.142072 (-0.465036) \| 0.651576 / 4.805227 (-4.153651) \| 0.116398 / 6.500664 (-6.384266) \| 0.041060 / 0.075469 (-0.034409) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.973278 / 1.841788 (-0.868509) \| 12.248332 / 8.074308 (4.174024) \| 10.830627 / 10.191392 (0.639235) \| 0.143146 / 0.680424 (-0.537278) \| 0.016249 / 0.534201 (-0.517952) \| 0.298563 / 0.579283 (-0.280720) \| 0.278643 / 0.434364 (-0.155721) \| 0.338206 / 0.540337 (-0.202132) \| 0.589485 / 1.386936 (-0.797451) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#da29ac32c57e079199c173e4404342cc105ed774 \"CML watermark\")\n" ]
2,023,617,877	6,468	Use auth to get parquet export	closed	2023-12-04T11:18:27	2023-12-04T17:21:22	2023-12-04T17:15:11	https://github.com/huggingface/datasets/pull/6468	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6468", "html_url": "https://github.com/huggingface/datasets/pull/6468", "diff_url": "https://github.com/huggingface/datasets/pull/6468.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6468.patch", "merged_at": "2023-12-04T17:15:11" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6468). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005076 / 0.011353 (-0.006277) \| 0.003510 / 0.011008 (-0.007499) \| 0.062939 / 0.038508 (0.024431) \| 0.049191 / 0.023109 (0.026082) \| 0.259088 / 0.275898 (-0.016810) \| 0.273523 / 0.323480 (-0.049957) \| 0.003902 / 0.007986 (-0.004083) \| 0.002699 / 0.004328 (-0.001630) \| 0.049077 / 0.004250 (0.044827) \| 0.037174 / 0.037052 (0.000121) \| 0.256467 / 0.258489 (-0.002022) \| 0.291235 / 0.293841 (-0.002606) \| 0.028119 / 0.128546 (-0.100427) \| 0.010404 / 0.075646 (-0.065243) \| 0.205825 / 0.419271 (-0.213446) \| 0.035741 / 0.043533 (-0.007792) \| 0.253219 / 0.255139 (-0.001920) \| 0.274986 / 0.283200 (-0.008214) \| 0.018379 / 0.141683 (-0.123304) \| 1.131139 / 1.452155 (-0.321016) \| 1.175875 / 1.492716 (-0.316841) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090717 / 0.018006 (0.072710) \| 0.299285 / 0.000490 (0.298796) \| 0.000217 / 0.000200 (0.000017) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018678 / 0.037411 (-0.018733) \| 0.060558 / 0.014526 (0.046032) \| 0.073828 / 0.176557 (-0.102728) \| 0.119302 / 0.737135 (-0.617833) \| 0.075261 / 0.296338 (-0.221078) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.277018 / 0.215209 (0.061809) \| 2.713255 / 2.077655 (0.635601) \| 1.427512 / 1.504120 (-0.076608) \| 1.311374 / 1.541195 (-0.229821) \| 1.348756 / 1.468490 (-0.119734) \| 0.561777 / 4.584777 (-4.023000) \| 2.393578 / 3.745712 (-1.352134) \| 2.798109 / 5.269862 (-2.471753) \| 1.754808 / 4.565676 (-2.810869) \| 0.062302 / 0.424275 (-0.361973) \| 0.004948 / 0.007607 (-0.002659) \| 0.328468 / 0.226044 (0.102423) \| 3.246558 / 2.268929 (0.977629) \| 1.786816 / 55.444624 (-53.657808) \| 1.482937 / 6.876477 (-5.393540) \| 1.516109 / 2.142072 (-0.625963) \| 0.634457 / 4.805227 (-4.170770) \| 0.116505 / 6.500664 (-6.384159) \| 0.042162 / 0.075469 (-0.033308) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.935312 / 1.841788 (-0.906476) \| 11.540599 / 8.074308 (3.466291) \| 10.512593 / 10.191392 (0.321201) \| 0.129638 / 0.680424 (-0.550786) \| 0.013994 / 0.534201 (-0.520207) \| 0.291490 / 0.579283 (-0.287793) \| 0.263641 / 0.434364 (-0.170722) \| 0.328718 / 0.540337 (-0.211619) \| 0.437598 / 1.386936 (-0.949338) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005192 / 0.011353 (-0.006161) \| 0.003454 / 0.011008 (-0.007554) \| 0.049448 / 0.038508 (0.010940) \| 0.050968 / 0.023109 (0.027859) \| 0.273702 / 0.275898 (-0.002196) \| 0.296934 / 0.323480 (-0.026545) \| 0.004066 / 0.007986 (-0.003920) \| 0.002611 / 0.004328 (-0.001718) \| 0.048284 / 0.004250 (0.044034) \| 0.041399 / 0.037052 (0.004346) \| 0.283000 / 0.258489 (0.024511) \| 0.302553 / 0.293841 (0.008712) \| 0.029086 / 0.128546 (-0.099460) \| 0.010510 / 0.075646 (-0.065137) \| 0.058097 / 0.419271 (-0.361175) \| 0.032992 / 0.043533 (-0.010541) \| 0.271752 / 0.255139 (0.016613) \| 0.293535 / 0.283200 (0.010335) \| 0.016958 / 0.141683 (-0.124725) \| 1.130126 / 1.452155 (-0.322028) \| 1.187228 / 1.492716 (-0.305488) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092321 / 0.018006 (0.074315) \| 0.302599 / 0.000490 (0.302109) \| 0.000215 / 0.000200 (0.000015) \| 0.000051 / 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) \| 0.021837 / 0.037411 (-0.015574) \| 0.071148 / 0.014526 (0.056622) \| 0.082448 / 0.176557 (-0.094108) \| 0.128083 / 0.737135 (-0.609053) \| 0.090864 / 0.296338 (-0.205474) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296248 / 0.215209 (0.081039) \| 2.881130 / 2.077655 (0.803476) \| 1.580360 / 1.504120 (0.076240) \| 1.454642 / 1.541195 (-0.086553) \| 1.461453 / 1.468490 (-0.007037) \| 0.567500 / 4.584777 (-4.017277) \| 2.493708 / 3.745712 (-1.252004) \| 2.756623 / 5.269862 (-2.513239) \| 1.771319 / 4.565676 (-2.794358) \| 0.062287 / 0.424275 (-0.361988) \| 0.004917 / 0.007607 (-0.002691) \| 0.348034 / 0.226044 (0.121990) \| 3.426938 / 2.268929 (1.158010) \| 1.954190 / 55.444624 (-53.490435) \| 1.660870 / 6.876477 (-5.215607) \| 1.675118 / 2.142072 (-0.466955) \| 0.636843 / 4.805227 (-4.168384) \| 0.115028 / 6.500664 (-6.385636) \| 0.040702 / 0.075469 (-0.034767) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.988076 / 1.841788 (-0.853711) \| 11.890867 / 8.074308 (3.816559) \| 10.621169 / 10.191392 (0.429777) \| 0.131568 / 0.680424 (-0.548856) \| 0.014994 / 0.534201 (-0.519207) \| 0.288900 / 0.579283 (-0.290384) \| 0.272092 / 0.434364 (-0.162272) \| 0.329397 / 0.540337 (-0.210940) \| 0.569337 / 1.386936 (-0.817599) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ae3b4a2268adc2f21568ff63891e9a83530c7e29 \"CML watermark\")\n" ]
2,023,174,233	6,467	New version release request	closed	2023-12-04T07:08:26	2023-12-04T15:42:22	2023-12-04T15:42:22	https://github.com/huggingface/datasets/issues/6467	null	LZHgrla	false	[ "We will publish it soon (we usually do it in intervals of 1-2 months, so probably next week)", "Thanks!" ]
2,022,601,176	6,466	Can't align optional features of struct	closed	2023-12-03T15:57:07	2024-02-15T15:19:33	2024-02-08T14:38:34	https://github.com/huggingface/datasets/issues/6466	null	Dref360	false	[ "Friendly bump, I would be happy to work on this issue once I get the go-ahead from the dev team. ", "Thanks for the PR!\r\n\r\nI'm struggling with this as well and would love to see this PR merged. My case is slightly different, with keys completely missing rather than being `None`:\r\n\r\n```\r\nds = Dataset.from_dict({'speaker': [{'name': 'Ben'}]})\r\nds2 = Dataset.from_dict({'speaker': [{'name': 'Fred', 'email': 'abc@aol.com'}]})\r\nprint(concatenate_datasets([ds, ds2]).features)\r\nprint(concatenate_datasets([ds, ds2]).to_dict())\r\n```\r\n\r\nI would expect this to work as well because other Dataset functions already handle this situation well. For example, this works just as expected:\r\n\r\n```\r\nds = Dataset.from_dict({'n': [1,2]})\r\nds_mapped = ds.map(lambda x: {\r\n 'speaker': {'name': 'Ben'} if x['n'] == 1 else {'name': 'Fred', 'email': 'abc@aol.com'}\r\n})\r\nprint(ds_mapped)\r\n```", "@vova-cyberhaven can you check with the new release if it fixes your issue? " ]
2,022,212,468	6,465	`load_dataset` uses out-of-date cache instead of re-downloading a changed dataset	open	2023-12-02T21:35:17	2024-08-20T08:32:11	null	https://github.com/huggingface/datasets/issues/6465	null	mnoukhov	false	[ "Hi, thanks for reporting! https://github.com/huggingface/datasets/pull/6459 will fix this.", "I meet a similar problem as using loading scripts. I have to set download_mode='force_redownload' to load the latest script." ]
2,020,860,462	6,464	Add concurrent loading of shards to datasets.load_from_disk	closed	2023-12-01T13:13:53	2024-01-26T15:17:43	2024-01-26T15:10:26	https://github.com/huggingface/datasets/pull/6464	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6464", "html_url": "https://github.com/huggingface/datasets/pull/6464", "diff_url": "https://github.com/huggingface/datasets/pull/6464.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6464.patch", "merged_at": "2024-01-26T15:10:26" }	kkoutini	true	[ "If we use multithreading no need to ask for `num_proc`. And maybe we the same numbers of threads as tqdm by default (IIRC it's `max(32, cpu_count() + 4)`) - you can even use `tqdm.contrib.concurrent.thread_map` directly to simplify the code\r\n\r\nAlso you can ignore the `IN_MEMORY_MAX_SIZE` config for this. This parameter is kinda legacy.\r\n\r\nHave you been able to run the benchmark on a fresh node ? The speed up doesn't seem that big in your first report", "I got some fresh nodes with the 32 threads I'm loading the dataset with around 315 seconds (without any preloading). Sequentially, it used to take around 1865 seconds. \r\nOk I'll roll back the changes and switch to `tqdm.contrib.concurrent.thread_map` without the `num_proc` parameter. ", "I switched to `tqdm.contrib.concurrent.thread_map` the code looks much simpler now.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6464). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Thanks for the update ! Btw you should tell Jack Morris that you added this :) see https://x.com/jxmnop/status/1749812573984461145?s=20 \r\n\r\nThe CI fail is unrelated to this PR - I'm trying to fix it on `main` right now", "> Thanks for the update ! Btw you should tell Jack Morris that you added this :) see https://x.com/jxmnop/status/1749812573984461145?s=20\r\n> \r\n> The CI fail is unrelated to this PR - I'm trying to fix it on `main` right now\r\n\r\nThank you! I'll let him know :)", "great work guys! letting you know here too", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005268 / 0.011353 (-0.006085) \| 0.003520 / 0.011008 (-0.007488) \| 0.063247 / 0.038508 (0.024739) \| 0.032337 / 0.023109 (0.009228) \| 0.243251 / 0.275898 (-0.032647) \| 0.265816 / 0.323480 (-0.057664) \| 0.002960 / 0.007986 (-0.005025) \| 0.002733 / 0.004328 (-0.001595) \| 0.048965 / 0.004250 (0.044715) \| 0.044341 / 0.037052 (0.007289) \| 0.260352 / 0.258489 (0.001863) \| 0.288546 / 0.293841 (-0.005295) \| 0.027903 / 0.128546 (-0.100643) \| 0.010897 / 0.075646 (-0.064749) \| 0.210852 / 0.419271 (-0.208419) \| 0.036302 / 0.043533 (-0.007231) \| 0.247440 / 0.255139 (-0.007699) \| 0.263024 / 0.283200 (-0.020176) \| 0.017732 / 0.141683 (-0.123951) \| 1.144206 / 1.452155 (-0.307949) \| 1.206135 / 1.492716 (-0.286581) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098404 / 0.018006 (0.080398) \| 0.310268 / 0.000490 (0.309778) \| 0.000231 / 0.000200 (0.000031) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018342 / 0.037411 (-0.019070) \| 0.060620 / 0.014526 (0.046094) \| 0.074248 / 0.176557 (-0.102308) \| 0.121025 / 0.737135 (-0.616110) \| 0.075331 / 0.296338 (-0.221008) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293721 / 0.215209 (0.078512) \| 2.854259 / 2.077655 (0.776605) \| 1.520735 / 1.504120 (0.016615) \| 1.393490 / 1.541195 (-0.147705) \| 1.494905 / 1.468490 (0.026415) \| 0.573812 / 4.584777 (-4.010965) \| 2.418383 / 3.745712 (-1.327329) \| 2.803916 / 5.269862 (-2.465945) \| 1.741646 / 4.565676 (-2.824030) \| 0.063341 / 0.424275 (-0.360934) \| 0.004950 / 0.007607 (-0.002658) \| 0.341758 / 0.226044 (0.115714) \| 3.392918 / 2.268929 (1.123989) \| 1.867037 / 55.444624 (-53.577587) \| 1.571381 / 6.876477 (-5.305096) \| 1.582883 / 2.142072 (-0.559190) \| 0.663660 / 4.805227 (-4.141567) \| 0.119587 / 6.500664 (-6.381077) \| 0.042071 / 0.075469 (-0.033398) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.940976 / 1.841788 (-0.900811) \| 11.841958 / 8.074308 (3.767650) \| 10.510954 / 10.191392 (0.319562) \| 0.131927 / 0.680424 (-0.548497) \| 0.015373 / 0.534201 (-0.518828) \| 0.294245 / 0.579283 (-0.285038) \| 0.269355 / 0.434364 (-0.165009) \| 0.330173 / 0.540337 (-0.210165) \| 0.436809 / 1.386936 (-0.950127) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005609 / 0.011353 (-0.005744) \| 0.003800 / 0.011008 (-0.007208) \| 0.055693 / 0.038508 (0.017185) \| 0.032606 / 0.023109 (0.009497) \| 0.302372 / 0.275898 (0.026474) \| 0.370530 / 0.323480 (0.047050) \| 0.004291 / 0.007986 (-0.003694) \| 0.002783 / 0.004328 (-0.001546) \| 0.049351 / 0.004250 (0.045101) \| 0.048186 / 0.037052 (0.011133) \| 0.290022 / 0.258489 (0.031533) \| 0.323358 / 0.293841 (0.029517) \| 0.053929 / 0.128546 (-0.074617) \| 0.011251 / 0.075646 (-0.064395) \| 0.058885 / 0.419271 (-0.360387) \| 0.033833 / 0.043533 (-0.009699) \| 0.283546 / 0.255139 (0.028407) \| 0.292416 / 0.283200 (0.009216) \| 0.017682 / 0.141683 (-0.124001) \| 1.141791 / 1.452155 (-0.310364) \| 1.202540 / 1.492716 (-0.290177) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.101240 / 0.018006 (0.083233) \| 0.313274 / 0.000490 (0.312784) \| 0.000255 / 0.000200 (0.000055) \| 0.000054 / 0.000054 (-0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023144 / 0.037411 (-0.014268) \| 0.078418 / 0.014526 (0.063892) \| 0.089716 / 0.176557 (-0.086840) \| 0.129065 / 0.737135 (-0.608070) \| 0.090976 / 0.296338 (-0.205362) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294585 / 0.215209 (0.079376) \| 2.921350 / 2.077655 (0.843695) \| 1.600977 / 1.504120 (0.096857) \| 1.483218 / 1.541195 (-0.057977) \| 1.533599 / 1.468490 (0.065109) \| 0.580064 / 4.584777 (-4.004712) \| 2.463501 / 3.745712 (-1.282211) \| 2.905853 / 5.269862 (-2.364009) \| 1.799701 / 4.565676 (-2.765975) \| 0.065057 / 0.424275 (-0.359218) \| 0.005080 / 0.007607 (-0.002527) \| 0.352292 / 0.226044 (0.126248) \| 3.429664 / 2.268929 (1.160735) \| 1.970752 / 55.444624 (-53.473872) \| 1.697151 / 6.876477 (-5.179326) \| 1.751678 / 2.142072 (-0.390394) \| 0.679264 / 4.805227 (-4.125963) \| 0.118197 / 6.500664 (-6.382467) \| 0.041834 / 0.075469 (-0.033635) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.985756 / 1.841788 (-0.856032) \| 13.335160 / 8.074308 (5.260852) \| 11.524807 / 10.191392 (1.333415) \| 0.134892 / 0.680424 (-0.545532) \| 0.016855 / 0.534201 (-0.517346) \| 0.294599 / 0.579283 (-0.284685) \| 0.285988 / 0.434364 (-0.148376) \| 0.331423 / 0.540337 (-0.208914) \| 0.418765 / 1.386936 (-0.968171) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#65434e449b6bb6c57121d9518d92abe9a97e0bb0 \"CML watermark\")\n" ]
2,020,702,967	6,463	Disable benchmarks in PRs	closed	2023-12-01T11:35:30	2023-12-01T12:09:09	2023-12-01T12:03:04	https://github.com/huggingface/datasets/pull/6463	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6463", "html_url": "https://github.com/huggingface/datasets/pull/6463", "diff_url": "https://github.com/huggingface/datasets/pull/6463.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6463.patch", "merged_at": "2023-12-01T12:03:04" }	lhoestq	true	[ "It's a way to detect regressions in performance sensitive methods like map, and find the commit that lead to the regression", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005357 / 0.011353 (-0.005996) \| 0.003295 / 0.011008 (-0.007713) \| 0.062354 / 0.038508 (0.023846) \| 0.054207 / 0.023109 (0.031098) \| 0.240030 / 0.275898 (-0.035869) \| 0.267863 / 0.323480 (-0.055617) \| 0.002925 / 0.007986 (-0.005061) \| 0.002634 / 0.004328 (-0.001695) \| 0.047952 / 0.004250 (0.043702) \| 0.038424 / 0.037052 (0.001372) \| 0.248059 / 0.258489 (-0.010430) \| 0.271923 / 0.293841 (-0.021918) \| 0.027513 / 0.128546 (-0.101034) \| 0.010344 / 0.075646 (-0.065302) \| 0.210864 / 0.419271 (-0.208407) \| 0.035911 / 0.043533 (-0.007622) \| 0.245166 / 0.255139 (-0.009973) \| 0.260914 / 0.283200 (-0.022285) \| 0.016709 / 0.141683 (-0.124974) \| 1.098324 / 1.452155 (-0.353830) \| 1.162638 / 1.492716 (-0.330079) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094419 / 0.018006 (0.076413) \| 0.303209 / 0.000490 (0.302719) \| 0.000214 / 0.000200 (0.000014) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018350 / 0.037411 (-0.019061) \| 0.060625 / 0.014526 (0.046099) \| 0.072545 / 0.176557 (-0.104012) \| 0.120905 / 0.737135 (-0.616231) \| 0.073858 / 0.296338 (-0.222480) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282011 / 0.215209 (0.066802) \| 2.758741 / 2.077655 (0.681086) \| 1.431691 / 1.504120 (-0.072429) \| 1.315883 / 1.541195 (-0.225312) \| 1.344235 / 1.468490 (-0.124255) \| 0.562117 / 4.584777 (-4.022660) \| 2.385641 / 3.745712 (-1.360071) \| 2.785402 / 5.269862 (-2.484460) \| 1.753912 / 4.565676 (-2.811764) \| 0.064054 / 0.424275 (-0.360221) \| 0.005050 / 0.007607 (-0.002557) \| 0.336452 / 0.226044 (0.110407) \| 3.302481 / 2.268929 (1.033553) \| 1.794105 / 55.444624 (-53.650519) \| 1.519346 / 6.876477 (-5.357131) \| 1.514911 / 2.142072 (-0.627161) \| 0.655779 / 4.805227 (-4.149449) \| 0.117913 / 6.500664 (-6.382751) \| 0.042229 / 0.075469 (-0.033240) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.935196 / 1.841788 (-0.906591) \| 11.490113 / 8.074308 (3.415805) \| 10.542446 / 10.191392 (0.351054) \| 0.129614 / 0.680424 (-0.550810) \| 0.014919 / 0.534201 (-0.519282) \| 0.288448 / 0.579283 (-0.290835) \| 0.266929 / 0.434364 (-0.167435) \| 0.328830 / 0.540337 (-0.211507) \| 0.475510 / 1.386936 (-0.911426) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005469 / 0.011353 (-0.005884) \| 0.003798 / 0.011008 (-0.007210) \| 0.049129 / 0.038508 (0.010621) \| 0.055490 / 0.023109 (0.032380) \| 0.265828 / 0.275898 (-0.010070) \| 0.286031 / 0.323480 (-0.037448) \| 0.004075 / 0.007986 (-0.003910) \| 0.002668 / 0.004328 (-0.001660) \| 0.047823 / 0.004250 (0.043573) \| 0.041946 / 0.037052 (0.004894) \| 0.270359 / 0.258489 (0.011869) \| 0.294287 / 0.293841 (0.000446) \| 0.029643 / 0.128546 (-0.098903) \| 0.010523 / 0.075646 (-0.065123) \| 0.057370 / 0.419271 (-0.361902) \| 0.033149 / 0.043533 (-0.010384) \| 0.264408 / 0.255139 (0.009269) \| 0.280413 / 0.283200 (-0.002787) \| 0.018313 / 0.141683 (-0.123370) \| 1.105982 / 1.452155 (-0.346173) \| 1.182486 / 1.492716 (-0.310230) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092643 / 0.018006 (0.074637) \| 0.301320 / 0.000490 (0.300831) \| 0.000221 / 0.000200 (0.000021) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021253 / 0.037411 (-0.016158) \| 0.068052 / 0.014526 (0.053527) \| 0.080821 / 0.176557 (-0.095736) \| 0.119320 / 0.737135 (-0.617816) \| 0.081952 / 0.296338 (-0.214387) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288536 / 0.215209 (0.073327) \| 2.819900 / 2.077655 (0.742245) \| 1.545210 / 1.504120 (0.041090) \| 1.422047 / 1.541195 (-0.119147) \| 1.439158 / 1.468490 (-0.029332) \| 0.564910 / 4.584777 (-4.019867) \| 2.430474 / 3.745712 (-1.315238) \| 2.763979 / 5.269862 (-2.505882) \| 1.732203 / 4.565676 (-2.833474) \| 0.062692 / 0.424275 (-0.361583) \| 0.004936 / 0.007607 (-0.002671) \| 0.341626 / 0.226044 (0.115582) \| 3.366623 / 2.268929 (1.097694) \| 1.917198 / 55.444624 (-53.527426) \| 1.637635 / 6.876477 (-5.238842) \| 1.625953 / 2.142072 (-0.516119) \| 0.634936 / 4.805227 (-4.170291) \| 0.115336 / 6.500664 (-6.385328) \| 0.040946 / 0.075469 (-0.034524) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.964865 / 1.841788 (-0.876922) \| 12.077233 / 8.074308 (4.002925) \| 10.664120 / 10.191392 (0.472728) \| 0.132084 / 0.680424 (-0.548340) \| 0.015931 / 0.534201 (-0.518270) \| 0.289181 / 0.579283 (-0.290102) \| 0.276943 / 0.434364 (-0.157420) \| 0.324884 / 0.540337 (-0.215453) \| 0.552570 / 1.386936 (-0.834366) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4ac3f2b3f6d867673e41a0253f9e1ad48db68a8e \"CML watermark\")\n" ]
2,019,238,388	6,462	Missing DatasetNotFoundError	closed	2023-11-30T18:09:43	2023-11-30T18:36:40	2023-11-30T18:30:30	https://github.com/huggingface/datasets/pull/6462	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6462", "html_url": "https://github.com/huggingface/datasets/pull/6462", "diff_url": "https://github.com/huggingface/datasets/pull/6462.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6462.patch", "merged_at": "2023-11-30T18:30:30" }	lhoestq	true	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005594 / 0.011353 (-0.005759) \| 0.003672 / 0.011008 (-0.007337) \| 0.062796 / 0.038508 (0.024288) \| 0.059432 / 0.023109 (0.036323) \| 0.253976 / 0.275898 (-0.021922) \| 0.281155 / 0.323480 (-0.042325) \| 0.003023 / 0.007986 (-0.004962) \| 0.003320 / 0.004328 (-0.001008) \| 0.049059 / 0.004250 (0.044809) \| 0.040252 / 0.037052 (0.003200) \| 0.259526 / 0.258489 (0.001037) \| 0.318798 / 0.293841 (0.024957) \| 0.027883 / 0.128546 (-0.100663) \| 0.010883 / 0.075646 (-0.064763) \| 0.206948 / 0.419271 (-0.212323) \| 0.036335 / 0.043533 (-0.007198) \| 0.253209 / 0.255139 (-0.001930) \| 0.275173 / 0.283200 (-0.008026) \| 0.020365 / 0.141683 (-0.121318) \| 1.121630 / 1.452155 (-0.330524) \| 1.174680 / 1.492716 (-0.318036) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098372 / 0.018006 (0.080366) \| 0.309949 / 0.000490 (0.309460) \| 0.000225 / 0.000200 (0.000025) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019495 / 0.037411 (-0.017916) \| 0.062321 / 0.014526 (0.047795) \| 0.074525 / 0.176557 (-0.102031) \| 0.121832 / 0.737135 (-0.615303) \| 0.077612 / 0.296338 (-0.218727) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288156 / 0.215209 (0.072947) \| 2.816411 / 2.077655 (0.738756) \| 1.497926 / 1.504120 (-0.006193) \| 1.378137 / 1.541195 (-0.163058) \| 1.446466 / 1.468490 (-0.022024) \| 0.566195 / 4.584777 (-4.018582) \| 2.391933 / 3.745712 (-1.353780) \| 2.929290 / 5.269862 (-2.340572) \| 1.828215 / 4.565676 (-2.737462) \| 0.063312 / 0.424275 (-0.360963) \| 0.005199 / 0.007607 (-0.002408) \| 0.342883 / 0.226044 (0.116838) \| 3.378388 / 2.268929 (1.109459) \| 1.865710 / 55.444624 (-53.578915) \| 1.573442 / 6.876477 (-5.303035) \| 1.631228 / 2.142072 (-0.510845) \| 0.651614 / 4.805227 (-4.153613) \| 0.118177 / 6.500664 (-6.382487) \| 0.043303 / 0.075469 (-0.032166) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.950694 / 1.841788 (-0.891094) \| 12.559851 / 8.074308 (4.485543) \| 10.751123 / 10.191392 (0.559731) \| 0.143107 / 0.680424 (-0.537317) \| 0.014469 / 0.534201 (-0.519732) \| 0.289531 / 0.579283 (-0.289752) \| 0.267316 / 0.434364 (-0.167047) \| 0.327748 / 0.540337 (-0.212590) \| 0.437758 / 1.386936 (-0.949178) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005669 / 0.011353 (-0.005684) \| 0.003831 / 0.011008 (-0.007177) \| 0.049096 / 0.038508 (0.010588) \| 0.061408 / 0.023109 (0.038299) \| 0.274571 / 0.275898 (-0.001327) \| 0.299978 / 0.323480 (-0.023501) \| 0.004216 / 0.007986 (-0.003769) \| 0.002848 / 0.004328 (-0.001480) \| 0.048755 / 0.004250 (0.044504) \| 0.042576 / 0.037052 (0.005524) \| 0.276781 / 0.258489 (0.018292) \| 0.300903 / 0.293841 (0.007062) \| 0.030243 / 0.128546 (-0.098303) \| 0.010967 / 0.075646 (-0.064679) \| 0.057879 / 0.419271 (-0.361392) \| 0.033206 / 0.043533 (-0.010327) \| 0.277620 / 0.255139 (0.022481) \| 0.296263 / 0.283200 (0.013064) \| 0.019022 / 0.141683 (-0.122660) \| 1.125615 / 1.452155 (-0.326539) \| 1.278016 / 1.492716 (-0.214700) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096836 / 0.018006 (0.078830) \| 0.307491 / 0.000490 (0.307001) \| 0.000230 / 0.000200 (0.000030) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021552 / 0.037411 (-0.015859) \| 0.071099 / 0.014526 (0.056573) \| 0.082432 / 0.176557 (-0.094124) \| 0.121826 / 0.737135 (-0.615310) \| 0.084902 / 0.296338 (-0.211437) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.328113 / 0.215209 (0.112904) \| 2.989613 / 2.077655 (0.911959) \| 1.604904 / 1.504120 (0.100784) \| 1.485459 / 1.541195 (-0.055735) \| 1.524829 / 1.468490 (0.056339) \| 0.580589 / 4.584777 (-4.004188) \| 2.440087 / 3.745712 (-1.305625) \| 2.944697 / 5.269862 (-2.325164) \| 1.832728 / 4.565676 (-2.732949) \| 0.064423 / 0.424275 (-0.359852) \| 0.004991 / 0.007607 (-0.002616) \| 0.357878 / 0.226044 (0.131834) \| 3.515415 / 2.268929 (1.246487) \| 1.964492 / 55.444624 (-53.480132) \| 1.684058 / 6.876477 (-5.192418) \| 1.730294 / 2.142072 (-0.411778) \| 0.661228 / 4.805227 (-4.143999) \| 0.122894 / 6.500664 (-6.377770) \| 0.041776 / 0.075469 (-0.033693) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.969849 / 1.841788 (-0.871939) \| 12.897067 / 8.074308 (4.822758) \| 10.908200 / 10.191392 (0.716808) \| 0.141139 / 0.680424 (-0.539285) \| 0.015377 / 0.534201 (-0.518824) \| 0.288625 / 0.579283 (-0.290658) \| 0.279020 / 0.434364 (-0.155344) \| 0.328386 / 0.540337 (-0.211951) \| 0.590833 / 1.386936 (-0.796103) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#39ea60eaabb05d8ee38c072f375816cf87fce1a9 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004986 / 0.011353 (-0.006367) \| 0.003070 / 0.011008 (-0.007938) \| 0.062433 / 0.038508 (0.023925) \| 0.050639 / 0.023109 (0.027530) \| 0.241807 / 0.275898 (-0.034091) \| 0.262517 / 0.323480 (-0.060963) \| 0.003826 / 0.007986 (-0.004160) \| 0.002602 / 0.004328 (-0.001727) \| 0.048508 / 0.004250 (0.044257) \| 0.037276 / 0.037052 (0.000224) \| 0.245757 / 0.258489 (-0.012732) \| 0.272969 / 0.293841 (-0.020871) \| 0.027139 / 0.128546 (-0.101407) \| 0.010265 / 0.075646 (-0.065381) \| 0.207279 / 0.419271 (-0.211992) \| 0.035312 / 0.043533 (-0.008221) \| 0.247535 / 0.255139 (-0.007604) \| 0.260668 / 0.283200 (-0.022532) \| 0.016496 / 0.141683 (-0.125187) \| 1.137510 / 1.452155 (-0.314645) \| 1.167870 / 1.492716 (-0.324847) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091743 / 0.018006 (0.073736) \| 0.298649 / 0.000490 (0.298159) \| 0.000208 / 0.000200 (0.000009) \| 0.000053 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019053 / 0.037411 (-0.018359) \| 0.060300 / 0.014526 (0.045774) \| 0.072154 / 0.176557 (-0.104402) \| 0.120293 / 0.737135 (-0.616842) \| 0.073923 / 0.296338 (-0.222415) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283058 / 0.215209 (0.067849) \| 2.769503 / 2.077655 (0.691849) \| 1.457016 / 1.504120 (-0.047104) \| 1.335753 / 1.541195 (-0.205441) \| 1.325986 / 1.468490 (-0.142504) \| 0.562553 / 4.584777 (-4.022224) \| 2.406144 / 3.745712 (-1.339568) \| 2.778063 / 5.269862 (-2.491799) \| 1.782199 / 4.565676 (-2.783477) \| 0.062490 / 0.424275 (-0.361785) \| 0.004912 / 0.007607 (-0.002695) \| 0.338500 / 0.226044 (0.112456) \| 3.309746 / 2.268929 (1.040818) \| 1.819693 / 55.444624 (-53.624931) \| 1.510295 / 6.876477 (-5.366182) \| 1.578402 / 2.142072 (-0.563671) \| 0.637517 / 4.805227 (-4.167710) \| 0.117018 / 6.500664 (-6.383647) \| 0.048149 / 0.075469 (-0.027320) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.939424 / 1.841788 (-0.902364) \| 11.494891 / 8.074308 (3.420583) \| 10.115194 / 10.191392 (-0.076198) \| 0.126751 / 0.680424 (-0.553673) \| 0.013567 / 0.534201 (-0.520634) \| 0.282501 / 0.579283 (-0.296782) \| 0.260594 / 0.434364 (-0.173770) \| 0.325940 / 0.540337 (-0.214397) \| 0.426186 / 1.386936 (-0.960750) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005405 / 0.011353 (-0.005948) \| 0.003557 / 0.011008 (-0.007451) \| 0.051139 / 0.038508 (0.012631) \| 0.053446 / 0.023109 (0.030337) \| 0.268051 / 0.275898 (-0.007847) \| 0.292343 / 0.323480 (-0.031136) \| 0.004716 / 0.007986 (-0.003269) \| 0.002677 / 0.004328 (-0.001651) \| 0.047634 / 0.004250 (0.043384) \| 0.041062 / 0.037052 (0.004009) \| 0.269225 / 0.258489 (0.010736) \| 0.297462 / 0.293841 (0.003621) \| 0.029292 / 0.128546 (-0.099254) \| 0.010947 / 0.075646 (-0.064699) \| 0.057845 / 0.419271 (-0.361426) \| 0.032793 / 0.043533 (-0.010740) \| 0.265308 / 0.255139 (0.010169) \| 0.288242 / 0.283200 (0.005043) \| 0.018311 / 0.141683 (-0.123372) \| 1.140957 / 1.452155 (-0.311197) \| 1.204883 / 1.492716 (-0.287833) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091375 / 0.018006 (0.073368) \| 0.285922 / 0.000490 (0.285432) \| 0.000238 / 0.000200 (0.000038) \| 0.000051 / 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) \| 0.021277 / 0.037411 (-0.016134) \| 0.068853 / 0.014526 (0.054328) \| 0.081002 / 0.176557 (-0.095555) \| 0.120998 / 0.737135 (-0.616138) \| 0.082741 / 0.296338 (-0.213598) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.299398 / 0.215209 (0.084189) \| 2.909622 / 2.077655 (0.831967) \| 1.624381 / 1.504120 (0.120261) \| 1.501683 / 1.541195 (-0.039512) \| 1.523045 / 1.468490 (0.054555) \| 0.548960 / 4.584777 (-4.035817) \| 2.413297 / 3.745712 (-1.332415) \| 2.817852 / 5.269862 (-2.452010) \| 1.754407 / 4.565676 (-2.811270) \| 0.061912 / 0.424275 (-0.362363) \| 0.004880 / 0.007607 (-0.002727) \| 0.353989 / 0.226044 (0.127944) \| 3.496147 / 2.268929 (1.227219) \| 2.003026 / 55.444624 (-53.441598) \| 1.702013 / 6.876477 (-5.174463) \| 1.680935 / 2.142072 (-0.461137) \| 0.630183 / 4.805227 (-4.175044) \| 0.113786 / 6.500664 (-6.386878) \| 0.040061 / 0.075469 (-0.035408) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.957218 / 1.841788 (-0.884569) \| 11.914469 / 8.074308 (3.840160) \| 10.488896 / 10.191392 (0.297504) \| 0.129292 / 0.680424 (-0.551132) \| 0.016603 / 0.534201 (-0.517598) \| 0.287367 / 0.579283 (-0.291916) \| 0.271332 / 0.434364 (-0.163032) \| 0.325577 / 0.540337 (-0.214761) \| 0.560553 / 1.386936 (-0.826383) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2d31e434bbeafdf6a70cb80539342d8fe5f5fd27 \"CML watermark\")\n" ]
2,018,850,731	6,461	Fix shard retry mechanism in `push_to_hub`	closed	2023-11-30T14:57:14	2023-12-01T17:57:39	2023-12-01T17:51:33	https://github.com/huggingface/datasets/pull/6461	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6461", "html_url": "https://github.com/huggingface/datasets/pull/6461", "diff_url": "https://github.com/huggingface/datasets/pull/6461.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6461.patch", "merged_at": "2023-12-01T17:51:33" }	mariosasko	true	[ "@Wauplin Maybe `504` should be added to the `retry_on_status_codes` tuple [here](https://github.com/huggingface/huggingface_hub/blob/5eefebee2c150a2df950ab710db350e96c711433/src/huggingface_hub/lfs.py#L300) to guard against https://github.com/huggingface/datasets/issues/3872", "We could but I'm not sure to have witness a 504 on S3 before. The issue reported in https://github.com/huggingface/datasets/issues/3872 is a 504 on the `/upload` endpoint on the Hub and this is not an endpoint that is retried on [this line](https://github.com/huggingface/huggingface_hub/blob/5eefebee2c150a2df950ab710db350e96c711433/src/huggingface_hub/lfs.py#L300).", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005110 / 0.011353 (-0.006243) \| 0.003307 / 0.011008 (-0.007701) \| 0.062601 / 0.038508 (0.024093) \| 0.049644 / 0.023109 (0.026534) \| 0.243195 / 0.275898 (-0.032703) \| 0.273543 / 0.323480 (-0.049936) \| 0.003862 / 0.007986 (-0.004123) \| 0.002624 / 0.004328 (-0.001705) \| 0.048273 / 0.004250 (0.044023) \| 0.037820 / 0.037052 (0.000768) \| 0.249134 / 0.258489 (-0.009355) \| 0.319359 / 0.293841 (0.025518) \| 0.027816 / 0.128546 (-0.100730) \| 0.010422 / 0.075646 (-0.065225) \| 0.206607 / 0.419271 (-0.212665) \| 0.035719 / 0.043533 (-0.007814) \| 0.250300 / 0.255139 (-0.004839) \| 0.290377 / 0.283200 (0.007177) \| 0.018459 / 0.141683 (-0.123224) \| 1.114664 / 1.452155 (-0.337490) \| 1.171429 / 1.492716 (-0.321288) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091483 / 0.018006 (0.073477) \| 0.302770 / 0.000490 (0.302281) \| 0.000203 / 0.000200 (0.000003) \| 0.000047 / 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) \| 0.018870 / 0.037411 (-0.018541) \| 0.062692 / 0.014526 (0.048166) \| 0.075381 / 0.176557 (-0.101176) \| 0.122338 / 0.737135 (-0.614797) \| 0.075608 / 0.296338 (-0.220730) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288115 / 0.215209 (0.072906) \| 2.816183 / 2.077655 (0.738528) \| 1.535601 / 1.504120 (0.031481) \| 1.409546 / 1.541195 (-0.131648) \| 1.438569 / 1.468490 (-0.029921) \| 0.561797 / 4.584777 (-4.022980) \| 2.373921 / 3.745712 (-1.371791) \| 2.739437 / 5.269862 (-2.530424) \| 1.750921 / 4.565676 (-2.814755) \| 0.062114 / 0.424275 (-0.362161) \| 0.004965 / 0.007607 (-0.002642) \| 0.348614 / 0.226044 (0.122569) \| 3.519631 / 2.268929 (1.250703) \| 1.910797 / 55.444624 (-53.533827) \| 1.610541 / 6.876477 (-5.265936) \| 1.617972 / 2.142072 (-0.524100) \| 0.639421 / 4.805227 (-4.165806) \| 0.117371 / 6.500664 (-6.383293) \| 0.041851 / 0.075469 (-0.033618) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945563 / 1.841788 (-0.896224) \| 11.362399 / 8.074308 (3.288090) \| 10.468468 / 10.191392 (0.277075) \| 0.128925 / 0.680424 (-0.551499) \| 0.013892 / 0.534201 (-0.520309) \| 0.285487 / 0.579283 (-0.293796) \| 0.269295 / 0.434364 (-0.165069) \| 0.324843 / 0.540337 (-0.215495) \| 0.438452 / 1.386936 (-0.948484) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005303 / 0.011353 (-0.006050) \| 0.003162 / 0.011008 (-0.007846) \| 0.048177 / 0.038508 (0.009669) \| 0.048708 / 0.023109 (0.025599) \| 0.271663 / 0.275898 (-0.004235) \| 0.289948 / 0.323480 (-0.033532) \| 0.003955 / 0.007986 (-0.004030) \| 0.002616 / 0.004328 (-0.001713) \| 0.047510 / 0.004250 (0.043260) \| 0.039938 / 0.037052 (0.002886) \| 0.277449 / 0.258489 (0.018960) \| 0.300315 / 0.293841 (0.006474) \| 0.029263 / 0.128546 (-0.099283) \| 0.010403 / 0.075646 (-0.065244) \| 0.056682 / 0.419271 (-0.362590) \| 0.032757 / 0.043533 (-0.010776) \| 0.273291 / 0.255139 (0.018152) \| 0.289023 / 0.283200 (0.005824) \| 0.017843 / 0.141683 (-0.123840) \| 1.124762 / 1.452155 (-0.327393) \| 1.176646 / 1.492716 (-0.316070) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004568 / 0.018006 (-0.013438) \| 0.300715 / 0.000490 (0.300225) \| 0.000212 / 0.000200 (0.000012) \| 0.000049 / 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) \| 0.021528 / 0.037411 (-0.015883) \| 0.068317 / 0.014526 (0.053792) \| 0.081358 / 0.176557 (-0.095199) \| 0.119297 / 0.737135 (-0.617838) \| 0.082445 / 0.296338 (-0.213893) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289681 / 0.215209 (0.074472) \| 2.843862 / 2.077655 (0.766208) \| 1.574257 / 1.504120 (0.070137) \| 1.454026 / 1.541195 (-0.087169) \| 1.478379 / 1.468490 (0.009889) \| 0.558259 / 4.584777 (-4.026518) \| 2.513261 / 3.745712 (-1.232451) \| 2.759751 / 5.269862 (-2.510111) \| 1.730335 / 4.565676 (-2.835341) \| 0.063805 / 0.424275 (-0.360470) \| 0.004991 / 0.007607 (-0.002616) \| 0.346586 / 0.226044 (0.120542) \| 3.369163 / 2.268929 (1.100234) \| 1.934734 / 55.444624 (-53.509890) \| 1.658864 / 6.876477 (-5.217613) \| 1.645621 / 2.142072 (-0.496452) \| 0.636633 / 4.805227 (-4.168594) \| 0.116839 / 6.500664 (-6.383825) \| 0.040863 / 0.075469 (-0.034606) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.960925 / 1.841788 (-0.880863) \| 11.769189 / 8.074308 (3.694881) \| 10.713662 / 10.191392 (0.522270) \| 0.140510 / 0.680424 (-0.539914) \| 0.015424 / 0.534201 (-0.518777) \| 0.288039 / 0.579283 (-0.291244) \| 0.277623 / 0.434364 (-0.156741) \| 0.322622 / 0.540337 (-0.217716) \| 0.539805 / 1.386936 (-0.847131) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#07ad81c15bd3b954defe779fc37ba5f432f5ff2a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005501 / 0.011353 (-0.005852) \| 0.003754 / 0.011008 (-0.007254) \| 0.062628 / 0.038508 (0.024120) \| 0.059951 / 0.023109 (0.036842) \| 0.254851 / 0.275898 (-0.021047) \| 0.272133 / 0.323480 (-0.051347) \| 0.003962 / 0.007986 (-0.004024) \| 0.002759 / 0.004328 (-0.001569) \| 0.048412 / 0.004250 (0.044161) \| 0.039349 / 0.037052 (0.002297) \| 0.253093 / 0.258489 (-0.005397) \| 0.287048 / 0.293841 (-0.006793) \| 0.027197 / 0.128546 (-0.101349) \| 0.010828 / 0.075646 (-0.064819) \| 0.206371 / 0.419271 (-0.212901) \| 0.035881 / 0.043533 (-0.007652) \| 0.254905 / 0.255139 (-0.000234) \| 0.273819 / 0.283200 (-0.009381) \| 0.018041 / 0.141683 (-0.123642) \| 1.103970 / 1.452155 (-0.348185) \| 1.166340 / 1.492716 (-0.326377) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093196 / 0.018006 (0.075190) \| 0.302690 / 0.000490 (0.302200) \| 0.000219 / 0.000200 (0.000019) \| 0.000045 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019552 / 0.037411 (-0.017860) \| 0.062337 / 0.014526 (0.047811) \| 0.074070 / 0.176557 (-0.102486) \| 0.120998 / 0.737135 (-0.616137) \| 0.076265 / 0.296338 (-0.220074) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.272637 / 0.215209 (0.057427) \| 2.693350 / 2.077655 (0.615696) \| 1.398020 / 1.504120 (-0.106100) \| 1.285706 / 1.541195 (-0.255488) \| 1.342810 / 1.468490 (-0.125680) \| 0.565378 / 4.584777 (-4.019399) \| 2.390131 / 3.745712 (-1.355581) \| 2.892137 / 5.269862 (-2.377725) \| 1.819840 / 4.565676 (-2.745836) \| 0.062789 / 0.424275 (-0.361486) \| 0.004920 / 0.007607 (-0.002687) \| 0.329281 / 0.226044 (0.103237) \| 3.261664 / 2.268929 (0.992735) \| 1.775102 / 55.444624 (-53.669523) \| 1.514341 / 6.876477 (-5.362136) \| 1.530805 / 2.142072 (-0.611267) \| 0.641009 / 4.805227 (-4.164218) \| 0.118626 / 6.500664 (-6.382038) \| 0.042732 / 0.075469 (-0.032737) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.933179 / 1.841788 (-0.908609) \| 12.085247 / 8.074308 (4.010939) \| 10.541596 / 10.191392 (0.350204) \| 0.140141 / 0.680424 (-0.540283) \| 0.014646 / 0.534201 (-0.519555) \| 0.289640 / 0.579283 (-0.289643) \| 0.281042 / 0.434364 (-0.153322) \| 0.326462 / 0.540337 (-0.213876) \| 0.441981 / 1.386936 (-0.944955) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005259 / 0.011353 (-0.006094) \| 0.003766 / 0.011008 (-0.007242) \| 0.048782 / 0.038508 (0.010273) \| 0.064946 / 0.023109 (0.041836) \| 0.264529 / 0.275898 (-0.011369) \| 0.289675 / 0.323480 (-0.033805) \| 0.004057 / 0.007986 (-0.003928) \| 0.002805 / 0.004328 (-0.001523) \| 0.047709 / 0.004250 (0.043459) \| 0.041149 / 0.037052 (0.004096) \| 0.271254 / 0.258489 (0.012765) \| 0.296685 / 0.293841 (0.002844) \| 0.029486 / 0.128546 (-0.099060) \| 0.010608 / 0.075646 (-0.065038) \| 0.056392 / 0.419271 (-0.362879) \| 0.033181 / 0.043533 (-0.010352) \| 0.267029 / 0.255139 (0.011890) \| 0.284987 / 0.283200 (0.001787) \| 0.018045 / 0.141683 (-0.123637) \| 1.137358 / 1.452155 (-0.314796) \| 1.184007 / 1.492716 (-0.308709) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004603 / 0.018006 (-0.013403) \| 0.303901 / 0.000490 (0.303411) \| 0.000225 / 0.000200 (0.000025) \| 0.000055 / 0.000054 (0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021957 / 0.037411 (-0.015454) \| 0.069427 / 0.014526 (0.054901) \| 0.082394 / 0.176557 (-0.094163) \| 0.120745 / 0.737135 (-0.616390) \| 0.084571 / 0.296338 (-0.211767) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292832 / 0.215209 (0.077623) \| 2.824295 / 2.077655 (0.746640) \| 1.563273 / 1.504120 (0.059153) \| 1.440202 / 1.541195 (-0.100992) \| 1.489810 / 1.468490 (0.021320) \| 0.561120 / 4.584777 (-4.023657) \| 2.439045 / 3.745712 (-1.306667) \| 2.867139 / 5.269862 (-2.402722) \| 1.793812 / 4.565676 (-2.771865) \| 0.062797 / 0.424275 (-0.361478) \| 0.005033 / 0.007607 (-0.002574) \| 0.343648 / 0.226044 (0.117604) \| 3.432285 / 2.268929 (1.163357) \| 1.918175 / 55.444624 (-53.526449) \| 1.637245 / 6.876477 (-5.239232) \| 1.709246 / 2.142072 (-0.432826) \| 0.634744 / 4.805227 (-4.170483) \| 0.115782 / 6.500664 (-6.384882) \| 0.041228 / 0.075469 (-0.034241) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.962369 / 1.841788 (-0.879418) \| 12.750819 / 8.074308 (4.676511) \| 10.927356 / 10.191392 (0.735964) \| 0.143454 / 0.680424 (-0.536970) \| 0.015348 / 0.534201 (-0.518853) \| 0.291207 / 0.579283 (-0.288076) \| 0.276924 / 0.434364 (-0.157440) \| 0.327287 / 0.540337 (-0.213050) \| 0.577439 / 1.386936 (-0.809497) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#544ad95f6b6da7fee44a2bc838e15a5e0156c946 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005070 / 0.011353 (-0.006283) \| 0.003475 / 0.011008 (-0.007533) \| 0.061985 / 0.038508 (0.023477) \| 0.048539 / 0.023109 (0.025430) \| 0.229935 / 0.275898 (-0.045963) \| 0.255247 / 0.323480 (-0.068233) \| 0.003919 / 0.007986 (-0.004066) \| 0.002664 / 0.004328 (-0.001664) \| 0.048892 / 0.004250 (0.044642) \| 0.037381 / 0.037052 (0.000328) \| 0.238517 / 0.258489 (-0.019972) \| 0.284069 / 0.293841 (-0.009772) \| 0.027513 / 0.128546 (-0.101033) \| 0.010778 / 0.075646 (-0.064868) \| 0.205004 / 0.419271 (-0.214268) \| 0.035553 / 0.043533 (-0.007980) \| 0.230117 / 0.255139 (-0.025022) \| 0.251150 / 0.283200 (-0.032050) \| 0.017951 / 0.141683 (-0.123732) \| 1.145548 / 1.452155 (-0.306607) \| 1.191659 / 1.492716 (-0.301057) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092335 / 0.018006 (0.074329) \| 0.300264 / 0.000490 (0.299774) \| 0.000206 / 0.000200 (0.000006) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018608 / 0.037411 (-0.018804) \| 0.060376 / 0.014526 (0.045850) \| 0.073551 / 0.176557 (-0.103006) \| 0.118840 / 0.737135 (-0.618295) \| 0.074447 / 0.296338 (-0.221892) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287033 / 0.215209 (0.071824) \| 2.770958 / 2.077655 (0.693303) \| 1.443986 / 1.504120 (-0.060134) \| 1.314627 / 1.541195 (-0.226567) \| 1.342287 / 1.468490 (-0.126203) \| 0.559607 / 4.584777 (-4.025170) \| 2.409678 / 3.745712 (-1.336034) \| 2.772566 / 5.269862 (-2.497295) \| 1.743511 / 4.565676 (-2.822165) \| 0.062277 / 0.424275 (-0.361998) \| 0.004952 / 0.007607 (-0.002655) \| 0.330581 / 0.226044 (0.104537) \| 3.280385 / 2.268929 (1.011456) \| 1.809599 / 55.444624 (-53.635025) \| 1.532186 / 6.876477 (-5.344290) \| 1.529689 / 2.142072 (-0.612383) \| 0.645213 / 4.805227 (-4.160014) \| 0.117564 / 6.500664 (-6.383100) \| 0.041657 / 0.075469 (-0.033812) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.943912 / 1.841788 (-0.897876) \| 11.414317 / 8.074308 (3.340009) \| 10.394915 / 10.191392 (0.203523) \| 0.129271 / 0.680424 (-0.551153) \| 0.013934 / 0.534201 (-0.520267) \| 0.288217 / 0.579283 (-0.291066) \| 0.267171 / 0.434364 (-0.167193) \| 0.327112 / 0.540337 (-0.213225) \| 0.446680 / 1.386936 (-0.940256) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005200 / 0.011353 (-0.006152) \| 0.003453 / 0.011008 (-0.007555) \| 0.048736 / 0.038508 (0.010228) \| 0.051073 / 0.023109 (0.027964) \| 0.276591 / 0.275898 (0.000693) \| 0.294495 / 0.323480 (-0.028985) \| 0.004069 / 0.007986 (-0.003917) \| 0.002945 / 0.004328 (-0.001383) \| 0.047090 / 0.004250 (0.042839) \| 0.040445 / 0.037052 (0.003393) \| 0.278464 / 0.258489 (0.019975) \| 0.304020 / 0.293841 (0.010179) \| 0.028811 / 0.128546 (-0.099736) \| 0.010388 / 0.075646 (-0.065259) \| 0.057214 / 0.419271 (-0.362057) \| 0.032588 / 0.043533 (-0.010945) \| 0.277694 / 0.255139 (0.022555) \| 0.294979 / 0.283200 (0.011779) \| 0.018384 / 0.141683 (-0.123299) \| 1.162332 / 1.452155 (-0.289822) \| 1.188355 / 1.492716 (-0.304361) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090501 / 0.018006 (0.072495) \| 0.303122 / 0.000490 (0.302632) \| 0.000222 / 0.000200 (0.000022) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022536 / 0.037411 (-0.014876) \| 0.068452 / 0.014526 (0.053926) \| 0.080932 / 0.176557 (-0.095625) \| 0.119185 / 0.737135 (-0.617950) \| 0.081513 / 0.296338 (-0.214825) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291522 / 0.215209 (0.076313) \| 2.849467 / 2.077655 (0.771812) \| 1.597395 / 1.504120 (0.093275) \| 1.512872 / 1.541195 (-0.028323) \| 1.488144 / 1.468490 (0.019654) \| 0.572436 / 4.584777 (-4.012341) \| 2.440129 / 3.745712 (-1.305583) \| 2.788045 / 5.269862 (-2.481817) \| 1.754246 / 4.565676 (-2.811430) \| 0.066706 / 0.424275 (-0.357569) \| 0.005035 / 0.007607 (-0.002573) \| 0.336621 / 0.226044 (0.110576) \| 3.322820 / 2.268929 (1.053891) \| 1.940494 / 55.444624 (-53.504130) \| 1.670022 / 6.876477 (-5.206454) \| 1.666353 / 2.142072 (-0.475720) \| 0.646180 / 4.805227 (-4.159047) \| 0.116676 / 6.500664 (-6.383988) \| 0.040559 / 0.075469 (-0.034910) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.971396 / 1.841788 (-0.870392) \| 11.782426 / 8.074308 (3.708118) \| 10.672034 / 10.191392 (0.480642) \| 0.137658 / 0.680424 (-0.542766) \| 0.016210 / 0.534201 (-0.517991) \| 0.288302 / 0.579283 (-0.290981) \| 0.280775 / 0.434364 (-0.153589) \| 0.326962 / 0.540337 (-0.213375) \| 0.558511 / 1.386936 (-0.828425) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#76020180407d7ea9a0b535758d8d1b241fd19d8c \"CML watermark\")\n" ]
2,017,433,899	6,460	jsonlines files don't load with `load_dataset`	closed	2023-11-29T21:20:11	2023-12-29T02:58:29	2023-12-05T13:30:53	https://github.com/huggingface/datasets/issues/6460	null	serenalotreck	false	[ "Hi @serenalotreck,\r\n\r\nWe use Apache Arrow `pyarrow` to read jsonlines and it throws an error when trying to load your data files:\r\n```python\r\nIn [1]: import pyarrow as pa\r\n\r\nIn [2]: data = pa.json.read_json(\"train.jsonl\")\r\n---------------------------------------------------------------------------\r\nArrowInvalid Traceback (most recent call last)\r\n<ipython-input-14-e9b104832528> in <module>\r\n----> 1 data = pa.json.read_json(\"train.jsonl\")\r\n\r\n.../huggingface/datasets/venv/lib/python3.9/site-packages/pyarrow/_json.pyx in pyarrow._json.read_json()\r\n\r\n.../huggingface/datasets/venv/lib/python3.9/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n.../huggingface/datasets/venv/lib/python3.9/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: JSON parse error: Column(/ner/[]/[]/[]) changed from number to string in row 0\r\n```\r\n\r\nI think it has to do with the data structure of the fields \"ner\" (and also \"relations\"):\r\n```json\r\n\"ner\": [\r\n [\r\n [0, 4, \"Biochemical_process\"], \r\n [15, 16, \"Protein\"]\r\n ], \r\n```\r\nArrow interprets this data structure as an array, an arrays contain just a single data type: \r\n- when reading sequentially, it finds first the `0` and infers that the data is of type `number`;\r\n- when it finds the string `\"Biochemical_process\"`, it cannot cast it to number and throws the `ArrowInvalid` error\r\n\r\nOne solution could be to change the data structure of your data files. Any other ideas, @huggingface/datasets ?", "Hi @albertvillanova, \r\n\r\nThanks for the explanation! To the best of my knowledge, arrays in a json [can contain multiple data types](https://docs.actian.com/ingres/11.2/index.html#page/SQLRef/Data_Types.htm), and I'm able to read these files with the `jsonlines` package. Is the requirement for arrays to only have one data type specific to PyArrow?\r\n\r\nI'd prefer to keep the data structure as is, since it's a specific input requirement for the models this data was generated for. Any thoughts on how to enable the use of `load_dataset` with this dataset would be great!", "Hi again @serenalotreck,\r\n\r\nYes, it is specific to PyArrow: as far as I know, Arrow does not support arrays with multiple data types.\r\n\r\nAs this is related specifically to your dataset structure (and not the `datasets` library), I have created a dedicated issue in your dataset page: https://huggingface.co/datasets/slotreck/pickle/discussions/1\r\n\r\nLet's continue the discussion there! :hugs: ", "> Hi again @serenalotreck,\r\n> \r\n> Yes, it is specific to PyArrow: as far as I know, Arrow does not support arrays with multiple data types.\r\n> \r\n> As this is related specifically to your dataset structure (and not the `datasets` library), I have created a dedicated issue in your dataset page: https://huggingface.co/datasets/slotreck/pickle/discussions/1\r\n> \r\n> Let's continue the discussion there! 🤗\r\n\r\nThis is really terrible. My JSONL format data is very simple, but I still report this error\r\n![image](https://github.com/huggingface/datasets/assets/58240629/e3fed922-ced4-406c-b5bc-90a4b891c4ee)\r\nThe error message is as follows：\r\n File \"pyarrow/_json.pyx\", line 290, in pyarrow._json.read_json\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 100, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: JSON parse error: Column(/inputs) changed from string to number in row 208\r\n" ]
2,017,029,380	6,459	Retrieve cached datasets that were pushed to hub when offline	closed	2023-11-29T16:56:15	2024-03-25T13:55:42	2024-03-25T13:55:42	https://github.com/huggingface/datasets/pull/6459	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6459", "html_url": "https://github.com/huggingface/datasets/pull/6459", "diff_url": "https://github.com/huggingface/datasets/pull/6459.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6459.patch", "merged_at": null }	lhoestq	true	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005292 / 0.011353 (-0.006061) \| 0.003811 / 0.011008 (-0.007197) \| 0.064912 / 0.038508 (0.026404) \| 0.061199 / 0.023109 (0.038090) \| 0.242953 / 0.275898 (-0.032945) \| 0.271789 / 0.323480 (-0.051691) \| 0.003994 / 0.007986 (-0.003991) \| 0.002723 / 0.004328 (-0.001606) \| 0.049952 / 0.004250 (0.045701) \| 0.039489 / 0.037052 (0.002437) \| 0.261143 / 0.258489 (0.002654) \| 0.288800 / 0.293841 (-0.005041) \| 0.028130 / 0.128546 (-0.100416) \| 0.010724 / 0.075646 (-0.064922) \| 0.208218 / 0.419271 (-0.211054) \| 0.036224 / 0.043533 (-0.007309) \| 0.247189 / 0.255139 (-0.007950) \| 0.274702 / 0.283200 (-0.008498) \| 0.019714 / 0.141683 (-0.121969) \| 1.134853 / 1.452155 (-0.317301) \| 1.192655 / 1.492716 (-0.300062) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096391 / 0.018006 (0.078385) \| 0.303802 / 0.000490 (0.303312) \| 0.000219 / 0.000200 (0.000019) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019530 / 0.037411 (-0.017881) \| 0.061588 / 0.014526 (0.047062) \| 0.075122 / 0.176557 (-0.101434) \| 0.120980 / 0.737135 (-0.616155) \| 0.075807 / 0.296338 (-0.220532) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281672 / 0.215209 (0.066463) \| 2.779884 / 2.077655 (0.702229) \| 1.502026 / 1.504120 (-0.002094) \| 1.369474 / 1.541195 (-0.171721) \| 1.402694 / 1.468490 (-0.065796) \| 0.559120 / 4.584777 (-4.025657) \| 2.355320 / 3.745712 (-1.390393) \| 2.823987 / 5.269862 (-2.445875) \| 1.763888 / 4.565676 (-2.801788) \| 0.061715 / 0.424275 (-0.362560) \| 0.005015 / 0.007607 (-0.002592) \| 0.342669 / 0.226044 (0.116625) \| 3.360651 / 2.268929 (1.091722) \| 1.887277 / 55.444624 (-53.557348) \| 1.555613 / 6.876477 (-5.320864) \| 1.614126 / 2.142072 (-0.527946) \| 0.643797 / 4.805227 (-4.161430) \| 0.118365 / 6.500664 (-6.382299) \| 0.042596 / 0.075469 (-0.032873) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.951383 / 1.841788 (-0.890405) \| 13.169812 / 8.074308 (5.095504) \| 10.772460 / 10.191392 (0.581068) \| 0.133248 / 0.680424 (-0.547176) \| 0.014597 / 0.534201 (-0.519604) \| 0.289758 / 0.579283 (-0.289525) \| 0.266324 / 0.434364 (-0.168040) \| 0.334811 / 0.540337 (-0.205526) \| 0.445566 / 1.386936 (-0.941370) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005668 / 0.011353 (-0.005684) \| 0.003583 / 0.011008 (-0.007425) \| 0.050681 / 0.038508 (0.012173) \| 0.063244 / 0.023109 (0.040135) \| 0.279624 / 0.275898 (0.003726) \| 0.308030 / 0.323480 (-0.015450) \| 0.004160 / 0.007986 (-0.003826) \| 0.002633 / 0.004328 (-0.001696) \| 0.048475 / 0.004250 (0.044225) \| 0.043106 / 0.037052 (0.006054) \| 0.283678 / 0.258489 (0.025189) \| 0.309730 / 0.293841 (0.015889) \| 0.030290 / 0.128546 (-0.098256) \| 0.011112 / 0.075646 (-0.064534) \| 0.058234 / 0.419271 (-0.361038) \| 0.033553 / 0.043533 (-0.009979) \| 0.279902 / 0.255139 (0.024763) \| 0.298041 / 0.283200 (0.014841) \| 0.019367 / 0.141683 (-0.122316) \| 1.142438 / 1.452155 (-0.309717) \| 1.197305 / 1.492716 (-0.295411) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090875 / 0.018006 (0.072869) \| 0.301174 / 0.000490 (0.300685) \| 0.000216 / 0.000200 (0.000016) \| 0.000053 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021544 / 0.037411 (-0.015867) \| 0.071371 / 0.014526 (0.056846) \| 0.080821 / 0.176557 (-0.095736) \| 0.120054 / 0.737135 (-0.617082) \| 0.082611 / 0.296338 (-0.213728) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293787 / 0.215209 (0.078578) \| 2.862610 / 2.077655 (0.784955) \| 1.597282 / 1.504120 (0.093162) \| 1.485094 / 1.541195 (-0.056101) \| 1.507384 / 1.468490 (0.038893) \| 0.558470 / 4.584777 (-4.026307) \| 2.414137 / 3.745712 (-1.331575) \| 2.863342 / 5.269862 (-2.406520) \| 1.776973 / 4.565676 (-2.788704) \| 0.062296 / 0.424275 (-0.361979) \| 0.004954 / 0.007607 (-0.002653) \| 0.346037 / 0.226044 (0.119993) \| 3.441864 / 2.268929 (1.172935) \| 1.969842 / 55.444624 (-53.474783) \| 1.714878 / 6.876477 (-5.161599) \| 1.738141 / 2.142072 (-0.403931) \| 0.645929 / 4.805227 (-4.159298) \| 0.117332 / 6.500664 (-6.383332) \| 0.041963 / 0.075469 (-0.033507) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.983229 / 1.841788 (-0.858559) \| 13.186932 / 8.074308 (5.112624) \| 11.220549 / 10.191392 (1.029157) \| 0.142105 / 0.680424 (-0.538319) \| 0.015210 / 0.534201 (-0.518991) \| 0.290055 / 0.579283 (-0.289228) \| 0.274513 / 0.434364 (-0.159851) \| 0.346834 / 0.540337 (-0.193504) \| 0.575897 / 1.386936 (-0.811039) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d3c0694d0c47a64a3cab5d468b4d9575ad7b1d96 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6459). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005308 / 0.011353 (-0.006045) \| 0.003135 / 0.011008 (-0.007873) \| 0.061820 / 0.038508 (0.023312) \| 0.052005 / 0.023109 (0.028895) \| 0.233507 / 0.275898 (-0.042391) \| 0.257790 / 0.323480 (-0.065690) \| 0.002848 / 0.007986 (-0.005138) \| 0.002645 / 0.004328 (-0.001683) \| 0.048379 / 0.004250 (0.044128) \| 0.038320 / 0.037052 (0.001268) \| 0.245470 / 0.258489 (-0.013019) \| 0.274854 / 0.293841 (-0.018987) \| 0.027335 / 0.128546 (-0.101211) \| 0.010349 / 0.075646 (-0.065297) \| 0.205872 / 0.419271 (-0.213400) \| 0.035896 / 0.043533 (-0.007637) \| 0.241645 / 0.255139 (-0.013494) \| 0.260033 / 0.283200 (-0.023167) \| 0.020325 / 0.141683 (-0.121358) \| 1.116768 / 1.452155 (-0.335387) \| 1.188067 / 1.492716 (-0.304649) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092622 / 0.018006 (0.074616) \| 0.302663 / 0.000490 (0.302173) \| 0.000227 / 0.000200 (0.000027) \| 0.000051 / 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) \| 0.018633 / 0.037411 (-0.018778) \| 0.060117 / 0.014526 (0.045592) \| 0.072713 / 0.176557 (-0.103844) \| 0.119955 / 0.737135 (-0.617180) \| 0.074698 / 0.296338 (-0.221640) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.277157 / 0.215209 (0.061948) \| 2.699650 / 2.077655 (0.621995) \| 1.413625 / 1.504120 (-0.090494) \| 1.295900 / 1.541195 (-0.245295) \| 1.306280 / 1.468490 (-0.162210) \| 0.555354 / 4.584777 (-4.029423) \| 2.386866 / 3.745712 (-1.358847) \| 2.794069 / 5.269862 (-2.475793) \| 1.736275 / 4.565676 (-2.829401) \| 0.061812 / 0.424275 (-0.362464) \| 0.004957 / 0.007607 (-0.002650) \| 0.334533 / 0.226044 (0.108488) \| 3.251096 / 2.268929 (0.982168) \| 1.768193 / 55.444624 (-53.676431) \| 1.473752 / 6.876477 (-5.402724) \| 1.476320 / 2.142072 (-0.665753) \| 0.642485 / 4.805227 (-4.162742) \| 0.116986 / 6.500664 (-6.383678) \| 0.042083 / 0.075469 (-0.033386) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.941364 / 1.841788 (-0.900424) \| 11.587408 / 8.074308 (3.513100) \| 10.500198 / 10.191392 (0.308806) \| 0.129126 / 0.680424 (-0.551298) \| 0.015206 / 0.534201 (-0.518995) \| 0.286580 / 0.579283 (-0.292703) \| 0.263566 / 0.434364 (-0.170798) \| 0.331662 / 0.540337 (-0.208676) \| 0.431423 / 1.386936 (-0.955513) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005151 / 0.011353 (-0.006202) \| 0.003425 / 0.011008 (-0.007583) \| 0.049301 / 0.038508 (0.010793) \| 0.052005 / 0.023109 (0.028895) \| 0.289594 / 0.275898 (0.013696) \| 0.312630 / 0.323480 (-0.010849) \| 0.003988 / 0.007986 (-0.003998) \| 0.002705 / 0.004328 (-0.001624) \| 0.048529 / 0.004250 (0.044279) \| 0.039645 / 0.037052 (0.002592) \| 0.293430 / 0.258489 (0.034941) \| 0.311697 / 0.293841 (0.017856) \| 0.029044 / 0.128546 (-0.099502) \| 0.010282 / 0.075646 (-0.065364) \| 0.057641 / 0.419271 (-0.361630) \| 0.032733 / 0.043533 (-0.010800) \| 0.293553 / 0.255139 (0.038414) \| 0.308850 / 0.283200 (0.025651) \| 0.018452 / 0.141683 (-0.123231) \| 1.147931 / 1.452155 (-0.304224) \| 1.173093 / 1.492716 (-0.319623) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.100862 / 0.018006 (0.082856) \| 0.309286 / 0.000490 (0.308796) \| 0.000223 / 0.000200 (0.000023) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021365 / 0.037411 (-0.016046) \| 0.068987 / 0.014526 (0.054461) \| 0.081092 / 0.176557 (-0.095465) \| 0.119852 / 0.737135 (-0.617283) \| 0.082850 / 0.296338 (-0.213489) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288477 / 0.215209 (0.073268) \| 2.833766 / 2.077655 (0.756111) \| 1.576670 / 1.504120 (0.072550) \| 1.431643 / 1.541195 (-0.109552) \| 1.442132 / 1.468490 (-0.026358) \| 0.556079 / 4.584777 (-4.028698) \| 2.465042 / 3.745712 (-1.280670) \| 2.786329 / 5.269862 (-2.483532) \| 1.779428 / 4.565676 (-2.786249) \| 0.062278 / 0.424275 (-0.361997) \| 0.004867 / 0.007607 (-0.002740) \| 0.348444 / 0.226044 (0.122399) \| 3.389824 / 2.268929 (1.120896) \| 1.919141 / 55.444624 (-53.525484) \| 1.635411 / 6.876477 (-5.241066) \| 1.654869 / 2.142072 (-0.487204) \| 0.634467 / 4.805227 (-4.170761) \| 0.114330 / 6.500664 (-6.386334) \| 0.039900 / 0.075469 (-0.035569) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.970851 / 1.841788 (-0.870937) \| 11.951660 / 8.074308 (3.877352) \| 10.571115 / 10.191392 (0.379723) \| 0.131040 / 0.680424 (-0.549384) \| 0.015299 / 0.534201 (-0.518902) \| 0.287851 / 0.579283 (-0.291432) \| 0.278366 / 0.434364 (-0.155998) \| 0.326468 / 0.540337 (-0.213870) \| 0.552288 / 1.386936 (-0.834648) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8214ff2a9f706427669a6c2a01ccabffa5bf0d2b \"CML watermark\")\n" ]
2,016,577,761	6,458	Lazy data files resolution	closed	2023-11-29T13:18:44	2024-02-08T14:41:35	2024-02-08T14:41:35	https://github.com/huggingface/datasets/pull/6458	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6458", "html_url": "https://github.com/huggingface/datasets/pull/6458", "diff_url": "https://github.com/huggingface/datasets/pull/6458.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6458.patch", "merged_at": null }	lhoestq	true	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005097 / 0.011353 (-0.006256) \| 0.003523 / 0.011008 (-0.007485) \| 0.062827 / 0.038508 (0.024319) \| 0.051677 / 0.023109 (0.028568) \| 0.248919 / 0.275898 (-0.026980) \| 0.275892 / 0.323480 (-0.047588) \| 0.003908 / 0.007986 (-0.004077) \| 0.002622 / 0.004328 (-0.001706) \| 0.048634 / 0.004250 (0.044383) \| 0.037903 / 0.037052 (0.000850) \| 0.255754 / 0.258489 (-0.002735) \| 0.283343 / 0.293841 (-0.010498) \| 0.027886 / 0.128546 (-0.100660) \| 0.010849 / 0.075646 (-0.064797) \| 0.208255 / 0.419271 (-0.211017) \| 0.035664 / 0.043533 (-0.007869) \| 0.254661 / 0.255139 (-0.000478) \| 0.274366 / 0.283200 (-0.008834) \| 0.017240 / 0.141683 (-0.124443) \| 1.092952 / 1.452155 (-0.359203) \| 1.148373 / 1.492716 (-0.344344) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091592 / 0.018006 (0.073586) \| 0.301926 / 0.000490 (0.301436) \| 0.000207 / 0.000200 (0.000007) \| 0.000051 / 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) \| 0.018525 / 0.037411 (-0.018887) \| 0.060539 / 0.014526 (0.046014) \| 0.073812 / 0.176557 (-0.102745) \| 0.120655 / 0.737135 (-0.616480) \| 0.076931 / 0.296338 (-0.219407) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282797 / 0.215209 (0.067588) \| 2.746573 / 2.077655 (0.668918) \| 1.477652 / 1.504120 (-0.026468) \| 1.349922 / 1.541195 (-0.191273) \| 1.374347 / 1.468490 (-0.094143) \| 0.574096 / 4.584777 (-4.010681) \| 2.383317 / 3.745712 (-1.362395) \| 2.809320 / 5.269862 (-2.460541) \| 1.758947 / 4.565676 (-2.806729) \| 0.064029 / 0.424275 (-0.360246) \| 0.004936 / 0.007607 (-0.002672) \| 0.331403 / 0.226044 (0.105358) \| 3.260908 / 2.268929 (0.991980) \| 1.817670 / 55.444624 (-53.626954) \| 1.525863 / 6.876477 (-5.350613) \| 1.542017 / 2.142072 (-0.600055) \| 0.638900 / 4.805227 (-4.166327) \| 0.119485 / 6.500664 (-6.381179) \| 0.042588 / 0.075469 (-0.032881) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.951583 / 1.841788 (-0.890205) \| 11.621917 / 8.074308 (3.547609) \| 10.511062 / 10.191392 (0.319670) \| 0.130137 / 0.680424 (-0.550287) \| 0.014048 / 0.534201 (-0.520153) \| 0.290621 / 0.579283 (-0.288662) \| 0.271665 / 0.434364 (-0.162699) \| 0.331260 / 0.540337 (-0.209077) \| 0.441621 / 1.386936 (-0.945316) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005272 / 0.011353 (-0.006081) \| 0.003656 / 0.011008 (-0.007352) \| 0.049245 / 0.038508 (0.010737) \| 0.054130 / 0.023109 (0.031021) \| 0.274775 / 0.275898 (-0.001123) \| 0.296664 / 0.323480 (-0.026816) \| 0.004870 / 0.007986 (-0.003115) \| 0.002728 / 0.004328 (-0.001601) \| 0.048087 / 0.004250 (0.043837) \| 0.041448 / 0.037052 (0.004396) \| 0.279110 / 0.258489 (0.020621) \| 0.303660 / 0.293841 (0.009819) \| 0.029767 / 0.128546 (-0.098779) \| 0.010799 / 0.075646 (-0.064848) \| 0.058650 / 0.419271 (-0.360622) \| 0.033088 / 0.043533 (-0.010445) \| 0.274456 / 0.255139 (0.019317) \| 0.290206 / 0.283200 (0.007007) \| 0.017259 / 0.141683 (-0.124424) \| 1.176501 / 1.452155 (-0.275654) \| 1.197552 / 1.492716 (-0.295165) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092865 / 0.018006 (0.074859) \| 0.302437 / 0.000490 (0.301947) \| 0.000209 / 0.000200 (0.000009) \| 0.000048 / 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) \| 0.021211 / 0.037411 (-0.016200) \| 0.068858 / 0.014526 (0.054332) \| 0.081783 / 0.176557 (-0.094773) \| 0.120472 / 0.737135 (-0.616663) \| 0.083900 / 0.296338 (-0.212438) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295157 / 0.215209 (0.079948) \| 2.910979 / 2.077655 (0.833324) \| 1.575772 / 1.504120 (0.071652) \| 1.456955 / 1.541195 (-0.084239) \| 1.468982 / 1.468490 (0.000492) \| 0.560309 / 4.584777 (-4.024468) \| 2.460171 / 3.745712 (-1.285541) \| 2.805713 / 5.269862 (-2.464149) \| 1.754074 / 4.565676 (-2.811603) \| 0.063333 / 0.424275 (-0.360942) \| 0.004940 / 0.007607 (-0.002667) \| 0.346141 / 0.226044 (0.120097) \| 3.463431 / 2.268929 (1.194502) \| 1.929135 / 55.444624 (-53.515490) \| 1.660191 / 6.876477 (-5.216286) \| 1.668327 / 2.142072 (-0.473746) \| 0.644183 / 4.805227 (-4.161044) \| 0.115738 / 6.500664 (-6.384926) \| 0.041347 / 0.075469 (-0.034122) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.961565 / 1.841788 (-0.880222) \| 12.232589 / 8.074308 (4.158281) \| 10.778774 / 10.191392 (0.587382) \| 0.132709 / 0.680424 (-0.547715) \| 0.015964 / 0.534201 (-0.518237) \| 0.286944 / 0.579283 (-0.292340) \| 0.279740 / 0.434364 (-0.154624) \| 0.333024 / 0.540337 (-0.207314) \| 0.438819 / 1.386936 (-0.948117) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#51002cb0325772adaf46d6f3ce01d41c01b51079 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6458). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005317 / 0.011353 (-0.006036) \| 0.003936 / 0.011008 (-0.007072) \| 0.063122 / 0.038508 (0.024614) \| 0.061274 / 0.023109 (0.038165) \| 0.251764 / 0.275898 (-0.024134) \| 0.274849 / 0.323480 (-0.048631) \| 0.004059 / 0.007986 (-0.003927) \| 0.002874 / 0.004328 (-0.001455) \| 0.048716 / 0.004250 (0.044465) \| 0.038281 / 0.037052 (0.001228) \| 0.265224 / 0.258489 (0.006735) \| 0.285962 / 0.293841 (-0.007878) \| 0.028522 / 0.128546 (-0.100024) \| 0.011150 / 0.075646 (-0.064496) \| 0.208362 / 0.419271 (-0.210910) \| 0.038900 / 0.043533 (-0.004633) \| 0.254113 / 0.255139 (-0.001026) \| 0.276721 / 0.283200 (-0.006478) \| 0.018372 / 0.141683 (-0.123311) \| 1.121336 / 1.452155 (-0.330818) \| 1.189548 / 1.492716 (-0.303168) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097633 / 0.018006 (0.079627) \| 0.304443 / 0.000490 (0.303953) \| 0.000218 / 0.000200 (0.000018) \| 0.000054 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021757 / 0.037411 (-0.015654) \| 0.061978 / 0.014526 (0.047453) \| 0.076296 / 0.176557 (-0.100260) \| 0.122320 / 0.737135 (-0.614816) \| 0.076738 / 0.296338 (-0.219601) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284328 / 0.215209 (0.069119) \| 2.793071 / 2.077655 (0.715417) \| 1.504768 / 1.504120 (0.000648) \| 1.386083 / 1.541195 (-0.155111) \| 1.457593 / 1.468490 (-0.010897) \| 0.575887 / 4.584777 (-4.008890) \| 2.419396 / 3.745712 (-1.326316) \| 2.931305 / 5.269862 (-2.338556) \| 1.840759 / 4.565676 (-2.724917) \| 0.063801 / 0.424275 (-0.360474) \| 0.004966 / 0.007607 (-0.002641) \| 0.341612 / 0.226044 (0.115568) \| 3.402842 / 2.268929 (1.133913) \| 1.860521 / 55.444624 (-53.584103) \| 1.603156 / 6.876477 (-5.273321) \| 1.665835 / 2.142072 (-0.476237) \| 0.655299 / 4.805227 (-4.149929) \| 0.124527 / 6.500664 (-6.376137) \| 0.044021 / 0.075469 (-0.031449) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.972068 / 1.841788 (-0.869720) \| 12.393202 / 8.074308 (4.318894) \| 10.420876 / 10.191392 (0.229484) \| 0.140684 / 0.680424 (-0.539740) \| 0.014442 / 0.534201 (-0.519759) \| 0.288182 / 0.579283 (-0.291101) \| 0.265029 / 0.434364 (-0.169334) \| 0.327133 / 0.540337 (-0.213204) \| 0.443403 / 1.386936 (-0.943533) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005559 / 0.011353 (-0.005794) \| 0.004046 / 0.011008 (-0.006962) \| 0.048991 / 0.038508 (0.010483) \| 0.059576 / 0.023109 (0.036467) \| 0.273596 / 0.275898 (-0.002302) \| 0.296658 / 0.323480 (-0.026822) \| 0.004089 / 0.007986 (-0.003897) \| 0.002777 / 0.004328 (-0.001551) \| 0.048216 / 0.004250 (0.043966) \| 0.043200 / 0.037052 (0.006148) \| 0.276815 / 0.258489 (0.018326) \| 0.300570 / 0.293841 (0.006729) \| 0.030250 / 0.128546 (-0.098296) \| 0.011322 / 0.075646 (-0.064324) \| 0.057843 / 0.419271 (-0.361429) \| 0.033366 / 0.043533 (-0.010167) \| 0.275636 / 0.255139 (0.020497) \| 0.293750 / 0.283200 (0.010550) \| 0.018551 / 0.141683 (-0.123132) \| 1.160919 / 1.452155 (-0.291236) \| 1.214519 / 1.492716 (-0.278197) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.100074 / 0.018006 (0.082068) \| 0.308434 / 0.000490 (0.307944) \| 0.000232 / 0.000200 (0.000032) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022600 / 0.037411 (-0.014811) \| 0.070506 / 0.014526 (0.055980) \| 0.081185 / 0.176557 (-0.095371) \| 0.120688 / 0.737135 (-0.616448) \| 0.082897 / 0.296338 (-0.213441) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.306661 / 0.215209 (0.091452) \| 2.989656 / 2.077655 (0.912001) \| 1.618868 / 1.504120 (0.114749) \| 1.485045 / 1.541195 (-0.056149) \| 1.549359 / 1.468490 (0.080869) \| 0.593596 / 4.584777 (-3.991181) \| 2.466215 / 3.745712 (-1.279497) \| 2.956570 / 5.269862 (-2.313292) \| 1.823160 / 4.565676 (-2.742516) \| 0.063442 / 0.424275 (-0.360833) \| 0.004928 / 0.007607 (-0.002679) \| 0.358464 / 0.226044 (0.132419) \| 3.566345 / 2.268929 (1.297417) \| 2.006784 / 55.444624 (-53.437840) \| 1.687091 / 6.876477 (-5.189386) \| 1.729464 / 2.142072 (-0.412609) \| 0.655656 / 4.805227 (-4.149572) \| 0.119044 / 6.500664 (-6.381620) \| 0.042782 / 0.075469 (-0.032687) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.974937 / 1.841788 (-0.866850) \| 12.992888 / 8.074308 (4.918580) \| 10.893713 / 10.191392 (0.702321) \| 0.133853 / 0.680424 (-0.546570) \| 0.016055 / 0.534201 (-0.518145) \| 0.289342 / 0.579283 (-0.289941) \| 0.286094 / 0.434364 (-0.148270) \| 0.328670 / 0.540337 (-0.211667) \| 0.444605 / 1.386936 (-0.942331) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5a5bb38bcc71ea21f2d7304aab374fdb81ded463 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005705 / 0.011353 (-0.005648) \| 0.003519 / 0.011008 (-0.007489) \| 0.062009 / 0.038508 (0.023501) \| 0.053481 / 0.023109 (0.030372) \| 0.262669 / 0.275898 (-0.013229) \| 0.280290 / 0.323480 (-0.043189) \| 0.002957 / 0.007986 (-0.005029) \| 0.002587 / 0.004328 (-0.001741) \| 0.047876 / 0.004250 (0.043626) \| 0.038868 / 0.037052 (0.001815) \| 0.267854 / 0.258489 (0.009365) \| 0.290430 / 0.293841 (-0.003411) \| 0.028120 / 0.128546 (-0.100427) \| 0.011042 / 0.075646 (-0.064605) \| 0.206113 / 0.419271 (-0.213158) \| 0.036039 / 0.043533 (-0.007494) \| 0.257715 / 0.255139 (0.002576) \| 0.281279 / 0.283200 (-0.001921) \| 0.019790 / 0.141683 (-0.121893) \| 1.114472 / 1.452155 (-0.337683) \| 1.192219 / 1.492716 (-0.300497) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091049 / 0.018006 (0.073043) \| 0.300846 / 0.000490 (0.300356) \| 0.000208 / 0.000200 (0.000008) \| 0.000051 / 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) \| 0.018569 / 0.037411 (-0.018843) \| 0.060075 / 0.014526 (0.045549) \| 0.073877 / 0.176557 (-0.102680) \| 0.120337 / 0.737135 (-0.616799) \| 0.075454 / 0.296338 (-0.220884) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290084 / 0.215209 (0.074875) \| 2.805712 / 2.077655 (0.728057) \| 1.459393 / 1.504120 (-0.044727) \| 1.327356 / 1.541195 (-0.213838) \| 1.384734 / 1.468490 (-0.083756) \| 0.574532 / 4.584777 (-4.010245) \| 2.419696 / 3.745712 (-1.326016) \| 2.805449 / 5.269862 (-2.464412) \| 1.764127 / 4.565676 (-2.801549) \| 0.063256 / 0.424275 (-0.361020) \| 0.004954 / 0.007607 (-0.002653) \| 0.344246 / 0.226044 (0.118202) \| 3.396050 / 2.268929 (1.127121) \| 1.807621 / 55.444624 (-53.637004) \| 1.536627 / 6.876477 (-5.339850) \| 1.552450 / 2.142072 (-0.589623) \| 0.651156 / 4.805227 (-4.154071) \| 0.119358 / 6.500664 (-6.381306) \| 0.042810 / 0.075469 (-0.032660) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.930646 / 1.841788 (-0.911142) \| 11.830454 / 8.074308 (3.756146) \| 10.615315 / 10.191392 (0.423923) \| 0.130617 / 0.680424 (-0.549807) \| 0.014081 / 0.534201 (-0.520120) \| 0.285027 / 0.579283 (-0.294256) \| 0.267296 / 0.434364 (-0.167068) \| 0.331478 / 0.540337 (-0.208859) \| 0.442676 / 1.386936 (-0.944260) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005340 / 0.011353 (-0.006013) \| 0.003745 / 0.011008 (-0.007264) \| 0.049011 / 0.038508 (0.010503) \| 0.051342 / 0.023109 (0.028233) \| 0.272482 / 0.275898 (-0.003416) \| 0.292816 / 0.323480 (-0.030663) \| 0.003977 / 0.007986 (-0.004008) \| 0.002642 / 0.004328 (-0.001687) \| 0.048213 / 0.004250 (0.043963) \| 0.040341 / 0.037052 (0.003289) \| 0.275176 / 0.258489 (0.016687) \| 0.301098 / 0.293841 (0.007257) \| 0.029052 / 0.128546 (-0.099495) \| 0.010796 / 0.075646 (-0.064850) \| 0.057654 / 0.419271 (-0.361618) \| 0.032914 / 0.043533 (-0.010619) \| 0.271235 / 0.255139 (0.016096) \| 0.289883 / 0.283200 (0.006684) \| 0.018548 / 0.141683 (-0.123135) \| 1.134072 / 1.452155 (-0.318083) \| 1.208228 / 1.492716 (-0.284488) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094524 / 0.018006 (0.076518) \| 0.310162 / 0.000490 (0.309672) \| 0.000237 / 0.000200 (0.000037) \| 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) \| 0.021090 / 0.037411 (-0.016321) \| 0.068351 / 0.014526 (0.053825) \| 0.082370 / 0.176557 (-0.094186) \| 0.121648 / 0.737135 (-0.615487) \| 0.083433 / 0.296338 (-0.212906) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294616 / 0.215209 (0.079407) \| 2.894194 / 2.077655 (0.816539) \| 1.619739 / 1.504120 (0.115619) \| 1.492466 / 1.541195 (-0.048729) \| 1.511662 / 1.468490 (0.043172) \| 0.557179 / 4.584777 (-4.027597) \| 2.400669 / 3.745712 (-1.345043) \| 2.781363 / 5.269862 (-2.488499) \| 1.769144 / 4.565676 (-2.796533) \| 0.063996 / 0.424275 (-0.360279) \| 0.004922 / 0.007607 (-0.002685) \| 0.354483 / 0.226044 (0.128438) \| 3.474795 / 2.268929 (1.205867) \| 1.985743 / 55.444624 (-53.458881) \| 1.693173 / 6.876477 (-5.183303) \| 1.695857 / 2.142072 (-0.446216) \| 0.654800 / 4.805227 (-4.150427) \| 0.117316 / 6.500664 (-6.383348) \| 0.040708 / 0.075469 (-0.034761) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.977678 / 1.841788 (-0.864109) \| 12.214098 / 8.074308 (4.139790) \| 10.741857 / 10.191392 (0.550465) \| 0.130308 / 0.680424 (-0.550116) \| 0.015053 / 0.534201 (-0.519148) \| 0.295496 / 0.579283 (-0.283787) \| 0.276348 / 0.434364 (-0.158015) \| 0.326568 / 0.540337 (-0.213769) \| 0.441902 / 1.386936 (-0.945034) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#214a3e6dcb66e9c1a8ff586553e8eee0f1c70710 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005218 / 0.011353 (-0.006135) \| 0.003270 / 0.011008 (-0.007738) \| 0.062380 / 0.038508 (0.023872) \| 0.052896 / 0.023109 (0.029787) \| 0.233060 / 0.275898 (-0.042838) \| 0.259194 / 0.323480 (-0.064286) \| 0.002880 / 0.007986 (-0.005106) \| 0.002643 / 0.004328 (-0.001686) \| 0.048084 / 0.004250 (0.043833) \| 0.038807 / 0.037052 (0.001755) \| 0.244925 / 0.258489 (-0.013564) \| 0.269619 / 0.293841 (-0.024222) \| 0.026901 / 0.128546 (-0.101646) \| 0.010150 / 0.075646 (-0.065497) \| 0.206854 / 0.419271 (-0.212417) \| 0.035618 / 0.043533 (-0.007915) \| 0.239577 / 0.255139 (-0.015562) \| 0.259684 / 0.283200 (-0.023516) \| 0.019823 / 0.141683 (-0.121860) \| 1.074472 / 1.452155 (-0.377682) \| 1.142911 / 1.492716 (-0.349805) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092616 / 0.018006 (0.074610) \| 0.301974 / 0.000490 (0.301485) \| 0.000201 / 0.000200 (0.000002) \| 0.000048 / 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) \| 0.018864 / 0.037411 (-0.018548) \| 0.061007 / 0.014526 (0.046481) \| 0.073228 / 0.176557 (-0.103328) \| 0.120719 / 0.737135 (-0.616416) \| 0.075686 / 0.296338 (-0.220653) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281404 / 0.215209 (0.066195) \| 2.777671 / 2.077655 (0.700017) \| 1.464689 / 1.504120 (-0.039431) \| 1.345357 / 1.541195 (-0.195838) \| 1.384273 / 1.468490 (-0.084217) \| 0.560298 / 4.584777 (-4.024479) \| 2.389877 / 3.745712 (-1.355835) \| 2.755564 / 5.269862 (-2.514297) \| 1.737754 / 4.565676 (-2.827922) \| 0.063025 / 0.424275 (-0.361251) \| 0.004975 / 0.007607 (-0.002632) \| 0.346741 / 0.226044 (0.120697) \| 3.321918 / 2.268929 (1.052989) \| 1.815700 / 55.444624 (-53.628924) \| 1.547333 / 6.876477 (-5.329144) \| 1.564809 / 2.142072 (-0.577263) \| 0.638645 / 4.805227 (-4.166582) \| 0.118157 / 6.500664 (-6.382507) \| 0.041605 / 0.075469 (-0.033864) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.942515 / 1.841788 (-0.899273) \| 11.400386 / 8.074308 (3.326078) \| 10.208763 / 10.191392 (0.017370) \| 0.138144 / 0.680424 (-0.542280) \| 0.014354 / 0.534201 (-0.519847) \| 0.288289 / 0.579283 (-0.290994) \| 0.265973 / 0.434364 (-0.168391) \| 0.327703 / 0.540337 (-0.212634) \| 0.435474 / 1.386936 (-0.951462) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005163 / 0.011353 (-0.006190) \| 0.003307 / 0.011008 (-0.007701) \| 0.048885 / 0.038508 (0.010377) \| 0.049044 / 0.023109 (0.025935) \| 0.261408 / 0.275898 (-0.014490) \| 0.284625 / 0.323480 (-0.038855) \| 0.003970 / 0.007986 (-0.004015) \| 0.002754 / 0.004328 (-0.001575) \| 0.048271 / 0.004250 (0.044021) \| 0.039849 / 0.037052 (0.002797) \| 0.266898 / 0.258489 (0.008409) \| 0.291445 / 0.293841 (-0.002396) \| 0.028477 / 0.128546 (-0.100069) \| 0.010656 / 0.075646 (-0.064990) \| 0.057732 / 0.419271 (-0.361539) \| 0.033298 / 0.043533 (-0.010235) \| 0.297773 / 0.255139 (0.042634) \| 0.281894 / 0.283200 (-0.001305) \| 0.018595 / 0.141683 (-0.123088) \| 1.168849 / 1.452155 (-0.283306) \| 1.183493 / 1.492716 (-0.309224) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092683 / 0.018006 (0.074677) \| 0.300387 / 0.000490 (0.299897) \| 0.000221 / 0.000200 (0.000021) \| 0.000052 / 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) \| 0.021356 / 0.037411 (-0.016055) \| 0.068095 / 0.014526 (0.053569) \| 0.079806 / 0.176557 (-0.096750) \| 0.118965 / 0.737135 (-0.618170) \| 0.082066 / 0.296338 (-0.214273) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293105 / 0.215209 (0.077896) \| 2.842800 / 2.077655 (0.765146) \| 1.572052 / 1.504120 (0.067932) \| 1.450156 / 1.541195 (-0.091038) \| 1.464227 / 1.468490 (-0.004263) \| 0.561215 / 4.584777 (-4.023562) \| 2.456117 / 3.745712 (-1.289596) \| 2.739766 / 5.269862 (-2.530095) \| 1.730354 / 4.565676 (-2.835323) \| 0.062636 / 0.424275 (-0.361639) \| 0.004933 / 0.007607 (-0.002674) \| 0.345800 / 0.226044 (0.119756) \| 3.415858 / 2.268929 (1.146929) \| 1.937288 / 55.444624 (-53.507336) \| 1.661975 / 6.876477 (-5.214502) \| 1.660347 / 2.142072 (-0.481726) \| 0.642780 / 4.805227 (-4.162448) \| 0.116643 / 6.500664 (-6.384021) \| 0.041282 / 0.075469 (-0.034187) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.976629 / 1.841788 (-0.865159) \| 11.900319 / 8.074308 (3.826011) \| 10.574198 / 10.191392 (0.382806) \| 0.129689 / 0.680424 (-0.550735) \| 0.015390 / 0.534201 (-0.518811) \| 0.286543 / 0.579283 (-0.292741) \| 0.277676 / 0.434364 (-0.156688) \| 0.325053 / 0.540337 (-0.215284) \| 0.439663 / 1.386936 (-0.947274) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b7a9674e17156ff10124632ba705125288de7442 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005382 / 0.011353 (-0.005971) \| 0.003606 / 0.011008 (-0.007402) \| 0.063234 / 0.038508 (0.024726) \| 0.053738 / 0.023109 (0.030629) \| 0.250405 / 0.275898 (-0.025493) \| 0.272244 / 0.323480 (-0.051236) \| 0.002896 / 0.007986 (-0.005090) \| 0.002684 / 0.004328 (-0.001644) \| 0.048394 / 0.004250 (0.044143) \| 0.039017 / 0.037052 (0.001964) \| 0.259554 / 0.258489 (0.001065) \| 0.287215 / 0.293841 (-0.006626) \| 0.028290 / 0.128546 (-0.100257) \| 0.011482 / 0.075646 (-0.064164) \| 0.214264 / 0.419271 (-0.205007) \| 0.036257 / 0.043533 (-0.007276) \| 0.252873 / 0.255139 (-0.002266) \| 0.271269 / 0.283200 (-0.011931) \| 0.017173 / 0.141683 (-0.124510) \| 1.137474 / 1.452155 (-0.314681) \| 1.161499 / 1.492716 (-0.331217) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092424 / 0.018006 (0.074418) \| 0.283703 / 0.000490 (0.283213) \| 0.000209 / 0.000200 (0.000009) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018307 / 0.037411 (-0.019105) \| 0.060780 / 0.014526 (0.046254) \| 0.073984 / 0.176557 (-0.102573) \| 0.120824 / 0.737135 (-0.616311) \| 0.074724 / 0.296338 (-0.221615) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297682 / 0.215209 (0.082473) \| 2.853267 / 2.077655 (0.775612) \| 1.567643 / 1.504120 (0.063523) \| 1.437218 / 1.541195 (-0.103976) \| 1.467187 / 1.468490 (-0.001304) \| 0.560552 / 4.584777 (-4.024225) \| 2.387848 / 3.745712 (-1.357864) \| 2.718946 / 5.269862 (-2.550916) \| 1.724107 / 4.565676 (-2.841570) \| 0.061923 / 0.424275 (-0.362352) \| 0.004828 / 0.007607 (-0.002779) \| 0.353916 / 0.226044 (0.127871) \| 3.404477 / 2.268929 (1.135548) \| 1.906078 / 55.444624 (-53.538546) \| 1.629686 / 6.876477 (-5.246791) \| 1.640839 / 2.142072 (-0.501233) \| 0.641082 / 4.805227 (-4.164145) \| 0.118078 / 6.500664 (-6.382586) \| 0.041881 / 0.075469 (-0.033588) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.936062 / 1.841788 (-0.905726) \| 11.397678 / 8.074308 (3.323370) \| 10.385159 / 10.191392 (0.193766) \| 0.127337 / 0.680424 (-0.553087) \| 0.013562 / 0.534201 (-0.520639) \| 0.290817 / 0.579283 (-0.288466) \| 0.259377 / 0.434364 (-0.174987) \| 0.324829 / 0.540337 (-0.215508) \| 0.434344 / 1.386936 (-0.952592) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005134 / 0.011353 (-0.006219) \| 0.003404 / 0.011008 (-0.007604) \| 0.048281 / 0.038508 (0.009772) \| 0.050952 / 0.023109 (0.027842) \| 0.277553 / 0.275898 (0.001655) \| 0.298855 / 0.323480 (-0.024625) \| 0.003928 / 0.007986 (-0.004058) \| 0.002642 / 0.004328 (-0.001687) \| 0.047374 / 0.004250 (0.043123) \| 0.039883 / 0.037052 (0.002831) \| 0.279808 / 0.258489 (0.021318) \| 0.301604 / 0.293841 (0.007763) \| 0.028708 / 0.128546 (-0.099838) \| 0.010949 / 0.075646 (-0.064697) \| 0.057090 / 0.419271 (-0.362181) \| 0.032438 / 0.043533 (-0.011095) \| 0.274690 / 0.255139 (0.019551) \| 0.290912 / 0.283200 (0.007712) \| 0.017556 / 0.141683 (-0.124127) \| 1.111091 / 1.452155 (-0.341064) \| 1.166063 / 1.492716 (-0.326653) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090557 / 0.018006 (0.072551) \| 0.298661 / 0.000490 (0.298171) \| 0.000228 / 0.000200 (0.000028) \| 0.000045 / 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) \| 0.021712 / 0.037411 (-0.015699) \| 0.068682 / 0.014526 (0.054156) \| 0.080108 / 0.176557 (-0.096449) \| 0.119480 / 0.737135 (-0.617655) \| 0.082703 / 0.296338 (-0.213636) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294095 / 0.215209 (0.078886) \| 2.884758 / 2.077655 (0.807103) \| 1.598312 / 1.504120 (0.094192) \| 1.480050 / 1.541195 (-0.061145) \| 1.488611 / 1.468490 (0.020121) \| 0.556052 / 4.584777 (-4.028724) \| 2.435484 / 3.745712 (-1.310228) \| 2.741592 / 5.269862 (-2.528270) \| 1.706223 / 4.565676 (-2.859454) \| 0.062214 / 0.424275 (-0.362061) \| 0.004901 / 0.007607 (-0.002706) \| 0.346301 / 0.226044 (0.120257) \| 3.474516 / 2.268929 (1.205587) \| 1.995205 / 55.444624 (-53.449419) \| 1.726349 / 6.876477 (-5.150128) \| 1.659600 / 2.142072 (-0.482472) \| 0.643560 / 4.805227 (-4.161667) \| 0.115222 / 6.500664 (-6.385442) \| 0.041137 / 0.075469 (-0.034332) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.974566 / 1.841788 (-0.867221) \| 11.872479 / 8.074308 (3.798171) \| 10.496919 / 10.191392 (0.305527) \| 0.129087 / 0.680424 (-0.551337) \| 0.014627 / 0.534201 (-0.519574) \| 0.289070 / 0.579283 (-0.290213) \| 0.269609 / 0.434364 (-0.164755) \| 0.327785 / 0.540337 (-0.212553) \| 0.444634 / 1.386936 (-0.942302) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#32e0960ea165a9481b1ff6eed31771475120cb38 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005080 / 0.011353 (-0.006273) \| 0.003782 / 0.011008 (-0.007226) \| 0.062816 / 0.038508 (0.024308) \| 0.056338 / 0.023109 (0.033229) \| 0.251317 / 0.275898 (-0.024581) \| 0.269414 / 0.323480 (-0.054066) \| 0.003984 / 0.007986 (-0.004001) \| 0.002749 / 0.004328 (-0.001580) \| 0.048126 / 0.004250 (0.043876) \| 0.038516 / 0.037052 (0.001464) \| 0.253809 / 0.258489 (-0.004680) \| 0.283309 / 0.293841 (-0.010532) \| 0.027015 / 0.128546 (-0.101531) \| 0.010610 / 0.075646 (-0.065037) \| 0.213024 / 0.419271 (-0.206247) \| 0.035734 / 0.043533 (-0.007799) \| 0.247909 / 0.255139 (-0.007230) \| 0.263539 / 0.283200 (-0.019660) \| 0.018408 / 0.141683 (-0.123275) \| 1.104366 / 1.452155 (-0.347789) \| 1.169668 / 1.492716 (-0.323048) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.114366 / 0.018006 (0.096360) \| 0.317674 / 0.000490 (0.317184) \| 0.000227 / 0.000200 (0.000027) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018955 / 0.037411 (-0.018457) \| 0.060716 / 0.014526 (0.046190) \| 0.072963 / 0.176557 (-0.103593) \| 0.121671 / 0.737135 (-0.615464) \| 0.073785 / 0.296338 (-0.222554) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292349 / 0.215209 (0.077140) \| 2.832049 / 2.077655 (0.754394) \| 1.504488 / 1.504120 (0.000368) \| 1.403418 / 1.541195 (-0.137777) \| 1.449223 / 1.468490 (-0.019267) \| 0.563846 / 4.584777 (-4.020931) \| 2.376726 / 3.745712 (-1.368986) \| 2.823304 / 5.269862 (-2.446558) \| 1.774858 / 4.565676 (-2.790818) \| 0.063229 / 0.424275 (-0.361046) \| 0.004923 / 0.007607 (-0.002684) \| 0.347240 / 0.226044 (0.121195) \| 3.486563 / 2.268929 (1.217634) \| 1.890516 / 55.444624 (-53.554109) \| 1.570620 / 6.876477 (-5.305857) \| 1.600842 / 2.142072 (-0.541231) \| 0.644287 / 4.805227 (-4.160940) \| 0.116931 / 6.500664 (-6.383733) \| 0.042068 / 0.075469 (-0.033401) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.935662 / 1.841788 (-0.906126) \| 11.950247 / 8.074308 (3.875939) \| 10.636225 / 10.191392 (0.444833) \| 0.139137 / 0.680424 (-0.541287) \| 0.014473 / 0.534201 (-0.519728) \| 0.294213 / 0.579283 (-0.285070) \| 0.273413 / 0.434364 (-0.160951) \| 0.325930 / 0.540337 (-0.214407) \| 0.444265 / 1.386936 (-0.942671) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005448 / 0.011353 (-0.005904) \| 0.003155 / 0.011008 (-0.007853) \| 0.048626 / 0.038508 (0.010117) \| 0.057427 / 0.023109 (0.034318) \| 0.270412 / 0.275898 (-0.005486) \| 0.290816 / 0.323480 (-0.032664) \| 0.004744 / 0.007986 (-0.003241) \| 0.002776 / 0.004328 (-0.001552) \| 0.047953 / 0.004250 (0.043703) \| 0.041126 / 0.037052 (0.004073) \| 0.276046 / 0.258489 (0.017557) \| 0.297548 / 0.293841 (0.003707) \| 0.029308 / 0.128546 (-0.099238) \| 0.010516 / 0.075646 (-0.065131) \| 0.056982 / 0.419271 (-0.362290) \| 0.032922 / 0.043533 (-0.010611) \| 0.271342 / 0.255139 (0.016203) \| 0.288963 / 0.283200 (0.005763) \| 0.019048 / 0.141683 (-0.122635) \| 1.130453 / 1.452155 (-0.321702) \| 1.206462 / 1.492716 (-0.286254) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.099249 / 0.018006 (0.081242) \| 0.312409 / 0.000490 (0.311919) \| 0.000224 / 0.000200 (0.000024) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021992 / 0.037411 (-0.015419) \| 0.068377 / 0.014526 (0.053851) \| 0.080749 / 0.176557 (-0.095807) \| 0.120534 / 0.737135 (-0.616602) \| 0.082549 / 0.296338 (-0.213790) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.299634 / 0.215209 (0.084425) \| 2.943496 / 2.077655 (0.865841) \| 1.602842 / 1.504120 (0.098722) \| 1.462140 / 1.541195 (-0.079055) \| 1.511082 / 1.468490 (0.042592) \| 0.574148 / 4.584777 (-4.010629) \| 2.492158 / 3.745712 (-1.253554) \| 2.921695 / 5.269862 (-2.348166) \| 1.812416 / 4.565676 (-2.753260) \| 0.064145 / 0.424275 (-0.360130) \| 0.005133 / 0.007607 (-0.002475) \| 0.357935 / 0.226044 (0.131891) \| 3.543728 / 2.268929 (1.274800) \| 1.948676 / 55.444624 (-53.495948) \| 1.664960 / 6.876477 (-5.211517) \| 1.678703 / 2.142072 (-0.463370) \| 0.645867 / 4.805227 (-4.159360) \| 0.117671 / 6.500664 (-6.382993) \| 0.040887 / 0.075469 (-0.034582) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.979127 / 1.841788 (-0.862661) \| 12.363904 / 8.074308 (4.289596) \| 10.673725 / 10.191392 (0.482333) \| 0.143358 / 0.680424 (-0.537066) \| 0.015375 / 0.534201 (-0.518825) \| 0.287590 / 0.579283 (-0.291694) \| 0.284742 / 0.434364 (-0.149622) \| 0.326901 / 0.540337 (-0.213437) \| 0.443962 / 1.386936 (-0.942974) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#68099ca55294bfc12a34781835dd73c533a764bd \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004994 / 0.011353 (-0.006359) \| 0.003368 / 0.011008 (-0.007640) \| 0.062803 / 0.038508 (0.024295) \| 0.050778 / 0.023109 (0.027669) \| 0.255955 / 0.275898 (-0.019943) \| 0.278215 / 0.323480 (-0.045265) \| 0.003801 / 0.007986 (-0.004184) \| 0.002703 / 0.004328 (-0.001626) \| 0.048369 / 0.004250 (0.044119) \| 0.037795 / 0.037052 (0.000743) \| 0.255634 / 0.258489 (-0.002855) \| 0.284226 / 0.293841 (-0.009615) \| 0.027252 / 0.128546 (-0.101294) \| 0.010686 / 0.075646 (-0.064961) \| 0.206139 / 0.419271 (-0.213133) \| 0.035543 / 0.043533 (-0.007990) \| 0.257167 / 0.255139 (0.002028) \| 0.277784 / 0.283200 (-0.005416) \| 0.016938 / 0.141683 (-0.124745) \| 1.108595 / 1.452155 (-0.343560) \| 1.188542 / 1.492716 (-0.304175) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090938 / 0.018006 (0.072932) \| 0.298463 / 0.000490 (0.297973) \| 0.000203 / 0.000200 (0.000003) \| 0.000048 / 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) \| 0.027762 / 0.037411 (-0.009649) \| 0.060539 / 0.014526 (0.046014) \| 0.075986 / 0.176557 (-0.100570) \| 0.133851 / 0.737135 (-0.603285) \| 0.074669 / 0.296338 (-0.221670) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285614 / 0.215209 (0.070405) \| 2.810529 / 2.077655 (0.732874) \| 1.537092 / 1.504120 (0.032973) \| 1.412211 / 1.541195 (-0.128983) \| 1.446395 / 1.468490 (-0.022095) \| 0.559008 / 4.584777 (-4.025769) \| 2.343445 / 3.745712 (-1.402267) \| 2.748113 / 5.269862 (-2.521748) \| 1.733593 / 4.565676 (-2.832083) \| 0.061720 / 0.424275 (-0.362555) \| 0.004930 / 0.007607 (-0.002677) \| 0.330646 / 0.226044 (0.104602) \| 3.314999 / 2.268929 (1.046071) \| 1.854527 / 55.444624 (-53.590098) \| 1.605819 / 6.876477 (-5.270657) \| 1.591406 / 2.142072 (-0.550667) \| 0.624239 / 4.805227 (-4.180988) \| 0.115352 / 6.500664 (-6.385312) \| 0.041600 / 0.075469 (-0.033869) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.933179 / 1.841788 (-0.908608) \| 11.456372 / 8.074308 (3.382064) \| 10.578042 / 10.191392 (0.386650) \| 0.128045 / 0.680424 (-0.552379) \| 0.014212 / 0.534201 (-0.519989) \| 0.284795 / 0.579283 (-0.294488) \| 0.266210 / 0.434364 (-0.168153) \| 0.344468 / 0.540337 (-0.195869) \| 0.434414 / 1.386936 (-0.952522) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005142 / 0.011353 (-0.006211) \| 0.003607 / 0.011008 (-0.007401) \| 0.048770 / 0.038508 (0.010262) \| 0.051147 / 0.023109 (0.028038) \| 0.277329 / 0.275898 (0.001430) \| 0.300863 / 0.323480 (-0.022617) \| 0.004005 / 0.007986 (-0.003980) \| 0.002624 / 0.004328 (-0.001705) \| 0.047740 / 0.004250 (0.043489) \| 0.040811 / 0.037052 (0.003759) \| 0.280020 / 0.258489 (0.021531) \| 0.303758 / 0.293841 (0.009918) \| 0.028273 / 0.128546 (-0.100274) \| 0.010379 / 0.075646 (-0.065267) \| 0.057503 / 0.419271 (-0.361768) \| 0.032717 / 0.043533 (-0.010816) \| 0.277560 / 0.255139 (0.022421) \| 0.300622 / 0.283200 (0.017422) \| 0.018142 / 0.141683 (-0.123541) \| 1.121890 / 1.452155 (-0.330265) \| 1.251481 / 1.492716 (-0.241235) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091523 / 0.018006 (0.073517) \| 0.300173 / 0.000490 (0.299683) \| 0.000216 / 0.000200 (0.000016) \| 0.000051 / 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) \| 0.026386 / 0.037411 (-0.011025) \| 0.078710 / 0.014526 (0.064184) \| 0.090594 / 0.176557 (-0.085962) \| 0.130623 / 0.737135 (-0.606512) \| 0.092637 / 0.296338 (-0.203701) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.299427 / 0.215209 (0.084218) \| 2.929463 / 2.077655 (0.851808) \| 1.608905 / 1.504120 (0.104785) \| 1.490863 / 1.541195 (-0.050331) \| 1.484286 / 1.468490 (0.015796) \| 0.568208 / 4.584777 (-4.016569) \| 2.447081 / 3.745712 (-1.298632) \| 2.801287 / 5.269862 (-2.468574) \| 1.744449 / 4.565676 (-2.821227) \| 0.064222 / 0.424275 (-0.360053) \| 0.004959 / 0.007607 (-0.002648) \| 0.350207 / 0.226044 (0.124162) \| 3.471944 / 2.268929 (1.203016) \| 1.951715 / 55.444624 (-53.492909) \| 1.668764 / 6.876477 (-5.207713) \| 1.675322 / 2.142072 (-0.466751) \| 0.642217 / 4.805227 (-4.163011) \| 0.116776 / 6.500664 (-6.383888) \| 0.040812 / 0.075469 (-0.034658) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.996478 / 1.841788 (-0.845310) \| 12.090647 / 8.074308 (4.016339) \| 10.723688 / 10.191392 (0.532296) \| 0.141770 / 0.680424 (-0.538653) \| 0.015578 / 0.534201 (-0.518623) \| 0.288236 / 0.579283 (-0.291047) \| 0.278542 / 0.434364 (-0.155822) \| 0.327411 / 0.540337 (-0.212927) \| 0.450309 / 1.386936 (-0.936627) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5dd4698f483d37afe243db0ffae774cbd34a4af4 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004967 / 0.011353 (-0.006385) \| 0.003382 / 0.011008 (-0.007627) \| 0.063436 / 0.038508 (0.024928) \| 0.050769 / 0.023109 (0.027659) \| 0.254214 / 0.275898 (-0.021684) \| 0.272076 / 0.323480 (-0.051404) \| 0.003815 / 0.007986 (-0.004170) \| 0.002618 / 0.004328 (-0.001711) \| 0.049021 / 0.004250 (0.044771) \| 0.037329 / 0.037052 (0.000277) \| 0.261112 / 0.258489 (0.002623) \| 0.284133 / 0.293841 (-0.009708) \| 0.026828 / 0.128546 (-0.101719) \| 0.010757 / 0.075646 (-0.064889) \| 0.208047 / 0.419271 (-0.211225) \| 0.035061 / 0.043533 (-0.008472) \| 0.250896 / 0.255139 (-0.004243) \| 0.273038 / 0.283200 (-0.010162) \| 0.016559 / 0.141683 (-0.125124) \| 1.128899 / 1.452155 (-0.323255) \| 1.188857 / 1.492716 (-0.303860) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.100121 / 0.018006 (0.082114) \| 0.298427 / 0.000490 (0.297937) \| 0.000218 / 0.000200 (0.000018) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018369 / 0.037411 (-0.019042) \| 0.060425 / 0.014526 (0.045899) \| 0.073501 / 0.176557 (-0.103055) \| 0.120254 / 0.737135 (-0.616881) \| 0.074889 / 0.296338 (-0.221450) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287153 / 0.215209 (0.071944) \| 2.797036 / 2.077655 (0.719382) \| 1.446216 / 1.504120 (-0.057904) \| 1.336015 / 1.541195 (-0.205179) \| 1.369841 / 1.468490 (-0.098650) \| 0.559424 / 4.584777 (-4.025353) \| 2.361344 / 3.745712 (-1.384368) \| 2.766619 / 5.269862 (-2.503243) \| 1.747235 / 4.565676 (-2.818441) \| 0.066243 / 0.424275 (-0.358032) \| 0.004974 / 0.007607 (-0.002633) \| 0.333565 / 0.226044 (0.107520) \| 3.319877 / 2.268929 (1.050948) \| 1.798024 / 55.444624 (-53.646601) \| 1.495896 / 6.876477 (-5.380580) \| 1.529243 / 2.142072 (-0.612830) \| 0.636609 / 4.805227 (-4.168618) \| 0.116151 / 6.500664 (-6.384514) \| 0.041779 / 0.075469 (-0.033690) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.952176 / 1.841788 (-0.889611) \| 11.559160 / 8.074308 (3.484852) \| 10.556771 / 10.191392 (0.365379) \| 0.127118 / 0.680424 (-0.553306) \| 0.014142 / 0.534201 (-0.520059) \| 0.286585 / 0.579283 (-0.292698) \| 0.260233 / 0.434364 (-0.174131) \| 0.324012 / 0.540337 (-0.216326) \| 0.435131 / 1.386936 (-0.951805) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005171 / 0.011353 (-0.006182) \| 0.003402 / 0.011008 (-0.007607) \| 0.048826 / 0.038508 (0.010318) \| 0.050455 / 0.023109 (0.027346) \| 0.272120 / 0.275898 (-0.003778) \| 0.290404 / 0.323480 (-0.033076) \| 0.003986 / 0.007986 (-0.003999) \| 0.002569 / 0.004328 (-0.001760) \| 0.047845 / 0.004250 (0.043595) \| 0.040203 / 0.037052 (0.003150) \| 0.278263 / 0.258489 (0.019774) \| 0.299255 / 0.293841 (0.005414) \| 0.028643 / 0.128546 (-0.099903) \| 0.010584 / 0.075646 (-0.065062) \| 0.056921 / 0.419271 (-0.362351) \| 0.032362 / 0.043533 (-0.011171) \| 0.274010 / 0.255139 (0.018871) \| 0.288601 / 0.283200 (0.005401) \| 0.017856 / 0.141683 (-0.123827) \| 1.154112 / 1.452155 (-0.298043) \| 1.216288 / 1.492716 (-0.276428) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091399 / 0.018006 (0.073392) \| 0.299966 / 0.000490 (0.299477) \| 0.000218 / 0.000200 (0.000018) \| 0.000054 / 0.000054 (-0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021728 / 0.037411 (-0.015683) \| 0.068285 / 0.014526 (0.053759) \| 0.081767 / 0.176557 (-0.094789) \| 0.120000 / 0.737135 (-0.617135) \| 0.082149 / 0.296338 (-0.214189) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289625 / 0.215209 (0.074416) \| 2.835114 / 2.077655 (0.757460) \| 1.583207 / 1.504120 (0.079087) \| 1.465251 / 1.541195 (-0.075944) \| 1.480691 / 1.468490 (0.012200) \| 0.569103 / 4.584777 (-4.015674) \| 2.416981 / 3.745712 (-1.328731) \| 2.761746 / 5.269862 (-2.508115) \| 1.720055 / 4.565676 (-2.845621) \| 0.063349 / 0.424275 (-0.360926) \| 0.004931 / 0.007607 (-0.002676) \| 0.343658 / 0.226044 (0.117614) \| 3.362996 / 2.268929 (1.094068) \| 1.948088 / 55.444624 (-53.496536) \| 1.659504 / 6.876477 (-5.216973) \| 1.660359 / 2.142072 (-0.481713) \| 0.647871 / 4.805227 (-4.157356) \| 0.117395 / 6.500664 (-6.383269) \| 0.041049 / 0.075469 (-0.034420) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.953971 / 1.841788 (-0.887817) \| 12.076998 / 8.074308 (4.002690) \| 10.549021 / 10.191392 (0.357629) \| 0.130026 / 0.680424 (-0.550398) \| 0.015697 / 0.534201 (-0.518504) \| 0.287125 / 0.579283 (-0.292158) \| 0.298402 / 0.434364 (-0.135962) \| 0.326005 / 0.540337 (-0.214332) \| 0.444065 / 1.386936 (-0.942871) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cf86d48792f585bf802bb2ff70e0d9c3a4de4bcf \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005053 / 0.011353 (-0.006300) \| 0.003537 / 0.011008 (-0.007472) \| 0.062923 / 0.038508 (0.024415) \| 0.053796 / 0.023109 (0.030687) \| 0.242523 / 0.275898 (-0.033375) \| 0.264014 / 0.323480 (-0.059466) \| 0.002879 / 0.007986 (-0.005106) \| 0.003273 / 0.004328 (-0.001055) \| 0.048735 / 0.004250 (0.044484) \| 0.037541 / 0.037052 (0.000488) \| 0.248587 / 0.258489 (-0.009902) \| 0.275531 / 0.293841 (-0.018310) \| 0.027215 / 0.128546 (-0.101331) \| 0.010466 / 0.075646 (-0.065180) \| 0.206508 / 0.419271 (-0.212763) \| 0.035606 / 0.043533 (-0.007927) \| 0.251044 / 0.255139 (-0.004095) \| 0.267183 / 0.283200 (-0.016016) \| 0.018357 / 0.141683 (-0.123326) \| 1.083513 / 1.452155 (-0.368642) \| 1.152988 / 1.492716 (-0.339728) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091749 / 0.018006 (0.073742) \| 0.299946 / 0.000490 (0.299456) \| 0.000212 / 0.000200 (0.000013) \| 0.000042 / 0.000054 (-0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018300 / 0.037411 (-0.019111) \| 0.060691 / 0.014526 (0.046166) \| 0.072998 / 0.176557 (-0.103559) \| 0.120581 / 0.737135 (-0.616554) \| 0.073912 / 0.296338 (-0.222427) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.277602 / 0.215209 (0.062393) \| 2.719181 / 2.077655 (0.641526) \| 1.450894 / 1.504120 (-0.053226) \| 1.314344 / 1.541195 (-0.226851) \| 1.351996 / 1.468490 (-0.116494) \| 0.586231 / 4.584777 (-3.998546) \| 2.349746 / 3.745712 (-1.395967) \| 2.810060 / 5.269862 (-2.459802) \| 1.761362 / 4.565676 (-2.804314) \| 0.062535 / 0.424275 (-0.361740) \| 0.004918 / 0.007607 (-0.002689) \| 0.336091 / 0.226044 (0.110047) \| 3.238139 / 2.268929 (0.969211) \| 1.769734 / 55.444624 (-53.674890) \| 1.505332 / 6.876477 (-5.371145) \| 1.527875 / 2.142072 (-0.614198) \| 0.640194 / 4.805227 (-4.165033) \| 0.116567 / 6.500664 (-6.384097) \| 0.042464 / 0.075469 (-0.033005) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.930919 / 1.841788 (-0.910869) \| 11.462498 / 8.074308 (3.388190) \| 10.575359 / 10.191392 (0.383967) \| 0.130567 / 0.680424 (-0.549857) \| 0.014203 / 0.534201 (-0.519998) \| 0.286944 / 0.579283 (-0.292339) \| 0.264706 / 0.434364 (-0.169658) \| 0.324820 / 0.540337 (-0.215517) \| 0.434579 / 1.386936 (-0.952357) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005164 / 0.011353 (-0.006189) \| 0.003442 / 0.011008 (-0.007567) \| 0.050146 / 0.038508 (0.011638) \| 0.050800 / 0.023109 (0.027691) \| 0.263405 / 0.275898 (-0.012493) \| 0.284876 / 0.323480 (-0.038604) \| 0.004011 / 0.007986 (-0.003975) \| 0.002602 / 0.004328 (-0.001726) \| 0.046742 / 0.004250 (0.042491) \| 0.040393 / 0.037052 (0.003341) \| 0.265052 / 0.258489 (0.006563) \| 0.294217 / 0.293841 (0.000377) \| 0.028429 / 0.128546 (-0.100118) \| 0.010418 / 0.075646 (-0.065228) \| 0.057285 / 0.419271 (-0.361987) \| 0.032137 / 0.043533 (-0.011396) \| 0.265867 / 0.255139 (0.010728) \| 0.284764 / 0.283200 (0.001564) \| 0.017448 / 0.141683 (-0.124235) \| 1.172830 / 1.452155 (-0.279325) \| 1.223982 / 1.492716 (-0.268735) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091859 / 0.018006 (0.073853) \| 0.285421 / 0.000490 (0.284931) \| 0.000220 / 0.000200 (0.000020) \| 0.000049 / 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) \| 0.021620 / 0.037411 (-0.015792) \| 0.069058 / 0.014526 (0.054532) \| 0.082560 / 0.176557 (-0.093997) \| 0.119511 / 0.737135 (-0.617624) \| 0.082318 / 0.296338 (-0.214021) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291499 / 0.215209 (0.076290) \| 2.863352 / 2.077655 (0.785698) \| 1.557242 / 1.504120 (0.053122) \| 1.430170 / 1.541195 (-0.111024) \| 1.432850 / 1.468490 (-0.035640) \| 0.559716 / 4.584777 (-4.025061) \| 2.385405 / 3.745712 (-1.360307) \| 2.748938 / 5.269862 (-2.520924) \| 1.740802 / 4.565676 (-2.824874) \| 0.061811 / 0.424275 (-0.362465) \| 0.005174 / 0.007607 (-0.002433) \| 0.348687 / 0.226044 (0.122642) \| 3.420120 / 2.268929 (1.151191) \| 1.918278 / 55.444624 (-53.526346) \| 1.631559 / 6.876477 (-5.244918) \| 1.635850 / 2.142072 (-0.506222) \| 0.644144 / 4.805227 (-4.161083) \| 0.115823 / 6.500664 (-6.384841) \| 0.041255 / 0.075469 (-0.034214) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.960066 / 1.841788 (-0.881722) \| 12.011372 / 8.074308 (3.937064) \| 10.580532 / 10.191392 (0.389140) \| 0.134763 / 0.680424 (-0.545661) \| 0.017027 / 0.534201 (-0.517174) \| 0.290484 / 0.579283 (-0.288799) \| 0.285171 / 0.434364 (-0.149193) \| 0.322453 / 0.540337 (-0.217884) \| 0.438088 / 1.386936 (-0.948848) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b3fc42882a2d84d7482c27063f1e19539e99b9d3 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005212 / 0.011353 (-0.006141) \| 0.003440 / 0.011008 (-0.007568) \| 0.063612 / 0.038508 (0.025104) \| 0.049070 / 0.023109 (0.025961) \| 0.269748 / 0.275898 (-0.006150) \| 0.283270 / 0.323480 (-0.040210) \| 0.002892 / 0.007986 (-0.005094) \| 0.002693 / 0.004328 (-0.001635) \| 0.049710 / 0.004250 (0.045459) \| 0.036707 / 0.037052 (-0.000345) \| 0.299035 / 0.258489 (0.040546) \| 0.296443 / 0.293841 (0.002602) \| 0.028095 / 0.128546 (-0.100451) \| 0.010682 / 0.075646 (-0.064964) \| 0.213914 / 0.419271 (-0.205358) \| 0.036210 / 0.043533 (-0.007323) \| 0.235720 / 0.255139 (-0.019419) \| 0.252687 / 0.283200 (-0.030512) \| 0.016985 / 0.141683 (-0.124698) \| 1.099024 / 1.452155 (-0.353130) \| 1.162970 / 1.492716 (-0.329746) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093114 / 0.018006 (0.075108) \| 0.305168 / 0.000490 (0.304678) \| 0.000216 / 0.000200 (0.000016) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018370 / 0.037411 (-0.019041) \| 0.060534 / 0.014526 (0.046008) \| 0.073960 / 0.176557 (-0.102596) \| 0.120325 / 0.737135 (-0.616810) \| 0.073754 / 0.296338 (-0.222585) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284244 / 0.215209 (0.069035) \| 2.756854 / 2.077655 (0.679199) \| 1.477304 / 1.504120 (-0.026816) \| 1.374635 / 1.541195 (-0.166560) \| 1.383284 / 1.468490 (-0.085206) \| 0.564656 / 4.584777 (-4.020121) \| 2.361719 / 3.745712 (-1.383993) \| 2.794822 / 5.269862 (-2.475039) \| 1.742981 / 4.565676 (-2.822696) \| 0.063443 / 0.424275 (-0.360832) \| 0.004952 / 0.007607 (-0.002655) \| 0.342058 / 0.226044 (0.116014) \| 3.351093 / 2.268929 (1.082164) \| 1.857375 / 55.444624 (-53.587250) \| 1.541680 / 6.876477 (-5.334797) \| 1.580147 / 2.142072 (-0.561926) \| 0.645216 / 4.805227 (-4.160012) \| 0.118768 / 6.500664 (-6.381896) \| 0.042115 / 0.075469 (-0.033354) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.925845 / 1.841788 (-0.915943) \| 11.444147 / 8.074308 (3.369839) \| 10.291297 / 10.191392 (0.099905) \| 0.128129 / 0.680424 (-0.552295) \| 0.013774 / 0.534201 (-0.520427) \| 0.289278 / 0.579283 (-0.290005) \| 0.262353 / 0.434364 (-0.172011) \| 0.328517 / 0.540337 (-0.211820) \| 0.436050 / 1.386936 (-0.950886) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005666 / 0.011353 (-0.005687) \| 0.003691 / 0.011008 (-0.007318) \| 0.049361 / 0.038508 (0.010853) \| 0.054245 / 0.023109 (0.031136) \| 0.274433 / 0.275898 (-0.001465) \| 0.285648 / 0.323480 (-0.037832) \| 0.004080 / 0.007986 (-0.003906) \| 0.002666 / 0.004328 (-0.001663) \| 0.047539 / 0.004250 (0.043288) \| 0.041001 / 0.037052 (0.003948) \| 0.296018 / 0.258489 (0.037529) \| 0.294542 / 0.293841 (0.000701) \| 0.030546 / 0.128546 (-0.098001) \| 0.010556 / 0.075646 (-0.065090) \| 0.058146 / 0.419271 (-0.361126) \| 0.033407 / 0.043533 (-0.010126) \| 0.263977 / 0.255139 (0.008838) \| 0.286228 / 0.283200 (0.003028) \| 0.018088 / 0.141683 (-0.123595) \| 1.121295 / 1.452155 (-0.330860) \| 1.182183 / 1.492716 (-0.310533) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.104540 / 0.018006 (0.086534) \| 0.303494 / 0.000490 (0.303004) \| 0.000222 / 0.000200 (0.000022) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021274 / 0.037411 (-0.016137) \| 0.070146 / 0.014526 (0.055621) \| 0.080343 / 0.176557 (-0.096213) \| 0.120017 / 0.737135 (-0.617119) \| 0.081303 / 0.296338 (-0.215036) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294390 / 0.215209 (0.079181) \| 2.883366 / 2.077655 (0.805711) \| 1.564629 / 1.504120 (0.060509) \| 1.432633 / 1.541195 (-0.108562) \| 1.438786 / 1.468490 (-0.029704) \| 0.569663 / 4.584777 (-4.015114) \| 2.448691 / 3.745712 (-1.297021) \| 2.817010 / 5.269862 (-2.452851) \| 1.757274 / 4.565676 (-2.808402) \| 0.064147 / 0.424275 (-0.360129) \| 0.004910 / 0.007607 (-0.002697) \| 0.344062 / 0.226044 (0.118018) \| 3.394223 / 2.268929 (1.125294) \| 1.927139 / 55.444624 (-53.517485) \| 1.624983 / 6.876477 (-5.251494) \| 1.629076 / 2.142072 (-0.512996) \| 0.654239 / 4.805227 (-4.150988) \| 0.117309 / 6.500664 (-6.383355) \| 0.041067 / 0.075469 (-0.034402) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.993184 / 1.841788 (-0.848604) \| 11.969985 / 8.074308 (3.895677) \| 10.363356 / 10.191392 (0.171964) \| 0.130708 / 0.680424 (-0.549716) \| 0.015577 / 0.534201 (-0.518624) \| 0.289579 / 0.579283 (-0.289704) \| 0.274875 / 0.434364 (-0.159488) \| 0.326736 / 0.540337 (-0.213601) \| 0.442770 / 1.386936 (-0.944166) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#796a47e388a5c5711a95bd649648608c18219ac5 \"CML watermark\")\n", "Getting the same windows error as in my other PR. I couldn't reproduce on my windows machine though 🧐 ", "`DataFilesList` is a list so we expect to be able to get its length with zero cost, which wouldn't be the case if we make it lazy no ? ", "But we don't call `len` on it, do we? And I couldn't find an instance of `DataFilesList` being used in GitHub's public repos.", "`DataFilesDict` is used in some repositories in dataset scripts when people want to list files from a repo using glob patterns", "Also making DataFilesList lazy would require to make the pickling more complex, since we don't want to resolve the data files when pickling. At the same time we want to get different hashes if the data files and origin metadata are different so revolving the patterns is needed in that case (we hash the data files when creating the config_id, used in the cache)", "> `DataFilesDict` is used in some repositories in dataset scripts when people want to list files from a repo using glob patterns\r\n\r\nWould be interesting to know how often these scripts call `len` or do random access on `DataFilesList`.\r\n\r\nStill, I think we should opt for a solution that makes more sense for us. To avoid the breaking change, we can define a `BuilderConfig.data_files` property that resolves this iterable. \r\n\r\n> Also making DataFilesList lazy would require to make the pickling more complex, since we don't want to resolve the data files when pickling. At the same time we want to get different hashes if the data files and origin metadata are different so revolving the patterns is needed in that case (we hash the data files when creating the config_id, used in the cache)\r\n\r\nThe `BuilderConfig.data_files` property suggested above should address this, no? \r\n\r\nI think we should be more careful not to make our API needlessly complex because of the YAML README feature. And if this can't be avoided, we should probably refactor the builder API.", "> The BuilderConfig.data_files property suggested above should address this, no?\r\n\r\nThat works indeed ! let me try something", "Implementing lazy DataFilesList and .data_files brings more complexity (less readable, more bad side effects) so I think the current solution is the best one", "I opened https://github.com/huggingface/datasets/pull/6493 to continue this and fix conflicts with https://github.com/huggingface/datasets/pull/6459" ]
2,015,650,563	6,457	`TypeError`: huggingface_hub.hf_file_system.HfFileSystem.find() got multiple values for keyword argument 'maxdepth'	closed	2023-11-29T01:57:36	2023-11-29T15:39:03	2023-11-29T02:02:38	https://github.com/huggingface/datasets/issues/6457	null	wasertech	false	[ "Updating `fsspec>=2023.10.0` did solve the issue.", "May be it should be pinned somewhere?", "> Maybe this should go in datasets directly... anyways you can easily fix this error by updating datasets>=2.15.1.dev0.\r\n\r\n@lhoestq @mariosasko for what I understand this is a bug fixed in `datasets` already, right? No need to do anything in `huggingface_hub`?", "I've opened a PR with a fix in `huggingface_hub`: https://github.com/huggingface/huggingface_hub/pull/1875", "Thanks! PR is merged and will be shipped in next release of `huggingface_hub`." ]
2,015,186,090	6,456	Don't require trust_remote_code in inspect_dataset	closed	2023-11-28T19:47:07	2023-11-30T10:40:23	2023-11-30T10:34:12	https://github.com/huggingface/datasets/pull/6456	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6456", "html_url": "https://github.com/huggingface/datasets/pull/6456", "diff_url": "https://github.com/huggingface/datasets/pull/6456.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6456.patch", "merged_at": "2023-11-30T10:34:12" }	lhoestq	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005705 / 0.011353 (-0.005648) \| 0.003536 / 0.011008 (-0.007473) \| 0.062852 / 0.038508 (0.024343) \| 0.053902 / 0.023109 (0.030793) \| 0.239465 / 0.275898 (-0.036433) \| 0.270829 / 0.323480 (-0.052651) \| 0.004052 / 0.007986 (-0.003934) \| 0.002775 / 0.004328 (-0.001554) \| 0.048475 / 0.004250 (0.044225) \| 0.039430 / 0.037052 (0.002377) \| 0.244318 / 0.258489 (-0.014171) \| 0.277539 / 0.293841 (-0.016302) \| 0.027637 / 0.128546 (-0.100909) \| 0.010875 / 0.075646 (-0.064771) \| 0.208839 / 0.419271 (-0.210432) \| 0.036984 / 0.043533 (-0.006549) \| 0.246355 / 0.255139 (-0.008784) \| 0.271200 / 0.283200 (-0.011999) \| 0.020636 / 0.141683 (-0.121047) \| 1.078472 / 1.452155 (-0.373683) \| 1.155701 / 1.492716 (-0.337015) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.100971 / 0.018006 (0.082965) \| 0.310996 / 0.000490 (0.310507) \| 0.000218 / 0.000200 (0.000018) \| 0.000055 / 0.000054 (0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019300 / 0.037411 (-0.018111) \| 0.060625 / 0.014526 (0.046099) \| 0.073778 / 0.176557 (-0.102778) \| 0.120280 / 0.737135 (-0.616855) \| 0.075288 / 0.296338 (-0.221051) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289838 / 0.215209 (0.074629) \| 2.859492 / 2.077655 (0.781837) \| 1.528478 / 1.504120 (0.024358) \| 1.417911 / 1.541195 (-0.123283) \| 1.444227 / 1.468490 (-0.024263) \| 0.566799 / 4.584777 (-4.017978) \| 2.402526 / 3.745712 (-1.343186) \| 2.805241 / 5.269862 (-2.464620) \| 1.798572 / 4.565676 (-2.767104) \| 0.062920 / 0.424275 (-0.361355) \| 0.004995 / 0.007607 (-0.002612) \| 0.340688 / 0.226044 (0.114644) \| 3.347967 / 2.268929 (1.079039) \| 1.898464 / 55.444624 (-53.546160) \| 1.604784 / 6.876477 (-5.271693) \| 1.648864 / 2.142072 (-0.493209) \| 0.642242 / 4.805227 (-4.162985) \| 0.117567 / 6.500664 (-6.383097) \| 0.041911 / 0.075469 (-0.033558) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.949099 / 1.841788 (-0.892689) \| 12.367323 / 8.074308 (4.293015) \| 10.694238 / 10.191392 (0.502846) \| 0.143424 / 0.680424 (-0.537000) \| 0.014569 / 0.534201 (-0.519632) \| 0.289127 / 0.579283 (-0.290156) \| 0.270490 / 0.434364 (-0.163874) \| 0.326470 / 0.540337 (-0.213867) \| 0.432223 / 1.386936 (-0.954713) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005380 / 0.011353 (-0.005973) \| 0.003582 / 0.011008 (-0.007426) \| 0.049341 / 0.038508 (0.010833) \| 0.053274 / 0.023109 (0.030165) \| 0.284319 / 0.275898 (0.008421) \| 0.334248 / 0.323480 (0.010768) \| 0.004032 / 0.007986 (-0.003953) \| 0.002682 / 0.004328 (-0.001646) \| 0.048317 / 0.004250 (0.044067) \| 0.040157 / 0.037052 (0.003105) \| 0.284594 / 0.258489 (0.026105) \| 0.341567 / 0.293841 (0.047726) \| 0.029639 / 0.128546 (-0.098908) \| 0.010780 / 0.075646 (-0.064867) \| 0.057990 / 0.419271 (-0.361282) \| 0.032730 / 0.043533 (-0.010803) \| 0.290328 / 0.255139 (0.035189) \| 0.298563 / 0.283200 (0.015363) \| 0.018546 / 0.141683 (-0.123137) \| 1.143157 / 1.452155 (-0.308998) \| 1.191391 / 1.492716 (-0.301326) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093802 / 0.018006 (0.075796) \| 0.312771 / 0.000490 (0.312282) \| 0.000221 / 0.000200 (0.000021) \| 0.000045 / 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) \| 0.021867 / 0.037411 (-0.015544) \| 0.069064 / 0.014526 (0.054538) \| 0.082270 / 0.176557 (-0.094287) \| 0.120222 / 0.737135 (-0.616913) \| 0.084628 / 0.296338 (-0.211710) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295505 / 0.215209 (0.080296) \| 2.891105 / 2.077655 (0.813450) \| 1.619480 / 1.504120 (0.115360) \| 1.498290 / 1.541195 (-0.042905) \| 1.547896 / 1.468490 (0.079406) \| 0.575188 / 4.584777 (-4.009589) \| 2.434426 / 3.745712 (-1.311286) \| 2.899286 / 5.269862 (-2.370576) \| 1.806085 / 4.565676 (-2.759591) \| 0.063660 / 0.424275 (-0.360616) \| 0.004933 / 0.007607 (-0.002674) \| 0.348274 / 0.226044 (0.122229) \| 3.447900 / 2.268929 (1.178971) \| 1.956237 / 55.444624 (-53.488387) \| 1.680416 / 6.876477 (-5.196061) \| 1.732307 / 2.142072 (-0.409766) \| 0.668428 / 4.805227 (-4.136799) \| 0.119161 / 6.500664 (-6.381503) \| 0.041694 / 0.075469 (-0.033775) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.973730 / 1.841788 (-0.868058) \| 12.082452 / 8.074308 (4.008144) \| 10.624836 / 10.191392 (0.433444) \| 0.144027 / 0.680424 (-0.536397) \| 0.014830 / 0.534201 (-0.519370) \| 0.289946 / 0.579283 (-0.289337) \| 0.281939 / 0.434364 (-0.152424) \| 0.325639 / 0.540337 (-0.214699) \| 0.551690 / 1.386936 (-0.835246) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9e1cf8526c9216b08b5431695d9f8e0eec64cc5f \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005279 / 0.011353 (-0.006074) \| 0.003506 / 0.011008 (-0.007502) \| 0.062579 / 0.038508 (0.024071) \| 0.052809 / 0.023109 (0.029700) \| 0.274693 / 0.275898 (-0.001205) \| 0.283917 / 0.323480 (-0.039563) \| 0.003950 / 0.007986 (-0.004036) \| 0.002772 / 0.004328 (-0.001557) \| 0.048127 / 0.004250 (0.043877) \| 0.037771 / 0.037052 (0.000719) \| 0.280595 / 0.258489 (0.022106) \| 0.292310 / 0.293841 (-0.001531) \| 0.027890 / 0.128546 (-0.100656) \| 0.010771 / 0.075646 (-0.064875) \| 0.207285 / 0.419271 (-0.211987) \| 0.036179 / 0.043533 (-0.007354) \| 0.253617 / 0.255139 (-0.001522) \| 0.276107 / 0.283200 (-0.007093) \| 0.018253 / 0.141683 (-0.123430) \| 1.112219 / 1.452155 (-0.339936) \| 1.166756 / 1.492716 (-0.325960) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095159 / 0.018006 (0.077152) \| 0.306097 / 0.000490 (0.305608) \| 0.000219 / 0.000200 (0.000019) \| 0.000042 / 0.000054 (-0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019056 / 0.037411 (-0.018355) \| 0.060445 / 0.014526 (0.045919) \| 0.073553 / 0.176557 (-0.103004) \| 0.120306 / 0.737135 (-0.616829) \| 0.075613 / 0.296338 (-0.220725) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.277839 / 0.215209 (0.062630) \| 2.761037 / 2.077655 (0.683382) \| 1.508524 / 1.504120 (0.004404) \| 1.368994 / 1.541195 (-0.172201) \| 1.415961 / 1.468490 (-0.052529) \| 0.570490 / 4.584777 (-4.014287) \| 2.356355 / 3.745712 (-1.389357) \| 2.806626 / 5.269862 (-2.463235) \| 1.757849 / 4.565676 (-2.807827) \| 0.063504 / 0.424275 (-0.360771) \| 0.005021 / 0.007607 (-0.002586) \| 0.338880 / 0.226044 (0.112836) \| 3.290947 / 2.268929 (1.022018) \| 1.818238 / 55.444624 (-53.626386) \| 1.529970 / 6.876477 (-5.346507) \| 1.557085 / 2.142072 (-0.584987) \| 0.645352 / 4.805227 (-4.159876) \| 0.123066 / 6.500664 (-6.377598) \| 0.043387 / 0.075469 (-0.032082) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.974512 / 1.841788 (-0.867276) \| 11.976411 / 8.074308 (3.902103) \| 10.361084 / 10.191392 (0.169692) \| 0.127171 / 0.680424 (-0.553253) \| 0.014091 / 0.534201 (-0.520110) \| 0.288608 / 0.579283 (-0.290675) \| 0.261886 / 0.434364 (-0.172478) \| 0.331632 / 0.540337 (-0.208705) \| 0.437002 / 1.386936 (-0.949934) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005129 / 0.011353 (-0.006224) \| 0.003490 / 0.011008 (-0.007518) \| 0.049005 / 0.038508 (0.010497) \| 0.054077 / 0.023109 (0.030968) \| 0.276653 / 0.275898 (0.000755) \| 0.298752 / 0.323480 (-0.024728) \| 0.003979 / 0.007986 (-0.004007) \| 0.002625 / 0.004328 (-0.001703) \| 0.047951 / 0.004250 (0.043701) \| 0.040969 / 0.037052 (0.003916) \| 0.279879 / 0.258489 (0.021390) \| 0.306244 / 0.293841 (0.012403) \| 0.029025 / 0.128546 (-0.099522) \| 0.010450 / 0.075646 (-0.065197) \| 0.056846 / 0.419271 (-0.362426) \| 0.033476 / 0.043533 (-0.010057) \| 0.273340 / 0.255139 (0.018201) \| 0.294783 / 0.283200 (0.011584) \| 0.019105 / 0.141683 (-0.122578) \| 1.126389 / 1.452155 (-0.325766) \| 1.183369 / 1.492716 (-0.309348) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094995 / 0.018006 (0.076989) \| 0.306984 / 0.000490 (0.306495) \| 0.000224 / 0.000200 (0.000024) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021880 / 0.037411 (-0.015532) \| 0.069674 / 0.014526 (0.055148) \| 0.082191 / 0.176557 (-0.094366) \| 0.120956 / 0.737135 (-0.616179) \| 0.083843 / 0.296338 (-0.212495) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295139 / 0.215209 (0.079929) \| 2.860520 / 2.077655 (0.782865) \| 1.578892 / 1.504120 (0.074772) \| 1.451003 / 1.541195 (-0.090192) \| 1.483099 / 1.468490 (0.014609) \| 0.550491 / 4.584777 (-4.034286) \| 2.430352 / 3.745712 (-1.315360) \| 2.874468 / 5.269862 (-2.395393) \| 1.741474 / 4.565676 (-2.824202) \| 0.062563 / 0.424275 (-0.361712) \| 0.004962 / 0.007607 (-0.002645) \| 0.343747 / 0.226044 (0.117703) \| 3.419046 / 2.268929 (1.150118) \| 1.943774 / 55.444624 (-53.500851) \| 1.650989 / 6.876477 (-5.225488) \| 1.704083 / 2.142072 (-0.437990) \| 0.645447 / 4.805227 (-4.159780) \| 0.125105 / 6.500664 (-6.375559) \| 0.041319 / 0.075469 (-0.034150) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.959708 / 1.841788 (-0.882079) \| 12.235906 / 8.074308 (4.161598) \| 10.575402 / 10.191392 (0.384010) \| 0.143619 / 0.680424 (-0.536805) \| 0.015517 / 0.534201 (-0.518684) \| 0.285231 / 0.579283 (-0.294052) \| 0.281549 / 0.434364 (-0.152815) \| 0.326649 / 0.540337 (-0.213689) \| 0.565706 / 1.386936 (-0.821230) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fb6985bc33277a3ece7f28c74ca742ba84655b0c \"CML watermark\")\n" ]
2,013,001,584	6,454	Refactor `dill` logic	closed	2023-11-27T20:01:25	2023-11-28T16:29:58	2023-11-28T16:29:31	https://github.com/huggingface/datasets/pull/6454	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6454", "html_url": "https://github.com/huggingface/datasets/pull/6454", "diff_url": "https://github.com/huggingface/datasets/pull/6454.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6454.patch", "merged_at": "2023-11-28T16:29:31" }	mariosasko	true	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005490 / 0.011353 (-0.005863) \| 0.003554 / 0.011008 (-0.007454) \| 0.062183 / 0.038508 (0.023675) \| 0.053093 / 0.023109 (0.029984) \| 0.245370 / 0.275898 (-0.030528) \| 0.271637 / 0.323480 (-0.051842) \| 0.002997 / 0.007986 (-0.004989) \| 0.002811 / 0.004328 (-0.001517) \| 0.047874 / 0.004250 (0.043623) \| 0.039673 / 0.037052 (0.002620) \| 0.253219 / 0.258489 (-0.005271) \| 0.280438 / 0.293841 (-0.013403) \| 0.028393 / 0.128546 (-0.100153) \| 0.010914 / 0.075646 (-0.064732) \| 0.207491 / 0.419271 (-0.211781) \| 0.037565 / 0.043533 (-0.005968) \| 0.252382 / 0.255139 (-0.002757) \| 0.272204 / 0.283200 (-0.010995) \| 0.019007 / 0.141683 (-0.122676) \| 1.099767 / 1.452155 (-0.352388) \| 1.173220 / 1.492716 (-0.319496) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098777 / 0.018006 (0.080771) \| 0.325912 / 0.000490 (0.325422) \| 0.000214 / 0.000200 (0.000014) \| 0.000051 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018815 / 0.037411 (-0.018596) \| 0.070031 / 0.014526 (0.055506) \| 0.075395 / 0.176557 (-0.101162) \| 0.122633 / 0.737135 (-0.614502) \| 0.077621 / 0.296338 (-0.218718) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290830 / 0.215209 (0.075621) \| 2.869214 / 2.077655 (0.791559) \| 1.507337 / 1.504120 (0.003217) \| 1.351391 / 1.541195 (-0.189804) \| 1.386642 / 1.468490 (-0.081848) \| 0.570318 / 4.584777 (-4.014459) \| 2.423442 / 3.745712 (-1.322270) \| 2.897812 / 5.269862 (-2.372050) \| 1.796458 / 4.565676 (-2.769219) \| 0.063649 / 0.424275 (-0.360626) \| 0.005038 / 0.007607 (-0.002570) \| 0.357819 / 0.226044 (0.131774) \| 3.535478 / 2.268929 (1.266549) \| 1.831764 / 55.444624 (-53.612861) \| 1.545035 / 6.876477 (-5.331442) \| 1.585919 / 2.142072 (-0.556154) \| 0.643333 / 4.805227 (-4.161894) \| 0.120319 / 6.500664 (-6.380345) \| 0.043031 / 0.075469 (-0.032438) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.981155 / 1.841788 (-0.860633) \| 12.136069 / 8.074308 (4.061760) \| 10.579923 / 10.191392 (0.388531) \| 0.152963 / 0.680424 (-0.527461) \| 0.014783 / 0.534201 (-0.519418) \| 0.289177 / 0.579283 (-0.290106) \| 0.271784 / 0.434364 (-0.162580) \| 0.322381 / 0.540337 (-0.217956) \| 0.420034 / 1.386936 (-0.966902) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005315 / 0.011353 (-0.006038) \| 0.003584 / 0.011008 (-0.007424) \| 0.048596 / 0.038508 (0.010088) \| 0.055940 / 0.023109 (0.032830) \| 0.277687 / 0.275898 (0.001789) \| 0.301545 / 0.323480 (-0.021935) \| 0.004150 / 0.007986 (-0.003836) \| 0.002699 / 0.004328 (-0.001629) \| 0.047661 / 0.004250 (0.043410) \| 0.040618 / 0.037052 (0.003565) \| 0.279173 / 0.258489 (0.020684) \| 0.306105 / 0.293841 (0.012264) \| 0.030099 / 0.128546 (-0.098447) \| 0.010784 / 0.075646 (-0.064862) \| 0.057418 / 0.419271 (-0.361853) \| 0.032632 / 0.043533 (-0.010901) \| 0.276064 / 0.255139 (0.020925) \| 0.307194 / 0.283200 (0.023995) \| 0.017416 / 0.141683 (-0.124267) \| 1.107749 / 1.452155 (-0.344406) \| 1.161104 / 1.492716 (-0.331612) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.102395 / 0.018006 (0.084389) \| 0.316933 / 0.000490 (0.316443) \| 0.000246 / 0.000200 (0.000046) \| 0.000042 / 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) \| 0.022833 / 0.037411 (-0.014579) \| 0.069372 / 0.014526 (0.054846) \| 0.082139 / 0.176557 (-0.094418) \| 0.121666 / 0.737135 (-0.615469) \| 0.084039 / 0.296338 (-0.212300) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298775 / 0.215209 (0.083566) \| 2.973898 / 2.077655 (0.896244) \| 1.614436 / 1.504120 (0.110316) \| 1.476112 / 1.541195 (-0.065083) \| 1.502031 / 1.468490 (0.033541) \| 0.580626 / 4.584777 (-4.004151) \| 2.493428 / 3.745712 (-1.252285) \| 2.931050 / 5.269862 (-2.338811) \| 1.823603 / 4.565676 (-2.742073) \| 0.064736 / 0.424275 (-0.359539) \| 0.004963 / 0.007607 (-0.002644) \| 0.355096 / 0.226044 (0.129052) \| 3.522801 / 2.268929 (1.253872) \| 1.968690 / 55.444624 (-53.475935) \| 1.698624 / 6.876477 (-5.177853) \| 1.714166 / 2.142072 (-0.427906) \| 0.681734 / 4.805227 (-4.123493) \| 0.118940 / 6.500664 (-6.381724) \| 0.041960 / 0.075469 (-0.033509) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.985311 / 1.841788 (-0.856476) \| 12.785393 / 8.074308 (4.711085) \| 11.289459 / 10.191392 (1.098067) \| 0.145297 / 0.680424 (-0.535127) \| 0.016125 / 0.534201 (-0.518076) \| 0.289445 / 0.579283 (-0.289838) \| 0.278974 / 0.434364 (-0.155390) \| 0.322456 / 0.540337 (-0.217881) \| 0.418218 / 1.386936 (-0.968718) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#66cef090c55d3561412468d94cb545b47fb000fb \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005142 / 0.011353 (-0.006211) \| 0.004180 / 0.011008 (-0.006829) \| 0.062647 / 0.038508 (0.024139) \| 0.055072 / 0.023109 (0.031962) \| 0.254681 / 0.275898 (-0.021217) \| 0.282650 / 0.323480 (-0.040830) \| 0.003950 / 0.007986 (-0.004035) \| 0.002862 / 0.004328 (-0.001466) \| 0.048420 / 0.004250 (0.044170) \| 0.038447 / 0.037052 (0.001394) \| 0.258160 / 0.258489 (-0.000329) \| 0.288596 / 0.293841 (-0.005245) \| 0.027898 / 0.128546 (-0.100648) \| 0.011165 / 0.075646 (-0.064482) \| 0.206844 / 0.419271 (-0.212427) \| 0.036312 / 0.043533 (-0.007221) \| 0.257957 / 0.255139 (0.002819) \| 0.277387 / 0.283200 (-0.005812) \| 0.018205 / 0.141683 (-0.123478) \| 1.109870 / 1.452155 (-0.342284) \| 1.175005 / 1.492716 (-0.317712) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096692 / 0.018006 (0.078686) \| 0.307463 / 0.000490 (0.306973) \| 0.000218 / 0.000200 (0.000018) \| 0.000042 / 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) \| 0.018602 / 0.037411 (-0.018809) \| 0.061489 / 0.014526 (0.046964) \| 0.072936 / 0.176557 (-0.103620) \| 0.119863 / 0.737135 (-0.617272) \| 0.073983 / 0.296338 (-0.222355) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291444 / 0.215209 (0.076235) \| 2.849024 / 2.077655 (0.771369) \| 1.533121 / 1.504120 (0.029001) \| 1.402148 / 1.541195 (-0.139046) \| 1.406397 / 1.468490 (-0.062094) \| 0.564241 / 4.584777 (-4.020536) \| 2.402052 / 3.745712 (-1.343660) \| 2.772639 / 5.269862 (-2.497223) \| 1.732342 / 4.565676 (-2.833334) \| 0.062361 / 0.424275 (-0.361914) \| 0.004945 / 0.007607 (-0.002662) \| 0.355841 / 0.226044 (0.129797) \| 3.426931 / 2.268929 (1.158003) \| 1.865412 / 55.444624 (-53.579212) \| 1.592628 / 6.876477 (-5.283849) \| 1.662364 / 2.142072 (-0.479708) \| 0.653278 / 4.805227 (-4.151949) \| 0.118626 / 6.500664 (-6.382038) \| 0.042961 / 0.075469 (-0.032508) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.956279 / 1.841788 (-0.885509) \| 11.635540 / 8.074308 (3.561232) \| 10.719590 / 10.191392 (0.528198) \| 0.130015 / 0.680424 (-0.550409) \| 0.014424 / 0.534201 (-0.519777) \| 0.288135 / 0.579283 (-0.291148) \| 0.270819 / 0.434364 (-0.163545) \| 0.320238 / 0.540337 (-0.220099) \| 0.421044 / 1.386936 (-0.965892) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005201 / 0.011353 (-0.006152) \| 0.003467 / 0.011008 (-0.007541) \| 0.048939 / 0.038508 (0.010431) \| 0.051841 / 0.023109 (0.028732) \| 0.273708 / 0.275898 (-0.002190) \| 0.293491 / 0.323480 (-0.029988) \| 0.004830 / 0.007986 (-0.003156) \| 0.002696 / 0.004328 (-0.001632) \| 0.047727 / 0.004250 (0.043476) \| 0.041319 / 0.037052 (0.004266) \| 0.273837 / 0.258489 (0.015348) \| 0.309860 / 0.293841 (0.016019) \| 0.029054 / 0.128546 (-0.099492) \| 0.010410 / 0.075646 (-0.065237) \| 0.058139 / 0.419271 (-0.361133) \| 0.032682 / 0.043533 (-0.010850) \| 0.273244 / 0.255139 (0.018105) \| 0.291579 / 0.283200 (0.008380) \| 0.018262 / 0.141683 (-0.123421) \| 1.144590 / 1.452155 (-0.307565) \| 1.202474 / 1.492716 (-0.290243) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097110 / 0.018006 (0.079104) \| 0.307344 / 0.000490 (0.306854) \| 0.000229 / 0.000200 (0.000029) \| 0.000045 / 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) \| 0.022263 / 0.037411 (-0.015148) \| 0.070140 / 0.014526 (0.055614) \| 0.081251 / 0.176557 (-0.095306) \| 0.120839 / 0.737135 (-0.616297) \| 0.083312 / 0.296338 (-0.213026) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297381 / 0.215209 (0.082172) \| 2.895530 / 2.077655 (0.817875) \| 1.608442 / 1.504120 (0.104322) \| 1.476237 / 1.541195 (-0.064958) \| 1.491306 / 1.468490 (0.022816) \| 0.567272 / 4.584777 (-4.017505) \| 2.463543 / 3.745712 (-1.282170) \| 2.814764 / 5.269862 (-2.455098) \| 1.725845 / 4.565676 (-2.839831) \| 0.064149 / 0.424275 (-0.360126) \| 0.004953 / 0.007607 (-0.002654) \| 0.359629 / 0.226044 (0.133585) \| 3.482414 / 2.268929 (1.213486) \| 1.949897 / 55.444624 (-53.494727) \| 1.677383 / 6.876477 (-5.199094) \| 1.683655 / 2.142072 (-0.458418) \| 0.645671 / 4.805227 (-4.159557) \| 0.115612 / 6.500664 (-6.385053) \| 0.041013 / 0.075469 (-0.034456) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.967843 / 1.841788 (-0.873945) \| 12.376877 / 8.074308 (4.302569) \| 10.988174 / 10.191392 (0.796782) \| 0.134660 / 0.680424 (-0.545764) \| 0.015801 / 0.534201 (-0.518400) \| 0.288699 / 0.579283 (-0.290584) \| 0.284887 / 0.434364 (-0.149477) \| 0.322000 / 0.540337 (-0.218337) \| 0.412360 / 1.386936 (-0.974576) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#148454d48b7c36507a283217c7c0e3bcc0539f75 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005407 / 0.011353 (-0.005946) \| 0.003496 / 0.011008 (-0.007512) \| 0.062730 / 0.038508 (0.024222) \| 0.051882 / 0.023109 (0.028773) \| 0.244766 / 0.275898 (-0.031132) \| 0.257963 / 0.323480 (-0.065516) \| 0.002894 / 0.007986 (-0.005092) \| 0.002567 / 0.004328 (-0.001761) \| 0.048756 / 0.004250 (0.044506) \| 0.039024 / 0.037052 (0.001971) \| 0.247303 / 0.258489 (-0.011186) \| 0.278341 / 0.293841 (-0.015500) \| 0.026725 / 0.128546 (-0.101821) \| 0.010577 / 0.075646 (-0.065069) \| 0.210483 / 0.419271 (-0.208789) \| 0.035230 / 0.043533 (-0.008303) \| 0.246125 / 0.255139 (-0.009014) \| 0.264039 / 0.283200 (-0.019160) \| 0.019881 / 0.141683 (-0.121802) \| 1.113475 / 1.452155 (-0.338679) \| 1.149606 / 1.492716 (-0.343110) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092946 / 0.018006 (0.074940) \| 0.299985 / 0.000490 (0.299495) \| 0.000215 / 0.000200 (0.000016) \| 0.000050 / 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) \| 0.018421 / 0.037411 (-0.018991) \| 0.060531 / 0.014526 (0.046005) \| 0.074459 / 0.176557 (-0.102098) \| 0.120369 / 0.737135 (-0.616766) \| 0.075505 / 0.296338 (-0.220833) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289497 / 0.215209 (0.074288) \| 2.783139 / 2.077655 (0.705485) \| 1.482533 / 1.504120 (-0.021587) \| 1.371013 / 1.541195 (-0.170182) \| 1.379114 / 1.468490 (-0.089376) \| 0.563953 / 4.584777 (-4.020824) \| 2.389996 / 3.745712 (-1.355716) \| 2.788067 / 5.269862 (-2.481795) \| 1.751772 / 4.565676 (-2.813904) \| 0.062680 / 0.424275 (-0.361595) \| 0.004901 / 0.007607 (-0.002706) \| 0.365193 / 0.226044 (0.139149) \| 3.389181 / 2.268929 (1.120252) \| 1.861659 / 55.444624 (-53.582965) \| 1.558899 / 6.876477 (-5.317577) \| 1.591079 / 2.142072 (-0.550993) \| 0.648300 / 4.805227 (-4.156927) \| 0.117486 / 6.500664 (-6.383178) \| 0.041961 / 0.075469 (-0.033508) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.944391 / 1.841788 (-0.897396) \| 11.500823 / 8.074308 (3.426515) \| 10.580430 / 10.191392 (0.389038) \| 0.142845 / 0.680424 (-0.537579) \| 0.014305 / 0.534201 (-0.519896) \| 0.290723 / 0.579283 (-0.288560) \| 0.266206 / 0.434364 (-0.168158) \| 0.325482 / 0.540337 (-0.214856) \| 0.416224 / 1.386936 (-0.970712) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005363 / 0.011353 (-0.005990) \| 0.003548 / 0.011008 (-0.007460) \| 0.048704 / 0.038508 (0.010196) \| 0.051025 / 0.023109 (0.027916) \| 0.273037 / 0.275898 (-0.002861) \| 0.297148 / 0.323480 (-0.026332) \| 0.003985 / 0.007986 (-0.004001) \| 0.002739 / 0.004328 (-0.001590) \| 0.048108 / 0.004250 (0.043857) \| 0.040244 / 0.037052 (0.003191) \| 0.277825 / 0.258489 (0.019336) \| 0.303704 / 0.293841 (0.009863) \| 0.029460 / 0.128546 (-0.099086) \| 0.010428 / 0.075646 (-0.065218) \| 0.057022 / 0.419271 (-0.362249) \| 0.032711 / 0.043533 (-0.010822) \| 0.274462 / 0.255139 (0.019323) \| 0.293499 / 0.283200 (0.010299) \| 0.018266 / 0.141683 (-0.123417) \| 1.158049 / 1.452155 (-0.294106) \| 1.170097 / 1.492716 (-0.322620) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093412 / 0.018006 (0.075406) \| 0.301538 / 0.000490 (0.301049) \| 0.000222 / 0.000200 (0.000022) \| 0.000051 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021698 / 0.037411 (-0.015713) \| 0.068735 / 0.014526 (0.054209) \| 0.083010 / 0.176557 (-0.093546) \| 0.127491 / 0.737135 (-0.609644) \| 0.083005 / 0.296338 (-0.213333) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298299 / 0.215209 (0.083090) \| 2.894209 / 2.077655 (0.816554) \| 1.597455 / 1.504120 (0.093335) \| 1.472953 / 1.541195 (-0.068241) \| 1.491553 / 1.468490 (0.023063) \| 0.556566 / 4.584777 (-4.028211) \| 2.419429 / 3.745712 (-1.326283) \| 2.788706 / 5.269862 (-2.481156) \| 1.759888 / 4.565676 (-2.805789) \| 0.062535 / 0.424275 (-0.361740) \| 0.004959 / 0.007607 (-0.002648) \| 0.345226 / 0.226044 (0.119182) \| 3.438539 / 2.268929 (1.169611) \| 1.943842 / 55.444624 (-53.500782) \| 1.661080 / 6.876477 (-5.215397) \| 1.687632 / 2.142072 (-0.454440) \| 0.639971 / 4.805227 (-4.165256) \| 0.116012 / 6.500664 (-6.384652) \| 0.041723 / 0.075469 (-0.033746) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.965143 / 1.841788 (-0.876645) \| 12.086547 / 8.074308 (4.012238) \| 10.708787 / 10.191392 (0.517395) \| 0.129506 / 0.680424 (-0.550918) \| 0.015254 / 0.534201 (-0.518947) \| 0.288326 / 0.579283 (-0.290957) \| 0.271976 / 0.434364 (-0.162388) \| 0.328402 / 0.540337 (-0.211936) \| 0.418102 / 1.386936 (-0.968834) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#18b6f13ede3dccedf335bb2d8ff04db306dc710a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005375 / 0.011353 (-0.005978) \| 0.003530 / 0.011008 (-0.007478) \| 0.062521 / 0.038508 (0.024013) \| 0.051514 / 0.023109 (0.028405) \| 0.241623 / 0.275898 (-0.034275) \| 0.269054 / 0.323480 (-0.054426) \| 0.002877 / 0.007986 (-0.005109) \| 0.002724 / 0.004328 (-0.001605) \| 0.049045 / 0.004250 (0.044794) \| 0.038560 / 0.037052 (0.001507) \| 0.248437 / 0.258489 (-0.010052) \| 0.276762 / 0.293841 (-0.017079) \| 0.027522 / 0.128546 (-0.101024) \| 0.010817 / 0.075646 (-0.064829) \| 0.208686 / 0.419271 (-0.210585) \| 0.035818 / 0.043533 (-0.007715) \| 0.249398 / 0.255139 (-0.005741) \| 0.268288 / 0.283200 (-0.014911) \| 0.019039 / 0.141683 (-0.122644) \| 1.135115 / 1.452155 (-0.317040) \| 1.195531 / 1.492716 (-0.297185) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093126 / 0.018006 (0.075120) \| 0.301028 / 0.000490 (0.300539) \| 0.000222 / 0.000200 (0.000023) \| 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) \| 0.018385 / 0.037411 (-0.019027) \| 0.060902 / 0.014526 (0.046376) \| 0.073168 / 0.176557 (-0.103389) \| 0.119216 / 0.737135 (-0.617919) \| 0.074225 / 0.296338 (-0.222114) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283749 / 0.215209 (0.068540) \| 2.741609 / 2.077655 (0.663954) \| 1.483439 / 1.504120 (-0.020681) \| 1.352896 / 1.541195 (-0.188299) \| 1.378824 / 1.468490 (-0.089667) \| 0.548731 / 4.584777 (-4.036046) \| 2.342717 / 3.745712 (-1.402995) \| 2.791592 / 5.269862 (-2.478269) \| 1.740605 / 4.565676 (-2.825071) \| 0.062059 / 0.424275 (-0.362216) \| 0.005028 / 0.007607 (-0.002579) \| 0.339205 / 0.226044 (0.113161) \| 3.353386 / 2.268929 (1.084458) \| 1.785717 / 55.444624 (-53.658907) \| 1.523390 / 6.876477 (-5.353086) \| 1.556999 / 2.142072 (-0.585073) \| 0.636745 / 4.805227 (-4.168483) \| 0.115821 / 6.500664 (-6.384843) \| 0.042200 / 0.075469 (-0.033269) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.948678 / 1.841788 (-0.893110) \| 11.588670 / 8.074308 (3.514362) \| 10.897130 / 10.191392 (0.705738) \| 0.140068 / 0.680424 (-0.540356) \| 0.014565 / 0.534201 (-0.519636) \| 0.286336 / 0.579283 (-0.292947) \| 0.265292 / 0.434364 (-0.169072) \| 0.324146 / 0.540337 (-0.216192) \| 0.413463 / 1.386936 (-0.973473) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005187 / 0.011353 (-0.006165) \| 0.003471 / 0.011008 (-0.007537) \| 0.048968 / 0.038508 (0.010460) \| 0.051285 / 0.023109 (0.028176) \| 0.283286 / 0.275898 (0.007388) \| 0.307046 / 0.323480 (-0.016434) \| 0.004017 / 0.007986 (-0.003969) \| 0.002655 / 0.004328 (-0.001673) \| 0.047762 / 0.004250 (0.043512) \| 0.039855 / 0.037052 (0.002803) \| 0.283101 / 0.258489 (0.024612) \| 0.312905 / 0.293841 (0.019064) \| 0.028188 / 0.128546 (-0.100358) \| 0.010849 / 0.075646 (-0.064797) \| 0.058112 / 0.419271 (-0.361159) \| 0.032163 / 0.043533 (-0.011369) \| 0.280825 / 0.255139 (0.025686) \| 0.300946 / 0.283200 (0.017747) \| 0.017409 / 0.141683 (-0.124274) \| 1.127360 / 1.452155 (-0.324795) \| 1.180409 / 1.492716 (-0.312307) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093186 / 0.018006 (0.075180) \| 0.300827 / 0.000490 (0.300338) \| 0.000220 / 0.000200 (0.000020) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021560 / 0.037411 (-0.015851) \| 0.069158 / 0.014526 (0.054632) \| 0.080953 / 0.176557 (-0.095603) \| 0.119071 / 0.737135 (-0.618064) \| 0.082817 / 0.296338 (-0.213521) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.307259 / 0.215209 (0.092050) \| 2.996058 / 2.077655 (0.918404) \| 1.627406 / 1.504120 (0.123286) \| 1.500715 / 1.541195 (-0.040480) \| 1.524278 / 1.468490 (0.055788) \| 0.569711 / 4.584777 (-4.015066) \| 2.436132 / 3.745712 (-1.309580) \| 2.796995 / 5.269862 (-2.472866) \| 1.760701 / 4.565676 (-2.804975) \| 0.063521 / 0.424275 (-0.360754) \| 0.004909 / 0.007607 (-0.002698) \| 0.359129 / 0.226044 (0.133085) \| 3.567278 / 2.268929 (1.298349) \| 2.013821 / 55.444624 (-53.430804) \| 1.708021 / 6.876477 (-5.168456) \| 1.738959 / 2.142072 (-0.403114) \| 0.648620 / 4.805227 (-4.156607) \| 0.122016 / 6.500664 (-6.378648) \| 0.041802 / 0.075469 (-0.033667) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.985208 / 1.841788 (-0.856579) \| 12.307785 / 8.074308 (4.233477) \| 10.587262 / 10.191392 (0.395870) \| 0.130468 / 0.680424 (-0.549956) \| 0.014912 / 0.534201 (-0.519289) \| 0.293822 / 0.579283 (-0.285461) \| 0.283021 / 0.434364 (-0.151343) \| 0.329560 / 0.540337 (-0.210777) \| 0.424741 / 1.386936 (-0.962195) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#04426d9c8e0aa5c97af2826064287f8cab6bece0 \"CML watermark\")\n" ]
2,011,907,787	6,453	Update hub-docs reference	closed	2023-11-27T09:57:20	2023-11-27T10:23:44	2023-11-27T10:17:34	https://github.com/huggingface/datasets/pull/6453	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6453", "html_url": "https://github.com/huggingface/datasets/pull/6453", "diff_url": "https://github.com/huggingface/datasets/pull/6453.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6453.patch", "merged_at": "2023-11-27T10:17:34" }	mishig25	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005119 / 0.011353 (-0.006234) \| 0.003469 / 0.011008 (-0.007540) \| 0.061791 / 0.038508 (0.023283) \| 0.051655 / 0.023109 (0.028545) \| 0.241157 / 0.275898 (-0.034741) \| 0.265930 / 0.323480 (-0.057549) \| 0.003851 / 0.007986 (-0.004134) \| 0.002412 / 0.004328 (-0.001916) \| 0.047498 / 0.004250 (0.043247) \| 0.037328 / 0.037052 (0.000276) \| 0.250418 / 0.258489 (-0.008071) \| 0.277842 / 0.293841 (-0.015999) \| 0.027626 / 0.128546 (-0.100920) \| 0.009947 / 0.075646 (-0.065699) \| 0.204549 / 0.419271 (-0.214722) \| 0.037546 / 0.043533 (-0.005987) \| 0.245383 / 0.255139 (-0.009756) \| 0.263486 / 0.283200 (-0.019713) \| 0.017792 / 0.141683 (-0.123891) \| 1.158900 / 1.452155 (-0.293255) \| 1.194060 / 1.492716 (-0.298657) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090607 / 0.018006 (0.072601) \| 0.299909 / 0.000490 (0.299419) \| 0.000206 / 0.000200 (0.000006) \| 0.000042 / 0.000054 (-0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018814 / 0.037411 (-0.018597) \| 0.062068 / 0.014526 (0.047542) \| 0.087221 / 0.176557 (-0.089336) \| 0.119594 / 0.737135 (-0.617541) \| 0.075485 / 0.296338 (-0.220853) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286093 / 0.215209 (0.070884) \| 2.767396 / 2.077655 (0.689741) \| 1.500472 / 1.504120 (-0.003648) \| 1.389514 / 1.541195 (-0.151680) \| 1.438933 / 1.468490 (-0.029557) \| 0.562545 / 4.584777 (-4.022232) \| 2.383330 / 3.745712 (-1.362382) \| 2.799215 / 5.269862 (-2.470647) \| 1.732618 / 4.565676 (-2.833058) \| 0.061282 / 0.424275 (-0.362993) \| 0.005007 / 0.007607 (-0.002601) \| 0.339769 / 0.226044 (0.113725) \| 3.337146 / 2.268929 (1.068218) \| 1.890789 / 55.444624 (-53.553836) \| 1.593555 / 6.876477 (-5.282922) \| 1.660016 / 2.142072 (-0.482057) \| 0.632452 / 4.805227 (-4.172775) \| 0.115503 / 6.500664 (-6.385161) \| 0.041590 / 0.075469 (-0.033880) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.941966 / 1.841788 (-0.899822) \| 11.470271 / 8.074308 (3.395963) \| 10.579454 / 10.191392 (0.388062) \| 0.140970 / 0.680424 (-0.539454) \| 0.014057 / 0.534201 (-0.520144) \| 0.289326 / 0.579283 (-0.289957) \| 0.265366 / 0.434364 (-0.168998) \| 0.324612 / 0.540337 (-0.215726) \| 0.415832 / 1.386936 (-0.971104) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005208 / 0.011353 (-0.006145) \| 0.003199 / 0.011008 (-0.007809) \| 0.048299 / 0.038508 (0.009791) \| 0.050727 / 0.023109 (0.027618) \| 0.274897 / 0.275898 (-0.001001) \| 0.298328 / 0.323480 (-0.025152) \| 0.003989 / 0.007986 (-0.003997) \| 0.002439 / 0.004328 (-0.001890) \| 0.047308 / 0.004250 (0.043058) \| 0.039726 / 0.037052 (0.002673) \| 0.276279 / 0.258489 (0.017790) \| 0.303679 / 0.293841 (0.009838) \| 0.028943 / 0.128546 (-0.099603) \| 0.010223 / 0.075646 (-0.065423) \| 0.056694 / 0.419271 (-0.362577) \| 0.032283 / 0.043533 (-0.011250) \| 0.275344 / 0.255139 (0.020205) \| 0.296358 / 0.283200 (0.013158) \| 0.017481 / 0.141683 (-0.124201) \| 1.131063 / 1.452155 (-0.321092) \| 1.181146 / 1.492716 (-0.311570) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092259 / 0.018006 (0.074253) \| 0.299381 / 0.000490 (0.298891) \| 0.000216 / 0.000200 (0.000016) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021693 / 0.037411 (-0.015718) \| 0.070441 / 0.014526 (0.055916) \| 0.080648 / 0.176557 (-0.095908) \| 0.119002 / 0.737135 (-0.618133) \| 0.081412 / 0.296338 (-0.214926) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296475 / 0.215209 (0.081266) \| 2.905098 / 2.077655 (0.827443) \| 1.596321 / 1.504120 (0.092201) \| 1.472640 / 1.541195 (-0.068555) \| 1.484453 / 1.468490 (0.015963) \| 0.565229 / 4.584777 (-4.019548) \| 2.390631 / 3.745712 (-1.355081) \| 2.765125 / 5.269862 (-2.504737) \| 1.738993 / 4.565676 (-2.826683) \| 0.063034 / 0.424275 (-0.361241) \| 0.004891 / 0.007607 (-0.002716) \| 0.350678 / 0.226044 (0.124633) \| 3.530919 / 2.268929 (1.261990) \| 1.943758 / 55.444624 (-53.500867) \| 1.665553 / 6.876477 (-5.210924) \| 1.656990 / 2.142072 (-0.485083) \| 0.647027 / 4.805227 (-4.158201) \| 0.116771 / 6.500664 (-6.383893) \| 0.041012 / 0.075469 (-0.034457) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.034226 / 1.841788 (-0.807561) \| 12.036726 / 8.074308 (3.962418) \| 10.934239 / 10.191392 (0.742847) \| 0.130142 / 0.680424 (-0.550281) \| 0.015537 / 0.534201 (-0.518664) \| 0.286020 / 0.579283 (-0.293263) \| 0.276739 / 0.434364 (-0.157625) \| 0.326284 / 0.540337 (-0.214054) \| 0.413392 / 1.386936 (-0.973544) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4787c0022c8b59c15256021478b444a6c51fa984 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005400 / 0.011353 (-0.005953) \| 0.003415 / 0.011008 (-0.007593) \| 0.062416 / 0.038508 (0.023908) \| 0.055962 / 0.023109 (0.032853) \| 0.234725 / 0.275898 (-0.041173) \| 0.261775 / 0.323480 (-0.061705) \| 0.002868 / 0.007986 (-0.005118) \| 0.002426 / 0.004328 (-0.001902) \| 0.047989 / 0.004250 (0.043738) \| 0.039214 / 0.037052 (0.002162) \| 0.246068 / 0.258489 (-0.012421) \| 0.270245 / 0.293841 (-0.023596) \| 0.027558 / 0.128546 (-0.100988) \| 0.010256 / 0.075646 (-0.065390) \| 0.210988 / 0.419271 (-0.208283) \| 0.035684 / 0.043533 (-0.007849) \| 0.245254 / 0.255139 (-0.009885) \| 0.255476 / 0.283200 (-0.027724) \| 0.018495 / 0.141683 (-0.123188) \| 1.115458 / 1.452155 (-0.336697) \| 1.166149 / 1.492716 (-0.326567) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092736 / 0.018006 (0.074730) \| 0.301040 / 0.000490 (0.300550) \| 0.000213 / 0.000200 (0.000013) \| 0.000053 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018607 / 0.037411 (-0.018805) \| 0.062189 / 0.014526 (0.047664) \| 0.073782 / 0.176557 (-0.102775) \| 0.119895 / 0.737135 (-0.617240) \| 0.074907 / 0.296338 (-0.221431) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283986 / 0.215209 (0.068777) \| 2.824498 / 2.077655 (0.746844) \| 1.505848 / 1.504120 (0.001728) \| 1.358879 / 1.541195 (-0.182316) \| 1.357087 / 1.468490 (-0.111403) \| 0.574307 / 4.584777 (-4.010470) \| 2.416478 / 3.745712 (-1.329234) \| 2.772909 / 5.269862 (-2.496953) \| 1.750395 / 4.565676 (-2.815282) \| 0.062465 / 0.424275 (-0.361810) \| 0.004983 / 0.007607 (-0.002624) \| 0.344490 / 0.226044 (0.118445) \| 3.405062 / 2.268929 (1.136134) \| 1.854972 / 55.444624 (-53.589653) \| 1.572789 / 6.876477 (-5.303687) \| 1.586109 / 2.142072 (-0.555963) \| 0.647431 / 4.805227 (-4.157797) \| 0.123079 / 6.500664 (-6.377585) \| 0.042766 / 0.075469 (-0.032703) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.950493 / 1.841788 (-0.891295) \| 11.814821 / 8.074308 (3.740513) \| 10.494768 / 10.191392 (0.303376) \| 0.131322 / 0.680424 (-0.549102) \| 0.015253 / 0.534201 (-0.518948) \| 0.287405 / 0.579283 (-0.291878) \| 0.269664 / 0.434364 (-0.164699) \| 0.322700 / 0.540337 (-0.217637) \| 0.424103 / 1.386936 (-0.962833) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005264 / 0.011353 (-0.006088) \| 0.003304 / 0.011008 (-0.007704) \| 0.048531 / 0.038508 (0.010023) \| 0.052752 / 0.023109 (0.029643) \| 0.274435 / 0.275898 (-0.001463) \| 0.297500 / 0.323480 (-0.025980) \| 0.003977 / 0.007986 (-0.004009) \| 0.002444 / 0.004328 (-0.001884) \| 0.048464 / 0.004250 (0.044214) \| 0.040192 / 0.037052 (0.003139) \| 0.278256 / 0.258489 (0.019767) \| 0.303627 / 0.293841 (0.009786) \| 0.028709 / 0.128546 (-0.099837) \| 0.010530 / 0.075646 (-0.065117) \| 0.057427 / 0.419271 (-0.361844) \| 0.032539 / 0.043533 (-0.010994) \| 0.272237 / 0.255139 (0.017098) \| 0.295288 / 0.283200 (0.012088) \| 0.018820 / 0.141683 (-0.122862) \| 1.116100 / 1.452155 (-0.336055) \| 1.180124 / 1.492716 (-0.312592) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092651 / 0.018006 (0.074644) \| 0.301481 / 0.000490 (0.300991) \| 0.000217 / 0.000200 (0.000017) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022461 / 0.037411 (-0.014951) \| 0.070623 / 0.014526 (0.056097) \| 0.082642 / 0.176557 (-0.093915) \| 0.120021 / 0.737135 (-0.617114) \| 0.083387 / 0.296338 (-0.212952) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291451 / 0.215209 (0.076242) \| 2.865602 / 2.077655 (0.787947) \| 1.592051 / 1.504120 (0.087931) \| 1.463521 / 1.541195 (-0.077673) \| 1.498899 / 1.468490 (0.030409) \| 0.570854 / 4.584777 (-4.013923) \| 2.410002 / 3.745712 (-1.335710) \| 2.768028 / 5.269862 (-2.501834) \| 1.740463 / 4.565676 (-2.825214) \| 0.063801 / 0.424275 (-0.360474) \| 0.005019 / 0.007607 (-0.002588) \| 0.348353 / 0.226044 (0.122309) \| 3.425793 / 2.268929 (1.156864) \| 1.957294 / 55.444624 (-53.487331) \| 1.696121 / 6.876477 (-5.180355) \| 1.691544 / 2.142072 (-0.450528) \| 0.645528 / 4.805227 (-4.159700) \| 0.118876 / 6.500664 (-6.381788) \| 0.041001 / 0.075469 (-0.034469) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.983805 / 1.841788 (-0.857983) \| 12.085909 / 8.074308 (4.011600) \| 10.835395 / 10.191392 (0.644003) \| 0.141971 / 0.680424 (-0.538453) \| 0.015534 / 0.534201 (-0.518667) \| 0.289289 / 0.579283 (-0.289994) \| 0.276316 / 0.434364 (-0.158048) \| 0.354577 / 0.540337 (-0.185761) \| 0.421824 / 1.386936 (-0.965112) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#27d1fe52857c6a25a29cac63a296405136b2797c \"CML watermark\")\n" ]
2,011,632,708	6,452	Praveen_repo_pull_req	closed	2023-11-27T07:07:50	2023-11-27T09:28:00	2023-11-27T09:28:00	https://github.com/huggingface/datasets/pull/6452	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6452", "html_url": "https://github.com/huggingface/datasets/pull/6452", "diff_url": "https://github.com/huggingface/datasets/pull/6452.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6452.patch", "merged_at": null }	Praveenhh	true	[]
2,010,693,912	6,451	Unable to read "marsyas/gtzan" data	closed	2023-11-25T15:13:17	2023-12-01T12:53:46	2023-11-27T09:36:25	https://github.com/huggingface/datasets/issues/6451	null	gerald-wrona	false	[ "Hi! We've merged a [PR](https://huggingface.co/datasets/marsyas/gtzan/discussions/1) that fixes the script's path logic on Windows.", "I have transferred the discussion to the corresponding dataset: https://huggingface.co/datasets/marsyas/gtzan/discussions/2\r\n\r\nLet's continue there.", "@mariosasko @albertvillanova \r\n\r\nThank you both very much for the speedy resolution :)" ]
2,009,491,386	6,450	Support multiple image/audio columns in ImageFolder/AudioFolder	closed	2023-11-24T10:34:09	2023-11-28T11:07:17	2023-11-24T17:24:38	https://github.com/huggingface/datasets/issues/6450	null	severo	false	[ "A duplicate of https://github.com/huggingface/datasets/issues/5760" ]
2,008,617,992	6,449	Fix metadata file resolution when inferred pattern is `**`	closed	2023-11-23T17:35:02	2023-11-27T10:02:56	2023-11-24T17:13:02	https://github.com/huggingface/datasets/pull/6449	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6449", "html_url": "https://github.com/huggingface/datasets/pull/6449", "diff_url": "https://github.com/huggingface/datasets/pull/6449.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6449.patch", "merged_at": "2023-11-24T17:13:02" }	mariosasko	true	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005551 / 0.011353 (-0.005802) \| 0.003297 / 0.011008 (-0.007711) \| 0.062524 / 0.038508 (0.024016) \| 0.058467 / 0.023109 (0.035358) \| 0.255703 / 0.275898 (-0.020195) \| 0.281420 / 0.323480 (-0.042060) \| 0.003857 / 0.007986 (-0.004129) \| 0.002460 / 0.004328 (-0.001868) \| 0.047762 / 0.004250 (0.043512) \| 0.038757 / 0.037052 (0.001705) \| 0.259937 / 0.258489 (0.001448) \| 0.290050 / 0.293841 (-0.003791) \| 0.028433 / 0.128546 (-0.100113) \| 0.010422 / 0.075646 (-0.065224) \| 0.207135 / 0.419271 (-0.212136) \| 0.036004 / 0.043533 (-0.007529) \| 0.268137 / 0.255139 (0.012998) \| 0.275020 / 0.283200 (-0.008179) \| 0.018301 / 0.141683 (-0.123382) \| 1.095479 / 1.452155 (-0.356676) \| 1.145452 / 1.492716 (-0.347265) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092046 / 0.018006 (0.074040) \| 0.299784 / 0.000490 (0.299294) \| 0.000214 / 0.000200 (0.000014) \| 0.000048 / 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) \| 0.019071 / 0.037411 (-0.018340) \| 0.072836 / 0.014526 (0.058310) \| 0.073974 / 0.176557 (-0.102583) \| 0.120903 / 0.737135 (-0.616232) \| 0.075740 / 0.296338 (-0.220599) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.276365 / 0.215209 (0.061156) \| 2.671217 / 2.077655 (0.593563) \| 1.438862 / 1.504120 (-0.065258) \| 1.327348 / 1.541195 (-0.213847) \| 1.349514 / 1.468490 (-0.118976) \| 0.548793 / 4.584777 (-4.035984) \| 2.364458 / 3.745712 (-1.381255) \| 2.716205 / 5.269862 (-2.553657) \| 1.735714 / 4.565676 (-2.829963) \| 0.061140 / 0.424275 (-0.363135) \| 0.004926 / 0.007607 (-0.002681) \| 0.330449 / 0.226044 (0.104404) \| 3.255243 / 2.268929 (0.986315) \| 1.824254 / 55.444624 (-53.620371) \| 1.540262 / 6.876477 (-5.336215) \| 1.535632 / 2.142072 (-0.606441) \| 0.635224 / 4.805227 (-4.170003) \| 0.116230 / 6.500664 (-6.384435) \| 0.042706 / 0.075469 (-0.032763) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.948796 / 1.841788 (-0.892992) \| 11.448403 / 8.074308 (3.374095) \| 10.523862 / 10.191392 (0.332470) \| 0.129694 / 0.680424 (-0.550730) \| 0.014146 / 0.534201 (-0.520055) \| 0.285706 / 0.579283 (-0.293577) \| 0.262572 / 0.434364 (-0.171792) \| 0.321251 / 0.540337 (-0.219087) \| 0.417130 / 1.386936 (-0.969806) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005266 / 0.011353 (-0.006086) \| 0.003339 / 0.011008 (-0.007670) \| 0.048411 / 0.038508 (0.009903) \| 0.053951 / 0.023109 (0.030842) \| 0.271228 / 0.275898 (-0.004670) \| 0.290066 / 0.323480 (-0.033414) \| 0.004087 / 0.007986 (-0.003898) \| 0.002446 / 0.004328 (-0.001882) \| 0.047049 / 0.004250 (0.042798) \| 0.040866 / 0.037052 (0.003813) \| 0.273711 / 0.258489 (0.015222) \| 0.298192 / 0.293841 (0.004351) \| 0.029025 / 0.128546 (-0.099521) \| 0.010479 / 0.075646 (-0.065167) \| 0.056941 / 0.419271 (-0.362330) \| 0.032914 / 0.043533 (-0.010619) \| 0.270432 / 0.255139 (0.015293) \| 0.291274 / 0.283200 (0.008074) \| 0.018602 / 0.141683 (-0.123081) \| 1.136707 / 1.452155 (-0.315447) \| 1.184704 / 1.492716 (-0.308012) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090041 / 0.018006 (0.072035) \| 0.300185 / 0.000490 (0.299696) \| 0.000221 / 0.000200 (0.000022) \| 0.000049 / 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) \| 0.022074 / 0.037411 (-0.015337) \| 0.070763 / 0.014526 (0.056237) \| 0.082141 / 0.176557 (-0.094415) \| 0.120286 / 0.737135 (-0.616850) \| 0.082680 / 0.296338 (-0.213659) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292223 / 0.215209 (0.077014) \| 2.856711 / 2.077655 (0.779056) \| 1.581194 / 1.504120 (0.077075) \| 1.496567 / 1.541195 (-0.044628) \| 1.485256 / 1.468490 (0.016766) \| 0.550633 / 4.584777 (-4.034144) \| 2.420281 / 3.745712 (-1.325431) \| 2.764373 / 5.269862 (-2.505489) \| 1.735958 / 4.565676 (-2.829719) \| 0.062562 / 0.424275 (-0.361714) \| 0.004918 / 0.007607 (-0.002689) \| 0.346038 / 0.226044 (0.119994) \| 3.443478 / 2.268929 (1.174550) \| 1.949366 / 55.444624 (-53.495259) \| 1.686140 / 6.876477 (-5.190337) \| 1.683038 / 2.142072 (-0.459034) \| 0.629270 / 4.805227 (-4.175958) \| 0.114947 / 6.500664 (-6.385717) \| 0.040635 / 0.075469 (-0.034834) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.969746 / 1.841788 (-0.872041) \| 11.922662 / 8.074308 (3.848354) \| 10.441432 / 10.191392 (0.250040) \| 0.128950 / 0.680424 (-0.551473) \| 0.015964 / 0.534201 (-0.518237) \| 0.289176 / 0.579283 (-0.290107) \| 0.279203 / 0.434364 (-0.155161) \| 0.323833 / 0.540337 (-0.216505) \| 0.540297 / 1.386936 (-0.846639) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3ed759d0f5aea6d166caa0532aa17c209bb3af79 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005288 / 0.011353 (-0.006065) \| 0.003383 / 0.011008 (-0.007625) \| 0.061926 / 0.038508 (0.023418) \| 0.049080 / 0.023109 (0.025971) \| 0.244852 / 0.275898 (-0.031046) \| 0.263957 / 0.323480 (-0.059523) \| 0.002810 / 0.007986 (-0.005175) \| 0.002384 / 0.004328 (-0.001945) \| 0.047807 / 0.004250 (0.043556) \| 0.038374 / 0.037052 (0.001321) \| 0.244414 / 0.258489 (-0.014075) \| 0.272257 / 0.293841 (-0.021584) \| 0.027356 / 0.128546 (-0.101190) \| 0.010235 / 0.075646 (-0.065411) \| 0.214896 / 0.419271 (-0.204375) \| 0.035604 / 0.043533 (-0.007929) \| 0.246584 / 0.255139 (-0.008555) \| 0.263281 / 0.283200 (-0.019918) \| 0.019689 / 0.141683 (-0.121994) \| 1.114100 / 1.452155 (-0.338054) \| 1.177644 / 1.492716 (-0.315073) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088892 / 0.018006 (0.070886) \| 0.298128 / 0.000490 (0.297639) \| 0.000199 / 0.000200 (-0.000001) \| 0.000046 / 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) \| 0.019337 / 0.037411 (-0.018075) \| 0.062096 / 0.014526 (0.047570) \| 0.073019 / 0.176557 (-0.103537) \| 0.118801 / 0.737135 (-0.618334) \| 0.074779 / 0.296338 (-0.221559) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289892 / 0.215209 (0.074683) \| 2.824131 / 2.077655 (0.746476) \| 1.466351 / 1.504120 (-0.037768) \| 1.339528 / 1.541195 (-0.201667) \| 1.369257 / 1.468490 (-0.099233) \| 0.561175 / 4.584777 (-4.023602) \| 2.394174 / 3.745712 (-1.351538) \| 2.749668 / 5.269862 (-2.520193) \| 1.747146 / 4.565676 (-2.818530) \| 0.063054 / 0.424275 (-0.361221) \| 0.004970 / 0.007607 (-0.002637) \| 0.342985 / 0.226044 (0.116941) \| 3.334894 / 2.268929 (1.065966) \| 1.838459 / 55.444624 (-53.606165) \| 1.579755 / 6.876477 (-5.296722) \| 1.560200 / 2.142072 (-0.581872) \| 0.642643 / 4.805227 (-4.162585) \| 0.117741 / 6.500664 (-6.382923) \| 0.042440 / 0.075469 (-0.033029) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.937476 / 1.841788 (-0.904312) \| 11.403556 / 8.074308 (3.329248) \| 10.317207 / 10.191392 (0.125815) \| 0.145277 / 0.680424 (-0.535147) \| 0.015297 / 0.534201 (-0.518904) \| 0.287511 / 0.579283 (-0.291772) \| 0.263516 / 0.434364 (-0.170848) \| 0.320803 / 0.540337 (-0.219534) \| 0.415580 / 1.386936 (-0.971356) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005239 / 0.011353 (-0.006114) \| 0.003506 / 0.011008 (-0.007502) \| 0.048635 / 0.038508 (0.010127) \| 0.052067 / 0.023109 (0.028957) \| 0.277526 / 0.275898 (0.001628) \| 0.300536 / 0.323480 (-0.022944) \| 0.003982 / 0.007986 (-0.004004) \| 0.002413 / 0.004328 (-0.001915) \| 0.046523 / 0.004250 (0.042273) \| 0.039383 / 0.037052 (0.002331) \| 0.281208 / 0.258489 (0.022719) \| 0.306199 / 0.293841 (0.012359) \| 0.028646 / 0.128546 (-0.099900) \| 0.010664 / 0.075646 (-0.064982) \| 0.057393 / 0.419271 (-0.361879) \| 0.032171 / 0.043533 (-0.011362) \| 0.277576 / 0.255139 (0.022437) \| 0.296039 / 0.283200 (0.012840) \| 0.017519 / 0.141683 (-0.124164) \| 1.153172 / 1.452155 (-0.298982) \| 1.180274 / 1.492716 (-0.312442) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088287 / 0.018006 (0.070280) \| 0.297922 / 0.000490 (0.297433) \| 0.000216 / 0.000200 (0.000016) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021936 / 0.037411 (-0.015475) \| 0.070181 / 0.014526 (0.055655) \| 0.082068 / 0.176557 (-0.094488) \| 0.119327 / 0.737135 (-0.617808) \| 0.083642 / 0.296338 (-0.212697) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.299449 / 0.215209 (0.084240) \| 2.914362 / 2.077655 (0.836707) \| 1.611906 / 1.504120 (0.107786) \| 1.488805 / 1.541195 (-0.052390) \| 1.536010 / 1.468490 (0.067520) \| 0.566772 / 4.584777 (-4.018004) \| 2.397897 / 3.745712 (-1.347815) \| 2.786048 / 5.269862 (-2.483814) \| 1.745153 / 4.565676 (-2.820523) \| 0.063870 / 0.424275 (-0.360405) \| 0.004968 / 0.007607 (-0.002640) \| 0.344455 / 0.226044 (0.118410) \| 3.465772 / 2.268929 (1.196844) \| 1.965761 / 55.444624 (-53.478863) \| 1.687960 / 6.876477 (-5.188516) \| 1.713987 / 2.142072 (-0.428085) \| 0.643760 / 4.805227 (-4.161467) \| 0.117623 / 6.500664 (-6.383042) \| 0.041086 / 0.075469 (-0.034383) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.985129 / 1.841788 (-0.856659) \| 11.986676 / 8.074308 (3.912368) \| 10.493440 / 10.191392 (0.302048) \| 0.130070 / 0.680424 (-0.550353) \| 0.015293 / 0.534201 (-0.518908) \| 0.285683 / 0.579283 (-0.293600) \| 0.275656 / 0.434364 (-0.158708) \| 0.328704 / 0.540337 (-0.211633) \| 0.537249 / 1.386936 (-0.849687) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d7ee58f322082d3af5f11863d1f809444910827a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005170 / 0.011353 (-0.006183) \| 0.003267 / 0.011008 (-0.007741) \| 0.061992 / 0.038508 (0.023484) \| 0.053414 / 0.023109 (0.030305) \| 0.245678 / 0.275898 (-0.030220) \| 0.261320 / 0.323480 (-0.062160) \| 0.003887 / 0.007986 (-0.004099) \| 0.002543 / 0.004328 (-0.001786) \| 0.048496 / 0.004250 (0.044246) \| 0.037392 / 0.037052 (0.000340) \| 0.243728 / 0.258489 (-0.014761) \| 0.272524 / 0.293841 (-0.021317) \| 0.027578 / 0.128546 (-0.100968) \| 0.010530 / 0.075646 (-0.065116) \| 0.206014 / 0.419271 (-0.213257) \| 0.035987 / 0.043533 (-0.007546) \| 0.243544 / 0.255139 (-0.011595) \| 0.263872 / 0.283200 (-0.019327) \| 0.017867 / 0.141683 (-0.123816) \| 1.105159 / 1.452155 (-0.346996) \| 1.186640 / 1.492716 (-0.306076) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092888 / 0.018006 (0.074882) \| 0.302024 / 0.000490 (0.301534) \| 0.000220 / 0.000200 (0.000020) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019329 / 0.037411 (-0.018083) \| 0.062135 / 0.014526 (0.047609) \| 0.075125 / 0.176557 (-0.101431) \| 0.120743 / 0.737135 (-0.616393) \| 0.078687 / 0.296338 (-0.217652) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279449 / 0.215209 (0.064240) \| 2.727310 / 2.077655 (0.649656) \| 1.442710 / 1.504120 (-0.061410) \| 1.315271 / 1.541195 (-0.225923) \| 1.360435 / 1.468490 (-0.108055) \| 0.567720 / 4.584777 (-4.017057) \| 2.397049 / 3.745712 (-1.348663) \| 2.891180 / 5.269862 (-2.378682) \| 1.774179 / 4.565676 (-2.791497) \| 0.063155 / 0.424275 (-0.361120) \| 0.004963 / 0.007607 (-0.002644) \| 0.337526 / 0.226044 (0.111482) \| 3.266016 / 2.268929 (0.997088) \| 1.808819 / 55.444624 (-53.635806) \| 1.525326 / 6.876477 (-5.351151) \| 1.566937 / 2.142072 (-0.575135) \| 0.654226 / 4.805227 (-4.151001) \| 0.118968 / 6.500664 (-6.381696) \| 0.042666 / 0.075469 (-0.032803) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.940792 / 1.841788 (-0.900996) \| 11.736380 / 8.074308 (3.662072) \| 10.709538 / 10.191392 (0.518146) \| 0.141390 / 0.680424 (-0.539034) \| 0.014204 / 0.534201 (-0.519996) \| 0.284842 / 0.579283 (-0.294441) \| 0.266315 / 0.434364 (-0.168049) \| 0.331619 / 0.540337 (-0.208718) \| 0.416446 / 1.386936 (-0.970491) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005298 / 0.011353 (-0.006055) \| 0.003507 / 0.011008 (-0.007501) \| 0.048315 / 0.038508 (0.009807) \| 0.054855 / 0.023109 (0.031746) \| 0.271558 / 0.275898 (-0.004340) \| 0.316851 / 0.323480 (-0.006628) \| 0.004054 / 0.007986 (-0.003932) \| 0.002433 / 0.004328 (-0.001896) \| 0.046442 / 0.004250 (0.042191) \| 0.040853 / 0.037052 (0.003801) \| 0.272537 / 0.258489 (0.014048) \| 0.293736 / 0.293841 (-0.000105) \| 0.029112 / 0.128546 (-0.099434) \| 0.010573 / 0.075646 (-0.065074) \| 0.056501 / 0.419271 (-0.362771) \| 0.032541 / 0.043533 (-0.010992) \| 0.271004 / 0.255139 (0.015865) \| 0.289276 / 0.283200 (0.006076) \| 0.018618 / 0.141683 (-0.123065) \| 1.149435 / 1.452155 (-0.302719) \| 1.205113 / 1.492716 (-0.287604) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094726 / 0.018006 (0.076720) \| 0.304347 / 0.000490 (0.303857) \| 0.000217 / 0.000200 (0.000017) \| 0.000051 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021374 / 0.037411 (-0.016037) \| 0.070574 / 0.014526 (0.056049) \| 0.081749 / 0.176557 (-0.094807) \| 0.119829 / 0.737135 (-0.617306) \| 0.082602 / 0.296338 (-0.213737) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293378 / 0.215209 (0.078169) \| 2.893607 / 2.077655 (0.815952) \| 1.577734 / 1.504120 (0.073614) \| 1.453670 / 1.541195 (-0.087525) \| 1.467354 / 1.468490 (-0.001136) \| 0.563415 / 4.584777 (-4.021362) \| 2.438330 / 3.745712 (-1.307382) \| 2.761822 / 5.269862 (-2.508040) \| 1.730944 / 4.565676 (-2.834732) \| 0.062251 / 0.424275 (-0.362024) \| 0.004969 / 0.007607 (-0.002638) \| 0.371238 / 0.226044 (0.145194) \| 3.399831 / 2.268929 (1.130903) \| 1.936156 / 55.444624 (-53.508469) \| 1.649716 / 6.876477 (-5.226761) \| 1.669107 / 2.142072 (-0.472965) \| 0.633696 / 4.805227 (-4.171531) \| 0.115857 / 6.500664 (-6.384807) \| 0.041012 / 0.075469 (-0.034457) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.964777 / 1.841788 (-0.877010) \| 12.037613 / 8.074308 (3.963305) \| 10.579241 / 10.191392 (0.387849) \| 0.130932 / 0.680424 (-0.549492) \| 0.015621 / 0.534201 (-0.518580) \| 0.286898 / 0.579283 (-0.292385) \| 0.281139 / 0.434364 (-0.153225) \| 0.325240 / 0.540337 (-0.215097) \| 0.554302 / 1.386936 (-0.832635) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#48d2378944a47987f96562ee856167aef1e78522 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005258 / 0.011353 (-0.006095) \| 0.003863 / 0.011008 (-0.007145) \| 0.064585 / 0.038508 (0.026077) \| 0.058013 / 0.023109 (0.034904) \| 0.249042 / 0.275898 (-0.026856) \| 0.273434 / 0.323480 (-0.050046) \| 0.004779 / 0.007986 (-0.003207) \| 0.002550 / 0.004328 (-0.001778) \| 0.048290 / 0.004250 (0.044040) \| 0.038777 / 0.037052 (0.001725) \| 0.253039 / 0.258489 (-0.005450) \| 0.285365 / 0.293841 (-0.008476) \| 0.028053 / 0.128546 (-0.100494) \| 0.010521 / 0.075646 (-0.065125) \| 0.210954 / 0.419271 (-0.208317) \| 0.035720 / 0.043533 (-0.007813) \| 0.252540 / 0.255139 (-0.002599) \| 0.264786 / 0.283200 (-0.018414) \| 0.018692 / 0.141683 (-0.122990) \| 1.108971 / 1.452155 (-0.343183) \| 1.201004 / 1.492716 (-0.291712) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095936 / 0.018006 (0.077930) \| 0.302979 / 0.000490 (0.302489) \| 0.000217 / 0.000200 (0.000017) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018859 / 0.037411 (-0.018552) \| 0.062559 / 0.014526 (0.048034) \| 0.073545 / 0.176557 (-0.103012) \| 0.120780 / 0.737135 (-0.616355) \| 0.074998 / 0.296338 (-0.221340) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.276728 / 0.215209 (0.061519) \| 2.715310 / 2.077655 (0.637655) \| 1.444927 / 1.504120 (-0.059193) \| 1.323867 / 1.541195 (-0.217328) \| 1.364962 / 1.468490 (-0.103528) \| 0.556792 / 4.584777 (-4.027985) \| 2.409151 / 3.745712 (-1.336561) \| 2.811836 / 5.269862 (-2.458026) \| 1.777369 / 4.565676 (-2.788308) \| 0.061398 / 0.424275 (-0.362877) \| 0.004924 / 0.007607 (-0.002683) \| 0.341228 / 0.226044 (0.115183) \| 3.369570 / 2.268929 (1.100641) \| 1.858151 / 55.444624 (-53.586474) \| 1.587352 / 6.876477 (-5.289125) \| 1.625004 / 2.142072 (-0.517068) \| 0.635317 / 4.805227 (-4.169910) \| 0.117197 / 6.500664 (-6.383467) \| 0.042672 / 0.075469 (-0.032797) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.940419 / 1.841788 (-0.901368) \| 12.156882 / 8.074308 (4.082574) \| 10.646780 / 10.191392 (0.455388) \| 0.129279 / 0.680424 (-0.551144) \| 0.013967 / 0.534201 (-0.520234) \| 0.287956 / 0.579283 (-0.291327) \| 0.265250 / 0.434364 (-0.169114) \| 0.323357 / 0.540337 (-0.216980) \| 0.412045 / 1.386936 (-0.974891) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005264 / 0.011353 (-0.006089) \| 0.003575 / 0.011008 (-0.007433) \| 0.049249 / 0.038508 (0.010741) \| 0.057069 / 0.023109 (0.033959) \| 0.327547 / 0.275898 (0.051649) \| 0.299027 / 0.323480 (-0.024453) \| 0.004768 / 0.007986 (-0.003217) \| 0.002522 / 0.004328 (-0.001807) \| 0.048020 / 0.004250 (0.043770) \| 0.041328 / 0.037052 (0.004275) \| 0.281385 / 0.258489 (0.022895) \| 0.304957 / 0.293841 (0.011116) \| 0.031371 / 0.128546 (-0.097175) \| 0.010523 / 0.075646 (-0.065124) \| 0.057073 / 0.419271 (-0.362198) \| 0.032913 / 0.043533 (-0.010620) \| 0.284963 / 0.255139 (0.029824) \| 0.291997 / 0.283200 (0.008798) \| 0.018325 / 0.141683 (-0.123357) \| 1.126681 / 1.452155 (-0.325473) \| 1.183011 / 1.492716 (-0.309705) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092544 / 0.018006 (0.074538) \| 0.299841 / 0.000490 (0.299351) \| 0.000221 / 0.000200 (0.000021) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022279 / 0.037411 (-0.015133) \| 0.072515 / 0.014526 (0.057989) \| 0.083068 / 0.176557 (-0.093488) \| 0.120600 / 0.737135 (-0.616536) \| 0.083574 / 0.296338 (-0.212765) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293393 / 0.215209 (0.078184) \| 2.865420 / 2.077655 (0.787765) \| 1.562419 / 1.504120 (0.058299) \| 1.440846 / 1.541195 (-0.100349) \| 1.471993 / 1.468490 (0.003503) \| 0.572510 / 4.584777 (-4.012267) \| 2.427417 / 3.745712 (-1.318295) \| 2.895347 / 5.269862 (-2.374515) \| 1.790578 / 4.565676 (-2.775098) \| 0.064489 / 0.424275 (-0.359786) \| 0.005044 / 0.007607 (-0.002564) \| 0.340774 / 0.226044 (0.114730) \| 3.391414 / 2.268929 (1.122486) \| 1.939980 / 55.444624 (-53.504644) \| 1.658514 / 6.876477 (-5.217963) \| 1.741406 / 2.142072 (-0.400667) \| 0.649033 / 4.805227 (-4.156194) \| 0.117587 / 6.500664 (-6.383077) \| 0.042042 / 0.075469 (-0.033427) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.980490 / 1.841788 (-0.861298) \| 12.664045 / 8.074308 (4.589737) \| 10.944437 / 10.191392 (0.753045) \| 0.142059 / 0.680424 (-0.538365) \| 0.015914 / 0.534201 (-0.518287) \| 0.288826 / 0.579283 (-0.290457) \| 0.282351 / 0.434364 (-0.152013) \| 0.325302 / 0.540337 (-0.215035) \| 0.416900 / 1.386936 (-0.970036) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#59750317ad258a4380ab6a6d206932b8d482ece1 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005591 / 0.011353 (-0.005762) \| 0.003445 / 0.011008 (-0.007563) \| 0.064290 / 0.038508 (0.025782) \| 0.053046 / 0.023109 (0.029936) \| 0.229101 / 0.275898 (-0.046797) \| 0.255515 / 0.323480 (-0.067964) \| 0.002912 / 0.007986 (-0.005073) \| 0.002466 / 0.004328 (-0.001863) \| 0.049348 / 0.004250 (0.045098) \| 0.039492 / 0.037052 (0.002440) \| 0.236301 / 0.258489 (-0.022188) \| 0.270109 / 0.293841 (-0.023732) \| 0.027506 / 0.128546 (-0.101040) \| 0.010381 / 0.075646 (-0.065265) \| 0.209999 / 0.419271 (-0.209273) \| 0.035827 / 0.043533 (-0.007705) \| 0.237231 / 0.255139 (-0.017908) \| 0.254345 / 0.283200 (-0.028854) \| 0.019689 / 0.141683 (-0.121994) \| 1.096103 / 1.452155 (-0.356052) \| 1.172393 / 1.492716 (-0.320323) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.101749 / 0.018006 (0.083743) \| 0.310913 / 0.000490 (0.310424) \| 0.000217 / 0.000200 (0.000017) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018743 / 0.037411 (-0.018669) \| 0.064190 / 0.014526 (0.049664) \| 0.074575 / 0.176557 (-0.101982) \| 0.124143 / 0.737135 (-0.612993) \| 0.077415 / 0.296338 (-0.218924) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286175 / 0.215209 (0.070965) \| 2.781169 / 2.077655 (0.703515) \| 1.495130 / 1.504120 (-0.008990) \| 1.379136 / 1.541195 (-0.162059) \| 1.397548 / 1.468490 (-0.070942) \| 0.564467 / 4.584777 (-4.020310) \| 2.408896 / 3.745712 (-1.336816) \| 2.857771 / 5.269862 (-2.412091) \| 1.776531 / 4.565676 (-2.789145) \| 0.062700 / 0.424275 (-0.361575) \| 0.004965 / 0.007607 (-0.002642) \| 0.344026 / 0.226044 (0.117982) \| 3.390829 / 2.268929 (1.121900) \| 1.875258 / 55.444624 (-53.569366) \| 1.602435 / 6.876477 (-5.274042) \| 1.613619 / 2.142072 (-0.528454) \| 0.639421 / 4.805227 (-4.165806) \| 0.117697 / 6.500664 (-6.382967) \| 0.042878 / 0.075469 (-0.032591) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.957694 / 1.841788 (-0.884094) \| 11.888917 / 8.074308 (3.814609) \| 10.643389 / 10.191392 (0.451997) \| 0.143358 / 0.680424 (-0.537066) \| 0.014382 / 0.534201 (-0.519819) \| 0.288731 / 0.579283 (-0.290552) \| 0.270040 / 0.434364 (-0.164324) \| 0.323586 / 0.540337 (-0.216751) \| 0.415743 / 1.386936 (-0.971193) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005228 / 0.011353 (-0.006125) \| 0.003445 / 0.011008 (-0.007563) \| 0.051072 / 0.038508 (0.012563) \| 0.053087 / 0.023109 (0.029978) \| 0.273116 / 0.275898 (-0.002782) \| 0.298633 / 0.323480 (-0.024847) \| 0.004067 / 0.007986 (-0.003919) \| 0.002537 / 0.004328 (-0.001791) \| 0.049326 / 0.004250 (0.045075) \| 0.041011 / 0.037052 (0.003959) \| 0.277748 / 0.258489 (0.019258) \| 0.304152 / 0.293841 (0.010311) \| 0.029012 / 0.128546 (-0.099534) \| 0.010589 / 0.075646 (-0.065057) \| 0.057564 / 0.419271 (-0.361707) \| 0.032785 / 0.043533 (-0.010747) \| 0.272508 / 0.255139 (0.017369) \| 0.294127 / 0.283200 (0.010927) \| 0.018466 / 0.141683 (-0.123217) \| 1.129341 / 1.452155 (-0.322814) \| 1.194631 / 1.492716 (-0.298086) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098558 / 0.018006 (0.080552) \| 0.312353 / 0.000490 (0.311863) \| 0.000269 / 0.000200 (0.000069) \| 0.000049 / 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) \| 0.022148 / 0.037411 (-0.015263) \| 0.070601 / 0.014526 (0.056075) \| 0.081780 / 0.176557 (-0.094777) \| 0.121993 / 0.737135 (-0.615142) \| 0.084263 / 0.296338 (-0.212076) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300501 / 0.215209 (0.085292) \| 2.927534 / 2.077655 (0.849879) \| 1.595527 / 1.504120 (0.091407) \| 1.475607 / 1.541195 (-0.065587) \| 1.496707 / 1.468490 (0.028217) \| 0.559051 / 4.584777 (-4.025726) \| 2.427126 / 3.745712 (-1.318586) \| 2.820908 / 5.269862 (-2.448953) \| 1.757492 / 4.565676 (-2.808185) \| 0.062391 / 0.424275 (-0.361884) \| 0.004950 / 0.007607 (-0.002657) \| 0.351204 / 0.226044 (0.125160) \| 3.485068 / 2.268929 (1.216139) \| 1.976418 / 55.444624 (-53.468207) \| 1.682715 / 6.876477 (-5.193761) \| 1.703457 / 2.142072 (-0.438616) \| 0.643476 / 4.805227 (-4.161751) \| 0.116321 / 6.500664 (-6.384343) \| 0.040776 / 0.075469 (-0.034694) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.974152 / 1.841788 (-0.867635) \| 12.390170 / 8.074308 (4.315862) \| 10.866283 / 10.191392 (0.674891) \| 0.145049 / 0.680424 (-0.535375) \| 0.016404 / 0.534201 (-0.517797) \| 0.288799 / 0.579283 (-0.290484) \| 0.285917 / 0.434364 (-0.148447) \| 0.328455 / 0.540337 (-0.211883) \| 0.417286 / 1.386936 (-0.969650) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#59750317ad258a4380ab6a6d206932b8d482ece1 \"CML watermark\")\n" ]
2,008,614,985	6,448	Use parquet export if possible	closed	2023-11-23T17:31:57	2023-12-01T17:57:17	2023-12-01T17:50:59	https://github.com/huggingface/datasets/pull/6448	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6448", "html_url": "https://github.com/huggingface/datasets/pull/6448", "diff_url": "https://github.com/huggingface/datasets/pull/6448.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6448.patch", "merged_at": "2023-12-01T17:50:59" }	lhoestq	true	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005177 / 0.011353 (-0.006176) \| 0.003002 / 0.011008 (-0.008006) \| 0.061915 / 0.038508 (0.023407) \| 0.052065 / 0.023109 (0.028956) \| 0.246114 / 0.275898 (-0.029784) \| 0.273974 / 0.323480 (-0.049506) \| 0.002983 / 0.007986 (-0.005003) \| 0.002444 / 0.004328 (-0.001885) \| 0.048424 / 0.004250 (0.044174) \| 0.039609 / 0.037052 (0.002557) \| 0.257771 / 0.258489 (-0.000718) \| 0.286228 / 0.293841 (-0.007613) \| 0.023925 / 0.128546 (-0.104621) \| 0.007248 / 0.075646 (-0.068398) \| 0.202205 / 0.419271 (-0.217067) \| 0.037124 / 0.043533 (-0.006409) \| 0.254872 / 0.255139 (-0.000267) \| 0.275252 / 0.283200 (-0.007947) \| 0.019251 / 0.141683 (-0.122432) \| 1.074921 / 1.452155 (-0.377234) \| 1.146515 / 1.492716 (-0.346202) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091998 / 0.018006 (0.073992) \| 0.299146 / 0.000490 (0.298656) \| 0.000240 / 0.000200 (0.000040) \| 0.000054 / 0.000054 (0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019266 / 0.037411 (-0.018145) \| 0.062560 / 0.014526 (0.048034) \| 0.075012 / 0.176557 (-0.101544) \| 0.120077 / 0.737135 (-0.617058) \| 0.077851 / 0.296338 (-0.218488) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290629 / 0.215209 (0.075420) \| 2.823847 / 2.077655 (0.746192) \| 1.516966 / 1.504120 (0.012846) \| 1.393383 / 1.541195 (-0.147812) \| 1.427688 / 1.468490 (-0.040802) \| 0.407456 / 4.584777 (-4.177321) \| 2.378280 / 3.745712 (-1.367433) \| 2.689800 / 5.269862 (-2.580061) \| 1.588037 / 4.565676 (-2.977640) \| 0.045837 / 0.424275 (-0.378438) \| 0.004884 / 0.007607 (-0.002724) \| 0.340464 / 0.226044 (0.114420) \| 3.377158 / 2.268929 (1.108230) \| 1.897854 / 55.444624 (-53.546771) \| 1.588285 / 6.876477 (-5.288191) \| 1.651708 / 2.142072 (-0.490364) \| 0.482018 / 4.805227 (-4.323209) \| 0.101583 / 6.500664 (-6.399081) \| 0.042306 / 0.075469 (-0.033163) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.948659 / 1.841788 (-0.893128) \| 11.809778 / 8.074308 (3.735470) \| 10.481896 / 10.191392 (0.290504) \| 0.143538 / 0.680424 (-0.536885) \| 0.014105 / 0.534201 (-0.520096) \| 0.272278 / 0.579283 (-0.307005) \| 0.264241 / 0.434364 (-0.170123) \| 0.307187 / 0.540337 (-0.233150) \| 0.401270 / 1.386936 (-0.985666) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004831 / 0.011353 (-0.006521) \| 0.002896 / 0.011008 (-0.008112) \| 0.047479 / 0.038508 (0.008971) \| 0.050665 / 0.023109 (0.027555) \| 0.275243 / 0.275898 (-0.000655) \| 0.296547 / 0.323480 (-0.026933) \| 0.004022 / 0.007986 (-0.003963) \| 0.002425 / 0.004328 (-0.001904) \| 0.047086 / 0.004250 (0.042836) \| 0.039611 / 0.037052 (0.002558) \| 0.275272 / 0.258489 (0.016783) \| 0.302429 / 0.293841 (0.008588) \| 0.024308 / 0.128546 (-0.104238) \| 0.007167 / 0.075646 (-0.068479) \| 0.052825 / 0.419271 (-0.366446) \| 0.032319 / 0.043533 (-0.011213) \| 0.273334 / 0.255139 (0.018195) \| 0.291161 / 0.283200 (0.007961) \| 0.017918 / 0.141683 (-0.123764) \| 1.110005 / 1.452155 (-0.342150) \| 1.176616 / 1.492716 (-0.316100) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092478 / 0.018006 (0.074471) \| 0.311431 / 0.000490 (0.310942) \| 0.000237 / 0.000200 (0.000037) \| 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) \| 0.021979 / 0.037411 (-0.015432) \| 0.080617 / 0.014526 (0.066091) \| 0.081534 / 0.176557 (-0.095023) \| 0.121073 / 0.737135 (-0.616062) \| 0.083235 / 0.296338 (-0.213104) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289527 / 0.215209 (0.074318) \| 2.839668 / 2.077655 (0.762013) \| 1.601737 / 1.504120 (0.097617) \| 1.496028 / 1.541195 (-0.045167) \| 1.511933 / 1.468490 (0.043443) \| 0.399819 / 4.584777 (-4.184958) \| 2.394147 / 3.745712 (-1.351565) \| 2.520767 / 5.269862 (-2.749095) \| 1.589496 / 4.565676 (-2.976180) \| 0.046673 / 0.424275 (-0.377602) \| 0.004858 / 0.007607 (-0.002749) \| 0.357986 / 0.226044 (0.131941) \| 3.376217 / 2.268929 (1.107289) \| 1.981853 / 55.444624 (-53.462771) \| 1.682240 / 6.876477 (-5.194236) \| 1.830643 / 2.142072 (-0.311429) \| 0.478286 / 4.805227 (-4.326941) \| 0.099589 / 6.500664 (-6.401075) \| 0.041173 / 0.075469 (-0.034296) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.985160 / 1.841788 (-0.856628) \| 12.312963 / 8.074308 (4.238655) \| 10.577225 / 10.191392 (0.385833) \| 0.130167 / 0.680424 (-0.550257) \| 0.016657 / 0.534201 (-0.517544) \| 0.271330 / 0.579283 (-0.307953) \| 0.276979 / 0.434364 (-0.157385) \| 0.304904 / 0.540337 (-0.235434) \| 0.412090 / 1.386936 (-0.974846) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1adc80151e892122ecb60f4e0b4572b136b2dd47 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6448). All of your documentation changes will be reflected on that endpoint.", "hooray! very excited about this", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005039 / 0.011353 (-0.006314) \| 0.003577 / 0.011008 (-0.007431) \| 0.062892 / 0.038508 (0.024384) \| 0.056334 / 0.023109 (0.033225) \| 0.252281 / 0.275898 (-0.023617) \| 0.274945 / 0.323480 (-0.048535) \| 0.003906 / 0.007986 (-0.004080) \| 0.002483 / 0.004328 (-0.001845) \| 0.049006 / 0.004250 (0.044756) \| 0.038375 / 0.037052 (0.001323) \| 0.257376 / 0.258489 (-0.001113) \| 0.292512 / 0.293841 (-0.001328) \| 0.027134 / 0.128546 (-0.101412) \| 0.010579 / 0.075646 (-0.065068) \| 0.212021 / 0.419271 (-0.207250) \| 0.035851 / 0.043533 (-0.007682) \| 0.258076 / 0.255139 (0.002937) \| 0.271758 / 0.283200 (-0.011442) \| 0.018222 / 0.141683 (-0.123461) \| 1.120481 / 1.452155 (-0.331674) \| 1.187007 / 1.492716 (-0.305710) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094986 / 0.018006 (0.076980) \| 0.302121 / 0.000490 (0.301631) \| 0.000211 / 0.000200 (0.000011) \| 0.000052 / 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) \| 0.019260 / 0.037411 (-0.018152) \| 0.062909 / 0.014526 (0.048383) \| 0.075644 / 0.176557 (-0.100912) \| 0.120966 / 0.737135 (-0.616170) \| 0.076678 / 0.296338 (-0.219661) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286754 / 0.215209 (0.071545) \| 2.797467 / 2.077655 (0.719812) \| 1.436798 / 1.504120 (-0.067322) \| 1.315032 / 1.541195 (-0.226163) \| 1.367841 / 1.468490 (-0.100649) \| 0.578917 / 4.584777 (-4.005860) \| 2.439773 / 3.745712 (-1.305939) \| 2.932779 / 5.269862 (-2.337082) \| 1.843895 / 4.565676 (-2.721782) \| 0.063351 / 0.424275 (-0.360925) \| 0.004998 / 0.007607 (-0.002610) \| 0.347385 / 0.226044 (0.121340) \| 3.449969 / 2.268929 (1.181040) \| 1.857734 / 55.444624 (-53.586890) \| 1.541341 / 6.876477 (-5.335136) \| 1.574915 / 2.142072 (-0.567158) \| 0.660178 / 4.805227 (-4.145049) \| 0.117686 / 6.500664 (-6.382978) \| 0.042602 / 0.075469 (-0.032867) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.937735 / 1.841788 (-0.904052) \| 11.962091 / 8.074308 (3.887783) \| 10.401715 / 10.191392 (0.210323) \| 0.142200 / 0.680424 (-0.538224) \| 0.014137 / 0.534201 (-0.520064) \| 0.289853 / 0.579283 (-0.289430) \| 0.267100 / 0.434364 (-0.167264) \| 0.323401 / 0.540337 (-0.216936) \| 0.418665 / 1.386936 (-0.968271) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005480 / 0.011353 (-0.005873) \| 0.003401 / 0.011008 (-0.007607) \| 0.049304 / 0.038508 (0.010796) \| 0.062043 / 0.023109 (0.038934) \| 0.270571 / 0.275898 (-0.005327) \| 0.295226 / 0.323480 (-0.028254) \| 0.004152 / 0.007986 (-0.003834) \| 0.002511 / 0.004328 (-0.001817) \| 0.048480 / 0.004250 (0.044229) \| 0.043964 / 0.037052 (0.006912) \| 0.273545 / 0.258489 (0.015056) \| 0.295152 / 0.293841 (0.001311) \| 0.029224 / 0.128546 (-0.099322) \| 0.010629 / 0.075646 (-0.065018) \| 0.057433 / 0.419271 (-0.361839) \| 0.033115 / 0.043533 (-0.010418) \| 0.269893 / 0.255139 (0.014754) \| 0.288658 / 0.283200 (0.005459) \| 0.018216 / 0.141683 (-0.123467) \| 1.123039 / 1.452155 (-0.329116) \| 1.182892 / 1.492716 (-0.309825) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095948 / 0.018006 (0.077942) \| 0.305811 / 0.000490 (0.305321) \| 0.000221 / 0.000200 (0.000021) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022996 / 0.037411 (-0.014415) \| 0.073836 / 0.014526 (0.059310) \| 0.082658 / 0.176557 (-0.093899) \| 0.121970 / 0.737135 (-0.615166) \| 0.086096 / 0.296338 (-0.210242) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291032 / 0.215209 (0.075823) \| 2.864613 / 2.077655 (0.786958) \| 1.567530 / 1.504120 (0.063410) \| 1.460291 / 1.541195 (-0.080903) \| 1.527066 / 1.468490 (0.058576) \| 0.571160 / 4.584777 (-4.013617) \| 2.465261 / 3.745712 (-1.280451) \| 2.915547 / 5.269862 (-2.354314) \| 1.835822 / 4.565676 (-2.729855) \| 0.064328 / 0.424275 (-0.359947) \| 0.005061 / 0.007607 (-0.002546) \| 0.357105 / 0.226044 (0.131061) \| 3.491363 / 2.268929 (1.222435) \| 1.943213 / 55.444624 (-53.501412) \| 1.675778 / 6.876477 (-5.200699) \| 1.719016 / 2.142072 (-0.423057) \| 0.658993 / 4.805227 (-4.146235) \| 0.122320 / 6.500664 (-6.378344) \| 0.049030 / 0.075469 (-0.026439) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.964762 / 1.841788 (-0.877025) \| 12.367251 / 8.074308 (4.292943) \| 10.886213 / 10.191392 (0.694821) \| 0.141533 / 0.680424 (-0.538891) \| 0.015646 / 0.534201 (-0.518555) \| 0.288583 / 0.579283 (-0.290700) \| 0.280353 / 0.434364 (-0.154010) \| 0.329095 / 0.540337 (-0.211242) \| 0.565118 / 1.386936 (-0.821818) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#493bf695dc3ee6cc81bfd0aae6a38f70547bb752 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006475 / 0.011353 (-0.004878) \| 0.004080 / 0.011008 (-0.006928) \| 0.066479 / 0.038508 (0.027971) \| 0.073270 / 0.023109 (0.050161) \| 0.244412 / 0.275898 (-0.031486) \| 0.273778 / 0.323480 (-0.049702) \| 0.003186 / 0.007986 (-0.004800) \| 0.003419 / 0.004328 (-0.000910) \| 0.049743 / 0.004250 (0.045492) \| 0.043581 / 0.037052 (0.006529) \| 0.248215 / 0.258489 (-0.010274) \| 0.280873 / 0.293841 (-0.012967) \| 0.029282 / 0.128546 (-0.099264) \| 0.011241 / 0.075646 (-0.064405) \| 0.215031 / 0.419271 (-0.204241) \| 0.038764 / 0.043533 (-0.004769) \| 0.259363 / 0.255139 (0.004224) \| 0.279253 / 0.283200 (-0.003946) \| 0.019524 / 0.141683 (-0.122159) \| 1.104735 / 1.452155 (-0.347420) \| 1.159823 / 1.492716 (-0.332894) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.108383 / 0.018006 (0.090377) \| 0.332904 / 0.000490 (0.332415) \| 0.000222 / 0.000200 (0.000022) \| 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) \| 0.020693 / 0.037411 (-0.016719) \| 0.071764 / 0.014526 (0.057238) \| 0.077073 / 0.176557 (-0.099484) \| 0.124604 / 0.737135 (-0.612532) \| 0.078057 / 0.296338 (-0.218282) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291014 / 0.215209 (0.075805) \| 2.865885 / 2.077655 (0.788231) \| 1.506141 / 1.504120 (0.002021) \| 1.435924 / 1.541195 (-0.105271) \| 1.461994 / 1.468490 (-0.006497) \| 0.571779 / 4.584777 (-4.012998) \| 2.461950 / 3.745712 (-1.283762) \| 3.079771 / 5.269862 (-2.190091) \| 1.933337 / 4.565676 (-2.632339) \| 0.063405 / 0.424275 (-0.360870) \| 0.005203 / 0.007607 (-0.002404) \| 0.345077 / 0.226044 (0.119032) \| 3.487189 / 2.268929 (1.218261) \| 1.903733 / 55.444624 (-53.540891) \| 1.705596 / 6.876477 (-5.170880) \| 1.718849 / 2.142072 (-0.423223) \| 0.658745 / 4.805227 (-4.146482) \| 0.120847 / 6.500664 (-6.379817) \| 0.045670 / 0.075469 (-0.029799) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.965969 / 1.841788 (-0.875819) \| 13.520489 / 8.074308 (5.446181) \| 12.322363 / 10.191392 (2.130971) \| 0.146605 / 0.680424 (-0.533819) \| 0.015061 / 0.534201 (-0.519140) \| 0.298125 / 0.579283 (-0.281159) \| 0.276864 / 0.434364 (-0.157500) \| 0.326787 / 0.540337 (-0.213550) \| 0.436897 / 1.386936 (-0.950039) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005862 / 0.011353 (-0.005491) \| 0.003716 / 0.011008 (-0.007292) \| 0.052849 / 0.038508 (0.014341) \| 0.072114 / 0.023109 (0.049005) \| 0.277800 / 0.275898 (0.001902) \| 0.325321 / 0.323480 (0.001841) \| 0.004428 / 0.007986 (-0.003557) \| 0.002527 / 0.004328 (-0.001801) \| 0.048847 / 0.004250 (0.044596) \| 0.047355 / 0.037052 (0.010303) \| 0.279331 / 0.258489 (0.020842) \| 0.310477 / 0.293841 (0.016636) \| 0.029661 / 0.128546 (-0.098886) \| 0.010812 / 0.075646 (-0.064834) \| 0.059803 / 0.419271 (-0.359469) \| 0.033554 / 0.043533 (-0.009978) \| 0.276890 / 0.255139 (0.021751) \| 0.308911 / 0.283200 (0.025712) \| 0.020752 / 0.141683 (-0.120931) \| 1.120896 / 1.452155 (-0.331259) \| 1.186428 / 1.492716 (-0.306288) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.106551 / 0.018006 (0.088545) \| 0.354455 / 0.000490 (0.353966) \| 0.000353 / 0.000200 (0.000153) \| 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) \| 0.023488 / 0.037411 (-0.013923) \| 0.080548 / 0.014526 (0.066022) \| 0.084431 / 0.176557 (-0.092126) \| 0.140698 / 0.737135 (-0.596438) \| 0.085692 / 0.296338 (-0.210647) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.314253 / 0.215209 (0.099044) \| 2.993236 / 2.077655 (0.915582) \| 1.639013 / 1.504120 (0.134893) \| 1.543966 / 1.541195 (0.002771) \| 1.567732 / 1.468490 (0.099242) \| 0.565857 / 4.584777 (-4.018920) \| 2.545339 / 3.745712 (-1.200373) \| 3.134546 / 5.269862 (-2.135316) \| 1.940350 / 4.565676 (-2.625326) \| 0.063847 / 0.424275 (-0.360429) \| 0.005079 / 0.007607 (-0.002528) \| 0.365762 / 0.226044 (0.139718) \| 3.610921 / 2.268929 (1.341993) \| 2.035151 / 55.444624 (-53.409473) \| 1.773409 / 6.876477 (-5.103068) \| 1.790332 / 2.142072 (-0.351741) \| 0.683019 / 4.805227 (-4.122209) \| 0.119566 / 6.500664 (-6.381099) \| 0.043578 / 0.075469 (-0.031891) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.996568 / 1.841788 (-0.845219) \| 14.094366 / 8.074308 (6.020058) \| 12.433600 / 10.191392 (2.242208) \| 0.139835 / 0.680424 (-0.540589) \| 0.016454 / 0.534201 (-0.517747) \| 0.294073 / 0.579283 (-0.285210) \| 0.309032 / 0.434364 (-0.125332) \| 0.330699 / 0.540337 (-0.209638) \| 0.619392 / 1.386936 (-0.767544) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#026fbce1c93a30188b6d0646bb975da8f56e2a2f \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005389 / 0.011353 (-0.005964) \| 0.003209 / 0.011008 (-0.007799) \| 0.061610 / 0.038508 (0.023102) \| 0.049781 / 0.023109 (0.026672) \| 0.240208 / 0.275898 (-0.035690) \| 0.263307 / 0.323480 (-0.060173) \| 0.002908 / 0.007986 (-0.005078) \| 0.002375 / 0.004328 (-0.001953) \| 0.047462 / 0.004250 (0.043212) \| 0.038643 / 0.037052 (0.001591) \| 0.246287 / 0.258489 (-0.012202) \| 0.278715 / 0.293841 (-0.015126) \| 0.027507 / 0.128546 (-0.101039) \| 0.010168 / 0.075646 (-0.065479) \| 0.204131 / 0.419271 (-0.215140) \| 0.035452 / 0.043533 (-0.008081) \| 0.251721 / 0.255139 (-0.003418) \| 0.266642 / 0.283200 (-0.016558) \| 0.017741 / 0.141683 (-0.123942) \| 1.094672 / 1.452155 (-0.357482) \| 1.162715 / 1.492716 (-0.330002) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092154 / 0.018006 (0.074148) \| 0.301376 / 0.000490 (0.300886) \| 0.000217 / 0.000200 (0.000017) \| 0.000051 / 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) \| 0.018534 / 0.037411 (-0.018877) \| 0.061995 / 0.014526 (0.047469) \| 0.072654 / 0.176557 (-0.103903) \| 0.119501 / 0.737135 (-0.617635) \| 0.073756 / 0.296338 (-0.222583) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280066 / 0.215209 (0.064857) \| 2.744207 / 2.077655 (0.666553) \| 1.483367 / 1.504120 (-0.020753) \| 1.386173 / 1.541195 (-0.155022) \| 1.381833 / 1.468490 (-0.086657) \| 0.552780 / 4.584777 (-4.031997) \| 2.395541 / 3.745712 (-1.350171) \| 2.747507 / 5.269862 (-2.522355) \| 1.735074 / 4.565676 (-2.830602) \| 0.062096 / 0.424275 (-0.362179) \| 0.004905 / 0.007607 (-0.002702) \| 0.338327 / 0.226044 (0.112283) \| 3.365391 / 2.268929 (1.096462) \| 1.839663 / 55.444624 (-53.604961) \| 1.577535 / 6.876477 (-5.298942) \| 1.558054 / 2.142072 (-0.584018) \| 0.636520 / 4.805227 (-4.168708) \| 0.116182 / 6.500664 (-6.384482) \| 0.042078 / 0.075469 (-0.033391) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.938512 / 1.841788 (-0.903276) \| 11.455749 / 8.074308 (3.381441) \| 10.510985 / 10.191392 (0.319593) \| 0.140865 / 0.680424 (-0.539559) \| 0.014073 / 0.534201 (-0.520128) \| 0.294747 / 0.579283 (-0.284536) \| 0.266147 / 0.434364 (-0.168217) \| 0.325354 / 0.540337 (-0.214984) \| 0.422182 / 1.386936 (-0.964754) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005231 / 0.011353 (-0.006122) \| 0.003032 / 0.011008 (-0.007977) \| 0.049608 / 0.038508 (0.011099) \| 0.051441 / 0.023109 (0.028332) \| 0.273812 / 0.275898 (-0.002086) \| 0.294318 / 0.323480 (-0.029162) \| 0.003958 / 0.007986 (-0.004028) \| 0.002384 / 0.004328 (-0.001944) \| 0.047942 / 0.004250 (0.043691) \| 0.039179 / 0.037052 (0.002127) \| 0.277504 / 0.258489 (0.019014) \| 0.299713 / 0.293841 (0.005872) \| 0.028989 / 0.128546 (-0.099557) \| 0.010267 / 0.075646 (-0.065379) \| 0.058318 / 0.419271 (-0.360954) \| 0.032214 / 0.043533 (-0.011318) \| 0.277964 / 0.255139 (0.022825) \| 0.293055 / 0.283200 (0.009856) \| 0.018532 / 0.141683 (-0.123151) \| 1.128620 / 1.452155 (-0.323535) \| 1.187365 / 1.492716 (-0.305351) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092137 / 0.018006 (0.074130) \| 0.299726 / 0.000490 (0.299236) \| 0.000222 / 0.000200 (0.000022) \| 0.000050 / 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) \| 0.021342 / 0.037411 (-0.016070) \| 0.069943 / 0.014526 (0.055417) \| 0.079862 / 0.176557 (-0.096694) \| 0.118917 / 0.737135 (-0.618218) \| 0.081861 / 0.296338 (-0.214477) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295883 / 0.215209 (0.080674) \| 2.881640 / 2.077655 (0.803986) \| 1.597705 / 1.504120 (0.093585) \| 1.473220 / 1.541195 (-0.067975) \| 1.501006 / 1.468490 (0.032516) \| 0.559409 / 4.584777 (-4.025368) \| 2.442709 / 3.745712 (-1.303003) \| 2.742139 / 5.269862 (-2.527723) \| 1.726002 / 4.565676 (-2.839674) \| 0.062436 / 0.424275 (-0.361840) \| 0.004896 / 0.007607 (-0.002711) \| 0.349203 / 0.226044 (0.123159) \| 3.435175 / 2.268929 (1.166247) \| 1.954888 / 55.444624 (-53.489737) \| 1.666233 / 6.876477 (-5.210243) \| 1.680852 / 2.142072 (-0.461221) \| 0.644271 / 4.805227 (-4.160956) \| 0.115160 / 6.500664 (-6.385504) \| 0.040681 / 0.075469 (-0.034788) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.963810 / 1.841788 (-0.877977) \| 11.860860 / 8.074308 (3.786552) \| 10.541703 / 10.191392 (0.350311) \| 0.131532 / 0.680424 (-0.548892) \| 0.016790 / 0.534201 (-0.517411) \| 0.286695 / 0.579283 (-0.292588) \| 0.279628 / 0.434364 (-0.154735) \| 0.324622 / 0.540337 (-0.215715) \| 0.535507 / 1.386936 (-0.851429) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#11217347e4bcfe1aaf794d164a5dd9f085b2f682 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005672 / 0.011353 (-0.005681) \| 0.003411 / 0.011008 (-0.007597) \| 0.062528 / 0.038508 (0.024020) \| 0.055209 / 0.023109 (0.032100) \| 0.248366 / 0.275898 (-0.027532) \| 0.279522 / 0.323480 (-0.043957) \| 0.002907 / 0.007986 (-0.005079) \| 0.002369 / 0.004328 (-0.001959) \| 0.047982 / 0.004250 (0.043731) \| 0.039009 / 0.037052 (0.001956) \| 0.256422 / 0.258489 (-0.002067) \| 0.288530 / 0.293841 (-0.005311) \| 0.028164 / 0.128546 (-0.100382) \| 0.010448 / 0.075646 (-0.065198) \| 0.208863 / 0.419271 (-0.210408) \| 0.036291 / 0.043533 (-0.007242) \| 0.251642 / 0.255139 (-0.003497) \| 0.275589 / 0.283200 (-0.007610) \| 0.019839 / 0.141683 (-0.121844) \| 1.092800 / 1.452155 (-0.359355) \| 1.147950 / 1.492716 (-0.344766) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094920 / 0.018006 (0.076914) \| 0.303049 / 0.000490 (0.302559) \| 0.000199 / 0.000200 (-0.000001) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018820 / 0.037411 (-0.018591) \| 0.063319 / 0.014526 (0.048793) \| 0.073644 / 0.176557 (-0.102912) \| 0.120045 / 0.737135 (-0.617091) \| 0.076219 / 0.296338 (-0.220119) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283897 / 0.215209 (0.068688) \| 2.822836 / 2.077655 (0.745182) \| 1.490505 / 1.504120 (-0.013615) \| 1.359777 / 1.541195 (-0.181418) \| 1.420536 / 1.468490 (-0.047954) \| 0.562308 / 4.584777 (-4.022469) \| 2.419249 / 3.745712 (-1.326463) \| 2.827620 / 5.269862 (-2.442241) \| 1.783171 / 4.565676 (-2.782505) \| 0.063206 / 0.424275 (-0.361069) \| 0.004966 / 0.007607 (-0.002641) \| 0.339647 / 0.226044 (0.113602) \| 3.378157 / 2.268929 (1.109229) \| 1.873221 / 55.444624 (-53.571403) \| 1.606367 / 6.876477 (-5.270109) \| 1.624976 / 2.142072 (-0.517096) \| 0.652653 / 4.805227 (-4.152574) \| 0.117997 / 6.500664 (-6.382667) \| 0.041955 / 0.075469 (-0.033514) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.961420 / 1.841788 (-0.880368) \| 11.807624 / 8.074308 (3.733316) \| 10.668249 / 10.191392 (0.476857) \| 0.141855 / 0.680424 (-0.538569) \| 0.014451 / 0.534201 (-0.519750) \| 0.289706 / 0.579283 (-0.289577) \| 0.268392 / 0.434364 (-0.165972) \| 0.323435 / 0.540337 (-0.216903) \| 0.420667 / 1.386936 (-0.966269) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005382 / 0.011353 (-0.005971) \| 0.003361 / 0.011008 (-0.007647) \| 0.048420 / 0.038508 (0.009912) \| 0.053702 / 0.023109 (0.030593) \| 0.286976 / 0.275898 (0.011078) \| 0.296708 / 0.323480 (-0.026772) \| 0.004013 / 0.007986 (-0.003972) \| 0.002444 / 0.004328 (-0.001884) \| 0.047797 / 0.004250 (0.043547) \| 0.042361 / 0.037052 (0.005309) \| 0.277543 / 0.258489 (0.019054) \| 0.300736 / 0.293841 (0.006896) \| 0.029894 / 0.128546 (-0.098653) \| 0.014119 / 0.075646 (-0.061527) \| 0.057636 / 0.419271 (-0.361636) \| 0.032533 / 0.043533 (-0.010999) \| 0.280963 / 0.255139 (0.025824) \| 0.291305 / 0.283200 (0.008106) \| 0.018391 / 0.141683 (-0.123292) \| 1.140042 / 1.452155 (-0.312113) \| 1.179485 / 1.492716 (-0.313231) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094668 / 0.018006 (0.076661) \| 0.301677 / 0.000490 (0.301187) \| 0.000245 / 0.000200 (0.000045) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021376 / 0.037411 (-0.016036) \| 0.070628 / 0.014526 (0.056102) \| 0.082249 / 0.176557 (-0.094308) \| 0.120423 / 0.737135 (-0.616712) \| 0.083792 / 0.296338 (-0.212546) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298884 / 0.215209 (0.083675) \| 2.931849 / 2.077655 (0.854194) \| 1.591888 / 1.504120 (0.087768) \| 1.455781 / 1.541195 (-0.085414) \| 1.500312 / 1.468490 (0.031822) \| 0.558466 / 4.584777 (-4.026311) \| 2.450449 / 3.745712 (-1.295263) \| 2.842768 / 5.269862 (-2.427094) \| 1.755614 / 4.565676 (-2.810062) \| 0.063200 / 0.424275 (-0.361075) \| 0.005022 / 0.007607 (-0.002585) \| 0.358282 / 0.226044 (0.132238) \| 3.575392 / 2.268929 (1.306464) \| 1.960258 / 55.444624 (-53.484366) \| 1.675518 / 6.876477 (-5.200959) \| 1.696630 / 2.142072 (-0.445442) \| 0.647185 / 4.805227 (-4.158042) \| 0.117038 / 6.500664 (-6.383626) \| 0.041622 / 0.075469 (-0.033848) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.962503 / 1.841788 (-0.879285) \| 12.194950 / 8.074308 (4.120642) \| 10.662233 / 10.191392 (0.470841) \| 0.131618 / 0.680424 (-0.548806) \| 0.016000 / 0.534201 (-0.518201) \| 0.291546 / 0.579283 (-0.287737) \| 0.279537 / 0.434364 (-0.154827) \| 0.328716 / 0.540337 (-0.211622) \| 0.547565 / 1.386936 (-0.839371) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4de8f5f09f60613d47b5d7eb901752321c7b6a49 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005209 / 0.011353 (-0.006144) \| 0.003017 / 0.011008 (-0.007991) \| 0.062017 / 0.038508 (0.023509) \| 0.048268 / 0.023109 (0.025158) \| 0.246384 / 0.275898 (-0.029514) \| 0.270441 / 0.323480 (-0.053039) \| 0.002763 / 0.007986 (-0.005222) \| 0.003140 / 0.004328 (-0.001188) \| 0.048720 / 0.004250 (0.044470) \| 0.038175 / 0.037052 (0.001123) \| 0.254184 / 0.258489 (-0.004306) \| 0.275515 / 0.293841 (-0.018326) \| 0.027309 / 0.128546 (-0.101238) \| 0.010507 / 0.075646 (-0.065140) \| 0.210315 / 0.419271 (-0.208956) \| 0.035203 / 0.043533 (-0.008329) \| 0.253015 / 0.255139 (-0.002124) \| 0.271465 / 0.283200 (-0.011734) \| 0.019543 / 0.141683 (-0.122140) \| 1.119242 / 1.452155 (-0.332913) \| 1.149359 / 1.492716 (-0.343357) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088935 / 0.018006 (0.070928) \| 0.293922 / 0.000490 (0.293432) \| 0.000202 / 0.000200 (0.000002) \| 0.000051 / 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) \| 0.018174 / 0.037411 (-0.019237) \| 0.060215 / 0.014526 (0.045689) \| 0.072868 / 0.176557 (-0.103689) \| 0.117998 / 0.737135 (-0.619137) \| 0.074159 / 0.296338 (-0.222179) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289229 / 0.215209 (0.074020) \| 2.840414 / 2.077655 (0.762759) \| 1.468357 / 1.504120 (-0.035763) \| 1.347714 / 1.541195 (-0.193481) \| 1.363704 / 1.468490 (-0.104786) \| 0.572059 / 4.584777 (-4.012718) \| 2.400631 / 3.745712 (-1.345081) \| 2.755779 / 5.269862 (-2.514083) \| 1.740937 / 4.565676 (-2.824739) \| 0.063473 / 0.424275 (-0.360802) \| 0.005012 / 0.007607 (-0.002595) \| 0.336057 / 0.226044 (0.110012) \| 3.382126 / 2.268929 (1.113197) \| 1.807838 / 55.444624 (-53.636786) \| 1.534594 / 6.876477 (-5.341883) \| 1.529951 / 2.142072 (-0.612121) \| 0.636661 / 4.805227 (-4.168566) \| 0.117090 / 6.500664 (-6.383574) \| 0.042310 / 0.075469 (-0.033160) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.924440 / 1.841788 (-0.917347) \| 11.120517 / 8.074308 (3.046209) \| 10.177210 / 10.191392 (-0.014182) \| 0.139060 / 0.680424 (-0.541364) \| 0.013818 / 0.534201 (-0.520383) \| 0.285634 / 0.579283 (-0.293649) \| 0.268657 / 0.434364 (-0.165706) \| 0.325842 / 0.540337 (-0.214496) \| 0.439902 / 1.386936 (-0.947034) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005202 / 0.011353 (-0.006150) \| 0.003002 / 0.011008 (-0.008006) \| 0.048729 / 0.038508 (0.010221) \| 0.048178 / 0.023109 (0.025069) \| 0.288573 / 0.275898 (0.012675) \| 0.311122 / 0.323480 (-0.012358) \| 0.003953 / 0.007986 (-0.004033) \| 0.002544 / 0.004328 (-0.001785) \| 0.047762 / 0.004250 (0.043511) \| 0.039711 / 0.037052 (0.002658) \| 0.308389 / 0.258489 (0.049900) \| 0.321913 / 0.293841 (0.028072) \| 0.029166 / 0.128546 (-0.099380) \| 0.010697 / 0.075646 (-0.064950) \| 0.057758 / 0.419271 (-0.361514) \| 0.032743 / 0.043533 (-0.010789) \| 0.290933 / 0.255139 (0.035794) \| 0.309404 / 0.283200 (0.026205) \| 0.017691 / 0.141683 (-0.123992) \| 1.157713 / 1.452155 (-0.294442) \| 1.210485 / 1.492716 (-0.282231) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088959 / 0.018006 (0.070953) \| 0.298531 / 0.000490 (0.298041) \| 0.000221 / 0.000200 (0.000021) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021129 / 0.037411 (-0.016283) \| 0.068419 / 0.014526 (0.053893) \| 0.079328 / 0.176557 (-0.097228) \| 0.118603 / 0.737135 (-0.618532) \| 0.080489 / 0.296338 (-0.215850) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292464 / 0.215209 (0.077254) \| 2.898221 / 2.077655 (0.820566) \| 1.600868 / 1.504120 (0.096748) \| 1.485128 / 1.541195 (-0.056067) \| 1.493091 / 1.468490 (0.024600) \| 0.576117 / 4.584777 (-4.008660) \| 2.450440 / 3.745712 (-1.295273) \| 2.746026 / 5.269862 (-2.523836) \| 1.722555 / 4.565676 (-2.843122) \| 0.062869 / 0.424275 (-0.361406) \| 0.004918 / 0.007607 (-0.002689) \| 0.348470 / 0.226044 (0.122425) \| 3.420267 / 2.268929 (1.151339) \| 1.942973 / 55.444624 (-53.501651) \| 1.667684 / 6.876477 (-5.208793) \| 1.669618 / 2.142072 (-0.472454) \| 0.630275 / 4.805227 (-4.174952) \| 0.115072 / 6.500664 (-6.385592) \| 0.040430 / 0.075469 (-0.035039) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.989827 / 1.841788 (-0.851961) \| 11.578068 / 8.074308 (3.503760) \| 10.636060 / 10.191392 (0.444668) \| 0.131943 / 0.680424 (-0.548481) \| 0.015915 / 0.534201 (-0.518286) \| 0.287277 / 0.579283 (-0.292006) \| 0.279451 / 0.434364 (-0.154913) \| 0.325485 / 0.540337 (-0.214852) \| 0.544635 / 1.386936 (-0.842301) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f22579be6c73867ac1a3c03e925abaf4872f8437 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005144 / 0.011353 (-0.006209) \| 0.003686 / 0.011008 (-0.007322) \| 0.064003 / 0.038508 (0.025495) \| 0.058962 / 0.023109 (0.035853) \| 0.233753 / 0.275898 (-0.042145) \| 0.255802 / 0.323480 (-0.067677) \| 0.003871 / 0.007986 (-0.004115) \| 0.002609 / 0.004328 (-0.001719) \| 0.048675 / 0.004250 (0.044425) \| 0.037550 / 0.037052 (0.000498) \| 0.240658 / 0.258489 (-0.017831) \| 0.272303 / 0.293841 (-0.021538) \| 0.027455 / 0.128546 (-0.101091) \| 0.010706 / 0.075646 (-0.064941) \| 0.210878 / 0.419271 (-0.208393) \| 0.035763 / 0.043533 (-0.007770) \| 0.239937 / 0.255139 (-0.015202) \| 0.262520 / 0.283200 (-0.020680) \| 0.017676 / 0.141683 (-0.124006) \| 1.095036 / 1.452155 (-0.357118) \| 1.178318 / 1.492716 (-0.314399) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095310 / 0.018006 (0.077304) \| 0.307485 / 0.000490 (0.306995) \| 0.000212 / 0.000200 (0.000013) \| 0.000047 / 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) \| 0.018630 / 0.037411 (-0.018781) \| 0.060461 / 0.014526 (0.045936) \| 0.073117 / 0.176557 (-0.103440) \| 0.119737 / 0.737135 (-0.617399) \| 0.073909 / 0.296338 (-0.222430) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280938 / 0.215209 (0.065729) \| 2.755333 / 2.077655 (0.677679) \| 1.468153 / 1.504120 (-0.035967) \| 1.350247 / 1.541195 (-0.190948) \| 1.379834 / 1.468490 (-0.088656) \| 0.564027 / 4.584777 (-4.020750) \| 2.387794 / 3.745712 (-1.357918) \| 2.768529 / 5.269862 (-2.501333) \| 1.761994 / 4.565676 (-2.803682) \| 0.062079 / 0.424275 (-0.362196) \| 0.005018 / 0.007607 (-0.002589) \| 0.337576 / 0.226044 (0.111532) \| 3.345347 / 2.268929 (1.076418) \| 1.821950 / 55.444624 (-53.622674) \| 1.545471 / 6.876477 (-5.331006) \| 1.534941 / 2.142072 (-0.607131) \| 0.626560 / 4.805227 (-4.178668) \| 0.116227 / 6.500664 (-6.384437) \| 0.041722 / 0.075469 (-0.033747) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.950480 / 1.841788 (-0.891307) \| 11.616355 / 8.074308 (3.542047) \| 10.426687 / 10.191392 (0.235295) \| 0.129967 / 0.680424 (-0.550457) \| 0.013977 / 0.534201 (-0.520224) \| 0.287150 / 0.579283 (-0.292133) \| 0.264028 / 0.434364 (-0.170336) \| 0.325061 / 0.540337 (-0.215277) \| 0.441281 / 1.386936 (-0.945655) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005436 / 0.011353 (-0.005917) \| 0.003567 / 0.011008 (-0.007441) \| 0.055275 / 0.038508 (0.016767) \| 0.053216 / 0.023109 (0.030107) \| 0.272826 / 0.275898 (-0.003072) \| 0.298399 / 0.323480 (-0.025081) \| 0.004803 / 0.007986 (-0.003183) \| 0.002681 / 0.004328 (-0.001648) \| 0.048704 / 0.004250 (0.044453) \| 0.040048 / 0.037052 (0.002996) \| 0.278200 / 0.258489 (0.019711) \| 0.331167 / 0.293841 (0.037326) \| 0.029282 / 0.128546 (-0.099265) \| 0.010766 / 0.075646 (-0.064881) \| 0.057370 / 0.419271 (-0.361902) \| 0.032674 / 0.043533 (-0.010859) \| 0.269430 / 0.255139 (0.014291) \| 0.288256 / 0.283200 (0.005056) \| 0.019340 / 0.141683 (-0.122343) \| 1.118058 / 1.452155 (-0.334097) \| 1.157811 / 1.492716 (-0.334906) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094091 / 0.018006 (0.076085) \| 0.301833 / 0.000490 (0.301343) \| 0.000216 / 0.000200 (0.000016) \| 0.000053 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021327 / 0.037411 (-0.016085) \| 0.068636 / 0.014526 (0.054110) \| 0.080246 / 0.176557 (-0.096311) \| 0.120524 / 0.737135 (-0.616611) \| 0.082226 / 0.296338 (-0.214113) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293579 / 0.215209 (0.078370) \| 2.880281 / 2.077655 (0.802626) \| 1.594647 / 1.504120 (0.090528) \| 1.477152 / 1.541195 (-0.064043) \| 1.498122 / 1.468490 (0.029632) \| 0.555073 / 4.584777 (-4.029704) \| 2.446743 / 3.745712 (-1.298970) \| 2.794971 / 5.269862 (-2.474890) \| 1.749730 / 4.565676 (-2.815947) \| 0.062537 / 0.424275 (-0.361738) \| 0.004908 / 0.007607 (-0.002699) \| 0.350772 / 0.226044 (0.124727) \| 3.486535 / 2.268929 (1.217607) \| 1.957414 / 55.444624 (-53.487210) \| 1.669169 / 6.876477 (-5.207308) \| 1.682396 / 2.142072 (-0.459676) \| 0.627379 / 4.805227 (-4.177848) \| 0.117218 / 6.500664 (-6.383446) \| 0.041000 / 0.075469 (-0.034469) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.958248 / 1.841788 (-0.883539) \| 12.022677 / 8.074308 (3.948369) \| 10.331661 / 10.191392 (0.140269) \| 0.129765 / 0.680424 (-0.550659) \| 0.015073 / 0.534201 (-0.519128) \| 0.287212 / 0.579283 (-0.292071) \| 0.278310 / 0.434364 (-0.156054) \| 0.328155 / 0.540337 (-0.212183) \| 0.564990 / 1.386936 (-0.821946) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0c16e56371e50adae771288945e3389cb81a31fd \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005576 / 0.011353 (-0.005777) \| 0.003430 / 0.011008 (-0.007578) \| 0.062714 / 0.038508 (0.024206) \| 0.051240 / 0.023109 (0.028131) \| 0.236637 / 0.275898 (-0.039261) \| 0.262660 / 0.323480 (-0.060820) \| 0.002924 / 0.007986 (-0.005061) \| 0.002712 / 0.004328 (-0.001616) \| 0.048680 / 0.004250 (0.044430) \| 0.038997 / 0.037052 (0.001945) \| 0.241426 / 0.258489 (-0.017063) \| 0.270652 / 0.293841 (-0.023189) \| 0.027355 / 0.128546 (-0.101192) \| 0.010640 / 0.075646 (-0.065006) \| 0.207754 / 0.419271 (-0.211517) \| 0.035921 / 0.043533 (-0.007612) \| 0.247645 / 0.255139 (-0.007494) \| 0.262933 / 0.283200 (-0.020266) \| 0.019658 / 0.141683 (-0.122025) \| 1.112576 / 1.452155 (-0.339578) \| 1.177362 / 1.492716 (-0.315354) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098100 / 0.018006 (0.080093) \| 0.310170 / 0.000490 (0.309680) \| 0.000220 / 0.000200 (0.000020) \| 0.000051 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019626 / 0.037411 (-0.017785) \| 0.065468 / 0.014526 (0.050942) \| 0.074767 / 0.176557 (-0.101789) \| 0.123619 / 0.737135 (-0.613516) \| 0.077159 / 0.296338 (-0.219179) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288585 / 0.215209 (0.073376) \| 2.771254 / 2.077655 (0.693599) \| 1.457091 / 1.504120 (-0.047029) \| 1.324341 / 1.541195 (-0.216854) \| 1.361960 / 1.468490 (-0.106530) \| 0.574197 / 4.584777 (-4.010580) \| 2.391440 / 3.745712 (-1.354273) \| 2.935060 / 5.269862 (-2.334802) \| 1.802792 / 4.565676 (-2.762884) \| 0.063530 / 0.424275 (-0.360745) \| 0.005129 / 0.007607 (-0.002478) \| 0.345977 / 0.226044 (0.119933) \| 3.368042 / 2.268929 (1.099113) \| 1.789575 / 55.444624 (-53.655050) \| 1.509165 / 6.876477 (-5.367312) \| 1.579792 / 2.142072 (-0.562280) \| 0.652136 / 4.805227 (-4.153091) \| 0.117014 / 6.500664 (-6.383650) \| 0.042385 / 0.075469 (-0.033084) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.963967 / 1.841788 (-0.877821) \| 11.847856 / 8.074308 (3.773548) \| 10.584088 / 10.191392 (0.392696) \| 0.143953 / 0.680424 (-0.536471) \| 0.014355 / 0.534201 (-0.519846) \| 0.286936 / 0.579283 (-0.292347) \| 0.269039 / 0.434364 (-0.165325) \| 0.324531 / 0.540337 (-0.215807) \| 0.443187 / 1.386936 (-0.943749) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005448 / 0.011353 (-0.005905) \| 0.003742 / 0.011008 (-0.007266) \| 0.048808 / 0.038508 (0.010300) \| 0.055409 / 0.023109 (0.032300) \| 0.271574 / 0.275898 (-0.004324) \| 0.295599 / 0.323480 (-0.027881) \| 0.004208 / 0.007986 (-0.003778) \| 0.002683 / 0.004328 (-0.001645) \| 0.048813 / 0.004250 (0.044562) \| 0.043672 / 0.037052 (0.006620) \| 0.282173 / 0.258489 (0.023684) \| 0.295447 / 0.293841 (0.001606) \| 0.030461 / 0.128546 (-0.098086) \| 0.010988 / 0.075646 (-0.064658) \| 0.057050 / 0.419271 (-0.362221) \| 0.033329 / 0.043533 (-0.010203) \| 0.269700 / 0.255139 (0.014561) \| 0.287099 / 0.283200 (0.003899) \| 0.018203 / 0.141683 (-0.123480) \| 1.142584 / 1.452155 (-0.309571) \| 1.181848 / 1.492716 (-0.310869) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096958 / 0.018006 (0.078952) \| 0.310563 / 0.000490 (0.310074) \| 0.000224 / 0.000200 (0.000024) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022213 / 0.037411 (-0.015199) \| 0.072054 / 0.014526 (0.057528) \| 0.086393 / 0.176557 (-0.090163) \| 0.122431 / 0.737135 (-0.614704) \| 0.085298 / 0.296338 (-0.211041) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290823 / 0.215209 (0.075614) \| 2.838026 / 2.077655 (0.760371) \| 1.541425 / 1.504120 (0.037305) \| 1.431903 / 1.541195 (-0.109292) \| 1.476567 / 1.468490 (0.008077) \| 0.557856 / 4.584777 (-4.026920) \| 2.449101 / 3.745712 (-1.296611) \| 2.924633 / 5.269862 (-2.345229) \| 1.824420 / 4.565676 (-2.741256) \| 0.063735 / 0.424275 (-0.360540) \| 0.005025 / 0.007607 (-0.002582) \| 0.349458 / 0.226044 (0.123413) \| 3.468627 / 2.268929 (1.199699) \| 1.925173 / 55.444624 (-53.519451) \| 1.655038 / 6.876477 (-5.221439) \| 1.698612 / 2.142072 (-0.443460) \| 0.643623 / 4.805227 (-4.161604) \| 0.116128 / 6.500664 (-6.384536) \| 0.042283 / 0.075469 (-0.033186) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.963029 / 1.841788 (-0.878758) \| 13.273985 / 8.074308 (5.199677) \| 11.400884 / 10.191392 (1.209492) \| 0.152635 / 0.680424 (-0.527788) \| 0.016442 / 0.534201 (-0.517759) \| 0.289272 / 0.579283 (-0.290012) \| 0.285286 / 0.434364 (-0.149078) \| 0.330028 / 0.540337 (-0.210310) \| 0.596500 / 1.386936 (-0.790436) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9c427c4b1dcf84c898ae62dc521bf446bb35e0e7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005124 / 0.011353 (-0.006229) \| 0.003832 / 0.011008 (-0.007176) \| 0.062806 / 0.038508 (0.024298) \| 0.053137 / 0.023109 (0.030028) \| 0.241155 / 0.275898 (-0.034743) \| 0.260521 / 0.323480 (-0.062959) \| 0.004005 / 0.007986 (-0.003981) \| 0.002754 / 0.004328 (-0.001575) \| 0.048934 / 0.004250 (0.044684) \| 0.039438 / 0.037052 (0.002385) \| 0.242534 / 0.258489 (-0.015955) \| 0.275498 / 0.293841 (-0.018343) \| 0.027338 / 0.128546 (-0.101208) \| 0.010809 / 0.075646 (-0.064837) \| 0.206986 / 0.419271 (-0.212285) \| 0.035614 / 0.043533 (-0.007919) \| 0.245780 / 0.255139 (-0.009359) \| 0.259793 / 0.283200 (-0.023407) \| 0.018108 / 0.141683 (-0.123575) \| 1.103412 / 1.452155 (-0.348742) \| 1.162940 / 1.492716 (-0.329776) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092463 / 0.018006 (0.074457) \| 0.299516 / 0.000490 (0.299026) \| 0.000210 / 0.000200 (0.000010) \| 0.000047 / 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) \| 0.018261 / 0.037411 (-0.019150) \| 0.060178 / 0.014526 (0.045652) \| 0.073043 / 0.176557 (-0.103513) \| 0.120541 / 0.737135 (-0.616594) \| 0.074972 / 0.296338 (-0.221367) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287288 / 0.215209 (0.072078) \| 2.814915 / 2.077655 (0.737260) \| 1.520221 / 1.504120 (0.016101) \| 1.396045 / 1.541195 (-0.145149) \| 1.419662 / 1.468490 (-0.048828) \| 0.589247 / 4.584777 (-3.995530) \| 2.411101 / 3.745712 (-1.334611) \| 2.777709 / 5.269862 (-2.492153) \| 1.750386 / 4.565676 (-2.815291) \| 0.063734 / 0.424275 (-0.360541) \| 0.005021 / 0.007607 (-0.002586) \| 0.338817 / 0.226044 (0.112773) \| 3.371218 / 2.268929 (1.102289) \| 1.892691 / 55.444624 (-53.551934) \| 1.599039 / 6.876477 (-5.277438) \| 1.574726 / 2.142072 (-0.567346) \| 0.665623 / 4.805227 (-4.139604) \| 0.118628 / 6.500664 (-6.382036) \| 0.041803 / 0.075469 (-0.033666) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.948696 / 1.841788 (-0.893092) \| 11.502916 / 8.074308 (3.428608) \| 10.301174 / 10.191392 (0.109782) \| 0.141752 / 0.680424 (-0.538672) \| 0.014064 / 0.534201 (-0.520137) \| 0.286701 / 0.579283 (-0.292583) \| 0.265805 / 0.434364 (-0.168559) \| 0.328420 / 0.540337 (-0.211917) \| 0.433619 / 1.386936 (-0.953317) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005262 / 0.011353 (-0.006091) \| 0.003361 / 0.011008 (-0.007648) \| 0.049525 / 0.038508 (0.011016) \| 0.048950 / 0.023109 (0.025841) \| 0.273617 / 0.275898 (-0.002281) \| 0.296614 / 0.323480 (-0.026866) \| 0.004014 / 0.007986 (-0.003971) \| 0.002630 / 0.004328 (-0.001698) \| 0.048203 / 0.004250 (0.043952) \| 0.040912 / 0.037052 (0.003860) \| 0.279736 / 0.258489 (0.021247) \| 0.301671 / 0.293841 (0.007830) \| 0.028546 / 0.128546 (-0.100000) \| 0.010440 / 0.075646 (-0.065206) \| 0.057869 / 0.419271 (-0.361402) \| 0.032876 / 0.043533 (-0.010657) \| 0.277649 / 0.255139 (0.022510) \| 0.296565 / 0.283200 (0.013365) \| 0.017558 / 0.141683 (-0.124125) \| 1.155005 / 1.452155 (-0.297149) \| 1.204827 / 1.492716 (-0.287889) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093248 / 0.018006 (0.075242) \| 0.302721 / 0.000490 (0.302231) \| 0.000218 / 0.000200 (0.000018) \| 0.000048 / 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) \| 0.021882 / 0.037411 (-0.015530) \| 0.068259 / 0.014526 (0.053733) \| 0.080982 / 0.176557 (-0.095574) \| 0.119386 / 0.737135 (-0.617750) \| 0.081745 / 0.296338 (-0.214593) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297812 / 0.215209 (0.082603) \| 2.909938 / 2.077655 (0.832283) \| 1.603736 / 1.504120 (0.099616) \| 1.482989 / 1.541195 (-0.058206) \| 1.495107 / 1.468490 (0.026617) \| 0.562275 / 4.584777 (-4.022502) \| 2.424812 / 3.745712 (-1.320901) \| 2.759127 / 5.269862 (-2.510735) \| 1.733283 / 4.565676 (-2.832394) \| 0.063144 / 0.424275 (-0.361131) \| 0.004949 / 0.007607 (-0.002658) \| 0.352756 / 0.226044 (0.126711) \| 3.496028 / 2.268929 (1.227100) \| 1.982804 / 55.444624 (-53.461820) \| 1.689787 / 6.876477 (-5.186690) \| 1.672699 / 2.142072 (-0.469373) \| 0.660169 / 4.805227 (-4.145059) \| 0.116535 / 6.500664 (-6.384129) \| 0.040616 / 0.075469 (-0.034853) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.975055 / 1.841788 (-0.866733) \| 11.919295 / 8.074308 (3.844986) \| 10.779188 / 10.191392 (0.587796) \| 0.143106 / 0.680424 (-0.537318) \| 0.015159 / 0.534201 (-0.519041) \| 0.289734 / 0.579283 (-0.289549) \| 0.278637 / 0.434364 (-0.155727) \| 0.328159 / 0.540337 (-0.212178) \| 0.570560 / 1.386936 (-0.816376) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#241500208da5fef64ad6ddc1cc5ab2be18f2f76d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005155 / 0.011353 (-0.006198) \| 0.003589 / 0.011008 (-0.007419) \| 0.064440 / 0.038508 (0.025932) \| 0.051020 / 0.023109 (0.027911) \| 0.246099 / 0.275898 (-0.029799) \| 0.273383 / 0.323480 (-0.050097) \| 0.003984 / 0.007986 (-0.004002) \| 0.002791 / 0.004328 (-0.001537) \| 0.049076 / 0.004250 (0.044826) \| 0.037975 / 0.037052 (0.000922) \| 0.253709 / 0.258489 (-0.004780) \| 0.281730 / 0.293841 (-0.012111) \| 0.028060 / 0.128546 (-0.100486) \| 0.010808 / 0.075646 (-0.064838) \| 0.206663 / 0.419271 (-0.212609) \| 0.035989 / 0.043533 (-0.007544) \| 0.252635 / 0.255139 (-0.002504) \| 0.280042 / 0.283200 (-0.003158) \| 0.016982 / 0.141683 (-0.124700) \| 1.098679 / 1.452155 (-0.353475) \| 1.157051 / 1.492716 (-0.335666) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098238 / 0.018006 (0.080232) \| 0.311990 / 0.000490 (0.311501) \| 0.000229 / 0.000200 (0.000029) \| 0.000052 / 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) \| 0.018270 / 0.037411 (-0.019141) \| 0.062711 / 0.014526 (0.048186) \| 0.074381 / 0.176557 (-0.102175) \| 0.119946 / 0.737135 (-0.617189) \| 0.075013 / 0.296338 (-0.221325) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282106 / 0.215209 (0.066897) \| 2.752653 / 2.077655 (0.674999) \| 1.488771 / 1.504120 (-0.015349) \| 1.372552 / 1.541195 (-0.168643) \| 1.390270 / 1.468490 (-0.078220) \| 0.558928 / 4.584777 (-4.025849) \| 2.411821 / 3.745712 (-1.333891) \| 2.771441 / 5.269862 (-2.498421) \| 1.747507 / 4.565676 (-2.818169) \| 0.061360 / 0.424275 (-0.362915) \| 0.004956 / 0.007607 (-0.002652) \| 0.332330 / 0.226044 (0.106286) \| 3.301405 / 2.268929 (1.032476) \| 1.786726 / 55.444624 (-53.657899) \| 1.529974 / 6.876477 (-5.346502) \| 1.538412 / 2.142072 (-0.603660) \| 0.637590 / 4.805227 (-4.167637) \| 0.117215 / 6.500664 (-6.383449) \| 0.042186 / 0.075469 (-0.033283) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945574 / 1.841788 (-0.896213) \| 11.616152 / 8.074308 (3.541844) \| 10.365114 / 10.191392 (0.173722) \| 0.130358 / 0.680424 (-0.550066) \| 0.013587 / 0.534201 (-0.520614) \| 0.306024 / 0.579283 (-0.273259) \| 0.270577 / 0.434364 (-0.163787) \| 0.340768 / 0.540337 (-0.199569) \| 0.460841 / 1.386936 (-0.926095) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005254 / 0.011353 (-0.006099) \| 0.003137 / 0.011008 (-0.007871) \| 0.048302 / 0.038508 (0.009794) \| 0.051952 / 0.023109 (0.028843) \| 0.269078 / 0.275898 (-0.006820) \| 0.292044 / 0.323480 (-0.031436) \| 0.003985 / 0.007986 (-0.004000) \| 0.002597 / 0.004328 (-0.001732) \| 0.049998 / 0.004250 (0.045747) \| 0.040227 / 0.037052 (0.003174) \| 0.274714 / 0.258489 (0.016225) \| 0.298160 / 0.293841 (0.004319) \| 0.028857 / 0.128546 (-0.099690) \| 0.010545 / 0.075646 (-0.065101) \| 0.057234 / 0.419271 (-0.362038) \| 0.032515 / 0.043533 (-0.011018) \| 0.271526 / 0.255139 (0.016387) \| 0.288556 / 0.283200 (0.005356) \| 0.018155 / 0.141683 (-0.123527) \| 1.201906 / 1.452155 (-0.250248) \| 1.220068 / 1.492716 (-0.272648) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.100098 / 0.018006 (0.082092) \| 0.311081 / 0.000490 (0.310591) \| 0.000231 / 0.000200 (0.000032) \| 0.000051 / 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) \| 0.022349 / 0.037411 (-0.015062) \| 0.069698 / 0.014526 (0.055172) \| 0.081334 / 0.176557 (-0.095222) \| 0.120847 / 0.737135 (-0.616289) \| 0.082091 / 0.296338 (-0.214248) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293810 / 0.215209 (0.078601) \| 2.844191 / 2.077655 (0.766536) \| 1.594494 / 1.504120 (0.090374) \| 1.486531 / 1.541195 (-0.054664) \| 1.506307 / 1.468490 (0.037817) \| 0.560247 / 4.584777 (-4.024530) \| 2.478309 / 3.745712 (-1.267403) \| 2.759024 / 5.269862 (-2.510837) \| 1.733063 / 4.565676 (-2.832613) \| 0.061838 / 0.424275 (-0.362438) \| 0.004869 / 0.007607 (-0.002738) \| 0.347267 / 0.226044 (0.121222) \| 3.407737 / 2.268929 (1.138808) \| 1.944420 / 55.444624 (-53.500204) \| 1.660060 / 6.876477 (-5.216417) \| 1.704219 / 2.142072 (-0.437854) \| 0.646969 / 4.805227 (-4.158258) \| 0.115750 / 6.500664 (-6.384914) \| 0.041614 / 0.075469 (-0.033855) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.972537 / 1.841788 (-0.869251) \| 12.013530 / 8.074308 (3.939222) \| 10.650215 / 10.191392 (0.458823) \| 0.132877 / 0.680424 (-0.547547) \| 0.016828 / 0.534201 (-0.517372) \| 0.288321 / 0.579283 (-0.290962) \| 0.284203 / 0.434364 (-0.150161) \| 0.324016 / 0.540337 (-0.216321) \| 0.575403 / 1.386936 (-0.811533) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#17ec1a7a610adba3db44f316a930b979872d4ef7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005925 / 0.011353 (-0.005427) \| 0.005138 / 0.011008 (-0.005870) \| 0.069865 / 0.038508 (0.031356) \| 0.067181 / 0.023109 (0.044072) \| 0.309642 / 0.275898 (0.033743) \| 0.302919 / 0.323480 (-0.020561) \| 0.003365 / 0.007986 (-0.004620) \| 0.003148 / 0.004328 (-0.001180) \| 0.054102 / 0.004250 (0.049852) \| 0.044196 / 0.037052 (0.007143) \| 0.306882 / 0.258489 (0.048393) \| 0.315153 / 0.293841 (0.021313) \| 0.030458 / 0.128546 (-0.098089) \| 0.011773 / 0.075646 (-0.063874) \| 0.235075 / 0.419271 (-0.184196) \| 0.040840 / 0.043533 (-0.002693) \| 0.279897 / 0.255139 (0.024758) \| 0.316334 / 0.283200 (0.033135) \| 0.020128 / 0.141683 (-0.121555) \| 1.237327 / 1.452155 (-0.214828) \| 1.290386 / 1.492716 (-0.202331) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.118540 / 0.018006 (0.100534) \| 0.363282 / 0.000490 (0.362792) \| 0.000266 / 0.000200 (0.000066) \| 0.000058 / 0.000054 (0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021435 / 0.037411 (-0.015977) \| 0.068124 / 0.014526 (0.053598) \| 0.082747 / 0.176557 (-0.093809) \| 0.137179 / 0.737135 (-0.599956) \| 0.084815 / 0.296338 (-0.211523) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.307836 / 0.215209 (0.092626) \| 2.983444 / 2.077655 (0.905790) \| 1.616430 / 1.504120 (0.112310) \| 1.466843 / 1.541195 (-0.074351) \| 1.512440 / 1.468490 (0.043950) \| 0.652311 / 4.584777 (-3.932466) \| 2.676420 / 3.745712 (-1.069292) \| 3.265747 / 5.269862 (-2.004115) \| 2.028586 / 4.565676 (-2.537090) \| 0.071997 / 0.424275 (-0.352278) \| 0.007068 / 0.007607 (-0.000539) \| 0.367199 / 0.226044 (0.141155) \| 3.617970 / 2.268929 (1.349042) \| 1.991345 / 55.444624 (-53.453280) \| 1.670015 / 6.876477 (-5.206462) \| 1.720515 / 2.142072 (-0.421557) \| 0.724649 / 4.805227 (-4.080579) \| 0.134888 / 6.500664 (-6.365776) \| 0.048325 / 0.075469 (-0.027144) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.051058 / 1.841788 (-0.790730) \| 13.772809 / 8.074308 (5.698501) \| 11.813879 / 10.191392 (1.622487) \| 0.160065 / 0.680424 (-0.520359) \| 0.016256 / 0.534201 (-0.517945) \| 0.320393 / 0.579283 (-0.258890) \| 0.314462 / 0.434364 (-0.119901) \| 0.371911 / 0.540337 (-0.168427) \| 0.506864 / 1.386936 (-0.880072) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005857 / 0.011353 (-0.005496) \| 0.004077 / 0.011008 (-0.006931) \| 0.056033 / 0.038508 (0.017525) \| 0.067622 / 0.023109 (0.044513) \| 0.298956 / 0.275898 (0.023058) \| 0.323484 / 0.323480 (0.000004) \| 0.004825 / 0.007986 (-0.003160) \| 0.003120 / 0.004328 (-0.001208) \| 0.055227 / 0.004250 (0.050976) \| 0.048439 / 0.037052 (0.011387) \| 0.303207 / 0.258489 (0.044718) \| 0.329478 / 0.293841 (0.035637) \| 0.032516 / 0.128546 (-0.096031) \| 0.012260 / 0.075646 (-0.063386) \| 0.065037 / 0.419271 (-0.354234) \| 0.038799 / 0.043533 (-0.004734) \| 0.299102 / 0.255139 (0.043963) \| 0.318248 / 0.283200 (0.035048) \| 0.020190 / 0.141683 (-0.121493) \| 1.263479 / 1.452155 (-0.188676) \| 1.329788 / 1.492716 (-0.162928) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.119801 / 0.018006 (0.101794) \| 0.359618 / 0.000490 (0.359129) \| 0.000260 / 0.000200 (0.000060) \| 0.000058 / 0.000054 (0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026876 / 0.037411 (-0.010535) \| 0.080637 / 0.014526 (0.066111) \| 0.092260 / 0.176557 (-0.084297) \| 0.137260 / 0.737135 (-0.599875) \| 0.093309 / 0.296338 (-0.203029) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.329327 / 0.215209 (0.114118) \| 3.193014 / 2.077655 (1.115359) \| 1.755838 / 1.504120 (0.251718) \| 1.612279 / 1.541195 (0.071084) \| 1.631958 / 1.468490 (0.163468) \| 0.630886 / 4.584777 (-3.953891) \| 2.739731 / 3.745712 (-1.005981) \| 3.186745 / 5.269862 (-2.083117) \| 1.987125 / 4.565676 (-2.578552) \| 0.070694 / 0.424275 (-0.353581) \| 0.006461 / 0.007607 (-0.001146) \| 0.386367 / 0.226044 (0.160323) \| 3.815837 / 2.268929 (1.546908) \| 2.155904 / 55.444624 (-53.288720) \| 1.832575 / 6.876477 (-5.043902) \| 1.842097 / 2.142072 (-0.299975) \| 0.716394 / 4.805227 (-4.088833) \| 0.130796 / 6.500664 (-6.369869) \| 0.045674 / 0.075469 (-0.029795) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.109117 / 1.841788 (-0.732671) \| 14.116582 / 8.074308 (6.042274) \| 11.926356 / 10.191392 (1.734964) \| 0.150543 / 0.680424 (-0.529881) \| 0.017426 / 0.534201 (-0.516775) \| 0.323058 / 0.579283 (-0.256225) \| 0.330228 / 0.434364 (-0.104136) \| 0.372533 / 0.540337 (-0.167804) \| 0.661348 / 1.386936 (-0.725588) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#04ffd22a30ecc7545234559edd9d23c85c6d84d9 \"CML watermark\")\n", "Thanks for the review, I took your comments into account !", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005477 / 0.011353 (-0.005876) \| 0.003509 / 0.011008 (-0.007499) \| 0.062884 / 0.038508 (0.024376) \| 0.051042 / 0.023109 (0.027933) \| 0.285180 / 0.275898 (0.009282) \| 0.315353 / 0.323480 (-0.008127) \| 0.002943 / 0.007986 (-0.005043) \| 0.003286 / 0.004328 (-0.001042) \| 0.048885 / 0.004250 (0.044635) \| 0.038591 / 0.037052 (0.001539) \| 0.288527 / 0.258489 (0.030038) \| 0.316102 / 0.293841 (0.022261) \| 0.028252 / 0.128546 (-0.100295) \| 0.010622 / 0.075646 (-0.065024) \| 0.205573 / 0.419271 (-0.213699) \| 0.035764 / 0.043533 (-0.007769) \| 0.285729 / 0.255139 (0.030590) \| 0.304578 / 0.283200 (0.021378) \| 0.019862 / 0.141683 (-0.121821) \| 1.102866 / 1.452155 (-0.349288) \| 1.175161 / 1.492716 (-0.317555) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095253 / 0.018006 (0.077246) \| 0.302290 / 0.000490 (0.301800) \| 0.000243 / 0.000200 (0.000043) \| 0.000061 / 0.000054 (0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018680 / 0.037411 (-0.018731) \| 0.060375 / 0.014526 (0.045849) \| 0.074033 / 0.176557 (-0.102524) \| 0.120290 / 0.737135 (-0.616845) \| 0.075350 / 0.296338 (-0.220989) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.277617 / 0.215209 (0.062408) \| 2.718201 / 2.077655 (0.640546) \| 1.462952 / 1.504120 (-0.041168) \| 1.339199 / 1.541195 (-0.201996) \| 1.375805 / 1.468490 (-0.092685) \| 0.559956 / 4.584777 (-4.024821) \| 2.373865 / 3.745712 (-1.371847) \| 2.795732 / 5.269862 (-2.474129) \| 1.755490 / 4.565676 (-2.810186) \| 0.062002 / 0.424275 (-0.362273) \| 0.004935 / 0.007607 (-0.002672) \| 0.334786 / 0.226044 (0.108741) \| 3.237499 / 2.268929 (0.968571) \| 1.787561 / 55.444624 (-53.657064) \| 1.513300 / 6.876477 (-5.363176) \| 1.549797 / 2.142072 (-0.592275) \| 0.643587 / 4.805227 (-4.161640) \| 0.117275 / 6.500664 (-6.383389) \| 0.042184 / 0.075469 (-0.033285) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.933366 / 1.841788 (-0.908421) \| 11.792282 / 8.074308 (3.717973) \| 10.466608 / 10.191392 (0.275216) \| 0.142148 / 0.680424 (-0.538275) \| 0.014084 / 0.534201 (-0.520117) \| 0.287233 / 0.579283 (-0.292050) \| 0.266022 / 0.434364 (-0.168342) \| 0.326854 / 0.540337 (-0.213483) \| 0.451348 / 1.386936 (-0.935588) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005384 / 0.011353 (-0.005969) \| 0.003562 / 0.011008 (-0.007446) \| 0.049014 / 0.038508 (0.010506) \| 0.057480 / 0.023109 (0.034371) \| 0.274456 / 0.275898 (-0.001442) \| 0.298387 / 0.323480 (-0.025093) \| 0.003909 / 0.007986 (-0.004076) \| 0.002646 / 0.004328 (-0.001683) \| 0.048374 / 0.004250 (0.044124) \| 0.040907 / 0.037052 (0.003854) \| 0.278267 / 0.258489 (0.019778) \| 0.299862 / 0.293841 (0.006021) \| 0.029108 / 0.128546 (-0.099439) \| 0.010752 / 0.075646 (-0.064894) \| 0.057523 / 0.419271 (-0.361749) \| 0.032692 / 0.043533 (-0.010841) \| 0.276288 / 0.255139 (0.021149) \| 0.291572 / 0.283200 (0.008372) \| 0.017818 / 0.141683 (-0.123865) \| 1.129517 / 1.452155 (-0.322638) \| 1.186630 / 1.492716 (-0.306086) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093405 / 0.018006 (0.075399) \| 0.301254 / 0.000490 (0.300764) \| 0.000225 / 0.000200 (0.000025) \| 0.000054 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021793 / 0.037411 (-0.015618) \| 0.069033 / 0.014526 (0.054508) \| 0.083502 / 0.176557 (-0.093055) \| 0.122149 / 0.737135 (-0.614986) \| 0.083801 / 0.296338 (-0.212537) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.299149 / 0.215209 (0.083940) \| 2.936550 / 2.077655 (0.858895) \| 1.595766 / 1.504120 (0.091647) \| 1.487117 / 1.541195 (-0.054078) \| 1.494606 / 1.468490 (0.026116) \| 0.569346 / 4.584777 (-4.015431) \| 2.445642 / 3.745712 (-1.300070) \| 2.805696 / 5.269862 (-2.464165) \| 1.743796 / 4.565676 (-2.821881) \| 0.062695 / 0.424275 (-0.361580) \| 0.004885 / 0.007607 (-0.002723) \| 0.354186 / 0.226044 (0.128142) \| 3.487926 / 2.268929 (1.218997) \| 1.965703 / 55.444624 (-53.478922) \| 1.682284 / 6.876477 (-5.194193) \| 1.705586 / 2.142072 (-0.436487) \| 0.655099 / 4.805227 (-4.150128) \| 0.116441 / 6.500664 (-6.384223) \| 0.040851 / 0.075469 (-0.034618) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.967361 / 1.841788 (-0.874427) \| 12.037718 / 8.074308 (3.963409) \| 10.599761 / 10.191392 (0.408369) \| 0.143127 / 0.680424 (-0.537297) \| 0.015063 / 0.534201 (-0.519138) \| 0.286894 / 0.579283 (-0.292389) \| 0.301505 / 0.434364 (-0.132859) \| 0.324339 / 0.540337 (-0.215999) \| 0.591782 / 1.386936 (-0.795154) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b96ff08d4aa6dbafc8a10a9d03dfabe236378bcd \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005337 / 0.011353 (-0.006015) \| 0.004074 / 0.011008 (-0.006934) \| 0.062653 / 0.038508 (0.024145) \| 0.054295 / 0.023109 (0.031186) \| 0.248284 / 0.275898 (-0.027614) \| 0.271604 / 0.323480 (-0.051876) \| 0.003931 / 0.007986 (-0.004055) \| 0.002907 / 0.004328 (-0.001422) \| 0.047991 / 0.004250 (0.043740) \| 0.042842 / 0.037052 (0.005790) \| 0.253648 / 0.258489 (-0.004841) \| 0.282546 / 0.293841 (-0.011295) \| 0.028005 / 0.128546 (-0.100541) \| 0.010734 / 0.075646 (-0.064912) \| 0.210023 / 0.419271 (-0.209248) \| 0.035940 / 0.043533 (-0.007592) \| 0.250766 / 0.255139 (-0.004373) \| 0.267644 / 0.283200 (-0.015556) \| 0.020451 / 0.141683 (-0.121232) \| 1.114972 / 1.452155 (-0.337183) \| 1.159823 / 1.492716 (-0.332893) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095527 / 0.018006 (0.077521) \| 0.303321 / 0.000490 (0.302831) \| 0.000216 / 0.000200 (0.000016) \| 0.000048 / 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) \| 0.018725 / 0.037411 (-0.018686) \| 0.062537 / 0.014526 (0.048011) \| 0.073091 / 0.176557 (-0.103466) \| 0.119570 / 0.737135 (-0.617565) \| 0.074863 / 0.296338 (-0.221476) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284936 / 0.215209 (0.069727) \| 2.802498 / 2.077655 (0.724843) \| 1.493316 / 1.504120 (-0.010804) \| 1.372319 / 1.541195 (-0.168875) \| 1.403657 / 1.468490 (-0.064833) \| 0.569303 / 4.584777 (-4.015474) \| 2.402498 / 3.745712 (-1.343214) \| 2.834778 / 5.269862 (-2.435084) \| 1.791312 / 4.565676 (-2.774365) \| 0.062526 / 0.424275 (-0.361749) \| 0.004947 / 0.007607 (-0.002660) \| 0.345141 / 0.226044 (0.119097) \| 3.371863 / 2.268929 (1.102934) \| 1.846023 / 55.444624 (-53.598602) \| 1.596368 / 6.876477 (-5.280109) \| 1.615902 / 2.142072 (-0.526170) \| 0.644333 / 4.805227 (-4.160894) \| 0.119460 / 6.500664 (-6.381204) \| 0.049122 / 0.075469 (-0.026347) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.951839 / 1.841788 (-0.889948) \| 11.677074 / 8.074308 (3.602766) \| 10.562586 / 10.191392 (0.371194) \| 0.143633 / 0.680424 (-0.536791) \| 0.014157 / 0.534201 (-0.520044) \| 0.289141 / 0.579283 (-0.290142) \| 0.264719 / 0.434364 (-0.169645) \| 0.327862 / 0.540337 (-0.212476) \| 0.451215 / 1.386936 (-0.935721) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005343 / 0.011353 (-0.006010) \| 0.003522 / 0.011008 (-0.007486) \| 0.049354 / 0.038508 (0.010846) \| 0.051441 / 0.023109 (0.028332) \| 0.259350 / 0.275898 (-0.016548) \| 0.288946 / 0.323480 (-0.034534) \| 0.004052 / 0.007986 (-0.003934) \| 0.002690 / 0.004328 (-0.001639) \| 0.049996 / 0.004250 (0.045746) \| 0.040224 / 0.037052 (0.003171) \| 0.264588 / 0.258489 (0.006099) \| 0.296474 / 0.293841 (0.002633) \| 0.028868 / 0.128546 (-0.099679) \| 0.010917 / 0.075646 (-0.064730) \| 0.057866 / 0.419271 (-0.361405) \| 0.032610 / 0.043533 (-0.010923) \| 0.260657 / 0.255139 (0.005518) \| 0.276947 / 0.283200 (-0.006253) \| 0.018877 / 0.141683 (-0.122806) \| 1.126205 / 1.452155 (-0.325949) \| 1.206173 / 1.492716 (-0.286543) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094464 / 0.018006 (0.076458) \| 0.304473 / 0.000490 (0.303984) \| 0.000231 / 0.000200 (0.000031) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021472 / 0.037411 (-0.015939) \| 0.070864 / 0.014526 (0.056338) \| 0.086607 / 0.176557 (-0.089950) \| 0.120679 / 0.737135 (-0.616456) \| 0.084271 / 0.296338 (-0.212068) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296448 / 0.215209 (0.081239) \| 2.893996 / 2.077655 (0.816341) \| 1.573409 / 1.504120 (0.069289) \| 1.438799 / 1.541195 (-0.102396) \| 1.461241 / 1.468490 (-0.007249) \| 0.566737 / 4.584777 (-4.018040) \| 2.425709 / 3.745712 (-1.320003) \| 2.826764 / 5.269862 (-2.443098) \| 1.785330 / 4.565676 (-2.780347) \| 0.063721 / 0.424275 (-0.360554) \| 0.005158 / 0.007607 (-0.002449) \| 0.354961 / 0.226044 (0.128916) \| 3.457499 / 2.268929 (1.188570) \| 1.931374 / 55.444624 (-53.513251) \| 1.646515 / 6.876477 (-5.229962) \| 1.629891 / 2.142072 (-0.512182) \| 0.648922 / 4.805227 (-4.156305) \| 0.114953 / 6.500664 (-6.385711) \| 0.040997 / 0.075469 (-0.034472) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.951049 / 1.841788 (-0.890739) \| 12.258298 / 8.074308 (4.183990) \| 10.663309 / 10.191392 (0.471917) \| 0.142933 / 0.680424 (-0.537491) \| 0.015927 / 0.534201 (-0.518273) \| 0.286914 / 0.579283 (-0.292369) \| 0.286600 / 0.434364 (-0.147764) \| 0.324464 / 0.540337 (-0.215874) \| 0.575075 / 1.386936 (-0.811861) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ed47b9d5e9c6aa03a0aa07d8abfd3fa8241da353 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005298 / 0.011353 (-0.006055) \| 0.003645 / 0.011008 (-0.007363) \| 0.061629 / 0.038508 (0.023121) \| 0.052322 / 0.023109 (0.029212) \| 0.242579 / 0.275898 (-0.033319) \| 0.263525 / 0.323480 (-0.059955) \| 0.002794 / 0.007986 (-0.005192) \| 0.002152 / 0.004328 (-0.002177) \| 0.048301 / 0.004250 (0.044050) \| 0.038177 / 0.037052 (0.001125) \| 0.247724 / 0.258489 (-0.010765) \| 0.274455 / 0.293841 (-0.019386) \| 0.026992 / 0.128546 (-0.101555) \| 0.010110 / 0.075646 (-0.065536) \| 0.205662 / 0.419271 (-0.213609) \| 0.034901 / 0.043533 (-0.008632) \| 0.241920 / 0.255139 (-0.013219) \| 0.262048 / 0.283200 (-0.021152) \| 0.019111 / 0.141683 (-0.122572) \| 1.127600 / 1.452155 (-0.324555) \| 1.193931 / 1.492716 (-0.298786) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090321 / 0.018006 (0.072315) \| 0.299046 / 0.000490 (0.298556) \| 0.000197 / 0.000200 (-0.000003) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018278 / 0.037411 (-0.019133) \| 0.060114 / 0.014526 (0.045588) \| 0.073602 / 0.176557 (-0.102954) \| 0.119676 / 0.737135 (-0.617459) \| 0.074786 / 0.296338 (-0.221552) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280385 / 0.215209 (0.065176) \| 2.764259 / 2.077655 (0.686604) \| 1.501027 / 1.504120 (-0.003093) \| 1.376900 / 1.541195 (-0.164295) \| 1.390587 / 1.468490 (-0.077903) \| 0.555180 / 4.584777 (-4.029597) \| 2.354307 / 3.745712 (-1.391405) \| 2.755862 / 5.269862 (-2.514000) \| 1.714771 / 4.565676 (-2.850906) \| 0.062507 / 0.424275 (-0.361768) \| 0.004974 / 0.007607 (-0.002633) \| 0.333900 / 0.226044 (0.107856) \| 3.266922 / 2.268929 (0.997994) \| 1.805401 / 55.444624 (-53.639223) \| 1.526970 / 6.876477 (-5.349507) \| 1.539425 / 2.142072 (-0.602647) \| 0.629364 / 4.805227 (-4.175863) \| 0.114929 / 6.500664 (-6.385735) \| 0.041258 / 0.075469 (-0.034211) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.968601 / 1.841788 (-0.873187) \| 11.260937 / 8.074308 (3.186629) \| 10.393839 / 10.191392 (0.202447) \| 0.127988 / 0.680424 (-0.552436) \| 0.014564 / 0.534201 (-0.519637) \| 0.286560 / 0.579283 (-0.292723) \| 0.260493 / 0.434364 (-0.173871) \| 0.330949 / 0.540337 (-0.209388) \| 0.435798 / 1.386936 (-0.951138) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005232 / 0.011353 (-0.006121) \| 0.003030 / 0.011008 (-0.007978) \| 0.048513 / 0.038508 (0.010005) \| 0.049501 / 0.023109 (0.026392) \| 0.270545 / 0.275898 (-0.005353) \| 0.289128 / 0.323480 (-0.034352) \| 0.003925 / 0.007986 (-0.004061) \| 0.002568 / 0.004328 (-0.001761) \| 0.047692 / 0.004250 (0.043442) \| 0.039854 / 0.037052 (0.002802) \| 0.272654 / 0.258489 (0.014165) \| 0.296275 / 0.293841 (0.002434) \| 0.029027 / 0.128546 (-0.099519) \| 0.010335 / 0.075646 (-0.065311) \| 0.056726 / 0.419271 (-0.362546) \| 0.033257 / 0.043533 (-0.010275) \| 0.272672 / 0.255139 (0.017533) \| 0.286298 / 0.283200 (0.003098) \| 0.017877 / 0.141683 (-0.123806) \| 1.150322 / 1.452155 (-0.301833) \| 1.221031 / 1.492716 (-0.271685) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.102838 / 0.018006 (0.084832) \| 0.298810 / 0.000490 (0.298320) \| 0.000207 / 0.000200 (0.000007) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021232 / 0.037411 (-0.016180) \| 0.067949 / 0.014526 (0.053423) \| 0.116487 / 0.176557 (-0.060070) \| 0.124035 / 0.737135 (-0.613100) \| 0.081075 / 0.296338 (-0.215263) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289098 / 0.215209 (0.073889) \| 2.844476 / 2.077655 (0.766821) \| 1.609576 / 1.504120 (0.105456) \| 1.480453 / 1.541195 (-0.060742) \| 1.489672 / 1.468490 (0.021182) \| 0.589661 / 4.584777 (-3.995116) \| 2.453804 / 3.745712 (-1.291908) \| 2.722381 / 5.269862 (-2.547480) \| 1.720251 / 4.565676 (-2.845425) \| 0.066085 / 0.424275 (-0.358190) \| 0.004943 / 0.007607 (-0.002664) \| 0.355149 / 0.226044 (0.129104) \| 3.444323 / 2.268929 (1.175395) \| 1.971157 / 55.444624 (-53.473467) \| 1.683029 / 6.876477 (-5.193448) \| 1.672798 / 2.142072 (-0.469274) \| 0.644812 / 4.805227 (-4.160416) \| 0.115098 / 6.500664 (-6.385566) \| 0.039883 / 0.075469 (-0.035586) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.960454 / 1.841788 (-0.881334) \| 11.604732 / 8.074308 (3.530424) \| 10.405481 / 10.191392 (0.214089) \| 0.129146 / 0.680424 (-0.551278) \| 0.014945 / 0.534201 (-0.519256) \| 0.286239 / 0.579283 (-0.293044) \| 0.281041 / 0.434364 (-0.153323) \| 0.320448 / 0.540337 (-0.219890) \| 0.554304 / 1.386936 (-0.832632) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b2cfb7859b029654829c4dfee230812ddab1f104 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005510 / 0.011353 (-0.005843) \| 0.003575 / 0.011008 (-0.007433) \| 0.062232 / 0.038508 (0.023724) \| 0.051115 / 0.023109 (0.028006) \| 0.250709 / 0.275898 (-0.025189) \| 0.274837 / 0.323480 (-0.048642) \| 0.002972 / 0.007986 (-0.005014) \| 0.002708 / 0.004328 (-0.001621) \| 0.048088 / 0.004250 (0.043838) \| 0.038588 / 0.037052 (0.001535) \| 0.252550 / 0.258489 (-0.005939) \| 0.285238 / 0.293841 (-0.008603) \| 0.027867 / 0.128546 (-0.100679) \| 0.011000 / 0.075646 (-0.064646) \| 0.206918 / 0.419271 (-0.212354) \| 0.035711 / 0.043533 (-0.007822) \| 0.255306 / 0.255139 (0.000167) \| 0.298636 / 0.283200 (0.015436) \| 0.018222 / 0.141683 (-0.123461) \| 1.122276 / 1.452155 (-0.329879) \| 1.196471 / 1.492716 (-0.296245) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092072 / 0.018006 (0.074066) \| 0.301469 / 0.000490 (0.300979) \| 0.000225 / 0.000200 (0.000025) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018672 / 0.037411 (-0.018739) \| 0.060235 / 0.014526 (0.045709) \| 0.074036 / 0.176557 (-0.102521) \| 0.119578 / 0.737135 (-0.617557) \| 0.073605 / 0.296338 (-0.222734) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286474 / 0.215209 (0.071264) \| 2.779427 / 2.077655 (0.701772) \| 1.478746 / 1.504120 (-0.025373) \| 1.362692 / 1.541195 (-0.178503) \| 1.388194 / 1.468490 (-0.080296) \| 0.560707 / 4.584777 (-4.024070) \| 2.352846 / 3.745712 (-1.392866) \| 2.784400 / 5.269862 (-2.485461) \| 1.775642 / 4.565676 (-2.790035) \| 0.062324 / 0.424275 (-0.361951) \| 0.004938 / 0.007607 (-0.002669) \| 0.334149 / 0.226044 (0.108105) \| 3.319446 / 2.268929 (1.050517) \| 1.810369 / 55.444624 (-53.634255) \| 1.559462 / 6.876477 (-5.317014) \| 1.611199 / 2.142072 (-0.530873) \| 0.655984 / 4.805227 (-4.149244) \| 0.118508 / 6.500664 (-6.382156) \| 0.043661 / 0.075469 (-0.031808) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.935046 / 1.841788 (-0.906742) \| 11.413501 / 8.074308 (3.339192) \| 10.392314 / 10.191392 (0.200922) \| 0.131507 / 0.680424 (-0.548917) \| 0.014827 / 0.534201 (-0.519374) \| 0.289069 / 0.579283 (-0.290214) \| 0.268288 / 0.434364 (-0.166076) \| 0.326843 / 0.540337 (-0.213495) \| 0.441283 / 1.386936 (-0.945653) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005375 / 0.011353 (-0.005978) \| 0.003549 / 0.011008 (-0.007459) \| 0.048996 / 0.038508 (0.010488) \| 0.051408 / 0.023109 (0.028298) \| 0.272265 / 0.275898 (-0.003633) \| 0.293228 / 0.323480 (-0.030252) \| 0.004147 / 0.007986 (-0.003839) \| 0.002673 / 0.004328 (-0.001655) \| 0.048116 / 0.004250 (0.043865) \| 0.039926 / 0.037052 (0.002874) \| 0.276987 / 0.258489 (0.018498) \| 0.302955 / 0.293841 (0.009115) \| 0.029488 / 0.128546 (-0.099058) \| 0.010797 / 0.075646 (-0.064849) \| 0.057552 / 0.419271 (-0.361720) \| 0.032827 / 0.043533 (-0.010706) \| 0.270888 / 0.255139 (0.015749) \| 0.289136 / 0.283200 (0.005937) \| 0.018815 / 0.141683 (-0.122868) \| 1.148624 / 1.452155 (-0.303530) \| 1.191184 / 1.492716 (-0.301532) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091712 / 0.018006 (0.073706) \| 0.311198 / 0.000490 (0.310708) \| 0.000226 / 0.000200 (0.000026) \| 0.000049 / 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) \| 0.022097 / 0.037411 (-0.015314) \| 0.070641 / 0.014526 (0.056116) \| 0.080084 / 0.176557 (-0.096472) \| 0.118998 / 0.737135 (-0.618137) \| 0.081827 / 0.296338 (-0.214512) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298599 / 0.215209 (0.083390) \| 2.884759 / 2.077655 (0.807105) \| 1.630794 / 1.504120 (0.126674) \| 1.454309 / 1.541195 (-0.086886) \| 1.466795 / 1.468490 (-0.001695) \| 0.565405 / 4.584777 (-4.019372) \| 2.460883 / 3.745712 (-1.284829) \| 2.764193 / 5.269862 (-2.505668) \| 1.734270 / 4.565676 (-2.831407) \| 0.063408 / 0.424275 (-0.360867) \| 0.004887 / 0.007607 (-0.002720) \| 0.347762 / 0.226044 (0.121717) \| 3.458385 / 2.268929 (1.189457) \| 1.965434 / 55.444624 (-53.479190) \| 1.671047 / 6.876477 (-5.205430) \| 1.665642 / 2.142072 (-0.476430) \| 0.640665 / 4.805227 (-4.164562) \| 0.116025 / 6.500664 (-6.384639) \| 0.040147 / 0.075469 (-0.035322) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.982194 / 1.841788 (-0.859593) \| 11.983487 / 8.074308 (3.909179) \| 10.660605 / 10.191392 (0.469213) \| 0.140647 / 0.680424 (-0.539777) \| 0.015870 / 0.534201 (-0.518331) \| 0.287032 / 0.579283 (-0.292251) \| 0.276629 / 0.434364 (-0.157735) \| 0.331171 / 0.540337 (-0.209166) \| 0.575346 / 1.386936 (-0.811590) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#56433c2f6a42d5fcc5acb46c6275911c29afc371 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005014 / 0.011353 (-0.006339) \| 0.003434 / 0.011008 (-0.007574) \| 0.063283 / 0.038508 (0.024775) \| 0.048068 / 0.023109 (0.024959) \| 0.239521 / 0.275898 (-0.036377) \| 0.265294 / 0.323480 (-0.058186) \| 0.003790 / 0.007986 (-0.004196) \| 0.002577 / 0.004328 (-0.001751) \| 0.048618 / 0.004250 (0.044368) \| 0.037427 / 0.037052 (0.000375) \| 0.245263 / 0.258489 (-0.013226) \| 0.276618 / 0.293841 (-0.017223) \| 0.026615 / 0.128546 (-0.101931) \| 0.010378 / 0.075646 (-0.065268) \| 0.205670 / 0.419271 (-0.213601) \| 0.035076 / 0.043533 (-0.008457) \| 0.245062 / 0.255139 (-0.010077) \| 0.264584 / 0.283200 (-0.018616) \| 0.017760 / 0.141683 (-0.123922) \| 1.148061 / 1.452155 (-0.304094) \| 1.192762 / 1.492716 (-0.299955) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090870 / 0.018006 (0.072864) \| 0.305458 / 0.000490 (0.304968) \| 0.000207 / 0.000200 (0.000007) \| 0.000052 / 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) \| 0.018597 / 0.037411 (-0.018814) \| 0.060349 / 0.014526 (0.045823) \| 0.074854 / 0.176557 (-0.101702) \| 0.123243 / 0.737135 (-0.613892) \| 0.075843 / 0.296338 (-0.220496) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275855 / 0.215209 (0.060645) \| 2.723965 / 2.077655 (0.646311) \| 1.436010 / 1.504120 (-0.068110) \| 1.323495 / 1.541195 (-0.217700) \| 1.356234 / 1.468490 (-0.112256) \| 0.564388 / 4.584777 (-4.020389) \| 2.390180 / 3.745712 (-1.355532) \| 2.782863 / 5.269862 (-2.486998) \| 1.765048 / 4.565676 (-2.800628) \| 0.062680 / 0.424275 (-0.361595) \| 0.004929 / 0.007607 (-0.002678) \| 0.337578 / 0.226044 (0.111533) \| 3.316780 / 2.268929 (1.047851) \| 1.803829 / 55.444624 (-53.640795) \| 1.524585 / 6.876477 (-5.351891) \| 1.549695 / 2.142072 (-0.592377) \| 0.638053 / 4.805227 (-4.167174) \| 0.116983 / 6.500664 (-6.383681) \| 0.042251 / 0.075469 (-0.033218) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.946978 / 1.841788 (-0.894810) \| 11.809483 / 8.074308 (3.735175) \| 10.459974 / 10.191392 (0.268582) \| 0.130015 / 0.680424 (-0.550409) \| 0.013843 / 0.534201 (-0.520358) \| 0.286972 / 0.579283 (-0.292311) \| 0.268904 / 0.434364 (-0.165460) \| 0.325591 / 0.540337 (-0.214746) \| 0.439233 / 1.386936 (-0.947703) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005804 / 0.011353 (-0.005549) \| 0.003431 / 0.011008 (-0.007577) \| 0.049041 / 0.038508 (0.010533) \| 0.054758 / 0.023109 (0.031649) \| 0.262330 / 0.275898 (-0.013568) \| 0.288872 / 0.323480 (-0.034608) \| 0.004016 / 0.007986 (-0.003970) \| 0.002606 / 0.004328 (-0.001722) \| 0.047878 / 0.004250 (0.043628) \| 0.045066 / 0.037052 (0.008013) \| 0.266310 / 0.258489 (0.007820) \| 0.290072 / 0.293841 (-0.003768) \| 0.028738 / 0.128546 (-0.099809) \| 0.010667 / 0.075646 (-0.064979) \| 0.057300 / 0.419271 (-0.361972) \| 0.032715 / 0.043533 (-0.010818) \| 0.264043 / 0.255139 (0.008904) \| 0.278652 / 0.283200 (-0.004547) \| 0.017873 / 0.141683 (-0.123810) \| 1.125981 / 1.452155 (-0.326174) \| 1.168548 / 1.492716 (-0.324168) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090997 / 0.018006 (0.072991) \| 0.300807 / 0.000490 (0.300317) \| 0.000223 / 0.000200 (0.000023) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021510 / 0.037411 (-0.015901) \| 0.068251 / 0.014526 (0.053725) \| 0.082073 / 0.176557 (-0.094484) \| 0.120071 / 0.737135 (-0.617064) \| 0.082245 / 0.296338 (-0.214093) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290601 / 0.215209 (0.075392) \| 2.871855 / 2.077655 (0.794200) \| 1.558239 / 1.504120 (0.054119) \| 1.447767 / 1.541195 (-0.093427) \| 1.446851 / 1.468490 (-0.021639) \| 0.573990 / 4.584777 (-4.010787) \| 2.439859 / 3.745712 (-1.305853) \| 2.795899 / 5.269862 (-2.473963) \| 1.746751 / 4.565676 (-2.818926) \| 0.062100 / 0.424275 (-0.362175) \| 0.004948 / 0.007607 (-0.002659) \| 0.344281 / 0.226044 (0.118236) \| 3.427499 / 2.268929 (1.158570) \| 1.940348 / 55.444624 (-53.504276) \| 1.660926 / 6.876477 (-5.215551) \| 1.669485 / 2.142072 (-0.472588) \| 0.634034 / 4.805227 (-4.171193) \| 0.114748 / 6.500664 (-6.385916) \| 0.041617 / 0.075469 (-0.033852) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.966411 / 1.841788 (-0.875376) \| 12.040753 / 8.074308 (3.966445) \| 10.506542 / 10.191392 (0.315150) \| 0.129659 / 0.680424 (-0.550764) \| 0.015691 / 0.534201 (-0.518510) \| 0.286911 / 0.579283 (-0.292372) \| 0.273588 / 0.434364 (-0.160776) \| 0.333642 / 0.540337 (-0.206695) \| 0.568550 / 1.386936 (-0.818386) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b38ed4705263df92ae06d89baab0932ae10065e0 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005023 / 0.011353 (-0.006330) \| 0.003492 / 0.011008 (-0.007516) \| 0.062808 / 0.038508 (0.024300) \| 0.051649 / 0.023109 (0.028540) \| 0.246871 / 0.275898 (-0.029027) \| 0.273430 / 0.323480 (-0.050050) \| 0.003851 / 0.007986 (-0.004135) \| 0.002643 / 0.004328 (-0.001686) \| 0.048499 / 0.004250 (0.044248) \| 0.037713 / 0.037052 (0.000661) \| 0.256431 / 0.258489 (-0.002058) \| 0.306956 / 0.293841 (0.013116) \| 0.027116 / 0.128546 (-0.101430) \| 0.010769 / 0.075646 (-0.064877) \| 0.206218 / 0.419271 (-0.213053) \| 0.035592 / 0.043533 (-0.007941) \| 0.249629 / 0.255139 (-0.005510) \| 0.268438 / 0.283200 (-0.014761) \| 0.018557 / 0.141683 (-0.123125) \| 1.123988 / 1.452155 (-0.328167) \| 1.158196 / 1.492716 (-0.334520) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090221 / 0.018006 (0.072215) \| 0.300892 / 0.000490 (0.300402) \| 0.000209 / 0.000200 (0.000009) \| 0.000046 / 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) \| 0.018305 / 0.037411 (-0.019106) \| 0.060294 / 0.014526 (0.045769) \| 0.073330 / 0.176557 (-0.103227) \| 0.119620 / 0.737135 (-0.617515) \| 0.074611 / 0.296338 (-0.221727) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285347 / 0.215209 (0.070138) \| 2.795144 / 2.077655 (0.717490) \| 1.468321 / 1.504120 (-0.035799) \| 1.343848 / 1.541195 (-0.197347) \| 1.388998 / 1.468490 (-0.079492) \| 0.559609 / 4.584777 (-4.025168) \| 2.355056 / 3.745712 (-1.390656) \| 2.798763 / 5.269862 (-2.471099) \| 1.764371 / 4.565676 (-2.801305) \| 0.062563 / 0.424275 (-0.361712) \| 0.005101 / 0.007607 (-0.002506) \| 0.339205 / 0.226044 (0.113161) \| 3.336729 / 2.268929 (1.067800) \| 1.801987 / 55.444624 (-53.642637) \| 1.526720 / 6.876477 (-5.349757) \| 1.539324 / 2.142072 (-0.602749) \| 0.635805 / 4.805227 (-4.169422) \| 0.138762 / 6.500664 (-6.361902) \| 0.042092 / 0.075469 (-0.033377) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.928755 / 1.841788 (-0.913032) \| 11.468224 / 8.074308 (3.393916) \| 10.784568 / 10.191392 (0.593176) \| 0.130332 / 0.680424 (-0.550092) \| 0.014203 / 0.534201 (-0.519998) \| 0.287125 / 0.579283 (-0.292158) \| 0.263921 / 0.434364 (-0.170443) \| 0.327824 / 0.540337 (-0.212513) \| 0.434679 / 1.386936 (-0.952257) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005194 / 0.011353 (-0.006159) \| 0.003411 / 0.011008 (-0.007598) \| 0.050122 / 0.038508 (0.011614) \| 0.049378 / 0.023109 (0.026269) \| 0.272980 / 0.275898 (-0.002918) \| 0.298047 / 0.323480 (-0.025433) \| 0.003945 / 0.007986 (-0.004041) \| 0.002633 / 0.004328 (-0.001696) \| 0.048935 / 0.004250 (0.044685) \| 0.040157 / 0.037052 (0.003104) \| 0.277056 / 0.258489 (0.018567) \| 0.299824 / 0.293841 (0.005983) \| 0.028997 / 0.128546 (-0.099550) \| 0.010868 / 0.075646 (-0.064779) \| 0.057895 / 0.419271 (-0.361377) \| 0.033522 / 0.043533 (-0.010010) \| 0.274912 / 0.255139 (0.019773) \| 0.288902 / 0.283200 (0.005702) \| 0.018016 / 0.141683 (-0.123667) \| 1.116669 / 1.452155 (-0.335485) \| 1.175007 / 1.492716 (-0.317710) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090169 / 0.018006 (0.072163) \| 0.310577 / 0.000490 (0.310087) \| 0.000215 / 0.000200 (0.000015) \| 0.000048 / 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) \| 0.020448 / 0.037411 (-0.016963) \| 0.068216 / 0.014526 (0.053690) \| 0.081798 / 0.176557 (-0.094759) \| 0.119151 / 0.737135 (-0.617985) \| 0.085197 / 0.296338 (-0.211142) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294957 / 0.215209 (0.079748) \| 2.874065 / 2.077655 (0.796410) \| 1.590963 / 1.504120 (0.086843) \| 1.459596 / 1.541195 (-0.081599) \| 1.467931 / 1.468490 (-0.000559) \| 0.562832 / 4.584777 (-4.021944) \| 2.426384 / 3.745712 (-1.319328) \| 2.767749 / 5.269862 (-2.502112) \| 1.746702 / 4.565676 (-2.818975) \| 0.063353 / 0.424275 (-0.360922) \| 0.005073 / 0.007607 (-0.002534) \| 0.348258 / 0.226044 (0.122213) \| 3.390351 / 2.268929 (1.121423) \| 1.950092 / 55.444624 (-53.494532) \| 1.671227 / 6.876477 (-5.205250) \| 1.683349 / 2.142072 (-0.458723) \| 0.637613 / 4.805227 (-4.167614) \| 0.115172 / 6.500664 (-6.385492) \| 0.040202 / 0.075469 (-0.035267) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.963085 / 1.841788 (-0.878702) \| 11.895384 / 8.074308 (3.821076) \| 10.609906 / 10.191392 (0.418513) \| 0.130865 / 0.680424 (-0.549559) \| 0.016020 / 0.534201 (-0.518181) \| 0.287540 / 0.579283 (-0.291743) \| 0.278204 / 0.434364 (-0.156160) \| 0.326007 / 0.540337 (-0.214330) \| 0.590881 / 1.386936 (-0.796055) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c291e330a7d460ff09d867377de1d4c53fd5394c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005266 / 0.011353 (-0.006087) \| 0.003751 / 0.011008 (-0.007257) \| 0.063835 / 0.038508 (0.025327) \| 0.052688 / 0.023109 (0.029579) \| 0.261957 / 0.275898 (-0.013941) \| 0.284264 / 0.323480 (-0.039216) \| 0.003958 / 0.007986 (-0.004027) \| 0.002696 / 0.004328 (-0.001633) \| 0.052791 / 0.004250 (0.048540) \| 0.038294 / 0.037052 (0.001242) \| 0.259488 / 0.258489 (0.000999) \| 0.298368 / 0.293841 (0.004528) \| 0.028309 / 0.128546 (-0.100237) \| 0.010819 / 0.075646 (-0.064827) \| 0.208221 / 0.419271 (-0.211050) \| 0.036373 / 0.043533 (-0.007160) \| 0.257000 / 0.255139 (0.001861) \| 0.273108 / 0.283200 (-0.010092) \| 0.019674 / 0.141683 (-0.122009) \| 1.119196 / 1.452155 (-0.332958) \| 1.161613 / 1.492716 (-0.331104) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093408 / 0.018006 (0.075401) \| 0.302278 / 0.000490 (0.301788) \| 0.000212 / 0.000200 (0.000012) \| 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) \| 0.019417 / 0.037411 (-0.017995) \| 0.060847 / 0.014526 (0.046321) \| 0.075399 / 0.176557 (-0.101158) \| 0.121233 / 0.737135 (-0.615902) \| 0.076916 / 0.296338 (-0.219422) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281265 / 0.215209 (0.066056) \| 2.651726 / 2.077655 (0.574072) \| 1.457726 / 1.504120 (-0.046394) \| 1.339250 / 1.541195 (-0.201945) \| 1.398529 / 1.468490 (-0.069961) \| 0.566574 / 4.584777 (-4.018203) \| 2.431576 / 3.745712 (-1.314136) \| 2.845884 / 5.269862 (-2.423977) \| 1.798051 / 4.565676 (-2.767626) \| 0.063619 / 0.424275 (-0.360656) \| 0.005286 / 0.007607 (-0.002321) \| 0.332834 / 0.226044 (0.106789) \| 3.293222 / 2.268929 (1.024293) \| 1.837810 / 55.444624 (-53.606815) \| 1.568511 / 6.876477 (-5.307966) \| 1.627518 / 2.142072 (-0.514555) \| 0.643520 / 4.805227 (-4.161708) \| 0.118482 / 6.500664 (-6.382182) \| 0.049563 / 0.075469 (-0.025906) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.947767 / 1.841788 (-0.894021) \| 11.994999 / 8.074308 (3.920691) \| 10.662651 / 10.191392 (0.471259) \| 0.142070 / 0.680424 (-0.538354) \| 0.014276 / 0.534201 (-0.519925) \| 0.288455 / 0.579283 (-0.290828) \| 0.266335 / 0.434364 (-0.168029) \| 0.328455 / 0.540337 (-0.211883) \| 0.440740 / 1.386936 (-0.946196) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005636 / 0.011353 (-0.005717) \| 0.003664 / 0.011008 (-0.007344) \| 0.050340 / 0.038508 (0.011832) \| 0.062795 / 0.023109 (0.039685) \| 0.280874 / 0.275898 (0.004976) \| 0.314056 / 0.323480 (-0.009424) \| 0.004089 / 0.007986 (-0.003897) \| 0.002780 / 0.004328 (-0.001548) \| 0.048468 / 0.004250 (0.044218) \| 0.042924 / 0.037052 (0.005871) \| 0.281381 / 0.258489 (0.022892) \| 0.308232 / 0.293841 (0.014391) \| 0.030294 / 0.128546 (-0.098252) \| 0.011098 / 0.075646 (-0.064548) \| 0.057535 / 0.419271 (-0.361736) \| 0.034217 / 0.043533 (-0.009316) \| 0.283022 / 0.255139 (0.027883) \| 0.298425 / 0.283200 (0.015225) \| 0.019285 / 0.141683 (-0.122398) \| 1.117722 / 1.452155 (-0.334433) \| 1.185878 / 1.492716 (-0.306839) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094915 / 0.018006 (0.076909) \| 0.311782 / 0.000490 (0.311293) \| 0.000217 / 0.000200 (0.000017) \| 0.000054 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022652 / 0.037411 (-0.014759) \| 0.069766 / 0.014526 (0.055240) \| 0.084495 / 0.176557 (-0.092061) \| 0.121295 / 0.737135 (-0.615841) \| 0.082447 / 0.296338 (-0.213891) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294286 / 0.215209 (0.079077) \| 2.863694 / 2.077655 (0.786039) \| 1.578338 / 1.504120 (0.074219) \| 1.478737 / 1.541195 (-0.062458) \| 1.528569 / 1.468490 (0.060079) \| 0.576944 / 4.584777 (-4.007833) \| 2.438730 / 3.745712 (-1.306982) \| 2.956138 / 5.269862 (-2.313723) \| 1.844484 / 4.565676 (-2.721192) \| 0.065980 / 0.424275 (-0.358295) \| 0.004998 / 0.007607 (-0.002609) \| 0.352063 / 0.226044 (0.126019) \| 3.456355 / 2.268929 (1.187426) \| 1.971582 / 55.444624 (-53.473042) \| 1.684536 / 6.876477 (-5.191940) \| 1.726823 / 2.142072 (-0.415250) \| 0.660235 / 4.805227 (-4.144992) \| 0.119029 / 6.500664 (-6.381635) \| 0.042497 / 0.075469 (-0.032972) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.971817 / 1.841788 (-0.869970) \| 12.900324 / 8.074308 (4.826015) \| 10.957495 / 10.191392 (0.766103) \| 0.133705 / 0.680424 (-0.546718) \| 0.015669 / 0.534201 (-0.518532) \| 0.287340 / 0.579283 (-0.291943) \| 0.280380 / 0.434364 (-0.153984) \| 0.330369 / 0.540337 (-0.209969) \| 0.581793 / 1.386936 (-0.805143) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c2af5efae1985499d6a0a1b6ab4120337eebf776 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005038 / 0.011353 (-0.006315) \| 0.003737 / 0.011008 (-0.007272) \| 0.063118 / 0.038508 (0.024610) \| 0.050120 / 0.023109 (0.027011) \| 0.240722 / 0.275898 (-0.035176) \| 0.263128 / 0.323480 (-0.060352) \| 0.003839 / 0.007986 (-0.004147) \| 0.002718 / 0.004328 (-0.001610) \| 0.047869 / 0.004250 (0.043618) \| 0.038092 / 0.037052 (0.001040) \| 0.245759 / 0.258489 (-0.012730) \| 0.277728 / 0.293841 (-0.016113) \| 0.027466 / 0.128546 (-0.101081) \| 0.011767 / 0.075646 (-0.063879) \| 0.205505 / 0.419271 (-0.213766) \| 0.035429 / 0.043533 (-0.008104) \| 0.241665 / 0.255139 (-0.013474) \| 0.260908 / 0.283200 (-0.022292) \| 0.017133 / 0.141683 (-0.124550) \| 1.107725 / 1.452155 (-0.344429) \| 1.169707 / 1.492716 (-0.323009) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094112 / 0.018006 (0.076106) \| 0.302596 / 0.000490 (0.302106) \| 0.000237 / 0.000200 (0.000037) \| 0.000041 / 0.000054 (-0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.017923 / 0.037411 (-0.019488) \| 0.060356 / 0.014526 (0.045830) \| 0.073708 / 0.176557 (-0.102849) \| 0.119952 / 0.737135 (-0.617183) \| 0.075350 / 0.296338 (-0.220989) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289253 / 0.215209 (0.074044) \| 2.800772 / 2.077655 (0.723117) \| 1.538368 / 1.504120 (0.034248) \| 1.401037 / 1.541195 (-0.140158) \| 1.427170 / 1.468490 (-0.041320) \| 0.560497 / 4.584777 (-4.024280) \| 2.417844 / 3.745712 (-1.327868) \| 2.798377 / 5.269862 (-2.471484) \| 1.756517 / 4.565676 (-2.809160) \| 0.063897 / 0.424275 (-0.360378) \| 0.005323 / 0.007607 (-0.002284) \| 0.339881 / 0.226044 (0.113836) \| 3.354858 / 2.268929 (1.085929) \| 1.877233 / 55.444624 (-53.567391) \| 1.578713 / 6.876477 (-5.297764) \| 1.631898 / 2.142072 (-0.510175) \| 0.640303 / 4.805227 (-4.164924) \| 0.116731 / 6.500664 (-6.383933) \| 0.041978 / 0.075469 (-0.033491) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.963259 / 1.841788 (-0.878529) \| 11.983646 / 8.074308 (3.909338) \| 10.561596 / 10.191392 (0.370204) \| 0.135863 / 0.680424 (-0.544561) \| 0.015607 / 0.534201 (-0.518594) \| 0.295164 / 0.579283 (-0.284119) \| 0.283366 / 0.434364 (-0.150998) \| 0.341848 / 0.540337 (-0.198489) \| 0.448359 / 1.386936 (-0.938577) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005366 / 0.011353 (-0.005987) \| 0.003621 / 0.011008 (-0.007387) \| 0.048615 / 0.038508 (0.010107) \| 0.053950 / 0.023109 (0.030841) \| 0.273112 / 0.275898 (-0.002786) \| 0.295655 / 0.323480 (-0.027825) \| 0.004066 / 0.007986 (-0.003920) \| 0.002700 / 0.004328 (-0.001628) \| 0.047899 / 0.004250 (0.043648) \| 0.041633 / 0.037052 (0.004581) \| 0.277760 / 0.258489 (0.019271) \| 0.302068 / 0.293841 (0.008227) \| 0.028879 / 0.128546 (-0.099668) \| 0.010756 / 0.075646 (-0.064891) \| 0.057190 / 0.419271 (-0.362082) \| 0.032555 / 0.043533 (-0.010978) \| 0.272045 / 0.255139 (0.016906) \| 0.289330 / 0.283200 (0.006130) \| 0.018466 / 0.141683 (-0.123216) \| 1.180435 / 1.452155 (-0.271720) \| 1.192228 / 1.492716 (-0.300488) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094871 / 0.018006 (0.076864) \| 0.302552 / 0.000490 (0.302062) \| 0.000224 / 0.000200 (0.000024) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022008 / 0.037411 (-0.015403) \| 0.068528 / 0.014526 (0.054002) \| 0.081735 / 0.176557 (-0.094821) \| 0.120990 / 0.737135 (-0.616145) \| 0.083155 / 0.296338 (-0.213184) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.305030 / 0.215209 (0.089821) \| 3.009812 / 2.077655 (0.932158) \| 1.677773 / 1.504120 (0.173654) \| 1.552280 / 1.541195 (0.011085) \| 1.606248 / 1.468490 (0.137758) \| 0.557093 / 4.584777 (-4.027684) \| 2.418292 / 3.745712 (-1.327420) \| 2.813049 / 5.269862 (-2.456813) \| 1.764507 / 4.565676 (-2.801169) \| 0.065089 / 0.424275 (-0.359186) \| 0.004944 / 0.007607 (-0.002663) \| 0.360672 / 0.226044 (0.134628) \| 3.525850 / 2.268929 (1.256921) \| 2.030091 / 55.444624 (-53.414533) \| 1.754669 / 6.876477 (-5.121807) \| 1.772673 / 2.142072 (-0.369399) \| 0.642904 / 4.805227 (-4.162324) \| 0.116018 / 6.500664 (-6.384646) \| 0.041308 / 0.075469 (-0.034161) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.986386 / 1.841788 (-0.855401) \| 12.291623 / 8.074308 (4.217315) \| 10.655932 / 10.191392 (0.464540) \| 0.141736 / 0.680424 (-0.538688) \| 0.016669 / 0.534201 (-0.517532) \| 0.286875 / 0.579283 (-0.292408) \| 0.281898 / 0.434364 (-0.152466) \| 0.325206 / 0.540337 (-0.215132) \| 0.577607 / 1.386936 (-0.809329) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1cf33502493fb9760ea8cc8e51622bf94d0c9e31 \"CML watermark\")\n", "Alright tests are passing (except one on temp dir cleanup windows but I don't think it's related to this PR ?)\r\n\r\n```\r\nFAILED tests/test_load.py::test_loading_from_the_datasets_hub - NotADirectoryError: [WinError 267] The directory name is invalid: 'C:\\\\Users\\\\RUNNER~1\\\\AppData\\\\Local\\\\Temp\\\\tmpqy3f2ft_\\\\hf-internal-testing___dataset_with_script\\\\default\\\\0.0.0\\\\c240e2be3370bdbd\\\\dataset_with_script-train.arrow'\r\n```", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005072 / 0.011353 (-0.006281) \| 0.003449 / 0.011008 (-0.007559) \| 0.062630 / 0.038508 (0.024122) \| 0.054276 / 0.023109 (0.031167) \| 0.253345 / 0.275898 (-0.022553) \| 0.273460 / 0.323480 (-0.050020) \| 0.003859 / 0.007986 (-0.004127) \| 0.002646 / 0.004328 (-0.001683) \| 0.048289 / 0.004250 (0.044038) \| 0.037943 / 0.037052 (0.000891) \| 0.256569 / 0.258489 (-0.001920) \| 0.287809 / 0.293841 (-0.006032) \| 0.027675 / 0.128546 (-0.100872) \| 0.010554 / 0.075646 (-0.065092) \| 0.205157 / 0.419271 (-0.214115) \| 0.035464 / 0.043533 (-0.008069) \| 0.254300 / 0.255139 (-0.000839) \| 0.272907 / 0.283200 (-0.010292) \| 0.018146 / 0.141683 (-0.123537) \| 1.110528 / 1.452155 (-0.341626) \| 1.170156 / 1.492716 (-0.322560) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093151 / 0.018006 (0.075144) \| 0.302087 / 0.000490 (0.301598) \| 0.000216 / 0.000200 (0.000016) \| 0.000042 / 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) \| 0.018744 / 0.037411 (-0.018667) \| 0.059843 / 0.014526 (0.045317) \| 0.073165 / 0.176557 (-0.103391) \| 0.120464 / 0.737135 (-0.616671) \| 0.074992 / 0.296338 (-0.221347) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285103 / 0.215209 (0.069894) \| 2.820254 / 2.077655 (0.742600) \| 1.505336 / 1.504120 (0.001216) \| 1.368631 / 1.541195 (-0.172564) \| 1.404140 / 1.468490 (-0.064350) \| 0.563906 / 4.584777 (-4.020871) \| 2.411871 / 3.745712 (-1.333841) \| 2.788390 / 5.269862 (-2.481471) \| 1.749788 / 4.565676 (-2.815888) \| 0.062171 / 0.424275 (-0.362104) \| 0.004918 / 0.007607 (-0.002689) \| 0.339615 / 0.226044 (0.113571) \| 3.337789 / 2.268929 (1.068861) \| 1.808445 / 55.444624 (-53.636180) \| 1.541015 / 6.876477 (-5.335462) \| 1.572389 / 2.142072 (-0.569683) \| 0.641739 / 4.805227 (-4.163488) \| 0.115844 / 6.500664 (-6.384820) \| 0.042504 / 0.075469 (-0.032965) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.942463 / 1.841788 (-0.899325) \| 11.602364 / 8.074308 (3.528056) \| 10.628921 / 10.191392 (0.437529) \| 0.136154 / 0.680424 (-0.544270) \| 0.013842 / 0.534201 (-0.520359) \| 0.287085 / 0.579283 (-0.292198) \| 0.269860 / 0.434364 (-0.164503) \| 0.329525 / 0.540337 (-0.210812) \| 0.441287 / 1.386936 (-0.945649) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005215 / 0.011353 (-0.006138) \| 0.003549 / 0.011008 (-0.007460) \| 0.049199 / 0.038508 (0.010691) \| 0.051655 / 0.023109 (0.028545) \| 0.272150 / 0.275898 (-0.003748) \| 0.291978 / 0.323480 (-0.031502) \| 0.003985 / 0.007986 (-0.004001) \| 0.002668 / 0.004328 (-0.001661) \| 0.048524 / 0.004250 (0.044274) \| 0.039824 / 0.037052 (0.002772) \| 0.275566 / 0.258489 (0.017077) \| 0.298076 / 0.293841 (0.004235) \| 0.029508 / 0.128546 (-0.099038) \| 0.010673 / 0.075646 (-0.064973) \| 0.057327 / 0.419271 (-0.361944) \| 0.032590 / 0.043533 (-0.010943) \| 0.273295 / 0.255139 (0.018156) \| 0.289127 / 0.283200 (0.005928) \| 0.017694 / 0.141683 (-0.123989) \| 1.134502 / 1.452155 (-0.317653) \| 1.185603 / 1.492716 (-0.307114) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098403 / 0.018006 (0.080396) \| 0.302735 / 0.000490 (0.302245) \| 0.000228 / 0.000200 (0.000028) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025192 / 0.037411 (-0.012219) \| 0.068149 / 0.014526 (0.053623) \| 0.082220 / 0.176557 (-0.094336) \| 0.119491 / 0.737135 (-0.617645) \| 0.082484 / 0.296338 (-0.213855) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295339 / 0.215209 (0.080130) \| 2.868411 / 2.077655 (0.790757) \| 1.590665 / 1.504120 (0.086545) \| 1.465995 / 1.541195 (-0.075200) \| 1.489205 / 1.468490 (0.020715) \| 0.562503 / 4.584777 (-4.022274) \| 2.480100 / 3.745712 (-1.265613) \| 2.774216 / 5.269862 (-2.495646) \| 1.733129 / 4.565676 (-2.832548) \| 0.062698 / 0.424275 (-0.361577) \| 0.004910 / 0.007607 (-0.002697) \| 0.354766 / 0.226044 (0.128722) \| 3.435541 / 2.268929 (1.166613) \| 1.953357 / 55.444624 (-53.491267) \| 1.673584 / 6.876477 (-5.202893) \| 1.677749 / 2.142072 (-0.464323) \| 0.632601 / 4.805227 (-4.172626) \| 0.114875 / 6.500664 (-6.385789) \| 0.040577 / 0.075469 (-0.034892) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.967003 / 1.841788 (-0.874785) \| 11.964490 / 8.074308 (3.890181) \| 10.493812 / 10.191392 (0.302420) \| 0.132177 / 0.680424 (-0.548247) \| 0.015149 / 0.534201 (-0.519052) \| 0.289011 / 0.579283 (-0.290272) \| 0.285479 / 0.434364 (-0.148885) \| 0.327090 / 0.540337 (-0.213248) \| 0.571747 / 1.386936 (-0.815189) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4c9b4cb7ee4720415261216d72051e2a3320fe41 \"CML watermark\")\n" ]
2,008,195,298	6,447	Support one dataset loader per config when using YAML	open	2023-11-23T13:03:07	2023-11-23T13:03:07	null	https://github.com/huggingface/datasets/issues/6447	null	severo	false	[]
2,007,092,708	6,446	Speech Commands v2 dataset doesn't match AST-v2 config	closed	2023-11-22T20:46:36	2023-11-28T14:46:08	2023-11-28T14:46:08	https://github.com/huggingface/datasets/issues/6446	null	vymao	false	[ "You can use `.align_labels_with_mapping` on the dataset to align the labels with the model config.\r\n\r\nRegarding the number of labels, only the special `_silence_` label corresponding to noise is missing, which is consistent with the model paper (reports training on 35 labels). You can run a `.filter` to drop it.\r\n\r\nPS: You should create a discussion on a model/dataset repo (on the Hub) for these kinds of questions", "Thanks, will keep that in mind. But I tried running `dataset_aligned = dataset.align_labels_with_mapping(model.config.id2label, 'label')`, and received this error: \r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/Users/victor/anaconda3/envs/transformers-v2/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 5928, in align_labels_with_mapping\r\n label2id = {k.lower(): v for k, v in label2id.items()}\r\n File \"/Users/victor/anaconda3/envs/transformers-v2/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 5928, in <dictcomp>\r\n label2id = {k.lower(): v for k, v in label2id.items()}\r\nAttributeError: 'int' object has no attribute 'lower'\r\n```\r\nMy guess is that the dataset `label` column is purely an int ID, and I'm not sure there's a way to identify which class label the ID belongs to in the dataset easily.", "Replacing `model.config.id2label` with `model.config.label2id` should fix the issue.\r\n\r\nSo, the full code to align the labels with the model config is as follows:\r\n```python\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoFeatureExtractor, AutoModelForAudioClassification\r\n\r\n# extractor = AutoFeatureExtractor.from_pretrained(\"MIT/ast-finetuned-speech-commands-v2\")\r\nmodel = AutoModelForAudioClassification.from_pretrained(\"MIT/ast-finetuned-speech-commands-v2\")\r\n\r\nds = load_dataset(\"speech_commands\", \"v0.02\")\r\nds = ds.filter(lambda label: label != ds[\"train\"].features[\"label\"].str2int(\"_silence_\"), input_columns=\"label\")\r\nds = ds.align_labels_with_mapping(model.config.label2id, \"label\")\r\n```" ]
2,006,958,595	6,445	Use `filelock` package for file locking	closed	2023-11-22T19:04:45	2023-11-23T18:47:30	2023-11-23T18:41:23	https://github.com/huggingface/datasets/pull/6445	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6445", "html_url": "https://github.com/huggingface/datasets/pull/6445", "diff_url": "https://github.com/huggingface/datasets/pull/6445.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6445.patch", "merged_at": "2023-11-23T18:41:22" }	mariosasko	true	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005431 / 0.011353 (-0.005922) \| 0.003255 / 0.011008 (-0.007753) \| 0.062867 / 0.038508 (0.024359) \| 0.051917 / 0.023109 (0.028808) \| 0.254229 / 0.275898 (-0.021669) \| 0.276949 / 0.323480 (-0.046531) \| 0.002868 / 0.007986 (-0.005117) \| 0.002539 / 0.004328 (-0.001789) \| 0.048366 / 0.004250 (0.044115) \| 0.038497 / 0.037052 (0.001445) \| 0.252158 / 0.258489 (-0.006332) \| 0.288868 / 0.293841 (-0.004973) \| 0.027956 / 0.128546 (-0.100591) \| 0.010500 / 0.075646 (-0.065147) \| 0.209263 / 0.419271 (-0.210008) \| 0.035415 / 0.043533 (-0.008118) \| 0.253104 / 0.255139 (-0.002035) \| 0.274646 / 0.283200 (-0.008554) \| 0.019923 / 0.141683 (-0.121760) \| 1.081870 / 1.452155 (-0.370285) \| 1.157159 / 1.492716 (-0.335557) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097420 / 0.018006 (0.079414) \| 0.315021 / 0.000490 (0.314531) \| 0.000218 / 0.000200 (0.000018) \| 0.000049 / 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) \| 0.018826 / 0.037411 (-0.018585) \| 0.061921 / 0.014526 (0.047395) \| 0.086825 / 0.176557 (-0.089731) \| 0.120606 / 0.737135 (-0.616529) \| 0.074344 / 0.296338 (-0.221994) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283238 / 0.215209 (0.068028) \| 2.771817 / 2.077655 (0.694162) \| 1.500194 / 1.504120 (-0.003926) \| 1.379286 / 1.541195 (-0.161908) \| 1.447747 / 1.468490 (-0.020743) \| 0.587176 / 4.584777 (-3.997601) \| 2.411260 / 3.745712 (-1.334452) \| 2.897682 / 5.269862 (-2.372180) \| 1.821720 / 4.565676 (-2.743957) \| 0.063299 / 0.424275 (-0.360976) \| 0.004969 / 0.007607 (-0.002639) \| 0.346417 / 0.226044 (0.120373) \| 3.432936 / 2.268929 (1.164007) \| 1.898662 / 55.444624 (-53.545963) \| 1.624339 / 6.876477 (-5.252138) \| 1.641653 / 2.142072 (-0.500419) \| 0.655773 / 4.805227 (-4.149454) \| 0.118588 / 6.500664 (-6.382076) \| 0.043919 / 0.075469 (-0.031551) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.949466 / 1.841788 (-0.892322) \| 12.378025 / 8.074308 (4.303717) \| 10.750942 / 10.191392 (0.559550) \| 0.146575 / 0.680424 (-0.533849) \| 0.015453 / 0.534201 (-0.518748) \| 0.290608 / 0.579283 (-0.288676) \| 0.273000 / 0.434364 (-0.161364) \| 0.328019 / 0.540337 (-0.212318) \| 0.417396 / 1.386936 (-0.969540) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005363 / 0.011353 (-0.005990) \| 0.003421 / 0.011008 (-0.007587) \| 0.049429 / 0.038508 (0.010920) \| 0.052774 / 0.023109 (0.029664) \| 0.274058 / 0.275898 (-0.001840) \| 0.297307 / 0.323480 (-0.026173) \| 0.004000 / 0.007986 (-0.003986) \| 0.002463 / 0.004328 (-0.001866) \| 0.048824 / 0.004250 (0.044574) \| 0.041064 / 0.037052 (0.004012) \| 0.279066 / 0.258489 (0.020577) \| 0.302420 / 0.293841 (0.008579) \| 0.029665 / 0.128546 (-0.098881) \| 0.010628 / 0.075646 (-0.065018) \| 0.057678 / 0.419271 (-0.361594) \| 0.032731 / 0.043533 (-0.010802) \| 0.274662 / 0.255139 (0.019523) \| 0.291878 / 0.283200 (0.008678) \| 0.018820 / 0.141683 (-0.122863) \| 1.124042 / 1.452155 (-0.328112) \| 1.175020 / 1.492716 (-0.317697) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.099419 / 0.018006 (0.081413) \| 0.311511 / 0.000490 (0.311022) \| 0.000228 / 0.000200 (0.000028) \| 0.000051 / 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) \| 0.022478 / 0.037411 (-0.014933) \| 0.071955 / 0.014526 (0.057429) \| 0.081423 / 0.176557 (-0.095134) \| 0.119574 / 0.737135 (-0.617561) \| 0.084724 / 0.296338 (-0.211615) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295537 / 0.215209 (0.080328) \| 2.893855 / 2.077655 (0.816201) \| 1.602065 / 1.504120 (0.097945) \| 1.478193 / 1.541195 (-0.063002) \| 1.508250 / 1.468490 (0.039760) \| 0.566140 / 4.584777 (-4.018637) \| 2.455474 / 3.745712 (-1.290238) \| 2.849525 / 5.269862 (-2.420337) \| 1.763830 / 4.565676 (-2.801846) \| 0.062375 / 0.424275 (-0.361900) \| 0.004992 / 0.007607 (-0.002615) \| 0.346068 / 0.226044 (0.120023) \| 3.452421 / 2.268929 (1.183492) \| 1.970346 / 55.444624 (-53.474278) \| 1.690865 / 6.876477 (-5.185612) \| 1.705358 / 2.142072 (-0.436714) \| 0.644261 / 4.805227 (-4.160967) \| 0.120596 / 6.500664 (-6.380068) \| 0.042699 / 0.075469 (-0.032770) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.980506 / 1.841788 (-0.861281) \| 12.401901 / 8.074308 (4.327593) \| 11.169413 / 10.191392 (0.978021) \| 0.142540 / 0.680424 (-0.537884) \| 0.015730 / 0.534201 (-0.518471) \| 0.288871 / 0.579283 (-0.290412) \| 0.287487 / 0.434364 (-0.146877) \| 0.325133 / 0.540337 (-0.215204) \| 0.417979 / 1.386936 (-0.968957) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#965685891db0d06979490aaebab72d5dc628e42b \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005062 / 0.011353 (-0.006291) \| 0.003024 / 0.011008 (-0.007984) \| 0.061801 / 0.038508 (0.023293) \| 0.048934 / 0.023109 (0.025825) \| 0.248024 / 0.275898 (-0.027874) \| 0.265665 / 0.323480 (-0.057815) \| 0.003885 / 0.007986 (-0.004100) \| 0.002371 / 0.004328 (-0.001957) \| 0.047895 / 0.004250 (0.043644) \| 0.039015 / 0.037052 (0.001963) \| 0.252320 / 0.258489 (-0.006169) \| 0.286533 / 0.293841 (-0.007308) \| 0.027694 / 0.128546 (-0.100852) \| 0.010254 / 0.075646 (-0.065392) \| 0.206586 / 0.419271 (-0.212685) \| 0.035681 / 0.043533 (-0.007852) \| 0.251645 / 0.255139 (-0.003494) \| 0.285462 / 0.283200 (0.002262) \| 0.017326 / 0.141683 (-0.124357) \| 1.086927 / 1.452155 (-0.365228) \| 1.153172 / 1.492716 (-0.339545) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093020 / 0.018006 (0.075014) \| 0.300018 / 0.000490 (0.299528) \| 0.000208 / 0.000200 (0.000008) \| 0.000047 / 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) \| 0.018828 / 0.037411 (-0.018584) \| 0.062569 / 0.014526 (0.048043) \| 0.074130 / 0.176557 (-0.102427) \| 0.119304 / 0.737135 (-0.617832) \| 0.076409 / 0.296338 (-0.219930) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285938 / 0.215209 (0.070729) \| 2.780662 / 2.077655 (0.703007) \| 1.522401 / 1.504120 (0.018281) \| 1.392475 / 1.541195 (-0.148720) \| 1.412517 / 1.468490 (-0.055973) \| 0.562768 / 4.584777 (-4.022009) \| 2.421406 / 3.745712 (-1.324306) \| 2.786271 / 5.269862 (-2.483591) \| 1.737193 / 4.565676 (-2.828484) \| 0.062775 / 0.424275 (-0.361500) \| 0.004908 / 0.007607 (-0.002699) \| 0.345070 / 0.226044 (0.119026) \| 3.383700 / 2.268929 (1.114771) \| 1.795974 / 55.444624 (-53.648651) \| 1.527656 / 6.876477 (-5.348820) \| 1.514035 / 2.142072 (-0.628037) \| 0.647652 / 4.805227 (-4.157575) \| 0.120121 / 6.500664 (-6.380543) \| 0.042259 / 0.075469 (-0.033210) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.948951 / 1.841788 (-0.892837) \| 11.514971 / 8.074308 (3.440663) \| 10.722668 / 10.191392 (0.531276) \| 0.143034 / 0.680424 (-0.537390) \| 0.014800 / 0.534201 (-0.519401) \| 0.286189 / 0.579283 (-0.293094) \| 0.270735 / 0.434364 (-0.163629) \| 0.323907 / 0.540337 (-0.216430) \| 0.417569 / 1.386936 (-0.969367) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005670 / 0.011353 (-0.005683) \| 0.003238 / 0.011008 (-0.007770) \| 0.048520 / 0.038508 (0.010012) \| 0.051341 / 0.023109 (0.028232) \| 0.273883 / 0.275898 (-0.002015) \| 0.295165 / 0.323480 (-0.028315) \| 0.004755 / 0.007986 (-0.003231) \| 0.002471 / 0.004328 (-0.001857) \| 0.047487 / 0.004250 (0.043237) \| 0.040225 / 0.037052 (0.003172) \| 0.276758 / 0.258489 (0.018269) \| 0.301182 / 0.293841 (0.007341) \| 0.029749 / 0.128546 (-0.098797) \| 0.010340 / 0.075646 (-0.065306) \| 0.057193 / 0.419271 (-0.362079) \| 0.033067 / 0.043533 (-0.010466) \| 0.272716 / 0.255139 (0.017577) \| 0.292301 / 0.283200 (0.009101) \| 0.019075 / 0.141683 (-0.122608) \| 1.101778 / 1.452155 (-0.350376) \| 1.173573 / 1.492716 (-0.319143) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091008 / 0.018006 (0.073002) \| 0.300749 / 0.000490 (0.300259) \| 0.000218 / 0.000200 (0.000018) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021760 / 0.037411 (-0.015651) \| 0.071407 / 0.014526 (0.056881) \| 0.081151 / 0.176557 (-0.095406) \| 0.120140 / 0.737135 (-0.616995) \| 0.082408 / 0.296338 (-0.213931) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294828 / 0.215209 (0.079619) \| 2.880701 / 2.077655 (0.803047) \| 1.604187 / 1.504120 (0.100068) \| 1.479236 / 1.541195 (-0.061959) \| 1.498875 / 1.468490 (0.030385) \| 0.561950 / 4.584777 (-4.022827) \| 2.462531 / 3.745712 (-1.283181) \| 2.800905 / 5.269862 (-2.468957) \| 1.746535 / 4.565676 (-2.819141) \| 0.062732 / 0.424275 (-0.361544) \| 0.004932 / 0.007607 (-0.002675) \| 0.347125 / 0.226044 (0.121081) \| 3.431343 / 2.268929 (1.162415) \| 1.964999 / 55.444624 (-53.479625) \| 1.669709 / 6.876477 (-5.206768) \| 1.675148 / 2.142072 (-0.466924) \| 0.635436 / 4.805227 (-4.169792) \| 0.116598 / 6.500664 (-6.384066) \| 0.041447 / 0.075469 (-0.034022) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.975751 / 1.841788 (-0.866037) \| 12.060246 / 8.074308 (3.985938) \| 10.871641 / 10.191392 (0.680249) \| 0.142936 / 0.680424 (-0.537488) \| 0.015779 / 0.534201 (-0.518422) \| 0.287120 / 0.579283 (-0.292163) \| 0.283963 / 0.434364 (-0.150401) \| 0.341231 / 0.540337 (-0.199107) \| 0.419518 / 1.386936 (-0.967418) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0943ff0072dcef473530d8a494f314048f3a3d51 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005105 / 0.011353 (-0.006248) \| 0.002855 / 0.011008 (-0.008153) \| 0.062044 / 0.038508 (0.023536) \| 0.052948 / 0.023109 (0.029839) \| 0.249841 / 0.275898 (-0.026057) \| 0.276687 / 0.323480 (-0.046792) \| 0.003792 / 0.007986 (-0.004194) \| 0.002385 / 0.004328 (-0.001943) \| 0.048648 / 0.004250 (0.044398) \| 0.038317 / 0.037052 (0.001264) \| 0.255235 / 0.258489 (-0.003254) \| 0.287870 / 0.293841 (-0.005971) \| 0.027429 / 0.128546 (-0.101117) \| 0.010182 / 0.075646 (-0.065464) \| 0.206980 / 0.419271 (-0.212291) \| 0.035444 / 0.043533 (-0.008089) \| 0.255073 / 0.255139 (-0.000066) \| 0.270636 / 0.283200 (-0.012563) \| 0.018003 / 0.141683 (-0.123680) \| 1.124691 / 1.452155 (-0.327463) \| 1.191872 / 1.492716 (-0.300844) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088824 / 0.018006 (0.070818) \| 0.302771 / 0.000490 (0.302281) \| 0.000210 / 0.000200 (0.000010) \| 0.000048 / 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) \| 0.018102 / 0.037411 (-0.019310) \| 0.062131 / 0.014526 (0.047605) \| 0.073230 / 0.176557 (-0.103327) \| 0.119789 / 0.737135 (-0.617346) \| 0.074804 / 0.296338 (-0.221534) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293244 / 0.215209 (0.078035) \| 2.891401 / 2.077655 (0.813746) \| 1.504481 / 1.504120 (0.000361) \| 1.381251 / 1.541195 (-0.159944) \| 1.387245 / 1.468490 (-0.081245) \| 0.552732 / 4.584777 (-4.032045) \| 2.386439 / 3.745712 (-1.359273) \| 2.718918 / 5.269862 (-2.550944) \| 1.725401 / 4.565676 (-2.840275) \| 0.061946 / 0.424275 (-0.362329) \| 0.004957 / 0.007607 (-0.002650) \| 0.342776 / 0.226044 (0.116731) \| 3.418911 / 2.268929 (1.149983) \| 1.838283 / 55.444624 (-53.606341) \| 1.538013 / 6.876477 (-5.338464) \| 1.545144 / 2.142072 (-0.596928) \| 0.637857 / 4.805227 (-4.167370) \| 0.116451 / 6.500664 (-6.384213) \| 0.042228 / 0.075469 (-0.033241) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.943575 / 1.841788 (-0.898212) \| 11.492939 / 8.074308 (3.418631) \| 10.601605 / 10.191392 (0.410212) \| 0.139084 / 0.680424 (-0.541340) \| 0.013691 / 0.534201 (-0.520510) \| 0.286696 / 0.579283 (-0.292587) \| 0.259979 / 0.434364 (-0.174385) \| 0.322578 / 0.540337 (-0.217759) \| 0.411950 / 1.386936 (-0.974986) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005168 / 0.011353 (-0.006185) \| 0.003238 / 0.011008 (-0.007770) \| 0.049028 / 0.038508 (0.010520) \| 0.052930 / 0.023109 (0.029821) \| 0.274750 / 0.275898 (-0.001148) \| 0.294023 / 0.323480 (-0.029457) \| 0.003829 / 0.007986 (-0.004157) \| 0.002372 / 0.004328 (-0.001956) \| 0.048689 / 0.004250 (0.044439) \| 0.040056 / 0.037052 (0.003003) \| 0.280147 / 0.258489 (0.021658) \| 0.304871 / 0.293841 (0.011030) \| 0.028734 / 0.128546 (-0.099812) \| 0.010624 / 0.075646 (-0.065022) \| 0.058705 / 0.419271 (-0.360566) \| 0.032140 / 0.043533 (-0.011393) \| 0.276702 / 0.255139 (0.021563) \| 0.293186 / 0.283200 (0.009987) \| 0.018124 / 0.141683 (-0.123559) \| 1.139398 / 1.452155 (-0.312757) \| 1.174862 / 1.492716 (-0.317855) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.087627 / 0.018006 (0.069620) \| 0.298376 / 0.000490 (0.297886) \| 0.000238 / 0.000200 (0.000038) \| 0.000052 / 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) \| 0.021344 / 0.037411 (-0.016067) \| 0.070208 / 0.014526 (0.055682) \| 0.081177 / 0.176557 (-0.095380) \| 0.120170 / 0.737135 (-0.616965) \| 0.082472 / 0.296338 (-0.213866) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293227 / 0.215209 (0.078018) \| 2.844619 / 2.077655 (0.766964) \| 1.586922 / 1.504120 (0.082803) \| 1.460256 / 1.541195 (-0.080938) \| 1.475955 / 1.468490 (0.007465) \| 0.553226 / 4.584777 (-4.031551) \| 2.418869 / 3.745712 (-1.326843) \| 2.709256 / 5.269862 (-2.560606) \| 1.705935 / 4.565676 (-2.859741) \| 0.062391 / 0.424275 (-0.361884) \| 0.004929 / 0.007607 (-0.002678) \| 0.350358 / 0.226044 (0.124313) \| 3.448824 / 2.268929 (1.179896) \| 1.929451 / 55.444624 (-53.515174) \| 1.669438 / 6.876477 (-5.207038) \| 1.660923 / 2.142072 (-0.481150) \| 0.633107 / 4.805227 (-4.172120) \| 0.114657 / 6.500664 (-6.386007) \| 0.041256 / 0.075469 (-0.034214) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.968408 / 1.841788 (-0.873380) \| 11.749754 / 8.074308 (3.675446) \| 10.796670 / 10.191392 (0.605278) \| 0.128881 / 0.680424 (-0.551543) \| 0.015326 / 0.534201 (-0.518875) \| 0.286407 / 0.579283 (-0.292876) \| 0.276324 / 0.434364 (-0.158040) \| 0.326201 / 0.540337 (-0.214136) \| 0.419854 / 1.386936 (-0.967082) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1731d5a8cd103533ef6b438b4429ab51d3a6a0ce \"CML watermark\")\n" ]
2,006,842,179	6,444	Remove `Table.__getstate__` and `Table.__setstate__`	closed	2023-11-22T17:55:10	2023-11-23T15:19:43	2023-11-23T15:13:28	https://github.com/huggingface/datasets/pull/6444	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6444", "html_url": "https://github.com/huggingface/datasets/pull/6444", "diff_url": "https://github.com/huggingface/datasets/pull/6444.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6444.patch", "merged_at": "2023-11-23T15:13:28" }	LZHgrla	true	[ "Thanks for working on this! The [issue](https://bugs.python.org/issue24658) with pickling objects larger than 4GB seems to be patched in Python 3.8 (the minimal supported version was 3.6 at the time of implementing this), so a simple solution would be removing the `Table.__setstate__` and `Table.__getstate__` overrides.", "@mariosasko \r\nCool!\r\nI removed these overrides, and it worked.\r\n\r\nAll modifications are committed. Ready for review!", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005251 / 0.011353 (-0.006102) \| 0.003804 / 0.011008 (-0.007204) \| 0.063143 / 0.038508 (0.024635) \| 0.059409 / 0.023109 (0.036300) \| 0.255319 / 0.275898 (-0.020579) \| 0.279194 / 0.323480 (-0.044285) \| 0.004643 / 0.007986 (-0.003343) \| 0.002560 / 0.004328 (-0.001768) \| 0.047490 / 0.004250 (0.043240) \| 0.039034 / 0.037052 (0.001982) \| 0.257352 / 0.258489 (-0.001137) \| 0.293029 / 0.293841 (-0.000812) \| 0.027548 / 0.128546 (-0.100998) \| 0.011307 / 0.075646 (-0.064339) \| 0.210325 / 0.419271 (-0.208946) \| 0.035161 / 0.043533 (-0.008372) \| 0.253491 / 0.255139 (-0.001648) \| 0.272085 / 0.283200 (-0.011115) \| 0.018924 / 0.141683 (-0.122759) \| 1.111148 / 1.452155 (-0.341007) \| 1.178076 / 1.492716 (-0.314641) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092447 / 0.018006 (0.074441) \| 0.303680 / 0.000490 (0.303190) \| 0.000208 / 0.000200 (0.000008) \| 0.000051 / 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) \| 0.019087 / 0.037411 (-0.018325) \| 0.062663 / 0.014526 (0.048137) \| 0.074651 / 0.176557 (-0.101905) \| 0.121334 / 0.737135 (-0.615802) \| 0.076703 / 0.296338 (-0.219636) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286505 / 0.215209 (0.071295) \| 2.804942 / 2.077655 (0.727287) \| 1.481930 / 1.504120 (-0.022190) \| 1.369485 / 1.541195 (-0.171710) \| 1.424467 / 1.468490 (-0.044023) \| 0.556810 / 4.584777 (-4.027967) \| 2.416338 / 3.745712 (-1.329374) \| 2.901869 / 5.269862 (-2.367992) \| 1.827007 / 4.565676 (-2.738669) \| 0.062252 / 0.424275 (-0.362024) \| 0.005076 / 0.007607 (-0.002531) \| 0.343850 / 0.226044 (0.117805) \| 3.377611 / 2.268929 (1.108683) \| 1.860214 / 55.444624 (-53.584410) \| 1.595146 / 6.876477 (-5.281331) \| 1.627234 / 2.142072 (-0.514838) \| 0.651027 / 4.805227 (-4.154200) \| 0.119214 / 6.500664 (-6.381450) \| 0.043342 / 0.075469 (-0.032127) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.942863 / 1.841788 (-0.898924) \| 12.484633 / 8.074308 (4.410324) \| 10.560668 / 10.191392 (0.369276) \| 0.144647 / 0.680424 (-0.535777) \| 0.014734 / 0.534201 (-0.519466) \| 0.286575 / 0.579283 (-0.292708) \| 0.270913 / 0.434364 (-0.163451) \| 0.323792 / 0.540337 (-0.216545) \| 0.419186 / 1.386936 (-0.967750) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005315 / 0.011353 (-0.006038) \| 0.003548 / 0.011008 (-0.007460) \| 0.049271 / 0.038508 (0.010763) \| 0.055198 / 0.023109 (0.032089) \| 0.275940 / 0.275898 (0.000042) \| 0.307637 / 0.323480 (-0.015843) \| 0.003997 / 0.007986 (-0.003988) \| 0.002544 / 0.004328 (-0.001785) \| 0.050381 / 0.004250 (0.046130) \| 0.041158 / 0.037052 (0.004105) \| 0.281519 / 0.258489 (0.023030) \| 0.308085 / 0.293841 (0.014244) \| 0.030464 / 0.128546 (-0.098083) \| 0.010690 / 0.075646 (-0.064957) \| 0.057458 / 0.419271 (-0.361814) \| 0.032814 / 0.043533 (-0.010719) \| 0.282435 / 0.255139 (0.027296) \| 0.301342 / 0.283200 (0.018142) \| 0.017556 / 0.141683 (-0.124127) \| 1.159423 / 1.452155 (-0.292732) \| 1.177344 / 1.492716 (-0.315372) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091086 / 0.018006 (0.073079) \| 0.305316 / 0.000490 (0.304826) \| 0.000218 / 0.000200 (0.000019) \| 0.000054 / 0.000054 (-0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021832 / 0.037411 (-0.015579) \| 0.071055 / 0.014526 (0.056529) \| 0.082982 / 0.176557 (-0.093574) \| 0.119966 / 0.737135 (-0.617169) \| 0.083539 / 0.296338 (-0.212800) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302501 / 0.215209 (0.087292) \| 2.936347 / 2.077655 (0.858692) \| 1.601658 / 1.504120 (0.097538) \| 1.467267 / 1.541195 (-0.073928) \| 1.514656 / 1.468490 (0.046166) \| 0.563934 / 4.584777 (-4.020843) \| 2.513715 / 3.745712 (-1.231997) \| 2.813014 / 5.269862 (-2.456847) \| 1.773243 / 4.565676 (-2.792433) \| 0.063208 / 0.424275 (-0.361067) \| 0.004979 / 0.007607 (-0.002628) \| 0.360694 / 0.226044 (0.134650) \| 3.520578 / 2.268929 (1.251650) \| 1.975369 / 55.444624 (-53.469255) \| 1.691257 / 6.876477 (-5.185220) \| 1.730872 / 2.142072 (-0.411200) \| 0.655366 / 4.805227 (-4.149861) \| 0.146043 / 6.500664 (-6.354621) \| 0.041386 / 0.075469 (-0.034083) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.979840 / 1.841788 (-0.861948) \| 12.456924 / 8.074308 (4.382616) \| 10.938595 / 10.191392 (0.747203) \| 0.133853 / 0.680424 (-0.546571) \| 0.015744 / 0.534201 (-0.518457) \| 0.289585 / 0.579283 (-0.289698) \| 0.291143 / 0.434364 (-0.143221) \| 0.328109 / 0.540337 (-0.212228) \| 0.561897 / 1.386936 (-0.825039) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#05ec66cc1abc20bd13d02c681b7be372ae084a4f \"CML watermark\")\n" ]
2,006,568,368	6,443	Trouble loading files defined in YAML explicitly	open	2023-11-22T15:18:10	2025-06-23T13:46:46	null	https://github.com/huggingface/datasets/issues/6443	null	severo	false	[ "There is a typo in one of the file names - `data/edf.csv` should be renamed to `data/def.csv` 🙂. ", "wow, I reviewed it twice to avoid being ashamed like that, but... I didn't notice the typo.\r\n\r\n---\r\n\r\nBesides this: do you think we would be able to improve the error message to make this clearer?", "Hi @severo 👋🏼\n\nI'd like to work on improving the error message when files listed in `data_files` inside the YAML config are not found in the dataset repository.\n\nMy plan is to:\n- Detect missing files explicitly by comparing listed paths in `data_files` with actual files in the repo\n- Update the raised error to clearly mention which files are missing, e.g.:\n `\"The following files were not found in the dataset repository: data/def.csv\"`\n\nLet me know if this direction makes sense — happy to start working on a PR!\n", "cc @lhoestq ", "Hi ! Doesn't it raise a `FileNotFoundError` when it's the case ? see in the source code:\n\nhttps://github.com/huggingface/datasets/blob/4a637bd963557bca42d3a2d8c54200fccb7ab91e/src/datasets/data_files.py#L379-L383", "Hi @lhoestq, thanks a lot for the pointer and for taking the time to review!\n\nYou're totally right — the FileNotFoundError does get raised from data_files.py when a file pattern isn't found.\n\nWhat I was hoping to improve is the clarity of the error message, especially when users define multiple paths in data_files via a YAML config. For example, instead of the generic:\n\n\"Unable to find 'data/def.csv'\"\n\nit could say something like:\n\n\"The following files listed in the data_files YAML config were not found in the dataset repository: data/def.csv\"\n\nThis could help users quickly catch typos or misnamed files in longer configs (like what happened in this issue 😅).\n\nIf that sounds like a helpful addition, I'd love to work on a small PR to enhance the error messaging.\n\nThanks again for all your help and feedback!" ]
2,006,086,907	6,442	Trouble loading image folder with additional features - metadata file ignored	closed	2023-11-22T11:01:35	2023-11-24T17:13:03	2023-11-24T17:13:03	https://github.com/huggingface/datasets/issues/6442	null	linoytsaban	false	[ "I reproduced too:\r\n- root: metadata file is ignored (https://huggingface.co/datasets/severo/doc-image-3)\r\n- data/ dir: metadata file is ignored (https://huggingface.co/datasets/severo/doc-image-4)\r\n- train/ dir: works (https://huggingface.co/datasets/severo/doc-image-5)" ]
2,004,985,857	6,441	Trouble Loading a Gated Dataset For User with Granted Permission	closed	2023-11-21T19:24:36	2023-12-13T08:27:16	2023-12-13T08:27:16	https://github.com/huggingface/datasets/issues/6441	null	e-trop	false	[ "> Also when they try to click the url link for the dataset they get a 404 error.\r\n\r\nThis seems to be a Hub error then (cc @SBrandeis)", "Could you report this to https://discuss.huggingface.co/c/hub/23, providing the URL of the dataset, or at least if the dataset is public or private?", "Thanks for the reply! I've created an issue on the hub's board here: https://discuss.huggingface.co/t/trouble-loading-a-gated-dataset-for-user-with-granted-permission/65565" ]
2,004,509,301	6,440	`.map` not hashing under python 3.9	closed	2023-11-21T15:14:54	2023-11-28T16:29:33	2023-11-28T16:29:33	https://github.com/huggingface/datasets/issues/6440	null	changyeli	false	[ "Tried to upgrade Python to 3.11 - still get this message. A partial solution is to NOT use `num_proc` at all. It will be considerably longer to finish the job.", "Hi! The `model = torch.compile(model)` line is problematic for our hashing logic. We would have to merge https://github.com/huggingface/datasets/pull/5867 to support hashing `torch.compile`-ed models/functions. \r\n\r\nI've started refactoring the hashing logic and plan to incorporate a fix for `torch.compile` as part of it, so this should be addressed soon (probably this or next week). " ]
2,002,916,514	6,439	Download + preparation speed of datasets.load_dataset is 20x slower than huggingface hub snapshot and manual loding	open	2023-11-20T20:07:23	2023-11-20T20:07:37	null	https://github.com/huggingface/datasets/issues/6439	null	AntreasAntoniou	false	[]
2,002,032,804	6,438	Support GeoParquet	open	2023-11-20T11:54:58	2024-02-07T08:36:51	null	https://github.com/huggingface/datasets/issues/6438	null	severo	false	[ "Thank you, @severo ! I would be more than happy to help in any way I can. I am not familiar with this repo's codebase, but I would be eager to contribute. :)\r\n\r\nFor the preview in Datasets Hub, I think it makes sense to just display the geospatial column as text. If there were a dataset loader, though, I think it should be able to support the geospatial components. Geopandas is probably the most user-friendly interface for that. I'm not sure if it's currently relevant in the context of geoparquet, but I think the pyogrio driver is faster than fiona.\r\n\r\nBut the whole gdal dependency thing can be a real pain. If anything, it would need to be an optional dependency. Maybe it would be best if the loader tries importing relevant geospatial libraries, and in the event of an ImportError, falls back to text for the geometry column.\r\n\r\nPlease let me know if I can be of assistance, and thanks again for creating this Issue. :)", "Just hitting into this same issue too showing GeoParquet files in Datasets Viewer. I tried to implement a custom reader for GeoParquet in https://huggingface.co/datasets/weiji14/clay_vector_embeddings/discussions/1, but it seems like HuggingFace has disabled datasets with custom loading scripts from using the dataset viewer according to https://discuss.huggingface.co/t/dataset-repo-requires-arbitrary-python-code-execution/59346 :frowning_face: \r\n\r\n![image](https://github.com/huggingface/datasets/assets/23487320/2f84d8ce-91c2-48cb-b72c-547ea8583892)\r\n\r\nI'm thinking now if there's a way to simply map files with GeoParquet extensions (.gpq, .geoparquet, etc) to use the Parquet reader. Maybe we could allowlist these geoparquet file extensions at https://github.com/huggingface/datasets/blame/0caf91285116ec910f409e82cc6e1f4cff7496e3/src/datasets/packaged_modules/__init__.py#L30-L51? Having the table columns show up would be a quick win.\r\n\r\nLonger term though, it would certainly be nice if the WKB geometry columns could be displayed in a nicer form. Geopandas' [read_parquet](https://geopandas.org/en/v0.14.1/docs/reference/api/geopandas.read_parquet.html) function is supposedly faster than `pyogrio.read_dataframe` according to https://github.com/geopandas/geopandas/discussions/2724#discussioncomment-4606048, but there's also [`pyogrio.raw.read_arrow`](https://pyogrio.readthedocs.io/en/latest/api.html#pyogrio.raw.read_arrow) now that can read into a `pyarrow.Table` directly.", "Update: It looks like renaming the GeoParquet file to have a file extension of `.parquet` works (see https://huggingface.co/datasets/weiji14/clay_vector_embeddings). HuggingFace's default parquet reader is able to read the GeoParquet file, though the geometry column is of an unknown type:\r\n\r\n![image](https://github.com/huggingface/datasets/assets/23487320/9060c300-d595-4409-9ccb-5e0207396883)\r\n\r\nI've opened a quick PR at #6508 to allow files with a `.geoparquet` or `*.gpq` extension to be read using the default Parquet reader. Let's see how that goes :smile:", "@joshuasundance-swca, @weiji14, If I'm understanding this correctly, the code below wouldn't be recommended to due to dependency headaches? If that's the case, what solution would there be to see the geometry features for .gpq files in huggingfaceHub? \r\n\r\ncode for dataset_loader.py\r\n```\r\nimport geopandas as gpd\r\n# ... (other imports remain the same)\r\n\r\nclass ClayVectorEmbeddings(datasets.ArrowBasedBuilder):\r\n # ... (other parts of the class remain the same)\r\n\r\n def _info(self):\r\n # Read the GeoParquet file to get the schema for the 'geometry' feature\r\n gdf = gpd.read_file(\"path/to/your/geoparquet/file.gpq\") # Replace with your file path\r\n geometry_schema = str(gdf.geometry.dtype)\r\n\r\n return datasets.DatasetInfo(\r\n # This is the description that will appear on the datasets page.\r\n description=\"Clay Vector Embeddings in GeoParquet format.\",\r\n # This defines the different columns of the dataset and their types\r\n features=datasets.Features(\r\n {\r\n \"source_url\": datasets.Value(dtype=\"string\"),\r\n \"date\": datasets.Value(dtype=\"date32\"),\r\n \"embeddings\": datasets.Value(\"string\"),\r\n \"geometry\": datasets.Value(dtype=geometry_schema), # Use the schema read by GeoPandas\r\n # ... (other features)\r\n }\r\n ),\r\n )\r\n\r\n# ... (rest of the script remains the same)\r\n\r\n```", "Hi @mehrdad-es, I'm not sure if HuggingFace would be keen to add `geopandas` to HuggingFace Hub (maybe a question for @severo?). Having a geometry viewer would be an even bigger task, and if you're thinking of a map-viewer, it might involve some redesign of the website UI. Some of my colleagues are working on streamlining GeoParquet visualization from cloud-hosted instances like HuggingFace (see e.g. https://github.com/developmentseed/lonboard/issues/314), and we could definitely come up with something if there's interest.", "I've created https://github.com/huggingface/datasets-server/issues/2416 to discuss the possibility of supporting (vectorial) geospatial columns in the dataset viewer, or in the converted parquet files.\r\n\r\nAt the same time, it would be super interesting to see what is already possible to do with a Hugging Face dataset that hosts geospatial data. \r\n\r\n> Some of my colleagues are working on streamlining GeoParquet visualization from cloud-hosted instances like HuggingFace (see e.g. https://github.com/developmentseed/lonboard/issues/314), and we could definitely come up with something if there's interest.\r\n\r\nIt would be awesome to show this inside a [Space](https://huggingface.co/docs/hub/spaces)." ]
2,001,272,606	6,437	Problem in training iterable dataset	open	2023-11-20T03:04:02	2024-05-22T03:14:13	null	https://github.com/huggingface/datasets/issues/6437	null	21Timothy	false	[ "Has anyone ever encountered this problem before?", "`split_dataset_by_node` doesn't give the exact same number of examples to each node in the case of iterable datasets, though it tries to be as equal as possible. In particular if your dataset is sharded and you have a number of shards that is a factor of the number of workers, then the shards will be evenly distributed among workers. If the shards don't contain the same number of examples, then some workers might end up with more examples than others.\r\n\r\nHowever if you use a Dataset you'll end up with the same amount of data, because we know the length of the dataset we can split it exactly where we want. Also Dataset objects don't load the full dataset in memory; instead it memory maps Arrow files from disk.", "> `split_dataset_by_node` doesn't give the exact same number of examples to each node in the case of iterable datasets, though it tries to be as equal as possible. In particular if your dataset is sharded and you have a number of shards that is a factor of the number of workers, then the shards will be evenly distributed among workers. If the shards don't contain the same number of examples, then some workers might end up with more examples than others.\r\n> \r\n> However if you use a Dataset you'll end up with the same amount of data, because we know the length of the dataset we can split it exactly where we want. Also Dataset objects don't load the full dataset in memory; instead it memory maps Arrow files from disk.\r\n\r\nThanks for your answer! I finally solve it by using the torch.distributed.algorithms.join.Join. I think maybe some rookie like me would face the same question the day after tomorrow hh.", "Great ! Maybe it can be worth having an example that we can include in the docs for other people, did you need anything else than the Join context manager used with the model and optimizer ?", "> Great ! Maybe it can be worth having an example that we can include in the docs for other people, did you need anything else than the Join context manager used with the model and optimizer ?\r\n\r\nI think it's none. I have tried barrier() to solve the problem but I failed. Maybe it's a tool for other situation." ]
2,000,844,474	6,436	TypeError: <lambda>() takes 0 positional arguments but 1 was given	closed	2023-11-19T13:10:20	2025-05-05T18:21:21	2023-11-29T16:28:34	https://github.com/huggingface/datasets/issues/6436	null	ahmadmustafaanis	false	[ "This looks like a problem with your environment rather than `datasets`.", "I meet the same problem,\r\nand originally use\r\n```python\r\nlocale.getpreferredencoding = lambda : \"UTF-8\"\r\n```\r\nand change to\r\n```\r\nlocale.getpreferredencoding = lambda x: \"UTF-8\"\r\n```\r\nand it works.", "> I meet the same problem, and originally use\n> \n> locale.getpreferredencoding = lambda : \"UTF-8\"\n> \n> and change to\n> \n> ```\n> locale.getpreferredencoding = lambda x: \"UTF-8\"\n> ```\n> \n> and it works.\n\nThanks, works for me too." ]
2,000,690,513	6,435	Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method	closed	2023-11-19T04:21:16	2024-01-27T17:14:20	2023-12-04T16:57:43	https://github.com/huggingface/datasets/issues/6435	null	kopyl	false	[ "[This doc section](https://huggingface.co/docs/datasets/main/en/process#multiprocessing) explains how to modify the script to avoid this error.", "@mariosasko thank you very much, i'll check it", "@mariosasko no it does not\r\n\r\n`Dataset.filter() got an unexpected keyword argument 'with_rank'`" ]
1,999,554,915	6,434	Use `ruff` for formatting	closed	2023-11-17T16:53:22	2023-11-21T14:19:21	2023-11-21T14:13:13	https://github.com/huggingface/datasets/pull/6434	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6434", "html_url": "https://github.com/huggingface/datasets/pull/6434", "diff_url": "https://github.com/huggingface/datasets/pull/6434.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6434.patch", "merged_at": "2023-11-21T14:13:13" }	mariosasko	true	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004293 / 0.011353 (-0.007060) \| 0.002953 / 0.011008 (-0.008055) \| 0.063712 / 0.038508 (0.025204) \| 0.029963 / 0.023109 (0.006854) \| 0.248574 / 0.275898 (-0.027324) \| 0.272757 / 0.323480 (-0.050723) \| 0.003878 / 0.007986 (-0.004108) \| 0.002456 / 0.004328 (-0.001872) \| 0.047959 / 0.004250 (0.043709) \| 0.043277 / 0.037052 (0.006224) \| 0.255071 / 0.258489 (-0.003418) \| 0.283934 / 0.293841 (-0.009907) \| 0.022870 / 0.128546 (-0.105676) \| 0.007224 / 0.075646 (-0.068422) \| 0.221595 / 0.419271 (-0.197677) \| 0.053468 / 0.043533 (0.009935) \| 0.249906 / 0.255139 (-0.005233) \| 0.274894 / 0.283200 (-0.008305) \| 0.017246 / 0.141683 (-0.124437) \| 1.112440 / 1.452155 (-0.339714) \| 1.167293 / 1.492716 (-0.325424) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092684 / 0.018006 (0.074677) \| 0.301721 / 0.000490 (0.301231) \| 0.000220 / 0.000200 (0.000020) \| 0.000050 / 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) \| 0.018289 / 0.037411 (-0.019122) \| 0.061898 / 0.014526 (0.047372) \| 0.072904 / 0.176557 (-0.103653) \| 0.118515 / 0.737135 (-0.618621) \| 0.074000 / 0.296338 (-0.222338) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287044 / 0.215209 (0.071835) \| 2.818091 / 2.077655 (0.740436) \| 1.502401 / 1.504120 (-0.001719) \| 1.374688 / 1.541195 (-0.166506) \| 1.410254 / 1.468490 (-0.058236) \| 0.407519 / 4.584777 (-4.177258) \| 2.379199 / 3.745712 (-1.366513) \| 2.585745 / 5.269862 (-2.684117) \| 1.562336 / 4.565676 (-3.003341) \| 0.045977 / 0.424275 (-0.378299) \| 0.004809 / 0.007607 (-0.002798) \| 0.347942 / 0.226044 (0.121897) \| 3.383318 / 2.268929 (1.114390) \| 1.844784 / 55.444624 (-53.599841) \| 1.561949 / 6.876477 (-5.314528) \| 1.571082 / 2.142072 (-0.570990) \| 0.482469 / 4.805227 (-4.322758) \| 0.099357 / 6.500664 (-6.401307) \| 0.041039 / 0.075469 (-0.034430) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.944236 / 1.841788 (-0.897551) \| 11.519623 / 8.074308 (3.445315) \| 10.353829 / 10.191392 (0.162437) \| 0.137530 / 0.680424 (-0.542894) \| 0.014454 / 0.534201 (-0.519747) \| 0.268657 / 0.579283 (-0.310626) \| 0.265165 / 0.434364 (-0.169199) \| 0.302626 / 0.540337 (-0.237712) \| 0.426923 / 1.386936 (-0.960013) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004711 / 0.011353 (-0.006641) \| 0.002504 / 0.011008 (-0.008504) \| 0.047671 / 0.038508 (0.009163) \| 0.051147 / 0.023109 (0.028037) \| 0.272848 / 0.275898 (-0.003050) \| 0.291705 / 0.323480 (-0.031775) \| 0.004002 / 0.007986 (-0.003984) \| 0.002382 / 0.004328 (-0.001947) \| 0.047583 / 0.004250 (0.043332) \| 0.038203 / 0.037052 (0.001150) \| 0.278536 / 0.258489 (0.020047) \| 0.305872 / 0.293841 (0.012031) \| 0.023890 / 0.128546 (-0.104657) \| 0.006954 / 0.075646 (-0.068693) \| 0.053716 / 0.419271 (-0.365556) \| 0.032158 / 0.043533 (-0.011375) \| 0.273939 / 0.255139 (0.018800) \| 0.290722 / 0.283200 (0.007522) \| 0.016946 / 0.141683 (-0.124737) \| 1.102726 / 1.452155 (-0.349429) \| 1.169356 / 1.492716 (-0.323360) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092520 / 0.018006 (0.074514) \| 0.301949 / 0.000490 (0.301459) \| 0.000248 / 0.000200 (0.000048) \| 0.000061 / 0.000054 (0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021013 / 0.037411 (-0.016399) \| 0.069965 / 0.014526 (0.055439) \| 0.080105 / 0.176557 (-0.096451) \| 0.119802 / 0.737135 (-0.617334) \| 0.081615 / 0.296338 (-0.214724) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.301170 / 0.215209 (0.085960) \| 2.884817 / 2.077655 (0.807162) \| 1.596376 / 1.504120 (0.092256) \| 1.471205 / 1.541195 (-0.069990) \| 1.499061 / 1.468490 (0.030571) \| 0.407729 / 4.584777 (-4.177048) \| 2.432824 / 3.745712 (-1.312888) \| 2.561905 / 5.269862 (-2.707957) \| 1.535364 / 4.565676 (-3.030313) \| 0.046592 / 0.424275 (-0.377683) \| 0.004773 / 0.007607 (-0.002834) \| 0.350872 / 0.226044 (0.124828) \| 3.474874 / 2.268929 (1.205945) \| 1.963114 / 55.444624 (-53.481510) \| 1.688213 / 6.876477 (-5.188263) \| 1.686325 / 2.142072 (-0.455748) \| 0.487151 / 4.805227 (-4.318076) \| 0.104253 / 6.500664 (-6.396411) \| 0.043499 / 0.075469 (-0.031970) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.980395 / 1.841788 (-0.861393) \| 11.907393 / 8.074308 (3.833085) \| 10.983688 / 10.191392 (0.792296) \| 0.142875 / 0.680424 (-0.537549) \| 0.015375 / 0.534201 (-0.518826) \| 0.270043 / 0.579283 (-0.309240) \| 0.295092 / 0.434364 (-0.139272) \| 0.309466 / 0.540337 (-0.230871) \| 0.409812 / 1.386936 (-0.977124) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#17f97ca8ec66f6664d3e9b7ceb84fe3ca49a9c18 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004703 / 0.011353 (-0.006650) \| 0.002767 / 0.011008 (-0.008241) \| 0.063162 / 0.038508 (0.024654) \| 0.052241 / 0.023109 (0.029132) \| 0.237138 / 0.275898 (-0.038760) \| 0.262793 / 0.323480 (-0.060687) \| 0.003873 / 0.007986 (-0.004113) \| 0.002433 / 0.004328 (-0.001896) \| 0.048647 / 0.004250 (0.044397) \| 0.037887 / 0.037052 (0.000834) \| 0.244939 / 0.258489 (-0.013551) \| 0.304015 / 0.293841 (0.010174) \| 0.022859 / 0.128546 (-0.105688) \| 0.006763 / 0.075646 (-0.068883) \| 0.202728 / 0.419271 (-0.216544) \| 0.035369 / 0.043533 (-0.008164) \| 0.240785 / 0.255139 (-0.014354) \| 0.255109 / 0.283200 (-0.028091) \| 0.017951 / 0.141683 (-0.123732) \| 1.096103 / 1.452155 (-0.356052) \| 1.167662 / 1.492716 (-0.325054) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092285 / 0.018006 (0.074279) \| 0.300201 / 0.000490 (0.299711) \| 0.000222 / 0.000200 (0.000022) \| 0.000049 / 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) \| 0.018271 / 0.037411 (-0.019140) \| 0.062306 / 0.014526 (0.047780) \| 0.072615 / 0.176557 (-0.103942) \| 0.119357 / 0.737135 (-0.617779) \| 0.073365 / 0.296338 (-0.222974) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278763 / 0.215209 (0.063554) \| 2.714943 / 2.077655 (0.637288) \| 1.426318 / 1.504120 (-0.077802) \| 1.313296 / 1.541195 (-0.227898) \| 1.330920 / 1.468490 (-0.137570) \| 0.391466 / 4.584777 (-4.193311) \| 2.380521 / 3.745712 (-1.365191) \| 2.545042 / 5.269862 (-2.724819) \| 1.549696 / 4.565676 (-3.015980) \| 0.044661 / 0.424275 (-0.379614) \| 0.005269 / 0.007607 (-0.002338) \| 0.331112 / 0.226044 (0.105068) \| 3.241120 / 2.268929 (0.972192) \| 1.783771 / 55.444624 (-53.660853) \| 1.506205 / 6.876477 (-5.370272) \| 1.521062 / 2.142072 (-0.621010) \| 0.462339 / 4.805227 (-4.342888) \| 0.097646 / 6.500664 (-6.403018) \| 0.041365 / 0.075469 (-0.034104) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.939653 / 1.841788 (-0.902135) \| 11.415472 / 8.074308 (3.341164) \| 10.338961 / 10.191392 (0.147569) \| 0.128543 / 0.680424 (-0.551881) \| 0.013997 / 0.534201 (-0.520204) \| 0.270034 / 0.579283 (-0.309249) \| 0.266766 / 0.434364 (-0.167598) \| 0.305290 / 0.540337 (-0.235047) \| 0.395969 / 1.386936 (-0.990967) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004869 / 0.011353 (-0.006484) \| 0.002445 / 0.011008 (-0.008563) \| 0.051256 / 0.038508 (0.012748) \| 0.050871 / 0.023109 (0.027761) \| 0.271044 / 0.275898 (-0.004854) \| 0.294138 / 0.323480 (-0.029342) \| 0.003974 / 0.007986 (-0.004012) \| 0.002423 / 0.004328 (-0.001906) \| 0.048277 / 0.004250 (0.044027) \| 0.039685 / 0.037052 (0.002632) \| 0.277092 / 0.258489 (0.018603) \| 0.302097 / 0.293841 (0.008256) \| 0.024515 / 0.128546 (-0.104031) \| 0.006892 / 0.075646 (-0.068754) \| 0.053528 / 0.419271 (-0.365744) \| 0.032243 / 0.043533 (-0.011290) \| 0.272098 / 0.255139 (0.016959) \| 0.291678 / 0.283200 (0.008479) \| 0.018368 / 0.141683 (-0.123315) \| 1.160151 / 1.452155 (-0.292004) \| 1.193643 / 1.492716 (-0.299073) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096669 / 0.018006 (0.078663) \| 0.299043 / 0.000490 (0.298553) \| 0.000227 / 0.000200 (0.000027) \| 0.000048 / 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) \| 0.021557 / 0.037411 (-0.015855) \| 0.069875 / 0.014526 (0.055349) \| 0.080952 / 0.176557 (-0.095605) \| 0.119509 / 0.737135 (-0.617626) \| 0.082030 / 0.296338 (-0.214308) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.303062 / 0.215209 (0.087853) \| 2.943823 / 2.077655 (0.866169) \| 1.607816 / 1.504120 (0.103696) \| 1.479773 / 1.541195 (-0.061422) \| 1.482663 / 1.468490 (0.014173) \| 0.411923 / 4.584777 (-4.172854) \| 2.450138 / 3.745712 (-1.295574) \| 2.466111 / 5.269862 (-2.803751) \| 1.543852 / 4.565676 (-3.021825) \| 0.046256 / 0.424275 (-0.378019) \| 0.004787 / 0.007607 (-0.002820) \| 0.353673 / 0.226044 (0.127628) \| 3.528218 / 2.268929 (1.259289) \| 1.984663 / 55.444624 (-53.459962) \| 1.675785 / 6.876477 (-5.200691) \| 1.775646 / 2.142072 (-0.366426) \| 0.483277 / 4.805227 (-4.321950) \| 0.097781 / 6.500664 (-6.402883) \| 0.040291 / 0.075469 (-0.035178) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.975458 / 1.841788 (-0.866330) \| 11.961966 / 8.074308 (3.887658) \| 10.558559 / 10.191392 (0.367167) \| 0.131372 / 0.680424 (-0.549052) \| 0.016156 / 0.534201 (-0.518045) \| 0.269254 / 0.579283 (-0.310029) \| 0.274896 / 0.434364 (-0.159468) \| 0.304672 / 0.540337 (-0.235665) \| 0.517652 / 1.386936 (-0.869284) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1a1e7416892dcb71097b47120bc9b26b3d90f06a \"CML watermark\")\n" ]
1,999,419,105	6,433	Better `tqdm` wrapper	closed	2023-11-17T15:45:15	2023-11-22T16:48:18	2023-11-22T16:42:08	https://github.com/huggingface/datasets/pull/6433	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6433", "html_url": "https://github.com/huggingface/datasets/pull/6433", "diff_url": "https://github.com/huggingface/datasets/pull/6433.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6433.patch", "merged_at": "2023-11-22T16:42:08" }	mariosasko	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005070 / 0.011353 (-0.006283) \| 0.003251 / 0.011008 (-0.007757) \| 0.061528 / 0.038508 (0.023020) \| 0.055386 / 0.023109 (0.032276) \| 0.248536 / 0.275898 (-0.027362) \| 0.272346 / 0.323480 (-0.051134) \| 0.003875 / 0.007986 (-0.004111) \| 0.002396 / 0.004328 (-0.001933) \| 0.047659 / 0.004250 (0.043409) \| 0.037448 / 0.037052 (0.000396) \| 0.251101 / 0.258489 (-0.007388) \| 0.282353 / 0.293841 (-0.011488) \| 0.027784 / 0.128546 (-0.100762) \| 0.010534 / 0.075646 (-0.065113) \| 0.206025 / 0.419271 (-0.213246) \| 0.035410 / 0.043533 (-0.008123) \| 0.250626 / 0.255139 (-0.004513) \| 0.266801 / 0.283200 (-0.016399) \| 0.017704 / 0.141683 (-0.123979) \| 1.089970 / 1.452155 (-0.362185) \| 1.171683 / 1.492716 (-0.321033) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092700 / 0.018006 (0.074694) \| 0.301314 / 0.000490 (0.300824) \| 0.000212 / 0.000200 (0.000012) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018385 / 0.037411 (-0.019026) \| 0.062364 / 0.014526 (0.047838) \| 0.075887 / 0.176557 (-0.100670) \| 0.119484 / 0.737135 (-0.617651) \| 0.074490 / 0.296338 (-0.221849) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283893 / 0.215209 (0.068684) \| 2.746772 / 2.077655 (0.669118) \| 1.486568 / 1.504120 (-0.017552) \| 1.376451 / 1.541195 (-0.164744) \| 1.377928 / 1.468490 (-0.090562) \| 0.572393 / 4.584777 (-4.012384) \| 2.383282 / 3.745712 (-1.362430) \| 2.791614 / 5.269862 (-2.478248) \| 1.753373 / 4.565676 (-2.812303) \| 0.063539 / 0.424275 (-0.360736) \| 0.005014 / 0.007607 (-0.002593) \| 0.341300 / 0.226044 (0.115256) \| 3.376960 / 2.268929 (1.108032) \| 1.914162 / 55.444624 (-53.530462) \| 1.590188 / 6.876477 (-5.286289) \| 1.618420 / 2.142072 (-0.523652) \| 0.648723 / 4.805227 (-4.156504) \| 0.117745 / 6.500664 (-6.382919) \| 0.048858 / 0.075469 (-0.026611) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.944422 / 1.841788 (-0.897366) \| 11.603590 / 8.074308 (3.529282) \| 10.707000 / 10.191392 (0.515608) \| 0.130779 / 0.680424 (-0.549645) \| 0.015126 / 0.534201 (-0.519075) \| 0.284869 / 0.579283 (-0.294414) \| 0.266778 / 0.434364 (-0.167585) \| 0.320646 / 0.540337 (-0.219691) \| 0.417167 / 1.386936 (-0.969769) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005384 / 0.011353 (-0.005969) \| 0.003311 / 0.011008 (-0.007698) \| 0.049933 / 0.038508 (0.011425) \| 0.052791 / 0.023109 (0.029681) \| 0.277061 / 0.275898 (0.001162) \| 0.302149 / 0.323480 (-0.021331) \| 0.004006 / 0.007986 (-0.003979) \| 0.002394 / 0.004328 (-0.001934) \| 0.049020 / 0.004250 (0.044770) \| 0.040168 / 0.037052 (0.003116) \| 0.278625 / 0.258489 (0.020136) \| 0.308641 / 0.293841 (0.014800) \| 0.029808 / 0.128546 (-0.098738) \| 0.010873 / 0.075646 (-0.064774) \| 0.058040 / 0.419271 (-0.361231) \| 0.032706 / 0.043533 (-0.010827) \| 0.277254 / 0.255139 (0.022115) \| 0.295208 / 0.283200 (0.012008) \| 0.017769 / 0.141683 (-0.123914) \| 1.126416 / 1.452155 (-0.325739) \| 1.169046 / 1.492716 (-0.323670) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094776 / 0.018006 (0.076770) \| 0.306262 / 0.000490 (0.305772) \| 0.000223 / 0.000200 (0.000023) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022279 / 0.037411 (-0.015132) \| 0.086784 / 0.014526 (0.072258) \| 0.082268 / 0.176557 (-0.094289) \| 0.120131 / 0.737135 (-0.617004) \| 0.082862 / 0.296338 (-0.213476) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300565 / 0.215209 (0.085356) \| 2.923424 / 2.077655 (0.845769) \| 1.594836 / 1.504120 (0.090716) \| 1.504323 / 1.541195 (-0.036872) \| 1.498495 / 1.468490 (0.030005) \| 0.570969 / 4.584777 (-4.013808) \| 2.476966 / 3.745712 (-1.268746) \| 2.785190 / 5.269862 (-2.484672) \| 1.749839 / 4.565676 (-2.815837) \| 0.062809 / 0.424275 (-0.361466) \| 0.004908 / 0.007607 (-0.002699) \| 0.361513 / 0.226044 (0.135469) \| 3.587135 / 2.268929 (1.318207) \| 1.952030 / 55.444624 (-53.492595) \| 1.661552 / 6.876477 (-5.214925) \| 1.678673 / 2.142072 (-0.463399) \| 0.645083 / 4.805227 (-4.160144) \| 0.117098 / 6.500664 (-6.383566) \| 0.041630 / 0.075469 (-0.033839) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.987883 / 1.841788 (-0.853904) \| 12.300764 / 8.074308 (4.226456) \| 10.962068 / 10.191392 (0.770675) \| 0.143200 / 0.680424 (-0.537224) \| 0.015743 / 0.534201 (-0.518458) \| 0.289733 / 0.579283 (-0.289550) \| 0.276384 / 0.434364 (-0.157979) \| 0.328542 / 0.540337 (-0.211795) \| 0.552175 / 1.386936 (-0.834761) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#81a65a57cf9fd0abdf85b664a144c9a06cb2896d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005110 / 0.011353 (-0.006243) \| 0.003311 / 0.011008 (-0.007697) \| 0.061962 / 0.038508 (0.023454) \| 0.050250 / 0.023109 (0.027140) \| 0.245313 / 0.275898 (-0.030585) \| 0.268748 / 0.323480 (-0.054732) \| 0.004693 / 0.007986 (-0.003293) \| 0.002465 / 0.004328 (-0.001863) \| 0.047698 / 0.004250 (0.043447) \| 0.037314 / 0.037052 (0.000262) \| 0.250370 / 0.258489 (-0.008119) \| 0.286023 / 0.293841 (-0.007818) \| 0.027891 / 0.128546 (-0.100655) \| 0.010574 / 0.075646 (-0.065072) \| 0.204895 / 0.419271 (-0.214376) \| 0.036014 / 0.043533 (-0.007519) \| 0.250959 / 0.255139 (-0.004180) \| 0.266710 / 0.283200 (-0.016489) \| 0.018492 / 0.141683 (-0.123191) \| 1.115340 / 1.452155 (-0.336815) \| 1.176488 / 1.492716 (-0.316229) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.099409 / 0.018006 (0.081402) \| 0.310151 / 0.000490 (0.309661) \| 0.000223 / 0.000200 (0.000023) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018132 / 0.037411 (-0.019279) \| 0.061820 / 0.014526 (0.047294) \| 0.074960 / 0.176557 (-0.101596) \| 0.119793 / 0.737135 (-0.617342) \| 0.074132 / 0.296338 (-0.222206) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286388 / 0.215209 (0.071179) \| 2.830791 / 2.077655 (0.753137) \| 1.514588 / 1.504120 (0.010468) \| 1.376514 / 1.541195 (-0.164681) \| 1.405080 / 1.468490 (-0.063410) \| 0.555297 / 4.584777 (-4.029480) \| 2.364838 / 3.745712 (-1.380874) \| 2.806050 / 5.269862 (-2.463812) \| 1.756114 / 4.565676 (-2.809562) \| 0.062254 / 0.424275 (-0.362022) \| 0.005020 / 0.007607 (-0.002588) \| 0.346272 / 0.226044 (0.120227) \| 3.453195 / 2.268929 (1.184266) \| 1.837810 / 55.444624 (-53.606814) \| 1.577984 / 6.876477 (-5.298493) \| 1.560821 / 2.142072 (-0.581251) \| 0.633930 / 4.805227 (-4.171297) \| 0.116414 / 6.500664 (-6.384250) \| 0.042007 / 0.075469 (-0.033462) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.941322 / 1.841788 (-0.900466) \| 11.740927 / 8.074308 (3.666618) \| 10.450543 / 10.191392 (0.259151) \| 0.128820 / 0.680424 (-0.551604) \| 0.014856 / 0.534201 (-0.519345) \| 0.285636 / 0.579283 (-0.293647) \| 0.270051 / 0.434364 (-0.164313) \| 0.321244 / 0.540337 (-0.219093) \| 0.415486 / 1.386936 (-0.971450) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005333 / 0.011353 (-0.006020) \| 0.003370 / 0.011008 (-0.007638) \| 0.049046 / 0.038508 (0.010538) \| 0.055767 / 0.023109 (0.032658) \| 0.273463 / 0.275898 (-0.002435) \| 0.292909 / 0.323480 (-0.030571) \| 0.004102 / 0.007986 (-0.003883) \| 0.002460 / 0.004328 (-0.001868) \| 0.048025 / 0.004250 (0.043775) \| 0.040342 / 0.037052 (0.003290) \| 0.275114 / 0.258489 (0.016625) \| 0.295988 / 0.293841 (0.002147) \| 0.029461 / 0.128546 (-0.099085) \| 0.010654 / 0.075646 (-0.064992) \| 0.057196 / 0.419271 (-0.362076) \| 0.033238 / 0.043533 (-0.010295) \| 0.275885 / 0.255139 (0.020746) \| 0.288566 / 0.283200 (0.005366) \| 0.018058 / 0.141683 (-0.123625) \| 1.130513 / 1.452155 (-0.321642) \| 1.173608 / 1.492716 (-0.319108) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097751 / 0.018006 (0.079745) \| 0.312106 / 0.000490 (0.311616) \| 0.000232 / 0.000200 (0.000032) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021201 / 0.037411 (-0.016211) \| 0.070150 / 0.014526 (0.055624) \| 0.081073 / 0.176557 (-0.095484) \| 0.119520 / 0.737135 (-0.617615) \| 0.084479 / 0.296338 (-0.211859) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292322 / 0.215209 (0.077113) \| 2.844070 / 2.077655 (0.766415) \| 1.581838 / 1.504120 (0.077718) \| 1.462665 / 1.541195 (-0.078530) \| 1.483013 / 1.468490 (0.014523) \| 0.558705 / 4.584777 (-4.026072) \| 2.422368 / 3.745712 (-1.323344) \| 2.772274 / 5.269862 (-2.497587) \| 1.725901 / 4.565676 (-2.839775) \| 0.062993 / 0.424275 (-0.361282) \| 0.004982 / 0.007607 (-0.002625) \| 0.344336 / 0.226044 (0.118292) \| 3.425230 / 2.268929 (1.156302) \| 1.947199 / 55.444624 (-53.497425) \| 1.670362 / 6.876477 (-5.206115) \| 1.674112 / 2.142072 (-0.467961) \| 0.633857 / 4.805227 (-4.171370) \| 0.114837 / 6.500664 (-6.385827) \| 0.042558 / 0.075469 (-0.032911) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.979474 / 1.841788 (-0.862314) \| 12.110856 / 8.074308 (4.036548) \| 10.605998 / 10.191392 (0.414606) \| 0.130769 / 0.680424 (-0.549654) \| 0.016057 / 0.534201 (-0.518144) \| 0.296448 / 0.579283 (-0.282835) \| 0.278078 / 0.434364 (-0.156286) \| 0.320809 / 0.540337 (-0.219528) \| 0.570756 / 1.386936 (-0.816180) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#eeb9727cc680a8f8172a012920bf84f285fec5a0 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005181 / 0.011353 (-0.006172) \| 0.003434 / 0.011008 (-0.007574) \| 0.062333 / 0.038508 (0.023825) \| 0.058544 / 0.023109 (0.035435) \| 0.233794 / 0.275898 (-0.042104) \| 0.258774 / 0.323480 (-0.064706) \| 0.003869 / 0.007986 (-0.004117) \| 0.002478 / 0.004328 (-0.001850) \| 0.047871 / 0.004250 (0.043620) \| 0.037997 / 0.037052 (0.000945) \| 0.241269 / 0.258489 (-0.017220) \| 0.270103 / 0.293841 (-0.023738) \| 0.027710 / 0.128546 (-0.100836) \| 0.010683 / 0.075646 (-0.064963) \| 0.213204 / 0.419271 (-0.206067) \| 0.036156 / 0.043533 (-0.007377) \| 0.240061 / 0.255139 (-0.015078) \| 0.253627 / 0.283200 (-0.029573) \| 0.017880 / 0.141683 (-0.123803) \| 1.102965 / 1.452155 (-0.349189) \| 1.176919 / 1.492716 (-0.315797) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093206 / 0.018006 (0.075200) \| 0.300960 / 0.000490 (0.300470) \| 0.000214 / 0.000200 (0.000014) \| 0.000042 / 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) \| 0.019417 / 0.037411 (-0.017994) \| 0.061948 / 0.014526 (0.047422) \| 0.073560 / 0.176557 (-0.102997) \| 0.120682 / 0.737135 (-0.616453) \| 0.074925 / 0.296338 (-0.221413) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280157 / 0.215209 (0.064948) \| 2.760648 / 2.077655 (0.682994) \| 1.482129 / 1.504120 (-0.021991) \| 1.364091 / 1.541195 (-0.177104) \| 1.415680 / 1.468490 (-0.052810) \| 0.564697 / 4.584777 (-4.020080) \| 2.364080 / 3.745712 (-1.381633) \| 2.794018 / 5.269862 (-2.475844) \| 1.752157 / 4.565676 (-2.813520) \| 0.062234 / 0.424275 (-0.362041) \| 0.004927 / 0.007607 (-0.002680) \| 0.337835 / 0.226044 (0.111790) \| 3.313819 / 2.268929 (1.044891) \| 1.834095 / 55.444624 (-53.610530) \| 1.559964 / 6.876477 (-5.316513) \| 1.598489 / 2.142072 (-0.543584) \| 0.636829 / 4.805227 (-4.168399) \| 0.116436 / 6.500664 (-6.384228) \| 0.042506 / 0.075469 (-0.032963) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.951508 / 1.841788 (-0.890280) \| 11.599532 / 8.074308 (3.525224) \| 10.492355 / 10.191392 (0.300963) \| 0.151582 / 0.680424 (-0.528842) \| 0.014356 / 0.534201 (-0.519845) \| 0.288448 / 0.579283 (-0.290835) \| 0.265607 / 0.434364 (-0.168757) \| 0.324455 / 0.540337 (-0.215883) \| 0.416718 / 1.386936 (-0.970218) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005489 / 0.011353 (-0.005864) \| 0.003481 / 0.011008 (-0.007527) \| 0.048952 / 0.038508 (0.010444) \| 0.054650 / 0.023109 (0.031540) \| 0.280853 / 0.275898 (0.004955) \| 0.298089 / 0.323480 (-0.025391) \| 0.004762 / 0.007986 (-0.003224) \| 0.002500 / 0.004328 (-0.001828) \| 0.048503 / 0.004250 (0.044253) \| 0.042048 / 0.037052 (0.004995) \| 0.281729 / 0.258489 (0.023240) \| 0.303625 / 0.293841 (0.009785) \| 0.028887 / 0.128546 (-0.099659) \| 0.010687 / 0.075646 (-0.064960) \| 0.058093 / 0.419271 (-0.361178) \| 0.032366 / 0.043533 (-0.011167) \| 0.281987 / 0.255139 (0.026848) \| 0.295554 / 0.283200 (0.012355) \| 0.019242 / 0.141683 (-0.122441) \| 1.127760 / 1.452155 (-0.324395) \| 1.166868 / 1.492716 (-0.325848) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092367 / 0.018006 (0.074361) \| 0.300195 / 0.000490 (0.299706) \| 0.000222 / 0.000200 (0.000022) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022062 / 0.037411 (-0.015349) \| 0.069955 / 0.014526 (0.055429) \| 0.081224 / 0.176557 (-0.095333) \| 0.120183 / 0.737135 (-0.616953) \| 0.082846 / 0.296338 (-0.213492) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295880 / 0.215209 (0.080671) \| 2.902508 / 2.077655 (0.824853) \| 1.616311 / 1.504120 (0.112191) \| 1.491320 / 1.541195 (-0.049875) \| 1.517333 / 1.468490 (0.048843) \| 0.566824 / 4.584777 (-4.017953) \| 2.428397 / 3.745712 (-1.317315) \| 2.807241 / 5.269862 (-2.462620) \| 1.786364 / 4.565676 (-2.779312) \| 0.065253 / 0.424275 (-0.359022) \| 0.004971 / 0.007607 (-0.002636) \| 0.350095 / 0.226044 (0.124051) \| 3.422226 / 2.268929 (1.153297) \| 1.972955 / 55.444624 (-53.471670) \| 1.686142 / 6.876477 (-5.190335) \| 1.694539 / 2.142072 (-0.447533) \| 0.645709 / 4.805227 (-4.159518) \| 0.117774 / 6.500664 (-6.382890) \| 0.041679 / 0.075469 (-0.033790) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.976835 / 1.841788 (-0.864952) \| 12.358039 / 8.074308 (4.283730) \| 10.774304 / 10.191392 (0.582912) \| 0.130442 / 0.680424 (-0.549982) \| 0.016071 / 0.534201 (-0.518130) \| 0.289911 / 0.579283 (-0.289372) \| 0.280693 / 0.434364 (-0.153671) \| 0.325598 / 0.540337 (-0.214739) \| 0.549618 / 1.386936 (-0.837318) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1570235228b67a15dce1ed535e905edd7442117f \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005176 / 0.011353 (-0.006177) \| 0.003297 / 0.011008 (-0.007711) \| 0.061673 / 0.038508 (0.023165) \| 0.052174 / 0.023109 (0.029065) \| 0.245897 / 0.275898 (-0.030001) \| 0.273102 / 0.323480 (-0.050377) \| 0.003870 / 0.007986 (-0.004115) \| 0.002385 / 0.004328 (-0.001943) \| 0.047675 / 0.004250 (0.043424) \| 0.037722 / 0.037052 (0.000670) \| 0.250780 / 0.258489 (-0.007709) \| 0.279464 / 0.293841 (-0.014376) \| 0.028107 / 0.128546 (-0.100439) \| 0.010460 / 0.075646 (-0.065187) \| 0.205522 / 0.419271 (-0.213750) \| 0.035781 / 0.043533 (-0.007752) \| 0.246526 / 0.255139 (-0.008613) \| 0.263919 / 0.283200 (-0.019281) \| 0.018634 / 0.141683 (-0.123049) \| 1.103845 / 1.452155 (-0.348310) \| 1.175536 / 1.492716 (-0.317181) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091696 / 0.018006 (0.073690) \| 0.301284 / 0.000490 (0.300794) \| 0.000213 / 0.000200 (0.000013) \| 0.000051 / 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) \| 0.019153 / 0.037411 (-0.018258) \| 0.063846 / 0.014526 (0.049320) \| 0.073635 / 0.176557 (-0.102922) \| 0.119625 / 0.737135 (-0.617511) \| 0.075161 / 0.296338 (-0.221177) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285637 / 0.215209 (0.070428) \| 2.751787 / 2.077655 (0.674132) \| 1.465098 / 1.504120 (-0.039022) \| 1.341676 / 1.541195 (-0.199519) \| 1.390636 / 1.468490 (-0.077854) \| 0.567663 / 4.584777 (-4.017114) \| 2.378183 / 3.745712 (-1.367529) \| 2.801830 / 5.269862 (-2.468032) \| 1.750125 / 4.565676 (-2.815551) \| 0.063705 / 0.424275 (-0.360570) \| 0.004967 / 0.007607 (-0.002640) \| 0.373302 / 0.226044 (0.147258) \| 3.301847 / 2.268929 (1.032918) \| 1.830117 / 55.444624 (-53.614508) \| 1.564360 / 6.876477 (-5.312117) \| 1.551766 / 2.142072 (-0.590306) \| 0.654424 / 4.805227 (-4.150803) \| 0.120656 / 6.500664 (-6.380008) \| 0.042383 / 0.075469 (-0.033086) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.931815 / 1.841788 (-0.909973) \| 11.755904 / 8.074308 (3.681596) \| 10.571707 / 10.191392 (0.380315) \| 0.131118 / 0.680424 (-0.549306) \| 0.015241 / 0.534201 (-0.518960) \| 0.287137 / 0.579283 (-0.292146) \| 0.265651 / 0.434364 (-0.168713) \| 0.329083 / 0.540337 (-0.211254) \| 0.417501 / 1.386936 (-0.969435) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005355 / 0.011353 (-0.005998) \| 0.003305 / 0.011008 (-0.007703) \| 0.048289 / 0.038508 (0.009781) \| 0.059223 / 0.023109 (0.036114) \| 0.267213 / 0.275898 (-0.008685) \| 0.290151 / 0.323480 (-0.033329) \| 0.004683 / 0.007986 (-0.003303) \| 0.002413 / 0.004328 (-0.001916) \| 0.047982 / 0.004250 (0.043732) \| 0.040943 / 0.037052 (0.003891) \| 0.270967 / 0.258489 (0.012478) \| 0.297644 / 0.293841 (0.003803) \| 0.029309 / 0.128546 (-0.099237) \| 0.010624 / 0.075646 (-0.065023) \| 0.057359 / 0.419271 (-0.361913) \| 0.032716 / 0.043533 (-0.010816) \| 0.268602 / 0.255139 (0.013463) \| 0.286016 / 0.283200 (0.002817) \| 0.018578 / 0.141683 (-0.123105) \| 1.120275 / 1.452155 (-0.331880) \| 1.195514 / 1.492716 (-0.297202) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092590 / 0.018006 (0.074584) \| 0.302589 / 0.000490 (0.302099) \| 0.000217 / 0.000200 (0.000017) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022439 / 0.037411 (-0.014972) \| 0.070914 / 0.014526 (0.056388) \| 0.084927 / 0.176557 (-0.091629) \| 0.123154 / 0.737135 (-0.613981) \| 0.085527 / 0.296338 (-0.210812) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292652 / 0.215209 (0.077443) \| 2.843736 / 2.077655 (0.766081) \| 1.561289 / 1.504120 (0.057169) \| 1.439500 / 1.541195 (-0.101695) \| 1.485074 / 1.468490 (0.016584) \| 0.570520 / 4.584777 (-4.014257) \| 2.436611 / 3.745712 (-1.309102) \| 2.925600 / 5.269862 (-2.344261) \| 1.796518 / 4.565676 (-2.769159) \| 0.065075 / 0.424275 (-0.359200) \| 0.004995 / 0.007607 (-0.002612) \| 0.349976 / 0.226044 (0.123932) \| 3.442535 / 2.268929 (1.173607) \| 1.919002 / 55.444624 (-53.525622) \| 1.659222 / 6.876477 (-5.217255) \| 1.648370 / 2.142072 (-0.493703) \| 0.643119 / 4.805227 (-4.162108) \| 0.118015 / 6.500664 (-6.382649) \| 0.041229 / 0.075469 (-0.034240) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.986226 / 1.841788 (-0.855562) \| 12.302487 / 8.074308 (4.228179) \| 10.528848 / 10.191392 (0.337456) \| 0.143911 / 0.680424 (-0.536513) \| 0.015265 / 0.534201 (-0.518936) \| 0.287692 / 0.579283 (-0.291591) \| 0.277011 / 0.434364 (-0.157353) \| 0.327650 / 0.540337 (-0.212688) \| 0.552951 / 1.386936 (-0.833985) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0af18e68664db94e863f0dcde4b0f3a7adcc80e7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005234 / 0.011353 (-0.006119) \| 0.003324 / 0.011008 (-0.007684) \| 0.062429 / 0.038508 (0.023921) \| 0.051619 / 0.023109 (0.028510) \| 0.256850 / 0.275898 (-0.019048) \| 0.260566 / 0.323480 (-0.062914) \| 0.002914 / 0.007986 (-0.005071) \| 0.003093 / 0.004328 (-0.001235) \| 0.047947 / 0.004250 (0.043696) \| 0.038753 / 0.037052 (0.001701) \| 0.246810 / 0.258489 (-0.011679) \| 0.275128 / 0.293841 (-0.018713) \| 0.027171 / 0.128546 (-0.101375) \| 0.010290 / 0.075646 (-0.065356) \| 0.206069 / 0.419271 (-0.213203) \| 0.035514 / 0.043533 (-0.008019) \| 0.240645 / 0.255139 (-0.014494) \| 0.259693 / 0.283200 (-0.023507) \| 0.019722 / 0.141683 (-0.121961) \| 1.128534 / 1.452155 (-0.323620) \| 1.139602 / 1.492716 (-0.353115) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095837 / 0.018006 (0.077830) \| 0.304754 / 0.000490 (0.304264) \| 0.000204 / 0.000200 (0.000004) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018349 / 0.037411 (-0.019063) \| 0.062763 / 0.014526 (0.048237) \| 0.074443 / 0.176557 (-0.102113) \| 0.120607 / 0.737135 (-0.616528) \| 0.077721 / 0.296338 (-0.218617) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281852 / 0.215209 (0.066643) \| 2.770806 / 2.077655 (0.693151) \| 1.466255 / 1.504120 (-0.037864) \| 1.349611 / 1.541195 (-0.191584) \| 1.385463 / 1.468490 (-0.083027) \| 0.566489 / 4.584777 (-4.018288) \| 2.420932 / 3.745712 (-1.324780) \| 2.809397 / 5.269862 (-2.460464) \| 1.749734 / 4.565676 (-2.815942) \| 0.063407 / 0.424275 (-0.360868) \| 0.005038 / 0.007607 (-0.002569) \| 0.379121 / 0.226044 (0.153077) \| 3.500938 / 2.268929 (1.232010) \| 1.852207 / 55.444624 (-53.592417) \| 1.570474 / 6.876477 (-5.306002) \| 1.555222 / 2.142072 (-0.586850) \| 0.657198 / 4.805227 (-4.148030) \| 0.119835 / 6.500664 (-6.380829) \| 0.042453 / 0.075469 (-0.033016) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.949953 / 1.841788 (-0.891835) \| 11.736811 / 8.074308 (3.662503) \| 10.558049 / 10.191392 (0.366657) \| 0.146230 / 0.680424 (-0.534194) \| 0.014922 / 0.534201 (-0.519279) \| 0.289100 / 0.579283 (-0.290183) \| 0.267130 / 0.434364 (-0.167234) \| 0.320055 / 0.540337 (-0.220282) \| 0.417244 / 1.386936 (-0.969692) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005309 / 0.011353 (-0.006044) \| 0.003329 / 0.011008 (-0.007679) \| 0.048576 / 0.038508 (0.010068) \| 0.055219 / 0.023109 (0.032110) \| 0.271522 / 0.275898 (-0.004376) \| 0.294435 / 0.323480 (-0.029045) \| 0.004018 / 0.007986 (-0.003968) \| 0.002456 / 0.004328 (-0.001873) \| 0.047939 / 0.004250 (0.043689) \| 0.041195 / 0.037052 (0.004143) \| 0.274819 / 0.258489 (0.016330) \| 0.299407 / 0.293841 (0.005566) \| 0.029145 / 0.128546 (-0.099401) \| 0.010680 / 0.075646 (-0.064966) \| 0.057238 / 0.419271 (-0.362034) \| 0.032722 / 0.043533 (-0.010810) \| 0.272066 / 0.255139 (0.016927) \| 0.289223 / 0.283200 (0.006023) \| 0.017826 / 0.141683 (-0.123857) \| 1.119079 / 1.452155 (-0.333076) \| 1.179109 / 1.492716 (-0.313608) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095662 / 0.018006 (0.077656) \| 0.307652 / 0.000490 (0.307162) \| 0.000213 / 0.000200 (0.000013) \| 0.000051 / 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) \| 0.022263 / 0.037411 (-0.015149) \| 0.070224 / 0.014526 (0.055698) \| 0.081477 / 0.176557 (-0.095079) \| 0.120763 / 0.737135 (-0.616372) \| 0.083152 / 0.296338 (-0.213187) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295780 / 0.215209 (0.080571) \| 2.926623 / 2.077655 (0.848968) \| 1.605901 / 1.504120 (0.101781) \| 1.482874 / 1.541195 (-0.058321) \| 1.501467 / 1.468490 (0.032977) \| 0.569566 / 4.584777 (-4.015211) \| 2.474948 / 3.745712 (-1.270764) \| 2.831877 / 5.269862 (-2.437985) \| 1.761229 / 4.565676 (-2.804448) \| 0.064129 / 0.424275 (-0.360147) \| 0.004964 / 0.007607 (-0.002643) \| 0.350081 / 0.226044 (0.124037) \| 3.446766 / 2.268929 (1.177837) \| 1.974998 / 55.444624 (-53.469627) \| 1.683381 / 6.876477 (-5.193095) \| 1.711543 / 2.142072 (-0.430530) \| 0.648695 / 4.805227 (-4.156532) \| 0.118224 / 6.500664 (-6.382440) \| 0.040895 / 0.075469 (-0.034574) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.960208 / 1.841788 (-0.881580) \| 12.164941 / 8.074308 (4.090633) \| 10.860573 / 10.191392 (0.669181) \| 0.133525 / 0.680424 (-0.546899) \| 0.015643 / 0.534201 (-0.518558) \| 0.290898 / 0.579283 (-0.288386) \| 0.289612 / 0.434364 (-0.144752) \| 0.325836 / 0.540337 (-0.214501) \| 0.565592 / 1.386936 (-0.821344) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9d19a315920c6d4293f8226273d99bf3de5c1d4e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006097 / 0.011353 (-0.005256) \| 0.004386 / 0.011008 (-0.006622) \| 0.064481 / 0.038508 (0.025973) \| 0.059983 / 0.023109 (0.036873) \| 0.268177 / 0.275898 (-0.007721) \| 0.296207 / 0.323480 (-0.027273) \| 0.002986 / 0.007986 (-0.005000) \| 0.002923 / 0.004328 (-0.001406) \| 0.048798 / 0.004250 (0.044547) \| 0.039945 / 0.037052 (0.002893) \| 0.271234 / 0.258489 (0.012745) \| 0.295461 / 0.293841 (0.001620) \| 0.028771 / 0.128546 (-0.099775) \| 0.011104 / 0.075646 (-0.064542) \| 0.207471 / 0.419271 (-0.211800) \| 0.036955 / 0.043533 (-0.006578) \| 0.254761 / 0.255139 (-0.000378) \| 0.275933 / 0.283200 (-0.007267) \| 0.021232 / 0.141683 (-0.120451) \| 1.170771 / 1.452155 (-0.281384) \| 1.188900 / 1.492716 (-0.303816) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092328 / 0.018006 (0.074322) \| 0.302591 / 0.000490 (0.302102) \| 0.000220 / 0.000200 (0.000020) \| 0.000052 / 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) \| 0.019207 / 0.037411 (-0.018204) \| 0.070247 / 0.014526 (0.055721) \| 0.074963 / 0.176557 (-0.101593) \| 0.124301 / 0.737135 (-0.612834) \| 0.077356 / 0.296338 (-0.218982) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283321 / 0.215209 (0.068112) \| 2.800448 / 2.077655 (0.722793) \| 1.510278 / 1.504120 (0.006158) \| 1.390353 / 1.541195 (-0.150842) \| 1.387881 / 1.468490 (-0.080609) \| 0.563927 / 4.584777 (-4.020850) \| 2.387753 / 3.745712 (-1.357959) \| 2.776655 / 5.269862 (-2.493207) \| 1.767383 / 4.565676 (-2.798293) \| 0.064864 / 0.424275 (-0.359411) \| 0.004999 / 0.007607 (-0.002608) \| 0.351173 / 0.226044 (0.125129) \| 3.459446 / 2.268929 (1.190517) \| 1.873078 / 55.444624 (-53.571547) \| 1.602831 / 6.876477 (-5.273646) \| 1.595612 / 2.142072 (-0.546460) \| 0.648786 / 4.805227 (-4.156441) \| 0.118720 / 6.500664 (-6.381944) \| 0.042821 / 0.075469 (-0.032649) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.970738 / 1.841788 (-0.871049) \| 12.273548 / 8.074308 (4.199240) \| 11.191375 / 10.191392 (0.999983) \| 0.131903 / 0.680424 (-0.548521) \| 0.014512 / 0.534201 (-0.519689) \| 0.289382 / 0.579283 (-0.289901) \| 0.269449 / 0.434364 (-0.164915) \| 0.327557 / 0.540337 (-0.212781) \| 0.427052 / 1.386936 (-0.959884) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005472 / 0.011353 (-0.005881) \| 0.003380 / 0.011008 (-0.007628) \| 0.050677 / 0.038508 (0.012169) \| 0.059606 / 0.023109 (0.036497) \| 0.275798 / 0.275898 (-0.000100) \| 0.303733 / 0.323480 (-0.019747) \| 0.004187 / 0.007986 (-0.003799) \| 0.002657 / 0.004328 (-0.001672) \| 0.048713 / 0.004250 (0.044463) \| 0.043501 / 0.037052 (0.006449) \| 0.278845 / 0.258489 (0.020356) \| 0.305322 / 0.293841 (0.011481) \| 0.030665 / 0.128546 (-0.097881) \| 0.010600 / 0.075646 (-0.065047) \| 0.058923 / 0.419271 (-0.360349) \| 0.032936 / 0.043533 (-0.010596) \| 0.272835 / 0.255139 (0.017696) \| 0.293975 / 0.283200 (0.010775) \| 0.018193 / 0.141683 (-0.123490) \| 1.144903 / 1.452155 (-0.307251) \| 1.192220 / 1.492716 (-0.300497) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094519 / 0.018006 (0.076513) \| 0.305591 / 0.000490 (0.305101) \| 0.000221 / 0.000200 (0.000021) \| 0.000056 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022108 / 0.037411 (-0.015303) \| 0.070184 / 0.014526 (0.055658) \| 0.081640 / 0.176557 (-0.094916) \| 0.124661 / 0.737135 (-0.612474) \| 0.082229 / 0.296338 (-0.214110) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.303710 / 0.215209 (0.088501) \| 2.966478 / 2.077655 (0.888824) \| 1.646066 / 1.504120 (0.141946) \| 1.551454 / 1.541195 (0.010259) \| 1.557995 / 1.468490 (0.089505) \| 0.577723 / 4.584777 (-4.007054) \| 2.510321 / 3.745712 (-1.235391) \| 2.951343 / 5.269862 (-2.318519) \| 1.857550 / 4.565676 (-2.708127) \| 0.064079 / 0.424275 (-0.360196) \| 0.004971 / 0.007607 (-0.002636) \| 0.359022 / 0.226044 (0.132978) \| 3.628716 / 2.268929 (1.359788) \| 2.011380 / 55.444624 (-53.433245) \| 1.710407 / 6.876477 (-5.166070) \| 1.756235 / 2.142072 (-0.385838) \| 0.659185 / 4.805227 (-4.146042) \| 0.120245 / 6.500664 (-6.380419) \| 0.042751 / 0.075469 (-0.032718) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.026794 / 1.841788 (-0.814993) \| 12.695125 / 8.074308 (4.620816) \| 10.864908 / 10.191392 (0.673516) \| 0.136128 / 0.680424 (-0.544295) \| 0.016824 / 0.534201 (-0.517377) \| 0.289717 / 0.579283 (-0.289567) \| 0.282919 / 0.434364 (-0.151445) \| 0.323345 / 0.540337 (-0.216992) \| 0.556375 / 1.386936 (-0.830561) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#52207295162f734235b71428d13e6a42c6fdc370 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005407 / 0.011353 (-0.005946) \| 0.003464 / 0.011008 (-0.007544) \| 0.062084 / 0.038508 (0.023576) \| 0.052582 / 0.023109 (0.029472) \| 0.251239 / 0.275898 (-0.024659) \| 0.276675 / 0.323480 (-0.046805) \| 0.002894 / 0.007986 (-0.005092) \| 0.003850 / 0.004328 (-0.000479) \| 0.047789 / 0.004250 (0.043538) \| 0.038955 / 0.037052 (0.001903) \| 0.258333 / 0.258489 (-0.000156) \| 0.290103 / 0.293841 (-0.003738) \| 0.027291 / 0.128546 (-0.101256) \| 0.010575 / 0.075646 (-0.065071) \| 0.207208 / 0.419271 (-0.212063) \| 0.035848 / 0.043533 (-0.007685) \| 0.253918 / 0.255139 (-0.001221) \| 0.269870 / 0.283200 (-0.013330) \| 0.019830 / 0.141683 (-0.121853) \| 1.085332 / 1.452155 (-0.366823) \| 1.171385 / 1.492716 (-0.321331) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094956 / 0.018006 (0.076950) \| 0.301104 / 0.000490 (0.300614) \| 0.000204 / 0.000200 (0.000004) \| 0.000049 / 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) \| 0.019045 / 0.037411 (-0.018367) \| 0.070815 / 0.014526 (0.056289) \| 0.073763 / 0.176557 (-0.102794) \| 0.120668 / 0.737135 (-0.616467) \| 0.075197 / 0.296338 (-0.221141) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286072 / 0.215209 (0.070863) \| 2.762868 / 2.077655 (0.685213) \| 1.504481 / 1.504120 (0.000361) \| 1.390301 / 1.541195 (-0.150894) \| 1.449571 / 1.468490 (-0.018919) \| 0.555598 / 4.584777 (-4.029179) \| 2.404975 / 3.745712 (-1.340737) \| 2.864359 / 5.269862 (-2.405503) \| 1.764913 / 4.565676 (-2.800763) \| 0.062956 / 0.424275 (-0.361320) \| 0.005116 / 0.007607 (-0.002491) \| 0.344027 / 0.226044 (0.117983) \| 3.426781 / 2.268929 (1.157852) \| 1.891040 / 55.444624 (-53.553584) \| 1.599972 / 6.876477 (-5.276505) \| 1.603464 / 2.142072 (-0.538608) \| 0.638136 / 4.805227 (-4.167091) \| 0.117808 / 6.500664 (-6.382857) \| 0.043740 / 0.075469 (-0.031730) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.934654 / 1.841788 (-0.907133) \| 12.243698 / 8.074308 (4.169390) \| 10.566791 / 10.191392 (0.375399) \| 0.130440 / 0.680424 (-0.549983) \| 0.014019 / 0.534201 (-0.520182) \| 0.285453 / 0.579283 (-0.293831) \| 0.266121 / 0.434364 (-0.168243) \| 0.325962 / 0.540337 (-0.214375) \| 0.422181 / 1.386936 (-0.964755) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005151 / 0.011353 (-0.006202) \| 0.003704 / 0.011008 (-0.007304) \| 0.049483 / 0.038508 (0.010975) \| 0.055147 / 0.023109 (0.032038) \| 0.277589 / 0.275898 (0.001691) \| 0.301274 / 0.323480 (-0.022206) \| 0.004031 / 0.007986 (-0.003955) \| 0.002568 / 0.004328 (-0.001760) \| 0.048830 / 0.004250 (0.044580) \| 0.040391 / 0.037052 (0.003339) \| 0.281031 / 0.258489 (0.022541) \| 0.304263 / 0.293841 (0.010422) \| 0.029237 / 0.128546 (-0.099309) \| 0.010598 / 0.075646 (-0.065048) \| 0.058089 / 0.419271 (-0.361182) \| 0.032529 / 0.043533 (-0.011004) \| 0.275761 / 0.255139 (0.020622) \| 0.294427 / 0.283200 (0.011227) \| 0.017227 / 0.141683 (-0.124456) \| 1.138036 / 1.452155 (-0.314119) \| 1.201946 / 1.492716 (-0.290770) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094241 / 0.018006 (0.076234) \| 0.301622 / 0.000490 (0.301132) \| 0.000229 / 0.000200 (0.000029) \| 0.000054 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022731 / 0.037411 (-0.014680) \| 0.071217 / 0.014526 (0.056691) \| 0.082619 / 0.176557 (-0.093937) \| 0.123308 / 0.737135 (-0.613827) \| 0.083552 / 0.296338 (-0.212787) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295770 / 0.215209 (0.080561) \| 2.886069 / 2.077655 (0.808414) \| 1.597686 / 1.504120 (0.093566) \| 1.458612 / 1.541195 (-0.082583) \| 1.501171 / 1.468490 (0.032680) \| 0.575653 / 4.584777 (-4.009124) \| 2.444021 / 3.745712 (-1.301691) \| 2.860192 / 5.269862 (-2.409669) \| 1.758896 / 4.565676 (-2.806780) \| 0.063334 / 0.424275 (-0.360941) \| 0.004913 / 0.007607 (-0.002694) \| 0.341828 / 0.226044 (0.115783) \| 3.420310 / 2.268929 (1.151381) \| 1.996099 / 55.444624 (-53.448525) \| 1.680112 / 6.876477 (-5.196365) \| 1.693418 / 2.142072 (-0.448654) \| 0.697321 / 4.805227 (-4.107906) \| 0.120474 / 6.500664 (-6.380190) \| 0.042192 / 0.075469 (-0.033277) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.975876 / 1.841788 (-0.865912) \| 12.174933 / 8.074308 (4.100625) \| 10.400906 / 10.191392 (0.209514) \| 0.162244 / 0.680424 (-0.518180) \| 0.016443 / 0.534201 (-0.517758) \| 0.293430 / 0.579283 (-0.285853) \| 0.285664 / 0.434364 (-0.148700) \| 0.332322 / 0.540337 (-0.208015) \| 0.609815 / 1.386936 (-0.777121) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f2c417d087d232b5abf9054ffb10305cc06c5440 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005155 / 0.011353 (-0.006198) \| 0.003226 / 0.011008 (-0.007782) \| 0.062651 / 0.038508 (0.024143) \| 0.051314 / 0.023109 (0.028205) \| 0.246075 / 0.275898 (-0.029823) \| 0.266859 / 0.323480 (-0.056621) \| 0.003895 / 0.007986 (-0.004091) \| 0.002462 / 0.004328 (-0.001866) \| 0.048097 / 0.004250 (0.043846) \| 0.037313 / 0.037052 (0.000261) \| 0.253208 / 0.258489 (-0.005281) \| 0.280255 / 0.293841 (-0.013585) \| 0.027052 / 0.128546 (-0.101494) \| 0.010276 / 0.075646 (-0.065370) \| 0.205663 / 0.419271 (-0.213608) \| 0.035111 / 0.043533 (-0.008422) \| 0.253757 / 0.255139 (-0.001382) \| 0.265466 / 0.283200 (-0.017733) \| 0.017873 / 0.141683 (-0.123810) \| 1.118906 / 1.452155 (-0.333249) \| 1.176384 / 1.492716 (-0.316332) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094921 / 0.018006 (0.076914) \| 0.300459 / 0.000490 (0.299970) \| 0.000214 / 0.000200 (0.000014) \| 0.000042 / 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) \| 0.018430 / 0.037411 (-0.018981) \| 0.062690 / 0.014526 (0.048165) \| 0.074215 / 0.176557 (-0.102342) \| 0.119969 / 0.737135 (-0.617166) \| 0.075846 / 0.296338 (-0.220493) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.273492 / 0.215209 (0.058283) \| 2.667937 / 2.077655 (0.590282) \| 1.405912 / 1.504120 (-0.098208) \| 1.269041 / 1.541195 (-0.272153) \| 1.313461 / 1.468490 (-0.155029) \| 0.554633 / 4.584777 (-4.030144) \| 2.325552 / 3.745712 (-1.420160) \| 2.825580 / 5.269862 (-2.444282) \| 1.745432 / 4.565676 (-2.820245) \| 0.062497 / 0.424275 (-0.361778) \| 0.004935 / 0.007607 (-0.002673) \| 0.337045 / 0.226044 (0.111001) \| 3.246360 / 2.268929 (0.977432) \| 1.775329 / 55.444624 (-53.669296) \| 1.491812 / 6.876477 (-5.384665) \| 1.499783 / 2.142072 (-0.642290) \| 0.636768 / 4.805227 (-4.168459) \| 0.116471 / 6.500664 (-6.384193) \| 0.041838 / 0.075469 (-0.033631) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.937388 / 1.841788 (-0.904400) \| 11.950930 / 8.074308 (3.876622) \| 10.532062 / 10.191392 (0.340670) \| 0.129490 / 0.680424 (-0.550934) \| 0.013907 / 0.534201 (-0.520294) \| 0.287503 / 0.579283 (-0.291780) \| 0.270548 / 0.434364 (-0.163816) \| 0.324321 / 0.540337 (-0.216016) \| 0.427639 / 1.386936 (-0.959297) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005272 / 0.011353 (-0.006081) \| 0.003413 / 0.011008 (-0.007595) \| 0.049800 / 0.038508 (0.011292) \| 0.055978 / 0.023109 (0.032868) \| 0.274365 / 0.275898 (-0.001533) \| 0.293414 / 0.323480 (-0.030066) \| 0.003994 / 0.007986 (-0.003992) \| 0.002480 / 0.004328 (-0.001848) \| 0.048787 / 0.004250 (0.044537) \| 0.040520 / 0.037052 (0.003468) \| 0.276198 / 0.258489 (0.017709) \| 0.301085 / 0.293841 (0.007244) \| 0.028352 / 0.128546 (-0.100194) \| 0.010631 / 0.075646 (-0.065015) \| 0.057103 / 0.419271 (-0.362168) \| 0.032277 / 0.043533 (-0.011256) \| 0.274472 / 0.255139 (0.019333) \| 0.289953 / 0.283200 (0.006754) \| 0.018048 / 0.141683 (-0.123635) \| 1.120329 / 1.452155 (-0.331826) \| 1.175784 / 1.492716 (-0.316932) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.102519 / 0.018006 (0.084512) \| 0.322030 / 0.000490 (0.321540) \| 0.000234 / 0.000200 (0.000034) \| 0.000045 / 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) \| 0.023084 / 0.037411 (-0.014327) \| 0.069592 / 0.014526 (0.055066) \| 0.081293 / 0.176557 (-0.095264) \| 0.119546 / 0.737135 (-0.617589) \| 0.083249 / 0.296338 (-0.213090) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294997 / 0.215209 (0.079788) \| 2.925517 / 2.077655 (0.847863) \| 1.607824 / 1.504120 (0.103705) \| 1.469586 / 1.541195 (-0.071608) \| 1.492350 / 1.468490 (0.023860) \| 0.561351 / 4.584777 (-4.023426) \| 2.446741 / 3.745712 (-1.298972) \| 2.842588 / 5.269862 (-2.427273) \| 1.789189 / 4.565676 (-2.776487) \| 0.064064 / 0.424275 (-0.360211) \| 0.005011 / 0.007607 (-0.002597) \| 0.351059 / 0.226044 (0.125015) \| 3.485277 / 2.268929 (1.216348) \| 1.981821 / 55.444624 (-53.462803) \| 1.671846 / 6.876477 (-5.204631) \| 1.702014 / 2.142072 (-0.440058) \| 0.645205 / 4.805227 (-4.160023) \| 0.117358 / 6.500664 (-6.383306) \| 0.041633 / 0.075469 (-0.033836) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.963281 / 1.841788 (-0.878506) \| 12.141256 / 8.074308 (4.066947) \| 10.595207 / 10.191392 (0.403815) \| 0.130401 / 0.680424 (-0.550023) \| 0.015490 / 0.534201 (-0.518710) \| 0.284201 / 0.579283 (-0.295082) \| 0.280244 / 0.434364 (-0.154120) \| 0.323545 / 0.540337 (-0.216792) \| 0.561246 / 1.386936 (-0.825690) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b3193829cf0dd9888c42bd7640a71d9d656cba2a \"CML watermark\")\n" ]
1,999,258,140	6,432	load_dataset does not load all of the data in my input file	open	2023-11-17T14:28:50	2023-11-22T17:34:58	null	https://github.com/huggingface/datasets/issues/6432	null	demongolem-biz2	false	[ "You should use `datasets.load_dataset` instead of `nlp.load_dataset`, as the `nlp` package is outdated.\r\n\r\nIf switching to `datasets.load_dataset` doesn't fix the issue, sharing the JSON file (feel free to replace the data with dummy data) would be nice so that we can reproduce it ourselves." ]
1,997,202,770	6,431	Create DatasetNotFoundError and DataFilesNotFoundError	closed	2023-11-16T16:02:55	2023-11-22T15:18:51	2023-11-22T15:12:33	https://github.com/huggingface/datasets/pull/6431	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6431", "html_url": "https://github.com/huggingface/datasets/pull/6431", "diff_url": "https://github.com/huggingface/datasets/pull/6431.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6431.patch", "merged_at": "2023-11-22T15:12:33" }	albertvillanova	true	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004459 / 0.011353 (-0.006894) \| 0.002883 / 0.011008 (-0.008125) \| 0.062434 / 0.038508 (0.023925) \| 0.030353 / 0.023109 (0.007244) \| 0.256696 / 0.275898 (-0.019202) \| 0.280557 / 0.323480 (-0.042923) \| 0.003903 / 0.007986 (-0.004083) \| 0.002424 / 0.004328 (-0.001905) \| 0.048509 / 0.004250 (0.044259) \| 0.043583 / 0.037052 (0.006531) \| 0.253900 / 0.258489 (-0.004590) \| 0.309146 / 0.293841 (0.015305) \| 0.023253 / 0.128546 (-0.105294) \| 0.007073 / 0.075646 (-0.068573) \| 0.204118 / 0.419271 (-0.215154) \| 0.056429 / 0.043533 (0.012897) \| 0.247331 / 0.255139 (-0.007808) \| 0.271581 / 0.283200 (-0.011619) \| 0.017021 / 0.141683 (-0.124662) \| 1.115057 / 1.452155 (-0.337098) \| 1.209947 / 1.492716 (-0.282770) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093141 / 0.018006 (0.075134) \| 0.295987 / 0.000490 (0.295497) \| 0.000221 / 0.000200 (0.000021) \| 0.000048 / 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) \| 0.019182 / 0.037411 (-0.018230) \| 0.062049 / 0.014526 (0.047523) \| 0.073824 / 0.176557 (-0.102733) \| 0.120175 / 0.737135 (-0.616960) \| 0.074700 / 0.296338 (-0.221639) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280036 / 0.215209 (0.064827) \| 2.731512 / 2.077655 (0.653857) \| 1.414606 / 1.504120 (-0.089514) \| 1.302433 / 1.541195 (-0.238761) \| 1.313012 / 1.468490 (-0.155478) \| 0.399722 / 4.584777 (-4.185055) \| 2.371249 / 3.745712 (-1.374463) \| 2.582520 / 5.269862 (-2.687342) \| 1.558505 / 4.565676 (-3.007171) \| 0.045765 / 0.424275 (-0.378510) \| 0.004748 / 0.007607 (-0.002859) \| 0.327623 / 0.226044 (0.101578) \| 3.258742 / 2.268929 (0.989814) \| 1.756798 / 55.444624 (-53.687826) \| 1.494551 / 6.876477 (-5.381925) \| 1.518161 / 2.142072 (-0.623911) \| 0.468560 / 4.805227 (-4.336667) \| 0.101034 / 6.500664 (-6.399630) \| 0.048259 / 0.075469 (-0.027210) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.938146 / 1.841788 (-0.903642) \| 11.636387 / 8.074308 (3.562078) \| 10.638909 / 10.191392 (0.447517) \| 0.128340 / 0.680424 (-0.552084) \| 0.015194 / 0.534201 (-0.519007) \| 0.275961 / 0.579283 (-0.303322) \| 0.264629 / 0.434364 (-0.169735) \| 0.308580 / 0.540337 (-0.231758) \| 0.433658 / 1.386936 (-0.953278) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004797 / 0.011353 (-0.006556) \| 0.002801 / 0.011008 (-0.008208) \| 0.048101 / 0.038508 (0.009593) \| 0.056406 / 0.023109 (0.033296) \| 0.274966 / 0.275898 (-0.000932) \| 0.298310 / 0.323480 (-0.025170) \| 0.004115 / 0.007986 (-0.003871) \| 0.002437 / 0.004328 (-0.001891) \| 0.047921 / 0.004250 (0.043671) \| 0.038812 / 0.037052 (0.001760) \| 0.279594 / 0.258489 (0.021105) \| 0.313703 / 0.293841 (0.019862) \| 0.024485 / 0.128546 (-0.104061) \| 0.007095 / 0.075646 (-0.068551) \| 0.053398 / 0.419271 (-0.365874) \| 0.032306 / 0.043533 (-0.011227) \| 0.278014 / 0.255139 (0.022875) \| 0.301156 / 0.283200 (0.017956) \| 0.017353 / 0.141683 (-0.124330) \| 1.150168 / 1.452155 (-0.301987) \| 1.190822 / 1.492716 (-0.301894) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092162 / 0.018006 (0.074156) \| 0.301031 / 0.000490 (0.300541) \| 0.000244 / 0.000200 (0.000044) \| 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) \| 0.020918 / 0.037411 (-0.016494) \| 0.072030 / 0.014526 (0.057504) \| 0.081813 / 0.176557 (-0.094743) \| 0.120233 / 0.737135 (-0.616903) \| 0.082874 / 0.296338 (-0.213465) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291659 / 0.215209 (0.076450) \| 2.841978 / 2.077655 (0.764323) \| 1.594207 / 1.504120 (0.090087) \| 1.473941 / 1.541195 (-0.067254) \| 1.514393 / 1.468490 (0.045903) \| 0.393393 / 4.584777 (-4.191384) \| 2.443663 / 3.745712 (-1.302050) \| 2.545747 / 5.269862 (-2.724114) \| 1.521130 / 4.565676 (-3.044546) \| 0.046246 / 0.424275 (-0.378030) \| 0.004826 / 0.007607 (-0.002781) \| 0.340909 / 0.226044 (0.114865) \| 3.319474 / 2.268929 (1.050546) \| 1.933110 / 55.444624 (-53.511515) \| 1.662463 / 6.876477 (-5.214014) \| 1.670331 / 2.142072 (-0.471742) \| 0.458062 / 4.805227 (-4.347165) \| 0.098397 / 6.500664 (-6.402267) \| 0.041339 / 0.075469 (-0.034130) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.973718 / 1.841788 (-0.868070) \| 12.095266 / 8.074308 (4.020957) \| 10.761212 / 10.191392 (0.569820) \| 0.142352 / 0.680424 (-0.538072) \| 0.015423 / 0.534201 (-0.518778) \| 0.270912 / 0.579283 (-0.308371) \| 0.276618 / 0.434364 (-0.157746) \| 0.309120 / 0.540337 (-0.231217) \| 0.415330 / 1.386936 (-0.971606) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cf4ba6f0e2641056774c01f62984aef5de5d68f1 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004676 / 0.011353 (-0.006677) \| 0.003101 / 0.011008 (-0.007907) \| 0.062260 / 0.038508 (0.023752) \| 0.030012 / 0.023109 (0.006903) \| 0.253704 / 0.275898 (-0.022194) \| 0.276404 / 0.323480 (-0.047075) \| 0.004060 / 0.007986 (-0.003926) \| 0.002467 / 0.004328 (-0.001861) \| 0.047921 / 0.004250 (0.043670) \| 0.045760 / 0.037052 (0.008708) \| 0.254529 / 0.258489 (-0.003960) \| 0.286283 / 0.293841 (-0.007558) \| 0.023301 / 0.128546 (-0.105246) \| 0.007407 / 0.075646 (-0.068239) \| 0.204541 / 0.419271 (-0.214730) \| 0.056387 / 0.043533 (0.012854) \| 0.252120 / 0.255139 (-0.003019) \| 0.275795 / 0.283200 (-0.007404) \| 0.018648 / 0.141683 (-0.123034) \| 1.113484 / 1.452155 (-0.338671) \| 1.168685 / 1.492716 (-0.324031) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098286 / 0.018006 (0.080280) \| 0.304619 / 0.000490 (0.304129) \| 0.000225 / 0.000200 (0.000025) \| 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) \| 0.019183 / 0.037411 (-0.018229) \| 0.062183 / 0.014526 (0.047657) \| 0.074288 / 0.176557 (-0.102269) \| 0.120576 / 0.737135 (-0.616560) \| 0.074833 / 0.296338 (-0.221505) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280512 / 0.215209 (0.065303) \| 2.770052 / 2.077655 (0.692397) \| 1.471234 / 1.504120 (-0.032886) \| 1.352080 / 1.541195 (-0.189114) \| 1.374518 / 1.468490 (-0.093973) \| 0.407108 / 4.584777 (-4.177669) \| 2.400581 / 3.745712 (-1.345131) \| 2.677507 / 5.269862 (-2.592355) \| 1.578042 / 4.565676 (-2.987635) \| 0.048539 / 0.424275 (-0.375736) \| 0.004905 / 0.007607 (-0.002703) \| 0.346676 / 0.226044 (0.120631) \| 3.367732 / 2.268929 (1.098803) \| 1.844405 / 55.444624 (-53.600220) \| 1.576883 / 6.876477 (-5.299594) \| 1.666986 / 2.142072 (-0.475086) \| 0.495872 / 4.805227 (-4.309355) \| 0.103142 / 6.500664 (-6.397522) \| 0.044037 / 0.075469 (-0.031432) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.980865 / 1.841788 (-0.860923) \| 12.268525 / 8.074308 (4.194217) \| 10.756554 / 10.191392 (0.565162) \| 0.129954 / 0.680424 (-0.550470) \| 0.013864 / 0.534201 (-0.520337) \| 0.267653 / 0.579283 (-0.311630) \| 0.265120 / 0.434364 (-0.169244) \| 0.309050 / 0.540337 (-0.231288) \| 0.423877 / 1.386936 (-0.963059) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005074 / 0.011353 (-0.006279) \| 0.003001 / 0.011008 (-0.008007) \| 0.048271 / 0.038508 (0.009763) \| 0.061206 / 0.023109 (0.038097) \| 0.279268 / 0.275898 (0.003370) \| 0.302592 / 0.323480 (-0.020888) \| 0.004177 / 0.007986 (-0.003809) \| 0.002452 / 0.004328 (-0.001876) \| 0.048259 / 0.004250 (0.044009) \| 0.040032 / 0.037052 (0.002979) \| 0.281398 / 0.258489 (0.022909) \| 0.314121 / 0.293841 (0.020280) \| 0.025137 / 0.128546 (-0.103409) \| 0.007230 / 0.075646 (-0.068416) \| 0.054537 / 0.419271 (-0.364735) \| 0.033266 / 0.043533 (-0.010267) \| 0.277305 / 0.255139 (0.022166) \| 0.295993 / 0.283200 (0.012794) \| 0.019278 / 0.141683 (-0.122405) \| 1.131700 / 1.452155 (-0.320454) \| 1.183848 / 1.492716 (-0.308868) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092258 / 0.018006 (0.074251) \| 0.310668 / 0.000490 (0.310178) \| 0.000219 / 0.000200 (0.000019) \| 0.000047 / 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) \| 0.021838 / 0.037411 (-0.015574) \| 0.071382 / 0.014526 (0.056857) \| 0.081389 / 0.176557 (-0.095168) \| 0.120389 / 0.737135 (-0.616746) \| 0.084135 / 0.296338 (-0.212203) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291676 / 0.215209 (0.076467) \| 2.840623 / 2.077655 (0.762968) \| 1.565748 / 1.504120 (0.061628) \| 1.452529 / 1.541195 (-0.088666) \| 1.490633 / 1.468490 (0.022143) \| 0.402878 / 4.584777 (-4.181899) \| 2.486192 / 3.745712 (-1.259520) \| 2.520563 / 5.269862 (-2.749299) \| 1.518550 / 4.565676 (-3.047127) \| 0.047423 / 0.424275 (-0.376852) \| 0.004823 / 0.007607 (-0.002784) \| 0.353122 / 0.226044 (0.127078) \| 3.452136 / 2.268929 (1.183208) \| 1.973798 / 55.444624 (-53.470827) \| 1.669569 / 6.876477 (-5.206907) \| 1.654910 / 2.142072 (-0.487163) \| 0.486746 / 4.805227 (-4.318481) \| 0.097260 / 6.500664 (-6.403404) \| 0.040608 / 0.075469 (-0.034861) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.989705 / 1.841788 (-0.852083) \| 12.114386 / 8.074308 (4.040077) \| 11.284551 / 10.191392 (1.093159) \| 0.141408 / 0.680424 (-0.539016) \| 0.015275 / 0.534201 (-0.518926) \| 0.267407 / 0.579283 (-0.311877) \| 0.281007 / 0.434364 (-0.153357) \| 0.309617 / 0.540337 (-0.230720) \| 0.414033 / 1.386936 (-0.972903) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6f3f3e3feec9d7d4d36111401787eb7b5fd51836 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004888 / 0.011353 (-0.006465) \| 0.002775 / 0.011008 (-0.008233) \| 0.062000 / 0.038508 (0.023492) \| 0.050694 / 0.023109 (0.027584) \| 0.257063 / 0.275898 (-0.018835) \| 0.282743 / 0.323480 (-0.040736) \| 0.002862 / 0.007986 (-0.005124) \| 0.002305 / 0.004328 (-0.002023) \| 0.049549 / 0.004250 (0.045299) \| 0.038754 / 0.037052 (0.001701) \| 0.264047 / 0.258489 (0.005558) \| 0.310162 / 0.293841 (0.016321) \| 0.022901 / 0.128546 (-0.105645) \| 0.006894 / 0.075646 (-0.068752) \| 0.202467 / 0.419271 (-0.216805) \| 0.035901 / 0.043533 (-0.007631) \| 0.262344 / 0.255139 (0.007205) \| 0.285563 / 0.283200 (0.002364) \| 0.017070 / 0.141683 (-0.124613) \| 1.113972 / 1.452155 (-0.338182) \| 1.176261 / 1.492716 (-0.316455) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092912 / 0.018006 (0.074906) \| 0.302610 / 0.000490 (0.302120) \| 0.000204 / 0.000200 (0.000005) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018232 / 0.037411 (-0.019179) \| 0.062367 / 0.014526 (0.047841) \| 0.074570 / 0.176557 (-0.101987) \| 0.120468 / 0.737135 (-0.616668) \| 0.075187 / 0.296338 (-0.221151) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279760 / 0.215209 (0.064551) \| 2.715372 / 2.077655 (0.637717) \| 1.461636 / 1.504120 (-0.042484) \| 1.324220 / 1.541195 (-0.216975) \| 1.350724 / 1.468490 (-0.117766) \| 0.395648 / 4.584777 (-4.189129) \| 2.376548 / 3.745712 (-1.369164) \| 2.594662 / 5.269862 (-2.675200) \| 1.553528 / 4.565676 (-3.012148) \| 0.047875 / 0.424275 (-0.376400) \| 0.005287 / 0.007607 (-0.002321) \| 0.334734 / 0.226044 (0.108689) \| 3.294753 / 2.268929 (1.025825) \| 1.797901 / 55.444624 (-53.646724) \| 1.510907 / 6.876477 (-5.365570) \| 1.536070 / 2.142072 (-0.606003) \| 0.474672 / 4.805227 (-4.330555) \| 0.099323 / 6.500664 (-6.401341) \| 0.041703 / 0.075469 (-0.033766) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.947441 / 1.841788 (-0.894347) \| 11.451378 / 8.074308 (3.377070) \| 10.283213 / 10.191392 (0.091821) \| 0.131032 / 0.680424 (-0.549392) \| 0.014423 / 0.534201 (-0.519777) \| 0.272568 / 0.579283 (-0.306715) \| 0.267127 / 0.434364 (-0.167237) \| 0.307361 / 0.540337 (-0.232976) \| 0.403858 / 1.386936 (-0.983078) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004836 / 0.011353 (-0.006517) \| 0.002544 / 0.011008 (-0.008464) \| 0.047979 / 0.038508 (0.009471) \| 0.052211 / 0.023109 (0.029102) \| 0.273394 / 0.275898 (-0.002504) \| 0.291202 / 0.323480 (-0.032277) \| 0.004094 / 0.007986 (-0.003891) \| 0.002415 / 0.004328 (-0.001914) \| 0.048057 / 0.004250 (0.043807) \| 0.039756 / 0.037052 (0.002703) \| 0.277301 / 0.258489 (0.018812) \| 0.297626 / 0.293841 (0.003785) \| 0.024641 / 0.128546 (-0.103905) \| 0.006957 / 0.075646 (-0.068690) \| 0.053574 / 0.419271 (-0.365697) \| 0.036532 / 0.043533 (-0.007001) \| 0.273753 / 0.255139 (0.018614) \| 0.294254 / 0.283200 (0.011054) \| 0.022252 / 0.141683 (-0.119431) \| 1.128609 / 1.452155 (-0.323546) \| 1.217322 / 1.492716 (-0.275394) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091050 / 0.018006 (0.073044) \| 0.300089 / 0.000490 (0.299600) \| 0.000215 / 0.000200 (0.000015) \| 0.000045 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021423 / 0.037411 (-0.015988) \| 0.069892 / 0.014526 (0.055366) \| 0.081125 / 0.176557 (-0.095432) \| 0.118725 / 0.737135 (-0.618411) \| 0.081357 / 0.296338 (-0.214981) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295046 / 0.215209 (0.079837) \| 2.868813 / 2.077655 (0.791159) \| 1.579613 / 1.504120 (0.075493) \| 1.449308 / 1.541195 (-0.091887) \| 1.478804 / 1.468490 (0.010314) \| 0.416916 / 4.584777 (-4.167861) \| 2.461093 / 3.745712 (-1.284619) \| 2.449792 / 5.269862 (-2.820070) \| 1.573930 / 4.565676 (-2.991746) \| 0.046808 / 0.424275 (-0.377467) \| 0.004811 / 0.007607 (-0.002796) \| 0.352805 / 0.226044 (0.126761) \| 3.495034 / 2.268929 (1.226105) \| 1.952019 / 55.444624 (-53.492606) \| 1.642607 / 6.876477 (-5.233869) \| 1.775235 / 2.142072 (-0.366837) \| 0.482196 / 4.805227 (-4.323032) \| 0.099562 / 6.500664 (-6.401102) \| 0.040709 / 0.075469 (-0.034760) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.972750 / 1.841788 (-0.869038) \| 11.905172 / 8.074308 (3.830864) \| 10.613847 / 10.191392 (0.422455) \| 0.129892 / 0.680424 (-0.550532) \| 0.015611 / 0.534201 (-0.518590) \| 0.271884 / 0.579283 (-0.307400) \| 0.275270 / 0.434364 (-0.159094) \| 0.303213 / 0.540337 (-0.237125) \| 0.402338 / 1.386936 (-0.984598) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bf8fa7ad7609ad34d4cc689f529ea606dd2560e0 \"CML watermark\")\n", "I think this PR can be merged.", "you already have an approval, feel free to merge!\r\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004826 / 0.011353 (-0.006527) \| 0.002979 / 0.011008 (-0.008029) \| 0.062055 / 0.038508 (0.023547) \| 0.056574 / 0.023109 (0.033465) \| 0.244342 / 0.275898 (-0.031556) \| 0.278040 / 0.323480 (-0.045439) \| 0.004020 / 0.007986 (-0.003965) \| 0.002474 / 0.004328 (-0.001855) \| 0.048451 / 0.004250 (0.044200) \| 0.038633 / 0.037052 (0.001580) \| 0.251389 / 0.258489 (-0.007100) \| 0.282739 / 0.293841 (-0.011102) \| 0.023298 / 0.128546 (-0.105248) \| 0.007513 / 0.075646 (-0.068134) \| 0.203014 / 0.419271 (-0.216257) \| 0.036216 / 0.043533 (-0.007317) \| 0.250988 / 0.255139 (-0.004151) \| 0.281228 / 0.283200 (-0.001972) \| 0.018259 / 0.141683 (-0.123424) \| 1.121200 / 1.452155 (-0.330955) \| 1.184298 / 1.492716 (-0.308419) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093730 / 0.018006 (0.075724) \| 0.301716 / 0.000490 (0.301226) \| 0.000223 / 0.000200 (0.000023) \| 0.000051 / 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) \| 0.019238 / 0.037411 (-0.018173) \| 0.064329 / 0.014526 (0.049803) \| 0.075657 / 0.176557 (-0.100899) \| 0.122616 / 0.737135 (-0.614519) \| 0.077459 / 0.296338 (-0.218880) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280153 / 0.215209 (0.064944) \| 2.715488 / 2.077655 (0.637833) \| 1.449666 / 1.504120 (-0.054454) \| 1.331903 / 1.541195 (-0.209292) \| 1.396200 / 1.468490 (-0.072290) \| 0.398861 / 4.584777 (-4.185916) \| 2.402814 / 3.745712 (-1.342898) \| 2.664033 / 5.269862 (-2.605829) \| 1.619589 / 4.565676 (-2.946088) \| 0.044798 / 0.424275 (-0.379477) \| 0.004989 / 0.007607 (-0.002618) \| 0.336822 / 0.226044 (0.110777) \| 3.245604 / 2.268929 (0.976676) \| 1.815633 / 55.444624 (-53.628991) \| 1.557975 / 6.876477 (-5.318501) \| 1.603655 / 2.142072 (-0.538417) \| 0.462980 / 4.805227 (-4.342247) \| 0.098340 / 6.500664 (-6.402324) \| 0.042750 / 0.075469 (-0.032719) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.973785 / 1.841788 (-0.868003) \| 12.379356 / 8.074308 (4.305048) \| 10.540164 / 10.191392 (0.348772) \| 0.144803 / 0.680424 (-0.535621) \| 0.013875 / 0.534201 (-0.520326) \| 0.270192 / 0.579283 (-0.309091) \| 0.264614 / 0.434364 (-0.169750) \| 0.313454 / 0.540337 (-0.226883) \| 0.402310 / 1.386936 (-0.984626) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004987 / 0.011353 (-0.006366) \| 0.003017 / 0.011008 (-0.007992) \| 0.048592 / 0.038508 (0.010084) \| 0.059370 / 0.023109 (0.036261) \| 0.277536 / 0.275898 (0.001638) \| 0.300592 / 0.323480 (-0.022888) \| 0.004870 / 0.007986 (-0.003115) \| 0.002452 / 0.004328 (-0.001876) \| 0.047972 / 0.004250 (0.043721) \| 0.042336 / 0.037052 (0.005283) \| 0.277570 / 0.258489 (0.019081) \| 0.304739 / 0.293841 (0.010898) \| 0.025313 / 0.128546 (-0.103233) \| 0.007219 / 0.075646 (-0.068427) \| 0.053967 / 0.419271 (-0.365304) \| 0.033314 / 0.043533 (-0.010219) \| 0.273908 / 0.255139 (0.018769) \| 0.291913 / 0.283200 (0.008713) \| 0.019440 / 0.141683 (-0.122243) \| 1.111047 / 1.452155 (-0.341107) \| 1.191276 / 1.492716 (-0.301440) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093985 / 0.018006 (0.075979) \| 0.303105 / 0.000490 (0.302615) \| 0.000235 / 0.000200 (0.000035) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022226 / 0.037411 (-0.015186) \| 0.072151 / 0.014526 (0.057625) \| 0.081700 / 0.176557 (-0.094857) \| 0.121407 / 0.737135 (-0.615729) \| 0.083217 / 0.296338 (-0.213121) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297286 / 0.215209 (0.082077) \| 2.913392 / 2.077655 (0.835738) \| 1.591758 / 1.504120 (0.087638) \| 1.463339 / 1.541195 (-0.077856) \| 1.495095 / 1.468490 (0.026605) \| 0.414341 / 4.584777 (-4.170436) \| 2.412438 / 3.745712 (-1.333275) \| 2.611452 / 5.269862 (-2.658410) \| 1.658545 / 4.565676 (-2.907132) \| 0.047269 / 0.424275 (-0.377007) \| 0.004872 / 0.007607 (-0.002735) \| 0.350746 / 0.226044 (0.124701) \| 3.491482 / 2.268929 (1.222554) \| 1.999009 / 55.444624 (-53.445616) \| 1.672862 / 6.876477 (-5.203615) \| 1.863095 / 2.142072 (-0.278977) \| 0.484746 / 4.805227 (-4.320481) \| 0.100774 / 6.500664 (-6.399890) \| 0.042519 / 0.075469 (-0.032950) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.984497 / 1.841788 (-0.857291) \| 12.972576 / 8.074308 (4.898268) \| 10.886021 / 10.191392 (0.694629) \| 0.141639 / 0.680424 (-0.538785) \| 0.015726 / 0.534201 (-0.518475) \| 0.284160 / 0.579283 (-0.295123) \| 0.291437 / 0.434364 (-0.142927) \| 0.314121 / 0.540337 (-0.226217) \| 0.420439 / 1.386936 (-0.966497) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#87ad7c7767b9cda62113c207f0ff42506a8f27c0 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004881 / 0.011353 (-0.006472) \| 0.002550 / 0.011008 (-0.008458) \| 0.062171 / 0.038508 (0.023663) \| 0.055341 / 0.023109 (0.032232) \| 0.243132 / 0.275898 (-0.032766) \| 0.265174 / 0.323480 (-0.058306) \| 0.002934 / 0.007986 (-0.005052) \| 0.002233 / 0.004328 (-0.002096) \| 0.049302 / 0.004250 (0.045052) \| 0.039491 / 0.037052 (0.002439) \| 0.252776 / 0.258489 (-0.005713) \| 0.280923 / 0.293841 (-0.012918) \| 0.022585 / 0.128546 (-0.105962) \| 0.006888 / 0.075646 (-0.068759) \| 0.202751 / 0.419271 (-0.216521) \| 0.035250 / 0.043533 (-0.008283) \| 0.251745 / 0.255139 (-0.003394) \| 0.267431 / 0.283200 (-0.015768) \| 0.019486 / 0.141683 (-0.122197) \| 1.161783 / 1.452155 (-0.290372) \| 1.194254 / 1.492716 (-0.298463) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097772 / 0.018006 (0.079766) \| 0.309137 / 0.000490 (0.308647) \| 0.000225 / 0.000200 (0.000025) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018719 / 0.037411 (-0.018693) \| 0.062211 / 0.014526 (0.047686) \| 0.074291 / 0.176557 (-0.102266) \| 0.119436 / 0.737135 (-0.617699) \| 0.075519 / 0.296338 (-0.220820) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279778 / 0.215209 (0.064569) \| 2.730678 / 2.077655 (0.653023) \| 1.413922 / 1.504120 (-0.090198) \| 1.286747 / 1.541195 (-0.254447) \| 1.299835 / 1.468490 (-0.168656) \| 0.392516 / 4.584777 (-4.192261) \| 2.381816 / 3.745712 (-1.363896) \| 2.616944 / 5.269862 (-2.652918) \| 1.606152 / 4.565676 (-2.959525) \| 0.044867 / 0.424275 (-0.379408) \| 0.004915 / 0.007607 (-0.002692) \| 0.334078 / 0.226044 (0.108034) \| 3.388096 / 2.268929 (1.119167) \| 1.756666 / 55.444624 (-53.687958) \| 1.497211 / 6.876477 (-5.379266) \| 1.496787 / 2.142072 (-0.645285) \| 0.469145 / 4.805227 (-4.336082) \| 0.097821 / 6.500664 (-6.402843) \| 0.041850 / 0.075469 (-0.033619) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.956878 / 1.841788 (-0.884910) \| 11.520184 / 8.074308 (3.445875) \| 10.659216 / 10.191392 (0.467824) \| 0.143687 / 0.680424 (-0.536737) \| 0.014118 / 0.534201 (-0.520083) \| 0.270990 / 0.579283 (-0.308293) \| 0.270057 / 0.434364 (-0.164306) \| 0.311109 / 0.540337 (-0.229229) \| 0.407042 / 1.386936 (-0.979894) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004816 / 0.011353 (-0.006537) \| 0.002898 / 0.011008 (-0.008110) \| 0.048540 / 0.038508 (0.010032) \| 0.055286 / 0.023109 (0.032176) \| 0.279086 / 0.275898 (0.003187) \| 0.298950 / 0.323480 (-0.024529) \| 0.004090 / 0.007986 (-0.003896) \| 0.002497 / 0.004328 (-0.001832) \| 0.049160 / 0.004250 (0.044910) \| 0.040612 / 0.037052 (0.003560) \| 0.287832 / 0.258489 (0.029343) \| 0.305617 / 0.293841 (0.011776) \| 0.023936 / 0.128546 (-0.104610) \| 0.007565 / 0.075646 (-0.068081) \| 0.054037 / 0.419271 (-0.365235) \| 0.032389 / 0.043533 (-0.011144) \| 0.283031 / 0.255139 (0.027892) \| 0.295411 / 0.283200 (0.012212) \| 0.018466 / 0.141683 (-0.123217) \| 1.134660 / 1.452155 (-0.317495) \| 1.196212 / 1.492716 (-0.296504) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.099961 / 0.018006 (0.081955) \| 0.310831 / 0.000490 (0.310342) \| 0.000238 / 0.000200 (0.000038) \| 0.000045 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021566 / 0.037411 (-0.015845) \| 0.070255 / 0.014526 (0.055729) \| 0.081221 / 0.176557 (-0.095336) \| 0.119404 / 0.737135 (-0.617732) \| 0.083005 / 0.296338 (-0.213333) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302788 / 0.215209 (0.087579) \| 2.928876 / 2.077655 (0.851221) \| 1.601221 / 1.504120 (0.097101) \| 1.485147 / 1.541195 (-0.056047) \| 1.508698 / 1.468490 (0.040207) \| 0.402783 / 4.584777 (-4.181994) \| 2.432151 / 3.745712 (-1.313561) \| 2.476848 / 5.269862 (-2.793013) \| 1.585487 / 4.565676 (-2.980189) \| 0.045965 / 0.424275 (-0.378310) \| 0.004818 / 0.007607 (-0.002789) \| 0.354847 / 0.226044 (0.128803) \| 3.500670 / 2.268929 (1.231742) \| 1.951904 / 55.444624 (-53.492720) \| 1.675152 / 6.876477 (-5.201325) \| 1.795971 / 2.142072 (-0.346101) \| 0.470625 / 4.805227 (-4.334602) \| 0.126080 / 6.500664 (-6.374584) \| 0.040506 / 0.075469 (-0.034963) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.985251 / 1.841788 (-0.856536) \| 12.316710 / 8.074308 (4.242402) \| 10.674437 / 10.191392 (0.483045) \| 0.133622 / 0.680424 (-0.546802) \| 0.016756 / 0.534201 (-0.517445) \| 0.269318 / 0.579283 (-0.309965) \| 0.282258 / 0.434364 (-0.152106) \| 0.309941 / 0.540337 (-0.230396) \| 0.403189 / 1.386936 (-0.983747) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#08ceb927025575c453228cab31291b74043dba1a \"CML watermark\")\n", "I am merging this PR because we need it by `datasets-server`.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004935 / 0.011353 (-0.006418) \| 0.002643 / 0.011008 (-0.008365) \| 0.064449 / 0.038508 (0.025941) \| 0.053110 / 0.023109 (0.030001) \| 0.261576 / 0.275898 (-0.014322) \| 0.270866 / 0.323480 (-0.052614) \| 0.002895 / 0.007986 (-0.005091) \| 0.002349 / 0.004328 (-0.001979) \| 0.047620 / 0.004250 (0.043370) \| 0.038699 / 0.037052 (0.001647) \| 0.246663 / 0.258489 (-0.011826) \| 0.282021 / 0.293841 (-0.011820) \| 0.022807 / 0.128546 (-0.105739) \| 0.007242 / 0.075646 (-0.068404) \| 0.204236 / 0.419271 (-0.215035) \| 0.035429 / 0.043533 (-0.008104) \| 0.241684 / 0.255139 (-0.013455) \| 0.262343 / 0.283200 (-0.020857) \| 0.020036 / 0.141683 (-0.121647) \| 1.112687 / 1.452155 (-0.339467) \| 1.167086 / 1.492716 (-0.325630) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.107059 / 0.018006 (0.089053) \| 0.301036 / 0.000490 (0.300546) \| 0.000224 / 0.000200 (0.000024) \| 0.000048 / 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) \| 0.018464 / 0.037411 (-0.018947) \| 0.063822 / 0.014526 (0.049296) \| 0.073562 / 0.176557 (-0.102994) \| 0.120136 / 0.737135 (-0.616999) \| 0.074934 / 0.296338 (-0.221405) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275474 / 0.215209 (0.060265) \| 2.714239 / 2.077655 (0.636584) \| 1.455535 / 1.504120 (-0.048585) \| 1.336530 / 1.541195 (-0.204665) \| 1.359607 / 1.468490 (-0.108883) \| 0.396303 / 4.584777 (-4.188474) \| 2.366076 / 3.745712 (-1.379636) \| 2.600755 / 5.269862 (-2.669107) \| 1.572382 / 4.565676 (-2.993294) \| 0.045795 / 0.424275 (-0.378480) \| 0.004932 / 0.007607 (-0.002675) \| 0.332175 / 0.226044 (0.106130) \| 3.257843 / 2.268929 (0.988915) \| 1.799021 / 55.444624 (-53.645603) \| 1.532813 / 6.876477 (-5.343663) \| 1.552279 / 2.142072 (-0.589794) \| 0.471369 / 4.805227 (-4.333858) \| 0.098931 / 6.500664 (-6.401733) \| 0.042735 / 0.075469 (-0.032734) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.960779 / 1.841788 (-0.881009) \| 11.741631 / 8.074308 (3.667322) \| 10.355721 / 10.191392 (0.164329) \| 0.129025 / 0.680424 (-0.551399) \| 0.013794 / 0.534201 (-0.520407) \| 0.267268 / 0.579283 (-0.312015) \| 0.265582 / 0.434364 (-0.168782) \| 0.306242 / 0.540337 (-0.234095) \| 0.400367 / 1.386936 (-0.986569) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004966 / 0.011353 (-0.006387) \| 0.002846 / 0.011008 (-0.008163) \| 0.049104 / 0.038508 (0.010596) \| 0.055436 / 0.023109 (0.032327) \| 0.273892 / 0.275898 (-0.002006) \| 0.300207 / 0.323480 (-0.023273) \| 0.004017 / 0.007986 (-0.003969) \| 0.002465 / 0.004328 (-0.001863) \| 0.048088 / 0.004250 (0.043837) \| 0.040037 / 0.037052 (0.002984) \| 0.279918 / 0.258489 (0.021429) \| 0.305378 / 0.293841 (0.011537) \| 0.024326 / 0.128546 (-0.104220) \| 0.006992 / 0.075646 (-0.068654) \| 0.053545 / 0.419271 (-0.365726) \| 0.032312 / 0.043533 (-0.011221) \| 0.272899 / 0.255139 (0.017760) \| 0.289683 / 0.283200 (0.006483) \| 0.019121 / 0.141683 (-0.122562) \| 1.133296 / 1.452155 (-0.318858) \| 1.220989 / 1.492716 (-0.271728) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093193 / 0.018006 (0.075187) \| 0.307658 / 0.000490 (0.307168) \| 0.000224 / 0.000200 (0.000024) \| 0.000045 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022906 / 0.037411 (-0.014506) \| 0.080931 / 0.014526 (0.066405) \| 0.081442 / 0.176557 (-0.095115) \| 0.121150 / 0.737135 (-0.615986) \| 0.083387 / 0.296338 (-0.212952) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294979 / 0.215209 (0.079770) \| 2.900090 / 2.077655 (0.822435) \| 1.610061 / 1.504120 (0.105941) \| 1.455118 / 1.541195 (-0.086077) \| 1.456599 / 1.468490 (-0.011891) \| 0.397919 / 4.584777 (-4.186858) \| 2.421010 / 3.745712 (-1.324702) \| 2.486527 / 5.269862 (-2.783334) \| 1.573854 / 4.565676 (-2.991822) \| 0.046199 / 0.424275 (-0.378076) \| 0.004888 / 0.007607 (-0.002719) \| 0.342183 / 0.226044 (0.116139) \| 3.392068 / 2.268929 (1.123140) \| 1.963688 / 55.444624 (-53.480936) \| 1.667611 / 6.876477 (-5.208866) \| 1.833706 / 2.142072 (-0.308367) \| 0.509421 / 4.805227 (-4.295806) \| 0.099669 / 6.500664 (-6.400995) \| 0.041004 / 0.075469 (-0.034465) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.956314 / 1.841788 (-0.885474) \| 12.190194 / 8.074308 (4.115886) \| 10.417839 / 10.191392 (0.226447) \| 0.144139 / 0.680424 (-0.536285) \| 0.015841 / 0.534201 (-0.518359) \| 0.270436 / 0.579283 (-0.308847) \| 0.273952 / 0.434364 (-0.160412) \| 0.303018 / 0.540337 (-0.237319) \| 0.410163 / 1.386936 (-0.976773) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aa8558fc7fe1f9f7675c7c5d21a14d1a19598296 \"CML watermark\")\n" ]
1,996,723,698	6,429	Add trust_remote_code argument	closed	2023-11-16T12:12:54	2023-11-28T16:10:39	2023-11-28T16:03:43	https://github.com/huggingface/datasets/pull/6429	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6429", "html_url": "https://github.com/huggingface/datasets/pull/6429", "diff_url": "https://github.com/huggingface/datasets/pull/6429.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6429.patch", "merged_at": "2023-11-28T16:03:43" }	lhoestq	true	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004947 / 0.011353 (-0.006405) \| 0.002961 / 0.011008 (-0.008047) \| 0.063474 / 0.038508 (0.024966) \| 0.030162 / 0.023109 (0.007053) \| 0.232388 / 0.275898 (-0.043511) \| 0.257654 / 0.323480 (-0.065826) \| 0.002969 / 0.007986 (-0.005017) \| 0.002336 / 0.004328 (-0.001993) \| 0.049724 / 0.004250 (0.045473) \| 0.045608 / 0.037052 (0.008555) \| 0.236079 / 0.258489 (-0.022410) \| 0.267809 / 0.293841 (-0.026032) \| 0.023805 / 0.128546 (-0.104741) \| 0.007177 / 0.075646 (-0.068470) \| 0.202167 / 0.419271 (-0.217104) \| 0.056181 / 0.043533 (0.012648) \| 0.256464 / 0.255139 (0.001325) \| 0.271908 / 0.283200 (-0.011292) \| 0.020211 / 0.141683 (-0.121472) \| 1.114112 / 1.452155 (-0.338042) \| 1.174879 / 1.492716 (-0.317837) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093457 / 0.018006 (0.075451) \| 0.307643 / 0.000490 (0.307154) \| 0.000212 / 0.000200 (0.000012) \| 0.000047 / 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) \| 0.018635 / 0.037411 (-0.018777) \| 0.062099 / 0.014526 (0.047573) \| 0.073619 / 0.176557 (-0.102938) \| 0.119986 / 0.737135 (-0.617149) \| 0.075439 / 0.296338 (-0.220899) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280142 / 0.215209 (0.064933) \| 2.733790 / 2.077655 (0.656136) \| 1.457633 / 1.504120 (-0.046487) \| 1.336288 / 1.541195 (-0.204907) \| 1.363191 / 1.468490 (-0.105299) \| 0.399331 / 4.584777 (-4.185446) \| 2.343099 / 3.745712 (-1.402614) \| 2.617059 / 5.269862 (-2.652802) \| 1.575912 / 4.565676 (-2.989765) \| 0.045621 / 0.424275 (-0.378655) \| 0.004825 / 0.007607 (-0.002782) \| 0.346669 / 0.226044 (0.120625) \| 3.225982 / 2.268929 (0.957054) \| 1.787067 / 55.444624 (-53.657557) \| 1.503883 / 6.876477 (-5.372593) \| 1.527593 / 2.142072 (-0.614479) \| 0.466806 / 4.805227 (-4.338421) \| 0.098537 / 6.500664 (-6.402127) \| 0.042028 / 0.075469 (-0.033441) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945040 / 1.841788 (-0.896748) \| 11.970022 / 8.074308 (3.895714) \| 10.261176 / 10.191392 (0.069784) \| 0.138231 / 0.680424 (-0.542193) \| 0.013933 / 0.534201 (-0.520268) \| 0.270640 / 0.579283 (-0.308643) \| 0.263185 / 0.434364 (-0.171178) \| 0.306686 / 0.540337 (-0.233651) \| 0.423164 / 1.386936 (-0.963772) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004765 / 0.011353 (-0.006588) \| 0.003158 / 0.011008 (-0.007850) \| 0.047813 / 0.038508 (0.009305) \| 0.053363 / 0.023109 (0.030254) \| 0.278570 / 0.275898 (0.002671) \| 0.291500 / 0.323480 (-0.031980) \| 0.003987 / 0.007986 (-0.003998) \| 0.002430 / 0.004328 (-0.001898) \| 0.048059 / 0.004250 (0.043809) \| 0.038595 / 0.037052 (0.001542) \| 0.276383 / 0.258489 (0.017894) \| 0.304234 / 0.293841 (0.010393) \| 0.024402 / 0.128546 (-0.104144) \| 0.007303 / 0.075646 (-0.068343) \| 0.055091 / 0.419271 (-0.364180) \| 0.032735 / 0.043533 (-0.010797) \| 0.270905 / 0.255139 (0.015766) \| 0.287181 / 0.283200 (0.003981) \| 0.018919 / 0.141683 (-0.122764) \| 1.153814 / 1.452155 (-0.298341) \| 1.197009 / 1.492716 (-0.295707) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093743 / 0.018006 (0.075737) \| 0.302877 / 0.000490 (0.302387) \| 0.000223 / 0.000200 (0.000023) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021279 / 0.037411 (-0.016133) \| 0.070886 / 0.014526 (0.056360) \| 0.081628 / 0.176557 (-0.094928) \| 0.119721 / 0.737135 (-0.617414) \| 0.083093 / 0.296338 (-0.213245) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297788 / 0.215209 (0.082579) \| 2.915235 / 2.077655 (0.837580) \| 1.587580 / 1.504120 (0.083460) \| 1.461699 / 1.541195 (-0.079495) \| 1.520609 / 1.468490 (0.052119) \| 0.398363 / 4.584777 (-4.186413) \| 2.408415 / 3.745712 (-1.337297) \| 2.552776 / 5.269862 (-2.717086) \| 1.508219 / 4.565676 (-3.057457) \| 0.045884 / 0.424275 (-0.378391) \| 0.004842 / 0.007607 (-0.002765) \| 0.341376 / 0.226044 (0.115331) \| 3.420192 / 2.268929 (1.151264) \| 1.974938 / 55.444624 (-53.469686) \| 1.678283 / 6.876477 (-5.198194) \| 1.702439 / 2.142072 (-0.439633) \| 0.467056 / 4.805227 (-4.338172) \| 0.098684 / 6.500664 (-6.401980) \| 0.041052 / 0.075469 (-0.034417) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.990145 / 1.841788 (-0.851643) \| 12.143198 / 8.074308 (4.068890) \| 10.911039 / 10.191392 (0.719647) \| 0.130384 / 0.680424 (-0.550040) \| 0.015602 / 0.534201 (-0.518599) \| 0.270799 / 0.579283 (-0.308484) \| 0.279060 / 0.434364 (-0.155304) \| 0.315108 / 0.540337 (-0.225230) \| 0.413576 / 1.386936 (-0.973360) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d99b8225e28cca88ed9c2d9b1d8e0342762c4ece \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004911 / 0.011353 (-0.006442) \| 0.002808 / 0.011008 (-0.008200) \| 0.061367 / 0.038508 (0.022859) \| 0.050154 / 0.023109 (0.027045) \| 0.250403 / 0.275898 (-0.025495) \| 0.273831 / 0.323480 (-0.049649) \| 0.002914 / 0.007986 (-0.005071) \| 0.002493 / 0.004328 (-0.001836) \| 0.048288 / 0.004250 (0.044037) \| 0.039219 / 0.037052 (0.002167) \| 0.260043 / 0.258489 (0.001554) \| 0.288177 / 0.293841 (-0.005664) \| 0.023123 / 0.128546 (-0.105423) \| 0.006981 / 0.075646 (-0.068666) \| 0.201306 / 0.419271 (-0.217965) \| 0.035670 / 0.043533 (-0.007863) \| 0.255237 / 0.255139 (0.000098) \| 0.283701 / 0.283200 (0.000502) \| 0.019349 / 0.141683 (-0.122334) \| 1.100963 / 1.452155 (-0.351192) \| 1.152725 / 1.492716 (-0.339992) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.106350 / 0.018006 (0.088344) \| 0.300577 / 0.000490 (0.300087) \| 0.000206 / 0.000200 (0.000006) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019028 / 0.037411 (-0.018384) \| 0.062643 / 0.014526 (0.048118) \| 0.072771 / 0.176557 (-0.103786) \| 0.119873 / 0.737135 (-0.617263) \| 0.074470 / 0.296338 (-0.221869) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287032 / 0.215209 (0.071823) \| 2.826134 / 2.077655 (0.748480) \| 1.507362 / 1.504120 (0.003242) \| 1.382929 / 1.541195 (-0.158266) \| 1.385361 / 1.468490 (-0.083129) \| 0.412081 / 4.584777 (-4.172696) \| 2.384289 / 3.745712 (-1.361423) \| 2.551316 / 5.269862 (-2.718546) \| 1.562954 / 4.565676 (-3.002722) \| 0.046669 / 0.424275 (-0.377606) \| 0.004804 / 0.007607 (-0.002803) \| 0.337751 / 0.226044 (0.111707) \| 3.378894 / 2.268929 (1.109965) \| 1.848817 / 55.444624 (-53.595807) \| 1.564560 / 6.876477 (-5.311917) \| 1.579577 / 2.142072 (-0.562496) \| 0.484531 / 4.805227 (-4.320697) \| 0.101157 / 6.500664 (-6.399507) \| 0.042272 / 0.075469 (-0.033197) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.948289 / 1.841788 (-0.893498) \| 11.490877 / 8.074308 (3.416569) \| 10.492787 / 10.191392 (0.301395) \| 0.128575 / 0.680424 (-0.551849) \| 0.013716 / 0.534201 (-0.520485) \| 0.271075 / 0.579283 (-0.308208) \| 0.269749 / 0.434364 (-0.164615) \| 0.306378 / 0.540337 (-0.233959) \| 0.400204 / 1.386936 (-0.986732) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004821 / 0.011353 (-0.006532) \| 0.002773 / 0.011008 (-0.008235) \| 0.048934 / 0.038508 (0.010426) \| 0.049490 / 0.023109 (0.026380) \| 0.271107 / 0.275898 (-0.004791) \| 0.291472 / 0.323480 (-0.032008) \| 0.004734 / 0.007986 (-0.003252) \| 0.002437 / 0.004328 (-0.001892) \| 0.048840 / 0.004250 (0.044590) \| 0.039757 / 0.037052 (0.002704) \| 0.276037 / 0.258489 (0.017548) \| 0.298220 / 0.293841 (0.004379) \| 0.024595 / 0.128546 (-0.103952) \| 0.007320 / 0.075646 (-0.068327) \| 0.054693 / 0.419271 (-0.364578) \| 0.032672 / 0.043533 (-0.010861) \| 0.271555 / 0.255139 (0.016416) \| 0.287685 / 0.283200 (0.004485) \| 0.017159 / 0.141683 (-0.124524) \| 1.118496 / 1.452155 (-0.333659) \| 1.177389 / 1.492716 (-0.315327) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090469 / 0.018006 (0.072463) \| 0.306014 / 0.000490 (0.305525) \| 0.000218 / 0.000200 (0.000018) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021452 / 0.037411 (-0.015960) \| 0.070014 / 0.014526 (0.055488) \| 0.081917 / 0.176557 (-0.094639) \| 0.120615 / 0.737135 (-0.616520) \| 0.081745 / 0.296338 (-0.214593) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294049 / 0.215209 (0.078840) \| 2.886802 / 2.077655 (0.809147) \| 1.607817 / 1.504120 (0.103697) \| 1.474172 / 1.541195 (-0.067023) \| 1.474744 / 1.468490 (0.006254) \| 0.398178 / 4.584777 (-4.186599) \| 2.455908 / 3.745712 (-1.289804) \| 2.463003 / 5.269862 (-2.806858) \| 1.560402 / 4.565676 (-3.005275) \| 0.046208 / 0.424275 (-0.378067) \| 0.004862 / 0.007607 (-0.002745) \| 0.350862 / 0.226044 (0.124817) \| 3.463958 / 2.268929 (1.195030) \| 1.934696 / 55.444624 (-53.509928) \| 1.660090 / 6.876477 (-5.216387) \| 1.770920 / 2.142072 (-0.371153) \| 0.468409 / 4.805227 (-4.336819) \| 0.096812 / 6.500664 (-6.403852) \| 0.040580 / 0.075469 (-0.034889) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.978102 / 1.841788 (-0.863686) \| 11.943265 / 8.074308 (3.868957) \| 10.684995 / 10.191392 (0.493603) \| 0.131554 / 0.680424 (-0.548870) \| 0.015608 / 0.534201 (-0.518593) \| 0.271449 / 0.579283 (-0.307834) \| 0.282485 / 0.434364 (-0.151879) \| 0.302376 / 0.540337 (-0.237962) \| 0.524908 / 1.386936 (-0.862028) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2bb0b21e37a57257a7d428f8744c862ca92c0c7e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004926 / 0.011353 (-0.006427) \| 0.003020 / 0.011008 (-0.007988) \| 0.061899 / 0.038508 (0.023391) \| 0.063836 / 0.023109 (0.040726) \| 0.239252 / 0.275898 (-0.036646) \| 0.268320 / 0.323480 (-0.055160) \| 0.003939 / 0.007986 (-0.004046) \| 0.002557 / 0.004328 (-0.001772) \| 0.048469 / 0.004250 (0.044219) \| 0.038707 / 0.037052 (0.001655) \| 0.247563 / 0.258489 (-0.010926) \| 0.281171 / 0.293841 (-0.012670) \| 0.023564 / 0.128546 (-0.104983) \| 0.007699 / 0.075646 (-0.067948) \| 0.207561 / 0.419271 (-0.211710) \| 0.036362 / 0.043533 (-0.007171) \| 0.248324 / 0.255139 (-0.006814) \| 0.269673 / 0.283200 (-0.013527) \| 0.018841 / 0.141683 (-0.122842) \| 1.123407 / 1.452155 (-0.328748) \| 1.170422 / 1.492716 (-0.322295) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096278 / 0.018006 (0.078272) \| 0.311477 / 0.000490 (0.310988) \| 0.000217 / 0.000200 (0.000017) \| 0.000042 / 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) \| 0.019470 / 0.037411 (-0.017942) \| 0.071888 / 0.014526 (0.057362) \| 0.074264 / 0.176557 (-0.102292) \| 0.124413 / 0.737135 (-0.612723) \| 0.075602 / 0.296338 (-0.220737) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284755 / 0.215209 (0.069546) \| 2.770789 / 2.077655 (0.693135) \| 1.478276 / 1.504120 (-0.025843) \| 1.375287 / 1.541195 (-0.165907) \| 1.398032 / 1.468490 (-0.070458) \| 0.420457 / 4.584777 (-4.164320) \| 2.445929 / 3.745712 (-1.299783) \| 2.819548 / 5.269862 (-2.450313) \| 1.628506 / 4.565676 (-2.937171) \| 0.047687 / 0.424275 (-0.376588) \| 0.004861 / 0.007607 (-0.002746) \| 0.340173 / 0.226044 (0.114129) \| 3.340703 / 2.268929 (1.071774) \| 1.882803 / 55.444624 (-53.561821) \| 1.587206 / 6.876477 (-5.289271) \| 1.645298 / 2.142072 (-0.496774) \| 0.490957 / 4.805227 (-4.314270) \| 0.102779 / 6.500664 (-6.397885) \| 0.048372 / 0.075469 (-0.027098) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.958311 / 1.841788 (-0.883477) \| 12.354981 / 8.074308 (4.280673) \| 10.864826 / 10.191392 (0.673434) \| 0.149053 / 0.680424 (-0.531371) \| 0.015078 / 0.534201 (-0.519123) \| 0.270117 / 0.579283 (-0.309166) \| 0.274495 / 0.434364 (-0.159869) \| 0.307584 / 0.540337 (-0.232753) \| 0.405603 / 1.386936 (-0.981333) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004996 / 0.011353 (-0.006357) \| 0.002995 / 0.011008 (-0.008014) \| 0.047897 / 0.038508 (0.009389) \| 0.056413 / 0.023109 (0.033303) \| 0.277669 / 0.275898 (0.001771) \| 0.300679 / 0.323480 (-0.022801) \| 0.004094 / 0.007986 (-0.003892) \| 0.002519 / 0.004328 (-0.001810) \| 0.049536 / 0.004250 (0.045285) \| 0.042341 / 0.037052 (0.005288) \| 0.281533 / 0.258489 (0.023044) \| 0.306771 / 0.293841 (0.012930) \| 0.025379 / 0.128546 (-0.103167) \| 0.007495 / 0.075646 (-0.068152) \| 0.054453 / 0.419271 (-0.364818) \| 0.032616 / 0.043533 (-0.010917) \| 0.277844 / 0.255139 (0.022705) \| 0.296265 / 0.283200 (0.013065) \| 0.019462 / 0.141683 (-0.122221) \| 1.115841 / 1.452155 (-0.336313) \| 1.169662 / 1.492716 (-0.323054) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095459 / 0.018006 (0.077453) \| 0.301590 / 0.000490 (0.301100) \| 0.000230 / 0.000200 (0.000030) \| 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) \| 0.022182 / 0.037411 (-0.015229) \| 0.085367 / 0.014526 (0.070842) \| 0.084006 / 0.176557 (-0.092550) \| 0.121260 / 0.737135 (-0.615876) \| 0.084137 / 0.296338 (-0.212202) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.310335 / 0.215209 (0.095126) \| 3.002531 / 2.077655 (0.924876) \| 1.642282 / 1.504120 (0.138162) \| 1.573044 / 1.541195 (0.031849) \| 1.572076 / 1.468490 (0.103586) \| 0.422037 / 4.584777 (-4.162740) \| 2.495295 / 3.745712 (-1.250417) \| 2.523707 / 5.269862 (-2.746155) \| 1.725824 / 4.565676 (-2.839853) \| 0.047814 / 0.424275 (-0.376461) \| 0.004868 / 0.007607 (-0.002739) \| 0.352833 / 0.226044 (0.126789) \| 3.477241 / 2.268929 (1.208313) \| 1.983888 / 55.444624 (-53.460736) \| 1.696883 / 6.876477 (-5.179594) \| 1.831665 / 2.142072 (-0.310407) \| 0.502976 / 4.805227 (-4.302251) \| 0.101264 / 6.500664 (-6.399400) \| 0.041779 / 0.075469 (-0.033690) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.981629 / 1.841788 (-0.860159) \| 12.550634 / 8.074308 (4.476326) \| 11.113382 / 10.191392 (0.921990) \| 0.136565 / 0.680424 (-0.543859) \| 0.016742 / 0.534201 (-0.517459) \| 0.274316 / 0.579283 (-0.304967) \| 0.284687 / 0.434364 (-0.149676) \| 0.309966 / 0.540337 (-0.230372) \| 0.557990 / 1.386936 (-0.828946) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b0c30facb87af83107a645eeffcd18c0775afe11 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004980 / 0.011353 (-0.006373) \| 0.002786 / 0.011008 (-0.008222) \| 0.062460 / 0.038508 (0.023952) \| 0.051811 / 0.023109 (0.028702) \| 0.231734 / 0.275898 (-0.044164) \| 0.254075 / 0.323480 (-0.069405) \| 0.002884 / 0.007986 (-0.005102) \| 0.002317 / 0.004328 (-0.002011) \| 0.049044 / 0.004250 (0.044793) \| 0.038984 / 0.037052 (0.001931) \| 0.241193 / 0.258489 (-0.017296) \| 0.272091 / 0.293841 (-0.021750) \| 0.023098 / 0.128546 (-0.105448) \| 0.007190 / 0.075646 (-0.068456) \| 0.201409 / 0.419271 (-0.217863) \| 0.036100 / 0.043533 (-0.007433) \| 0.238185 / 0.255139 (-0.016954) \| 0.257127 / 0.283200 (-0.026072) \| 0.019542 / 0.141683 (-0.122141) \| 1.127925 / 1.452155 (-0.324230) \| 1.174354 / 1.492716 (-0.318362) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.099608 / 0.018006 (0.081601) \| 0.315046 / 0.000490 (0.314556) \| 0.000282 / 0.000200 (0.000082) \| 0.000042 / 0.000054 (-0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018710 / 0.037411 (-0.018701) \| 0.062557 / 0.014526 (0.048031) \| 0.074021 / 0.176557 (-0.102536) \| 0.119670 / 0.737135 (-0.617465) \| 0.076491 / 0.296338 (-0.219847) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282940 / 0.215209 (0.067731) \| 2.788542 / 2.077655 (0.710887) \| 1.496039 / 1.504120 (-0.008080) \| 1.367542 / 1.541195 (-0.173653) \| 1.393705 / 1.468490 (-0.074785) \| 0.405910 / 4.584777 (-4.178867) \| 2.422544 / 3.745712 (-1.323168) \| 2.602822 / 5.269862 (-2.667039) \| 1.586853 / 4.565676 (-2.978823) \| 0.045440 / 0.424275 (-0.378836) \| 0.004792 / 0.007607 (-0.002815) \| 0.342059 / 0.226044 (0.116015) \| 3.366880 / 2.268929 (1.097952) \| 1.810566 / 55.444624 (-53.634058) \| 1.527112 / 6.876477 (-5.349364) \| 1.548906 / 2.142072 (-0.593166) \| 0.479491 / 4.805227 (-4.325736) \| 0.099807 / 6.500664 (-6.400857) \| 0.041951 / 0.075469 (-0.033518) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.953723 / 1.841788 (-0.888065) \| 11.837240 / 8.074308 (3.762932) \| 10.562979 / 10.191392 (0.371587) \| 0.145064 / 0.680424 (-0.535360) \| 0.014285 / 0.534201 (-0.519916) \| 0.270605 / 0.579283 (-0.308678) \| 0.264086 / 0.434364 (-0.170278) \| 0.308000 / 0.540337 (-0.232337) \| 0.403916 / 1.386936 (-0.983020) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004796 / 0.011353 (-0.006557) \| 0.002997 / 0.011008 (-0.008011) \| 0.048702 / 0.038508 (0.010193) \| 0.053377 / 0.023109 (0.030267) \| 0.271852 / 0.275898 (-0.004046) \| 0.293366 / 0.323480 (-0.030114) \| 0.004041 / 0.007986 (-0.003945) \| 0.002459 / 0.004328 (-0.001869) \| 0.048197 / 0.004250 (0.043947) \| 0.040094 / 0.037052 (0.003042) \| 0.275837 / 0.258489 (0.017348) \| 0.301174 / 0.293841 (0.007333) \| 0.024433 / 0.128546 (-0.104113) \| 0.007203 / 0.075646 (-0.068444) \| 0.054080 / 0.419271 (-0.365192) \| 0.033237 / 0.043533 (-0.010295) \| 0.271177 / 0.255139 (0.016038) \| 0.293062 / 0.283200 (0.009862) \| 0.018399 / 0.141683 (-0.123284) \| 1.149527 / 1.452155 (-0.302628) \| 1.202717 / 1.492716 (-0.290000) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093168 / 0.018006 (0.075162) \| 0.290536 / 0.000490 (0.290046) \| 0.000290 / 0.000200 (0.000090) \| 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) \| 0.021191 / 0.037411 (-0.016221) \| 0.069990 / 0.014526 (0.055465) \| 0.080636 / 0.176557 (-0.095920) \| 0.120151 / 0.737135 (-0.616984) \| 0.082944 / 0.296338 (-0.213395) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289673 / 0.215209 (0.074463) \| 2.828419 / 2.077655 (0.750764) \| 1.590741 / 1.504120 (0.086621) \| 1.480969 / 1.541195 (-0.060226) \| 1.512761 / 1.468490 (0.044271) \| 0.398328 / 4.584777 (-4.186449) \| 2.441134 / 3.745712 (-1.304578) \| 2.487606 / 5.269862 (-2.782256) \| 1.586604 / 4.565676 (-2.979073) \| 0.045578 / 0.424275 (-0.378697) \| 0.004842 / 0.007607 (-0.002766) \| 0.344556 / 0.226044 (0.118512) \| 3.395982 / 2.268929 (1.127053) \| 1.963354 / 55.444624 (-53.481271) \| 1.680496 / 6.876477 (-5.195980) \| 1.827916 / 2.142072 (-0.314157) \| 0.476203 / 4.805227 (-4.329024) \| 0.098016 / 6.500664 (-6.402648) \| 0.041234 / 0.075469 (-0.034235) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.977820 / 1.841788 (-0.863968) \| 12.139614 / 8.074308 (4.065306) \| 10.643071 / 10.191392 (0.451679) \| 0.130928 / 0.680424 (-0.549496) \| 0.015341 / 0.534201 (-0.518860) \| 0.271304 / 0.579283 (-0.307979) \| 0.284671 / 0.434364 (-0.149693) \| 0.306210 / 0.540337 (-0.234128) \| 0.546498 / 1.386936 (-0.840438) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1bf7408a171db4a744d1760a9e32ba21deb8d41d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004748 / 0.011353 (-0.006605) \| 0.002942 / 0.011008 (-0.008066) \| 0.061298 / 0.038508 (0.022790) \| 0.052873 / 0.023109 (0.029764) \| 0.250573 / 0.275898 (-0.025325) \| 0.270636 / 0.323480 (-0.052844) \| 0.002925 / 0.007986 (-0.005061) \| 0.003126 / 0.004328 (-0.001203) \| 0.047340 / 0.004250 (0.043090) \| 0.038662 / 0.037052 (0.001609) \| 0.252151 / 0.258489 (-0.006338) \| 0.284700 / 0.293841 (-0.009141) \| 0.025145 / 0.128546 (-0.103402) \| 0.007075 / 0.075646 (-0.068572) \| 0.200501 / 0.419271 (-0.218771) \| 0.035623 / 0.043533 (-0.007910) \| 0.249657 / 0.255139 (-0.005482) \| 0.272384 / 0.283200 (-0.010815) \| 0.018331 / 0.141683 (-0.123351) \| 1.095064 / 1.452155 (-0.357091) \| 1.145304 / 1.492716 (-0.347412) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092548 / 0.018006 (0.074542) \| 0.299338 / 0.000490 (0.298848) \| 0.000212 / 0.000200 (0.000012) \| 0.000046 / 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) \| 0.018723 / 0.037411 (-0.018688) \| 0.062226 / 0.014526 (0.047700) \| 0.072840 / 0.176557 (-0.103717) \| 0.120073 / 0.737135 (-0.617063) \| 0.074536 / 0.296338 (-0.221802) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284862 / 0.215209 (0.069653) \| 2.791842 / 2.077655 (0.714188) \| 1.506481 / 1.504120 (0.002361) \| 1.368952 / 1.541195 (-0.172243) \| 1.372555 / 1.468490 (-0.095935) \| 0.408292 / 4.584777 (-4.176485) \| 2.381155 / 3.745712 (-1.364558) \| 2.613617 / 5.269862 (-2.656244) \| 1.575892 / 4.565676 (-2.989785) \| 0.047526 / 0.424275 (-0.376749) \| 0.004792 / 0.007607 (-0.002815) \| 0.344818 / 0.226044 (0.118773) \| 3.344965 / 2.268929 (1.076036) \| 1.883659 / 55.444624 (-53.560965) \| 1.596039 / 6.876477 (-5.280437) \| 1.584410 / 2.142072 (-0.557662) \| 0.486672 / 4.805227 (-4.318555) \| 0.101464 / 6.500664 (-6.399200) \| 0.041824 / 0.075469 (-0.033645) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.930491 / 1.841788 (-0.911296) \| 11.636526 / 8.074308 (3.562218) \| 10.371829 / 10.191392 (0.180437) \| 0.138181 / 0.680424 (-0.542243) \| 0.014307 / 0.534201 (-0.519894) \| 0.268322 / 0.579283 (-0.310961) \| 0.264173 / 0.434364 (-0.170191) \| 0.303649 / 0.540337 (-0.236688) \| 0.399958 / 1.386936 (-0.986978) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004802 / 0.011353 (-0.006551) \| 0.002861 / 0.011008 (-0.008147) \| 0.048843 / 0.038508 (0.010335) \| 0.053887 / 0.023109 (0.030778) \| 0.278690 / 0.275898 (0.002792) \| 0.302729 / 0.323480 (-0.020751) \| 0.003929 / 0.007986 (-0.004057) \| 0.002376 / 0.004328 (-0.001953) \| 0.048146 / 0.004250 (0.043896) \| 0.039842 / 0.037052 (0.002790) \| 0.281595 / 0.258489 (0.023106) \| 0.305813 / 0.293841 (0.011972) \| 0.024214 / 0.128546 (-0.104333) \| 0.007201 / 0.075646 (-0.068446) \| 0.053604 / 0.419271 (-0.365667) \| 0.032841 / 0.043533 (-0.010691) \| 0.276168 / 0.255139 (0.021029) \| 0.293869 / 0.283200 (0.010669) \| 0.017550 / 0.141683 (-0.124132) \| 1.121508 / 1.452155 (-0.330647) \| 1.177694 / 1.492716 (-0.315022) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091805 / 0.018006 (0.073799) \| 0.299026 / 0.000490 (0.298536) \| 0.000219 / 0.000200 (0.000019) \| 0.000051 / 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) \| 0.021094 / 0.037411 (-0.016318) \| 0.069769 / 0.014526 (0.055243) \| 0.081191 / 0.176557 (-0.095366) \| 0.118884 / 0.737135 (-0.618252) \| 0.081955 / 0.296338 (-0.214383) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292159 / 0.215209 (0.076950) \| 2.874473 / 2.077655 (0.796819) \| 1.614695 / 1.504120 (0.110575) \| 1.492123 / 1.541195 (-0.049071) \| 1.505293 / 1.468490 (0.036803) \| 0.394498 / 4.584777 (-4.190279) \| 2.455539 / 3.745712 (-1.290173) \| 2.458184 / 5.269862 (-2.811677) \| 1.569108 / 4.565676 (-2.996569) \| 0.046576 / 0.424275 (-0.377699) \| 0.005093 / 0.007607 (-0.002514) \| 0.346142 / 0.226044 (0.120098) \| 3.398171 / 2.268929 (1.129242) \| 1.971953 / 55.444624 (-53.472672) \| 1.695275 / 6.876477 (-5.181201) \| 1.840511 / 2.142072 (-0.301562) \| 0.465932 / 4.805227 (-4.339295) \| 0.098578 / 6.500664 (-6.402086) \| 0.040456 / 0.075469 (-0.035013) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.977636 / 1.841788 (-0.864152) \| 12.083585 / 8.074308 (4.009277) \| 10.509082 / 10.191392 (0.317690) \| 0.130717 / 0.680424 (-0.549707) \| 0.015958 / 0.534201 (-0.518243) \| 0.273504 / 0.579283 (-0.305780) \| 0.276498 / 0.434364 (-0.157866) \| 0.306139 / 0.540337 (-0.234199) \| 0.522521 / 1.386936 (-0.864415) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6e17dd8acec9a958ba82a5f753276b842eaadf52 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004859 / 0.011353 (-0.006493) \| 0.002423 / 0.011008 (-0.008585) \| 0.060969 / 0.038508 (0.022461) \| 0.048758 / 0.023109 (0.025649) \| 0.245400 / 0.275898 (-0.030498) \| 0.263686 / 0.323480 (-0.059794) \| 0.002852 / 0.007986 (-0.005134) \| 0.002273 / 0.004328 (-0.002055) \| 0.047648 / 0.004250 (0.043398) \| 0.038310 / 0.037052 (0.001258) \| 0.249849 / 0.258489 (-0.008640) \| 0.279305 / 0.293841 (-0.014536) \| 0.022897 / 0.128546 (-0.105649) \| 0.006882 / 0.075646 (-0.068764) \| 0.202793 / 0.419271 (-0.216478) \| 0.034557 / 0.043533 (-0.008976) \| 0.252147 / 0.255139 (-0.002992) \| 0.267414 / 0.283200 (-0.015785) \| 0.019956 / 0.141683 (-0.121727) \| 1.106181 / 1.452155 (-0.345973) \| 1.158423 / 1.492716 (-0.334293) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.086848 / 0.018006 (0.068842) \| 0.295235 / 0.000490 (0.294745) \| 0.000211 / 0.000200 (0.000011) \| 0.000041 / 0.000054 (-0.000014) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018209 / 0.037411 (-0.019203) \| 0.061967 / 0.014526 (0.047441) \| 0.071551 / 0.176557 (-0.105005) \| 0.117525 / 0.737135 (-0.619611) \| 0.073401 / 0.296338 (-0.222937) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.272388 / 0.215209 (0.057179) \| 2.689797 / 2.077655 (0.612143) \| 1.440897 / 1.504120 (-0.063223) \| 1.334689 / 1.541195 (-0.206505) \| 1.356395 / 1.468490 (-0.112095) \| 0.387201 / 4.584777 (-4.197576) \| 2.342908 / 3.745712 (-1.402804) \| 2.480156 / 5.269862 (-2.789706) \| 1.512342 / 4.565676 (-3.053335) \| 0.042324 / 0.424275 (-0.381951) \| 0.004744 / 0.007607 (-0.002863) \| 0.323568 / 0.226044 (0.097523) \| 3.190021 / 2.268929 (0.921093) \| 1.765046 / 55.444624 (-53.679578) \| 1.513958 / 6.876477 (-5.362519) \| 1.504943 / 2.142072 (-0.637129) \| 0.452302 / 4.805227 (-4.352925) \| 0.094728 / 6.500664 (-6.405936) \| 0.038641 / 0.075469 (-0.036828) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.939721 / 1.841788 (-0.902067) \| 11.174180 / 8.074308 (3.099872) \| 10.046717 / 10.191392 (-0.144675) \| 0.124877 / 0.680424 (-0.555547) \| 0.013687 / 0.534201 (-0.520514) \| 0.261002 / 0.579283 (-0.318282) \| 0.267349 / 0.434364 (-0.167015) \| 0.306545 / 0.540337 (-0.233792) \| 0.389322 / 1.386936 (-0.997614) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004702 / 0.011353 (-0.006651) \| 0.002431 / 0.011008 (-0.008577) \| 0.046138 / 0.038508 (0.007630) \| 0.048356 / 0.023109 (0.025246) \| 0.272154 / 0.275898 (-0.003744) \| 0.292676 / 0.323480 (-0.030804) \| 0.003870 / 0.007986 (-0.004115) \| 0.002294 / 0.004328 (-0.002035) \| 0.048129 / 0.004250 (0.043879) \| 0.039026 / 0.037052 (0.001974) \| 0.273900 / 0.258489 (0.015411) \| 0.295927 / 0.293841 (0.002086) \| 0.024044 / 0.128546 (-0.104502) \| 0.006906 / 0.075646 (-0.068740) \| 0.053268 / 0.419271 (-0.366004) \| 0.032360 / 0.043533 (-0.011173) \| 0.273470 / 0.255139 (0.018331) \| 0.286207 / 0.283200 (0.003007) \| 0.017580 / 0.141683 (-0.124103) \| 1.091064 / 1.452155 (-0.361091) \| 1.159645 / 1.492716 (-0.333071) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.087149 / 0.018006 (0.069143) \| 0.293489 / 0.000490 (0.293000) \| 0.000217 / 0.000200 (0.000017) \| 0.000052 / 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) \| 0.021779 / 0.037411 (-0.015632) \| 0.066453 / 0.014526 (0.051928) \| 0.078517 / 0.176557 (-0.098039) \| 0.117317 / 0.737135 (-0.619819) \| 0.079828 / 0.296338 (-0.216511) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287605 / 0.215209 (0.072396) \| 2.811094 / 2.077655 (0.733439) \| 1.572474 / 1.504120 (0.068354) \| 1.450294 / 1.541195 (-0.090900) \| 1.456052 / 1.468490 (-0.012438) \| 0.402095 / 4.584777 (-4.182682) \| 2.444709 / 3.745712 (-1.301003) \| 2.390837 / 5.269862 (-2.879024) \| 1.530519 / 4.565676 (-3.035157) \| 0.043520 / 0.424275 (-0.380755) \| 0.004788 / 0.007607 (-0.002819) \| 0.337436 / 0.226044 (0.111391) \| 3.326111 / 2.268929 (1.057182) \| 1.889273 / 55.444624 (-53.555352) \| 1.624423 / 6.876477 (-5.252054) \| 1.715766 / 2.142072 (-0.426307) \| 0.484570 / 4.805227 (-4.320657) \| 0.091691 / 6.500664 (-6.408973) \| 0.038278 / 0.075469 (-0.037191) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.961708 / 1.841788 (-0.880079) \| 11.496471 / 8.074308 (3.422162) \| 10.211589 / 10.191392 (0.020197) \| 0.127584 / 0.680424 (-0.552840) \| 0.015178 / 0.534201 (-0.519023) \| 0.267290 / 0.579283 (-0.311993) \| 0.259305 / 0.434364 (-0.175059) \| 0.303433 / 0.540337 (-0.236905) \| 0.508016 / 1.386936 (-0.878920) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#72880aa8a3e4b49438db72b13fb9a2541331820b \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004558 / 0.011353 (-0.006795) \| 0.002563 / 0.011008 (-0.008445) \| 0.061314 / 0.038508 (0.022806) \| 0.049312 / 0.023109 (0.026203) \| 0.240988 / 0.275898 (-0.034910) \| 0.260548 / 0.323480 (-0.062932) \| 0.002817 / 0.007986 (-0.005169) \| 0.002904 / 0.004328 (-0.001425) \| 0.048515 / 0.004250 (0.044264) \| 0.037511 / 0.037052 (0.000459) \| 0.244880 / 0.258489 (-0.013609) \| 0.276118 / 0.293841 (-0.017723) \| 0.022636 / 0.128546 (-0.105910) \| 0.006694 / 0.075646 (-0.068953) \| 0.201336 / 0.419271 (-0.217936) \| 0.035228 / 0.043533 (-0.008305) \| 0.242424 / 0.255139 (-0.012715) \| 0.260178 / 0.283200 (-0.023022) \| 0.017836 / 0.141683 (-0.123847) \| 1.122296 / 1.452155 (-0.329859) \| 1.189024 / 1.492716 (-0.303692) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090051 / 0.018006 (0.072045) \| 0.298562 / 0.000490 (0.298073) \| 0.000216 / 0.000200 (0.000016) \| 0.000051 / 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) \| 0.018228 / 0.037411 (-0.019184) \| 0.062379 / 0.014526 (0.047853) \| 0.073482 / 0.176557 (-0.103075) \| 0.120341 / 0.737135 (-0.616794) \| 0.073868 / 0.296338 (-0.222470) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280195 / 0.215209 (0.064986) \| 2.743333 / 2.077655 (0.665678) \| 1.470078 / 1.504120 (-0.034042) \| 1.335874 / 1.541195 (-0.205321) \| 1.342961 / 1.468490 (-0.125529) \| 0.409203 / 4.584777 (-4.175574) \| 2.392217 / 3.745712 (-1.353495) \| 2.544161 / 5.269862 (-2.725701) \| 1.544016 / 4.565676 (-3.021660) \| 0.059485 / 0.424275 (-0.364790) \| 0.004833 / 0.007607 (-0.002775) \| 0.335114 / 0.226044 (0.109070) \| 3.289009 / 2.268929 (1.020080) \| 1.854666 / 55.444624 (-53.589959) \| 1.566282 / 6.876477 (-5.310195) \| 1.561287 / 2.142072 (-0.580786) \| 0.484961 / 4.805227 (-4.320267) \| 0.099651 / 6.500664 (-6.401013) \| 0.041408 / 0.075469 (-0.034061) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.941743 / 1.841788 (-0.900044) \| 11.165692 / 8.074308 (3.091383) \| 10.236693 / 10.191392 (0.045301) \| 0.129694 / 0.680424 (-0.550730) \| 0.014879 / 0.534201 (-0.519322) \| 0.275120 / 0.579283 (-0.304163) \| 0.263822 / 0.434364 (-0.170542) \| 0.306429 / 0.540337 (-0.233909) \| 0.397611 / 1.386936 (-0.989325) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004714 / 0.011353 (-0.006639) \| 0.002430 / 0.011008 (-0.008578) \| 0.047644 / 0.038508 (0.009136) \| 0.049710 / 0.023109 (0.026601) \| 0.271950 / 0.275898 (-0.003948) \| 0.290996 / 0.323480 (-0.032483) \| 0.003888 / 0.007986 (-0.004097) \| 0.002367 / 0.004328 (-0.001962) \| 0.047623 / 0.004250 (0.043372) \| 0.039574 / 0.037052 (0.002522) \| 0.274540 / 0.258489 (0.016051) \| 0.298065 / 0.293841 (0.004224) \| 0.024677 / 0.128546 (-0.103869) \| 0.006844 / 0.075646 (-0.068802) \| 0.053180 / 0.419271 (-0.366091) \| 0.032391 / 0.043533 (-0.011141) \| 0.273222 / 0.255139 (0.018083) \| 0.290336 / 0.283200 (0.007136) \| 0.017911 / 0.141683 (-0.123772) \| 1.105879 / 1.452155 (-0.346276) \| 1.176979 / 1.492716 (-0.315737) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089563 / 0.018006 (0.071557) \| 0.296392 / 0.000490 (0.295903) \| 0.000214 / 0.000200 (0.000014) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021588 / 0.037411 (-0.015824) \| 0.069951 / 0.014526 (0.055425) \| 0.080397 / 0.176557 (-0.096160) \| 0.118772 / 0.737135 (-0.618363) \| 0.080356 / 0.296338 (-0.215983) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288492 / 0.215209 (0.073283) \| 2.839553 / 2.077655 (0.761898) \| 1.597504 / 1.504120 (0.093384) \| 1.475001 / 1.541195 (-0.066193) \| 1.481561 / 1.468490 (0.013071) \| 0.411851 / 4.584777 (-4.172926) \| 2.397322 / 3.745712 (-1.348390) \| 2.444078 / 5.269862 (-2.825784) \| 1.557106 / 4.565676 (-3.008571) \| 0.047159 / 0.424275 (-0.377116) \| 0.004842 / 0.007607 (-0.002765) \| 0.346221 / 0.226044 (0.120177) \| 3.387900 / 2.268929 (1.118972) \| 1.962167 / 55.444624 (-53.482457) \| 1.675017 / 6.876477 (-5.201460) \| 1.788745 / 2.142072 (-0.353328) \| 0.488063 / 4.805227 (-4.317164) \| 0.098878 / 6.500664 (-6.401786) \| 0.040369 / 0.075469 (-0.035100) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.977999 / 1.841788 (-0.863789) \| 11.671558 / 8.074308 (3.597250) \| 10.327847 / 10.191392 (0.136455) \| 0.129317 / 0.680424 (-0.551107) \| 0.015600 / 0.534201 (-0.518601) \| 0.267967 / 0.579283 (-0.311316) \| 0.273811 / 0.434364 (-0.160553) \| 0.301749 / 0.540337 (-0.238588) \| 0.515493 / 1.386936 (-0.871443) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5394939b0b3d124674f938e1f1cd9e8de3cbdbf7 \"CML watermark\")\n", "I added tests and docs @mariosasko @albertvillanova let le know what you think !", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004867 / 0.011353 (-0.006486) \| 0.002952 / 0.011008 (-0.008056) \| 0.062008 / 0.038508 (0.023500) \| 0.055279 / 0.023109 (0.032170) \| 0.248160 / 0.275898 (-0.027738) \| 0.276173 / 0.323480 (-0.047307) \| 0.003945 / 0.007986 (-0.004041) \| 0.002371 / 0.004328 (-0.001958) \| 0.048385 / 0.004250 (0.044134) \| 0.038997 / 0.037052 (0.001945) \| 0.257465 / 0.258489 (-0.001024) \| 0.286920 / 0.293841 (-0.006921) \| 0.023031 / 0.128546 (-0.105515) \| 0.007075 / 0.075646 (-0.068571) \| 0.201897 / 0.419271 (-0.217375) \| 0.035637 / 0.043533 (-0.007896) \| 0.252050 / 0.255139 (-0.003089) \| 0.272580 / 0.283200 (-0.010620) \| 0.018578 / 0.141683 (-0.123105) \| 1.129427 / 1.452155 (-0.322727) \| 1.172182 / 1.492716 (-0.320534) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091806 / 0.018006 (0.073800) \| 0.298632 / 0.000490 (0.298143) \| 0.000202 / 0.000200 (0.000002) \| 0.000047 / 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) \| 0.019123 / 0.037411 (-0.018288) \| 0.062603 / 0.014526 (0.048077) \| 0.074352 / 0.176557 (-0.102205) \| 0.120431 / 0.737135 (-0.616704) \| 0.074622 / 0.296338 (-0.221717) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.276019 / 0.215209 (0.060810) \| 2.701610 / 2.077655 (0.623955) \| 1.398388 / 1.504120 (-0.105732) \| 1.270902 / 1.541195 (-0.270292) \| 1.307992 / 1.468490 (-0.160499) \| 0.396350 / 4.584777 (-4.188427) \| 2.351064 / 3.745712 (-1.394648) \| 2.606229 / 5.269862 (-2.663632) \| 1.591075 / 4.565676 (-2.974601) \| 0.046429 / 0.424275 (-0.377846) \| 0.004832 / 0.007607 (-0.002775) \| 0.327887 / 0.226044 (0.101843) \| 3.277847 / 2.268929 (1.008918) \| 1.767210 / 55.444624 (-53.677414) \| 1.483997 / 6.876477 (-5.392479) \| 1.515689 / 2.142072 (-0.626383) \| 0.471326 / 4.805227 (-4.333902) \| 0.098821 / 6.500664 (-6.401843) \| 0.041914 / 0.075469 (-0.033555) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.956278 / 1.841788 (-0.885510) \| 11.924373 / 8.074308 (3.850065) \| 10.493926 / 10.191392 (0.302534) \| 0.140214 / 0.680424 (-0.540210) \| 0.013679 / 0.534201 (-0.520522) \| 0.270304 / 0.579283 (-0.308979) \| 0.266518 / 0.434364 (-0.167846) \| 0.310113 / 0.540337 (-0.230224) \| 0.399811 / 1.386936 (-0.987125) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004793 / 0.011353 (-0.006560) \| 0.002879 / 0.011008 (-0.008130) \| 0.048632 / 0.038508 (0.010124) \| 0.051413 / 0.023109 (0.028304) \| 0.272704 / 0.275898 (-0.003194) \| 0.291541 / 0.323480 (-0.031939) \| 0.003913 / 0.007986 (-0.004072) \| 0.002387 / 0.004328 (-0.001941) \| 0.049045 / 0.004250 (0.044795) \| 0.040164 / 0.037052 (0.003112) \| 0.273052 / 0.258489 (0.014563) \| 0.300139 / 0.293841 (0.006298) \| 0.024225 / 0.128546 (-0.104321) \| 0.007060 / 0.075646 (-0.068587) \| 0.054360 / 0.419271 (-0.364911) \| 0.032882 / 0.043533 (-0.010650) \| 0.270295 / 0.255139 (0.015157) \| 0.312253 / 0.283200 (0.029054) \| 0.017413 / 0.141683 (-0.124270) \| 1.137306 / 1.452155 (-0.314849) \| 1.203705 / 1.492716 (-0.289011) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091083 / 0.018006 (0.073077) \| 0.301607 / 0.000490 (0.301117) \| 0.000219 / 0.000200 (0.000019) \| 0.000052 / 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) \| 0.021753 / 0.037411 (-0.015658) \| 0.069693 / 0.014526 (0.055167) \| 0.080481 / 0.176557 (-0.096075) \| 0.118581 / 0.737135 (-0.618555) \| 0.082231 / 0.296338 (-0.214108) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300014 / 0.215209 (0.084805) \| 2.885934 / 2.077655 (0.808279) \| 1.594120 / 1.504120 (0.090000) \| 1.472312 / 1.541195 (-0.068883) \| 1.491663 / 1.468490 (0.023173) \| 0.412946 / 4.584777 (-4.171831) \| 2.494168 / 3.745712 (-1.251544) \| 2.527987 / 5.269862 (-2.741875) \| 1.589187 / 4.565676 (-2.976490) \| 0.046594 / 0.424275 (-0.377681) \| 0.004810 / 0.007607 (-0.002797) \| 0.345496 / 0.226044 (0.119452) \| 3.428850 / 2.268929 (1.159921) \| 1.952696 / 55.444624 (-53.491929) \| 1.663285 / 6.876477 (-5.213191) \| 1.822187 / 2.142072 (-0.319885) \| 0.483798 / 4.805227 (-4.321430) \| 0.101403 / 6.500664 (-6.399261) \| 0.041773 / 0.075469 (-0.033696) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.974247 / 1.841788 (-0.867541) \| 12.459980 / 8.074308 (4.385672) \| 10.354792 / 10.191392 (0.163400) \| 0.129083 / 0.680424 (-0.551341) \| 0.015225 / 0.534201 (-0.518976) \| 0.267673 / 0.579283 (-0.311610) \| 0.281011 / 0.434364 (-0.153352) \| 0.303054 / 0.540337 (-0.237283) \| 0.405719 / 1.386936 (-0.981217) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33dc51fc1a8122b842bb7839ff0eda32f173c325 \"CML watermark\")\n", "I switched to using `deepmind/code_contests` in examples in the docs to avoid having to pass trust_remote_code, and remove the DEFAULT naming stuff :)", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005169 / 0.011353 (-0.006184) \| 0.003066 / 0.011008 (-0.007942) \| 0.068884 / 0.038508 (0.030376) \| 0.060345 / 0.023109 (0.037236) \| 0.243050 / 0.275898 (-0.032848) \| 0.265523 / 0.323480 (-0.057957) \| 0.002918 / 0.007986 (-0.005067) \| 0.002495 / 0.004328 (-0.001834) \| 0.051538 / 0.004250 (0.047288) \| 0.040010 / 0.037052 (0.002957) \| 0.249603 / 0.258489 (-0.008886) \| 0.287955 / 0.293841 (-0.005886) \| 0.024003 / 0.128546 (-0.104543) \| 0.007111 / 0.075646 (-0.068535) \| 0.205041 / 0.419271 (-0.214231) \| 0.036296 / 0.043533 (-0.007237) \| 0.246135 / 0.255139 (-0.009004) \| 0.268801 / 0.283200 (-0.014399) \| 0.018451 / 0.141683 (-0.123232) \| 1.130387 / 1.452155 (-0.321767) \| 1.162041 / 1.492716 (-0.330675) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096370 / 0.018006 (0.078364) \| 0.309867 / 0.000490 (0.309377) \| 0.000229 / 0.000200 (0.000029) \| 0.000051 / 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) \| 0.018688 / 0.037411 (-0.018723) \| 0.062859 / 0.014526 (0.048333) \| 0.076383 / 0.176557 (-0.100173) \| 0.120385 / 0.737135 (-0.616750) \| 0.080192 / 0.296338 (-0.216147) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282994 / 0.215209 (0.067785) \| 2.742341 / 2.077655 (0.664686) \| 1.432041 / 1.504120 (-0.072079) \| 1.303282 / 1.541195 (-0.237913) \| 1.347198 / 1.468490 (-0.121292) \| 0.399145 / 4.584777 (-4.185632) \| 2.359766 / 3.745712 (-1.385947) \| 2.753577 / 5.269862 (-2.516285) \| 1.639953 / 4.565676 (-2.925724) \| 0.047111 / 0.424275 (-0.377164) \| 0.004946 / 0.007607 (-0.002661) \| 0.338857 / 0.226044 (0.112813) \| 3.328709 / 2.268929 (1.059781) \| 1.794729 / 55.444624 (-53.649895) \| 1.508514 / 6.876477 (-5.367963) \| 1.550737 / 2.142072 (-0.591335) \| 0.484227 / 4.805227 (-4.321000) \| 0.101001 / 6.500664 (-6.399663) \| 0.042792 / 0.075469 (-0.032677) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.956471 / 1.841788 (-0.885317) \| 12.031362 / 8.074308 (3.957054) \| 10.512914 / 10.191392 (0.321522) \| 0.141841 / 0.680424 (-0.538583) \| 0.014343 / 0.534201 (-0.519858) \| 0.273916 / 0.579283 (-0.305367) \| 0.266150 / 0.434364 (-0.168214) \| 0.312020 / 0.540337 (-0.228317) \| 0.410465 / 1.386936 (-0.976471) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004945 / 0.011353 (-0.006408) \| 0.003288 / 0.011008 (-0.007720) \| 0.048247 / 0.038508 (0.009739) \| 0.057892 / 0.023109 (0.034783) \| 0.269741 / 0.275898 (-0.006157) \| 0.293728 / 0.323480 (-0.029752) \| 0.004789 / 0.007986 (-0.003197) \| 0.002477 / 0.004328 (-0.001852) \| 0.047825 / 0.004250 (0.043575) \| 0.040780 / 0.037052 (0.003727) \| 0.273355 / 0.258489 (0.014865) \| 0.300057 / 0.293841 (0.006216) \| 0.024481 / 0.128546 (-0.104066) \| 0.007285 / 0.075646 (-0.068361) \| 0.053046 / 0.419271 (-0.366226) \| 0.032342 / 0.043533 (-0.011190) \| 0.272293 / 0.255139 (0.017154) \| 0.290842 / 0.283200 (0.007642) \| 0.017546 / 0.141683 (-0.124137) \| 1.155816 / 1.452155 (-0.296339) \| 1.195839 / 1.492716 (-0.296878) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094177 / 0.018006 (0.076170) \| 0.305122 / 0.000490 (0.304632) \| 0.000237 / 0.000200 (0.000037) \| 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) \| 0.021817 / 0.037411 (-0.015595) \| 0.070711 / 0.014526 (0.056185) \| 0.084028 / 0.176557 (-0.092528) \| 0.120160 / 0.737135 (-0.616975) \| 0.083085 / 0.296338 (-0.213254) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289127 / 0.215209 (0.073918) \| 2.826365 / 2.077655 (0.748710) \| 1.582910 / 1.504120 (0.078790) \| 1.472796 / 1.541195 (-0.068399) \| 1.497491 / 1.468490 (0.029000) \| 0.412276 / 4.584777 (-4.172501) \| 2.430692 / 3.745712 (-1.315020) \| 2.556444 / 5.269862 (-2.713418) \| 1.625782 / 4.565676 (-2.939895) \| 0.047921 / 0.424275 (-0.376354) \| 0.004809 / 0.007607 (-0.002798) \| 0.345569 / 0.226044 (0.119524) \| 3.417785 / 2.268929 (1.148856) \| 1.959223 / 55.444624 (-53.485401) \| 1.672765 / 6.876477 (-5.203712) \| 1.852444 / 2.142072 (-0.289628) \| 0.489225 / 4.805227 (-4.316002) \| 0.100624 / 6.500664 (-6.400040) \| 0.041242 / 0.075469 (-0.034227) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.971130 / 1.841788 (-0.870658) \| 12.652204 / 8.074308 (4.577896) \| 10.661821 / 10.191392 (0.470429) \| 0.147636 / 0.680424 (-0.532787) \| 0.015738 / 0.534201 (-0.518463) \| 0.272763 / 0.579283 (-0.306520) \| 0.282623 / 0.434364 (-0.151741) \| 0.341303 / 0.540337 (-0.199035) \| 0.412149 / 1.386936 (-0.974787) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9499908c97ceef1792f69b71e93e36602880a4ae \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004589 / 0.011353 (-0.006764) \| 0.002730 / 0.011008 (-0.008279) \| 0.061862 / 0.038508 (0.023353) \| 0.050945 / 0.023109 (0.027836) \| 0.240776 / 0.275898 (-0.035122) \| 0.266000 / 0.323480 (-0.057480) \| 0.003823 / 0.007986 (-0.004162) \| 0.002345 / 0.004328 (-0.001983) \| 0.047821 / 0.004250 (0.043571) \| 0.037813 / 0.037052 (0.000761) \| 0.251075 / 0.258489 (-0.007415) \| 0.279430 / 0.293841 (-0.014411) \| 0.022957 / 0.128546 (-0.105590) \| 0.007294 / 0.075646 (-0.068353) \| 0.206092 / 0.419271 (-0.213180) \| 0.035308 / 0.043533 (-0.008225) \| 0.247197 / 0.255139 (-0.007942) \| 0.264988 / 0.283200 (-0.018212) \| 0.017588 / 0.141683 (-0.124095) \| 1.093291 / 1.452155 (-0.358864) \| 1.165477 / 1.492716 (-0.327240) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.104057 / 0.018006 (0.086051) \| 0.303424 / 0.000490 (0.302934) \| 0.000223 / 0.000200 (0.000023) \| 0.000051 / 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) \| 0.019040 / 0.037411 (-0.018371) \| 0.063161 / 0.014526 (0.048635) \| 0.085333 / 0.176557 (-0.091224) \| 0.155973 / 0.737135 (-0.581162) \| 0.077528 / 0.296338 (-0.218810) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.276104 / 0.215209 (0.060895) \| 2.738174 / 2.077655 (0.660519) \| 1.479484 / 1.504120 (-0.024636) \| 1.354094 / 1.541195 (-0.187100) \| 1.385312 / 1.468490 (-0.083178) \| 0.401398 / 4.584777 (-4.183379) \| 2.368503 / 3.745712 (-1.377209) \| 2.586405 / 5.269862 (-2.683457) \| 1.573978 / 4.565676 (-2.991699) \| 0.046969 / 0.424275 (-0.377306) \| 0.004874 / 0.007607 (-0.002733) \| 0.334028 / 0.226044 (0.107984) \| 3.269645 / 2.268929 (1.000717) \| 1.834528 / 55.444624 (-53.610096) \| 1.559883 / 6.876477 (-5.316594) \| 1.581380 / 2.142072 (-0.560693) \| 0.479580 / 4.805227 (-4.325647) \| 0.099077 / 6.500664 (-6.401587) \| 0.041166 / 0.075469 (-0.034303) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.918810 / 1.841788 (-0.922978) \| 11.505017 / 8.074308 (3.430709) \| 10.331934 / 10.191392 (0.140542) \| 0.128079 / 0.680424 (-0.552345) \| 0.013716 / 0.534201 (-0.520485) \| 0.271567 / 0.579283 (-0.307716) \| 0.264846 / 0.434364 (-0.169518) \| 0.305245 / 0.540337 (-0.235092) \| 0.401391 / 1.386936 (-0.985546) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004860 / 0.011353 (-0.006493) \| 0.002854 / 0.011008 (-0.008155) \| 0.048327 / 0.038508 (0.009819) \| 0.051377 / 0.023109 (0.028268) \| 0.264344 / 0.275898 (-0.011554) \| 0.286800 / 0.323480 (-0.036680) \| 0.003969 / 0.007986 (-0.004016) \| 0.002415 / 0.004328 (-0.001914) \| 0.048498 / 0.004250 (0.044247) \| 0.040399 / 0.037052 (0.003347) \| 0.267254 / 0.258489 (0.008765) \| 0.292049 / 0.293841 (-0.001792) \| 0.024730 / 0.128546 (-0.103817) \| 0.007275 / 0.075646 (-0.068371) \| 0.053725 / 0.419271 (-0.365546) \| 0.033142 / 0.043533 (-0.010391) \| 0.265418 / 0.255139 (0.010279) \| 0.286242 / 0.283200 (0.003042) \| 0.017824 / 0.141683 (-0.123859) \| 1.135978 / 1.452155 (-0.316176) \| 1.192506 / 1.492716 (-0.300210) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091907 / 0.018006 (0.073900) \| 0.307152 / 0.000490 (0.306663) \| 0.000223 / 0.000200 (0.000023) \| 0.000046 / 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) \| 0.021909 / 0.037411 (-0.015502) \| 0.070676 / 0.014526 (0.056150) \| 0.081651 / 0.176557 (-0.094906) \| 0.120915 / 0.737135 (-0.616220) \| 0.085882 / 0.296338 (-0.210456) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288008 / 0.215209 (0.072799) \| 2.861352 / 2.077655 (0.783697) \| 1.539045 / 1.504120 (0.034925) \| 1.412175 / 1.541195 (-0.129019) \| 1.421236 / 1.468490 (-0.047254) \| 0.404921 / 4.584777 (-4.179856) \| 2.480211 / 3.745712 (-1.265501) \| 2.473083 / 5.269862 (-2.796779) \| 1.558894 / 4.565676 (-3.006783) \| 0.046692 / 0.424275 (-0.377584) \| 0.004802 / 0.007607 (-0.002805) \| 0.346046 / 0.226044 (0.120001) \| 3.464387 / 2.268929 (1.195459) \| 1.937298 / 55.444624 (-53.507326) \| 1.593701 / 6.876477 (-5.282776) \| 1.730688 / 2.142072 (-0.411385) \| 0.481069 / 4.805227 (-4.324158) \| 0.098991 / 6.500664 (-6.401673) \| 0.040491 / 0.075469 (-0.034978) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.967809 / 1.841788 (-0.873979) \| 11.952335 / 8.074308 (3.878027) \| 10.616711 / 10.191392 (0.425319) \| 0.128938 / 0.680424 (-0.551486) \| 0.015455 / 0.534201 (-0.518746) \| 0.272100 / 0.579283 (-0.307183) \| 0.278275 / 0.434364 (-0.156089) \| 0.309711 / 0.540337 (-0.230627) \| 0.411026 / 1.386936 (-0.975910) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#495bc04226a67983f523d12d42b680172f8d4893 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008470 / 0.011353 (-0.002883) \| 0.003201 / 0.011008 (-0.007808) \| 0.063193 / 0.038508 (0.024685) \| 0.064174 / 0.023109 (0.041064) \| 0.248316 / 0.275898 (-0.027582) \| 0.281598 / 0.323480 (-0.041882) \| 0.004076 / 0.007986 (-0.003909) \| 0.002397 / 0.004328 (-0.001932) \| 0.048834 / 0.004250 (0.044584) \| 0.056517 / 0.037052 (0.019465) \| 0.254164 / 0.258489 (-0.004326) \| 0.289800 / 0.293841 (-0.004041) \| 0.031092 / 0.128546 (-0.097454) \| 0.010885 / 0.075646 (-0.064762) \| 0.219198 / 0.419271 (-0.200073) \| 0.040087 / 0.043533 (-0.003446) \| 0.250900 / 0.255139 (-0.004239) \| 0.267787 / 0.283200 (-0.015413) \| 0.019666 / 0.141683 (-0.122017) \| 1.114960 / 1.452155 (-0.337194) \| 1.266675 / 1.492716 (-0.226041) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091429 / 0.018006 (0.073422) \| 0.301804 / 0.000490 (0.301314) \| 0.000212 / 0.000200 (0.000012) \| 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) \| 0.021053 / 0.037411 (-0.016358) \| 0.062407 / 0.014526 (0.047881) \| 0.073166 / 0.176557 (-0.103391) \| 0.119642 / 0.737135 (-0.617493) \| 0.074771 / 0.296338 (-0.221567) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278582 / 0.215209 (0.063373) \| 2.773023 / 2.077655 (0.695368) \| 1.459977 / 1.504120 (-0.044143) \| 1.330453 / 1.541195 (-0.210742) \| 1.372797 / 1.468490 (-0.095693) \| 0.628845 / 4.584777 (-3.955932) \| 3.428779 / 3.745712 (-0.316933) \| 3.138967 / 5.269862 (-2.130895) \| 2.126891 / 4.565676 (-2.438785) \| 0.062340 / 0.424275 (-0.361935) \| 0.004939 / 0.007607 (-0.002668) \| 0.336058 / 0.226044 (0.110014) \| 3.463741 / 2.268929 (1.194813) \| 1.847504 / 55.444624 (-53.597120) \| 1.984173 / 6.876477 (-4.892304) \| 1.602962 / 2.142072 (-0.539110) \| 0.637683 / 4.805227 (-4.167545) \| 0.117898 / 6.500664 (-6.382766) \| 0.043308 / 0.075469 (-0.032161) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.087773 / 1.841788 (-0.754014) \| 14.959526 / 8.074308 (6.885218) \| 10.886003 / 10.191392 (0.694611) \| 0.163385 / 0.680424 (-0.517039) \| 0.016679 / 0.534201 (-0.517522) \| 0.351913 / 0.579283 (-0.227370) \| 0.359007 / 0.434364 (-0.075357) \| 0.323824 / 0.540337 (-0.216513) \| 0.549268 / 1.386936 (-0.837668) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005265 / 0.011353 (-0.006088) \| 0.003367 / 0.011008 (-0.007641) \| 0.062741 / 0.038508 (0.024233) \| 0.068463 / 0.023109 (0.045354) \| 0.258497 / 0.275898 (-0.017401) \| 0.355360 / 0.323480 (0.031880) \| 0.003910 / 0.007986 (-0.004075) \| 0.002399 / 0.004328 (-0.001929) \| 0.055564 / 0.004250 (0.051313) \| 0.039644 / 0.037052 (0.002591) \| 0.258313 / 0.258489 (-0.000176) \| 0.328927 / 0.293841 (0.035086) \| 0.035634 / 0.128546 (-0.092912) \| 0.010378 / 0.075646 (-0.065268) \| 0.073109 / 0.419271 (-0.346163) \| 0.039752 / 0.043533 (-0.003781) \| 0.258237 / 0.255139 (0.003098) \| 0.330329 / 0.283200 (0.047129) \| 0.023924 / 0.141683 (-0.117759) \| 1.198639 / 1.452155 (-0.253515) \| 1.202307 / 1.492716 (-0.290409) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091297 / 0.018006 (0.073290) \| 0.298729 / 0.000490 (0.298240) \| 0.000210 / 0.000200 (0.000010) \| 0.000049 / 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) \| 0.022381 / 0.037411 (-0.015030) \| 0.070226 / 0.014526 (0.055700) \| 0.080549 / 0.176557 (-0.096007) \| 0.119677 / 0.737135 (-0.617458) \| 0.082612 / 0.296338 (-0.213727) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289270 / 0.215209 (0.074061) \| 2.853830 / 2.077655 (0.776175) \| 1.528938 / 1.504120 (0.024818) \| 1.398429 / 1.541195 (-0.142766) \| 1.472465 / 1.468490 (0.003975) \| 0.779015 / 4.584777 (-3.805762) \| 3.287724 / 3.745712 (-0.457988) \| 3.020908 / 5.269862 (-2.248953) \| 1.926094 / 4.565676 (-2.639583) \| 0.063163 / 0.424275 (-0.361112) \| 0.005175 / 0.007607 (-0.002432) \| 0.342884 / 0.226044 (0.116840) \| 3.476837 / 2.268929 (1.207908) \| 1.880683 / 55.444624 (-53.563942) \| 1.613845 / 6.876477 (-5.262632) \| 1.624734 / 2.142072 (-0.517338) \| 0.626220 / 4.805227 (-4.179007) \| 0.114976 / 6.500664 (-6.385689) \| 0.040670 / 0.075469 (-0.034799) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.116815 / 1.841788 (-0.724973) \| 15.388426 / 8.074308 (7.314118) \| 10.825276 / 10.191392 (0.633884) \| 0.172659 / 0.680424 (-0.507765) \| 0.015468 / 0.534201 (-0.518733) \| 0.285552 / 0.579283 (-0.293731) \| 0.346886 / 0.434364 (-0.087478) \| 0.348696 / 0.540337 (-0.191641) \| 0.729335 / 1.386936 (-0.657601) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d7bbf346dc268b8084dee406b2a6e2b96d44bc3b \"CML watermark\")\n" ]
1,996,306,394	6,428	Set dev version	closed	2023-11-16T08:12:55	2023-11-16T08:19:39	2023-11-16T08:13:28	https://github.com/huggingface/datasets/pull/6428	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6428", "html_url": "https://github.com/huggingface/datasets/pull/6428", "diff_url": "https://github.com/huggingface/datasets/pull/6428.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6428.patch", "merged_at": "2023-11-16T08:13:28" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6428). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004839 / 0.011353 (-0.006514) \| 0.002928 / 0.011008 (-0.008080) \| 0.061730 / 0.038508 (0.023221) \| 0.030523 / 0.023109 (0.007414) \| 0.252679 / 0.275898 (-0.023219) \| 0.281597 / 0.323480 (-0.041883) \| 0.003025 / 0.007986 (-0.004961) \| 0.002374 / 0.004328 (-0.001955) \| 0.048134 / 0.004250 (0.043884) \| 0.045843 / 0.037052 (0.008791) \| 0.256274 / 0.258489 (-0.002215) \| 0.288704 / 0.293841 (-0.005137) \| 0.023486 / 0.128546 (-0.105060) \| 0.007186 / 0.075646 (-0.068461) \| 0.202519 / 0.419271 (-0.216753) \| 0.058192 / 0.043533 (0.014659) \| 0.256448 / 0.255139 (0.001309) \| 0.279417 / 0.283200 (-0.003783) \| 0.019942 / 0.141683 (-0.121740) \| 1.100954 / 1.452155 (-0.351201) \| 1.168183 / 1.492716 (-0.324533) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091314 / 0.018006 (0.073308) \| 0.298614 / 0.000490 (0.298124) \| 0.000232 / 0.000200 (0.000032) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018071 / 0.037411 (-0.019340) \| 0.062265 / 0.014526 (0.047740) \| 0.073228 / 0.176557 (-0.103328) \| 0.119163 / 0.737135 (-0.617972) \| 0.074717 / 0.296338 (-0.221622) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.273906 / 0.215209 (0.058697) \| 2.683995 / 2.077655 (0.606340) \| 1.418773 / 1.504120 (-0.085347) \| 1.310473 / 1.541195 (-0.230722) \| 1.303152 / 1.468490 (-0.165339) \| 0.390846 / 4.584777 (-4.193931) \| 2.346407 / 3.745712 (-1.399305) \| 2.582945 / 5.269862 (-2.686916) \| 1.569549 / 4.565676 (-2.996128) \| 0.044893 / 0.424275 (-0.379383) \| 0.004754 / 0.007607 (-0.002853) \| 0.323491 / 0.226044 (0.097447) \| 3.229736 / 2.268929 (0.960808) \| 1.783551 / 55.444624 (-53.661074) \| 1.499685 / 6.876477 (-5.376792) \| 1.515826 / 2.142072 (-0.626246) \| 0.475768 / 4.805227 (-4.329460) \| 0.099579 / 6.500664 (-6.401085) \| 0.042709 / 0.075469 (-0.032760) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.926120 / 1.841788 (-0.915667) \| 11.597189 / 8.074308 (3.522881) \| 10.327055 / 10.191392 (0.135663) \| 0.127479 / 0.680424 (-0.552945) \| 0.014844 / 0.534201 (-0.519357) \| 0.261181 / 0.579283 (-0.318102) \| 0.258407 / 0.434364 (-0.175957) \| 0.303192 / 0.540337 (-0.237146) \| 0.416665 / 1.386936 (-0.970271) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004759 / 0.011353 (-0.006594) \| 0.002780 / 0.011008 (-0.008228) \| 0.047991 / 0.038508 (0.009483) \| 0.052263 / 0.023109 (0.029153) \| 0.261228 / 0.275898 (-0.014670) \| 0.287779 / 0.323480 (-0.035701) \| 0.003961 / 0.007986 (-0.004024) \| 0.002357 / 0.004328 (-0.001971) \| 0.047755 / 0.004250 (0.043505) \| 0.038066 / 0.037052 (0.001014) \| 0.269502 / 0.258489 (0.011013) \| 0.298348 / 0.293841 (0.004507) \| 0.024398 / 0.128546 (-0.104149) \| 0.007189 / 0.075646 (-0.068457) \| 0.053356 / 0.419271 (-0.365915) \| 0.032459 / 0.043533 (-0.011074) \| 0.266389 / 0.255139 (0.011250) \| 0.305367 / 0.283200 (0.022168) \| 0.017629 / 0.141683 (-0.124054) \| 1.145789 / 1.452155 (-0.306366) \| 1.204778 / 1.492716 (-0.287938) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091347 / 0.018006 (0.073341) \| 0.298671 / 0.000490 (0.298181) \| 0.000229 / 0.000200 (0.000029) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021374 / 0.037411 (-0.016037) \| 0.068869 / 0.014526 (0.054344) \| 0.080443 / 0.176557 (-0.096113) \| 0.118759 / 0.737135 (-0.618376) \| 0.081646 / 0.296338 (-0.214692) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295274 / 0.215209 (0.080065) \| 2.889349 / 2.077655 (0.811695) \| 1.561020 / 1.504120 (0.056900) \| 1.425025 / 1.541195 (-0.116170) \| 1.495446 / 1.468490 (0.026956) \| 0.403825 / 4.584777 (-4.180952) \| 2.404905 / 3.745712 (-1.340807) \| 2.590104 / 5.269862 (-2.679758) \| 1.570559 / 4.565676 (-2.995118) \| 0.046342 / 0.424275 (-0.377933) \| 0.004799 / 0.007607 (-0.002809) \| 0.349981 / 0.226044 (0.123937) \| 3.437341 / 2.268929 (1.168412) \| 1.948155 / 55.444624 (-53.496469) \| 1.637765 / 6.876477 (-5.238711) \| 1.671521 / 2.142072 (-0.470551) \| 0.479500 / 4.805227 (-4.325727) \| 0.098305 / 6.500664 (-6.402359) \| 0.040864 / 0.075469 (-0.034605) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.979986 / 1.841788 (-0.861801) \| 12.169722 / 8.074308 (4.095413) \| 11.297345 / 10.191392 (1.105953) \| 0.129123 / 0.680424 (-0.551301) \| 0.015389 / 0.534201 (-0.518812) \| 0.270964 / 0.579283 (-0.308319) \| 0.269590 / 0.434364 (-0.164774) \| 0.310662 / 0.540337 (-0.229675) \| 0.406272 / 1.386936 (-0.980664) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#31873f1e9acbe013e6d396d1ed5492db8cd59dd3 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004620 / 0.011353 (-0.006733) \| 0.002971 / 0.011008 (-0.008038) \| 0.062864 / 0.038508 (0.024355) \| 0.028743 / 0.023109 (0.005634) \| 0.246729 / 0.275898 (-0.029169) \| 0.271165 / 0.323480 (-0.052315) \| 0.003930 / 0.007986 (-0.004056) \| 0.002422 / 0.004328 (-0.001906) \| 0.047430 / 0.004250 (0.043180) \| 0.044895 / 0.037052 (0.007843) \| 0.249128 / 0.258489 (-0.009361) \| 0.283384 / 0.293841 (-0.010457) \| 0.023288 / 0.128546 (-0.105259) \| 0.007241 / 0.075646 (-0.068405) \| 0.207551 / 0.419271 (-0.211720) \| 0.055008 / 0.043533 (0.011475) \| 0.252781 / 0.255139 (-0.002358) \| 0.296924 / 0.283200 (0.013724) \| 0.017860 / 0.141683 (-0.123822) \| 1.094597 / 1.452155 (-0.357558) \| 1.162314 / 1.492716 (-0.330402) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091423 / 0.018006 (0.073417) \| 0.302833 / 0.000490 (0.302343) \| 0.000242 / 0.000200 (0.000042) \| 0.000042 / 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) \| 0.018143 / 0.037411 (-0.019268) \| 0.066371 / 0.014526 (0.051845) \| 0.072774 / 0.176557 (-0.103783) \| 0.119062 / 0.737135 (-0.618073) \| 0.102836 / 0.296338 (-0.193502) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280117 / 0.215209 (0.064908) \| 2.757955 / 2.077655 (0.680301) \| 1.494994 / 1.504120 (-0.009126) \| 1.375325 / 1.541195 (-0.165870) \| 1.384179 / 1.468490 (-0.084311) \| 0.399824 / 4.584777 (-4.184953) \| 2.368575 / 3.745712 (-1.377137) \| 2.574035 / 5.269862 (-2.695827) \| 1.548738 / 4.565676 (-3.016939) \| 0.045841 / 0.424275 (-0.378434) \| 0.004799 / 0.007607 (-0.002808) \| 0.331522 / 0.226044 (0.105478) \| 3.324471 / 2.268929 (1.055543) \| 1.838637 / 55.444624 (-53.605987) \| 1.562854 / 6.876477 (-5.313623) \| 1.581736 / 2.142072 (-0.560336) \| 0.468832 / 4.805227 (-4.336396) \| 0.099309 / 6.500664 (-6.401355) \| 0.042078 / 0.075469 (-0.033391) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.928468 / 1.841788 (-0.913320) \| 11.331143 / 8.074308 (3.256835) \| 10.296213 / 10.191392 (0.104821) \| 0.138912 / 0.680424 (-0.541511) \| 0.014044 / 0.534201 (-0.520157) \| 0.267293 / 0.579283 (-0.311991) \| 0.267267 / 0.434364 (-0.167097) \| 0.306560 / 0.540337 (-0.233778) \| 0.423926 / 1.386936 (-0.963010) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004842 / 0.011353 (-0.006511) \| 0.002917 / 0.011008 (-0.008091) \| 0.048263 / 0.038508 (0.009755) \| 0.051453 / 0.023109 (0.028344) \| 0.278330 / 0.275898 (0.002432) \| 0.298569 / 0.323480 (-0.024911) \| 0.003936 / 0.007986 (-0.004049) \| 0.002479 / 0.004328 (-0.001850) \| 0.048281 / 0.004250 (0.044031) \| 0.038925 / 0.037052 (0.001872) \| 0.285258 / 0.258489 (0.026769) \| 0.313701 / 0.293841 (0.019860) \| 0.024916 / 0.128546 (-0.103630) \| 0.007142 / 0.075646 (-0.068504) \| 0.053634 / 0.419271 (-0.365638) \| 0.032842 / 0.043533 (-0.010690) \| 0.279373 / 0.255139 (0.024234) \| 0.295844 / 0.283200 (0.012644) \| 0.018142 / 0.141683 (-0.123541) \| 1.136960 / 1.452155 (-0.315195) \| 1.184438 / 1.492716 (-0.308278) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090271 / 0.018006 (0.072264) \| 0.299940 / 0.000490 (0.299450) \| 0.000234 / 0.000200 (0.000034) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021175 / 0.037411 (-0.016237) \| 0.070924 / 0.014526 (0.056398) \| 0.080584 / 0.176557 (-0.095972) \| 0.119278 / 0.737135 (-0.617857) \| 0.082361 / 0.296338 (-0.213977) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298312 / 0.215209 (0.083103) \| 2.895361 / 2.077655 (0.817706) \| 1.616120 / 1.504120 (0.112001) \| 1.484444 / 1.541195 (-0.056750) \| 1.541893 / 1.468490 (0.073403) \| 0.409968 / 4.584777 (-4.174809) \| 2.423639 / 3.745712 (-1.322073) \| 2.585122 / 5.269862 (-2.684740) \| 1.540343 / 4.565676 (-3.025333) \| 0.046604 / 0.424275 (-0.377671) \| 0.004742 / 0.007607 (-0.002865) \| 0.341659 / 0.226044 (0.115614) \| 3.409259 / 2.268929 (1.140330) \| 2.007068 / 55.444624 (-53.437556) \| 1.681348 / 6.876477 (-5.195129) \| 1.719253 / 2.142072 (-0.422819) \| 0.482301 / 4.805227 (-4.322926) \| 0.099619 / 6.500664 (-6.401045) \| 0.041247 / 0.075469 (-0.034222) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.971783 / 1.841788 (-0.870004) \| 12.208000 / 8.074308 (4.133692) \| 10.948230 / 10.191392 (0.756838) \| 0.131824 / 0.680424 (-0.548599) \| 0.015696 / 0.534201 (-0.518505) \| 0.272265 / 0.579283 (-0.307018) \| 0.276093 / 0.434364 (-0.158270) \| 0.305897 / 0.540337 (-0.234441) \| 0.411632 / 1.386936 (-0.975304) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2bf75fe522c6fedd16d00b4a928f613dee11f73c \"CML watermark\")\n" ]
1,996,248,605	6,427	Release: 2.15.0	closed	2023-11-16T07:37:20	2023-11-16T08:12:12	2023-11-16T07:43:05	https://github.com/huggingface/datasets/pull/6427	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6427", "html_url": "https://github.com/huggingface/datasets/pull/6427", "diff_url": "https://github.com/huggingface/datasets/pull/6427.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6427.patch", "merged_at": "2023-11-16T07:43:05" }	albertvillanova	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004331 / 0.011353 (-0.007022) \| 0.002573 / 0.011008 (-0.008435) \| 0.061002 / 0.038508 (0.022494) \| 0.029259 / 0.023109 (0.006149) \| 0.242983 / 0.275898 (-0.032915) \| 0.267629 / 0.323480 (-0.055851) \| 0.003906 / 0.007986 (-0.004080) \| 0.002383 / 0.004328 (-0.001946) \| 0.047574 / 0.004250 (0.043323) \| 0.042153 / 0.037052 (0.005101) \| 0.245821 / 0.258489 (-0.012668) \| 0.276479 / 0.293841 (-0.017362) \| 0.022498 / 0.128546 (-0.106049) \| 0.006775 / 0.075646 (-0.068871) \| 0.201795 / 0.419271 (-0.217477) \| 0.052443 / 0.043533 (0.008910) \| 0.248320 / 0.255139 (-0.006819) \| 0.261964 / 0.283200 (-0.021235) \| 0.016764 / 0.141683 (-0.124919) \| 1.118702 / 1.452155 (-0.333453) \| 1.203079 / 1.492716 (-0.289638) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088808 / 0.018006 (0.070801) \| 0.296526 / 0.000490 (0.296037) \| 0.000203 / 0.000200 (0.000003) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018816 / 0.037411 (-0.018595) \| 0.062295 / 0.014526 (0.047769) \| 0.075228 / 0.176557 (-0.101329) \| 0.119916 / 0.737135 (-0.617219) \| 0.077206 / 0.296338 (-0.219132) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.276723 / 0.215209 (0.061514) \| 2.711431 / 2.077655 (0.633776) \| 1.425590 / 1.504120 (-0.078530) \| 1.301383 / 1.541195 (-0.239812) \| 1.316314 / 1.468490 (-0.152176) \| 0.402709 / 4.584777 (-4.182068) \| 2.347229 / 3.745712 (-1.398483) \| 2.596937 / 5.269862 (-2.672925) \| 1.560658 / 4.565676 (-3.005018) \| 0.046162 / 0.424275 (-0.378113) \| 0.004760 / 0.007607 (-0.002848) \| 0.330522 / 0.226044 (0.104478) \| 3.244072 / 2.268929 (0.975143) \| 1.747603 / 55.444624 (-53.697021) \| 1.475534 / 6.876477 (-5.400943) \| 1.485135 / 2.142072 (-0.656938) \| 0.476794 / 4.805227 (-4.328433) \| 0.098496 / 6.500664 (-6.402168) \| 0.040740 / 0.075469 (-0.034729) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.939020 / 1.841788 (-0.902768) \| 11.235187 / 8.074308 (3.160878) \| 10.194975 / 10.191392 (0.003583) \| 0.126241 / 0.680424 (-0.554182) \| 0.013990 / 0.534201 (-0.520211) \| 0.269149 / 0.579283 (-0.310134) \| 0.256950 / 0.434364 (-0.177414) \| 0.301282 / 0.540337 (-0.239056) \| 0.421490 / 1.386936 (-0.965446) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004956 / 0.011353 (-0.006397) \| 0.002478 / 0.011008 (-0.008530) \| 0.047773 / 0.038508 (0.009265) \| 0.050076 / 0.023109 (0.026967) \| 0.261915 / 0.275898 (-0.013983) \| 0.282553 / 0.323480 (-0.040927) \| 0.003881 / 0.007986 (-0.004105) \| 0.002329 / 0.004328 (-0.001999) \| 0.048091 / 0.004250 (0.043841) \| 0.038188 / 0.037052 (0.001135) \| 0.265502 / 0.258489 (0.007013) \| 0.292568 / 0.293841 (-0.001273) \| 0.024172 / 0.128546 (-0.104374) \| 0.006865 / 0.075646 (-0.068781) \| 0.053199 / 0.419271 (-0.366072) \| 0.032201 / 0.043533 (-0.011332) \| 0.265774 / 0.255139 (0.010635) \| 0.277954 / 0.283200 (-0.005245) \| 0.017798 / 0.141683 (-0.123885) \| 1.121503 / 1.452155 (-0.330652) \| 1.176319 / 1.492716 (-0.316398) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.087027 / 0.018006 (0.069020) \| 0.296182 / 0.000490 (0.295693) \| 0.000216 / 0.000200 (0.000017) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020990 / 0.037411 (-0.016421) \| 0.069693 / 0.014526 (0.055168) \| 0.081098 / 0.176557 (-0.095459) \| 0.117760 / 0.737135 (-0.619375) \| 0.081493 / 0.296338 (-0.214845) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295078 / 0.215209 (0.079869) \| 2.876602 / 2.077655 (0.798947) \| 1.558011 / 1.504120 (0.053891) \| 1.426715 / 1.541195 (-0.114480) \| 1.443785 / 1.468490 (-0.024705) \| 0.400826 / 4.584777 (-4.183951) \| 2.378903 / 3.745712 (-1.366810) \| 2.473128 / 5.269862 (-2.796734) \| 1.500785 / 4.565676 (-3.064891) \| 0.045438 / 0.424275 (-0.378837) \| 0.004953 / 0.007607 (-0.002654) \| 0.348182 / 0.226044 (0.122137) \| 3.427751 / 2.268929 (1.158822) \| 1.925173 / 55.444624 (-53.519451) \| 1.633354 / 6.876477 (-5.243123) \| 1.651573 / 2.142072 (-0.490499) \| 0.473260 / 4.805227 (-4.331968) \| 0.097613 / 6.500664 (-6.403051) \| 0.040196 / 0.075469 (-0.035273) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.951780 / 1.841788 (-0.890008) \| 11.709342 / 8.074308 (3.635034) \| 10.571831 / 10.191392 (0.380439) \| 0.134344 / 0.680424 (-0.546079) \| 0.022116 / 0.534201 (-0.512084) \| 0.269651 / 0.579283 (-0.309632) \| 0.272310 / 0.434364 (-0.162054) \| 0.306434 / 0.540337 (-0.233903) \| 0.408320 / 1.386936 (-0.978616) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7ea64b77079cf76675421917472c05d06ace63fc \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004402 / 0.011353 (-0.006951) \| 0.002732 / 0.011008 (-0.008277) \| 0.062799 / 0.038508 (0.024291) \| 0.029155 / 0.023109 (0.006046) \| 0.241925 / 0.275898 (-0.033973) \| 0.275694 / 0.323480 (-0.047786) \| 0.003989 / 0.007986 (-0.003997) \| 0.002528 / 0.004328 (-0.001801) \| 0.048410 / 0.004250 (0.044160) \| 0.043729 / 0.037052 (0.006677) \| 0.248843 / 0.258489 (-0.009646) \| 0.282980 / 0.293841 (-0.010860) \| 0.023828 / 0.128546 (-0.104718) \| 0.006972 / 0.075646 (-0.068675) \| 0.213222 / 0.419271 (-0.206049) \| 0.054883 / 0.043533 (0.011350) \| 0.251353 / 0.255139 (-0.003786) \| 0.269818 / 0.283200 (-0.013381) \| 0.016906 / 0.141683 (-0.124777) \| 1.114109 / 1.452155 (-0.338045) \| 1.162942 / 1.492716 (-0.329774) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093724 / 0.018006 (0.075718) \| 0.301989 / 0.000490 (0.301499) \| 0.000213 / 0.000200 (0.000014) \| 0.000049 / 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) \| 0.018245 / 0.037411 (-0.019166) \| 0.062237 / 0.014526 (0.047712) \| 0.075644 / 0.176557 (-0.100913) \| 0.119655 / 0.737135 (-0.617480) \| 0.074525 / 0.296338 (-0.221814) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.274534 / 0.215209 (0.059324) \| 2.683678 / 2.077655 (0.606024) \| 1.453306 / 1.504120 (-0.050814) \| 1.347630 / 1.541195 (-0.193564) \| 1.352875 / 1.468490 (-0.115615) \| 0.398425 / 4.584777 (-4.186352) \| 2.375738 / 3.745712 (-1.369974) \| 2.591573 / 5.269862 (-2.678289) \| 1.555527 / 4.565676 (-3.010150) \| 0.045656 / 0.424275 (-0.378619) \| 0.004898 / 0.007607 (-0.002709) \| 0.330591 / 0.226044 (0.104547) \| 3.247638 / 2.268929 (0.978710) \| 1.816676 / 55.444624 (-53.627948) \| 1.531754 / 6.876477 (-5.344723) \| 1.543196 / 2.142072 (-0.598877) \| 0.472489 / 4.805227 (-4.332739) \| 0.099311 / 6.500664 (-6.401353) \| 0.042139 / 0.075469 (-0.033330) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945472 / 1.841788 (-0.896316) \| 11.476550 / 8.074308 (3.402242) \| 10.281157 / 10.191392 (0.089765) \| 0.141062 / 0.680424 (-0.539362) \| 0.013634 / 0.534201 (-0.520567) \| 0.268778 / 0.579283 (-0.310505) \| 0.263542 / 0.434364 (-0.170822) \| 0.307918 / 0.540337 (-0.232420) \| 0.421231 / 1.386936 (-0.965705) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005090 / 0.011353 (-0.006263) \| 0.003135 / 0.011008 (-0.007873) \| 0.048058 / 0.038508 (0.009550) \| 0.052898 / 0.023109 (0.029789) \| 0.273233 / 0.275898 (-0.002665) \| 0.299516 / 0.323480 (-0.023964) \| 0.004126 / 0.007986 (-0.003860) \| 0.002331 / 0.004328 (-0.001997) \| 0.047627 / 0.004250 (0.043376) \| 0.039076 / 0.037052 (0.002023) \| 0.276625 / 0.258489 (0.018136) \| 0.308180 / 0.293841 (0.014340) \| 0.024929 / 0.128546 (-0.103618) \| 0.007396 / 0.075646 (-0.068251) \| 0.053408 / 0.419271 (-0.365863) \| 0.032896 / 0.043533 (-0.010637) \| 0.275412 / 0.255139 (0.020273) \| 0.292014 / 0.283200 (0.008814) \| 0.018336 / 0.141683 (-0.123347) \| 1.123565 / 1.452155 (-0.328589) \| 1.175382 / 1.492716 (-0.317334) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093799 / 0.018006 (0.075793) \| 0.304219 / 0.000490 (0.303729) \| 0.000231 / 0.000200 (0.000031) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021034 / 0.037411 (-0.016377) \| 0.069961 / 0.014526 (0.055435) \| 0.080311 / 0.176557 (-0.096246) \| 0.118603 / 0.737135 (-0.618532) \| 0.084003 / 0.296338 (-0.212335) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.305610 / 0.215209 (0.090401) \| 2.962027 / 2.077655 (0.884372) \| 1.598604 / 1.504120 (0.094484) \| 1.476227 / 1.541195 (-0.064967) \| 1.528960 / 1.468490 (0.060470) \| 0.404545 / 4.584777 (-4.180232) \| 2.423147 / 3.745712 (-1.322565) \| 2.516632 / 5.269862 (-2.753229) \| 1.529000 / 4.565676 (-3.036677) \| 0.045780 / 0.424275 (-0.378495) \| 0.004784 / 0.007607 (-0.002823) \| 0.358836 / 0.226044 (0.132792) \| 3.508782 / 2.268929 (1.239853) \| 1.954513 / 55.444624 (-53.490111) \| 1.672824 / 6.876477 (-5.203653) \| 1.683482 / 2.142072 (-0.458590) \| 0.479014 / 4.805227 (-4.326213) \| 0.098325 / 6.500664 (-6.402340) \| 0.040934 / 0.075469 (-0.034536) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.974770 / 1.841788 (-0.867017) \| 11.956137 / 8.074308 (3.881829) \| 10.956458 / 10.191392 (0.765066) \| 0.141800 / 0.680424 (-0.538624) \| 0.015439 / 0.534201 (-0.518762) \| 0.271783 / 0.579283 (-0.307500) \| 0.278058 / 0.434364 (-0.156306) \| 0.305823 / 0.540337 (-0.234514) \| 0.415677 / 1.386936 (-0.971259) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0caf91285116ec910f409e82cc6e1f4cff7496e3 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004483 / 0.011353 (-0.006870) \| 0.002560 / 0.011008 (-0.008448) \| 0.061428 / 0.038508 (0.022920) \| 0.029460 / 0.023109 (0.006351) \| 0.238971 / 0.275898 (-0.036927) \| 0.271768 / 0.323480 (-0.051712) \| 0.003970 / 0.007986 (-0.004016) \| 0.002408 / 0.004328 (-0.001921) \| 0.047755 / 0.004250 (0.043505) \| 0.043358 / 0.037052 (0.006306) \| 0.245543 / 0.258489 (-0.012946) \| 0.278230 / 0.293841 (-0.015611) \| 0.023819 / 0.128546 (-0.104727) \| 0.006856 / 0.075646 (-0.068790) \| 0.204603 / 0.419271 (-0.214668) \| 0.054521 / 0.043533 (0.010989) \| 0.246277 / 0.255139 (-0.008862) \| 0.271230 / 0.283200 (-0.011969) \| 0.017283 / 0.141683 (-0.124400) \| 1.088955 / 1.452155 (-0.363200) \| 1.245141 / 1.492716 (-0.247575) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091534 / 0.018006 (0.073528) \| 0.299517 / 0.000490 (0.299027) \| 0.000215 / 0.000200 (0.000015) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018105 / 0.037411 (-0.019306) \| 0.061860 / 0.014526 (0.047334) \| 0.074494 / 0.176557 (-0.102063) \| 0.120107 / 0.737135 (-0.617029) \| 0.073406 / 0.296338 (-0.222932) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278140 / 0.215209 (0.062931) \| 2.746208 / 2.077655 (0.668553) \| 1.476264 / 1.504120 (-0.027856) \| 1.356498 / 1.541195 (-0.184697) \| 1.362998 / 1.468490 (-0.105492) \| 0.401884 / 4.584777 (-4.182893) \| 2.409836 / 3.745712 (-1.335877) \| 2.579087 / 5.269862 (-2.690775) \| 1.545021 / 4.565676 (-3.020656) \| 0.046001 / 0.424275 (-0.378274) \| 0.004812 / 0.007607 (-0.002795) \| 0.339767 / 0.226044 (0.113722) \| 3.341599 / 2.268929 (1.072670) \| 1.821498 / 55.444624 (-53.623127) \| 1.559311 / 6.876477 (-5.317166) \| 1.570368 / 2.142072 (-0.571704) \| 0.472688 / 4.805227 (-4.332539) \| 0.099549 / 6.500664 (-6.401115) \| 0.041644 / 0.075469 (-0.033825) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.951988 / 1.841788 (-0.889799) \| 11.371459 / 8.074308 (3.297150) \| 10.229446 / 10.191392 (0.038054) \| 0.128105 / 0.680424 (-0.552319) \| 0.014418 / 0.534201 (-0.519783) \| 0.268615 / 0.579283 (-0.310668) \| 0.263956 / 0.434364 (-0.170407) \| 0.302213 / 0.540337 (-0.238125) \| 0.427224 / 1.386936 (-0.959712) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005150 / 0.011353 (-0.006203) \| 0.002557 / 0.011008 (-0.008451) \| 0.048092 / 0.038508 (0.009584) \| 0.050522 / 0.023109 (0.027413) \| 0.272195 / 0.275898 (-0.003703) \| 0.294191 / 0.323480 (-0.029289) \| 0.004098 / 0.007986 (-0.003887) \| 0.002350 / 0.004328 (-0.001978) \| 0.048682 / 0.004250 (0.044432) \| 0.038381 / 0.037052 (0.001328) \| 0.275530 / 0.258489 (0.017041) \| 0.303991 / 0.293841 (0.010150) \| 0.024734 / 0.128546 (-0.103812) \| 0.006926 / 0.075646 (-0.068720) \| 0.053683 / 0.419271 (-0.365588) \| 0.032675 / 0.043533 (-0.010858) \| 0.272816 / 0.255139 (0.017677) \| 0.291754 / 0.283200 (0.008554) \| 0.018290 / 0.141683 (-0.123392) \| 1.127696 / 1.452155 (-0.324459) \| 1.187080 / 1.492716 (-0.305636) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091224 / 0.018006 (0.073218) \| 0.288838 / 0.000490 (0.288348) \| 0.000226 / 0.000200 (0.000026) \| 0.000045 / 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) \| 0.021409 / 0.037411 (-0.016003) \| 0.069846 / 0.014526 (0.055320) \| 0.079962 / 0.176557 (-0.096594) \| 0.118575 / 0.737135 (-0.618560) \| 0.080223 / 0.296338 (-0.216115) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290835 / 0.215209 (0.075626) \| 2.831787 / 2.077655 (0.754133) \| 1.587728 / 1.504120 (0.083608) \| 1.461939 / 1.541195 (-0.079256) \| 1.495257 / 1.468490 (0.026767) \| 0.397653 / 4.584777 (-4.187124) \| 2.399903 / 3.745712 (-1.345809) \| 2.527615 / 5.269862 (-2.742247) \| 1.501555 / 4.565676 (-3.064121) \| 0.045742 / 0.424275 (-0.378533) \| 0.004797 / 0.007607 (-0.002811) \| 0.339139 / 0.226044 (0.113094) \| 3.358340 / 2.268929 (1.089412) \| 1.968955 / 55.444624 (-53.475670) \| 1.663598 / 6.876477 (-5.212879) \| 1.673995 / 2.142072 (-0.468078) \| 0.463444 / 4.805227 (-4.341783) \| 0.098008 / 6.500664 (-6.402656) \| 0.040836 / 0.075469 (-0.034633) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.974033 / 1.841788 (-0.867755) \| 11.863206 / 8.074308 (3.788897) \| 10.892389 / 10.191392 (0.700997) \| 0.128884 / 0.680424 (-0.551540) \| 0.015319 / 0.534201 (-0.518882) \| 0.268931 / 0.579283 (-0.310353) \| 0.274148 / 0.434364 (-0.160216) \| 0.305407 / 0.540337 (-0.234930) \| 0.410574 / 1.386936 (-0.976362) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0caf91285116ec910f409e82cc6e1f4cff7496e3 \"CML watermark\")\n" ]
1,995,363,264	6,426	More robust temporary directory deletion	closed	2023-11-15T19:06:42	2023-12-01T15:37:32	2023-12-01T15:31:19	https://github.com/huggingface/datasets/pull/6426	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6426", "html_url": "https://github.com/huggingface/datasets/pull/6426", "diff_url": "https://github.com/huggingface/datasets/pull/6426.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6426.patch", "merged_at": "2023-12-01T15:31:19" }	mariosasko	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6426). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004750 / 0.011353 (-0.006603) \| 0.002928 / 0.011008 (-0.008080) \| 0.061962 / 0.038508 (0.023454) \| 0.029878 / 0.023109 (0.006768) \| 0.233380 / 0.275898 (-0.042518) \| 0.262221 / 0.323480 (-0.061259) \| 0.002982 / 0.007986 (-0.005004) \| 0.003698 / 0.004328 (-0.000630) \| 0.048565 / 0.004250 (0.044314) \| 0.046107 / 0.037052 (0.009055) \| 0.240090 / 0.258489 (-0.018399) \| 0.267294 / 0.293841 (-0.026547) \| 0.023335 / 0.128546 (-0.105211) \| 0.007221 / 0.075646 (-0.068425) \| 0.200903 / 0.419271 (-0.218369) \| 0.059237 / 0.043533 (0.015705) \| 0.234929 / 0.255139 (-0.020210) \| 0.256326 / 0.283200 (-0.026874) \| 0.018549 / 0.141683 (-0.123134) \| 1.103519 / 1.452155 (-0.348635) \| 1.156573 / 1.492716 (-0.336143) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091205 / 0.018006 (0.073199) \| 0.303533 / 0.000490 (0.303043) \| 0.000204 / 0.000200 (0.000004) \| 0.000042 / 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) \| 0.018572 / 0.037411 (-0.018839) \| 0.062323 / 0.014526 (0.047797) \| 0.074528 / 0.176557 (-0.102029) \| 0.120295 / 0.737135 (-0.616841) \| 0.076786 / 0.296338 (-0.219552) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278814 / 0.215209 (0.063605) \| 2.745483 / 2.077655 (0.667829) \| 1.486073 / 1.504120 (-0.018047) \| 1.385334 / 1.541195 (-0.155861) \| 1.386351 / 1.468490 (-0.082139) \| 0.395545 / 4.584777 (-4.189232) \| 2.409468 / 3.745712 (-1.336244) \| 2.670702 / 5.269862 (-2.599159) \| 1.629245 / 4.565676 (-2.936432) \| 0.045990 / 0.424275 (-0.378286) \| 0.004782 / 0.007607 (-0.002825) \| 0.332912 / 0.226044 (0.106867) \| 3.249277 / 2.268929 (0.980349) \| 1.888690 / 55.444624 (-53.555934) \| 1.533462 / 6.876477 (-5.343015) \| 1.576045 / 2.142072 (-0.566027) \| 0.473090 / 4.805227 (-4.332138) \| 0.099448 / 6.500664 (-6.401216) \| 0.042613 / 0.075469 (-0.032857) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.944229 / 1.841788 (-0.897559) \| 12.103621 / 8.074308 (4.029313) \| 10.643471 / 10.191392 (0.452079) \| 0.143004 / 0.680424 (-0.537420) \| 0.013872 / 0.534201 (-0.520329) \| 0.272026 / 0.579283 (-0.307257) \| 0.298701 / 0.434364 (-0.135663) \| 0.310299 / 0.540337 (-0.230038) \| 0.420934 / 1.386936 (-0.966002) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004904 / 0.011353 (-0.006449) \| 0.003064 / 0.011008 (-0.007945) \| 0.047982 / 0.038508 (0.009474) \| 0.056354 / 0.023109 (0.033245) \| 0.292893 / 0.275898 (0.016995) \| 0.348744 / 0.323480 (0.025264) \| 0.003988 / 0.007986 (-0.003997) \| 0.002431 / 0.004328 (-0.001898) \| 0.049108 / 0.004250 (0.044857) \| 0.039055 / 0.037052 (0.002002) \| 0.278129 / 0.258489 (0.019640) \| 0.318547 / 0.293841 (0.024706) \| 0.025040 / 0.128546 (-0.103507) \| 0.007166 / 0.075646 (-0.068480) \| 0.053967 / 0.419271 (-0.365305) \| 0.033128 / 0.043533 (-0.010405) \| 0.272849 / 0.255139 (0.017710) \| 0.312143 / 0.283200 (0.028943) \| 0.017942 / 0.141683 (-0.123741) \| 1.192297 / 1.452155 (-0.259857) \| 1.328102 / 1.492716 (-0.164615) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090903 / 0.018006 (0.072896) \| 0.301260 / 0.000490 (0.300770) \| 0.000215 / 0.000200 (0.000015) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021112 / 0.037411 (-0.016300) \| 0.070181 / 0.014526 (0.055656) \| 0.082431 / 0.176557 (-0.094126) \| 0.121973 / 0.737135 (-0.615163) \| 0.083617 / 0.296338 (-0.212721) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289587 / 0.215209 (0.074378) \| 2.877895 / 2.077655 (0.800240) \| 1.721417 / 1.504120 (0.217297) \| 1.536023 / 1.541195 (-0.005171) \| 1.550917 / 1.468490 (0.082427) \| 0.402978 / 4.584777 (-4.181799) \| 2.431767 / 3.745712 (-1.313946) \| 2.544419 / 5.269862 (-2.725442) \| 1.554562 / 4.565676 (-3.011115) \| 0.046260 / 0.424275 (-0.378015) \| 0.004923 / 0.007607 (-0.002684) \| 0.341584 / 0.226044 (0.115540) \| 3.362133 / 2.268929 (1.093205) \| 1.928741 / 55.444624 (-53.515884) \| 1.654798 / 6.876477 (-5.221679) \| 1.715111 / 2.142072 (-0.426962) \| 0.471029 / 4.805227 (-4.334198) \| 0.098912 / 6.500664 (-6.401752) \| 0.041018 / 0.075469 (-0.034451) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.992880 / 1.841788 (-0.848907) \| 12.083890 / 8.074308 (4.009582) \| 11.023833 / 10.191392 (0.832441) \| 0.139217 / 0.680424 (-0.541207) \| 0.015183 / 0.534201 (-0.519018) \| 0.271637 / 0.579283 (-0.307646) \| 0.278910 / 0.434364 (-0.155454) \| 0.306891 / 0.540337 (-0.233447) \| 0.424412 / 1.386936 (-0.962524) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2d51f37eb9996d4c52250ee6e987ccce0d74f2f4 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004545 / 0.011353 (-0.006808) \| 0.002955 / 0.011008 (-0.008054) \| 0.062119 / 0.038508 (0.023611) \| 0.029357 / 0.023109 (0.006248) \| 0.240068 / 0.275898 (-0.035830) \| 0.273376 / 0.323480 (-0.050104) \| 0.003884 / 0.007986 (-0.004102) \| 0.002390 / 0.004328 (-0.001938) \| 0.048621 / 0.004250 (0.044371) \| 0.043867 / 0.037052 (0.006815) \| 0.247240 / 0.258489 (-0.011249) \| 0.279187 / 0.293841 (-0.014654) \| 0.023377 / 0.128546 (-0.105169) \| 0.007261 / 0.075646 (-0.068385) \| 0.201913 / 0.419271 (-0.217359) \| 0.057063 / 0.043533 (0.013530) \| 0.245698 / 0.255139 (-0.009441) \| 0.265644 / 0.283200 (-0.017556) \| 0.018077 / 0.141683 (-0.123606) \| 1.133225 / 1.452155 (-0.318930) \| 1.186380 / 1.492716 (-0.306336) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089639 / 0.018006 (0.071632) \| 0.298918 / 0.000490 (0.298428) \| 0.000198 / 0.000200 (-0.000002) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019037 / 0.037411 (-0.018374) \| 0.062580 / 0.014526 (0.048055) \| 0.072974 / 0.176557 (-0.103582) \| 0.119909 / 0.737135 (-0.617226) \| 0.075021 / 0.296338 (-0.221317) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.276561 / 0.215209 (0.061352) \| 2.697281 / 2.077655 (0.619626) \| 1.419772 / 1.504120 (-0.084348) \| 1.302079 / 1.541195 (-0.239115) \| 1.329143 / 1.468490 (-0.139347) \| 0.395528 / 4.584777 (-4.189249) \| 2.365788 / 3.745712 (-1.379925) \| 2.583802 / 5.269862 (-2.686059) \| 1.561983 / 4.565676 (-3.003694) \| 0.045269 / 0.424275 (-0.379006) \| 0.004826 / 0.007607 (-0.002781) \| 0.331041 / 0.226044 (0.104996) \| 3.292523 / 2.268929 (1.023595) \| 1.797865 / 55.444624 (-53.646759) \| 1.509229 / 6.876477 (-5.367248) \| 1.498884 / 2.142072 (-0.643188) \| 0.458518 / 4.805227 (-4.346709) \| 0.098076 / 6.500664 (-6.402588) \| 0.042290 / 0.075469 (-0.033179) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.922331 / 1.841788 (-0.919457) \| 11.605041 / 8.074308 (3.530732) \| 10.471664 / 10.191392 (0.280272) \| 0.130325 / 0.680424 (-0.550098) \| 0.014084 / 0.534201 (-0.520117) \| 0.278877 / 0.579283 (-0.300406) \| 0.263104 / 0.434364 (-0.171259) \| 0.306723 / 0.540337 (-0.233615) \| 0.416238 / 1.386936 (-0.970698) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005094 / 0.011353 (-0.006259) \| 0.002794 / 0.011008 (-0.008214) \| 0.048189 / 0.038508 (0.009680) \| 0.050409 / 0.023109 (0.027300) \| 0.272618 / 0.275898 (-0.003280) \| 0.293589 / 0.323480 (-0.029891) \| 0.003995 / 0.007986 (-0.003991) \| 0.002373 / 0.004328 (-0.001956) \| 0.048269 / 0.004250 (0.044018) \| 0.038751 / 0.037052 (0.001698) \| 0.273495 / 0.258489 (0.015006) \| 0.309244 / 0.293841 (0.015403) \| 0.024681 / 0.128546 (-0.103866) \| 0.007390 / 0.075646 (-0.068256) \| 0.053844 / 0.419271 (-0.365427) \| 0.032395 / 0.043533 (-0.011137) \| 0.271963 / 0.255139 (0.016824) \| 0.289557 / 0.283200 (0.006357) \| 0.018659 / 0.141683 (-0.123024) \| 1.154478 / 1.452155 (-0.297676) \| 1.199772 / 1.492716 (-0.292944) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089771 / 0.018006 (0.071764) \| 0.299468 / 0.000490 (0.298978) \| 0.000219 / 0.000200 (0.000020) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021854 / 0.037411 (-0.015558) \| 0.070280 / 0.014526 (0.055754) \| 0.080956 / 0.176557 (-0.095600) \| 0.119430 / 0.737135 (-0.617705) \| 0.082778 / 0.296338 (-0.213561) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.304273 / 0.215209 (0.089064) \| 2.968264 / 2.077655 (0.890609) \| 1.592363 / 1.504120 (0.088243) \| 1.460795 / 1.541195 (-0.080400) \| 1.501545 / 1.468490 (0.033055) \| 0.411001 / 4.584777 (-4.173776) \| 2.464273 / 3.745712 (-1.281439) \| 2.524585 / 5.269862 (-2.745277) \| 1.537443 / 4.565676 (-3.028234) \| 0.046163 / 0.424275 (-0.378112) \| 0.004783 / 0.007607 (-0.002824) \| 0.354251 / 0.226044 (0.128206) \| 3.512087 / 2.268929 (1.243158) \| 1.968156 / 55.444624 (-53.476468) \| 1.664966 / 6.876477 (-5.211510) \| 1.685013 / 2.142072 (-0.457060) \| 0.485793 / 4.805227 (-4.319435) \| 0.099789 / 6.500664 (-6.400875) \| 0.040705 / 0.075469 (-0.034764) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.966570 / 1.841788 (-0.875218) \| 12.023188 / 8.074308 (3.948880) \| 11.122602 / 10.191392 (0.931210) \| 0.141002 / 0.680424 (-0.539422) \| 0.015955 / 0.534201 (-0.518246) \| 0.270293 / 0.579283 (-0.308990) \| 0.281839 / 0.434364 (-0.152525) \| 0.307279 / 0.540337 (-0.233058) \| 0.434687 / 1.386936 (-0.952249) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7eaad71464e85c7358eaa36494227a43257ffcd8 \"CML watermark\")\n", "What would be the impact for non-windows users ?\r\n\r\nAlso I wonder if a gc.collect() after the `del` could help to remove the PermissionError ? Or register the dataset for deletion on copy/pickle maybe ?", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004973 / 0.011353 (-0.006380) \| 0.002753 / 0.011008 (-0.008256) \| 0.061489 / 0.038508 (0.022981) \| 0.051122 / 0.023109 (0.028012) \| 0.228783 / 0.275898 (-0.047115) \| 0.256982 / 0.323480 (-0.066498) \| 0.002873 / 0.007986 (-0.005112) \| 0.003544 / 0.004328 (-0.000784) \| 0.048721 / 0.004250 (0.044471) \| 0.039137 / 0.037052 (0.002085) \| 0.244988 / 0.258489 (-0.013501) \| 0.275230 / 0.293841 (-0.018611) \| 0.023034 / 0.128546 (-0.105513) \| 0.006988 / 0.075646 (-0.068658) \| 0.202780 / 0.419271 (-0.216492) \| 0.035325 / 0.043533 (-0.008207) \| 0.241722 / 0.255139 (-0.013417) \| 0.259671 / 0.283200 (-0.023528) \| 0.019875 / 0.141683 (-0.121808) \| 1.098667 / 1.452155 (-0.353488) \| 1.161444 / 1.492716 (-0.331272) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093591 / 0.018006 (0.075585) \| 0.298703 / 0.000490 (0.298213) \| 0.000219 / 0.000200 (0.000019) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018319 / 0.037411 (-0.019092) \| 0.062993 / 0.014526 (0.048467) \| 0.074313 / 0.176557 (-0.102244) \| 0.123089 / 0.737135 (-0.614046) \| 0.075177 / 0.296338 (-0.221162) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.268584 / 0.215209 (0.053375) \| 2.633116 / 2.077655 (0.555461) \| 1.390743 / 1.504120 (-0.113377) \| 1.277385 / 1.541195 (-0.263810) \| 1.287934 / 1.468490 (-0.180556) \| 0.387934 / 4.584777 (-4.196843) \| 2.345819 / 3.745712 (-1.399893) \| 2.558169 / 5.269862 (-2.711693) \| 1.569812 / 4.565676 (-2.995865) \| 0.045297 / 0.424275 (-0.378978) \| 0.005238 / 0.007607 (-0.002369) \| 0.359704 / 0.226044 (0.133659) \| 3.204688 / 2.268929 (0.935759) \| 1.753321 / 55.444624 (-53.691303) \| 1.492223 / 6.876477 (-5.384254) \| 1.498207 / 2.142072 (-0.643865) \| 0.459830 / 4.805227 (-4.345397) \| 0.098194 / 6.500664 (-6.402470) \| 0.042632 / 0.075469 (-0.032837) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.963020 / 1.841788 (-0.878768) \| 11.500470 / 8.074308 (3.426161) \| 10.451882 / 10.191392 (0.260490) \| 0.127706 / 0.680424 (-0.552718) \| 0.014084 / 0.534201 (-0.520117) \| 0.269728 / 0.579283 (-0.309555) \| 0.260283 / 0.434364 (-0.174080) \| 0.303717 / 0.540337 (-0.236620) \| 0.397028 / 1.386936 (-0.989908) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004823 / 0.011353 (-0.006529) \| 0.002751 / 0.011008 (-0.008257) \| 0.048719 / 0.038508 (0.010211) \| 0.051409 / 0.023109 (0.028300) \| 0.267139 / 0.275898 (-0.008759) \| 0.287659 / 0.323480 (-0.035821) \| 0.003959 / 0.007986 (-0.004027) \| 0.002376 / 0.004328 (-0.001953) \| 0.047942 / 0.004250 (0.043692) \| 0.039742 / 0.037052 (0.002690) \| 0.268348 / 0.258489 (0.009859) \| 0.297201 / 0.293841 (0.003360) \| 0.024226 / 0.128546 (-0.104320) \| 0.007103 / 0.075646 (-0.068544) \| 0.053310 / 0.419271 (-0.365961) \| 0.032716 / 0.043533 (-0.010816) \| 0.269469 / 0.255139 (0.014330) \| 0.287752 / 0.283200 (0.004553) \| 0.018191 / 0.141683 (-0.123492) \| 1.114086 / 1.452155 (-0.338069) \| 1.188054 / 1.492716 (-0.304662) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091072 / 0.018006 (0.073066) \| 0.300367 / 0.000490 (0.299877) \| 0.000218 / 0.000200 (0.000018) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020970 / 0.037411 (-0.016441) \| 0.070356 / 0.014526 (0.055830) \| 0.081339 / 0.176557 (-0.095218) \| 0.120741 / 0.737135 (-0.616394) \| 0.081677 / 0.296338 (-0.214662) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290405 / 0.215209 (0.075196) \| 2.863877 / 2.077655 (0.786222) \| 1.524603 / 1.504120 (0.020483) \| 1.397917 / 1.541195 (-0.143278) \| 1.402635 / 1.468490 (-0.065855) \| 0.405525 / 4.584777 (-4.179252) \| 2.432474 / 3.745712 (-1.313239) \| 2.446277 / 5.269862 (-2.823585) \| 1.550300 / 4.565676 (-3.015377) \| 0.046545 / 0.424275 (-0.377730) \| 0.004824 / 0.007607 (-0.002783) \| 0.343578 / 0.226044 (0.117534) \| 3.436850 / 2.268929 (1.167922) \| 1.897200 / 55.444624 (-53.547425) \| 1.625222 / 6.876477 (-5.251255) \| 1.730488 / 2.142072 (-0.411585) \| 0.482099 / 4.805227 (-4.323129) \| 0.097828 / 6.500664 (-6.402836) \| 0.040385 / 0.075469 (-0.035084) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.950975 / 1.841788 (-0.890812) \| 11.875024 / 8.074308 (3.800715) \| 10.430301 / 10.191392 (0.238909) \| 0.130546 / 0.680424 (-0.549878) \| 0.015423 / 0.534201 (-0.518778) \| 0.269592 / 0.579283 (-0.309691) \| 0.282505 / 0.434364 (-0.151859) \| 0.305567 / 0.540337 (-0.234771) \| 0.522142 / 1.386936 (-0.864794) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c166692aa955528180dd4d55474a984f6044896d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004983 / 0.011353 (-0.006369) \| 0.003346 / 0.011008 (-0.007662) \| 0.062233 / 0.038508 (0.023725) \| 0.050246 / 0.023109 (0.027137) \| 0.305738 / 0.275898 (0.029839) \| 0.321863 / 0.323480 (-0.001617) \| 0.003870 / 0.007986 (-0.004116) \| 0.002610 / 0.004328 (-0.001718) \| 0.047734 / 0.004250 (0.043483) \| 0.037611 / 0.037052 (0.000559) \| 0.299121 / 0.258489 (0.040632) \| 0.327370 / 0.293841 (0.033529) \| 0.027009 / 0.128546 (-0.101537) \| 0.010816 / 0.075646 (-0.064830) \| 0.204627 / 0.419271 (-0.214645) \| 0.035708 / 0.043533 (-0.007825) \| 0.291837 / 0.255139 (0.036698) \| 0.313646 / 0.283200 (0.030447) \| 0.017277 / 0.141683 (-0.124405) \| 1.097907 / 1.452155 (-0.354248) \| 1.163203 / 1.492716 (-0.329513) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091933 / 0.018006 (0.073926) \| 0.298787 / 0.000490 (0.298297) \| 0.000204 / 0.000200 (0.000004) \| 0.000051 / 0.000054 (-0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018349 / 0.037411 (-0.019062) \| 0.061520 / 0.014526 (0.046994) \| 0.073159 / 0.176557 (-0.103397) \| 0.118657 / 0.737135 (-0.618478) \| 0.073601 / 0.296338 (-0.222737) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.276297 / 0.215209 (0.061088) \| 2.725668 / 2.077655 (0.648013) \| 1.458079 / 1.504120 (-0.046041) \| 1.331236 / 1.541195 (-0.209959) \| 1.347919 / 1.468490 (-0.120571) \| 0.565954 / 4.584777 (-4.018823) \| 2.380883 / 3.745712 (-1.364829) \| 2.800533 / 5.269862 (-2.469329) \| 1.740534 / 4.565676 (-2.825142) \| 0.065617 / 0.424275 (-0.358658) \| 0.004907 / 0.007607 (-0.002700) \| 0.335973 / 0.226044 (0.109929) \| 3.337405 / 2.268929 (1.068476) \| 1.819852 / 55.444624 (-53.624772) \| 1.542724 / 6.876477 (-5.333752) \| 1.509508 / 2.142072 (-0.632565) \| 0.648618 / 4.805227 (-4.156609) \| 0.116812 / 6.500664 (-6.383852) \| 0.041561 / 0.075469 (-0.033909) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.943488 / 1.841788 (-0.898299) \| 11.184770 / 8.074308 (3.110462) \| 10.406311 / 10.191392 (0.214919) \| 0.129841 / 0.680424 (-0.550583) \| 0.013736 / 0.534201 (-0.520465) \| 0.287281 / 0.579283 (-0.292002) \| 0.267403 / 0.434364 (-0.166961) \| 0.325319 / 0.540337 (-0.215019) \| 0.454207 / 1.386936 (-0.932729) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005169 / 0.011353 (-0.006183) \| 0.003155 / 0.011008 (-0.007854) \| 0.048101 / 0.038508 (0.009593) \| 0.048726 / 0.023109 (0.025617) \| 0.275768 / 0.275898 (-0.000130) \| 0.291209 / 0.323480 (-0.032271) \| 0.003984 / 0.007986 (-0.004001) \| 0.002586 / 0.004328 (-0.001742) \| 0.047751 / 0.004250 (0.043500) \| 0.040176 / 0.037052 (0.003124) \| 0.279161 / 0.258489 (0.020672) \| 0.297371 / 0.293841 (0.003530) \| 0.028502 / 0.128546 (-0.100044) \| 0.010103 / 0.075646 (-0.065544) \| 0.056920 / 0.419271 (-0.362351) \| 0.032174 / 0.043533 (-0.011359) \| 0.271925 / 0.255139 (0.016786) \| 0.289572 / 0.283200 (0.006372) \| 0.017981 / 0.141683 (-0.123702) \| 1.192972 / 1.452155 (-0.259183) \| 1.223231 / 1.492716 (-0.269485) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091363 / 0.018006 (0.073356) \| 0.298106 / 0.000490 (0.297616) \| 0.000216 / 0.000200 (0.000016) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021509 / 0.037411 (-0.015902) \| 0.068377 / 0.014526 (0.053851) \| 0.079798 / 0.176557 (-0.096759) \| 0.120546 / 0.737135 (-0.616589) \| 0.080602 / 0.296338 (-0.215737) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300809 / 0.215209 (0.085600) \| 2.921144 / 2.077655 (0.843489) \| 1.621096 / 1.504120 (0.116976) \| 1.504265 / 1.541195 (-0.036930) \| 1.508050 / 1.468490 (0.039560) \| 0.554291 / 4.584777 (-4.030486) \| 2.418798 / 3.745712 (-1.326914) \| 2.768088 / 5.269862 (-2.501773) \| 1.728267 / 4.565676 (-2.837410) \| 0.062943 / 0.424275 (-0.361332) \| 0.004891 / 0.007607 (-0.002716) \| 0.350298 / 0.226044 (0.124254) \| 3.442782 / 2.268929 (1.173853) \| 1.960163 / 55.444624 (-53.484461) \| 1.682000 / 6.876477 (-5.194477) \| 1.680311 / 2.142072 (-0.461761) \| 0.631201 / 4.805227 (-4.174026) \| 0.115211 / 6.500664 (-6.385453) \| 0.041279 / 0.075469 (-0.034190) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.962478 / 1.841788 (-0.879310) \| 11.671463 / 8.074308 (3.597155) \| 10.640129 / 10.191392 (0.448737) \| 0.130649 / 0.680424 (-0.549775) \| 0.016169 / 0.534201 (-0.518032) \| 0.286894 / 0.579283 (-0.292389) \| 0.269319 / 0.434364 (-0.165045) \| 0.324512 / 0.540337 (-0.215825) \| 0.550874 / 1.386936 (-0.836062) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#69f135121beb1616f1d7c7584b317d4e41e21275 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005078 / 0.011353 (-0.006275) \| 0.003950 / 0.011008 (-0.007058) \| 0.063345 / 0.038508 (0.024837) \| 0.054486 / 0.023109 (0.031377) \| 0.243213 / 0.275898 (-0.032685) \| 0.264079 / 0.323480 (-0.059401) \| 0.003922 / 0.007986 (-0.004064) \| 0.002631 / 0.004328 (-0.001698) \| 0.048660 / 0.004250 (0.044409) \| 0.037205 / 0.037052 (0.000153) \| 0.244577 / 0.258489 (-0.013912) \| 0.276025 / 0.293841 (-0.017816) \| 0.027134 / 0.128546 (-0.101412) \| 0.010921 / 0.075646 (-0.064726) \| 0.209792 / 0.419271 (-0.209479) \| 0.035999 / 0.043533 (-0.007534) \| 0.245671 / 0.255139 (-0.009468) \| 0.262807 / 0.283200 (-0.020393) \| 0.018173 / 0.141683 (-0.123510) \| 1.084417 / 1.452155 (-0.367738) \| 1.148284 / 1.492716 (-0.344432) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093128 / 0.018006 (0.075122) \| 0.301606 / 0.000490 (0.301117) \| 0.000221 / 0.000200 (0.000021) \| 0.000050 / 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) \| 0.018718 / 0.037411 (-0.018693) \| 0.060819 / 0.014526 (0.046293) \| 0.073050 / 0.176557 (-0.103507) \| 0.120043 / 0.737135 (-0.617092) \| 0.075374 / 0.296338 (-0.220965) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291080 / 0.215209 (0.075871) \| 2.808802 / 2.077655 (0.731148) \| 1.485686 / 1.504120 (-0.018434) \| 1.354356 / 1.541195 (-0.186839) \| 1.347863 / 1.468490 (-0.120627) \| 0.571501 / 4.584777 (-4.013276) \| 2.377960 / 3.745712 (-1.367752) \| 2.768023 / 5.269862 (-2.501839) \| 1.754360 / 4.565676 (-2.811316) \| 0.063115 / 0.424275 (-0.361160) \| 0.004941 / 0.007607 (-0.002666) \| 0.338281 / 0.226044 (0.112237) \| 3.340587 / 2.268929 (1.071658) \| 1.849479 / 55.444624 (-53.595145) \| 1.551846 / 6.876477 (-5.324631) \| 1.539090 / 2.142072 (-0.602983) \| 0.644522 / 4.805227 (-4.160705) \| 0.117398 / 6.500664 (-6.383266) \| 0.042239 / 0.075469 (-0.033230) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.949496 / 1.841788 (-0.892291) \| 11.548352 / 8.074308 (3.474044) \| 10.478065 / 10.191392 (0.286673) \| 0.129534 / 0.680424 (-0.550890) \| 0.015378 / 0.534201 (-0.518822) \| 0.287221 / 0.579283 (-0.292062) \| 0.262944 / 0.434364 (-0.171419) \| 0.321727 / 0.540337 (-0.218611) \| 0.432354 / 1.386936 (-0.954582) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005256 / 0.011353 (-0.006097) \| 0.003491 / 0.011008 (-0.007517) \| 0.048647 / 0.038508 (0.010139) \| 0.054011 / 0.023109 (0.030901) \| 0.271786 / 0.275898 (-0.004112) \| 0.291964 / 0.323480 (-0.031516) \| 0.004035 / 0.007986 (-0.003950) \| 0.002671 / 0.004328 (-0.001657) \| 0.048108 / 0.004250 (0.043857) \| 0.040421 / 0.037052 (0.003368) \| 0.278594 / 0.258489 (0.020105) \| 0.300707 / 0.293841 (0.006867) \| 0.028924 / 0.128546 (-0.099623) \| 0.010600 / 0.075646 (-0.065047) \| 0.057649 / 0.419271 (-0.361623) \| 0.034221 / 0.043533 (-0.009312) \| 0.276692 / 0.255139 (0.021553) \| 0.293545 / 0.283200 (0.010345) \| 0.017908 / 0.141683 (-0.123775) \| 1.135108 / 1.452155 (-0.317047) \| 1.190823 / 1.492716 (-0.301893) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095243 / 0.018006 (0.077237) \| 0.301885 / 0.000490 (0.301396) \| 0.000235 / 0.000200 (0.000035) \| 0.000056 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021561 / 0.037411 (-0.015850) \| 0.069054 / 0.014526 (0.054529) \| 0.080466 / 0.176557 (-0.096091) \| 0.121323 / 0.737135 (-0.615812) \| 0.081891 / 0.296338 (-0.214448) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293957 / 0.215209 (0.078748) \| 2.869035 / 2.077655 (0.791380) \| 1.608837 / 1.504120 (0.104717) \| 1.440594 / 1.541195 (-0.100601) \| 1.464775 / 1.468490 (-0.003715) \| 0.565663 / 4.584777 (-4.019114) \| 2.439456 / 3.745712 (-1.306256) \| 2.794775 / 5.269862 (-2.475087) \| 1.750026 / 4.565676 (-2.815651) \| 0.063291 / 0.424275 (-0.360984) \| 0.004930 / 0.007607 (-0.002677) \| 0.347169 / 0.226044 (0.121125) \| 3.408260 / 2.268929 (1.139331) \| 1.920933 / 55.444624 (-53.523691) \| 1.648821 / 6.876477 (-5.227656) \| 1.639022 / 2.142072 (-0.503051) \| 0.642870 / 4.805227 (-4.162357) \| 0.117077 / 6.500664 (-6.383587) \| 0.040784 / 0.075469 (-0.034685) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.993501 / 1.841788 (-0.848287) \| 12.012423 / 8.074308 (3.938115) \| 10.740932 / 10.191392 (0.549540) \| 0.132409 / 0.680424 (-0.548015) \| 0.015294 / 0.534201 (-0.518907) \| 0.287902 / 0.579283 (-0.291381) \| 0.281350 / 0.434364 (-0.153014) \| 0.329201 / 0.540337 (-0.211137) \| 0.553199 / 1.386936 (-0.833737) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ecd3a22c5dec2133491a320515e12956512439eb \"CML watermark\")\n" ]
1,995,269,382	6,425	Fix deprecation warning when building conda package	closed	2023-11-15T18:00:11	2023-12-13T14:22:30	2023-12-13T14:16:00	https://github.com/huggingface/datasets/pull/6425	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6425", "html_url": "https://github.com/huggingface/datasets/pull/6425", "diff_url": "https://github.com/huggingface/datasets/pull/6425.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6425.patch", "merged_at": "2023-12-13T14:16:00" }	albertvillanova	true	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004811 / 0.011353 (-0.006542) \| 0.002478 / 0.011008 (-0.008530) \| 0.062241 / 0.038508 (0.023733) \| 0.031153 / 0.023109 (0.008044) \| 0.248896 / 0.275898 (-0.027002) \| 0.276860 / 0.323480 (-0.046620) \| 0.002934 / 0.007986 (-0.005052) \| 0.002428 / 0.004328 (-0.001901) \| 0.048507 / 0.004250 (0.044257) \| 0.044567 / 0.037052 (0.007515) \| 0.253570 / 0.258489 (-0.004919) \| 0.280762 / 0.293841 (-0.013079) \| 0.023549 / 0.128546 (-0.104997) \| 0.006985 / 0.075646 (-0.068661) \| 0.206227 / 0.419271 (-0.213044) \| 0.054027 / 0.043533 (0.010494) \| 0.257655 / 0.255139 (0.002516) \| 0.273498 / 0.283200 (-0.009702) \| 0.018997 / 0.141683 (-0.122685) \| 1.111732 / 1.452155 (-0.340422) \| 1.162078 / 1.492716 (-0.330639) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091816 / 0.018006 (0.073810) \| 0.299428 / 0.000490 (0.298938) \| 0.000211 / 0.000200 (0.000012) \| 0.000048 / 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) \| 0.018503 / 0.037411 (-0.018908) \| 0.062933 / 0.014526 (0.048407) \| 0.076349 / 0.176557 (-0.100208) \| 0.123291 / 0.737135 (-0.613844) \| 0.077491 / 0.296338 (-0.218847) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280770 / 0.215209 (0.065561) \| 2.762185 / 2.077655 (0.684530) \| 1.429124 / 1.504120 (-0.074996) \| 1.303162 / 1.541195 (-0.238033) \| 1.307523 / 1.468490 (-0.160967) \| 0.405593 / 4.584777 (-4.179184) \| 2.396992 / 3.745712 (-1.348721) \| 2.550968 / 5.269862 (-2.718894) \| 1.557358 / 4.565676 (-3.008318) \| 0.046149 / 0.424275 (-0.378126) \| 0.004808 / 0.007607 (-0.002799) \| 0.341870 / 0.226044 (0.115825) \| 3.362478 / 2.268929 (1.093550) \| 1.786360 / 55.444624 (-53.658264) \| 1.483419 / 6.876477 (-5.393058) \| 1.493463 / 2.142072 (-0.648609) \| 0.470605 / 4.805227 (-4.334623) \| 0.098372 / 6.500664 (-6.402292) \| 0.041722 / 0.075469 (-0.033748) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.938148 / 1.841788 (-0.903640) \| 11.219184 / 8.074308 (3.144876) \| 10.454439 / 10.191392 (0.263047) \| 0.139645 / 0.680424 (-0.540778) \| 0.014453 / 0.534201 (-0.519748) \| 0.268975 / 0.579283 (-0.310308) \| 0.262060 / 0.434364 (-0.172304) \| 0.313652 / 0.540337 (-0.226686) \| 0.423992 / 1.386936 (-0.962944) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004829 / 0.011353 (-0.006524) \| 0.002426 / 0.011008 (-0.008582) \| 0.049064 / 0.038508 (0.010555) \| 0.049728 / 0.023109 (0.026619) \| 0.273263 / 0.275898 (-0.002635) \| 0.295645 / 0.323480 (-0.027835) \| 0.004156 / 0.007986 (-0.003830) \| 0.002397 / 0.004328 (-0.001932) \| 0.048902 / 0.004250 (0.044652) \| 0.038414 / 0.037052 (0.001362) \| 0.276176 / 0.258489 (0.017687) \| 0.306844 / 0.293841 (0.013003) \| 0.024546 / 0.128546 (-0.104000) \| 0.006946 / 0.075646 (-0.068701) \| 0.054024 / 0.419271 (-0.365247) \| 0.032444 / 0.043533 (-0.011089) \| 0.274125 / 0.255139 (0.018986) \| 0.293226 / 0.283200 (0.010027) \| 0.018003 / 0.141683 (-0.123680) \| 1.130402 / 1.452155 (-0.321752) \| 1.195969 / 1.492716 (-0.296748) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090043 / 0.018006 (0.072037) \| 0.298699 / 0.000490 (0.298209) \| 0.000214 / 0.000200 (0.000014) \| 0.000047 / 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) \| 0.021284 / 0.037411 (-0.016127) \| 0.069954 / 0.014526 (0.055428) \| 0.080445 / 0.176557 (-0.096111) \| 0.119461 / 0.737135 (-0.617674) \| 0.080632 / 0.296338 (-0.215706) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302246 / 0.215209 (0.087037) \| 2.991936 / 2.077655 (0.914281) \| 1.662969 / 1.504120 (0.158850) \| 1.533141 / 1.541195 (-0.008054) \| 1.583183 / 1.468490 (0.114693) \| 0.402864 / 4.584777 (-4.181913) \| 2.424119 / 3.745712 (-1.321593) \| 2.489558 / 5.269862 (-2.780303) \| 1.502196 / 4.565676 (-3.063481) \| 0.045980 / 0.424275 (-0.378295) \| 0.004768 / 0.007607 (-0.002839) \| 0.356089 / 0.226044 (0.130044) \| 3.481333 / 2.268929 (1.212404) \| 2.009713 / 55.444624 (-53.434912) \| 1.730021 / 6.876477 (-5.146455) \| 1.704656 / 2.142072 (-0.437416) \| 0.470832 / 4.805227 (-4.334395) \| 0.097473 / 6.500664 (-6.403191) \| 0.040437 / 0.075469 (-0.035032) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.981497 / 1.841788 (-0.860291) \| 11.827242 / 8.074308 (3.752933) \| 10.888324 / 10.191392 (0.696932) \| 0.129249 / 0.680424 (-0.551174) \| 0.015812 / 0.534201 (-0.518389) \| 0.269657 / 0.579283 (-0.309626) \| 0.275585 / 0.434364 (-0.158779) \| 0.305698 / 0.540337 (-0.234639) \| 0.411497 / 1.386936 (-0.975439) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bcde318293af04fd5044b42ddfcb650f9b092d45 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6425). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005402 / 0.011353 (-0.005951) \| 0.003955 / 0.011008 (-0.007053) \| 0.064096 / 0.038508 (0.025588) \| 0.062330 / 0.023109 (0.039221) \| 0.254729 / 0.275898 (-0.021169) \| 0.276259 / 0.323480 (-0.047221) \| 0.003052 / 0.007986 (-0.004934) \| 0.003474 / 0.004328 (-0.000854) \| 0.048938 / 0.004250 (0.044687) \| 0.038635 / 0.037052 (0.001583) \| 0.267953 / 0.258489 (0.009464) \| 0.293725 / 0.293841 (-0.000116) \| 0.028266 / 0.128546 (-0.100280) \| 0.011188 / 0.075646 (-0.064458) \| 0.221204 / 0.419271 (-0.198067) \| 0.036549 / 0.043533 (-0.006984) \| 0.252484 / 0.255139 (-0.002655) \| 0.273855 / 0.283200 (-0.009345) \| 0.017975 / 0.141683 (-0.123708) \| 1.112265 / 1.452155 (-0.339890) \| 1.185647 / 1.492716 (-0.307069) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096223 / 0.018006 (0.078217) \| 0.305010 / 0.000490 (0.304520) \| 0.000227 / 0.000200 (0.000027) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018924 / 0.037411 (-0.018488) \| 0.061910 / 0.014526 (0.047384) \| 0.073751 / 0.176557 (-0.102806) \| 0.120956 / 0.737135 (-0.616179) \| 0.075090 / 0.296338 (-0.221249) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293277 / 0.215209 (0.078068) \| 2.867468 / 2.077655 (0.789813) \| 1.518218 / 1.504120 (0.014098) \| 1.393741 / 1.541195 (-0.147454) \| 1.424979 / 1.468490 (-0.043511) \| 0.579766 / 4.584777 (-4.005011) \| 2.434951 / 3.745712 (-1.310761) \| 2.909924 / 5.269862 (-2.359937) \| 1.838123 / 4.565676 (-2.727554) \| 0.064260 / 0.424275 (-0.360015) \| 0.005169 / 0.007607 (-0.002438) \| 0.348228 / 0.226044 (0.122184) \| 3.447558 / 2.268929 (1.178629) \| 1.884988 / 55.444624 (-53.559636) \| 1.570921 / 6.876477 (-5.305556) \| 1.646341 / 2.142072 (-0.495732) \| 0.660189 / 4.805227 (-4.145038) \| 0.120026 / 6.500664 (-6.380638) \| 0.043715 / 0.075469 (-0.031754) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.953253 / 1.841788 (-0.888535) \| 12.576112 / 8.074308 (4.501804) \| 11.132637 / 10.191392 (0.941245) \| 0.132870 / 0.680424 (-0.547553) \| 0.014720 / 0.534201 (-0.519481) \| 0.291866 / 0.579283 (-0.287417) \| 0.265456 / 0.434364 (-0.168908) \| 0.338629 / 0.540337 (-0.201709) \| 0.456323 / 1.386936 (-0.930613) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005644 / 0.011353 (-0.005709) \| 0.003624 / 0.011008 (-0.007384) \| 0.049043 / 0.038508 (0.010535) \| 0.059572 / 0.023109 (0.036463) \| 0.277159 / 0.275898 (0.001261) \| 0.303933 / 0.323480 (-0.019547) \| 0.004294 / 0.007986 (-0.003692) \| 0.002744 / 0.004328 (-0.001584) \| 0.048187 / 0.004250 (0.043937) \| 0.043655 / 0.037052 (0.006603) \| 0.282441 / 0.258489 (0.023952) \| 0.317130 / 0.293841 (0.023289) \| 0.030159 / 0.128546 (-0.098387) \| 0.011300 / 0.075646 (-0.064346) \| 0.057451 / 0.419271 (-0.361821) \| 0.033666 / 0.043533 (-0.009866) \| 0.274554 / 0.255139 (0.019415) \| 0.292470 / 0.283200 (0.009270) \| 0.018757 / 0.141683 (-0.122926) \| 1.170094 / 1.452155 (-0.282060) \| 1.244626 / 1.492716 (-0.248090) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094920 / 0.018006 (0.076914) \| 0.304156 / 0.000490 (0.303666) \| 0.000226 / 0.000200 (0.000026) \| 0.000045 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022297 / 0.037411 (-0.015115) \| 0.068908 / 0.014526 (0.054383) \| 0.081520 / 0.176557 (-0.095037) \| 0.122422 / 0.737135 (-0.614714) \| 0.082533 / 0.296338 (-0.213806) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296080 / 0.215209 (0.080871) \| 2.883120 / 2.077655 (0.805465) \| 1.607950 / 1.504120 (0.103830) \| 1.496191 / 1.541195 (-0.045004) \| 1.520549 / 1.468490 (0.052059) \| 0.562081 / 4.584777 (-4.022696) \| 2.453447 / 3.745712 (-1.292265) \| 2.943676 / 5.269862 (-2.326186) \| 1.820581 / 4.565676 (-2.745096) \| 0.064518 / 0.424275 (-0.359757) \| 0.005406 / 0.007607 (-0.002201) \| 0.349022 / 0.226044 (0.122978) \| 3.472117 / 2.268929 (1.203188) \| 2.006928 / 55.444624 (-53.437696) \| 1.704800 / 6.876477 (-5.171677) \| 1.719025 / 2.142072 (-0.423048) \| 0.643719 / 4.805227 (-4.161508) \| 0.117723 / 6.500664 (-6.382941) \| 0.043158 / 0.075469 (-0.032311) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.981229 / 1.841788 (-0.860559) \| 12.637620 / 8.074308 (4.563312) \| 10.848775 / 10.191392 (0.657383) \| 0.143981 / 0.680424 (-0.536443) \| 0.015950 / 0.534201 (-0.518251) \| 0.287542 / 0.579283 (-0.291741) \| 0.278989 / 0.434364 (-0.155375) \| 0.331786 / 0.540337 (-0.208552) \| 0.607238 / 1.386936 (-0.779698) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#06fb2f9973962ee97d1af7888209819b8ba7de37 \"CML watermark\")\n" ]
1,995,224,516	6,424	[docs] troubleshooting guide	closed	2023-11-15T17:28:14	2023-11-30T17:29:55	2023-11-30T17:23:46	https://github.com/huggingface/datasets/pull/6424	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6424", "html_url": "https://github.com/huggingface/datasets/pull/6424", "diff_url": "https://github.com/huggingface/datasets/pull/6424.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6424.patch", "merged_at": "2023-11-30T17:23:46" }	MKhalusova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6424). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005323 / 0.011353 (-0.006030) \| 0.003560 / 0.011008 (-0.007448) \| 0.062572 / 0.038508 (0.024064) \| 0.049549 / 0.023109 (0.026440) \| 0.236522 / 0.275898 (-0.039376) \| 0.260601 / 0.323480 (-0.062879) \| 0.002887 / 0.007986 (-0.005099) \| 0.003225 / 0.004328 (-0.001103) \| 0.048210 / 0.004250 (0.043960) \| 0.038783 / 0.037052 (0.001731) \| 0.242506 / 0.258489 (-0.015983) \| 0.273906 / 0.293841 (-0.019935) \| 0.027202 / 0.128546 (-0.101344) \| 0.010577 / 0.075646 (-0.065069) \| 0.211669 / 0.419271 (-0.207603) \| 0.035727 / 0.043533 (-0.007806) \| 0.242303 / 0.255139 (-0.012836) \| 0.260468 / 0.283200 (-0.022732) \| 0.020109 / 0.141683 (-0.121573) \| 1.089603 / 1.452155 (-0.362552) \| 1.149899 / 1.492716 (-0.342817) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088768 / 0.018006 (0.070761) \| 0.300300 / 0.000490 (0.299810) \| 0.000212 / 0.000200 (0.000013) \| 0.000050 / 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) \| 0.018758 / 0.037411 (-0.018653) \| 0.060097 / 0.014526 (0.045571) \| 0.074060 / 0.176557 (-0.102496) \| 0.119977 / 0.737135 (-0.617158) \| 0.075298 / 0.296338 (-0.221040) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278640 / 0.215209 (0.063431) \| 2.715574 / 2.077655 (0.637919) \| 1.466644 / 1.504120 (-0.037476) \| 1.344470 / 1.541195 (-0.196725) \| 1.386984 / 1.468490 (-0.081506) \| 0.575796 / 4.584777 (-4.008981) \| 2.392324 / 3.745712 (-1.353388) \| 2.826284 / 5.269862 (-2.443578) \| 1.758997 / 4.565676 (-2.806679) \| 0.062474 / 0.424275 (-0.361801) \| 0.004930 / 0.007607 (-0.002678) \| 0.332595 / 0.226044 (0.106551) \| 3.240076 / 2.268929 (0.971147) \| 1.785283 / 55.444624 (-53.659341) \| 1.527594 / 6.876477 (-5.348882) \| 1.562840 / 2.142072 (-0.579233) \| 0.655474 / 4.805227 (-4.149754) \| 0.116682 / 6.500664 (-6.383983) \| 0.042664 / 0.075469 (-0.032805) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.936306 / 1.841788 (-0.905481) \| 11.561239 / 8.074308 (3.486931) \| 10.341918 / 10.191392 (0.150526) \| 0.140602 / 0.680424 (-0.539822) \| 0.013857 / 0.534201 (-0.520344) \| 0.294241 / 0.579283 (-0.285042) \| 0.268359 / 0.434364 (-0.166005) \| 0.326344 / 0.540337 (-0.213993) \| 0.430936 / 1.386936 (-0.956000) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005197 / 0.011353 (-0.006156) \| 0.003543 / 0.011008 (-0.007465) \| 0.049051 / 0.038508 (0.010542) \| 0.052742 / 0.023109 (0.029633) \| 0.277032 / 0.275898 (0.001134) \| 0.300799 / 0.323480 (-0.022681) \| 0.003922 / 0.007986 (-0.004064) \| 0.002573 / 0.004328 (-0.001755) \| 0.047270 / 0.004250 (0.043019) \| 0.039782 / 0.037052 (0.002730) \| 0.282780 / 0.258489 (0.024291) \| 0.308858 / 0.293841 (0.015017) \| 0.028641 / 0.128546 (-0.099905) \| 0.010516 / 0.075646 (-0.065131) \| 0.056367 / 0.419271 (-0.362904) \| 0.032346 / 0.043533 (-0.011186) \| 0.277591 / 0.255139 (0.022452) \| 0.298539 / 0.283200 (0.015339) \| 0.018168 / 0.141683 (-0.123515) \| 1.104331 / 1.452155 (-0.347823) \| 1.187691 / 1.492716 (-0.305025) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089511 / 0.018006 (0.071505) \| 0.301309 / 0.000490 (0.300820) \| 0.000213 / 0.000200 (0.000013) \| 0.000049 / 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) \| 0.021466 / 0.037411 (-0.015945) \| 0.069917 / 0.014526 (0.055391) \| 0.081105 / 0.176557 (-0.095452) \| 0.119619 / 0.737135 (-0.617516) \| 0.083928 / 0.296338 (-0.212410) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296471 / 0.215209 (0.081262) \| 2.912139 / 2.077655 (0.834484) \| 1.588861 / 1.504120 (0.084741) \| 1.452148 / 1.541195 (-0.089047) \| 1.475388 / 1.468490 (0.006898) \| 0.555779 / 4.584777 (-4.028998) \| 2.425599 / 3.745712 (-1.320113) \| 2.792848 / 5.269862 (-2.477013) \| 1.718757 / 4.565676 (-2.846919) \| 0.077687 / 0.424275 (-0.346588) \| 0.007522 / 0.007607 (-0.000085) \| 0.348254 / 0.226044 (0.122210) \| 3.439315 / 2.268929 (1.170386) \| 1.925907 / 55.444624 (-53.518717) \| 1.646163 / 6.876477 (-5.230314) \| 1.662148 / 2.142072 (-0.479924) \| 0.637277 / 4.805227 (-4.167950) \| 0.116159 / 6.500664 (-6.384505) \| 0.041518 / 0.075469 (-0.033952) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.966358 / 1.841788 (-0.875430) \| 12.125201 / 8.074308 (4.050892) \| 10.629939 / 10.191392 (0.438547) \| 0.132439 / 0.680424 (-0.547984) \| 0.015622 / 0.534201 (-0.518579) \| 0.288824 / 0.579283 (-0.290459) \| 0.277634 / 0.434364 (-0.156730) \| 0.327200 / 0.540337 (-0.213138) \| 0.549679 / 1.386936 (-0.837257) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0850f663f5498e0f296461e99a345dfd65e3358f \"CML watermark\")\n" ]
1,994,946,847	6,423	Fix conda release by adding pyarrow-hotfix dependency	closed	2023-11-15T14:57:12	2023-11-15T17:15:33	2023-11-15T17:09:24	https://github.com/huggingface/datasets/pull/6423	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6423", "html_url": "https://github.com/huggingface/datasets/pull/6423", "diff_url": "https://github.com/huggingface/datasets/pull/6423.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6423.patch", "merged_at": "2023-11-15T17:09:24" }	albertvillanova	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004476 / 0.011353 (-0.006877) \| 0.002691 / 0.011008 (-0.008317) \| 0.061400 / 0.038508 (0.022892) \| 0.030096 / 0.023109 (0.006986) \| 0.279868 / 0.275898 (0.003970) \| 0.310320 / 0.323480 (-0.013159) \| 0.003873 / 0.007986 (-0.004112) \| 0.002394 / 0.004328 (-0.001935) \| 0.048307 / 0.004250 (0.044056) \| 0.043326 / 0.037052 (0.006273) \| 0.288256 / 0.258489 (0.029767) \| 0.311449 / 0.293841 (0.017609) \| 0.022970 / 0.128546 (-0.105576) \| 0.006714 / 0.075646 (-0.068932) \| 0.201656 / 0.419271 (-0.217615) \| 0.052811 / 0.043533 (0.009278) \| 0.285123 / 0.255139 (0.029984) \| 0.301495 / 0.283200 (0.018295) \| 0.017531 / 0.141683 (-0.124152) \| 1.097660 / 1.452155 (-0.354494) \| 1.161986 / 1.492716 (-0.330731) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089223 / 0.018006 (0.071217) \| 0.297815 / 0.000490 (0.297326) \| 0.000205 / 0.000200 (0.000005) \| 0.000042 / 0.000054 (-0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018679 / 0.037411 (-0.018732) \| 0.062742 / 0.014526 (0.048216) \| 0.072869 / 0.176557 (-0.103687) \| 0.120730 / 0.737135 (-0.616406) \| 0.074526 / 0.296338 (-0.221813) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.299977 / 0.215209 (0.084768) \| 2.921029 / 2.077655 (0.843375) \| 1.632283 / 1.504120 (0.128163) \| 1.508008 / 1.541195 (-0.033187) \| 1.513967 / 1.468490 (0.045477) \| 0.403056 / 4.584777 (-4.181721) \| 2.340011 / 3.745712 (-1.405701) \| 2.552319 / 5.269862 (-2.717543) \| 1.549741 / 4.565676 (-3.015935) \| 0.046303 / 0.424275 (-0.377972) \| 0.004768 / 0.007607 (-0.002839) \| 0.356921 / 0.226044 (0.130877) \| 3.506410 / 2.268929 (1.237482) \| 1.975394 / 55.444624 (-53.469230) \| 1.688683 / 6.876477 (-5.187794) \| 1.715502 / 2.142072 (-0.426571) \| 0.471016 / 4.805227 (-4.334212) \| 0.099552 / 6.500664 (-6.401112) \| 0.042095 / 0.075469 (-0.033374) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.955784 / 1.841788 (-0.886004) \| 11.191802 / 8.074308 (3.117494) \| 10.127818 / 10.191392 (-0.063574) \| 0.141225 / 0.680424 (-0.539199) \| 0.014486 / 0.534201 (-0.519715) \| 0.267204 / 0.579283 (-0.312079) \| 0.289108 / 0.434364 (-0.145256) \| 0.309458 / 0.540337 (-0.230880) \| 0.422802 / 1.386936 (-0.964134) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004797 / 0.011353 (-0.006556) \| 0.002907 / 0.011008 (-0.008101) \| 0.047666 / 0.038508 (0.009158) \| 0.051183 / 0.023109 (0.028074) \| 0.266315 / 0.275898 (-0.009583) \| 0.286429 / 0.323480 (-0.037051) \| 0.003954 / 0.007986 (-0.004031) \| 0.002041 / 0.004328 (-0.002288) \| 0.047652 / 0.004250 (0.043401) \| 0.038211 / 0.037052 (0.001158) \| 0.272210 / 0.258489 (0.013721) \| 0.299425 / 0.293841 (0.005584) \| 0.024266 / 0.128546 (-0.104280) \| 0.006747 / 0.075646 (-0.068900) \| 0.052959 / 0.419271 (-0.366312) \| 0.032094 / 0.043533 (-0.011439) \| 0.265677 / 0.255139 (0.010538) \| 0.285373 / 0.283200 (0.002174) \| 0.017577 / 0.141683 (-0.124106) \| 1.114514 / 1.452155 (-0.337640) \| 1.212970 / 1.492716 (-0.279746) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088347 / 0.018006 (0.070341) \| 0.296678 / 0.000490 (0.296188) \| 0.000209 / 0.000200 (0.000009) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021159 / 0.037411 (-0.016253) \| 0.069886 / 0.014526 (0.055360) \| 0.079832 / 0.176557 (-0.096725) \| 0.115512 / 0.737135 (-0.621623) \| 0.081600 / 0.296338 (-0.214739) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292659 / 0.215209 (0.077450) \| 2.872556 / 2.077655 (0.794901) \| 1.573017 / 1.504120 (0.068897) \| 1.445122 / 1.541195 (-0.096072) \| 1.485584 / 1.468490 (0.017094) \| 0.388638 / 4.584777 (-4.196139) \| 2.434847 / 3.745712 (-1.310865) \| 2.518167 / 5.269862 (-2.751695) \| 1.503000 / 4.565676 (-3.062676) \| 0.045123 / 0.424275 (-0.379153) \| 0.004778 / 0.007607 (-0.002829) \| 0.347955 / 0.226044 (0.121910) \| 3.384819 / 2.268929 (1.115891) \| 1.920185 / 55.444624 (-53.524439) \| 1.646910 / 6.876477 (-5.229567) \| 1.638092 / 2.142072 (-0.503980) \| 0.450535 / 4.805227 (-4.354692) \| 0.095301 / 6.500664 (-6.405363) \| 0.040275 / 0.075469 (-0.035194) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.956088 / 1.841788 (-0.885700) \| 11.776642 / 8.074308 (3.702334) \| 10.651063 / 10.191392 (0.459671) \| 0.127079 / 0.680424 (-0.553345) \| 0.015080 / 0.534201 (-0.519121) \| 0.273737 / 0.579283 (-0.305546) \| 0.271434 / 0.434364 (-0.162929) \| 0.308448 / 0.540337 (-0.231889) \| 0.412467 / 1.386936 (-0.974469) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#af014830363401a0166a2b8435ca2f863cb468d4 \"CML watermark\")\n", "Once this PR is merged, we should upload the missing version to conda.\r\n\r\n@lhoestq you did this in the past. If you tell me your approach (I see a tag called `VERSION`...), I could do it myself.", "Maybe open a PR against the 2.14 branch and update `release-conda.yml` like this ?\r\n\r\n```diff\r\n- on:\r\n- push:\r\n- tags:\r\n- - \"[0-9]+.[0-9]+.[0-9]+*\"\r\n+ on: push\r\n```\r\n\r\nand then set it back to normal after the release is done", "After having cherry-picked the commit in this PR, I have released the conda package. See: \r\n- https://github.com/huggingface/datasets/actions/runs/6880182419/job/18713812449\r\n- https://anaconda.org/HuggingFace/datasets/files?version=2.14.7\r\n\r\nI am merging this PR.\r\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004993 / 0.011353 (-0.006360) \| 0.002964 / 0.011008 (-0.008044) \| 0.062588 / 0.038508 (0.024080) \| 0.030794 / 0.023109 (0.007685) \| 0.234856 / 0.275898 (-0.041042) \| 0.264807 / 0.323480 (-0.058673) \| 0.003139 / 0.007986 (-0.004847) \| 0.002498 / 0.004328 (-0.001831) \| 0.048058 / 0.004250 (0.043807) \| 0.048349 / 0.037052 (0.011296) \| 0.238210 / 0.258489 (-0.020279) \| 0.278144 / 0.293841 (-0.015697) \| 0.023219 / 0.128546 (-0.105327) \| 0.007296 / 0.075646 (-0.068351) \| 0.203263 / 0.419271 (-0.216008) \| 0.058844 / 0.043533 (0.015311) \| 0.246330 / 0.255139 (-0.008809) \| 0.264550 / 0.283200 (-0.018649) \| 0.018580 / 0.141683 (-0.123103) \| 1.084163 / 1.452155 (-0.367992) \| 1.154891 / 1.492716 (-0.337825) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092393 / 0.018006 (0.074387) \| 0.300545 / 0.000490 (0.300055) \| 0.000203 / 0.000200 (0.000003) \| 0.000047 / 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) \| 0.018648 / 0.037411 (-0.018763) \| 0.063151 / 0.014526 (0.048625) \| 0.074206 / 0.176557 (-0.102350) \| 0.120929 / 0.737135 (-0.616207) \| 0.075970 / 0.296338 (-0.220368) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278489 / 0.215209 (0.063279) \| 2.664804 / 2.077655 (0.587150) \| 1.433040 / 1.504120 (-0.071080) \| 1.321416 / 1.541195 (-0.219779) \| 1.320964 / 1.468490 (-0.147526) \| 0.401289 / 4.584777 (-4.183488) \| 2.365310 / 3.745712 (-1.380402) \| 2.635798 / 5.269862 (-2.634063) \| 1.584384 / 4.565676 (-2.981293) \| 0.045675 / 0.424275 (-0.378600) \| 0.004854 / 0.007607 (-0.002753) \| 0.337592 / 0.226044 (0.111548) \| 3.330462 / 2.268929 (1.061534) \| 1.794507 / 55.444624 (-53.650117) \| 1.531284 / 6.876477 (-5.345193) \| 1.507165 / 2.142072 (-0.634908) \| 0.478622 / 4.805227 (-4.326606) \| 0.099105 / 6.500664 (-6.401560) \| 0.041575 / 0.075469 (-0.033894) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.941790 / 1.841788 (-0.899997) \| 11.609871 / 8.074308 (3.535563) \| 10.770869 / 10.191392 (0.579477) \| 0.138931 / 0.680424 (-0.541493) \| 0.014406 / 0.534201 (-0.519795) \| 0.269681 / 0.579283 (-0.309602) \| 0.260556 / 0.434364 (-0.173808) \| 0.308244 / 0.540337 (-0.232093) \| 0.428867 / 1.386936 (-0.958069) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004803 / 0.011353 (-0.006550) \| 0.003263 / 0.011008 (-0.007745) \| 0.049143 / 0.038508 (0.010635) \| 0.052033 / 0.023109 (0.028924) \| 0.267815 / 0.275898 (-0.008083) \| 0.288733 / 0.323480 (-0.034747) \| 0.004159 / 0.007986 (-0.003826) \| 0.002407 / 0.004328 (-0.001921) \| 0.048978 / 0.004250 (0.044728) \| 0.038994 / 0.037052 (0.001942) \| 0.264028 / 0.258489 (0.005539) \| 0.303930 / 0.293841 (0.010090) \| 0.024283 / 0.128546 (-0.104263) \| 0.007201 / 0.075646 (-0.068446) \| 0.053810 / 0.419271 (-0.365461) \| 0.032611 / 0.043533 (-0.010922) \| 0.266730 / 0.255139 (0.011591) \| 0.281564 / 0.283200 (-0.001635) \| 0.018720 / 0.141683 (-0.122963) \| 1.140676 / 1.452155 (-0.311479) \| 1.206604 / 1.492716 (-0.286113) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.109390 / 0.018006 (0.091384) \| 0.313783 / 0.000490 (0.313294) \| 0.000228 / 0.000200 (0.000028) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021228 / 0.037411 (-0.016183) \| 0.070505 / 0.014526 (0.055979) \| 0.081961 / 0.176557 (-0.094595) \| 0.119943 / 0.737135 (-0.617193) \| 0.083582 / 0.296338 (-0.212757) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295702 / 0.215209 (0.080493) \| 2.886865 / 2.077655 (0.809210) \| 1.583206 / 1.504120 (0.079086) \| 1.451129 / 1.541195 (-0.090065) \| 1.486253 / 1.468490 (0.017763) \| 0.403207 / 4.584777 (-4.181570) \| 2.408889 / 3.745712 (-1.336824) \| 2.578480 / 5.269862 (-2.691381) \| 1.533066 / 4.565676 (-3.032610) \| 0.046075 / 0.424275 (-0.378200) \| 0.004877 / 0.007607 (-0.002730) \| 0.345995 / 0.226044 (0.119950) \| 3.377039 / 2.268929 (1.108110) \| 1.944614 / 55.444624 (-53.500010) \| 1.677691 / 6.876477 (-5.198786) \| 1.672828 / 2.142072 (-0.469244) \| 0.468426 / 4.805227 (-4.336802) \| 0.097290 / 6.500664 (-6.403374) \| 0.040695 / 0.075469 (-0.034774) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.965778 / 1.841788 (-0.876010) \| 12.092639 / 8.074308 (4.018331) \| 11.210968 / 10.191392 (1.019576) \| 0.131212 / 0.680424 (-0.549212) \| 0.015865 / 0.534201 (-0.518336) \| 0.285702 / 0.579283 (-0.293581) \| 0.278319 / 0.434364 (-0.156045) \| 0.336063 / 0.540337 (-0.204275) \| 0.426265 / 1.386936 (-0.960671) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d122b3ddc67705cc2b622bcbd79de9ff943a5742 \"CML watermark\")\n" ]
1,994,579,267	6,422	Allow to choose the `writer_batch_size` when using `save_to_disk`	open	2023-11-15T11:18:34	2023-11-16T10:00:21	null	https://github.com/huggingface/datasets/issues/6422	null	NathanGodey	false	[ "We have a config variable that controls the batch size in `save_to_disk`:\r\n```python\r\nimport datasets\r\ndatasets.config.DEFAULT_MAX_BATCH_SIZE = <smaller_batch_size>\r\n...\r\nds.save_to_disk(...)\r\n```", "Thank you for your answer!\r\n\r\nFrom what I am reading in `https://github.com/huggingface/datasets/blob/2.14.5/src/datasets/arrow_dataset.py`, every function involved (`select`, `shard`, ...) has a default hardcoded batch size of 1000, as such:\r\n```python\r\ndef select(\r\n self,\r\n indices: Iterable,\r\n keep_in_memory: bool = False,\r\n indices_cache_file_name: Optional[str] = None,\r\n writer_batch_size: Optional[int] = 1000,\r\n new_fingerprint: Optional[str] = None,\r\n ) -> \"Dataset\":\r\n...\r\n```\r\nThen, `ArrowWriter` is instantiated with the specified `writer_batch_size`. In `ArrowWriter`, `writer_batch_size` is set to `datasets.config.DEFAULT_MAX_BATCH_SIZE` if it is `None`(https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_writer.py#L345C14-L345C31). However, in our case, it is already set to 1000 by \"parent\" methods, so it won't happen.\r\n\r\nNevertheless, due to this: \r\n```python\r\ndef _save_to_disk_single(job_id: int, shard: \"Dataset\", fpath: str, storage_options: Optional[dict]):\r\n batch_size = config.DEFAULT_MAX_BATCH_SIZE\r\n...\r\n```\r\nit seems to work. I will use it as such, but it should maybe be added to documentation? And maybe improved in next versions?" ]
1,994,451,553	6,421	Add pyarrow-hotfix to release docs	closed	2023-11-15T10:06:44	2023-11-15T13:49:55	2023-11-15T13:38:22	https://github.com/huggingface/datasets/pull/6421	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6421", "html_url": "https://github.com/huggingface/datasets/pull/6421", "diff_url": "https://github.com/huggingface/datasets/pull/6421.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6421.patch", "merged_at": "2023-11-15T13:38:22" }	albertvillanova	true	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004755 / 0.011353 (-0.006598) \| 0.002683 / 0.011008 (-0.008325) \| 0.061701 / 0.038508 (0.023193) \| 0.030123 / 0.023109 (0.007013) \| 0.238186 / 0.275898 (-0.037712) \| 0.266570 / 0.323480 (-0.056910) \| 0.002898 / 0.007986 (-0.005088) \| 0.002381 / 0.004328 (-0.001948) \| 0.048033 / 0.004250 (0.043782) \| 0.044529 / 0.037052 (0.007477) \| 0.246728 / 0.258489 (-0.011761) \| 0.302066 / 0.293841 (0.008225) \| 0.024008 / 0.128546 (-0.104539) \| 0.006626 / 0.075646 (-0.069020) \| 0.202000 / 0.419271 (-0.217272) \| 0.056492 / 0.043533 (0.012959) \| 0.243417 / 0.255139 (-0.011722) \| 0.263947 / 0.283200 (-0.019253) \| 0.020481 / 0.141683 (-0.121202) \| 1.130635 / 1.452155 (-0.321520) \| 1.180570 / 1.492716 (-0.312146) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095541 / 0.018006 (0.077535) \| 0.306152 / 0.000490 (0.305662) \| 0.000217 / 0.000200 (0.000017) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018593 / 0.037411 (-0.018818) \| 0.063029 / 0.014526 (0.048503) \| 0.074312 / 0.176557 (-0.102245) \| 0.119882 / 0.737135 (-0.617254) \| 0.074066 / 0.296338 (-0.222273) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275409 / 0.215209 (0.060200) \| 2.727061 / 2.077655 (0.649407) \| 1.415632 / 1.504120 (-0.088488) \| 1.294922 / 1.541195 (-0.246273) \| 1.341636 / 1.468490 (-0.126854) \| 0.403250 / 4.584777 (-4.181527) \| 2.384657 / 3.745712 (-1.361055) \| 2.604131 / 5.269862 (-2.665731) \| 1.558888 / 4.565676 (-3.006789) \| 0.046008 / 0.424275 (-0.378267) \| 0.004819 / 0.007607 (-0.002789) \| 0.331046 / 0.226044 (0.105002) \| 3.340950 / 2.268929 (1.072021) \| 1.801077 / 55.444624 (-53.643548) \| 1.479162 / 6.876477 (-5.397315) \| 1.503713 / 2.142072 (-0.638359) \| 0.474931 / 4.805227 (-4.330296) \| 0.101869 / 6.500664 (-6.398795) \| 0.041946 / 0.075469 (-0.033523) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.955641 / 1.841788 (-0.886147) \| 11.441032 / 8.074308 (3.366724) \| 10.267731 / 10.191392 (0.076339) \| 0.128735 / 0.680424 (-0.551689) \| 0.013942 / 0.534201 (-0.520259) \| 0.266620 / 0.579283 (-0.312663) \| 0.262334 / 0.434364 (-0.172029) \| 0.302713 / 0.540337 (-0.237624) \| 0.430323 / 1.386936 (-0.956613) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004670 / 0.011353 (-0.006683) \| 0.002671 / 0.011008 (-0.008338) \| 0.048949 / 0.038508 (0.010441) \| 0.052520 / 0.023109 (0.029411) \| 0.272614 / 0.275898 (-0.003284) \| 0.292618 / 0.323480 (-0.030862) \| 0.004016 / 0.007986 (-0.003969) \| 0.002430 / 0.004328 (-0.001899) \| 0.048313 / 0.004250 (0.044063) \| 0.038647 / 0.037052 (0.001595) \| 0.279893 / 0.258489 (0.021404) \| 0.305371 / 0.293841 (0.011530) \| 0.023710 / 0.128546 (-0.104836) \| 0.006999 / 0.075646 (-0.068648) \| 0.053315 / 0.419271 (-0.365956) \| 0.032417 / 0.043533 (-0.011115) \| 0.272066 / 0.255139 (0.016927) \| 0.291717 / 0.283200 (0.008518) \| 0.018127 / 0.141683 (-0.123556) \| 1.173611 / 1.452155 (-0.278544) \| 1.183659 / 1.492716 (-0.309057) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094831 / 0.018006 (0.076824) \| 0.304911 / 0.000490 (0.304421) \| 0.000225 / 0.000200 (0.000025) \| 0.000049 / 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) \| 0.020948 / 0.037411 (-0.016463) \| 0.070255 / 0.014526 (0.055729) \| 0.081371 / 0.176557 (-0.095186) \| 0.118932 / 0.737135 (-0.618203) \| 0.082207 / 0.296338 (-0.214132) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294067 / 0.215209 (0.078858) \| 2.856981 / 2.077655 (0.779326) \| 1.598392 / 1.504120 (0.094273) \| 1.479093 / 1.541195 (-0.062102) \| 1.509495 / 1.468490 (0.041005) \| 0.396303 / 4.584777 (-4.188473) \| 2.429077 / 3.745712 (-1.316635) \| 2.525037 / 5.269862 (-2.744824) \| 1.503332 / 4.565676 (-3.062345) \| 0.046191 / 0.424275 (-0.378084) \| 0.004858 / 0.007607 (-0.002750) \| 0.349528 / 0.226044 (0.123484) \| 3.401451 / 2.268929 (1.132522) \| 1.989613 / 55.444624 (-53.455012) \| 1.664528 / 6.876477 (-5.211949) \| 1.669076 / 2.142072 (-0.472997) \| 0.467090 / 4.805227 (-4.338137) \| 0.098137 / 6.500664 (-6.402527) \| 0.040448 / 0.075469 (-0.035021) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.969578 / 1.841788 (-0.872210) \| 12.064705 / 8.074308 (3.990396) \| 10.991438 / 10.191392 (0.800046) \| 0.130149 / 0.680424 (-0.550275) \| 0.015357 / 0.534201 (-0.518844) \| 0.266567 / 0.579283 (-0.312717) \| 0.270619 / 0.434364 (-0.163744) \| 0.305978 / 0.540337 (-0.234359) \| 0.411164 / 1.386936 (-0.975772) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#86a2cf3174c55899535ee5f1707892a430ee53bc \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009810 / 0.011353 (-0.001543) \| 0.005411 / 0.011008 (-0.005598) \| 0.111670 / 0.038508 (0.073162) \| 0.050288 / 0.023109 (0.027179) \| 0.415625 / 0.275898 (0.139727) \| 0.479382 / 0.323480 (0.155902) \| 0.005104 / 0.007986 (-0.002882) \| 0.007122 / 0.004328 (0.002793) \| 0.079626 / 0.004250 (0.075375) \| 0.079421 / 0.037052 (0.042369) \| 0.406722 / 0.258489 (0.148233) \| 0.461511 / 0.293841 (0.167670) \| 0.053812 / 0.128546 (-0.074734) \| 0.014315 / 0.075646 (-0.061331) \| 0.389636 / 0.419271 (-0.029636) \| 0.111859 / 0.043533 (0.068326) \| 0.411703 / 0.255139 (0.156564) \| 0.457072 / 0.283200 (0.173872) \| 0.039807 / 0.141683 (-0.101876) \| 1.744064 / 1.452155 (0.291909) \| 1.968321 / 1.492716 (0.475604) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.341839 / 0.018006 (0.323833) \| 0.628083 / 0.000490 (0.627593) \| 0.023787 / 0.000200 (0.023587) \| 0.000601 / 0.000054 (0.000547) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034170 / 0.037411 (-0.003241) \| 0.091159 / 0.014526 (0.076633) \| 0.108993 / 0.176557 (-0.067563) \| 0.186906 / 0.737135 (-0.550229) \| 0.109753 / 0.296338 (-0.186586) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.684138 / 0.215209 (0.468929) \| 6.634852 / 2.077655 (4.557198) \| 3.102870 / 1.504120 (1.598750) \| 2.831023 / 1.541195 (1.289828) \| 2.831597 / 1.468490 (1.363107) \| 0.903584 / 4.584777 (-3.681193) \| 5.503341 / 3.745712 (1.757629) \| 4.970283 / 5.269862 (-0.299579) \| 3.139413 / 4.565676 (-1.426264) \| 0.109848 / 0.424275 (-0.314427) \| 0.008501 / 0.007607 (0.000894) \| 0.823815 / 0.226044 (0.597770) \| 7.963355 / 2.268929 (5.694426) \| 4.002010 / 55.444624 (-51.442614) \| 3.229390 / 6.876477 (-3.647087) \| 3.166413 / 2.142072 (1.024341) \| 1.030313 / 4.805227 (-3.774914) \| 0.219394 / 6.500664 (-6.281270) \| 0.077760 / 0.075469 (0.002291) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.580309 / 1.841788 (-0.261479) \| 24.279185 / 8.074308 (16.204877) \| 22.305293 / 10.191392 (12.113901) \| 0.235711 / 0.680424 (-0.444713) \| 0.030342 / 0.534201 (-0.503859) \| 0.498137 / 0.579283 (-0.081146) \| 0.619173 / 0.434364 (0.184809) \| 0.529904 / 0.540337 (-0.010434) \| 0.822547 / 1.386936 (-0.564389) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009375 / 0.011353 (-0.001978) \| 0.006009 / 0.011008 (-0.004999) \| 0.074080 / 0.038508 (0.035572) \| 0.089454 / 0.023109 (0.066345) \| 0.473458 / 0.275898 (0.197560) \| 0.462558 / 0.323480 (0.139078) \| 0.006415 / 0.007986 (-0.001571) \| 0.004777 / 0.004328 (0.000448) \| 0.076563 / 0.004250 (0.072313) \| 0.062793 / 0.037052 (0.025741) \| 0.455860 / 0.258489 (0.197371) \| 0.485281 / 0.293841 (0.191440) \| 0.052966 / 0.128546 (-0.075580) \| 0.021600 / 0.075646 (-0.054046) \| 0.090407 / 0.419271 (-0.328864) \| 0.063951 / 0.043533 (0.020418) \| 0.487561 / 0.255139 (0.232422) \| 0.479958 / 0.283200 (0.196758) \| 0.039263 / 0.141683 (-0.102420) \| 1.727215 / 1.452155 (0.275061) \| 1.962039 / 1.492716 (0.469323) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296267 / 0.018006 (0.278261) \| 0.604982 / 0.000490 (0.604493) \| 0.007842 / 0.000200 (0.007642) \| 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) \| 0.034317 / 0.037411 (-0.003094) \| 0.097796 / 0.014526 (0.083270) \| 0.126034 / 0.176557 (-0.050522) \| 0.180873 / 0.737135 (-0.556262) \| 0.125410 / 0.296338 (-0.170928) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.608278 / 0.215209 (0.393069) \| 6.154006 / 2.077655 (4.076351) \| 2.822342 / 1.504120 (1.318222) \| 2.568263 / 1.541195 (1.027068) \| 2.518545 / 1.468490 (1.050055) \| 0.863186 / 4.584777 (-3.721591) \| 5.367969 / 3.745712 (1.622257) \| 4.737691 / 5.269862 (-0.532170) \| 2.917620 / 4.565676 (-1.648056) \| 0.100731 / 0.424275 (-0.323544) \| 0.008611 / 0.007607 (0.001004) \| 0.735523 / 0.226044 (0.509479) \| 7.552790 / 2.268929 (5.283862) \| 3.821835 / 55.444624 (-51.622789) \| 2.878259 / 6.876477 (-3.998217) \| 2.957686 / 2.142072 (0.815613) \| 0.964630 / 4.805227 (-3.840598) \| 0.207098 / 6.500664 (-6.293566) \| 0.084215 / 0.075469 (0.008746) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.711020 / 1.841788 (-0.130768) \| 24.034122 / 8.074308 (15.959814) \| 21.378504 / 10.191392 (11.187112) \| 0.233433 / 0.680424 (-0.446990) \| 0.037214 / 0.534201 (-0.496987) \| 0.511952 / 0.579283 (-0.067332) \| 0.591486 / 0.434364 (0.157123) \| 0.606549 / 0.540337 (0.066211) \| 0.833773 / 1.386936 (-0.553163) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#671f9b32fc559a35996c1b9070fad1a2647a7fef \"CML watermark\")\n" ]
1,994,278,903	6,420	Set dev version	closed	2023-11-15T08:22:19	2023-11-15T08:33:36	2023-11-15T08:22:33	https://github.com/huggingface/datasets/pull/6420	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6420", "html_url": "https://github.com/huggingface/datasets/pull/6420", "diff_url": "https://github.com/huggingface/datasets/pull/6420.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6420.patch", "merged_at": "2023-11-15T08:22:33" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6420). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004536 / 0.011353 (-0.006816) \| 0.002979 / 0.011008 (-0.008030) \| 0.061984 / 0.038508 (0.023476) \| 0.029382 / 0.023109 (0.006273) \| 0.245237 / 0.275898 (-0.030661) \| 0.270571 / 0.323480 (-0.052909) \| 0.003956 / 0.007986 (-0.004029) \| 0.002453 / 0.004328 (-0.001876) \| 0.047967 / 0.004250 (0.043717) \| 0.043695 / 0.037052 (0.006643) \| 0.248457 / 0.258489 (-0.010032) \| 0.283293 / 0.293841 (-0.010548) \| 0.023603 / 0.128546 (-0.104943) \| 0.007225 / 0.075646 (-0.068422) \| 0.200533 / 0.419271 (-0.218739) \| 0.055310 / 0.043533 (0.011777) \| 0.245152 / 0.255139 (-0.009987) \| 0.267187 / 0.283200 (-0.016012) \| 0.018158 / 0.141683 (-0.123525) \| 1.126079 / 1.452155 (-0.326075) \| 1.185137 / 1.492716 (-0.307580) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092436 / 0.018006 (0.074430) \| 0.300132 / 0.000490 (0.299642) \| 0.000206 / 0.000200 (0.000006) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018476 / 0.037411 (-0.018935) \| 0.062827 / 0.014526 (0.048301) \| 0.074605 / 0.176557 (-0.101952) \| 0.119768 / 0.737135 (-0.617368) \| 0.076044 / 0.296338 (-0.220294) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279717 / 0.215209 (0.064508) \| 2.752308 / 2.077655 (0.674654) \| 1.434954 / 1.504120 (-0.069166) \| 1.314700 / 1.541195 (-0.226495) \| 1.347689 / 1.468490 (-0.120802) \| 0.400332 / 4.584777 (-4.184445) \| 2.383024 / 3.745712 (-1.362689) \| 2.583130 / 5.269862 (-2.686732) \| 1.567670 / 4.565676 (-2.998007) \| 0.045446 / 0.424275 (-0.378829) \| 0.004813 / 0.007607 (-0.002794) \| 0.336191 / 0.226044 (0.110147) \| 3.319837 / 2.268929 (1.050909) \| 1.816808 / 55.444624 (-53.627817) \| 1.539052 / 6.876477 (-5.337424) \| 1.550765 / 2.142072 (-0.591307) \| 0.484253 / 4.805227 (-4.320974) \| 0.100494 / 6.500664 (-6.400170) \| 0.041614 / 0.075469 (-0.033855) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.940857 / 1.841788 (-0.900931) \| 11.784946 / 8.074308 (3.710638) \| 10.397038 / 10.191392 (0.205646) \| 0.141458 / 0.680424 (-0.538965) \| 0.014193 / 0.534201 (-0.520008) \| 0.268304 / 0.579283 (-0.310979) \| 0.267059 / 0.434364 (-0.167305) \| 0.309389 / 0.540337 (-0.230949) \| 0.420628 / 1.386936 (-0.966308) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004776 / 0.011353 (-0.006577) \| 0.002941 / 0.011008 (-0.008067) \| 0.048659 / 0.038508 (0.010151) \| 0.053334 / 0.023109 (0.030225) \| 0.273342 / 0.275898 (-0.002556) \| 0.302278 / 0.323480 (-0.021202) \| 0.004001 / 0.007986 (-0.003984) \| 0.002414 / 0.004328 (-0.001914) \| 0.047504 / 0.004250 (0.043254) \| 0.038581 / 0.037052 (0.001529) \| 0.277768 / 0.258489 (0.019279) \| 0.306772 / 0.293841 (0.012931) \| 0.024146 / 0.128546 (-0.104400) \| 0.007233 / 0.075646 (-0.068413) \| 0.053308 / 0.419271 (-0.365964) \| 0.032617 / 0.043533 (-0.010916) \| 0.277390 / 0.255139 (0.022251) \| 0.296015 / 0.283200 (0.012816) \| 0.018733 / 0.141683 (-0.122950) \| 1.124895 / 1.452155 (-0.327260) \| 1.182579 / 1.492716 (-0.310137) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093375 / 0.018006 (0.075369) \| 0.301555 / 0.000490 (0.301066) \| 0.000217 / 0.000200 (0.000017) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021284 / 0.037411 (-0.016127) \| 0.070158 / 0.014526 (0.055632) \| 0.080187 / 0.176557 (-0.096370) \| 0.119282 / 0.737135 (-0.617854) \| 0.081672 / 0.296338 (-0.214666) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.314396 / 0.215209 (0.099187) \| 2.975114 / 2.077655 (0.897459) \| 1.724658 / 1.504120 (0.220539) \| 1.604464 / 1.541195 (0.063269) \| 1.652736 / 1.468490 (0.184246) \| 0.395064 / 4.584777 (-4.189713) \| 2.412768 / 3.745712 (-1.332944) \| 2.564427 / 5.269862 (-2.705435) \| 1.507627 / 4.565676 (-3.058050) \| 0.045463 / 0.424275 (-0.378812) \| 0.004797 / 0.007607 (-0.002810) \| 0.383115 / 0.226044 (0.157071) \| 3.501976 / 2.268929 (1.233048) \| 2.087512 / 55.444624 (-53.357113) \| 1.793132 / 6.876477 (-5.083345) \| 1.804178 / 2.142072 (-0.337895) \| 0.468287 / 4.805227 (-4.336940) \| 0.097247 / 6.500664 (-6.403417) \| 0.041139 / 0.075469 (-0.034330) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.976034 / 1.841788 (-0.865754) \| 12.431248 / 8.074308 (4.356940) \| 10.896064 / 10.191392 (0.704672) \| 0.129137 / 0.680424 (-0.551287) \| 0.015636 / 0.534201 (-0.518565) \| 0.268219 / 0.579283 (-0.311064) \| 0.278345 / 0.434364 (-0.156019) \| 0.302696 / 0.540337 (-0.237642) \| 0.408465 / 1.386936 (-0.978471) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#51c53e94acd7a273c24899c045446df021314cd2 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007703 / 0.011353 (-0.003650) \| 0.004614 / 0.011008 (-0.006394) \| 0.101425 / 0.038508 (0.062917) \| 0.040122 / 0.023109 (0.017013) \| 0.398890 / 0.275898 (0.122992) \| 0.424392 / 0.323480 (0.100912) \| 0.005411 / 0.007986 (-0.002575) \| 0.003747 / 0.004328 (-0.000582) \| 0.080494 / 0.004250 (0.076243) \| 0.059392 / 0.037052 (0.022340) \| 0.398025 / 0.258489 (0.139536) \| 0.454293 / 0.293841 (0.160452) \| 0.043662 / 0.128546 (-0.084884) \| 0.013726 / 0.075646 (-0.061920) \| 0.352910 / 0.419271 (-0.066362) \| 0.088572 / 0.043533 (0.045039) \| 0.401677 / 0.255139 (0.146538) \| 0.421774 / 0.283200 (0.138575) \| 0.033377 / 0.141683 (-0.108305) \| 1.728499 / 1.452155 (0.276344) \| 1.821557 / 1.492716 (0.328841) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230744 / 0.018006 (0.212738) \| 0.496188 / 0.000490 (0.495698) \| 0.010315 / 0.000200 (0.010115) \| 0.000402 / 0.000054 (0.000348) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028859 / 0.037411 (-0.008552) \| 0.089688 / 0.014526 (0.075163) \| 0.111697 / 0.176557 (-0.064860) \| 0.183238 / 0.737135 (-0.553898) \| 0.112407 / 0.296338 (-0.183931) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.558394 / 0.215209 (0.343185) \| 5.643048 / 2.077655 (3.565393) \| 2.454622 / 1.504120 (0.950502) \| 2.183338 / 1.541195 (0.642143) \| 2.324793 / 1.468490 (0.856303) \| 0.859482 / 4.584777 (-3.725295) \| 4.959346 / 3.745712 (1.213634) \| 4.599224 / 5.269862 (-0.670638) \| 2.764382 / 4.565676 (-1.801295) \| 0.089976 / 0.424275 (-0.334299) \| 0.008144 / 0.007607 (0.000537) \| 0.634675 / 0.226044 (0.408631) \| 6.555693 / 2.268929 (4.286765) \| 3.080252 / 55.444624 (-52.364373) \| 2.442715 / 6.876477 (-4.433762) \| 2.475126 / 2.142072 (0.333053) \| 0.986459 / 4.805227 (-3.818768) \| 0.193859 / 6.500664 (-6.306805) \| 0.063652 / 0.075469 (-0.011817) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.545318 / 1.841788 (-0.296469) \| 21.928751 / 8.074308 (13.854442) \| 20.598229 / 10.191392 (10.406837) \| 0.234046 / 0.680424 (-0.446377) \| 0.025947 / 0.534201 (-0.508254) \| 0.459773 / 0.579283 (-0.119510) \| 0.598026 / 0.434364 (0.163662) \| 0.555260 / 0.540337 (0.014922) \| 0.782767 / 1.386936 (-0.604169) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009322 / 0.011353 (-0.002030) \| 0.004650 / 0.011008 (-0.006358) \| 0.079326 / 0.038508 (0.040818) \| 0.079112 / 0.023109 (0.056003) \| 0.428708 / 0.275898 (0.152810) \| 0.481647 / 0.323480 (0.158168) \| 0.006419 / 0.007986 (-0.001566) \| 0.003878 / 0.004328 (-0.000450) \| 0.079013 / 0.004250 (0.074762) \| 0.058107 / 0.037052 (0.021055) \| 0.436967 / 0.258489 (0.178478) \| 0.501120 / 0.293841 (0.207279) \| 0.052972 / 0.128546 (-0.075574) \| 0.014414 / 0.075646 (-0.061232) \| 0.098587 / 0.419271 (-0.320685) \| 0.061626 / 0.043533 (0.018093) \| 0.451623 / 0.255139 (0.196484) \| 0.468893 / 0.283200 (0.185693) \| 0.032479 / 0.141683 (-0.109203) \| 1.911743 / 1.452155 (0.459588) \| 1.969024 / 1.492716 (0.476308) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.232015 / 0.018006 (0.214009) \| 0.508637 / 0.000490 (0.508147) \| 0.005470 / 0.000200 (0.005270) \| 0.000131 / 0.000054 (0.000076) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035345 / 0.037411 (-0.002066) \| 0.106319 / 0.014526 (0.091794) \| 0.117205 / 0.176557 (-0.059352) \| 0.176527 / 0.737135 (-0.560608) \| 0.121566 / 0.296338 (-0.174773) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.584920 / 0.215209 (0.369711) \| 5.745688 / 2.077655 (3.668034) \| 2.519875 / 1.504120 (1.015755) \| 2.197593 / 1.541195 (0.656398) \| 2.296670 / 1.468490 (0.828180) \| 0.831938 / 4.584777 (-3.752839) \| 5.130594 / 3.745712 (1.384882) \| 4.581385 / 5.269862 (-0.688476) \| 2.829516 / 4.565676 (-1.736161) \| 0.099015 / 0.424275 (-0.325260) \| 0.011468 / 0.007607 (0.003861) \| 0.702717 / 0.226044 (0.476672) \| 6.856099 / 2.268929 (4.587170) \| 3.372966 / 55.444624 (-52.071658) \| 2.567664 / 6.876477 (-4.308812) \| 2.699200 / 2.142072 (0.557127) \| 0.992316 / 4.805227 (-3.812911) \| 0.190463 / 6.500664 (-6.310201) \| 0.063305 / 0.075469 (-0.012165) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.591491 / 1.841788 (-0.250296) \| 21.696492 / 8.074308 (13.622184) \| 19.695404 / 10.191392 (9.504012) \| 0.222853 / 0.680424 (-0.457571) \| 0.032936 / 0.534201 (-0.501265) \| 0.431209 / 0.579283 (-0.148074) \| 0.543101 / 0.434364 (0.108737) \| 0.543427 / 0.540337 (0.003089) \| 0.742102 / 1.386936 (-0.644834) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#534a227179265df9093230885613c95390325705 \"CML watermark\")\n" ]
1,994,257,873	6,419	Release: 2.14.7	closed	2023-11-15T08:07:37	2023-11-15T17:35:30	2023-11-15T08:12:59	https://github.com/huggingface/datasets/pull/6419	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6419", "html_url": "https://github.com/huggingface/datasets/pull/6419", "diff_url": "https://github.com/huggingface/datasets/pull/6419.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6419.patch", "merged_at": "2023-11-15T08:12:59" }	albertvillanova	true	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004943 / 0.011353 (-0.006410) \| 0.002900 / 0.011008 (-0.008109) \| 0.061495 / 0.038508 (0.022987) \| 0.053575 / 0.023109 (0.030466) \| 0.249318 / 0.275898 (-0.026580) \| 0.271773 / 0.323480 (-0.051706) \| 0.003074 / 0.007986 (-0.004911) \| 0.003738 / 0.004328 (-0.000590) \| 0.047624 / 0.004250 (0.043373) \| 0.045141 / 0.037052 (0.008089) \| 0.255467 / 0.258489 (-0.003022) \| 0.286577 / 0.293841 (-0.007264) \| 0.023113 / 0.128546 (-0.105433) \| 0.007189 / 0.075646 (-0.068458) \| 0.204441 / 0.419271 (-0.214830) \| 0.036829 / 0.043533 (-0.006704) \| 0.252474 / 0.255139 (-0.002665) \| 0.270960 / 0.283200 (-0.012239) \| 0.019666 / 0.141683 (-0.122017) \| 1.095139 / 1.452155 (-0.357015) \| 1.158659 / 1.492716 (-0.334057) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091046 / 0.018006 (0.073040) \| 0.298346 / 0.000490 (0.297856) \| 0.000215 / 0.000200 (0.000015) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018702 / 0.037411 (-0.018709) \| 0.062213 / 0.014526 (0.047687) \| 0.073364 / 0.176557 (-0.103193) \| 0.119841 / 0.737135 (-0.617294) \| 0.074070 / 0.296338 (-0.222268) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282388 / 0.215209 (0.067179) \| 2.792029 / 2.077655 (0.714375) \| 1.471483 / 1.504120 (-0.032637) \| 1.386236 / 1.541195 (-0.154959) \| 1.377489 / 1.468490 (-0.091001) \| 0.410335 / 4.584777 (-4.174442) \| 2.424866 / 3.745712 (-1.320846) \| 2.610609 / 5.269862 (-2.659253) \| 1.574636 / 4.565676 (-2.991041) \| 0.046716 / 0.424275 (-0.377559) \| 0.004768 / 0.007607 (-0.002839) \| 0.339831 / 0.226044 (0.113787) \| 3.297579 / 2.268929 (1.028651) \| 1.851410 / 55.444624 (-53.593214) \| 1.550048 / 6.876477 (-5.326428) \| 1.576647 / 2.142072 (-0.565425) \| 0.482538 / 4.805227 (-4.322689) \| 0.101381 / 6.500664 (-6.399283) \| 0.042066 / 0.075469 (-0.033403) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.972664 / 1.841788 (-0.869123) \| 11.580700 / 8.074308 (3.506392) \| 10.586747 / 10.191392 (0.395355) \| 0.127844 / 0.680424 (-0.552580) \| 0.014270 / 0.534201 (-0.519931) \| 0.269678 / 0.579283 (-0.309605) \| 0.264022 / 0.434364 (-0.170342) \| 0.309395 / 0.540337 (-0.230942) \| 0.429228 / 1.386936 (-0.957708) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004815 / 0.011353 (-0.006538) \| 0.002890 / 0.011008 (-0.008119) \| 0.048039 / 0.038508 (0.009531) \| 0.053029 / 0.023109 (0.029920) \| 0.271346 / 0.275898 (-0.004552) \| 0.294488 / 0.323480 (-0.028992) \| 0.003983 / 0.007986 (-0.004003) \| 0.002439 / 0.004328 (-0.001889) \| 0.048250 / 0.004250 (0.044000) \| 0.038855 / 0.037052 (0.001803) \| 0.284723 / 0.258489 (0.026234) \| 0.303604 / 0.293841 (0.009763) \| 0.024384 / 0.128546 (-0.104163) \| 0.007021 / 0.075646 (-0.068625) \| 0.053850 / 0.419271 (-0.365422) \| 0.032177 / 0.043533 (-0.011356) \| 0.270039 / 0.255139 (0.014900) \| 0.289669 / 0.283200 (0.006469) \| 0.018840 / 0.141683 (-0.122842) \| 1.122191 / 1.452155 (-0.329963) \| 1.187083 / 1.492716 (-0.305634) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090609 / 0.018006 (0.072603) \| 0.298915 / 0.000490 (0.298425) \| 0.000216 / 0.000200 (0.000016) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020919 / 0.037411 (-0.016492) \| 0.070474 / 0.014526 (0.055948) \| 0.082421 / 0.176557 (-0.094135) \| 0.126967 / 0.737135 (-0.610168) \| 0.083447 / 0.296338 (-0.212892) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300153 / 0.215209 (0.084944) \| 2.958992 / 2.077655 (0.881337) \| 1.631228 / 1.504120 (0.127108) \| 1.497991 / 1.541195 (-0.043204) \| 1.536963 / 1.468490 (0.068473) \| 0.403047 / 4.584777 (-4.181730) \| 2.448782 / 3.745712 (-1.296930) \| 2.571954 / 5.269862 (-2.697908) \| 1.556346 / 4.565676 (-3.009331) \| 0.045992 / 0.424275 (-0.378283) \| 0.004785 / 0.007607 (-0.002822) \| 0.357448 / 0.226044 (0.131404) \| 3.558808 / 2.268929 (1.289880) \| 1.992624 / 55.444624 (-53.452001) \| 1.695027 / 6.876477 (-5.181450) \| 1.695183 / 2.142072 (-0.446889) \| 0.477001 / 4.805227 (-4.328226) \| 0.097485 / 6.500664 (-6.403179) \| 0.040530 / 0.075469 (-0.034939) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.976342 / 1.841788 (-0.865445) \| 12.141698 / 8.074308 (4.067390) \| 10.881101 / 10.191392 (0.689709) \| 0.142443 / 0.680424 (-0.537981) \| 0.015583 / 0.534201 (-0.518618) \| 0.269727 / 0.579283 (-0.309556) \| 0.275890 / 0.434364 (-0.158474) \| 0.306351 / 0.540337 (-0.233987) \| 0.412003 / 1.386936 (-0.974933) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7c744261000fd684f54c54de8ac4f15a726092d7 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004946 / 0.011353 (-0.006407) \| 0.002863 / 0.011008 (-0.008146) \| 0.061888 / 0.038508 (0.023380) \| 0.050664 / 0.023109 (0.027554) \| 0.242635 / 0.275898 (-0.033263) \| 0.271741 / 0.323480 (-0.051739) \| 0.003023 / 0.007986 (-0.004963) \| 0.003088 / 0.004328 (-0.001241) \| 0.049286 / 0.004250 (0.045036) \| 0.044699 / 0.037052 (0.007647) \| 0.249581 / 0.258489 (-0.008908) \| 0.285633 / 0.293841 (-0.008208) \| 0.023048 / 0.128546 (-0.105499) \| 0.007235 / 0.075646 (-0.068412) \| 0.202989 / 0.419271 (-0.216282) \| 0.036357 / 0.043533 (-0.007175) \| 0.245980 / 0.255139 (-0.009159) \| 0.277486 / 0.283200 (-0.005713) \| 0.019215 / 0.141683 (-0.122468) \| 1.096456 / 1.452155 (-0.355699) \| 1.152196 / 1.492716 (-0.340520) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092026 / 0.018006 (0.074020) \| 0.303038 / 0.000490 (0.302549) \| 0.000209 / 0.000200 (0.000009) \| 0.000048 / 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) \| 0.018670 / 0.037411 (-0.018741) \| 0.061972 / 0.014526 (0.047446) \| 0.072963 / 0.176557 (-0.103594) \| 0.119984 / 0.737135 (-0.617151) \| 0.074074 / 0.296338 (-0.222265) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282444 / 0.215209 (0.067235) \| 2.754571 / 2.077655 (0.676916) \| 1.482635 / 1.504120 (-0.021485) \| 1.352039 / 1.541195 (-0.189155) \| 1.359333 / 1.468490 (-0.109157) \| 0.399690 / 4.584777 (-4.185087) \| 2.364844 / 3.745712 (-1.380868) \| 2.603942 / 5.269862 (-2.665919) \| 1.569512 / 4.565676 (-2.996164) \| 0.046074 / 0.424275 (-0.378201) \| 0.004745 / 0.007607 (-0.002862) \| 0.339066 / 0.226044 (0.113022) \| 3.363456 / 2.268929 (1.094527) \| 1.822213 / 55.444624 (-53.622411) \| 1.536622 / 6.876477 (-5.339854) \| 1.574772 / 2.142072 (-0.567300) \| 0.474418 / 4.805227 (-4.330809) \| 0.099572 / 6.500664 (-6.401092) \| 0.041824 / 0.075469 (-0.033645) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.956300 / 1.841788 (-0.885487) \| 11.648886 / 8.074308 (3.574578) \| 10.645700 / 10.191392 (0.454308) \| 0.138924 / 0.680424 (-0.541499) \| 0.013936 / 0.534201 (-0.520265) \| 0.270319 / 0.579283 (-0.308964) \| 0.269735 / 0.434364 (-0.164629) \| 0.309699 / 0.540337 (-0.230639) \| 0.429139 / 1.386936 (-0.957797) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004838 / 0.011353 (-0.006515) \| 0.002937 / 0.011008 (-0.008072) \| 0.048094 / 0.038508 (0.009586) \| 0.053131 / 0.023109 (0.030022) \| 0.271893 / 0.275898 (-0.004005) \| 0.291025 / 0.323480 (-0.032454) \| 0.004058 / 0.007986 (-0.003928) \| 0.002410 / 0.004328 (-0.001919) \| 0.047939 / 0.004250 (0.043689) \| 0.038996 / 0.037052 (0.001944) \| 0.274983 / 0.258489 (0.016494) \| 0.306175 / 0.293841 (0.012334) \| 0.024388 / 0.128546 (-0.104159) \| 0.007242 / 0.075646 (-0.068404) \| 0.054011 / 0.419271 (-0.365261) \| 0.032750 / 0.043533 (-0.010783) \| 0.271147 / 0.255139 (0.016008) \| 0.288163 / 0.283200 (0.004963) \| 0.018383 / 0.141683 (-0.123299) \| 1.116134 / 1.452155 (-0.336021) \| 1.185964 / 1.492716 (-0.306752) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093289 / 0.018006 (0.075283) \| 0.303058 / 0.000490 (0.302568) \| 0.000241 / 0.000200 (0.000041) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021422 / 0.037411 (-0.015990) \| 0.069974 / 0.014526 (0.055449) \| 0.081164 / 0.176557 (-0.095392) \| 0.119991 / 0.737135 (-0.617144) \| 0.082154 / 0.296338 (-0.214184) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292298 / 0.215209 (0.077089) \| 2.851475 / 2.077655 (0.773821) \| 1.558283 / 1.504120 (0.054163) \| 1.432431 / 1.541195 (-0.108764) \| 1.479282 / 1.468490 (0.010792) \| 0.413124 / 4.584777 (-4.171653) \| 2.473005 / 3.745712 (-1.272707) \| 2.548779 / 5.269862 (-2.721082) \| 1.520776 / 4.565676 (-3.044900) \| 0.046476 / 0.424275 (-0.377799) \| 0.004814 / 0.007607 (-0.002794) \| 0.347036 / 0.226044 (0.120992) \| 3.424928 / 2.268929 (1.155999) \| 1.963274 / 55.444624 (-53.481351) \| 1.653794 / 6.876477 (-5.222683) \| 1.643874 / 2.142072 (-0.498198) \| 0.469086 / 4.805227 (-4.336141) \| 0.097417 / 6.500664 (-6.403247) \| 0.040468 / 0.075469 (-0.035002) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.972783 / 1.841788 (-0.869005) \| 12.122994 / 8.074308 (4.048686) \| 10.876396 / 10.191392 (0.685004) \| 0.130573 / 0.680424 (-0.549850) \| 0.016693 / 0.534201 (-0.517508) \| 0.270952 / 0.579283 (-0.308331) \| 0.273834 / 0.434364 (-0.160530) \| 0.305049 / 0.540337 (-0.235289) \| 0.408776 / 1.386936 (-0.978160) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e4216e5d57ea07e6b1ed73a3ec2cf845c6e59f70 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004606 / 0.011353 (-0.006747) \| 0.002433 / 0.011008 (-0.008576) \| 0.061985 / 0.038508 (0.023477) \| 0.048853 / 0.023109 (0.025744) \| 0.244506 / 0.275898 (-0.031392) \| 0.270159 / 0.323480 (-0.053321) \| 0.003962 / 0.007986 (-0.004024) \| 0.002376 / 0.004328 (-0.001952) \| 0.048067 / 0.004250 (0.043817) \| 0.041864 / 0.037052 (0.004812) \| 0.249743 / 0.258489 (-0.008746) \| 0.287723 / 0.293841 (-0.006117) \| 0.022954 / 0.128546 (-0.105593) \| 0.006845 / 0.075646 (-0.068801) \| 0.206313 / 0.419271 (-0.212959) \| 0.035780 / 0.043533 (-0.007753) \| 0.244286 / 0.255139 (-0.010853) \| 0.270026 / 0.283200 (-0.013173) \| 0.018177 / 0.141683 (-0.123506) \| 1.083998 / 1.452155 (-0.368157) \| 1.156086 / 1.492716 (-0.336630) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093754 / 0.018006 (0.075748) \| 0.302157 / 0.000490 (0.301667) \| 0.000215 / 0.000200 (0.000015) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018745 / 0.037411 (-0.018666) \| 0.061707 / 0.014526 (0.047181) \| 0.074356 / 0.176557 (-0.102200) \| 0.121643 / 0.737135 (-0.615492) \| 0.075885 / 0.296338 (-0.220454) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289156 / 0.215209 (0.073947) \| 2.881327 / 2.077655 (0.803672) \| 1.483568 / 1.504120 (-0.020552) \| 1.355933 / 1.541195 (-0.185262) \| 1.389693 / 1.468490 (-0.078797) \| 0.402834 / 4.584777 (-4.181943) \| 2.390634 / 3.745712 (-1.355078) \| 2.596761 / 5.269862 (-2.673101) \| 1.527602 / 4.565676 (-3.038074) \| 0.046434 / 0.424275 (-0.377841) \| 0.004783 / 0.007607 (-0.002824) \| 0.341017 / 0.226044 (0.114972) \| 3.429023 / 2.268929 (1.160095) \| 1.832988 / 55.444624 (-53.611637) \| 1.526510 / 6.876477 (-5.349967) \| 1.539382 / 2.142072 (-0.602690) \| 0.475734 / 4.805227 (-4.329493) \| 0.098710 / 6.500664 (-6.401954) \| 0.041136 / 0.075469 (-0.034333) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.922023 / 1.841788 (-0.919765) \| 11.428215 / 8.074308 (3.353907) \| 10.356668 / 10.191392 (0.165276) \| 0.139575 / 0.680424 (-0.540848) \| 0.014541 / 0.534201 (-0.519660) \| 0.271359 / 0.579283 (-0.307924) \| 0.266701 / 0.434364 (-0.167663) \| 0.309449 / 0.540337 (-0.230888) \| 0.422047 / 1.386936 (-0.964889) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004892 / 0.011353 (-0.006461) \| 0.002792 / 0.011008 (-0.008216) \| 0.048027 / 0.038508 (0.009519) \| 0.059256 / 0.023109 (0.036147) \| 0.270150 / 0.275898 (-0.005748) \| 0.294530 / 0.323480 (-0.028950) \| 0.004162 / 0.007986 (-0.003823) \| 0.002470 / 0.004328 (-0.001858) \| 0.047993 / 0.004250 (0.043743) \| 0.040380 / 0.037052 (0.003328) \| 0.275247 / 0.258489 (0.016758) \| 0.305684 / 0.293841 (0.011843) \| 0.025072 / 0.128546 (-0.103474) \| 0.007183 / 0.075646 (-0.068463) \| 0.054875 / 0.419271 (-0.364397) \| 0.033053 / 0.043533 (-0.010480) \| 0.271281 / 0.255139 (0.016142) \| 0.288057 / 0.283200 (0.004858) \| 0.018692 / 0.141683 (-0.122991) \| 1.125224 / 1.452155 (-0.326930) \| 1.171083 / 1.492716 (-0.321633) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.103102 / 0.018006 (0.085096) \| 0.309099 / 0.000490 (0.308609) \| 0.000232 / 0.000200 (0.000032) \| 0.000052 / 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) \| 0.021532 / 0.037411 (-0.015879) \| 0.069927 / 0.014526 (0.055401) \| 0.080920 / 0.176557 (-0.095637) \| 0.122214 / 0.737135 (-0.614921) \| 0.082268 / 0.296338 (-0.214071) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298121 / 0.215209 (0.082912) \| 2.933000 / 2.077655 (0.855345) \| 1.608782 / 1.504120 (0.104662) \| 1.554083 / 1.541195 (0.012889) \| 1.552700 / 1.468490 (0.084209) \| 0.400576 / 4.584777 (-4.184201) \| 2.412914 / 3.745712 (-1.332798) \| 2.545706 / 5.269862 (-2.724155) \| 1.548797 / 4.565676 (-3.016879) \| 0.045553 / 0.424275 (-0.378722) \| 0.004751 / 0.007607 (-0.002857) \| 0.343002 / 0.226044 (0.116958) \| 3.402866 / 2.268929 (1.133937) \| 1.969910 / 55.444624 (-53.474715) \| 1.686639 / 6.876477 (-5.189838) \| 1.768474 / 2.142072 (-0.373599) \| 0.471299 / 4.805227 (-4.333928) \| 0.097696 / 6.500664 (-6.402968) \| 0.041693 / 0.075469 (-0.033776) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.971380 / 1.841788 (-0.870408) \| 12.686033 / 8.074308 (4.611725) \| 11.370946 / 10.191392 (1.179554) \| 0.138377 / 0.680424 (-0.542047) \| 0.015623 / 0.534201 (-0.518578) \| 0.270935 / 0.579283 (-0.308348) \| 0.276235 / 0.434364 (-0.158129) \| 0.310196 / 0.540337 (-0.230141) \| 0.416908 / 1.386936 (-0.970028) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bf02cff8d70180a9e89328961ded9e3d8510fd22 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004581 / 0.011353 (-0.006772) \| 0.002468 / 0.011008 (-0.008541) \| 0.061420 / 0.038508 (0.022912) \| 0.047685 / 0.023109 (0.024575) \| 0.237756 / 0.275898 (-0.038142) \| 0.267548 / 0.323480 (-0.055932) \| 0.003899 / 0.007986 (-0.004086) \| 0.002338 / 0.004328 (-0.001990) \| 0.048794 / 0.004250 (0.044543) \| 0.042485 / 0.037052 (0.005433) \| 0.250165 / 0.258489 (-0.008324) \| 0.278791 / 0.293841 (-0.015050) \| 0.022371 / 0.128546 (-0.106175) \| 0.006923 / 0.075646 (-0.068723) \| 0.201401 / 0.419271 (-0.217870) \| 0.035867 / 0.043533 (-0.007665) \| 0.244628 / 0.255139 (-0.010511) \| 0.271137 / 0.283200 (-0.012063) \| 0.017257 / 0.141683 (-0.124426) \| 1.097261 / 1.452155 (-0.354894) \| 1.163314 / 1.492716 (-0.329402) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089060 / 0.018006 (0.071054) \| 0.297489 / 0.000490 (0.296999) \| 0.000207 / 0.000200 (0.000007) \| 0.000050 / 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) \| 0.018583 / 0.037411 (-0.018828) \| 0.061974 / 0.014526 (0.047449) \| 0.073300 / 0.176557 (-0.103256) \| 0.118871 / 0.737135 (-0.618264) \| 0.075513 / 0.296338 (-0.220826) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285544 / 0.215209 (0.070335) \| 2.799871 / 2.077655 (0.722216) \| 1.479871 / 1.504120 (-0.024249) \| 1.351128 / 1.541195 (-0.190067) \| 1.377540 / 1.468490 (-0.090950) \| 0.393056 / 4.584777 (-4.191721) \| 2.341791 / 3.745712 (-1.403921) \| 2.546854 / 5.269862 (-2.723007) \| 1.547368 / 4.565676 (-3.018309) \| 0.046056 / 0.424275 (-0.378219) \| 0.004765 / 0.007607 (-0.002842) \| 0.336384 / 0.226044 (0.110339) \| 3.283277 / 2.268929 (1.014348) \| 1.784535 / 55.444624 (-53.660089) \| 1.557809 / 6.876477 (-5.318667) \| 1.581728 / 2.142072 (-0.560344) \| 0.470527 / 4.805227 (-4.334700) \| 0.098383 / 6.500664 (-6.402281) \| 0.041563 / 0.075469 (-0.033906) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.946924 / 1.841788 (-0.894863) \| 11.202775 / 8.074308 (3.128467) \| 10.249760 / 10.191392 (0.058368) \| 0.142337 / 0.680424 (-0.538087) \| 0.013784 / 0.534201 (-0.520417) \| 0.267237 / 0.579283 (-0.312046) \| 0.264142 / 0.434364 (-0.170222) \| 0.306343 / 0.540337 (-0.233994) \| 0.423681 / 1.386936 (-0.963255) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004786 / 0.011353 (-0.006567) \| 0.002398 / 0.011008 (-0.008610) \| 0.047325 / 0.038508 (0.008817) \| 0.050753 / 0.023109 (0.027644) \| 0.271132 / 0.275898 (-0.004766) \| 0.290854 / 0.323480 (-0.032626) \| 0.003953 / 0.007986 (-0.004033) \| 0.002238 / 0.004328 (-0.002090) \| 0.047463 / 0.004250 (0.043213) \| 0.038504 / 0.037052 (0.001451) \| 0.273182 / 0.258489 (0.014693) \| 0.303449 / 0.293841 (0.009608) \| 0.024069 / 0.128546 (-0.104477) \| 0.006712 / 0.075646 (-0.068934) \| 0.053032 / 0.419271 (-0.366239) \| 0.032221 / 0.043533 (-0.011312) \| 0.271770 / 0.255139 (0.016631) \| 0.287876 / 0.283200 (0.004677) \| 0.018040 / 0.141683 (-0.123643) \| 1.138749 / 1.452155 (-0.313405) \| 1.192048 / 1.492716 (-0.300668) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089132 / 0.018006 (0.071126) \| 0.298636 / 0.000490 (0.298146) \| 0.000220 / 0.000200 (0.000020) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020808 / 0.037411 (-0.016603) \| 0.069506 / 0.014526 (0.054980) \| 0.079412 / 0.176557 (-0.097145) \| 0.118188 / 0.737135 (-0.618947) \| 0.083044 / 0.296338 (-0.213294) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293502 / 0.215209 (0.078293) \| 2.863692 / 2.077655 (0.786037) \| 1.590877 / 1.504120 (0.086757) \| 1.483634 / 1.541195 (-0.057561) \| 1.502113 / 1.468490 (0.033623) \| 0.402170 / 4.584777 (-4.182607) \| 2.414188 / 3.745712 (-1.331524) \| 2.500146 / 5.269862 (-2.769716) \| 1.506977 / 4.565676 (-3.058699) \| 0.045849 / 0.424275 (-0.378426) \| 0.004755 / 0.007607 (-0.002852) \| 0.343073 / 0.226044 (0.117029) \| 3.354985 / 2.268929 (1.086056) \| 1.952594 / 55.444624 (-53.492030) \| 1.664084 / 6.876477 (-5.212392) \| 1.664203 / 2.142072 (-0.477869) \| 0.475858 / 4.805227 (-4.329370) \| 0.097539 / 6.500664 (-6.403125) \| 0.040201 / 0.075469 (-0.035268) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.980051 / 1.841788 (-0.861736) \| 11.615291 / 8.074308 (3.540983) \| 10.492092 / 10.191392 (0.300700) \| 0.130450 / 0.680424 (-0.549974) \| 0.015883 / 0.534201 (-0.518318) \| 0.267575 / 0.579283 (-0.311708) \| 0.276981 / 0.434364 (-0.157383) \| 0.310221 / 0.540337 (-0.230116) \| 0.417143 / 1.386936 (-0.969793) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bf02cff8d70180a9e89328961ded9e3d8510fd22 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004721 / 0.011353 (-0.006632) \| 0.002931 / 0.011008 (-0.008077) \| 0.061948 / 0.038508 (0.023440) \| 0.051066 / 0.023109 (0.027957) \| 0.245431 / 0.275898 (-0.030467) \| 0.295627 / 0.323480 (-0.027852) \| 0.003997 / 0.007986 (-0.003988) \| 0.002408 / 0.004328 (-0.001920) \| 0.048292 / 0.004250 (0.044041) \| 0.044716 / 0.037052 (0.007664) \| 0.255119 / 0.258489 (-0.003371) \| 0.287384 / 0.293841 (-0.006457) \| 0.022835 / 0.128546 (-0.105711) \| 0.007162 / 0.075646 (-0.068484) \| 0.201352 / 0.419271 (-0.217920) \| 0.036626 / 0.043533 (-0.006906) \| 0.249590 / 0.255139 (-0.005549) \| 0.270822 / 0.283200 (-0.012378) \| 0.018152 / 0.141683 (-0.123531) \| 1.097046 / 1.452155 (-0.355109) \| 1.160461 / 1.492716 (-0.332255) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091712 / 0.018006 (0.073705) \| 0.299121 / 0.000490 (0.298631) \| 0.000244 / 0.000200 (0.000044) \| 0.000055 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018998 / 0.037411 (-0.018413) \| 0.062811 / 0.014526 (0.048285) \| 0.076348 / 0.176557 (-0.100209) \| 0.123898 / 0.737135 (-0.613238) \| 0.076249 / 0.296338 (-0.220090) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282780 / 0.215209 (0.067571) \| 2.739028 / 2.077655 (0.661373) \| 1.472564 / 1.504120 (-0.031556) \| 1.347343 / 1.541195 (-0.193852) \| 1.387130 / 1.468490 (-0.081360) \| 0.403348 / 4.584777 (-4.181429) \| 2.369924 / 3.745712 (-1.375788) \| 2.612875 / 5.269862 (-2.656987) \| 1.588079 / 4.565676 (-2.977598) \| 0.045233 / 0.424275 (-0.379042) \| 0.004767 / 0.007607 (-0.002840) \| 0.336614 / 0.226044 (0.110570) \| 3.300485 / 2.268929 (1.031556) \| 1.834365 / 55.444624 (-53.610259) \| 1.559799 / 6.876477 (-5.316677) \| 1.601265 / 2.142072 (-0.540808) \| 0.468158 / 4.805227 (-4.337069) \| 0.099811 / 6.500664 (-6.400853) \| 0.042688 / 0.075469 (-0.032782) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.934097 / 1.841788 (-0.907691) \| 11.687713 / 8.074308 (3.613405) \| 10.412723 / 10.191392 (0.221331) \| 0.139276 / 0.680424 (-0.541148) \| 0.014042 / 0.534201 (-0.520159) \| 0.270306 / 0.579283 (-0.308978) \| 0.266609 / 0.434364 (-0.167755) \| 0.314179 / 0.540337 (-0.226158) \| 0.437744 / 1.386936 (-0.949192) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004893 / 0.011353 (-0.006460) \| 0.002952 / 0.011008 (-0.008056) \| 0.050441 / 0.038508 (0.011933) \| 0.051838 / 0.023109 (0.028729) \| 0.271163 / 0.275898 (-0.004735) \| 0.293031 / 0.323480 (-0.030449) \| 0.003976 / 0.007986 (-0.004010) \| 0.002396 / 0.004328 (-0.001933) \| 0.048103 / 0.004250 (0.043852) \| 0.038732 / 0.037052 (0.001680) \| 0.274276 / 0.258489 (0.015787) \| 0.305112 / 0.293841 (0.011271) \| 0.024112 / 0.128546 (-0.104434) \| 0.007203 / 0.075646 (-0.068443) \| 0.053502 / 0.419271 (-0.365770) \| 0.032360 / 0.043533 (-0.011173) \| 0.270154 / 0.255139 (0.015015) \| 0.286689 / 0.283200 (0.003489) \| 0.018285 / 0.141683 (-0.123397) \| 1.141421 / 1.452155 (-0.310734) \| 1.244062 / 1.492716 (-0.248654) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090960 / 0.018006 (0.072954) \| 0.286134 / 0.000490 (0.285644) \| 0.000207 / 0.000200 (0.000007) \| 0.000045 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020789 / 0.037411 (-0.016622) \| 0.070850 / 0.014526 (0.056324) \| 0.080750 / 0.176557 (-0.095807) \| 0.120046 / 0.737135 (-0.617089) \| 0.083630 / 0.296338 (-0.212708) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290654 / 0.215209 (0.075445) \| 2.846669 / 2.077655 (0.769014) \| 1.561752 / 1.504120 (0.057632) \| 1.442968 / 1.541195 (-0.098227) \| 1.503551 / 1.468490 (0.035061) \| 0.399731 / 4.584777 (-4.185046) \| 2.430099 / 3.745712 (-1.315613) \| 2.556169 / 5.269862 (-2.713692) \| 1.545591 / 4.565676 (-3.020085) \| 0.045967 / 0.424275 (-0.378309) \| 0.004851 / 0.007607 (-0.002756) \| 0.340167 / 0.226044 (0.114122) \| 3.392738 / 2.268929 (1.123809) \| 1.943577 / 55.444624 (-53.501047) \| 1.650057 / 6.876477 (-5.226420) \| 1.686872 / 2.142072 (-0.455201) \| 0.470305 / 4.805227 (-4.334923) \| 0.097296 / 6.500664 (-6.403368) \| 0.041399 / 0.075469 (-0.034070) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.985660 / 1.841788 (-0.856128) \| 12.300826 / 8.074308 (4.226518) \| 10.972591 / 10.191392 (0.781199) \| 0.131512 / 0.680424 (-0.548912) \| 0.015742 / 0.534201 (-0.518459) \| 0.270630 / 0.579283 (-0.308653) \| 0.276039 / 0.434364 (-0.158325) \| 0.302288 / 0.540337 (-0.238050) \| 0.409415 / 1.386936 (-0.977521) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bf02cff8d70180a9e89328961ded9e3d8510fd22 \"CML watermark\")\n" ]
1,993,224,629	6,418	Remove token value from warnings	closed	2023-11-14T17:34:06	2023-11-14T22:26:04	2023-11-14T22:19:45	https://github.com/huggingface/datasets/pull/6418	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6418", "html_url": "https://github.com/huggingface/datasets/pull/6418", "diff_url": "https://github.com/huggingface/datasets/pull/6418.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6418.patch", "merged_at": "2023-11-14T22:19:45" }	mariosasko	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005135 / 0.011353 (-0.006218) \| 0.002950 / 0.011008 (-0.008058) \| 0.062316 / 0.038508 (0.023808) \| 0.030068 / 0.023109 (0.006959) \| 0.251998 / 0.275898 (-0.023900) \| 0.274806 / 0.323480 (-0.048674) \| 0.003067 / 0.007986 (-0.004919) \| 0.003082 / 0.004328 (-0.001247) \| 0.048503 / 0.004250 (0.044253) \| 0.045167 / 0.037052 (0.008114) \| 0.254277 / 0.258489 (-0.004212) \| 0.290528 / 0.293841 (-0.003313) \| 0.023666 / 0.128546 (-0.104880) \| 0.007049 / 0.075646 (-0.068597) \| 0.202367 / 0.419271 (-0.216905) \| 0.056291 / 0.043533 (0.012758) \| 0.251923 / 0.255139 (-0.003216) \| 0.273595 / 0.283200 (-0.009605) \| 0.019065 / 0.141683 (-0.122618) \| 1.100832 / 1.452155 (-0.351322) \| 1.266758 / 1.492716 (-0.225959) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094311 / 0.018006 (0.076305) \| 0.303199 / 0.000490 (0.302709) \| 0.000238 / 0.000200 (0.000039) \| 0.000051 / 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) \| 0.019413 / 0.037411 (-0.017999) \| 0.062618 / 0.014526 (0.048092) \| 0.072850 / 0.176557 (-0.103707) \| 0.119124 / 0.737135 (-0.618012) \| 0.074044 / 0.296338 (-0.222294) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.273660 / 0.215209 (0.058451) \| 2.682371 / 2.077655 (0.604716) \| 1.426041 / 1.504120 (-0.078079) \| 1.317186 / 1.541195 (-0.224009) \| 1.332385 / 1.468490 (-0.136106) \| 0.394599 / 4.584777 (-4.190178) \| 2.368167 / 3.745712 (-1.377545) \| 2.683728 / 5.269862 (-2.586134) \| 1.668348 / 4.565676 (-2.897329) \| 0.046177 / 0.424275 (-0.378098) \| 0.004833 / 0.007607 (-0.002774) \| 0.331413 / 0.226044 (0.105369) \| 3.278984 / 2.268929 (1.010055) \| 1.797600 / 55.444624 (-53.647024) \| 1.492202 / 6.876477 (-5.384274) \| 1.536039 / 2.142072 (-0.606034) \| 0.470601 / 4.805227 (-4.334626) \| 0.100833 / 6.500664 (-6.399831) \| 0.042787 / 0.075469 (-0.032682) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.959036 / 1.841788 (-0.882752) \| 11.632956 / 8.074308 (3.558648) \| 10.384574 / 10.191392 (0.193182) \| 0.127477 / 0.680424 (-0.552946) \| 0.014072 / 0.534201 (-0.520129) \| 0.269534 / 0.579283 (-0.309749) \| 0.259753 / 0.434364 (-0.174611) \| 0.313450 / 0.540337 (-0.226888) \| 0.431799 / 1.386936 (-0.955137) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004964 / 0.011353 (-0.006389) \| 0.002906 / 0.011008 (-0.008102) \| 0.048145 / 0.038508 (0.009637) \| 0.056457 / 0.023109 (0.033348) \| 0.274131 / 0.275898 (-0.001767) \| 0.298534 / 0.323480 (-0.024946) \| 0.004145 / 0.007986 (-0.003841) \| 0.002415 / 0.004328 (-0.001913) \| 0.048558 / 0.004250 (0.044308) \| 0.039031 / 0.037052 (0.001978) \| 0.278948 / 0.258489 (0.020459) \| 0.312358 / 0.293841 (0.018517) \| 0.024902 / 0.128546 (-0.103645) \| 0.007286 / 0.075646 (-0.068360) \| 0.053839 / 0.419271 (-0.365433) \| 0.032510 / 0.043533 (-0.011023) \| 0.272023 / 0.255139 (0.016884) \| 0.293420 / 0.283200 (0.010221) \| 0.018932 / 0.141683 (-0.122750) \| 1.122792 / 1.452155 (-0.329362) \| 1.167385 / 1.492716 (-0.325331) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094574 / 0.018006 (0.076567) \| 0.303810 / 0.000490 (0.303321) \| 0.000227 / 0.000200 (0.000027) \| 0.000056 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021675 / 0.037411 (-0.015737) \| 0.070289 / 0.014526 (0.055763) \| 0.080345 / 0.176557 (-0.096211) \| 0.120220 / 0.737135 (-0.616915) \| 0.084080 / 0.296338 (-0.212259) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300134 / 0.215209 (0.084925) \| 2.945831 / 2.077655 (0.868176) \| 1.605303 / 1.504120 (0.101183) \| 1.480135 / 1.541195 (-0.061059) \| 1.526039 / 1.468490 (0.057549) \| 0.398264 / 4.584777 (-4.186512) \| 2.461391 / 3.745712 (-1.284321) \| 2.559929 / 5.269862 (-2.709933) \| 1.541391 / 4.565676 (-3.024286) \| 0.045319 / 0.424275 (-0.378957) \| 0.004834 / 0.007607 (-0.002773) \| 0.352186 / 0.226044 (0.126141) \| 3.500108 / 2.268929 (1.231180) \| 1.966394 / 55.444624 (-53.478230) \| 1.675500 / 6.876477 (-5.200977) \| 1.683134 / 2.142072 (-0.458938) \| 0.465085 / 4.805227 (-4.340142) \| 0.097235 / 6.500664 (-6.403429) \| 0.040764 / 0.075469 (-0.034705) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.982813 / 1.841788 (-0.858975) \| 12.382529 / 8.074308 (4.308221) \| 11.082660 / 10.191392 (0.891268) \| 0.129113 / 0.680424 (-0.551310) \| 0.015718 / 0.534201 (-0.518483) \| 0.272776 / 0.579283 (-0.306507) \| 0.275513 / 0.434364 (-0.158850) \| 0.304933 / 0.540337 (-0.235404) \| 0.414591 / 1.386936 (-0.972345) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8723b129a64928eba40baf70ffd462060ade9f97 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004400 / 0.011353 (-0.006953) \| 0.002580 / 0.011008 (-0.008428) \| 0.060975 / 0.038508 (0.022467) \| 0.029337 / 0.023109 (0.006228) \| 0.248643 / 0.275898 (-0.027255) \| 0.274476 / 0.323480 (-0.049004) \| 0.003925 / 0.007986 (-0.004061) \| 0.002332 / 0.004328 (-0.001997) \| 0.049501 / 0.004250 (0.045251) \| 0.042730 / 0.037052 (0.005678) \| 0.255823 / 0.258489 (-0.002666) \| 0.281748 / 0.293841 (-0.012093) \| 0.023118 / 0.128546 (-0.105428) \| 0.006957 / 0.075646 (-0.068690) \| 0.201630 / 0.419271 (-0.217641) \| 0.054258 / 0.043533 (0.010725) \| 0.252289 / 0.255139 (-0.002850) \| 0.267561 / 0.283200 (-0.015639) \| 0.016903 / 0.141683 (-0.124780) \| 1.104322 / 1.452155 (-0.347833) \| 1.160027 / 1.492716 (-0.332689) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096340 / 0.018006 (0.078333) \| 0.305187 / 0.000490 (0.304697) \| 0.000222 / 0.000200 (0.000022) \| 0.000050 / 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) \| 0.018733 / 0.037411 (-0.018678) \| 0.062382 / 0.014526 (0.047856) \| 0.072309 / 0.176557 (-0.104248) \| 0.119772 / 0.737135 (-0.617364) \| 0.074655 / 0.296338 (-0.221683) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286150 / 0.215209 (0.070941) \| 2.770328 / 2.077655 (0.692673) \| 1.494593 / 1.504120 (-0.009527) \| 1.358611 / 1.541195 (-0.182583) \| 1.396308 / 1.468490 (-0.072182) \| 0.394806 / 4.584777 (-4.189971) \| 2.349100 / 3.745712 (-1.396613) \| 2.600541 / 5.269862 (-2.669321) \| 1.568975 / 4.565676 (-2.996701) \| 0.046212 / 0.424275 (-0.378063) \| 0.004821 / 0.007607 (-0.002786) \| 0.332286 / 0.226044 (0.106242) \| 3.302643 / 2.268929 (1.033714) \| 1.838992 / 55.444624 (-53.605633) \| 1.571919 / 6.876477 (-5.304557) \| 1.574956 / 2.142072 (-0.567117) \| 0.464156 / 4.805227 (-4.341071) \| 0.097983 / 6.500664 (-6.402681) \| 0.042243 / 0.075469 (-0.033226) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.941675 / 1.841788 (-0.900113) \| 11.450326 / 8.074308 (3.376017) \| 10.169943 / 10.191392 (-0.021449) \| 0.137879 / 0.680424 (-0.542545) \| 0.013765 / 0.534201 (-0.520436) \| 0.268633 / 0.579283 (-0.310650) \| 0.265083 / 0.434364 (-0.169281) \| 0.302099 / 0.540337 (-0.238238) \| 0.423033 / 1.386936 (-0.963903) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004998 / 0.011353 (-0.006355) \| 0.003174 / 0.011008 (-0.007834) \| 0.047924 / 0.038508 (0.009416) \| 0.057598 / 0.023109 (0.034489) \| 0.278823 / 0.275898 (0.002925) \| 0.334349 / 0.323480 (0.010869) \| 0.004053 / 0.007986 (-0.003932) \| 0.002554 / 0.004328 (-0.001774) \| 0.047797 / 0.004250 (0.043547) \| 0.039802 / 0.037052 (0.002749) \| 0.278295 / 0.258489 (0.019806) \| 0.319597 / 0.293841 (0.025757) \| 0.024802 / 0.128546 (-0.103744) \| 0.007362 / 0.075646 (-0.068284) \| 0.066983 / 0.419271 (-0.352288) \| 0.032707 / 0.043533 (-0.010826) \| 0.277350 / 0.255139 (0.022211) \| 0.296829 / 0.283200 (0.013629) \| 0.017902 / 0.141683 (-0.123781) \| 1.129765 / 1.452155 (-0.322390) \| 1.201940 / 1.492716 (-0.290777) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095631 / 0.018006 (0.077625) \| 0.296999 / 0.000490 (0.296510) \| 0.000234 / 0.000200 (0.000034) \| 0.000051 / 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) \| 0.021547 / 0.037411 (-0.015865) \| 0.070003 / 0.014526 (0.055477) \| 0.083173 / 0.176557 (-0.093384) \| 0.121676 / 0.737135 (-0.615459) \| 0.082974 / 0.296338 (-0.213364) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298982 / 0.215209 (0.083773) \| 2.918666 / 2.077655 (0.841011) \| 1.582054 / 1.504120 (0.077934) \| 1.463804 / 1.541195 (-0.077391) \| 1.484384 / 1.468490 (0.015893) \| 0.399443 / 4.584777 (-4.185334) \| 2.393515 / 3.745712 (-1.352197) \| 2.533004 / 5.269862 (-2.736858) \| 1.490411 / 4.565676 (-3.075266) \| 0.045274 / 0.424275 (-0.379002) \| 0.004783 / 0.007607 (-0.002824) \| 0.350510 / 0.226044 (0.124465) \| 3.437927 / 2.268929 (1.168998) \| 1.940115 / 55.444624 (-53.504509) \| 1.662025 / 6.876477 (-5.214452) \| 1.640621 / 2.142072 (-0.501452) \| 0.464014 / 4.805227 (-4.341214) \| 0.095506 / 6.500664 (-6.405158) \| 0.040172 / 0.075469 (-0.035297) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.975618 / 1.841788 (-0.866169) \| 12.561067 / 8.074308 (4.486759) \| 11.408037 / 10.191392 (1.216645) \| 0.130699 / 0.680424 (-0.549725) \| 0.016796 / 0.534201 (-0.517405) \| 0.271130 / 0.579283 (-0.308153) \| 0.283506 / 0.434364 (-0.150857) \| 0.304482 / 0.540337 (-0.235856) \| 0.413673 / 1.386936 (-0.973263) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#723038a73248dd12dc0673d2b341e9295c441ea3 \"CML watermark\")\n" ]
1,993,149,416	6,417	Bug: LayoutLMv3 finetuning on FUNSD Notebook; Arrow Error	closed	2023-11-14T16:53:20	2023-11-16T20:23:41	2023-11-16T20:23:41	https://github.com/huggingface/datasets/issues/6417	null	Davo00	false	[ "Very strange: `datasets-cli env`\r\n> \r\n> Copy-and-paste the text below in your GitHub issue.\r\n> \r\n> - `datasets` version: 2.9.0\r\n> - Platform: macOS-14.0-arm64-arm-64bit\r\n> - Python version: 3.9.13\r\n> - PyArrow version: 8.0.0\r\n> - Pandas version: 1.3.5\r\n\r\nAfter updating datasets and pyarrow on base environment, although I am using a different one called layoutLM\r\n\r\n> Copy-and-paste the text below in your GitHub issue.\r\n> \r\n> - `datasets` version: 2.14.6\r\n> - Platform: macOS-14.0-arm64-arm-64bit\r\n> - Python version: 3.9.18\r\n> - Huggingface_hub version: 0.17.3\r\n> - PyArrow version: 14.0.1\r\n> - Pandas version: 2.1.3", "Hi! The latest (patch) release (published a few hours ago) includes a fix for this [PyArrow security issue](https://github.com/advisories/GHSA-5wvp-7f3h-6wmm). To install it, run `pip install -U datasets`.", "> Hi! The latest (patch) release (published a few hours ago) includes a fix for this [PyArrow security issue](https://github.com/advisories/GHSA-5wvp-7f3h-6wmm). To install it, run `pip install -U datasets`.\r\n\r\nThanks for the info and the latest release, it seems this has also solved my issue. First run after the update worked and I am training right now :D\r\nWill close the Issu" ]
1,992,954,723	6,416	Rename audio_classificiation.py to audio_classification.py	closed	2023-11-14T15:15:29	2023-11-15T11:59:32	2023-11-15T11:53:20	https://github.com/huggingface/datasets/pull/6416	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6416", "html_url": "https://github.com/huggingface/datasets/pull/6416", "diff_url": "https://github.com/huggingface/datasets/pull/6416.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6416.patch", "merged_at": "2023-11-15T11:53:20" }	carlthome	true	[ "Oh good catch. Can you also rename it in `src/datasets/tasks/__init__.py` ?", "Fixed! \r\n\r\n(I think, tough word to spell right TBH)", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004737 / 0.011353 (-0.006616) \| 0.002446 / 0.011008 (-0.008563) \| 0.060928 / 0.038508 (0.022420) \| 0.030479 / 0.023109 (0.007370) \| 0.238385 / 0.275898 (-0.037513) \| 0.265563 / 0.323480 (-0.057917) \| 0.002910 / 0.007986 (-0.005076) \| 0.002325 / 0.004328 (-0.002004) \| 0.047817 / 0.004250 (0.043566) \| 0.044243 / 0.037052 (0.007191) \| 0.245190 / 0.258489 (-0.013299) \| 0.275449 / 0.293841 (-0.018392) \| 0.023384 / 0.128546 (-0.105162) \| 0.006820 / 0.075646 (-0.068826) \| 0.201488 / 0.419271 (-0.217783) \| 0.057758 / 0.043533 (0.014225) \| 0.245279 / 0.255139 (-0.009860) \| 0.266094 / 0.283200 (-0.017106) \| 0.019254 / 0.141683 (-0.122429) \| 1.107497 / 1.452155 (-0.344658) \| 1.161412 / 1.492716 (-0.331304) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094909 / 0.018006 (0.076903) \| 0.305185 / 0.000490 (0.304695) \| 0.000221 / 0.000200 (0.000021) \| 0.000042 / 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) \| 0.018352 / 0.037411 (-0.019059) \| 0.062441 / 0.014526 (0.047915) \| 0.072386 / 0.176557 (-0.104171) \| 0.118836 / 0.737135 (-0.618299) \| 0.074514 / 0.296338 (-0.221824) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283632 / 0.215209 (0.068423) \| 2.751845 / 2.077655 (0.674190) \| 1.478620 / 1.504120 (-0.025499) \| 1.357221 / 1.541195 (-0.183974) \| 1.415297 / 1.468490 (-0.053194) \| 0.400093 / 4.584777 (-4.184684) \| 2.404607 / 3.745712 (-1.341105) \| 2.617572 / 5.269862 (-2.652289) \| 1.587622 / 4.565676 (-2.978055) \| 0.045997 / 0.424275 (-0.378278) \| 0.004872 / 0.007607 (-0.002735) \| 0.338901 / 0.226044 (0.112856) \| 3.371362 / 2.268929 (1.102434) \| 1.870469 / 55.444624 (-53.574155) \| 1.561670 / 6.876477 (-5.314807) \| 1.573186 / 2.142072 (-0.568886) \| 0.478735 / 4.805227 (-4.326492) \| 0.098743 / 6.500664 (-6.401921) \| 0.041780 / 0.075469 (-0.033689) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945422 / 1.841788 (-0.896366) \| 11.563464 / 8.074308 (3.489156) \| 10.368731 / 10.191392 (0.177339) \| 0.129910 / 0.680424 (-0.550513) \| 0.014014 / 0.534201 (-0.520187) \| 0.269036 / 0.579283 (-0.310247) \| 0.265516 / 0.434364 (-0.168848) \| 0.311082 / 0.540337 (-0.229255) \| 0.431510 / 1.386936 (-0.955426) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005068 / 0.011353 (-0.006284) \| 0.002989 / 0.011008 (-0.008019) \| 0.048213 / 0.038508 (0.009705) \| 0.056133 / 0.023109 (0.033024) \| 0.283347 / 0.275898 (0.007449) \| 0.307505 / 0.323480 (-0.015975) \| 0.004041 / 0.007986 (-0.003944) \| 0.002477 / 0.004328 (-0.001852) \| 0.047771 / 0.004250 (0.043521) \| 0.039361 / 0.037052 (0.002309) \| 0.283764 / 0.258489 (0.025275) \| 0.320644 / 0.293841 (0.026803) \| 0.024972 / 0.128546 (-0.103575) \| 0.007599 / 0.075646 (-0.068048) \| 0.054732 / 0.419271 (-0.364539) \| 0.032774 / 0.043533 (-0.010759) \| 0.285594 / 0.255139 (0.030455) \| 0.301500 / 0.283200 (0.018300) \| 0.018181 / 0.141683 (-0.123501) \| 1.126311 / 1.452155 (-0.325843) \| 1.187147 / 1.492716 (-0.305569) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097397 / 0.018006 (0.079391) \| 0.315112 / 0.000490 (0.314622) \| 0.000224 / 0.000200 (0.000024) \| 0.000056 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021529 / 0.037411 (-0.015882) \| 0.073208 / 0.014526 (0.058682) \| 0.081683 / 0.176557 (-0.094874) \| 0.120475 / 0.737135 (-0.616660) \| 0.083265 / 0.296338 (-0.213073) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289976 / 0.215209 (0.074767) \| 2.839860 / 2.077655 (0.762205) \| 1.592635 / 1.504120 (0.088515) \| 1.466722 / 1.541195 (-0.074472) \| 1.552850 / 1.468490 (0.084360) \| 0.418693 / 4.584777 (-4.166084) \| 2.526620 / 3.745712 (-1.219093) \| 2.706182 / 5.269862 (-2.563680) \| 1.618514 / 4.565676 (-2.947162) \| 0.046303 / 0.424275 (-0.377972) \| 0.004873 / 0.007607 (-0.002734) \| 0.345146 / 0.226044 (0.119102) \| 3.378448 / 2.268929 (1.109520) \| 1.986393 / 55.444624 (-53.458231) \| 1.681838 / 6.876477 (-5.194639) \| 1.738093 / 2.142072 (-0.403980) \| 0.484386 / 4.805227 (-4.320842) \| 0.100693 / 6.500664 (-6.399971) \| 0.043084 / 0.075469 (-0.032385) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.976399 / 1.841788 (-0.865389) \| 13.122968 / 8.074308 (5.048660) \| 11.245031 / 10.191392 (1.053639) \| 0.134433 / 0.680424 (-0.545991) \| 0.017439 / 0.534201 (-0.516762) \| 0.274083 / 0.579283 (-0.305200) \| 0.287353 / 0.434364 (-0.147011) \| 0.309231 / 0.540337 (-0.231106) \| 0.418003 / 1.386936 (-0.968933) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#939f136f255eab68a5bf6441db2a395f8af78511 \"CML watermark\")\n" ]
1,992,917,248	6,415	Fix multi gpu map example	closed	2023-11-14T14:57:18	2024-01-31T00:49:15	2023-11-22T15:42:19	https://github.com/huggingface/datasets/pull/6415	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6415", "html_url": "https://github.com/huggingface/datasets/pull/6415", "diff_url": "https://github.com/huggingface/datasets/pull/6415.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6415.patch", "merged_at": "2023-11-22T15:42:19" }	lhoestq	true	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004537 / 0.011353 (-0.006816) \| 0.002844 / 0.011008 (-0.008164) \| 0.062506 / 0.038508 (0.023998) \| 0.029675 / 0.023109 (0.006566) \| 0.238080 / 0.275898 (-0.037818) \| 0.259858 / 0.323480 (-0.063622) \| 0.004015 / 0.007986 (-0.003970) \| 0.002432 / 0.004328 (-0.001897) \| 0.049477 / 0.004250 (0.045227) \| 0.045383 / 0.037052 (0.008331) \| 0.241934 / 0.258489 (-0.016555) \| 0.270759 / 0.293841 (-0.023082) \| 0.023207 / 0.128546 (-0.105339) \| 0.007107 / 0.075646 (-0.068539) \| 0.207626 / 0.419271 (-0.211645) \| 0.056706 / 0.043533 (0.013173) \| 0.239713 / 0.255139 (-0.015426) \| 0.256639 / 0.283200 (-0.026560) \| 0.017514 / 0.141683 (-0.124169) \| 1.105201 / 1.452155 (-0.346953) \| 1.173087 / 1.492716 (-0.319629) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093391 / 0.018006 (0.075384) \| 0.302673 / 0.000490 (0.302184) \| 0.000218 / 0.000200 (0.000018) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019447 / 0.037411 (-0.017965) \| 0.063349 / 0.014526 (0.048823) \| 0.075600 / 0.176557 (-0.100957) \| 0.121098 / 0.737135 (-0.616037) \| 0.075028 / 0.296338 (-0.221311) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291479 / 0.215209 (0.076270) \| 2.787231 / 2.077655 (0.709576) \| 1.480205 / 1.504120 (-0.023915) \| 1.417656 / 1.541195 (-0.123538) \| 1.394529 / 1.468490 (-0.073962) \| 0.408843 / 4.584777 (-4.175934) \| 2.398691 / 3.745712 (-1.347021) \| 2.635457 / 5.269862 (-2.634404) \| 1.591722 / 4.565676 (-2.973955) \| 0.048445 / 0.424275 (-0.375830) \| 0.004864 / 0.007607 (-0.002743) \| 0.349014 / 0.226044 (0.122969) \| 3.436962 / 2.268929 (1.168033) \| 1.839266 / 55.444624 (-53.605359) \| 1.535252 / 6.876477 (-5.341225) \| 1.581048 / 2.142072 (-0.561025) \| 0.491150 / 4.805227 (-4.314078) \| 0.101279 / 6.500664 (-6.399385) \| 0.041938 / 0.075469 (-0.033532) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.946986 / 1.841788 (-0.894801) \| 11.766196 / 8.074308 (3.691888) \| 10.425615 / 10.191392 (0.234223) \| 0.129957 / 0.680424 (-0.550467) \| 0.014859 / 0.534201 (-0.519342) \| 0.268046 / 0.579283 (-0.311237) \| 0.263724 / 0.434364 (-0.170640) \| 0.311028 / 0.540337 (-0.229309) \| 0.434715 / 1.386936 (-0.952221) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004874 / 0.011353 (-0.006479) \| 0.002942 / 0.011008 (-0.008067) \| 0.048250 / 0.038508 (0.009742) \| 0.053726 / 0.023109 (0.030617) \| 0.268870 / 0.275898 (-0.007028) \| 0.289152 / 0.323480 (-0.034328) \| 0.003982 / 0.007986 (-0.004004) \| 0.002488 / 0.004328 (-0.001840) \| 0.047902 / 0.004250 (0.043652) \| 0.038732 / 0.037052 (0.001680) \| 0.271021 / 0.258489 (0.012532) \| 0.299967 / 0.293841 (0.006126) \| 0.024672 / 0.128546 (-0.103874) \| 0.007311 / 0.075646 (-0.068336) \| 0.053721 / 0.419271 (-0.365550) \| 0.032407 / 0.043533 (-0.011126) \| 0.266604 / 0.255139 (0.011465) \| 0.286816 / 0.283200 (0.003617) \| 0.018973 / 0.141683 (-0.122710) \| 1.122460 / 1.452155 (-0.329695) \| 1.177720 / 1.492716 (-0.314997) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093968 / 0.018006 (0.075962) \| 0.304010 / 0.000490 (0.303521) \| 0.000228 / 0.000200 (0.000028) \| 0.000056 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021203 / 0.037411 (-0.016208) \| 0.070318 / 0.014526 (0.055793) \| 0.081688 / 0.176557 (-0.094869) \| 0.120916 / 0.737135 (-0.616219) \| 0.083452 / 0.296338 (-0.212886) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293961 / 0.215209 (0.078752) \| 2.858514 / 2.077655 (0.780860) \| 1.556169 / 1.504120 (0.052049) \| 1.431523 / 1.541195 (-0.109671) \| 1.478145 / 1.468490 (0.009654) \| 0.408927 / 4.584777 (-4.175850) \| 2.440630 / 3.745712 (-1.305082) \| 2.586327 / 5.269862 (-2.683534) \| 1.529495 / 4.565676 (-3.036182) \| 0.047387 / 0.424275 (-0.376888) \| 0.004817 / 0.007607 (-0.002790) \| 0.345009 / 0.226044 (0.118965) \| 3.386313 / 2.268929 (1.117384) \| 1.922361 / 55.444624 (-53.522264) \| 1.640814 / 6.876477 (-5.235663) \| 1.657005 / 2.142072 (-0.485068) \| 0.483844 / 4.805227 (-4.321383) \| 0.099470 / 6.500664 (-6.401194) \| 0.040735 / 0.075469 (-0.034734) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.986311 / 1.841788 (-0.855476) \| 12.327425 / 8.074308 (4.253117) \| 10.995135 / 10.191392 (0.803743) \| 0.146814 / 0.680424 (-0.533610) \| 0.015820 / 0.534201 (-0.518381) \| 0.272319 / 0.579283 (-0.306964) \| 0.274858 / 0.434364 (-0.159506) \| 0.305728 / 0.540337 (-0.234609) \| 0.421400 / 1.386936 (-0.965536) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#611a03d70378d6e48a19fac89e7616cf556b920a \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007995 / 0.011353 (-0.003358) \| 0.004596 / 0.011008 (-0.006412) \| 0.099818 / 0.038508 (0.061310) \| 0.053539 / 0.023109 (0.030429) \| 0.367757 / 0.275898 (0.091859) \| 0.409351 / 0.323480 (0.085871) \| 0.007423 / 0.007986 (-0.000563) \| 0.003770 / 0.004328 (-0.000558) \| 0.075635 / 0.004250 (0.071385) \| 0.078844 / 0.037052 (0.041791) \| 0.374523 / 0.258489 (0.116034) \| 0.423378 / 0.293841 (0.129537) \| 0.038901 / 0.128546 (-0.089645) \| 0.009985 / 0.075646 (-0.065661) \| 0.342793 / 0.419271 (-0.076479) \| 0.098045 / 0.043533 (0.054512) \| 0.368077 / 0.255139 (0.112938) \| 0.394251 / 0.283200 (0.111051) \| 0.030624 / 0.141683 (-0.111059) \| 1.782728 / 1.452155 (0.330574) \| 1.867571 / 1.492716 (0.374855) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.265550 / 0.018006 (0.247544) \| 0.504045 / 0.000490 (0.503555) \| 0.016523 / 0.000200 (0.016323) \| 0.000757 / 0.000054 (0.000702) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034239 / 0.037411 (-0.003172) \| 0.099953 / 0.014526 (0.085427) \| 0.113728 / 0.176557 (-0.062829) \| 0.180113 / 0.737135 (-0.557023) \| 0.114506 / 0.296338 (-0.181833) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.507186 / 0.215209 (0.291977) \| 5.033590 / 2.077655 (2.955935) \| 2.480111 / 1.504120 (0.975991) \| 2.258966 / 1.541195 (0.717771) \| 2.316045 / 1.468490 (0.847555) \| 0.622482 / 4.584777 (-3.962295) \| 4.400909 / 3.745712 (0.655197) \| 4.012443 / 5.269862 (-1.257419) \| 2.408294 / 4.565676 (-2.157383) \| 0.067608 / 0.424275 (-0.356668) \| 0.008638 / 0.007607 (0.001031) \| 0.546558 / 0.226044 (0.320513) \| 5.472973 / 2.268929 (3.204044) \| 2.795147 / 55.444624 (-52.649477) \| 2.371153 / 6.876477 (-4.505324) \| 2.440883 / 2.142072 (0.298811) \| 0.682380 / 4.805227 (-4.122847) \| 0.156819 / 6.500664 (-6.343845) \| 0.071969 / 0.075469 (-0.003500) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.500200 / 1.841788 (-0.341588) \| 22.854103 / 8.074308 (14.779795) \| 16.691945 / 10.191392 (6.500553) \| 0.210945 / 0.680424 (-0.469479) \| 0.023234 / 0.534201 (-0.510967) \| 0.475641 / 0.579283 (-0.103642) \| 0.491553 / 0.434364 (0.057189) \| 0.549311 / 0.540337 (0.008974) \| 0.858498 / 1.386936 (-0.528439) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009020 / 0.011353 (-0.002333) \| 0.004768 / 0.011008 (-0.006240) \| 0.082841 / 0.038508 (0.044333) \| 0.095111 / 0.023109 (0.072002) \| 0.486050 / 0.275898 (0.210151) \| 0.527074 / 0.323480 (0.203594) \| 0.006622 / 0.007986 (-0.001364) \| 0.003961 / 0.004328 (-0.000367) \| 0.083361 / 0.004250 (0.079111) \| 0.068571 / 0.037052 (0.031518) \| 0.494575 / 0.258489 (0.236086) \| 0.545593 / 0.293841 (0.251752) \| 0.047671 / 0.128546 (-0.080875) \| 0.010715 / 0.075646 (-0.064932) \| 0.096239 / 0.419271 (-0.323033) \| 0.061556 / 0.043533 (0.018023) \| 0.484301 / 0.255139 (0.229162) \| 0.492189 / 0.283200 (0.208989) \| 0.029374 / 0.141683 (-0.112309) \| 1.911833 / 1.452155 (0.459678) \| 2.005744 / 1.492716 (0.513028) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.265402 / 0.018006 (0.247396) \| 0.501034 / 0.000490 (0.500545) \| 0.004039 / 0.000200 (0.003839) \| 0.000105 / 0.000054 (0.000051) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.041005 / 0.037411 (0.003594) \| 0.119204 / 0.014526 (0.104678) \| 0.134583 / 0.176557 (-0.041973) \| 0.195995 / 0.737135 (-0.541140) \| 0.133125 / 0.296338 (-0.163214) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.503012 / 0.215209 (0.287803) \| 5.021972 / 2.077655 (2.944318) \| 2.912987 / 1.504120 (1.408867) \| 2.707637 / 1.541195 (1.166442) \| 2.824065 / 1.468490 (1.355575) \| 0.664285 / 4.584777 (-3.920492) \| 4.341905 / 3.745712 (0.596193) \| 4.152839 / 5.269862 (-1.117022) \| 2.438138 / 4.565676 (-2.127539) \| 0.076169 / 0.424275 (-0.348106) \| 0.010471 / 0.007607 (0.002864) \| 0.680918 / 0.226044 (0.454874) \| 6.424209 / 2.268929 (4.155281) \| 3.285353 / 55.444624 (-52.159271) \| 2.865458 / 6.876477 (-4.011019) \| 2.946246 / 2.142072 (0.804173) \| 0.700051 / 4.805227 (-4.105176) \| 0.155299 / 6.500664 (-6.345365) \| 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) \| 1.749517 / 1.841788 (-0.092271) \| 23.382582 / 8.074308 (15.308274) \| 17.708718 / 10.191392 (7.517326) \| 0.197042 / 0.680424 (-0.483382) \| 0.023874 / 0.534201 (-0.510327) \| 0.471631 / 0.579283 (-0.107652) \| 0.512649 / 0.434364 (0.078285) \| 0.614479 / 0.540337 (0.074142) \| 0.771859 / 1.386936 (-0.615077) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4f084b2d85f5004ed969d2387027093b2d765a4f \"CML watermark\")\n", "Merging this one, but lmk if you have more comments for subsequent improvements @NielsRogge ", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004874 / 0.011353 (-0.006479) \| 0.002866 / 0.011008 (-0.008142) \| 0.061761 / 0.038508 (0.023253) \| 0.052185 / 0.023109 (0.029076) \| 0.242264 / 0.275898 (-0.033634) \| 0.267816 / 0.323480 (-0.055664) \| 0.002844 / 0.007986 (-0.005142) \| 0.002349 / 0.004328 (-0.001979) \| 0.048393 / 0.004250 (0.044142) \| 0.038590 / 0.037052 (0.001538) \| 0.257483 / 0.258489 (-0.001006) \| 0.279704 / 0.293841 (-0.014137) \| 0.023125 / 0.128546 (-0.105421) \| 0.007044 / 0.075646 (-0.068602) \| 0.203606 / 0.419271 (-0.215665) \| 0.035489 / 0.043533 (-0.008044) \| 0.248419 / 0.255139 (-0.006719) \| 0.266357 / 0.283200 (-0.016843) \| 0.020178 / 0.141683 (-0.121505) \| 1.163674 / 1.452155 (-0.288481) \| 1.191340 / 1.492716 (-0.301376) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092972 / 0.018006 (0.074966) \| 0.295260 / 0.000490 (0.294770) \| 0.000214 / 0.000200 (0.000014) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018109 / 0.037411 (-0.019302) \| 0.061743 / 0.014526 (0.047217) \| 0.073965 / 0.176557 (-0.102592) \| 0.119493 / 0.737135 (-0.617642) \| 0.075646 / 0.296338 (-0.220692) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275700 / 0.215209 (0.060491) \| 2.666846 / 2.077655 (0.589191) \| 1.401452 / 1.504120 (-0.102668) \| 1.276009 / 1.541195 (-0.265186) \| 1.309914 / 1.468490 (-0.158576) \| 0.396411 / 4.584777 (-4.188365) \| 2.347193 / 3.745712 (-1.398519) \| 2.568006 / 5.269862 (-2.701856) \| 1.564572 / 4.565676 (-3.001105) \| 0.045450 / 0.424275 (-0.378825) \| 0.004827 / 0.007607 (-0.002780) \| 0.333092 / 0.226044 (0.107048) \| 3.284295 / 2.268929 (1.015367) \| 1.809928 / 55.444624 (-53.634696) \| 1.486041 / 6.876477 (-5.390436) \| 1.528198 / 2.142072 (-0.613875) \| 0.470053 / 4.805227 (-4.335174) \| 0.098559 / 6.500664 (-6.402105) \| 0.041637 / 0.075469 (-0.033832) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.948915 / 1.841788 (-0.892873) \| 11.513211 / 8.074308 (3.438903) \| 10.386419 / 10.191392 (0.195027) \| 0.129513 / 0.680424 (-0.550910) \| 0.021772 / 0.534201 (-0.512429) \| 0.295627 / 0.579283 (-0.283656) \| 0.261008 / 0.434364 (-0.173355) \| 0.305869 / 0.540337 (-0.234469) \| 0.399676 / 1.386936 (-0.987260) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004799 / 0.011353 (-0.006553) \| 0.002764 / 0.011008 (-0.008244) \| 0.048469 / 0.038508 (0.009961) \| 0.051346 / 0.023109 (0.028236) \| 0.274853 / 0.275898 (-0.001045) \| 0.300770 / 0.323480 (-0.022710) \| 0.003986 / 0.007986 (-0.003999) \| 0.002376 / 0.004328 (-0.001952) \| 0.048545 / 0.004250 (0.044294) \| 0.039854 / 0.037052 (0.002801) \| 0.280053 / 0.258489 (0.021564) \| 0.312797 / 0.293841 (0.018957) \| 0.024513 / 0.128546 (-0.104033) \| 0.006971 / 0.075646 (-0.068675) \| 0.053030 / 0.419271 (-0.366241) \| 0.035580 / 0.043533 (-0.007953) \| 0.276078 / 0.255139 (0.020939) \| 0.299345 / 0.283200 (0.016145) \| 0.020423 / 0.141683 (-0.121260) \| 1.103053 / 1.452155 (-0.349102) \| 1.179747 / 1.492716 (-0.312969) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093042 / 0.018006 (0.075036) \| 0.299421 / 0.000490 (0.298932) \| 0.000232 / 0.000200 (0.000033) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021966 / 0.037411 (-0.015445) \| 0.070978 / 0.014526 (0.056452) \| 0.083841 / 0.176557 (-0.092715) \| 0.121223 / 0.737135 (-0.615912) \| 0.082829 / 0.296338 (-0.213510) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289436 / 0.215209 (0.074227) \| 2.838074 / 2.077655 (0.760419) \| 1.597013 / 1.504120 (0.092893) \| 1.476888 / 1.541195 (-0.064307) \| 1.504582 / 1.468490 (0.036092) \| 0.398050 / 4.584777 (-4.186727) \| 2.434446 / 3.745712 (-1.311266) \| 2.493545 / 5.269862 (-2.776316) \| 1.584159 / 4.565676 (-2.981517) \| 0.046461 / 0.424275 (-0.377814) \| 0.004876 / 0.007607 (-0.002731) \| 0.344166 / 0.226044 (0.118122) \| 3.388530 / 2.268929 (1.119602) \| 1.939585 / 55.444624 (-53.505039) \| 1.672495 / 6.876477 (-5.203982) \| 1.811825 / 2.142072 (-0.330247) \| 0.470798 / 4.805227 (-4.334429) \| 0.097522 / 6.500664 (-6.403142) \| 0.040887 / 0.075469 (-0.034582) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.990081 / 1.841788 (-0.851707) \| 12.619827 / 8.074308 (4.545519) \| 10.748062 / 10.191392 (0.556670) \| 0.130409 / 0.680424 (-0.550015) \| 0.016624 / 0.534201 (-0.517577) \| 0.272381 / 0.579283 (-0.306902) \| 0.270597 / 0.434364 (-0.163767) \| 0.306458 / 0.540337 (-0.233879) \| 0.408700 / 1.386936 (-0.978236) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bc44d2188a1baac50d28a6c8110d6e5497f409de \"CML watermark\")\n", "This is a little hard to follow — where is the documentation currently? I am trying to follow from snippets, here is what I have based on your convo in this thread:\r\n\r\n```>>> import os\r\n>>>\r\n>>> for i in range(torch.cuda.device_count()): # send model to every GPU\r\n... model.to(f\"cuda:{i}\")\r\n>>>\r\n>>> def gpu_computation(example, rank):\r\n... torch.cuda.set_device(f\"cuda:{rank}\") # use one GPU\r\n... inputs = tokenizer(texts, truncation=True, return_tensors=\"pt\").to(f\"cuda:{rank}\")\r\n... outputs = model(*inputs)\r\n... .... \r\n```\r\n\r\nbut I'm getting device errors (data is on device 3, but it thinks model is on device 0, despite setting `torch.cuda.set_device`\r\n\r\nIs this correct? What version of Torch are you using for this? ", "Anyway, this didn't work for me:\r\n\r\n`torch.cuda.set_device(f\"cuda:{rank}\") # use one GPU`\r\n\r\nbut substituting it for:\r\n\r\n`model.to(f\"cuda:{rank}\")`\r\n\r\n(`.to` doesn't make a million copies of the model on the device, which I was worried it would do... so you can use it in an inner process)\r\n\r\n(btw, versions: `torch==2.1.1`, `cuda=12.2`)", "Yeah for me this issue isn't resolved yet, we need a better code example", "Hi @alex2awesome, could you open a PR with your suggestion to improve this code snippet ?", "i'm happy to when i get it fully working, but i feel like there are some fundamentals that I'm not fully understanding...\r\n\r\nI've set it up twice now, for 2 GPU-processing pipelines. \r\n\r\nIn one pipelines, my memory usage is fine, it delivers me a huge speedup, and everything is great. In the second pipeline, I keep getting OOM errors when `num_proc > 1` that I don't get when `num_proc=1`. \r\n\r\nThere is a discussion here: https://github.com/pytorch/pytorch/issues/44156 about CUDA memory leaks in multiprocessing setups, and I haven't had the time to fully read the source code to `datasets.map` to understand whether the situations are parallel. Also, if they are, then I don't know what the solution is, not really knowing how it is implemented under the hood. In that discussion, one guy offers a work-around, but it doesn't look great.\r\n\r\nSo, I haven't fully tested out enough to see what the issue. If I feel comfortable over the next several days to generate a slimmed-down example that will generalize to real-world cases such as those I'm working with now, then I will contribute it.\r\n\r\n", "@lhoestq do you know how `datasets` does multiprocessing? Do we use:\r\nhttps://pytorch.org/docs/stable/multiprocessing.html#module-torch.multiprocessing?\r\n\r\nIf so, there are lots of points around memory usage, here:\r\nhttps://pytorch.org/docs/stable/notes/multiprocessing.html\r\n\r\nEDIT: ahh I see it is using python's native multiprocessing library: https://github.com/huggingface/datasets/blob/2.15.0/src/datasets/arrow_dataset.py#L3172-L3189", "After some more research and playing around, I can't pinpoint the source of my CUDA memory leak nor can I determine with confidence what works and what doesn't in this setup.\r\n\r\nI'm not really an expert on multiprocessing in general, but my gut is that the current set-up isn't ideal for multiprocessing and I'm not sure I would recommend users to do this. \r\n\r\nKinda unfortunate, because I don't see any great tools for distributed inference out there, and in theory, `datasets.map` could be the standard.\r\n\r\nAre either of you more experienced in this?", "Not sure about your GPU's OOM :/\r\n\r\nStill, I opened a PR with your suggestion here: https://github.com/huggingface/datasets/pull/6550", "I still get only 0 rank...\r\n\r\nHere is my code: https://pastebin.com/c6du8jaM\r\n\r\nfrom this ^ i just improt one function:\r\n\r\n\r\n```\r\nfrom test import map_train\r\nfrom multiprocess import set_start_method\r\n\r\n\r\nset_start_method(\"spawn\")\r\nmap_train()\r\n```\r\n\r\nAnd here is the traceback:\r\nhttps://pastebin.com/YijspwQK ", "Also this code from your docs is not valid (source: https://huggingface.co/docs/datasets/main/en/process#multiprocessing):\r\n```\r\nfor i in range(torch.cuda.device_count()):\r\n model.to(f\"cuda:{i}\")\r\n```\r\n\r\n\r\nThis for me sends the model only to the second GPU\r\n```\r\nvae = AutoencoderKL.from_pretrained(\r\n pretrained_model_name_or_path, subfolder=\"vae\"\r\n)\r\nvae.to(\"cuda:0\")\r\nvae.to(\"cuda:1\")\r\n```", "Could you please provide a working example of multi-GPU mapping?\r\n\r\nNot just an example in docs, but a real working example starting from all imports loading datasets and models.", "@alex2awesome the same issue with CUDA OOM. It should not be happening, since it should 2 different GPUs be handling different loads. But in fact something wrong is happening.", "I haven't experimented much with the multi-GPU code documentation.\r\n\r\nCan you try using the code example at https://github.com/huggingface/datasets/pull/6550 instead ? That would be super helpful if you could confirm that it works on your side\r\n\r\nThough if you have some fixes/improvements ideas feel free to open a PR !", "@lhoestq the mapping does not start at all in this case:\r\n<img width=\"855\" alt=\"image\" src=\"https://github.com/huggingface/datasets/assets/17604849/7f29a3c1-c6dc-4bab-9955-5311256aa217\">\r\n\r\nHere is the updated code: https://pastebin.com/Kn9aGfZr", "@lhoestq with this code: https://pastebin.com/muDm78kp\r\ni now getting this error:\r\n\r\n```\r\nMap (num_proc=2): 1%\| \| 26288/3043663 [06:11<11:51:08, 70.72 examples/s]\r\nTraceback (most recent call last):\r\n File \"/workspace/compute.py\", line 229, in <module>\r\n map_train()\r\n File \"/workspace/compute.py\", line 224, in map_train\r\n return train_dataset.map(compute_embeddings_fn, batched=True, batch_size=16, with_rank=True, num_proc=2, keep_in_memory=True)\r\n File \"/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\", line 593, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, args, *kwargs)\r\n File \"/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\", line 558, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, args, **kwargs)\r\n File \"/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\", line 3193, in map\r\n for rank, done, content in iflatmap_unordered(\r\n File \"/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py\", line 658, in iflatmap_unordered\r\n raise RuntimeError(\r\nRuntimeError: One of the subprocesses has abruptly died during map operation.To debug the error, disable multiprocessing.\r\n```\r\n\r\nAlso when trying to download my dataset there were no issues from one machine, but from another:\r\n```\r\nSSLError: (MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /api/datasets/kopyl/3M_icons_monochrome_only_no_captioning/revision/753dca4be462dad7022f34cc273555ab6deb5832 (Caused by SSLError(SSLEOFError(8, '[SSL: UNEXPECTED_EOF_WHILE_READING] EOF occurred in violation of protocol (_ssl.c:1007)')))\"), '(Request ID: 7d0881f3-1b93-4d73-bcb6-52e816d84529)')\r\n```\r\n\r\nCan't download my dataset at all...", "Hmm this is not good, do you know a way to make it work ?\r\n\r\nBasically `map` creates two subprocesses and runs the function in the subprocesses. Since each function has a parameter `rank` it should be possible to choose which GPU to use", "I can confirm that PR #6550 works. All GPUs are at full throttle. You have to manually move the model to all GPUs. \r\n\r\n> I haven't experimented much with the multi-GPU code documentation.\r\n> \r\n> Can you try using the code example at #6550 instead ? That would be super helpful if you could confirm that it works on your side\r\n> \r\n> Though if you have some fixes/improvements ideas feel free to open a PR !\r\n\r\n", "I wrote a [blog post](https://forrestbao.github.io/2024/01/30/datasets_map_with_rank_multiple_GPUs.html) with a complete example by compiling information from several PRs and issues here. Hope it can help. Let me know how it works. \r\n\r\n> Could you please provide a working example of multi-GPU mapping?\r\n> \r\n> Not just an example in docs, but a real working example starting from all imports loading datasets and models.\r\n\r\n" ]
1,992,482,491	6,414	Set `usedforsecurity=False` in hashlib methods (FIPS compliance)	closed	2023-11-14T10:47:09	2023-11-17T14:23:20	2023-11-17T14:17:00	https://github.com/huggingface/datasets/pull/6414	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6414", "html_url": "https://github.com/huggingface/datasets/pull/6414", "diff_url": "https://github.com/huggingface/datasets/pull/6414.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6414.patch", "merged_at": "2023-11-17T14:17:00" }	Wauplin	true	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008434 / 0.011353 (-0.002919) \| 0.006755 / 0.011008 (-0.004253) \| 0.106169 / 0.038508 (0.067661) \| 0.049329 / 0.023109 (0.026220) \| 0.433610 / 0.275898 (0.157712) \| 0.441993 / 0.323480 (0.118513) \| 0.004703 / 0.007986 (-0.003282) \| 0.006996 / 0.004328 (0.002667) \| 0.080330 / 0.004250 (0.076080) \| 0.066098 / 0.037052 (0.029045) \| 0.435444 / 0.258489 (0.176955) \| 0.490442 / 0.293841 (0.196601) \| 0.047050 / 0.128546 (-0.081496) \| 0.014520 / 0.075646 (-0.061127) \| 0.339805 / 0.419271 (-0.079467) \| 0.101161 / 0.043533 (0.057629) \| 0.423236 / 0.255139 (0.168097) \| 0.455627 / 0.283200 (0.172427) \| 0.036218 / 0.141683 (-0.105465) \| 1.766128 / 1.452155 (0.313973) \| 1.923919 / 1.492716 (0.431203) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.242939 / 0.018006 (0.224933) \| 0.515582 / 0.000490 (0.515093) \| 0.020271 / 0.000200 (0.020071) \| 0.000383 / 0.000054 (0.000328) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030927 / 0.037411 (-0.006484) \| 0.093951 / 0.014526 (0.079425) \| 0.109028 / 0.176557 (-0.067529) \| 0.174947 / 0.737135 (-0.562188) \| 0.120538 / 0.296338 (-0.175800) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.553884 / 0.215209 (0.338675) \| 5.424566 / 2.077655 (3.346911) \| 2.439420 / 1.504120 (0.935301) \| 2.019324 / 1.541195 (0.478129) \| 2.170781 / 1.468490 (0.702290) \| 0.924424 / 4.584777 (-3.660353) \| 5.706029 / 3.745712 (1.960317) \| 5.096911 / 5.269862 (-0.172951) \| 3.168261 / 4.565676 (-1.397416) \| 0.094336 / 0.424275 (-0.329940) \| 0.015899 / 0.007607 (0.008292) \| 0.709684 / 0.226044 (0.483639) \| 7.476865 / 2.268929 (5.207936) \| 3.350983 / 55.444624 (-52.093641) \| 2.653419 / 6.876477 (-4.223058) \| 2.802201 / 2.142072 (0.660129) \| 1.081442 / 4.805227 (-3.723785) \| 0.217025 / 6.500664 (-6.283639) \| 0.077248 / 0.075469 (0.001779) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.598621 / 1.841788 (-0.243167) \| 23.490338 / 8.074308 (15.416030) \| 21.853488 / 10.191392 (11.662096) \| 0.209625 / 0.680424 (-0.470799) \| 0.028166 / 0.534201 (-0.506035) \| 0.473883 / 0.579283 (-0.105400) \| 0.584226 / 0.434364 (0.149862) \| 0.538605 / 0.540337 (-0.001732) \| 0.837060 / 1.386936 (-0.549876) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009029 / 0.011353 (-0.002324) \| 0.004945 / 0.011008 (-0.006063) \| 0.084539 / 0.038508 (0.046031) \| 0.081014 / 0.023109 (0.057905) \| 0.431291 / 0.275898 (0.155393) \| 0.478913 / 0.323480 (0.155433) \| 0.006107 / 0.007986 (-0.001879) \| 0.003939 / 0.004328 (-0.000390) \| 0.079932 / 0.004250 (0.075682) \| 0.057936 / 0.037052 (0.020884) \| 0.437295 / 0.258489 (0.178806) \| 0.489790 / 0.293841 (0.195949) \| 0.049544 / 0.128546 (-0.079003) \| 0.013675 / 0.075646 (-0.061972) \| 0.093143 / 0.419271 (-0.326128) \| 0.064104 / 0.043533 (0.020571) \| 0.444699 / 0.255139 (0.189560) \| 0.443688 / 0.283200 (0.160489) \| 0.034331 / 0.141683 (-0.107352) \| 1.753014 / 1.452155 (0.300859) \| 1.877274 / 1.492716 (0.384558) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.250460 / 0.018006 (0.232454) \| 0.527241 / 0.000490 (0.526752) \| 0.007679 / 0.000200 (0.007479) \| 0.000115 / 0.000054 (0.000061) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033269 / 0.037411 (-0.004142) \| 0.111262 / 0.014526 (0.096736) \| 0.133503 / 0.176557 (-0.043053) \| 0.177998 / 0.737135 (-0.559137) \| 0.117899 / 0.296338 (-0.178440) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.633588 / 0.215209 (0.418379) \| 6.105283 / 2.077655 (4.027628) \| 2.779309 / 1.504120 (1.275189) \| 2.445788 / 1.541195 (0.904594) \| 2.396443 / 1.468490 (0.927953) \| 0.925928 / 4.584777 (-3.658849) \| 5.266142 / 3.745712 (1.520430) \| 4.868830 / 5.269862 (-0.401031) \| 2.998768 / 4.565676 (-1.566909) \| 0.103135 / 0.424275 (-0.321140) \| 0.008059 / 0.007607 (0.000452) \| 0.753159 / 0.226044 (0.527115) \| 7.532170 / 2.268929 (5.263242) \| 3.563941 / 55.444624 (-51.880683) \| 2.829208 / 6.876477 (-4.047269) \| 2.913954 / 2.142072 (0.771881) \| 1.085843 / 4.805227 (-3.719384) \| 0.214195 / 6.500664 (-6.286469) \| 0.071509 / 0.075469 (-0.003960) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.544819 / 1.841788 (-0.296968) \| 23.790149 / 8.074308 (15.715841) \| 23.086019 / 10.191392 (12.894627) \| 0.242695 / 0.680424 (-0.437729) \| 0.041706 / 0.534201 (-0.492495) \| 0.552402 / 0.579283 (-0.026881) \| 0.652518 / 0.434364 (0.218154) \| 0.581876 / 0.540337 (0.041539) \| 0.795425 / 1.386936 (-0.591511) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#117fdfccc8523fe150521ad74e478459fe2f297c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004573 / 0.011353 (-0.006780) \| 0.002965 / 0.011008 (-0.008043) \| 0.061913 / 0.038508 (0.023405) \| 0.029474 / 0.023109 (0.006365) \| 0.258117 / 0.275898 (-0.017781) \| 0.279854 / 0.323480 (-0.043626) \| 0.003954 / 0.007986 (-0.004031) \| 0.002479 / 0.004328 (-0.001850) \| 0.048685 / 0.004250 (0.044434) \| 0.044733 / 0.037052 (0.007681) \| 0.256659 / 0.258489 (-0.001830) \| 0.285235 / 0.293841 (-0.008606) \| 0.023566 / 0.128546 (-0.104981) \| 0.007291 / 0.075646 (-0.068355) \| 0.202701 / 0.419271 (-0.216570) \| 0.055706 / 0.043533 (0.012173) \| 0.258790 / 0.255139 (0.003651) \| 0.278675 / 0.283200 (-0.004525) \| 0.018574 / 0.141683 (-0.123109) \| 1.109359 / 1.452155 (-0.342796) \| 1.184434 / 1.492716 (-0.308282) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095048 / 0.018006 (0.077042) \| 0.305027 / 0.000490 (0.304537) \| 0.000310 / 0.000200 (0.000110) \| 0.000066 / 0.000054 (0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018183 / 0.037411 (-0.019228) \| 0.066130 / 0.014526 (0.051604) \| 0.073948 / 0.176557 (-0.102608) \| 0.120458 / 0.737135 (-0.616678) \| 0.075995 / 0.296338 (-0.220343) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279419 / 0.215209 (0.064210) \| 2.728591 / 2.077655 (0.650936) \| 1.439016 / 1.504120 (-0.065104) \| 1.325798 / 1.541195 (-0.215397) \| 1.352050 / 1.468490 (-0.116440) \| 0.395041 / 4.584777 (-4.189736) \| 2.377651 / 3.745712 (-1.368061) \| 2.618473 / 5.269862 (-2.651389) \| 1.587580 / 4.565676 (-2.978096) \| 0.045910 / 0.424275 (-0.378365) \| 0.004843 / 0.007607 (-0.002764) \| 0.335491 / 0.226044 (0.109447) \| 3.378441 / 2.268929 (1.109512) \| 1.827757 / 55.444624 (-53.616868) \| 1.502360 / 6.876477 (-5.374117) \| 1.508460 / 2.142072 (-0.633612) \| 0.471309 / 4.805227 (-4.333918) \| 0.098934 / 6.500664 (-6.401730) \| 0.041705 / 0.075469 (-0.033764) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945067 / 1.841788 (-0.896720) \| 11.548209 / 8.074308 (3.473900) \| 10.422628 / 10.191392 (0.231236) \| 0.141494 / 0.680424 (-0.538929) \| 0.014345 / 0.534201 (-0.519856) \| 0.267750 / 0.579283 (-0.311533) \| 0.261488 / 0.434364 (-0.172876) \| 0.307192 / 0.540337 (-0.233145) \| 0.427926 / 1.386936 (-0.959010) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004831 / 0.011353 (-0.006522) \| 0.002876 / 0.011008 (-0.008132) \| 0.048629 / 0.038508 (0.010121) \| 0.055090 / 0.023109 (0.031981) \| 0.271381 / 0.275898 (-0.004517) \| 0.292350 / 0.323480 (-0.031130) \| 0.004001 / 0.007986 (-0.003985) \| 0.002389 / 0.004328 (-0.001939) \| 0.047527 / 0.004250 (0.043277) \| 0.038065 / 0.037052 (0.001012) \| 0.277387 / 0.258489 (0.018898) \| 0.307209 / 0.293841 (0.013368) \| 0.025136 / 0.128546 (-0.103411) \| 0.007309 / 0.075646 (-0.068338) \| 0.054483 / 0.419271 (-0.364789) \| 0.032807 / 0.043533 (-0.010726) \| 0.274364 / 0.255139 (0.019225) \| 0.290280 / 0.283200 (0.007080) \| 0.017855 / 0.141683 (-0.123828) \| 1.185912 / 1.452155 (-0.266243) \| 1.228141 / 1.492716 (-0.264576) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094787 / 0.018006 (0.076781) \| 0.314191 / 0.000490 (0.313701) \| 0.000217 / 0.000200 (0.000017) \| 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) \| 0.020920 / 0.037411 (-0.016491) \| 0.070446 / 0.014526 (0.055920) \| 0.081371 / 0.176557 (-0.095186) \| 0.119127 / 0.737135 (-0.618009) \| 0.085658 / 0.296338 (-0.210680) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290601 / 0.215209 (0.075392) \| 2.874091 / 2.077655 (0.796436) \| 1.598934 / 1.504120 (0.094814) \| 1.464329 / 1.541195 (-0.076866) \| 1.504943 / 1.468490 (0.036453) \| 0.410457 / 4.584777 (-4.174320) \| 2.428706 / 3.745712 (-1.317006) \| 2.596510 / 5.269862 (-2.673352) \| 1.547084 / 4.565676 (-3.018592) \| 0.047546 / 0.424275 (-0.376729) \| 0.004740 / 0.007607 (-0.002867) \| 0.351168 / 0.226044 (0.125123) \| 3.424554 / 2.268929 (1.155626) \| 1.969792 / 55.444624 (-53.474832) \| 1.676731 / 6.876477 (-5.199745) \| 1.668769 / 2.142072 (-0.473304) \| 0.482486 / 4.805227 (-4.322741) \| 0.100018 / 6.500664 (-6.400646) \| 0.040956 / 0.075469 (-0.034513) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.966306 / 1.841788 (-0.875482) \| 12.158909 / 8.074308 (4.084601) \| 10.926447 / 10.191392 (0.735055) \| 0.130359 / 0.680424 (-0.550065) \| 0.016162 / 0.534201 (-0.518039) \| 0.269977 / 0.579283 (-0.309306) \| 0.283366 / 0.434364 (-0.150997) \| 0.304517 / 0.540337 (-0.235821) \| 0.410398 / 1.386936 (-0.976539) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53d5d6e57913465c22bb8074b0c0f968252cb12b \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004686 / 0.011353 (-0.006667) \| 0.002764 / 0.011008 (-0.008244) \| 0.061411 / 0.038508 (0.022902) \| 0.030450 / 0.023109 (0.007341) \| 0.247648 / 0.275898 (-0.028250) \| 0.278033 / 0.323480 (-0.045447) \| 0.002903 / 0.007986 (-0.005082) \| 0.002350 / 0.004328 (-0.001979) \| 0.047514 / 0.004250 (0.043264) \| 0.044446 / 0.037052 (0.007393) \| 0.256170 / 0.258489 (-0.002319) \| 0.285977 / 0.293841 (-0.007864) \| 0.023407 / 0.128546 (-0.105139) \| 0.007223 / 0.075646 (-0.068423) \| 0.201274 / 0.419271 (-0.217997) \| 0.054022 / 0.043533 (0.010489) \| 0.253841 / 0.255139 (-0.001298) \| 0.278219 / 0.283200 (-0.004980) \| 0.017796 / 0.141683 (-0.123886) \| 1.105950 / 1.452155 (-0.346205) \| 1.182021 / 1.492716 (-0.310695) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089584 / 0.018006 (0.071578) \| 0.299338 / 0.000490 (0.298849) \| 0.000202 / 0.000200 (0.000003) \| 0.000050 / 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) \| 0.018974 / 0.037411 (-0.018437) \| 0.062352 / 0.014526 (0.047826) \| 0.073667 / 0.176557 (-0.102889) \| 0.119225 / 0.737135 (-0.617911) \| 0.075393 / 0.296338 (-0.220945) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282749 / 0.215209 (0.067540) \| 2.795822 / 2.077655 (0.718167) \| 1.492946 / 1.504120 (-0.011174) \| 1.382340 / 1.541195 (-0.158855) \| 1.377281 / 1.468490 (-0.091209) \| 0.397361 / 4.584777 (-4.187415) \| 2.379416 / 3.745712 (-1.366296) \| 2.552967 / 5.269862 (-2.716895) \| 1.546347 / 4.565676 (-3.019330) \| 0.045851 / 0.424275 (-0.378424) \| 0.004830 / 0.007607 (-0.002777) \| 0.351194 / 0.226044 (0.125150) \| 3.407406 / 2.268929 (1.138478) \| 1.852983 / 55.444624 (-53.591641) \| 1.536381 / 6.876477 (-5.340095) \| 1.542786 / 2.142072 (-0.599287) \| 0.471960 / 4.805227 (-4.333267) \| 0.098336 / 6.500664 (-6.402328) \| 0.041569 / 0.075469 (-0.033900) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.912718 / 1.841788 (-0.929070) \| 11.339404 / 8.074308 (3.265095) \| 10.480593 / 10.191392 (0.289201) \| 0.139508 / 0.680424 (-0.540916) \| 0.014210 / 0.534201 (-0.519991) \| 0.268152 / 0.579283 (-0.311131) \| 0.260503 / 0.434364 (-0.173860) \| 0.304735 / 0.540337 (-0.235602) \| 0.422155 / 1.386936 (-0.964781) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004714 / 0.011353 (-0.006638) \| 0.002638 / 0.011008 (-0.008370) \| 0.047967 / 0.038508 (0.009459) \| 0.050758 / 0.023109 (0.027649) \| 0.265619 / 0.275898 (-0.010279) \| 0.286920 / 0.323480 (-0.036560) \| 0.003936 / 0.007986 (-0.004050) \| 0.002351 / 0.004328 (-0.001977) \| 0.047642 / 0.004250 (0.043392) \| 0.038412 / 0.037052 (0.001360) \| 0.269561 / 0.258489 (0.011072) \| 0.302057 / 0.293841 (0.008216) \| 0.023893 / 0.128546 (-0.104653) \| 0.006793 / 0.075646 (-0.068854) \| 0.053091 / 0.419271 (-0.366180) \| 0.032228 / 0.043533 (-0.011305) \| 0.267110 / 0.255139 (0.011971) \| 0.287211 / 0.283200 (0.004011) \| 0.017945 / 0.141683 (-0.123738) \| 1.191770 / 1.452155 (-0.260384) \| 1.269644 / 1.492716 (-0.223072) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088067 / 0.018006 (0.070061) \| 0.298383 / 0.000490 (0.297893) \| 0.000202 / 0.000200 (0.000002) \| 0.000048 / 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) \| 0.020685 / 0.037411 (-0.016726) \| 0.069883 / 0.014526 (0.055357) \| 0.080107 / 0.176557 (-0.096450) \| 0.119311 / 0.737135 (-0.617825) \| 0.080791 / 0.296338 (-0.215548) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295781 / 0.215209 (0.080572) \| 2.905536 / 2.077655 (0.827881) \| 1.579184 / 1.504120 (0.075064) \| 1.475937 / 1.541195 (-0.065258) \| 1.533708 / 1.468490 (0.065218) \| 0.409851 / 4.584777 (-4.174926) \| 2.443217 / 3.745712 (-1.302496) \| 2.543980 / 5.269862 (-2.725882) \| 1.512187 / 4.565676 (-3.053489) \| 0.046390 / 0.424275 (-0.377885) \| 0.004762 / 0.007607 (-0.002845) \| 0.345066 / 0.226044 (0.119021) \| 3.485133 / 2.268929 (1.216204) \| 1.954690 / 55.444624 (-53.489934) \| 1.671104 / 6.876477 (-5.205372) \| 1.655330 / 2.142072 (-0.486743) \| 0.487910 / 4.805227 (-4.317317) \| 0.097707 / 6.500664 (-6.402957) \| 0.040379 / 0.075469 (-0.035090) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.981620 / 1.841788 (-0.860168) \| 11.806530 / 8.074308 (3.732222) \| 10.868275 / 10.191392 (0.676883) \| 0.141230 / 0.680424 (-0.539194) \| 0.015785 / 0.534201 (-0.518416) \| 0.271416 / 0.579283 (-0.307867) \| 0.276048 / 0.434364 (-0.158316) \| 0.310988 / 0.540337 (-0.229349) \| 0.410078 / 1.386936 (-0.976858) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ec565740dee10c466ade16f81dee2783e442ba55 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004803 / 0.011353 (-0.006550) \| 0.002961 / 0.011008 (-0.008047) \| 0.061431 / 0.038508 (0.022923) \| 0.030189 / 0.023109 (0.007080) \| 0.255755 / 0.275898 (-0.020143) \| 0.277841 / 0.323480 (-0.045639) \| 0.003083 / 0.007986 (-0.004902) \| 0.002432 / 0.004328 (-0.001896) \| 0.047674 / 0.004250 (0.043424) \| 0.045066 / 0.037052 (0.008014) \| 0.268701 / 0.258489 (0.010211) \| 0.286673 / 0.293841 (-0.007168) \| 0.023663 / 0.128546 (-0.104883) \| 0.007148 / 0.075646 (-0.068499) \| 0.201962 / 0.419271 (-0.217310) \| 0.054953 / 0.043533 (0.011420) \| 0.257155 / 0.255139 (0.002016) \| 0.277769 / 0.283200 (-0.005431) \| 0.017803 / 0.141683 (-0.123880) \| 1.100270 / 1.452155 (-0.351884) \| 1.146975 / 1.492716 (-0.345741) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092776 / 0.018006 (0.074770) \| 0.303786 / 0.000490 (0.303296) \| 0.000237 / 0.000200 (0.000037) \| 0.000055 / 0.000054 (0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019647 / 0.037411 (-0.017765) \| 0.063211 / 0.014526 (0.048686) \| 0.076684 / 0.176557 (-0.099873) \| 0.121952 / 0.737135 (-0.615184) \| 0.077202 / 0.296338 (-0.219137) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282087 / 0.215209 (0.066878) \| 2.789204 / 2.077655 (0.711550) \| 1.510376 / 1.504120 (0.006256) \| 1.384241 / 1.541195 (-0.156954) \| 1.414949 / 1.468490 (-0.053541) \| 0.402206 / 4.584777 (-4.182570) \| 2.377601 / 3.745712 (-1.368111) \| 2.585354 / 5.269862 (-2.684508) \| 1.592937 / 4.565676 (-2.972740) \| 0.045217 / 0.424275 (-0.379058) \| 0.004772 / 0.007607 (-0.002835) \| 0.339584 / 0.226044 (0.113539) \| 3.373184 / 2.268929 (1.104256) \| 1.855196 / 55.444624 (-53.589428) \| 1.599559 / 6.876477 (-5.276918) \| 1.604421 / 2.142072 (-0.537651) \| 0.467754 / 4.805227 (-4.337474) \| 0.098244 / 6.500664 (-6.402420) \| 0.042631 / 0.075469 (-0.032838) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.947680 / 1.841788 (-0.894108) \| 11.539875 / 8.074308 (3.465567) \| 10.340830 / 10.191392 (0.149438) \| 0.145591 / 0.680424 (-0.534833) \| 0.014367 / 0.534201 (-0.519834) \| 0.270506 / 0.579283 (-0.308777) \| 0.268825 / 0.434364 (-0.165539) \| 0.308372 / 0.540337 (-0.231966) \| 0.425039 / 1.386936 (-0.961897) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004813 / 0.011353 (-0.006540) \| 0.002931 / 0.011008 (-0.008078) \| 0.047997 / 0.038508 (0.009489) \| 0.050753 / 0.023109 (0.027644) \| 0.272704 / 0.275898 (-0.003194) \| 0.294045 / 0.323480 (-0.029435) \| 0.004059 / 0.007986 (-0.003927) \| 0.002491 / 0.004328 (-0.001838) \| 0.047621 / 0.004250 (0.043371) \| 0.038824 / 0.037052 (0.001772) \| 0.275322 / 0.258489 (0.016833) \| 0.306447 / 0.293841 (0.012606) \| 0.024402 / 0.128546 (-0.104145) \| 0.007252 / 0.075646 (-0.068394) \| 0.053346 / 0.419271 (-0.365925) \| 0.032224 / 0.043533 (-0.011309) \| 0.271468 / 0.255139 (0.016329) \| 0.289429 / 0.283200 (0.006229) \| 0.018285 / 0.141683 (-0.123398) \| 1.116743 / 1.452155 (-0.335412) \| 1.182724 / 1.492716 (-0.309993) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091899 / 0.018006 (0.073893) \| 0.299161 / 0.000490 (0.298671) \| 0.000224 / 0.000200 (0.000024) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021823 / 0.037411 (-0.015588) \| 0.071227 / 0.014526 (0.056701) \| 0.080503 / 0.176557 (-0.096053) \| 0.120243 / 0.737135 (-0.616892) \| 0.082328 / 0.296338 (-0.214010) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.324951 / 0.215209 (0.109742) \| 2.842358 / 2.077655 (0.764703) \| 1.602317 / 1.504120 (0.098197) \| 1.481103 / 1.541195 (-0.060091) \| 1.497557 / 1.468490 (0.029067) \| 0.406523 / 4.584777 (-4.178254) \| 2.402743 / 3.745712 (-1.342970) \| 2.545435 / 5.269862 (-2.724427) \| 1.534071 / 4.565676 (-3.031605) \| 0.046914 / 0.424275 (-0.377361) \| 0.004728 / 0.007607 (-0.002879) \| 0.341544 / 0.226044 (0.115499) \| 3.412017 / 2.268929 (1.143089) \| 1.937442 / 55.444624 (-53.507182) \| 1.668774 / 6.876477 (-5.207703) \| 1.668908 / 2.142072 (-0.473165) \| 0.477398 / 4.805227 (-4.327829) \| 0.098531 / 6.500664 (-6.402133) \| 0.041077 / 0.075469 (-0.034392) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.983888 / 1.841788 (-0.857900) \| 12.072703 / 8.074308 (3.998395) \| 11.028622 / 10.191392 (0.837230) \| 0.148097 / 0.680424 (-0.532327) \| 0.015869 / 0.534201 (-0.518332) \| 0.267609 / 0.579283 (-0.311674) \| 0.272345 / 0.434364 (-0.162019) \| 0.303840 / 0.540337 (-0.236497) \| 0.409199 / 1.386936 (-0.977737) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1487df064580bd23458234fab2e85876d9364e03 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005016 / 0.011353 (-0.006337) \| 0.002931 / 0.011008 (-0.008077) \| 0.062142 / 0.038508 (0.023634) \| 0.030758 / 0.023109 (0.007648) \| 0.251689 / 0.275898 (-0.024209) \| 0.272114 / 0.323480 (-0.051366) \| 0.004102 / 0.007986 (-0.003884) \| 0.002500 / 0.004328 (-0.001828) \| 0.049187 / 0.004250 (0.044937) \| 0.047150 / 0.037052 (0.010098) \| 0.256497 / 0.258489 (-0.001992) \| 0.288069 / 0.293841 (-0.005772) \| 0.023915 / 0.128546 (-0.104632) \| 0.007204 / 0.075646 (-0.068442) \| 0.204257 / 0.419271 (-0.215015) \| 0.063879 / 0.043533 (0.020346) \| 0.253008 / 0.255139 (-0.002131) \| 0.266554 / 0.283200 (-0.016645) \| 0.018929 / 0.141683 (-0.122754) \| 1.140547 / 1.452155 (-0.311608) \| 1.197049 / 1.492716 (-0.295668) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094111 / 0.018006 (0.076105) \| 0.301618 / 0.000490 (0.301128) \| 0.000219 / 0.000200 (0.000019) \| 0.000042 / 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) \| 0.018614 / 0.037411 (-0.018797) \| 0.062426 / 0.014526 (0.047900) \| 0.073079 / 0.176557 (-0.103477) \| 0.120313 / 0.737135 (-0.616823) \| 0.076445 / 0.296338 (-0.219894) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285151 / 0.215209 (0.069942) \| 2.754272 / 2.077655 (0.676617) \| 1.485254 / 1.504120 (-0.018866) \| 1.368412 / 1.541195 (-0.172783) \| 1.402819 / 1.468490 (-0.065671) \| 0.396561 / 4.584777 (-4.188216) \| 2.375708 / 3.745712 (-1.370004) \| 2.656088 / 5.269862 (-2.613773) \| 1.588676 / 4.565676 (-2.977001) \| 0.048662 / 0.424275 (-0.375613) \| 0.004963 / 0.007607 (-0.002644) \| 0.339747 / 0.226044 (0.113702) \| 3.315841 / 2.268929 (1.046912) \| 1.841439 / 55.444624 (-53.603186) \| 1.547803 / 6.876477 (-5.328674) \| 1.601872 / 2.142072 (-0.540200) \| 0.468637 / 4.805227 (-4.336591) \| 0.099423 / 6.500664 (-6.401241) \| 0.041926 / 0.075469 (-0.033543) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.933058 / 1.841788 (-0.908730) \| 11.680870 / 8.074308 (3.606561) \| 10.239009 / 10.191392 (0.047617) \| 0.129974 / 0.680424 (-0.550450) \| 0.014081 / 0.534201 (-0.520120) \| 0.273076 / 0.579283 (-0.306207) \| 0.261914 / 0.434364 (-0.172450) \| 0.305982 / 0.540337 (-0.234356) \| 0.430623 / 1.386936 (-0.956313) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004969 / 0.011353 (-0.006384) \| 0.003084 / 0.011008 (-0.007924) \| 0.048686 / 0.038508 (0.010178) \| 0.057234 / 0.023109 (0.034125) \| 0.295408 / 0.275898 (0.019510) \| 0.323774 / 0.323480 (0.000294) \| 0.004014 / 0.007986 (-0.003972) \| 0.002423 / 0.004328 (-0.001905) \| 0.048000 / 0.004250 (0.043749) \| 0.039872 / 0.037052 (0.002820) \| 0.294717 / 0.258489 (0.036228) \| 0.331149 / 0.293841 (0.037309) \| 0.027884 / 0.128546 (-0.100662) \| 0.007155 / 0.075646 (-0.068491) \| 0.053812 / 0.419271 (-0.365460) \| 0.032483 / 0.043533 (-0.011050) \| 0.293402 / 0.255139 (0.038263) \| 0.312553 / 0.283200 (0.029354) \| 0.017848 / 0.141683 (-0.123835) \| 1.125600 / 1.452155 (-0.326554) \| 1.189469 / 1.492716 (-0.303248) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096198 / 0.018006 (0.078191) \| 0.305096 / 0.000490 (0.304607) \| 0.000229 / 0.000200 (0.000029) \| 0.000045 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021992 / 0.037411 (-0.015419) \| 0.072082 / 0.014526 (0.057556) \| 0.082704 / 0.176557 (-0.093853) \| 0.124512 / 0.737135 (-0.612624) \| 0.084541 / 0.296338 (-0.211797) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296440 / 0.215209 (0.081231) \| 2.923392 / 2.077655 (0.845738) \| 1.599057 / 1.504120 (0.094937) \| 1.480473 / 1.541195 (-0.060722) \| 1.551837 / 1.468490 (0.083347) \| 0.418618 / 4.584777 (-4.166159) \| 2.472727 / 3.745712 (-1.272985) \| 2.796141 / 5.269862 (-2.473721) \| 1.629139 / 4.565676 (-2.936538) \| 0.047703 / 0.424275 (-0.376572) \| 0.004971 / 0.007607 (-0.002636) \| 0.354453 / 0.226044 (0.128408) \| 3.514861 / 2.268929 (1.245932) \| 1.993597 / 55.444624 (-53.451028) \| 1.694386 / 6.876477 (-5.182090) \| 1.748562 / 2.142072 (-0.393510) \| 0.487158 / 4.805227 (-4.318070) \| 0.102021 / 6.500664 (-6.398643) \| 0.042648 / 0.075469 (-0.032821) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.974950 / 1.841788 (-0.866837) \| 13.391204 / 8.074308 (5.316896) \| 11.474696 / 10.191392 (1.283304) \| 0.142618 / 0.680424 (-0.537806) \| 0.016163 / 0.534201 (-0.518038) \| 0.271453 / 0.579283 (-0.307830) \| 0.287049 / 0.434364 (-0.147315) \| 0.309069 / 0.540337 (-0.231268) \| 0.417117 / 1.386936 (-0.969819) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#35a3422cfcebfef5b09ae70c22843ffadaf44c46 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004974 / 0.011353 (-0.006379) \| 0.002950 / 0.011008 (-0.008058) \| 0.061856 / 0.038508 (0.023348) \| 0.030539 / 0.023109 (0.007429) \| 0.250105 / 0.275898 (-0.025793) \| 0.276687 / 0.323480 (-0.046793) \| 0.003077 / 0.007986 (-0.004908) \| 0.002412 / 0.004328 (-0.001916) \| 0.048336 / 0.004250 (0.044086) \| 0.045849 / 0.037052 (0.008797) \| 0.251757 / 0.258489 (-0.006732) \| 0.284914 / 0.293841 (-0.008927) \| 0.024033 / 0.128546 (-0.104513) \| 0.007343 / 0.075646 (-0.068303) \| 0.202867 / 0.419271 (-0.216405) \| 0.061294 / 0.043533 (0.017762) \| 0.263590 / 0.255139 (0.008451) \| 0.272744 / 0.283200 (-0.010455) \| 0.019613 / 0.141683 (-0.122070) \| 1.104263 / 1.452155 (-0.347892) \| 1.164128 / 1.492716 (-0.328588) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094261 / 0.018006 (0.076255) \| 0.303340 / 0.000490 (0.302850) \| 0.000215 / 0.000200 (0.000015) \| 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) \| 0.018381 / 0.037411 (-0.019030) \| 0.062727 / 0.014526 (0.048201) \| 0.074955 / 0.176557 (-0.101602) \| 0.124810 / 0.737135 (-0.612326) \| 0.074335 / 0.296338 (-0.222004) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279368 / 0.215209 (0.064159) \| 2.721641 / 2.077655 (0.643986) \| 1.510773 / 1.504120 (0.006653) \| 1.364349 / 1.541195 (-0.176845) \| 1.386044 / 1.468490 (-0.082446) \| 0.403051 / 4.584777 (-4.181726) \| 2.416525 / 3.745712 (-1.329187) \| 2.623198 / 5.269862 (-2.646663) \| 1.560869 / 4.565676 (-3.004808) \| 0.046613 / 0.424275 (-0.377662) \| 0.004861 / 0.007607 (-0.002746) \| 0.337875 / 0.226044 (0.111830) \| 3.289956 / 2.268929 (1.021028) \| 1.851707 / 55.444624 (-53.592917) \| 1.571092 / 6.876477 (-5.305385) \| 1.600328 / 2.142072 (-0.541745) \| 0.480766 / 4.805227 (-4.324461) \| 0.099138 / 6.500664 (-6.401526) \| 0.041691 / 0.075469 (-0.033779) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.941162 / 1.841788 (-0.900626) \| 11.745335 / 8.074308 (3.671027) \| 10.645509 / 10.191392 (0.454117) \| 0.132506 / 0.680424 (-0.547918) \| 0.015192 / 0.534201 (-0.519009) \| 0.272483 / 0.579283 (-0.306800) \| 0.270269 / 0.434364 (-0.164094) \| 0.309580 / 0.540337 (-0.230758) \| 0.431513 / 1.386936 (-0.955423) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005068 / 0.011353 (-0.006285) \| 0.003069 / 0.011008 (-0.007939) \| 0.048605 / 0.038508 (0.010097) \| 0.059557 / 0.023109 (0.036448) \| 0.275092 / 0.275898 (-0.000806) \| 0.298910 / 0.323480 (-0.024570) \| 0.004198 / 0.007986 (-0.003788) \| 0.002499 / 0.004328 (-0.001830) \| 0.048248 / 0.004250 (0.043997) \| 0.040302 / 0.037052 (0.003249) \| 0.279539 / 0.258489 (0.021050) \| 0.312500 / 0.293841 (0.018659) \| 0.025407 / 0.128546 (-0.103140) \| 0.007364 / 0.075646 (-0.068282) \| 0.053086 / 0.419271 (-0.366186) \| 0.033291 / 0.043533 (-0.010242) \| 0.276521 / 0.255139 (0.021382) \| 0.292943 / 0.283200 (0.009743) \| 0.019416 / 0.141683 (-0.122267) \| 1.151734 / 1.452155 (-0.300421) \| 1.205021 / 1.492716 (-0.287695) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094112 / 0.018006 (0.076106) \| 0.309534 / 0.000490 (0.309044) \| 0.000219 / 0.000200 (0.000019) \| 0.000052 / 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) \| 0.021539 / 0.037411 (-0.015872) \| 0.070325 / 0.014526 (0.055799) \| 0.080468 / 0.176557 (-0.096089) \| 0.121095 / 0.737135 (-0.616040) \| 0.082008 / 0.296338 (-0.214331) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302591 / 0.215209 (0.087382) \| 2.943475 / 2.077655 (0.865820) \| 1.597970 / 1.504120 (0.093850) \| 1.468774 / 1.541195 (-0.072421) \| 1.504812 / 1.468490 (0.036322) \| 0.413715 / 4.584777 (-4.171062) \| 2.418319 / 3.745712 (-1.327393) \| 2.616656 / 5.269862 (-2.653206) \| 1.558165 / 4.565676 (-3.007512) \| 0.047169 / 0.424275 (-0.377106) \| 0.004761 / 0.007607 (-0.002846) \| 0.347225 / 0.226044 (0.121180) \| 3.479624 / 2.268929 (1.210696) \| 1.961253 / 55.444624 (-53.483371) \| 1.673532 / 6.876477 (-5.202944) \| 1.698900 / 2.142072 (-0.443172) \| 0.488373 / 4.805227 (-4.316855) \| 0.098322 / 6.500664 (-6.402342) \| 0.040832 / 0.075469 (-0.034637) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.009133 / 1.841788 (-0.832655) \| 13.373258 / 8.074308 (5.298949) \| 11.327360 / 10.191392 (1.135968) \| 0.135778 / 0.680424 (-0.544646) \| 0.015813 / 0.534201 (-0.518388) \| 0.275404 / 0.579283 (-0.303879) \| 0.282564 / 0.434364 (-0.151799) \| 0.311830 / 0.540337 (-0.228507) \| 0.419008 / 1.386936 (-0.967928) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4592709e5399f91b5b392f4fd73687985365c909 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004899 / 0.011353 (-0.006454) \| 0.002780 / 0.011008 (-0.008229) \| 0.061997 / 0.038508 (0.023489) \| 0.029909 / 0.023109 (0.006800) \| 0.233445 / 0.275898 (-0.042453) \| 0.254128 / 0.323480 (-0.069351) \| 0.002927 / 0.007986 (-0.005058) \| 0.002396 / 0.004328 (-0.001932) \| 0.048118 / 0.004250 (0.043868) \| 0.044520 / 0.037052 (0.007468) \| 0.237594 / 0.258489 (-0.020895) \| 0.268407 / 0.293841 (-0.025434) \| 0.023517 / 0.128546 (-0.105029) \| 0.007035 / 0.075646 (-0.068612) \| 0.202803 / 0.419271 (-0.216469) \| 0.057692 / 0.043533 (0.014159) \| 0.237058 / 0.255139 (-0.018081) \| 0.252966 / 0.283200 (-0.030233) \| 0.017934 / 0.141683 (-0.123748) \| 1.096406 / 1.452155 (-0.355749) \| 1.153509 / 1.492716 (-0.339207) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091812 / 0.018006 (0.073806) \| 0.298410 / 0.000490 (0.297920) \| 0.000228 / 0.000200 (0.000028) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018333 / 0.037411 (-0.019078) \| 0.062685 / 0.014526 (0.048159) \| 0.073295 / 0.176557 (-0.103261) \| 0.119234 / 0.737135 (-0.617901) \| 0.074603 / 0.296338 (-0.221736) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279078 / 0.215209 (0.063869) \| 2.768535 / 2.077655 (0.690880) \| 1.457049 / 1.504120 (-0.047071) \| 1.326870 / 1.541195 (-0.214325) \| 1.349657 / 1.468490 (-0.118833) \| 0.405003 / 4.584777 (-4.179774) \| 2.428726 / 3.745712 (-1.316986) \| 2.595776 / 5.269862 (-2.674086) \| 1.557879 / 4.565676 (-3.007797) \| 0.045985 / 0.424275 (-0.378291) \| 0.004854 / 0.007607 (-0.002753) \| 0.336437 / 0.226044 (0.110392) \| 3.317330 / 2.268929 (1.048401) \| 1.784525 / 55.444624 (-53.660100) \| 1.500295 / 6.876477 (-5.376182) \| 1.529869 / 2.142072 (-0.612203) \| 0.473426 / 4.805227 (-4.331801) \| 0.099609 / 6.500664 (-6.401055) \| 0.042054 / 0.075469 (-0.033415) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.937154 / 1.841788 (-0.904633) \| 11.482383 / 8.074308 (3.408075) \| 10.468769 / 10.191392 (0.277377) \| 0.132724 / 0.680424 (-0.547700) \| 0.015242 / 0.534201 (-0.518959) \| 0.281124 / 0.579283 (-0.298159) \| 0.268603 / 0.434364 (-0.165761) \| 0.311410 / 0.540337 (-0.228928) \| 0.431817 / 1.386936 (-0.955119) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004695 / 0.011353 (-0.006658) \| 0.002873 / 0.011008 (-0.008135) \| 0.048133 / 0.038508 (0.009625) \| 0.052505 / 0.023109 (0.029396) \| 0.271679 / 0.275898 (-0.004219) \| 0.292530 / 0.323480 (-0.030950) \| 0.003844 / 0.007986 (-0.004142) \| 0.002417 / 0.004328 (-0.001912) \| 0.048619 / 0.004250 (0.044369) \| 0.039152 / 0.037052 (0.002100) \| 0.276575 / 0.258489 (0.018086) \| 0.307836 / 0.293841 (0.013995) \| 0.023877 / 0.128546 (-0.104669) \| 0.006897 / 0.075646 (-0.068749) \| 0.053241 / 0.419271 (-0.366031) \| 0.032487 / 0.043533 (-0.011046) \| 0.274205 / 0.255139 (0.019066) \| 0.289701 / 0.283200 (0.006502) \| 0.018250 / 0.141683 (-0.123432) \| 1.137902 / 1.452155 (-0.314253) \| 1.202043 / 1.492716 (-0.290673) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091453 / 0.018006 (0.073446) \| 0.297032 / 0.000490 (0.296543) \| 0.000224 / 0.000200 (0.000024) \| 0.000056 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021062 / 0.037411 (-0.016349) \| 0.069848 / 0.014526 (0.055322) \| 0.084337 / 0.176557 (-0.092219) \| 0.119951 / 0.737135 (-0.617184) \| 0.082805 / 0.296338 (-0.213533) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297056 / 0.215209 (0.081846) \| 2.890110 / 2.077655 (0.812456) \| 1.609918 / 1.504120 (0.105798) \| 1.491184 / 1.541195 (-0.050011) \| 1.529433 / 1.468490 (0.060943) \| 0.396081 / 4.584777 (-4.188696) \| 2.408310 / 3.745712 (-1.337402) \| 2.567905 / 5.269862 (-2.701957) \| 1.514465 / 4.565676 (-3.051212) \| 0.045329 / 0.424275 (-0.378946) \| 0.004738 / 0.007607 (-0.002869) \| 0.344373 / 0.226044 (0.118328) \| 3.428333 / 2.268929 (1.159404) \| 1.981401 / 55.444624 (-53.463223) \| 1.688007 / 6.876477 (-5.188470) \| 1.685542 / 2.142072 (-0.456531) \| 0.478045 / 4.805227 (-4.327182) \| 0.096664 / 6.500664 (-6.404001) \| 0.040335 / 0.075469 (-0.035135) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.972912 / 1.841788 (-0.868876) \| 12.055045 / 8.074308 (3.980737) \| 10.821073 / 10.191392 (0.629681) \| 0.139177 / 0.680424 (-0.541247) \| 0.015046 / 0.534201 (-0.519155) \| 0.275670 / 0.579283 (-0.303613) \| 0.280366 / 0.434364 (-0.153998) \| 0.315781 / 0.540337 (-0.224556) \| 0.424536 / 1.386936 (-0.962400) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0684b471d6ca8a235162f5575f624b6eda7956c5 \"CML watermark\")\n", "I'm finally merging as `transformers`/`tokenizers` dependency pins have been removed + `huggingface_hub 0.19.4` has fixed the deps incompatibility issue. All good now :)", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004435 / 0.011353 (-0.006918) \| 0.002924 / 0.011008 (-0.008084) \| 0.062159 / 0.038508 (0.023651) \| 0.029639 / 0.023109 (0.006529) \| 0.237470 / 0.275898 (-0.038428) \| 0.269641 / 0.323480 (-0.053839) \| 0.004124 / 0.007986 (-0.003862) \| 0.002528 / 0.004328 (-0.001800) \| 0.048114 / 0.004250 (0.043864) \| 0.046055 / 0.037052 (0.009002) \| 0.245844 / 0.258489 (-0.012645) \| 0.278085 / 0.293841 (-0.015756) \| 0.023152 / 0.128546 (-0.105394) \| 0.007194 / 0.075646 (-0.068452) \| 0.206493 / 0.419271 (-0.212778) \| 0.055687 / 0.043533 (0.012155) \| 0.243301 / 0.255139 (-0.011838) \| 0.267645 / 0.283200 (-0.015555) \| 0.017413 / 0.141683 (-0.124270) \| 1.113071 / 1.452155 (-0.339083) \| 1.201436 / 1.492716 (-0.291280) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092576 / 0.018006 (0.074570) \| 0.303516 / 0.000490 (0.303027) \| 0.000213 / 0.000200 (0.000013) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019108 / 0.037411 (-0.018303) \| 0.062326 / 0.014526 (0.047800) \| 0.073711 / 0.176557 (-0.102846) \| 0.120414 / 0.737135 (-0.616721) \| 0.075837 / 0.296338 (-0.220501) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278267 / 0.215209 (0.063058) \| 2.766231 / 2.077655 (0.688576) \| 1.455613 / 1.504120 (-0.048507) \| 1.337128 / 1.541195 (-0.204066) \| 1.357659 / 1.468490 (-0.110831) \| 0.404549 / 4.584777 (-4.180228) \| 2.409084 / 3.745712 (-1.336628) \| 2.645000 / 5.269862 (-2.624861) \| 1.600475 / 4.565676 (-2.965201) \| 0.046680 / 0.424275 (-0.377595) \| 0.004887 / 0.007607 (-0.002720) \| 0.340338 / 0.226044 (0.114294) \| 3.332647 / 2.268929 (1.063719) \| 1.852529 / 55.444624 (-53.592096) \| 1.532442 / 6.876477 (-5.344035) \| 1.550383 / 2.142072 (-0.591689) \| 0.482702 / 4.805227 (-4.322525) \| 0.101067 / 6.500664 (-6.399597) \| 0.042132 / 0.075469 (-0.033337) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945481 / 1.841788 (-0.896307) \| 11.886240 / 8.074308 (3.811932) \| 10.484620 / 10.191392 (0.293228) \| 0.130906 / 0.680424 (-0.549518) \| 0.014880 / 0.534201 (-0.519321) \| 0.268836 / 0.579283 (-0.310447) \| 0.268112 / 0.434364 (-0.166251) \| 0.304300 / 0.540337 (-0.236038) \| 0.440262 / 1.386936 (-0.946674) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005028 / 0.011353 (-0.006325) \| 0.002937 / 0.011008 (-0.008071) \| 0.049038 / 0.038508 (0.010530) \| 0.057763 / 0.023109 (0.034653) \| 0.273196 / 0.275898 (-0.002702) \| 0.295519 / 0.323480 (-0.027961) \| 0.004102 / 0.007986 (-0.003883) \| 0.002487 / 0.004328 (-0.001841) \| 0.049148 / 0.004250 (0.044898) \| 0.040303 / 0.037052 (0.003251) \| 0.279187 / 0.258489 (0.020698) \| 0.311086 / 0.293841 (0.017245) \| 0.024961 / 0.128546 (-0.103585) \| 0.007264 / 0.075646 (-0.068382) \| 0.055711 / 0.419271 (-0.363561) \| 0.032355 / 0.043533 (-0.011178) \| 0.274304 / 0.255139 (0.019165) \| 0.290953 / 0.283200 (0.007753) \| 0.018358 / 0.141683 (-0.123325) \| 1.115984 / 1.452155 (-0.336170) \| 1.190409 / 1.492716 (-0.302308) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095765 / 0.018006 (0.077759) \| 0.287947 / 0.000490 (0.287457) \| 0.000242 / 0.000200 (0.000042) \| 0.000047 / 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) \| 0.022165 / 0.037411 (-0.015246) \| 0.070465 / 0.014526 (0.055940) \| 0.082078 / 0.176557 (-0.094479) \| 0.120209 / 0.737135 (-0.616926) \| 0.084573 / 0.296338 (-0.211765) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298492 / 0.215209 (0.083283) \| 2.924981 / 2.077655 (0.847327) \| 1.597326 / 1.504120 (0.093206) \| 1.459132 / 1.541195 (-0.082062) \| 1.511471 / 1.468490 (0.042981) \| 0.406671 / 4.584777 (-4.178106) \| 2.443154 / 3.745712 (-1.302558) \| 2.591131 / 5.269862 (-2.678731) \| 1.549931 / 4.565676 (-3.015745) \| 0.047042 / 0.424275 (-0.377234) \| 0.004891 / 0.007607 (-0.002716) \| 0.346274 / 0.226044 (0.120230) \| 3.456050 / 2.268929 (1.187121) \| 1.959328 / 55.444624 (-53.485296) \| 1.647631 / 6.876477 (-5.228845) \| 1.692024 / 2.142072 (-0.450049) \| 0.478307 / 4.805227 (-4.326920) \| 0.098738 / 6.500664 (-6.401926) \| 0.041743 / 0.075469 (-0.033726) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.984619 / 1.841788 (-0.857168) \| 12.403984 / 8.074308 (4.329676) \| 10.974347 / 10.191392 (0.782955) \| 0.132893 / 0.680424 (-0.547530) \| 0.015504 / 0.534201 (-0.518697) \| 0.275354 / 0.579283 (-0.303929) \| 0.283312 / 0.434364 (-0.151052) \| 0.313661 / 0.540337 (-0.226677) \| 0.419065 / 1.386936 (-0.967871) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c65315e4a8308f04fcb025039afe2a2e43b5684e \"CML watermark\")\n" ]
1,992,401,594	6,412	User token is printed out!	closed	2023-11-14T10:01:34	2023-11-14T22:19:46	2023-11-14T22:19:46	https://github.com/huggingface/datasets/issues/6412	null	mohsen-goodarzi	false	[ "Indeed, this is not a good practice. I've opened a PR that removes the token value from the (deprecation) warning." ]
1,992,386,630	6,411	Fix dependency conflict within CI build documentation	closed	2023-11-14T09:52:51	2023-11-14T10:05:59	2023-11-14T10:05:35	https://github.com/huggingface/datasets/pull/6411	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6411", "html_url": "https://github.com/huggingface/datasets/pull/6411", "diff_url": "https://github.com/huggingface/datasets/pull/6411.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6411.patch", "merged_at": "2023-11-14T10:05:34" }	albertvillanova	true	[ "_The documentation is not available anymore as the PR was closed or merged._" ]
1,992,100,209	6,410	Datasets does not load HuggingFace Repository properly	open	2023-11-14T06:50:49	2023-11-16T06:54:36	null	https://github.com/huggingface/datasets/issues/6410	null	MikeDoes	false	[ "Hi! You can avoid the error by requesting only the `jsonl` files. `dataset = load_dataset(\"ai4privacy/pii-masking-200k\", data_files=[\"*.jsonl\"])`.\r\n\r\nOur data file inference does not filter out (incompatible) `json` files because `json` and `jsonl` use the same builder. Still, I think the inference should differentiate these extensions because it's safe to assume that loading them together will lead to an error. WDYT @lhoestq? ", "Raising an error if there is a mix of json and jsonl in the builder makes sense yea" ]
1,991,960,865	6,409	using DownloadManager to download from local filesystem and disable_progress_bar, there will be an exception	closed	2023-11-14T04:21:01	2023-11-22T16:42:09	2023-11-22T16:42:09	https://github.com/huggingface/datasets/issues/6409	null	neiblegy	false	[]
1,991,902,972	6,408	`IterableDataset` lost but not keep columns when map function adding columns with names in `remove_columns`	open	2023-11-14T03:12:08	2023-11-16T06:24:10	null	https://github.com/huggingface/datasets/issues/6408	null	shmily326	false	[]
1,991,514,079	6,407	Loading the dataset from private S3 bucket gives "TypeError: cannot pickle '_contextvars.Context' object"	open	2023-11-13T21:27:43	2024-07-30T12:35:09	null	https://github.com/huggingface/datasets/issues/6407	null	eawer	false	[ "I have encountered the same problem with `datasets-2.20.0`. \r\n\r\nI found the following workaround for this issue (including the fix from #6598):\r\n1. specify the AWS profile name in the `storage_options` instead of passing an existing session object\r\n2. use a custom `DownloadConfig` object to fix #6598\r\n3. pass the `storage_options` to the `DownloadConfig`\r\n```python\r\nfrom datasets import load_dataset, DownloadConfig\r\n\r\n# Fix for DownloadConfig from https://github.com/huggingface/datasets/issues/6598#issuecomment-1986699619\r\nclass ReviseDownloadConfig(DownloadConfig):\r\n def __post_init__(self, use_auth_token):\r\n if use_auth_token != \"deprecated\":\r\n warnings.warn(\r\n \"'use_auth_token' was deprecated in favor of 'token' in version 2.14.0 and will be removed in 3.0.0.\\n\"\r\n f\"You can remove this warning by passing 'token={use_auth_token}' instead.\",\r\n FutureWarning,\r\n )\r\n self.token = use_auth_token\r\n\r\nstorage_options={\"profile\": \"my-aws-profile-name\"}\r\n\r\nds = load_dataset(\r\n \"parquet\", \r\n data_files={\"train\": DATA_PATH}, \r\n storage_options=storage_options,\r\n download_config=ReviseDownloadConfig(storage_options=storage_options)\r\n)\r\n```" ]
1,990,469,045	6,406	CI Build PR Documentation is broken: ImportError: cannot import name 'TypeAliasType' from 'typing_extensions'	closed	2023-11-13T11:36:10	2023-11-14T10:05:36	2023-11-14T10:05:36	https://github.com/huggingface/datasets/issues/6406	null	albertvillanova	false	[]
1,990,358,743	6,405	ConfigNamesError on a simple CSV file	closed	2023-11-13T10:28:29	2023-11-13T20:01:24	2023-11-13T20:01:24	https://github.com/huggingface/datasets/issues/6405	null	severo	false	[ "The viewer is working now. \r\n\r\nBased on the repo commit history, the bug was due to the incorrect format of the `features` field in the README YAML (`Value` requires `dtype`, e.g., `Value(\"string\")`, but it was not specified)", "Feel free to close the issue", "Oh, OK! Thanks. So, there was no reason to open an issue" ]
1,990,211,901	6,404	Support pyarrow 14.0.1 and fix vulnerability CVE-2023-47248	closed	2023-11-13T09:15:39	2023-11-14T10:29:48	2023-11-14T10:23:29	https://github.com/huggingface/datasets/pull/6404	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6404", "html_url": "https://github.com/huggingface/datasets/pull/6404", "diff_url": "https://github.com/huggingface/datasets/pull/6404.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6404.patch", "merged_at": "2023-11-14T10:23:29" }	albertvillanova	true	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005974 / 0.011353 (-0.005378) \| 0.003707 / 0.011008 (-0.007301) \| 0.079908 / 0.038508 (0.041399) \| 0.036891 / 0.023109 (0.013781) \| 0.390355 / 0.275898 (0.114457) \| 0.424439 / 0.323480 (0.100960) \| 0.004936 / 0.007986 (-0.003050) \| 0.002886 / 0.004328 (-0.001442) \| 0.062793 / 0.004250 (0.058542) \| 0.054192 / 0.037052 (0.017139) \| 0.394697 / 0.258489 (0.136208) \| 0.437775 / 0.293841 (0.143934) \| 0.027596 / 0.128546 (-0.100950) \| 0.008006 / 0.075646 (-0.067640) \| 0.262515 / 0.419271 (-0.156757) \| 0.071014 / 0.043533 (0.027481) \| 0.392964 / 0.255139 (0.137825) \| 0.417449 / 0.283200 (0.134249) \| 0.021819 / 0.141683 (-0.119864) \| 1.458083 / 1.452155 (0.005929) \| 1.489042 / 1.492716 (-0.003674) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230303 / 0.018006 (0.212297) \| 0.439361 / 0.000490 (0.438871) \| 0.010615 / 0.000200 (0.010415) \| 0.000303 / 0.000054 (0.000249) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026600 / 0.037411 (-0.010811) \| 0.078605 / 0.014526 (0.064079) \| 0.088552 / 0.176557 (-0.088005) \| 0.149429 / 0.737135 (-0.587706) \| 0.087921 / 0.296338 (-0.208417) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.422063 / 0.215209 (0.206854) \| 4.201333 / 2.077655 (2.123678) \| 1.982284 / 1.504120 (0.478164) \| 1.779625 / 1.541195 (0.238431) \| 1.872454 / 1.468490 (0.403964) \| 0.502713 / 4.584777 (-4.082063) \| 3.103372 / 3.745712 (-0.642340) \| 3.030516 / 5.269862 (-2.239346) \| 1.909123 / 4.565676 (-2.656554) \| 0.057134 / 0.424275 (-0.367141) \| 0.006405 / 0.007607 (-0.001202) \| 0.494452 / 0.226044 (0.268408) \| 4.839345 / 2.268929 (2.570417) \| 2.424721 / 55.444624 (-53.019904) \| 2.028618 / 6.876477 (-4.847859) \| 2.082528 / 2.142072 (-0.059545) \| 0.587396 / 4.805227 (-4.217831) \| 0.125013 / 6.500664 (-6.375651) \| 0.061369 / 0.075469 (-0.014100) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.235799 / 1.841788 (-0.605989) \| 17.919977 / 8.074308 (9.845669) \| 13.868524 / 10.191392 (3.677132) \| 0.146058 / 0.680424 (-0.534366) \| 0.016826 / 0.534201 (-0.517375) \| 0.337512 / 0.579283 (-0.241771) \| 0.390263 / 0.434364 (-0.044101) \| 0.385336 / 0.540337 (-0.155001) \| 0.566004 / 1.386936 (-0.820932) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006537 / 0.011353 (-0.004816) \| 0.003787 / 0.011008 (-0.007221) \| 0.062568 / 0.038508 (0.024060) \| 0.066672 / 0.023109 (0.043563) \| 0.420447 / 0.275898 (0.144549) \| 0.457260 / 0.323480 (0.133780) \| 0.005005 / 0.007986 (-0.002981) \| 0.003037 / 0.004328 (-0.001291) \| 0.062095 / 0.004250 (0.057844) \| 0.049619 / 0.037052 (0.012567) \| 0.429935 / 0.258489 (0.171446) \| 0.471566 / 0.293841 (0.177725) \| 0.029688 / 0.128546 (-0.098859) \| 0.008028 / 0.075646 (-0.067619) \| 0.067915 / 0.419271 (-0.351356) \| 0.042066 / 0.043533 (-0.001467) \| 0.419275 / 0.255139 (0.164136) \| 0.444819 / 0.283200 (0.161619) \| 0.020100 / 0.141683 (-0.121583) \| 1.439057 / 1.452155 (-0.013098) \| 1.495657 / 1.492716 (0.002940) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.211148 / 0.018006 (0.193142) \| 0.423777 / 0.000490 (0.423288) \| 0.005892 / 0.000200 (0.005693) \| 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) \| 0.026469 / 0.037411 (-0.010942) \| 0.081438 / 0.014526 (0.066912) \| 0.092007 / 0.176557 (-0.084550) \| 0.143433 / 0.737135 (-0.593703) \| 0.093039 / 0.296338 (-0.203300) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.410468 / 0.215209 (0.195259) \| 4.083783 / 2.077655 (2.006128) \| 2.234501 / 1.504120 (0.730381) \| 2.122323 / 1.541195 (0.581128) \| 2.255036 / 1.468490 (0.786546) \| 0.497712 / 4.584777 (-4.087065) \| 3.231187 / 3.745712 (-0.514525) \| 3.005399 / 5.269862 (-2.264463) \| 1.909516 / 4.565676 (-2.656161) \| 0.057529 / 0.424275 (-0.366746) \| 0.006475 / 0.007607 (-0.001132) \| 0.477282 / 0.226044 (0.251238) \| 4.799566 / 2.268929 (2.530637) \| 2.497070 / 55.444624 (-52.947554) \| 2.206359 / 6.876477 (-4.670118) \| 2.281614 / 2.142072 (0.139541) \| 0.581710 / 4.805227 (-4.223518) \| 0.121572 / 6.500664 (-6.379092) \| 0.058774 / 0.075469 (-0.016695) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.301880 / 1.841788 (-0.539908) \| 18.287330 / 8.074308 (10.213021) \| 14.939642 / 10.191392 (4.748250) \| 0.153941 / 0.680424 (-0.526483) \| 0.018345 / 0.534201 (-0.515856) \| 0.335986 / 0.579283 (-0.243297) \| 0.384264 / 0.434364 (-0.050099) \| 0.393115 / 0.540337 (-0.147223) \| 0.573343 / 1.386936 (-0.813594) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d54b6459f4ed0b2519ddec605dd71956d2d1d3e4 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004805 / 0.011353 (-0.006548) \| 0.003261 / 0.011008 (-0.007747) \| 0.061585 / 0.038508 (0.023077) \| 0.030236 / 0.023109 (0.007127) \| 0.234767 / 0.275898 (-0.041131) \| 0.260478 / 0.323480 (-0.063002) \| 0.004121 / 0.007986 (-0.003865) \| 0.002525 / 0.004328 (-0.001803) \| 0.048213 / 0.004250 (0.043962) \| 0.045229 / 0.037052 (0.008176) \| 0.245143 / 0.258489 (-0.013346) \| 0.271818 / 0.293841 (-0.022023) \| 0.023594 / 0.128546 (-0.104952) \| 0.007335 / 0.075646 (-0.068311) \| 0.206246 / 0.419271 (-0.213026) \| 0.060783 / 0.043533 (0.017250) \| 0.238588 / 0.255139 (-0.016551) \| 0.274985 / 0.283200 (-0.008214) \| 0.018342 / 0.141683 (-0.123341) \| 1.135445 / 1.452155 (-0.316710) \| 1.184836 / 1.492716 (-0.307881) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095603 / 0.018006 (0.077597) \| 0.290340 / 0.000490 (0.289850) \| 0.000219 / 0.000200 (0.000019) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018804 / 0.037411 (-0.018607) \| 0.062525 / 0.014526 (0.047999) \| 0.074797 / 0.176557 (-0.101760) \| 0.120360 / 0.737135 (-0.616775) \| 0.076182 / 0.296338 (-0.220156) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.274981 / 0.215209 (0.059772) \| 2.684931 / 2.077655 (0.607276) \| 1.453845 / 1.504120 (-0.050275) \| 1.348361 / 1.541195 (-0.192834) \| 1.402820 / 1.468490 (-0.065670) \| 0.396311 / 4.584777 (-4.188466) \| 2.396314 / 3.745712 (-1.349398) \| 2.744379 / 5.269862 (-2.525482) \| 1.615268 / 4.565676 (-2.950409) \| 0.045920 / 0.424275 (-0.378355) \| 0.004844 / 0.007607 (-0.002763) \| 0.331132 / 0.226044 (0.105087) \| 3.325484 / 2.268929 (1.056556) \| 1.845734 / 55.444624 (-53.598890) \| 1.537268 / 6.876477 (-5.339209) \| 1.565155 / 2.142072 (-0.576918) \| 0.480032 / 4.805227 (-4.325195) \| 0.099917 / 6.500664 (-6.400747) \| 0.042276 / 0.075469 (-0.033193) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.973128 / 1.841788 (-0.868660) \| 12.643790 / 8.074308 (4.569482) \| 10.319586 / 10.191392 (0.128194) \| 0.131733 / 0.680424 (-0.548691) \| 0.014849 / 0.534201 (-0.519352) \| 0.270960 / 0.579283 (-0.308323) \| 0.265409 / 0.434364 (-0.168955) \| 0.309073 / 0.540337 (-0.231264) \| 0.466204 / 1.386936 (-0.920732) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005067 / 0.011353 (-0.006286) \| 0.003344 / 0.011008 (-0.007665) \| 0.047917 / 0.038508 (0.009409) \| 0.059556 / 0.023109 (0.036447) \| 0.275777 / 0.275898 (-0.000121) \| 0.299703 / 0.323480 (-0.023777) \| 0.004185 / 0.007986 (-0.003801) \| 0.002602 / 0.004328 (-0.001726) \| 0.048723 / 0.004250 (0.044472) \| 0.040686 / 0.037052 (0.003634) \| 0.281078 / 0.258489 (0.022589) \| 0.314725 / 0.293841 (0.020885) \| 0.024645 / 0.128546 (-0.103901) \| 0.007465 / 0.075646 (-0.068182) \| 0.053827 / 0.419271 (-0.365445) \| 0.033395 / 0.043533 (-0.010138) \| 0.273675 / 0.255139 (0.018536) \| 0.291261 / 0.283200 (0.008062) \| 0.019733 / 0.141683 (-0.121950) \| 1.134084 / 1.452155 (-0.318071) \| 1.189186 / 1.492716 (-0.303531) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.114960 / 0.018006 (0.096954) \| 0.308800 / 0.000490 (0.308311) \| 0.000237 / 0.000200 (0.000037) \| 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) \| 0.021633 / 0.037411 (-0.015778) \| 0.073192 / 0.014526 (0.058666) \| 0.081598 / 0.176557 (-0.094959) \| 0.123085 / 0.737135 (-0.614050) \| 0.088677 / 0.296338 (-0.207661) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300865 / 0.215209 (0.085656) \| 2.956847 / 2.077655 (0.879192) \| 1.613890 / 1.504120 (0.109770) \| 1.494074 / 1.541195 (-0.047121) \| 1.550345 / 1.468490 (0.081855) \| 0.408880 / 4.584777 (-4.175897) \| 2.422848 / 3.745712 (-1.322865) \| 2.690623 / 5.269862 (-2.579239) \| 1.546922 / 4.565676 (-3.018755) \| 0.047192 / 0.424275 (-0.377083) \| 0.004882 / 0.007607 (-0.002725) \| 0.360625 / 0.226044 (0.134580) \| 3.512678 / 2.268929 (1.243749) \| 1.978633 / 55.444624 (-53.465992) \| 1.686927 / 6.876477 (-5.189549) \| 1.748387 / 2.142072 (-0.393685) \| 0.480780 / 4.805227 (-4.324447) \| 0.099163 / 6.500664 (-6.401501) \| 0.041194 / 0.075469 (-0.034275) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.989087 / 1.841788 (-0.852700) \| 12.341951 / 8.074308 (4.267643) \| 11.109329 / 10.191392 (0.917936) \| 0.143329 / 0.680424 (-0.537095) \| 0.015565 / 0.534201 (-0.518636) \| 0.269532 / 0.579283 (-0.309751) \| 0.274899 / 0.434364 (-0.159465) \| 0.309308 / 0.540337 (-0.231030) \| 0.439651 / 1.386936 (-0.947285) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#04a3f006a1a88c894ea10610d66dfddd73ad1490 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007880 / 0.011353 (-0.003473) \| 0.004386 / 0.011008 (-0.006622) \| 0.099067 / 0.038508 (0.060559) \| 0.048036 / 0.023109 (0.024927) \| 0.368349 / 0.275898 (0.092451) \| 0.400052 / 0.323480 (0.076572) \| 0.004493 / 0.007986 (-0.003493) \| 0.003732 / 0.004328 (-0.000597) \| 0.076153 / 0.004250 (0.071902) \| 0.071024 / 0.037052 (0.033972) \| 0.379771 / 0.258489 (0.121282) \| 0.425005 / 0.293841 (0.131164) \| 0.036092 / 0.128546 (-0.092454) \| 0.009825 / 0.075646 (-0.065822) \| 0.340217 / 0.419271 (-0.079055) \| 0.089571 / 0.043533 (0.046038) \| 0.371426 / 0.255139 (0.116287) \| 0.397864 / 0.283200 (0.114664) \| 0.029440 / 0.141683 (-0.112243) \| 1.778100 / 1.452155 (0.325945) \| 1.857202 / 1.492716 (0.364486) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.254022 / 0.018006 (0.236015) \| 0.549844 / 0.000490 (0.549354) \| 0.012824 / 0.000200 (0.012624) \| 0.000378 / 0.000054 (0.000324) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032334 / 0.037411 (-0.005077) \| 0.096101 / 0.014526 (0.081576) \| 0.117825 / 0.176557 (-0.058731) \| 0.179277 / 0.737135 (-0.557858) \| 0.112614 / 0.296338 (-0.183724) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.455051 / 0.215209 (0.239842) \| 4.537086 / 2.077655 (2.459431) \| 2.198662 / 1.504120 (0.694542) \| 1.982772 / 1.541195 (0.441578) \| 2.058673 / 1.468490 (0.590182) \| 0.569268 / 4.584777 (-4.015509) \| 4.095000 / 3.745712 (0.349288) \| 3.891680 / 5.269862 (-1.378182) \| 2.345129 / 4.565676 (-2.220548) \| 0.066974 / 0.424275 (-0.357301) \| 0.008557 / 0.007607 (0.000950) \| 0.545290 / 0.226044 (0.319245) \| 5.453377 / 2.268929 (3.184448) \| 2.858688 / 55.444624 (-52.585936) \| 2.502367 / 6.876477 (-4.374109) \| 2.515658 / 2.142072 (0.373586) \| 0.681423 / 4.805227 (-4.123804) \| 0.155975 / 6.500664 (-6.344689) \| 0.070872 / 0.075469 (-0.004597) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.474674 / 1.841788 (-0.367114) \| 21.653619 / 8.074308 (13.579311) \| 16.277111 / 10.191392 (6.085719) \| 0.166445 / 0.680424 (-0.513979) \| 0.021676 / 0.534201 (-0.512525) \| 0.466949 / 0.579283 (-0.112334) \| 0.500953 / 0.434364 (0.066589) \| 0.540413 / 0.540337 (0.000076) \| 0.792989 / 1.386936 (-0.593947) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007633 / 0.011353 (-0.003720) \| 0.004468 / 0.011008 (-0.006540) \| 0.075573 / 0.038508 (0.037065) \| 0.081174 / 0.023109 (0.058064) \| 0.440741 / 0.275898 (0.164843) \| 0.489493 / 0.323480 (0.166013) \| 0.006180 / 0.007986 (-0.001805) \| 0.003693 / 0.004328 (-0.000636) \| 0.074692 / 0.004250 (0.070441) \| 0.061732 / 0.037052 (0.024680) \| 0.460391 / 0.258489 (0.201902) \| 0.505575 / 0.293841 (0.211734) \| 0.037692 / 0.128546 (-0.090854) \| 0.009870 / 0.075646 (-0.065776) \| 0.083830 / 0.419271 (-0.335442) \| 0.056255 / 0.043533 (0.012723) \| 0.439330 / 0.255139 (0.184191) \| 0.475598 / 0.283200 (0.192399) \| 0.026626 / 0.141683 (-0.115056) \| 1.794410 / 1.452155 (0.342255) \| 1.882510 / 1.492716 (0.389794) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.236194 / 0.018006 (0.218187) \| 0.486109 / 0.000490 (0.485619) \| 0.006652 / 0.000200 (0.006453) \| 0.000108 / 0.000054 (0.000053) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037277 / 0.037411 (-0.000134) \| 0.108904 / 0.014526 (0.094378) \| 0.122699 / 0.176557 (-0.053857) \| 0.182388 / 0.737135 (-0.554747) \| 0.122826 / 0.296338 (-0.173512) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.485989 / 0.215209 (0.270780) \| 4.913263 / 2.077655 (2.835609) \| 2.571618 / 1.504120 (1.067498) \| 2.401248 / 1.541195 (0.860054) \| 2.501117 / 1.468490 (1.032627) \| 0.570989 / 4.584777 (-4.013788) \| 4.107420 / 3.745712 (0.361708) \| 3.814977 / 5.269862 (-1.454885) \| 2.282539 / 4.565676 (-2.283138) \| 0.067765 / 0.424275 (-0.356511) \| 0.008561 / 0.007607 (0.000954) \| 0.584515 / 0.226044 (0.358471) \| 5.817821 / 2.268929 (3.548893) \| 3.211202 / 55.444624 (-52.233422) \| 2.764480 / 6.876477 (-4.111996) \| 2.807301 / 2.142072 (0.665229) \| 0.676882 / 4.805227 (-4.128346) \| 0.150124 / 6.500664 (-6.350540) \| 0.067205 / 0.075469 (-0.008265) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.594945 / 1.841788 (-0.246843) \| 22.533511 / 8.074308 (14.459203) \| 17.099693 / 10.191392 (6.908301) \| 0.195954 / 0.680424 (-0.484470) \| 0.023968 / 0.534201 (-0.510233) \| 0.471337 / 0.579283 (-0.107946) \| 0.491017 / 0.434364 (0.056653) \| 0.561342 / 0.540337 (0.021004) \| 0.797116 / 1.386936 (-0.589820) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#98871b9ba46e89e75e9d0dddc49f4241373c575d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006235 / 0.011353 (-0.005118) \| 0.003688 / 0.011008 (-0.007321) \| 0.080801 / 0.038508 (0.042293) \| 0.036243 / 0.023109 (0.013134) \| 0.312173 / 0.275898 (0.036275) \| 0.346239 / 0.323480 (0.022759) \| 0.003429 / 0.007986 (-0.004556) \| 0.003806 / 0.004328 (-0.000523) \| 0.063236 / 0.004250 (0.058986) \| 0.053229 / 0.037052 (0.016177) \| 0.315184 / 0.258489 (0.056695) \| 0.360124 / 0.293841 (0.066283) \| 0.027447 / 0.128546 (-0.101099) \| 0.008029 / 0.075646 (-0.067618) \| 0.262766 / 0.419271 (-0.156505) \| 0.068421 / 0.043533 (0.024888) \| 0.309028 / 0.255139 (0.053889) \| 0.345859 / 0.283200 (0.062659) \| 0.021388 / 0.141683 (-0.120295) \| 1.452807 / 1.452155 (0.000652) \| 1.502803 / 1.492716 (0.010087) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.211297 / 0.018006 (0.193291) \| 0.423364 / 0.000490 (0.422874) \| 0.004574 / 0.000200 (0.004374) \| 0.000272 / 0.000054 (0.000218) \|\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 (-0.013606) \| 0.072309 / 0.014526 (0.057783) \| 0.083274 / 0.176557 (-0.093283) \| 0.143594 / 0.737135 (-0.593541) \| 0.083777 / 0.296338 (-0.212561) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.415691 / 0.215209 (0.200482) \| 4.128621 / 2.077655 (2.050967) \| 1.931128 / 1.504120 (0.427008) \| 1.737486 / 1.541195 (0.196292) \| 1.806314 / 1.468490 (0.337823) \| 0.501405 / 4.584777 (-4.083372) \| 3.082042 / 3.745712 (-0.663670) \| 2.980224 / 5.269862 (-2.289637) \| 1.879780 / 4.565676 (-2.685897) \| 0.057546 / 0.424275 (-0.366729) \| 0.006422 / 0.007607 (-0.001186) \| 0.479813 / 0.226044 (0.253768) \| 4.854497 / 2.268929 (2.585568) \| 2.529674 / 55.444624 (-52.914950) \| 2.283041 / 6.876477 (-4.593436) \| 2.377173 / 2.142072 (0.235101) \| 0.589654 / 4.805227 (-4.215573) \| 0.126190 / 6.500664 (-6.374474) \| 0.062391 / 0.075469 (-0.013079) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.232023 / 1.841788 (-0.609764) \| 17.576621 / 8.074308 (9.502313) \| 13.437075 / 10.191392 (3.245683) \| 0.143367 / 0.680424 (-0.537057) \| 0.016638 / 0.534201 (-0.517563) \| 0.332806 / 0.579283 (-0.246477) \| 0.356029 / 0.434364 (-0.078335) \| 0.385610 / 0.540337 (-0.154727) \| 0.563268 / 1.386936 (-0.823668) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006293 / 0.011353 (-0.005060) \| 0.003692 / 0.011008 (-0.007317) \| 0.062075 / 0.038508 (0.023567) \| 0.062104 / 0.023109 (0.038995) \| 0.407478 / 0.275898 (0.131580) \| 0.434982 / 0.323480 (0.111502) \| 0.004889 / 0.007986 (-0.003097) \| 0.002915 / 0.004328 (-0.001413) \| 0.061426 / 0.004250 (0.057176) \| 0.048027 / 0.037052 (0.010974) \| 0.410504 / 0.258489 (0.152015) \| 0.435383 / 0.293841 (0.141542) \| 0.029419 / 0.128546 (-0.099127) \| 0.008275 / 0.075646 (-0.067371) \| 0.067796 / 0.419271 (-0.351476) \| 0.041696 / 0.043533 (-0.001837) \| 0.398882 / 0.255139 (0.143743) \| 0.419480 / 0.283200 (0.136281) \| 0.021519 / 0.141683 (-0.120164) \| 1.436961 / 1.452155 (-0.015194) \| 1.507961 / 1.492716 (0.015245) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223190 / 0.018006 (0.205184) \| 0.416281 / 0.000490 (0.415791) \| 0.003370 / 0.000200 (0.003170) \| 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) \| 0.025923 / 0.037411 (-0.011488) \| 0.079989 / 0.014526 (0.065463) \| 0.091289 / 0.176557 (-0.085268) \| 0.141212 / 0.737135 (-0.595923) \| 0.091717 / 0.296338 (-0.204622) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.434640 / 0.215209 (0.219431) \| 4.326154 / 2.077655 (2.248500) \| 2.364845 / 1.504120 (0.860725) \| 2.194040 / 1.541195 (0.652846) \| 2.276665 / 1.468490 (0.808175) \| 0.501879 / 4.584777 (-4.082898) \| 3.073307 / 3.745712 (-0.672405) \| 2.893823 / 5.269862 (-2.376039) \| 1.820594 / 4.565676 (-2.745083) \| 0.057595 / 0.424275 (-0.366680) \| 0.006516 / 0.007607 (-0.001091) \| 0.513633 / 0.226044 (0.287589) \| 5.104799 / 2.268929 (2.835870) \| 2.845025 / 55.444624 (-52.599599) \| 2.513852 / 6.876477 (-4.362624) \| 2.561044 / 2.142072 (0.418972) \| 0.582711 / 4.805227 (-4.222516) \| 0.120631 / 6.500664 (-6.380034) \| 0.056738 / 0.075469 (-0.018731) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.303370 / 1.841788 (-0.538418) \| 18.023568 / 8.074308 (9.949259) \| 14.637973 / 10.191392 (4.446581) \| 0.145182 / 0.680424 (-0.535241) \| 0.018061 / 0.534201 (-0.516140) \| 0.333219 / 0.579283 (-0.246065) \| 0.373184 / 0.434364 (-0.061180) \| 0.388176 / 0.540337 (-0.152161) \| 0.564752 / 1.386936 (-0.822184) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aecdc94580d105d4b70c94e8e238ce097f97af90 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007230 / 0.011353 (-0.004122) \| 0.003727 / 0.011008 (-0.007281) \| 0.078893 / 0.038508 (0.040385) \| 0.042600 / 0.023109 (0.019491) \| 0.301905 / 0.275898 (0.026007) \| 0.328478 / 0.323480 (0.004998) \| 0.003960 / 0.007986 (-0.004026) \| 0.004530 / 0.004328 (0.000201) \| 0.059446 / 0.004250 (0.055196) \| 0.061241 / 0.037052 (0.024189) \| 0.301878 / 0.258489 (0.043389) \| 0.340935 / 0.293841 (0.047095) \| 0.030559 / 0.128546 (-0.097988) \| 0.008016 / 0.075646 (-0.067630) \| 0.305174 / 0.419271 (-0.114097) \| 0.080374 / 0.043533 (0.036842) \| 0.307162 / 0.255139 (0.052023) \| 0.342459 / 0.283200 (0.059259) \| 0.025881 / 0.141683 (-0.115801) \| 1.443311 / 1.452155 (-0.008844) \| 1.631060 / 1.492716 (0.138344) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.242676 / 0.018006 (0.224670) \| 0.463941 / 0.000490 (0.463451) \| 0.007762 / 0.000200 (0.007562) \| 0.000582 / 0.000054 (0.000527) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027334 / 0.037411 (-0.010077) \| 0.078910 / 0.014526 (0.064384) \| 0.091399 / 0.176557 (-0.085157) \| 0.143318 / 0.737135 (-0.593818) \| 0.089761 / 0.296338 (-0.206577) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.463002 / 0.215209 (0.247793) \| 4.627235 / 2.077655 (2.549580) \| 2.256699 / 1.504120 (0.752579) \| 2.057615 / 1.541195 (0.516421) \| 2.126424 / 1.468490 (0.657934) \| 0.571969 / 4.584777 (-4.012808) \| 4.130260 / 3.745712 (0.384548) \| 3.833521 / 5.269862 (-1.436341) \| 2.320141 / 4.565676 (-2.245535) \| 0.067587 / 0.424275 (-0.356688) \| 0.008452 / 0.007607 (0.000845) \| 0.546478 / 0.226044 (0.320433) \| 5.070678 / 2.268929 (2.801750) \| 2.325387 / 55.444624 (-53.119237) \| 2.044041 / 6.876477 (-4.832435) \| 2.019714 / 2.142072 (-0.122358) \| 0.563589 / 4.805227 (-4.241639) \| 0.135269 / 6.500664 (-6.365395) \| 0.058208 / 0.075469 (-0.017261) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.283156 / 1.841788 (-0.558631) \| 18.617776 / 8.074308 (10.543468) \| 13.360700 / 10.191392 (3.169308) \| 0.160001 / 0.680424 (-0.520423) \| 0.021538 / 0.534201 (-0.512663) \| 0.384169 / 0.579283 (-0.195114) \| 0.407517 / 0.434364 (-0.026847) \| 0.427295 / 0.540337 (-0.113042) \| 0.655288 / 1.386936 (-0.731648) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006854 / 0.011353 (-0.004499) \| 0.003442 / 0.011008 (-0.007566) \| 0.060622 / 0.038508 (0.022114) \| 0.074649 / 0.023109 (0.051540) \| 0.341733 / 0.275898 (0.065835) \| 0.360096 / 0.323480 (0.036616) \| 0.006235 / 0.007986 (-0.001751) \| 0.003447 / 0.004328 (-0.000882) \| 0.057301 / 0.004250 (0.053051) \| 0.059022 / 0.037052 (0.021970) \| 0.369523 / 0.258489 (0.111034) \| 0.386280 / 0.293841 (0.092439) \| 0.034319 / 0.128546 (-0.094228) \| 0.008291 / 0.075646 (-0.067355) \| 0.070403 / 0.419271 (-0.348868) \| 0.050433 / 0.043533 (0.006901) \| 0.347262 / 0.255139 (0.092123) \| 0.380543 / 0.283200 (0.097343) \| 0.024492 / 0.141683 (-0.117191) \| 1.446721 / 1.452155 (-0.005433) \| 1.541614 / 1.492716 (0.048898) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.226148 / 0.018006 (0.208142) \| 0.442150 / 0.000490 (0.441660) \| 0.004997 / 0.000200 (0.004797) \| 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) \| 0.032866 / 0.037411 (-0.004546) \| 0.088097 / 0.014526 (0.073571) \| 0.102178 / 0.176557 (-0.074379) \| 0.151129 / 0.737135 (-0.586006) \| 0.103953 / 0.296338 (-0.192386) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.376701 / 0.215209 (0.161492) \| 3.886997 / 2.077655 (1.809342) \| 2.027143 / 1.504120 (0.523023) \| 1.808647 / 1.541195 (0.267453) \| 1.867664 / 1.468490 (0.399173) \| 0.459487 / 4.584777 (-4.125290) \| 3.640801 / 3.745712 (-0.104911) \| 3.242512 / 5.269862 (-2.027350) \| 1.889174 / 4.565676 (-2.676503) \| 0.052415 / 0.424275 (-0.371860) \| 0.007479 / 0.007607 (-0.000128) \| 0.457706 / 0.226044 (0.231662) \| 4.815041 / 2.268929 (2.546112) \| 2.542470 / 55.444624 (-52.902154) \| 2.137084 / 6.876477 (-4.739392) \| 2.122867 / 2.142072 (-0.019205) \| 0.553756 / 4.805227 (-4.251471) \| 0.118902 / 6.500664 (-6.381763) \| 0.058149 / 0.075469 (-0.017320) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.272615 / 1.841788 (-0.569173) \| 19.455709 / 8.074308 (11.381401) \| 14.111693 / 10.191392 (3.920301) \| 0.165741 / 0.680424 (-0.514683) \| 0.023680 / 0.534201 (-0.510521) \| 0.431458 / 0.579283 (-0.147825) \| 0.433612 / 0.434364 (-0.000752) \| 0.465615 / 0.540337 (-0.074722) \| 0.678177 / 1.386936 (-0.708759) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#998623fa51991320740b945d0853ee20807304d7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004870 / 0.011353 (-0.006483) \| 0.002834 / 0.011008 (-0.008175) \| 0.061359 / 0.038508 (0.022851) \| 0.031286 / 0.023109 (0.008177) \| 0.236701 / 0.275898 (-0.039197) \| 0.258139 / 0.323480 (-0.065341) \| 0.002943 / 0.007986 (-0.005043) \| 0.002989 / 0.004328 (-0.001339) \| 0.048046 / 0.004250 (0.043796) \| 0.044927 / 0.037052 (0.007874) \| 0.241339 / 0.258489 (-0.017151) \| 0.273912 / 0.293841 (-0.019929) \| 0.023427 / 0.128546 (-0.105119) \| 0.007251 / 0.075646 (-0.068395) \| 0.202730 / 0.419271 (-0.216542) \| 0.056223 / 0.043533 (0.012691) \| 0.239908 / 0.255139 (-0.015231) \| 0.254723 / 0.283200 (-0.028476) \| 0.018223 / 0.141683 (-0.123460) \| 1.119691 / 1.452155 (-0.332464) \| 1.163802 / 1.492716 (-0.328915) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091303 / 0.018006 (0.073297) \| 0.302097 / 0.000490 (0.301607) \| 0.000214 / 0.000200 (0.000014) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018201 / 0.037411 (-0.019210) \| 0.062092 / 0.014526 (0.047566) \| 0.074806 / 0.176557 (-0.101751) \| 0.119625 / 0.737135 (-0.617510) \| 0.074680 / 0.296338 (-0.221659) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281140 / 0.215209 (0.065931) \| 2.752094 / 2.077655 (0.674439) \| 1.436813 / 1.504120 (-0.067307) \| 1.312947 / 1.541195 (-0.228247) \| 1.331022 / 1.468490 (-0.137468) \| 0.396579 / 4.584777 (-4.188198) \| 2.406181 / 3.745712 (-1.339531) \| 2.597180 / 5.269862 (-2.672682) \| 1.565879 / 4.565676 (-2.999798) \| 0.046330 / 0.424275 (-0.377945) \| 0.004776 / 0.007607 (-0.002831) \| 0.339681 / 0.226044 (0.113637) \| 3.279533 / 2.268929 (1.010605) \| 1.793352 / 55.444624 (-53.651272) \| 1.493910 / 6.876477 (-5.382567) \| 1.514494 / 2.142072 (-0.627579) \| 0.467955 / 4.805227 (-4.337272) \| 0.097764 / 6.500664 (-6.402900) \| 0.041659 / 0.075469 (-0.033810) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.943204 / 1.841788 (-0.898583) \| 11.350848 / 8.074308 (3.276540) \| 10.169944 / 10.191392 (-0.021448) \| 0.130882 / 0.680424 (-0.549542) \| 0.013804 / 0.534201 (-0.520397) \| 0.269107 / 0.579283 (-0.310177) \| 0.261685 / 0.434364 (-0.172679) \| 0.305610 / 0.540337 (-0.234727) \| 0.430586 / 1.386936 (-0.956350) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004835 / 0.011353 (-0.006518) \| 0.002530 / 0.011008 (-0.008479) \| 0.047383 / 0.038508 (0.008875) \| 0.052559 / 0.023109 (0.029450) \| 0.265015 / 0.275898 (-0.010883) \| 0.286955 / 0.323480 (-0.036525) \| 0.003931 / 0.007986 (-0.004054) \| 0.002038 / 0.004328 (-0.002290) \| 0.047458 / 0.004250 (0.043207) \| 0.038257 / 0.037052 (0.001205) \| 0.270569 / 0.258489 (0.012080) \| 0.298968 / 0.293841 (0.005127) \| 0.024615 / 0.128546 (-0.103932) \| 0.006969 / 0.075646 (-0.068677) \| 0.052361 / 0.419271 (-0.366911) \| 0.032701 / 0.043533 (-0.010832) \| 0.269126 / 0.255139 (0.013987) \| 0.285934 / 0.283200 (0.002735) \| 0.018121 / 0.141683 (-0.123562) \| 1.129796 / 1.452155 (-0.322359) \| 1.272831 / 1.492716 (-0.219885) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092058 / 0.018006 (0.074051) \| 0.303544 / 0.000490 (0.303054) \| 0.000232 / 0.000200 (0.000032) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020983 / 0.037411 (-0.016428) \| 0.069798 / 0.014526 (0.055272) \| 0.081410 / 0.176557 (-0.095146) \| 0.120403 / 0.737135 (-0.616732) \| 0.082813 / 0.296338 (-0.213525) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295943 / 0.215209 (0.080734) \| 2.895761 / 2.077655 (0.818106) \| 1.583534 / 1.504120 (0.079414) \| 1.458397 / 1.541195 (-0.082798) \| 1.492113 / 1.468490 (0.023623) \| 0.402364 / 4.584777 (-4.182413) \| 2.469777 / 3.745712 (-1.275935) \| 2.565262 / 5.269862 (-2.704599) \| 1.525914 / 4.565676 (-3.039763) \| 0.047168 / 0.424275 (-0.377107) \| 0.004800 / 0.007607 (-0.002808) \| 0.348356 / 0.226044 (0.122311) \| 3.463184 / 2.268929 (1.194255) \| 1.930240 / 55.444624 (-53.514385) \| 1.644312 / 6.876477 (-5.232165) \| 1.625477 / 2.142072 (-0.516596) \| 0.480781 / 4.805227 (-4.324446) \| 0.098431 / 6.500664 (-6.402233) \| 0.041071 / 0.075469 (-0.034398) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.973633 / 1.841788 (-0.868154) \| 11.952261 / 8.074308 (3.877953) \| 11.038222 / 10.191392 (0.846830) \| 0.142755 / 0.680424 (-0.537669) \| 0.015389 / 0.534201 (-0.518812) \| 0.274144 / 0.579283 (-0.305139) \| 0.282319 / 0.434364 (-0.152045) \| 0.314330 / 0.540337 (-0.226007) \| 0.435315 / 1.386936 (-0.951621) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#05200c0a4f8f02c3890ab79a10b44ab0bcf11629 \"CML watermark\")\n", "The red CI job is unrelated to this PR. It appeared 5 days ago. See:\r\n- https://github.com/huggingface/datasets/pull/6390#pullrequestreview-1721070927\r\n- https://github.com/huggingface/datasets/issues/6406", "Let's do a new release once this is merged ? cc @mariosasko as well let us know if the fix sounds good to you", "@lhoestq Yes, this sounds good to me!", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004932 / 0.011353 (-0.006421) \| 0.002956 / 0.011008 (-0.008052) \| 0.061999 / 0.038508 (0.023491) \| 0.030174 / 0.023109 (0.007065) \| 0.241483 / 0.275898 (-0.034415) \| 0.261578 / 0.323480 (-0.061902) \| 0.002881 / 0.007986 (-0.005105) \| 0.002451 / 0.004328 (-0.001878) \| 0.048176 / 0.004250 (0.043925) \| 0.045028 / 0.037052 (0.007976) \| 0.244304 / 0.258489 (-0.014185) \| 0.275834 / 0.293841 (-0.018007) \| 0.023312 / 0.128546 (-0.105234) \| 0.007361 / 0.075646 (-0.068286) \| 0.204433 / 0.419271 (-0.214838) \| 0.054561 / 0.043533 (0.011028) \| 0.236902 / 0.255139 (-0.018237) \| 0.269358 / 0.283200 (-0.013842) \| 0.017736 / 0.141683 (-0.123947) \| 1.112444 / 1.452155 (-0.339711) \| 1.170260 / 1.492716 (-0.322456) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093081 / 0.018006 (0.075074) \| 0.311470 / 0.000490 (0.310981) \| 0.000212 / 0.000200 (0.000013) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018654 / 0.037411 (-0.018757) \| 0.063239 / 0.014526 (0.048714) \| 0.073759 / 0.176557 (-0.102798) \| 0.120279 / 0.737135 (-0.616857) \| 0.076214 / 0.296338 (-0.220124) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287219 / 0.215209 (0.072010) \| 2.765378 / 2.077655 (0.687723) \| 1.459733 / 1.504120 (-0.044387) \| 1.325999 / 1.541195 (-0.215196) \| 1.349957 / 1.468490 (-0.118533) \| 0.413093 / 4.584777 (-4.171684) \| 2.394758 / 3.745712 (-1.350954) \| 2.633916 / 5.269862 (-2.635945) \| 1.621629 / 4.565676 (-2.944047) \| 0.046839 / 0.424275 (-0.377436) \| 0.004786 / 0.007607 (-0.002822) \| 0.336261 / 0.226044 (0.110217) \| 3.348196 / 2.268929 (1.079267) \| 1.853050 / 55.444624 (-53.591574) \| 1.543926 / 6.876477 (-5.332551) \| 1.573675 / 2.142072 (-0.568398) \| 0.484088 / 4.805227 (-4.321139) \| 0.100820 / 6.500664 (-6.399845) \| 0.042194 / 0.075469 (-0.033275) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945186 / 1.841788 (-0.896601) \| 11.859855 / 8.074308 (3.785547) \| 10.459883 / 10.191392 (0.268491) \| 0.142024 / 0.680424 (-0.538400) \| 0.013882 / 0.534201 (-0.520319) \| 0.269584 / 0.579283 (-0.309699) \| 0.264353 / 0.434364 (-0.170011) \| 0.307988 / 0.540337 (-0.232349) \| 0.423655 / 1.386936 (-0.963281) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004891 / 0.011353 (-0.006461) \| 0.003087 / 0.011008 (-0.007921) \| 0.048206 / 0.038508 (0.009697) \| 0.058570 / 0.023109 (0.035461) \| 0.268552 / 0.275898 (-0.007346) \| 0.287839 / 0.323480 (-0.035641) \| 0.004044 / 0.007986 (-0.003942) \| 0.002388 / 0.004328 (-0.001940) \| 0.048186 / 0.004250 (0.043935) \| 0.038719 / 0.037052 (0.001667) \| 0.271940 / 0.258489 (0.013451) \| 0.299716 / 0.293841 (0.005875) \| 0.027166 / 0.128546 (-0.101380) \| 0.007388 / 0.075646 (-0.068258) \| 0.053885 / 0.419271 (-0.365387) \| 0.032804 / 0.043533 (-0.010729) \| 0.271664 / 0.255139 (0.016525) \| 0.284613 / 0.283200 (0.001414) \| 0.018488 / 0.141683 (-0.123195) \| 1.125854 / 1.452155 (-0.326301) \| 1.195896 / 1.492716 (-0.296820) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092438 / 0.018006 (0.074431) \| 0.315265 / 0.000490 (0.314775) \| 0.000228 / 0.000200 (0.000028) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021373 / 0.037411 (-0.016038) \| 0.070611 / 0.014526 (0.056085) \| 0.080391 / 0.176557 (-0.096165) \| 0.118749 / 0.737135 (-0.618386) \| 0.082340 / 0.296338 (-0.213999) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295583 / 0.215209 (0.080374) \| 2.882152 / 2.077655 (0.804497) \| 1.565088 / 1.504120 (0.060968) \| 1.451954 / 1.541195 (-0.089241) \| 1.505783 / 1.468490 (0.037293) \| 0.404699 / 4.584777 (-4.180078) \| 2.451703 / 3.745712 (-1.294009) \| 2.596301 / 5.269862 (-2.673560) \| 1.547014 / 4.565676 (-3.018662) \| 0.047750 / 0.424275 (-0.376525) \| 0.004850 / 0.007607 (-0.002757) \| 0.346893 / 0.226044 (0.120849) \| 3.383355 / 2.268929 (1.114426) \| 1.943933 / 55.444624 (-53.500692) \| 1.657513 / 6.876477 (-5.218964) \| 1.687166 / 2.142072 (-0.454906) \| 0.478543 / 4.805227 (-4.326685) \| 0.097804 / 6.500664 (-6.402860) \| 0.041392 / 0.075469 (-0.034078) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.983894 / 1.841788 (-0.857893) \| 12.446443 / 8.074308 (4.372135) \| 10.973461 / 10.191392 (0.782069) \| 0.131630 / 0.680424 (-0.548794) \| 0.017196 / 0.534201 (-0.517005) \| 0.270873 / 0.579283 (-0.308411) \| 0.284379 / 0.434364 (-0.149985) \| 0.306103 / 0.540337 (-0.234234) \| 0.413762 / 1.386936 (-0.973174) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#980ad4c6e6e33f0129db8745e84de8c298741aa2 \"CML watermark\")\n", "Note I had to add `pa.ExtensionType.__reduce__` because this is used by `copy.deepcopy` when using `.with_format`. See error below.\r\n\r\nThis method was added in pyarrow-13.0.0: https://github.com/apache/arrow/pull/36170\r\n- We need to re-implement it as long we support lower pyarrow versions\r\n\r\nErrors: https://github.com/huggingface/datasets/actions/runs/6861278161/job/18656665772\r\n```\r\n ____________________________ test_dataset_map[True] ____________________________\r\n[gw1] linux -- Python 3.8.18 /opt/hostedtoolcache/Python/3.8.18/x64/bin/python\r\n\r\n> ???\r\nE KeyError: 'extension<datasets.features.features.array3dextensiontype<array3dextensiontype>>'\r\n\r\npyarrow/types.pxi:3155: KeyError\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nwith_none = True\r\n\r\n @pytest.mark.parametrize(\"with_none\", [False, True])\r\n def test_dataset_map(with_none):\r\n ds = datasets.Dataset.from_dict({\"path\": [\"path1\", \"path2\"]})\r\n \r\n def process_data(batch):\r\n batch = {\r\n \"image\": [\r\n np.array(\r\n [\r\n [[1, 2, 3], [4, 5, 6], [7, 8, 9]],\r\n [[10, 20, 30], [40, 50, 60], [70, 80, 90]],\r\n [[100, 200, 300], [400, 500, 600], [700, 800, 900]],\r\n ]\r\n )\r\n for _ in batch[\"path\"]\r\n ]\r\n }\r\n if with_none:\r\n batch[\"image\"][0] = None\r\n return batch\r\n \r\n features = datasets.Features({\"image\": Array3D(dtype=\"int32\", shape=(3, 3, 3))})\r\n processed_ds = ds.map(process_data, batched=True, remove_columns=ds.column_names, features=features)\r\n assert processed_ds.shape == (2, 1)\r\n> with processed_ds.with_format(\"numpy\") as pds:\r\n\r\ntests/features/test_array_xd.py:459: \r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/arrow_dataset.py:2669: in with_format\r\n dataset = copy.deepcopy(self)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:172: in deepcopy\r\n y = _reconstruct(x, memo, rv)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:270: in _reconstruct\r\n state = deepcopy(state, memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:146: in deepcopy\r\n y = copier(x, memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:230: in _deepcopy_dict\r\n y[deepcopy(key, memo)] = deepcopy(value, memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:153: in deepcopy\r\n y = copier(memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/table.py:188: in __deepcopy__\r\n return _deepcopy(self, memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/table.py:86: in _deepcopy\r\n setattr(result, k, copy.deepcopy(v, memo))\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:172: in deepcopy\r\n y = _reconstruct(x, memo, rv)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:264: in _reconstruct\r\n y = func(args)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:263: in <genexpr>\r\n args = (deepcopy(arg, memo) for arg in args)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:146: in deepcopy\r\n y = copier(x, memo)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:205: in _deepcopy_list\r\n append(deepcopy(a, memo))\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:172: in deepcopy\r\n y = _reconstruct(x, memo, rv)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:264: in _reconstruct\r\n y = func(args)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:263: in <genexpr>\r\n args = (deepcopy(arg, memo) for arg in args)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:172: in deepcopy\r\n y = _reconstruct(x, memo, rv)\r\n/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/copy.py:264: in _reconstruct\r\n y = func(*args)\r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\n\r\n> ???\r\nE ValueError: No type alias for extension<datasets.features.features.array3dextensiontype<array3dextensiontype>>\r\n\r\npyarrow/types.pxi:3157: ValueError\r\n```\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_class_encode_column_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_dummy_dataset_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_tf_dataset_conversion_in_memory - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_tf_dataset_conversion_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_tf_dataset_options_in_memory - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_tf_dataset_options_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_to_csv_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_to_parquet_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_to_sql_on_disk - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::test_map_cases[True] - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::test_map_cases[False] - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/test_arrow_dataset.py::test_map_cases[mix] - ValueError: No type alias for extension<datasets.features.features.array2dextensiontype<array2dextensiontype>>\r\nFAILED tests/features/test_array_xd.py::ArrayXDDynamicTest::test_map_dataset - ValueError: No type alias for extension<datasets.features.features.array3dextensiontype<array3dextensiontype>>\r\nFAILED tests/features/test_array_xd.py::test_dataset_map[False] - ValueError: No type alias for extension<datasets.features.features.array3dextensiontype<array3dextensiontype>>\r\nFAILED tests/features/test_array_xd.py::test_dataset_map[True] - ValueError: No type alias for extension<datasets.features.features.array3dextensiontype<array3dextensiontype>>\r\n===== 15 failed,\r\n```", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007338 / 0.011353 (-0.004015) \| 0.004308 / 0.011008 (-0.006700) \| 0.088788 / 0.038508 (0.050280) \| 0.039369 / 0.023109 (0.016260) \| 0.334527 / 0.275898 (0.058629) \| 0.373748 / 0.323480 (0.050268) \| 0.005550 / 0.007986 (-0.002435) \| 0.003606 / 0.004328 (-0.000723) \| 0.072238 / 0.004250 (0.067988) \| 0.061271 / 0.037052 (0.024218) \| 0.336333 / 0.258489 (0.077844) \| 0.398256 / 0.293841 (0.104415) \| 0.041941 / 0.128546 (-0.086605) \| 0.013372 / 0.075646 (-0.062274) \| 0.336221 / 0.419271 (-0.083050) \| 0.083013 / 0.043533 (0.039480) \| 0.334743 / 0.255139 (0.079604) \| 0.362572 / 0.283200 (0.079373) \| 0.031161 / 0.141683 (-0.110521) \| 1.563441 / 1.452155 (0.111287) \| 1.704059 / 1.492716 (0.211343) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.252978 / 0.018006 (0.234972) \| 0.506348 / 0.000490 (0.505859) \| 0.011679 / 0.000200 (0.011479) \| 0.000104 / 0.000054 (0.000049) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026257 / 0.037411 (-0.011154) \| 0.085936 / 0.014526 (0.071410) \| 0.098542 / 0.176557 (-0.078015) \| 0.154507 / 0.737135 (-0.582628) \| 0.111493 / 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) \| 0.575941 / 0.215209 (0.360732) \| 5.590230 / 2.077655 (3.512576) \| 2.463330 / 1.504120 (0.959211) \| 2.125760 / 1.541195 (0.584565) \| 2.095933 / 1.468490 (0.627443) \| 0.844768 / 4.584777 (-3.740009) \| 4.768995 / 3.745712 (1.023282) \| 4.670484 / 5.269862 (-0.599377) \| 2.630386 / 4.565676 (-1.935290) \| 0.085996 / 0.424275 (-0.338279) \| 0.007900 / 0.007607 (0.000293) \| 0.685463 / 0.226044 (0.459419) \| 6.699310 / 2.268929 (4.430381) \| 3.132542 / 55.444624 (-52.312083) \| 2.527963 / 6.876477 (-4.348513) \| 2.381835 / 2.142072 (0.239763) \| 0.909668 / 4.805227 (-3.895559) \| 0.209979 / 6.500664 (-6.290685) \| 0.079222 / 0.075469 (0.003753) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.444895 / 1.841788 (-0.396892) \| 20.388140 / 8.074308 (12.313832) \| 19.354148 / 10.191392 (9.162756) \| 0.222433 / 0.680424 (-0.457991) \| 0.029710 / 0.534201 (-0.504491) \| 0.427153 / 0.579283 (-0.152130) \| 0.537500 / 0.434364 (0.103136) \| 0.506917 / 0.540337 (-0.033421) \| 0.726088 / 1.386936 (-0.660848) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007652 / 0.011353 (-0.003701) \| 0.004320 / 0.011008 (-0.006688) \| 0.072721 / 0.038508 (0.034212) \| 0.068204 / 0.023109 (0.045095) \| 0.392087 / 0.275898 (0.116189) \| 0.431638 / 0.323480 (0.108158) \| 0.005419 / 0.007986 (-0.002566) \| 0.004305 / 0.004328 (-0.000023) \| 0.069042 / 0.004250 (0.064791) \| 0.051555 / 0.037052 (0.014503) \| 0.412141 / 0.258489 (0.153651) \| 0.438802 / 0.293841 (0.144961) \| 0.043631 / 0.128546 (-0.084915) \| 0.014169 / 0.075646 (-0.061478) \| 0.079571 / 0.419271 (-0.339701) \| 0.056707 / 0.043533 (0.013174) \| 0.413698 / 0.255139 (0.158559) \| 0.414127 / 0.283200 (0.130928) \| 0.031380 / 0.141683 (-0.110303) \| 1.677157 / 1.452155 (0.225003) \| 1.755155 / 1.492716 (0.262439) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.257236 / 0.018006 (0.239230) \| 0.521347 / 0.000490 (0.520858) \| 0.006282 / 0.000200 (0.006082) \| 0.000139 / 0.000054 (0.000085) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028433 / 0.037411 (-0.008978) \| 0.087698 / 0.014526 (0.073172) \| 0.108840 / 0.176557 (-0.067716) \| 0.157432 / 0.737135 (-0.579704) \| 0.103144 / 0.296338 (-0.193195) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.598745 / 0.215209 (0.383536) \| 5.981460 / 2.077655 (3.903805) \| 2.556931 / 1.504120 (1.052811) \| 2.179915 / 1.541195 (0.638720) \| 2.240841 / 1.468490 (0.772351) \| 0.811501 / 4.584777 (-3.773276) \| 4.718282 / 3.745712 (0.972570) \| 4.365738 / 5.269862 (-0.904124) \| 2.669798 / 4.565676 (-1.895878) \| 0.099135 / 0.424275 (-0.325140) \| 0.007369 / 0.007607 (-0.000238) \| 0.669491 / 0.226044 (0.443447) \| 6.700389 / 2.268929 (4.431461) \| 3.155328 / 55.444624 (-52.289296) \| 2.563375 / 6.876477 (-4.313102) \| 2.545191 / 2.142072 (0.403119) \| 0.961359 / 4.805227 (-3.843868) \| 0.189391 / 6.500664 (-6.311273) \| 0.061597 / 0.075469 (-0.013873) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.564008 / 1.841788 (-0.277780) \| 21.401307 / 8.074308 (13.326999) \| 20.693441 / 10.191392 (10.502049) \| 0.229340 / 0.680424 (-0.451084) \| 0.033637 / 0.534201 (-0.500564) \| 0.429394 / 0.579283 (-0.149889) \| 0.557202 / 0.434364 (0.122838) \| 0.510284 / 0.540337 (-0.030054) \| 0.725661 / 1.386936 (-0.661276) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#45abe297c178b829afcee853f9958b0c5a6469aa \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004820 / 0.011353 (-0.006533) \| 0.003152 / 0.011008 (-0.007856) \| 0.061842 / 0.038508 (0.023334) \| 0.030127 / 0.023109 (0.007018) \| 0.257409 / 0.275898 (-0.018489) \| 0.269382 / 0.323480 (-0.054097) \| 0.004288 / 0.007986 (-0.003698) \| 0.002500 / 0.004328 (-0.001829) \| 0.048520 / 0.004250 (0.044270) \| 0.046815 / 0.037052 (0.009763) \| 0.245858 / 0.258489 (-0.012631) \| 0.289636 / 0.293841 (-0.004205) \| 0.023983 / 0.128546 (-0.104563) \| 0.007336 / 0.075646 (-0.068310) \| 0.202347 / 0.419271 (-0.216924) \| 0.057737 / 0.043533 (0.014204) \| 0.245922 / 0.255139 (-0.009217) \| 0.268788 / 0.283200 (-0.014412) \| 0.017819 / 0.141683 (-0.123864) \| 1.149889 / 1.452155 (-0.302265) \| 1.227192 / 1.492716 (-0.265524) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092234 / 0.018006 (0.074228) \| 0.310259 / 0.000490 (0.309769) \| 0.000223 / 0.000200 (0.000023) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019059 / 0.037411 (-0.018352) \| 0.064904 / 0.014526 (0.050378) \| 0.073531 / 0.176557 (-0.103026) \| 0.120879 / 0.737135 (-0.616257) \| 0.075410 / 0.296338 (-0.220929) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275364 / 0.215209 (0.060155) \| 2.724379 / 2.077655 (0.646725) \| 1.447617 / 1.504120 (-0.056503) \| 1.366794 / 1.541195 (-0.174401) \| 1.345849 / 1.468490 (-0.122641) \| 0.411205 / 4.584777 (-4.173572) \| 2.412712 / 3.745712 (-1.333000) \| 2.612469 / 5.269862 (-2.657393) \| 1.552113 / 4.565676 (-3.013564) \| 0.045783 / 0.424275 (-0.378492) \| 0.004782 / 0.007607 (-0.002825) \| 0.339218 / 0.226044 (0.113174) \| 3.359540 / 2.268929 (1.090612) \| 1.821369 / 55.444624 (-53.623256) \| 1.540742 / 6.876477 (-5.335734) \| 1.531845 / 2.142072 (-0.610227) \| 0.462009 / 4.805227 (-4.343218) \| 0.097794 / 6.500664 (-6.402870) \| 0.041222 / 0.075469 (-0.034247) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.938319 / 1.841788 (-0.903469) \| 11.712003 / 8.074308 (3.637695) \| 10.325317 / 10.191392 (0.133925) \| 0.126812 / 0.680424 (-0.553612) \| 0.013734 / 0.534201 (-0.520467) \| 0.279509 / 0.579283 (-0.299774) \| 0.269265 / 0.434364 (-0.165099) \| 0.322033 / 0.540337 (-0.218304) \| 0.441610 / 1.386936 (-0.945326) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004882 / 0.011353 (-0.006471) \| 0.002984 / 0.011008 (-0.008024) \| 0.048318 / 0.038508 (0.009810) \| 0.054642 / 0.023109 (0.031533) \| 0.268599 / 0.275898 (-0.007299) \| 0.292916 / 0.323480 (-0.030564) \| 0.004108 / 0.007986 (-0.003878) \| 0.002500 / 0.004328 (-0.001829) \| 0.048452 / 0.004250 (0.044202) \| 0.038835 / 0.037052 (0.001782) \| 0.275410 / 0.258489 (0.016921) \| 0.307284 / 0.293841 (0.013443) \| 0.024720 / 0.128546 (-0.103826) \| 0.007274 / 0.075646 (-0.068372) \| 0.054419 / 0.419271 (-0.364853) \| 0.032815 / 0.043533 (-0.010718) \| 0.273660 / 0.255139 (0.018521) \| 0.289183 / 0.283200 (0.005984) \| 0.017746 / 0.141683 (-0.123937) \| 1.153876 / 1.452155 (-0.298278) \| 1.212778 / 1.492716 (-0.279938) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095286 / 0.018006 (0.077280) \| 0.305185 / 0.000490 (0.304696) \| 0.000230 / 0.000200 (0.000030) \| 0.000054 / 0.000054 (-0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021556 / 0.037411 (-0.015855) \| 0.071029 / 0.014526 (0.056503) \| 0.081914 / 0.176557 (-0.094643) \| 0.120553 / 0.737135 (-0.616582) \| 0.086696 / 0.296338 (-0.209642) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289750 / 0.215209 (0.074541) \| 2.794247 / 2.077655 (0.716592) \| 1.577105 / 1.504120 (0.072985) \| 1.457706 / 1.541195 (-0.083489) \| 1.500481 / 1.468490 (0.031991) \| 0.403834 / 4.584777 (-4.180943) \| 2.466810 / 3.745712 (-1.278902) \| 2.701008 / 5.269862 (-2.568854) \| 1.634821 / 4.565676 (-2.930856) \| 0.046954 / 0.424275 (-0.377322) \| 0.004811 / 0.007607 (-0.002796) \| 0.347622 / 0.226044 (0.121578) \| 3.407125 / 2.268929 (1.138197) \| 1.987121 / 55.444624 (-53.457504) \| 1.689978 / 6.876477 (-5.186499) \| 1.731801 / 2.142072 (-0.410271) \| 0.478926 / 4.805227 (-4.326301) \| 0.100730 / 6.500664 (-6.399934) \| 0.043078 / 0.075469 (-0.032391) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.963575 / 1.841788 (-0.878212) \| 12.675331 / 8.074308 (4.601023) \| 11.167584 / 10.191392 (0.976192) \| 0.131199 / 0.680424 (-0.549225) \| 0.016030 / 0.534201 (-0.518171) \| 0.277783 / 0.579283 (-0.301500) \| 0.278693 / 0.434364 (-0.155671) \| 0.315141 / 0.540337 (-0.225196) \| 0.429104 / 1.386936 (-0.957832) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#825c1d25835b64fc3533a63d60bd237f4465f15e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004807 / 0.011353 (-0.006546) \| 0.002925 / 0.011008 (-0.008083) \| 0.062560 / 0.038508 (0.024052) \| 0.029926 / 0.023109 (0.006817) \| 0.264708 / 0.275898 (-0.011190) \| 0.273464 / 0.323480 (-0.050016) \| 0.003197 / 0.007986 (-0.004788) \| 0.002544 / 0.004328 (-0.001784) \| 0.048230 / 0.004250 (0.043980) \| 0.046552 / 0.037052 (0.009500) \| 0.249553 / 0.258489 (-0.008936) \| 0.282078 / 0.293841 (-0.011762) \| 0.023201 / 0.128546 (-0.105346) \| 0.007306 / 0.075646 (-0.068340) \| 0.241361 / 0.419271 (-0.177910) \| 0.058286 / 0.043533 (0.014753) \| 0.245854 / 0.255139 (-0.009285) \| 0.266053 / 0.283200 (-0.017146) \| 0.020294 / 0.141683 (-0.121388) \| 1.102215 / 1.452155 (-0.349939) \| 1.170733 / 1.492716 (-0.321984) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094647 / 0.018006 (0.076641) \| 0.303819 / 0.000490 (0.303329) \| 0.000250 / 0.000200 (0.000050) \| 0.000055 / 0.000054 (0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019036 / 0.037411 (-0.018375) \| 0.064729 / 0.014526 (0.050203) \| 0.074143 / 0.176557 (-0.102414) \| 0.120082 / 0.737135 (-0.617054) \| 0.076835 / 0.296338 (-0.219503) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283786 / 0.215209 (0.068577) \| 2.751446 / 2.077655 (0.673791) \| 1.473789 / 1.504120 (-0.030331) \| 1.336968 / 1.541195 (-0.204226) \| 1.384148 / 1.468490 (-0.084342) \| 0.397452 / 4.584777 (-4.187325) \| 2.388042 / 3.745712 (-1.357670) \| 2.661291 / 5.269862 (-2.608571) \| 1.595454 / 4.565676 (-2.970223) \| 0.045919 / 0.424275 (-0.378356) \| 0.004879 / 0.007607 (-0.002728) \| 0.337862 / 0.226044 (0.111818) \| 3.355665 / 2.268929 (1.086737) \| 1.875261 / 55.444624 (-53.569363) \| 1.540874 / 6.876477 (-5.335603) \| 1.653632 / 2.142072 (-0.488440) \| 0.473090 / 4.805227 (-4.332138) \| 0.100151 / 6.500664 (-6.400513) \| 0.042357 / 0.075469 (-0.033112) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.959550 / 1.841788 (-0.882238) \| 12.307145 / 8.074308 (4.232837) \| 10.719321 / 10.191392 (0.527929) \| 0.128376 / 0.680424 (-0.552048) \| 0.014406 / 0.534201 (-0.519795) \| 0.295208 / 0.579283 (-0.284075) \| 0.268891 / 0.434364 (-0.165473) \| 0.305446 / 0.540337 (-0.234892) \| 0.429591 / 1.386936 (-0.957345) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005189 / 0.011353 (-0.006164) \| 0.003082 / 0.011008 (-0.007926) \| 0.048956 / 0.038508 (0.010448) \| 0.063403 / 0.023109 (0.040294) \| 0.272858 / 0.275898 (-0.003040) \| 0.295207 / 0.323480 (-0.028273) \| 0.004253 / 0.007986 (-0.003733) \| 0.002552 / 0.004328 (-0.001776) \| 0.048042 / 0.004250 (0.043792) \| 0.040429 / 0.037052 (0.003377) \| 0.269614 / 0.258489 (0.011125) \| 0.307205 / 0.293841 (0.013364) \| 0.027912 / 0.128546 (-0.100634) \| 0.007621 / 0.075646 (-0.068026) \| 0.054020 / 0.419271 (-0.365251) \| 0.036958 / 0.043533 (-0.006574) \| 0.272457 / 0.255139 (0.017318) \| 0.287966 / 0.283200 (0.004766) \| 0.019542 / 0.141683 (-0.122141) \| 1.116742 / 1.452155 (-0.335413) \| 1.194739 / 1.492716 (-0.297977) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093532 / 0.018006 (0.075526) \| 0.303262 / 0.000490 (0.302773) \| 0.000217 / 0.000200 (0.000017) \| 0.000042 / 0.000054 (-0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021984 / 0.037411 (-0.015428) \| 0.075024 / 0.014526 (0.060498) \| 0.080959 / 0.176557 (-0.095598) \| 0.121780 / 0.737135 (-0.615356) \| 0.082817 / 0.296338 (-0.213522) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292766 / 0.215209 (0.077557) \| 2.857457 / 2.077655 (0.779802) \| 1.621860 / 1.504120 (0.117740) \| 1.473783 / 1.541195 (-0.067412) \| 1.535211 / 1.468490 (0.066721) \| 0.402212 / 4.584777 (-4.182565) \| 2.467143 / 3.745712 (-1.278569) \| 2.618162 / 5.269862 (-2.651700) \| 1.568682 / 4.565676 (-2.996994) \| 0.047123 / 0.424275 (-0.377152) \| 0.004780 / 0.007607 (-0.002827) \| 0.346959 / 0.226044 (0.120914) \| 3.395196 / 2.268929 (1.126268) \| 1.957835 / 55.444624 (-53.486789) \| 1.674287 / 6.876477 (-5.202190) \| 1.715879 / 2.142072 (-0.426193) \| 0.479481 / 4.805227 (-4.325746) \| 0.100043 / 6.500664 (-6.400621) \| 0.041289 / 0.075469 (-0.034180) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.965418 / 1.841788 (-0.876370) \| 12.703830 / 8.074308 (4.629522) \| 11.301401 / 10.191392 (1.110009) \| 0.131429 / 0.680424 (-0.548995) \| 0.016597 / 0.534201 (-0.517604) \| 0.273290 / 0.579283 (-0.305993) \| 0.285400 / 0.434364 (-0.148964) \| 0.307327 / 0.540337 (-0.233011) \| 0.434186 / 1.386936 (-0.952750) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c096bd288d07ed86f340ae090e5d4d9c5351f76f \"CML watermark\")\n" ]
1,990,098,817	6,403	Cannot import datasets on google colab (python 3.10.12)	closed	2023-11-13T08:14:43	2023-11-16T05:04:22	2023-11-16T05:04:21	https://github.com/huggingface/datasets/issues/6403	null	nabilaannisa	false	[ "You are most likely using an outdated version of `datasets` in the notebook, which can be verified with the `!datasets-cli env` command. You can run `!pip install -U datasets` to update the installation.", "okay, it works! thank you so much! 😄 " ]
1,989,094,542	6,402	Update torch_formatter.py	closed	2023-11-11T19:40:41	2024-03-15T11:31:53	2024-03-15T11:25:37	https://github.com/huggingface/datasets/pull/6402	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6402", "html_url": "https://github.com/huggingface/datasets/pull/6402", "diff_url": "https://github.com/huggingface/datasets/pull/6402.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6402.patch", "merged_at": "2024-03-15T11:25:36" }	varunneal	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6402). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005391 / 0.011353 (-0.005962) \| 0.003908 / 0.011008 (-0.007100) \| 0.064342 / 0.038508 (0.025834) \| 0.031385 / 0.023109 (0.008275) \| 0.251869 / 0.275898 (-0.024030) \| 0.294875 / 0.323480 (-0.028605) \| 0.003053 / 0.007986 (-0.004933) \| 0.002881 / 0.004328 (-0.001448) \| 0.050072 / 0.004250 (0.045822) \| 0.044463 / 0.037052 (0.007411) \| 0.264646 / 0.258489 (0.006157) \| 0.296024 / 0.293841 (0.002183) \| 0.027832 / 0.128546 (-0.100714) \| 0.010937 / 0.075646 (-0.064710) \| 0.207799 / 0.419271 (-0.211472) \| 0.036423 / 0.043533 (-0.007110) \| 0.251022 / 0.255139 (-0.004117) \| 0.271366 / 0.283200 (-0.011833) \| 0.019780 / 0.141683 (-0.121903) \| 1.149634 / 1.452155 (-0.302521) \| 1.196476 / 1.492716 (-0.296240) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094857 / 0.018006 (0.076850) \| 0.312159 / 0.000490 (0.311669) \| 0.000215 / 0.000200 (0.000015) \| 0.000042 / 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) \| 0.018858 / 0.037411 (-0.018553) \| 0.062022 / 0.014526 (0.047496) \| 0.078302 / 0.176557 (-0.098255) \| 0.122199 / 0.737135 (-0.614936) \| 0.076044 / 0.296338 (-0.220294) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286414 / 0.215209 (0.071205) \| 2.785446 / 2.077655 (0.707791) \| 1.438248 / 1.504120 (-0.065872) \| 1.307558 / 1.541195 (-0.233636) \| 1.337172 / 1.468490 (-0.131318) \| 0.582228 / 4.584777 (-4.002549) \| 2.457207 / 3.745712 (-1.288505) \| 2.906692 / 5.269862 (-2.363169) \| 1.833020 / 4.565676 (-2.732656) \| 0.063549 / 0.424275 (-0.360726) \| 0.005080 / 0.007607 (-0.002527) \| 0.333178 / 0.226044 (0.107133) \| 3.332463 / 2.268929 (1.063534) \| 1.797209 / 55.444624 (-53.647415) \| 1.514446 / 6.876477 (-5.362031) \| 1.601252 / 2.142072 (-0.540820) \| 0.664933 / 4.805227 (-4.140294) \| 0.120140 / 6.500664 (-6.380524) \| 0.042769 / 0.075469 (-0.032700) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.983738 / 1.841788 (-0.858050) \| 11.996685 / 8.074308 (3.922376) \| 9.594757 / 10.191392 (-0.596635) \| 0.146680 / 0.680424 (-0.533744) \| 0.014455 / 0.534201 (-0.519746) \| 0.292546 / 0.579283 (-0.286737) \| 0.270381 / 0.434364 (-0.163983) \| 0.326759 / 0.540337 (-0.213579) \| 0.423387 / 1.386936 (-0.963549) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005659 / 0.011353 (-0.005694) \| 0.004841 / 0.011008 (-0.006167) \| 0.049598 / 0.038508 (0.011090) \| 0.031796 / 0.023109 (0.008687) \| 0.265967 / 0.275898 (-0.009931) \| 0.287648 / 0.323480 (-0.035832) \| 0.004264 / 0.007986 (-0.003721) \| 0.002759 / 0.004328 (-0.001569) \| 0.049249 / 0.004250 (0.044999) \| 0.049526 / 0.037052 (0.012474) \| 0.357765 / 0.258489 (0.099276) \| 0.307563 / 0.293841 (0.013722) \| 0.030329 / 0.128546 (-0.098217) \| 0.010536 / 0.075646 (-0.065111) \| 0.057547 / 0.419271 (-0.361724) \| 0.059874 / 0.043533 (0.016341) \| 0.264946 / 0.255139 (0.009807) \| 0.283666 / 0.283200 (0.000466) \| 0.021005 / 0.141683 (-0.120677) \| 1.170959 / 1.452155 (-0.281195) \| 1.206232 / 1.492716 (-0.286484) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097567 / 0.018006 (0.079561) \| 0.308585 / 0.000490 (0.308095) \| 0.000220 / 0.000200 (0.000020) \| 0.000052 / 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) \| 0.023267 / 0.037411 (-0.014144) \| 0.075896 / 0.014526 (0.061370) \| 0.087341 / 0.176557 (-0.089216) \| 0.130270 / 0.737135 (-0.606866) \| 0.091086 / 0.296338 (-0.205252) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298728 / 0.215209 (0.083519) \| 2.876498 / 2.077655 (0.798843) \| 1.591584 / 1.504120 (0.087464) \| 1.461251 / 1.541195 (-0.079944) \| 1.483276 / 1.468490 (0.014786) \| 0.572344 / 4.584777 (-4.012433) \| 2.474050 / 3.745712 (-1.271662) \| 2.707051 / 5.269862 (-2.562811) \| 1.759210 / 4.565676 (-2.806466) \| 0.063420 / 0.424275 (-0.360855) \| 0.005014 / 0.007607 (-0.002593) \| 0.344490 / 0.226044 (0.118446) \| 3.411065 / 2.268929 (1.142137) \| 1.937232 / 55.444624 (-53.507392) \| 1.665826 / 6.876477 (-5.210650) \| 1.824377 / 2.142072 (-0.317696) \| 0.631630 / 4.805227 (-4.173597) \| 0.115791 / 6.500664 (-6.384873) \| 0.040846 / 0.075469 (-0.034623) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.015248 / 1.841788 (-0.826540) \| 12.738696 / 8.074308 (4.664388) \| 10.324303 / 10.191392 (0.132911) \| 0.153597 / 0.680424 (-0.526827) \| 0.015396 / 0.534201 (-0.518805) \| 0.287160 / 0.579283 (-0.292123) \| 0.279886 / 0.434364 (-0.154478) \| 0.324128 / 0.540337 (-0.216210) \| 0.456089 / 1.386936 (-0.930847) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a02997de6e990eb78dccd905f05e12404a919156 \"CML watermark\")\n" ]
1,988,710,061	6,401	dataset = load_dataset("Hyperspace-Technologies/scp-wiki-text") not working	closed	2023-11-11T04:09:07	2023-11-20T17:45:20	2023-11-20T17:45:20	https://github.com/huggingface/datasets/issues/6401	null	userbox020	false	[ "Seems like it's a problem with the dataset, since in the [README](https://huggingface.co/datasets/Hyperspace-Technologies/scp-wiki-text/blob/main/README.md) the validation is not specified. Try cloning the dataset, removing the README (or validation split), and loading it locally/ ", "@VarunNSrivastava thanks brother, working beautiful now\r\n\r\n```\r\nC:\\_Work\\_datasets>py dataset.py\r\nDownloading data files: 100%\|████████████████████████████████████████████████████████████████████\| 3/3 [00:00<?, ?it/s]\r\nExtracting data files: 100%\|████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 599.90it/s]\r\nGenerating train split: 314294 examples [00:00, 1293222.03 examples/s]\r\nGenerating validation split: 120 examples [00:00, 59053.91 examples/s]\r\nGenerating test split: 34922 examples [00:00, 1343275.84 examples/s]\r\n```" ]

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