Split (1)

train · 1.5k rows

id int64 1.9B 3.25B	title stringlengths 2 244	state stringclasses 2 values	body stringlengths 3 58.6k ⌀	created_at timestamp[s]date 2023-09-15 14:23:33 2025-07-22 09:33:54	updated_at timestamp[s]date 2023-09-18 16:20:09 2025-07-22 10:44:03	closed_at timestamp[s]date 2023-09-18 16:20:09 2025-07-19 22:45:08 ⌀	html_url stringlengths 49 51	pull_request dict	number int64 6.24k 7.7k	is_pull_request bool 2 classes	comments listlengths 0 24
2,009,491,386	Support multiple image/audio columns in ImageFolder/AudioFolder	closed	### Feature request Have a metadata.csv file with multiple columns that point to relative image or audio files. ### Motivation Currently, ImageFolder allows one column, called `file_name`, pointing to relative image files. On the same model, AudioFolder allows one column, called `file_name`, pointing to relative audio files. But it's not possible to have two image columns, or to have two audio column, or to have one audio column and one image column. ### Your contribution no specific contribution	2023-11-24T10:34:09	2023-11-28T11:07:17	2023-11-24T17:24:38	https://github.com/huggingface/datasets/issues/6450	null	6,450	false	[ "A duplicate of https://github.com/huggingface/datasets/issues/5760" ]
2,008,617,992	Fix metadata file resolution when inferred pattern is `**`	closed	Refetch metadata files in case they were dropped by `filter_extensions` in the previous step. Fix #6442	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" }	6,449	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	Use parquet export if possible	closed	The idea is to make this code work for datasets with scripts if they have a Parquet export ```python ds = load_dataset("squad", trust_remote_code=False) ``` And more generally, it means we use the Parquet export whenever it's possible (it's safer and faster than dataset scripts). I also added a `config.USE_PARQUET_EXPORT` variable to use in the datasets-server parquet conversion job - [x] Needs https://github.com/huggingface/datasets/pull/6429 to be merged first cc @severo I use the /parquet and /info endpoints from datasets-server	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" }	6,448	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	Support one dataset loader per config when using YAML	open	### Feature request See https://huggingface.co/datasets/datasets-examples/doc-unsupported-1 I would like to use CSV loader for the "csv" config, JSONL loader for the "jsonl" config, etc. ### Motivation It would be more flexible for the users ### Your contribution No specific contribution	2023-11-23T13:03:07	2023-11-23T13:03:07	null	https://github.com/huggingface/datasets/issues/6447	null	6,447	false	[]
2,007,092,708	Speech Commands v2 dataset doesn't match AST-v2 config	closed	### Describe the bug [According](https://huggingface.co/MIT/ast-finetuned-speech-commands-v2) to `MIT/ast-finetuned-speech-commands-v2`, the model was trained on the Speech Commands v2 dataset. However, while the model config says the model should have 35 class labels, the dataset itself has 36 class labels. Moreover, the class labels themselves don't match between the model config and the dataset. It is difficult to reproduce the data used to fine tune `MIT/ast-finetuned-speech-commands-v2`. ### Steps to reproduce the bug ``` >>> model = ASTForAudioClassification.from_pretrained("MIT/ast-finetuned-speech-commands-v2") >>> model.config.id2label {0: 'backward', 1: 'follow', 2: 'five', 3: 'bed', 4: 'zero', 5: 'on', 6: 'learn', 7: 'two', 8: 'house', 9: 'tree', 10: 'dog', 11: 'stop', 12: 'seven', 13: 'eight', 14: 'down', 15: 'six', 16: 'forward', 17: 'cat', 18: 'right', 19: 'visual', 20: 'four', 21: 'wow', 22: 'no', 23: 'nine', 24: 'off', 25: 'three', 26: 'left', 27: 'marvin', 28: 'yes', 29: 'up', 30: 'sheila', 31: 'happy', 32: 'bird', 33: 'go', 34: 'one'} >>> dataset = load_dataset("speech_commands", "v0.02", split="test") >>> torch.unique(torch.Tensor(dataset['label'])) tensor([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35.]) ``` If you try to explore the [dataset itself](https://huggingface.co/datasets/speech_commands/viewer/v0.02/test), you can see that the id to label does not match what is provided by `model.config.id2label`. ### Expected behavior The labels should match completely and there should be the same number of label classes between the model config and the dataset itself. ### Environment info datasets = 2.14.6, transformers = 4.33.3	2023-11-22T20:46:36	2023-11-28T14:46:08	2023-11-28T14:46:08	https://github.com/huggingface/datasets/issues/6446	null	6,446	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	Use `filelock` package for file locking	closed	Use the `filelock` package instead of `datasets.utils.filelock` for file locking to be consistent with `huggingface_hub` and not to be responsible for improving the `filelock` capabilities 🙂. (Reverts https://github.com/huggingface/datasets/pull/859, but these `INFO` logs are not printed by default (anymore?), so this should be okay)	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" }	6,445	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	Remove `Table.__getstate__` and `Table.__setstate__`	closed	When using distributed training, the code of `os.remove(filename)` may be executed separately by each rank, leading to `FileNotFoundError: [Errno 2] No such file or directory: '/tmp/tmprxxxxxxx.arrow'` ```python from torch import distributed as dist if dist.get_rank() == 0: dataset = process_dataset(args, *kwargs) objects = [dataset] else: objects = [None] dist.broadcast_object_list(objects, src=0) dataset = objects[0] ```	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" }	6,444	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	Trouble loading files defined in YAML explicitly	open	Look at https://huggingface.co/datasets/severo/doc-yaml-2 It's a reproduction of the example given in the docs at https://huggingface.co/docs/hub/datasets-manual-configuration ``` You can select multiple files per split using a list of paths: my_dataset_repository/ ├── README.md ├── data/ │ ├── abc.csv │ └── def.csv └── holdout/ └── ghi.csv --- configs: - config_name: default data_files: - split: train path: - "data/abc.csv" - "data/def.csv" - split: test path: "holdout/ghi.csv" --- ``` It raises the following error: ``` Error code: ConfigNamesError Exception: FileNotFoundError Message: Couldn't find a dataset script at /src/services/worker/severo/doc-yaml-2/doc-yaml-2.py or any data file in the same directory. Couldn't find 'severo/doc-yaml-2' on the Hugging Face Hub either: FileNotFoundError: Unable to find 'hf://datasets/severo/doc-yaml-2@938a0578fb4c6bc9da7d80b06a3ba39c2834b0c2/data/def.csv' with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.arrow', '.txt', '.blp', '.bmp', '.dib', '.bufr', '.cur', '.pcx', '.dcx', '.dds', '.ps', '.eps', '.fit', '.fits', '.fli', '.flc', '.ftc', '.ftu', '.gbr', '.gif', '.grib', '.h5', '.hdf', '.png', '.apng', '.jp2', '.j2k', '.jpc', '.jpf', '.jpx', '.j2c', '.icns', '.ico', '.im', '.iim', '.tif', '.tiff', '.jfif', '.jpe', '.jpg', '.jpeg', '.mpg', '.mpeg', '.msp', '.pcd', '.pxr', '.pbm', '.pgm', '.ppm', '.pnm', '.psd', '.bw', '.rgb', '.rgba', '.sgi', '.ras', '.tga', '.icb', '.vda', '.vst', '.webp', '.wmf', '.emf', '.xbm', '.xpm', '.BLP', '.BMP', '.DIB', '.BUFR', '.CUR', '.PCX', '.DCX', '.DDS', '.PS', '.EPS', '.FIT', '.FITS', '.FLI', '.FLC', '.FTC', '.FTU', '.GBR', '.GIF', '.GRIB', '.H5', '.HDF', '.PNG', '.APNG', '.JP2', '.J2K', '.JPC', '.JPF', '.JPX', '.J2C', '.ICNS', '.ICO', '.IM', '.IIM', '.TIF', '.TIFF', '.JFIF', '.JPE', '.JPG', '.JPEG', '.MPG', '.MPEG', '.MSP', '.PCD', '.PXR', '.PBM', '.PGM', '.PPM', '.PNM', '.PSD', '.BW', '.RGB', '.RGBA', '.SGI', '.RAS', '.TGA', '.ICB', '.VDA', '.VST', '.WEBP', '.WMF', '.EMF', '.XBM', '.XPM', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip'] Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 65, in compute_config_names_response for config in sorted(get_dataset_config_names(path=dataset, token=hf_token)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names dataset_module = dataset_module_factory( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1507, in dataset_module_factory raise FileNotFoundError( FileNotFoundError: Couldn't find a dataset script at /src/services/worker/severo/doc-yaml-2/doc-yaml-2.py or any data file in the same directory. Couldn't find 'severo/doc-yaml-2' on the Hugging Face Hub either: FileNotFoundError: Unable to find 'hf://datasets/severo/doc-yaml-2@938a0578fb4c6bc9da7d80b06a3ba39c2834b0c2/data/def.csv' with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.arrow', '.txt', '.blp', '.bmp', '.dib', '.bufr', '.cur', '.pcx', '.dcx', '.dds', '.ps', '.eps', '.fit', '.fits', '.fli', '.flc', '.ftc', '.ftu', '.gbr', '.gif', '.grib', '.h5', '.hdf', '.png', '.apng', '.jp2', '.j2k', '.jpc', '.jpf', '.jpx', '.j2c', '.icns', '.ico', '.im', '.iim', '.tif', '.tiff', '.jfif', '.jpe', '.jpg', '.jpeg', '.mpg', '.mpeg', '.msp', '.pcd', '.pxr', '.pbm', '.pgm', '.ppm', '.pnm', '.psd', '.bw', '.rgb', '.rgba', '.sgi', '.ras', '.tga', '.icb', '.vda', '.vst', '.webp', '.wmf', '.emf', '.xbm', '.xpm', '.BLP', '.BMP', '.DIB', '.BUFR', '.CUR', '.PCX', '.DCX', '.DDS', '.PS', '.EPS', '.FIT', '.FITS', '.FLI', '.FLC', '.FTC', '.FTU', '.GBR', '.GIF', '.GRIB', '.H5', '.HDF', '.PNG', '.APNG', '.JP2', '.J2K', '.JPC', '.JPF', '.JPX', '.J2C', '.ICNS', '.ICO', '.IM', '.IIM', '.TIF', '.TIFF', '.JFIF', '.JPE', '.JPG', '.JPEG', '.MPG', '.MPEG', '.MSP', '.PCD', '.PXR', '.PBM', '.PGM', '.PPM', '.PNM', '.PSD', '.BW', '.RGB', '.RGBA', '.SGI', '.RAS', '.TGA', '.ICB', '.VDA', '.VST', '.WEBP', '.WMF', '.EMF', '.XBM', '.XPM', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip'] ```	2023-11-22T15:18:10	2025-06-23T13:46:46	null	https://github.com/huggingface/datasets/issues/6443	null	6,443	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	Trouble loading image folder with additional features - metadata file ignored	closed	### Describe the bug Loading image folder with a caption column using `load_dataset(<image_folder_path>)` doesn't load the captions. When loading a local image folder with captions using `datasets==2.13.0` ``` from datasets import load_dataset data = load_dataset(<image_folder_path>) data.column_names ``` yields `{'train': ['image', 'prompt']}` but when using `datasets==2.15.0` yeilds `{'train': ['image']}` Putting the images and `metadata.jsonl` file into a nested `train` folder or loading with `load_dataset("imagefolder", data_dir=<image_folder_path>)` solves the issue and yields `{'train': ['image', 'prompt']}` ### Steps to reproduce the bug 1. create a folder `<image_folder_path>` that contains images and a metadata file with additional features- e.g. "prompt" 2. run: ``` from datasets import load_dataset data = load_dataset("<image_folder_path>") data.column_names ``` ### Expected behavior `{'train': ['image', 'prompt']}` ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-5.15.120+-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.19.4 - PyArrow version: 9.0.0 - Pandas version: 1.5.3 - `fsspec` version: 2023.6.0	2023-11-22T11:01:35	2023-11-24T17:13:03	2023-11-24T17:13:03	https://github.com/huggingface/datasets/issues/6442	null	6,442	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	Trouble Loading a Gated Dataset For User with Granted Permission	closed	### Describe the bug I have granted permissions to several users to access a gated huggingface dataset. The users accepted the invite and when trying to load the dataset using their access token they get `FileNotFoundError: Couldn't find a dataset script at .....` . Also when they try to click the url link for the dataset they get a 404 error. ### Steps to reproduce the bug 1. Grant access to gated dataset for specific users 2. Users accept invitation 3. Users login to hugging face hub using cli login 4. Users run load_dataset ### Expected behavior Dataset is loaded normally for users who were granted access to the gated dataset. ### Environment info datasets==2.15.0	2023-11-21T19:24:36	2023-12-13T08:27:16	2023-12-13T08:27:16	https://github.com/huggingface/datasets/issues/6441	null	6,441	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	`.map` not hashing under python 3.9	closed	### Describe the bug The `.map` function cannot hash under python 3.9. Tried to use [the solution here](https://github.com/huggingface/datasets/issues/4521#issuecomment-1205166653), but still get the same message: `Parameter 'function'=<function map_to_pred at 0x7fa0b49ead30> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.` ### Steps to reproduce the bug ```python def map_to_pred(batch): """ Perform inference on an audio batch Parameters: batch (dict): A dictionary containing audio data and other related information. Returns: dict: The input batch dictionary with added prediction and transcription fields. """ audio = batch['audio'] input_features = processor( audio['array'], sampling_rate=audio['sampling_rate'], return_tensors="pt").input_features input_features = input_features.to('cuda') with torch.no_grad(): predicted_ids = model.generate(input_features) preds = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0] batch['prediction'] = processor.tokenizer._normalize(preds) batch["transcription"] = processor.tokenizer._normalize(batch['transcription']) return batch MODEL_CARD = "openai/whisper-small" MODEL_NAME = MODEL_CARD.rsplit('/', maxsplit=1)[-1] model = WhisperForConditionalGeneration.from_pretrained(MODEL_CARD) processor = AutoProcessor.from_pretrained( MODEL_CARD, language="english", task="transcribe") model = torch.compile(model) dt = load_dataset("audiofolder", data_dir=config['DATA']['dataset'], split="test") dt = dt.cast_column("audio", Audio(sampling_rate=16000)) result = coraal_dt.map(map_to_pred, num_proc=16) ``` ### Expected behavior Hashed and cached dataset starts inferencing ### Environment info - `transformers` version: 4.35.0 - Platform: Linux-5.14.0-284.30.1.el9_2.x86_64-x86_64-with-glibc2.34 - Python version: 3.9.18 - Huggingface_hub version: 0.17.3 - Safetensors version: 0.4.0 - Accelerate version: 0.24.1 - Accelerate config: not found - PyTorch version (GPU?): 2.1.0 (True) - Tensorflow version (GPU?): not installed (NA) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: yes - Using distributed or parallel set-up in script?: no	2023-11-21T15:14:54	2023-11-28T16:29:33	2023-11-28T16:29:33	https://github.com/huggingface/datasets/issues/6440	null	6,440	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	Download + preparation speed of datasets.load_dataset is 20x slower than huggingface hub snapshot and manual loding	open	### Describe the bug I am working with a dataset I am trying to publish. The path is Antreas/TALI. It's a fairly large dataset, and contains images, video, audio and text. I have been having multiple problems when the dataset is being downloaded using the load_dataset function -- even with 64 workers taking more than 7 days to process. With snapshot download it takes 12 hours, and that includes the dataset preparation done using load_dataset and passing the dataset parquet file paths. Find the script I am using below: ```python import multiprocessing as mp import pathlib from typing import Optional import datasets from rich import print from tqdm import tqdm def download_dataset_via_hub( dataset_name: str, dataset_download_path: pathlib.Path, num_download_workers: int = mp.cpu_count(), ): import huggingface_hub as hf_hub download_folder = hf_hub.snapshot_download( repo_id=dataset_name, repo_type="dataset", cache_dir=dataset_download_path, resume_download=True, max_workers=num_download_workers, ignore_patterns=[], ) return pathlib.Path(download_folder) / "data" def load_dataset_via_hub( dataset_download_path: pathlib.Path, num_download_workers: int = mp.cpu_count(), dataset_name: Optional[str] = None, ): from dataclasses import dataclass, field from datasets import ClassLabel, Features, Image, Sequence, Value dataset_path = download_dataset_via_hub( dataset_download_path=dataset_download_path, num_download_workers=num_download_workers, dataset_name=dataset_name, ) # Building a list of file paths for validation set train_files = [ file.as_posix() for file in pathlib.Path(dataset_path).glob(".parquet") if "train" in file.as_posix() ] val_files = [ file.as_posix() for file in pathlib.Path(dataset_path).glob(".parquet") if "val" in file.as_posix() ] test_files = [ file.as_posix() for file in pathlib.Path(dataset_path).glob("*.parquet") if "test" in file.as_posix() ] print( f"Found {len(test_files)} files for testing set, {len(train_files)} for training set and {len(val_files)} for validation set" ) data_files = { "test": test_files, "val": val_files, "train": train_files, } features = Features( { "image": Image( decode=True ), # Set `decode=True` if you want to decode the images, otherwise `decode=False` "image_url": Value("string"), "item_idx": Value("int64"), "wit_features": Sequence( { "attribution_passes_lang_id": Value("bool"), "caption_alt_text_description": Value("string"), "caption_reference_description": Value("string"), "caption_title_and_reference_description": Value("string"), "context_page_description": Value("string"), "context_section_description": Value("string"), "hierarchical_section_title": Value("string"), "is_main_image": Value("bool"), "language": Value("string"), "page_changed_recently": Value("bool"), "page_title": Value("string"), "page_url": Value("string"), "section_title": Value("string"), } ), "wit_idx": Value("int64"), "youtube_title_text": Value("string"), "youtube_description_text": Value("string"), "youtube_video_content": Value("binary"), "youtube_video_starting_time": Value("string"), "youtube_subtitle_text": Value("string"), "youtube_video_size": Value("int64"), "youtube_video_file_path": Value("string"), } ) dataset = datasets.load_dataset( "parquet" if dataset_name is None else dataset_name, data_files=data_files, features=features, num_proc=1, cache_dir=dataset_download_path / "cache", ) return dataset if __name__ == "__main__": dataset_cache = pathlib.Path("/disk/scratch_fast0/tali/") dataset = load_dataset_via_hub(dataset_cache, dataset_name="Antreas/TALI")[ "test" ] for sample in tqdm(dataset): print(list(sample.keys())) ``` Also, streaming this dataset has been a very painfully slow process. Streaming the train set takes 15m to start, and streaming the test and val sets takes 3 hours to start! ### Steps to reproduce the bug 1. Run the code I provided to get a sense of how fast snapshot + manual is 2. Run datasets.load_dataset("Antreas/TALI") to get a sense of the speed of that OP. 3. You should now have an appreciation of how long these things take. ### Expected behavior The load dataset function should be at least as fast as the huggingface snapshot download function in terms of downloading dataset files. Not 20 times slower. ### Environment info - `datasets` version: 2.14.5 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.35 - Python version: 3.10.13 - Huggingface_hub version: 0.17.3 - PyArrow version: 13.0.0 - Pandas version: 2.1.1	2023-11-20T20:07:23	2023-11-20T20:07:37	null	https://github.com/huggingface/datasets/issues/6439	null	6,439	false	[]
2,002,032,804	Support GeoParquet	open	### Feature request Support the GeoParquet format ### Motivation GeoParquet (https://geoparquet.org/) is a common format for sharing vectorial geospatial data on the cloud, along with "traditional" data columns. It would be nice to be able to load this format with datasets, and more generally, in the Datasets Hub (see https://huggingface.co/datasets/joshuasundance/govgis_nov2023-slim-spatial/discussions/1). ### Your contribution I would be happy to help work on a PR (but I don't think I can do one on my own). Also, we have to define what we want to support: - load all the columns, but get the "geospatial" column in text-only mode for now - or, fully support the spatial features, maybe taking inspiration from (or depending upon) https://geopandas.org/en/stable/index.html (which itself depends on https://fiona.readthedocs.io/en/stable/, which requires a local install of https://gdal.org/)	2023-11-20T11:54:58	2024-02-07T08:36:51	null	https://github.com/huggingface/datasets/issues/6438	null	6,438	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	Problem in training iterable dataset	open	### Describe the bug I am using PyTorch DDP (Distributed Data Parallel) to train my model. Since the data is too large to load into memory at once, I am using load_dataset to read the data as an iterable dataset. I have used datasets.distributed.split_dataset_by_node to distribute the dataset. However, I have noticed that this distribution results in different processes having different amounts of data to train on. As a result, when the earliest process finishes training and starts predicting on the test set, other processes are still training, causing the overall training speed to be very slow. ### Steps to reproduce the bug ``` def train(args, model, device, train_loader, optimizer, criterion, epoch, length): model.train() idx_length = 0 for batch_idx, data in enumerate(train_loader): s_time = time.time() X = data['X'] target = data['y'].reshape(-1, 28) X, target = X.to(device), target.to(device) optimizer.zero_grad() output = model(X) loss = criterion(output, target) loss.backward() optimizer.step() idx_length += 1 if batch_idx % args.log_interval == 0: # print('Train Epoch: {} Batch_idx: {} Process: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( # epoch, batch_idx, torch.distributed.get_rank(), batch_idx * len(X), length / torch.distributed.get_world_size(), # 100. * batch_idx * len( # X) * torch.distributed.get_world_size() / length, loss.item())) print('Train Epoch: {} Batch_idx: {} Process: {} [{}/{} ({:.0f}%)]\t'.format( epoch, batch_idx, torch.distributed.get_rank(), batch_idx * len(X), length / torch.distributed.get_world_size(), 100. * batch_idx * len( X) * torch.distributed.get_world_size() / length)) if args.dry_run: break print('Process %s length: %s time: %s' % (torch.distributed.get_rank(), idx_length, datetime.datetime.now())) train_iterable_dataset = load_dataset("parquet", data_files=data_files, split="train", streaming=True) test_iterable_dataset = load_dataset("parquet", data_files=data_files, split="test", streaming=True) train_iterable_dataset = train_iterable_dataset.map(process_fn) test_iterable_dataset = test_iterable_dataset.map(process_fn) train_iterable_dataset = train_iterable_dataset.map(scale) test_iterable_dataset = test_iterable_dataset.map(scale) train_iterable_dataset = datasets.distributed.split_dataset_by_node(train_iterable_dataset, world_size=world_size, rank=local_rank).shuffle(seed=1234) test_iterable_dataset = datasets.distributed.split_dataset_by_node(test_iterable_dataset, world_size=world_size, rank=local_rank).shuffle(seed=1234) print(torch.distributed.get_rank(), train_iterable_dataset.n_shards, test_iterable_dataset.n_shards) train_kwargs = {'batch_size': args.batch_size} test_kwargs = {'batch_size': args.test_batch_size} if use_cuda: cuda_kwargs = {'num_workers': 3,#ngpus_per_node, 'pin_memory': True, 'shuffle': False} train_kwargs.update(cuda_kwargs) test_kwargs.update(cuda_kwargs) train_loader = torch.utils.data.DataLoader(train_iterable_dataset, train_kwargs, # sampler=torch.utils.data.distributed.DistributedSampler( # train_iterable_dataset, # num_replicas=ngpus_per_node, # rank=0) ) test_loader = torch.utils.data.DataLoader(test_iterable_dataset, test_kwargs, # sampler=torch.utils.data.distributed.DistributedSampler( # test_iterable_dataset, # num_replicas=ngpus_per_node, # rank=0) ) for epoch in range(1, args.epochs + 1): start_time = time.time() train_iterable_dataset.set_epoch(epoch) test_iterable_dataset.set_epoch(epoch) train(args, model, device, train_loader, optimizer, criterion, epoch, train_len) test(args, model, device, criterion2, test_loader) ``` And here’s the part of output: ``` Train Epoch: 1 Batch_idx: 5000 Process: 0 [320000/4710975.0 (7%)] Train Epoch: 1 Batch_idx: 5000 Process: 1 [320000/4710975.0 (7%)] Train Epoch: 1 Batch_idx: 5000 Process: 2 [320000/4710975.0 (7%)] Train Epoch: 1 Batch_idx: 5862 Process: 3 Data_length: 12 coststime: 0.04095172882080078 Train Epoch: 1 Batch_idx: 5862 Process: 0 Data_length: 3 coststime: 0.0751960277557373 Train Epoch: 1 Batch_idx: 5867 Process: 3 Data_length: 49 coststime: 0.0032558441162109375 Train Epoch: 1 Batch_idx: 5872 Process: 1 Data_length: 2 coststime: 0.022842884063720703 Train Epoch: 1 Batch_idx: 5876 Process: 3 Data_length: 63 coststime: 0.002694845199584961 Process 3 length: 5877 time: 2023-11-17 17:03:26.582317 Train epoch 1 costTime: 241.72063446044922s . Process 3 Start to test. 3 0 tensor(45508.8516, device='cuda:3') 3 100 tensor(45309.0469, device='cuda:3') 3 200 tensor(45675.3047, device='cuda:3') 3 300 tensor(45263.0273, device='cuda:3') Process 3 Reduce metrics. Train Epoch: 2 Batch_idx: 0 Process: 3 [0/4710975.0 (0%)] Train Epoch: 1 Batch_idx: 5882 Process: 1 Data_length: 63 coststime: 0.05185818672180176 Train Epoch: 1 Batch_idx: 5887 Process: 1 Data_length: 12 coststime: 0.006895303726196289 Process 1 length: 5888 time: 2023-11-17 17:20:48.578204 Train epoch 1 costTime: 1285.7279663085938s . Process 1 Start to test. 1 0 tensor(45265.9141, device='cuda:1') ``` ### Expected behavior I'd like to know how to fix this problem. ### Environment info ``` torch==2.0 datasets==2.14.0 ```	2023-11-20T03:04:02	2024-05-22T03:14:13	null	https://github.com/huggingface/datasets/issues/6437	null	6,437	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	TypeError: <lambda>() takes 0 positional arguments but 1 was given	closed	### Describe the bug ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) [<ipython-input-35-7b6becee3685>](https://localhost:8080/#) in <cell line: 1>() ----> 1 from datasets import Dataset 9 frames [/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module> 20 __version__ = "2.15.0" 21 ---> 22 from .arrow_dataset import Dataset 23 from .arrow_reader import ReadInstruction 24 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder [/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module> 61 import pyarrow.compute as pc 62 from huggingface_hub import CommitOperationAdd, CommitOperationDelete, DatasetCard, DatasetCardData, HfApi ---> 63 from multiprocess import Pool 64 from requests import HTTPError 65 [/usr/local/lib/python3.10/dist-packages/multiprocess/__init__.py](https://localhost:8080/#) in <module> 31 32 import sys ---> 33 from . import context 34 35 # [/usr/local/lib/python3.10/dist-packages/multiprocess/context.py](https://localhost:8080/#) in <module> 4 5 from . import process ----> 6 from . import reduction 7 8 __all__ = () [/usr/local/lib/python3.10/dist-packages/multiprocess/reduction.py](https://localhost:8080/#) in <module> 14 import os 15 try: ---> 16 import dill as pickle 17 except ImportError: 18 import pickle [/usr/local/lib/python3.10/dist-packages/dill/__init__.py](https://localhost:8080/#) in <module> 24 25 ---> 26 from ._dill import ( 27 dump, dumps, load, loads, copy, 28 Pickler, Unpickler, register, pickle, pickles, check, [/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in <module> 166 try: 167 from _pyio import open as _open --> 168 PyTextWrapperType = get_file_type('r', buffering=-1, open=_open) 169 PyBufferedRandomType = get_file_type('r+b', buffering=-1, open=_open) 170 PyBufferedReaderType = get_file_type('rb', buffering=-1, open=_open) [/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in get_file_type(args, kwargs) 154 def get_file_type(args, *kwargs): 155 open = kwargs.pop("open", __builtin__.open) --> 156 f = open(os.devnull, args, *kwargs) 157 t = type(f) 158 f.close() [/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in open(file, mode, buffering, encoding, errors, newline, closefd, opener) 280 return result 281 encoding = text_encoding(encoding) --> 282 text = TextIOWrapper(buffer, encoding, errors, newline, line_buffering) 283 result = text 284 text.mode = mode [/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in __init__(self, buffer, encoding, errors, newline, line_buffering, write_through) 2043 encoding = "utf-8" 2044 else: -> 2045 encoding = locale.getpreferredencoding(False) 2046 2047 if not isinstance(encoding, str): TypeError: <lambda>() takes 0 positional arguments but 1 was given ``` or ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) [<ipython-input-36-652e886d387f>](https://localhost:8080/#) in <cell line: 1>() ----> 1 import datasets 9 frames [/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module> 20 __version__ = "2.15.0" 21 ---> 22 from .arrow_dataset import Dataset 23 from .arrow_reader import ReadInstruction 24 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder [/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module> 61 import pyarrow.compute as pc 62 from huggingface_hub import CommitOperationAdd, CommitOperationDelete, DatasetCard, DatasetCardData, HfApi ---> 63 from multiprocess import Pool 64 from requests import HTTPError 65 [/usr/local/lib/python3.10/dist-packages/multiprocess/__init__.py](https://localhost:8080/#) in <module> 31 32 import sys ---> 33 from . import context 34 35 # [/usr/local/lib/python3.10/dist-packages/multiprocess/context.py](https://localhost:8080/#) in <module> 4 5 from . import process ----> 6 from . import reduction 7 8 __all__ = () [/usr/local/lib/python3.10/dist-packages/multiprocess/reduction.py](https://localhost:8080/#) in <module> 14 import os 15 try: ---> 16 import dill as pickle 17 except ImportError: 18 import pickle [/usr/local/lib/python3.10/dist-packages/dill/__init__.py](https://localhost:8080/#) in <module> 24 25 ---> 26 from ._dill import ( 27 dump, dumps, load, loads, copy, 28 Pickler, Unpickler, register, pickle, pickles, check, [/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in <module> 166 try: 167 from _pyio import open as _open --> 168 PyTextWrapperType = get_file_type('r', buffering=-1, open=_open) 169 PyBufferedRandomType = get_file_type('r+b', buffering=-1, open=_open) 170 PyBufferedReaderType = get_file_type('rb', buffering=-1, open=_open) [/usr/local/lib/python3.10/dist-packages/dill/_dill.py](https://localhost:8080/#) in get_file_type(args, *kwargs) 154 def get_file_type(args, *kwargs): 155 open = kwargs.pop("open", __builtin__.open) --> 156 f = open(os.devnull, args, **kwargs) 157 t = type(f) 158 f.close() [/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in open(file, mode, buffering, encoding, errors, newline, closefd, opener) 280 return result 281 encoding = text_encoding(encoding) --> 282 text = TextIOWrapper(buffer, encoding, errors, newline, line_buffering) 283 result = text 284 text.mode = mode [/usr/lib/python3.10/_pyio.py](https://localhost:8080/#) in __init__(self, buffer, encoding, errors, newline, line_buffering, write_through) 2043 encoding = "utf-8" 2044 else: -> 2045 encoding = locale.getpreferredencoding(False) 2046 2047 if not isinstance(encoding, str): TypeError: <lambda>() takes 0 positional arguments but 1 was given ``` ### Steps to reproduce the bug `import datasets` on colab ### Expected behavior work fine ### Environment info colab `!pip install datasets`	2023-11-19T13:10:20	2025-05-05T18:21:21	2023-11-29T16:28:34	https://github.com/huggingface/datasets/issues/6436	null	6,436	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	Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method	closed	### Describe the bug 1. I ran dataset mapping with `num_proc=6` in it and got this error: `RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method` I can't actually find a way to run multi-GPU dataset mapping. Can you help? ### Steps to reproduce the bug 1. Rund SDXL training with `num_proc=6`: https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py ### Expected behavior Should work well ### Environment info 6x A100 SXM, Linux	2023-11-19T04:21:16	2024-01-27T17:14:20	2023-12-04T16:57:43	https://github.com/huggingface/datasets/issues/6435	null	6,435	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	Use `ruff` for formatting	closed	Use `ruff` instead of `black` for formatting to be consistent with `transformers` ([PR](https://github.com/huggingface/transformers/pull/27144)) and `huggingface_hub` ([PR 1](https://github.com/huggingface/huggingface_hub/pull/1783) and [PR 2](https://github.com/huggingface/huggingface_hub/pull/1789)).	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" }	6,434	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	Better `tqdm` wrapper	closed	This PR aligns the `tqdm` logic with `huggingface_hub` (without introducing breaking changes), as the current one is error-prone. Additionally, it improves the doc page about the `datasets`' utilities, and the handling of local `fsspec` paths in `cached_path`. Fix #6409	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" }	6,433	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	load_dataset does not load all of the data in my input file	open	### Describe the bug I have 127 elements in my input dataset. When I do a len on the dataset after loaded, it is only 124 elements. ### Steps to reproduce the bug train_dataset = nlp.load_dataset(data_args.dataset_path, name=data_args.qg_format, split=nlp.Split.TRAIN) valid_dataset = nlp.load_dataset(data_args.dataset_path, name=data_args.qg_format, split=nlp.Split.VALIDATION) logger.info(len(train_dataset)) logger.info(len(valid_dataset)) Both train and valid input are 127 items. However, they both only load 124 items. The input format is in json. At the end of the day, I am trying to create .pt files. ### Expected behavior I see all 127 elements in my dataset when performing len ### Environment info Python 3.10. CentOS operating system. nlp==0.40, datasets==2.14.5, transformers==4.26.1	2023-11-17T14:28:50	2023-11-22T17:34:58	null	https://github.com/huggingface/datasets/issues/6432	null	6,432	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	Create DatasetNotFoundError and DataFilesNotFoundError	closed	Create `DatasetNotFoundError` and `DataFilesNotFoundError`. Fix #6397. CC: @severo	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" }	6,431	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	Add trust_remote_code argument	closed	Draft about adding `trust_remote_code` to `load_dataset`. ```python ds = load_dataset(..., trust_remote_code=True) # run remote code (current default) ``` It would default to `True` (current behavior) and in the next major release it will prompt the user to check the code before running it (we'll communicate on this before doing it of course). ```python # in the future ds = load_dataset(...) # prompt the user to check the code before running it (future default) ds = load_dataset(..., trust_remote_code=True) # run remote code ds = load_dataset(..., trust_remote_code=False) # disallow remote code ``` Related to https://github.com/huggingface/datasets/issues/6400 Will do a separate PR to use the parquet export when possible	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" }	6,429	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	Set dev version	closed	null	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" }	6,428	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	Release: 2.15.0	closed	null	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" }	6,427	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	More robust temporary directory deletion	closed	While fixing the Windows errors in #6362, I noticed that `PermissionError` can still easily be thrown on the session exit by the temporary cache directory's finalizer (we would also have to keep track of intermediate datasets, copies, etc.). ~~Due to the low usage of `datasets` on Windows, this PR takes a simpler approach to the issue than https://github.com/huggingface/datasets/pull/2403 - it tries to delete the temporary cache directory, and if this fails, logs a warning message about using a `delete-temp-cache` CLI command to delete it manually. The problematic references are freed after the session exits, so the CLI command should then succeed.~~ This PR implements `Dataset.__setstate__` to register datasets with temporary cache files for deletion.	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" }	6,426	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	Fix deprecation warning when building conda package	closed	When building/releasing conda package, we get this deprecation warning: ``` /usr/share/miniconda/envs/build-datasets/bin/conda-build:11: DeprecationWarning: conda_build.cli.main_build.main is deprecated and will be removed in 4.0.0. Use `conda build` instead. ``` This PR fixes the deprecation warning by using `conda build` instead.	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" }	6,425	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	[docs] troubleshooting guide	closed	Hi all! This is a PR adding a troubleshooting guide for Datasets docs. I went through the library's GitHub Issues and Forum questions and identified a few issues that are common enough that I think it would be valuable to include them in the troubleshooting guide. These are: - creating a dataset from a folder and not following the required format - authentication issues when using `push_to_hub` - `Too Many Requests` with `push_to_hub` - Pickling issues when using Dataset.from_generator() There's also a section on asking for help. Please let me know if there are other common issues or advice that we can include here.	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" }	6,424	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	Fix conda release by adding pyarrow-hotfix dependency	closed	Fix conda release by adding pyarrow-hotfix dependency. Note that conda release failed in latest 2.14.7 release: https://github.com/huggingface/datasets/actions/runs/6874667214/job/18696761723 ``` Traceback (most recent call last): File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/test_tmp/run_test.py", line 2, in <module> import datasets File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.12/site-packages/datasets/__init__.py", line 22, in <module> from .arrow_dataset import Dataset File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.12/site-packages/datasets/arrow_dataset.py", line 67, in <module> from .arrow_writer import ArrowWriter, OptimizedTypedSequence File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.12/site-packages/datasets/arrow_writer.py", line 27, in <module> from .features import Features, Image, Value File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.12/site-packages/datasets/features/__init__.py", line 18, in <module> from .features import Array2D, Array3D, Array4D, Array5D, ClassLabel, Features, Sequence, Value File "/usr/share/miniconda/envs/build-datasets/conda-bld/datasets_1700036460222/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/python3.12/site-packages/datasets/features/features.py", line 34, in <module> import pyarrow_hotfix # noqa: F401 # to fix vulnerability on pyarrow<14.0.1 ^^^^^^^^^^^^^^^^^^^^^ ModuleNotFoundError: No module named 'pyarrow_hotfix' ```	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" }	6,423	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	Allow to choose the `writer_batch_size` when using `save_to_disk`	open	### Feature request Add an argument in `save_to_disk` regarding batch size, which would be passed to `shard` and other methods. ### Motivation The `Dataset.save_to_disk` method currently calls `shard` without passing a `writer_batch_size` argument, thus implicitly using the default value (1000). This can result in RAM saturation when using a lot of processes on long text sequences or other modalities, or for specific IO configs. ### Your contribution I would be glad to submit a PR, as long as it does not imply extensive tests refactoring.	2023-11-15T11:18:34	2023-11-16T10:00:21	null	https://github.com/huggingface/datasets/issues/6422	null	6,422	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	Add pyarrow-hotfix to release docs	closed	Add `pyarrow-hotfix` to release docs.	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" }	6,421	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	Set dev version	closed	null	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" }	6,420	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	Release: 2.14.7	closed	Release 2.14.7.	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" }	6,419	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	Remove token value from warnings	closed	Fix #6412	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" }	6,418	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	Bug: LayoutLMv3 finetuning on FUNSD Notebook; Arrow Error	closed	### Describe the bug Arrow issues when running the example Notebook laptop locally on Mac with M1. Works on Google Collab. Notebook: https://github.com/NielsRogge/Transformers-Tutorials/blob/master/LayoutLMv3/Fine_tune_LayoutLMv3_on_FUNSD_(HuggingFace_Trainer).ipynb Error: `ValueError: Arrow type extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>> does not have a datasets dtype equivalent.` Caused by: ``` # we need to define custom features for `set_format` (used later on) to work properly features = Features({ 'pixel_values': Array3D(dtype="float32", shape=(3, 224, 224)), 'input_ids': Sequence(feature=Value(dtype='int64')), 'attention_mask': Sequence(Value(dtype='int64')), 'bbox': Array2D(dtype="int64", shape=(512, 4)), 'labels': Sequence(feature=Value(dtype='int64')), }) ``` ### Steps to reproduce the bug Run the notebook provided, locally. If possible also on M1. ### Expected behavior The cell where features are mapped to Array2D and Array3D should work without any issues. ### Environment info Tried with Python 3.9 and 3.10 conda envs. Running Mac M1. `pip show datasets` > Name: datasets Version: 2.14.6 Summary: HuggingFace community-driven open-source library of datasets `pip list` > Package Version > ------------------------- ------------ > accelerate 0.24.1 > aiohttp 3.8.6 > aiosignal 1.3.1 > anyio 3.5.0 > appnope 0.1.2 > argon2-cffi 21.3.0 > argon2-cffi-bindings 21.2.0 > asttokens 2.0.5 > async-timeout 4.0.3 > attrs 23.1.0 > backcall 0.2.0 > beautifulsoup4 4.12.2 > bleach 4.1.0 > certifi 2023.7.22 > cffi 1.15.1 > charset-normalizer 3.3.2 > comm 0.1.2 > datasets 2.14.6 > debugpy 1.6.7 > decorator 5.1.1 > defusedxml 0.7.1 > dill 0.3.7 > entrypoints 0.4 > exceptiongroup 1.0.4 > executing 0.8.3 > fastjsonschema 2.16.2 > filelock 3.13.1 > frozenlist 1.4.0 > fsspec 2023.10.0 > huggingface-hub 0.17.3 > idna 3.4 > importlib-metadata 6.0.0 > IProgress 0.4 > ipykernel 6.25.0 > ipython 8.15.0 > ipython-genutils 0.2.0 > jedi 0.18.1 > Jinja2 3.1.2 > joblib 1.3.2 > jsonschema 4.19.2 > jsonschema-specifications 2023.7.1 > jupyter_client 7.4.9 > jupyter_core 5.5.0 > jupyter-server 1.23.4 > jupyterlab-pygments 0.1.2 > MarkupSafe 2.1.1 > matplotlib-inline 0.1.6 > mistune 2.0.4 > mpmath 1.3.0 > multidict 6.0.4 > multiprocess 0.70.15 > nbclassic 1.0.0 > nbclient 0.8.0 > nbconvert 7.10.0 > nbformat 5.9.2 > nest-asyncio 1.5.6 > networkx 3.2.1 > notebook 6.5.4 > notebook_shim 0.2.3 > numpy 1.26.1 > packaging 23.1 > pandas 2.1.3 > pandocfilters 1.5.0 > parso 0.8.3 > pexpect 4.8.0 > pickleshare 0.7.5 > Pillow 10.1.0 > pip 23.3 > platformdirs 3.10.0 > prometheus-client 0.14.1 > prompt-toolkit 3.0.36 > psutil 5.9.0 > ptyprocess 0.7.0 > pure-eval 0.2.2 > pyarrow 14.0.1 > pycparser 2.21 > Pygments 2.15.1 > python-dateutil 2.8.2 > pytz 2023.3.post1 > PyYAML 6.0.1 > pyzmq 23.2.0 > referencing 0.30.2 > regex 2023.10.3 > requests 2.31.0 > rpds-py 0.10.6 > safetensors 0.4.0 > scikit-learn 1.3.2 > scipy 1.11.3 > Send2Trash 1.8.2 > seqeval 1.2.2 > setuptools 68.0.0 > six 1.16.0 > sniffio 1.2.0 > soupsieve 2.5 > stack-data 0.2.0 > sympy 1.12 > terminado 0.17.1 > threadpoolctl 3.2.0 > tinycss2 1.2.1 > tokenizers 0.14.1 > torch 2.1.0 > tornado 6.3.3 > tqdm 4.66.1 > traitlets 5.7.1 > transformers 4.36.0.dev0 > typing_extensions 4.7.1 > tzdata 2023.3 > urllib3 2.0.7 > wcwidth 0.2.5 > webencodings 0.5.1 > websocket-client 0.58.0 > wheel 0.41.2 > xxhash 3.4.1 > yarl 1.9.2 > zipp 3.11.0	2023-11-14T16:53:20	2023-11-16T20:23:41	2023-11-16T20:23:41	https://github.com/huggingface/datasets/issues/6417	null	6,417	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	Rename audio_classificiation.py to audio_classification.py	closed	null	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" }	6,416	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	Fix multi gpu map example	closed	- use `orch.cuda.set_device` instead of `CUDA_VISIBLE_DEVICES ` - add `if __name__ == "__main__"` fix https://github.com/huggingface/datasets/issues/6186	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" }	6,415	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	Set `usedforsecurity=False` in hashlib methods (FIPS compliance)	closed	Related to https://github.com/huggingface/transformers/issues/27034 and https://github.com/huggingface/huggingface_hub/pull/1782. TL;DR: `hashlib` is not a secure library for cryptography-related stuff. We are only using `hashlib` for non-security-related purposes in `datasets` so it's fine. From Python 3.9 we set can `usedforsecurity=False` in any `hashlib` method which is mandatory for companies that forbid the use of `hashlib` for security purposes. This PR fixes that. Note: before merging this we need to release a new tokenizers version that would allow the newest `huggingface_hub` version (see https://github.com/huggingface/tokenizers/pull/1385). Otherwise it might create friction to users that want to install `datasets` + `tokenizers` at the same time.	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" }	6,414	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	User token is printed out!	closed	This line prints user token on command line! Is it safe? https://github.com/huggingface/datasets/blob/12ebe695b4748c5a26e08b44ed51955f74f5801d/src/datasets/load.py#L2091	2023-11-14T10:01:34	2023-11-14T22:19:46	2023-11-14T22:19:46	https://github.com/huggingface/datasets/issues/6412	null	6,412	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	Fix dependency conflict within CI build documentation	closed	Manually fix dependency conflict on `typing-extensions` version originated by `apache-beam` + `pydantic` (now a dependency of `huggingface-hub`). This is a temporary hot fix of our CI build documentation until we stop using `apache-beam`. Fix #6406.	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" }	6,411	true	[ "_The documentation is not available anymore as the PR was closed or merged._" ]
1,992,100,209	Datasets does not load HuggingFace Repository properly	open	### Describe the bug Dear Datasets team, We just have published a dataset on Huggingface: https://huggingface.co/ai4privacy However, when trying to read it using the Dataset library we get an error. As I understand jsonl files are compatible, could you please clarify how we can solve the issue? Please let me know and we would be more than happy to adapt the structure of the repository or meta data so it works easier: ```python from datasets import load_dataset dataset = load_dataset("ai4privacy/pii-masking-200k") ``` ``` Downloading readme: 100% 11.8k/11.8k [00:00<00:00, 512kB/s] Downloading data files: 100% 1/1 [00:11<00:00, 11.16s/it] Downloading data: 100% 64.3M/64.3M [00:02<00:00, 32.9MB/s] Downloading data: 100% 113M/113M [00:03<00:00, 35.0MB/s] Downloading data: 100% 97.7M/97.7M [00:02<00:00, 46.1MB/s] Downloading data: 100% 90.8M/90.8M [00:02<00:00, 44.9MB/s] Downloading data: 100% 7.63k/7.63k [00:00<00:00, 41.0kB/s] Downloading data: 100% 1.03k/1.03k [00:00<00:00, 9.44kB/s] Extracting data files: 100% 1/1 [00:00<00:00, 29.26it/s] Generating train split: 209261/0 [00:05<00:00, 41201.25 examples/s] --------------------------------------------------------------------------- ValueError Traceback (most recent call last) [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1939 ) -> 1940 writer.write_table(table) 1941 num_examples_progress_update += len(table) 8 frames [/usr/local/lib/python3.10/dist-packages/datasets/arrow_writer.py](https://localhost:8080/#) in write_table(self, pa_table, writer_batch_size) 571 pa_table = pa_table.combine_chunks() --> 572 pa_table = table_cast(pa_table, self._schema) 573 if self.embed_local_files: [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in table_cast(table, schema) 2327 if table.schema != schema: -> 2328 return cast_table_to_schema(table, schema) 2329 elif table.schema.metadata != schema.metadata: [/usr/local/lib/python3.10/dist-packages/datasets/table.py](https://localhost:8080/#) in cast_table_to_schema(table, schema) 2285 if sorted(table.column_names) != sorted(features): -> 2286 raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nbecause column names don't match") 2287 arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] ValueError: Couldn't cast JOBTYPE: int64 PHONEIMEI: int64 ACCOUNTNAME: int64 VEHICLEVIN: int64 GENDER: int64 CURRENCYCODE: int64 CREDITCARDISSUER: int64 JOBTITLE: int64 SEX: int64 CURRENCYSYMBOL: int64 IP: int64 EYECOLOR: int64 MASKEDNUMBER: int64 SECONDARYADDRESS: int64 JOBAREA: int64 ACCOUNTNUMBER: int64 language: string BITCOINADDRESS: int64 MAC: int64 SSN: int64 EMAIL: int64 ETHEREUMADDRESS: int64 DOB: int64 VEHICLEVRM: int64 IPV6: int64 AMOUNT: int64 URL: int64 PHONENUMBER: int64 PIN: int64 TIME: int64 CREDITCARDNUMBER: int64 FIRSTNAME: int64 IBAN: int64 BIC: int64 COUNTY: int64 STATE: int64 LASTNAME: int64 ZIPCODE: int64 HEIGHT: int64 ORDINALDIRECTION: int64 MIDDLENAME: int64 STREET: int64 USERNAME: int64 CURRENCY: int64 PREFIX: int64 USERAGENT: int64 CURRENCYNAME: int64 LITECOINADDRESS: int64 CREDITCARDCVV: int64 AGE: int64 CITY: int64 PASSWORD: int64 BUILDINGNUMBER: int64 IPV4: int64 NEARBYGPSCOORDINATE: int64 DATE: int64 COMPANYNAME: int64 to {'masked_text': Value(dtype='string', id=None), 'unmasked_text': Value(dtype='string', id=None), 'privacy_mask': Value(dtype='string', id=None), 'span_labels': Value(dtype='string', id=None), 'bio_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'tokenised_text': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)} because column names don't match The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) [<ipython-input-2-f1c6811e9c83>](https://localhost:8080/#) in <cell line: 3>() 1 from datasets import load_dataset 2 ----> 3 dataset = load_dataset("ai4privacy/pii-masking-200k") [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, config_kwargs) 2151 2152 # Download and prepare data -> 2153 builder_instance.download_and_prepare( 2154 download_config=download_config, 2155 download_mode=download_mode, [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, download_and_prepare_kwargs) 952 if num_proc is not None: 953 prepare_split_kwargs["num_proc"] = num_proc --> 954 self._download_and_prepare( 955 dl_manager=dl_manager, 956 verification_mode=verification_mode, [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verification_mode, prepare_split_kwargs) 1047 try: 1048 # Prepare split will record examples associated to the split -> 1049 self._prepare_split(split_generator, prepare_split_kwargs) 1050 except OSError as e: 1051 raise OSError( [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split(self, split_generator, file_format, num_proc, max_shard_size) 1811 job_id = 0 1812 with pbar: -> 1813 for job_id, done, content in self._prepare_split_single( 1814 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args 1815 ): [/usr/local/lib/python3.10/dist-packages/datasets/builder.py](https://localhost:8080/#) in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1956 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1957 e = e.__context__ -> 1958 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1959 1960 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` Thank you and have a great day ahead ### Steps to reproduce the bug Open Google Colab Notebook: Run command: !pip3 install datasets Run code: from datasets import load_dataset dataset = load_dataset("ai4privacy/pii-masking-200k") ### Expected behavior Download the dataset successfully from HuggingFace to the notebook so that we can start working with it ### Environment info - `datasets` version: 2.14.6 - Platform: Linux-5.15.120+-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.19.1 - PyArrow version: 9.0.0 - Pandas version: 1.5.3	2023-11-14T06:50:49	2023-11-16T06:54:36	null	https://github.com/huggingface/datasets/issues/6410	null	6,410	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	using DownloadManager to download from local filesystem and disable_progress_bar, there will be an exception	closed	### Describe the bug i'm using datasets.download.download_manager.DownloadManager to download files like "file:///a/b/c.txt", and i disable_progress_bar() to disable bar. there will be an exception as follows: `AttributeError: 'function' object has no attribute 'close' Exception ignored in: <function TqdmCallback.__del__ at 0x7fa8683d84c0> Traceback (most recent call last): File "/home/protoss.gao/.local/lib/python3.9/site-packages/fsspec/callbacks.py", line 233, in __del__ self.tqdm.close()` i check your source code in datasets/utils/file_utils.py:348 you define TqdmCallback derive from fsspec.callbacks.TqdmCallback but in the newest fsspec code [https://github.com/fsspec/filesystem_spec/blob/master/fsspec/callbacks.py](url) , line 146, in this case, _DEFAULT_CALLBACK will take effect, but in line 234, it calls "close()" function which _DEFAULT_CALLBACK don't have such thing. so i think the class "TqdmCallback" in datasets/utils/file_utils.py may override "__del__" function or report this bug to fsspec. ### Steps to reproduce the bug as i said ### Expected behavior no exception ### Environment info datasets: 2.14.4 python: 3.9 platform: x86_64	2023-11-14T04:21:01	2023-11-22T16:42:09	2023-11-22T16:42:09	https://github.com/huggingface/datasets/issues/6409	null	6,409	false	[]
1,991,902,972	`IterableDataset` lost but not keep columns when map function adding columns with names in `remove_columns`	open	### Describe the bug IterableDataset lost but not keep columns when map function adding columns with names in remove_columns, Dataset not. May be related to the code below: https://github.com/huggingface/datasets/blob/06c3ffb8d068b6307b247164b10f7c7311cefed4/src/datasets/iterable_dataset.py#L750-L756 ### Steps to reproduce the bug ```python dataset: IterableDataset = load_dataset("Anthropic/hh-rlhf", streaming=True, split="train") column_names = list(next(iter(dataset)).keys()) # ['chosen', 'rejected'] # map_fn will return dict {"chosen": xxx, "rejected": xxx, "prompt": xxx, "history": xxxx} dataset = dataset.map(map_fn, batched=True, remove_columns=column_names) next(iter(dataset)) # output # {'prompt': 'xxx, 'history': xxx} ``` ```python # when load_dataset with streaming=False, the column_names are kept: dataset: Dataset = load_dataset("Anthropic/hh-rlhf", streaming=False, split="train") column_names = list(next(iter(dataset)).keys()) # ['chosen', 'rejected'] # map_fn will return dict {"chosen": xxx, "rejected": xxx, "prompt": xxx, "history": xxxx} dataset = dataset.map(map_fn, batched=True, remove_columns=column_names) next(iter(dataset)) # output # {'prompt': 'xxx, 'history': xxx, "chosen": xxx, "rejected": xxx} ``` ### Expected behavior IterableDataset keep columns when map function adding columns with names in remove_columns ### Environment info datasets==2.14.6	2023-11-14T03:12:08	2023-11-16T06:24:10	null	https://github.com/huggingface/datasets/issues/6408	null	6,408	false	[]
1,991,514,079	Loading the dataset from private S3 bucket gives "TypeError: cannot pickle '_contextvars.Context' object"	open	### Describe the bug I'm trying to read the parquet file from the private s3 bucket using the `load_dataset` function, but I receive `TypeError: cannot pickle '_contextvars.Context' object` error I'm working on a machine with `~/.aws/credentials` file. I can't give credentials and the path to a file in a private bucket for obvious reasons, but I'll try to give all possible outputs. ### Steps to reproduce the bug ```python import s3fs from datasets import load_dataset from aiobotocore.session import get_session DATA_PATH = "s3://bucket_name/path/validation.parquet" fs = s3fs.S3FileSystem(session=get_session()) ``` `fs.stat` returns the data, so we can say that fs is working and we have all permissions ```python fs.stat(DATA_PATH) # Returns: # {'ETag': '"123123a-19"', # 'LastModified': datetime.datetime(2023, 11, 1, 10, 16, 57, tzinfo=tzutc()), # 'size': 312237170, # 'name': 'bucket_name/path/validation.parquet', # 'type': 'file', # 'StorageClass': 'STANDARD', # 'VersionId': 'Abc.HtmsC9h.as', # 'ContentType': 'binary/octet-stream'} ``` ```python fs.storage_options # Returns: # {'session': <aiobotocore.session.AioSession at 0x7f9193fa53c0>} ``` ```python ds = load_dataset("parquet", data_files={"train": DATA_PATH}, storage_options=fs.storage_options) ``` <details> <summary>Returns such error (expandable)</summary> ```python --------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[88], line 1 ----> 1 ds = load_dataset("parquet", data_files={"train": DATA_PATH}, storage_options=fs.storage_options) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/load.py:2153, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, config_kwargs) 2150 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 2152 # Download and prepare data -> 2153 builder_instance.download_and_prepare( 2154 download_config=download_config, 2155 download_mode=download_mode, 2156 verification_mode=verification_mode, 2157 try_from_hf_gcs=try_from_hf_gcs, 2158 num_proc=num_proc, 2159 storage_options=storage_options, 2160 ) 2162 # Build dataset for splits 2163 keep_in_memory = ( 2164 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 2165 ) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/builder.py:954, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, download_and_prepare_kwargs) 952 if num_proc is not None: 953 prepare_split_kwargs["num_proc"] = num_proc --> 954 self._download_and_prepare( 955 dl_manager=dl_manager, 956 verification_mode=verification_mode, 957 prepare_split_kwargs, 958 download_and_prepare_kwargs, 959 ) 960 # Sync info 961 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/builder.py:1027, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, prepare_split_kwargs) 1025 split_dict = SplitDict(dataset_name=self.dataset_name) 1026 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) -> 1027 split_generators = self._split_generators(dl_manager, split_generators_kwargs) 1029 # Checksums verification 1030 if verification_mode == VerificationMode.ALL_CHECKS and dl_manager.record_checksums: File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py:34, in Parquet._split_generators(self, dl_manager) 32 if not self.config.data_files: 33 raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}") ---> 34 data_files = dl_manager.download_and_extract(self.config.data_files) 35 if isinstance(data_files, (str, list, tuple)): 36 files = data_files File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/download/download_manager.py:565, in DownloadManager.download_and_extract(self, url_or_urls) 549 def download_and_extract(self, url_or_urls): 550 """Download and extract given `url_or_urls`. 551 552 Is roughly equivalent to: (...) 563 extracted_path(s): `str`, extracted paths of given URL(s). 564 """ --> 565 return self.extract(self.download(url_or_urls)) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/download/download_manager.py:420, in DownloadManager.download(self, url_or_urls) 401 def download(self, url_or_urls): 402 """Download given URL(s). 403 404 By default, only one process is used for download. Pass customized `download_config.num_proc` to change this behavior. (...) 418 ``` 419 """ --> 420 download_config = self.download_config.copy() 421 download_config.extract_compressed_file = False 422 if download_config.download_desc is None: File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/download/download_config.py:94, in DownloadConfig.copy(self) 93 def copy(self) -> "DownloadConfig": ---> 94 return self.__class__({k: copy.deepcopy(v) for k, v in self.__dict__.items()}) File ~/miniconda3/envs/test-env/lib/python3.10/site-packages/datasets/download/download_config.py:94, in <dictcomp>(.0) 93 def copy(self) -> "DownloadConfig": ---> 94 return self.__class__({k: copy.deepcopy(v) for k, v in self.__dict__.items()}) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) [... skipping similar frames: _deepcopy_dict at line 231 (2 times), deepcopy at line 146 (2 times)] File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) [... skipping similar frames: deepcopy at line 146 (1 times)] File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:206, in _deepcopy_list(x, memo, deepcopy) 204 append = y.append 205 for a in x: --> 206 append(deepcopy(a, memo)) 207 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:238, in _deepcopy_method(x, memo) 237 def _deepcopy_method(x, memo): # Copy instance methods --> 238 return type(x)(x.__func__, deepcopy(x.__self__, memo)) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) [... skipping similar frames: _deepcopy_dict at line 231 (3 times), deepcopy at line 146 (3 times), deepcopy at line 172 (3 times), _reconstruct at line 271 (2 times)] File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) [... skipping similar frames: _deepcopy_dict at line 231 (1 times), deepcopy at line 146 (1 times)] File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:265, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 263 if deep and args: 264 args = (deepcopy(arg, memo) for arg in args) --> 265 y = func(args) 266 if deep: 267 memo[id(x)] = y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:264, in <genexpr>(.0) 262 deep = memo is not None 263 if deep and args: --> 264 args = (deepcopy(arg, memo) for arg in args) 265 y = func(args) 266 if deep: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:211, in _deepcopy_tuple(x, memo, deepcopy) 210 def _deepcopy_tuple(x, memo, deepcopy=deepcopy): --> 211 y = [deepcopy(a, memo) for a in x] 212 # We're not going to put the tuple in the memo, but it's still important we 213 # check for it, in case the tuple contains recursive mutable structures. 214 try: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:211, in <listcomp>(.0) 210 def _deepcopy_tuple(x, memo, deepcopy=deepcopy): --> 211 y = [deepcopy(a, memo) for a in x] 212 # We're not going to put the tuple in the memo, but it's still important we 213 # check for it, in case the tuple contains recursive mutable structures. 214 try: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:211, in _deepcopy_tuple(x, memo, deepcopy) 210 def _deepcopy_tuple(x, memo, deepcopy=deepcopy): --> 211 y = [deepcopy(a, memo) for a in x] 212 # We're not going to put the tuple in the memo, but it's still important we 213 # check for it, in case the tuple contains recursive mutable structures. 214 try: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:211, in <listcomp>(.0) 210 def _deepcopy_tuple(x, memo, deepcopy=deepcopy): --> 211 y = [deepcopy(a, memo) for a in x] 212 # We're not going to put the tuple in the memo, but it's still important we 213 # check for it, in case the tuple contains recursive mutable structures. 214 try: File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File ~/miniconda3/envs/test-env/lib/python3.10/copy.py:161, in deepcopy(x, memo, _nil) 159 reductor = getattr(x, "__reduce_ex__", None) 160 if reductor is not None: --> 161 rv = reductor(4) 162 else: 163 reductor = getattr(x, "__reduce__", None) TypeError: cannot pickle '_contextvars.Context' object ``` </details> ### Expected behavior If I choose to load the file from the public bucket with `anon=True` passed - everything works, so I expected loading from the private bucket to work as well ### Environment info - `datasets` version: 2.14.6 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.10.13 - Huggingface_hub version: 0.19.1 - PyArrow version: 14.0.1 - Pandas version: 1.5.3 - s3fs version: 2023.10.0 - fsspec version: 2023.10.0 - aiobotocore version: 2.7.0	2023-11-13T21:27:43	2024-07-30T12:35:09	null	https://github.com/huggingface/datasets/issues/6407	null	6,407	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	CI Build PR Documentation is broken: ImportError: cannot import name 'TypeAliasType' from 'typing_extensions'	closed	Our CI Build PR Documentation is broken. See: https://github.com/huggingface/datasets/actions/runs/6799554060/job/18486828777?pr=6390 ``` ImportError: cannot import name 'TypeAliasType' from 'typing_extensions' ```	2023-11-13T11:36:10	2023-11-14T10:05:36	2023-11-14T10:05:36	https://github.com/huggingface/datasets/issues/6406	null	6,406	false	[]
1,990,358,743	ConfigNamesError on a simple CSV file	closed	See https://huggingface.co/datasets/Nguyendo1999/mmath/discussions/1 ``` Error code: ConfigNamesError Exception: TypeError Message: __init__() missing 1 required positional argument: 'dtype' Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 65, in compute_config_names_response for config in sorted(get_dataset_config_names(path=dataset, token=hf_token)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names dataset_module = dataset_module_factory( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1512, in dataset_module_factory raise e1 from None File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1489, in dataset_module_factory return HubDatasetModuleFactoryWithoutScript( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1039, in get_module dataset_infos = DatasetInfosDict.from_dataset_card_data(dataset_card_data) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 468, in from_dataset_card_data dataset_info = DatasetInfo._from_yaml_dict(dataset_card_data["dataset_info"]) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 399, in _from_yaml_dict yaml_data["features"] = Features._from_yaml_list(yaml_data["features"]) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1838, in _from_yaml_list return cls.from_dict(from_yaml_inner(yaml_data)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1690, in from_dict obj = generate_from_dict(dic) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1345, in generate_from_dict return {key: generate_from_dict(value) for key, value in obj.items()} File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1345, in <dictcomp> return {key: generate_from_dict(value) for key, value in obj.items()} File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1353, in generate_from_dict return class_type(**{k: v for k, v in obj.items() if k in field_names}) TypeError: __init__() missing 1 required positional argument: 'dtype' ``` This is the CSV file: https://huggingface.co/datasets/Nguyendo1999/mmath/blob/dbcdd7c2c6fc447f852ec136a7532292802bb46f/math_train.csv	2023-11-13T10:28:29	2023-11-13T20:01:24	2023-11-13T20:01:24	https://github.com/huggingface/datasets/issues/6405	null	6,405	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	Support pyarrow 14.0.1 and fix vulnerability CVE-2023-47248	closed	Support `pyarrow` 14.0.1 and fix vulnerability [CVE-2023-47248](https://github.com/advisories/GHSA-5wvp-7f3h-6wmm). Fix #6396.	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" }	6,404	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	Cannot import datasets on google colab (python 3.10.12)	closed	### Describe the bug I'm trying A full colab demo notebook of zero-shot-distillation from https://github.com/huggingface/transformers/tree/main/examples/research_projects/zero-shot-distillation but i got this type of error when importing datasets on my google colab (python version is 3.10.12) ![image](https://github.com/huggingface/datasets/assets/15389235/6f7758a2-681d-4436-87d0-5e557838e368) I found the same problem that have been solved in [#3326 ] but it seem still error on the google colab. I can't try on my local using jupyter notebook because of my laptop resource doesn't fulfill the requirements. Please can anyone help me solve this problem. Thank you 😅 ### Steps to reproduce the bug Error: ``` --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) [<ipython-input-8-b6e092f83978>](https://localhost:8080/#) in <cell line: 1>() ----> 1 from datasets import load_dataset 2 3 # Print all the available datasets 4 from huggingface_hub import list_datasets 5 print([dataset.id for dataset in list_datasets()]) 6 frames [/usr/lib/python3.10/functools.py](https://localhost:8080/#) in update_wrapper(wrapper, wrapped, assigned, updated) 59 # Issue #17482: set __wrapped__ last so we don't inadvertently copy it 60 # from the wrapped function when updating __dict__ ---> 61 wrapper.__wrapped__ = wrapped 62 # Return the wrapper so this can be used as a decorator via partial() 63 return wrapper AttributeError: readonly attribute ``` ### Expected behavior Run success on Google Colab (free) ### Environment info Windows 11 x64, Google Colab free	2023-11-13T08:14:43	2023-11-16T05:04:22	2023-11-16T05:04:21	https://github.com/huggingface/datasets/issues/6403	null	6,403	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	Update torch_formatter.py	closed	Ensure PyTorch images are converted to (C, H, W) instead of (H, W, C). See #6394 for motivation.	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" }	6,402	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	dataset = load_dataset("Hyperspace-Technologies/scp-wiki-text") not working	closed	### Describe the bug ``` (datasets) mruserbox@guru-X99:/media/10TB_HHD/_LLM_DATASETS$ python dataset.py Downloading readme: 100%\|███████████████████████████████████\| 360/360 [00:00<00:00, 2.16MB/s] Downloading data: 100%\|█████████████████████████████████\| 65.1M/65.1M [00:19<00:00, 3.38MB/s] Downloading data: 100%\|█████████████████████████████████\| 6.35k/6.35k [00:00<00:00, 20.7kB/s] Downloading data: 100%\|█████████████████████████████████\| 7.29M/7.29M [00:01<00:00, 3.99MB/s] Downloading data files: 100%\|██████████████████████████████████\| 3/3 [00:21<00:00, 7.14s/it] Extracting data files: 100%\|█████████████████████████████████\| 3/3 [00:00<00:00, 1624.23it/s] Generating train split: 100%\|█████████████\| 314294/314294 [00:00<00:00, 668186.58 examples/s] Generating validation split: 120 examples [00:00, 100422.28 examples/s] Generating test split: 100%\|████████████████\| 34922/34922 [00:00<00:00, 754683.41 examples/s] Traceback (most recent call last): File "/media/10TB_HHD/_LLM_DATASETS/dataset.py", line 3, in <module> dataset = load_dataset("Hyperspace-Technologies/scp-wiki-text") File "/home/mruserbox/miniconda3/envs/datasets/lib/python3.10/site-packages/datasets/load.py", line 2153, in load_dataset builder_instance.download_and_prepare( File "/home/mruserbox/miniconda3/envs/datasets/lib/python3.10/site-packages/datasets/builder.py", line 954, in download_and_prepare self._download_and_prepare( File "/home/mruserbox/miniconda3/envs/datasets/lib/python3.10/site-packages/datasets/builder.py", line 1067, in _download_and_prepare verify_splits(self.info.splits, split_dict) File "/home/mruserbox/miniconda3/envs/datasets/lib/python3.10/site-packages/datasets/utils/info_utils.py", line 93, in verify_splits raise UnexpectedSplits(str(set(recorded_splits) - set(expected_splits))) datasets.utils.info_utils.UnexpectedSplits: {'validation'} ``` ### Steps to reproduce the bug Name: `dataset.py` Code: ``` from datasets import load_dataset dataset = load_dataset("Hyperspace-Technologies/scp-wiki-text") ``` ### Expected behavior Run without errors ### Environment info ``` name: datasets channels: - defaults dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - bzip2=1.0.8=h7b6447c_0 - ca-certificates=2023.08.22=h06a4308_0 - ld_impl_linux-64=2.38=h1181459_1 - libffi=3.4.4=h6a678d5_0 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - libuuid=1.41.5=h5eee18b_0 - ncurses=6.4=h6a678d5_0 - openssl=3.0.12=h7f8727e_0 - python=3.10.13=h955ad1f_0 - readline=8.2=h5eee18b_0 - setuptools=68.0.0=py310h06a4308_0 - sqlite=3.41.2=h5eee18b_0 - tk=8.6.12=h1ccaba5_0 - wheel=0.41.2=py310h06a4308_0 - xz=5.4.2=h5eee18b_0 - zlib=1.2.13=h5eee18b_0 - pip: - aiohttp==3.8.6 - aiosignal==1.3.1 - async-timeout==4.0.3 - attrs==23.1.0 - certifi==2023.7.22 - charset-normalizer==3.3.2 - click==8.1.7 - datasets==2.14.6 - dill==0.3.7 - filelock==3.13.1 - frozenlist==1.4.0 - fsspec==2023.10.0 - huggingface-hub==0.19.0 - idna==3.4 - multidict==6.0.4 - multiprocess==0.70.15 - numpy==1.26.1 - openai==0.27.8 - packaging==23.2 - pandas==2.1.3 - pip==23.3.1 - platformdirs==4.0.0 - pyarrow==14.0.1 - python-dateutil==2.8.2 - pytz==2023.3.post1 - pyyaml==6.0.1 - requests==2.31.0 - six==1.16.0 - tomli==2.0.1 - tqdm==4.66.1 - typer==0.9.0 - typing-extensions==4.8.0 - tzdata==2023.3 - urllib3==2.0.7 - xxhash==3.4.1 - yarl==1.9.2 prefix: /home/mruserbox/miniconda3/envs/datasets ```	2023-11-11T04:09:07	2023-11-20T17:45:20	2023-11-20T17:45:20	https://github.com/huggingface/datasets/issues/6401	null	6,401	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```" ]
1,988,571,317	Safely load datasets by disabling execution of dataset loading script	closed	### Feature request Is there a way to disable execution of dataset loading script using `load_dataset`? This is a security vulnerability that could lead to arbitrary code execution. Any suggested workarounds are welcome as well. ### Motivation This is a security vulnerability that could lead to arbitrary code execution. ### Your contribution n/a	2023-11-10T23:48:29	2024-06-13T15:56:13	2024-06-13T15:56:13	https://github.com/huggingface/datasets/issues/6400	null	6,400	false	[ "great idea IMO\r\n\r\nthis could be a `trust_remote_code=True` flag like in transformers. We could also default to loading the Parquet conversion rather than executing code (for dataset repos that have both)", "@julien-c that would be great!", "We added the `trust_remote_code` argument to `load_dataset()` in `datasets` 2.16:\r\n- in the future users will have to pass trust_remote_code=True to use datasets with a script\r\n- for now we just show a warning when a dataset script is used\r\n- we fallback on the Hugging Face Parquet exports when possible (to keep compatibility with old datasets with scripts)\r\n\r\nSo feel free to use `trust_remote_code=False` in the meantime to disable loading from dataset loading scripts :)", "Passing `trust_remote_code=True` explicitly is now mandatory to load a dataset with a script since https://github.com/huggingface/datasets/pull/6954" ]
1,988,368,503	TypeError: Cannot convert pyarrow.lib.ChunkedArray to pyarrow.lib.Array	open	### Describe the bug Hi, I am preprocessing a large custom dataset with numpy arrays. I am running into this TypeError during writing in a dataset.map() function. I've tried decreasing writer batch size, but this error persists. This error does not occur for smaller datasets. Thank you! ### Steps to reproduce the bug Traceback (most recent call last): File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/multiprocess/pool.py", line 125, in worker result = (True, func(args, kwds)) File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 1354, in _write_generator_to_queue for i, result in enumerate(func(*kwargs)): File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 3493, in _map_single writer.write_batch(batch) File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/arrow_writer.py", line 555, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 243, in pyarrow.lib.array File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/arrow_writer.py", line 184, in __arrow_array__ out = numpy_to_pyarrow_listarray(data) File "/n/home12/yhwang/.conda/envs/lib/python3.10/site-packages/datasets/features/features.py", line 1394, in numpy_to_pyarrow_listarray values = pa.ListArray.from_arrays(offsets, values) File "pyarrow/array.pxi", line 2004, in pyarrow.lib.ListArray.from_arrays TypeError: Cannot convert pyarrow.lib.ChunkedArray to pyarrow.lib.Array ### Expected behavior Type should not be a ChunkedArray ### Environment info datasets v2.14.5 arrow v1.2.3 pyarrow v12.0.1	2023-11-10T20:48:46	2024-06-22T00:13:48	null	https://github.com/huggingface/datasets/issues/6399	null	6,399	false	[ "Seconding encountering this issue." ]
1,987,786,446	Remove redundant condition in builders	closed	Minor refactoring to remove redundant condition.	2023-11-10T14:56:43	2023-11-14T10:49:15	2023-11-14T10:43:00	https://github.com/huggingface/datasets/pull/6398	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6398", "html_url": "https://github.com/huggingface/datasets/pull/6398", "diff_url": "https://github.com/huggingface/datasets/pull/6398.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6398.patch", "merged_at": "2023-11-14T10:43:00" }	6,398	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) \| 0.004475 / 0.011353 (-0.006878) \| 0.002840 / 0.011008 (-0.008168) \| 0.061544 / 0.038508 (0.023036) \| 0.031237 / 0.023109 (0.008128) \| 0.243270 / 0.275898 (-0.032628) \| 0.271903 / 0.323480 (-0.051577) \| 0.002906 / 0.007986 (-0.005080) \| 0.003118 / 0.004328 (-0.001210) \| 0.047362 / 0.004250 (0.043112) \| 0.047840 / 0.037052 (0.010788) \| 0.244044 / 0.258489 (-0.014445) \| 0.279310 / 0.293841 (-0.014531) \| 0.023408 / 0.128546 (-0.105138) \| 0.007110 / 0.075646 (-0.068536) \| 0.207328 / 0.419271 (-0.211943) \| 0.058463 / 0.043533 (0.014930) \| 0.245631 / 0.255139 (-0.009508) \| 0.267755 / 0.283200 (-0.015445) \| 0.018147 / 0.141683 (-0.123536) \| 1.086877 / 1.452155 (-0.365278) \| 1.155380 / 1.492716 (-0.337337) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091925 / 0.018006 (0.073919) \| 0.299858 / 0.000490 (0.299368) \| 0.000232 / 0.000200 (0.000032) \| 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) \| 0.018416 / 0.037411 (-0.018995) \| 0.062608 / 0.014526 (0.048082) \| 0.073897 / 0.176557 (-0.102660) \| 0.120216 / 0.737135 (-0.616919) \| 0.075788 / 0.296338 (-0.220550) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287823 / 0.215209 (0.072614) \| 2.797546 / 2.077655 (0.719891) \| 1.470878 / 1.504120 (-0.033242) \| 1.347497 / 1.541195 (-0.193698) \| 1.363837 / 1.468490 (-0.104653) \| 0.400069 / 4.584777 (-4.184708) \| 2.338870 / 3.745712 (-1.406842) \| 2.564075 / 5.269862 (-2.705787) \| 1.568454 / 4.565676 (-2.997222) \| 0.047103 / 0.424275 (-0.377172) \| 0.004783 / 0.007607 (-0.002824) \| 0.345244 / 0.226044 (0.119200) \| 3.407752 / 2.268929 (1.138823) \| 1.826552 / 55.444624 (-53.618073) \| 1.536714 / 6.876477 (-5.339763) \| 1.543138 / 2.142072 (-0.598934) \| 0.478996 / 4.805227 (-4.326232) \| 0.099580 / 6.500664 (-6.401085) \| 0.041994 / 0.075469 (-0.033475) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.947106 / 1.841788 (-0.894682) \| 11.391262 / 8.074308 (3.316954) \| 10.531141 / 10.191392 (0.339749) \| 0.141497 / 0.680424 (-0.538927) \| 0.014214 / 0.534201 (-0.519987) \| 0.269346 / 0.579283 (-0.309937) \| 0.268129 / 0.434364 (-0.166235) \| 0.309496 / 0.540337 (-0.230841) \| 0.429207 / 1.386936 (-0.957729) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004770 / 0.011353 (-0.006583) \| 0.002878 / 0.011008 (-0.008130) \| 0.048248 / 0.038508 (0.009740) \| 0.051068 / 0.023109 (0.027959) \| 0.272076 / 0.275898 (-0.003822) \| 0.292423 / 0.323480 (-0.031057) \| 0.004016 / 0.007986 (-0.003970) \| 0.002522 / 0.004328 (-0.001807) \| 0.047617 / 0.004250 (0.043367) \| 0.038168 / 0.037052 (0.001115) \| 0.275236 / 0.258489 (0.016746) \| 0.303811 / 0.293841 (0.009970) \| 0.023816 / 0.128546 (-0.104730) \| 0.007177 / 0.075646 (-0.068469) \| 0.053453 / 0.419271 (-0.365818) \| 0.032425 / 0.043533 (-0.011108) \| 0.271620 / 0.255139 (0.016481) \| 0.289618 / 0.283200 (0.006418) \| 0.017986 / 0.141683 (-0.123697) \| 1.154225 / 1.452155 (-0.297930) \| 1.224244 / 1.492716 (-0.268472) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090477 / 0.018006 (0.072471) \| 0.299461 / 0.000490 (0.298971) \| 0.000224 / 0.000200 (0.000024) \| 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) \| 0.022043 / 0.037411 (-0.015369) \| 0.070327 / 0.014526 (0.055801) \| 0.080132 / 0.176557 (-0.096425) \| 0.120007 / 0.737135 (-0.617128) \| 0.083037 / 0.296338 (-0.213301) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294538 / 0.215209 (0.079329) \| 2.882791 / 2.077655 (0.805136) \| 1.582923 / 1.504120 (0.078803) \| 1.457091 / 1.541195 (-0.084104) \| 1.536149 / 1.468490 (0.067659) \| 0.401539 / 4.584777 (-4.183238) \| 2.440919 / 3.745712 (-1.304793) \| 2.503108 / 5.269862 (-2.766753) \| 1.509216 / 4.565676 (-3.056460) \| 0.046267 / 0.424275 (-0.378008) \| 0.004790 / 0.007607 (-0.002817) \| 0.336137 / 0.226044 (0.110093) \| 3.331655 / 2.268929 (1.062726) \| 1.954228 / 55.444624 (-53.490396) \| 1.686637 / 6.876477 (-5.189840) \| 1.650278 / 2.142072 (-0.491794) \| 0.473895 / 4.805227 (-4.331333) \| 0.096908 / 6.500664 (-6.403756) \| 0.040387 / 0.075469 (-0.035082) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.972999 / 1.841788 (-0.868789) \| 11.978367 / 8.074308 (3.904059) \| 10.861092 / 10.191392 (0.669699) \| 0.129054 / 0.680424 (-0.551369) \| 0.015988 / 0.534201 (-0.518213) \| 0.268827 / 0.579283 (-0.310456) \| 0.271714 / 0.434364 (-0.162649) \| 0.304045 / 0.540337 (-0.236293) \| 0.413158 / 1.386936 (-0.973778) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9e4348a233a75907c305b3159ac9cb183cf30ea5 \"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) \| 0.005286 / 0.011353 (-0.006067) \| 0.002860 / 0.011008 (-0.008149) \| 0.062449 / 0.038508 (0.023941) \| 0.035346 / 0.023109 (0.012237) \| 0.241685 / 0.275898 (-0.034213) \| 0.268116 / 0.323480 (-0.055364) \| 0.003050 / 0.007986 (-0.004935) \| 0.003134 / 0.004328 (-0.001194) \| 0.048818 / 0.004250 (0.044567) \| 0.049187 / 0.037052 (0.012135) \| 0.247395 / 0.258489 (-0.011094) \| 0.280301 / 0.293841 (-0.013540) \| 0.023801 / 0.128546 (-0.104745) \| 0.007653 / 0.075646 (-0.067994) \| 0.204185 / 0.419271 (-0.215087) \| 0.071251 / 0.043533 (0.027718) \| 0.244409 / 0.255139 (-0.010730) \| 0.262363 / 0.283200 (-0.020836) \| 0.018631 / 0.141683 (-0.123052) \| 1.110152 / 1.452155 (-0.342003) \| 1.165093 / 1.492716 (-0.327624) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.099536 / 0.018006 (0.081530) \| 0.309598 / 0.000490 (0.309109) \| 0.000207 / 0.000200 (0.000007) \| 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) \| 0.019213 / 0.037411 (-0.018198) \| 0.069296 / 0.014526 (0.054770) \| 0.074752 / 0.176557 (-0.101804) \| 0.121314 / 0.737135 (-0.615822) \| 0.081274 / 0.296338 (-0.215065) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281345 / 0.215209 (0.066136) \| 2.755435 / 2.077655 (0.677780) \| 1.453358 / 1.504120 (-0.050762) \| 1.328222 / 1.541195 (-0.212973) \| 1.392281 / 1.468490 (-0.076209) \| 0.410539 / 4.584777 (-4.174238) \| 2.452072 / 3.745712 (-1.293640) \| 2.777757 / 5.269862 (-2.492105) \| 1.656719 / 4.565676 (-2.908958) \| 0.046844 / 0.424275 (-0.377431) \| 0.004785 / 0.007607 (-0.002822) \| 0.336567 / 0.226044 (0.110522) \| 3.317564 / 2.268929 (1.048635) \| 1.830737 / 55.444624 (-53.613888) \| 1.528464 / 6.876477 (-5.348013) \| 1.620527 / 2.142072 (-0.521545) \| 0.480662 / 4.805227 (-4.324565) \| 0.100819 / 6.500664 (-6.399845) \| 0.042501 / 0.075469 (-0.032968) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.962593 / 1.841788 (-0.879195) \| 12.508048 / 8.074308 (4.433740) \| 11.117398 / 10.191392 (0.926006) \| 0.131265 / 0.680424 (-0.549159) \| 0.014469 / 0.534201 (-0.519732) \| 0.271627 / 0.579283 (-0.307656) \| 0.274966 / 0.434364 (-0.159398) \| 0.313260 / 0.540337 (-0.227077) \| 0.444741 / 1.386936 (-0.942195) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated 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) \| 0.003383 / 0.011008 (-0.007626) \| 0.048792 / 0.038508 (0.010284) \| 0.052821 / 0.023109 (0.029712) \| 0.267123 / 0.275898 (-0.008775) \| 0.293604 / 0.323480 (-0.029876) \| 0.003968 / 0.007986 (-0.004018) \| 0.002594 / 0.004328 (-0.001735) \| 0.047690 / 0.004250 (0.043439) \| 0.040236 / 0.037052 (0.003183) \| 0.267805 / 0.258489 (0.009315) \| 0.310543 / 0.293841 (0.016702) \| 0.025707 / 0.128546 (-0.102839) \| 0.008012 / 0.075646 (-0.067634) \| 0.054460 / 0.419271 (-0.364812) \| 0.033545 / 0.043533 (-0.009988) \| 0.270166 / 0.255139 (0.015027) \| 0.285965 / 0.283200 (0.002765) \| 0.019391 / 0.141683 (-0.122292) \| 1.144991 / 1.452155 (-0.307164) \| 1.198491 / 1.492716 (-0.294225) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094757 / 0.018006 (0.076751) \| 0.306712 / 0.000490 (0.306222) \| 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) \| 0.020995 / 0.037411 (-0.016417) \| 0.070293 / 0.014526 (0.055767) \| 0.081441 / 0.176557 (-0.095116) \| 0.119538 / 0.737135 (-0.617597) \| 0.081454 / 0.296338 (-0.214885) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293451 / 0.215209 (0.078242) \| 2.880378 / 2.077655 (0.802723) \| 1.572547 / 1.504120 (0.068427) \| 1.439172 / 1.541195 (-0.102023) \| 1.506343 / 1.468490 (0.037853) \| 0.402764 / 4.584777 (-4.182013) \| 2.501341 / 3.745712 (-1.244371) \| 2.538494 / 5.269862 (-2.731367) \| 1.524306 / 4.565676 (-3.041371) \| 0.046401 / 0.424275 (-0.377874) \| 0.004781 / 0.007607 (-0.002826) \| 0.349448 / 0.226044 (0.123404) \| 3.416181 / 2.268929 (1.147252) \| 1.964204 / 55.444624 (-53.480420) \| 1.648564 / 6.876477 (-5.227912) \| 1.675977 / 2.142072 (-0.466095) \| 0.475717 / 4.805227 (-4.329511) \| 0.098416 / 6.500664 (-6.402248) \| 0.041212 / 0.075469 (-0.034257) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.975928 / 1.841788 (-0.865860) \| 12.066648 / 8.074308 (3.992340) \| 10.943181 / 10.191392 (0.751789) \| 0.149687 / 0.680424 (-0.530736) \| 0.015107 / 0.534201 (-0.519094) \| 0.268950 / 0.579283 (-0.310333) \| 0.280419 / 0.434364 (-0.153945) \| 0.305263 / 0.540337 (-0.235074) \| 0.408486 / 1.386936 (-0.978450) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#344086a7a1707ef20b57399f813ef64ce679e956 \"CML watermark\")\n" ]
1,987,622,152	Raise a different exception for inexisting dataset vs files without known extension	closed	See https://github.com/huggingface/datasets-server/issues/2082#issuecomment-1805716557 We have the same error for: - https://huggingface.co/datasets/severo/a_dataset_that_does_not_exist: a dataset that does not exist - https://huggingface.co/datasets/severo/test_files_without_extension: a dataset with files without a known extension ``` >>> import datasets >>> datasets.get_dataset_config_names('severo/a_dataset_that_does_not_exist') Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names dataset_module = dataset_module_factory( File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1508, in dataset_module_factory raise FileNotFoundError( FileNotFoundError: Couldn't find a dataset script at /home/slesage/hf/datasets-server/services/worker/severo/a_dataset_that_does_not_exist/a_dataset_that_does_not_exist.py or any data file in the same directory. Couldn't find 'severo/a_dataset_that_does_not_exist' on the Hugging Face Hub either: FileNotFoundError: Dataset 'severo/a_dataset_that_does_not_exist' doesn't exist on the Hub. If the repo is private or gated, make sure to log in with `huggingface-cli login`. >>> datasets.get_dataset_config_names('severo/test_files_without_extension') Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names dataset_module = dataset_module_factory( File "/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1508, in dataset_module_factory raise FileNotFoundError( FileNotFoundError: Couldn't find a dataset script at /home/slesage/hf/datasets-server/services/worker/severo/test_files_without_extension/test_files_without_extension.py or any data file in the same directory. Couldn't find 'severo/test_files_without_extension' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in severo/test_files_without_extension. ``` To differentiate, we must parse the error message (only the end is different). We should have a different exception for these two errors.	2023-11-10T13:22:14	2023-11-22T15:12:34	2023-11-22T15:12:34	https://github.com/huggingface/datasets/issues/6397	null	6,397	false	[]
1,987,308,077	Issue with pyarrow 14.0.1	closed	See https://github.com/huggingface/datasets-server/pull/2089 for reference ``` from datasets import (Array2D, Dataset, Features) feature_type = Array2D(shape=(2, 2), dtype="float32") content = [[0.0, 0.0], [0.0, 0.0]] features = Features({"col": feature_type}) dataset = Dataset.from_dict({"col": [content]}, features=features) ``` generates ``` /home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py:648: FutureWarning: pyarrow.PyExtensionType is deprecated and will refuse deserialization by default. Instead, please derive from pyarrow.ExtensionType and implement your own serialization mechanism. pa.PyExtensionType.__init__(self, self.storage_dtype) /home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py:1661: RuntimeWarning: pickle-based deserialization of pyarrow.PyExtensionType subclasses is disabled by default; if you only ingest trusted data files, you may re-enable this using `pyarrow.PyExtensionType.set_auto_load(True)`. In the future, Python-defined extension subclasses should derive from pyarrow.ExtensionType (not pyarrow.PyExtensionType) and implement their own serialization mechanism. obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema} /home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py:1661: FutureWarning: pyarrow.PyExtensionType is deprecated and will refuse deserialization by default. Instead, please derive from pyarrow.ExtensionType and implement your own serialization mechanism. obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema} Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 924, in from_dict return cls(pa_table, info=info, split=split) File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 693, in __init__ inferred_features = Features.from_arrow_schema(arrow_table.schema) File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1661, in from_arrow_schema obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema} File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1661, in <dictcomp> obj = {field.name: generate_from_arrow_type(field.type) for field in pa_schema} File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1381, in generate_from_arrow_type return Value(dtype=_arrow_to_datasets_dtype(pa_type)) File "/home/slesage/hf/datasets-server/libs/libcommon/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 111, in _arrow_to_datasets_dtype raise ValueError(f"Arrow type {arrow_type} does not have a datasets dtype equivalent.") ValueError: Arrow type extension<arrow.py_extension_type<pyarrow.lib.UnknownExtensionType>> does not have a datasets dtype equivalent. ```	2023-11-10T10:02:12	2023-11-14T10:23:30	2023-11-14T10:23:30	https://github.com/huggingface/datasets/issues/6396	null	6,396	false	[ "Looks like we should stop using `PyExtensionType` and use `ExtensionType` instead\r\n\r\nsee https://github.com/apache/arrow/commit/f14170976372436ec1d03a724d8d3f3925484ecf", "https://github.com/huggingface/datasets-server/pull/2089#pullrequestreview-1724449532\r\n\r\n> Yes, I understand now: they have disabled their `PyExtensionType` and we use it in `datasets` for arrays... ", "related?\r\n\r\nhttps://huggingface.co/datasets/ssbuild/tools_data/discussions/1#654e663b77c8ec680d10479c", "> related?\r\n>\r\n> https://huggingface.co/datasets/ssbuild/tools_data/discussions/1#654e663b77c8ec680d10479c\r\n\r\nNo, related to https://github.com/huggingface/datasets/issues/5706", "Running the following is a workaround:\r\n\r\n```\r\nimport pyarrow\r\npyarrow.PyExtensionType.set_auto_load(True)\r\n```" ]
1,986,484,124	Add ability to set lock type	closed	### Feature request Allow setting file lock type, maybe from an environment variable Currently, it only depends on whether fnctl is available: https://github.com/huggingface/datasets/blob/12ebe695b4748c5a26e08b44ed51955f74f5801d/src/datasets/utils/filelock.py#L463-L470C16 ### Motivation In my environment, flock isn't supported on a network attached drive ### Your contribution I'll be happy to submit a pr.	2023-11-09T22:12:30	2023-11-23T18:50:00	2023-11-23T18:50:00	https://github.com/huggingface/datasets/issues/6395	null	6,395	false	[ "We've replaced our filelock implementation with the `filelock` package, so their repo is the right place to request this feature.\r\n\r\nIn the meantime, the following should work: \r\n```python\r\nimport filelock\r\nfilelock.FileLock = filelock.SoftFileLock\r\n\r\nimport datasets\r\n...\r\n```" ]
1,985,947,116	TorchFormatter images (H, W, C) instead of (C, H, W) format	closed	### Describe the bug Using .set_format("torch") leads to images having shape (H, W, C), the same as in numpy. However, pytorch normally uses (C, H, W) format. Maybe I'm missing something but this makes the format a lot less useful as I then have to permute it anyways. If not using the format it is possible to directly use torchvision transforms but any non-transformed value will not be a tensor. Is there a reason for this choice? ### Steps to reproduce the bug ```python from datasets import Dataset, Features, Audio, Image images = ["path/to/image.png"] * 10 features = Features({"image": Image()}) ds = Dataset.from_dict({"image": images}, features=features) ds = ds.with_format("torch") ds[0]["image"].shape ``` ```python torch.Size([512, 512, 4]) ``` ### Expected behavior ```python from datasets import Dataset, Features, Audio, Image images = ["path/to/image.png"] * 10 features = Features({"image": Image()}) ds = Dataset.from_dict({"image": images}, features=features) ds = ds.with_format("torch") ds[0]["image"].shape ``` ```python torch.Size([4, 512, 512]) ``` ### Environment info - `datasets` version: 2.14.6 - Platform: Linux-6.5.9-100.fc37.x86_64-x86_64-with-glibc2.31 - Python version: 3.11.6 - Huggingface_hub version: 0.18.0 - PyArrow version: 14.0.1 - Pandas version: 2.1.2	2023-11-09T16:02:15	2024-04-11T12:40:16	2024-04-11T12:40:16	https://github.com/huggingface/datasets/issues/6394	null	6,394	false	[ "Here's a PR for that. https://github.com/huggingface/datasets/pull/6402\r\n\r\nIt's not backward compatible, unfortunately. ", "Just ran into this working on data lib that's attempting to achieve common interfaces across hf datasets, webdataset, native torch style datasets. The defacto standards for image tensors are numpy == HWC, torch.Tensor == CHW. \r\n\r\nI had to drop use of 'torch' formatting because as is (H, W, C) makes it incompatible with pretty much all standard torch vision processing (torchvision, etc) including model inputs themselves... not sure what the breakage scope would be, but might be worth considering a breaking change since I'm not aware of many use cases where a torch.Tensor image is expected to be in HWC form. And if I set the format to 'torch', I'd expect to be able to apply torchvision transforms, etc directly to the output...\r\n\r\nEDIT: For 'torch' output to be compatible with torch conventions (namely torchvision for images), should follow this https://pytorch.org/vision/0.17/transforms.html#supported-input-types-and-conventions\r\n\r\nattn @lhoestq \r\n\r\n", "We can define something like `.with_format(\"torch\", image_data_format=\"channels_first\")` and recommend using this in the docs maybe ? also cc @NielsRogge ", "Sounds good to me. I guess it's not allowed to use the channels first format by default for backwards compatibility purposes?", "This works, but am wondering how widespread the use of the function is for image datasets? My hunch would be that it's not used widely enough with image datasets to favour backwards compat (keeping default channels_last) over clumsiness of needing this to be 'correct' for typical use.. but don't have the data to back that up.", "I see. I just checked in the HF libraries and it shouldn't break anything. And to be consistent with them we should actually use C H W. For example `transformers` image processors use C H W by default too.\r\n\r\nSo I'm ok with doing a breaking change to make it consistent with `transformers`, `torchvision`, etc.", "Since it is quite connected, the proposed PR #6402 will not work for monochrome `PIL` images since they only have 2 dimensions as `numpy `arrays. [Torchvision ](https://pytorch.org/vision/stable/_modules/torchvision/transforms/functional.html#pil_to_tensor) adds a channel before permuting. Would that make sense here as well?", "@Modexus yes, indeed that would make sense as torch expects 1, H, W for monochrome, not H,W as you'd often see in numpy (via PIL), OpenCV, etc.\r\n\r\nThe reference should be the torchvision fn https://pytorch.org/vision/main/_modules/torchvision/transforms/functional.html#pil_to_tensor", "My PR now should handle monochrome PIL image. Thanks for the heads up :)" ]
1,984,913,259	Filter occasionally hangs	closed	### Describe the bug A call to `.filter` occasionally hangs (after the filter is complete, according to tqdm) There is a trace produced ``` Exception ignored in: <function Dataset.__del__ at 0x7efb48130c10> Traceback (most recent call last): File "/usr/lib/python3/dist-packages/datasets/arrow_dataset.py", line 1366, in __del__ if hasattr(self, "_indices"): File "/usr/lib/python3/dist-packages/composer/core/engine.py", line 123, in sigterm_handler sys.exit(128 + signal) SystemExit: 143 ``` but I'm not sure if the trace is actually from `datasets`, or from surrounding code that is trying to clean up after datasets gets stuck. Unfortunately I can't reproduce this issue anywhere close to reliably. It happens infrequently when using `num_procs > 1`. Anecdotally I started seeing it when using larger datasets (~10M samples). ### Steps to reproduce the bug N/A see description ### Expected behavior map/filter calls always complete sucessfully ### Environment info - `datasets` version: 2.14.6 - Platform: Linux-5.4.0-137-generic-x86_64-with-glibc2.31 - Python version: 3.10.13 - Huggingface_hub version: 0.17.3 - PyArrow version: 13.0.0 - Pandas version: 2.1.2	2023-11-09T06:18:30	2025-02-22T00:49:19	2025-02-22T00:49:19	https://github.com/huggingface/datasets/issues/6393	null	6,393	false	[ "It looks like I may not be the first to encounter this: https://github.com/huggingface/datasets/issues/3172", "Adding some more information, it seems to occur more frequently with large (millions of samples) datasets.", "More information. My code is structured as (1) load (2) map (3) filter (4) filter. It was always the second filter that failed. Combining the two filters into one seems to reliably work.", "@lhoestq it'd be great if someone had a chance to look at this. I suspect it is impacting many users given the other issue that I linked.", "Hi ! Sorry for the late response. Was it happening after the first or the second filter ?\r\n\r\nIt looks like an issue with the garbage collector (which makes it random). Maybe datasets created with `filter` are not always handled properly ? cc @mariosasko", "It was after the second filter (and combining the two filters into one seemingly resolved it). I obviously haven't tried all settings to know that these details are causal, but it did work for me.", "Thanks, that's good to know.\r\n\r\nThe stacktrace suggests an issue when `del self._indices` is called, which happens when a filtered dataset falls out of scope. The indices are a PyArrow table memory mapped from disk, so I'm not quite sure how calling `del` on it can cause this issue. We do `del self._indices` to make sure the file on disk is not used anymore by the current process and avoid e.g. permission errors.\r\n\r\nHopefully we can find a way to reproduce this error, otherwise it will be quite hard to understand what happened", "Yeah, I have a reliable repro, but it is not even close to minimal and uses a dataset I can't share. Perhaps you could try getting close to my setting.\r\n\r\n(1) make a large (~20GB) jsonl with prompt/response pairs\r\n(2) load it on a linux machine (`dataset = load_dataset(...)`)\r\n(3) map a tokenizer to it, with multiprocessing (`tokenized_dataset = dataset.map(...)`)\r\n(4) filter it once based on something, with multiprocessing (`filtered_1 = tokenized_dataset.filter(...)`)\r\n(5) filter it again based on something, with multiprocessing (`filtered_2 = filtered_1.filter(...)`)\r\n\r\nI included the variable names just in case it is relevant that I was creating new datasets each time, not overwriting the same variable.", "@lhoestq I have another version of the repro that seems fairly reliably. I have lots of jsonl files, and I iteratively load each one with `load_dataset('json', data_files='path/to/my/file.jsonl', streaming=False, split='train')` and then `dataset.map(..., num_proc=<int>)`. This iteration hangs in a random place each time. So seems like there is a bug that hits with _some_ frequency.", "With `num_proc=None` it works fine.", "I am also having similar issue to #3172 when trying to tokenize the data. My dataset contains 10M samples. Is there anything that could be done without having to split up the processing into multiple datasets?", "https://github.com/huggingface/datasets/pull/7411 seems to have fixed the issue for me, curious if it resolves others issues too." ]
1,984,369,545	`push_to_hub` is not robust to hub closing connection	closed	### Describe the bug Like to #6172, `push_to_hub` will crash if Hub resets the connection and raise the following error: ``` Pushing dataset shards to the dataset hub: 32%\|███▏ \| 54/171 [06:38<14:23, 7.38s/it] Traceback (most recent call last): File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 715, in urlopen httplib_response = self._make_request( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 467, in _make_request six.raise_from(e, None) File "<string>", line 3, in raise_from File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 462, in _make_request httplib_response = conn.getresponse() File "/usr/lib/python3.8/http/client.py", line 1348, in getresponse response.begin() File "/usr/lib/python3.8/http/client.py", line 316, in begin version, status, reason = self._read_status() File "/usr/lib/python3.8/http/client.py", line 285, in _read_status raise RemoteDisconnected("Remote end closed connection without" http.client.RemoteDisconnected: Remote end closed connection without response During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/adapters.py", line 486, in send resp = conn.urlopen( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 799, in urlopen retries = retries.increment( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/util/retry.py", line 550, in increment raise six.reraise(type(error), error, _stacktrace) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/packages/six.py", line 769, in reraise raise value.with_traceback(tb) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 715, in urlopen httplib_response = self._make_request( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 467, in _make_request six.raise_from(e, None) File "<string>", line 3, in raise_from File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/urllib3/connectionpool.py", line 462, in _make_request httplib_response = conn.getresponse() File "/usr/lib/python3.8/http/client.py", line 1348, in getresponse response.begin() File "/usr/lib/python3.8/http/client.py", line 316, in begin version, status, reason = self._read_status() File "/usr/lib/python3.8/http/client.py", line 285, in _read_status raise RemoteDisconnected("Remote end closed connection without" urllib3.exceptions.ProtocolError: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response')) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py", line 383, in _wrapped_lfs_upload lfs_upload(operation=operation, lfs_batch_action=batch_action, token=token) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py", line 223, in lfs_upload _upload_multi_part(operation=operation, header=header, chunk_size=chunk_size, upload_url=upload_action["href"]) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py", line 319, in _upload_multi_part else _upload_parts_iteratively(operation=operation, sorted_parts_urls=sorted_parts_urls, chunk_size=chunk_size) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py", line 375, in _upload_parts_iteratively part_upload_res = http_backoff("PUT", part_upload_url, data=fileobj_slice) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_http.py", line 258, in http_backoff response = session.request(method=method, url=url, kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/sessions.py", line 589, in request resp = self.send(prep, send_kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/sessions.py", line 703, in send r = adapter.send(request, *kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_http.py", line 63, in send return super().send(request, args, *kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/adapters.py", line 501, in send raise ConnectionError(err, request=request) requests.exceptions.ConnectionError: (ProtocolError('Connection aborted.', RemoteDisconnected('Remote end closed connection without response')), '(Request ID: 2bab8c06-b701-4266-aead-fe2e0dc0e3ed)') The above exception was the direct cause of the following exception: Traceback (most recent call last): File "convert_to_hf.py", line 116, in <module> main() File "convert_to_hf.py", line 108, in main audio_dataset.push_to_hub( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/dataset_dict.py", line 1641, in push_to_hub repo_id, split, uploaded_size, dataset_nbytes, _, _ = self[split]._push_parquet_shards_to_hub( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 5308, in _push_parquet_shards_to_hub _retry( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 290, in _retry return func(func_args, *func_kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn return fn(args, *kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 828, in _inner return fn(self, args, *kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 3221, in upload_file commit_info = self.create_commit( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn return fn(args, *kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 828, in _inner return fn(self, args, *kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 2695, in create_commit upload_lfs_files( File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn return fn(args, **kwargs) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py", line 393, in upload_lfs_files _wrapped_lfs_upload(filtered_actions[0]) File "/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py", line 385, in _wrapped_lfs_upload raise RuntimeError(f"Error while uploading '{operation.path_in_repo}' to the Hub.") from exc RuntimeError: Error while uploading 'batch_19/train-00054-of-00171-932beb4082c034bf.parquet' to the Hub. ``` The function should retry if the operations fails, or at least offer a way to recover after such a failure. Right now, calling the function again will start sending all the parquets files leading to duplicates in the repository, with no guarantee that it will actually be pushed. Previously, it would crash with an error 400 #4677 . ### Steps to reproduce the bug Any large dataset pushed the hub: ```py audio_dataset.push_to_hub( repo_id="org/dataset", ) ``` ### Expected behavior `push_to_hub` should have an option for max retries or resume. ### Environment info - `datasets` version: 2.14.6 - Platform: Linux-5.15.0-1044-aws-x86_64-with-glibc2.29 - Python version: 3.8.10 - Huggingface_hub version: 0.16.4 - PyArrow version: 13.0.0 - Pandas version: 2.0.3	2023-11-08T20:44:53	2023-12-20T07:28:24	2023-12-01T17:51:34	https://github.com/huggingface/datasets/issues/6392	null	6,392	false	[ "Hi! We made some improvements to `push_to_hub` to make it more robust a couple of weeks ago but haven't published a release in the meantime, so it would help if you could install `datasets` from `main` (`pip install https://github.com/huggingface/datasets`) and let us know if this improved version of `push_to_hub` resolves the issue (in case the `ConnectionError` happens, re-running `push_to_hub` should be faster now).\r\n\r\nAlso, note that the previous implementation retries the upload, but sometimes this is not enough, so re-running the op is the only option.", "The update helped push more data.\r\nHowever it still crashed a little later:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 270, in hf_raise_for_status\r\n response.raise_for_status()\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/models.py\", line 1021, in raise_for_status\r\n raise HTTPError(http_error_msg, response=self)\r\nrequests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://hf-hub-lfs-us-east-1.s3.us-east-1.amazonaws.com/repos/6c/33/6c33b3be1463a656e43c7a4f2d43c4a1cdae6e9d81fff87f69167ef25ccb1b88/5f53cb57cf2a52ca0d4c2166a69a6714c64fcdbb7cb8936dfa5b11ac60058e5f?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA2JU7TKAQFN2FTF47%2F20231110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20231110T011254Z&X-Amz-Expires=86400&X-Amz-Signature=74e3e33c09ac4e7c6ac887aaee8d489f068869abbe1ee6d58a910fb18d0601d4&X-Amz-SignedHeaders=host&partNumber=13&uploadId=kQwunNkunfmT9D8GulQu_ufw1BTZtRA6wEUI4hnYOjytfdf.GKxDETgMr4wm8_0WNF2yGaNco_0h3JAGm4l9KV1N0nqr5XXyUCbs1ROmHP475fn9FIhc1umWQLEDc97V&x-id=UploadPart\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 391, in _wrapped_lfs_upload\r\n lfs_upload(operation=operation, lfs_batch_action=batch_action, token=token)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 223, in lfs_upload\r\n _upload_multi_part(operation=operation, header=header, chunk_size=chunk_size, upload_url=upload_action[\"href\"])\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 319, in _upload_multi_part\r\n else _upload_parts_iteratively(operation=operation, sorted_parts_urls=sorted_parts_urls, chunk_size=chunk_size)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 376, in _upload_parts_iteratively\r\n hf_raise_for_status(part_upload_res)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 330, in hf_raise_for_status\r\n raise HfHubHTTPError(str(e), response=response) from e\r\nhuggingface_hub.utils._errors.HfHubHTTPError: 500 Server Error: Internal Server Error for url: https://hf-hub-lfs-us-east-1.s3.us-east-1.amazonaws.com/repos/6c/33/6c33b3be1463a656e43c7a4f2d43c4a1cdae6e9d81fff87f69167ef25ccb1b88/5f53cb57cf2a52ca0d4c2166a69a6714c64fcdbb7cb8936dfa5b11ac60058e5f?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA2JU7TKAQFN2FTF47%2F20231110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20231110T011254Z&X-Amz-Expires=86400&X-Amz-Signature=74e3e33c09ac4e7c6ac887aaee8d489f068869abbe1ee6d58a910fb18d0601d4&X-Amz-SignedHeaders=host&partNumber=13&uploadId=kQwunNkunfmT9D8GulQu_ufw1BTZtRA6wEUI4hnYOjytfdf.GKxDETgMr4wm8_0WNF2yGaNco_0h3JAGm4l9KV1N0nqr5XXyUCbs1ROmHP475fn9FIhc1umWQLEDc97V&x-id=UploadPart\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"convert_to_hf.py\", line 121, in <module>\r\n main()\r\n File \"convert_to_hf.py\", line 109, in main\r\n audio_dataset.push_to_hub(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/dataset_dict.py\", line 1699, in push_to_hub\r\n split_additions, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 5215, in _push_parquet_shards_to_hub\r\n _retry(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 290, in _retry\r\n return func(func_args, func_kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 3665, in preupload_lfs_files\r\n _upload_lfs_files(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn\r\n return fn(args, *kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 401, in _upload_lfs_files\r\n _wrapped_lfs_upload(filtered_actions[0])\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 393, in _wrapped_lfs_upload\r\n raise RuntimeError(f\"Error while uploading '{operation.path_in_repo}' to the Hub.\") from exc\r\nRuntimeError: Error while uploading 'batch_20/train-00206-of-00261.parquet' to the Hub.\r\n```", "I think the previous implementation was actually better: it pushes to the hub every shard. So if it fails, as long as the shards have the same checksum, it will skip the ones that have been pushed.\r\n\r\nThe implementation in `main` pushes commits at the end, so when it fails, there are no commits and therefore restarts from the beginning every time.\r\n\r\nBelow is the another error log from another run with `main`. I've reverting back to the current release as it does the job for me.\r\n\r\n```\r\nUploading the dataset shards: 86%\|████████▌ \| 224/261 [21:46<03:35, 5.83s/it]s]\r\nTraceback (most recent call last):\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 270, in hf_raise_for_status\r\n response.raise_for_status()\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/models.py\", line 1021, in raise_for_status\r\n raise HTTPError(http_error_msg, response=self)\r\nrequests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://hf-hub-lfs-us-east-1.s3.us-east-1.amazonaws.com/repos/6c/33/6c33b3be1463a656e43c7a4f2d43c4a1cdae6e9d81fff87f69167ef25ccb1b88/97e68d7a5d4a747ffaa249fc09798e961d621fe4170599e6100197f7733f321d?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA2JU7TKAQFN2FTF47%2F20231110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20231110T145155Z&X-Amz-Expires=86400&X-Amz-Signature=5341e4b34dc325737f92dc9005c4a31e4d3f9a3d3d853b267e01915260acf629&X-Amz-SignedHeaders=host&partNumber=27&uploadId=NRD0izEWv7MPtC2bYrm5VJ4XgIbHctKNguR7zS1UhGOOrXwBJvigrOywBvQBnS9sxiy0J0ma9sNog8S13nIdTdE9p60MIITTstUFeKvLHSxpU.a527QED1JVYzJ.9xA0&x-id=UploadPart\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 391, in _wrapped_lfs_upload\r\n lfs_upload(operation=operation, lfs_batch_action=batch_action, token=token)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 223, in lfs_upload\r\n _upload_multi_part(operation=operation, header=header, chunk_size=chunk_size, upload_url=upload_action[\"href\"])\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 319, in _upload_multi_part\r\n else _upload_parts_iteratively(operation=operation, sorted_parts_urls=sorted_parts_urls, chunk_size=chunk_size)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 376, in _upload_parts_iteratively\r\n hf_raise_for_status(part_upload_res)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 330, in hf_raise_for_status\r\n raise HfHubHTTPError(str(e), response=response) from e\r\nhuggingface_hub.utils._errors.HfHubHTTPError: 500 Server Error: Internal Server Error for url: https://hf-hub-lfs-us-east-1.s3.us-east-1.amazonaws.com/repos/6c/33/6c33b3be1463a656e43c7a4f2d43c4a1cdae6e9d81fff87f69167ef25ccb1b88/97e68d7a5d4a747ffaa249fc09798e961d621fe4170599e6100197f7733f321d?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA2JU7TKAQFN2FTF47%2F20231110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20231110T145155Z&X-Amz-Expires=86400&X-Amz-Signature=5341e4b34dc325737f92dc9005c4a31e4d3f9a3d3d853b267e01915260acf629&X-Amz-SignedHeaders=host&partNumber=27&uploadId=NRD0izEWv7MPtC2bYrm5VJ4XgIbHctKNguR7zS1UhGOOrXwBJvigrOywBvQBnS9sxiy0J0ma9sNog8S13nIdTdE9p60MIITTstUFeKvLHSxpU.a527QED1JVYzJ.9xA0&x-id=UploadPart\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"convert_to_hf.py\", line 121, in <module>\r\n main()\r\n File \"convert_to_hf.py\", line 109, in main\r\n audio_dataset.push_to_hub(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/dataset_dict.py\", line 1699, in push_to_hub\r\n p, glob_pattern_to_regex(PUSH_TO_HUB_WITHOUT_METADATA_CONFIGS_SPLIT_PATTERN_SHARDED)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 5215, in _push_parquet_shards_to_hub\r\n token = token if token is not None else HfFolder.get_token()\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 290, in _retry\r\n return func(func_args, *func_kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 3665, in preupload_lfs_files\r\n _upload_lfs_files(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn\r\n return fn(args, *kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 401, in _upload_lfs_files\r\n _wrapped_lfs_upload(filtered_actions[0])\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 393, in _wrapped_lfs_upload\r\n raise RuntimeError(f\"Error while uploading '{operation.path_in_repo}' to the Hub.\") from exc\r\nRuntimeError: Error while uploading 'batch_20/train-00224-of-00261.parquet' to the Hub.\r\n```", "There's a new error from the hub now:\r\n```\r\nPushing dataset shards to the dataset hub: 49%\|████▉ \| 128/261 [11:38<12:05, 5.45s/it]\r\nTraceback (most recent call last):\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 270, in hf_raise_for_status\r\n response.raise_for_status()\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/models.py\", line 1021, in raise_for_status\r\n raise HTTPError(http_error_msg, response=self)\r\nrequests.exceptions.HTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/api/datasets/tarteel-ai/tawseem/commit/main\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"convert_to_hf.py\", line 121, in <module>\r\n main()\r\n File \"convert_to_hf.py\", line 109, in main\r\n audio_dataset.push_to_hub(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/dataset_dict.py\", line 1641, in push_to_hub\r\n repo_id, split, uploaded_size, dataset_nbytes, _, _ = self[split]._push_parquet_shards_to_hub(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 5308, in _push_parquet_shards_to_hub\r\n _retry(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 293, in _retry\r\n raise err\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 290, in _retry\r\n return func(func_args, *func_kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn\r\n return fn(args, *kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 1045, in _inner\r\n return fn(self, args, *kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 3850, in upload_file\r\n commit_info = self.create_commit(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn\r\n return fn(args, *kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 1045, in _inner\r\n return fn(self, args, **kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 3237, in create_commit\r\n hf_raise_for_status(commit_resp, endpoint_name=\"commit\")\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 330, in hf_raise_for_status\r\n raise HfHubHTTPError(str(e), response=response) from e\r\nhuggingface_hub.utils._errors.HfHubHTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/api/datasets/tarteel-ai/tawseem/commit/main (Request ID: Root=1-654e48e6-598511b14413bb293fa67084;783522b4-66f9-4f8a-8a74-2accf7cabd17)\r\n\r\nYou have exceeded our hourly quotas for action: commit. We invite you to retry later.\r\n```\r\n\r\nAt least this is more explicit from the server side.", "> think the previous implementation was actually better: it pushes to the hub every shard. So if it fails, as long as the shards have the same checksum, it will skip the ones that have been pushed.\r\n>\r\n>The implementation in main pushes commits at the end, so when it fails, there are no commits and therefore restarts from the beginning every time.\r\n>\r\n>Below is the another error log from another run with main. I've reverting back to the current release as it does the job for me.\r\n\r\nThe `preupload` step is instant for the already uploaded shards, so only the Parquet conversion is repeated without uploading the actual Parquet data (only to check the SHAs). The previous implementation manually checks the Parquet shard's fingerprint to resume uploading, so the current implementation is cleaner.\r\n\r\n> You have exceeded our hourly quotas for action: commit. We invite you to retry later.\r\n\r\nThis is the problem with the previous implementation. If the number of shards is large, it creates too many commits for the Hub in a short period.", "But I agree that the `500 Server Error` returned by the Hub is annoying. Earlier today, I also got it on a small 5GB dataset (with 500 MB shards).\r\n\r\n@Wauplin @julien-c Is there something we can do about this?", "@mariosasko can't do much if AWS raises a HTTP 500 unfortunately (we are simply pushing data to a S3 bucket).\r\nWhat we can do is to add a retry mechanism in the multi-part upload logic here: https://github.com/huggingface/huggingface_hub/blob/c972cba1fecb456a7b3325cdd1fdbcc425f21f94/src/huggingface_hub/lfs.py#L370 :confused: ", "@Wauplin That code already retries the request using `http_backoff`, no?", "> That code already retries the request using http_backoff, no?\r\n\r\nCurrently only on HTTP 503 by default. We should add 500 as well (and hope it is a transient error from AWS)", "Opened a PR to retry in case S3 raises HTTP 500. Will also retry on any `ConnectionError` (connection reset by peer, connection lost,...). Hopefully this should make the upload process more robust to transient errors.", "I still get the same error, using `push_to_hub`. Using `git lfs` and pushing the files solved it for me.", "@BEpresent the fix has not been released yet. You can expect a release of `huggingface_hub` (with this fix) today or tomorrow :)" ]
1,984,091,776	Webdataset dataset builder	closed	Allow `load_dataset` to support the Webdataset format. It allows users to download/stream data from local files or from the Hugging Face Hub. Moreover it will enable the Dataset Viewer for Webdataset datasets on HF. ## Implementation details - I added a new Webdataset builder - dataset with TAR files are now read using the Webdataset builder - Basic decoding from `webdataset` is used by default, except unsafe ones like pickle - HF authentication support is done with `xopen` ## TODOS - [x] tests - [x] docs	2023-11-08T17:31:59	2024-05-22T16:51:08	2023-11-28T16:33:10	https://github.com/huggingface/datasets/pull/6391	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6391", "html_url": "https://github.com/huggingface/datasets/pull/6391", "diff_url": "https://github.com/huggingface/datasets/pull/6391.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6391.patch", "merged_at": "2023-11-28T16:33:10" }	6,391	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "I added an error message if the first examples don't appear to be in webdataset format\r\n```\r\n\"The TAR archives of the dataset should be in Webdataset format, \"\r\n\"but the files in the archive don't share the same prefix or the same types.\"\r\n```", "@mariosasko could you review this ? I think it's fine to have webdataset as an optional dependency for now, then depending on usage and user feedbacks see if it makes sense to have our own implementation or not", "I just removed the dependency on `webdataset` @mariosasko :)", "took your comments into account, lmk if you see anything else" ]
1,983,725,707	handle future deprecation argument	closed	getting this error: ``` /root/miniconda3/envs/py3.10/lib/python3.10/site-packages/datasets/table.py:1387: FutureWarning: promote has been superseded by mode='default'. return cls._concat_blocks(pa_tables_to_concat_vertically, axis=0) ``` Since datasets supports arrow greater than 8.0.0, we need to handle both cases. [Arrow v14 docs](https://arrow.apache.org/docs/python/generated/pyarrow.concat_tables.html) [Arrow v13 docs](https://arrow.apache.org/docs/13.0/python/generated/pyarrow.concat_tables.html)	2023-11-08T14:21:25	2023-11-21T02:10:24	2023-11-14T15:15:59	https://github.com/huggingface/datasets/pull/6390	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6390", "html_url": "https://github.com/huggingface/datasets/pull/6390", "diff_url": "https://github.com/huggingface/datasets/pull/6390.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6390.patch", "merged_at": "2023-11-14T15:15:59" }	6,390	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) \| 0.004368 / 0.011353 (-0.006985) \| 0.002613 / 0.011008 (-0.008396) \| 0.061365 / 0.038508 (0.022856) \| 0.029553 / 0.023109 (0.006444) \| 0.240535 / 0.275898 (-0.035363) \| 0.280634 / 0.323480 (-0.042845) \| 0.002923 / 0.007986 (-0.005063) \| 0.003696 / 0.004328 (-0.000632) \| 0.049824 / 0.004250 (0.045573) \| 0.044935 / 0.037052 (0.007882) \| 0.246870 / 0.258489 (-0.011619) \| 0.317248 / 0.293841 (0.023407) \| 0.022717 / 0.128546 (-0.105829) \| 0.006933 / 0.075646 (-0.068713) \| 0.201118 / 0.419271 (-0.218154) \| 0.053422 / 0.043533 (0.009890) \| 0.266262 / 0.255139 (0.011123) \| 0.269114 / 0.283200 (-0.014086) \| 0.016908 / 0.141683 (-0.124775) \| 1.154296 / 1.452155 (-0.297859) \| 1.218825 / 1.492716 (-0.273892) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089908 / 0.018006 (0.071902) \| 0.300029 / 0.000490 (0.299539) \| 0.000209 / 0.000200 (0.000009) \| 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) \| 0.018518 / 0.037411 (-0.018894) \| 0.062246 / 0.014526 (0.047720) \| 0.073542 / 0.176557 (-0.103014) \| 0.119386 / 0.737135 (-0.617749) \| 0.075256 / 0.296338 (-0.221082) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280812 / 0.215209 (0.065603) \| 2.701282 / 2.077655 (0.623628) \| 1.455146 / 1.504120 (-0.048974) \| 1.310198 / 1.541195 (-0.230996) \| 1.335287 / 1.468490 (-0.133203) \| 0.388245 / 4.584777 (-4.196532) \| 2.357770 / 3.745712 (-1.387942) \| 2.534640 / 5.269862 (-2.735222) \| 1.541382 / 4.565676 (-3.024295) \| 0.045597 / 0.424275 (-0.378678) \| 0.004842 / 0.007607 (-0.002765) \| 0.325416 / 0.226044 (0.099371) \| 3.221873 / 2.268929 (0.952944) \| 1.791061 / 55.444624 (-53.653563) \| 1.485094 / 6.876477 (-5.391382) \| 1.512354 / 2.142072 (-0.629718) \| 0.471241 / 4.805227 (-4.333986) \| 0.098672 / 6.500664 (-6.401992) \| 0.041668 / 0.075469 (-0.033801) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.953553 / 1.841788 (-0.888234) \| 11.378394 / 8.074308 (3.304086) \| 10.355970 / 10.191392 (0.164578) \| 0.126891 / 0.680424 (-0.553533) \| 0.013808 / 0.534201 (-0.520393) \| 0.267800 / 0.579283 (-0.311484) \| 0.266436 / 0.434364 (-0.167928) \| 0.306668 / 0.540337 (-0.233670) \| 0.427666 / 1.386936 (-0.959270) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004908 / 0.011353 (-0.006445) \| 0.002698 / 0.011008 (-0.008310) \| 0.047492 / 0.038508 (0.008984) \| 0.049906 / 0.023109 (0.026797) \| 0.271466 / 0.275898 (-0.004432) \| 0.291030 / 0.323480 (-0.032449) \| 0.003938 / 0.007986 (-0.004047) \| 0.002457 / 0.004328 (-0.001871) \| 0.047347 / 0.004250 (0.043096) \| 0.038599 / 0.037052 (0.001547) \| 0.269950 / 0.258489 (0.011461) \| 0.303026 / 0.293841 (0.009185) \| 0.024196 / 0.128546 (-0.104351) \| 0.006889 / 0.075646 (-0.068757) \| 0.053357 / 0.419271 (-0.365914) \| 0.032249 / 0.043533 (-0.011284) \| 0.271660 / 0.255139 (0.016521) \| 0.286395 / 0.283200 (0.003196) \| 0.017914 / 0.141683 (-0.123769) \| 1.128762 / 1.452155 (-0.323393) \| 1.206495 / 1.492716 (-0.286221) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093384 / 0.018006 (0.075378) \| 0.305504 / 0.000490 (0.305014) \| 0.000227 / 0.000200 (0.000027) \| 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) \| 0.021183 / 0.037411 (-0.016229) \| 0.070113 / 0.014526 (0.055587) \| 0.080288 / 0.176557 (-0.096269) \| 0.120798 / 0.737135 (-0.616337) \| 0.082896 / 0.296338 (-0.213442) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292758 / 0.215209 (0.077549) \| 2.893975 / 2.077655 (0.816320) \| 1.584909 / 1.504120 (0.080789) \| 1.455509 / 1.541195 (-0.085686) \| 1.501625 / 1.468490 (0.033135) \| 0.400772 / 4.584777 (-4.184005) \| 2.446319 / 3.745712 (-1.299393) \| 2.530690 / 5.269862 (-2.739172) \| 1.525957 / 4.565676 (-3.039719) \| 0.046070 / 0.424275 (-0.378205) \| 0.004756 / 0.007607 (-0.002851) \| 0.343039 / 0.226044 (0.116995) \| 3.366772 / 2.268929 (1.097844) \| 1.948895 / 55.444624 (-53.495729) \| 1.666419 / 6.876477 (-5.210058) \| 1.658258 / 2.142072 (-0.483814) \| 0.470835 / 4.805227 (-4.334392) \| 0.098008 / 6.500664 (-6.402656) \| 0.040743 / 0.075469 (-0.034726) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.978025 / 1.841788 (-0.863763) \| 11.945229 / 8.074308 (3.870920) \| 11.025810 / 10.191392 (0.834418) \| 0.129706 / 0.680424 (-0.550717) \| 0.015148 / 0.534201 (-0.519053) \| 0.269160 / 0.579283 (-0.310123) \| 0.284306 / 0.434364 (-0.150058) \| 0.307154 / 0.540337 (-0.233183) \| 0.409153 / 1.386936 (-0.977783) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9c75c104fd79cbf53be25f0fbbeb001e535f7e9b \"CML watermark\")\n" ]
1,983,545,744	Index 339 out of range for dataset of size 339 <-- save_to_file()	open	### Describe the bug When saving out some Audio() data. The data is audio recordings with associated 'sentences'. (They use the audio 'bytes' approach because they're clips within audio files). Code is below the traceback (I can't upload the voice audio/text (it's not even me)). ``` Traceback (most recent call last): File "/mnt/ddrive/prj/voice/voice-training-dataset-create/./dataset.py", line 156, in <module> create_dataset(args) File "/mnt/ddrive/prj/voice/voice-training-dataset-create/./dataset.py", line 138, in create_dataset hf_dataset.save_to_disk(args.outds, max_shard_size='50MB') File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 1531, in save_to_disk for kwargs in kwargs_per_job: File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 1508, in <genexpr> "shard": self.shard(num_shards=num_shards, index=shard_idx, contiguous=True), ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 4609, in shard return self.select( ^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 556, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, args, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/fingerprint.py", line 511, in wrapper out = func(dataset, args, *kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 3797, in select return self._select_contiguous(start, length, new_fingerprint=new_fingerprint) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 556, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, args, *kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/fingerprint.py", line 511, in wrapper out = func(dataset, args, *kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 3857, in _select_contiguous _check_valid_indices_value(start, len(self)) File "/home/j/src/py/datasets/src/datasets/arrow_dataset.py", line 648, in _check_valid_indices_value raise IndexError(f"Index {index} out of range for dataset of size {size}.") IndexError: Index 339 out of range for dataset of size 339. ``` ### Steps to reproduce the bug (I had to set the default max batch size down due to a different bug... or maybe it's related: https://github.com/huggingface/datasets/issues/5717) ```python3 #!/usr/bin/env python3 import argparse import os from pathlib import Path import soundfile as sf import datasets datasets.config.DEFAULT_MAX_BATCH_SIZE=35 from datasets import Features, Array2D, Value, Dataset, Sequence, Audio import numpy as np import librosa import sys import soundfile as sf import io import logging logging.basicConfig(level=logging.DEBUG, filename='debug.log', filemode='w', format='%(name)s - %(levelname)s - %(message)s') # Define the arguments for the command-line interface def parse_args(): parser = argparse.ArgumentParser(description="Create a Huggingface dataset from labeled audio files.") parser.add_argument("--indir_labeled", action="append", help="Directory containing labeled audio files.", required=True) parser.add_argument("--outds", help="Path to save the dataset file.", required=True) parser.add_argument("--max_clips", type=int, help="Max count of audio samples to add to the dataset.", default=None) parser.add_argument("-r", "--sr", type=int, help="Sample rate for the audio files.", default=16000) parser.add_argument("--no-resample", action="store_true", help="Disable resampling of the audio files.") parser.add_argument("--max_clip_secs", type=float, help="Max length of audio clips in seconds.", default=3.0) parser.add_argument("-v", "--verbose", action='count', default=1, help="Increase verbosity") return parser.parse_args() # Convert the NumPy arrays to audio bytes in WAV format def numpy_to_bytes(audio_array, sampling_rate=16000): with io.BytesIO() as bytes_io: sf.write(bytes_io, audio_array, samplerate=sampling_rate, format='wav', subtype='FLOAT') # float32 return bytes_io.getvalue() # Function to find audio and label files in a directory def find_audio_label_pairs(indir_labeled): audio_label_pairs = [] for root, _, files in os.walk(indir_labeled): for file in files: if file.endswith(('.mp3', '.wav', '.aac', '.flac')): audio_path = Path(root) / file if args.verbose>1: print(f'File: {audio_path}') label_path = audio_path.with_suffix('.labels.txt') if label_path.exists(): if args.verbose>0: print(f' Pair: {audio_path}') audio_label_pairs.append((audio_path, label_path)) return audio_label_pairs def process_audio_label_pair(audio_path, label_path, sampling_rate, no_resample, max_clip_secs): # Read the label file with open(label_path, 'r') as label_file: labels = label_file.readlines() # Load the full audio file full_audio, current_sr = sf.read(audio_path) if not no_resample and current_sr != sampling_rate: # You can use librosa.resample here if librosa is available full_audio = librosa.resample(full_audio, orig_sr=current_sr, target_sr=sampling_rate) audio_segments = [] sentences = [] # Process each label for label in labels: start_secs, end_secs, label_text = label.strip().split('\t') start_sample = int(float(start_secs) sampling_rate) end_sample = int(float(end_secs) * sampling_rate) # Extract segment and truncate or pad to max_clip_secs audio_segment = full_audio[start_sample:end_sample] max_samples = int(max_clip_secs * sampling_rate) if len(audio_segment) > max_samples: # Truncate audio_segment = audio_segment[:max_samples] elif len(audio_segment) < max_samples: # Pad padding = np.zeros(max_samples - len(audio_segment), dtype=audio_segment.dtype) audio_segment = np.concatenate((audio_segment, padding)) audio_segment = numpy_to_bytes(audio_segment) audio_data = { 'path': str(audio_path), 'bytes': audio_segment, } audio_segments.append(audio_data) sentences.append(label_text) return audio_segments, sentences # Main function to create the dataset def create_dataset(args): audio_label_pairs = [] for indir in args.indir_labeled: audio_label_pairs.extend(find_audio_label_pairs(indir)) # Initialize our dataset data dataset_data = { 'path': [], # This will be a list of strings 'audio': [], # This will be a list of dictionaries 'sentence': [], # This will be a list of strings } # Process each audio-label pair and add the data to the dataset for audio_path, label_path in audio_label_pairs[:args.max_clips]: audio_segments, sentences = process_audio_label_pair(audio_path, label_path, args.sr, args.no_resample, args.max_clip_secs) if audio_segments and sentences: for audio_data, sentence in zip(audio_segments, sentences): if args.verbose>1: print(f'Appending {audio_data["path"]}') dataset_data['path'].append(audio_data['path']) dataset_data['audio'].append({ 'path': audio_data['path'], 'bytes': audio_data['bytes'], }) dataset_data['sentence'].append(sentence) features = Features({ 'path': Value('string'), # Path is redundant in common voice set also 'audio': Audio(sampling_rate=16000), 'sentence': Value('string'), }) hf_dataset = Dataset.from_dict(dataset_data, features=features) for key in dataset_data: for i, item in enumerate(dataset_data[key]): if item is None or (isinstance(item, bytes) and len(item) == 0): logging.error(f"Invalid {key} at index {i}: {item}") import ipdb; ipdb.set_trace(context=16); pass hf_dataset.save_to_disk(args.outds, max_shard_size='50MB') # try: # hf_dataset.save_to_disk(args.outds) # except TypeError as e: # # If there's a TypeError, log the exception and the dataset data that might have caused it # logging.exception("An error occurred while saving the dataset.") # import ipdb; ipdb.set_trace(context=16); pass # for key in dataset_data: # logging.debug(f"{key} length: {len(dataset_data[key])}") # if key == 'audio': # # Log the first 100 bytes of the audio data to avoid huge log files # for i, audio in enumerate(dataset_data[key]): # logging.debug(f"Audio {i}: {audio['bytes'][:100]}") # raise # Run the script if __name__ == "__main__": args = parse_args() create_dataset(args) ``` ### Expected behavior It shouldn't fail. ### Environment info - `datasets` version: 2.14.7.dev0 - Platform: Linux-6.1.0-13-amd64-x86_64-with-glibc2.36 - Python version: 3.11.2 - `huggingface_hub` version: 0.17.3 - PyArrow version: 13.0.0 - Pandas version: 2.1.2 - `fsspec` version: 2023.9.2	2023-11-08T12:52:09	2023-11-24T09:14:13	null	https://github.com/huggingface/datasets/issues/6389	null	6,389	false	[ "Hi! Can you make the above reproducer self-contained by adding code that generates the data?", "I managed a workaround eventually but I don't know what it was (I made a lot of changes to seq2seq). I'll try to include generating code in the future. (If I close, I don't know if you see it. Feel free to close; I'll re-open if I encounter it again (if I can))." ]
1,981,136,093	How to create 3d medical imgae dataset?	open	### Feature request I am newer to huggingface, after i look up `datasets` docs, I can't find how to create the dataset contains 3d medical image (ends with '.mhd', '.dcm', '.nii') ### Motivation help us to upload 3d medical dataset to huggingface! ### Your contribution I'll submit a PR if I find a way to add this feature	2023-11-07T11:27:36	2023-11-07T11:28:53	null	https://github.com/huggingface/datasets/issues/6388	null	6,388	false	[]
1,980,224,020	How to load existing downloaded dataset ?	closed	Hi @mariosasko @lhoestq @katielink Thanks for your contribution and hard work. ### Feature request First, I download a dataset as normal by: ``` from datasets import load_dataset dataset = load_dataset('username/data_name', cache_dir='data') ``` The dataset format in `data` directory will be: ``` -data \|-data_name \|-test-00000-of-00001-bf4c733542e35fcb.parquet \|-train-00000-of-00001-2a1df75c6bce91ab.parquet ``` Then I use SCP to clone this dataset into another machine, and then try: ``` from datasets import load_dataset dataset = load_dataset('data/data_name') # load from local path ``` This leads to re-generating training and validation split for each time, and the disk quota will be duplicated occupation. How can I just load the dataset without generating and saving these splits again? ### Motivation I do not want to download the same dataset in two machines, scp is much faster and better than HuggingFace API. I hope we can directly load the downloaded datasets (.parquest) ### Your contribution Please refer to the feature	2023-11-06T22:51:44	2023-11-16T18:07:01	2023-11-16T18:07:01	https://github.com/huggingface/datasets/issues/6387	null	6,387	false	[ "Feel free to use `dataset.save_to_disk(...)`, then scp the directory containing the saved dataset and reload it on your other machine using `dataset = load_from_disk(...)`" ]
1,979,878,014	Formatting overhead	closed	### Describe the bug Hi! I very recently noticed that my training time is dominated by batch formatting. Using Lightning's profilers, I located the bottleneck within `datasets.formatting.formatting` and then narrowed it down with `line-profiler`. It turns out that almost all of the overhead is due to creating new instances of `self.python_arrow_extractor`. I admit I'm confused why that could be the case - as far as I can tell there's no complex `__init__` logic to execute. ![image](https://github.com/huggingface/datasets/assets/320321/5e022e0b-0d21-43d0-8e6f-9e641142e96b) ### Steps to reproduce the bug 1. Set up a dataset `ds` with potentially several (4+) columns (not sure if this is necessary, but it did at one point of the investigation make overhead worse) 2. Process it using a custom transform, `ds = ds.with_transform(transform_func)` 3. Decorate this function https://github.com/huggingface/datasets/blob/main/src/datasets/formatting/formatting.py#L512 with `@profile` from https://pypi.org/project/line-profiler/ 4. Profile with `$ kernprof -l script_to_profile.py` ### Expected behavior Batch formatting should have acceptable overhead. ### Environment info ``` datasets=2.14.6 pyarrow=14.0.0 ```	2023-11-06T19:06:38	2023-11-06T23:56:12	2023-11-06T23:56:12	https://github.com/huggingface/datasets/issues/6386	null	6,386	false	[ "Ah I think the `line-profiler` log is off-by-one and it is in fact the `extract_batch` method that's taking forever. Will investigate further.", "I tracked it down to a quirk of my setup. Apologies." ]
1,979,308,338	Get an error when i try to concatenate the squad dataset with my own dataset	closed	### Describe the bug Hello, I'm new here and I need to concatenate the squad dataset with my own dataset i created. I find the following error when i try to do it: Traceback (most recent call last): Cell In[9], line 1 concatenated_dataset = concatenate_datasets([train_dataset, dataset1]) File ~\anaconda3\Lib\site-packages\datasets\combine.py:213 in concatenate_datasets return _concatenate_map_style_datasets(dsets, info=info, split=split, axis=axis) File ~\anaconda3\Lib\site-packages\datasets\arrow_dataset.py:6002 in _concatenate_map_style_datasets _check_if_features_can_be_aligned([dset.features for dset in dsets]) File ~\anaconda3\Lib\site-packages\datasets\features\features.py:2122 in _check_if_features_can_be_aligned raise ValueError( ValueError: The features can't be aligned because the key answers of features {'id': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'context': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), 'answers': Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None)} has unexpected type - Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None) (expected either {'answer_start': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'text': Value(dtype='string', id=None)} or Value("null"). ### Steps to reproduce the bug ```python from huggingface_hub import notebook_login from datasets import load_dataset notebook_login("mymailadresse", "mypassword") squad = load_dataset("squad", split="train[:5000]") squad = squad.train_test_split(test_size=0.2) dataset1 = squad["train"] import json mybase = [ { "id": "1", "context": "She lives in Nantes", "question": "Where does she live?", "answers": { "text": "Nantes", "answer_start": [13], } } ] # Save the data to a JSON file json_file_path = r"C:\Users\mypath\thefile.json" with open(json_file_path, "w", encoding= "utf-8") as json_file: json.dump(mybase, json_file, indent=4) # Load the JSON file as a dataset custom_dataset = load_dataset("json", data_files=json_file_path) # Access the train split train_dataset = custom_dataset["train"] from datasets import concatenate_datasets # Concatenate the datasets concatenated_dataset = concatenate_datasets([train_dataset, dataset1]) ``` ### Expected behavior I would expect the two datasets to be concatenated without error. The len(dataset1) is equal to 4000 and the len(train_dataset) is equal to 1 so I would exepect concatenated_dataset to be created and having lenght 4001. ### Environment info Python 3.11.4 and using windows Thank you for your help	2023-11-06T14:29:22	2023-11-06T16:50:45	2023-11-06T16:50:45	https://github.com/huggingface/datasets/issues/6385	null	6,385	false	[ "The `answers.text` field in the JSON dataset needs to be a list of strings, not a string.\r\n\r\nSo, here is the fixed code:\r\n```python\r\nfrom huggingface_hub import notebook_login\r\nfrom datasets import load_dataset\r\n\r\n\r\n\r\nnotebook_login(\"mymailadresse\", \"mypassword\")\r\nsquad = load_dataset(\"squad\", split=\"train[:5000]\")\r\nsquad = squad.train_test_split(test_size=0.2)\r\ndataset1 = squad[\"train\"]\r\n\r\n\r\n\r\n\r\nimport json\r\n\r\nmybase = [\r\n {\r\n \"id\": \"1\",\r\n \"context\": \"She lives in Nantes\",\r\n \"question\": \"Where does she live?\",\r\n \"answers\": {\r\n \"text\": [\"Nantes\"],\r\n \"answer_start\": [13],\r\n }\r\n }\r\n]\r\n\r\n\r\n\r\n\r\n# Save the data to a JSON file\r\njson_file_path = r\"data\"\r\nwith open(json_file_path, \"w\", encoding= \"utf-8\") as json_file:\r\n json.dump(mybase, json_file, indent=4)\r\n\r\n\r\n\r\n\r\n# Load the JSON file as a dataset\r\ncustom_dataset = load_dataset(\"json\", data_files=json_file_path, features=dataset1.features)\r\n\r\n\r\n# Access the train split\r\ntrain_dataset = custom_dataset[\"train\"]\r\n\r\n\r\nfrom datasets import concatenate_datasets\r\n\r\n\r\n# Concatenate the datasets\r\nconcatenated_dataset = concatenate_datasets([train_dataset, dataset1])\r\n```", "Thank you @mariosasko for your help ! It works !" ]
1,979,117,069	Load the local dataset folder from other place	closed	This is from https://github.com/huggingface/diffusers/issues/5573	2023-11-06T13:07:04	2023-11-19T05:42:06	2023-11-19T05:42:05	https://github.com/huggingface/datasets/issues/6384	null	6,384	false	[ "Solved" ]
1,978,189,389	imagenet-1k downloads over and over	closed	### Describe the bug What could be causing this? ``` $ python3 Python 3.8.13 (default, Mar 28 2022, 11:38:47) [GCC 7.5.0] :: Anaconda, Inc. on linux Type "help", "copyright", "credits" or "license" for more information. >>> from datasets import load_dataset >>> load_dataset("imagenet-1k") Downloading builder script: 100%\|██████████\| 4.72k/4.72k [00:00<00:00, 7.51MB/s] Downloading readme: 100%\|███████████████████\| 85.4k/85.4k [00:00<00:00, 510kB/s] Downloading extra modules: 100%\|████████████\| 46.4k/46.4k [00:00<00:00, 300kB/s] Downloading data: 100%\|████████████████████\| 29.1G/29.1G [19:36<00:00, 24.8MB/s] Downloading data: 100%\|████████████████████\| 29.3G/29.3G [08:38<00:00, 56.5MB/s] Downloading data: 100%\|████████████████████\| 29.0G/29.0G [09:26<00:00, 51.2MB/s] Downloading data: 100%\|████████████████████\| 29.2G/29.2G [09:38<00:00, 50.6MB/s] Downloading data: 100%\|███████████████████▉\| 29.2G/29.2G [09:37<00:00, 44.1MB/s^Downloading data: 0%\| \| 106M/29.1G [00:05<23:49, 20.3MB/s] ``` ### Steps to reproduce the bug See above commands/code ### Expected behavior imagenet-1k is downloaded ### Environment info - `datasets` version: 2.14.6 - Platform: Linux-6.2.0-34-generic-x86_64-with-glibc2.17 - Python version: 3.8.13 - Huggingface_hub version: 0.15.1 - PyArrow version: 14.0.0 - Pandas version: 1.5.2	2023-11-06T02:58:58	2024-06-12T13:15:00	2023-11-06T06:02:39	https://github.com/huggingface/datasets/issues/6383	null	6,383	false	[ "Have you solved this problem?" ]
1,977,400,799	Add CheXpert dataset for vision	open	### Feature request ### Name CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison ### Paper https://arxiv.org/abs/1901.07031 ### Data https://stanfordaimi.azurewebsites.net/datasets/8cbd9ed4-2eb9-4565-affc-111cf4f7ebe2 ### Motivation CheXpert is one of the fundamental models in medical image classification and can serve as a viable pre-training dataset for radiology classification or low-scale ablation / exploratory studies. This could also serve as a good pre-training dataset for Kaggle competitions. ### Your contribution Would love to make a PR and pre-process / get this into 🤗	2023-11-04T15:36:11	2024-01-10T11:53:52	null	https://github.com/huggingface/datasets/issues/6382	null	6,382	false	[ "Hey @SauravMaheshkar ! Just responded to your email.\r\n\r\n_For transparency, copying part of my response here:_\r\nI agree, it would be really great to have this and other BenchMD datasets easily accessible on the hub.\r\n\r\nI think the main limiting factor is that the ChexPert dataset is currently hosted on the Stanford AIMI Shared Datasets website, with a license that does not permit redistribution IIRC. Thus, I believe we would need to create a [dataset loading script](https://huggingface.co/docs/datasets/image_dataset#loading-script) that would check authentication with the Stanford AIMI site before downloading and extracting the data. \r\n\r\nI've started a HF dataset repo [here](https://huggingface.co/datasets/katielink/CheXpert), in case you want to collaborate on writing up this loading script! I'm also happy to take a stab when I have some more time next week.", "Hey @katielink I would love to try this out. Please guide me.", "Hi @katielink , I would also love to be on board and contribute to this loading script/project if it is still being developed. I'm interested because I personally would like to gain access to the CheXpert dataset and am facing some weird issues, so I'd like to sort it out for me, and potentially others. Please keep me updated and guide me on this as well!!!" ]
1,975,028,470	Add my dataset	closed	## medical data Description: This dataset, named "medical data," is a collection of text data from various sources, carefully curated and cleaned for use in natural language processing (NLP) tasks. It consists of a diverse range of text, including articles, books, and online content, covering topics from science to literature. Citation: If applicable, please include a citation for this dataset to give credit to the original sources or contributors. Key Features: - Language: The text is primarily in English, but it may include content in other languages as well. - Use Cases: This dataset is suitable for text classification, language modeling, sentiment analysis, and other NLP tasks. Usage: To access this dataset, use the `load_your_dataset` function provided in the `your_dataset.py` script within this repository. You can specify the dataset split you need, such as "train," "test," or "validation," to get the data for your specific task. Contributors: - [Keyur Chaudhari] Contact: If you have any questions or need assistance regarding this dataset, please feel free to contact [keyurchaudhari536@gmail.com]. Please note that this dataset is shared under a specific license, which can be found in the [LICENSE](link to your dataset's license) file. Make sure to review and adhere to the terms of the license when using this dataset for your projects.	2023-11-02T20:59:52	2023-11-08T14:37:46	2023-11-06T15:50:14	https://github.com/huggingface/datasets/pull/6381	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6381", "html_url": "https://github.com/huggingface/datasets/pull/6381", "diff_url": "https://github.com/huggingface/datasets/pull/6381.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6381.patch", "merged_at": null }	6,381	true	[ "Hi! We do not host datasets in this repo. Instead, you should use `dataset.push_to_hub` to upload the dataset to the HF Hub.", "@mariosasko could you provide me proper guide to push data on HF hub ", "You can find this info here: https://huggingface.co/docs/datasets/upload_dataset. Also, check https://huggingface.co/docs/datasets/loading for how to load a local dataset (before pushing it to the Hub)." ]
1,974,741,221	Fix for continuation behaviour on broken dataset archives due to starving download connections via HTTP-GET	open	This PR proposes a (slightly hacky) fix for an Issue that can occur when downloading large dataset parts over unstable connections. The underlying issue is also being discussed in https://github.com/huggingface/datasets/issues/5594. Issue Symptoms & Behaviour: - Download of a large archive file during dataset download via HTTP-GET fails. - An silent net exception (which I was unable to identify) is thrown within the `tqdm` download progress. - Due to missing exception catch code, the above process just continues processing, assuming `http_get` completed successfully. - Pending Archive file gets renamed to remove the `.incomplete` extension, despite not all data has been downloaded. - Also, for reasons I did not investigate, there seems to be no real integrity check for the downloaded files; or it does not detect this problem. This is especially problematic, since the downloader script won't retry downloading this archive after CRC-Checking, even if it is being manually restarted / executed again after running into errors on extraction. Fix proposal: Adding a retry mechanic for HTTP-GET downloads, which adds the following behaviour: - Download Progress Thread checks for download size validity in case the HTTP connection starves mid download. If the check fails, a RuntimeError is thrown - Cache Downloader code with retry mechanic monitors for an exception thrown by the download progress thread, and retries download with updated `resume_size`. - Cache Downloader will not mark incomplete files which have thrown an exception during download, and exceeded retries, as complete.	2023-11-02T17:28:23	2023-11-02T17:31:19	null	https://github.com/huggingface/datasets/pull/6380	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6380", "html_url": "https://github.com/huggingface/datasets/pull/6380", "diff_url": "https://github.com/huggingface/datasets/pull/6380.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6380.patch", "merged_at": null }	6,380	true	[]
1,974,638,850	Avoid redundant warning when encoding NumPy array as `Image`	closed	Avoid a redundant warning in `encode_np_array` by removing the identity check as NumPy `dtype`s can be equal without having identical `id`s. Additionally, fix "unreachable" checks in `encode_np_array`.	2023-11-02T16:37:58	2023-11-06T17:53:27	2023-11-02T17:08:07	https://github.com/huggingface/datasets/pull/6379	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6379", "html_url": "https://github.com/huggingface/datasets/pull/6379", "diff_url": "https://github.com/huggingface/datasets/pull/6379.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6379.patch", "merged_at": "2023-11-02T17:08:07" }	6,379	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) \| 0.008649 / 0.011353 (-0.002704) \| 0.005754 / 0.011008 (-0.005254) \| 0.101992 / 0.038508 (0.063484) \| 0.084932 / 0.023109 (0.061823) \| 0.393928 / 0.275898 (0.118030) \| 0.414059 / 0.323480 (0.090579) \| 0.006564 / 0.007986 (-0.001422) \| 0.004746 / 0.004328 (0.000418) \| 0.078624 / 0.004250 (0.074373) \| 0.060465 / 0.037052 (0.023412) \| 0.420767 / 0.258489 (0.162278) \| 0.497797 / 0.293841 (0.203956) \| 0.047031 / 0.128546 (-0.081516) \| 0.014316 / 0.075646 (-0.061330) \| 0.340347 / 0.419271 (-0.078925) \| 0.067126 / 0.043533 (0.023593) \| 0.390806 / 0.255139 (0.135667) \| 0.413711 / 0.283200 (0.130512) \| 0.037838 / 0.141683 (-0.103845) \| 1.713547 / 1.452155 (0.261393) \| 1.825591 / 1.492716 (0.332874) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.316357 / 0.018006 (0.298350) \| 0.594279 / 0.000490 (0.593789) \| 0.013659 / 0.000200 (0.013459) \| 0.000547 / 0.000054 (0.000492) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031310 / 0.037411 (-0.006101) \| 0.090410 / 0.014526 (0.075884) \| 0.114620 / 0.176557 (-0.061936) \| 0.183036 / 0.737135 (-0.554099) \| 0.112700 / 0.296338 (-0.183638) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.582424 / 0.215209 (0.367215) \| 5.670424 / 2.077655 (3.592769) \| 2.444326 / 1.504120 (0.940206) \| 2.108555 / 1.541195 (0.567360) \| 2.091594 / 1.468490 (0.623104) \| 0.839067 / 4.584777 (-3.745710) \| 5.280942 / 3.745712 (1.535230) \| 4.611059 / 5.269862 (-0.658803) \| 2.911145 / 4.565676 (-1.654531) \| 0.091929 / 0.424275 (-0.332346) \| 0.008774 / 0.007607 (0.001167) \| 0.657948 / 0.226044 (0.431904) \| 6.816300 / 2.268929 (4.547371) \| 3.232260 / 55.444624 (-52.212364) \| 2.479626 / 6.876477 (-4.396851) \| 2.497886 / 2.142072 (0.355813) \| 0.959160 / 4.805227 (-3.846068) \| 0.222306 / 6.500664 (-6.278358) \| 0.072962 / 0.075469 (-0.002507) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.580415 / 1.841788 (-0.261372) \| 23.689597 / 8.074308 (15.615289) \| 20.430709 / 10.191392 (10.239317) \| 0.237891 / 0.680424 (-0.442533) \| 0.028194 / 0.534201 (-0.506007) \| 0.464915 / 0.579283 (-0.114368) \| 0.611512 / 0.434364 (0.177148) \| 0.556564 / 0.540337 (0.016227) \| 0.811075 / 1.386936 (-0.575861) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008703 / 0.011353 (-0.002649) \| 0.005030 / 0.011008 (-0.005978) \| 0.079251 / 0.038508 (0.040743) \| 0.079054 / 0.023109 (0.055945) \| 0.440220 / 0.275898 (0.164322) \| 0.479824 / 0.323480 (0.156344) \| 0.006312 / 0.007986 (-0.001673) \| 0.004506 / 0.004328 (0.000177) \| 0.078454 / 0.004250 (0.074203) \| 0.061041 / 0.037052 (0.023989) \| 0.490104 / 0.258489 (0.231615) \| 0.480925 / 0.293841 (0.187084) \| 0.049601 / 0.128546 (-0.078945) \| 0.013114 / 0.075646 (-0.062532) \| 0.092576 / 0.419271 (-0.326696) \| 0.059516 / 0.043533 (0.015983) \| 0.433728 / 0.255139 (0.178589) \| 0.490039 / 0.283200 (0.206839) \| 0.035359 / 0.141683 (-0.106324) \| 1.823618 / 1.452155 (0.371463) \| 1.980894 / 1.492716 (0.488178) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284679 / 0.018006 (0.266673) \| 0.606623 / 0.000490 (0.606133) \| 0.007531 / 0.000200 (0.007331) \| 0.000109 / 0.000054 (0.000055) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033261 / 0.037411 (-0.004150) \| 0.102908 / 0.014526 (0.088382) \| 0.123912 / 0.176557 (-0.052644) \| 0.169893 / 0.737135 (-0.567242) \| 0.115366 / 0.296338 (-0.180973) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.598239 / 0.215209 (0.383030) \| 6.003464 / 2.077655 (3.925809) \| 2.828483 / 1.504120 (1.324363) \| 2.485996 / 1.541195 (0.944802) \| 2.434986 / 1.468490 (0.966496) \| 0.832718 / 4.584777 (-3.752058) \| 5.327407 / 3.745712 (1.581694) \| 4.732271 / 5.269862 (-0.537590) \| 3.047555 / 4.565676 (-1.518121) \| 0.103576 / 0.424275 (-0.320699) \| 0.009795 / 0.007607 (0.002188) \| 0.755443 / 0.226044 (0.529399) \| 7.465857 / 2.268929 (5.196928) \| 3.564923 / 55.444624 (-51.879701) \| 2.740483 / 6.876477 (-4.135994) \| 3.044993 / 2.142072 (0.902920) \| 1.012925 / 4.805227 (-3.792302) \| 0.207498 / 6.500664 (-6.293167) \| 0.073361 / 0.075469 (-0.002108) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.704988 / 1.841788 (-0.136800) \| 24.669992 / 8.074308 (16.595684) \| 21.103096 / 10.191392 (10.911704) \| 0.253759 / 0.680424 (-0.426665) \| 0.040109 / 0.534201 (-0.494092) \| 0.465646 / 0.579283 (-0.113637) \| 0.619696 / 0.434364 (0.185332) \| 0.552228 / 0.540337 (0.011890) \| 0.794907 / 1.386936 (-0.592029) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#85bba8991f6a2d9ed9fd4769d945eeaf318d3aa6 \"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) \| 0.006347 / 0.011353 (-0.005006) \| 0.003725 / 0.011008 (-0.007283) \| 0.080233 / 0.038508 (0.041725) \| 0.061013 / 0.023109 (0.037904) \| 0.390046 / 0.275898 (0.114148) \| 0.420526 / 0.323480 (0.097046) \| 0.003579 / 0.007986 (-0.004407) \| 0.002837 / 0.004328 (-0.001491) \| 0.062929 / 0.004250 (0.058678) \| 0.048781 / 0.037052 (0.011729) \| 0.400722 / 0.258489 (0.142233) \| 0.435022 / 0.293841 (0.141182) \| 0.027560 / 0.128546 (-0.100986) \| 0.007981 / 0.075646 (-0.067666) \| 0.262838 / 0.419271 (-0.156433) \| 0.045480 / 0.043533 (0.001947) \| 0.394443 / 0.255139 (0.139304) \| 0.413828 / 0.283200 (0.130628) \| 0.023375 / 0.141683 (-0.118307) \| 1.412865 / 1.452155 (-0.039290) \| 1.495761 / 1.492716 (0.003044) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224876 / 0.018006 (0.206870) \| 0.424234 / 0.000490 (0.423745) \| 0.007502 / 0.000200 (0.007302) \| 0.000220 / 0.000054 (0.000166) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024246 / 0.037411 (-0.013165) \| 0.073982 / 0.014526 (0.059456) \| 0.082704 / 0.176557 (-0.093852) \| 0.143137 / 0.737135 (-0.593998) \| 0.083398 / 0.296338 (-0.212941) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.400220 / 0.215209 (0.185010) \| 3.973037 / 2.077655 (1.895382) \| 2.025903 / 1.504120 (0.521783) \| 1.912888 / 1.541195 (0.371693) \| 1.999578 / 1.468490 (0.531088) \| 0.499378 / 4.584777 (-4.085399) \| 3.025715 / 3.745712 (-0.719997) \| 2.992338 / 5.269862 (-2.277524) \| 1.851155 / 4.565676 (-2.714522) \| 0.057528 / 0.424275 (-0.366747) \| 0.006802 / 0.007607 (-0.000805) \| 0.469516 / 0.226044 (0.243471) \| 4.675630 / 2.268929 (2.406702) \| 2.472166 / 55.444624 (-52.972458) \| 2.238052 / 6.876477 (-4.638424) \| 2.288255 / 2.142072 (0.146183) \| 0.584906 / 4.805227 (-4.220321) \| 0.125902 / 6.500664 (-6.374762) \| 0.060681 / 0.075469 (-0.014788) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.236383 / 1.841788 (-0.605404) \| 17.554238 / 8.074308 (9.479930) \| 13.749298 / 10.191392 (3.557906) \| 0.144715 / 0.680424 (-0.535708) \| 0.017449 / 0.534201 (-0.516752) \| 0.334831 / 0.579283 (-0.244452) \| 0.362660 / 0.434364 (-0.071704) \| 0.385295 / 0.540337 (-0.155043) \| 0.541173 / 1.386936 (-0.845763) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006118 / 0.011353 (-0.005235) \| 0.003660 / 0.011008 (-0.007348) \| 0.062373 / 0.038508 (0.023865) \| 0.063404 / 0.023109 (0.040295) \| 0.354149 / 0.275898 (0.078251) \| 0.410324 / 0.323480 (0.086844) \| 0.004826 / 0.007986 (-0.003160) \| 0.002881 / 0.004328 (-0.001448) \| 0.061631 / 0.004250 (0.057381) \| 0.048052 / 0.037052 (0.010999) \| 0.352905 / 0.258489 (0.094416) \| 0.400096 / 0.293841 (0.106255) \| 0.028472 / 0.128546 (-0.100075) \| 0.008076 / 0.075646 (-0.067571) \| 0.067910 / 0.419271 (-0.351362) \| 0.040671 / 0.043533 (-0.002862) \| 0.352131 / 0.255139 (0.096992) \| 0.402140 / 0.283200 (0.118940) \| 0.020065 / 0.141683 (-0.121618) \| 1.456938 / 1.452155 (0.004783) \| 1.506484 / 1.492716 (0.013767) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.222295 / 0.018006 (0.204288) \| 0.416672 / 0.000490 (0.416183) \| 0.003015 / 0.000200 (0.002815) \| 0.000079 / 0.000054 (0.000025) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026428 / 0.037411 (-0.010983) \| 0.080072 / 0.014526 (0.065547) \| 0.089992 / 0.176557 (-0.086564) \| 0.141739 / 0.737135 (-0.595397) \| 0.092281 / 0.296338 (-0.204058) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.417758 / 0.215209 (0.202549) \| 4.175673 / 2.077655 (2.098018) \| 2.262369 / 1.504120 (0.758249) \| 2.100440 / 1.541195 (0.559246) \| 2.075827 / 1.468490 (0.607337) \| 0.505673 / 4.584777 (-4.079104) \| 3.129020 / 3.745712 (-0.616692) \| 2.843255 / 5.269862 (-2.426607) \| 1.853288 / 4.565676 (-2.712389) \| 0.058337 / 0.424275 (-0.365938) \| 0.006461 / 0.007607 (-0.001147) \| 0.491797 / 0.226044 (0.265753) \| 4.933327 / 2.268929 (2.664399) \| 2.675374 / 55.444624 (-52.769250) \| 2.358103 / 6.876477 (-4.518374) \| 2.540436 / 2.142072 (0.398363) \| 0.591550 / 4.805227 (-4.213677) \| 0.121572 / 6.500664 (-6.379092) \| 0.057311 / 0.075469 (-0.018158) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.365368 / 1.841788 (-0.476419) \| 17.763413 / 8.074308 (9.689105) \| 14.368754 / 10.191392 (4.177362) \| 0.132979 / 0.680424 (-0.547445) \| 0.017957 / 0.534201 (-0.516244) \| 0.334035 / 0.579283 (-0.245248) \| 0.385349 / 0.434364 (-0.049015) \| 0.392636 / 0.540337 (-0.147702) \| 0.537957 / 1.386936 (-0.848979) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#92503c94839b31125b4d5288d0a49d81b9b9b3cc \"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) \| 0.008053 / 0.011353 (-0.003300) \| 0.004966 / 0.011008 (-0.006043) \| 0.102219 / 0.038508 (0.063711) \| 0.099319 / 0.023109 (0.076210) \| 0.418458 / 0.275898 (0.142559) \| 0.459344 / 0.323480 (0.135864) \| 0.004756 / 0.007986 (-0.003229) \| 0.003940 / 0.004328 (-0.000388) \| 0.076824 / 0.004250 (0.072573) \| 0.068090 / 0.037052 (0.031038) \| 0.428689 / 0.258489 (0.170200) \| 0.476153 / 0.293841 (0.182312) \| 0.036927 / 0.128546 (-0.091619) \| 0.010232 / 0.075646 (-0.065414) \| 0.345126 / 0.419271 (-0.074145) \| 0.063182 / 0.043533 (0.019649) \| 0.416633 / 0.255139 (0.161494) \| 0.437418 / 0.283200 (0.154218) \| 0.028192 / 0.141683 (-0.113491) \| 1.768869 / 1.452155 (0.316715) \| 1.847022 / 1.492716 (0.354306) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.269997 / 0.018006 (0.251991) \| 0.544246 / 0.000490 (0.543756) \| 0.012940 / 0.000200 (0.012740) \| 0.000754 / 0.000054 (0.000699) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035570 / 0.037411 (-0.001842) \| 0.104318 / 0.014526 (0.089792) \| 0.115263 / 0.176557 (-0.061294) \| 0.184693 / 0.737135 (-0.552442) \| 0.116023 / 0.296338 (-0.180315) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.472361 / 0.215209 (0.257152) \| 4.714327 / 2.077655 (2.636673) \| 2.405434 / 1.504120 (0.901314) \| 2.197871 / 1.541195 (0.656677) \| 2.312901 / 1.468490 (0.844411) \| 0.569736 / 4.584777 (-4.015041) \| 4.600008 / 3.745712 (0.854296) \| 4.127967 / 5.269862 (-1.141895) \| 2.462232 / 4.565676 (-2.103445) \| 0.067759 / 0.424275 (-0.356516) \| 0.009277 / 0.007607 (0.001670) \| 0.569658 / 0.226044 (0.343614) \| 5.694050 / 2.268929 (3.425121) \| 3.041495 / 55.444624 (-52.403129) \| 2.688418 / 6.876477 (-4.188059) \| 2.762175 / 2.142072 (0.620102) \| 0.683250 / 4.805227 (-4.121977) \| 0.158772 / 6.500664 (-6.341892) \| 0.073364 / 0.075469 (-0.002105) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.627241 / 1.841788 (-0.214547) \| 23.054465 / 8.074308 (14.980157) \| 17.122451 / 10.191392 (6.931059) \| 0.170272 / 0.680424 (-0.510152) \| 0.021678 / 0.534201 (-0.512523) \| 0.467301 / 0.579283 (-0.111982) \| 0.509480 / 0.434364 (0.075116) \| 0.555077 / 0.540337 (0.014740) \| 0.816199 / 1.386936 (-0.570737) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008499 / 0.011353 (-0.002854) \| 0.004724 / 0.011008 (-0.006284) \| 0.077519 / 0.038508 (0.039011) \| 0.103237 / 0.023109 (0.080127) \| 0.447470 / 0.275898 (0.171572) \| 0.484778 / 0.323480 (0.161298) \| 0.006475 / 0.007986 (-0.001511) \| 0.003946 / 0.004328 (-0.000383) \| 0.075596 / 0.004250 (0.071346) \| 0.069265 / 0.037052 (0.032213) \| 0.454185 / 0.258489 (0.195696) \| 0.491039 / 0.293841 (0.197198) \| 0.038611 / 0.128546 (-0.089935) \| 0.009889 / 0.075646 (-0.065758) \| 0.084012 / 0.419271 (-0.335260) \| 0.057265 / 0.043533 (0.013732) \| 0.448622 / 0.255139 (0.193483) \| 0.470961 / 0.283200 (0.187762) \| 0.029220 / 0.141683 (-0.112463) \| 1.773347 / 1.452155 (0.321192) \| 1.872669 / 1.492716 (0.379953) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.272429 / 0.018006 (0.254423) \| 0.569907 / 0.000490 (0.569418) \| 0.013359 / 0.000200 (0.013159) \| 0.000187 / 0.000054 (0.000133) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.038784 / 0.037411 (0.001373) \| 0.114958 / 0.014526 (0.100432) \| 0.132745 / 0.176557 (-0.043811) \| 0.186283 / 0.737135 (-0.550852) \| 0.126652 / 0.296338 (-0.169686) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.482753 / 0.215209 (0.267544) \| 4.827287 / 2.077655 (2.749633) \| 2.539959 / 1.504120 (1.035839) \| 2.348483 / 1.541195 (0.807288) \| 2.421739 / 1.468490 (0.953249) \| 0.586064 / 4.584777 (-3.998713) \| 4.579865 / 3.745712 (0.834152) \| 3.950617 / 5.269862 (-1.319244) \| 2.528447 / 4.565676 (-2.037229) \| 0.070280 / 0.424275 (-0.353995) \| 0.008801 / 0.007607 (0.001194) \| 0.568857 / 0.226044 (0.342812) \| 5.692739 / 2.268929 (3.423810) \| 3.192045 / 55.444624 (-52.252579) \| 2.768092 / 6.876477 (-4.108384) \| 3.002934 / 2.142072 (0.860862) \| 0.701887 / 4.805227 (-4.103340) \| 0.155563 / 6.500664 (-6.345102) \| 0.069397 / 0.075469 (-0.006072) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.607991 / 1.841788 (-0.233796) \| 24.658060 / 8.074308 (16.583752) \| 17.616229 / 10.191392 (7.424837) \| 0.209730 / 0.680424 (-0.470693) \| 0.024052 / 0.534201 (-0.510149) \| 0.476648 / 0.579283 (-0.102635) \| 0.534452 / 0.434364 (0.100089) \| 0.567702 / 0.540337 (0.027365) \| 0.772933 / 1.386936 (-0.614003) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a49e78ede85c2a680adddacbb6b9638cba4062f3 \"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) \| 0.004684 / 0.011353 (-0.006669) \| 0.002944 / 0.011008 (-0.008064) \| 0.063065 / 0.038508 (0.024557) \| 0.051627 / 0.023109 (0.028518) \| 0.243485 / 0.275898 (-0.032413) \| 0.275144 / 0.323480 (-0.048336) \| 0.002934 / 0.007986 (-0.005052) \| 0.002395 / 0.004328 (-0.001934) \| 0.048579 / 0.004250 (0.044328) \| 0.038940 / 0.037052 (0.001887) \| 0.250244 / 0.258489 (-0.008245) \| 0.287404 / 0.293841 (-0.006437) \| 0.022958 / 0.128546 (-0.105588) \| 0.007189 / 0.075646 (-0.068458) \| 0.202483 / 0.419271 (-0.216788) \| 0.035477 / 0.043533 (-0.008056) \| 0.243793 / 0.255139 (-0.011346) \| 0.265990 / 0.283200 (-0.017209) \| 0.019675 / 0.141683 (-0.122008) \| 1.119127 / 1.452155 (-0.333028) \| 1.183230 / 1.492716 (-0.309486) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097090 / 0.018006 (0.079084) \| 0.305815 / 0.000490 (0.305325) \| 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) \| 0.019233 / 0.037411 (-0.018178) \| 0.061743 / 0.014526 (0.047217) \| 0.077033 / 0.176557 (-0.099524) \| 0.119786 / 0.737135 (-0.617349) \| 0.074740 / 0.296338 (-0.221598) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284361 / 0.215209 (0.069152) \| 2.761501 / 2.077655 (0.683846) \| 1.464980 / 1.504120 (-0.039140) \| 1.348026 / 1.541195 (-0.193169) \| 1.362690 / 1.468490 (-0.105800) \| 0.392022 / 4.584777 (-4.192755) \| 2.401330 / 3.745712 (-1.344382) \| 2.618999 / 5.269862 (-2.650863) \| 1.599526 / 4.565676 (-2.966150) \| 0.045621 / 0.424275 (-0.378654) \| 0.005153 / 0.007607 (-0.002454) \| 0.337279 / 0.226044 (0.111234) \| 3.330135 / 2.268929 (1.061206) \| 1.803544 / 55.444624 (-53.641081) \| 1.515545 / 6.876477 (-5.360932) \| 1.561745 / 2.142072 (-0.580327) \| 0.468735 / 4.805227 (-4.336492) \| 0.098882 / 6.500664 (-6.401782) \| 0.042923 / 0.075469 (-0.032546) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.961106 / 1.841788 (-0.880682) \| 12.030489 / 8.074308 (3.956181) \| 10.824166 / 10.191392 (0.632774) \| 0.132135 / 0.680424 (-0.548289) \| 0.015320 / 0.534201 (-0.518881) \| 0.269691 / 0.579283 (-0.309592) \| 0.270700 / 0.434364 (-0.163664) \| 0.308317 / 0.540337 (-0.232020) \| 0.397871 / 1.386936 (-0.989065) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated 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 (-0.006494) \| 0.003400 / 0.011008 (-0.007609) \| 0.048095 / 0.038508 (0.009587) \| 0.054885 / 0.023109 (0.031776) \| 0.276976 / 0.275898 (0.001078) \| 0.302298 / 0.323480 (-0.021182) \| 0.004084 / 0.007986 (-0.003902) \| 0.002647 / 0.004328 (-0.001681) \| 0.048570 / 0.004250 (0.044319) \| 0.040683 / 0.037052 (0.003631) \| 0.279828 / 0.258489 (0.021339) \| 0.306037 / 0.293841 (0.012196) \| 0.024263 / 0.128546 (-0.104283) \| 0.007336 / 0.075646 (-0.068310) \| 0.053768 / 0.419271 (-0.365503) \| 0.032284 / 0.043533 (-0.011248) \| 0.276706 / 0.255139 (0.021567) \| 0.294706 / 0.283200 (0.011506) \| 0.018092 / 0.141683 (-0.123591) \| 1.153430 / 1.452155 (-0.298725) \| 1.208783 / 1.492716 (-0.283933) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.096946 / 0.018006 (0.078939) \| 0.308118 / 0.000490 (0.307628) \| 0.000234 / 0.000200 (0.000034) \| 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) \| 0.021834 / 0.037411 (-0.015577) \| 0.070934 / 0.014526 (0.056408) \| 0.080310 / 0.176557 (-0.096247) \| 0.123299 / 0.737135 (-0.613836) \| 0.081591 / 0.296338 (-0.214748) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302242 / 0.215209 (0.087033) \| 2.934477 / 2.077655 (0.856822) \| 1.623768 / 1.504120 (0.119648) \| 1.493868 / 1.541195 (-0.047326) \| 1.516553 / 1.468490 (0.048063) \| 0.410319 / 4.584777 (-4.174458) \| 2.471346 / 3.745712 (-1.274366) \| 2.667371 / 5.269862 (-2.602491) \| 1.625390 / 4.565676 (-2.940286) \| 0.046465 / 0.424275 (-0.377810) \| 0.004867 / 0.007607 (-0.002740) \| 0.355516 / 0.226044 (0.129471) \| 3.442294 / 2.268929 (1.173365) \| 1.973859 / 55.444624 (-53.470765) \| 1.682089 / 6.876477 (-5.194388) \| 1.865253 / 2.142072 (-0.276819) \| 0.475750 / 4.805227 (-4.329477) \| 0.098298 / 6.500664 (-6.402366) \| 0.041025 / 0.075469 (-0.034445) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.969864 / 1.841788 (-0.871924) \| 12.437806 / 8.074308 (4.363498) \| 10.461262 / 10.191392 (0.269870) \| 0.131051 / 0.680424 (-0.549373) \| 0.016232 / 0.534201 (-0.517969) \| 0.273968 / 0.579283 (-0.305315) \| 0.285369 / 0.434364 (-0.148995) \| 0.309046 / 0.540337 (-0.231291) \| 0.398776 / 1.386936 (-0.988160) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a49e78ede85c2a680adddacbb6b9638cba4062f3 \"CML watermark\")\n" ]
1,973,942,770	Support pyarrow 14.0.0	closed	Support `pyarrow` 14.0.0. Fix #6377 and fix #6374 (root cause). This fix is analog to a previous one: - #6175	2023-11-02T10:25:10	2023-11-02T15:24:28	2023-11-02T15:15:44	https://github.com/huggingface/datasets/pull/6378	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6378", "html_url": "https://github.com/huggingface/datasets/pull/6378", "diff_url": "https://github.com/huggingface/datasets/pull/6378.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6378.patch", "merged_at": "2023-11-02T15:15:44" }	6,378	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) \| 0.007561 / 0.011353 (-0.003792) \| 0.004824 / 0.011008 (-0.006184) \| 0.110372 / 0.038508 (0.071864) \| 0.076767 / 0.023109 (0.053657) \| 0.357094 / 0.275898 (0.081196) \| 0.420566 / 0.323480 (0.097086) \| 0.004753 / 0.007986 (-0.003232) \| 0.004734 / 0.004328 (0.000405) \| 0.072926 / 0.004250 (0.068675) \| 0.058045 / 0.037052 (0.020992) \| 0.401109 / 0.258489 (0.142620) \| 0.444585 / 0.293841 (0.150744) \| 0.046492 / 0.128546 (-0.082055) \| 0.013948 / 0.075646 (-0.061698) \| 0.305188 / 0.419271 (-0.114083) \| 0.063112 / 0.043533 (0.019579) \| 0.384711 / 0.255139 (0.129572) \| 0.411375 / 0.283200 (0.128175) \| 0.048147 / 0.141683 (-0.093536) \| 1.632357 / 1.452155 (0.180202) \| 1.661021 / 1.492716 (0.168304) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281104 / 0.018006 (0.263098) \| 0.567152 / 0.000490 (0.566662) \| 0.007178 / 0.000200 (0.006978) \| 0.000121 / 0.000054 (0.000066) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029337 / 0.037411 (-0.008075) \| 0.081644 / 0.014526 (0.067118) \| 0.103326 / 0.176557 (-0.073230) \| 0.155299 / 0.737135 (-0.581836) \| 0.093518 / 0.296338 (-0.202821) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.517979 / 0.215209 (0.302769) \| 5.250052 / 2.077655 (3.172397) \| 2.220543 / 1.504120 (0.716424) \| 1.901087 / 1.541195 (0.359892) \| 1.920564 / 1.468490 (0.452073) \| 0.766289 / 4.584777 (-3.818488) \| 5.130968 / 3.745712 (1.385256) \| 4.561874 / 5.269862 (-0.707988) \| 2.702808 / 4.565676 (-1.862868) \| 0.078929 / 0.424275 (-0.345346) \| 0.007834 / 0.007607 (0.000226) \| 0.636628 / 0.226044 (0.410583) \| 6.309391 / 2.268929 (4.040463) \| 2.942180 / 55.444624 (-52.502445) \| 2.369557 / 6.876477 (-4.506920) \| 2.347528 / 2.142072 (0.205456) \| 0.911110 / 4.805227 (-3.894117) \| 0.189102 / 6.500664 (-6.311562) \| 0.068012 / 0.075469 (-0.007457) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.494431 / 1.841788 (-0.347356) \| 22.161476 / 8.074308 (14.087168) \| 19.426403 / 10.191392 (9.235011) \| 0.211154 / 0.680424 (-0.469270) \| 0.030655 / 0.534201 (-0.503546) \| 0.440449 / 0.579283 (-0.138834) \| 0.526522 / 0.434364 (0.092158) \| 0.517494 / 0.540337 (-0.022844) \| 0.727387 / 1.386936 (-0.659549) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008354 / 0.011353 (-0.002999) \| 0.006108 / 0.011008 (-0.004900) \| 0.069079 / 0.038508 (0.030571) \| 0.080402 / 0.023109 (0.057292) \| 0.452166 / 0.275898 (0.176268) \| 0.440264 / 0.323480 (0.116784) \| 0.005942 / 0.007986 (-0.002043) \| 0.003397 / 0.004328 (-0.000932) \| 0.079856 / 0.004250 (0.075606) \| 0.056329 / 0.037052 (0.019276) \| 0.424261 / 0.258489 (0.165772) \| 0.464362 / 0.293841 (0.170521) \| 0.051968 / 0.128546 (-0.076578) \| 0.015204 / 0.075646 (-0.060442) \| 0.085940 / 0.419271 (-0.333332) \| 0.066673 / 0.043533 (0.023140) \| 0.436481 / 0.255139 (0.181342) \| 0.445285 / 0.283200 (0.162085) \| 0.035188 / 0.141683 (-0.106495) \| 1.579442 / 1.452155 (0.127288) \| 1.686120 / 1.492716 (0.193404) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.319039 / 0.018006 (0.301032) \| 0.655080 / 0.000490 (0.654591) \| 0.005445 / 0.000200 (0.005245) \| 0.000112 / 0.000054 (0.000057) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028566 / 0.037411 (-0.008845) \| 0.092131 / 0.014526 (0.077605) \| 0.103654 / 0.176557 (-0.072902) \| 0.158082 / 0.737135 (-0.579054) \| 0.107520 / 0.296338 (-0.188819) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.573479 / 0.215209 (0.358270) \| 5.629751 / 2.077655 (3.552096) \| 2.501722 / 1.504120 (0.997602) \| 2.156255 / 1.541195 (0.615061) \| 2.251296 / 1.468490 (0.782805) \| 0.767686 / 4.584777 (-3.817091) \| 5.080866 / 3.745712 (1.335154) \| 4.353351 / 5.269862 (-0.916510) \| 2.818707 / 4.565676 (-1.746970) \| 0.082617 / 0.424275 (-0.341658) \| 0.008045 / 0.007607 (0.000438) \| 0.665462 / 0.226044 (0.439417) \| 6.961380 / 2.268929 (4.692452) \| 3.308717 / 55.444624 (-52.135907) \| 2.664239 / 6.876477 (-4.212238) \| 2.782790 / 2.142072 (0.640718) \| 0.919567 / 4.805227 (-3.885660) \| 0.186731 / 6.500664 (-6.313933) \| 0.063437 / 0.075469 (-0.012032) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.668076 / 1.841788 (-0.173712) \| 22.720187 / 8.074308 (14.645879) \| 19.803359 / 10.191392 (9.611967) \| 0.237201 / 0.680424 (-0.443223) \| 0.041156 / 0.534201 (-0.493045) \| 0.458974 / 0.579283 (-0.120309) \| 0.620276 / 0.434364 (0.185912) \| 0.544079 / 0.540337 (0.003741) \| 0.722715 / 1.386936 (-0.664221) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1ed9306b6c512befb721b681fba3222221c8468e \"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) \| 0.006882 / 0.011353 (-0.004471) \| 0.004238 / 0.011008 (-0.006770) \| 0.084042 / 0.038508 (0.045534) \| 0.074175 / 0.023109 (0.051065) \| 0.308771 / 0.275898 (0.032873) \| 0.346300 / 0.323480 (0.022820) \| 0.005455 / 0.007986 (-0.002530) \| 0.003638 / 0.004328 (-0.000690) \| 0.065326 / 0.004250 (0.061076) \| 0.056080 / 0.037052 (0.019028) \| 0.326324 / 0.258489 (0.067834) \| 0.360133 / 0.293841 (0.066292) \| 0.031577 / 0.128546 (-0.096969) \| 0.008675 / 0.075646 (-0.066971) \| 0.288051 / 0.419271 (-0.131221) \| 0.052769 / 0.043533 (0.009236) \| 0.308689 / 0.255139 (0.053550) \| 0.328270 / 0.283200 (0.045070) \| 0.025028 / 0.141683 (-0.116655) \| 1.520670 / 1.452155 (0.068515) \| 1.585229 / 1.492716 (0.092513) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284078 / 0.018006 (0.266072) \| 0.558134 / 0.000490 (0.557644) \| 0.015042 / 0.000200 (0.014842) \| 0.000429 / 0.000054 (0.000375) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028747 / 0.037411 (-0.008664) \| 0.083816 / 0.014526 (0.069290) \| 0.207467 / 0.176557 (0.030911) \| 0.163527 / 0.737135 (-0.573608) \| 0.100148 / 0.296338 (-0.196190) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.376109 / 0.215209 (0.160900) \| 3.749639 / 2.077655 (1.671984) \| 1.827081 / 1.504120 (0.322961) \| 1.662021 / 1.541195 (0.120827) \| 1.734655 / 1.468490 (0.266165) \| 0.483701 / 4.584777 (-4.101075) \| 3.454772 / 3.745712 (-0.290941) \| 3.465079 / 5.269862 (-1.804783) \| 2.070874 / 4.565676 (-2.494802) \| 0.056714 / 0.424275 (-0.367561) \| 0.007786 / 0.007607 (0.000179) \| 0.455980 / 0.226044 (0.229936) \| 4.530612 / 2.268929 (2.261683) \| 2.345757 / 55.444624 (-53.098867) \| 2.030289 / 6.876477 (-4.846188) \| 2.068440 / 2.142072 (-0.073632) \| 0.576502 / 4.805227 (-4.228725) \| 0.131787 / 6.500664 (-6.368878) \| 0.060038 / 0.075469 (-0.015431) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.272225 / 1.841788 (-0.569563) \| 19.373635 / 8.074308 (11.299327) \| 14.167831 / 10.191392 (3.976439) \| 0.166336 / 0.680424 (-0.514088) \| 0.018420 / 0.534201 (-0.515781) \| 0.387878 / 0.579283 (-0.191405) \| 0.413105 / 0.434364 (-0.021259) \| 0.458618 / 0.540337 (-0.081720) \| 0.639031 / 1.386936 (-0.747905) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007122 / 0.011353 (-0.004230) \| 0.004193 / 0.011008 (-0.006815) \| 0.066194 / 0.038508 (0.027686) \| 0.077775 / 0.023109 (0.054666) \| 0.349780 / 0.275898 (0.073882) \| 0.383417 / 0.323480 (0.059937) \| 0.006416 / 0.007986 (-0.001570) \| 0.003651 / 0.004328 (-0.000677) \| 0.064837 / 0.004250 (0.060587) \| 0.058012 / 0.037052 (0.020959) \| 0.351085 / 0.258489 (0.092596) \| 0.387302 / 0.293841 (0.093462) \| 0.032447 / 0.128546 (-0.096099) \| 0.008636 / 0.075646 (-0.067011) \| 0.071962 / 0.419271 (-0.347309) \| 0.047839 / 0.043533 (0.004306) \| 0.349508 / 0.255139 (0.094369) \| 0.361892 / 0.283200 (0.078693) \| 0.024129 / 0.141683 (-0.117554) \| 1.523828 / 1.452155 (0.071673) \| 1.607371 / 1.492716 (0.114655) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.245928 / 0.018006 (0.227922) \| 0.567708 / 0.000490 (0.567218) \| 0.003789 / 0.000200 (0.003589) \| 0.000092 / 0.000054 (0.000037) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034107 / 0.037411 (-0.003304) \| 0.092539 / 0.014526 (0.078014) \| 0.110735 / 0.176557 (-0.065821) \| 0.163251 / 0.737135 (-0.573884) \| 0.110353 / 0.296338 (-0.185985) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.399992 / 0.215209 (0.184783) \| 3.976526 / 2.077655 (1.898872) \| 2.056182 / 1.504120 (0.552062) \| 1.856624 / 1.541195 (0.315429) \| 1.941540 / 1.468490 (0.473050) \| 0.484662 / 4.584777 (-4.100115) \| 3.548228 / 3.745712 (-0.197484) \| 3.352900 / 5.269862 (-1.916962) \| 2.056310 / 4.565676 (-2.509366) \| 0.056952 / 0.424275 (-0.367323) \| 0.007284 / 0.007607 (-0.000323) \| 0.473749 / 0.226044 (0.247704) \| 4.736510 / 2.268929 (2.467581) \| 2.570711 / 55.444624 (-52.873913) \| 2.204237 / 6.876477 (-4.672239) \| 2.438512 / 2.142072 (0.296439) \| 0.575542 / 4.805227 (-4.229685) \| 0.129260 / 6.500664 (-6.371404) \| 0.057704 / 0.075469 (-0.017765) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.316659 / 1.841788 (-0.525128) \| 20.103340 / 8.074308 (12.029032) \| 14.488385 / 10.191392 (4.296993) \| 0.171841 / 0.680424 (-0.508583) \| 0.020148 / 0.534201 (-0.514053) \| 0.398456 / 0.579283 (-0.180828) \| 0.443516 / 0.434364 (0.009152) \| 0.479597 / 0.540337 (-0.060741) \| 0.643665 / 1.386936 (-0.743271) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#370be814b0c18769ea8e699e3647fadcf431e6df \"CML watermark\")\n" ]
1,973,937,612	Support pyarrow 14.0.0	closed	Support pyarrow 14.0.0 by fixing the root cause of: - #6374 and revert: - #6375	2023-11-02T10:22:08	2023-11-02T15:15:45	2023-11-02T15:15:45	https://github.com/huggingface/datasets/issues/6377	null	6,377	false	[]
1,973,927,468	Caching problem when deleting a dataset	closed	### Describe the bug Pushing a dataset with n + m features to a repo which was deleted, but contained n features, will fail. ### Steps to reproduce the bug 1. Create a dataset with n features per row 2. `dataset.push_to_hub(YOUR_PATH, SPLIT, token=TOKEN)` 3. Go on the hub, delete the repo at `YOUR_PATH` 4. Update your local dataset to have n + m features per row 5. `dataset.push_to_hub(YOUR_PATH, SPLIT, token=TOKEN)` will fail because of a mismatch in features number ### Expected behavior Step 5 should work or display a message to indicate the cache has not been cleared ### Environment info - `datasets` version: 2.12.0 - Platform: Linux-5.15.0-88-generic-x86_64-with-glibc2.31 - Python version: 3.10.10 - Huggingface_hub version: 0.16.4 - PyArrow version: 11.0.0 - Pandas version: 2.0.0	2023-11-02T10:15:58	2023-12-04T16:53:34	2023-12-04T16:53:33	https://github.com/huggingface/datasets/issues/6376	null	6,376	false	[ "Thanks for reporting! Can you also share the error message printed in step 5?", "I did not store it at the time but I'll try to re-do a mwe next week to get it again", "I haven't managed to reproduce this issue using a [notebook](https://colab.research.google.com/drive/1m6eduYun7pFTkigrCJAFgw0BghlbvXIL?usp=sharing) that follows the steps to reproduce the bug. So, I'm closing it.\r\n\r\nBut feel free to re-open it if you have a better reproducer." ]
1,973,877,879	Temporarily pin pyarrow < 14.0.0	closed	Temporarily pin `pyarrow` < 14.0.0 until permanent solution is found. Hot fix #6374.	2023-11-02T09:48:58	2023-11-02T10:22:33	2023-11-02T10:11:19	https://github.com/huggingface/datasets/pull/6375	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6375", "html_url": "https://github.com/huggingface/datasets/pull/6375", "diff_url": "https://github.com/huggingface/datasets/pull/6375.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6375.patch", "merged_at": "2023-11-02T10:11:19" }	6,375	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) \| 0.008947 / 0.011353 (-0.002406) \| 0.005602 / 0.011008 (-0.005406) \| 0.111208 / 0.038508 (0.072700) \| 0.082750 / 0.023109 (0.059641) \| 0.453277 / 0.275898 (0.177379) \| 0.480072 / 0.323480 (0.156592) \| 0.005254 / 0.007986 (-0.002731) \| 0.005421 / 0.004328 (0.001092) \| 0.082899 / 0.004250 (0.078648) \| 0.062859 / 0.037052 (0.025807) \| 0.466703 / 0.258489 (0.208214) \| 0.478241 / 0.293841 (0.184400) \| 0.050754 / 0.128546 (-0.077792) \| 0.017726 / 0.075646 (-0.057920) \| 0.374830 / 0.419271 (-0.044442) \| 0.068577 / 0.043533 (0.025044) \| 0.453643 / 0.255139 (0.198504) \| 0.453736 / 0.283200 (0.170537) \| 0.037313 / 0.141683 (-0.104369) \| 1.741215 / 1.452155 (0.289060) \| 1.862247 / 1.492716 (0.369531) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.314174 / 0.018006 (0.296168) \| 0.644439 / 0.000490 (0.643949) \| 0.013914 / 0.000200 (0.013715) \| 0.000478 / 0.000054 (0.000424) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030462 / 0.037411 (-0.006949) \| 0.096789 / 0.014526 (0.082263) \| 0.109999 / 0.176557 (-0.066557) \| 0.184610 / 0.737135 (-0.552525) \| 0.113846 / 0.296338 (-0.182493) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.586508 / 0.215209 (0.371299) \| 5.785138 / 2.077655 (3.707484) \| 2.578512 / 1.504120 (1.074392) \| 2.266981 / 1.541195 (0.725786) \| 2.442463 / 1.468490 (0.973973) \| 0.880973 / 4.584777 (-3.703804) \| 5.410327 / 3.745712 (1.664615) \| 4.976842 / 5.269862 (-0.293020) \| 3.020535 / 4.565676 (-1.545142) \| 0.089640 / 0.424275 (-0.334635) \| 0.009126 / 0.007607 (0.001519) \| 0.682364 / 0.226044 (0.456319) \| 6.840507 / 2.268929 (4.571579) \| 3.313314 / 55.444624 (-52.131310) \| 2.815313 / 6.876477 (-4.061164) \| 2.851787 / 2.142072 (0.709715) \| 1.044916 / 4.805227 (-3.760312) \| 0.218346 / 6.500664 (-6.282318) \| 0.075655 / 0.075469 (0.000186) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.641767 / 1.841788 (-0.200020) \| 24.618096 / 8.074308 (16.543788) \| 21.557652 / 10.191392 (11.366260) \| 0.211622 / 0.680424 (-0.468801) \| 0.028775 / 0.534201 (-0.505426) \| 0.480469 / 0.579283 (-0.098814) \| 0.593311 / 0.434364 (0.158948) \| 0.560620 / 0.540337 (0.020283) \| 0.827026 / 1.386936 (-0.559910) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009347 / 0.011353 (-0.002006) \| 0.005184 / 0.011008 (-0.005824) \| 0.078878 / 0.038508 (0.040370) \| 0.083067 / 0.023109 (0.059957) \| 0.446591 / 0.275898 (0.170693) \| 0.512934 / 0.323480 (0.189454) \| 0.006614 / 0.007986 (-0.001372) \| 0.004477 / 0.004328 (0.000148) \| 0.087403 / 0.004250 (0.083153) \| 0.060710 / 0.037052 (0.023658) \| 0.451811 / 0.258489 (0.193322) \| 0.482031 / 0.293841 (0.188190) \| 0.051685 / 0.128546 (-0.076862) \| 0.013436 / 0.075646 (-0.062210) \| 0.109012 / 0.419271 (-0.310259) \| 0.059654 / 0.043533 (0.016121) \| 0.439041 / 0.255139 (0.183902) \| 0.481708 / 0.283200 (0.198508) \| 0.037393 / 0.141683 (-0.104290) \| 1.761704 / 1.452155 (0.309549) \| 1.946711 / 1.492716 (0.453995) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287981 / 0.018006 (0.269975) \| 0.610219 / 0.000490 (0.609729) \| 0.006733 / 0.000200 (0.006533) \| 0.000128 / 0.000054 (0.000074) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.038999 / 0.037411 (0.001588) \| 0.100613 / 0.014526 (0.086087) \| 0.126445 / 0.176557 (-0.050111) \| 0.187596 / 0.737135 (-0.549540) \| 0.122130 / 0.296338 (-0.174208) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.647686 / 0.215209 (0.432477) \| 6.176079 / 2.077655 (4.098424) \| 2.800232 / 1.504120 (1.296112) \| 2.434625 / 1.541195 (0.893430) \| 2.460646 / 1.468490 (0.992155) \| 0.923736 / 4.584777 (-3.661041) \| 5.480197 / 3.745712 (1.734485) \| 4.849250 / 5.269862 (-0.420612) \| 3.031576 / 4.565676 (-1.534101) \| 0.102525 / 0.424275 (-0.321750) \| 0.008688 / 0.007607 (0.001081) \| 0.766097 / 0.226044 (0.540052) \| 7.626822 / 2.268929 (5.357893) \| 3.719155 / 55.444624 (-51.725469) \| 2.967121 / 6.876477 (-3.909356) \| 3.182464 / 2.142072 (1.040392) \| 1.018315 / 4.805227 (-3.786912) \| 0.211300 / 6.500664 (-6.289364) \| 0.083055 / 0.075469 (0.007586) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.731619 / 1.841788 (-0.110168) \| 25.315978 / 8.074308 (17.241669) \| 22.736306 / 10.191392 (12.544914) \| 0.270330 / 0.680424 (-0.410094) \| 0.034790 / 0.534201 (-0.499411) \| 0.488675 / 0.579283 (-0.090608) \| 0.603426 / 0.434364 (0.169062) \| 0.572547 / 0.540337 (0.032210) \| 0.825719 / 1.386936 (-0.561217) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1eaa85a4ad79aa0e411218d61a8894cc14a75fa0 \"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) \| 0.008992 / 0.011353 (-0.002360) \| 0.005086 / 0.011008 (-0.005923) \| 0.107400 / 0.038508 (0.068892) \| 0.091894 / 0.023109 (0.068785) \| 0.382347 / 0.275898 (0.106449) \| 0.446581 / 0.323480 (0.123101) \| 0.005179 / 0.007986 (-0.002807) \| 0.006356 / 0.004328 (0.002028) \| 0.084979 / 0.004250 (0.080729) \| 0.060647 / 0.037052 (0.023594) \| 0.385940 / 0.258489 (0.127451) \| 0.444817 / 0.293841 (0.150976) \| 0.049484 / 0.128546 (-0.079062) \| 0.014173 / 0.075646 (-0.061473) \| 0.345704 / 0.419271 (-0.073567) \| 0.068082 / 0.043533 (0.024550) \| 0.377170 / 0.255139 (0.122031) \| 0.411816 / 0.283200 (0.128616) \| 0.043049 / 0.141683 (-0.098633) \| 1.681499 / 1.452155 (0.229344) \| 1.805428 / 1.492716 (0.312712) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.323170 / 0.018006 (0.305164) \| 0.693845 / 0.000490 (0.693355) \| 0.015499 / 0.000200 (0.015299) \| 0.000603 / 0.000054 (0.000548) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031629 / 0.037411 (-0.005783) \| 0.093511 / 0.014526 (0.078985) \| 0.112400 / 0.176557 (-0.064157) \| 0.173731 / 0.737135 (-0.563405) \| 0.116013 / 0.296338 (-0.180325) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.576724 / 0.215209 (0.361515) \| 5.775055 / 2.077655 (3.697400) \| 2.755869 / 1.504120 (1.251749) \| 2.430253 / 1.541195 (0.889058) \| 2.479629 / 1.468490 (1.011139) \| 0.841472 / 4.584777 (-3.743305) \| 5.120536 / 3.745712 (1.374824) \| 4.813281 / 5.269862 (-0.456581) \| 3.054617 / 4.565676 (-1.511059) \| 0.091459 / 0.424275 (-0.332816) \| 0.009072 / 0.007607 (0.001465) \| 0.742674 / 0.226044 (0.516629) \| 7.137861 / 2.268929 (4.868933) \| 3.497568 / 55.444624 (-51.947056) \| 2.814658 / 6.876477 (-4.061819) \| 2.934415 / 2.142072 (0.792343) \| 0.970855 / 4.805227 (-3.834372) \| 0.213366 / 6.500664 (-6.287299) \| 0.078763 / 0.075469 (0.003293) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.584716 / 1.841788 (-0.257072) \| 24.098173 / 8.074308 (16.023865) \| 20.746352 / 10.191392 (10.554960) \| 0.215313 / 0.680424 (-0.465111) \| 0.029538 / 0.534201 (-0.504663) \| 0.448672 / 0.579283 (-0.130611) \| 0.580023 / 0.434364 (0.145659) \| 0.537867 / 0.540337 (-0.002471) \| 0.804622 / 1.386936 (-0.582314) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008965 / 0.011353 (-0.002388) \| 0.005544 / 0.011008 (-0.005464) \| 0.076806 / 0.038508 (0.038298) \| 0.085333 / 0.023109 (0.062224) \| 0.509974 / 0.275898 (0.234076) \| 0.511548 / 0.323480 (0.188068) \| 0.007136 / 0.007986 (-0.000849) \| 0.004491 / 0.004328 (0.000163) \| 0.086687 / 0.004250 (0.082437) \| 0.066539 / 0.037052 (0.029486) \| 0.483663 / 0.258489 (0.225174) \| 0.529480 / 0.293841 (0.235639) \| 0.046296 / 0.128546 (-0.082250) \| 0.014736 / 0.075646 (-0.060910) \| 0.088261 / 0.419271 (-0.331010) \| 0.056753 / 0.043533 (0.013220) \| 0.511698 / 0.255139 (0.256559) \| 0.497956 / 0.283200 (0.214756) \| 0.034753 / 0.141683 (-0.106930) \| 1.828354 / 1.452155 (0.376199) \| 1.799211 / 1.492716 (0.306494) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.389652 / 0.018006 (0.371645) \| 0.602522 / 0.000490 (0.602033) \| 0.068363 / 0.000200 (0.068163) \| 0.000493 / 0.000054 (0.000439) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036431 / 0.037411 (-0.000980) \| 0.102162 / 0.014526 (0.087636) \| 0.122466 / 0.176557 (-0.054091) \| 0.181001 / 0.737135 (-0.556134) \| 0.125743 / 0.296338 (-0.170596) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.583847 / 0.215209 (0.368638) \| 5.913008 / 2.077655 (3.835354) \| 2.716088 / 1.504120 (1.211968) \| 2.328631 / 1.541195 (0.787437) \| 2.459953 / 1.468490 (0.991463) \| 0.792829 / 4.584777 (-3.791948) \| 5.183965 / 3.745712 (1.438253) \| 4.508264 / 5.269862 (-0.761598) \| 2.855444 / 4.565676 (-1.710232) \| 0.090704 / 0.424275 (-0.333571) \| 0.009303 / 0.007607 (0.001696) \| 0.694303 / 0.226044 (0.468258) \| 6.951876 / 2.268929 (4.682947) \| 3.418244 / 55.444624 (-52.026381) \| 2.799743 / 6.876477 (-4.076734) \| 3.043657 / 2.142072 (0.901584) \| 0.921537 / 4.805227 (-3.883691) \| 0.191774 / 6.500664 (-6.308890) \| 0.068602 / 0.075469 (-0.006867) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.624842 / 1.841788 (-0.216946) \| 24.570622 / 8.074308 (16.496314) \| 21.207566 / 10.191392 (11.016174) \| 0.217734 / 0.680424 (-0.462689) \| 0.033109 / 0.534201 (-0.501091) \| 0.451651 / 0.579283 (-0.127632) \| 0.590890 / 0.434364 (0.156526) \| 0.546195 / 0.540337 (0.005858) \| 0.730298 / 1.386936 (-0.656638) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f6bdecff73303cf97f279a4e36622faf53133f9c \"CML watermark\")\n" ]
1,973,857,428	CI is broken: TypeError: Couldn't cast array	closed	See: https://github.com/huggingface/datasets/actions/runs/6730567226/job/18293518039 ``` FAILED tests/test_table.py::test_cast_sliced_fixed_size_array_to_features - TypeError: Couldn't cast array of type fixed_size_list<item: int32>[3] to Sequence(feature=Value(dtype='int64', id=None), length=3, id=None) ```	2023-11-02T09:37:06	2023-11-02T10:11:20	2023-11-02T10:11:20	https://github.com/huggingface/datasets/issues/6374	null	6,374	false	[]
1,973,349,695	Fix typo in `Dataset.map` docstring	closed	null	2023-11-02T01:36:49	2023-11-02T15:18:22	2023-11-02T10:11:38	https://github.com/huggingface/datasets/pull/6373	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6373", "html_url": "https://github.com/huggingface/datasets/pull/6373", "diff_url": "https://github.com/huggingface/datasets/pull/6373.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6373.patch", "merged_at": "2023-11-02T10:11:38" }	6,373	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) \| 0.006709 / 0.011353 (-0.004643) \| 0.004102 / 0.011008 (-0.006906) \| 0.084449 / 0.038508 (0.045941) \| 0.076078 / 0.023109 (0.052969) \| 0.319831 / 0.275898 (0.043933) \| 0.359918 / 0.323480 (0.036438) \| 0.006092 / 0.007986 (-0.001894) \| 0.003402 / 0.004328 (-0.000926) \| 0.064715 / 0.004250 (0.060465) \| 0.054541 / 0.037052 (0.017488) \| 0.330394 / 0.258489 (0.071905) \| 0.366048 / 0.293841 (0.072207) \| 0.031594 / 0.128546 (-0.096952) \| 0.008591 / 0.075646 (-0.067056) \| 0.292983 / 0.419271 (-0.126288) \| 0.052986 / 0.043533 (0.009453) \| 0.322253 / 0.255139 (0.067114) \| 0.340082 / 0.283200 (0.056882) \| 0.023390 / 0.141683 (-0.118293) \| 1.459038 / 1.452155 (0.006883) \| 1.536256 / 1.492716 (0.043540) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.233527 / 0.018006 (0.215521) \| 0.459145 / 0.000490 (0.458655) \| 0.007471 / 0.000200 (0.007271) \| 0.000281 / 0.000054 (0.000227) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028158 / 0.037411 (-0.009253) \| 0.083079 / 0.014526 (0.068553) \| 0.097159 / 0.176557 (-0.079397) \| 0.151927 / 0.737135 (-0.585208) \| 0.098024 / 0.296338 (-0.198314) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.386882 / 0.215209 (0.171673) \| 3.849635 / 2.077655 (1.771981) \| 1.832885 / 1.504120 (0.328765) \| 1.668356 / 1.541195 (0.127162) \| 1.745066 / 1.468490 (0.276576) \| 0.484476 / 4.584777 (-4.100301) \| 3.547604 / 3.745712 (-0.198108) \| 3.480338 / 5.269862 (-1.789523) \| 2.066837 / 4.565676 (-2.498840) \| 0.056755 / 0.424275 (-0.367520) \| 0.007747 / 0.007607 (0.000140) \| 0.467999 / 0.226044 (0.241955) \| 4.678875 / 2.268929 (2.409946) \| 2.341930 / 55.444624 (-53.102695) \| 1.985632 / 6.876477 (-4.890844) \| 2.046998 / 2.142072 (-0.095074) \| 0.579860 / 4.805227 (-4.225367) \| 0.131488 / 6.500664 (-6.369176) \| 0.060193 / 0.075469 (-0.015276) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.249656 / 1.841788 (-0.592132) \| 19.079517 / 8.074308 (11.005209) \| 14.328827 / 10.191392 (4.137435) \| 0.173707 / 0.680424 (-0.506717) \| 0.018250 / 0.534201 (-0.515951) \| 0.392225 / 0.579283 (-0.187058) \| 0.413920 / 0.434364 (-0.020444) \| 0.464124 / 0.540337 (-0.076214) \| 0.640283 / 1.386936 (-0.746653) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006859 / 0.011353 (-0.004494) \| 0.004068 / 0.011008 (-0.006940) \| 0.063936 / 0.038508 (0.025428) \| 0.077187 / 0.023109 (0.054078) \| 0.365098 / 0.275898 (0.089200) \| 0.391003 / 0.323480 (0.067523) \| 0.005571 / 0.007986 (-0.002415) \| 0.003425 / 0.004328 (-0.000904) \| 0.063220 / 0.004250 (0.058970) \| 0.056964 / 0.037052 (0.019912) \| 0.367793 / 0.258489 (0.109304) \| 0.398776 / 0.293841 (0.104935) \| 0.033182 / 0.128546 (-0.095364) \| 0.008601 / 0.075646 (-0.067045) \| 0.070276 / 0.419271 (-0.348996) \| 0.048383 / 0.043533 (0.004850) \| 0.360414 / 0.255139 (0.105275) \| 0.368171 / 0.283200 (0.084971) \| 0.023114 / 0.141683 (-0.118569) \| 1.503503 / 1.452155 (0.051349) \| 1.567279 / 1.492716 (0.074562) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224296 / 0.018006 (0.206290) \| 0.455138 / 0.000490 (0.454648) \| 0.004014 / 0.000200 (0.003814) \| 0.000104 / 0.000054 (0.000050) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032337 / 0.037411 (-0.005074) \| 0.094385 / 0.014526 (0.079859) \| 0.109870 / 0.176557 (-0.066687) \| 0.156978 / 0.737135 (-0.580157) \| 0.107559 / 0.296338 (-0.188780) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.427409 / 0.215209 (0.212200) \| 4.261772 / 2.077655 (2.184117) \| 2.276106 / 1.504120 (0.771986) \| 2.115232 / 1.541195 (0.574038) \| 2.192048 / 1.468490 (0.723558) \| 0.488459 / 4.584777 (-4.096318) \| 3.675463 / 3.745712 (-0.070249) \| 3.322475 / 5.269862 (-1.947387) \| 2.072253 / 4.565676 (-2.493424) \| 0.058259 / 0.424275 (-0.366017) \| 0.007319 / 0.007607 (-0.000288) \| 0.499513 / 0.226044 (0.273469) \| 4.994774 / 2.268929 (2.725845) \| 2.760927 / 55.444624 (-52.683697) \| 2.391947 / 6.876477 (-4.484530) \| 2.600557 / 2.142072 (0.458484) \| 0.587597 / 4.805227 (-4.217630) \| 0.131444 / 6.500664 (-6.369220) \| 0.057334 / 0.075469 (-0.018135) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.354636 / 1.841788 (-0.487152) \| 19.685735 / 8.074308 (11.611427) \| 14.295920 / 10.191392 (4.104528) \| 0.171921 / 0.680424 (-0.508503) \| 0.019926 / 0.534201 (-0.514274) \| 0.395216 / 0.579283 (-0.184068) \| 0.432791 / 0.434364 (-0.001573) \| 0.473055 / 0.540337 (-0.067282) \| 0.638633 / 1.386936 (-0.748303) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fad7c899ec9218a717311223aa6ef5c09a6c7885 \"CML watermark\")\n" ]
1,972,837,794	do not try to download from HF GCS for generator	closed	attempt to fix https://github.com/huggingface/datasets/issues/6371	2023-11-01T17:57:11	2023-11-02T16:02:52	2023-11-02T15:52:09	https://github.com/huggingface/datasets/pull/6372	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6372", "html_url": "https://github.com/huggingface/datasets/pull/6372", "diff_url": "https://github.com/huggingface/datasets/pull/6372.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6372.patch", "merged_at": "2023-11-02T15:52:09" }	6,372	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) \| 0.007617 / 0.011353 (-0.003735) \| 0.005371 / 0.011008 (-0.005638) \| 0.092110 / 0.038508 (0.053602) \| 0.070654 / 0.023109 (0.047544) \| 0.362501 / 0.275898 (0.086603) \| 0.412835 / 0.323480 (0.089355) \| 0.006752 / 0.007986 (-0.001234) \| 0.003752 / 0.004328 (-0.000576) \| 0.075644 / 0.004250 (0.071394) \| 0.055666 / 0.037052 (0.018614) \| 0.355906 / 0.258489 (0.097417) \| 0.405078 / 0.293841 (0.111237) \| 0.045767 / 0.128546 (-0.082779) \| 0.013778 / 0.075646 (-0.061868) \| 0.324696 / 0.419271 (-0.094575) \| 0.062200 / 0.043533 (0.018667) \| 0.359571 / 0.255139 (0.104432) \| 0.387274 / 0.283200 (0.104075) \| 0.035323 / 0.141683 (-0.106360) \| 1.586294 / 1.452155 (0.134139) \| 1.707564 / 1.492716 (0.214847) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.303940 / 0.018006 (0.285934) \| 0.583349 / 0.000490 (0.582859) \| 0.014845 / 0.000200 (0.014645) \| 0.000698 / 0.000054 (0.000643) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028994 / 0.037411 (-0.008417) \| 0.085555 / 0.014526 (0.071029) \| 0.097856 / 0.176557 (-0.078701) \| 0.161480 / 0.737135 (-0.575655) \| 0.098573 / 0.296338 (-0.197766) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.591294 / 0.215209 (0.376085) \| 5.751350 / 2.077655 (3.673695) \| 2.241620 / 1.504120 (0.737500) \| 1.991083 / 1.541195 (0.449888) \| 2.006711 / 1.468490 (0.538221) \| 0.832339 / 4.584777 (-3.752438) \| 5.213808 / 3.745712 (1.468095) \| 4.650355 / 5.269862 (-0.619506) \| 2.860494 / 4.565676 (-1.705182) \| 0.093090 / 0.424275 (-0.331185) \| 0.009740 / 0.007607 (0.002133) \| 0.693509 / 0.226044 (0.467464) \| 6.828735 / 2.268929 (4.559807) \| 2.967763 / 55.444624 (-52.476862) \| 2.311461 / 6.876477 (-4.565016) \| 2.400051 / 2.142072 (0.257979) \| 0.914753 / 4.805227 (-3.890474) \| 0.202804 / 6.500664 (-6.297860) \| 0.076905 / 0.075469 (0.001436) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.576424 / 1.841788 (-0.265363) \| 22.963472 / 8.074308 (14.889164) \| 19.948105 / 10.191392 (9.756713) \| 0.228982 / 0.680424 (-0.451442) \| 0.029038 / 0.534201 (-0.505163) \| 0.477715 / 0.579283 (-0.101568) \| 0.554924 / 0.434364 (0.120560) \| 0.532118 / 0.540337 (-0.008219) \| 0.775096 / 1.386936 (-0.611840) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009127 / 0.011353 (-0.002226) \| 0.004978 / 0.011008 (-0.006030) \| 0.084166 / 0.038508 (0.045658) \| 0.083391 / 0.023109 (0.060282) \| 0.420760 / 0.275898 (0.144862) \| 0.459072 / 0.323480 (0.135592) \| 0.007102 / 0.007986 (-0.000883) \| 0.004175 / 0.004328 (-0.000154) \| 0.082922 / 0.004250 (0.078672) \| 0.059010 / 0.037052 (0.021957) \| 0.416959 / 0.258489 (0.158470) \| 0.472220 / 0.293841 (0.178379) \| 0.049999 / 0.128546 (-0.078547) \| 0.014126 / 0.075646 (-0.061520) \| 0.096894 / 0.419271 (-0.322378) \| 0.057920 / 0.043533 (0.014387) \| 0.405779 / 0.255139 (0.150640) \| 0.464286 / 0.283200 (0.181087) \| 0.034957 / 0.141683 (-0.106726) \| 1.637921 / 1.452155 (0.185767) \| 1.768231 / 1.492716 (0.275515) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.354875 / 0.018006 (0.336868) \| 0.554667 / 0.000490 (0.554177) \| 0.074127 / 0.000200 (0.073927) \| 0.000411 / 0.000054 (0.000357) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027681 / 0.037411 (-0.009730) \| 0.087746 / 0.014526 (0.073220) \| 0.093714 / 0.176557 (-0.082843) \| 0.145380 / 0.737135 (-0.591755) \| 0.095686 / 0.296338 (-0.200652) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.522079 / 0.215209 (0.306870) \| 5.197366 / 2.077655 (3.119711) \| 2.300744 / 1.504120 (0.796624) \| 2.056846 / 1.541195 (0.515652) \| 2.009897 / 1.468490 (0.541407) \| 0.813025 / 4.584777 (-3.771751) \| 5.177732 / 3.745712 (1.432020) \| 4.076749 / 5.269862 (-1.193112) \| 2.545588 / 4.565676 (-2.020088) \| 0.083507 / 0.424275 (-0.340769) \| 0.007011 / 0.007607 (-0.000596) \| 0.598820 / 0.226044 (0.372776) \| 6.203730 / 2.268929 (3.934801) \| 2.945385 / 55.444624 (-52.499239) \| 2.304849 / 6.876477 (-4.571628) \| 2.599035 / 2.142072 (0.456962) \| 1.002721 / 4.805227 (-3.802506) \| 0.191781 / 6.500664 (-6.308883) \| 0.064178 / 0.075469 (-0.011292) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.549560 / 1.841788 (-0.292228) \| 22.395727 / 8.074308 (14.321418) \| 20.537895 / 10.191392 (10.346503) \| 0.246542 / 0.680424 (-0.433882) \| 0.031673 / 0.534201 (-0.502528) \| 0.442490 / 0.579283 (-0.136793) \| 0.589838 / 0.434364 (0.155474) \| 0.535201 / 0.540337 (-0.005136) \| 0.733660 / 1.386936 (-0.653276) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e742718e504a372cce0b1f87c2cac65eb8c35792 \"CML watermark\")\n" ]
1,972,807,579	`Dataset.from_generator` should not try to download from HF GCS	closed	### Describe the bug When using [`Dataset.from_generator`](https://github.com/huggingface/datasets/blob/c9c1166e1cf81d38534020f9c167b326585339e5/src/datasets/arrow_dataset.py#L1072) with `streaming=False`, the internal logic will call [`download_and_prepare`](https://github.com/huggingface/datasets/blob/main/src/datasets/io/generator.py#L47) which will attempt to download from HF GCS which is redundant, because user has already provided the generator from which the data should be drawn. If someone attempts to call `Dataset.from_generator` from an environment that doesn't have external internet access (for example internal production machine) and doesn't set `HF_DATASETS_OFFLINE=1`, this will result in process being stuck at building connection. ### Steps to reproduce the bug ```python import datasets def gen(): for _ in range(100): yield {"text": "dummy text"} dataset = datasets.Dataset.from_generator(gen) ``` A minimum example executed on any environment that doesn't have access to HF GCS can result in the error ### Expected behavior `try_from_hf_gcs` should be set to False here https://github.com/huggingface/datasets/blob/c9c1166e1cf81d38534020f9c167b326585339e5/src/datasets/io/generator.py#L51 ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-3.10.0-1160.90.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.12 - Huggingface_hub version: 0.17.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.3	2023-11-01T17:36:17	2023-11-02T15:52:10	2023-11-02T15:52:10	https://github.com/huggingface/datasets/issues/6371	null	6,371	false	[ "Indeed, setting `try_from_gcs` to `False` makes sense for `from_generator`.\r\n\r\nWe plan to deprecate and remove `try_from_hf_gcs` soon, as we can use Hub for file hosting now, but this is a good temporary fix.\r\n" ]
1,972,073,909	TensorDataset format does not work with Trainer from transformers	closed	### Describe the bug The model was built to do fine tunning on BERT model for relation extraction. trainer.train() returns an error message ```TypeError: vars() argument must have __dict__ attribute``` when it has `train_dataset` generated from `torch.utils.data.TensorDataset` However, in the document, the required data format is `torch.utils.data.TensorDataset`. ![image](https://github.com/huggingface/datasets/assets/49014051/36fa34ac-3127-4c64-9580-9ab736136d83) Transformers trainer is supposed to accept the train_dataset in the format of torch.utils.data.TensorDataset, but it returns error message "TypeError: vars() argument must have __dict__ attribute" ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-30-5df728c929a2> in <cell line: 1>() ----> 1 trainer.train() 2 trainer.evaluate(test_dataset) 9 frames /usr/local/lib/python3.10/dist-packages/transformers/data/data_collator.py in <listcomp>(.0) 107 108 if not isinstance(features[0], Mapping): --> 109 features = [vars(f) for f in features] 110 first = features[0] 111 batch = {} TypeError: vars() argument must have __dict__ attribute ``` ### Steps to reproduce the bug Create train_dataset using `torch.utils.data.TensorDataset`, for instance, ```train_dataset = torch.utils.data.TensorDataset(train_input_ids, train_attention_masks, train_labels)``` Feed this `train_dataset` to your trainer and run trainer.train ``` trainer = Trainer(model, training_args, train_dataset=train_dataset, eval_dataset=dev_dataset, compute_metrics=compute_metrics, ) ``` ### Expected behavior Trainer should start training ### Environment info It is running on Google Colab - `datasets` version: 2.14.6 - Platform: Linux-5.15.120+-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.17.3 - PyArrow version: 9.0.0 - Pandas version: 1.5.3	2023-11-01T10:09:54	2023-11-29T16:31:08	2023-11-29T16:31:08	https://github.com/huggingface/datasets/issues/6370	null	6,370	false	[ "I figured it out. I found that `Trainer` does not work with TensorDataset even though the document says it uses it. Instead, I ended up creating a dictionary and converting it to a dataset using `dataset.Dataset.from_dict()`.\r\n\r\nI will leave this post open for a while. If someone knows a better approach, please leave a comment.", "Only issues directly related to the HF datasets library should be reported here. ~So, I'm transferring this issue to the `transformers` repo.~ I'm not a `transformers` maintainer, so GitHub doesn't let me transfer it there :(. This means you need to do it manually." ]
1,971,794,108	Multi process map did not load cache file correctly	closed	### Describe the bug When I was training model on Multiple GPUs by DDP, the dataset is tokenized multiple times after main process. ![1698820541284](https://github.com/huggingface/datasets/assets/14285786/0b2fe054-54d8-4e00-96e6-6ca5b69e662b) ![1698820501568](https://github.com/huggingface/datasets/assets/14285786/dd62bf6f-a58f-41bf-9848-ea4fb3b62b9b) Code is modified from [run_clm.py](https://github.com/huggingface/transformers/blob/7d8ff3629b2725ec43ace99c1a6e87ac1978d433/examples/pytorch/language-modeling/run_clm.py#L484) ### Steps to reproduce the bug ``` block_size = data_args.block_size IGNORE_INDEX = -100 Ignore_Input = False def tokenize_function(examples): sources = [] targets = [] for instruction, inputs, output in zip(examples['instruction'], examples['input'], examples['output']): source = instruction + inputs target = f"{output}{tokenizer.eos_token}" sources.append(source) targets.append(target) tokenized_sources = tokenizer(sources, return_attention_mask=False) tokenized_targets = tokenizer(targets, return_attention_mask=False, add_special_tokens=False ) all_input_ids = [] all_labels = [] for s, t in zip(tokenized_sources['input_ids'], tokenized_targets['input_ids']): if len(s) > block_size and Ignore_Input == False: # print(s) continue input_ids = torch.LongTensor(s + t)[:block_size] if Ignore_Input: labels = torch.LongTensor([IGNORE_INDEX] * len(s) + t)[:block_size] else: labels = input_ids assert len(input_ids) == len(labels) all_input_ids.append(input_ids) all_labels.append(labels) results = { 'input_ids': all_input_ids, 'labels': all_labels, } return results with training_args.main_process_first(desc="dataset map tokenization ", local=False): # print('local_rank',training_args.local_rank) if not data_args.streaming: tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not data_args.overwrite_cache, desc="Running tokenizer on dataset ", ) else: tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, desc="Running tokenizer on dataset " ) ``` ### Expected behavior This code should only tokenize the dataset in the main process, and the other processes load the dataset after waiting ### Environment info transformers == 4.34.1 datasets == 2.14.5	2023-11-01T06:36:54	2023-11-30T16:04:46	2023-11-30T16:04:45	https://github.com/huggingface/datasets/issues/6369	null	6,369	false	[ "The inconsistency may be caused by the usage of \"update_fingerprint\" and setting \"trust_remote_code\" to \"True.\"\r\nWhen the tokenizer employs \"trust_remote_code,\" the behavior of the map function varies with each code execution. Even if the remote code of the tokenizer remains the same, the result of \"asher.hexdigest()\" is found to be inconsistent each time.\r\nThis may result in different processes executing multiple maps\r\n![1698841094290](https://github.com/huggingface/datasets/assets/14285786/21fc3c65-e9fd-4a79-b12e-a1d4b9c6cf32)\r\n![1698841117416](https://github.com/huggingface/datasets/assets/14285786/c3e5a530-54d2-4ae6-b902-ce9f85de373b)\r\n\r\n", "The issue may be related to problems previously discussed in GitHub issues [#3847](https://github.com/huggingface/datasets/issues/3847) and [#6318](https://github.com/huggingface/datasets/pull/6318). \r\nThis arises from the fact that tokenizer.tokens_trie._tokens is an unordered set, leading to varying hash results:\r\n`value = hash_bytes(dumps(tokenizer.tokens_trie._tokens))`\r\nConsequently, this results in different outcomes each time for:\r\n`new_fingerprint = update_fingerprint(datasets._fingerprint, transform, kwargs_for_fingerprint)`\r\n\r\nTo address this issue, it's essential to make `Trie._tokens` a deterministic set while ensuring a consistent order after the final update of `_tokens`.\r\n", "We now sort `set` and `dict` items to make their hashes deterministic (install from `main` with `pip install git+https://github.com/huggingface/datasets` to test this). Consequently, this should also make the `tokenizer.tokens_trie`'s hash deterministic. Feel free to re-open the issue if this is not the case." ]
1,971,193,692	Fix python formatting for complex types in `format_table`	closed	Fix #6366	2023-10-31T19:48:08	2023-11-02T14:42:28	2023-11-02T14:21:16	https://github.com/huggingface/datasets/pull/6368	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6368", "html_url": "https://github.com/huggingface/datasets/pull/6368", "diff_url": "https://github.com/huggingface/datasets/pull/6368.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6368.patch", "merged_at": "2023-11-02T14:21:16" }	6,368	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) \| 0.008047 / 0.011353 (-0.003305) \| 0.004649 / 0.011008 (-0.006359) \| 0.100275 / 0.038508 (0.061767) \| 0.089551 / 0.023109 (0.066442) \| 0.369831 / 0.275898 (0.093933) \| 0.431023 / 0.323480 (0.107544) \| 0.004721 / 0.007986 (-0.003265) \| 0.004904 / 0.004328 (0.000575) \| 0.076345 / 0.004250 (0.072095) \| 0.066902 / 0.037052 (0.029849) \| 0.377208 / 0.258489 (0.118718) \| 0.430989 / 0.293841 (0.137148) \| 0.036260 / 0.128546 (-0.092287) \| 0.010158 / 0.075646 (-0.065488) \| 0.344923 / 0.419271 (-0.074349) \| 0.062504 / 0.043533 (0.018971) \| 0.373038 / 0.255139 (0.117899) \| 0.399918 / 0.283200 (0.116718) \| 0.028257 / 0.141683 (-0.113425) \| 1.782546 / 1.452155 (0.330391) \| 1.920010 / 1.492716 (0.427293) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.277670 / 0.018006 (0.259664) \| 0.500543 / 0.000490 (0.500053) \| 0.018256 / 0.000200 (0.018056) \| 0.000343 / 0.000054 (0.000289) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033337 / 0.037411 (-0.004074) \| 0.100542 / 0.014526 (0.086017) \| 0.114903 / 0.176557 (-0.061654) \| 0.181267 / 0.737135 (-0.555868) \| 0.115019 / 0.296338 (-0.181320) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.457333 / 0.215209 (0.242124) \| 4.542082 / 2.077655 (2.464427) \| 2.231817 / 1.504120 (0.727697) \| 2.028523 / 1.541195 (0.487328) \| 2.110715 / 1.468490 (0.642225) \| 0.583162 / 4.584777 (-4.001615) \| 4.179413 / 3.745712 (0.433701) \| 4.145620 / 5.269862 (-1.124241) \| 2.452458 / 4.565676 (-2.113218) \| 0.068229 / 0.424275 (-0.356046) \| 0.009027 / 0.007607 (0.001420) \| 0.549002 / 0.226044 (0.322957) \| 5.485707 / 2.268929 (3.216779) \| 2.789467 / 55.444624 (-52.655157) \| 2.397499 / 6.876477 (-4.478977) \| 2.492083 / 2.142072 (0.350010) \| 0.692445 / 4.805227 (-4.112782) \| 0.160527 / 6.500664 (-6.340137) \| 0.071597 / 0.075469 (-0.003872) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.486043 / 1.841788 (-0.355744) \| 22.377207 / 8.074308 (14.302899) \| 16.443719 / 10.191392 (6.252327) \| 0.170740 / 0.680424 (-0.509684) \| 0.021511 / 0.534201 (-0.512690) \| 0.470798 / 0.579283 (-0.108485) \| 0.511851 / 0.434364 (0.077487) \| 0.551154 / 0.540337 (0.010817) \| 0.768420 / 1.386936 (-0.618516) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008049 / 0.011353 (-0.003303) \| 0.004676 / 0.011008 (-0.006332) \| 0.076360 / 0.038508 (0.037852) \| 0.093648 / 0.023109 (0.070539) \| 0.480597 / 0.275898 (0.204699) \| 0.524674 / 0.323480 (0.201194) \| 0.006242 / 0.007986 (-0.001744) \| 0.003827 / 0.004328 (-0.000501) \| 0.077039 / 0.004250 (0.072788) \| 0.067992 / 0.037052 (0.030940) \| 0.480287 / 0.258489 (0.221798) \| 0.528546 / 0.293841 (0.234706) \| 0.038347 / 0.128546 (-0.090199) \| 0.010036 / 0.075646 (-0.065611) \| 0.084386 / 0.419271 (-0.334885) \| 0.057211 / 0.043533 (0.013678) \| 0.475993 / 0.255139 (0.220854) \| 0.504881 / 0.283200 (0.221682) \| 0.026658 / 0.141683 (-0.115025) \| 1.777095 / 1.452155 (0.324940) \| 1.896446 / 1.492716 (0.403730) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.242450 / 0.018006 (0.224443) \| 0.488864 / 0.000490 (0.488374) \| 0.007329 / 0.000200 (0.007129) \| 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) \| 0.039093 / 0.037411 (0.001682) \| 0.114724 / 0.014526 (0.100198) \| 0.124965 / 0.176557 (-0.051591) \| 0.188165 / 0.737135 (-0.548971) \| 0.125336 / 0.296338 (-0.171002) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.515718 / 0.215209 (0.300509) \| 5.150865 / 2.077655 (3.073210) \| 2.767866 / 1.504120 (1.263746) \| 2.571003 / 1.541195 (1.029808) \| 2.656224 / 1.468490 (1.187734) \| 0.583771 / 4.584777 (-4.001006) \| 4.268713 / 3.745712 (0.523001) \| 3.938699 / 5.269862 (-1.331163) \| 2.413569 / 4.565676 (-2.152108) \| 0.068848 / 0.424275 (-0.355427) \| 0.008758 / 0.007607 (0.001151) \| 0.610831 / 0.226044 (0.384786) \| 6.099965 / 2.268929 (3.831037) \| 3.337530 / 55.444624 (-52.107095) \| 2.910962 / 6.876477 (-3.965514) \| 3.149813 / 2.142072 (1.007740) \| 0.700576 / 4.805227 (-4.104651) \| 0.157569 / 6.500664 (-6.343095) \| 0.072237 / 0.075469 (-0.003232) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.655840 / 1.841788 (-0.185947) \| 23.639061 / 8.074308 (15.564753) \| 17.301593 / 10.191392 (7.110201) \| 0.201717 / 0.680424 (-0.478707) \| 0.023836 / 0.534201 (-0.510365) \| 0.470941 / 0.579283 (-0.108342) \| 0.498157 / 0.434364 (0.063794) \| 0.581195 / 0.540337 (0.040857) \| 0.788304 / 1.386936 (-0.598632) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f657900acfd8ea1afaf47267e552a7ad2c6ef28b \"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) \| 0.004823 / 0.011353 (-0.006530) \| 0.002976 / 0.011008 (-0.008032) \| 0.062070 / 0.038508 (0.023562) \| 0.051623 / 0.023109 (0.028513) \| 0.242249 / 0.275898 (-0.033649) \| 0.271223 / 0.323480 (-0.052257) \| 0.003906 / 0.007986 (-0.004079) \| 0.002709 / 0.004328 (-0.001620) \| 0.047874 / 0.004250 (0.043624) \| 0.038123 / 0.037052 (0.001071) \| 0.253737 / 0.258489 (-0.004752) \| 0.281942 / 0.293841 (-0.011899) \| 0.023750 / 0.128546 (-0.104797) \| 0.007227 / 0.075646 (-0.068420) \| 0.203137 / 0.419271 (-0.216134) \| 0.036254 / 0.043533 (-0.007278) \| 0.243923 / 0.255139 (-0.011216) \| 0.263908 / 0.283200 (-0.019291) \| 0.017795 / 0.141683 (-0.123888) \| 1.105680 / 1.452155 (-0.346475) \| 1.166804 / 1.492716 (-0.325912) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097388 / 0.018006 (0.079381) \| 0.305481 / 0.000490 (0.304991) \| 0.000210 / 0.000200 (0.000010) \| 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) \| 0.020096 / 0.037411 (-0.017315) \| 0.063990 / 0.014526 (0.049464) \| 0.073694 / 0.176557 (-0.102863) \| 0.122909 / 0.737135 (-0.614227) \| 0.076199 / 0.296338 (-0.220140) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285612 / 0.215209 (0.070403) \| 2.770524 / 2.077655 (0.692869) \| 1.451624 / 1.504120 (-0.052496) \| 1.329223 / 1.541195 (-0.211972) \| 1.369980 / 1.468490 (-0.098510) \| 0.398269 / 4.584777 (-4.186507) \| 2.418740 / 3.745712 (-1.326972) \| 2.796384 / 5.269862 (-2.473478) \| 1.686490 / 4.565676 (-2.879186) \| 0.046417 / 0.424275 (-0.377858) \| 0.005414 / 0.007607 (-0.002193) \| 0.345505 / 0.226044 (0.119460) \| 3.391857 / 2.268929 (1.122929) \| 1.856696 / 55.444624 (-53.587929) \| 1.538061 / 6.876477 (-5.338416) \| 1.631489 / 2.142072 (-0.510584) \| 0.479188 / 4.805227 (-4.326039) \| 0.101549 / 6.500664 (-6.399116) \| 0.042150 / 0.075469 (-0.033319) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.957961 / 1.841788 (-0.883827) \| 12.349371 / 8.074308 (4.275063) \| 10.778214 / 10.191392 (0.586822) \| 0.141265 / 0.680424 (-0.539158) \| 0.014559 / 0.534201 (-0.519642) \| 0.272071 / 0.579283 (-0.307212) \| 0.262493 / 0.434364 (-0.171871) \| 0.310351 / 0.540337 (-0.229986) \| 0.399220 / 1.386936 (-0.987716) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005127 / 0.011353 (-0.006226) \| 0.002926 / 0.011008 (-0.008082) \| 0.048320 / 0.038508 (0.009812) \| 0.063082 / 0.023109 (0.039973) \| 0.269846 / 0.275898 (-0.006052) \| 0.294470 / 0.323480 (-0.029010) \| 0.004201 / 0.007986 (-0.003784) \| 0.002434 / 0.004328 (-0.001894) \| 0.048020 / 0.004250 (0.043770) \| 0.043909 / 0.037052 (0.006856) \| 0.271328 / 0.258489 (0.012839) \| 0.298820 / 0.293841 (0.004979) \| 0.024565 / 0.128546 (-0.103981) \| 0.007752 / 0.075646 (-0.067894) \| 0.054171 / 0.419271 (-0.365101) \| 0.033147 / 0.043533 (-0.010386) \| 0.266628 / 0.255139 (0.011489) \| 0.288651 / 0.283200 (0.005452) \| 0.018910 / 0.141683 (-0.122773) \| 1.153679 / 1.452155 (-0.298476) \| 1.214979 / 1.492716 (-0.277737) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097064 / 0.018006 (0.079057) \| 0.307504 / 0.000490 (0.307014) \| 0.000230 / 0.000200 (0.000030) \| 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) \| 0.021848 / 0.037411 (-0.015563) \| 0.071159 / 0.014526 (0.056633) \| 0.081310 / 0.176557 (-0.095247) \| 0.120175 / 0.737135 (-0.616961) \| 0.082619 / 0.296338 (-0.213720) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296606 / 0.215209 (0.081397) \| 2.908495 / 2.077655 (0.830840) \| 1.606522 / 1.504120 (0.102402) \| 1.528599 / 1.541195 (-0.012596) \| 1.508332 / 1.468490 (0.039842) \| 0.396336 / 4.584777 (-4.188441) \| 2.449163 / 3.745712 (-1.296549) \| 2.533372 / 5.269862 (-2.736490) \| 1.623061 / 4.565676 (-2.942615) \| 0.046723 / 0.424275 (-0.377552) \| 0.005120 / 0.007607 (-0.002487) \| 0.345763 / 0.226044 (0.119718) \| 3.427382 / 2.268929 (1.158454) \| 1.962806 / 55.444624 (-53.481819) \| 1.678548 / 6.876477 (-5.197929) \| 1.865773 / 2.142072 (-0.276300) \| 0.477932 / 4.805227 (-4.327295) \| 0.100994 / 6.500664 (-6.399670) \| 0.042212 / 0.075469 (-0.033258) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.992766 / 1.841788 (-0.849022) \| 12.764885 / 8.074308 (4.690577) \| 10.892094 / 10.191392 (0.700702) \| 0.143211 / 0.680424 (-0.537213) \| 0.016347 / 0.534201 (-0.517853) \| 0.270181 / 0.579283 (-0.309102) \| 0.278658 / 0.434364 (-0.155706) \| 0.307134 / 0.540337 (-0.233203) \| 0.396792 / 1.386936 (-0.990144) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6d2f2a5e0fea3827eccfd1717d8021c15fc4292a \"CML watermark\")\n", "Thanks for the fix ! It was probably my mistake (forgot to re-apply the features)" ]
1,971,015,861	Fix time measuring snippet in docs	closed	Fix https://discuss.huggingface.co/t/attributeerror-enter/60509	2023-10-31T17:57:17	2023-10-31T18:35:53	2023-10-31T18:24:02	https://github.com/huggingface/datasets/pull/6367	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6367", "html_url": "https://github.com/huggingface/datasets/pull/6367", "diff_url": "https://github.com/huggingface/datasets/pull/6367.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6367.patch", "merged_at": "2023-10-31T18:24:02" }	6,367	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) \| 0.007683 / 0.011353 (-0.003670) \| 0.004159 / 0.011008 (-0.006849) \| 0.097017 / 0.038508 (0.058509) \| 0.074216 / 0.023109 (0.051107) \| 0.323115 / 0.275898 (0.047217) \| 0.412836 / 0.323480 (0.089356) \| 0.005151 / 0.007986 (-0.002834) \| 0.004037 / 0.004328 (-0.000292) \| 0.067881 / 0.004250 (0.063631) \| 0.051395 / 0.037052 (0.014342) \| 0.356391 / 0.258489 (0.097901) \| 0.386744 / 0.293841 (0.092903) \| 0.043571 / 0.128546 (-0.084975) \| 0.012844 / 0.075646 (-0.062803) \| 0.369440 / 0.419271 (-0.049832) \| 0.056944 / 0.043533 (0.013411) \| 0.316159 / 0.255139 (0.061020) \| 0.435530 / 0.283200 (0.152330) \| 0.033622 / 0.141683 (-0.108061) \| 1.379602 / 1.452155 (-0.072553) \| 1.766400 / 1.492716 (0.273683) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.304151 / 0.018006 (0.286145) \| 0.616365 / 0.000490 (0.615875) \| 0.013588 / 0.000200 (0.013389) \| 0.000441 / 0.000054 (0.000387) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032812 / 0.037411 (-0.004600) \| 0.100914 / 0.014526 (0.086388) \| 0.124004 / 0.176557 (-0.052552) \| 0.195087 / 0.737135 (-0.542048) \| 0.124388 / 0.296338 (-0.171951) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.575649 / 0.215209 (0.360440) \| 5.665461 / 2.077655 (3.587806) \| 2.474892 / 1.504120 (0.970773) \| 2.142687 / 1.541195 (0.601492) \| 2.254962 / 1.468490 (0.786472) \| 0.816635 / 4.584777 (-3.768141) \| 5.044279 / 3.745712 (1.298567) \| 4.566728 / 5.269862 (-0.703134) \| 2.867146 / 4.565676 (-1.698531) \| 0.092994 / 0.424275 (-0.331281) \| 0.008395 / 0.007607 (0.000788) \| 0.680346 / 0.226044 (0.454302) \| 6.909875 / 2.268929 (4.640946) \| 3.275602 / 55.444624 (-52.169022) \| 2.556000 / 6.876477 (-4.320477) \| 2.581337 / 2.142072 (0.439264) \| 0.997883 / 4.805227 (-3.807344) \| 0.204109 / 6.500664 (-6.296555) \| 0.069705 / 0.075469 (-0.005764) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.504573 / 1.841788 (-0.337215) \| 22.219363 / 8.074308 (14.145055) \| 19.078040 / 10.191392 (8.886648) \| 0.234970 / 0.680424 (-0.445454) \| 0.027324 / 0.534201 (-0.506877) \| 0.427960 / 0.579283 (-0.151323) \| 0.570258 / 0.434364 (0.135894) \| 0.502335 / 0.540337 (-0.038003) \| 0.788078 / 1.386936 (-0.598858) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008370 / 0.011353 (-0.002982) \| 0.004573 / 0.011008 (-0.006435) \| 0.073080 / 0.038508 (0.034572) \| 0.068752 / 0.023109 (0.045643) \| 0.439648 / 0.275898 (0.163750) \| 0.499700 / 0.323480 (0.176220) \| 0.006119 / 0.007986 (-0.001866) \| 0.004300 / 0.004328 (-0.000028) \| 0.073173 / 0.004250 (0.068923) \| 0.055676 / 0.037052 (0.018624) \| 0.464152 / 0.258489 (0.205663) \| 0.476954 / 0.293841 (0.183113) \| 0.046335 / 0.128546 (-0.082211) \| 0.013373 / 0.075646 (-0.062274) \| 0.092006 / 0.419271 (-0.327265) \| 0.054802 / 0.043533 (0.011269) \| 0.456594 / 0.255139 (0.201455) \| 0.491931 / 0.283200 (0.208732) \| 0.034021 / 0.141683 (-0.107662) \| 1.575200 / 1.452155 (0.123045) \| 1.689742 / 1.492716 (0.197026) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.299432 / 0.018006 (0.281426) \| 0.605643 / 0.000490 (0.605153) \| 0.006280 / 0.000200 (0.006080) \| 0.000120 / 0.000054 (0.000066) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028414 / 0.037411 (-0.008997) \| 0.085812 / 0.014526 (0.071286) \| 0.109142 / 0.176557 (-0.067414) \| 0.163458 / 0.737135 (-0.573677) \| 0.100837 / 0.296338 (-0.195501) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.615557 / 0.215209 (0.400348) \| 6.051599 / 2.077655 (3.973944) \| 2.872353 / 1.504120 (1.368234) \| 2.508322 / 1.541195 (0.967128) \| 2.550073 / 1.468490 (1.081583) \| 0.835793 / 4.584777 (-3.748983) \| 5.208484 / 3.745712 (1.462772) \| 4.361846 / 5.269862 (-0.908016) \| 2.776164 / 4.565676 (-1.789513) \| 0.090831 / 0.424275 (-0.333444) \| 0.007320 / 0.007607 (-0.000287) \| 0.725533 / 0.226044 (0.499488) \| 7.051321 / 2.268929 (4.782393) \| 3.515464 / 55.444624 (-51.929160) \| 2.798193 / 6.876477 (-4.078284) \| 3.022512 / 2.142072 (0.880440) \| 0.986744 / 4.805227 (-3.818484) \| 0.198050 / 6.500664 (-6.302615) \| 0.069200 / 0.075469 (-0.006269) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.623759 / 1.841788 (-0.218029) \| 22.269700 / 8.074308 (14.195392) \| 19.577429 / 10.191392 (9.386037) \| 0.215990 / 0.680424 (-0.464434) \| 0.033005 / 0.534201 (-0.501196) \| 0.436848 / 0.579283 (-0.142435) \| 0.591442 / 0.434364 (0.157078) \| 0.547701 / 0.540337 (0.007364) \| 0.741695 / 1.386936 (-0.645241) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7e17e139b1323aca3321a5d2c2da40d82c458bae \"CML watermark\")\n", "CI failures are unrelated", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009027 / 0.011353 (-0.002326) \| 0.006118 / 0.011008 (-0.004890) \| 0.118939 / 0.038508 (0.080431) \| 0.089979 / 0.023109 (0.066869) \| 0.412425 / 0.275898 (0.136527) \| 0.455706 / 0.323480 (0.132227) \| 0.006762 / 0.007986 (-0.001224) \| 0.004409 / 0.004328 (0.000080) \| 0.088002 / 0.004250 (0.083751) \| 0.063708 / 0.037052 (0.026656) \| 0.417373 / 0.258489 (0.158884) \| 0.489582 / 0.293841 (0.195741) \| 0.050222 / 0.128546 (-0.078324) \| 0.014386 / 0.075646 (-0.061260) \| 0.435363 / 0.419271 (0.016092) \| 0.069375 / 0.043533 (0.025842) \| 0.410242 / 0.255139 (0.155103) \| 0.436439 / 0.283200 (0.153239) \| 0.039318 / 0.141683 (-0.102365) \| 1.857574 / 1.452155 (0.405419) \| 1.919402 / 1.492716 (0.426686) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.343916 / 0.018006 (0.325910) \| 0.633639 / 0.000490 (0.633150) \| 0.014756 / 0.000200 (0.014557) \| 0.000707 / 0.000054 (0.000652) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031983 / 0.037411 (-0.005429) \| 0.097222 / 0.014526 (0.082697) \| 0.114644 / 0.176557 (-0.061912) \| 0.187787 / 0.737135 (-0.549348) \| 0.120595 / 0.296338 (-0.175743) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.605861 / 0.215209 (0.390652) \| 6.039318 / 2.077655 (3.961664) \| 2.699251 / 1.504120 (1.195132) \| 2.436398 / 1.541195 (0.895203) \| 2.493653 / 1.468490 (1.025163) \| 0.889423 / 4.584777 (-3.695354) \| 5.384769 / 3.745712 (1.639056) \| 5.033033 / 5.269862 (-0.236829) \| 3.056894 / 4.565676 (-1.508783) \| 0.100683 / 0.424275 (-0.323592) \| 0.009103 / 0.007607 (0.001495) \| 0.737066 / 0.226044 (0.511021) \| 7.370485 / 2.268929 (5.101556) \| 3.422670 / 55.444624 (-52.021954) \| 2.830392 / 6.876477 (-4.046084) \| 2.985789 / 2.142072 (0.843717) \| 0.999239 / 4.805227 (-3.805989) \| 0.203506 / 6.500664 (-6.297158) \| 0.076135 / 0.075469 (0.000666) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.697001 / 1.841788 (-0.144787) \| 24.653975 / 8.074308 (16.579667) \| 22.241622 / 10.191392 (12.050230) \| 0.257075 / 0.680424 (-0.423349) \| 0.029159 / 0.534201 (-0.505041) \| 0.493329 / 0.579283 (-0.085954) \| 0.596661 / 0.434364 (0.162297) \| 0.569431 / 0.540337 (0.029094) \| 0.812231 / 1.386936 (-0.574705) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009815 / 0.011353 (-0.001538) \| 0.005136 / 0.011008 (-0.005872) \| 0.078224 / 0.038508 (0.039716) \| 0.103276 / 0.023109 (0.080166) \| 0.512742 / 0.275898 (0.236844) \| 0.544010 / 0.323480 (0.220530) \| 0.007957 / 0.007986 (-0.000029) \| 0.004629 / 0.004328 (0.000300) \| 0.074983 / 0.004250 (0.070733) \| 0.071831 / 0.037052 (0.034778) \| 0.542752 / 0.258489 (0.284262) \| 0.573176 / 0.293841 (0.279335) \| 0.053939 / 0.128546 (-0.074607) \| 0.015007 / 0.075646 (-0.060640) \| 0.085389 / 0.419271 (-0.333882) \| 0.063587 / 0.043533 (0.020055) \| 0.509580 / 0.255139 (0.254441) \| 0.563374 / 0.283200 (0.280174) \| 0.037575 / 0.141683 (-0.104108) \| 1.840740 / 1.452155 (0.388585) \| 1.836414 / 1.492716 (0.343698) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.310188 / 0.018006 (0.292182) \| 0.641478 / 0.000490 (0.640988) \| 0.011057 / 0.000200 (0.010857) \| 0.000173 / 0.000054 (0.000119) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.043280 / 0.037411 (0.005869) \| 0.109256 / 0.014526 (0.094730) \| 0.126701 / 0.176557 (-0.049856) \| 0.199172 / 0.737135 (-0.537963) \| 0.123584 / 0.296338 (-0.172755) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.649272 / 0.215209 (0.434063) \| 6.487501 / 2.077655 (4.409846) \| 3.170330 / 1.504120 (1.666210) \| 2.960912 / 1.541195 (1.419718) \| 3.024531 / 1.468490 (1.556041) \| 0.905112 / 4.584777 (-3.679665) \| 5.560961 / 3.745712 (1.815249) \| 4.920463 / 5.269862 (-0.349399) \| 3.158989 / 4.565676 (-1.406687) \| 0.095444 / 0.424275 (-0.328831) \| 0.008264 / 0.007607 (0.000657) \| 0.819292 / 0.226044 (0.593247) \| 7.982695 / 2.268929 (5.713767) \| 4.098704 / 55.444624 (-51.345921) \| 3.442330 / 6.876477 (-3.434147) \| 3.763426 / 2.142072 (1.621354) \| 1.065464 / 4.805227 (-3.739763) \| 0.215089 / 6.500664 (-6.285575) \| 0.085280 / 0.075469 (0.009811) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.881770 / 1.841788 (0.039983) \| 25.671479 / 8.074308 (17.597171) \| 22.367019 / 10.191392 (12.175627) \| 0.241377 / 0.680424 (-0.439047) \| 0.033555 / 0.534201 (-0.500646) \| 0.501786 / 0.579283 (-0.077497) \| 0.596376 / 0.434364 (0.162012) \| 0.579674 / 0.540337 (0.039337) \| 0.855534 / 1.386936 (-0.531402) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c9c1166e1cf81d38534020f9c167b326585339e5 \"CML watermark\")\n" ]
1,970,213,490	with_format() function returns bytes instead of PIL images even when image column is not part of "columns"	closed	### Describe the bug When using the with_format() function on a dataset containing images, even if the image column is not part of the columns provided in the function, its type will be changed to bytes. Here is a minimal reproduction of the bug: https://colab.research.google.com/drive/1hyaOspgyhB41oiR1-tXE3k_gJCdJUQCf?usp=sharing ### Steps to reproduce the bug 1. Load the image dataset 2. apply with_format(columns=["text"]) 3. Check the type of images in the "image" column before and after applying with_format ### Expected behavior The type should stay the same, but it does not ### Environment info datasets==2.14.6	2023-10-31T11:10:48	2023-11-02T14:21:17	2023-11-02T14:21:17	https://github.com/huggingface/datasets/issues/6366	null	6,366	false	[ "Thanks for reporting! I've opened a PR with a fix." ]
1,970,140,392	Parquet size grows exponential for categorical data	closed	### Describe the bug It seems that when saving a data frame with a categorical column inside the size can grow exponentially. This seems to happen because when we save the categorical data to parquet, we are saving the data + all the categories existing in the original data. This happens even when the categories are not present in the original data. ### Steps to reproduce the bug To reproduce the bug, it is enough to run this script: ``` import pandas as pd import os if __name__ == "__main__": for n in [10, 1e2, 1e3, 1e4, 1e5]: for n_col in [1, 10, 100, 1000, 10000]: input = pd.DataFrame([{"{i}": f"{i}_cat" for col in range(n_col)} for i in range(int(n))]) input.iloc[0:100].to_parquet("a.parquet") for col in input.columns: input[col] = input[col].astype("category") input.iloc[0:100].to_parquet("b.parquet") a_size_mb = os.stat("a.parquet").st_size / (1024 * 1024) b_size_mb = os.stat("b.parquet").st_size / (1024 * 1024) print(f"{n} {n_col} {a_size_mb} {b_size_mb} {100*b_size_mb/a_size_mb:.2f}") ``` That produces this output: <img width="464" alt="Screenshot 2023-10-31 at 11 25 25" src="https://github.com/huggingface/datasets/assets/82567957/2b8a9284-7f9e-4c10-a006-0a27236ebd15"> ### Expected behavior In my opinion either: 1. The two file should have (almost) the same size 2. There should be warning telling the user that such difference in size is possible ### Environment info Python 3.8.18 pandas==2.0.3 numpy==1.24.4	2023-10-31T10:29:02	2023-10-31T10:49:17	2023-10-31T10:49:17	https://github.com/huggingface/datasets/issues/6365	null	6,365	false	[ "Wrong repo." ]
1,969,136,106	ArrowNotImplementedError: Unsupported cast from string to list using function cast_list	closed	Hi, I am trying to load a local csv dataset(similar to explodinggradients_fiqa) using load_dataset. When I try to pass features, I am facing the mentioned issue. CSV Data sample(golden_dataset.csv): Question \| Context \| answer \| groundtruth "what is abc?" \| "abc is this and that" \| "abc is this " \| "abc is this and that" ``` import csv # built it based on https://huggingface.co/datasets/explodinggradients/fiqa/viewer/ragas_eval?row=0 mydict = [ {'question' : "what is abc?", 'contexts': ["abc is this and that"], 'answer': "abc is this " , 'groundtruth': ["abc is this and that"]}, {'question' : "what is abc?", 'contexts': ["abc is this and that"], 'answer': "abc is this " , 'groundtruth': ["abc is this and that"]}, {'question' : "what is abc?", 'contexts': ["abc is this and that"], 'answer': "abc is this " , 'groundtruth': ["abc is this and that"]} ] fields = ['question', 'contexts', 'answer', 'ground_truths'] with open('golden_dataset.csv', 'w', newline='\n') as file: writer = csv.DictWriter(file, fieldnames = fields) writer.writeheader() for row in mydict: writer.writerow(row) ``` Retrieved dataset: DatasetDict({ train: Dataset({ features: ['question', 'contexts', 'answer', 'ground_truths'], num_rows: 1 }) }) Code to reproduce issue: ``` from datasets import load_dataset, Features, Sequence, Value encode_features = Features( { "question": Value(dtype='string', id=0), "contexts": Sequence(feature=Value(dtype='string', id=1)), "answer": Value(dtype='string', id=2), "ground_truths": Sequence(feature=Value(dtype='string',id=3)), } ) eval_dataset = load_dataset('csv', data_files='/golden_dataset.csv', features = encode_features ) ``` Error trace: ``` --------------------------------------------------------------------------- ArrowNotImplementedError Traceback (most recent call last) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1925, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1924 _time = time.time() -> 1925 for _, table in generator: 1926 if max_shard_size is not None and writer._num_bytes > max_shard_size: File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/packaged_modules/csv/csv.py:192, in Csv._generate_tables(self, files) 189 # Uncomment for debugging (will print the Arrow table size and elements) 190 # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}") 191 # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows))) --> 192 yield (file_idx, batch_idx), self._cast_table(pa_table) 193 except ValueError as e: File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/packaged_modules/csv/csv.py:167, in Csv._cast_table(self, pa_table) 165 if all(not require_storage_cast(feature) for feature in self.config.features.values()): 166 # cheaper cast --> 167 pa_table = pa.Table.from_arrays([pa_table[field.name] for field in schema], schema=schema) 168 else: 169 # more expensive cast; allows str <-> int/float or str to Audio for example File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/table.pxi:3781, in pyarrow.lib.Table.from_arrays() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/table.pxi:1449, in pyarrow.lib._sanitize_arrays() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/array.pxi:354, in pyarrow.lib.asarray() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/table.pxi:551, in pyarrow.lib.ChunkedArray.cast() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/compute.py:400, in cast(arr, target_type, safe, options, memory_pool) 399 options = CastOptions.safe(target_type) --> 400 return call_function("cast", [arr], options, memory_pool) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/_compute.pyx:572, in pyarrow._compute.call_function() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/_compute.pyx:367, in pyarrow._compute.Function.call() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/error.pxi:144, in pyarrow.lib.pyarrow_internal_check_status() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/error.pxi:121, in pyarrow.lib.check_status() ArrowNotImplementedError: Unsupported cast from string to list using function cast_list The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) Cell In[57], line 1 ----> 1 eval_dataset = load_dataset('csv', data_files='/golden_dataset.csv', features = encode_features ) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/load.py:2153, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, config_kwargs) 2150 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 2152 # Download and prepare data -> 2153 builder_instance.download_and_prepare( 2154 download_config=download_config, 2155 download_mode=download_mode, 2156 verification_mode=verification_mode, 2157 try_from_hf_gcs=try_from_hf_gcs, 2158 num_proc=num_proc, 2159 storage_options=storage_options, 2160 ) 2162 # Build dataset for splits 2163 keep_in_memory = ( 2164 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 2165 ) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:954, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, download_and_prepare_kwargs) 952 if num_proc is not None: 953 prepare_split_kwargs["num_proc"] = num_proc --> 954 self._download_and_prepare( 955 dl_manager=dl_manager, 956 verification_mode=verification_mode, 957 prepare_split_kwargs, 958 download_and_prepare_kwargs, 959 ) 960 # Sync info 961 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1049, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, prepare_split_kwargs) 1045 split_dict.add(split_generator.split_info) 1047 try: 1048 # Prepare split will record examples associated to the split -> 1049 self._prepare_split(split_generator, prepare_split_kwargs) 1050 except OSError as e: 1051 raise OSError( 1052 "Cannot find data file. " 1053 + (self.manual_download_instructions or "") 1054 + "\nOriginal error:\n" 1055 + str(e) 1056 ) from None File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1813, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, num_proc, max_shard_size) 1811 job_id = 0 1812 with pbar: -> 1813 for job_id, done, content in self._prepare_split_single( 1814 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args 1815 ): 1816 if done: 1817 result = content File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1958, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1956 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1957 e = e.__context__ -> 1958 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1960 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` Environment Info: datasets version: 2.14.5 Python version: 3.10.8 PyArrow version: 12.0.1 Pandas version: 2.0.3 I have also tried to load dataset first and then use cast_column, or save_to_disk and load_from_disk.	2023-10-30T20:14:01	2023-10-31T19:21:23	2023-10-31T19:21:23	https://github.com/huggingface/datasets/issues/6364	null	6,364	false	[ "You can use the following code to load this CSV with the list values preserved:\r\n```python\r\nfrom datasets import load_dataset\r\nimport ast\r\n\r\nconverters = {\r\n \"contexts\" : ast.literal_eval,\r\n \"ground_truths\" : ast.literal_eval,\r\n}\r\n\r\nds = load_dataset(\"csv\", data_files=\"golden_dataset.csv\", converters=converters)\r\n```", "Thank you! it worked :)" ]
1,968,891,277	dataset.transform() hangs indefinitely while finetuning the stable diffusion XL	closed	### Describe the bug Multi-GPU fine-tuning the stable diffusion X by following https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/README_sdxl.md hangs indefinitely. ### Steps to reproduce the bug accelerate launch train_text_to_image_sdxl.py --pretrained_model_name_or_path=$MODEL_NAME --pretrained_vae_model_name_or_path=$VAE_NAME --dataset_name=$DATASET_NAME --enable_xformers_memory_efficient_attention --resolution=512 --center_crop --random_flip --proportion_empty_prompts=0.2 --train_batch_size=1 --gradient_accumulation_steps=4 --gradient_checkpointing --max_train_steps=10000 --use_8bit_adam --learning_rate=1e-06 --lr_scheduler="constant" --lr_warmup_steps=0 --mixed_precision="fp16" --report_to="wandb" --validation_prompt="a cute Sundar Pichai creature" --validation_epochs 5 --checkpointing_steps=5000 --output_dir="sdxl-pokemon-model" ### Expected behavior It should start the training as it does for the single GPU training. I opened the issue in diffusers **https://github.com/huggingface/diffusers/issues/5534 but it does seem to be an issue with the Pokemon dataset. I added some debug prints ``` print("==========HERE3=============") with accelerator.main_process_first(): print(accelerator.is_main_process) print("===========Here3.1===========") if args.max_train_samples is not None: dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples)) print("===========Here3.2===========") # Set the training transforms train_dataset = dataset["train"].with_transform(preprocess_train) print("==========HERE4=============") Corresponding Output Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher. 10/25/2023 21:18:04 - INFO - main - Distributed environment: MULTI_GPU Backend: nccl Num processes: 3 Process index: 1 Local process index: 1 Device: cuda:1 Mixed precision type: fp16 10/25/2023 21:18:04 - INFO - main - Distributed environment: MULTI_GPU Backend: nccl Num processes: 3 Process index: 2 Local process index: 2 Device: cuda:2 Mixed precision type: fp16 10/25/2023 21:18:04 - INFO - main - Distributed environment: MULTI_GPU Backend: nccl Num processes: 3 Process index: 0 Local process index: 0 Device: cuda:0 Mixed precision type: fp16 You are using a model of type clip_text_model to instantiate a model of type . This is not supported for all configurations of models and can yield errors. You are using a model of type clip_text_model to instantiate a model of type . This is not supported for all configurations of models and can yield errors. {‘variance_type’, ‘clip_sample_range’, ‘thresholding’, ‘dynamic_thresholding_ratio’} was not found in config. Values will be initialized to default values. {‘attention_type’, ‘reverse_transformer_layers_per_block’, ‘dropout’} was not found in config. Values will be initialized to default values. ==========HERE1============= ==========HERE1============= ==========HERE1============= ==========HERE2============= ==========HERE2============= ==========HERE2============= ==========HERE3============= True ===========Here3.1=========== ===========Here3.2=========== ==========HERE3============= ==========HERE3========= ``` ### Environment info _libgcc_mutex 0.1 conda_forge conda-forge _openmp_mutex 4.5 2_kmp_llvm conda-forge absl-py 2.0.0 pypi_0 pypi accelerate 0.24.0 pypi_0 pypi aiohttp 3.8.6 pypi_0 pypi aiosignal 1.3.1 pypi_0 pypi appdirs 1.4.4 pyh9f0ad1d_0 conda-forge async-timeout 4.0.3 pypi_0 pypi attrs 23.1.0 pypi_0 pypi bitsandbytes 0.41.1 pypi_0 pypi blas 1.0 mkl blessings 1.7 py39h06a4308_1002 brotli-python 1.0.9 py39h6a678d5_7 bzip2 1.0.8 h7b6447c_0 ca-certificates 2023.08.22 h06a4308_0 cachetools 5.3.2 pypi_0 pypi certifi 2023.7.22 py39h06a4308_0 cffi 1.15.1 py39h5eee18b_3 charset-normalizer 2.0.4 pyhd3eb1b0_0 click 8.1.7 unix_pyh707e725_0 conda-forge cryptography 41.0.3 py39hdda0065_0 cuda-cudart 11.7.99 0 nvidia cuda-cupti 11.7.101 0 nvidia cuda-libraries 11.7.1 0 nvidia cuda-nvrtc 11.7.99 0 nvidia cuda-nvtx 11.7.91 0 nvidia cuda-runtime 11.7.1 0 nvidia datasets 2.14.6 pypi_0 pypi diffusers 0.22.0.dev0 pypi_0 pypi dill 0.3.7 pypi_0 pypi docker-pycreds 0.4.0 py_0 conda-forge ffmpeg 4.3 hf484d3e_0 pytorch filelock 3.12.4 pypi_0 pypi freetype 2.12.1 h4a9f257_0 frozenlist 1.4.0 pypi_0 pypi fsspec 2023.10.0 pypi_0 pypi ftfy 6.1.1 pypi_0 pypi giflib 5.2.1 h5eee18b_3 gitdb 4.0.11 pyhd8ed1ab_0 conda-forge gitpython 3.1.40 pyhd8ed1ab_0 conda-forge gmp 6.2.1 h295c915_3 gnutls 3.6.15 he1e5248_0 google-auth 2.23.3 pypi_0 pypi google-auth-oauthlib 1.1.0 pypi_0 pypi gpustat 0.6.0 pyhd3eb1b0_1 grpcio 1.59.0 pypi_0 pypi huggingface-hub 0.17.3 pypi_0 pypi idna 3.4 py39h06a4308_0 importlib-metadata 6.8.0 pypi_0 pypi intel-openmp 2023.1.0 hdb19cb5_46305 jinja2 3.1.2 pypi_0 pypi jpeg 9e h5eee18b_1 lame 3.100 h7b6447c_0 lcms2 2.12 h3be6417_0 ld_impl_linux-64 2.38 h1181459_1 lerc 3.0 h295c915_0 libcublas 11.10.3.66 0 nvidia libcufft 10.7.2.124 h4fbf590_0 nvidia libcufile 1.8.0.34 0 nvidia libcurand 10.3.4.52 0 nvidia libcusolver 11.4.0.1 0 nvidia libcusparse 11.7.4.91 0 nvidia libdeflate 1.17 h5eee18b_1 libffi 3.4.4 h6a678d5_0 libgcc-ng 13.2.0 h807b86a_2 conda-forge libgfortran-ng 13.2.0 h69a702a_2 conda-forge libgfortran5 13.2.0 ha4646dd_2 conda-forge libiconv 1.16 h7f8727e_2 libidn2 2.3.4 h5eee18b_0 libnpp 11.7.4.75 0 nvidia libnvjpeg 11.8.0.2 0 nvidia libpng 1.6.39 h5eee18b_0 libprotobuf 3.20.3 he621ea3_0 libstdcxx-ng 13.2.0 h7e041cc_2 conda-forge libtasn1 4.19.0 h5eee18b_0 libtiff 4.5.1 h6a678d5_0 libunistring 0.9.10 h27cfd23_0 libwebp 1.3.2 h11a3e52_0 libwebp-base 1.3.2 h5eee18b_0 llvm-openmp 14.0.6 h9e868ea_0 lz4-c 1.9.4 h6a678d5_0 markdown 3.5 pypi_0 pypi markupsafe 2.1.3 pypi_0 pypi mkl 2023.1.0 h213fc3f_46343 mkl-service 2.4.0 py39h5eee18b_1 mkl_fft 1.3.8 py39h5eee18b_0 mkl_random 1.2.4 py39hdb19cb5_0 multidict 6.0.4 pypi_0 pypi multiprocess 0.70.15 pypi_0 pypi ncurses 6.4 h6a678d5_0 nettle 3.7.3 hbbd107a_1 numpy 1.26.0 py39h5f9d8c6_0 numpy-base 1.26.0 py39hb5e798b_0 nvidia-ml 7.352.0 pyhd3eb1b0_0 oauthlib 3.2.2 pypi_0 pypi openh264 2.1.1 h4ff587b_0 openjpeg 2.4.0 h3ad879b_0 openssl 3.0.11 h7f8727e_2 packaging 23.2 pypi_0 pypi pandas 2.1.1 pypi_0 pypi pathtools 0.1.2 py_1 conda-forge pillow 10.0.1 py39ha6cbd5a_0 pip 23.3 py39h06a4308_0 protobuf 4.23.4 pypi_0 pypi psutil 5.9.6 pypi_0 pypi pyarrow 13.0.0 pypi_0 pypi pyasn1 0.5.0 pypi_0 pypi pyasn1-modules 0.3.0 pypi_0 pypi pycparser 2.21 pyhd3eb1b0_0 pyopenssl 23.2.0 py39h06a4308_0 pysocks 1.7.1 py39h06a4308_0 python 3.9.18 h955ad1f_0 python-dateutil 2.8.2 pypi_0 pypi python_abi 3.9 2_cp39 conda-forge pytorch 1.13.1 py3.9_cuda11.7_cudnn8.5.0_0 pytorch pytorch-cuda 11.7 h778d358_5 pytorch pytorch-mutex 1.0 cuda pytorch pytz 2023.3.post1 pypi_0 pypi pyyaml 6.0.1 pypi_0 pypi readline 8.2 h5eee18b_0 regex 2023.10.3 pypi_0 pypi requests 2.31.0 py39h06a4308_0 requests-oauthlib 1.3.1 pypi_0 pypi rsa 4.9 pypi_0 pypi safetensors 0.4.0 pypi_0 pypi scipy 1.11.3 py39h5f9d8c6_0 sentry-sdk 1.32.0 pyhd8ed1ab_0 conda-forge setproctitle 1.1.10 py39h3811e60_1004 conda-forge setuptools 68.0.0 py39h06a4308_0 six 1.16.0 pyh6c4a22f_0 conda-forge smmap 5.0.0 pyhd8ed1ab_0 conda-forge sqlite 3.41.2 h5eee18b_0 tbb 2021.8.0 hdb19cb5_0 tensorboard 2.15.0 pypi_0 pypi tensorboard-data-server 0.7.2 pypi_0 pypi tk 8.6.12 h1ccaba5_0 tokenizers 0.14.1 pypi_0 pypi torchaudio 0.13.1 py39_cu117 pytorch torchtriton 2.1.0 py39 pytorch torchvision 0.14.1 py39_cu117 pytorch tqdm 4.66.1 pypi_0 pypi transformers 4.34.1 pypi_0 pypi typing_extensions 4.7.1 py39h06a4308_0 tzdata 2023.3 pypi_0 pypi urllib3 1.26.18 py39h06a4308_0 wandb 0.15.12 pyhd8ed1ab_0 conda-forge wcwidth 0.2.8 pypi_0 pypi werkzeug 3.0.1 pypi_0 pypi wheel 0.41.2 py39h06a4308_0 xformers 0.0.22.post7 py39_cu11.7.1_pyt1.13.1 xformers xxhash 3.4.1 pypi_0 pypi xz 5.4.2 h5eee18b_0 yaml 0.2.5 h7f98852_2 conda-forge yarl 1.9.2 pypi_0 pypi zipp 3.17.0 pypi_0 pypi zlib 1.2.13 h5eee18b_0 zstd 1.5.5 hc292b87_0	2023-10-30T17:34:05	2023-11-22T00:29:21	2023-11-22T00:29:21	https://github.com/huggingface/datasets/issues/6363	null	6,363	false	[ "I think the code hangs on the `accelerator.main_process_first()` context manager exit. To verify this, you can append a print statement to the end of the `accelerator.main_process_first()` block. \r\n\r\n\r\nIf the problem is in `with_transform`, it would help if you could share the error stack trace printed when you interrupt the process (while it hangs)", "@bhosalems Were you able to fix that ? I face this issue as well", "@matankley No I am not able to resolve this issue yet.", "@mariosasko yes the problem seems to be to exit from accelerator.main_process_first(). Is there any known problem?", "NCCL debug info I get below output, if it helps.\r\n```\r\n11/09/2023 13:36:44 - INFO - __main__ - Distributed environment: MULTI_GPU Backend: nccl\r\nNum processes: 2\r\nProcess index: 1\r\nLocal process index: 1\r\nDevice: cuda:1\r\n\r\nMixed precision type: fp16\r\n\r\nDetected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\r\n11/09/2023 13:36:44 - INFO - __main__ - Distributed environment: MULTI_GPU Backend: nccl\r\nNum processes: 2\r\nProcess index: 0\r\nLocal process index: 0\r\nDevice: cuda:0\r\n\r\nMixed precision type: fp16\r\n\r\n{'timestep_spacing', 'thresholding', 'variance_type', 'clip_sample_range', 'prediction_type', 'dynamic_thresholding_ratio', 'sample_max_value'} was not found in config. Values will be initialized to default values.\r\n{'norm_num_groups', 'force_upcast'} was not found in config. Values will be initialized to default values.\r\n{'num_attention_heads', 'projection_class_embeddings_input_dim', 'addition_embed_type_num_heads', 'mid_block_only_cross_attention', 'addition_embed_type', 'num_class_embeds', 'upcast_attention', 'cross_attention_norm', 'addition_time_embed_dim', 'time_embedding_dim', 'class_embeddings_concat', 'encoder_hid_dim', 'encoder_hid_dim_type', 'resnet_out_scale_factor', 'attention_type', 'conv_out_kernel', 'only_cross_attention', 'resnet_time_scale_shift', 'resnet_skip_time_act', 'reverse_transformer_layers_per_block', 'conv_in_kernel', 'time_cond_proj_dim', 'use_linear_projection', 'mid_block_type', 'time_embedding_act_fn', 'dropout', 'timestep_post_act', 'dual_cross_attention', 'class_embed_type', 'transformer_layers_per_block', 'time_embedding_type'} was not found in config. Values will be initialized to default values.\r\n{'num_attention_heads', 'projection_class_embeddings_input_dim', 'addition_embed_type_num_heads', 'mid_block_only_cross_attention', 'addition_embed_type', 'num_class_embeds', 'upcast_attention', 'cross_attention_norm', 'addition_time_embed_dim', 'time_embedding_dim', 'class_embeddings_concat', 'encoder_hid_dim', 'encoder_hid_dim_type', 'resnet_out_scale_factor', 'attention_type', 'conv_out_kernel', 'only_cross_attention', 'resnet_time_scale_shift', 'resnet_skip_time_act', 'reverse_transformer_layers_per_block', 'conv_in_kernel', 'time_cond_proj_dim', 'use_linear_projection', 'mid_block_type', 'time_embedding_act_fn', 'dropout', 'timestep_post_act', 'dual_cross_attention', 'class_embed_type', 'transformer_layers_per_block', 'time_embedding_type'} was not found in config. Values will be initialized to default values.\r\ndeepbull5:1311249:1311249 [0] NCCL INFO Bootstrap : Using enp194s0f0:128.205.43.171<0>\r\ndeepbull5:1311249:1311249 [0] NCCL INFO NET/Plugin : No plugin found (libnccl-net.so), using internal implementation\r\ndeepbull5:1311249:1311249 [0] NCCL INFO cudaDriverVersion 11070\r\nNCCL version 2.14.3+cuda11.7\r\ndeepbull5:1311250:1311250 [1] NCCL INFO cudaDriverVersion 11070\r\ndeepbull5:1311249:1311365 [0] NCCL INFO NET/IB : No device found.\r\ndeepbull5:1311249:1311365 [0] NCCL INFO NET/Socket : Using [0]enp194s0f0:128.205.43.171<0>\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Using network Socket\r\ndeepbull5:1311250:1311250 [1] NCCL INFO Bootstrap : Using enp194s0f0:128.205.43.171<0>\r\ndeepbull5:1311250:1311250 [1] NCCL INFO NET/Plugin : No plugin found (libnccl-net.so), using internal implementation\r\ndeepbull5:1311250:1311366 [1] NCCL INFO NET/IB : No device found.\r\ndeepbull5:1311250:1311366 [1] NCCL INFO NET/Socket : Using [0]enp194s0f0:128.205.43.171<0>\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Using network Socket\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Setting affinity for GPU 1 to ff,ffff0000,00ffffff\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Setting affinity for GPU 0 to ff,ffff0000,00ffffff\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 00/04 : 0 1\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Trees [0] -1/-1/-1->1->0 [1] 0/-1/-1->1->-1 [2] -1/-1/-1->1->0 [3] 0/-1/-1->1->-1\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 01/04 : 0 1\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 02/04 : 0 1\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 03/04 : 0 1\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] -1/-1/-1->0->1 [2] 1/-1/-1->0->-1 [3] -1/-1/-1->0->1\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 00/0 : 0[1000] -> 1[24000] via P2P/IPC\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Channel 00/0 : 1[24000] -> 0[1000] via P2P/IPC\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 01/0 : 0[1000] -> 1[24000] via P2P/IPC\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Channel 01/0 : 1[24000] -> 0[1000] via P2P/IPC\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Channel 02/0 : 1[24000] -> 0[1000] via P2P/IPC\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 02/0 : 0[1000] -> 1[24000] via P2P/IPC\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Channel 03/0 : 1[24000] -> 0[1000] via P2P/IPC\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 03/0 : 0[1000] -> 1[24000] via P2P/IPC\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Connected all rings\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Connected all trees\r\ndeepbull5:1311249:1311365 [0] NCCL INFO threadThresholds 8/8/64 \| 16/8/64 \| 512 \| 512\r\ndeepbull5:1311249:1311365 [0] NCCL INFO 4 coll channels, 4 p2p channels, 2 p2p channels per peer\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Connected all rings\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Connected all trees\r\ndeepbull5:1311250:1311366 [1] NCCL INFO threadThresholds 8/8/64 \| 16/8/64 \| 512 \| 512\r\ndeepbull5:1311250:1311366 [1] NCCL INFO 4 coll channels, 4 p2p channels, 2 p2p channels per peer\r\ndeepbull5:1311249:1311365 [0] NCCL INFO comm 0x88a84ee0 rank 0 nranks 2 cudaDev 0 busId 1000 - Init COMPLETE\r\ndeepbull5:1311250:1311366 [1] NCCL INFO comm 0x89a42f60 rank 1 nranks 2 cudaDev 1 busId 24000 - Init COMPLETE\r\n\r\n```", "Maybe @muellerzr can help as an `accelerate` maintainer.", "I don't know what the issue was, but after going through the thread here I loved the issue with https://github.com/huggingface/accelerate/issues/314#issuecomment-1565259831" ]
1,965,794,569	Simplify filesystem logic	closed	Simplifies the existing filesystem logic (e.g., to avoid unnecessary if-else as mentioned in https://github.com/huggingface/datasets/pull/6098#issue-1827655071)	2023-10-27T15:54:18	2023-11-15T14:08:29	2023-11-15T14:02:02	https://github.com/huggingface/datasets/pull/6362	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6362", "html_url": "https://github.com/huggingface/datasets/pull/6362", "diff_url": "https://github.com/huggingface/datasets/pull/6362.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6362.patch", "merged_at": "2023-11-15T14:02:02" }	6,362	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) \| 0.008852 / 0.011353 (-0.002501) \| 0.004613 / 0.011008 (-0.006396) \| 0.096153 / 0.038508 (0.057645) \| 0.074945 / 0.023109 (0.051836) \| 0.365960 / 0.275898 (0.090062) \| 0.385450 / 0.323480 (0.061970) \| 0.004757 / 0.007986 (-0.003229) \| 0.003453 / 0.004328 (-0.000876) \| 0.069944 / 0.004250 (0.065693) \| 0.057781 / 0.037052 (0.020729) \| 0.361056 / 0.258489 (0.102567) \| 0.409218 / 0.293841 (0.115377) \| 0.045714 / 0.128546 (-0.082833) \| 0.013776 / 0.075646 (-0.061871) \| 0.328797 / 0.419271 (-0.090474) \| 0.063431 / 0.043533 (0.019899) \| 0.370799 / 0.255139 (0.115660) \| 0.370701 / 0.283200 (0.087502) \| 0.034894 / 0.141683 (-0.106789) \| 1.730290 / 1.452155 (0.278136) \| 1.863600 / 1.492716 (0.370883) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.245571 / 0.018006 (0.227565) \| 0.509666 / 0.000490 (0.509176) \| 0.008051 / 0.000200 (0.007851) \| 0.000104 / 0.000054 (0.000050) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027854 / 0.037411 (-0.009557) \| 0.090735 / 0.014526 (0.076209) \| 0.100100 / 0.176557 (-0.076457) \| 0.158267 / 0.737135 (-0.578868) \| 0.107537 / 0.296338 (-0.188801) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.565455 / 0.215209 (0.350246) \| 5.671436 / 2.077655 (3.593781) \| 2.438078 / 1.504120 (0.933958) \| 2.072403 / 1.541195 (0.531208) \| 2.127830 / 1.468490 (0.659340) \| 0.840101 / 4.584777 (-3.744675) \| 4.945952 / 3.745712 (1.200240) \| 4.840904 / 5.269862 (-0.428957) \| 3.037936 / 4.565676 (-1.527740) \| 0.099027 / 0.424275 (-0.325248) \| 0.008448 / 0.007607 (0.000841) \| 0.703315 / 0.226044 (0.477271) \| 6.837550 / 2.268929 (4.568621) \| 3.204232 / 55.444624 (-52.240393) \| 2.492985 / 6.876477 (-4.383492) \| 2.426792 / 2.142072 (0.284720) \| 0.998430 / 4.805227 (-3.806797) \| 0.203854 / 6.500664 (-6.296811) \| 0.072386 / 0.075469 (-0.003083) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.606627 / 1.841788 (-0.235161) \| 22.287391 / 8.074308 (14.213082) \| 20.245654 / 10.191392 (10.054262) \| 0.229377 / 0.680424 (-0.451046) \| 0.028399 / 0.534201 (-0.505802) \| 0.446567 / 0.579283 (-0.132716) \| 0.565277 / 0.434364 (0.130913) \| 0.502957 / 0.540337 (-0.037381) \| 0.749268 / 1.386936 (-0.637668) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008253 / 0.011353 (-0.003100) \| 0.004432 / 0.011008 (-0.006576) \| 0.081995 / 0.038508 (0.043487) \| 0.075443 / 0.023109 (0.052334) \| 0.442139 / 0.275898 (0.166241) \| 0.507308 / 0.323480 (0.183829) \| 0.007343 / 0.007986 (-0.000643) \| 0.003850 / 0.004328 (-0.000478) \| 0.072656 / 0.004250 (0.068406) \| 0.054585 / 0.037052 (0.017533) \| 0.430057 / 0.258489 (0.171568) \| 0.466953 / 0.293841 (0.173112) \| 0.050350 / 0.128546 (-0.078196) \| 0.013682 / 0.075646 (-0.061965) \| 0.088164 / 0.419271 (-0.331107) \| 0.061726 / 0.043533 (0.018193) \| 0.444420 / 0.255139 (0.189281) \| 0.470406 / 0.283200 (0.187206) \| 0.033258 / 0.141683 (-0.108425) \| 1.635977 / 1.452155 (0.183823) \| 1.732767 / 1.492716 (0.240051) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227350 / 0.018006 (0.209344) \| 0.500805 / 0.000490 (0.500316) \| 0.006473 / 0.000200 (0.006273) \| 0.000110 / 0.000054 (0.000055) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034456 / 0.037411 (-0.002955) \| 0.094832 / 0.014526 (0.080306) \| 0.118549 / 0.176557 (-0.058008) \| 0.177971 / 0.737135 (-0.559164) \| 0.114165 / 0.296338 (-0.182174) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.664805 / 0.215209 (0.449596) \| 6.509756 / 2.077655 (4.432101) \| 2.936840 / 1.504120 (1.432720) \| 2.662645 / 1.541195 (1.121450) \| 2.659957 / 1.468490 (1.191467) \| 0.903019 / 4.584777 (-3.681758) \| 5.237191 / 3.745712 (1.491479) \| 4.791917 / 5.269862 (-0.477945) \| 3.130905 / 4.565676 (-1.434772) \| 0.100953 / 0.424275 (-0.323322) \| 0.008388 / 0.007607 (0.000781) \| 0.776393 / 0.226044 (0.550348) \| 7.726230 / 2.268929 (5.457301) \| 3.669223 / 55.444624 (-51.775401) \| 2.904556 / 6.876477 (-3.971921) \| 3.205546 / 2.142072 (1.063473) \| 1.058899 / 4.805227 (-3.746329) \| 0.213733 / 6.500664 (-6.286931) \| 0.071374 / 0.075469 (-0.004096) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.713384 / 1.841788 (-0.128403) \| 23.325498 / 8.074308 (15.251190) \| 20.140510 / 10.191392 (9.949118) \| 0.211565 / 0.680424 (-0.468859) \| 0.032916 / 0.534201 (-0.501285) \| 0.460504 / 0.579283 (-0.118779) \| 0.594352 / 0.434364 (0.159988) \| 0.556384 / 0.540337 (0.016047) \| 0.788586 / 1.386936 (-0.598350) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3c7249c11d8330ce49b1fe119c34fc6100f10774 \"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) \| 0.008840 / 0.011353 (-0.002513) \| 0.005045 / 0.011008 (-0.005963) \| 0.110777 / 0.038508 (0.072269) \| 0.100495 / 0.023109 (0.077386) \| 0.420302 / 0.275898 (0.144404) \| 0.456423 / 0.323480 (0.132943) \| 0.006873 / 0.007986 (-0.001113) \| 0.005230 / 0.004328 (0.000902) \| 0.081316 / 0.004250 (0.077066) \| 0.063047 / 0.037052 (0.025995) \| 0.439469 / 0.258489 (0.180979) \| 0.488477 / 0.293841 (0.194636) \| 0.048553 / 0.128546 (-0.079994) \| 0.014984 / 0.075646 (-0.060662) \| 0.401317 / 0.419271 (-0.017955) \| 0.074578 / 0.043533 (0.031045) \| 0.435298 / 0.255139 (0.180159) \| 0.464406 / 0.283200 (0.181206) \| 0.048788 / 0.141683 (-0.092895) \| 1.836166 / 1.452155 (0.384011) \| 1.959808 / 1.492716 (0.467091) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.321419 / 0.018006 (0.303412) \| 0.595736 / 0.000490 (0.595246) \| 0.021144 / 0.000200 (0.020944) \| 0.000626 / 0.000054 (0.000571) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033033 / 0.037411 (-0.004379) \| 0.112621 / 0.014526 (0.098095) \| 0.118736 / 0.176557 (-0.057821) \| 0.195533 / 0.737135 (-0.541602) \| 0.120807 / 0.296338 (-0.175531) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.616692 / 0.215209 (0.401483) \| 6.033674 / 2.077655 (3.956019) \| 2.630106 / 1.504120 (1.125986) \| 2.316739 / 1.541195 (0.775544) \| 2.387525 / 1.468490 (0.919035) \| 0.863385 / 4.584777 (-3.721392) \| 5.288193 / 3.745712 (1.542481) \| 5.115766 / 5.269862 (-0.154096) \| 3.083055 / 4.565676 (-1.482621) \| 0.104885 / 0.424275 (-0.319391) \| 0.012233 / 0.007607 (0.004626) \| 0.739924 / 0.226044 (0.513880) \| 7.422996 / 2.268929 (5.154067) \| 3.403316 / 55.444624 (-52.041309) \| 2.778740 / 6.876477 (-4.097736) \| 2.836937 / 2.142072 (0.694864) \| 1.059683 / 4.805227 (-3.745544) \| 0.235838 / 6.500664 (-6.264826) \| 0.083725 / 0.075469 (0.008256) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.755843 / 1.841788 (-0.085944) \| 25.186642 / 8.074308 (17.112334) \| 24.133582 / 10.191392 (13.942190) \| 0.240511 / 0.680424 (-0.439913) \| 0.029563 / 0.534201 (-0.504638) \| 0.486049 / 0.579283 (-0.093234) \| 0.610064 / 0.434364 (0.175700) \| 0.559521 / 0.540337 (0.019184) \| 0.828289 / 1.386936 (-0.558647) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.012134 / 0.011353 (0.000781) \| 0.005133 / 0.011008 (-0.005875) \| 0.084521 / 0.038508 (0.046013) \| 0.095172 / 0.023109 (0.072063) \| 0.527298 / 0.275898 (0.251400) \| 0.558915 / 0.323480 (0.235435) \| 0.006996 / 0.007986 (-0.000989) \| 0.004283 / 0.004328 (-0.000045) \| 0.082975 / 0.004250 (0.078725) \| 0.067976 / 0.037052 (0.030924) \| 0.534020 / 0.258489 (0.275531) \| 0.560810 / 0.293841 (0.266969) \| 0.051603 / 0.128546 (-0.076943) \| 0.013330 / 0.075646 (-0.062316) \| 0.094093 / 0.419271 (-0.325178) \| 0.068967 / 0.043533 (0.025434) \| 0.512527 / 0.255139 (0.257388) \| 0.542182 / 0.283200 (0.258982) \| 0.039159 / 0.141683 (-0.102524) \| 1.858841 / 1.452155 (0.406686) \| 1.915450 / 1.492716 (0.422734) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.269013 / 0.018006 (0.251007) \| 0.601711 / 0.000490 (0.601222) \| 0.013950 / 0.000200 (0.013750) \| 0.000166 / 0.000054 (0.000112) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.038817 / 0.037411 (0.001405) \| 0.138528 / 0.014526 (0.124002) \| 0.130691 / 0.176557 (-0.045865) \| 0.192825 / 0.737135 (-0.544310) \| 0.128337 / 0.296338 (-0.168002) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.678725 / 0.215209 (0.463516) \| 6.869763 / 2.077655 (4.792108) \| 3.416224 / 1.504120 (1.912104) \| 3.106971 / 1.541195 (1.565776) \| 3.117248 / 1.468490 (1.648757) \| 0.895004 / 4.584777 (-3.689773) \| 5.551618 / 3.745712 (1.805906) \| 4.964811 / 5.269862 (-0.305051) \| 3.239555 / 4.565676 (-1.326121) \| 0.099776 / 0.424275 (-0.324500) \| 0.008723 / 0.007607 (0.001116) \| 0.818554 / 0.226044 (0.592510) \| 8.015976 / 2.268929 (5.747047) \| 4.200392 / 55.444624 (-51.244232) \| 3.566942 / 6.876477 (-3.309535) \| 3.766249 / 2.142072 (1.624177) \| 1.083428 / 4.805227 (-3.721799) \| 0.214614 / 6.500664 (-6.286050) \| 0.081951 / 0.075469 (0.006482) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.854400 / 1.841788 (0.012612) \| 26.002556 / 8.074308 (17.928248) \| 24.315194 / 10.191392 (14.123802) \| 0.249012 / 0.680424 (-0.431412) \| 0.032681 / 0.534201 (-0.501520) \| 0.502360 / 0.579283 (-0.076923) \| 0.606014 / 0.434364 (0.171650) \| 0.616852 / 0.540337 (0.076514) \| 0.861785 / 1.386936 (-0.525151) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53c5c01583153cc112a507082aff4679433a1cce \"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) \| 0.006723 / 0.011353 (-0.004630) \| 0.004135 / 0.011008 (-0.006873) \| 0.079241 / 0.038508 (0.040733) \| 0.065484 / 0.023109 (0.042374) \| 0.302831 / 0.275898 (0.026933) \| 0.343747 / 0.323480 (0.020268) \| 0.005910 / 0.007986 (-0.002076) \| 0.006028 / 0.004328 (0.001699) \| 0.064000 / 0.004250 (0.059750) \| 0.047872 / 0.037052 (0.010820) \| 0.336928 / 0.258489 (0.078439) \| 0.357726 / 0.293841 (0.063885) \| 0.039375 / 0.128546 (-0.089171) \| 0.010439 / 0.075646 (-0.065207) \| 0.310453 / 0.419271 (-0.108819) \| 0.055320 / 0.043533 (0.011787) \| 0.294722 / 0.255139 (0.039583) \| 0.314649 / 0.283200 (0.031450) \| 0.033223 / 0.141683 (-0.108460) \| 1.386705 / 1.452155 (-0.065450) \| 1.420546 / 1.492716 (-0.072170) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.262649 / 0.018006 (0.244643) \| 0.536764 / 0.000490 (0.536274) \| 0.011090 / 0.000200 (0.010891) \| 0.000118 / 0.000054 (0.000063) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023822 / 0.037411 (-0.013590) \| 0.074279 / 0.014526 (0.059753) \| 0.081295 / 0.176557 (-0.095262) \| 0.135853 / 0.737135 (-0.601282) \| 0.080193 / 0.296338 (-0.216146) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.468577 / 0.215209 (0.253368) \| 4.615975 / 2.077655 (2.538321) \| 2.059232 / 1.504120 (0.555112) \| 1.798578 / 1.541195 (0.257383) \| 1.801436 / 1.468490 (0.332946) \| 0.660489 / 4.584777 (-3.924288) \| 4.394652 / 3.745712 (0.648940) \| 3.956277 / 5.269862 (-1.313585) \| 2.406700 / 4.565676 (-2.158976) \| 0.077174 / 0.424275 (-0.347101) \| 0.007121 / 0.007607 (-0.000486) \| 0.568213 / 0.226044 (0.342168) \| 5.721217 / 2.268929 (3.452289) \| 2.662741 / 55.444624 (-52.781883) \| 2.207333 / 6.876477 (-4.669144) \| 2.165279 / 2.142072 (0.023206) \| 0.772566 / 4.805227 (-4.032661) \| 0.162845 / 6.500664 (-6.337819) \| 0.057515 / 0.075469 (-0.017954) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.313565 / 1.841788 (-0.528223) \| 19.298926 / 8.074308 (11.224618) \| 17.194320 / 10.191392 (7.002928) \| 0.223404 / 0.680424 (-0.457020) \| 0.024735 / 0.534201 (-0.509466) \| 0.388452 / 0.579283 (-0.190831) \| 0.489354 / 0.434364 (0.054990) \| 0.427962 / 0.540337 (-0.112375) \| 0.629483 / 1.386936 (-0.757453) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007404 / 0.011353 (-0.003949) \| 0.004434 / 0.011008 (-0.006574) \| 0.061633 / 0.038508 (0.023125) \| 0.058446 / 0.023109 (0.035336) \| 0.386107 / 0.275898 (0.110209) \| 0.397676 / 0.323480 (0.074197) \| 0.005463 / 0.007986 (-0.002523) \| 0.003797 / 0.004328 (-0.000531) \| 0.067323 / 0.004250 (0.063072) \| 0.053826 / 0.037052 (0.016774) \| 0.387910 / 0.258489 (0.129421) \| 0.409364 / 0.293841 (0.115523) \| 0.039836 / 0.128546 (-0.088710) \| 0.011940 / 0.075646 (-0.063706) \| 0.071812 / 0.419271 (-0.347459) \| 0.047952 / 0.043533 (0.004419) \| 0.386826 / 0.255139 (0.131687) \| 0.392845 / 0.283200 (0.109645) \| 0.029430 / 0.141683 (-0.112253) \| 1.390961 / 1.452155 (-0.061194) \| 1.482744 / 1.492716 (-0.009972) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.258814 / 0.018006 (0.240807) \| 0.535505 / 0.000490 (0.535015) \| 0.006097 / 0.000200 (0.005897) \| 0.000130 / 0.000054 (0.000075) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028046 / 0.037411 (-0.009365) \| 0.078077 / 0.014526 (0.063552) \| 0.087713 / 0.176557 (-0.088843) \| 0.140856 / 0.737135 (-0.596279) \| 0.090565 / 0.296338 (-0.205773) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.504375 / 0.215209 (0.289165) \| 5.133472 / 2.077655 (3.055817) \| 2.368968 / 1.504120 (0.864848) \| 2.176939 / 1.541195 (0.635744) \| 2.151976 / 1.468490 (0.683486) \| 0.720566 / 4.584777 (-3.864211) \| 5.050505 / 3.745712 (1.304793) \| 3.993614 / 5.269862 (-1.276248) \| 2.492234 / 4.565676 (-2.073443) \| 0.089629 / 0.424275 (-0.334646) \| 0.008074 / 0.007607 (0.000467) \| 0.677706 / 0.226044 (0.451661) \| 6.208332 / 2.268929 (3.939403) \| 3.058299 / 55.444624 (-52.386325) \| 2.461078 / 6.876477 (-4.415399) \| 2.622681 / 2.142072 (0.480609) \| 0.873573 / 4.805227 (-3.931654) \| 0.176321 / 6.500664 (-6.324343) \| 0.062410 / 0.075469 (-0.013059) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.454767 / 1.841788 (-0.387021) \| 19.544225 / 8.074308 (11.469917) \| 17.365997 / 10.191392 (7.174605) \| 0.225461 / 0.680424 (-0.454963) \| 0.027679 / 0.534201 (-0.506522) \| 0.396419 / 0.579283 (-0.182864) \| 0.513244 / 0.434364 (0.078880) \| 0.469054 / 0.540337 (-0.071283) \| 0.676458 / 1.386936 (-0.710478) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d44d8a649b541cd0b10ea99fbfe7a02c3ba50a63 \"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) \| 0.007606 / 0.011353 (-0.003747) \| 0.004692 / 0.011008 (-0.006317) \| 0.100525 / 0.038508 (0.062017) \| 0.085426 / 0.023109 (0.062317) \| 0.378568 / 0.275898 (0.102670) \| 0.412268 / 0.323480 (0.088788) \| 0.004756 / 0.007986 (-0.003230) \| 0.003871 / 0.004328 (-0.000457) \| 0.075244 / 0.004250 (0.070994) \| 0.064969 / 0.037052 (0.027916) \| 0.385569 / 0.258489 (0.127079) \| 0.429117 / 0.293841 (0.135276) \| 0.035798 / 0.128546 (-0.092749) \| 0.009999 / 0.075646 (-0.065647) \| 0.351380 / 0.419271 (-0.067891) \| 0.060850 / 0.043533 (0.017317) \| 0.381327 / 0.255139 (0.126188) \| 0.403663 / 0.283200 (0.120464) \| 0.028103 / 0.141683 (-0.113580) \| 1.814143 / 1.452155 (0.361988) \| 1.895062 / 1.492716 (0.402346) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.263581 / 0.018006 (0.245575) \| 0.506988 / 0.000490 (0.506499) \| 0.012775 / 0.000200 (0.012575) \| 0.000456 / 0.000054 (0.000402) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033452 / 0.037411 (-0.003959) \| 0.104950 / 0.014526 (0.090425) \| 0.114803 / 0.176557 (-0.061754) \| 0.182465 / 0.737135 (-0.554671) \| 0.116156 / 0.296338 (-0.180183) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.441574 / 0.215209 (0.226365) \| 4.394601 / 2.077655 (2.316946) \| 2.170797 / 1.504120 (0.666677) \| 1.926675 / 1.541195 (0.385480) \| 1.974867 / 1.468490 (0.506377) \| 0.546777 / 4.584777 (-4.038000) \| 4.053612 / 3.745712 (0.307900) \| 3.934278 / 5.269862 (-1.335583) \| 2.354660 / 4.565676 (-2.211017) \| 0.067706 / 0.424275 (-0.356569) \| 0.009217 / 0.007607 (0.001610) \| 0.539261 / 0.226044 (0.313217) \| 5.409552 / 2.268929 (3.140623) \| 2.835739 / 55.444624 (-52.608886) \| 2.282246 / 6.876477 (-4.594230) \| 2.359930 / 2.142072 (0.217858) \| 0.696363 / 4.805227 (-4.108864) \| 0.155947 / 6.500664 (-6.344717) \| 0.071293 / 0.075469 (-0.004176) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.495512 / 1.841788 (-0.346275) \| 22.027128 / 8.074308 (13.952820) \| 16.226068 / 10.191392 (6.034676) \| 0.180281 / 0.680424 (-0.500142) \| 0.021839 / 0.534201 (-0.512362) \| 0.446151 / 0.579283 (-0.133132) \| 0.476872 / 0.434364 (0.042508) \| 0.515171 / 0.540337 (-0.025166) \| 0.731372 / 1.386936 (-0.655564) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006843 / 0.011353 (-0.004510) \| 0.004286 / 0.011008 (-0.006722) \| 0.074104 / 0.038508 (0.035596) \| 0.076789 / 0.023109 (0.053680) \| 0.441506 / 0.275898 (0.165608) \| 0.500999 / 0.323480 (0.177519) \| 0.006041 / 0.007986 (-0.001945) \| 0.003718 / 0.004328 (-0.000610) \| 0.074189 / 0.004250 (0.069938) \| 0.060513 / 0.037052 (0.023461) \| 0.460812 / 0.258489 (0.202323) \| 0.503631 / 0.293841 (0.209790) \| 0.037026 / 0.128546 (-0.091520) \| 0.009611 / 0.075646 (-0.066035) \| 0.077037 / 0.419271 (-0.342234) \| 0.052191 / 0.043533 (0.008658) \| 0.444567 / 0.255139 (0.189428) \| 0.486730 / 0.283200 (0.203530) \| 0.023846 / 0.141683 (-0.117837) \| 1.692422 / 1.452155 (0.240267) \| 1.809648 / 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) \| 0.240007 / 0.018006 (0.222001) \| 0.481980 / 0.000490 (0.481490) \| 0.006945 / 0.000200 (0.006746) \| 0.000120 / 0.000054 (0.000065) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037198 / 0.037411 (-0.000213) \| 0.119413 / 0.014526 (0.104887) \| 0.137409 / 0.176557 (-0.039148) \| 0.199130 / 0.737135 (-0.538005) \| 0.133137 / 0.296338 (-0.163202) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.521747 / 0.215209 (0.306538) \| 4.955653 / 2.077655 (2.877999) \| 2.694323 / 1.504120 (1.190203) \| 2.496629 / 1.541195 (0.955434) \| 2.661151 / 1.468490 (1.192660) \| 0.576687 / 4.584777 (-4.008089) \| 4.251437 / 3.745712 (0.505725) \| 3.683020 / 5.269862 (-1.586842) \| 2.363951 / 4.565676 (-2.201726) \| 0.064631 / 0.424275 (-0.359644) \| 0.007958 / 0.007607 (0.000351) \| 0.616498 / 0.226044 (0.390454) \| 5.919424 / 2.268929 (3.650496) \| 3.255936 / 55.444624 (-52.188689) \| 2.866167 / 6.876477 (-4.010309) \| 3.007272 / 2.142072 (0.865199) \| 0.660259 / 4.805227 (-4.144968) \| 0.152469 / 6.500664 (-6.348195) \| 0.065254 / 0.075469 (-0.010215) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.547912 / 1.841788 (-0.293876) \| 22.494611 / 8.074308 (14.420303) \| 16.400746 / 10.191392 (6.209354) \| 0.184137 / 0.680424 (-0.496287) \| 0.023615 / 0.534201 (-0.510586) \| 0.473923 / 0.579283 (-0.105360) \| 0.473030 / 0.434364 (0.038666) \| 0.534264 / 0.540337 (-0.006073) \| 0.770178 / 1.386936 (-0.616758) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#565efb7f43839072ef01247681645ca404ba0b94 \"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) \| 0.006812 / 0.011353 (-0.004541) \| 0.004254 / 0.011008 (-0.006754) \| 0.084271 / 0.038508 (0.045763) \| 0.084299 / 0.023109 (0.061189) \| 0.317437 / 0.275898 (0.041539) \| 0.350855 / 0.323480 (0.027375) \| 0.004296 / 0.007986 (-0.003690) \| 0.003610 / 0.004328 (-0.000718) \| 0.065205 / 0.004250 (0.060955) \| 0.057734 / 0.037052 (0.020682) \| 0.324049 / 0.258489 (0.065560) \| 0.365042 / 0.293841 (0.071201) \| 0.031454 / 0.128546 (-0.097092) \| 0.008703 / 0.075646 (-0.066943) \| 0.286603 / 0.419271 (-0.132668) \| 0.052251 / 0.043533 (0.008719) \| 0.312404 / 0.255139 (0.057265) \| 0.335902 / 0.283200 (0.052703) \| 0.025087 / 0.141683 (-0.116595) \| 1.478573 / 1.452155 (0.026418) \| 1.559548 / 1.492716 (0.066831) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.307637 / 0.018006 (0.289631) \| 0.567169 / 0.000490 (0.566679) \| 0.006782 / 0.000200 (0.006582) \| 0.000235 / 0.000054 (0.000180) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030979 / 0.037411 (-0.006433) \| 0.089972 / 0.014526 (0.075446) \| 0.101689 / 0.176557 (-0.074868) \| 0.162038 / 0.737135 (-0.575097) \| 0.103107 / 0.296338 (-0.193232) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.382458 / 0.215209 (0.167248) \| 3.813105 / 2.077655 (1.735450) \| 1.855198 / 1.504120 (0.351078) \| 1.699850 / 1.541195 (0.158656) \| 1.902818 / 1.468490 (0.434328) \| 0.478654 / 4.584777 (-4.106123) \| 3.536926 / 3.745712 (-0.208786) \| 3.558557 / 5.269862 (-1.711304) \| 2.121098 / 4.565676 (-2.444579) \| 0.056584 / 0.424275 (-0.367691) \| 0.007693 / 0.007607 (0.000086) \| 0.471157 / 0.226044 (0.245112) \| 4.717742 / 2.268929 (2.448813) \| 2.389033 / 55.444624 (-53.055591) \| 2.102898 / 6.876477 (-4.773579) \| 2.233404 / 2.142072 (0.091332) \| 0.585829 / 4.805227 (-4.219398) \| 0.133784 / 6.500664 (-6.366880) \| 0.063963 / 0.075469 (-0.011506) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.272234 / 1.841788 (-0.569554) \| 19.897647 / 8.074308 (11.823339) \| 14.808090 / 10.191392 (4.616698) \| 0.167199 / 0.680424 (-0.513224) \| 0.018357 / 0.534201 (-0.515844) \| 0.391635 / 0.579283 (-0.187648) \| 0.409603 / 0.434364 (-0.024761) \| 0.467670 / 0.540337 (-0.072668) \| 0.639763 / 1.386936 (-0.747173) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006794 / 0.011353 (-0.004559) \| 0.004317 / 0.011008 (-0.006692) \| 0.065434 / 0.038508 (0.026926) \| 0.079066 / 0.023109 (0.055957) \| 0.415486 / 0.275898 (0.139588) \| 0.448072 / 0.323480 (0.124593) \| 0.005705 / 0.007986 (-0.002281) \| 0.003589 / 0.004328 (-0.000739) \| 0.065195 / 0.004250 (0.060945) \| 0.058951 / 0.037052 (0.021899) \| 0.414466 / 0.258489 (0.155977) \| 0.453844 / 0.293841 (0.160003) \| 0.032437 / 0.128546 (-0.096110) \| 0.008805 / 0.075646 (-0.066841) \| 0.071741 / 0.419271 (-0.347530) \| 0.048051 / 0.043533 (0.004518) \| 0.413197 / 0.255139 (0.158058) \| 0.430071 / 0.283200 (0.146872) \| 0.023144 / 0.141683 (-0.118539) \| 1.507756 / 1.452155 (0.055601) \| 1.572180 / 1.492716 (0.079464) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.326556 / 0.018006 (0.308550) \| 0.533664 / 0.000490 (0.533174) \| 0.007400 / 0.000200 (0.007200) \| 0.000119 / 0.000054 (0.000065) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033397 / 0.037411 (-0.004014) \| 0.092486 / 0.014526 (0.077960) \| 0.108454 / 0.176557 (-0.068103) \| 0.163885 / 0.737135 (-0.573250) \| 0.109682 / 0.296338 (-0.186657) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.429283 / 0.215209 (0.214074) \| 4.285774 / 2.077655 (2.208119) \| 2.245646 / 1.504120 (0.741526) \| 2.088460 / 1.541195 (0.547265) \| 2.217908 / 1.468490 (0.749418) \| 0.500126 / 4.584777 (-4.084651) \| 3.640253 / 3.745712 (-0.105459) \| 3.435069 / 5.269862 (-1.834793) \| 2.158015 / 4.565676 (-2.407662) \| 0.059087 / 0.424275 (-0.365188) \| 0.007479 / 0.007607 (-0.000128) \| 0.518067 / 0.226044 (0.292023) \| 5.181891 / 2.268929 (2.912963) \| 2.759156 / 55.444624 (-52.685468) \| 2.452164 / 6.876477 (-4.424313) \| 2.712764 / 2.142072 (0.570692) \| 0.604871 / 4.805227 (-4.200356) \| 0.137810 / 6.500664 (-6.362854) \| 0.061999 / 0.075469 (-0.013470) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.338081 / 1.841788 (-0.503706) \| 19.934668 / 8.074308 (11.860360) \| 14.482526 / 10.191392 (4.291134) \| 0.167615 / 0.680424 (-0.512809) \| 0.020257 / 0.534201 (-0.513944) \| 0.399103 / 0.579283 (-0.180180) \| 0.431785 / 0.434364 (-0.002579) \| 0.475470 / 0.540337 (-0.064868) \| 0.648003 / 1.386936 (-0.738933) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#153a6c0b0897fc30203ded5a6d6c358c53aa3a0e \"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) \| 0.011916 / 0.011353 (0.000563) \| 0.004696 / 0.011008 (-0.006313) \| 0.101061 / 0.038508 (0.062553) \| 0.093383 / 0.023109 (0.070274) \| 0.391517 / 0.275898 (0.115619) \| 0.434374 / 0.323480 (0.110894) \| 0.006193 / 0.007986 (-0.001792) \| 0.003840 / 0.004328 (-0.000489) \| 0.077946 / 0.004250 (0.073696) \| 0.066332 / 0.037052 (0.029280) \| 0.413103 / 0.258489 (0.154614) \| 0.452988 / 0.293841 (0.159148) \| 0.044899 / 0.128546 (-0.083647) \| 0.009969 / 0.075646 (-0.065677) \| 0.344569 / 0.419271 (-0.074703) \| 0.064688 / 0.043533 (0.021155) \| 0.388042 / 0.255139 (0.132903) \| 0.417615 / 0.283200 (0.134416) \| 0.032899 / 0.141683 (-0.108784) \| 1.738834 / 1.452155 (0.286679) \| 1.837562 / 1.492716 (0.344845) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.255265 / 0.018006 (0.237259) \| 0.547550 / 0.000490 (0.547061) \| 0.009018 / 0.000200 (0.008818) \| 0.001232 / 0.000054 (0.001178) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033171 / 0.037411 (-0.004241) \| 0.102569 / 0.014526 (0.088043) \| 0.113611 / 0.176557 (-0.062946) \| 0.181805 / 0.737135 (-0.555330) \| 0.115015 / 0.296338 (-0.181323) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.456430 / 0.215209 (0.241221) \| 4.536000 / 2.077655 (2.458346) \| 2.220554 / 1.504120 (0.716434) \| 2.037965 / 1.541195 (0.496770) \| 2.223780 / 1.468490 (0.755290) \| 0.565732 / 4.584777 (-4.019045) \| 4.574917 / 3.745712 (0.829205) \| 4.085683 / 5.269862 (-1.184178) \| 2.529052 / 4.565676 (-2.036624) \| 0.067061 / 0.424275 (-0.357214) \| 0.009161 / 0.007607 (0.001554) \| 0.551377 / 0.226044 (0.325332) \| 5.510422 / 2.268929 (3.241493) \| 2.788264 / 55.444624 (-52.656360) \| 2.432821 / 6.876477 (-4.443656) \| 2.500835 / 2.142072 (0.358762) \| 0.683645 / 4.805227 (-4.121582) \| 0.155595 / 6.500664 (-6.345069) \| 0.072265 / 0.075469 (-0.003204) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.512571 / 1.841788 (-0.329217) \| 23.752582 / 8.074308 (15.678273) \| 16.798834 / 10.191392 (6.607442) \| 0.210325 / 0.680424 (-0.470099) \| 0.023446 / 0.534201 (-0.510755) \| 0.472964 / 0.579283 (-0.106319) \| 0.518003 / 0.434364 (0.083639) \| 0.588422 / 0.540337 (0.048085) \| 0.830762 / 1.386936 (-0.556174) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008075 / 0.011353 (-0.003278) \| 0.004569 / 0.011008 (-0.006439) \| 0.079786 / 0.038508 (0.041278) \| 0.092741 / 0.023109 (0.069632) \| 0.500732 / 0.275898 (0.224834) \| 0.544108 / 0.323480 (0.220628) \| 0.006305 / 0.007986 (-0.001680) \| 0.003843 / 0.004328 (-0.000486) \| 0.078347 / 0.004250 (0.074096) \| 0.066969 / 0.037052 (0.029916) \| 0.504116 / 0.258489 (0.245627) \| 0.548109 / 0.293841 (0.254268) \| 0.038263 / 0.128546 (-0.090283) \| 0.010006 / 0.075646 (-0.065640) \| 0.085582 / 0.419271 (-0.333690) \| 0.056937 / 0.043533 (0.013404) \| 0.502861 / 0.255139 (0.247722) \| 0.532002 / 0.283200 (0.248802) \| 0.027003 / 0.141683 (-0.114679) \| 1.811658 / 1.452155 (0.359503) \| 1.878863 / 1.492716 (0.386147) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.242297 / 0.018006 (0.224291) \| 0.489060 / 0.000490 (0.488570) \| 0.005770 / 0.000200 (0.005570) \| 0.000129 / 0.000054 (0.000075) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.040368 / 0.037411 (0.002956) \| 0.116221 / 0.014526 (0.101695) \| 0.125195 / 0.176557 (-0.051361) \| 0.188616 / 0.737135 (-0.548519) \| 0.126473 / 0.296338 (-0.169866) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.513975 / 0.215209 (0.298766) \| 5.122407 / 2.077655 (3.044752) \| 2.854024 / 1.504120 (1.349904) \| 2.611101 / 1.541195 (1.069906) \| 2.704880 / 1.468490 (1.236390) \| 0.581568 / 4.584777 (-4.003209) \| 4.628965 / 3.745712 (0.883253) \| 4.069359 / 5.269862 (-1.200503) \| 2.433793 / 4.565676 (-2.131883) \| 0.068624 / 0.424275 (-0.355651) \| 0.008843 / 0.007607 (0.001235) \| 0.609147 / 0.226044 (0.383102) \| 6.096923 / 2.268929 (3.827995) \| 3.411687 / 55.444624 (-52.032937) \| 2.972037 / 6.876477 (-3.904440) \| 3.210266 / 2.142072 (1.068194) \| 0.697935 / 4.805227 (-4.107292) \| 0.156855 / 6.500664 (-6.343809) \| 0.072600 / 0.075469 (-0.002869) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.673126 / 1.841788 (-0.168661) \| 24.782231 / 8.074308 (16.707923) \| 17.945937 / 10.191392 (7.754545) \| 0.229063 / 0.680424 (-0.451361) \| 0.024264 / 0.534201 (-0.509937) \| 0.474904 / 0.579283 (-0.104379) \| 0.616602 / 0.434364 (0.182238) \| 0.587687 / 0.540337 (0.047350) \| 0.875600 / 1.386936 (-0.511336) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a46ae63ad72c0733c947fa0f2996fa739d80e1ef \"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) \| 0.004866 / 0.011353 (-0.006487) \| 0.002877 / 0.011008 (-0.008132) \| 0.061786 / 0.038508 (0.023277) \| 0.051555 / 0.023109 (0.028446) \| 0.262182 / 0.275898 (-0.013716) \| 0.288908 / 0.323480 (-0.034572) \| 0.002929 / 0.007986 (-0.005057) \| 0.002358 / 0.004328 (-0.001971) \| 0.048246 / 0.004250 (0.043995) \| 0.040391 / 0.037052 (0.003339) \| 0.268165 / 0.258489 (0.009675) \| 0.304844 / 0.293841 (0.011003) \| 0.023280 / 0.128546 (-0.105266) \| 0.007274 / 0.075646 (-0.068372) \| 0.200698 / 0.419271 (-0.218574) \| 0.036181 / 0.043533 (-0.007352) \| 0.267292 / 0.255139 (0.012153) \| 0.286981 / 0.283200 (0.003781) \| 0.018686 / 0.141683 (-0.122996) \| 1.131903 / 1.452155 (-0.320251) \| 1.196631 / 1.492716 (-0.296086) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092158 / 0.018006 (0.074152) \| 0.300621 / 0.000490 (0.300132) \| 0.000205 / 0.000200 (0.000006) \| 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) \| 0.018101 / 0.037411 (-0.019310) \| 0.062478 / 0.014526 (0.047952) \| 0.073092 / 0.176557 (-0.103464) \| 0.119397 / 0.737135 (-0.617738) \| 0.073768 / 0.296338 (-0.222570) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286711 / 0.215209 (0.071502) \| 2.766663 / 2.077655 (0.689008) \| 1.431238 / 1.504120 (-0.072882) \| 1.308312 / 1.541195 (-0.232883) \| 1.344886 / 1.468490 (-0.123605) \| 0.396719 / 4.584777 (-4.188058) \| 2.371154 / 3.745712 (-1.374558) \| 2.626471 / 5.269862 (-2.643391) \| 1.574837 / 4.565676 (-2.990840) \| 0.046344 / 0.424275 (-0.377931) \| 0.005108 / 0.007607 (-0.002499) \| 0.334200 / 0.226044 (0.108156) \| 3.277034 / 2.268929 (1.008106) \| 1.789338 / 55.444624 (-53.655286) \| 1.527584 / 6.876477 (-5.348892) \| 1.570417 / 2.142072 (-0.571656) \| 0.472663 / 4.805227 (-4.332564) \| 0.100825 / 6.500664 (-6.399839) \| 0.042270 / 0.075469 (-0.033199) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.965416 / 1.841788 (-0.876372) \| 11.827406 / 8.074308 (3.753098) \| 10.820703 / 10.191392 (0.629311) \| 0.128636 / 0.680424 (-0.551788) \| 0.014696 / 0.534201 (-0.519505) \| 0.271019 / 0.579283 (-0.308264) \| 0.270077 / 0.434364 (-0.164287) \| 0.313054 / 0.540337 (-0.227284) \| 0.402941 / 1.386936 (-0.983995) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005204 / 0.011353 (-0.006149) \| 0.002976 / 0.011008 (-0.008032) \| 0.047723 / 0.038508 (0.009215) \| 0.056180 / 0.023109 (0.033071) \| 0.277751 / 0.275898 (0.001853) \| 0.304109 / 0.323480 (-0.019371) \| 0.004254 / 0.007986 (-0.003732) \| 0.002386 / 0.004328 (-0.001943) \| 0.047815 / 0.004250 (0.043564) \| 0.041553 / 0.037052 (0.004501) \| 0.280958 / 0.258489 (0.022469) \| 0.308639 / 0.293841 (0.014799) \| 0.023549 / 0.128546 (-0.104997) \| 0.007846 / 0.075646 (-0.067800) \| 0.053762 / 0.419271 (-0.365509) \| 0.031763 / 0.043533 (-0.011770) \| 0.278208 / 0.255139 (0.023069) \| 0.294024 / 0.283200 (0.010825) \| 0.018648 / 0.141683 (-0.123035) \| 1.140664 / 1.452155 (-0.311490) \| 1.206706 / 1.492716 (-0.286010) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093211 / 0.018006 (0.075205) \| 0.303067 / 0.000490 (0.302577) \| 0.000222 / 0.000200 (0.000022) \| 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) \| 0.021745 / 0.037411 (-0.015666) \| 0.070400 / 0.014526 (0.055874) \| 0.083250 / 0.176557 (-0.093307) \| 0.119745 / 0.737135 (-0.617391) \| 0.083004 / 0.296338 (-0.213335) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.305841 / 0.215209 (0.090632) \| 2.958171 / 2.077655 (0.880516) \| 1.596990 / 1.504120 (0.092870) \| 1.466522 / 1.541195 (-0.074673) \| 1.487050 / 1.468490 (0.018560) \| 0.402866 / 4.584777 (-4.181911) \| 2.425415 / 3.745712 (-1.320297) \| 2.545245 / 5.269862 (-2.724617) \| 1.569719 / 4.565676 (-2.995958) \| 0.046344 / 0.424275 (-0.377931) \| 0.005275 / 0.007607 (-0.002332) \| 0.362024 / 0.226044 (0.135980) \| 3.556721 / 2.268929 (1.287792) \| 1.961359 / 55.444624 (-53.483266) \| 1.672835 / 6.876477 (-5.203641) \| 1.814036 / 2.142072 (-0.328036) \| 0.482012 / 4.805227 (-4.323215) \| 0.099275 / 6.500664 (-6.401389) \| 0.040988 / 0.075469 (-0.034481) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.984368 / 1.841788 (-0.857420) \| 12.251555 / 8.074308 (4.177247) \| 10.645975 / 10.191392 (0.454583) \| 0.128955 / 0.680424 (-0.551468) \| 0.015355 / 0.534201 (-0.518846) \| 0.272498 / 0.579283 (-0.306785) \| 0.279342 / 0.434364 (-0.155022) \| 0.303055 / 0.540337 (-0.237282) \| 0.392437 / 1.386936 (-0.994499) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#56458fa98d2670ff6bf47a782b6f418785c017fd \"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) \| 0.009502 / 0.011353 (-0.001851) \| 0.004957 / 0.011008 (-0.006052) \| 0.111062 / 0.038508 (0.072553) \| 0.100012 / 0.023109 (0.076903) \| 0.415747 / 0.275898 (0.139849) \| 0.453910 / 0.323480 (0.130430) \| 0.006030 / 0.007986 (-0.001956) \| 0.004271 / 0.004328 (-0.000057) \| 0.088694 / 0.004250 (0.084444) \| 0.064529 / 0.037052 (0.027477) \| 0.414999 / 0.258489 (0.156510) \| 0.477115 / 0.293841 (0.183274) \| 0.047565 / 0.128546 (-0.080982) \| 0.013352 / 0.075646 (-0.062294) \| 0.367948 / 0.419271 (-0.051324) \| 0.067577 / 0.043533 (0.024044) \| 0.405107 / 0.255139 (0.149968) \| 0.430281 / 0.283200 (0.147081) \| 0.041629 / 0.141683 (-0.100054) \| 1.784746 / 1.452155 (0.332591) \| 1.901539 / 1.492716 (0.408822) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.308456 / 0.018006 (0.290450) \| 0.623253 / 0.000490 (0.622763) \| 0.014966 / 0.000200 (0.014766) \| 0.000393 / 0.000054 (0.000338) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031538 / 0.037411 (-0.005873) \| 0.100321 / 0.014526 (0.085796) \| 0.112788 / 0.176557 (-0.063769) \| 0.180998 / 0.737135 (-0.556138) \| 0.111589 / 0.296338 (-0.184750) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.603121 / 0.215209 (0.387912) \| 5.769795 / 2.077655 (3.692140) \| 2.501168 / 1.504120 (0.997048) \| 2.240982 / 1.541195 (0.699787) \| 2.333123 / 1.468490 (0.864633) \| 0.799246 / 4.584777 (-3.785531) \| 5.148529 / 3.745712 (1.402817) \| 4.737782 / 5.269862 (-0.532080) \| 3.003032 / 4.565676 (-1.562644) \| 0.087457 / 0.424275 (-0.336818) \| 0.008777 / 0.007607 (0.001170) \| 0.692961 / 0.226044 (0.466916) \| 7.235537 / 2.268929 (4.966608) \| 3.464074 / 55.444624 (-51.980551) \| 2.817360 / 6.876477 (-4.059116) \| 2.903121 / 2.142072 (0.761049) \| 1.026150 / 4.805227 (-3.779077) \| 0.231814 / 6.500664 (-6.268850) \| 0.088358 / 0.075469 (0.012888) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.527889 / 1.841788 (-0.313898) \| 24.374770 / 8.074308 (16.300462) \| 21.720415 / 10.191392 (11.529023) \| 0.209357 / 0.680424 (-0.471067) \| 0.027587 / 0.534201 (-0.506614) \| 0.479136 / 0.579283 (-0.100147) \| 0.573005 / 0.434364 (0.138641) \| 0.537713 / 0.540337 (-0.002625) \| 0.753628 / 1.386936 (-0.633308) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009724 / 0.011353 (-0.001629) \| 0.004798 / 0.011008 (-0.006210) \| 0.076423 / 0.038508 (0.037915) \| 0.085693 / 0.023109 (0.062584) \| 0.446864 / 0.275898 (0.170966) \| 0.482700 / 0.323480 (0.159220) \| 0.006448 / 0.007986 (-0.001537) \| 0.004451 / 0.004328 (0.000122) \| 0.078295 / 0.004250 (0.074045) \| 0.061940 / 0.037052 (0.024888) \| 0.446091 / 0.258489 (0.187601) \| 0.478567 / 0.293841 (0.184726) \| 0.047206 / 0.128546 (-0.081340) \| 0.012608 / 0.075646 (-0.063038) \| 0.089719 / 0.419271 (-0.329552) \| 0.057791 / 0.043533 (0.014258) \| 0.438357 / 0.255139 (0.183218) \| 0.475060 / 0.283200 (0.191860) \| 0.035466 / 0.141683 (-0.106216) \| 1.691982 / 1.452155 (0.239827) \| 1.773834 / 1.492716 (0.281118) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290053 / 0.018006 (0.272047) \| 0.595465 / 0.000490 (0.594976) \| 0.007531 / 0.000200 (0.007331) \| 0.000179 / 0.000054 (0.000124) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034625 / 0.037411 (-0.002786) \| 0.098725 / 0.014526 (0.084200) \| 0.111248 / 0.176557 (-0.065308) \| 0.172113 / 0.737135 (-0.565022) \| 0.111299 / 0.296338 (-0.185040) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.581773 / 0.215209 (0.366564) \| 6.150993 / 2.077655 (4.073338) \| 2.761099 / 1.504120 (1.256980) \| 2.431459 / 1.541195 (0.890264) \| 2.501471 / 1.468490 (1.032981) \| 0.805751 / 4.584777 (-3.779026) \| 5.375406 / 3.745712 (1.629693) \| 4.829323 / 5.269862 (-0.440538) \| 3.095235 / 4.565676 (-1.470442) \| 0.103336 / 0.424275 (-0.320939) \| 0.012678 / 0.007607 (0.005071) \| 0.730121 / 0.226044 (0.504077) \| 7.272025 / 2.268929 (5.003097) \| 3.607889 / 55.444624 (-51.836735) \| 2.904797 / 6.876477 (-3.971680) \| 3.179139 / 2.142072 (1.037067) \| 0.997510 / 4.805227 (-3.807717) \| 0.219023 / 6.500664 (-6.281641) \| 0.076680 / 0.075469 (0.001211) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.712838 / 1.841788 (-0.128950) \| 24.242240 / 8.074308 (16.167932) \| 19.746825 / 10.191392 (9.555433) \| 0.234590 / 0.680424 (-0.445833) \| 0.032015 / 0.534201 (-0.502186) \| 0.462554 / 0.579283 (-0.116729) \| 0.604529 / 0.434364 (0.170165) \| 0.537779 / 0.540337 (-0.002558) \| 0.777386 / 1.386936 (-0.609550) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#89c1b13d85b8971925440deb84f558e23c224a47 \"CML watermark\")\n", "Cool ! Nice to simplify 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) \| 0.004659 / 0.011353 (-0.006693) \| 0.002672 / 0.011008 (-0.008337) \| 0.062385 / 0.038508 (0.023877) \| 0.030581 / 0.023109 (0.007471) \| 0.243210 / 0.275898 (-0.032688) \| 0.271441 / 0.323480 (-0.052039) \| 0.002909 / 0.007986 (-0.005076) \| 0.002371 / 0.004328 (-0.001957) \| 0.049213 / 0.004250 (0.044962) \| 0.043952 / 0.037052 (0.006900) \| 0.250257 / 0.258489 (-0.008232) \| 0.280470 / 0.293841 (-0.013371) \| 0.023048 / 0.128546 (-0.105499) \| 0.006893 / 0.075646 (-0.068754) \| 0.204026 / 0.419271 (-0.215245) \| 0.054067 / 0.043533 (0.010534) \| 0.248730 / 0.255139 (-0.006409) \| 0.272325 / 0.283200 (-0.010874) \| 0.019028 / 0.141683 (-0.122655) \| 1.103477 / 1.452155 (-0.348678) \| 1.185775 / 1.492716 (-0.306942) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097295 / 0.018006 (0.079289) \| 0.302997 / 0.000490 (0.302507) \| 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) \| 0.018653 / 0.037411 (-0.018759) \| 0.062604 / 0.014526 (0.048079) \| 0.075652 / 0.176557 (-0.100904) \| 0.121298 / 0.737135 (-0.615838) \| 0.074129 / 0.296338 (-0.222209) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283315 / 0.215209 (0.068106) \| 2.833975 / 2.077655 (0.756320) \| 1.463877 / 1.504120 (-0.040243) \| 1.352197 / 1.541195 (-0.188998) \| 1.337623 / 1.468490 (-0.130867) \| 0.405282 / 4.584777 (-4.179495) \| 2.371381 / 3.745712 (-1.374331) \| 2.584853 / 5.269862 (-2.685009) \| 1.565902 / 4.565676 (-2.999775) \| 0.046398 / 0.424275 (-0.377877) \| 0.004795 / 0.007607 (-0.002812) \| 0.345949 / 0.226044 (0.119905) \| 3.326662 / 2.268929 (1.057733) \| 1.778394 / 55.444624 (-53.666230) \| 1.520788 / 6.876477 (-5.355688) \| 1.526517 / 2.142072 (-0.615556) \| 0.471788 / 4.805227 (-4.333439) \| 0.099236 / 6.500664 (-6.401428) \| 0.041886 / 0.075469 (-0.033583) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.958183 / 1.841788 (-0.883605) \| 11.474476 / 8.074308 (3.400168) \| 10.547550 / 10.191392 (0.356158) \| 0.129316 / 0.680424 (-0.551108) \| 0.013969 / 0.534201 (-0.520232) \| 0.272028 / 0.579283 (-0.307255) \| 0.271027 / 0.434364 (-0.163337) \| 0.312124 / 0.540337 (-0.228214) \| 0.423879 / 1.386936 (-0.963057) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004743 / 0.011353 (-0.006610) \| 0.002724 / 0.011008 (-0.008284) \| 0.049526 / 0.038508 (0.011018) \| 0.051429 / 0.023109 (0.028319) \| 0.265202 / 0.275898 (-0.010696) \| 0.287498 / 0.323480 (-0.035981) \| 0.004034 / 0.007986 (-0.003951) \| 0.002460 / 0.004328 (-0.001868) \| 0.049367 / 0.004250 (0.045116) \| 0.038526 / 0.037052 (0.001474) \| 0.271496 / 0.258489 (0.013007) \| 0.300969 / 0.293841 (0.007128) \| 0.024159 / 0.128546 (-0.104387) \| 0.006959 / 0.075646 (-0.068687) \| 0.055316 / 0.419271 (-0.363955) \| 0.032409 / 0.043533 (-0.011124) \| 0.267524 / 0.255139 (0.012385) \| 0.284667 / 0.283200 (0.001467) \| 0.017305 / 0.141683 (-0.124378) \| 1.127560 / 1.452155 (-0.324595) \| 1.188271 / 1.492716 (-0.304445) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093587 / 0.018006 (0.075581) \| 0.301834 / 0.000490 (0.301344) \| 0.000211 / 0.000200 (0.000011) \| 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) \| 0.020899 / 0.037411 (-0.016512) \| 0.069999 / 0.014526 (0.055473) \| 0.081434 / 0.176557 (-0.095123) \| 0.120538 / 0.737135 (-0.616598) \| 0.082708 / 0.296338 (-0.213630) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291845 / 0.215209 (0.076636) \| 2.872476 / 2.077655 (0.794822) \| 1.579330 / 1.504120 (0.075210) \| 1.453083 / 1.541195 (-0.088112) \| 1.496675 / 1.468490 (0.028185) \| 0.406178 / 4.584777 (-4.178599) \| 2.434121 / 3.745712 (-1.311592) \| 2.519760 / 5.269862 (-2.750101) \| 1.535781 / 4.565676 (-3.029895) \| 0.046331 / 0.424275 (-0.377944) \| 0.004749 / 0.007607 (-0.002858) \| 0.340862 / 0.226044 (0.114817) \| 3.362750 / 2.268929 (1.093822) \| 1.924707 / 55.444624 (-53.519917) \| 1.646820 / 6.876477 (-5.229657) \| 1.630885 / 2.142072 (-0.511188) \| 0.478623 / 4.805227 (-4.326605) \| 0.098235 / 6.500664 (-6.402429) \| 0.040741 / 0.075469 (-0.034728) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.989858 / 1.841788 (-0.851929) \| 12.111035 / 8.074308 (4.036727) \| 11.065284 / 10.191392 (0.873892) \| 0.143443 / 0.680424 (-0.536981) \| 0.015873 / 0.534201 (-0.518328) \| 0.271932 / 0.579283 (-0.307351) \| 0.281440 / 0.434364 (-0.152924) \| 0.309518 / 0.540337 (-0.230819) \| 0.414701 / 1.386936 (-0.972235) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#322ee4bd7d460a5789f9991a45453b9fb5f5aed1 \"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) \| 0.005840 / 0.011353 (-0.005513) \| 0.003580 / 0.011008 (-0.007428) \| 0.079921 / 0.038508 (0.041413) \| 0.036316 / 0.023109 (0.013206) \| 0.321065 / 0.275898 (0.045167) \| 0.348594 / 0.323480 (0.025115) \| 0.004662 / 0.007986 (-0.003324) \| 0.002884 / 0.004328 (-0.001444) \| 0.062964 / 0.004250 (0.058714) \| 0.052856 / 0.037052 (0.015804) \| 0.322087 / 0.258489 (0.063598) \| 0.355546 / 0.293841 (0.061705) \| 0.027025 / 0.128546 (-0.101521) \| 0.007969 / 0.075646 (-0.067678) \| 0.261416 / 0.419271 (-0.157855) \| 0.066612 / 0.043533 (0.023079) \| 0.314631 / 0.255139 (0.059492) \| 0.340939 / 0.283200 (0.057739) \| 0.019710 / 0.141683 (-0.121972) \| 1.446068 / 1.452155 (-0.006086) \| 1.510342 / 1.492716 (0.017625) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.219742 / 0.018006 (0.201736) \| 0.431794 / 0.000490 (0.431304) \| 0.005717 / 0.000200 (0.005517) \| 0.000195 / 0.000054 (0.000141) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024486 / 0.037411 (-0.012926) \| 0.073231 / 0.014526 (0.058706) \| 0.084053 / 0.176557 (-0.092503) \| 0.145857 / 0.737135 (-0.591279) \| 0.083050 / 0.296338 (-0.213289) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.400532 / 0.215209 (0.185323) \| 3.989293 / 2.077655 (1.911638) \| 1.935520 / 1.504120 (0.431400) \| 1.754146 / 1.541195 (0.212951) \| 1.821060 / 1.468490 (0.352570) \| 0.512603 / 4.584777 (-4.072173) \| 3.070974 / 3.745712 (-0.674738) \| 2.984617 / 5.269862 (-2.285245) \| 1.875790 / 4.565676 (-2.689886) \| 0.057881 / 0.424275 (-0.366394) \| 0.006403 / 0.007607 (-0.001204) \| 0.465542 / 0.226044 (0.239498) \| 4.659589 / 2.268929 (2.390661) \| 2.349637 / 55.444624 (-53.094987) \| 2.011511 / 6.876477 (-4.864965) \| 2.071893 / 2.142072 (-0.070179) \| 0.591113 / 4.805227 (-4.214114) \| 0.125000 / 6.500664 (-6.375664) \| 0.061372 / 0.075469 (-0.014097) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.237068 / 1.841788 (-0.604720) \| 17.493192 / 8.074308 (9.418884) \| 13.600688 / 10.191392 (3.409296) \| 0.142508 / 0.680424 (-0.537916) \| 0.017305 / 0.534201 (-0.516896) \| 0.333352 / 0.579283 (-0.245931) \| 0.366699 / 0.434364 (-0.067665) \| 0.381104 / 0.540337 (-0.159233) \| 0.562645 / 1.386936 (-0.824291) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006337 / 0.011353 (-0.005016) \| 0.003584 / 0.011008 (-0.007424) \| 0.063351 / 0.038508 (0.024843) \| 0.061351 / 0.023109 (0.038242) \| 0.430690 / 0.275898 (0.154792) \| 0.462158 / 0.323480 (0.138678) \| 0.004922 / 0.007986 (-0.003064) \| 0.002898 / 0.004328 (-0.001430) \| 0.063722 / 0.004250 (0.059472) \| 0.046970 / 0.037052 (0.009918) \| 0.436340 / 0.258489 (0.177851) \| 0.472842 / 0.293841 (0.179001) \| 0.029238 / 0.128546 (-0.099309) \| 0.008079 / 0.075646 (-0.067568) \| 0.068425 / 0.419271 (-0.350846) \| 0.041272 / 0.043533 (-0.002261) \| 0.429150 / 0.255139 (0.174011) \| 0.451859 / 0.283200 (0.168659) \| 0.020135 / 0.141683 (-0.121547) \| 1.440388 / 1.452155 (-0.011767) \| 1.506784 / 1.492716 (0.014068) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.225810 / 0.018006 (0.207804) \| 0.408447 / 0.000490 (0.407957) \| 0.002484 / 0.000200 (0.002284) \| 0.000079 / 0.000054 (0.000024) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026162 / 0.037411 (-0.011250) \| 0.079292 / 0.014526 (0.064766) \| 0.091126 / 0.176557 (-0.085431) \| 0.141607 / 0.737135 (-0.595528) \| 0.090073 / 0.296338 (-0.206266) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.420689 / 0.215209 (0.205479) \| 4.207631 / 2.077655 (2.129976) \| 2.163469 / 1.504120 (0.659350) \| 2.098208 / 1.541195 (0.557013) \| 2.217340 / 1.468490 (0.748850) \| 0.502599 / 4.584777 (-4.082178) \| 3.128151 / 3.745712 (-0.617561) \| 2.921041 / 5.269862 (-2.348820) \| 1.808352 / 4.565676 (-2.757325) \| 0.057724 / 0.424275 (-0.366551) \| 0.006423 / 0.007607 (-0.001184) \| 0.490631 / 0.226044 (0.264587) \| 4.878761 / 2.268929 (2.609833) \| 2.614831 / 55.444624 (-52.829793) \| 2.214611 / 6.876477 (-4.661866) \| 2.253313 / 2.142072 (0.111241) \| 0.585643 / 4.805227 (-4.219584) \| 0.122436 / 6.500664 (-6.378228) \| 0.057974 / 0.075469 (-0.017495) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.334290 / 1.841788 (-0.507498) \| 17.778981 / 8.074308 (9.704672) \| 14.982837 / 10.191392 (4.791445) \| 0.135731 / 0.680424 (-0.544693) \| 0.018314 / 0.534201 (-0.515887) \| 0.332318 / 0.579283 (-0.246966) \| 0.380185 / 0.434364 (-0.054179) \| 0.391430 / 0.540337 (-0.148907) \| 0.554577 / 1.386936 (-0.832359) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1715b61a096cafb20caeb136111522432aba04f5 \"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) \| 0.005248 / 0.011353 (-0.006105) \| 0.003188 / 0.011008 (-0.007820) \| 0.063045 / 0.038508 (0.024537) \| 0.033620 / 0.023109 (0.010511) \| 0.244725 / 0.275898 (-0.031173) \| 0.283259 / 0.323480 (-0.040220) \| 0.003013 / 0.007986 (-0.004973) \| 0.002486 / 0.004328 (-0.001842) \| 0.048873 / 0.004250 (0.044623) \| 0.049431 / 0.037052 (0.012379) \| 0.245297 / 0.258489 (-0.013192) \| 0.283127 / 0.293841 (-0.010714) \| 0.024204 / 0.128546 (-0.104342) \| 0.007542 / 0.075646 (-0.068104) \| 0.204831 / 0.419271 (-0.214440) \| 0.067487 / 0.043533 (0.023954) \| 0.251477 / 0.255139 (-0.003662) \| 0.273108 / 0.283200 (-0.010091) \| 0.021035 / 0.141683 (-0.120648) \| 1.108361 / 1.452155 (-0.343793) \| 1.172923 / 1.492716 (-0.319793) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094729 / 0.018006 (0.076722) \| 0.301877 / 0.000490 (0.301388) \| 0.000223 / 0.000200 (0.000023) \| 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) \| 0.019901 / 0.037411 (-0.017511) \| 0.068059 / 0.014526 (0.053534) \| 0.075333 / 0.176557 (-0.101224) \| 0.123276 / 0.737135 (-0.613859) \| 0.076810 / 0.296338 (-0.219528) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283421 / 0.215209 (0.068211) \| 2.775511 / 2.077655 (0.697857) \| 1.430927 / 1.504120 (-0.073193) \| 1.317334 / 1.541195 (-0.223860) \| 1.359483 / 1.468490 (-0.109007) \| 0.403186 / 4.584777 (-4.181591) \| 2.405789 / 3.745712 (-1.339923) \| 2.773039 / 5.269862 (-2.496823) \| 1.666722 / 4.565676 (-2.898954) \| 0.047937 / 0.424275 (-0.376338) \| 0.004879 / 0.007607 (-0.002728) \| 0.347225 / 0.226044 (0.121180) \| 3.380860 / 2.268929 (1.111931) \| 1.838532 / 55.444624 (-53.606092) \| 1.597681 / 6.876477 (-5.278796) \| 1.600123 / 2.142072 (-0.541949) \| 0.478836 / 4.805227 (-4.326391) \| 0.100332 / 6.500664 (-6.400332) \| 0.043334 / 0.075469 (-0.032135) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.942591 / 1.841788 (-0.899196) \| 12.588886 / 8.074308 (4.514578) \| 11.375666 / 10.191392 (1.184274) \| 0.143460 / 0.680424 (-0.536964) \| 0.014990 / 0.534201 (-0.519211) \| 0.271068 / 0.579283 (-0.308216) \| 0.265478 / 0.434364 (-0.168885) \| 0.310914 / 0.540337 (-0.229423) \| 0.428310 / 1.386936 (-0.958626) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated 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) \| 0.003263 / 0.011008 (-0.007745) \| 0.049076 / 0.038508 (0.010567) \| 0.063665 / 0.023109 (0.040556) \| 0.270352 / 0.275898 (-0.005546) \| 0.298849 / 0.323480 (-0.024631) \| 0.004083 / 0.007986 (-0.003903) \| 0.002503 / 0.004328 (-0.001826) \| 0.048586 / 0.004250 (0.044335) \| 0.040701 / 0.037052 (0.003648) \| 0.274082 / 0.258489 (0.015593) \| 0.308279 / 0.293841 (0.014438) \| 0.024734 / 0.128546 (-0.103812) \| 0.007535 / 0.075646 (-0.068111) \| 0.054670 / 0.419271 (-0.364602) \| 0.032828 / 0.043533 (-0.010705) \| 0.276226 / 0.255139 (0.021087) \| 0.289322 / 0.283200 (0.006122) \| 0.018789 / 0.141683 (-0.122893) \| 1.279837 / 1.452155 (-0.172318) \| 1.203010 / 1.492716 (-0.289706) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095674 / 0.018006 (0.077667) \| 0.309754 / 0.000490 (0.309265) \| 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) \| 0.021733 / 0.037411 (-0.015678) \| 0.074858 / 0.014526 (0.060332) \| 0.081845 / 0.176557 (-0.094711) \| 0.121991 / 0.737135 (-0.615145) \| 0.084057 / 0.296338 (-0.212281) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298456 / 0.215209 (0.083246) \| 2.884930 / 2.077655 (0.807276) \| 1.574875 / 1.504120 (0.070755) \| 1.451598 / 1.541195 (-0.089597) \| 1.548106 / 1.468490 (0.079616) \| 0.408662 / 4.584777 (-4.176115) \| 2.444306 / 3.745712 (-1.301406) \| 2.737027 / 5.269862 (-2.532835) \| 1.633085 / 4.565676 (-2.932592) \| 0.047349 / 0.424275 (-0.376926) \| 0.004864 / 0.007607 (-0.002744) \| 0.355434 / 0.226044 (0.129389) \| 3.495531 / 2.268929 (1.226603) \| 1.972737 / 55.444624 (-53.471888) \| 1.706973 / 6.876477 (-5.169504) \| 1.798985 / 2.142072 (-0.343087) \| 0.490353 / 4.805227 (-4.314874) \| 0.099533 / 6.500664 (-6.401131) \| 0.042397 / 0.075469 (-0.033073) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.978092 / 1.841788 (-0.863696) \| 13.166220 / 8.074308 (5.091912) \| 11.673518 / 10.191392 (1.482126) \| 0.134253 / 0.680424 (-0.546171) \| 0.016478 / 0.534201 (-0.517723) \| 0.271629 / 0.579283 (-0.307654) \| 0.284082 / 0.434364 (-0.150282) \| 0.313352 / 0.540337 (-0.226986) \| 0.416913 / 1.386936 (-0.970023) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#01e7144a02825fea3418872c51a8ca93950f3080 \"CML watermark\")\n" ]
1,965,672,950	Add support for `Sequence(Audio/Image)` feature in `push_to_hub`	closed	### Feature request Allow for `Sequence` of `Image` (or `Audio`) to be embedded inside the shards. ### Motivation Currently, thanks to #3685, when `embed_external_files` is set to True (which is the default) in `push_to_hub`, features of type `Image` and `Audio` are embedded inside the arrow/parquet shards, instead of only storing paths to the files. I've noticed that this behavior does not extend to `Sequence` of `Image`, when working with a [dataset of timelapse images](https://huggingface.co/datasets/1aurent/Human-Embryo-Timelapse). ### Your contribution I'll submit a PR if I find a way to add this feature	2023-10-27T14:39:57	2024-02-06T19:24:20	2024-02-06T19:24:20	https://github.com/huggingface/datasets/issues/6360	null	6,360	false	[ "This issue stems from https://github.com/huggingface/datasets/blob/6d2f2a5e0fea3827eccfd1717d8021c15fc4292a/src/datasets/table.py#L2203-L2205\r\n\r\nI'll address it as part of https://github.com/huggingface/datasets/pull/6283.\r\n\r\nIn the meantime, this should work\r\n\r\n```python\r\nimport pyarrow as pa\r\nfrom datasets import Image\r\n\r\ndataset = dataset.with_format(\"arrow\")\r\n\r\ndef embed_images(pa_table):\r\n images_arr = pa.chunked_array(\r\n [\r\n pa.ListArray.from_arrays(chunk.offsets, Image().embed_storage(chunk.values), mask=chunk.is_null())\r\n for chunk in pa_table[\"images\"].chunks\r\n ]\r\n )\r\n return pa_table.set_column(pa_table.schema.get_field_index(\"images\"), \"images\", images_arr)\r\n\r\ndataset = dataset.map(embed_images, batched=True)\r\n\r\ndataset = dataset.with_format(\"python\")\r\n\r\ndataset.push_to_hub(...)\r\n```" ]
1,965,378,583	Stuck in "Resolving data files..."	open	### Describe the bug I have an image dataset with 300k images, the size of image is 768 * 768. When I run `dataset = load_dataset("imagefolder", data_dir="/path/to/img_dir", split='train')` in second time, it takes 50 minutes to finish "Resolving data files" part, what's going on in this part? From my understand, after Arrow files been created in the first run, the second run should not take time longer than one or two minutes. ### Steps to reproduce the bug # Run following code two times dataset = load_dataset("imagefolder", data_dir="/path/to/img_dir", split='train') ### Expected behavior Fast dataset building ### Environment info - `datasets` version: 2.14.5 - Platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.35 - Python version: 3.10.11 - Huggingface_hub version: 0.17.3 - PyArrow version: 10.0.1 - Pandas version: 1.5.3	2023-10-27T12:01:51	2025-03-09T02:18:19	null	https://github.com/huggingface/datasets/issues/6359	null	6,359	false	[ "Most likely, the data file inference logic is the problem here.\r\n\r\nYou can run the following code to verify this:\r\n```python\r\nimport time\r\nfrom datasets.data_files import get_data_patterns\r\nstart_time = time.time()\r\nget_data_patterns(\"/path/to/img_dir\")\r\nend_time = time.time()\r\nprint(f\"Elapsed time: {end_time - start_time:.2f}s\")\r\n```\r\n \r\nWe plan to optimize this for the next version (or version after that). In the meantime, specifying the split patterns manually should give better performance:\r\n```python\r\nds = load_dataset(\"imagefolder\", data_files={\"train\": \"path/to/img_dir/train/**\", ...}, split=\"train\")\r\n```", "Hi, @mariosasko, you are right; data file inference logic is extremely slow.\r\n\r\nI have done a similar test, that is I modify the source code of datasets/load.py to measure the cost of two suspicious operations:\r\n```python\r\ndef get_module(self) -> DatasetModule:\r\n base_path = Path(self.data_dir or \"\").expanduser().resolve().as_posix()\r\n start = time.time()\r\n patterns = sanitize_patterns(self.data_files) if self.data_files is not None else get_data_patterns(base_path)\r\n print(f\"patterns: {time.time() - start}\")\r\n start = time.time()\r\n data_files = DataFilesDict.from_patterns(\r\n patterns,\r\n download_config=self.download_config,\r\n base_path=base_path,\r\n )\r\n print(f\"data_files: {time.time() - start}\")\r\n```\r\nIt gaves:\r\npatterns: 3062.2050700187683\r\ndata_files: 413.9576675891876\r\n\r\nThus, these two operations contribute to almost all of load time. What's going on in them?", "Furthermore, what's my current workaround about this problem? Should I save it by `save_to_disk()` and load dataset through `load_from_disk`?", "were you able to solve this issue?, I am facing the same issue", "Still suffering from this issue. For me, I cannot download Emilia dataset, and stucked at 'Resolving data files' forever.\n``` e = load_dataset('amphion/Emilia-Dataset', token=my_token)\nResolving data files: 0%\| \| 1/4343 [00:00<11:27, 6.32it/s]\nResolving data files: 100%\|█████████████████████████████████████████████████████████████████████████████\| 4343/4343 [00:01<00:00, 3844.15it/s]\n```" ]
1,965,014,595	Mounting datasets cache fails due to absolute paths.	closed	### Describe the bug Creating a datasets cache and mounting this into, for example, a docker container, renders the data unreadable due to absolute paths written into the cache. ### Steps to reproduce the bug 1. Create a datasets cache by downloading some data 2. Mount the dataset folder into a docker container or remote system. 3. (Edit) Set `HF_HOME` or `HF_DATASET_CACHE` to point to the mounted cache. 4. Attempt to access the data from within the docker container. 5. An error is thrown saying no file exists at \<absolute path to original cache location\> ### Expected behavior The data is loaded without error ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.4.0-162-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - Huggingface_hub version: 0.16.4 - PyArrow version: 13.0.0 - Pandas version: 2.0.3	2023-10-27T08:20:27	2024-04-10T08:50:06	2023-11-28T14:47:12	https://github.com/huggingface/datasets/issues/6358	null	6,358	false	[ "You may be able to make it work by tweaking some environment variables, such as [`HF_HOME`](https://huggingface.co/docs/huggingface_hub/main/en/package_reference/environment_variables#hfhome) or [`HF_DATASETS_CACHE`](https://huggingface.co/docs/datasets/cache#cache-directory).", "> You may be able to make it work by tweaking some environment variables, such as [`HF_HOME`](https://huggingface.co/docs/huggingface_hub/main/en/package_reference/environment_variables#hfhome) or [`HF_DATASETS_CACHE`](https://huggingface.co/docs/datasets/cache#cache-directory).\r\n\r\nI am already doing this. The problem is that, while this seemingly allows flexibility, the absolute paths written into the cache still have the old cache directory. The paths written into the cache should be relative to the cache location to allow this sort of flexibility. Sorry, I omitted this in the reproduction steps, I have now added it.", "I'm unable to reproduce this with the cache\r\n```bash\r\nexport HF_CACHE=$PWD/hf_cache\r\npython -c \"import datasets; datasets.load_dataset('imdb')\"\r\n```\r\nimported inside a dummy container that is built from\r\n```bash\r\nFROM python:3.9\r\n\r\nWORKDIR /usr/src/app\r\n\r\nRUN pip install datasets\r\n\r\nCOPY ./hf_cache ./hf_cache\r\n\r\nENV HF_HOME=./hf_cache\r\nENV HF_DATASETS_OFFLINE=1\r\n\r\nCMD [\"python\"]\r\n```\r\nWhat do you mean by \"absolute paths written into the cache\"? Paths inside the HF cache paths are based on hash (hashed URL of the downloaded files, etc.)", "@mariosasko Same problem: the absolute paths written into the cache still have the old cache directory. Like:\r\n\r\n{'bytes': None, 'path': 'E:\\\\work-20240321\\\\datasets\\\\downloads\\\\extracted\\\\9752883596854dc57e01c74cc3f494b2ba63754dadd9e77f9d1932deddbd2273\\\\58f33a03-026f-4adc-b69f-b89d16b9f35a.webp'}\r\n\r\nWhen I move this cached directory to another directory, these datasets cannot be used casue path changes. So, the paths written into the cache should be relative to the cache location to allow this sort of flexibility. ", "Sorry, the reply on this thread escaped my attention. The problem with @mariosasko's attempted reproduction is the absolute path `./hf_cache` is the same in the host system and the docker container, so naturally the paths would be correct. Modifying the docker image as below should reproduce the error...\r\n\r\n```\r\nFROM python:3.9\r\n\r\nWORKDIR /usr/src/app\r\n\r\nRUN pip install datasets\r\n\r\nCOPY ./hf_cache ./my_cache/\r\n\r\nENV HF_HOME=./my_cache/\r\nENV HF_DATASETS_OFFLINE=1\r\n\r\nCMD [\"python\"]\r\n```\r\n\r\nThe paths written inside the cache will still have `./hf_cache` prefixing all the paths. If they were relative paths (relative to the top level of the cache) this would be avoided." ]
1,964,653,995	Allow passing a multiprocessing context to functions that support `num_proc`	open	### Feature request Allow specifying [a multiprocessing context](https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods) to functions that support `num_proc` or use multiprocessing pools. For example, the following could be done: ```python dataset = dataset.map(_func, num_proc=2, mp_context=multiprocess.get_context("spawn")) ``` Or at least the multiprocessing start method ("fork", "spawn", "fork_server" or `None`): ```python dataset = dataset.map(_func, num_proc=2, mp_start_method="spawn") ``` Another option could be passing the `pool` as an argument. ### Motivation By default, `multiprocess` (the `multiprocessing`-fork library that this repo uses) uses the "fork" start method for multiprocessing pools (for the default context). It could be changed by using `set_start_method`. However, this conditions the multiprocessing start method from all processing in a Python program that uses the default context, because [you can't call that function more than once](https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods:~:text=set_start_method()%20should%20not%20be%20used%20more%20than%20once%20in%20the%20program.). My proposal is to allow using a different multiprocessing context, not to condition the whole Python program. One reason to change the start method is that "fork" (the default) makes child processes likely deadlock if thread pools were created before (and also this is not supported by POSIX). For example, this happens when using PyTorch because OpenMP threads are used for CPU intra-op parallelism, which is enabled by default (e.g., for context see [`torch.set_num_threads`](https://pytorch.org/docs/stable/generated/torch.set_num_threads.html)). This can also be fixed by setting `torch.set_num_threads(1)` (or similarly by other methods) but this conditions the whole Python program as it can only be set once to guarantee its behavior (similarly to). There are noticeable performance differences when setting this number to 1 even when using GPU(s). Using, e.g., a "spawn" start method would solve this issue. For more context, see: * https://discuss.huggingface.co/t/dataset-map-stuck-with-torch-set-num-threads-set-to-2-or-larger/37984 * https://discuss.huggingface.co/t/using-num-proc-1-in-dataset-map-hangs/44310 ### Your contribution I'd be happy to review a PR that makes such a change. And if you really don't have the bandwidth for it, I'd consider creating one.	2023-10-27T02:31:16	2023-10-27T02:31:16	null	https://github.com/huggingface/datasets/issues/6357	null	6,357	false	[]
1,964,015,802	Add `fsspec` version to the `datasets-cli env` command output	closed	... to make debugging issues easier, as `fsspec`'s releases often introduce breaking changes.	2023-10-26T17:19:25	2023-10-26T18:42:56	2023-10-26T18:32:21	https://github.com/huggingface/datasets/pull/6356	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6356", "html_url": "https://github.com/huggingface/datasets/pull/6356", "diff_url": "https://github.com/huggingface/datasets/pull/6356.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6356.patch", "merged_at": "2023-10-26T18:32:21" }	6,356	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) \| 0.008775 / 0.011353 (-0.002578) \| 0.005304 / 0.011008 (-0.005704) \| 0.108912 / 0.038508 (0.070404) \| 0.075589 / 0.023109 (0.052479) \| 0.456612 / 0.275898 (0.180713) \| 0.502303 / 0.323480 (0.178823) \| 0.006695 / 0.007986 (-0.001291) \| 0.004404 / 0.004328 (0.000076) \| 0.084802 / 0.004250 (0.080552) \| 0.062711 / 0.037052 (0.025659) \| 0.465062 / 0.258489 (0.206573) \| 0.505321 / 0.293841 (0.211480) \| 0.049401 / 0.128546 (-0.079146) \| 0.014784 / 0.075646 (-0.060862) \| 0.378202 / 0.419271 (-0.041069) \| 0.069826 / 0.043533 (0.026293) \| 0.461161 / 0.255139 (0.206022) \| 0.484616 / 0.283200 (0.201416) \| 0.035998 / 0.141683 (-0.105685) \| 1.846343 / 1.452155 (0.394189) \| 1.999439 / 1.492716 (0.506723) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.317779 / 0.018006 (0.299773) \| 0.605967 / 0.000490 (0.605477) \| 0.011412 / 0.000200 (0.011212) \| 0.000410 / 0.000054 (0.000356) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031118 / 0.037411 (-0.006293) \| 0.095425 / 0.014526 (0.080900) \| 0.108002 / 0.176557 (-0.068554) \| 0.184625 / 0.737135 (-0.552511) \| 0.108180 / 0.296338 (-0.188159) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.587497 / 0.215209 (0.372288) \| 5.818632 / 2.077655 (3.740977) \| 2.629776 / 1.504120 (1.125656) \| 2.266129 / 1.541195 (0.724934) \| 2.324618 / 1.468490 (0.856128) \| 0.830049 / 4.584777 (-3.754728) \| 5.380062 / 3.745712 (1.634350) \| 4.808525 / 5.269862 (-0.461336) \| 2.960368 / 4.565676 (-1.605309) \| 0.093637 / 0.424275 (-0.330638) \| 0.009187 / 0.007607 (0.001580) \| 0.703468 / 0.226044 (0.477424) \| 6.924509 / 2.268929 (4.655580) \| 3.380582 / 55.444624 (-52.064043) \| 2.689118 / 6.876477 (-4.187358) \| 2.712418 / 2.142072 (0.570345) \| 1.017144 / 4.805227 (-3.788084) \| 0.212874 / 6.500664 (-6.287791) \| 0.080053 / 0.075469 (0.004584) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.623663 / 1.841788 (-0.218125) \| 23.668872 / 8.074308 (15.594564) \| 20.245972 / 10.191392 (10.054580) \| 0.236448 / 0.680424 (-0.443976) \| 0.029730 / 0.534201 (-0.504470) \| 0.491525 / 0.579283 (-0.087758) \| 0.593780 / 0.434364 (0.159416) \| 0.548776 / 0.540337 (0.008438) \| 0.799370 / 1.386936 (-0.587566) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009714 / 0.011353 (-0.001639) \| 0.005328 / 0.011008 (-0.005681) \| 0.078460 / 0.038508 (0.039952) \| 0.077791 / 0.023109 (0.054682) \| 0.510124 / 0.275898 (0.234226) \| 0.547769 / 0.323480 (0.224289) \| 0.006868 / 0.007986 (-0.001118) \| 0.004145 / 0.004328 (-0.000183) \| 0.088696 / 0.004250 (0.084445) \| 0.072387 / 0.037052 (0.035334) \| 0.527373 / 0.258489 (0.268884) \| 0.561948 / 0.293841 (0.268107) \| 0.049769 / 0.128546 (-0.078777) \| 0.014401 / 0.075646 (-0.061246) \| 0.097541 / 0.419271 (-0.321731) \| 0.062237 / 0.043533 (0.018705) \| 0.531001 / 0.255139 (0.275862) \| 0.561797 / 0.283200 (0.278597) \| 0.038482 / 0.141683 (-0.103201) \| 1.783558 / 1.452155 (0.331404) \| 1.864339 / 1.492716 (0.371622) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289389 / 0.018006 (0.271383) \| 0.595326 / 0.000490 (0.594836) \| 0.004583 / 0.000200 (0.004383) \| 0.000114 / 0.000054 (0.000060) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034492 / 0.037411 (-0.002919) \| 0.102934 / 0.014526 (0.088409) \| 0.121689 / 0.176557 (-0.054868) \| 0.182121 / 0.737135 (-0.555015) \| 0.127087 / 0.296338 (-0.169252) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.645726 / 0.215209 (0.430517) \| 6.462235 / 2.077655 (4.384580) \| 3.044176 / 1.504120 (1.540056) \| 2.731181 / 1.541195 (1.189986) \| 2.805508 / 1.468490 (1.337018) \| 0.846324 / 4.584777 (-3.738453) \| 5.341074 / 3.745712 (1.595362) \| 4.687111 / 5.269862 (-0.582751) \| 3.035472 / 4.565676 (-1.530205) \| 0.099193 / 0.424275 (-0.325082) \| 0.008825 / 0.007607 (0.001218) \| 0.795102 / 0.226044 (0.569058) \| 7.895770 / 2.268929 (5.626842) \| 3.826752 / 55.444624 (-51.617873) \| 3.112217 / 6.876477 (-3.764259) \| 3.526878 / 2.142072 (1.384806) \| 1.011352 / 4.805227 (-3.793875) \| 0.213424 / 6.500664 (-6.287240) \| 0.076228 / 0.075469 (0.000759) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.805232 / 1.841788 (-0.036556) \| 24.049100 / 8.074308 (15.974792) \| 23.056011 / 10.191392 (12.864619) \| 0.261656 / 0.680424 (-0.418767) \| 0.032021 / 0.534201 (-0.502179) \| 0.483829 / 0.579283 (-0.095454) \| 0.602208 / 0.434364 (0.167844) \| 0.565848 / 0.540337 (0.025511) \| 0.818678 / 1.386936 (-0.568258) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#71fc5e2ca41f5f725b9117f4cf99f348534902f3 \"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) \| 0.008043 / 0.011353 (-0.003310) \| 0.004642 / 0.011008 (-0.006366) \| 0.102592 / 0.038508 (0.064084) \| 0.099508 / 0.023109 (0.076399) \| 0.377692 / 0.275898 (0.101794) \| 0.409929 / 0.323480 (0.086450) \| 0.006363 / 0.007986 (-0.001622) \| 0.003881 / 0.004328 (-0.000447) \| 0.076636 / 0.004250 (0.072386) \| 0.067021 / 0.037052 (0.029969) \| 0.371454 / 0.258489 (0.112964) \| 0.423637 / 0.293841 (0.129796) \| 0.038632 / 0.128546 (-0.089914) \| 0.010055 / 0.075646 (-0.065591) \| 0.352021 / 0.419271 (-0.067251) \| 0.064988 / 0.043533 (0.021456) \| 0.369614 / 0.255139 (0.114475) \| 0.396972 / 0.283200 (0.113773) \| 0.028866 / 0.141683 (-0.112817) \| 1.757620 / 1.452155 (0.305465) \| 1.886283 / 1.492716 (0.393567) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.257579 / 0.018006 (0.239572) \| 0.529859 / 0.000490 (0.529369) \| 0.011720 / 0.000200 (0.011520) \| 0.000455 / 0.000054 (0.000401) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034163 / 0.037411 (-0.003248) \| 0.101422 / 0.014526 (0.086896) \| 0.114858 / 0.176557 (-0.061698) \| 0.180265 / 0.737135 (-0.556870) \| 0.116034 / 0.296338 (-0.180305) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.477609 / 0.215209 (0.262400) \| 4.830116 / 2.077655 (2.752461) \| 2.323844 / 1.504120 (0.819724) \| 2.174496 / 1.541195 (0.633301) \| 2.268594 / 1.468490 (0.800104) \| 0.612429 / 4.584777 (-3.972348) \| 4.265277 / 3.745712 (0.519565) \| 4.095741 / 5.269862 (-1.174121) \| 2.561532 / 4.565676 (-2.004144) \| 0.068043 / 0.424275 (-0.356233) \| 0.009139 / 0.007607 (0.001532) \| 0.545512 / 0.226044 (0.319467) \| 5.456403 / 2.268929 (3.187475) \| 2.778937 / 55.444624 (-52.665688) \| 2.428560 / 6.876477 (-4.447917) \| 2.557483 / 2.142072 (0.415411) \| 0.696721 / 4.805227 (-4.108506) \| 0.157217 / 6.500664 (-6.343447) \| 0.071334 / 0.075469 (-0.004135) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.617755 / 1.841788 (-0.224032) \| 23.368508 / 8.074308 (15.294200) \| 17.028591 / 10.191392 (6.837199) \| 0.195881 / 0.680424 (-0.484542) \| 0.021788 / 0.534201 (-0.512413) \| 0.468484 / 0.579283 (-0.110799) \| 0.474604 / 0.434364 (0.040240) \| 0.544738 / 0.540337 (0.004400) \| 0.771722 / 1.386936 (-0.615214) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007939 / 0.011353 (-0.003414) \| 0.004684 / 0.011008 (-0.006324) \| 0.077273 / 0.038508 (0.038765) \| 0.088763 / 0.023109 (0.065654) \| 0.489178 / 0.275898 (0.213280) \| 0.531547 / 0.323480 (0.208067) \| 0.006214 / 0.007986 (-0.001772) \| 0.003988 / 0.004328 (-0.000340) \| 0.076685 / 0.004250 (0.072434) \| 0.066628 / 0.037052 (0.029576) \| 0.497153 / 0.258489 (0.238664) \| 0.538301 / 0.293841 (0.244460) \| 0.037939 / 0.128546 (-0.090607) \| 0.010054 / 0.075646 (-0.065592) \| 0.084642 / 0.419271 (-0.334629) \| 0.057140 / 0.043533 (0.013608) \| 0.487701 / 0.255139 (0.232562) \| 0.519676 / 0.283200 (0.236477) \| 0.026560 / 0.141683 (-0.115123) \| 1.809676 / 1.452155 (0.357521) \| 1.864884 / 1.492716 (0.372168) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.259005 / 0.018006 (0.240998) \| 0.522900 / 0.000490 (0.522410) \| 0.006885 / 0.000200 (0.006685) \| 0.000156 / 0.000054 (0.000102) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.039838 / 0.037411 (0.002426) \| 0.117777 / 0.014526 (0.103251) \| 0.129189 / 0.176557 (-0.047368) \| 0.198584 / 0.737135 (-0.538552) \| 0.129753 / 0.296338 (-0.166586) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.543366 / 0.215209 (0.328157) \| 5.241502 / 2.077655 (3.163847) \| 2.719079 / 1.504120 (1.214959) \| 2.525337 / 1.541195 (0.984142) \| 2.648908 / 1.468490 (1.180418) \| 0.589239 / 4.584777 (-3.995538) \| 4.379856 / 3.745712 (0.634144) \| 4.139919 / 5.269862 (-1.129943) \| 2.633412 / 4.565676 (-1.932264) \| 0.074582 / 0.424275 (-0.349693) \| 0.009106 / 0.007607 (0.001499) \| 0.635540 / 0.226044 (0.409495) \| 6.072965 / 2.268929 (3.804037) \| 3.327233 / 55.444624 (-52.117391) \| 3.012637 / 6.876477 (-3.863840) \| 3.113226 / 2.142072 (0.971154) \| 0.712705 / 4.805227 (-4.092523) \| 0.159550 / 6.500664 (-6.341114) \| 0.073446 / 0.075469 (-0.002023) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.718732 / 1.841788 (-0.123055) \| 23.249445 / 8.074308 (15.175137) \| 17.630643 / 10.191392 (7.439251) \| 0.201017 / 0.680424 (-0.479407) \| 0.024162 / 0.534201 (-0.510039) \| 0.475054 / 0.579283 (-0.104229) \| 0.492348 / 0.434364 (0.057985) \| 0.587118 / 0.540337 (0.046781) \| 0.777462 / 1.386936 (-0.609474) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#feb036956a592b9a9ecdf048cc801549f233dbef \"CML watermark\")\n" ]
1,963,979,896	More hub centric docs	closed	Let's have more hub-centric documentation in the datasets docs Tutorials - Add “Configure the dataset viewer” page - Change order: - Overview - and more focused on the Hub rather than the library - Then all the hub related things - and mention how to read/write with other tools like pandas - Then all the datasets lib related things in a subsection Also: - Rename “know your dataset” page to “Explore your dataset” - Remove “Evaluate Predictions” page since it's 'evaluate' stuff (or move to legacy section ?) TODO: - [ ] write the “Configure the dataset viewer” page	2023-10-26T16:54:46	2024-01-11T06:34:16	2023-10-30T17:32:57	https://github.com/huggingface/datasets/pull/6355	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6355", "html_url": "https://github.com/huggingface/datasets/pull/6355", "diff_url": "https://github.com/huggingface/datasets/pull/6355.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6355.patch", "merged_at": null }	6,355	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) \| 0.006941 / 0.011353 (-0.004412) \| 0.004255 / 0.011008 (-0.006753) \| 0.085237 / 0.038508 (0.046729) \| 0.080962 / 0.023109 (0.057853) \| 0.312016 / 0.275898 (0.036118) \| 0.353161 / 0.323480 (0.029681) \| 0.005756 / 0.007986 (-0.002230) \| 0.003591 / 0.004328 (-0.000738) \| 0.065416 / 0.004250 (0.061166) \| 0.057837 / 0.037052 (0.020785) \| 0.316169 / 0.258489 (0.057680) \| 0.372345 / 0.293841 (0.078504) \| 0.031958 / 0.128546 (-0.096588) \| 0.008798 / 0.075646 (-0.066848) \| 0.294764 / 0.419271 (-0.124507) \| 0.053954 / 0.043533 (0.010421) \| 0.310961 / 0.255139 (0.055822) \| 0.330063 / 0.283200 (0.046864) \| 0.025298 / 0.141683 (-0.116385) \| 1.454715 / 1.452155 (0.002560) \| 1.557915 / 1.492716 (0.065198) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.274830 / 0.018006 (0.256824) \| 0.565890 / 0.000490 (0.565400) \| 0.009242 / 0.000200 (0.009042) \| 0.000321 / 0.000054 (0.000266) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031092 / 0.037411 (-0.006320) \| 0.087558 / 0.014526 (0.073033) \| 0.103395 / 0.176557 (-0.073162) \| 0.160078 / 0.737135 (-0.577057) \| 0.102356 / 0.296338 (-0.193983) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.402912 / 0.215209 (0.187703) \| 4.029374 / 2.077655 (1.951719) \| 2.048237 / 1.504120 (0.544117) \| 1.887470 / 1.541195 (0.346276) \| 1.994807 / 1.468490 (0.526316) \| 0.491109 / 4.584777 (-4.093668) \| 3.645059 / 3.745712 (-0.100653) \| 3.516376 / 5.269862 (-1.753486) \| 2.103267 / 4.565676 (-2.462409) \| 0.058072 / 0.424275 (-0.366203) \| 0.007796 / 0.007607 (0.000189) \| 0.480544 / 0.226044 (0.254499) \| 4.795422 / 2.268929 (2.526494) \| 2.507770 / 55.444624 (-52.936854) \| 2.187106 / 6.876477 (-4.689371) \| 2.271005 / 2.142072 (0.128933) \| 0.585376 / 4.805227 (-4.219851) \| 0.134741 / 6.500664 (-6.365923) \| 0.060684 / 0.075469 (-0.014785) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.264349 / 1.841788 (-0.577439) \| 19.448735 / 8.074308 (11.374427) \| 14.521197 / 10.191392 (4.329805) \| 0.167295 / 0.680424 (-0.513129) \| 0.018352 / 0.534201 (-0.515849) \| 0.396345 / 0.579283 (-0.182938) \| 0.418690 / 0.434364 (-0.015674) \| 0.469703 / 0.540337 (-0.070635) \| 0.637852 / 1.386936 (-0.749084) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006939 / 0.011353 (-0.004414) \| 0.004196 / 0.011008 (-0.006812) \| 0.064719 / 0.038508 (0.026211) \| 0.077517 / 0.023109 (0.054407) \| 0.401977 / 0.275898 (0.126079) \| 0.431089 / 0.323480 (0.107609) \| 0.005624 / 0.007986 (-0.002362) \| 0.003680 / 0.004328 (-0.000649) \| 0.065817 / 0.004250 (0.061567) \| 0.058297 / 0.037052 (0.021245) \| 0.399614 / 0.258489 (0.141125) \| 0.440089 / 0.293841 (0.146248) \| 0.032492 / 0.128546 (-0.096054) \| 0.008974 / 0.075646 (-0.066672) \| 0.071311 / 0.419271 (-0.347961) \| 0.048001 / 0.043533 (0.004468) \| 0.394763 / 0.255139 (0.139624) \| 0.416754 / 0.283200 (0.133554) \| 0.023730 / 0.141683 (-0.117953) \| 1.509677 / 1.452155 (0.057522) \| 1.605711 / 1.492716 (0.112994) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.265490 / 0.018006 (0.247483) \| 0.561745 / 0.000490 (0.561255) \| 0.004616 / 0.000200 (0.004417) \| 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) \| 0.033371 / 0.037411 (-0.004040) \| 0.092763 / 0.014526 (0.078238) \| 0.108905 / 0.176557 (-0.067652) \| 0.160380 / 0.737135 (-0.576756) \| 0.106968 / 0.296338 (-0.189370) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.430268 / 0.215209 (0.215059) \| 4.299313 / 2.077655 (2.221658) \| 2.308971 / 1.504120 (0.804851) \| 2.155855 / 1.541195 (0.614661) \| 2.392698 / 1.468490 (0.924208) \| 0.498464 / 4.584777 (-4.086313) \| 3.694473 / 3.745712 (-0.051239) \| 3.409625 / 5.269862 (-1.860236) \| 2.106144 / 4.565676 (-2.459532) \| 0.058992 / 0.424275 (-0.365283) \| 0.007395 / 0.007607 (-0.000212) \| 0.511291 / 0.226044 (0.285247) \| 5.101806 / 2.268929 (2.832877) \| 2.853100 / 55.444624 (-52.591524) \| 2.527216 / 6.876477 (-4.349260) \| 2.819380 / 2.142072 (0.677308) \| 0.635155 / 4.805227 (-4.170072) \| 0.135816 / 6.500664 (-6.364848) \| 0.062056 / 0.075469 (-0.013413) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.353479 / 1.841788 (-0.488308) \| 20.318513 / 8.074308 (12.244205) \| 15.105336 / 10.191392 (4.913944) \| 0.166186 / 0.680424 (-0.514238) \| 0.020742 / 0.534201 (-0.513459) \| 0.399286 / 0.579283 (-0.179997) \| 0.431785 / 0.434364 (-0.002579) \| 0.478667 / 0.540337 (-0.061671) \| 0.654683 / 1.386936 (-0.732253) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b39d1ce0b8f231649752f28cb724971f4df1c7ae \"CML watermark\")\n", "Yea I think some of it should be in the Hub docs indeed, let me open a new PR there.\r\n\r\nThen I'll update the `datasets` docs anyway to avoid redundant stuff and add redirects instead" ]
1,963,483,324	`IterableDataset.from_spark` does not support multiple workers in pytorch `Dataloader`	open	### Describe the bug Looks like `IterableDataset.from_spark` does not support multiple workers in pytorch `Dataloader` if I'm not missing anything. Also, returns not consistent error messages, which probably depend on the nondeterministic order of worker executions Some exampes I've encountered: ``` File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 79, in __iter__ yield from self.generate_examples_fn() File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 49, in generate_fn df_with_partition_id = df.select("", pyspark.sql.functions.spark_partition_id().alias("part_id")) File "/databricks/spark/python/pyspark/instrumentation_utils.py", line 54, in wrapper logger.log_failure( File "/databricks/spark/python/pyspark/databricks/usage_logger.py", line 70, in log_failure self.logger.recordFunctionCallFailureEvent( File "/databricks/spark/python/lib/py4j-0.10.9.7-src.zip/py4j/java_gateway.py", line 1322, in __call__ return_value = get_return_value( File "/databricks/spark/python/pyspark/errors/exceptions/captured.py", line 188, in deco return f(a, *kw) File "/databricks/spark/python/lib/py4j-0.10.9.7-src.zip/py4j/protocol.py", line 342, in get_return_value return OUTPUT_CONVERTER[type](answer[2:], gateway_client) KeyError: 'c' ``` ``` File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 79, in __iter__ yield from self.generate_examples_fn() File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 49, in generate_fn df_with_partition_id = df.select("", pyspark.sql.functions.spark_partition_id().alias("part_id")) File "/databricks/spark/python/pyspark/sql/utils.py", line 162, in wrapped return f(args, kwargs) File "/databricks/spark/python/pyspark/sql/functions.py", line 4893, in spark_partition_id return _invoke_function("spark_partition_id") File "/databricks/spark/python/pyspark/sql/functions.py", line 98, in _invoke_function return Column(jf(args)) File "/databricks/spark/python/lib/py4j-0.10.9.7-src.zip/py4j/java_gateway.py", line 1322, in __call__ return_value = get_return_value( File "/databricks/spark/python/pyspark/errors/exceptions/captured.py", line 188, in deco return f(a, kw) File "/databricks/spark/python/lib/py4j-0.10.9.7-src.zip/py4j/protocol.py", line 342, in get_return_value return OUTPUT_CONVERTER[type](answer[2:], gateway_client) KeyError: 'm' ``` ``` File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 79, in __iter__ yield from self.generate_examples_fn() File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 49, in generate_fn df_with_partition_id = df.select("", pyspark.sql.functions.spark_partition_id().alias("part_id")) File "/databricks/spark/python/pyspark/sql/utils.py", line 162, in wrapped return f(args, kwargs) File "/databricks/spark/python/pyspark/sql/functions.py", line 4893, in spark_partition_id return _invoke_function("spark_partition_id") File "/databricks/spark/python/pyspark/sql/functions.py", line 97, in _invoke_function jf = _get_jvm_function(name, SparkContext._active_spark_context) File "/databricks/spark/python/pyspark/sql/functions.py", line 88, in _get_jvm_function return getattr(sc._jvm.functions, name) File "/databricks/spark/python/lib/py4j-0.10.9.7-src.zip/py4j/java_gateway.py", line 1725, in __getattr__ raise Py4JError(message) py4j.protocol.Py4JError: functions does not exist in the JVM ``` ### Steps to reproduce the bug ```python import pandas as pd import numpy as np batch_size = 16 pdf = pd.DataFrame({ key: np.random.rand(16100) for key in ['feature', 'target'] }) test_df = spark.createDataFrame(pdf) from datasets import IterableDataset from torch.utils.data import DataLoader ids = IterableDataset.from_spark(test_df) for batch in DataLoader(ids, batch_size=16, num_workers=4): for k, b in batch.items(): print(k, b.shape, sep='\t') print('\n') ``` ### Expected behavior For `num_workers` equal to 0 or 1 works fine as expected: ``` feature torch.Size([16]) target torch.Size([16]) feature torch.Size([16]) target torch.Size([16]) .... ``` Expected to support workers >1. ### Environment info Databricks 13.3 LTS ML runtime - Spark 3.4.1 pyspark==3.4.1 py4j==0.10.9.7 datasets==2.13.1 and also tested with datasets==2.14.6	2023-10-26T12:43:36	2024-12-10T14:06:06	null	https://github.com/huggingface/datasets/issues/6354	null	6,354	false	[ "I am having issues as well with this. \r\n\r\nHowever, the error I am getting is :\r\n`RuntimeError: It appears that you are attempting to reference SparkContext from a broadcast variable, action, or transformation. SparkContext can only be used on the driver, not in code that it run on workers. For more information, see SPARK-5063.`\r\n\r\nAlso did not work with pyspark==3.3.0 and py4j==0.10.9.5", "Hi, may you have some solution of this bug now?", "cc @maddiedawson if you have an idea ?" ]
1,962,646,450	load_dataset save_to_disk load_from_disk error	closed	### Describe the bug datasets version： 2.10.1 I `load_dataset `and `save_to_disk` sucessfully on windows10( and I `load_from_disk(/LLM/data/wiki)` succcesfully on windows10), and I copy the dataset `/LLM/data/wiki` into a ubuntu system, but when I `load_from_disk(/LLM/data/wiki)` on ubuntu, something weird happens: ``` load_from_disk('/LLM/data/wiki') File "/usr/local/miniconda3/lib/python3.8/site-packages/datasets/load.py", line 1874, in load_from_disk return DatasetDict.load_from_disk(dataset_path, keep_in_memory=keep_in_memory, storage_options=storage_options) File "/usr/local/miniconda3/lib/python3.8/site-packages/datasets/dataset_dict.py", line 1309, in load_from_disk dataset_dict[k] = Dataset.load_from_disk( File "/usr/local/miniconda3/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1543, in load_from_disk fs_token_paths = fsspec.get_fs_token_paths(dataset_path, storage_options=storage_options) File "/usr/local/miniconda3/lib/python3.8/site-packages/fsspec/core.py", line 610, in get_fs_token_paths chain = _un_chain(urlpath0, storage_options or {}) File "/usr/local/miniconda3/lib/python3.8/site-packages/fsspec/core.py", line 325, in _un_chain cls = get_filesystem_class(protocol) File "/usr/local/miniconda3/lib/python3.8/site-packages/fsspec/registry.py", line 232, in get_filesystem_class raise ValueError(f"Protocol not known: {protocol}") ValueError: Protocol not known: /LLM/data/wiki ``` It seems that something went wrong on the arrow file? How can I solve this , since currently I can not save_to_disk on ubuntu system ### Steps to reproduce the bug datasets version： 2.10.1 ### Expected behavior datasets version： 2.10.1 ### Environment info datasets version： 2.10.1	2023-10-26T03:47:06	2024-04-03T05:31:01	2023-10-26T10:18:04	https://github.com/huggingface/datasets/issues/6353	null	6,353	false	[ "solved.\r\nfsspec version problem", "I'm using the latest datasets and fsspec , but still got this error!\r\n\r\ndatasets : Version: 2.13.0\r\n\r\nfsspec Version: 2023.10.0\r\n\r\n```\r\nFile \"/home/guoby/app/Anaconda3-2021.05/envs/news/lib/python3.8/site-packages/datasets/load.py\", line 1892, in load_from_disk\r\n return DatasetDict.load_from_disk(dataset_path, keep_in_memory=keep_in_memory, storage_options=storage_options)\r\n File \"/home/guoby/app/Anaconda3-2021.05/envs/news/lib/python3.8/site-packages/datasets/dataset_dict.py\", line 1371, in load_from_disk\r\n dataset_dict[k] = Dataset.load_from_disk(\r\n File \"/home/guoby/app/Anaconda3-2021.05/envs/news/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 1639, in load_from_disk\r\n fs_token_paths = fsspec.get_fs_token_paths(dataset_path, storage_options=storage_options)\r\n File \"/home/guoby/app/Anaconda3-2021.05/envs/news/lib/python3.8/site-packages/fsspec/core.py\", line 610, in get_fs_token_paths\r\n chain = _un_chain(urlpath0, storage_options or {})\r\n File \"/home/guoby/app/Anaconda3-2021.05/envs/news/lib/python3.8/site-packages/fsspec/core.py\", line 325, in _un_chain\r\n cls = get_filesystem_class(protocol)\r\n File \"/home/guoby/app/Anaconda3-2021.05/envs/news/lib/python3.8/site-packages/fsspec/registry.py\", line 232, in get_filesystem_class\r\n raise ValueError(f\"Protocol not known: {protocol}\")\r\n```", "These two versions work.\r\n<img width=\"807\" alt=\"截圖 2023-11-22 下午5 55 28\" src=\"https://github.com/huggingface/datasets/assets/77866896/faa8333f-0519-4d69-b243-a8880cd7fc1f\">\r\n", "datasets==2.10.1 and fsspec==2023.6.0 also works for me.", "确实" ]
1,962,296,057	Error loading wikitext data raise NotImplementedError(f"Loading a dataset cached in a {type(self._fs).__name__} is not supported.")	closed	I was trying to load the wiki dataset, but i got this error traindata = load_dataset('wikitext', 'wikitext-2-raw-v1', split='train') File "/home/aelkordy/.conda/envs/prune_llm/lib/python3.9/site-packages/datasets/load.py", line 1804, in load_dataset ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory) File "/home/aelkordy/.conda/envs/prune_llm/lib/python3.9/site-packages/datasets/builder.py", line 1108, in as_dataset raise NotImplementedError(f"Loading a dataset cached in a {type(self._fs).__name__} is not supported.") NotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported.	2023-10-25T21:55:31	2024-03-19T16:46:22	2023-11-07T07:26:54	https://github.com/huggingface/datasets/issues/6352	null	6,352	false	[ "+1 \r\n```\r\nFound cached dataset csv (file:///home/ubuntu/.cache/huggingface/datasets/theSquarePond___csv/theSquarePond--XXXXX-bbf0a8365d693d2c/0.0.0/eea64c71ca8b46dd3f537ed218fc9bf495d5707789152eb2764f5c78fa66d59d)\r\n---------------------------------------------------------------------------\r\nNotImplementedError Traceback (most recent call last)\r\nCell In[14], line 4\r\n 1 get_ipython().system('pip install -U datasets')\r\n 3 # Load dataset from the hub\r\n----> 4 dataset = load_dataset(dataset_name)\r\n\r\nFile ~/anaconda3/envs/python38-env/lib/python3.8/site-packages/datasets/load.py:1810, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, config_kwargs)\r\n 1806 # Build dataset for splits\r\n 1807 keep_in_memory = (\r\n 1808 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)\r\n 1809 )\r\n-> 1810 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)\r\n 1811 # Rename and cast features to match task schema\r\n 1812 if task is not None:\r\n\r\nFile ~/anaconda3/envs/python38-env/lib/python3.8/site-packages/datasets/builder.py:1128, in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory)\r\n 1126 is_local = not is_remote_filesystem(self._fs)\r\n 1127 if not is_local:\r\n-> 1128 raise NotImplementedError(f\"Loading a dataset cached in a {type(self._fs).__name__} is not supported.\")\r\n 1129 if not os.path.exists(self._output_dir):\r\n 1130 raise FileNotFoundError(\r\n 1131 f\"Dataset {self.name}: could not find data in {self._output_dir}. Please make sure to call \"\r\n 1132 \"builder.download_and_prepare(), or use \"\r\n 1133 \"datasets.load_dataset() before trying to access the Dataset object.\"\r\n 1134 )\r\n\r\nNotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported.\r\n```", "+1\r\n\r\n```\r\nFound cached dataset csv ([file://C:/Users/Shady/.cache/huggingface/datasets/knkarthick___csv/knkarthick--dialogsum-cd36827d3490488d/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1](file:///C:/Users/Shady/.cache/huggingface/datasets/knkarthick___csv/knkarthick--dialogsum-cd36827d3490488d/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1))\r\n---------------------------------------------------------------------------\r\nNotImplementedError Traceback (most recent call last)\r\nCell In[38], line 3\r\n 1 huggingface_dataset_name = \"knkarthick/dialogsum\"\r\n----> 3 dataset = load_dataset(huggingface_dataset_name)\r\n\r\nFile D:\\Desktop\\Workspace\\GenAI\\genai\\lib\\site-packages\\datasets\\load.py:1804, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, config_kwargs)\r\n 1800 # Build dataset for splits\r\n 1801 keep_in_memory = (\r\n 1802 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)\r\n 1803 )\r\n-> 1804 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)\r\n 1805 # Rename and cast features to match task schema\r\n 1806 if task is not None:\r\n\r\nFile D:\\Desktop\\Workspace\\GenAI\\genai\\lib\\site-packages\\datasets\\builder.py:1108, in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory)\r\n 1106 is_local = not is_remote_filesystem(self._fs)\r\n 1107 if not is_local:\r\n-> 1108 raise NotImplementedError(f\"Loading a dataset cached in a {type(self._fs).__name__} is not supported.\")\r\n 1109 if not os.path.exists(self._output_dir):\r\n 1110 raise FileNotFoundError(\r\n 1111 f\"Dataset {self.name}: could not find data in {self._output_dir}. Please make sure to call \"\r\n 1112 \"builder.download_and_prepare(), or use \"\r\n 1113 \"datasets.load_dataset() before trying to access the Dataset object.\"\r\n 1114 )\r\n\r\nNotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported.\r\n```", "This error stems from a breaking change in `fsspec`. It has been fixed in the latest `datasets` release (`2.14.6`). Updating the installation with `pip install -U datasets` should fix the issue.\r\n", "> 此错误源于中的重大更改。此问题已在最新版本（）中修复。更新安装应该可以解决此问题。`fsspec``datasets``2.14.6``pip install -U datasets`\r\n\r\nthanks , 太好啦，刚好解决了我的问题，GPT都没解决了，终于被你搞定了", "https://stackoverflow.com/questions/77433096/notimplementederror-loading-a-dataset-cached-in-a-localfilesystem-is-not-suppor/77433141#77433141", "Fixed by:\r\n- https://github.com/huggingface/datasets/pull/6334\r\n\r\nThe fix was released in `datasets-2.14.6`.", "this is fixed in 2.15.0, but broken again in 2.17.0. Can someone verify?", "I'm on `2.17.1` and can confirm it's broken again. Downgrading to `2.16` helped.", "> 2.14.6\r\n\r\ni update the version but the error still exist \r\n", "The issue seems to persist in 2.18.0", "same problem in 2.18.0", "Which version of `fsspec` and OS are you using ?", "> Which version of `fsspec` and OS are you using ?\r\n\r\n`fsspec-2023.10.0` and Windows 10, guess fsspec version too old..." ]
1,961,982,988	Fix use_dataset.mdx	closed	The current example isn't working because it can't find `labels` inside the Dataset object. So I've added an extra step to the process. Tested and working in Colab.	2023-10-25T18:21:08	2023-10-26T17:19:49	2023-10-26T17:10:27	https://github.com/huggingface/datasets/pull/6351	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6351", "html_url": "https://github.com/huggingface/datasets/pull/6351", "diff_url": "https://github.com/huggingface/datasets/pull/6351.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6351.patch", "merged_at": "2023-10-26T17:10:27" }	6,351	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) \| 0.007718 / 0.011353 (-0.003635) \| 0.004730 / 0.011008 (-0.006278) \| 0.097262 / 0.038508 (0.058754) \| 0.077880 / 0.023109 (0.054771) \| 0.363855 / 0.275898 (0.087957) \| 0.394470 / 0.323480 (0.070990) \| 0.006416 / 0.007986 (-0.001570) \| 0.003596 / 0.004328 (-0.000732) \| 0.076494 / 0.004250 (0.072243) \| 0.062656 / 0.037052 (0.025603) \| 0.366160 / 0.258489 (0.107671) \| 0.421383 / 0.293841 (0.127542) \| 0.035756 / 0.128546 (-0.092791) \| 0.009430 / 0.075646 (-0.066217) \| 0.327722 / 0.419271 (-0.091550) \| 0.061252 / 0.043533 (0.017719) \| 0.352167 / 0.255139 (0.097028) \| 0.385166 / 0.283200 (0.101966) \| 0.026656 / 0.141683 (-0.115027) \| 1.718533 / 1.452155 (0.266378) \| 1.886646 / 1.492716 (0.393930) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.254564 / 0.018006 (0.236558) \| 0.490942 / 0.000490 (0.490452) \| 0.011656 / 0.000200 (0.011456) \| 0.000313 / 0.000054 (0.000259) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028753 / 0.037411 (-0.008659) \| 0.093076 / 0.014526 (0.078550) \| 0.096441 / 0.176557 (-0.080116) \| 0.154848 / 0.737135 (-0.582287) \| 0.092903 / 0.296338 (-0.203435) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.395611 / 0.215209 (0.180402) \| 3.860736 / 2.077655 (1.783082) \| 1.908808 / 1.504120 (0.404688) \| 1.708975 / 1.541195 (0.167781) \| 1.848173 / 1.468490 (0.379683) \| 0.527022 / 4.584777 (-4.057755) \| 3.815171 / 3.745712 (0.069459) \| 3.621132 / 5.269862 (-1.648730) \| 2.220238 / 4.565676 (-2.345439) \| 0.063169 / 0.424275 (-0.361106) \| 0.008906 / 0.007607 (0.001299) \| 0.510478 / 0.226044 (0.284433) \| 4.828116 / 2.268929 (2.559187) \| 2.340801 / 55.444624 (-53.103824) \| 2.040834 / 6.876477 (-4.835642) \| 2.092316 / 2.142072 (-0.049757) \| 0.579194 / 4.805227 (-4.226033) \| 0.135525 / 6.500664 (-6.365139) \| 0.062720 / 0.075469 (-0.012749) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.393091 / 1.841788 (-0.448697) \| 19.751526 / 8.074308 (11.677218) \| 14.161795 / 10.191392 (3.970403) \| 0.163340 / 0.680424 (-0.517084) \| 0.021504 / 0.534201 (-0.512697) \| 0.393183 / 0.579283 (-0.186100) \| 0.448407 / 0.434364 (0.014043) \| 0.504169 / 0.540337 (-0.036169) \| 0.663698 / 1.386936 (-0.723238) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007390 / 0.011353 (-0.003962) \| 0.004381 / 0.011008 (-0.006628) \| 0.074501 / 0.038508 (0.035993) \| 0.078242 / 0.023109 (0.055133) \| 0.481108 / 0.275898 (0.205210) \| 0.512111 / 0.323480 (0.188631) \| 0.006280 / 0.007986 (-0.001705) \| 0.003820 / 0.004328 (-0.000509) \| 0.071602 / 0.004250 (0.067351) \| 0.068359 / 0.037052 (0.031307) \| 0.478484 / 0.258489 (0.219995) \| 0.519543 / 0.293841 (0.225702) \| 0.036211 / 0.128546 (-0.092335) \| 0.009433 / 0.075646 (-0.066213) \| 0.086140 / 0.419271 (-0.333132) \| 0.054177 / 0.043533 (0.010644) \| 0.466726 / 0.255139 (0.211587) \| 0.514085 / 0.283200 (0.230885) \| 0.026729 / 0.141683 (-0.114954) \| 1.743770 / 1.452155 (0.291615) \| 1.833469 / 1.492716 (0.340753) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.251339 / 0.018006 (0.233333) \| 0.472294 / 0.000490 (0.471804) \| 0.013381 / 0.000200 (0.013181) \| 0.000117 / 0.000054 (0.000062) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037845 / 0.037411 (0.000433) \| 0.105977 / 0.014526 (0.091451) \| 0.124446 / 0.176557 (-0.052111) \| 0.180432 / 0.737135 (-0.556703) \| 0.120844 / 0.296338 (-0.175495) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.470928 / 0.215209 (0.255719) \| 4.738154 / 2.077655 (2.660499) \| 2.558618 / 1.504120 (1.054498) \| 2.359745 / 1.541195 (0.818550) \| 2.458438 / 1.468490 (0.989948) \| 0.548580 / 4.584777 (-4.036197) \| 3.912145 / 3.745712 (0.166433) \| 3.764174 / 5.269862 (-1.505687) \| 2.325265 / 4.565676 (-2.240411) \| 0.078022 / 0.424275 (-0.346254) \| 0.008279 / 0.007607 (0.000672) \| 0.571635 / 0.226044 (0.345590) \| 5.672445 / 2.268929 (3.403517) \| 2.760577 / 55.444624 (-52.684047) \| 2.544229 / 6.876477 (-4.332248) \| 2.537509 / 2.142072 (0.395436) \| 0.609858 / 4.805227 (-4.195369) \| 0.131053 / 6.500664 (-6.369611) \| 0.056433 / 0.075469 (-0.019036) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.567231 / 1.841788 (-0.274556) \| 21.415586 / 8.074308 (13.341278) \| 15.982328 / 10.191392 (5.790936) \| 0.167648 / 0.680424 (-0.512776) \| 0.023562 / 0.534201 (-0.510639) \| 0.477307 / 0.579283 (-0.101976) \| 0.471929 / 0.434364 (0.037566) \| 0.549996 / 0.540337 (0.009659) \| 0.753927 / 1.386936 (-0.633009) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1fb2785be9198997e8b9006225b0e231f4d8ed31 \"CML watermark\")\n" ]
1,961,869,203	Different objects are returned from calls that should be returning the same kind of object.	open	### Describe the bug 1. dataset = load_dataset("togethercomputer/RedPajama-Data-1T-Sample", cache_dir=training_args.cache_dir, split='train[:1%]') 2. dataset = load_dataset("togethercomputer/RedPajama-Data-1T-Sample", cache_dir=training_args.cache_dir) The only difference I would expect these calls to have is the size of the dataset. But, while 2. returns a dictionary with "train" key in it, 1. returns a dataset WITHOUT any initial "train" keyword. Both calls are to be used within exactly the same context. They should return identically structured datasets of different size. ### Steps to reproduce the bug See above. ### Expected behavior Expect both calls to return the same structured Dataset structure but with different number of elements, i.e. call 1. should have 1% of the data of the call 2.0 ### Environment info Ubuntu 20.04 gcc 9.x.x. It is really irrelevant.	2023-10-25T17:08:39	2023-10-26T21:03:06	null	https://github.com/huggingface/datasets/issues/6350	null	6,350	false	[ "`load_dataset` returns a `DatasetDict` object unless `split` is defined, in which case it returns a `Dataset` (or a list of datasets if `split` is a list). We've discussed dropping `DatasetDict` from the API in https://github.com/huggingface/datasets/issues/5189 to always return the same type in `load_dataset` and support datasets without (explicit) splits. IIRC the main discussion point is deciding what to return when loading a dataset with multiple splits, but `split` is not specified. What would you expect as a return value in that scenario?", "> `load_dataset` returns a `DatasetDict` object unless `split` is defined, in which case it returns a `Dataset` (or a list of datasets if `split` is a list). We've discussed dropping `DatasetDict` from the API in #5189 to always return the same type in `load_dataset` and support datasets without (explicit) splits. IIRC the main discussion point is deciding what to return when loading a dataset with multiple splits, but `split` is not specified. What would you expect as a return value in that scenario?\r\n\r\nWouldn't a dataset with multiple splits already have keys and their related data arrays?\r\n\r\nLets say the dataset has \"train\" : trainset, \"valid\": validset and \"test\": testset\r\n\r\nSo a dictionary can be returned,, i.e.\r\n\r\n{ \r\n\"train\": trainset,\r\n\"valid\": validset,\r\n\"test\": testset\r\n}\r\n\r\nif a split is provided split=['train[:80%]', 'valid[80%:90%]', 'test[90%:100%]']\r\n\r\nwould also return the same dictionary as above.\r\n\r\nsplit='train[:10%]' should return the same value as split=['train[:10%]']\r\n\r\n{\r\n\"train\": trainset\r\n}\r\n " ]
1,961,435,673	Can't load ds = load_dataset("imdb")	closed	### Describe the bug I did `from datasets import load_dataset, load_metric` and then `ds = load_dataset("imdb")` and it gave me the error: ExpectedMoreDownloadedFiles: {'http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz'} I tried doing `ds = load_dataset("imdb",download_mode="force_redownload")` as well as reinstalling dataset. I still face this problem. ### Steps to reproduce the bug 1. from datasets import load_dataset, load_metric 2. ds = load_dataset("imdb") ### Expected behavior It should load and give me this when I run `ds` DatasetDict({ train: Dataset({ features: ['text', 'label'], num_rows: 25000 }) test: Dataset({ features: ['text', 'label'], num_rows: 25000 }) unsupervised: Dataset({ features: ['text', 'label'], num_rows: 50000 }) }) ### Environment info - `datasets` version: 2.14.6 - Platform: Linux-5.4.0-164-generic-x86_64-with-glibc2.17 - Python version: 3.8.18 - Huggingface_hub version: 0.16.2 - PyArrow version: 13.0.0 - Pandas version: 2.0.2	2023-10-25T13:29:51	2024-03-20T15:09:53	2023-10-31T19:59:35	https://github.com/huggingface/datasets/issues/6349	null	6,349	false	[ "I'm unable to reproduce this error. The server hosting the files may have been down temporarily, so try again.", "getting the same error", "I am getting the following error:\r\nEnv: Python3.10\r\ndatasets: 2.10.1\r\nLinux: Amazon Linux2\r\n\r\n`Traceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/ec2-user/anaconda3/envs/JupyterSystemEnv/lib/python3.10/site-packages/datasets/load.py\", line 1759, in load_dataset\r\n builder_instance = load_dataset_builder(\r\n File \"/home/ec2-user/anaconda3/envs/JupyterSystemEnv/lib/python3.10/site-packages/datasets/load.py\", line 1496, in load_dataset_builder\r\n dataset_module = dataset_module_factory(\r\n File \"/home/ec2-user/anaconda3/envs/JupyterSystemEnv/lib/python3.10/site-packages/datasets/load.py\", line 1218, in dataset_module_factory\r\n raise e1 from None\r\n File \"/home/ec2-user/anaconda3/envs/JupyterSystemEnv/lib/python3.10/site-packages/datasets/load.py\", line 1202, in dataset_module_factory\r\n ).get_module()\r\n File \"/home/ec2-user/anaconda3/envs/JupyterSystemEnv/lib/python3.10/site-packages/datasets/load.py\", line 767, in get_module\r\n else get_data_patterns_in_dataset_repository(hfh_dataset_info, self.data_dir)\r\n File \"/home/ec2-user/anaconda3/envs/JupyterSystemEnv/lib/python3.10/site-packages/datasets/data_files.py\", line 675, in get_data_patterns_in_dataset_repository\r\n return _get_data_files_patterns(resolver)\r\n File \"/home/ec2-user/anaconda3/envs/JupyterSystemEnv/lib/python3.10/site-packages/datasets/data_files.py\", line 236, in _get_data_files_patterns\r\n data_files = pattern_resolver(pattern)\r\n File \"/home/ec2-user/anaconda3/envs/JupyterSystemEnv/lib/python3.10/site-packages/datasets/data_files.py\", line 486, in _resolve_single_pattern_in_dataset_repository\r\n glob_iter = [PurePath(filepath) for filepath in fs.glob(PurePath(pattern).as_posix()) if fs.isfile(filepath)]\r\n File \"/home/ec2-user/anaconda3/envs/JupyterSystemEnv/lib/python3.10/site-packages/fsspec/spec.py\", line 606, in glob\r\n pattern = glob_translate(path + (\"/\" if ends_with_sep else \"\"))\r\n File \"/home/ec2-user/anaconda3/envs/JupyterSystemEnv/lib/python3.10/site-packages/fsspec/utils.py\", line 734, in glob_translate\r\n raise ValueError(\r\nValueError: Invalid pattern: '**' can only be an entire path component`", "Resolved by upgrading datasets version to 2.18.0" ]
1,961,268,504	Parquet stream-conversion fails to embed images/audio files from gated repos	open	it seems to be an issue with datasets not passing the token to embed_table_storage when generating a dataset See https://github.com/huggingface/datasets-server/issues/2010	2023-10-25T12:12:44	2025-04-17T12:21:43	null	https://github.com/huggingface/datasets/issues/6348	null	6,348	false	[ "I have created a project to stream audio in the datasets viewer on Hugging Face using Parquet.\n\nhttps://github.com/pr0mila/ParquetToHuggingFace" ]

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