Split (1)

train · 5k rows

id int64 953M 3.35B	number int64 2.72k 7.75k	title stringlengths 1 290	state stringclasses 2 values	created_at timestamp[s]date 2021-07-26 12:21:17 2025-08-23 00:18:43	updated_at timestamp[s]date 2021-07-26 13:27:59 2025-08-23 12:34:39	closed_at timestamp[s]date 2021-07-26 13:27:59 2025-08-20 16:35:55 ⌀	html_url stringlengths 49 51	pull_request dict	user_login stringlengths 3 26	is_pull_request bool 2 classes	comments listlengths 0 30
1,557,510,618	5,465	audiofolder creates empty dataset even though the dataset passed in follows the correct structure	closed	2023-01-26T01:45:45	2023-01-26T08:48:45	2023-01-26T08:48:45	https://github.com/huggingface/datasets/issues/5465	null	jcho19	false	[]
1,557,462,104	5,464	NonMatchingChecksumError for hendrycks_test	closed	2023-01-26T00:43:23	2023-01-27T05:44:31	2023-01-26T07:41:58	https://github.com/huggingface/datasets/issues/5464	null	sarahwie	false	[ "Thanks for reporting, @sarahwie.\r\n\r\nPlease note this issue was already fixed in `datasets` 2.6.0 version:\r\n- #5040\r\n\r\nIf you update your `datasets` version, you will be able to load the dataset:\r\n```\r\npip install -U datasets\r\n```", "Oops, missed that I needed to upgrade. Thanks!" ]
1,557,021,041	5,463	Imagefolder docs: mention support of CSV and ZIP	closed	2023-01-25T17:24:01	2023-01-25T18:33:35	2023-01-25T18:26:15	https://github.com/huggingface/datasets/pull/5463	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5463", "html_url": "https://github.com/huggingface/datasets/pull/5463", "diff_url": "https://github.com/huggingface/datasets/pull/5463.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5463.patch", "merged_at": "2023-01-25T18:26:15" }	lhoestq	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009559 / 0.011353 (-0.001794) \| 0.006425 / 0.011008 (-0.004583) \| 0.112951 / 0.038508 (0.074443) \| 0.030835 / 0.023109 (0.007725) \| 0.313846 / 0.275898 (0.037948) \| 0.352780 / 0.323480 (0.029301) \| 0.007740 / 0.007986 (-0.000246) \| 0.006843 / 0.004328 (0.002515) \| 0.082632 / 0.004250 (0.078382) \| 0.039704 / 0.037052 (0.002652) \| 0.328526 / 0.258489 (0.070037) \| 0.369162 / 0.293841 (0.075321) \| 0.047603 / 0.128546 (-0.080943) \| 0.015834 / 0.075646 (-0.059812) \| 0.385912 / 0.419271 (-0.033360) \| 0.053838 / 0.043533 (0.010306) \| 0.325778 / 0.255139 (0.070639) \| 0.361863 / 0.283200 (0.078663) \| 0.097388 / 0.141683 (-0.044295) \| 1.510132 / 1.452155 (0.057978) \| 1.555980 / 1.492716 (0.063264) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.210792 / 0.018006 (0.192786) \| 0.507270 / 0.000490 (0.506780) \| 0.002383 / 0.000200 (0.002183) \| 0.000095 / 0.000054 (0.000041) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023057 / 0.037411 (-0.014355) \| 0.103471 / 0.014526 (0.088945) \| 0.111671 / 0.176557 (-0.064885) \| 0.145665 / 0.737135 (-0.591470) \| 0.131447 / 0.296338 (-0.164891) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.502979 / 0.215209 (0.287770) \| 5.111471 / 2.077655 (3.033816) \| 2.093604 / 1.504120 (0.589484) \| 1.761342 / 1.541195 (0.220148) \| 1.919485 / 1.468490 (0.450995) \| 1.065672 / 4.584777 (-3.519105) \| 5.109746 / 3.745712 (1.364034) \| 4.694027 / 5.269862 (-0.575835) \| 2.438401 / 4.565676 (-2.127275) \| 0.133579 / 0.424275 (-0.290696) \| 0.012355 / 0.007607 (0.004748) \| 0.669077 / 0.226044 (0.443033) \| 6.533905 / 2.268929 (4.264976) \| 2.698832 / 55.444624 (-52.745792) \| 2.146377 / 6.876477 (-4.730100) \| 2.220563 / 2.142072 (0.078491) \| 1.287855 / 4.805227 (-3.517372) \| 0.238221 / 6.500664 (-6.262443) \| 0.071426 / 0.075469 (-0.004043) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.332659 / 1.841788 (-0.509129) \| 15.610100 / 8.074308 (7.535791) \| 16.691117 / 10.191392 (6.499725) \| 0.226338 / 0.680424 (-0.454086) \| 0.039964 / 0.534201 (-0.494237) \| 0.462911 / 0.579283 (-0.116372) \| 0.575923 / 0.434364 (0.141560) \| 0.592583 / 0.540337 (0.052245) \| 0.658552 / 1.386936 (-0.728384) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008388 / 0.011353 (-0.002965) \| 0.005360 / 0.011008 (-0.005648) \| 0.104574 / 0.038508 (0.066066) \| 0.030109 / 0.023109 (0.007000) \| 0.389294 / 0.275898 (0.113396) \| 0.424813 / 0.323480 (0.101333) \| 0.006629 / 0.007986 (-0.001356) \| 0.005222 / 0.004328 (0.000893) \| 0.080157 / 0.004250 (0.075907) \| 0.045811 / 0.037052 (0.008759) \| 0.398708 / 0.258489 (0.140219) \| 0.429449 / 0.293841 (0.135608) \| 0.052242 / 0.128546 (-0.076304) \| 0.017439 / 0.075646 (-0.058207) \| 0.362678 / 0.419271 (-0.056593) \| 0.054151 / 0.043533 (0.010618) \| 0.387932 / 0.255139 (0.132793) \| 0.410544 / 0.283200 (0.127344) \| 0.101210 / 0.141683 (-0.040473) \| 1.486496 / 1.452155 (0.034341) \| 1.576404 / 1.492716 (0.083687) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.259468 / 0.018006 (0.241461) \| 0.521661 / 0.000490 (0.521172) \| 0.000456 / 0.000200 (0.000256) \| 0.000078 / 0.000054 (0.000024) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027045 / 0.037411 (-0.010366) \| 0.107615 / 0.014526 (0.093089) \| 0.133228 / 0.176557 (-0.043329) \| 0.156807 / 0.737135 (-0.580328) \| 0.125226 / 0.296338 (-0.171113) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.528804 / 0.215209 (0.313595) \| 5.516402 / 2.077655 (3.438748) \| 2.387531 / 1.504120 (0.883412) \| 2.084734 / 1.541195 (0.543539) \| 2.091894 / 1.468490 (0.623404) \| 1.089761 / 4.584777 (-3.495016) \| 5.093067 / 3.745712 (1.347355) \| 2.670349 / 5.269862 (-2.599512) \| 1.784723 / 4.565676 (-2.780953) \| 0.125528 / 0.424275 (-0.298747) \| 0.013702 / 0.007607 (0.006095) \| 0.667755 / 0.226044 (0.441710) \| 6.653900 / 2.268929 (4.384972) \| 3.006058 / 55.444624 (-52.438567) \| 2.512919 / 6.876477 (-4.363558) \| 2.546824 / 2.142072 (0.404751) \| 1.269008 / 4.805227 (-3.536219) \| 0.234388 / 6.500664 (-6.266276) \| 0.065675 / 0.075469 (-0.009795) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.372222 / 1.841788 (-0.469566) \| 15.565156 / 8.074308 (7.490848) \| 16.800666 / 10.191392 (6.609274) \| 0.220656 / 0.680424 (-0.459768) \| 0.023690 / 0.534201 (-0.510511) \| 0.450049 / 0.579283 (-0.129234) \| 0.580433 / 0.434364 (0.146069) \| 0.558899 / 0.540337 (0.018561) \| 0.676799 / 1.386936 (-0.710137) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6cc5dcacecf41efc566385b323a3ca72ab44db36 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009440 / 0.011353 (-0.001913) \| 0.005159 / 0.011008 (-0.005849) \| 0.099152 / 0.038508 (0.060644) \| 0.035939 / 0.023109 (0.012830) \| 0.300968 / 0.275898 (0.025070) \| 0.365676 / 0.323480 (0.042196) \| 0.008220 / 0.007986 (0.000235) \| 0.004071 / 0.004328 (-0.000257) \| 0.075216 / 0.004250 (0.070965) \| 0.042173 / 0.037052 (0.005121) \| 0.315055 / 0.258489 (0.056566) \| 0.338287 / 0.293841 (0.044446) \| 0.037789 / 0.128546 (-0.090758) \| 0.011856 / 0.075646 (-0.063791) \| 0.332975 / 0.419271 (-0.086297) \| 0.047087 / 0.043533 (0.003554) \| 0.295107 / 0.255139 (0.039968) \| 0.315416 / 0.283200 (0.032217) \| 0.102273 / 0.141683 (-0.039410) \| 1.464908 / 1.452155 (0.012754) \| 1.500281 / 1.492716 (0.007565) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.208522 / 0.018006 (0.190516) \| 0.446576 / 0.000490 (0.446086) \| 0.005766 / 0.000200 (0.005566) \| 0.000084 / 0.000054 (0.000029) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027924 / 0.037411 (-0.009487) \| 0.111296 / 0.014526 (0.096771) \| 0.119055 / 0.176557 (-0.057502) \| 0.157755 / 0.737135 (-0.579381) \| 0.125539 / 0.296338 (-0.170799) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.395683 / 0.215209 (0.180474) \| 3.962696 / 2.077655 (1.885042) \| 1.789511 / 1.504120 (0.285391) \| 1.591541 / 1.541195 (0.050346) \| 1.661276 / 1.468490 (0.192786) \| 0.693524 / 4.584777 (-3.891253) \| 3.836526 / 3.745712 (0.090813) \| 2.187284 / 5.269862 (-3.082578) \| 1.521420 / 4.565676 (-3.044257) \| 0.084370 / 0.424275 (-0.339905) \| 0.012083 / 0.007607 (0.004476) \| 0.498017 / 0.226044 (0.271972) \| 4.982356 / 2.268929 (2.713428) \| 2.235881 / 55.444624 (-53.208743) \| 1.912067 / 6.876477 (-4.964410) \| 2.052172 / 2.142072 (-0.089900) \| 0.836232 / 4.805227 (-3.968995) \| 0.165234 / 6.500664 (-6.335431) \| 0.062933 / 0.075469 (-0.012536) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.197785 / 1.841788 (-0.644003) \| 15.233655 / 8.074308 (7.159347) \| 14.254450 / 10.191392 (4.063058) \| 0.169149 / 0.680424 (-0.511274) \| 0.028794 / 0.534201 (-0.505407) \| 0.437214 / 0.579283 (-0.142069) \| 0.434836 / 0.434364 (0.000472) \| 0.531594 / 0.540337 (-0.008744) \| 0.626266 / 1.386936 (-0.760670) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007394 / 0.011353 (-0.003959) \| 0.005305 / 0.011008 (-0.005703) \| 0.098888 / 0.038508 (0.060380) \| 0.033069 / 0.023109 (0.009959) \| 0.388427 / 0.275898 (0.112529) \| 0.415216 / 0.323480 (0.091736) \| 0.005610 / 0.007986 (-0.002375) \| 0.004922 / 0.004328 (0.000593) \| 0.073694 / 0.004250 (0.069443) \| 0.047368 / 0.037052 (0.010315) \| 0.379604 / 0.258489 (0.121115) \| 0.424876 / 0.293841 (0.131035) \| 0.039471 / 0.128546 (-0.089075) \| 0.012219 / 0.075646 (-0.063427) \| 0.345925 / 0.419271 (-0.073346) \| 0.048981 / 0.043533 (0.005448) \| 0.379303 / 0.255139 (0.124164) \| 0.404682 / 0.283200 (0.121483) \| 0.103932 / 0.141683 (-0.037751) \| 1.490852 / 1.452155 (0.038697) \| 1.578900 / 1.492716 (0.086183) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.201393 / 0.018006 (0.183387) \| 0.452484 / 0.000490 (0.451994) \| 0.005627 / 0.000200 (0.005428) \| 0.000129 / 0.000054 (0.000075) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029317 / 0.037411 (-0.008094) \| 0.114904 / 0.014526 (0.100378) \| 0.126678 / 0.176557 (-0.049878) \| 0.178315 / 0.737135 (-0.558820) \| 0.131603 / 0.296338 (-0.164736) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.459830 / 0.215209 (0.244621) \| 4.595358 / 2.077655 (2.517703) \| 2.383582 / 1.504120 (0.879462) \| 2.181945 / 1.541195 (0.640750) \| 2.309517 / 1.468490 (0.841027) \| 0.704803 / 4.584777 (-3.879974) \| 3.820411 / 3.745712 (0.074698) \| 4.872173 / 5.269862 (-0.397689) \| 2.266090 / 4.565676 (-2.299586) \| 0.085805 / 0.424275 (-0.338470) \| 0.012488 / 0.007607 (0.004881) \| 0.557500 / 0.226044 (0.331456) \| 5.570830 / 2.268929 (3.301901) \| 2.836202 / 55.444624 (-52.608422) \| 2.530534 / 6.876477 (-4.345943) \| 2.599792 / 2.142072 (0.457720) \| 0.843852 / 4.805227 (-3.961376) \| 0.169427 / 6.500664 (-6.331237) \| 0.065521 / 0.075469 (-0.009948) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.246014 / 1.841788 (-0.595774) \| 15.455336 / 8.074308 (7.381028) \| 13.559111 / 10.191392 (3.367719) \| 0.169131 / 0.680424 (-0.511293) \| 0.017812 / 0.534201 (-0.516389) \| 0.421161 / 0.579283 (-0.158122) \| 0.458286 / 0.434364 (0.023922) \| 0.534692 / 0.540337 (-0.005645) \| 0.639299 / 1.386936 (-0.747637) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2b7558953b5a071194356bbe4c596a2890a3b847 \"CML watermark\")\n" ]
1,556,572,144	5,462	Concatenate on axis=1 with misaligned blocks	closed	2023-01-25T12:33:22	2023-01-26T09:37:00	2023-01-26T09:27:19	https://github.com/huggingface/datasets/pull/5462	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5462", "html_url": "https://github.com/huggingface/datasets/pull/5462", "diff_url": "https://github.com/huggingface/datasets/pull/5462.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5462.patch", "merged_at": "2023-01-26T09:27:19" }	lhoestq	true	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008860 / 0.011353 (-0.002493) \| 0.004564 / 0.011008 (-0.006444) \| 0.101556 / 0.038508 (0.063048) \| 0.030000 / 0.023109 (0.006891) \| 0.304404 / 0.275898 (0.028506) \| 0.366247 / 0.323480 (0.042767) \| 0.007182 / 0.007986 (-0.000804) \| 0.003583 / 0.004328 (-0.000746) \| 0.079665 / 0.004250 (0.075415) \| 0.036529 / 0.037052 (-0.000523) \| 0.310998 / 0.258489 (0.052509) \| 0.346954 / 0.293841 (0.053113) \| 0.034098 / 0.128546 (-0.094448) \| 0.011576 / 0.075646 (-0.064070) \| 0.320448 / 0.419271 (-0.098824) \| 0.043328 / 0.043533 (-0.000205) \| 0.307317 / 0.255139 (0.052178) \| 0.325071 / 0.283200 (0.041871) \| 0.096406 / 0.141683 (-0.045277) \| 1.540331 / 1.452155 (0.088176) \| 1.589533 / 1.492716 (0.096817) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.011034 / 0.018006 (-0.006972) \| 0.422066 / 0.000490 (0.421577) \| 0.002409 / 0.000200 (0.002209) \| 0.000071 / 0.000054 (0.000017) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023703 / 0.037411 (-0.013708) \| 0.099935 / 0.014526 (0.085409) \| 0.105966 / 0.176557 (-0.070591) \| 0.142259 / 0.737135 (-0.594876) \| 0.109327 / 0.296338 (-0.187011) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.418381 / 0.215209 (0.203172) \| 4.177564 / 2.077655 (2.099909) \| 1.880196 / 1.504120 (0.376076) \| 1.669169 / 1.541195 (0.127974) \| 1.725989 / 1.468490 (0.257499) \| 0.689384 / 4.584777 (-3.895393) \| 3.380963 / 3.745712 (-0.364749) \| 1.884192 / 5.269862 (-3.385670) \| 1.162409 / 4.565676 (-3.403268) \| 0.082045 / 0.424275 (-0.342230) \| 0.012575 / 0.007607 (0.004968) \| 0.525824 / 0.226044 (0.299779) \| 5.272574 / 2.268929 (3.003646) \| 2.283492 / 55.444624 (-53.161132) \| 1.947390 / 6.876477 (-4.929087) \| 2.013790 / 2.142072 (-0.128283) \| 0.806280 / 4.805227 (-3.998948) \| 0.149267 / 6.500664 (-6.351397) \| 0.066967 / 0.075469 (-0.008502) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.216511 / 1.841788 (-0.625277) \| 13.869829 / 8.074308 (5.795521) \| 14.189967 / 10.191392 (3.998575) \| 0.148716 / 0.680424 (-0.531708) \| 0.028324 / 0.534201 (-0.505877) \| 0.390856 / 0.579283 (-0.188427) \| 0.404389 / 0.434364 (-0.029975) \| 0.456050 / 0.540337 (-0.084287) \| 0.544139 / 1.386936 (-0.842797) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006727 / 0.011353 (-0.004626) \| 0.004515 / 0.011008 (-0.006494) \| 0.098791 / 0.038508 (0.060283) \| 0.027596 / 0.023109 (0.004487) \| 0.439066 / 0.275898 (0.163168) \| 0.480555 / 0.323480 (0.157076) \| 0.005066 / 0.007986 (-0.002920) \| 0.004669 / 0.004328 (0.000341) \| 0.075334 / 0.004250 (0.071084) \| 0.039779 / 0.037052 (0.002726) \| 0.439860 / 0.258489 (0.181371) \| 0.480787 / 0.293841 (0.186946) \| 0.031550 / 0.128546 (-0.096996) \| 0.011668 / 0.075646 (-0.063978) \| 0.317348 / 0.419271 (-0.101923) \| 0.041312 / 0.043533 (-0.002220) \| 0.442934 / 0.255139 (0.187795) \| 0.463677 / 0.283200 (0.180478) \| 0.090066 / 0.141683 (-0.051617) \| 1.544152 / 1.452155 (0.091998) \| 1.584455 / 1.492716 (0.091738) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224284 / 0.018006 (0.206278) \| 0.406982 / 0.000490 (0.406492) \| 0.000427 / 0.000200 (0.000227) \| 0.000061 / 0.000054 (0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024914 / 0.037411 (-0.012497) \| 0.102608 / 0.014526 (0.088082) \| 0.106931 / 0.176557 (-0.069626) \| 0.140828 / 0.737135 (-0.596308) \| 0.112015 / 0.296338 (-0.184324) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.471078 / 0.215209 (0.255869) \| 4.705742 / 2.077655 (2.628088) \| 2.437442 / 1.504120 (0.933322) \| 2.242768 / 1.541195 (0.701573) \| 2.302158 / 1.468490 (0.833668) \| 0.697314 / 4.584777 (-3.887462) \| 3.357730 / 3.745712 (-0.387982) \| 1.913306 / 5.269862 (-3.356556) \| 1.173879 / 4.565676 (-3.391798) \| 0.083257 / 0.424275 (-0.341018) \| 0.012480 / 0.007607 (0.004873) \| 0.573407 / 0.226044 (0.347362) \| 5.728650 / 2.268929 (3.459721) \| 2.868863 / 55.444624 (-52.575761) \| 2.548640 / 6.876477 (-4.327837) \| 2.596622 / 2.142072 (0.454549) \| 0.805563 / 4.805227 (-3.999664) \| 0.150860 / 6.500664 (-6.349804) \| 0.068344 / 0.075469 (-0.007125) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.300368 / 1.841788 (-0.541420) \| 13.920451 / 8.074308 (5.846143) \| 14.222430 / 10.191392 (4.031038) \| 0.152497 / 0.680424 (-0.527927) \| 0.017415 / 0.534201 (-0.516786) \| 0.378827 / 0.579283 (-0.200456) \| 0.384165 / 0.434364 (-0.050199) \| 0.439364 / 0.540337 (-0.100973) \| 0.525710 / 1.386936 (-0.861226) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2cd22277fa87e02ad9970483f5b75aacdfbf9a70 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008482 / 0.011353 (-0.002871) \| 0.004405 / 0.011008 (-0.006604) \| 0.099662 / 0.038508 (0.061154) \| 0.029062 / 0.023109 (0.005953) \| 0.298329 / 0.275898 (0.022431) \| 0.332837 / 0.323480 (0.009357) \| 0.006760 / 0.007986 (-0.001225) \| 0.003290 / 0.004328 (-0.001039) \| 0.077659 / 0.004250 (0.073409) \| 0.034745 / 0.037052 (-0.002307) \| 0.303134 / 0.258489 (0.044644) \| 0.346402 / 0.293841 (0.052561) \| 0.033511 / 0.128546 (-0.095035) \| 0.011464 / 0.075646 (-0.064183) \| 0.322932 / 0.419271 (-0.096340) \| 0.040697 / 0.043533 (-0.002836) \| 0.301951 / 0.255139 (0.046812) \| 0.328961 / 0.283200 (0.045761) \| 0.084802 / 0.141683 (-0.056881) \| 1.506247 / 1.452155 (0.054092) \| 1.547631 / 1.492716 (0.054915) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.190370 / 0.018006 (0.172363) \| 0.405786 / 0.000490 (0.405297) \| 0.002196 / 0.000200 (0.001997) \| 0.000072 / 0.000054 (0.000018) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022958 / 0.037411 (-0.014453) \| 0.095736 / 0.014526 (0.081210) \| 0.103684 / 0.176557 (-0.072872) \| 0.138200 / 0.737135 (-0.598936) \| 0.105618 / 0.296338 (-0.190721) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.415239 / 0.215209 (0.200030) \| 4.147223 / 2.077655 (2.069569) \| 1.850322 / 1.504120 (0.346202) \| 1.662815 / 1.541195 (0.121620) \| 1.671563 / 1.468490 (0.203073) \| 0.693806 / 4.584777 (-3.890971) \| 3.352938 / 3.745712 (-0.392774) \| 1.849257 / 5.269862 (-3.420604) \| 1.161603 / 4.565676 (-3.404074) \| 0.081884 / 0.424275 (-0.342391) \| 0.012726 / 0.007607 (0.005119) \| 0.521105 / 0.226044 (0.295061) \| 5.231910 / 2.268929 (2.962981) \| 2.306073 / 55.444624 (-53.138551) \| 1.950449 / 6.876477 (-4.926028) \| 1.988433 / 2.142072 (-0.153640) \| 0.811168 / 4.805227 (-3.994059) \| 0.149960 / 6.500664 (-6.350704) \| 0.064845 / 0.075469 (-0.010624) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.221487 / 1.841788 (-0.620301) \| 13.756534 / 8.074308 (5.682226) \| 13.825369 / 10.191392 (3.633977) \| 0.155641 / 0.680424 (-0.524783) \| 0.028444 / 0.534201 (-0.505757) \| 0.390364 / 0.579283 (-0.188919) \| 0.397592 / 0.434364 (-0.036772) \| 0.455905 / 0.540337 (-0.084433) \| 0.534606 / 1.386936 (-0.852330) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006281 / 0.011353 (-0.005071) \| 0.004533 / 0.011008 (-0.006475) \| 0.098328 / 0.038508 (0.059820) \| 0.026998 / 0.023109 (0.003889) \| 0.424814 / 0.275898 (0.148915) \| 0.457653 / 0.323480 (0.134173) \| 0.004617 / 0.007986 (-0.003368) \| 0.003320 / 0.004328 (-0.001009) \| 0.075884 / 0.004250 (0.071634) \| 0.035865 / 0.037052 (-0.001187) \| 0.431674 / 0.258489 (0.173185) \| 0.468286 / 0.293841 (0.174445) \| 0.031915 / 0.128546 (-0.096631) \| 0.011680 / 0.075646 (-0.063967) \| 0.319575 / 0.419271 (-0.099696) \| 0.047792 / 0.043533 (0.004259) \| 0.428191 / 0.255139 (0.173052) \| 0.445657 / 0.283200 (0.162458) \| 0.090464 / 0.141683 (-0.051218) \| 1.465480 / 1.452155 (0.013326) \| 1.548985 / 1.492716 (0.056268) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.185671 / 0.018006 (0.167664) \| 0.399274 / 0.000490 (0.398784) \| 0.002822 / 0.000200 (0.002622) \| 0.000083 / 0.000054 (0.000028) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025934 / 0.037411 (-0.011477) \| 0.099480 / 0.014526 (0.084954) \| 0.110264 / 0.176557 (-0.066293) \| 0.140558 / 0.737135 (-0.596577) \| 0.110832 / 0.296338 (-0.185507) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.473491 / 0.215209 (0.258282) \| 4.722507 / 2.077655 (2.644852) \| 2.456242 / 1.504120 (0.952122) \| 2.255999 / 1.541195 (0.714804) \| 2.300816 / 1.468490 (0.832326) \| 0.698226 / 4.584777 (-3.886551) \| 3.397296 / 3.745712 (-0.348416) \| 2.741674 / 5.269862 (-2.528187) \| 1.462103 / 4.565676 (-3.103573) \| 0.082736 / 0.424275 (-0.341539) \| 0.012183 / 0.007607 (0.004576) \| 0.580144 / 0.226044 (0.354099) \| 5.794351 / 2.268929 (3.525422) \| 2.881201 / 55.444624 (-52.563423) \| 2.544384 / 6.876477 (-4.332093) \| 2.555227 / 2.142072 (0.413154) \| 0.805849 / 4.805227 (-3.999378) \| 0.151822 / 6.500664 (-6.348842) \| 0.067477 / 0.075469 (-0.007992) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.300224 / 1.841788 (-0.541564) \| 13.595361 / 8.074308 (5.521053) \| 13.967622 / 10.191392 (3.776230) \| 0.129222 / 0.680424 (-0.551202) \| 0.016939 / 0.534201 (-0.517262) \| 0.375190 / 0.579283 (-0.204094) \| 0.383511 / 0.434364 (-0.050853) \| 0.437179 / 0.540337 (-0.103158) \| 0.525674 / 1.386936 (-0.861262) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7ed52db3d67cc8d0f2adfe53b2ec8d1124a174b8 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.012364 / 0.011353 (0.001011) \| 0.006098 / 0.011008 (-0.004911) \| 0.158908 / 0.038508 (0.120400) \| 0.039798 / 0.023109 (0.016689) \| 0.383786 / 0.275898 (0.107888) \| 0.533961 / 0.323480 (0.210481) \| 0.012079 / 0.007986 (0.004094) \| 0.006483 / 0.004328 (0.002155) \| 0.109660 / 0.004250 (0.105410) \| 0.048391 / 0.037052 (0.011339) \| 0.447426 / 0.258489 (0.188937) \| 0.477292 / 0.293841 (0.183451) \| 0.066492 / 0.128546 (-0.062054) \| 0.021155 / 0.075646 (-0.054492) \| 0.474473 / 0.419271 (0.055202) \| 0.063520 / 0.043533 (0.019987) \| 0.444941 / 0.255139 (0.189802) \| 0.450675 / 0.283200 (0.167475) \| 0.129236 / 0.141683 (-0.012447) \| 2.009362 / 1.452155 (0.557207) \| 1.912067 / 1.492716 (0.419350) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.260384 / 0.018006 (0.242378) \| 0.577654 / 0.000490 (0.577165) \| 0.004977 / 0.000200 (0.004777) \| 0.000110 / 0.000054 (0.000056) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028101 / 0.037411 (-0.009310) \| 0.161680 / 0.014526 (0.147154) \| 0.146107 / 0.176557 (-0.030450) \| 0.173878 / 0.737135 (-0.563257) \| 0.186149 / 0.296338 (-0.110190) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.689835 / 0.215209 (0.474626) \| 6.775888 / 2.077655 (4.698234) \| 2.885499 / 1.504120 (1.381379) \| 2.486855 / 1.541195 (0.945660) \| 2.540831 / 1.468490 (1.072341) \| 1.328135 / 4.584777 (-3.256642) \| 5.964983 / 3.745712 (2.219271) \| 3.400713 / 5.269862 (-1.869149) \| 2.423257 / 4.565676 (-2.142419) \| 0.129767 / 0.424275 (-0.294508) \| 0.017936 / 0.007607 (0.010328) \| 0.909284 / 0.226044 (0.683239) \| 8.778791 / 2.268929 (6.509863) \| 3.890757 / 55.444624 (-51.553867) \| 3.072116 / 6.876477 (-3.804360) \| 3.085390 / 2.142072 (0.943318) \| 1.571710 / 4.805227 (-3.233517) \| 0.279290 / 6.500664 (-6.221374) \| 0.087775 / 0.075469 (0.012306) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.751223 / 1.841788 (-0.090564) \| 20.313135 / 8.074308 (12.238827) \| 22.793800 / 10.191392 (12.602408) \| 0.296052 / 0.680424 (-0.384372) \| 0.053420 / 0.534201 (-0.480781) \| 0.600626 / 0.579283 (0.021343) \| 0.634505 / 0.434364 (0.200142) \| 0.724000 / 0.540337 (0.183663) \| 0.869283 / 1.386936 (-0.517653) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.014876 / 0.011353 (0.003523) \| 0.008113 / 0.011008 (-0.002895) \| 0.177038 / 0.038508 (0.138530) \| 0.050825 / 0.023109 (0.027716) \| 0.473989 / 0.275898 (0.198091) \| 0.601058 / 0.323480 (0.277578) \| 0.007536 / 0.007986 (-0.000450) \| 0.006761 / 0.004328 (0.002432) \| 0.105260 / 0.004250 (0.101010) \| 0.073960 / 0.037052 (0.036908) \| 0.447711 / 0.258489 (0.189222) \| 0.609998 / 0.293841 (0.316157) \| 0.061280 / 0.128546 (-0.067267) \| 0.019370 / 0.075646 (-0.056276) \| 0.510466 / 0.419271 (0.091194) \| 0.062695 / 0.043533 (0.019162) \| 0.436778 / 0.255139 (0.181639) \| 0.489916 / 0.283200 (0.206717) \| 0.137305 / 0.141683 (-0.004378) \| 1.801554 / 1.452155 (0.349399) \| 2.082409 / 1.492716 (0.589692) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291304 / 0.018006 (0.273298) \| 0.599041 / 0.000490 (0.598551) \| 0.008017 / 0.000200 (0.007817) \| 0.000127 / 0.000054 (0.000072) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031243 / 0.037411 (-0.006169) \| 0.139689 / 0.014526 (0.125163) \| 0.138678 / 0.176557 (-0.037878) \| 0.180458 / 0.737135 (-0.556677) \| 0.149753 / 0.296338 (-0.146585) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.699692 / 0.215209 (0.484482) \| 7.273327 / 2.077655 (5.195672) \| 3.222650 / 1.504120 (1.718530) \| 2.679424 / 1.541195 (1.138229) \| 2.842378 / 1.468490 (1.373888) \| 1.394633 / 4.584777 (-3.190143) \| 6.379970 / 3.745712 (2.634258) \| 5.944663 / 5.269862 (0.674801) \| 3.105214 / 4.565676 (-1.460462) \| 0.138790 / 0.424275 (-0.285485) \| 0.014211 / 0.007607 (0.006604) \| 0.815275 / 0.226044 (0.589230) \| 8.549334 / 2.268929 (6.280405) \| 3.754795 / 55.444624 (-51.689829) \| 3.125222 / 6.876477 (-3.751255) \| 3.269639 / 2.142072 (1.127566) \| 1.464187 / 4.805227 (-3.341040) \| 0.314557 / 6.500664 (-6.186107) \| 0.107354 / 0.075469 (0.031885) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.480793 / 1.841788 (-0.360995) \| 16.770328 / 8.074308 (8.696019) \| 18.054861 / 10.191392 (7.863469) \| 0.198257 / 0.680424 (-0.482167) \| 0.026493 / 0.534201 (-0.507708) \| 0.489701 / 0.579283 (-0.089582) \| 0.540890 / 0.434364 (0.106526) \| 0.566675 / 0.540337 (0.026337) \| 0.661918 / 1.386936 (-0.725018) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c4b839b50e9a81693e065f5299990026b97f6580 \"CML watermark\")\n" ]
1,555,532,719	5,461	Discrepancy in `nyu_depth_v2` dataset	open	2023-01-24T19:15:46	2023-02-06T20:52:00	null	https://github.com/huggingface/datasets/issues/5461	null	awsaf49	false	[ "Ccing @dwofk (the author of `fast-depth`). \r\n\r\nThanks, @awsaf49 for reporting this. I believe this is because the NYU Depth V2 shipped from `fast-depth` is already preprocessed. \r\n\r\nIf you think it might be better to have the NYU Depth V2 dataset from BTS [here](https://huggingface.co/datasets/sayakpaul/nyu_depth_v2) feel free to open a PR, I am happy to provide guidance :) ", "Good catch ! Ideally it would be nice to have the datasets in the raw form, this way users can choose whatever processing they want to apply", "> Ccing @dwofk (the author of `fast-depth`).\r\n> \r\n> Thanks, @awsaf49 for reporting this. I believe this is because the NYU Depth V2 shipped from `fast-depth` is already preprocessed.\r\n> \r\n> If you think it might be better to have the NYU Depth V2 dataset from BTS [here](https://huggingface.co/datasets/sayakpaul/nyu_depth_v2) feel free to open a PR, I am happy to provide guidance :)\r\n\r\n@sayakpaul I would love to create a PR on this. As this will be my first PR here, some guidance would be helpful.\r\n\r\nNeed a bit of advice on the dataset, there are three publicly available datasets. Which one should I consider for PR?\r\n1. [BTS](https://github.com/cleinc/bts): Containst train/test: 36K/654 data, dtype = `uint16` hence more precise\r\n2. [DenseDepth](https://github.com/ialhashim/DenseDepth) It contains train/test: 50K/654 data, dtype = `uint8` hence less precise\r\n3. [Official](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html#raw_parts): Size is big 400GB+, requires MatLab code for fixing projection and sync, DataType: `pgm` and `dump` hence can't be used directly.\r\n\r\ncc: @lhoestq\r\n\r\n", "I think BTS. Repositories like https://github.com/vinvino02/GLPDepth usually use BTS. Also, just for clarity, the PR will be to https://huggingface.co/datasets/sayakpaul/nyu_depth_v2. Once we have worked it out, we can update the following things:\r\n\r\n* https://github.com/huggingface/blog/pull/718\r\n* https://huggingface.co/docs/datasets/main/en/depth_estimation\r\n\r\nDon't worry about it if it seems overwhelming. We will work it out together :) \r\n\r\n@lhoestq what do you think? ", "@sayakpaul If I get this right I have to,\r\n1. Create a PR on https://huggingface.co/datasets/sayakpaul/nyu_depth_v2\r\n2. Create a PR on https://github.com/huggingface/blog\r\n3. Create a PR on https://github.com/huggingface/datasets to update https://github.com/huggingface/datasets/blob/main/docs/source/depth_estimation.mdx", "The last two are low-hanging fruits. Don't worry about them. ", "Yup opening a PR to use BTS on https://huggingface.co/datasets/sayakpaul/nyu_depth_v2 sounds good :) Thanks for the help !", "Finally, I have found the origin of the discretized depth map. When I first loaded the datasets from HF I noticed it was 30GB but in DenseDepth data is only 4GB with dtype=uint8. This means data from fast-depth (before loading to HF) must have high precision. So when I tried to dig deeper by directly loading depth_map from `h5py`, I found depth_map from `h5py` came with `float32`. But when the data is processed in HF with `datasets.Image()` it was directly converted to `uint8` from `float32` hence the discretized depth map.\r\nhttps://github.com/huggingface/datasets/blob/c78559cacbb0ca6e0bc8bfc313cc0359f8c23ead/src/datasets/features/image.py#L91-L93\r\n\r\n## Solutions:\r\n\r\n#### 1. Array2D\r\nUse `Array2D` feature with `float32` for depth_map \r\n\r\n* Code:\r\n```py\r\nFeatures({'depth_map': Array2D(shape=(480, 640), dtype='float32')})\r\n```\r\n* Pros:\r\nNo precision loss.\r\n\r\n* Cons:\r\nAs depth_map is saved as Array I think it can't be visuzlied in [hf.co/dataset](https://huggingface.co/datasets/sayakpaul/nyu_depth_v2) page like segmentation mask.\r\n\r\n#### 2. Uint16\r\nUse `uint16` as dtype for Image in `_h5_loader` for saving depth maps and accept `uint16` dtype in `datasets.Image()` feature.\r\n\r\n* Code\r\n```py\r\ndepth = np.array(h5f[\"depth\"])\r\ndepth /= 10.0 # [0, max_depth] -> [0, 1]\r\ndepth = (216 -1) # transform from [0, 1] -> [0, 2^16 - 1]\r\ndepth = depth.astype('uint16')\r\n```\r\n Pros:\r\n * We can visualize depth map in hf.co/datasets page like segmentation mask.\r\n * No need for post-processing.\r\n\r\n* Cons:\r\n * We need to make two change\r\n * Modify `_h5_loader` in https://huggingface.co/datasets/sayakpaul/nyu_depth_v2 to convert depth_map from `float32` to `uint16`.\r\n * Make sure `datasets.Image()` converts `np.ndarray` to `uint16` checking max value\r\n * Precision loss due to `float32` to `uint16`\r\n * Post-processing required for depth_map to transform from `[0, 2^16 - 1]` to `[0, max_depth]` before feeding them to model.", "Thanks so much for digging into this. \r\n\r\nSince the second solution entails changes to core datatypes in `datasets`, I think it's better to go with the first solution. \r\n\r\n@lhoestq WDYT?", "@sayakpaul Yes, Solution 1 requires minimal change and provides no precision loss. But I think support for `uint16` image would be a great addition as many datasets come with `uint16` image. For example [UW-Madison GI Tract Image Segmentation](https://www.kaggle.com/competitions/uw-madison-gi-tract-image-segmentation) dataset, here the image itself comes with `uint16` dtype rather than mask. So, saving `uint16` image with `uint8` will result in precision loss.\r\n\r\nPerhaps we can adapt solution 1 for this issue and Add support for `uint16` image separately?", "Using Array2D makes it not practical to use to train a model - in `transformers` we expect an image type.\r\n\r\nThere is a pull request to support more precision than uint8 in Image() here: https://github.com/huggingface/datasets/pull/5365/files\r\n\r\nwe can probably merge it today and do a release right away", "Fantastic, @lhoestq! \r\n\r\n@awsaf49 then let's wait for the PR to get merged and then take the next steps? ", "Sure", "The PR adds support for uint16 which is ok for BTS if I understand correctly, would it be ok for you ?", "If the main issue with the current version of NYU we have on the Hub is related to the precision loss stemming from `Image()`, I'd prefer if `Image()` supported float32 as well. ", "I also prefer `float32` as it offers more precision. But I'm not sure if we'll be able to visualize image with `float32` precision.", "We could have a separate loading for the float32 one using Array2D, but I feel like it's less convenient to use due to the amount of disk space and because it's not an Image() type. That's why I think uint16 is a better solution for users", "A bit confused here, If https://github.com/huggingface/datasets/pull/5365 gets merged won't this issue will be resolved automatically?", "Yes in theory :)", "actually float32 also seems to work in this PR (it just doesn't work for multi-channel)", "In that case, a new PR isn't necessary, right?", "Yep. I just tested from the PR and it works:\r\n```python\r\n>>> train_dataset = load_dataset(\"sayakpaul/nyu_depth_v2\", split=\"train\", streaming=True) \r\nDownloading readme: 100%\|██████████████████\| 8.71k/8.71k [00:00<00:00, 3.60MB/s]\r\n>>> next(iter(train_dataset))\r\n{'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=640x480 at 0x1382ED7F0>,\r\n 'depth_map': <PIL.TiffImagePlugin.TiffImageFile image mode=F size=640x480 at 0x1382EDF28>}\r\n>>> x = next(iter(train_dataset))\r\n>>> np.asarray(x[\"depth_map\"]) \r\narray([[0. , 0. , 0. , ..., 0. , 0. ,\r\n 0. ],\r\n [0. , 0. , 0. , ..., 0. , 0. ,\r\n 0. ],\r\n [0. , 0. , 0. , ..., 0. , 0. ,\r\n 0. ],\r\n ...,\r\n [0. , 2.2861192, 2.2861192, ..., 2.234162 , 2.234162 ,\r\n 0. ],\r\n [0. , 2.2861192, 2.2861192, ..., 2.234162 , 2.234162 ,\r\n 0. ],\r\n [0. , 2.2861192, 2.2861192, ..., 2.234162 , 2.234162 ,\r\n 0. ]], dtype=float32)\r\n```", "Great! the case is closed! This issue has been solved and I have to say, it was quite the thrill ride. I felt like Sherlock Holmes, solving a mystery and finding the bug🕵️‍♂️. But in all seriousness, it was a pleasure working on this issue and I'm glad we could get to the bottom of it.\r\n\r\nOn another note, should I consider closing the issue? I think we still need to make updates on https://github.com/huggingface/blog and https://github.com/huggingface/datasets/blob/main/docs/source/depth_estimation.mdx", "Haha thanks Mr Holmes :p\r\n\r\nmaybe let's close this issue when we're done updating the blog post and the documentation", "@awsaf49 thank you for your hard work! \r\n\r\nI am a little unsure why the other links need to be updated, though. They all rely on datasets internally. ", "I think depth_map still shows discretized version. It would be nice to have corrected one.\r\n<img src=\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/datasets/depth_est_target_viz.png\" width = 300>", "Also, I think we need to make some changes in the code to visualize depth_map as it is `float32` . `plot.imshow()` supports either [0, 1] + float32 or [0. 255] + uint8", "Oh yes! Do you want to start with the fixes? Please feel free to say no but I wanted to make sure your contributions are reflected properly in our doc and the blog :)", "Yes I think that would be nice :)", "I'll make the changes tomorrow. I hope it's okay..." ]
1,555,387,532	5,460	Document that removing all the columns returns an empty document and the num_row is lost	closed	2023-01-24T17:33:38	2023-01-25T16:11:10	2023-01-25T16:04:03	https://github.com/huggingface/datasets/pull/5460	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5460", "html_url": "https://github.com/huggingface/datasets/pull/5460", "diff_url": "https://github.com/huggingface/datasets/pull/5460.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5460.patch", "merged_at": "2023-01-25T16:04:03" }	thomasw21	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.011812 / 0.011353 (0.000459) \| 0.006878 / 0.011008 (-0.004130) \| 0.128720 / 0.038508 (0.090212) \| 0.038506 / 0.023109 (0.015397) \| 0.359670 / 0.275898 (0.083772) \| 0.422908 / 0.323480 (0.099428) \| 0.010115 / 0.007986 (0.002129) \| 0.004332 / 0.004328 (0.000004) \| 0.096281 / 0.004250 (0.092031) \| 0.048850 / 0.037052 (0.011798) \| 0.373795 / 0.258489 (0.115306) \| 0.414643 / 0.293841 (0.120802) \| 0.057568 / 0.128546 (-0.070978) \| 0.024135 / 0.075646 (-0.051512) \| 0.411764 / 0.419271 (-0.007507) \| 0.060167 / 0.043533 (0.016634) \| 0.367119 / 0.255139 (0.111980) \| 0.391813 / 0.283200 (0.108613) \| 0.112125 / 0.141683 (-0.029558) \| 1.869560 / 1.452155 (0.417406) \| 1.845649 / 1.492716 (0.352932) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.211449 / 0.018006 (0.193443) \| 0.522453 / 0.000490 (0.521963) \| 0.003984 / 0.000200 (0.003784) \| 0.000096 / 0.000054 (0.000042) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026015 / 0.037411 (-0.011397) \| 0.117747 / 0.014526 (0.103221) \| 0.125037 / 0.176557 (-0.051520) \| 0.168351 / 0.737135 (-0.568785) \| 0.132390 / 0.296338 (-0.163949) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.605653 / 0.215209 (0.390444) \| 5.883452 / 2.077655 (3.805798) \| 2.367052 / 1.504120 (0.862932) \| 2.137671 / 1.541195 (0.596476) \| 2.042370 / 1.468490 (0.573880) \| 1.168442 / 4.584777 (-3.416335) \| 5.205236 / 3.745712 (1.459524) \| 2.992514 / 5.269862 (-2.277348) \| 2.191829 / 4.565676 (-2.373847) \| 0.137702 / 0.424275 (-0.286574) \| 0.015898 / 0.007607 (0.008291) \| 0.783987 / 0.226044 (0.557942) \| 7.768965 / 2.268929 (5.500036) \| 3.249149 / 55.444624 (-52.195476) \| 2.530687 / 6.876477 (-4.345790) \| 2.675212 / 2.142072 (0.533140) \| 1.482804 / 4.805227 (-3.322423) \| 0.276845 / 6.500664 (-6.223819) \| 0.080597 / 0.075469 (0.005128) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.519086 / 1.841788 (-0.322701) \| 17.394093 / 8.074308 (9.319785) \| 19.613554 / 10.191392 (9.422162) \| 0.253291 / 0.680424 (-0.427133) \| 0.047746 / 0.534201 (-0.486455) \| 0.547114 / 0.579283 (-0.032170) \| 0.623873 / 0.434364 (0.189509) \| 0.631924 / 0.540337 (0.091586) \| 0.744390 / 1.386936 (-0.642546) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009229 / 0.011353 (-0.002124) \| 0.006206 / 0.011008 (-0.004802) \| 0.121866 / 0.038508 (0.083357) \| 0.033629 / 0.023109 (0.010519) \| 0.435172 / 0.275898 (0.159274) \| 0.472093 / 0.323480 (0.148613) \| 0.006946 / 0.007986 (-0.001039) \| 0.004848 / 0.004328 (0.000519) \| 0.097289 / 0.004250 (0.093038) \| 0.046982 / 0.037052 (0.009930) \| 0.447365 / 0.258489 (0.188876) \| 0.491213 / 0.293841 (0.197372) \| 0.055486 / 0.128546 (-0.073060) \| 0.019788 / 0.075646 (-0.055858) \| 0.399830 / 0.419271 (-0.019441) \| 0.058943 / 0.043533 (0.015411) \| 0.447658 / 0.255139 (0.192519) \| 0.465752 / 0.283200 (0.182552) \| 0.110441 / 0.141683 (-0.031242) \| 1.773155 / 1.452155 (0.321001) \| 1.899370 / 1.492716 (0.406653) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.191188 / 0.018006 (0.173181) \| 0.523721 / 0.000490 (0.523232) \| 0.004008 / 0.000200 (0.003808) \| 0.000126 / 0.000054 (0.000072) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032579 / 0.037411 (-0.004833) \| 0.120870 / 0.014526 (0.106344) \| 0.154991 / 0.176557 (-0.021565) \| 0.175450 / 0.737135 (-0.561685) \| 0.136526 / 0.296338 (-0.159813) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.627262 / 0.215209 (0.412052) \| 6.457989 / 2.077655 (4.380334) \| 2.935188 / 1.504120 (1.431068) \| 2.558705 / 1.541195 (1.017510) \| 2.669455 / 1.468490 (1.200965) \| 1.228791 / 4.584777 (-3.355985) \| 5.621262 / 3.745712 (1.875549) \| 3.181775 / 5.269862 (-2.088086) \| 2.115116 / 4.565676 (-2.450560) \| 0.159348 / 0.424275 (-0.264927) \| 0.013598 / 0.007607 (0.005991) \| 0.834732 / 0.226044 (0.608687) \| 8.051097 / 2.268929 (5.782168) \| 3.761681 / 55.444624 (-51.682943) \| 2.898158 / 6.876477 (-3.978319) \| 2.936289 / 2.142072 (0.794217) \| 1.476307 / 4.805227 (-3.328920) \| 0.269845 / 6.500664 (-6.230819) \| 0.087225 / 0.075469 (0.011756) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.632522 / 1.841788 (-0.209266) \| 17.615297 / 8.074308 (9.540989) \| 20.501172 / 10.191392 (10.309780) \| 0.248845 / 0.680424 (-0.431579) \| 0.024852 / 0.534201 (-0.509349) \| 0.498957 / 0.579283 (-0.080326) \| 0.588566 / 0.434364 (0.154202) \| 0.611051 / 0.540337 (0.070714) \| 0.726321 / 1.386936 (-0.660615) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#adaaf0b5ad596538c744d41bb56ce472834b6573 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008920 / 0.011353 (-0.002433) \| 0.004666 / 0.011008 (-0.006342) \| 0.098584 / 0.038508 (0.060076) \| 0.030213 / 0.023109 (0.007103) \| 0.298180 / 0.275898 (0.022282) \| 0.358932 / 0.323480 (0.035452) \| 0.007182 / 0.007986 (-0.000804) \| 0.005430 / 0.004328 (0.001102) \| 0.077962 / 0.004250 (0.073712) \| 0.038516 / 0.037052 (0.001463) \| 0.308840 / 0.258489 (0.050351) \| 0.343678 / 0.293841 (0.049837) \| 0.033701 / 0.128546 (-0.094845) \| 0.011460 / 0.075646 (-0.064186) \| 0.319809 / 0.419271 (-0.099462) \| 0.040731 / 0.043533 (-0.002802) \| 0.299772 / 0.255139 (0.044633) \| 0.324292 / 0.283200 (0.041092) \| 0.087755 / 0.141683 (-0.053928) \| 1.493077 / 1.452155 (0.040922) \| 1.527462 / 1.492716 (0.034746) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.187927 / 0.018006 (0.169921) \| 0.412785 / 0.000490 (0.412296) \| 0.003235 / 0.000200 (0.003035) \| 0.000080 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023313 / 0.037411 (-0.014098) \| 0.095663 / 0.014526 (0.081137) \| 0.105094 / 0.176557 (-0.071463) \| 0.140389 / 0.737135 (-0.596746) \| 0.108477 / 0.296338 (-0.187861) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.410680 / 0.215209 (0.195471) \| 4.109287 / 2.077655 (2.031632) \| 1.833214 / 1.504120 (0.329094) \| 1.622837 / 1.541195 (0.081642) \| 1.679899 / 1.468490 (0.211409) \| 0.686920 / 4.584777 (-3.897857) \| 3.463267 / 3.745712 (-0.282445) \| 1.867035 / 5.269862 (-3.402826) \| 1.150631 / 4.565676 (-3.415046) \| 0.081209 / 0.424275 (-0.343066) \| 0.012384 / 0.007607 (0.004777) \| 0.521070 / 0.226044 (0.295026) \| 5.208829 / 2.268929 (2.939900) \| 2.289032 / 55.444624 (-53.155592) \| 1.942976 / 6.876477 (-4.933501) \| 1.990660 / 2.142072 (-0.151413) \| 0.802976 / 4.805227 (-4.002252) \| 0.148199 / 6.500664 (-6.352465) \| 0.064644 / 0.075469 (-0.010825) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.277029 / 1.841788 (-0.564759) \| 13.915489 / 8.074308 (5.841181) \| 14.035486 / 10.191392 (3.844094) \| 0.138205 / 0.680424 (-0.542219) \| 0.028968 / 0.534201 (-0.505232) \| 0.394275 / 0.579283 (-0.185008) \| 0.399967 / 0.434364 (-0.034397) \| 0.460595 / 0.540337 (-0.079742) \| 0.537625 / 1.386936 (-0.849311) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006485 / 0.011353 (-0.004868) \| 0.004534 / 0.011008 (-0.006474) \| 0.097742 / 0.038508 (0.059234) \| 0.027231 / 0.023109 (0.004122) \| 0.431321 / 0.275898 (0.155423) \| 0.469212 / 0.323480 (0.145732) \| 0.004894 / 0.007986 (-0.003092) \| 0.004147 / 0.004328 (-0.000181) \| 0.073650 / 0.004250 (0.069400) \| 0.037052 / 0.037052 (-0.000000) \| 0.434196 / 0.258489 (0.175707) \| 0.480539 / 0.293841 (0.186698) \| 0.031923 / 0.128546 (-0.096623) \| 0.011522 / 0.075646 (-0.064124) \| 0.317062 / 0.419271 (-0.102209) \| 0.041124 / 0.043533 (-0.002409) \| 0.432013 / 0.255139 (0.176874) \| 0.456760 / 0.283200 (0.173560) \| 0.089757 / 0.141683 (-0.051925) \| 1.497752 / 1.452155 (0.045597) \| 1.585342 / 1.492716 (0.092626) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227784 / 0.018006 (0.209778) \| 0.404570 / 0.000490 (0.404080) \| 0.000556 / 0.000200 (0.000356) \| 0.000065 / 0.000054 (0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025201 / 0.037411 (-0.012210) \| 0.099348 / 0.014526 (0.084822) \| 0.114984 / 0.176557 (-0.061573) \| 0.147039 / 0.737135 (-0.590097) \| 0.109727 / 0.296338 (-0.186611) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.468415 / 0.215209 (0.253206) \| 4.692228 / 2.077655 (2.614573) \| 2.403382 / 1.504120 (0.899262) \| 2.196026 / 1.541195 (0.654832) \| 2.234736 / 1.468490 (0.766246) \| 0.703011 / 4.584777 (-3.881766) \| 3.451513 / 3.745712 (-0.294199) \| 2.596811 / 5.269862 (-2.673051) \| 1.544079 / 4.565676 (-3.021598) \| 0.083153 / 0.424275 (-0.341123) \| 0.012605 / 0.007607 (0.004998) \| 0.570265 / 0.226044 (0.344220) \| 5.735996 / 2.268929 (3.467067) \| 2.865336 / 55.444624 (-52.579288) \| 2.508340 / 6.876477 (-4.368137) \| 2.547144 / 2.142072 (0.405072) \| 0.813018 / 4.805227 (-3.992210) \| 0.150327 / 6.500664 (-6.350337) \| 0.065837 / 0.075469 (-0.009632) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.268941 / 1.841788 (-0.572847) \| 13.835698 / 8.074308 (5.761390) \| 13.992726 / 10.191392 (3.801334) \| 0.127751 / 0.680424 (-0.552673) \| 0.016673 / 0.534201 (-0.517528) \| 0.381921 / 0.579283 (-0.197362) \| 0.390688 / 0.434364 (-0.043676) \| 0.446234 / 0.540337 (-0.094103) \| 0.532631 / 1.386936 (-0.854305) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1492df3311bfeac55aaedf34c93c014630c4403e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008486 / 0.011353 (-0.002867) \| 0.004573 / 0.011008 (-0.006435) \| 0.100096 / 0.038508 (0.061588) \| 0.029449 / 0.023109 (0.006340) \| 0.298384 / 0.275898 (0.022486) \| 0.361886 / 0.323480 (0.038406) \| 0.006813 / 0.007986 (-0.001173) \| 0.003394 / 0.004328 (-0.000935) \| 0.077563 / 0.004250 (0.073312) \| 0.035605 / 0.037052 (-0.001447) \| 0.306864 / 0.258489 (0.048375) \| 0.346438 / 0.293841 (0.052597) \| 0.033156 / 0.128546 (-0.095390) \| 0.011567 / 0.075646 (-0.064079) \| 0.322189 / 0.419271 (-0.097083) \| 0.040161 / 0.043533 (-0.003372) \| 0.299329 / 0.255139 (0.044190) \| 0.326375 / 0.283200 (0.043175) \| 0.086572 / 0.141683 (-0.055111) \| 1.502473 / 1.452155 (0.050319) \| 1.528539 / 1.492716 (0.035823) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008502 / 0.018006 (-0.009505) \| 0.411045 / 0.000490 (0.410555) \| 0.003179 / 0.000200 (0.002980) \| 0.000073 / 0.000054 (0.000018) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023177 / 0.037411 (-0.014234) \| 0.096948 / 0.014526 (0.082422) \| 0.104068 / 0.176557 (-0.072489) \| 0.138739 / 0.737135 (-0.598396) \| 0.108241 / 0.296338 (-0.188097) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.411156 / 0.215209 (0.195947) \| 4.092992 / 2.077655 (2.015337) \| 1.841903 / 1.504120 (0.337783) \| 1.637449 / 1.541195 (0.096254) \| 1.670968 / 1.468490 (0.202478) \| 0.697301 / 4.584777 (-3.887476) \| 3.354717 / 3.745712 (-0.390995) \| 1.851518 / 5.269862 (-3.418344) \| 1.160367 / 4.565676 (-3.405309) \| 0.082613 / 0.424275 (-0.341662) \| 0.012477 / 0.007607 (0.004870) \| 0.524839 / 0.226044 (0.298795) \| 5.264173 / 2.268929 (2.995245) \| 2.294530 / 55.444624 (-53.150094) \| 1.933233 / 6.876477 (-4.943244) \| 1.968959 / 2.142072 (-0.173113) \| 0.817104 / 4.805227 (-3.988123) \| 0.149072 / 6.500664 (-6.351592) \| 0.064911 / 0.075469 (-0.010558) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.222215 / 1.841788 (-0.619573) \| 13.607545 / 8.074308 (5.533237) \| 13.990230 / 10.191392 (3.798838) \| 0.150855 / 0.680424 (-0.529568) \| 0.028844 / 0.534201 (-0.505357) \| 0.396169 / 0.579283 (-0.183114) \| 0.406957 / 0.434364 (-0.027407) \| 0.464069 / 0.540337 (-0.076268) \| 0.554027 / 1.386936 (-0.832909) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006296 / 0.011353 (-0.005057) \| 0.004563 / 0.011008 (-0.006445) \| 0.097719 / 0.038508 (0.059211) \| 0.027106 / 0.023109 (0.003996) \| 0.409333 / 0.275898 (0.133435) \| 0.445397 / 0.323480 (0.121917) \| 0.004906 / 0.007986 (-0.003080) \| 0.003316 / 0.004328 (-0.001012) \| 0.075363 / 0.004250 (0.071112) \| 0.039366 / 0.037052 (0.002314) \| 0.412710 / 0.258489 (0.154221) \| 0.451789 / 0.293841 (0.157948) \| 0.031810 / 0.128546 (-0.096736) \| 0.011681 / 0.075646 (-0.063965) \| 0.318484 / 0.419271 (-0.100788) \| 0.046741 / 0.043533 (0.003208) \| 0.411631 / 0.255139 (0.156492) \| 0.435274 / 0.283200 (0.152074) \| 0.092366 / 0.141683 (-0.049317) \| 1.492243 / 1.452155 (0.040089) \| 1.617603 / 1.492716 (0.124887) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.217376 / 0.018006 (0.199369) \| 0.400940 / 0.000490 (0.400450) \| 0.003700 / 0.000200 (0.003500) \| 0.000075 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023733 / 0.037411 (-0.013678) \| 0.098553 / 0.014526 (0.084027) \| 0.105790 / 0.176557 (-0.070767) \| 0.139537 / 0.737135 (-0.597598) \| 0.109862 / 0.296338 (-0.186477) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.476562 / 0.215209 (0.261353) \| 4.773469 / 2.077655 (2.695814) \| 2.447302 / 1.504120 (0.943182) \| 2.240596 / 1.541195 (0.699401) \| 2.271370 / 1.468490 (0.802880) \| 0.698913 / 4.584777 (-3.885864) \| 3.345648 / 3.745712 (-0.400064) \| 1.845008 / 5.269862 (-3.424854) \| 1.163213 / 4.565676 (-3.402464) \| 0.082456 / 0.424275 (-0.341819) \| 0.012315 / 0.007607 (0.004708) \| 0.575881 / 0.226044 (0.349836) \| 5.769575 / 2.268929 (3.500647) \| 2.909759 / 55.444624 (-52.534865) \| 2.580259 / 6.876477 (-4.296218) \| 2.590473 / 2.142072 (0.448401) \| 0.802765 / 4.805227 (-4.002462) \| 0.151514 / 6.500664 (-6.349150) \| 0.067718 / 0.075469 (-0.007751) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.293014 / 1.841788 (-0.548773) \| 13.934072 / 8.074308 (5.859763) \| 13.538760 / 10.191392 (3.347368) \| 0.126490 / 0.680424 (-0.553934) \| 0.016653 / 0.534201 (-0.517548) \| 0.381220 / 0.579283 (-0.198064) \| 0.387571 / 0.434364 (-0.046793) \| 0.444674 / 0.540337 (-0.095663) \| 0.550802 / 1.386936 (-0.836134) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bed576f2205c96f6cb26b5c6522345cb8b06ecfc \"CML watermark\")\n" ]
1,555,367,504	5,459	Disable aiohttp requoting of redirection URL	closed	2023-01-24T17:18:59	2024-09-01T18:08:31	2023-01-31T08:37:54	https://github.com/huggingface/datasets/pull/5459	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5459", "html_url": "https://github.com/huggingface/datasets/pull/5459", "diff_url": "https://github.com/huggingface/datasets/pull/5459.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5459.patch", "merged_at": "2023-01-31T08:37:54" }	albertvillanova	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "Comment by @lhoestq:\r\n> Do you think we need this in `datasets` if it's fixed on the moon landing side ? In the aiohttp doc they consider those symbols as \"non-safe\" ", "The lib `requests` does not perform that requote on redirect URLs.", "Indeed, the `requests` library does perform a requoting, but this does not unquote `%27`:\r\n```python\r\nIn [1]: from requests.utils import requote_uri\r\n\r\nIn [2]: url = \"https://netloc/path?param=param%27%27value\"\r\n\r\nIn [3]: url\r\nOut[3]: 'https://netloc/path?param=param%27%27value'\r\n\r\nIn [4]: requote_uri(url)\r\nOut[4]: 'https://netloc/path?param=param%27%27value'\r\n```\r\n\r\nHowever, the `aiohttp` library uses `yarl.ULR` and this does unquote `%27`:\r\n```python\r\nIn [5]: from yarl import URL\r\n\r\nIn [6]: url\r\nOut[6]: 'https://netloc/path?param=param%27%27value'\r\n\r\nIn [7]: str(URL(url))\r\nOut[7]: \"https://netloc/path?param=param''value\"\r\n```\r\n\r\nIf we pass `requote_redirect_url=False` to `aiohttp`, then it passes `encoded=True` to `yarl.ULR`: https://github.com/aio-libs/aiohttp/blob/4635161ee8e7ad321cca46e01ce5bfeb1ad8bf26/aiohttp/client.py#L578-L580\r\n```python\r\nparsed_url = URL(\r\n r_url, encoded=not self._requote_redirect_url\r\n)\r\n```\r\nwhich does not unquote `%27`:\r\n```python\r\nIn [8]: url\r\nOut[8]: 'https://netloc/path?param=param%27%27value'\r\n\r\nIn [9]: str(URL(url, encoded=True))\r\nOut[9]: 'https://netloc/path?param=param%27%27value'\r\n```", "See the issues we opened in the respective libraries:\r\n- aiohttp\r\n - aio-libs/yarl#1077\r\n- requests\r\n - psf/requests#6341", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.012399 / 0.011353 (0.001047) \| 0.006388 / 0.011008 (-0.004620) \| 0.134173 / 0.038508 (0.095665) \| 0.037059 / 0.023109 (0.013949) \| 0.420697 / 0.275898 (0.144799) \| 0.473981 / 0.323480 (0.150502) \| 0.009857 / 0.007986 (0.001871) \| 0.004791 / 0.004328 (0.000463) \| 0.106886 / 0.004250 (0.102636) \| 0.044871 / 0.037052 (0.007818) \| 0.429843 / 0.258489 (0.171354) \| 0.461569 / 0.293841 (0.167728) \| 0.057285 / 0.128546 (-0.071261) \| 0.018809 / 0.075646 (-0.056837) \| 0.432613 / 0.419271 (0.013342) \| 0.058086 / 0.043533 (0.014553) \| 0.413064 / 0.255139 (0.157925) \| 0.444407 / 0.283200 (0.161207) \| 0.119102 / 0.141683 (-0.022581) \| 1.875954 / 1.452155 (0.423799) \| 1.916392 / 1.492716 (0.423676) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.267489 / 0.018006 (0.249483) \| 0.567554 / 0.000490 (0.567064) \| 0.005901 / 0.000200 (0.005701) \| 0.000134 / 0.000054 (0.000079) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031248 / 0.037411 (-0.006164) \| 0.123014 / 0.014526 (0.108489) \| 0.140001 / 0.176557 (-0.036556) \| 0.191476 / 0.737135 (-0.545659) \| 0.141687 / 0.296338 (-0.154652) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.637481 / 0.215209 (0.422272) \| 6.255969 / 2.077655 (4.178314) \| 2.559811 / 1.504120 (1.055691) \| 2.118154 / 1.541195 (0.576960) \| 2.079487 / 1.468490 (0.610997) \| 1.201079 / 4.584777 (-3.383698) \| 5.592625 / 3.745712 (1.846913) \| 5.143344 / 5.269862 (-0.126517) \| 2.764716 / 4.565676 (-1.800960) \| 0.142539 / 0.424275 (-0.281736) \| 0.015541 / 0.007607 (0.007934) \| 0.771407 / 0.226044 (0.545363) \| 7.631657 / 2.268929 (5.362728) \| 3.279684 / 55.444624 (-52.164940) \| 2.587566 / 6.876477 (-4.288911) \| 2.624622 / 2.142072 (0.482549) \| 1.427878 / 4.805227 (-3.377350) \| 0.257759 / 6.500664 (-6.242906) \| 0.078616 / 0.075469 (0.003147) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.609305 / 1.841788 (-0.232483) \| 18.258792 / 8.074308 (10.184484) \| 20.345242 / 10.191392 (10.153850) \| 0.267366 / 0.680424 (-0.413058) \| 0.047035 / 0.534201 (-0.487166) \| 0.568881 / 0.579283 (-0.010402) \| 0.662763 / 0.434364 (0.228399) \| 0.668927 / 0.540337 (0.128590) \| 0.755766 / 1.386936 (-0.631170) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010017 / 0.011353 (-0.001336) \| 0.006816 / 0.011008 (-0.004192) \| 0.105038 / 0.038508 (0.066529) \| 0.038689 / 0.023109 (0.015580) \| 0.482113 / 0.275898 (0.206215) \| 0.540072 / 0.323480 (0.216592) \| 0.007738 / 0.007986 (-0.000248) \| 0.005134 / 0.004328 (0.000806) \| 0.102203 / 0.004250 (0.097953) \| 0.054080 / 0.037052 (0.017028) \| 0.501057 / 0.258489 (0.242568) \| 0.567186 / 0.293841 (0.273345) \| 0.060330 / 0.128546 (-0.068217) \| 0.020059 / 0.075646 (-0.055587) \| 0.123102 / 0.419271 (-0.296170) \| 0.063426 / 0.043533 (0.019893) \| 0.494171 / 0.255139 (0.239032) \| 0.538238 / 0.283200 (0.255039) \| 0.119613 / 0.141683 (-0.022069) \| 1.853728 / 1.452155 (0.401574) \| 1.984621 / 1.492716 (0.491904) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282511 / 0.018006 (0.264505) \| 0.563190 / 0.000490 (0.562700) \| 0.000465 / 0.000200 (0.000265) \| 0.000086 / 0.000054 (0.000032) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029267 / 0.037411 (-0.008144) \| 0.135618 / 0.014526 (0.121093) \| 0.146286 / 0.176557 (-0.030271) \| 0.188570 / 0.737135 (-0.548565) \| 0.155839 / 0.296338 (-0.140499) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.671660 / 0.215209 (0.456451) \| 6.718775 / 2.077655 (4.641120) \| 3.004601 / 1.504120 (1.500481) \| 2.640504 / 1.541195 (1.099309) \| 2.666788 / 1.468490 (1.198298) \| 1.242655 / 4.584777 (-3.342122) \| 5.780119 / 3.745712 (2.034407) \| 3.247935 / 5.269862 (-2.021927) \| 2.114007 / 4.565676 (-2.451669) \| 0.147546 / 0.424275 (-0.276729) \| 0.014408 / 0.007607 (0.006801) \| 0.824407 / 0.226044 (0.598362) \| 8.278185 / 2.268929 (6.009257) \| 3.733463 / 55.444624 (-51.711161) \| 2.976732 / 6.876477 (-3.899745) \| 3.132758 / 2.142072 (0.990686) \| 1.446095 / 4.805227 (-3.359132) \| 0.258628 / 6.500664 (-6.242036) \| 0.085513 / 0.075469 (0.010043) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.702681 / 1.841788 (-0.139106) \| 18.725123 / 8.074308 (10.650815) \| 19.622808 / 10.191392 (9.431416) \| 0.215845 / 0.680424 (-0.464579) \| 0.029246 / 0.534201 (-0.504955) \| 0.554819 / 0.579283 (-0.024464) \| 0.630926 / 0.434364 (0.196562) \| 0.637663 / 0.540337 (0.097325) \| 0.837948 / 1.386936 (-0.548988) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c4a4f96ef0a4ec4b25f0872f160fa1eb9d2e711c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008540 / 0.011353 (-0.002813) \| 0.004538 / 0.011008 (-0.006470) \| 0.101507 / 0.038508 (0.062999) \| 0.029751 / 0.023109 (0.006641) \| 0.292608 / 0.275898 (0.016710) \| 0.354734 / 0.323480 (0.031254) \| 0.007430 / 0.007986 (-0.000556) \| 0.003365 / 0.004328 (-0.000964) \| 0.078703 / 0.004250 (0.074452) \| 0.034858 / 0.037052 (-0.002194) \| 0.303518 / 0.258489 (0.045029) \| 0.336523 / 0.293841 (0.042682) \| 0.033741 / 0.128546 (-0.094805) \| 0.011460 / 0.075646 (-0.064186) \| 0.319551 / 0.419271 (-0.099721) \| 0.041102 / 0.043533 (-0.002431) \| 0.295914 / 0.255139 (0.040775) \| 0.322142 / 0.283200 (0.038943) \| 0.084694 / 0.141683 (-0.056989) \| 1.481308 / 1.452155 (0.029153) \| 1.530271 / 1.492716 (0.037554) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.180516 / 0.018006 (0.162510) \| 0.405741 / 0.000490 (0.405251) \| 0.002806 / 0.000200 (0.002606) \| 0.000072 / 0.000054 (0.000018) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023359 / 0.037411 (-0.014052) \| 0.096950 / 0.014526 (0.082424) \| 0.103991 / 0.176557 (-0.072566) \| 0.143700 / 0.737135 (-0.593435) \| 0.106764 / 0.296338 (-0.189575) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.416966 / 0.215209 (0.201757) \| 4.145601 / 2.077655 (2.067946) \| 1.838258 / 1.504120 (0.334139) \| 1.629396 / 1.541195 (0.088201) \| 1.649707 / 1.468490 (0.181217) \| 0.689624 / 4.584777 (-3.895153) \| 3.414584 / 3.745712 (-0.331129) \| 1.874295 / 5.269862 (-3.395566) \| 1.251930 / 4.565676 (-3.313746) \| 0.081782 / 0.424275 (-0.342493) \| 0.012868 / 0.007607 (0.005261) \| 0.523904 / 0.226044 (0.297859) \| 5.251032 / 2.268929 (2.982104) \| 2.301549 / 55.444624 (-53.143075) \| 1.942110 / 6.876477 (-4.934367) \| 2.023014 / 2.142072 (-0.119058) \| 0.816492 / 4.805227 (-3.988736) \| 0.150107 / 6.500664 (-6.350558) \| 0.065118 / 0.075469 (-0.010351) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.226433 / 1.841788 (-0.615355) \| 13.852569 / 8.074308 (5.778261) \| 13.862779 / 10.191392 (3.671387) \| 0.146361 / 0.680424 (-0.534062) \| 0.028652 / 0.534201 (-0.505549) \| 0.398251 / 0.579283 (-0.181032) \| 0.403590 / 0.434364 (-0.030774) \| 0.492184 / 0.540337 (-0.048154) \| 0.581040 / 1.386936 (-0.805896) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006859 / 0.011353 (-0.004494) \| 0.004632 / 0.011008 (-0.006376) \| 0.076653 / 0.038508 (0.038145) \| 0.027865 / 0.023109 (0.004755) \| 0.354472 / 0.275898 (0.078573) \| 0.385462 / 0.323480 (0.061982) \| 0.005125 / 0.007986 (-0.002861) \| 0.003420 / 0.004328 (-0.000909) \| 0.076018 / 0.004250 (0.071768) \| 0.040197 / 0.037052 (0.003144) \| 0.353675 / 0.258489 (0.095186) \| 0.394911 / 0.293841 (0.101070) \| 0.032909 / 0.128546 (-0.095637) \| 0.011713 / 0.075646 (-0.063933) \| 0.085921 / 0.419271 (-0.333350) \| 0.044462 / 0.043533 (0.000929) \| 0.349997 / 0.255139 (0.094858) \| 0.375207 / 0.283200 (0.092008) \| 0.091288 / 0.141683 (-0.050394) \| 1.536515 / 1.452155 (0.084361) \| 1.581878 / 1.492716 (0.089162) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.273284 / 0.018006 (0.255277) \| 0.424457 / 0.000490 (0.423967) \| 0.044659 / 0.000200 (0.044459) \| 0.000247 / 0.000054 (0.000192) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025473 / 0.037411 (-0.011938) \| 0.100014 / 0.014526 (0.085488) \| 0.108551 / 0.176557 (-0.068006) \| 0.147913 / 0.737135 (-0.589223) \| 0.112729 / 0.296338 (-0.183610) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.448162 / 0.215209 (0.232953) \| 4.472701 / 2.077655 (2.395046) \| 2.078384 / 1.504120 (0.574264) \| 1.861292 / 1.541195 (0.320097) \| 1.920482 / 1.468490 (0.451991) \| 0.706968 / 4.584777 (-3.877809) \| 3.433109 / 3.745712 (-0.312603) \| 1.898684 / 5.269862 (-3.371178) \| 1.174375 / 4.565676 (-3.391302) \| 0.083666 / 0.424275 (-0.340609) \| 0.012388 / 0.007607 (0.004781) \| 0.546011 / 0.226044 (0.319966) \| 5.487514 / 2.268929 (3.218585) \| 2.534124 / 55.444624 (-52.910500) \| 2.168441 / 6.876477 (-4.708036) \| 2.203458 / 2.142072 (0.061386) \| 0.813333 / 4.805227 (-3.991894) \| 0.153169 / 6.500664 (-6.347495) \| 0.067151 / 0.075469 (-0.008318) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.277815 / 1.841788 (-0.563972) \| 13.920545 / 8.074308 (5.846237) \| 13.473801 / 10.191392 (3.282409) \| 0.129035 / 0.680424 (-0.551389) \| 0.016737 / 0.534201 (-0.517464) \| 0.388413 / 0.579283 (-0.190870) \| 0.388785 / 0.434364 (-0.045579) \| 0.481735 / 0.540337 (-0.058602) \| 0.576390 / 1.386936 (-0.810546) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c4a4f96ef0a4ec4b25f0872f160fa1eb9d2e711c \"CML watermark\")\n" ]
1,555,054,737	5,458	slice split while streaming	closed	2023-01-24T14:08:17	2023-01-24T15:11:47	2023-01-24T15:11:47	https://github.com/huggingface/datasets/issues/5458	null	SvenDS9	false	[ "Hi! Yes, that's correct. When `streaming` is `True`, only split names can be specified as `split`, and for slicing, you have to use `.skip`/`.take` instead.\r\n\r\nE.g. \r\n`load_dataset(\"lhoestq/demo1\",revision=None, streaming=True, split=\"train[:3]\")`\r\n\r\nrewritten with `.skip`/`.take`:\r\n`load_dataset(\"lhoestq/demo1\",revision=None, streaming=True, split=\"train\").take(3)`\r\n\r\n\r\n", "Thank you for your quick response!" ]
1,554,171,264	5,457	prebuilt dataset relies on `downloads/extracted`	open	2023-01-24T02:09:32	2024-11-18T07:43:51	null	https://github.com/huggingface/datasets/issues/5457	null	stas00	false	[ "Hi! \r\n\r\nThis issue is due to our audio/image datasets not being self-contained. This allows us to save disk space (files are written only once) but also leads to the issues like this one. We plan to make all our datasets self-contained in Datasets 3.0.\r\n\r\nIn the meantime, you can run the following map to ensure your dataset is self-contained:\r\n```python\r\nfrom datasets.table import embed_table_storage\r\n# load_dataset ...\r\ndset = dset.with_format(\"arrow\")\r\ndset.map(embed_table_storage, batched=True)\r\ndset = dset.with_format(\"python\")\r\n```\r\n", "Understood. Thank you, Mario.\r\n\r\nPerhaps the solution could be very simple - move the extracted files into the directory of the cached dataset? Which would make it self-contained already and won't require waiting for a new major release. Unless I'm missing some back-compat nuance.\r\n\r\nBut regardless if X relies on Y - it could check if Y is still there when loading X. so not checking full consistency but just the top-level directory it relies on.", "Hello, \r\n\r\nI also face some problem with prebuilt dataset that relies on the same directory on \r\n\r\n`.cache\\\\huggingface\\\\datasets\\\\downloads\\\\extracted\\\\b557ce52f22c65030869d849d199d7b3fd5af18b335143729c717d29f6221baa\\\\ADEChallengeData2016\\\\annotations\\\\training\\\\ADE_train_00000023.png'`\r\n\r\nThe images exist but the training function somehow cannot reached it. Is this also related to the same problem?\r\n\r\nCurrently the directory map looked like this:\r\n```\r\n\r\n> (hf-pretrain38) C:\\Users\\Len\\.cache\\huggingface>tree\r\n> Folder PATH listing\r\n> C:.\r\n> ├───datasets\r\n> │ ├───downloads\r\n> │ │ └───extracted\r\n> │ │ ├───64c6a0967481dbc192dceabeac06c02b47b992a106357d49e1916dfcdc23a2ea\r\n> │ │ │ └───release_test\r\n> │ │ │ └───testing\r\n> │ │ └───b557ce52f22c65030869d849d199d7b3fd5af18b335143729c717d29f6221baa\r\n> │ │ └───ADEChallengeData2016\r\n> │ │ ├───annotations\r\n> │ │ │ ├───training\r\n> │ │ │ └───validation\r\n> │ │ └───images\r\n> │ │ ├───training\r\n> │ │ └───validation\r\n> │ ├───parquet\r\n> │ │ └───yelp_review_full-66f1f8c8d1a2da02\r\n> │ │ └───0.0.0\r\n> │ │ └───14a00e99c0d15a23649d0db8944380ac81082d4b021f398733dd84f3a6c569a7\r\n> │ └───scene_parse_150\r\n> │ └───scene_parsing\r\n> │ └───1.0.0\r\n> │ └───d998c54e1b5c5bad12b4d2ec7e1a5f74eee4c153bc1b089a0001677ae9b3fd75\r\n> ├───evaluate\r\n> │ └───downloads\r\n> ├───hub\r\n> │ ├───.locks\r\n> │ │ ├───datasets--scene_parse_150\r\n> │ │ ├───models--facebook--mask2former-swin-large-cityscapes-instance\r\n> │ │ ├───models--facebook--mask2former-swin-large-cityscapes-panoptic\r\n> │ │ ├───models--nvidia--mit-b0\r\n> │ │ └───models--nvidia--segformer-b1-finetuned-cityscapes-1024-1024\r\n> │ ├───datasets--huggingface--label-files\r\n> │ │ ├───blobs\r\n> │ │ ├───refs\r\n> │ │ └───snapshots\r\n> │ │ └───9462154cba99c3c7f569d3b4f1ba26614afd558c\r\n> │ ├───datasets--scene_parse_150\r\n> │ │ ├───.no_exist\r\n> │ │ │ └───ac1c0c0e23875e74cd77aca0fd725fd6a35c3667\r\n> │ │ ├───blobs\r\n> │ │ ├───refs\r\n> │ │ └───snapshots\r\n> │ │ └───ac1c0c0e23875e74cd77aca0fd725fd6a35c3667\r\n> │ ├───models--bert-base-cased\r\n> │ │ ├───.no_exist\r\n> │ │ │ └───cd5ef92a9fb2f889e972770a36d4ed042daf221e\r\n> │ │ ├───blobs\r\n> │ │ ├───refs\r\n> │ │ └───snapshots\r\n> │ │ └───cd5ef92a9fb2f889e972770a36d4ed042daf221e\r\n> │ ├───models--bert-case-cased\r\n> │ ├───models--facebook--detr-resnet-50-panoptic\r\n> │ │ ├───blobs\r\n> │ │ ├───refs\r\n> │ │ └───snapshots\r\n> │ │ └───d53b52a799403a8867920f82c869e40732b47037\r\n> │ ├───models--facebook--mask2former-swin-base-coco-panoptic\r\n> │ │ ├───blobs\r\n> │ │ ├───refs\r\n> │ │ └───snapshots\r\n> │ │ └───8351ef9576a965d65196da91a5015dcaf6c6b5d2\r\n> │ ├───models--facebook--mask2former-swin-large-cityscapes-instance\r\n> │ │ ├───blobs\r\n> │ │ ├───refs\r\n> │ │ └───snapshots\r\n> │ │ └───70fed72d02a138560da931a1c6a2dcfbb56cd2ff\r\n> │ ├───models--facebook--mask2former-swin-large-cityscapes-panoptic\r\n> │ │ ├───blobs\r\n> │ │ ├───refs\r\n> │ │ └───snapshots\r\n> │ │ └───544d76fe93971ee046dacae19b6d4f6ecb5d9088\r\n> │ ├───models--google_bert--bert-base-cased\r\n> │ ├───models--nvidia--mit-b0\r\n> │ │ ├───.no_exist\r\n> │ │ │ └───80983a413c30d36a39c20203974ae7807835e2b4\r\n> │ │ ├───blobs\r\n> │ │ ├───refs\r\n> │ │ │ └───refs\r\n> │ │ │ └───pr\r\n> │ │ └───snapshots\r\n> │ │ ├───25ce79d97e6d9d509ed12e17cb2eb89b0a83a2dc\r\n> │ │ └───80983a413c30d36a39c20203974ae7807835e2b4\r\n> │ ├───models--nvidia--segformer-b0-finetuned-cityscapes-768-768\r\n> │ │ ├───blobs\r\n> │ │ ├───refs\r\n> │ │ └───snapshots\r\n> │ │ └───d3b7801ed329668d5bff04cd33365fa37f538c3b\r\n> │ └───models--nvidia--segformer-b1-finetuned-cityscapes-1024-1024\r\n> │ ├───.no_exist\r\n> │ │ └───ec86afeba68e656629ccf47e0c8d2902f964917b\r\n> │ ├───blobs\r\n> │ ├───refs\r\n> │ │ └───refs\r\n> │ │ └───pr\r\n> │ └───snapshots\r\n> │ ├───ad2bb0101129289844ea62577e6a22adc2752004\r\n> │ └───ec86afeba68e656629ccf47e0c8d2902f964917b\r\n> ├───metrics\r\n> │ └───mean_io_u\r\n> │ └───default\r\n> └───modules\r\n> ├───datasets_modules\r\n> │ ├───datasets\r\n> │ │ ├───scene_parse_150\r\n> │ │ │ ├───d998c54e1b5c5bad12b4d2ec7e1a5f74eee4c153bc1b089a0001677ae9b3fd75\r\n> │ │ │ │ └───__pycache__\r\n> │ │ │ └───__pycache__\r\n> │ │ └───__pycache__\r\n> │ └───__pycache__\r\n> └───evaluate_modules\r\n> ├───metrics\r\n> │ ├───evaluate-metric--mean_iou\r\n> │ │ ├───9e450724f21f05592bfb0255fe2fa576df8171fa060d11121d8aecfff0db80d0\r\n> │ │ │ └───__pycache__\r\n> │ │ └───__pycache__\r\n> │ └───__pycache__\r\n> └───__pycache__\r\n```\r\n\r\nWill appreciate for some help and will help in completing further details, thanks in advance" ]
1,553,905,148	5,456	feat: tqdm for `to_parquet`	closed	2023-01-23T22:05:38	2023-01-24T11:26:47	2023-01-24T11:17:12	https://github.com/huggingface/datasets/pull/5456	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5456", "html_url": "https://github.com/huggingface/datasets/pull/5456", "diff_url": "https://github.com/huggingface/datasets/pull/5456.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5456.patch", "merged_at": "2023-01-24T11:17:12" }	zanussbaum	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.012395 / 0.011353 (0.001042) \| 0.006466 / 0.011008 (-0.004542) \| 0.127605 / 0.038508 (0.089097) \| 0.044929 / 0.023109 (0.021820) \| 0.399856 / 0.275898 (0.123958) \| 0.491341 / 0.323480 (0.167861) \| 0.009193 / 0.007986 (0.001207) \| 0.005419 / 0.004328 (0.001090) \| 0.100577 / 0.004250 (0.096327) \| 0.045338 / 0.037052 (0.008286) \| 0.409970 / 0.258489 (0.151481) \| 0.452941 / 0.293841 (0.159100) \| 0.054350 / 0.128546 (-0.074197) \| 0.019069 / 0.075646 (-0.056578) \| 0.427036 / 0.419271 (0.007765) \| 0.073616 / 0.043533 (0.030083) \| 0.395384 / 0.255139 (0.140245) \| 0.442381 / 0.283200 (0.159181) \| 0.123185 / 0.141683 (-0.018498) \| 1.797640 / 1.452155 (0.345485) \| 1.888860 / 1.492716 (0.396143) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.211041 / 0.018006 (0.193035) \| 0.539350 / 0.000490 (0.538860) \| 0.001683 / 0.000200 (0.001483) \| 0.000118 / 0.000054 (0.000064) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031699 / 0.037411 (-0.005712) \| 0.132696 / 0.014526 (0.118170) \| 0.133710 / 0.176557 (-0.042846) \| 0.190074 / 0.737135 (-0.547061) \| 0.142919 / 0.296338 (-0.153420) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.643521 / 0.215209 (0.428312) \| 6.137350 / 2.077655 (4.059695) \| 2.463894 / 1.504120 (0.959774) \| 2.120043 / 1.541195 (0.578848) \| 2.121898 / 1.468490 (0.653408) \| 1.287319 / 4.584777 (-3.297458) \| 5.517864 / 3.745712 (1.772151) \| 5.070820 / 5.269862 (-0.199042) \| 2.948967 / 4.565676 (-1.616710) \| 0.175861 / 0.424275 (-0.248415) \| 0.015292 / 0.007607 (0.007685) \| 0.843195 / 0.226044 (0.617150) \| 7.884275 / 2.268929 (5.615347) \| 3.182821 / 55.444624 (-52.261803) \| 2.576093 / 6.876477 (-4.300384) \| 2.537160 / 2.142072 (0.395088) \| 1.510029 / 4.805227 (-3.295198) \| 0.249404 / 6.500664 (-6.251260) \| 0.080434 / 0.075469 (0.004965) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.618695 / 1.841788 (-0.223093) \| 18.879207 / 8.074308 (10.804899) \| 21.075272 / 10.191392 (10.883880) \| 0.260781 / 0.680424 (-0.419643) \| 0.046387 / 0.534201 (-0.487813) \| 0.570709 / 0.579283 (-0.008574) \| 0.619050 / 0.434364 (0.184686) \| 0.642295 / 0.540337 (0.101958) \| 0.780070 / 1.386936 (-0.606866) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010418 / 0.011353 (-0.000935) \| 0.006104 / 0.011008 (-0.004905) \| 0.133609 / 0.038508 (0.095101) \| 0.035101 / 0.023109 (0.011992) \| 0.471931 / 0.275898 (0.196033) \| 0.504498 / 0.323480 (0.181018) \| 0.007388 / 0.007986 (-0.000598) \| 0.004852 / 0.004328 (0.000523) \| 0.094535 / 0.004250 (0.090284) \| 0.056832 / 0.037052 (0.019779) \| 0.470513 / 0.258489 (0.212024) \| 0.531285 / 0.293841 (0.237444) \| 0.058271 / 0.128546 (-0.070276) \| 0.020523 / 0.075646 (-0.055123) \| 0.437398 / 0.419271 (0.018126) \| 0.065390 / 0.043533 (0.021857) \| 0.503702 / 0.255139 (0.248563) \| 0.515876 / 0.283200 (0.232677) \| 0.118615 / 0.141683 (-0.023068) \| 1.865380 / 1.452155 (0.413225) \| 1.990316 / 1.492716 (0.497600) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.246772 / 0.018006 (0.228766) \| 0.560607 / 0.000490 (0.560118) \| 0.005675 / 0.000200 (0.005475) \| 0.000142 / 0.000054 (0.000088) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034692 / 0.037411 (-0.002719) \| 0.174016 / 0.014526 (0.159490) \| 0.179838 / 0.176557 (0.003282) \| 0.217118 / 0.737135 (-0.520018) \| 0.184811 / 0.296338 (-0.111527) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.675970 / 0.215209 (0.460760) \| 6.787039 / 2.077655 (4.709384) \| 2.932619 / 1.504120 (1.428499) \| 2.545076 / 1.541195 (1.003882) \| 2.566705 / 1.468490 (1.098215) \| 1.287365 / 4.584777 (-3.297412) \| 5.468441 / 3.745712 (1.722729) \| 5.227726 / 5.269862 (-0.042136) \| 2.868970 / 4.565676 (-1.696706) \| 0.153535 / 0.424275 (-0.270740) \| 0.020087 / 0.007607 (0.012480) \| 0.860562 / 0.226044 (0.634518) \| 8.656109 / 2.268929 (6.387180) \| 3.749424 / 55.444624 (-51.695200) \| 3.011337 / 6.876477 (-3.865139) \| 3.119045 / 2.142072 (0.976973) \| 1.562174 / 4.805227 (-3.243053) \| 0.279161 / 6.500664 (-6.221504) \| 0.084905 / 0.075469 (0.009436) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.638684 / 1.841788 (-0.203104) \| 18.834760 / 8.074308 (10.760452) \| 21.554310 / 10.191392 (11.362918) \| 0.274518 / 0.680424 (-0.405906) \| 0.030343 / 0.534201 (-0.503858) \| 0.539094 / 0.579283 (-0.040189) \| 0.627258 / 0.434364 (0.192895) \| 0.624638 / 0.540337 (0.084301) \| 0.742776 / 1.386936 (-0.644160) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#98c9b27be45e1f5bc8c18d8bb2414478efe68055 \"CML watermark\")\n" ]
1,553,040,080	5,455	Single TQDM bar in multi-proc map	closed	2023-01-23T12:49:40	2023-02-13T20:23:34	2023-02-13T20:16:38	https://github.com/huggingface/datasets/pull/5455	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5455", "html_url": "https://github.com/huggingface/datasets/pull/5455", "diff_url": "https://github.com/huggingface/datasets/pull/5455.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5455.patch", "merged_at": "2023-02-13T20:16:38" }	mariosasko	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008372 / 0.011353 (-0.002981) \| 0.004658 / 0.011008 (-0.006350) \| 0.102005 / 0.038508 (0.063497) \| 0.029030 / 0.023109 (0.005920) \| 0.296968 / 0.275898 (0.021070) \| 0.364898 / 0.323480 (0.041418) \| 0.006899 / 0.007986 (-0.001087) \| 0.003410 / 0.004328 (-0.000919) \| 0.079705 / 0.004250 (0.075455) \| 0.034265 / 0.037052 (-0.002787) \| 0.305695 / 0.258489 (0.047206) \| 0.343275 / 0.293841 (0.049434) \| 0.033783 / 0.128546 (-0.094763) \| 0.011604 / 0.075646 (-0.064042) \| 0.322577 / 0.419271 (-0.096694) \| 0.040540 / 0.043533 (-0.002993) \| 0.299176 / 0.255139 (0.044037) \| 0.333157 / 0.283200 (0.049957) \| 0.087460 / 0.141683 (-0.054223) \| 1.494392 / 1.452155 (0.042237) \| 1.539580 / 1.492716 (0.046863) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.176206 / 0.018006 (0.158200) \| 0.413702 / 0.000490 (0.413212) \| 0.002625 / 0.000200 (0.002425) \| 0.000071 / 0.000054 (0.000017) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023886 / 0.037411 (-0.013525) \| 0.099758 / 0.014526 (0.085232) \| 0.104349 / 0.176557 (-0.072208) \| 0.147138 / 0.737135 (-0.589998) \| 0.108682 / 0.296338 (-0.187657) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.411957 / 0.215209 (0.196748) \| 4.110004 / 2.077655 (2.032349) \| 1.820951 / 1.504120 (0.316831) \| 1.629726 / 1.541195 (0.088532) \| 1.672573 / 1.468490 (0.204083) \| 0.686627 / 4.584777 (-3.898150) \| 3.382665 / 3.745712 (-0.363047) \| 2.875908 / 5.269862 (-2.393954) \| 1.475331 / 4.565676 (-3.090345) \| 0.081353 / 0.424275 (-0.342922) \| 0.012521 / 0.007607 (0.004914) \| 0.516226 / 0.226044 (0.290182) \| 5.157658 / 2.268929 (2.888729) \| 2.302012 / 55.444624 (-53.142612) \| 1.950831 / 6.876477 (-4.925646) \| 1.962081 / 2.142072 (-0.179992) \| 0.800007 / 4.805227 (-4.005221) \| 0.148462 / 6.500664 (-6.352202) \| 0.064448 / 0.075469 (-0.011021) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.227977 / 1.841788 (-0.613810) \| 13.776087 / 8.074308 (5.701779) \| 13.749825 / 10.191392 (3.558433) \| 0.137034 / 0.680424 (-0.543390) \| 0.028461 / 0.534201 (-0.505740) \| 0.392335 / 0.579283 (-0.186948) \| 0.397404 / 0.434364 (-0.036960) \| 0.450831 / 0.540337 (-0.089507) \| 0.533716 / 1.386936 (-0.853220) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006883 / 0.011353 (-0.004470) \| 0.004625 / 0.011008 (-0.006383) \| 0.099039 / 0.038508 (0.060531) \| 0.028068 / 0.023109 (0.004958) \| 0.419988 / 0.275898 (0.144090) \| 0.449543 / 0.323480 (0.126063) \| 0.005232 / 0.007986 (-0.002753) \| 0.003527 / 0.004328 (-0.000801) \| 0.076308 / 0.004250 (0.072057) \| 0.040523 / 0.037052 (0.003471) \| 0.420165 / 0.258489 (0.161676) \| 0.463220 / 0.293841 (0.169379) \| 0.032368 / 0.128546 (-0.096178) \| 0.011784 / 0.075646 (-0.063863) \| 0.320675 / 0.419271 (-0.098597) \| 0.041861 / 0.043533 (-0.001672) \| 0.424903 / 0.255139 (0.169764) \| 0.443528 / 0.283200 (0.160328) \| 0.090869 / 0.141683 (-0.050814) \| 1.504757 / 1.452155 (0.052602) \| 1.557824 / 1.492716 (0.065108) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224020 / 0.018006 (0.206014) \| 0.404090 / 0.000490 (0.403601) \| 0.000403 / 0.000200 (0.000203) \| 0.000058 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024556 / 0.037411 (-0.012855) \| 0.101280 / 0.014526 (0.086754) \| 0.108017 / 0.176557 (-0.068540) \| 0.146679 / 0.737135 (-0.590456) \| 0.111468 / 0.296338 (-0.184870) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.478955 / 0.215209 (0.263746) \| 4.769628 / 2.077655 (2.691973) \| 2.473238 / 1.504120 (0.969118) \| 2.263588 / 1.541195 (0.722393) \| 2.285425 / 1.468490 (0.816935) \| 0.699051 / 4.584777 (-3.885726) \| 3.390495 / 3.745712 (-0.355217) \| 1.858569 / 5.269862 (-3.411293) \| 1.162081 / 4.565676 (-3.403596) \| 0.083294 / 0.424275 (-0.340981) \| 0.012410 / 0.007607 (0.004803) \| 0.580786 / 0.226044 (0.354741) \| 5.866868 / 2.268929 (3.597940) \| 2.944358 / 55.444624 (-52.500266) \| 2.596241 / 6.876477 (-4.280235) \| 2.664464 / 2.142072 (0.522392) \| 0.806751 / 4.805227 (-3.998476) \| 0.152389 / 6.500664 (-6.348275) \| 0.066945 / 0.075469 (-0.008524) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.290545 / 1.841788 (-0.551243) \| 14.005727 / 8.074308 (5.931419) \| 14.478951 / 10.191392 (4.287559) \| 0.127488 / 0.680424 (-0.552935) \| 0.016929 / 0.534201 (-0.517272) \| 0.378380 / 0.579283 (-0.200904) \| 0.387499 / 0.434364 (-0.046865) \| 0.440816 / 0.540337 (-0.099522) \| 0.525794 / 1.386936 (-0.861142) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#07549c6fcb2dced59d7614b4b8264d54ef573407 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008704 / 0.011353 (-0.002649) \| 0.004474 / 0.011008 (-0.006534) \| 0.101720 / 0.038508 (0.063212) \| 0.030426 / 0.023109 (0.007317) \| 0.298944 / 0.275898 (0.023046) \| 0.371491 / 0.323480 (0.048011) \| 0.007042 / 0.007986 (-0.000944) \| 0.003479 / 0.004328 (-0.000850) \| 0.078086 / 0.004250 (0.073835) \| 0.037014 / 0.037052 (-0.000038) \| 0.312964 / 0.258489 (0.054475) \| 0.351251 / 0.293841 (0.057410) \| 0.033286 / 0.128546 (-0.095260) \| 0.011468 / 0.075646 (-0.064179) \| 0.321784 / 0.419271 (-0.097488) \| 0.040700 / 0.043533 (-0.002832) \| 0.303799 / 0.255139 (0.048660) \| 0.336982 / 0.283200 (0.053782) \| 0.089448 / 0.141683 (-0.052235) \| 1.462430 / 1.452155 (0.010275) \| 1.524448 / 1.492716 (0.031732) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.178390 / 0.018006 (0.160384) \| 0.402474 / 0.000490 (0.401984) \| 0.002697 / 0.000200 (0.002497) \| 0.000078 / 0.000054 (0.000023) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022679 / 0.037411 (-0.014733) \| 0.097759 / 0.014526 (0.083234) \| 0.105102 / 0.176557 (-0.071454) \| 0.140720 / 0.737135 (-0.596415) \| 0.109119 / 0.296338 (-0.187219) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.414153 / 0.215209 (0.198944) \| 4.131799 / 2.077655 (2.054144) \| 1.852325 / 1.504120 (0.348205) \| 1.646955 / 1.541195 (0.105760) \| 1.662880 / 1.468490 (0.194390) \| 0.693823 / 4.584777 (-3.890954) \| 3.378843 / 3.745712 (-0.366869) \| 1.861324 / 5.269862 (-3.408538) \| 1.156916 / 4.565676 (-3.408761) \| 0.082385 / 0.424275 (-0.341890) \| 0.012166 / 0.007607 (0.004559) \| 0.528690 / 0.226044 (0.302646) \| 5.286388 / 2.268929 (3.017459) \| 2.319941 / 55.444624 (-53.124684) \| 1.959462 / 6.876477 (-4.917014) \| 1.995102 / 2.142072 (-0.146970) \| 0.817158 / 4.805227 (-3.988069) \| 0.149479 / 6.500664 (-6.351185) \| 0.065668 / 0.075469 (-0.009801) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.240228 / 1.841788 (-0.601560) \| 13.770357 / 8.074308 (5.696048) \| 13.940638 / 10.191392 (3.749246) \| 0.152589 / 0.680424 (-0.527835) \| 0.028498 / 0.534201 (-0.505703) \| 0.392579 / 0.579283 (-0.186704) \| 0.402843 / 0.434364 (-0.031521) \| 0.455429 / 0.540337 (-0.084909) \| 0.541090 / 1.386936 (-0.845846) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006692 / 0.011353 (-0.004661) \| 0.004514 / 0.011008 (-0.006495) \| 0.097058 / 0.038508 (0.058550) \| 0.027780 / 0.023109 (0.004671) \| 0.415806 / 0.275898 (0.139908) \| 0.443079 / 0.323480 (0.119599) \| 0.005181 / 0.007986 (-0.002805) \| 0.003408 / 0.004328 (-0.000921) \| 0.075263 / 0.004250 (0.071013) \| 0.038169 / 0.037052 (0.001116) \| 0.417292 / 0.258489 (0.158803) \| 0.461875 / 0.293841 (0.168034) \| 0.032280 / 0.128546 (-0.096266) \| 0.011571 / 0.075646 (-0.064075) \| 0.319091 / 0.419271 (-0.100181) \| 0.048295 / 0.043533 (0.004762) \| 0.423619 / 0.255139 (0.168480) \| 0.435064 / 0.283200 (0.151864) \| 0.094869 / 0.141683 (-0.046814) \| 1.523000 / 1.452155 (0.070846) \| 1.583097 / 1.492716 (0.090381) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.214326 / 0.018006 (0.196320) \| 0.391623 / 0.000490 (0.391134) \| 0.004602 / 0.000200 (0.004403) \| 0.000078 / 0.000054 (0.000024) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024306 / 0.037411 (-0.013106) \| 0.101178 / 0.014526 (0.086652) \| 0.108504 / 0.176557 (-0.068053) \| 0.144114 / 0.737135 (-0.593022) \| 0.111088 / 0.296338 (-0.185250) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.472573 / 0.215209 (0.257364) \| 4.748929 / 2.077655 (2.671274) \| 2.441602 / 1.504120 (0.937482) \| 2.238841 / 1.541195 (0.697647) \| 2.303303 / 1.468490 (0.834813) \| 0.696618 / 4.584777 (-3.888159) \| 3.373867 / 3.745712 (-0.371845) \| 2.809009 / 5.269862 (-2.460852) \| 1.337240 / 4.565676 (-3.228437) \| 0.082682 / 0.424275 (-0.341593) \| 0.012834 / 0.007607 (0.005227) \| 0.569686 / 0.226044 (0.343642) \| 5.723407 / 2.268929 (3.454478) \| 2.882944 / 55.444624 (-52.561680) \| 2.543530 / 6.876477 (-4.332947) \| 2.581856 / 2.142072 (0.439784) \| 0.802353 / 4.805227 (-4.002874) \| 0.149947 / 6.500664 (-6.350717) \| 0.065865 / 0.075469 (-0.009604) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.282146 / 1.841788 (-0.559642) \| 13.831344 / 8.074308 (5.757036) \| 14.081550 / 10.191392 (3.890157) \| 0.141735 / 0.680424 (-0.538689) \| 0.016677 / 0.534201 (-0.517524) \| 0.378967 / 0.579283 (-0.200316) \| 0.383775 / 0.434364 (-0.050589) \| 0.432892 / 0.540337 (-0.107446) \| 0.518042 / 1.386936 (-0.868894) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#01b4a5a18b56fa7b648b0f131f6b5568b1fd436a \"CML watermark\")\n", "Omg I love this ! cc @TevenLeScao @thomasw21 this will save your terminals from infinite streams of progress bars", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008680 / 0.011353 (-0.002673) \| 0.004597 / 0.011008 (-0.006411) \| 0.101154 / 0.038508 (0.062646) \| 0.029831 / 0.023109 (0.006722) \| 0.300619 / 0.275898 (0.024721) \| 0.358259 / 0.323480 (0.034779) \| 0.007284 / 0.007986 (-0.000701) \| 0.003511 / 0.004328 (-0.000817) \| 0.078805 / 0.004250 (0.074555) \| 0.037192 / 0.037052 (0.000140) \| 0.307241 / 0.258489 (0.048752) \| 0.354648 / 0.293841 (0.060807) \| 0.033696 / 0.128546 (-0.094851) \| 0.011660 / 0.075646 (-0.063986) \| 0.324266 / 0.419271 (-0.095006) \| 0.043393 / 0.043533 (-0.000140) \| 0.297503 / 0.255139 (0.042364) \| 0.326037 / 0.283200 (0.042838) \| 0.091165 / 0.141683 (-0.050517) \| 1.479970 / 1.452155 (0.027816) \| 1.508507 / 1.492716 (0.015791) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.179995 / 0.018006 (0.161989) \| 0.464282 / 0.000490 (0.463793) \| 0.003953 / 0.000200 (0.003753) \| 0.000077 / 0.000054 (0.000023) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022696 / 0.037411 (-0.014715) \| 0.099510 / 0.014526 (0.084984) \| 0.103741 / 0.176557 (-0.072816) \| 0.137837 / 0.737135 (-0.599299) \| 0.108776 / 0.296338 (-0.187563) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.417034 / 0.215209 (0.201825) \| 4.183479 / 2.077655 (2.105824) \| 1.855329 / 1.504120 (0.351209) \| 1.660675 / 1.541195 (0.119481) \| 1.723936 / 1.468490 (0.255446) \| 0.687815 / 4.584777 (-3.896962) \| 3.331280 / 3.745712 (-0.414432) \| 2.821430 / 5.269862 (-2.448432) \| 1.542394 / 4.565676 (-3.023283) \| 0.081665 / 0.424275 (-0.342610) \| 0.012483 / 0.007607 (0.004875) \| 0.524758 / 0.226044 (0.298713) \| 5.277285 / 2.268929 (3.008357) \| 2.278067 / 55.444624 (-53.166557) \| 1.923232 / 6.876477 (-4.953245) \| 1.978645 / 2.142072 (-0.163428) \| 0.806225 / 4.805227 (-3.999002) \| 0.147568 / 6.500664 (-6.353096) \| 0.064206 / 0.075469 (-0.011263) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.175079 / 1.841788 (-0.666708) \| 13.677443 / 8.074308 (5.603135) \| 14.064103 / 10.191392 (3.872711) \| 0.167462 / 0.680424 (-0.512962) \| 0.028677 / 0.534201 (-0.505524) \| 0.399090 / 0.579283 (-0.180193) \| 0.398930 / 0.434364 (-0.035433) \| 0.461604 / 0.540337 (-0.078733) \| 0.540978 / 1.386936 (-0.845958) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006846 / 0.011353 (-0.004507) \| 0.004452 / 0.011008 (-0.006556) \| 0.076169 / 0.038508 (0.037661) \| 0.028290 / 0.023109 (0.005181) \| 0.341105 / 0.275898 (0.065207) \| 0.381465 / 0.323480 (0.057986) \| 0.005038 / 0.007986 (-0.002948) \| 0.003298 / 0.004328 (-0.001031) \| 0.075794 / 0.004250 (0.071544) \| 0.039225 / 0.037052 (0.002173) \| 0.342995 / 0.258489 (0.084506) \| 0.384878 / 0.293841 (0.091037) \| 0.031766 / 0.128546 (-0.096780) \| 0.011597 / 0.075646 (-0.064049) \| 0.084849 / 0.419271 (-0.334423) \| 0.041795 / 0.043533 (-0.001737) \| 0.341770 / 0.255139 (0.086631) \| 0.383142 / 0.283200 (0.099942) \| 0.088854 / 0.141683 (-0.052829) \| 1.465116 / 1.452155 (0.012961) \| 1.566888 / 1.492716 (0.074171) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.225129 / 0.018006 (0.207123) \| 0.394290 / 0.000490 (0.393801) \| 0.000397 / 0.000200 (0.000197) \| 0.000060 / 0.000054 (0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025492 / 0.037411 (-0.011919) \| 0.100494 / 0.014526 (0.085968) \| 0.110587 / 0.176557 (-0.065969) \| 0.142715 / 0.737135 (-0.594420) \| 0.110962 / 0.296338 (-0.185376) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.437240 / 0.215209 (0.222031) \| 4.379191 / 2.077655 (2.301536) \| 2.055059 / 1.504120 (0.550939) \| 1.844643 / 1.541195 (0.303448) \| 1.914678 / 1.468490 (0.446188) \| 0.695607 / 4.584777 (-3.889170) \| 3.353845 / 3.745712 (-0.391867) \| 1.837403 / 5.269862 (-3.432459) \| 1.155518 / 4.565676 (-3.410158) \| 0.082753 / 0.424275 (-0.341523) \| 0.012812 / 0.007607 (0.005205) \| 0.537304 / 0.226044 (0.311260) \| 5.387425 / 2.268929 (3.118497) \| 2.506986 / 55.444624 (-52.937638) \| 2.159031 / 6.876477 (-4.717445) \| 2.187844 / 2.142072 (0.045772) \| 0.796880 / 4.805227 (-4.008347) \| 0.151850 / 6.500664 (-6.348815) \| 0.067577 / 0.075469 (-0.007892) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.257779 / 1.841788 (-0.584009) \| 13.968842 / 8.074308 (5.894534) \| 13.544220 / 10.191392 (3.352828) \| 0.149962 / 0.680424 (-0.530462) \| 0.016875 / 0.534201 (-0.517326) \| 0.394714 / 0.579283 (-0.184570) \| 0.387845 / 0.434364 (-0.046519) \| 0.481674 / 0.540337 (-0.058664) \| 0.569820 / 1.386936 (-0.817116) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#71e50283422a93e805ea76722ce2520d1aae39c2 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009745 / 0.011353 (-0.001607) \| 0.005307 / 0.011008 (-0.005702) \| 0.104230 / 0.038508 (0.065722) \| 0.039745 / 0.023109 (0.016635) \| 0.306102 / 0.275898 (0.030204) \| 0.384390 / 0.323480 (0.060910) \| 0.008265 / 0.007986 (0.000279) \| 0.005516 / 0.004328 (0.001187) \| 0.076023 / 0.004250 (0.071772) \| 0.048266 / 0.037052 (0.011213) \| 0.315380 / 0.258489 (0.056891) \| 0.365735 / 0.293841 (0.071895) \| 0.038222 / 0.128546 (-0.090324) \| 0.012397 / 0.075646 (-0.063249) \| 0.348964 / 0.419271 (-0.070307) \| 0.047668 / 0.043533 (0.004135) \| 0.301037 / 0.255139 (0.045898) \| 0.322982 / 0.283200 (0.039783) \| 0.109307 / 0.141683 (-0.032376) \| 1.420777 / 1.452155 (-0.031378) \| 1.468290 / 1.492716 (-0.024426) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.262386 / 0.018006 (0.244380) \| 0.557151 / 0.000490 (0.556661) \| 0.000352 / 0.000200 (0.000152) \| 0.000062 / 0.000054 (0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029508 / 0.037411 (-0.007903) \| 0.113960 / 0.014526 (0.099434) \| 0.123176 / 0.176557 (-0.053381) \| 0.161928 / 0.737135 (-0.575207) \| 0.129196 / 0.296338 (-0.167142) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.407051 / 0.215209 (0.191842) \| 4.072550 / 2.077655 (1.994895) \| 1.899809 / 1.504120 (0.395689) \| 1.751981 / 1.541195 (0.210786) \| 1.841361 / 1.468490 (0.372871) \| 0.713908 / 4.584777 (-3.870869) \| 3.703339 / 3.745712 (-0.042373) \| 2.091283 / 5.269862 (-3.178578) \| 1.323810 / 4.565676 (-3.241866) \| 0.084691 / 0.424275 (-0.339584) \| 0.012685 / 0.007607 (0.005078) \| 0.511301 / 0.226044 (0.285257) \| 5.109741 / 2.268929 (2.840813) \| 2.315073 / 55.444624 (-53.129551) \| 2.012746 / 6.876477 (-4.863731) \| 2.160074 / 2.142072 (0.018002) \| 0.853025 / 4.805227 (-3.952202) \| 0.165301 / 6.500664 (-6.335363) \| 0.062244 / 0.075469 (-0.013225) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.219727 / 1.841788 (-0.622061) \| 15.319675 / 8.074308 (7.245367) \| 13.100883 / 10.191392 (2.909491) \| 0.173451 / 0.680424 (-0.506973) \| 0.029173 / 0.534201 (-0.505028) \| 0.440162 / 0.579283 (-0.139122) \| 0.429771 / 0.434364 (-0.004593) \| 0.518689 / 0.540337 (-0.021648) \| 0.608590 / 1.386936 (-0.778346) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007839 / 0.011353 (-0.003514) \| 0.005409 / 0.011008 (-0.005599) \| 0.076468 / 0.038508 (0.037960) \| 0.036568 / 0.023109 (0.013459) \| 0.337568 / 0.275898 (0.061670) \| 0.379353 / 0.323480 (0.055873) \| 0.006208 / 0.007986 (-0.001778) \| 0.005971 / 0.004328 (0.001643) \| 0.073765 / 0.004250 (0.069514) \| 0.056609 / 0.037052 (0.019556) \| 0.344578 / 0.258489 (0.086089) \| 0.405249 / 0.293841 (0.111408) \| 0.037652 / 0.128546 (-0.090894) \| 0.012549 / 0.075646 (-0.063097) \| 0.087086 / 0.419271 (-0.332186) \| 0.056669 / 0.043533 (0.013136) \| 0.334121 / 0.255139 (0.078983) \| 0.354582 / 0.283200 (0.071383) \| 0.113293 / 0.141683 (-0.028390) \| 1.437327 / 1.452155 (-0.014828) \| 1.574400 / 1.492716 (0.081684) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.325235 / 0.018006 (0.307229) \| 0.535405 / 0.000490 (0.534915) \| 0.014119 / 0.000200 (0.013919) \| 0.000278 / 0.000054 (0.000224) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030826 / 0.037411 (-0.006585) \| 0.114077 / 0.014526 (0.099552) \| 0.128799 / 0.176557 (-0.047758) \| 0.172164 / 0.737135 (-0.564971) \| 0.133665 / 0.296338 (-0.162673) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.430898 / 0.215209 (0.215689) \| 4.285507 / 2.077655 (2.207853) \| 2.089767 / 1.504120 (0.585647) \| 1.899457 / 1.541195 (0.358262) \| 2.042875 / 1.468490 (0.574385) \| 0.690575 / 4.584777 (-3.894202) \| 3.815905 / 3.745712 (0.070192) \| 3.371085 / 5.269862 (-1.898776) \| 1.865748 / 4.565676 (-2.699929) \| 0.086678 / 0.424275 (-0.337597) \| 0.013172 / 0.007607 (0.005565) \| 0.552038 / 0.226044 (0.325994) \| 5.275093 / 2.268929 (3.006165) \| 2.561102 / 55.444624 (-52.883522) \| 2.224235 / 6.876477 (-4.652242) \| 2.330315 / 2.142072 (0.188243) \| 0.845163 / 4.805227 (-3.960064) \| 0.170675 / 6.500664 (-6.329989) \| 0.068446 / 0.075469 (-0.007023) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.261213 / 1.841788 (-0.580575) \| 15.354959 / 8.074308 (7.280651) \| 15.034302 / 10.191392 (4.842910) \| 0.146704 / 0.680424 (-0.533720) \| 0.017986 / 0.534201 (-0.516215) \| 0.425978 / 0.579283 (-0.153305) \| 0.421806 / 0.434364 (-0.012558) \| 0.494844 / 0.540337 (-0.045493) \| 0.587870 / 1.386936 (-0.799066) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0933901bb757e9a386095aef0fb11de9f9a04085 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.012765 / 0.011353 (0.001412) \| 0.006429 / 0.011008 (-0.004579) \| 0.133669 / 0.038508 (0.095161) \| 0.041420 / 0.023109 (0.018311) \| 0.419990 / 0.275898 (0.144092) \| 0.505218 / 0.323480 (0.181738) \| 0.010189 / 0.007986 (0.002204) \| 0.005134 / 0.004328 (0.000805) \| 0.100890 / 0.004250 (0.096640) \| 0.045639 / 0.037052 (0.008587) \| 0.440593 / 0.258489 (0.182103) \| 0.476966 / 0.293841 (0.183125) \| 0.059270 / 0.128546 (-0.069276) \| 0.018625 / 0.075646 (-0.057021) \| 0.444957 / 0.419271 (0.025686) \| 0.060669 / 0.043533 (0.017136) \| 0.415373 / 0.255139 (0.160234) \| 0.461810 / 0.283200 (0.178610) \| 0.116119 / 0.141683 (-0.025564) \| 1.873691 / 1.452155 (0.421536) \| 1.939891 / 1.492716 (0.447175) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.259529 / 0.018006 (0.241523) \| 0.587213 / 0.000490 (0.586723) \| 0.003729 / 0.000200 (0.003529) \| 0.000115 / 0.000054 (0.000060) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032064 / 0.037411 (-0.005347) \| 0.140228 / 0.014526 (0.125702) \| 0.147139 / 0.176557 (-0.029417) \| 0.193731 / 0.737135 (-0.543405) \| 0.162126 / 0.296338 (-0.134213) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.639262 / 0.215209 (0.424053) \| 6.496491 / 2.077655 (4.418836) \| 2.602044 / 1.504120 (1.097924) \| 2.245891 / 1.541195 (0.704696) \| 2.301321 / 1.468490 (0.832831) \| 1.234088 / 4.584777 (-3.350689) \| 5.883315 / 3.745712 (2.137603) \| 3.166902 / 5.269862 (-2.102959) \| 2.258279 / 4.565676 (-2.307398) \| 0.146203 / 0.424275 (-0.278072) \| 0.015490 / 0.007607 (0.007883) \| 0.800188 / 0.226044 (0.574144) \| 8.150866 / 2.268929 (5.881938) \| 3.419508 / 55.444624 (-52.025117) \| 2.712174 / 6.876477 (-4.164302) \| 2.805059 / 2.142072 (0.662987) \| 1.421047 / 4.805227 (-3.384180) \| 0.254274 / 6.500664 (-6.246390) \| 0.083886 / 0.075469 (0.008417) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.651962 / 1.841788 (-0.189826) \| 19.453202 / 8.074308 (11.378894) \| 24.643881 / 10.191392 (14.452489) \| 0.263612 / 0.680424 (-0.416812) \| 0.046913 / 0.534201 (-0.487288) \| 0.579861 / 0.579283 (0.000578) \| 0.695137 / 0.434364 (0.260773) \| 0.705479 / 0.540337 (0.165142) \| 0.806073 / 1.386936 (-0.580863) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010384 / 0.011353 (-0.000969) \| 0.007460 / 0.011008 (-0.003548) \| 0.107830 / 0.038508 (0.069322) \| 0.036792 / 0.023109 (0.013682) \| 0.469585 / 0.275898 (0.193687) \| 0.521278 / 0.323480 (0.197798) \| 0.007472 / 0.007986 (-0.000513) \| 0.007774 / 0.004328 (0.003446) \| 0.105405 / 0.004250 (0.101154) \| 0.053732 / 0.037052 (0.016680) \| 0.486299 / 0.258489 (0.227810) \| 0.537067 / 0.293841 (0.243226) \| 0.053378 / 0.128546 (-0.075168) \| 0.022018 / 0.075646 (-0.053628) \| 0.127765 / 0.419271 (-0.291507) \| 0.063844 / 0.043533 (0.020311) \| 0.479724 / 0.255139 (0.224585) \| 0.511243 / 0.283200 (0.228043) \| 0.123223 / 0.141683 (-0.018460) \| 1.934167 / 1.452155 (0.482013) \| 2.003168 / 1.492716 (0.510451) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227670 / 0.018006 (0.209664) \| 0.609125 / 0.000490 (0.608635) \| 0.004408 / 0.000200 (0.004208) \| 0.000147 / 0.000054 (0.000092) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035905 / 0.037411 (-0.001506) \| 0.142207 / 0.014526 (0.127681) \| 0.154749 / 0.176557 (-0.021808) \| 0.216191 / 0.737135 (-0.520944) \| 0.156577 / 0.296338 (-0.139761) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.665085 / 0.215209 (0.449876) \| 6.510923 / 2.077655 (4.433269) \| 2.902438 / 1.504120 (1.398318) \| 2.561427 / 1.541195 (1.020232) \| 2.669556 / 1.468490 (1.201066) \| 1.190340 / 4.584777 (-3.394437) \| 5.933066 / 3.745712 (2.187354) \| 5.627784 / 5.269862 (0.357922) \| 2.971922 / 4.565676 (-1.593755) \| 0.140884 / 0.424275 (-0.283391) \| 0.015382 / 0.007607 (0.007775) \| 0.810441 / 0.226044 (0.584396) \| 8.255538 / 2.268929 (5.986609) \| 3.819014 / 55.444624 (-51.625611) \| 3.222479 / 6.876477 (-3.653998) \| 3.181700 / 2.142072 (1.039627) \| 1.483403 / 4.805227 (-3.321824) \| 0.262726 / 6.500664 (-6.237939) \| 0.090252 / 0.075469 (0.014783) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.748566 / 1.841788 (-0.093222) \| 19.566894 / 8.074308 (11.492586) \| 24.382155 / 10.191392 (14.190763) \| 0.260118 / 0.680424 (-0.420305) \| 0.028725 / 0.534201 (-0.505476) \| 0.564875 / 0.579283 (-0.014408) \| 0.666708 / 0.434364 (0.232344) \| 0.691165 / 0.540337 (0.150827) \| 0.837061 / 1.386936 (-0.549875) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fe6bf908e9f12e0b69b4059c392da8264881525d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010098 / 0.011353 (-0.001255) \| 0.005797 / 0.011008 (-0.005211) \| 0.111262 / 0.038508 (0.072754) \| 0.039687 / 0.023109 (0.016578) \| 0.331081 / 0.275898 (0.055183) \| 0.395878 / 0.323480 (0.072398) \| 0.009244 / 0.007986 (0.001259) \| 0.004498 / 0.004328 (0.000170) \| 0.086129 / 0.004250 (0.081879) \| 0.046662 / 0.037052 (0.009610) \| 0.361926 / 0.258489 (0.103437) \| 0.386155 / 0.293841 (0.092314) \| 0.043657 / 0.128546 (-0.084889) \| 0.013545 / 0.075646 (-0.062101) \| 0.383735 / 0.419271 (-0.035537) \| 0.055727 / 0.043533 (0.012194) \| 0.355356 / 0.255139 (0.100217) \| 0.358749 / 0.283200 (0.075550) \| 0.123219 / 0.141683 (-0.018463) \| 1.707982 / 1.452155 (0.255828) \| 1.773342 / 1.492716 (0.280626) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.238902 / 0.018006 (0.220896) \| 0.495525 / 0.000490 (0.495036) \| 0.001742 / 0.000200 (0.001542) \| 0.000096 / 0.000054 (0.000041) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031276 / 0.037411 (-0.006135) \| 0.124286 / 0.014526 (0.109760) \| 0.136236 / 0.176557 (-0.040321) \| 0.180257 / 0.737135 (-0.556879) \| 0.141047 / 0.296338 (-0.155292) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.465075 / 0.215209 (0.249865) \| 4.543997 / 2.077655 (2.466342) \| 2.036632 / 1.504120 (0.532512) \| 1.820356 / 1.541195 (0.279161) \| 1.860692 / 1.468490 (0.392202) \| 0.807549 / 4.584777 (-3.777227) \| 4.400369 / 3.745712 (0.654657) \| 2.423372 / 5.269862 (-2.846490) \| 1.741338 / 4.565676 (-2.824339) \| 0.099457 / 0.424275 (-0.324818) \| 0.014464 / 0.007607 (0.006857) \| 0.599442 / 0.226044 (0.373398) \| 5.867798 / 2.268929 (3.598870) \| 2.641859 / 55.444624 (-52.802766) \| 2.294246 / 6.876477 (-4.582231) \| 2.329639 / 2.142072 (0.187567) \| 0.981897 / 4.805227 (-3.823331) \| 0.189278 / 6.500664 (-6.311386) \| 0.071868 / 0.075469 (-0.003601) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.471800 / 1.841788 (-0.369988) \| 17.149150 / 8.074308 (9.074841) \| 15.818942 / 10.191392 (5.627550) \| 0.174760 / 0.680424 (-0.505664) \| 0.033507 / 0.534201 (-0.500694) \| 0.511055 / 0.579283 (-0.068228) \| 0.517107 / 0.434364 (0.082743) \| 0.650813 / 0.540337 (0.110476) \| 0.752515 / 1.386936 (-0.634421) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008651 / 0.011353 (-0.002702) \| 0.005935 / 0.011008 (-0.005073) \| 0.088589 / 0.038508 (0.050081) \| 0.038796 / 0.023109 (0.015687) \| 0.415430 / 0.275898 (0.139532) \| 0.443693 / 0.323480 (0.120213) \| 0.006631 / 0.007986 (-0.001354) \| 0.004638 / 0.004328 (0.000309) \| 0.085779 / 0.004250 (0.081529) \| 0.053994 / 0.037052 (0.016942) \| 0.408349 / 0.258489 (0.149860) \| 0.475441 / 0.293841 (0.181600) \| 0.042792 / 0.128546 (-0.085754) \| 0.013938 / 0.075646 (-0.061709) \| 0.102173 / 0.419271 (-0.317098) \| 0.057940 / 0.043533 (0.014407) \| 0.408967 / 0.255139 (0.153828) \| 0.422741 / 0.283200 (0.139541) \| 0.121844 / 0.141683 (-0.019839) \| 1.772779 / 1.452155 (0.320625) \| 1.837706 / 1.492716 (0.344989) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.228896 / 0.018006 (0.210890) \| 0.497964 / 0.000490 (0.497475) \| 0.004402 / 0.000200 (0.004202) \| 0.000112 / 0.000054 (0.000057) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035626 / 0.037411 (-0.001786) \| 0.132021 / 0.014526 (0.117495) \| 0.145599 / 0.176557 (-0.030957) \| 0.192317 / 0.737135 (-0.544818) \| 0.150165 / 0.296338 (-0.146174) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.500216 / 0.215209 (0.285007) \| 5.002916 / 2.077655 (2.925262) \| 2.502439 / 1.504120 (0.998319) \| 2.353019 / 1.541195 (0.811825) \| 2.485082 / 1.468490 (1.016592) \| 0.827694 / 4.584777 (-3.757083) \| 4.569319 / 3.745712 (0.823607) \| 3.739820 / 5.269862 (-1.530042) \| 2.097857 / 4.565676 (-2.467819) \| 0.098636 / 0.424275 (-0.325639) \| 0.014608 / 0.007607 (0.007001) \| 0.604411 / 0.226044 (0.378366) \| 6.131702 / 2.268929 (3.862774) \| 3.043988 / 55.444624 (-52.400637) \| 2.642427 / 6.876477 (-4.234050) \| 2.687223 / 2.142072 (0.545151) \| 0.968808 / 4.805227 (-3.836419) \| 0.193876 / 6.500664 (-6.306788) \| 0.076931 / 0.075469 (0.001462) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.511820 / 1.841788 (-0.329968) \| 17.971574 / 8.074308 (9.897265) \| 16.512738 / 10.191392 (6.321346) \| 0.223702 / 0.680424 (-0.456722) \| 0.020191 / 0.534201 (-0.514010) \| 0.511045 / 0.579283 (-0.068238) \| 0.499813 / 0.434364 (0.065449) \| 0.642147 / 0.540337 (0.101810) \| 0.756029 / 1.386936 (-0.630907) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1f6c7b9eb4bca89ec90c465623f7a2e6f5251062 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008909 / 0.011353 (-0.002444) \| 0.005096 / 0.011008 (-0.005912) \| 0.098568 / 0.038508 (0.060060) \| 0.034548 / 0.023109 (0.011438) \| 0.294762 / 0.275898 (0.018864) \| 0.366093 / 0.323480 (0.042613) \| 0.007476 / 0.007986 (-0.000510) \| 0.003982 / 0.004328 (-0.000347) \| 0.075975 / 0.004250 (0.071725) \| 0.040499 / 0.037052 (0.003446) \| 0.315050 / 0.258489 (0.056561) \| 0.351273 / 0.293841 (0.057433) \| 0.038327 / 0.128546 (-0.090219) \| 0.011943 / 0.075646 (-0.063703) \| 0.332148 / 0.419271 (-0.087124) \| 0.047648 / 0.043533 (0.004115) \| 0.295817 / 0.255139 (0.040678) \| 0.322704 / 0.283200 (0.039504) \| 0.100830 / 0.141683 (-0.040853) \| 1.422162 / 1.452155 (-0.029993) \| 1.468972 / 1.492716 (-0.023744) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.201164 / 0.018006 (0.183158) \| 0.435425 / 0.000490 (0.434935) \| 0.001576 / 0.000200 (0.001376) \| 0.000218 / 0.000054 (0.000163) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026667 / 0.037411 (-0.010744) \| 0.106161 / 0.014526 (0.091636) \| 0.115836 / 0.176557 (-0.060720) \| 0.151511 / 0.737135 (-0.585624) \| 0.122248 / 0.296338 (-0.174091) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.395974 / 0.215209 (0.180765) \| 3.952958 / 2.077655 (1.875303) \| 1.772111 / 1.504120 (0.267991) \| 1.581370 / 1.541195 (0.040175) \| 1.602811 / 1.468490 (0.134321) \| 0.694072 / 4.584777 (-3.890705) \| 3.640238 / 3.745712 (-0.105474) \| 2.028865 / 5.269862 (-3.240997) \| 1.419182 / 4.565676 (-3.146495) \| 0.084078 / 0.424275 (-0.340197) \| 0.012248 / 0.007607 (0.004641) \| 0.499768 / 0.226044 (0.273723) \| 4.997449 / 2.268929 (2.728521) \| 2.280711 / 55.444624 (-53.163913) \| 1.971701 / 6.876477 (-4.904776) \| 1.983248 / 2.142072 (-0.158824) \| 0.831030 / 4.805227 (-3.974198) \| 0.163008 / 6.500664 (-6.337656) \| 0.061887 / 0.075469 (-0.013582) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.191744 / 1.841788 (-0.650043) \| 14.424546 / 8.074308 (6.350238) \| 14.530127 / 10.191392 (4.338735) \| 0.165793 / 0.680424 (-0.514631) \| 0.029099 / 0.534201 (-0.505102) \| 0.447830 / 0.579283 (-0.131453) \| 0.441036 / 0.434364 (0.006672) \| 0.554697 / 0.540337 (0.014360) \| 0.668854 / 1.386936 (-0.718082) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006825 / 0.011353 (-0.004528) \| 0.004998 / 0.011008 (-0.006010) \| 0.074197 / 0.038508 (0.035689) \| 0.032381 / 0.023109 (0.009272) \| 0.335745 / 0.275898 (0.059847) \| 0.360474 / 0.323480 (0.036994) \| 0.005420 / 0.007986 (-0.002566) \| 0.005121 / 0.004328 (0.000792) \| 0.074980 / 0.004250 (0.070730) \| 0.046392 / 0.037052 (0.009340) \| 0.338693 / 0.258489 (0.080204) \| 0.383679 / 0.293841 (0.089838) \| 0.035380 / 0.128546 (-0.093166) \| 0.012197 / 0.075646 (-0.063449) \| 0.085738 / 0.419271 (-0.333533) \| 0.049990 / 0.043533 (0.006458) \| 0.342640 / 0.255139 (0.087501) \| 0.355139 / 0.283200 (0.071939) \| 0.102992 / 0.141683 (-0.038690) \| 1.451900 / 1.452155 (-0.000254) \| 1.550919 / 1.492716 (0.058202) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223241 / 0.018006 (0.205235) \| 0.436954 / 0.000490 (0.436464) \| 0.003319 / 0.000200 (0.003120) \| 0.000088 / 0.000054 (0.000034) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028042 / 0.037411 (-0.009370) \| 0.106079 / 0.014526 (0.091554) \| 0.122713 / 0.176557 (-0.053843) \| 0.156543 / 0.737135 (-0.580593) \| 0.122424 / 0.296338 (-0.173914) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.439482 / 0.215209 (0.224273) \| 4.283112 / 2.077655 (2.205457) \| 2.139705 / 1.504120 (0.635585) \| 1.940898 / 1.541195 (0.399703) \| 2.003906 / 1.468490 (0.535416) \| 0.703269 / 4.584777 (-3.881508) \| 3.780391 / 3.745712 (0.034679) \| 2.079963 / 5.269862 (-3.189898) \| 1.330669 / 4.565676 (-3.235007) \| 0.086582 / 0.424275 (-0.337693) \| 0.012497 / 0.007607 (0.004890) \| 0.519329 / 0.226044 (0.293284) \| 5.218117 / 2.268929 (2.949189) \| 2.635982 / 55.444624 (-52.808643) \| 2.301111 / 6.876477 (-4.575366) \| 2.341312 / 2.142072 (0.199239) \| 0.840157 / 4.805227 (-3.965070) \| 0.166174 / 6.500664 (-6.334490) \| 0.062890 / 0.075469 (-0.012579) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.257672 / 1.841788 (-0.584116) \| 14.983374 / 8.074308 (6.909066) \| 14.284441 / 10.191392 (4.093049) \| 0.176077 / 0.680424 (-0.504347) \| 0.017544 / 0.534201 (-0.516657) \| 0.429619 / 0.579283 (-0.149664) \| 0.426371 / 0.434364 (-0.007993) \| 0.534832 / 0.540337 (-0.005506) \| 0.643322 / 1.386936 (-0.743614) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0409b1435876fa97b3674b0275285e84b49d83f8 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010622 / 0.011353 (-0.000731) \| 0.005856 / 0.011008 (-0.005152) \| 0.108608 / 0.038508 (0.070100) \| 0.039868 / 0.023109 (0.016759) \| 0.327853 / 0.275898 (0.051955) \| 0.396721 / 0.323480 (0.073241) \| 0.008916 / 0.007986 (0.000930) \| 0.004590 / 0.004328 (0.000261) \| 0.085020 / 0.004250 (0.080770) \| 0.046608 / 0.037052 (0.009555) \| 0.356369 / 0.258489 (0.097880) \| 0.391142 / 0.293841 (0.097301) \| 0.040579 / 0.128546 (-0.087967) \| 0.012249 / 0.075646 (-0.063397) \| 0.387740 / 0.419271 (-0.031532) \| 0.057794 / 0.043533 (0.014262) \| 0.335763 / 0.255139 (0.080624) \| 0.369847 / 0.283200 (0.086647) \| 0.121276 / 0.141683 (-0.020407) \| 1.605406 / 1.452155 (0.153251) \| 1.709524 / 1.492716 (0.216808) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.226688 / 0.018006 (0.208681) \| 0.493320 / 0.000490 (0.492831) \| 0.002825 / 0.000200 (0.002626) \| 0.000088 / 0.000054 (0.000033) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031874 / 0.037411 (-0.005538) \| 0.117365 / 0.014526 (0.102840) \| 0.127697 / 0.176557 (-0.048859) \| 0.175589 / 0.737135 (-0.561546) \| 0.137731 / 0.296338 (-0.158608) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.472563 / 0.215209 (0.257354) \| 4.744383 / 2.077655 (2.666728) \| 2.152015 / 1.504120 (0.647895) \| 1.925398 / 1.541195 (0.384203) \| 2.054613 / 1.468490 (0.586123) \| 0.821703 / 4.584777 (-3.763074) \| 4.468177 / 3.745712 (0.722465) \| 4.687682 / 5.269862 (-0.582179) \| 2.379674 / 4.565676 (-2.186003) \| 0.101325 / 0.424275 (-0.322950) \| 0.014891 / 0.007607 (0.007284) \| 0.593161 / 0.226044 (0.367117) \| 5.641670 / 2.268929 (3.372741) \| 2.460206 / 55.444624 (-52.984419) \| 2.131148 / 6.876477 (-4.745329) \| 2.351067 / 2.142072 (0.208994) \| 0.997634 / 4.805227 (-3.807593) \| 0.195338 / 6.500664 (-6.305326) \| 0.075540 / 0.075469 (0.000071) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.411585 / 1.841788 (-0.430203) \| 17.055689 / 8.074308 (8.981381) \| 16.544028 / 10.191392 (6.352636) \| 0.180840 / 0.680424 (-0.499584) \| 0.034549 / 0.534201 (-0.499652) \| 0.510256 / 0.579283 (-0.069027) \| 0.525632 / 0.434364 (0.091268) \| 0.601206 / 0.540337 (0.060868) \| 0.668468 / 1.386936 (-0.718469) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008989 / 0.011353 (-0.002364) \| 0.006065 / 0.011008 (-0.004943) \| 0.088294 / 0.038508 (0.049786) \| 0.040404 / 0.023109 (0.017295) \| 0.405622 / 0.275898 (0.129724) \| 0.454519 / 0.323480 (0.131039) \| 0.006919 / 0.007986 (-0.001067) \| 0.004545 / 0.004328 (0.000217) \| 0.087023 / 0.004250 (0.082772) \| 0.055962 / 0.037052 (0.018910) \| 0.400942 / 0.258489 (0.142453) \| 0.490670 / 0.293841 (0.196829) \| 0.044086 / 0.128546 (-0.084461) \| 0.014485 / 0.075646 (-0.061162) \| 0.103333 / 0.419271 (-0.315938) \| 0.059663 / 0.043533 (0.016130) \| 0.404944 / 0.255139 (0.149805) \| 0.425763 / 0.283200 (0.142563) \| 0.123989 / 0.141683 (-0.017694) \| 1.777244 / 1.452155 (0.325089) \| 1.879884 / 1.492716 (0.387167) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.226440 / 0.018006 (0.208434) \| 0.492688 / 0.000490 (0.492198) \| 0.004691 / 0.000200 (0.004491) \| 0.000110 / 0.000054 (0.000055) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035123 / 0.037411 (-0.002288) \| 0.134288 / 0.014526 (0.119762) \| 0.145542 / 0.176557 (-0.031015) \| 0.195372 / 0.737135 (-0.541764) \| 0.152551 / 0.296338 (-0.143787) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.468615 / 0.215209 (0.253406) \| 4.813363 / 2.077655 (2.735708) \| 2.333606 / 1.504120 (0.829486) \| 2.107344 / 1.541195 (0.566149) \| 2.109109 / 1.468490 (0.640619) \| 0.783779 / 4.584777 (-3.800998) \| 4.521448 / 3.745712 (0.775736) \| 2.290532 / 5.269862 (-2.979329) \| 1.553488 / 4.565676 (-3.012189) \| 0.088786 / 0.424275 (-0.335489) \| 0.013091 / 0.007607 (0.005484) \| 0.567165 / 0.226044 (0.341120) \| 5.974315 / 2.268929 (3.705386) \| 2.815018 / 55.444624 (-52.629606) \| 2.488954 / 6.876477 (-4.387522) \| 2.461849 / 2.142072 (0.319776) \| 0.934487 / 4.805227 (-3.870740) \| 0.190209 / 6.500664 (-6.310455) \| 0.074811 / 0.075469 (-0.000658) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.513476 / 1.841788 (-0.328311) \| 17.902599 / 8.074308 (9.828291) \| 14.308027 / 10.191392 (4.116635) \| 0.201992 / 0.680424 (-0.478432) \| 0.018678 / 0.534201 (-0.515523) \| 0.454707 / 0.579283 (-0.124576) \| 0.470643 / 0.434364 (0.036279) \| 0.612534 / 0.540337 (0.072197) \| 0.685773 / 1.386936 (-0.701163) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c4a66da3633a811eb8ea01d23469c41dfec0ffb8 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009385 / 0.011353 (-0.001968) \| 0.005220 / 0.011008 (-0.005788) \| 0.098722 / 0.038508 (0.060214) \| 0.035382 / 0.023109 (0.012273) \| 0.297114 / 0.275898 (0.021216) \| 0.371443 / 0.323480 (0.047963) \| 0.008070 / 0.007986 (0.000084) \| 0.004204 / 0.004328 (-0.000125) \| 0.075621 / 0.004250 (0.071370) \| 0.046015 / 0.037052 (0.008963) \| 0.304569 / 0.258489 (0.046080) \| 0.345598 / 0.293841 (0.051757) \| 0.037946 / 0.128546 (-0.090600) \| 0.011972 / 0.075646 (-0.063674) \| 0.331993 / 0.419271 (-0.087279) \| 0.047250 / 0.043533 (0.003717) \| 0.296588 / 0.255139 (0.041449) \| 0.316070 / 0.283200 (0.032870) \| 0.108211 / 0.141683 (-0.033472) \| 1.447619 / 1.452155 (-0.004535) \| 1.481243 / 1.492716 (-0.011473) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.274860 / 0.018006 (0.256854) \| 0.503139 / 0.000490 (0.502649) \| 0.003598 / 0.000200 (0.003398) \| 0.000081 / 0.000054 (0.000027) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026752 / 0.037411 (-0.010660) \| 0.109008 / 0.014526 (0.094482) \| 0.119109 / 0.176557 (-0.057448) \| 0.158462 / 0.737135 (-0.578673) \| 0.126171 / 0.296338 (-0.170168) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.396396 / 0.215209 (0.181187) \| 3.963055 / 2.077655 (1.885400) \| 1.796308 / 1.504120 (0.292188) \| 1.600565 / 1.541195 (0.059370) \| 1.742409 / 1.468490 (0.273919) \| 0.690942 / 4.584777 (-3.893835) \| 3.713343 / 3.745712 (-0.032369) \| 2.066804 / 5.269862 (-3.203058) \| 1.292946 / 4.565676 (-3.272730) \| 0.084344 / 0.424275 (-0.339931) \| 0.012473 / 0.007607 (0.004865) \| 0.513109 / 0.226044 (0.287065) \| 5.175141 / 2.268929 (2.906213) \| 2.266559 / 55.444624 (-53.178066) \| 1.935737 / 6.876477 (-4.940740) \| 2.028911 / 2.142072 (-0.113161) \| 0.831191 / 4.805227 (-3.974036) \| 0.163155 / 6.500664 (-6.337509) \| 0.063414 / 0.075469 (-0.012055) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.195429 / 1.841788 (-0.646358) \| 15.257933 / 8.074308 (7.183625) \| 14.358815 / 10.191392 (4.167423) \| 0.152677 / 0.680424 (-0.527747) \| 0.028890 / 0.534201 (-0.505311) \| 0.455342 / 0.579283 (-0.123941) \| 0.442602 / 0.434364 (0.008238) \| 0.526833 / 0.540337 (-0.013505) \| 0.618296 / 1.386936 (-0.768640) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007613 / 0.011353 (-0.003740) \| 0.005515 / 0.011008 (-0.005493) \| 0.073759 / 0.038508 (0.035251) \| 0.033944 / 0.023109 (0.010835) \| 0.347764 / 0.275898 (0.071866) \| 0.371143 / 0.323480 (0.047664) \| 0.005997 / 0.007986 (-0.001988) \| 0.004322 / 0.004328 (-0.000006) \| 0.073002 / 0.004250 (0.068751) \| 0.053051 / 0.037052 (0.015999) \| 0.340345 / 0.258489 (0.081856) \| 0.383761 / 0.293841 (0.089920) \| 0.037734 / 0.128546 (-0.090813) \| 0.012815 / 0.075646 (-0.062831) \| 0.086998 / 0.419271 (-0.332273) \| 0.050165 / 0.043533 (0.006632) \| 0.343864 / 0.255139 (0.088725) \| 0.356734 / 0.283200 (0.073534) \| 0.108955 / 0.141683 (-0.032728) \| 1.464558 / 1.452155 (0.012403) \| 1.560084 / 1.492716 (0.067368) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.327885 / 0.018006 (0.309878) \| 0.515515 / 0.000490 (0.515025) \| 0.000439 / 0.000200 (0.000239) \| 0.000059 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030741 / 0.037411 (-0.006670) \| 0.107634 / 0.014526 (0.093108) \| 0.127121 / 0.176557 (-0.049436) \| 0.164044 / 0.737135 (-0.573092) \| 0.129097 / 0.296338 (-0.167242) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.435690 / 0.215209 (0.220481) \| 4.350705 / 2.077655 (2.273050) \| 2.199597 / 1.504120 (0.695477) \| 2.022715 / 1.541195 (0.481521) \| 2.265907 / 1.468490 (0.797417) \| 0.695817 / 4.584777 (-3.888960) \| 3.795207 / 3.745712 (0.049494) \| 3.061587 / 5.269862 (-2.208274) \| 1.872213 / 4.565676 (-2.693463) \| 0.085265 / 0.424275 (-0.339010) \| 0.012243 / 0.007607 (0.004636) \| 0.547209 / 0.226044 (0.321164) \| 5.383626 / 2.268929 (3.114698) \| 2.707439 / 55.444624 (-52.737185) \| 2.393773 / 6.876477 (-4.482703) \| 2.481385 / 2.142072 (0.339312) \| 0.826169 / 4.805227 (-3.979059) \| 0.166643 / 6.500664 (-6.334021) \| 0.065817 / 0.075469 (-0.009652) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.274469 / 1.841788 (-0.567318) \| 15.565025 / 8.074308 (7.490717) \| 14.254192 / 10.191392 (4.062800) \| 0.166785 / 0.680424 (-0.513639) \| 0.017830 / 0.534201 (-0.516371) \| 0.430406 / 0.579283 (-0.148877) \| 0.435655 / 0.434364 (0.001292) \| 0.530605 / 0.540337 (-0.009732) \| 0.636355 / 1.386936 (-0.750581) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#146983fdc70b9fe2cc38109368e185b6ffa7a05e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008466 / 0.011353 (-0.002887) \| 0.004679 / 0.011008 (-0.006329) \| 0.100534 / 0.038508 (0.062025) \| 0.029513 / 0.023109 (0.006403) \| 0.302866 / 0.275898 (0.026968) \| 0.352816 / 0.323480 (0.029336) \| 0.006912 / 0.007986 (-0.001074) \| 0.003513 / 0.004328 (-0.000815) \| 0.078625 / 0.004250 (0.074375) \| 0.036725 / 0.037052 (-0.000327) \| 0.312135 / 0.258489 (0.053646) \| 0.344579 / 0.293841 (0.050738) \| 0.033870 / 0.128546 (-0.094677) \| 0.011563 / 0.075646 (-0.064083) \| 0.318982 / 0.419271 (-0.100290) \| 0.043002 / 0.043533 (-0.000531) \| 0.301956 / 0.255139 (0.046817) \| 0.330798 / 0.283200 (0.047599) \| 0.091755 / 0.141683 (-0.049927) \| 1.458577 / 1.452155 (0.006422) \| 1.532642 / 1.492716 (0.039926) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.194853 / 0.018006 (0.176847) \| 0.396844 / 0.000490 (0.396354) \| 0.004401 / 0.000200 (0.004201) \| 0.000076 / 0.000054 (0.000022) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022971 / 0.037411 (-0.014441) \| 0.096595 / 0.014526 (0.082069) \| 0.106104 / 0.176557 (-0.070452) \| 0.144815 / 0.737135 (-0.592320) \| 0.110036 / 0.296338 (-0.186303) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.415025 / 0.215209 (0.199816) \| 4.138136 / 2.077655 (2.060481) \| 1.861253 / 1.504120 (0.357133) \| 1.653420 / 1.541195 (0.112226) \| 1.703784 / 1.468490 (0.235294) \| 0.698261 / 4.584777 (-3.886516) \| 3.357240 / 3.745712 (-0.388472) \| 3.025790 / 5.269862 (-2.244072) \| 1.637191 / 4.565676 (-2.928485) \| 0.085620 / 0.424275 (-0.338655) \| 0.012454 / 0.007607 (0.004846) \| 0.524708 / 0.226044 (0.298663) \| 5.269234 / 2.268929 (3.000306) \| 2.290612 / 55.444624 (-53.154012) \| 1.936107 / 6.876477 (-4.940370) \| 1.968216 / 2.142072 (-0.173856) \| 0.810438 / 4.805227 (-3.994789) \| 0.154133 / 6.500664 (-6.346531) \| 0.064978 / 0.075469 (-0.010491) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.231782 / 1.841788 (-0.610006) \| 13.545573 / 8.074308 (5.471264) \| 14.558765 / 10.191392 (4.367373) \| 0.140763 / 0.680424 (-0.539661) \| 0.029259 / 0.534201 (-0.504942) \| 0.407776 / 0.579283 (-0.171507) \| 0.410244 / 0.434364 (-0.024120) \| 0.477313 / 0.540337 (-0.063024) \| 0.551465 / 1.386936 (-0.835471) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006272 / 0.011353 (-0.005081) \| 0.004397 / 0.011008 (-0.006611) \| 0.077496 / 0.038508 (0.038988) \| 0.026946 / 0.023109 (0.003837) \| 0.342992 / 0.275898 (0.067094) \| 0.374407 / 0.323480 (0.050927) \| 0.004849 / 0.007986 (-0.003136) \| 0.004549 / 0.004328 (0.000220) \| 0.076439 / 0.004250 (0.072189) \| 0.035829 / 0.037052 (-0.001224) \| 0.343483 / 0.258489 (0.084994) \| 0.385581 / 0.293841 (0.091740) \| 0.031745 / 0.128546 (-0.096801) \| 0.011617 / 0.075646 (-0.064030) \| 0.087207 / 0.419271 (-0.332064) \| 0.042252 / 0.043533 (-0.001281) \| 0.343223 / 0.255139 (0.088084) \| 0.368707 / 0.283200 (0.085508) \| 0.093259 / 0.141683 (-0.048424) \| 1.506904 / 1.452155 (0.054750) \| 1.567583 / 1.492716 (0.074867) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.158962 / 0.018006 (0.140955) \| 0.395982 / 0.000490 (0.395492) \| 0.003604 / 0.000200 (0.003404) \| 0.000078 / 0.000054 (0.000023) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025003 / 0.037411 (-0.012408) \| 0.101176 / 0.014526 (0.086650) \| 0.104494 / 0.176557 (-0.072062) \| 0.140414 / 0.737135 (-0.596722) \| 0.108398 / 0.296338 (-0.187941) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.436849 / 0.215209 (0.221640) \| 4.369428 / 2.077655 (2.291774) \| 2.070613 / 1.504120 (0.566493) \| 1.867511 / 1.541195 (0.326317) \| 1.866589 / 1.468490 (0.398099) \| 0.700036 / 4.584777 (-3.884741) \| 3.407513 / 3.745712 (-0.338199) \| 3.022409 / 5.269862 (-2.247453) \| 1.581423 / 4.565676 (-2.984253) \| 0.083425 / 0.424275 (-0.340850) \| 0.012380 / 0.007607 (0.004773) \| 0.535087 / 0.226044 (0.309043) \| 5.374814 / 2.268929 (3.105886) \| 2.504841 / 55.444624 (-52.939784) \| 2.166484 / 6.876477 (-4.709993) \| 2.166363 / 2.142072 (0.024291) \| 0.803692 / 4.805227 (-4.001535) \| 0.150873 / 6.500664 (-6.349791) \| 0.066253 / 0.075469 (-0.009216) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.291256 / 1.841788 (-0.550532) \| 13.827843 / 8.074308 (5.753535) \| 13.839334 / 10.191392 (3.647942) \| 0.153530 / 0.680424 (-0.526894) \| 0.016896 / 0.534201 (-0.517305) \| 0.379937 / 0.579283 (-0.199346) \| 0.396241 / 0.434364 (-0.038123) \| 0.461808 / 0.540337 (-0.078530) \| 0.553023 / 1.386936 (-0.833913) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#779ddc5c7ebbd406b2a6c9092c3f455a2cc7f5e7 \"CML watermark\")\n" ]
1,552,890,419	5,454	Save and resume the state of a DataLoader	open	2023-01-23T10:58:54	2024-11-27T01:19:21	null	https://github.com/huggingface/datasets/issues/5454	null	lhoestq	false	[ "Something that'd be nice to have is \"manual update of state\". One of the learning from training LLMs is the ability to skip some batches whenever we notice huge spike might be handy.", "Your outline spec is very sound and clear, @lhoestq - thank you!\r\n\r\n@thomasw21, indeed that would be a wonderful extra feature. In Megatron-Deepspeed we manually drained the dataloader for the range we wanted. I wasn't very satisfied with the way we did it, since its behavior would change if you were to do multiple range skips. I think it should remember all the ranges it skipped and not just skip the last range - since otherwise the data is inconsistent (but we probably should discuss this in a separate issue not to derail this much bigger one).", "Hi there! I think this is a critical issue and have an urgent need for it, in my attempt to train on a super large-scale dataset using `datasets`. It is impossible to resume a time-consuming (like one month) experiment by iterating all seen data again, which could possibly cost several days.\r\n\r\n@stas00 @thomasw21 @lhoestq Any updates on this problem after 1 year passed?", "any update？", "No update so far, I wonder if someone implemented a resumable pytorch Sampler somwhere.\r\n\r\nThen regarding resuming a streaming dataset, we'd first like to have an efficient way to skip shards automatically but this is not implemented yet", "I opened a draft here for IterableDataset: https://github.com/huggingface/datasets/pull/6658\r\n\r\n\r\n\r\n```python\r\n\"\"\"Requires https://github.com/huggingface/datasets/pull/6658 (WIP)\"\"\"\r\nfrom datasets import load_dataset\r\nfrom torch.utils.data import DataLoader\r\n\r\nds = load_dataset(..., streaming=True)\r\n# ds = ds.map(tokenize)\r\n# ds = ds.shuffle(seed=42, buffer_size=1000)\r\n\r\n# Init the dataset state_dict, or load it from a checkpoint\r\ndataset_state_dict = ds.state_dict()\r\n\r\n# Resumable training loop\r\nds.load_state_dict(dataset_state_dict)\r\ndataloader = DataLoader(ds, batch_size=batch_size)\r\nfor step, batch in enumerate(dataloader):\r\n ...\r\n if step % save_steps == 0:\r\n dataset_state_dict = ds.state_dict()\r\n```", "Hi @lhoestq - can you provide more information and how to implement on saving and restoring vanilla DataLoader states with map-style datasets?\r\n\r\n", "For now the easiest is probably to use the vanilla DataLoader only for batching and multiprocessing, and implement the resuming logic using a `Dataset` (it has `.select()` to skip examples) and a `dataset_state_dict`:\r\n\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nfrom torch.utils.data import DataLoader\r\n\r\nds = load_dataset(...)\r\n# ds = ds.map(tokenize)\r\n# ds = ds.shuffle(seed=42)\r\n\r\n# Init the dataset state_dict, or load it from a checkpoint\r\ndataset_state_dict = {\"step\": 0} \r\n\r\n# Resumable training loop\r\nstart_step = dataset_state_dict[\"step\"]\r\ndataloader = DataLoader(ds.select(range(start_step * batch_size, len(ds))), batch_size=batch_size)\r\nfor step, batch in enumerate(dataloader, start=start_step):\r\n ...\r\n if step % save_steps == 0:\r\n dataset_state_dict = {\"step\": step}\r\n```", "Hello, I found a similar implementation online that seems to solve your problem. https://github.com/facebookresearch/vissl/blob/main/vissl/data/data_helper.py#L93\r\nit looks like we can set_start_iter in StatefulDistributedSampler to implement the stateful resume requirement we want.\r\n\r\n", "Hi y'all, @lhoestq I wanted to flag that we currently have a StatefulDataLoader in `pytorch/data/torchdata` that has state_dict/load_state_dict methods, which will call a dataset's state_dict/load_state_dict methods but also handle multiprocessing under the hood. Any chance we can collaborate on this and try to get them to work well together? Please have a look here for some basic examples: https://github.com/pytorch/data/tree/main/torchdata/stateful_dataloader#saving-and-loading-state ", "Fantastic ! This will help pushing our IterableDataset state_dict implementation at https://github.com/huggingface/datasets/pull/6658 :) I'll check if there is anything missing to maker them work together, and add tests and some docs referring to the StatefulDataLoader :)", "Ah I just saw this disclaimer in the torchdata README and it feels like people should not rely on it. Should the StatefulDataLoader live elsewhere @andrewkho ?\r\n\r\n> ⚠️ As of July 2023, we have paused active development on TorchData and have paused new releases. We have learnt a lot from building it and hearing from users, but also believe we need to re-evaluate the technical design and approach given how much the industry has changed since we began the project. During the rest of 2023 we will be re-evaluating our plans in this space. Please reach out if you suggestions or comments (please use https://github.com/pytorch/data/issues/1196 for feedback).", "@lhoestq Good find, we are in the midst of updating this disclaimer as we're re-starting development and regular releases, though our approach will be to iterate on DL V1 (ie StatefulDataLoader) instead of continuing development on datapipes+DLV2. Let's discuss on a call at some point to figure out the best path forward! ", "As a heads up, `IterableDataset` state_dict has been added in https://github.com/huggingface/datasets/pull/6658\r\n\r\n...and it works out of the box with the `torchdata` `StatefulDataLoader` :)\r\n\r\nSee the docs at https://huggingface.co/docs/datasets/main/en/use_with_pytorch#checkpoint-and-resume", "amazing! Thank you, @lhoestq \r\n\r\ndoes it work with non-iterable dataset as well? the docs only mention iterable dataset", "It's for iterable dataset only. For regular dataset I believe the sampler should implement state_dict, but maybe @andrewkho might know best how to resume a regular dataset with torchdata", "@stas00 stateful dataloader will save and resume samplers for map style datasets. If no state_dict/load_state_dict is provided by the sampler, it will naively skip samples to fast forward. See here for more details https://github.com/pytorch/data/blob/main/torchdata/stateful_dataloader/README.md \n\nHope this helps! ", "Thank you very much for clarifying that, Andrew.\r\n\r\n", "👋 I am trying to use `HF Streaming Dataset + TorchDDP + Stateful Dataloader`, to train using multiple nodes and large datasets. \r\n\r\nSo far, I have been able to use HF Streaming Dataset + TorchDDP with Vanilla Datasets. To do so, I implemented a custom iterable to make sure that shards are distributed across the multiple nodes, while letting the `dataset` take care of the multiple workers. The implementation uses `split_dataset_by_node`:\r\n\r\n```\r\nimport torch\r\nfrom torch.distributed import get_rank, get_world_size\r\nfrom torch.utils.data import DataLoader, IterableDataset\r\n\r\nclass MyIterableDataset(IterableDataset):\r\n def __init__(self, dataset):\r\n super().__init__()\r\n self.dataset = dataset\r\n self._iterable_by_node = None\r\n\r\n def __iter__(self):\r\n if torch.distributed.is_available() and torch.distributed.is_initialized():\r\n world_size = get_world_size()\r\n process_rank = get_rank()\r\n else:\r\n world_size = 1\r\n process_rank = 0\r\n\r\n if world_size > 1:\r\n self._iterable_by_node = split_dataset_by_node(\r\n self.dataset, rank=process_rank, world_size=world_size\r\n )\r\n else:\r\n self._iterable_by_node = self.dataset\r\n\r\n for example in self._iterable_by_node:\r\n # Trying with _state_dict, since `.state_dict()` creates a copy\r\n self._state_dict.update(self._iterable_by_node._state_dict)\r\n yield example\r\n\r\n def state_dict(self):\r\n return self._state_dict\r\n\r\n def load_state_dict(self, state):\r\n pass # Not implemented yet\r\n\r\n```\r\n \r\nThis doesn't seem to work with `StatefulDataLoader` though. I can see the state of the worker's dataset being updated in its corresponding workers' processes, but somehow the updates are not propagated back to the main process. I have tried with different variants of the above code without success. \r\n\r\nI confirmed that if I skip the custom class and pass `dataset` directly to the loader as in the [docs](https://huggingface.co/docs/datasets/main/en/use_with_pytorch#checkpoint-and-resume), the StatefulDataLoader sees the updates for each worker. However, if I do this, multiple nodes will see the same examples, which I definitely don't want.\r\n\r\nIs there something I am missing? It would be nice if streaming `dataset`s would support by default the multinode training (unless it already does it and I am missing something).\r\n\r\n\r\n", "Hi ! Have you tried using `split_dataset_by_node()` and pass the result to the StatefulDataLoader ?\r\n\r\n```python\r\ndataloader = StatefulDataLoader(split_dataset_by_node(dataset, rank=process_rank, world_size=world_size))\r\n```", "> Hi ! Have you tried using split_dataset_by_node() and pass the result to the StatefulDataLoader ?\r\n\r\n@lhoestq it took me some time to test, but it works like a charm. Thanks for the pointer. Totally missed this 🤦. " ]
1,552,727,425	5,453	Fix base directory while extracting insecure TAR files	closed	2023-01-23T08:57:40	2023-01-24T01:34:20	2023-01-23T10:10:42	https://github.com/huggingface/datasets/pull/5453	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5453", "html_url": "https://github.com/huggingface/datasets/pull/5453", "diff_url": "https://github.com/huggingface/datasets/pull/5453.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5453.patch", "merged_at": "2023-01-23T10:10:42" }	albertvillanova	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008215 / 0.011353 (-0.003138) \| 0.004510 / 0.011008 (-0.006498) \| 0.099270 / 0.038508 (0.060761) \| 0.028682 / 0.023109 (0.005573) \| 0.332726 / 0.275898 (0.056827) \| 0.371025 / 0.323480 (0.047545) \| 0.006665 / 0.007986 (-0.001320) \| 0.003329 / 0.004328 (-0.001000) \| 0.078509 / 0.004250 (0.074259) \| 0.032388 / 0.037052 (-0.004664) \| 0.348540 / 0.258489 (0.090051) \| 0.382212 / 0.293841 (0.088371) \| 0.033307 / 0.128546 (-0.095239) \| 0.011642 / 0.075646 (-0.064004) \| 0.322573 / 0.419271 (-0.096699) \| 0.041297 / 0.043533 (-0.002236) \| 0.322710 / 0.255139 (0.067571) \| 0.361593 / 0.283200 (0.078394) \| 0.082276 / 0.141683 (-0.059407) \| 1.481932 / 1.452155 (0.029777) \| 1.531677 / 1.492716 (0.038961) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.194964 / 0.018006 (0.176958) \| 0.406002 / 0.000490 (0.405512) \| 0.001015 / 0.000200 (0.000815) \| 0.000075 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023317 / 0.037411 (-0.014095) \| 0.097231 / 0.014526 (0.082705) \| 0.103898 / 0.176557 (-0.072659) \| 0.139864 / 0.737135 (-0.597271) \| 0.106785 / 0.296338 (-0.189554) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.419036 / 0.215209 (0.203827) \| 4.193985 / 2.077655 (2.116330) \| 1.879069 / 1.504120 (0.374949) \| 1.675384 / 1.541195 (0.134190) \| 1.696225 / 1.468490 (0.227735) \| 0.695257 / 4.584777 (-3.889520) \| 3.437971 / 3.745712 (-0.307741) \| 2.656037 / 5.269862 (-2.613824) \| 1.463320 / 4.565676 (-3.102356) \| 0.082575 / 0.424275 (-0.341700) \| 0.012593 / 0.007607 (0.004986) \| 0.526643 / 0.226044 (0.300599) \| 5.278366 / 2.268929 (3.009437) \| 2.288106 / 55.444624 (-53.156518) \| 1.954875 / 6.876477 (-4.921602) \| 1.950641 / 2.142072 (-0.191431) \| 0.808289 / 4.805227 (-3.996938) \| 0.148790 / 6.500664 (-6.351875) \| 0.064775 / 0.075469 (-0.010694) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.215219 / 1.841788 (-0.626569) \| 13.551467 / 8.074308 (5.477159) \| 13.841547 / 10.191392 (3.650155) \| 0.153610 / 0.680424 (-0.526814) \| 0.028308 / 0.534201 (-0.505893) \| 0.397087 / 0.579283 (-0.182196) \| 0.401724 / 0.434364 (-0.032640) \| 0.458042 / 0.540337 (-0.082296) \| 0.544955 / 1.386936 (-0.841981) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006321 / 0.011353 (-0.005032) \| 0.004336 / 0.011008 (-0.006673) \| 0.097196 / 0.038508 (0.058688) \| 0.026933 / 0.023109 (0.003824) \| 0.416520 / 0.275898 (0.140622) \| 0.450703 / 0.323480 (0.127223) \| 0.004831 / 0.007986 (-0.003155) \| 0.003252 / 0.004328 (-0.001076) \| 0.074981 / 0.004250 (0.070730) \| 0.036136 / 0.037052 (-0.000917) \| 0.423166 / 0.258489 (0.164677) \| 0.460936 / 0.293841 (0.167095) \| 0.031859 / 0.128546 (-0.096687) \| 0.011500 / 0.075646 (-0.064146) \| 0.318197 / 0.419271 (-0.101074) \| 0.041472 / 0.043533 (-0.002061) \| 0.419227 / 0.255139 (0.164088) \| 0.444712 / 0.283200 (0.161512) \| 0.088841 / 0.141683 (-0.052841) \| 1.497237 / 1.452155 (0.045083) \| 1.572111 / 1.492716 (0.079395) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.239261 / 0.018006 (0.221255) \| 0.400358 / 0.000490 (0.399868) \| 0.003460 / 0.000200 (0.003261) \| 0.000078 / 0.000054 (0.000024) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024016 / 0.037411 (-0.013395) \| 0.098414 / 0.014526 (0.083888) \| 0.107220 / 0.176557 (-0.069337) \| 0.143538 / 0.737135 (-0.593598) \| 0.108607 / 0.296338 (-0.187731) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.473896 / 0.215209 (0.258687) \| 4.740386 / 2.077655 (2.662731) \| 2.458046 / 1.504120 (0.953926) \| 2.260895 / 1.541195 (0.719700) \| 2.280218 / 1.468490 (0.811728) \| 0.694843 / 4.584777 (-3.889934) \| 3.349795 / 3.745712 (-0.395917) \| 1.846970 / 5.269862 (-3.422892) \| 1.151481 / 4.565676 (-3.414195) \| 0.082054 / 0.424275 (-0.342221) \| 0.012664 / 0.007607 (0.005057) \| 0.573400 / 0.226044 (0.347355) \| 5.750648 / 2.268929 (3.481720) \| 2.904257 / 55.444624 (-52.540367) \| 2.555181 / 6.876477 (-4.321295) \| 2.595830 / 2.142072 (0.453758) \| 0.799580 / 4.805227 (-4.005647) \| 0.151088 / 6.500664 (-6.349576) \| 0.066639 / 0.075469 (-0.008831) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.251413 / 1.841788 (-0.590375) \| 13.743368 / 8.074308 (5.669060) \| 13.808729 / 10.191392 (3.617337) \| 0.144765 / 0.680424 (-0.535659) \| 0.016606 / 0.534201 (-0.517594) \| 0.376503 / 0.579283 (-0.202780) \| 0.381510 / 0.434364 (-0.052854) \| 0.440295 / 0.540337 (-0.100043) \| 0.524248 / 1.386936 (-0.862688) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#eea1226779993687845da5ecd264cf047e46a128 \"CML watermark\")\n", "Thanks a lot, @albertvillanova - I validated that your fix solves the original problem!" ]
1,552,655,939	5,452	Swap log messages for symbolic/hard links in tar extractor	closed	2023-01-23T07:53:38	2023-01-23T09:40:55	2023-01-23T08:31:17	https://github.com/huggingface/datasets/pull/5452	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5452", "html_url": "https://github.com/huggingface/datasets/pull/5452", "diff_url": "https://github.com/huggingface/datasets/pull/5452.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5452.patch", "merged_at": "2023-01-23T08:31:17" }	albertvillanova	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.011848 / 0.011353 (0.000495) \| 0.006988 / 0.011008 (-0.004020) \| 0.138078 / 0.038508 (0.099570) \| 0.040310 / 0.023109 (0.017201) \| 0.411857 / 0.275898 (0.135959) \| 0.509496 / 0.323480 (0.186016) \| 0.010695 / 0.007986 (0.002709) \| 0.005275 / 0.004328 (0.000946) \| 0.107157 / 0.004250 (0.102907) \| 0.050987 / 0.037052 (0.013935) \| 0.432387 / 0.258489 (0.173898) \| 0.495136 / 0.293841 (0.201295) \| 0.055273 / 0.128546 (-0.073273) \| 0.019573 / 0.075646 (-0.056074) \| 0.460356 / 0.419271 (0.041084) \| 0.060916 / 0.043533 (0.017383) \| 0.426140 / 0.255139 (0.171002) \| 0.430461 / 0.283200 (0.147261) \| 0.124569 / 0.141683 (-0.017114) \| 1.989404 / 1.452155 (0.537250) \| 1.942052 / 1.492716 (0.449335) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287233 / 0.018006 (0.269227) \| 0.606056 / 0.000490 (0.605566) \| 0.004435 / 0.000200 (0.004235) \| 0.000144 / 0.000054 (0.000090) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032353 / 0.037411 (-0.005058) \| 0.124237 / 0.014526 (0.109711) \| 0.143280 / 0.176557 (-0.033276) \| 0.182081 / 0.737135 (-0.555055) \| 0.148085 / 0.296338 (-0.148253) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.613550 / 0.215209 (0.398341) \| 6.172421 / 2.077655 (4.094766) \| 2.466018 / 1.504120 (0.961898) \| 2.166433 / 1.541195 (0.625238) \| 2.192511 / 1.468490 (0.724021) \| 1.248777 / 4.584777 (-3.336000) \| 5.746150 / 3.745712 (2.000438) \| 3.097184 / 5.269862 (-2.172678) \| 2.078176 / 4.565676 (-2.487501) \| 0.144351 / 0.424275 (-0.279924) \| 0.014830 / 0.007607 (0.007223) \| 0.761699 / 0.226044 (0.535655) \| 7.713201 / 2.268929 (5.444272) \| 3.359647 / 55.444624 (-52.084977) \| 2.652595 / 6.876477 (-4.223882) \| 2.721952 / 2.142072 (0.579880) \| 1.493036 / 4.805227 (-3.312192) \| 0.252336 / 6.500664 (-6.248328) \| 0.082906 / 0.075469 (0.007436) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.643887 / 1.841788 (-0.197901) \| 18.762775 / 8.074308 (10.688466) \| 22.003583 / 10.191392 (11.812191) \| 0.256361 / 0.680424 (-0.424062) \| 0.048048 / 0.534201 (-0.486153) \| 0.601971 / 0.579283 (0.022688) \| 0.712801 / 0.434364 (0.278438) \| 0.684473 / 0.540337 (0.144136) \| 0.802566 / 1.386936 (-0.584370) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010410 / 0.011353 (-0.000943) \| 0.006719 / 0.011008 (-0.004289) \| 0.132862 / 0.038508 (0.094354) \| 0.036973 / 0.023109 (0.013863) \| 0.470925 / 0.275898 (0.195027) \| 0.502864 / 0.323480 (0.179384) \| 0.007447 / 0.007986 (-0.000539) \| 0.005629 / 0.004328 (0.001301) \| 0.091985 / 0.004250 (0.087734) \| 0.057537 / 0.037052 (0.020485) \| 0.458362 / 0.258489 (0.199873) \| 0.518324 / 0.293841 (0.224483) \| 0.056540 / 0.128546 (-0.072007) \| 0.021266 / 0.075646 (-0.054380) \| 0.448289 / 0.419271 (0.029018) \| 0.064211 / 0.043533 (0.020678) \| 0.492596 / 0.255139 (0.237457) \| 0.495030 / 0.283200 (0.211830) \| 0.121858 / 0.141683 (-0.019825) \| 1.823821 / 1.452155 (0.371667) \| 2.012165 / 1.492716 (0.519449) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296252 / 0.018006 (0.278245) \| 0.601688 / 0.000490 (0.601198) \| 0.006369 / 0.000200 (0.006169) \| 0.000107 / 0.000054 (0.000053) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035821 / 0.037411 (-0.001590) \| 0.132722 / 0.014526 (0.118196) \| 0.141819 / 0.176557 (-0.034738) \| 0.205115 / 0.737135 (-0.532020) \| 0.148917 / 0.296338 (-0.147422) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.678207 / 0.215209 (0.462998) \| 6.969918 / 2.077655 (4.892263) \| 3.077831 / 1.504120 (1.573711) \| 2.689296 / 1.541195 (1.148102) \| 2.706462 / 1.468490 (1.237972) \| 1.249125 / 4.584777 (-3.335652) \| 5.793917 / 3.745712 (2.048205) \| 3.137565 / 5.269862 (-2.132297) \| 2.056880 / 4.565676 (-2.508796) \| 0.151918 / 0.424275 (-0.272357) \| 0.015029 / 0.007607 (0.007422) \| 0.833975 / 0.226044 (0.607930) \| 8.575649 / 2.268929 (6.306720) \| 3.812115 / 55.444624 (-51.632509) \| 3.124219 / 6.876477 (-3.752258) \| 3.178645 / 2.142072 (1.036572) \| 1.488260 / 4.805227 (-3.316967) \| 0.268239 / 6.500664 (-6.232425) \| 0.089463 / 0.075469 (0.013993) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.645461 / 1.841788 (-0.196327) \| 19.074412 / 8.074308 (11.000104) \| 21.626726 / 10.191392 (11.435334) \| 0.210525 / 0.680424 (-0.469899) \| 0.032166 / 0.534201 (-0.502035) \| 0.555572 / 0.579283 (-0.023711) \| 0.654667 / 0.434364 (0.220303) \| 0.632471 / 0.540337 (0.092133) \| 0.756510 / 1.386936 (-0.630426) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6681c36bbaae9b8b1daa3dbbd4a96b35aaae271b \"CML watermark\")\n" ]
1,552,336,300	5,451	ImageFolder BadZipFile: Bad offset for central directory	closed	2023-01-22T23:50:12	2023-05-23T10:35:48	2023-02-10T16:31:36	https://github.com/huggingface/datasets/issues/5451	null	hmartiro	false	[ "Hi ! Could you share the full stack trace ? Which dataset did you try to load ?\r\n\r\nit may be related to https://github.com/huggingface/datasets/pull/5640", "The `BadZipFile` error means the ZIP file is corrupted, so I'm closing this issue as it's not directly related to `datasets`.", "For others that find this issue following a `BadZipFile` error, I had the same problem because I had a file in a folder dataset `my-image.target` and the datasets library was incorrectly determining that the (PNG) file was a zip archive. When it tried to extract the file, this error occurred. \r\n\r\nUpdating to `datasets==2.12.0` fixed the problem for me." ]
1,551,109,365	5,450	to_tf_dataset with a TF collator causes bizarrely persistent slowdown	closed	2023-01-20T16:08:37	2023-02-13T14:13:34	2023-02-13T14:13:34	https://github.com/huggingface/datasets/issues/5450	null	Rocketknight1	false	[ "wtf", "Couldn't find what's causing this, this will need more investigation", "A possible hint: The function it seems to be spending a lot of time in (when iterating over the original dataset) is `_get_mp` in the PIL JPEG decoder: \r\n![image](https://user-images.githubusercontent.com/12866554/214057267-c889f05e-efaf-4036-b805-c5381fa62f4a.png)\r\n", "If \"mp\" is multiprocessing, this might suggest some kind of negative interaction between the JPEG decoder and TF's handling of processes/threads. Note that we haven't merged the parallel `to_tf_dataset` PR yet, so it's not caused by that PR!", "Update: MP isn't multiprocessing at all, it's an internal PIL method for loading metadata from JPEG files. No idea why that would be a bottleneck, but I'll see if a Python profiler can't figure out where the time is actually being spent.", "After further profiling, the slowdown is in the C methods for JPEG decoding that are included as part of PIL. Because Python profilers can't inspect inside that, I don't have any further information on which lines exactly are responsible for the slowdown or why.\r\n\r\nIn the meantime, I'm going to suggest switching from `return_tensors=\"tf\"` to `return_tensors=\"np\"` in most of our `transformers` code - this generally works better for pre-processing. Two relevant PRs are [here](https://github.com/huggingface/transformers/pull/21266) and [here](https://github.com/huggingface/notebooks/pull/308).", "Closing this issue as we've done what we can with this one! " ]
1,550,801,453	5,449	Support fsspec 2023.1.0 in CI	closed	2023-01-20T12:53:17	2023-01-20T13:32:50	2023-01-20T13:26:03	https://github.com/huggingface/datasets/pull/5449	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5449", "html_url": "https://github.com/huggingface/datasets/pull/5449", "diff_url": "https://github.com/huggingface/datasets/pull/5449.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5449.patch", "merged_at": "2023-01-20T13:26:03" }	albertvillanova	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008227 / 0.011353 (-0.003126) \| 0.004496 / 0.011008 (-0.006512) \| 0.099319 / 0.038508 (0.060811) \| 0.029929 / 0.023109 (0.006820) \| 0.296686 / 0.275898 (0.020788) \| 0.355372 / 0.323480 (0.031892) \| 0.006864 / 0.007986 (-0.001122) \| 0.003458 / 0.004328 (-0.000871) \| 0.077234 / 0.004250 (0.072983) \| 0.037072 / 0.037052 (0.000020) \| 0.311675 / 0.258489 (0.053186) \| 0.338965 / 0.293841 (0.045124) \| 0.033562 / 0.128546 (-0.094985) \| 0.011399 / 0.075646 (-0.064248) \| 0.322406 / 0.419271 (-0.096865) \| 0.043034 / 0.043533 (-0.000499) \| 0.298083 / 0.255139 (0.042944) \| 0.323661 / 0.283200 (0.040462) \| 0.089380 / 0.141683 (-0.052303) \| 1.479363 / 1.452155 (0.027208) \| 1.518337 / 1.492716 (0.025620) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.177822 / 0.018006 (0.159816) \| 0.400806 / 0.000490 (0.400317) \| 0.002121 / 0.000200 (0.001921) \| 0.000074 / 0.000054 (0.000019) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021986 / 0.037411 (-0.015426) \| 0.096749 / 0.014526 (0.082223) \| 0.101443 / 0.176557 (-0.075113) \| 0.137519 / 0.737135 (-0.599616) \| 0.105558 / 0.296338 (-0.190780) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.418983 / 0.215209 (0.203774) \| 4.189579 / 2.077655 (2.111924) \| 1.877831 / 1.504120 (0.373711) \| 1.666213 / 1.541195 (0.125019) \| 1.680735 / 1.468490 (0.212245) \| 0.693033 / 4.584777 (-3.891744) \| 3.420553 / 3.745712 (-0.325160) \| 1.819647 / 5.269862 (-3.450214) \| 1.144934 / 4.565676 (-3.420743) \| 0.082209 / 0.424275 (-0.342066) \| 0.012433 / 0.007607 (0.004826) \| 0.526781 / 0.226044 (0.300737) \| 5.273689 / 2.268929 (3.004760) \| 2.323468 / 55.444624 (-53.121156) \| 1.960508 / 6.876477 (-4.915969) \| 2.035338 / 2.142072 (-0.106735) \| 0.812789 / 4.805227 (-3.992438) \| 0.148429 / 6.500664 (-6.352235) \| 0.064727 / 0.075469 (-0.010742) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.253218 / 1.841788 (-0.588569) \| 13.303426 / 8.074308 (5.229118) \| 13.651074 / 10.191392 (3.459682) \| 0.135178 / 0.680424 (-0.545246) \| 0.028483 / 0.534201 (-0.505717) \| 0.393284 / 0.579283 (-0.185999) \| 0.401957 / 0.434364 (-0.032407) \| 0.457136 / 0.540337 (-0.083201) \| 0.535835 / 1.386936 (-0.851101) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006335 / 0.011353 (-0.005017) \| 0.004454 / 0.011008 (-0.006554) \| 0.097565 / 0.038508 (0.059057) \| 0.026917 / 0.023109 (0.003808) \| 0.350779 / 0.275898 (0.074881) \| 0.391979 / 0.323480 (0.068499) \| 0.004648 / 0.007986 (-0.003337) \| 0.003204 / 0.004328 (-0.001124) \| 0.076987 / 0.004250 (0.072737) \| 0.035257 / 0.037052 (-0.001796) \| 0.347193 / 0.258489 (0.088704) \| 0.391462 / 0.293841 (0.097621) \| 0.031244 / 0.128546 (-0.097302) \| 0.011460 / 0.075646 (-0.064186) \| 0.321606 / 0.419271 (-0.097665) \| 0.041218 / 0.043533 (-0.002315) \| 0.341884 / 0.255139 (0.086745) \| 0.374920 / 0.283200 (0.091720) \| 0.086383 / 0.141683 (-0.055300) \| 1.501750 / 1.452155 (0.049595) \| 1.565060 / 1.492716 (0.072344) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.165447 / 0.018006 (0.147441) \| 0.401885 / 0.000490 (0.401395) \| 0.000975 / 0.000200 (0.000775) \| 0.000070 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024494 / 0.037411 (-0.012917) \| 0.097334 / 0.014526 (0.082808) \| 0.105324 / 0.176557 (-0.071232) \| 0.142430 / 0.737135 (-0.594705) \| 0.107249 / 0.296338 (-0.189089) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.441632 / 0.215209 (0.226423) \| 4.407729 / 2.077655 (2.330074) \| 2.078167 / 1.504120 (0.574047) \| 1.864210 / 1.541195 (0.323015) \| 1.885948 / 1.468490 (0.417458) \| 0.693974 / 4.584777 (-3.890803) \| 3.386837 / 3.745712 (-0.358875) \| 1.840291 / 5.269862 (-3.429571) \| 1.150524 / 4.565676 (-3.415153) \| 0.082240 / 0.424275 (-0.342035) \| 0.012488 / 0.007607 (0.004881) \| 0.537589 / 0.226044 (0.311545) \| 5.404007 / 2.268929 (3.135078) \| 2.537467 / 55.444624 (-52.907157) \| 2.190775 / 6.876477 (-4.685702) \| 2.224746 / 2.142072 (0.082674) \| 0.799524 / 4.805227 (-4.005703) \| 0.150639 / 6.500664 (-6.350025) \| 0.066473 / 0.075469 (-0.008997) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.258559 / 1.841788 (-0.583228) \| 13.773583 / 8.074308 (5.699275) \| 13.964322 / 10.191392 (3.772930) \| 0.156295 / 0.680424 (-0.524129) \| 0.016824 / 0.534201 (-0.517377) \| 0.377476 / 0.579283 (-0.201807) \| 0.390163 / 0.434364 (-0.044201) \| 0.442541 / 0.540337 (-0.097796) \| 0.529404 / 1.386936 (-0.857532) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8f500a5c554b213aafe87293bd593920567742c3 \"CML watermark\")\n" ]
1,550,618,514	5,448	Support fsspec 2023.1.0 in CI	closed	2023-01-20T10:26:31	2023-01-20T13:26:05	2023-01-20T13:26:05	https://github.com/huggingface/datasets/issues/5448	null	albertvillanova	false	[]
1,550,599,193	5,447	Fix CI by temporarily pinning fsspec < 2023.1.0	closed	2023-01-20T10:11:02	2023-01-20T10:38:13	2023-01-20T10:28:43	https://github.com/huggingface/datasets/pull/5447	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5447", "html_url": "https://github.com/huggingface/datasets/pull/5447", "diff_url": "https://github.com/huggingface/datasets/pull/5447.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5447.patch", "merged_at": "2023-01-20T10:28:43" }	albertvillanova	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.011875 / 0.011353 (0.000522) \| 0.008188 / 0.011008 (-0.002821) \| 0.131137 / 0.038508 (0.092629) \| 0.038127 / 0.023109 (0.015018) \| 0.383864 / 0.275898 (0.107966) \| 0.458617 / 0.323480 (0.135137) \| 0.010989 / 0.007986 (0.003003) \| 0.004892 / 0.004328 (0.000563) \| 0.101955 / 0.004250 (0.097704) \| 0.045081 / 0.037052 (0.008029) \| 0.409768 / 0.258489 (0.151279) \| 0.446597 / 0.293841 (0.152756) \| 0.058588 / 0.128546 (-0.069958) \| 0.020872 / 0.075646 (-0.054774) \| 0.432982 / 0.419271 (0.013711) \| 0.075875 / 0.043533 (0.032342) \| 0.380923 / 0.255139 (0.125784) \| 0.432994 / 0.283200 (0.149795) \| 0.122678 / 0.141683 (-0.019005) \| 1.857865 / 1.452155 (0.405710) \| 1.927801 / 1.492716 (0.435085) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.212941 / 0.018006 (0.194935) \| 0.527977 / 0.000490 (0.527488) \| 0.002996 / 0.000200 (0.002797) \| 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) \| 0.030046 / 0.037411 (-0.007366) \| 0.126384 / 0.014526 (0.111858) \| 0.138307 / 0.176557 (-0.038250) \| 0.185338 / 0.737135 (-0.551797) \| 0.144733 / 0.296338 (-0.151606) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.627096 / 0.215209 (0.411887) \| 6.418014 / 2.077655 (4.340360) \| 2.547675 / 1.504120 (1.043555) \| 2.195552 / 1.541195 (0.654357) \| 2.200377 / 1.468490 (0.731887) \| 1.289935 / 4.584777 (-3.294842) \| 5.670839 / 3.745712 (1.925127) \| 5.252597 / 5.269862 (-0.017265) \| 2.878470 / 4.565676 (-1.687207) \| 0.143754 / 0.424275 (-0.280521) \| 0.014814 / 0.007607 (0.007207) \| 0.810073 / 0.226044 (0.584028) \| 8.183757 / 2.268929 (5.914829) \| 3.375525 / 55.444624 (-52.069099) \| 2.594048 / 6.876477 (-4.282428) \| 2.598095 / 2.142072 (0.456023) \| 1.554493 / 4.805227 (-3.250734) \| 0.263159 / 6.500664 (-6.237505) \| 0.089822 / 0.075469 (0.014353) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.660847 / 1.841788 (-0.180941) \| 18.434283 / 8.074308 (10.359975) \| 21.764887 / 10.191392 (11.573495) \| 0.264524 / 0.680424 (-0.415900) \| 0.048519 / 0.534201 (-0.485682) \| 0.587468 / 0.579283 (0.008185) \| 0.634142 / 0.434364 (0.199778) \| 0.675374 / 0.540337 (0.135037) \| 0.777510 / 1.386936 (-0.609426) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010021 / 0.011353 (-0.001332) \| 0.006207 / 0.011008 (-0.004801) \| 0.130490 / 0.038508 (0.091982) \| 0.037957 / 0.023109 (0.014848) \| 0.489381 / 0.275898 (0.213483) \| 0.536522 / 0.323480 (0.213042) \| 0.008611 / 0.007986 (0.000626) \| 0.004894 / 0.004328 (0.000565) \| 0.101617 / 0.004250 (0.097367) \| 0.052629 / 0.037052 (0.015577) \| 0.509211 / 0.258489 (0.250721) \| 0.545023 / 0.293841 (0.251182) \| 0.057468 / 0.128546 (-0.071078) \| 0.023393 / 0.075646 (-0.052253) \| 0.431408 / 0.419271 (0.012137) \| 0.064967 / 0.043533 (0.021434) \| 0.495261 / 0.255139 (0.240122) \| 0.527098 / 0.283200 (0.243898) \| 0.113172 / 0.141683 (-0.028511) \| 1.937072 / 1.452155 (0.484918) \| 2.048413 / 1.492716 (0.555697) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.245406 / 0.018006 (0.227399) \| 0.526772 / 0.000490 (0.526283) \| 0.004379 / 0.000200 (0.004179) \| 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) \| 0.031785 / 0.037411 (-0.005626) \| 0.130949 / 0.014526 (0.116424) \| 0.145660 / 0.176557 (-0.030896) \| 0.186991 / 0.737135 (-0.550144) \| 0.151000 / 0.296338 (-0.145338) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.708643 / 0.215209 (0.493434) \| 7.179252 / 2.077655 (5.101597) \| 3.143375 / 1.504120 (1.639255) \| 2.714298 / 1.541195 (1.173103) \| 2.773441 / 1.468490 (1.304951) \| 1.312821 / 4.584777 (-3.271956) \| 5.798396 / 3.745712 (2.052684) \| 3.253215 / 5.269862 (-2.016646) \| 2.147260 / 4.565676 (-2.418416) \| 0.154673 / 0.424275 (-0.269602) \| 0.014918 / 0.007607 (0.007311) \| 0.860618 / 0.226044 (0.634573) \| 8.774455 / 2.268929 (6.505527) \| 3.925020 / 55.444624 (-51.519604) \| 3.139361 / 6.876477 (-3.737115) \| 3.208883 / 2.142072 (1.066810) \| 1.547305 / 4.805227 (-3.257922) \| 0.268814 / 6.500664 (-6.231850) \| 0.084578 / 0.075469 (0.009109) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.694990 / 1.841788 (-0.146798) \| 18.619183 / 8.074308 (10.544875) \| 21.929886 / 10.191392 (11.738494) \| 0.265763 / 0.680424 (-0.414661) \| 0.028325 / 0.534201 (-0.505876) \| 0.552910 / 0.579283 (-0.026373) \| 0.616864 / 0.434364 (0.182500) \| 0.637858 / 0.540337 (0.097521) \| 0.744508 / 1.386936 (-0.642428) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5f819ba3d0306748aaf9fd8ea040b981dd08e5e5 \"CML watermark\")\n" ]
1,550,591,588	5,446	test v0.12.0.rc0	closed	2023-01-20T10:05:19	2023-01-20T10:43:22	2023-01-20T10:13:48	https://github.com/huggingface/datasets/pull/5446	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5446", "html_url": "https://github.com/huggingface/datasets/pull/5446", "diff_url": "https://github.com/huggingface/datasets/pull/5446.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5446.patch", "merged_at": null }	Wauplin	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "@Wauplin I was testing it in a dedicated branch without opening a PR: https://github.com/huggingface/datasets/commits/test-hfh-0.12.0rc0", "Oops, sorry @albertvillanova. I thought for next time I'll start the CIs before pinging everyone.\r\nI'm closing this one.", "@Wauplin in your Slack message, you asked people from every major dependent library to check that our CI work. That is why I am checking it... :)\r\n\r\nAlso, I think for this purpose it is better to test it in a dedicated branch, rather than opening and closing a PR.", "Yes, yes I know. Completely my fault on this one" ]
1,550,588,703	5,445	CI tests are broken: AttributeError: 'mappingproxy' object has no attribute 'target'	closed	2023-01-20T10:03:10	2023-01-20T10:28:44	2023-01-20T10:28:44	https://github.com/huggingface/datasets/issues/5445	null	albertvillanova	false	[]
1,550,185,071	5,444	info messages logged as warnings	closed	2023-01-20T01:19:18	2023-07-12T17:19:31	2023-07-12T17:19:31	https://github.com/huggingface/datasets/issues/5444	null	davidgilbertson	false	[ "Looks like a duplicate of https://github.com/huggingface/datasets/issues/1948. \r\n\r\nI also think these should be logged as INFO messages, but let's see what @lhoestq thinks.", "It can be considered unexpected to see a `map` function return instantaneously. The warning is here to explain this case by mentioning that the cache was used. I don't expect first time users (only seeing warnings) to guess that the cache works this way", "Oh, so it's intentional? Do all Hugging Face packages use `warning` when using cache?\r\nI guess feel free to close this issue then.", "Yes it's intentional for `map`. For `load_dataset` it's also intentional but for a different reason: it shows where in the cache the dataset is located, in case the user wants to clear the cache.", "OK I see. It's surprising to me that these are considered \"something unexpected happened\", the concept of cache is pretty common.\r\n\r\nHas a user every actually complained that they ran their code once, and it took a minute while the data downloaded, then ran their code again and it ran really fast (and completed successfully) but they were so baffled by the fact that it ran quickly, _and_ didn't set the log level to INFO, _and_ hadn't read the docs (or thought about it) to know that datasets are cached, that they logged an issue asking that this information be output as a warning every time they run their code?\r\n\r\nThat seems like a very niche scenario to cater for, given that the side effect is to flood the console with irrelevant warnings for every other user every other time they run a bit of `datasets` code. And the real world impact is that people TURN OFF warnings, which is a pretty bad habit to get into.\r\n\r\nAnyhoo, if there's no chance I'm going to change your mind, please close the issue :)", "I see your point and I'm not closed to switching to INFO, but I think those logs are important to make the library less opaque. I also just checked `transformers` scripts and they default to INFO which is nice. However for colab users the default is still WARNING iirc, and it counts as one of the main env where `datasets` is used.\r\n\r\nWe also use progress bars a lot in `datasets`, that are shown if the logger is at the WARNING level. But we offer a function to disable the progress bars if necessary.", "These kinds of messages are logged as INFO in Transformers, so we should probably be consistent with them" ]
1,550,178,914	5,443	Update share tutorial	closed	2023-01-20T01:09:14	2023-01-20T15:44:45	2023-01-20T15:37:30	https://github.com/huggingface/datasets/pull/5443	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5443", "html_url": "https://github.com/huggingface/datasets/pull/5443", "diff_url": "https://github.com/huggingface/datasets/pull/5443.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5443.patch", "merged_at": "2023-01-20T15:37:30" }	stevhliu	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009885 / 0.011353 (-0.001468) \| 0.005338 / 0.011008 (-0.005670) \| 0.099967 / 0.038508 (0.061459) \| 0.036860 / 0.023109 (0.013751) \| 0.295283 / 0.275898 (0.019385) \| 0.369504 / 0.323480 (0.046024) \| 0.008267 / 0.007986 (0.000281) \| 0.004375 / 0.004328 (0.000046) \| 0.076294 / 0.004250 (0.072043) \| 0.047058 / 0.037052 (0.010006) \| 0.314463 / 0.258489 (0.055974) \| 0.348125 / 0.293841 (0.054284) \| 0.038334 / 0.128546 (-0.090213) \| 0.012102 / 0.075646 (-0.063544) \| 0.333049 / 0.419271 (-0.086223) \| 0.050727 / 0.043533 (0.007195) \| 0.299244 / 0.255139 (0.044105) \| 0.318210 / 0.283200 (0.035010) \| 0.112609 / 0.141683 (-0.029074) \| 1.450377 / 1.452155 (-0.001778) \| 1.485177 / 1.492716 (-0.007539) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287083 / 0.018006 (0.269077) \| 0.564268 / 0.000490 (0.563778) \| 0.003578 / 0.000200 (0.003378) \| 0.000093 / 0.000054 (0.000039) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026755 / 0.037411 (-0.010657) \| 0.105857 / 0.014526 (0.091331) \| 0.118291 / 0.176557 (-0.058266) \| 0.155735 / 0.737135 (-0.581401) \| 0.122527 / 0.296338 (-0.173812) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.396992 / 0.215209 (0.181783) \| 3.958562 / 2.077655 (1.880908) \| 1.781570 / 1.504120 (0.277451) \| 1.617743 / 1.541195 (0.076549) \| 1.753504 / 1.468490 (0.285013) \| 0.681509 / 4.584777 (-3.903268) \| 3.816910 / 3.745712 (0.071198) \| 2.087359 / 5.269862 (-3.182503) \| 1.328380 / 4.565676 (-3.237297) \| 0.083542 / 0.424275 (-0.340733) \| 0.012081 / 0.007607 (0.004473) \| 0.505127 / 0.226044 (0.279082) \| 5.075136 / 2.268929 (2.806208) \| 2.259871 / 55.444624 (-53.184753) \| 1.944302 / 6.876477 (-4.932175) \| 2.102624 / 2.142072 (-0.039449) \| 0.819779 / 4.805227 (-3.985448) \| 0.165584 / 6.500664 (-6.335080) \| 0.061774 / 0.075469 (-0.013695) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.208258 / 1.841788 (-0.633530) \| 14.841635 / 8.074308 (6.767327) \| 14.484515 / 10.191392 (4.293123) \| 0.156464 / 0.680424 (-0.523959) \| 0.028839 / 0.534201 (-0.505362) \| 0.440860 / 0.579283 (-0.138423) \| 0.433892 / 0.434364 (-0.000472) \| 0.515339 / 0.540337 (-0.024998) \| 0.608838 / 1.386936 (-0.778098) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007548 / 0.011353 (-0.003804) \| 0.005464 / 0.011008 (-0.005544) \| 0.096987 / 0.038508 (0.058479) \| 0.034472 / 0.023109 (0.011363) \| 0.391249 / 0.275898 (0.115351) \| 0.432779 / 0.323480 (0.109299) \| 0.006170 / 0.007986 (-0.001816) \| 0.004316 / 0.004328 (-0.000013) \| 0.074184 / 0.004250 (0.069934) \| 0.054254 / 0.037052 (0.017202) \| 0.397947 / 0.258489 (0.139458) \| 0.451253 / 0.293841 (0.157412) \| 0.037098 / 0.128546 (-0.091449) \| 0.012649 / 0.075646 (-0.062997) \| 0.333533 / 0.419271 (-0.085739) \| 0.050247 / 0.043533 (0.006714) \| 0.390446 / 0.255139 (0.135307) \| 0.410547 / 0.283200 (0.127347) \| 0.110888 / 0.141683 (-0.030795) \| 1.452160 / 1.452155 (0.000006) \| 1.596331 / 1.492716 (0.103615) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.256061 / 0.018006 (0.238055) \| 0.552674 / 0.000490 (0.552184) \| 0.003362 / 0.000200 (0.003162) \| 0.000095 / 0.000054 (0.000040) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030199 / 0.037411 (-0.007213) \| 0.110288 / 0.014526 (0.095762) \| 0.127412 / 0.176557 (-0.049145) \| 0.165428 / 0.737135 (-0.571707) \| 0.131658 / 0.296338 (-0.164680) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.441946 / 0.215209 (0.226737) \| 4.414209 / 2.077655 (2.336555) \| 2.284530 / 1.504120 (0.780410) \| 2.110752 / 1.541195 (0.569557) \| 2.210751 / 1.468490 (0.742260) \| 0.698829 / 4.584777 (-3.885948) \| 3.819044 / 3.745712 (0.073332) \| 3.274021 / 5.269862 (-1.995840) \| 1.781284 / 4.565676 (-2.784393) \| 0.085264 / 0.424275 (-0.339011) \| 0.012360 / 0.007607 (0.004753) \| 0.553519 / 0.226044 (0.327475) \| 5.466395 / 2.268929 (3.197467) \| 2.825839 / 55.444624 (-52.618786) \| 2.439451 / 6.876477 (-4.437026) \| 2.582534 / 2.142072 (0.440462) \| 0.841644 / 4.805227 (-3.963583) \| 0.172288 / 6.500664 (-6.328376) \| 0.067215 / 0.075469 (-0.008254) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.283623 / 1.841788 (-0.558165) \| 15.753163 / 8.074308 (7.678855) \| 14.983263 / 10.191392 (4.791871) \| 0.187584 / 0.680424 (-0.492840) \| 0.017999 / 0.534201 (-0.516202) \| 0.427157 / 0.579283 (-0.152126) \| 0.435456 / 0.434364 (0.001092) \| 0.496800 / 0.540337 (-0.043537) \| 0.592557 / 1.386936 (-0.794379) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8a72676689a4a3fb466cc5077884446c7302e605 \"CML watermark\")\n" ]
1,550,084,450	5,442	OneDrive Integrations with HF Datasets	closed	2023-01-19T23:12:08	2023-02-24T16:17:51	2023-02-24T16:17:51	https://github.com/huggingface/datasets/issues/5442	null	Mohammed20201991	false	[ "Hi! \r\n\r\nWe use [`fsspec`](https://github.com/fsspec/filesystem_spec) to integrate with storage providers. You can find more info (and the usage examples) in [our docs](https://huggingface.co/docs/datasets/v2.8.0/filesystems#download-and-prepare-a-dataset-into-a-cloud-storage).\r\n\r\n[`gdrivefs`](https://github.com/fsspec/gdrivefs) makes it possible to use Google Drive as a storage service in Datasets, but this is not the case for OneDrive, since its[ Python SDK](https://github.com/OneDrive/onedrive-sdk-python) is not integrated with `fsspec`. Can you please request the integration with `fsspec` in their repo to address this limitation?", "I'm closing this issue as implementing a fsspec-compliant OneDrive filesystem is not our responsibility." ]
1,548,417,594	5,441	resolving a weird tar extract issue	open	2023-01-19T02:17:21	2023-01-20T16:49:22	null	https://github.com/huggingface/datasets/pull/5441	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5441", "html_url": "https://github.com/huggingface/datasets/pull/5441", "diff_url": "https://github.com/huggingface/datasets/pull/5441.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5441.patch", "merged_at": null }	stas00	true	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.011815 / 0.011353 (0.000463) \| 0.006407 / 0.011008 (-0.004601) \| 0.132937 / 0.038508 (0.094429) \| 0.040634 / 0.023109 (0.017525) \| 0.398049 / 0.275898 (0.122151) \| 0.498207 / 0.323480 (0.174727) \| 0.010111 / 0.007986 (0.002126) \| 0.007282 / 0.004328 (0.002954) \| 0.103661 / 0.004250 (0.099411) \| 0.046223 / 0.037052 (0.009171) \| 0.411490 / 0.258489 (0.153001) \| 0.480973 / 0.293841 (0.187132) \| 0.058397 / 0.128546 (-0.070149) \| 0.019952 / 0.075646 (-0.055695) \| 0.440734 / 0.419271 (0.021463) \| 0.064585 / 0.043533 (0.021052) \| 0.392556 / 0.255139 (0.137417) \| 0.437842 / 0.283200 (0.154643) \| 0.130684 / 0.141683 (-0.010999) \| 1.910552 / 1.452155 (0.458397) \| 1.984644 / 1.492716 (0.491927) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.264417 / 0.018006 (0.246411) \| 0.676519 / 0.000490 (0.676030) \| 0.003369 / 0.000200 (0.003169) \| 0.000125 / 0.000054 (0.000071) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034558 / 0.037411 (-0.002854) \| 0.126561 / 0.014526 (0.112035) \| 0.134478 / 0.176557 (-0.042079) \| 0.202125 / 0.737135 (-0.535010) \| 0.143273 / 0.296338 (-0.153066) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.618592 / 0.215209 (0.403383) \| 6.224435 / 2.077655 (4.146780) \| 2.636689 / 1.504120 (1.132569) \| 2.243507 / 1.541195 (0.702313) \| 2.312449 / 1.468490 (0.843959) \| 1.188499 / 4.584777 (-3.396277) \| 5.738347 / 3.745712 (1.992635) \| 4.891933 / 5.269862 (-0.377929) \| 2.697631 / 4.565676 (-1.868046) \| 0.140200 / 0.424275 (-0.284076) \| 0.015484 / 0.007607 (0.007877) \| 0.781947 / 0.226044 (0.555903) \| 7.946600 / 2.268929 (5.677671) \| 3.365574 / 55.444624 (-52.079050) \| 2.783443 / 6.876477 (-4.093034) \| 2.738634 / 2.142072 (0.596561) \| 1.487247 / 4.805227 (-3.317980) \| 0.255681 / 6.500664 (-6.244983) \| 0.084607 / 0.075469 (0.009138) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.717846 / 1.841788 (-0.123941) \| 18.405566 / 8.074308 (10.331258) \| 20.508578 / 10.191392 (10.317186) \| 0.262364 / 0.680424 (-0.418060) \| 0.050881 / 0.534201 (-0.483319) \| 0.587516 / 0.579283 (0.008232) \| 0.650900 / 0.434364 (0.216536) \| 0.656168 / 0.540337 (0.115830) \| 0.778876 / 1.386936 (-0.608061) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010817 / 0.011353 (-0.000536) \| 0.007338 / 0.011008 (-0.003670) \| 0.131949 / 0.038508 (0.093441) \| 0.037244 / 0.023109 (0.014135) \| 0.565994 / 0.275898 (0.290096) \| 0.567434 / 0.323480 (0.243954) \| 0.007733 / 0.007986 (-0.000252) \| 0.005216 / 0.004328 (0.000887) \| 0.096578 / 0.004250 (0.092328) \| 0.056001 / 0.037052 (0.018949) \| 0.538209 / 0.258489 (0.279720) \| 0.580385 / 0.293841 (0.286544) \| 0.053654 / 0.128546 (-0.074892) \| 0.019471 / 0.075646 (-0.056176) \| 0.448781 / 0.419271 (0.029509) \| 0.064774 / 0.043533 (0.021241) \| 0.540222 / 0.255139 (0.285083) \| 0.563058 / 0.283200 (0.279858) \| 0.122716 / 0.141683 (-0.018967) \| 1.839402 / 1.452155 (0.387247) \| 1.915523 / 1.492716 (0.422806) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.310448 / 0.018006 (0.292442) \| 0.603664 / 0.000490 (0.603175) \| 0.004833 / 0.000200 (0.004633) \| 0.000145 / 0.000054 (0.000090) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032340 / 0.037411 (-0.005072) \| 0.130115 / 0.014526 (0.115589) \| 0.154192 / 0.176557 (-0.022364) \| 0.200655 / 0.737135 (-0.536480) \| 0.144961 / 0.296338 (-0.151377) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.671588 / 0.215209 (0.456379) \| 6.691642 / 2.077655 (4.613988) \| 2.915230 / 1.504120 (1.411110) \| 2.573337 / 1.541195 (1.032143) \| 2.578204 / 1.468490 (1.109714) \| 1.249028 / 4.584777 (-3.335749) \| 5.808539 / 3.745712 (2.062827) \| 3.079317 / 5.269862 (-2.190545) \| 2.033308 / 4.565676 (-2.532369) \| 0.142411 / 0.424275 (-0.281864) \| 0.015525 / 0.007607 (0.007918) \| 0.800389 / 0.226044 (0.574345) \| 8.228236 / 2.268929 (5.959308) \| 3.660207 / 55.444624 (-51.784417) \| 3.021033 / 6.876477 (-3.855444) \| 3.088335 / 2.142072 (0.946263) \| 1.380137 / 4.805227 (-3.425091) \| 0.252065 / 6.500664 (-6.248599) \| 0.084302 / 0.075469 (0.008833) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.709429 / 1.841788 (-0.132359) \| 18.358770 / 8.074308 (10.284462) \| 21.109844 / 10.191392 (10.918452) \| 0.231549 / 0.680424 (-0.448875) \| 0.029251 / 0.534201 (-0.504950) \| 0.560719 / 0.579283 (-0.018564) \| 0.610125 / 0.434364 (0.175761) \| 0.630015 / 0.540337 (0.089678) \| 0.751656 / 1.386936 (-0.635280) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#18baf4eebf71c0db1d9980f7ee164f1272ff8f26 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5441). All of your documentation changes will be reflected on that endpoint.", "I think I managed to reproduce it:\r\n\r\n```\r\nrm -rf ~/.cache/huggingface/datasets/HuggingFaceM4___cm4-synthetic-testing\r\nmkdir -p /tmp/xxx/hf-data\r\nsudo ln -s /tmp/xxx /test\r\nmkdir -p /tmp/yyy\r\nln -sf /test/hf-data /tmp/yyy/data\r\ncd /tmp/yyy\r\npython -c 'import sys; from datasets import load_dataset; ds=load_dataset(sys.argv[1])' HuggingFaceM4/cm4-synthetic-testing\r\n```\r\n\r\nPlease note it includes a creation of a symlink from the `/` (so `sudo`) - may be there is a simpler way but I'm just trying to replicate the real setup. Of course please be careful - it's mostly under `/tmp` not to destroy anything if you try to run this.\r\n\r\nthis fails with:\r\n\r\n```\r\nNo config specified, defaulting to: cm4-synthetic-testing/100.unique\r\nDownloading and preparing dataset cm4-synthetic-testing/100.unique (download: 20.71 KiB, generated: 49.99 MiB, post-processed: Unknown size, total: 50.01 MiB) to /home/stas/.cache/huggingface/datasets/HuggingFaceM4___cm4-synthetic-testing/100.unique/1.1.1/2e33dcc086c7209b8ccff4b19e44f1d41b5be53262e7d793142b96c2e984602b...\r\nExtraction of data is blocked (illegal path: /tmp/yyy)\r\n[...]\r\nExtraction of data/115/texts_03.txt is blocked (illegal path: /tmp/yyy)\r\nGenerating 100.unique split: 0%\| \| 0/100 [00:00<?, ? examples/s]Generating 100-long unique records split\r\n\r\nTraceback (most recent call last):\r\n File \"/mnt/nvme0/code/huggingface/datasets-master/src/datasets/builder.py\", line 1571, in _prepare_split_single\r\n for key, record in generator:\r\n File \"/home/stas/.cache/huggingface/modules/datasets_modules/datasets/HuggingFaceM4--cm4-synthetic-testing/2e33dcc086c7209b8ccff4b19e44f1d41b5be53262e7d793142b96c2e984602b/cm4-synthetic-testing.py\", line 190, in _generate_examples\r\n raise ValueError(f\"can't find any data - check {data_path}\")\r\nValueError: can't find any data - check /home/stas/.cache/huggingface/datasets/downloads/extracted/134227b9b94c4eccf19b205bf3021d4492d0227b9be6c2ddb6bf517d8d55a8cb/data\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 \"<string>\", line 1, in <module>\r\n File \"/mnt/nvme0/code/huggingface/datasets-master/src/datasets/load.py\", line 1757, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/mnt/nvme0/code/huggingface/datasets-master/src/datasets/builder.py\", line 860, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/mnt/nvme0/code/huggingface/datasets-master/src/datasets/builder.py\", line 1612, in _download_and_prepare\r\n super()._download_and_prepare(\r\n File \"/mnt/nvme0/code/huggingface/datasets-master/src/datasets/builder.py\", line 953, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/mnt/nvme0/code/huggingface/datasets-master/src/datasets/builder.py\", line 1450, in _prepare_split\r\n for job_id, done, content in self._prepare_split_single(\r\n File \"/mnt/nvme0/code/huggingface/datasets-master/src/datasets/builder.py\", line 1607, in _prepare_split_single\r\n raise DatasetGenerationError(\"An error occurred while generating the dataset\") from e\r\ndatasets.builder.DatasetGenerationError: An error occurred while generating the dataset\r\n```\r\n\r\nnote that `illegal path: /tmp/yyy` is now with the mods of this PR.\r\n\r\n----------------------\r\n\r\nAlso I think the whole thing should have failed at the first `illegal path` and not continue running. But as it continued and gave:\r\n\r\n\r\n> ValueError: can't find any data - check /home/stas/.cache/huggingface/datasets/downloads/extracted/134227b9b94c4eccf19b205bf3021d4492d0227b9be6c2ddb6bf517d8d55a8cb/data\r\n\r\nwhat can a user do with that other than confirming that that dir is indeed empty, but no clue is given to why and it's far from obvious that one needs to scroll up and discover earlier issues. Most users won't do that.\r\n\r\n(my apologies for writing out so much - was trying to make the situation clear)", "Thank you, Albert, for the explanation.\r\n\r\nTo summarize I think what's needed is:\r\n\r\n1. add a comment in the code to why this is done for someone being puzzled over the odd code\r\n2. and to use an actionable by the user error message\r\n3. perform an untrapped assert on that tar extract error and not continue, so that the user will not get a later misleading error that the folder is empty and is completely not actionable and it's is far from obvious that one needs to scroll up to find earlier errors, which were trapped.\r\n\r\nAfter reading the advisory I'm still not sure why `cwd` is used and not a designated `~/.cache/huggingface/datasets/downloads/extracted`, I can't see what difference does it make since I could `chdir` to the designated directory and it would be `cwd`. The security solution is trying to ensure that `/etc/passwd` won't get overriden. So why is the check done in `.` and not the real target base directory, since the extraction isn't done in the current working dir. By not using `.` you lower the chances that the user will have all sorts of local symlinks that could trigger the issue since `datasets` typically is the only one managing it's `~/.cache/huggingface/datasets` domain and 99.9% of the time the user won't manually create files in it.\r\n\r\nthank you!\r\n" ]
1,538,361,143	5,440	Fix documentation about batch samplers	closed	2023-01-18T17:04:27	2023-01-18T17:57:29	2023-01-18T17:50:04	https://github.com/huggingface/datasets/pull/5440	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5440", "html_url": "https://github.com/huggingface/datasets/pull/5440", "diff_url": "https://github.com/huggingface/datasets/pull/5440.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5440.patch", "merged_at": "2023-01-18T17:50:04" }	thomasw21	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008874 / 0.011353 (-0.002479) \| 0.004685 / 0.011008 (-0.006323) \| 0.101478 / 0.038508 (0.062970) \| 0.031409 / 0.023109 (0.008300) \| 0.305429 / 0.275898 (0.029531) \| 0.371777 / 0.323480 (0.048297) \| 0.007282 / 0.007986 (-0.000704) \| 0.005545 / 0.004328 (0.001217) \| 0.078583 / 0.004250 (0.074333) \| 0.037171 / 0.037052 (0.000118) \| 0.320186 / 0.258489 (0.061696) \| 0.347881 / 0.293841 (0.054040) \| 0.034005 / 0.128546 (-0.094541) \| 0.011534 / 0.075646 (-0.064113) \| 0.326079 / 0.419271 (-0.093193) \| 0.040856 / 0.043533 (-0.002677) \| 0.307327 / 0.255139 (0.052188) \| 0.323521 / 0.283200 (0.040321) \| 0.090407 / 0.141683 (-0.051276) \| 1.481994 / 1.452155 (0.029840) \| 1.490372 / 1.492716 (-0.002345) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.175161 / 0.018006 (0.157155) \| 0.447009 / 0.000490 (0.446519) \| 0.003570 / 0.000200 (0.003370) \| 0.000072 / 0.000054 (0.000017) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023868 / 0.037411 (-0.013543) \| 0.100791 / 0.014526 (0.086265) \| 0.108131 / 0.176557 (-0.068425) \| 0.147993 / 0.737135 (-0.589142) \| 0.111205 / 0.296338 (-0.185133) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.425369 / 0.215209 (0.210160) \| 4.241694 / 2.077655 (2.164040) \| 2.145403 / 1.504120 (0.641283) \| 1.913517 / 1.541195 (0.372322) \| 1.887307 / 1.468490 (0.418817) \| 0.691615 / 4.584777 (-3.893162) \| 3.402233 / 3.745712 (-0.343480) \| 1.992532 / 5.269862 (-3.277330) \| 1.322292 / 4.565676 (-3.243385) \| 0.082862 / 0.424275 (-0.341413) \| 0.012595 / 0.007607 (0.004988) \| 0.528490 / 0.226044 (0.302445) \| 5.313338 / 2.268929 (3.044409) \| 2.645037 / 55.444624 (-52.799587) \| 2.326279 / 6.876477 (-4.550198) \| 2.396955 / 2.142072 (0.254883) \| 0.819354 / 4.805227 (-3.985873) \| 0.150889 / 6.500664 (-6.349775) \| 0.066517 / 0.075469 (-0.008952) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.233673 / 1.841788 (-0.608114) \| 14.563293 / 8.074308 (6.488985) \| 14.317989 / 10.191392 (4.126597) \| 0.150767 / 0.680424 (-0.529657) \| 0.028972 / 0.534201 (-0.505229) \| 0.400547 / 0.579283 (-0.178736) \| 0.402267 / 0.434364 (-0.032097) \| 0.459375 / 0.540337 (-0.080962) \| 0.544419 / 1.386936 (-0.842517) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006817 / 0.011353 (-0.004536) \| 0.004588 / 0.011008 (-0.006421) \| 0.099224 / 0.038508 (0.060716) \| 0.027730 / 0.023109 (0.004621) \| 0.412310 / 0.275898 (0.136412) \| 0.445731 / 0.323480 (0.122252) \| 0.005197 / 0.007986 (-0.002788) \| 0.003601 / 0.004328 (-0.000728) \| 0.076200 / 0.004250 (0.071950) \| 0.041813 / 0.037052 (0.004761) \| 0.415282 / 0.258489 (0.156793) \| 0.457182 / 0.293841 (0.163341) \| 0.031920 / 0.128546 (-0.096626) \| 0.011712 / 0.075646 (-0.063934) \| 0.320859 / 0.419271 (-0.098412) \| 0.041466 / 0.043533 (-0.002067) \| 0.418156 / 0.255139 (0.163017) \| 0.435501 / 0.283200 (0.152302) \| 0.090727 / 0.141683 (-0.050955) \| 1.484014 / 1.452155 (0.031859) \| 1.568072 / 1.492716 (0.075356) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.263356 / 0.018006 (0.245350) \| 0.410768 / 0.000490 (0.410278) \| 0.015983 / 0.000200 (0.015783) \| 0.000301 / 0.000054 (0.000246) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024522 / 0.037411 (-0.012889) \| 0.103986 / 0.014526 (0.089460) \| 0.109253 / 0.176557 (-0.067303) \| 0.142308 / 0.737135 (-0.594827) \| 0.114037 / 0.296338 (-0.182302) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.452617 / 0.215209 (0.237407) \| 4.505215 / 2.077655 (2.427560) \| 2.185546 / 1.504120 (0.681426) \| 1.995540 / 1.541195 (0.454345) \| 1.962875 / 1.468490 (0.494385) \| 0.690237 / 4.584777 (-3.894540) \| 3.448311 / 3.745712 (-0.297401) \| 1.901572 / 5.269862 (-3.368289) \| 1.170832 / 4.565676 (-3.394844) \| 0.082333 / 0.424275 (-0.341942) \| 0.012569 / 0.007607 (0.004962) \| 0.547822 / 0.226044 (0.321778) \| 5.504180 / 2.268929 (3.235251) \| 2.693981 / 55.444624 (-52.750644) \| 2.320710 / 6.876477 (-4.555767) \| 2.270508 / 2.142072 (0.128435) \| 0.803145 / 4.805227 (-4.002083) \| 0.152168 / 6.500664 (-6.348496) \| 0.067408 / 0.075469 (-0.008061) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.260689 / 1.841788 (-0.581099) \| 14.281112 / 8.074308 (6.206804) \| 14.549742 / 10.191392 (4.358350) \| 0.129337 / 0.680424 (-0.551087) \| 0.017181 / 0.534201 (-0.517020) \| 0.380473 / 0.579283 (-0.198810) \| 0.387689 / 0.434364 (-0.046675) \| 0.446734 / 0.540337 (-0.093603) \| 0.532479 / 1.386936 (-0.854457) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7972a0b5f1ad2c36023a79686f6ef026f4ffa64f \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008953 / 0.011353 (-0.002400) \| 0.004917 / 0.011008 (-0.006091) \| 0.098699 / 0.038508 (0.060191) \| 0.034460 / 0.023109 (0.011351) \| 0.294604 / 0.275898 (0.018706) \| 0.322709 / 0.323480 (-0.000770) \| 0.007780 / 0.007986 (-0.000206) \| 0.004061 / 0.004328 (-0.000267) \| 0.076134 / 0.004250 (0.071883) \| 0.043786 / 0.037052 (0.006734) \| 0.302155 / 0.258489 (0.043666) \| 0.339779 / 0.293841 (0.045938) \| 0.038305 / 0.128546 (-0.090241) \| 0.012131 / 0.075646 (-0.063515) \| 0.332656 / 0.419271 (-0.086615) \| 0.048029 / 0.043533 (0.004496) \| 0.303859 / 0.255139 (0.048720) \| 0.315861 / 0.283200 (0.032662) \| 0.100758 / 0.141683 (-0.040925) \| 1.468072 / 1.452155 (0.015918) \| 1.521325 / 1.492716 (0.028609) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.244975 / 0.018006 (0.226969) \| 0.524392 / 0.000490 (0.523902) \| 0.003720 / 0.000200 (0.003520) \| 0.000087 / 0.000054 (0.000032) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027704 / 0.037411 (-0.009707) \| 0.109048 / 0.014526 (0.094522) \| 0.118298 / 0.176557 (-0.058259) \| 0.158748 / 0.737135 (-0.578388) \| 0.125654 / 0.296338 (-0.170684) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.406973 / 0.215209 (0.191764) \| 4.057502 / 2.077655 (1.979847) \| 1.939847 / 1.504120 (0.435727) \| 1.746457 / 1.541195 (0.205262) \| 1.698866 / 1.468490 (0.230376) \| 0.692884 / 4.584777 (-3.891893) \| 3.736988 / 3.745712 (-0.008724) \| 2.050122 / 5.269862 (-3.219740) \| 1.299808 / 4.565676 (-3.265868) \| 0.085285 / 0.424275 (-0.338990) \| 0.012768 / 0.007607 (0.005161) \| 0.510814 / 0.226044 (0.284770) \| 5.105319 / 2.268929 (2.836391) \| 2.304003 / 55.444624 (-53.140621) \| 1.951123 / 6.876477 (-4.925354) \| 1.998504 / 2.142072 (-0.143568) \| 0.840235 / 4.805227 (-3.964993) \| 0.164521 / 6.500664 (-6.336143) \| 0.064215 / 0.075469 (-0.011254) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.272520 / 1.841788 (-0.569268) \| 14.648110 / 8.074308 (6.573802) \| 14.573754 / 10.191392 (4.382362) \| 0.170053 / 0.680424 (-0.510371) \| 0.029389 / 0.534201 (-0.504811) \| 0.438924 / 0.579283 (-0.140359) \| 0.433572 / 0.434364 (-0.000792) \| 0.517702 / 0.540337 (-0.022635) \| 0.600389 / 1.386936 (-0.786547) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007362 / 0.011353 (-0.003991) \| 0.005451 / 0.011008 (-0.005557) \| 0.099336 / 0.038508 (0.060828) \| 0.033284 / 0.023109 (0.010174) \| 0.377143 / 0.275898 (0.101245) \| 0.423724 / 0.323480 (0.100244) \| 0.006194 / 0.007986 (-0.001792) \| 0.004208 / 0.004328 (-0.000121) \| 0.074473 / 0.004250 (0.070223) \| 0.049874 / 0.037052 (0.012821) \| 0.376012 / 0.258489 (0.117523) \| 0.439942 / 0.293841 (0.146101) \| 0.037860 / 0.128546 (-0.090686) \| 0.012546 / 0.075646 (-0.063100) \| 0.349123 / 0.419271 (-0.070148) \| 0.048980 / 0.043533 (0.005447) \| 0.391205 / 0.255139 (0.136066) \| 0.396474 / 0.283200 (0.113274) \| 0.105846 / 0.141683 (-0.035836) \| 1.502475 / 1.452155 (0.050321) \| 1.612303 / 1.492716 (0.119587) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300815 / 0.018006 (0.282809) \| 0.542171 / 0.000490 (0.541681) \| 0.005465 / 0.000200 (0.005265) \| 0.000094 / 0.000054 (0.000039) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028904 / 0.037411 (-0.008508) \| 0.110352 / 0.014526 (0.095827) \| 0.123275 / 0.176557 (-0.053282) \| 0.161958 / 0.737135 (-0.575178) \| 0.133595 / 0.296338 (-0.162743) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.438724 / 0.215209 (0.223515) \| 4.373633 / 2.077655 (2.295979) \| 2.178981 / 1.504120 (0.674861) \| 1.992442 / 1.541195 (0.451247) \| 2.063149 / 1.468490 (0.594659) \| 0.696688 / 4.584777 (-3.888089) \| 3.849370 / 3.745712 (0.103658) \| 3.509495 / 5.269862 (-1.760367) \| 1.923320 / 4.565676 (-2.642356) \| 0.085554 / 0.424275 (-0.338721) \| 0.012510 / 0.007607 (0.004903) \| 0.535953 / 0.226044 (0.309909) \| 5.365684 / 2.268929 (3.096755) \| 2.686902 / 55.444624 (-52.757723) \| 2.330922 / 6.876477 (-4.545554) \| 2.353445 / 2.142072 (0.211373) \| 0.878336 / 4.805227 (-3.926891) \| 0.167296 / 6.500664 (-6.333368) \| 0.064564 / 0.075469 (-0.010905) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.244696 / 1.841788 (-0.597091) \| 15.027981 / 8.074308 (6.953673) \| 14.545797 / 10.191392 (4.354405) \| 0.147229 / 0.680424 (-0.533194) \| 0.018007 / 0.534201 (-0.516194) \| 0.446196 / 0.579283 (-0.133087) \| 0.437418 / 0.434364 (0.003054) \| 0.510732 / 0.540337 (-0.029606) \| 0.594814 / 1.386936 (-0.792122) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#80eb8db74f49b7ee9c0f73a819c22177fabd61db \"CML watermark\")\n" ]
1,537,973,564	5,439	[dataset request] Add Common Voice 12.0	closed	2023-01-18T13:07:05	2023-07-21T14:26:10	2023-07-21T14:26:09	https://github.com/huggingface/datasets/issues/5439	null	MohammedRakib	false	[ "@polinaeterna any tentative date on when the Common Voice 12.0 dataset will be added ?", "This dataset is now hosted on the Hub here: https://huggingface.co/datasets/mozilla-foundation/common_voice_12_0" ]
1,537,489,730	5,438	Update actions/checkout in CD Conda release	closed	2023-01-18T06:53:15	2023-01-18T13:49:51	2023-01-18T13:42:49	https://github.com/huggingface/datasets/pull/5438	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5438", "html_url": "https://github.com/huggingface/datasets/pull/5438", "diff_url": "https://github.com/huggingface/datasets/pull/5438.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5438.patch", "merged_at": "2023-01-18T13:42:48" }	albertvillanova	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008470 / 0.011353 (-0.002883) \| 0.004721 / 0.011008 (-0.006287) \| 0.099024 / 0.038508 (0.060516) \| 0.029831 / 0.023109 (0.006722) \| 0.325887 / 0.275898 (0.049989) \| 0.380753 / 0.323480 (0.057273) \| 0.007101 / 0.007986 (-0.000885) \| 0.004734 / 0.004328 (0.000406) \| 0.077576 / 0.004250 (0.073326) \| 0.037207 / 0.037052 (0.000154) \| 0.320463 / 0.258489 (0.061974) \| 0.369284 / 0.293841 (0.075443) \| 0.033411 / 0.128546 (-0.095135) \| 0.011610 / 0.075646 (-0.064037) \| 0.321460 / 0.419271 (-0.097811) \| 0.041315 / 0.043533 (-0.002217) \| 0.349186 / 0.255139 (0.094047) \| 0.384546 / 0.283200 (0.101347) \| 0.088045 / 0.141683 (-0.053637) \| 1.536341 / 1.452155 (0.084186) \| 1.527806 / 1.492716 (0.035089) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.193435 / 0.018006 (0.175429) \| 0.451732 / 0.000490 (0.451243) \| 0.003165 / 0.000200 (0.002965) \| 0.000082 / 0.000054 (0.000027) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023203 / 0.037411 (-0.014208) \| 0.096211 / 0.014526 (0.081685) \| 0.105665 / 0.176557 (-0.070891) \| 0.141074 / 0.737135 (-0.596061) \| 0.108584 / 0.296338 (-0.187755) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.419041 / 0.215209 (0.203832) \| 4.187915 / 2.077655 (2.110261) \| 1.855336 / 1.504120 (0.351216) \| 1.660046 / 1.541195 (0.118851) \| 1.674646 / 1.468490 (0.206156) \| 0.692257 / 4.584777 (-3.892520) \| 3.466853 / 3.745712 (-0.278860) \| 1.900925 / 5.269862 (-3.368936) \| 1.294696 / 4.565676 (-3.270980) \| 0.082792 / 0.424275 (-0.341483) \| 0.012808 / 0.007607 (0.005201) \| 0.529622 / 0.226044 (0.303578) \| 5.337025 / 2.268929 (3.068096) \| 2.326558 / 55.444624 (-53.118066) \| 1.956256 / 6.876477 (-4.920221) \| 2.035911 / 2.142072 (-0.106161) \| 0.815824 / 4.805227 (-3.989403) \| 0.148720 / 6.500664 (-6.351944) \| 0.064226 / 0.075469 (-0.011243) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.231347 / 1.841788 (-0.610440) \| 13.724596 / 8.074308 (5.650288) \| 13.933878 / 10.191392 (3.742486) \| 0.150913 / 0.680424 (-0.529511) \| 0.028460 / 0.534201 (-0.505741) \| 0.393564 / 0.579283 (-0.185719) \| 0.407185 / 0.434364 (-0.027179) \| 0.458250 / 0.540337 (-0.082087) \| 0.547993 / 1.386936 (-0.838943) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006653 / 0.011353 (-0.004699) \| 0.004615 / 0.011008 (-0.006393) \| 0.098062 / 0.038508 (0.059554) \| 0.027849 / 0.023109 (0.004740) \| 0.409116 / 0.275898 (0.133218) \| 0.448770 / 0.323480 (0.125290) \| 0.004856 / 0.007986 (-0.003130) \| 0.003427 / 0.004328 (-0.000901) \| 0.075748 / 0.004250 (0.071498) \| 0.037942 / 0.037052 (0.000889) \| 0.410232 / 0.258489 (0.151743) \| 0.457394 / 0.293841 (0.163553) \| 0.031927 / 0.128546 (-0.096620) \| 0.011618 / 0.075646 (-0.064028) \| 0.321231 / 0.419271 (-0.098040) \| 0.041416 / 0.043533 (-0.002117) \| 0.413535 / 0.255139 (0.158396) \| 0.438196 / 0.283200 (0.154997) \| 0.089551 / 0.141683 (-0.052132) \| 1.459298 / 1.452155 (0.007143) \| 1.552594 / 1.492716 (0.059878) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.228186 / 0.018006 (0.210180) \| 0.404393 / 0.000490 (0.403904) \| 0.006944 / 0.000200 (0.006744) \| 0.000081 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025167 / 0.037411 (-0.012244) \| 0.101282 / 0.014526 (0.086756) \| 0.107282 / 0.176557 (-0.069275) \| 0.139797 / 0.737135 (-0.597339) \| 0.110477 / 0.296338 (-0.185861) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.479121 / 0.215209 (0.263912) \| 4.778210 / 2.077655 (2.700555) \| 2.464687 / 1.504120 (0.960567) \| 2.255312 / 1.541195 (0.714118) \| 2.287348 / 1.468490 (0.818858) \| 0.694769 / 4.584777 (-3.890008) \| 3.460860 / 3.745712 (-0.284852) \| 3.078881 / 5.269862 (-2.190980) \| 1.297726 / 4.565676 (-3.267950) \| 0.082699 / 0.424275 (-0.341576) \| 0.012652 / 0.007607 (0.005045) \| 0.583308 / 0.226044 (0.357263) \| 5.839199 / 2.268929 (3.570271) \| 2.893724 / 55.444624 (-52.550900) \| 2.546503 / 6.876477 (-4.329974) \| 2.559570 / 2.142072 (0.417498) \| 0.802357 / 4.805227 (-4.002870) \| 0.151890 / 6.500664 (-6.348774) \| 0.068593 / 0.075469 (-0.006876) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.262421 / 1.841788 (-0.579367) \| 13.771848 / 8.074308 (5.697540) \| 14.046017 / 10.191392 (3.854625) \| 0.140950 / 0.680424 (-0.539474) \| 0.016839 / 0.534201 (-0.517362) \| 0.378870 / 0.579283 (-0.200413) \| 0.385908 / 0.434364 (-0.048456) \| 0.438539 / 0.540337 (-0.101799) \| 0.522761 / 1.386936 (-0.864175) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8145ebfd4fc3508d0be0de9a0f9c58877f2b32f8 \"CML watermark\")\n" ]
1,536,837,144	5,437	Can't load png dataset with 4 channel (RGBA)	closed	2023-01-17T18:22:27	2023-01-18T20:20:15	2023-01-18T20:20:15	https://github.com/huggingface/datasets/issues/5437	null	WiNE-iNEFF	false	[ "Hi! Can you please share the directory structure of your image folder and the `load_dataset` call? We decode images with Pillow, and Pillow supports RGBA PNGs, so this shouldn't be a problem.\r\n\r\n", "> Hi! Can you please share the directory structure of your image folder and the `load_dataset` call? We decode images with Pillow, and Pillow supports RGBA PNGs, so this shouldn't be a problem.\n> \n> \n\nI have only 1 folder that I use in the load_dataset function with the name \"IMGDATA\" and all my 9000 images are located in this folder.\n`\nfrom datasets import load_dataset\n\ndataset = load_dataset(\"IMGDATA\")\n`\nAt the same time, using another data set with images consisting of 3 RGB channels, everything works", "Okay, I figured out what was wrong. When uploading my dataset via Google Drive, the images broke and Pillow couldn't open them. As a result, I solved the problem by downloading the ZIP archive" ]
1,536,633,173	5,436	Revert container image pin in CI benchmarks	closed	2023-01-17T15:59:50	2023-01-18T09:05:49	2023-01-18T06:29:06	https://github.com/huggingface/datasets/pull/5436	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5436", "html_url": "https://github.com/huggingface/datasets/pull/5436", "diff_url": "https://github.com/huggingface/datasets/pull/5436.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5436.patch", "merged_at": "2023-01-18T06:29:06" }	0x2b3bfa0	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.013736 / 0.011353 (0.002383) \| 0.006253 / 0.011008 (-0.004755) \| 0.127076 / 0.038508 (0.088568) \| 0.040997 / 0.023109 (0.017888) \| 0.394744 / 0.275898 (0.118846) \| 0.454285 / 0.323480 (0.130805) \| 0.009864 / 0.007986 (0.001878) \| 0.005093 / 0.004328 (0.000765) \| 0.098714 / 0.004250 (0.094464) \| 0.044308 / 0.037052 (0.007255) \| 0.421951 / 0.258489 (0.163462) \| 0.462280 / 0.293841 (0.168439) \| 0.059979 / 0.128546 (-0.068567) \| 0.020607 / 0.075646 (-0.055039) \| 0.443593 / 0.419271 (0.024321) \| 0.062332 / 0.043533 (0.018799) \| 0.411335 / 0.255139 (0.156196) \| 0.426524 / 0.283200 (0.143324) \| 0.118233 / 0.141683 (-0.023450) \| 1.877681 / 1.452155 (0.425527) \| 1.865271 / 1.492716 (0.372555) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.234791 / 0.018006 (0.216784) \| 0.557322 / 0.000490 (0.556833) \| 0.000528 / 0.000200 (0.000328) \| 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) \| 0.030260 / 0.037411 (-0.007151) \| 0.122594 / 0.014526 (0.108068) \| 0.142142 / 0.176557 (-0.034414) \| 0.197098 / 0.737135 (-0.540037) \| 0.150978 / 0.296338 (-0.145360) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.622644 / 0.215209 (0.407435) \| 6.320078 / 2.077655 (4.242423) \| 2.552755 / 1.504120 (1.048635) \| 2.188647 / 1.541195 (0.647453) \| 2.226602 / 1.468490 (0.758112) \| 1.288083 / 4.584777 (-3.296694) \| 5.624143 / 3.745712 (1.878431) \| 3.208382 / 5.269862 (-2.061480) \| 2.115222 / 4.565676 (-2.450455) \| 0.146420 / 0.424275 (-0.277856) \| 0.014464 / 0.007607 (0.006857) \| 0.816470 / 0.226044 (0.590425) \| 7.984049 / 2.268929 (5.715120) \| 3.364942 / 55.444624 (-52.079682) \| 2.552306 / 6.876477 (-4.324171) \| 2.664575 / 2.142072 (0.522503) \| 1.556177 / 4.805227 (-3.249050) \| 0.263389 / 6.500664 (-6.237275) \| 0.076861 / 0.075469 (0.001391) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.553734 / 1.841788 (-0.288054) \| 18.365029 / 8.074308 (10.290721) \| 20.993993 / 10.191392 (10.802601) \| 0.235642 / 0.680424 (-0.444782) \| 0.047084 / 0.534201 (-0.487117) \| 0.555610 / 0.579283 (-0.023673) \| 0.659413 / 0.434364 (0.225049) \| 0.639284 / 0.540337 (0.098947) \| 0.756317 / 1.386936 (-0.630620) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.014709 / 0.011353 (0.003356) \| 0.006673 / 0.011008 (-0.004335) \| 0.133718 / 0.038508 (0.095210) \| 0.035699 / 0.023109 (0.012590) \| 0.459089 / 0.275898 (0.183191) \| 0.538071 / 0.323480 (0.214591) \| 0.007376 / 0.007986 (-0.000610) \| 0.004688 / 0.004328 (0.000360) \| 0.104909 / 0.004250 (0.100659) \| 0.064942 / 0.037052 (0.027890) \| 0.466158 / 0.258489 (0.207669) \| 0.566100 / 0.293841 (0.272259) \| 0.057368 / 0.128546 (-0.071178) \| 0.021572 / 0.075646 (-0.054075) \| 0.413826 / 0.419271 (-0.005446) \| 0.079543 / 0.043533 (0.036010) \| 0.493313 / 0.255139 (0.238174) \| 0.517787 / 0.283200 (0.234587) \| 0.119836 / 0.141683 (-0.021847) \| 1.833956 / 1.452155 (0.381801) \| 2.003288 / 1.492716 (0.510572) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.276013 / 0.018006 (0.258007) \| 0.549194 / 0.000490 (0.548704) \| 0.010939 / 0.000200 (0.010739) \| 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) \| 0.034983 / 0.037411 (-0.002428) \| 0.131576 / 0.014526 (0.117050) \| 0.140651 / 0.176557 (-0.035906) \| 0.186455 / 0.737135 (-0.550681) \| 0.146309 / 0.296338 (-0.150029) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.675973 / 0.215209 (0.460763) \| 6.821862 / 2.077655 (4.744208) \| 3.090307 / 1.504120 (1.586187) \| 2.710679 / 1.541195 (1.169484) \| 2.891577 / 1.468490 (1.423087) \| 1.306160 / 4.584777 (-3.278617) \| 5.629763 / 3.745712 (1.884051) \| 4.662578 / 5.269862 (-0.607283) \| 2.670195 / 4.565676 (-1.895482) \| 0.153867 / 0.424275 (-0.270408) \| 0.016028 / 0.007607 (0.008421) \| 0.878702 / 0.226044 (0.652658) \| 8.801612 / 2.268929 (6.532683) \| 4.005520 / 55.444624 (-51.439104) \| 3.124755 / 6.876477 (-3.751721) \| 3.382132 / 2.142072 (1.240060) \| 1.525951 / 4.805227 (-3.279277) \| 0.263350 / 6.500664 (-6.237315) \| 0.079285 / 0.075469 (0.003815) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.647591 / 1.841788 (-0.194197) \| 18.281646 / 8.074308 (10.207338) \| 21.072142 / 10.191392 (10.880750) \| 0.232236 / 0.680424 (-0.448188) \| 0.026126 / 0.534201 (-0.508075) \| 0.546926 / 0.579283 (-0.032357) \| 0.634496 / 0.434364 (0.200132) \| 0.604345 / 0.540337 (0.064007) \| 0.730159 / 1.386936 (-0.656777) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cfe8a6aa4cd2d3d0d7067f390152d1a4aeb4c710 \"CML watermark\")\n" ]
1,536,099,300	5,435	Wrong statement in "Load a Dataset in Streaming mode" leads to data leakage	closed	2023-01-17T10:04:16	2023-01-19T09:56:03	2023-01-19T09:56:03	https://github.com/huggingface/datasets/issues/5435	null	DanielYang59	false	[ "Just for your information, Tensorflow confirmed this issue [here.](https://github.com/tensorflow/tensorflow/issues/59279)", "Thanks for reporting, @HaoyuYang59.\r\n\r\nPlease note that these are different \"dataset\" objects: our docs refer to Hugging Face `datasets.Dataset` and not to TensorFlow `tf.data.Dataset`.\r\n\r\nOur `datasets.Dataset.shuffle` method does not have a `reshuffle_each_iteration` argument. Therefore, I would say the statement in our docs is True because they refer to `datasets.Dataset.shuffle`, `datasets.Dataset.skip` and `datasets.Dataset.take`.\r\n\r\nI think this issue is restricted to TensorFlow dataset, and this would be addressed by them in the issue you opened in their repo: https://github.com/tensorflow/tensorflow/issues/59279", "Also note that you are referring to an outdated documentation page: datasets 1.10.2 version\r\n\r\nCurrent datasets version is 2.8.0 and the corresponding documentation page is: https://huggingface.co/docs/datasets/stream#split-dataset", "Hi @albertvillanova thanks for your reply and your explaination here. \r\n\r\nSorry for the confusion as I'm not actually a user of your repo and I just happen to find the thread by Google (and didn't read carefully).\r\n\r\nGreat to know that and you made everything very clear now.\r\n\r\nThanks for your time and sorry for the consusion.\r\n\r\nWishing you a wonderful time. \r\n\r\nRegards" ]
1,536,090,042	5,434	sample_dataset module not found	closed	2023-01-17T09:57:54	2023-01-19T13:52:12	2023-01-19T07:55:11	https://github.com/huggingface/datasets/issues/5434	null	nickums	false	[ "Hi! Can you describe what the actual error is?", "working on the setfit example script\r\n\r\n from setfit import SetFitModel, SetFitTrainer, sample_dataset\r\n\r\nImportError: cannot import name 'sample_dataset' from 'setfit' (C:\\Python\\Python38\\lib\\site-packages\\setfit\\__init__.py)\r\n\r\n apart from that, I also had to hack these loads to import thses modules:\r\n from datasets.load import load_dataset \r\n from datasets.arrow_dataset import Dataset\r\n from datasets.dataset_dict import DatasetDict", "Hi! This issue is related to the [SetFit](https://github.com/huggingface/setfit) project, so can you please open it there?" ]
1,536,017,901	5,433	Support latest Docker image in CI benchmarks	closed	2023-01-17T09:06:08	2023-01-18T06:29:08	2023-01-18T06:29:08	https://github.com/huggingface/datasets/issues/5433	null	albertvillanova	false	[ "Sorry, it was us:[^1] https://github.com/iterative/cml/pull/1317 & https://github.com/iterative/cml/issues/1319#issuecomment-1385599559; should be fixed with [v0.18.17](https://github.com/iterative/cml/releases/tag/v0.18.17).\r\n\r\n[^1]: More or less, see https://github.com/yargs/yargs/issues/873.", "Opened https://github.com/huggingface/datasets/pull/5436 unpinning again the container image.", "Hi @0x2b3bfa0, thanks a lot for the investigation, the context about the the root cause and for fixing it!!\r\n\r\nWe are reviewing your PR to unpin the container image." ]
1,535,893,019	5,432	Fix CI benchmarks by temporarily pinning Docker image version	closed	2023-01-17T07:15:31	2023-01-17T08:58:22	2023-01-17T08:51:17	https://github.com/huggingface/datasets/pull/5432	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5432", "html_url": "https://github.com/huggingface/datasets/pull/5432", "diff_url": "https://github.com/huggingface/datasets/pull/5432.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5432.patch", "merged_at": "2023-01-17T08:51:17" }	albertvillanova	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008519 / 0.011353 (-0.002834) \| 0.004451 / 0.011008 (-0.006558) \| 0.102401 / 0.038508 (0.063893) \| 0.029779 / 0.023109 (0.006669) \| 0.302654 / 0.275898 (0.026756) \| 0.366002 / 0.323480 (0.042522) \| 0.007044 / 0.007986 (-0.000942) \| 0.003350 / 0.004328 (-0.000978) \| 0.078213 / 0.004250 (0.073963) \| 0.035208 / 0.037052 (-0.001844) \| 0.312980 / 0.258489 (0.054491) \| 0.344217 / 0.293841 (0.050376) \| 0.033089 / 0.128546 (-0.095457) \| 0.011443 / 0.075646 (-0.064203) \| 0.353143 / 0.419271 (-0.066128) \| 0.040851 / 0.043533 (-0.002682) \| 0.304501 / 0.255139 (0.049362) \| 0.329118 / 0.283200 (0.045918) \| 0.087399 / 0.141683 (-0.054284) \| 1.500200 / 1.452155 (0.048046) \| 1.536176 / 1.492716 (0.043459) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.209626 / 0.018006 (0.191619) \| 0.425551 / 0.000490 (0.425061) \| 0.001168 / 0.000200 (0.000968) \| 0.000069 / 0.000054 (0.000014) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023664 / 0.037411 (-0.013748) \| 0.096792 / 0.014526 (0.082266) \| 0.105652 / 0.176557 (-0.070905) \| 0.140796 / 0.737135 (-0.596340) \| 0.109319 / 0.296338 (-0.187019) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.414802 / 0.215209 (0.199593) \| 4.152619 / 2.077655 (2.074964) \| 1.814403 / 1.504120 (0.310283) \| 1.611392 / 1.541195 (0.070198) \| 1.667350 / 1.468490 (0.198860) \| 0.691855 / 4.584777 (-3.892922) \| 3.406584 / 3.745712 (-0.339128) \| 1.940332 / 5.269862 (-3.329530) \| 1.279061 / 4.565676 (-3.286615) \| 0.082938 / 0.424275 (-0.341337) \| 0.012388 / 0.007607 (0.004781) \| 0.521738 / 0.226044 (0.295693) \| 5.233764 / 2.268929 (2.964835) \| 2.306573 / 55.444624 (-53.138051) \| 1.954631 / 6.876477 (-4.921845) \| 2.048315 / 2.142072 (-0.093757) \| 0.816921 / 4.805227 (-3.988306) \| 0.150983 / 6.500664 (-6.349681) \| 0.066628 / 0.075469 (-0.008842) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.235939 / 1.841788 (-0.605849) \| 14.047114 / 8.074308 (5.972806) \| 14.149842 / 10.191392 (3.958450) \| 0.152836 / 0.680424 (-0.527588) \| 0.028837 / 0.534201 (-0.505364) \| 0.396232 / 0.579283 (-0.183051) \| 0.409950 / 0.434364 (-0.024414) \| 0.460296 / 0.540337 (-0.080041) \| 0.556787 / 1.386936 (-0.830149) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006582 / 0.011353 (-0.004771) \| 0.004491 / 0.011008 (-0.006518) \| 0.100093 / 0.038508 (0.061585) \| 0.026826 / 0.023109 (0.003717) \| 0.413971 / 0.275898 (0.138073) \| 0.445625 / 0.323480 (0.122145) \| 0.004892 / 0.007986 (-0.003094) \| 0.003295 / 0.004328 (-0.001034) \| 0.077879 / 0.004250 (0.073628) \| 0.039177 / 0.037052 (0.002125) \| 0.353299 / 0.258489 (0.094810) \| 0.406566 / 0.293841 (0.112725) \| 0.031633 / 0.128546 (-0.096913) \| 0.011517 / 0.075646 (-0.064130) \| 0.320939 / 0.419271 (-0.098332) \| 0.041487 / 0.043533 (-0.002046) \| 0.353735 / 0.255139 (0.098596) \| 0.434786 / 0.283200 (0.151586) \| 0.087722 / 0.141683 (-0.053961) \| 1.515134 / 1.452155 (0.062979) \| 1.588908 / 1.492716 (0.096191) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.225312 / 0.018006 (0.207305) \| 0.398324 / 0.000490 (0.397834) \| 0.000453 / 0.000200 (0.000253) \| 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) \| 0.024645 / 0.037411 (-0.012766) \| 0.099399 / 0.014526 (0.084873) \| 0.107006 / 0.176557 (-0.069550) \| 0.145090 / 0.737135 (-0.592045) \| 0.110046 / 0.296338 (-0.186292) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.450573 / 0.215209 (0.235364) \| 4.498030 / 2.077655 (2.420375) \| 2.193164 / 1.504120 (0.689044) \| 1.940103 / 1.541195 (0.398908) \| 1.957137 / 1.468490 (0.488647) \| 0.697599 / 4.584777 (-3.887178) \| 3.465146 / 3.745712 (-0.280566) \| 1.918209 / 5.269862 (-3.351653) \| 1.183921 / 4.565676 (-3.381756) \| 0.082540 / 0.424275 (-0.341735) \| 0.012495 / 0.007607 (0.004888) \| 0.549702 / 0.226044 (0.323658) \| 5.526841 / 2.268929 (3.257912) \| 2.658611 / 55.444624 (-52.786014) \| 2.259542 / 6.876477 (-4.616935) \| 2.310139 / 2.142072 (0.168066) \| 0.810550 / 4.805227 (-3.994677) \| 0.152369 / 6.500664 (-6.348295) \| 0.066295 / 0.075469 (-0.009174) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.289240 / 1.841788 (-0.552547) \| 14.032143 / 8.074308 (5.957834) \| 13.973492 / 10.191392 (3.782100) \| 0.140082 / 0.680424 (-0.540342) \| 0.017113 / 0.534201 (-0.517088) \| 0.386534 / 0.579283 (-0.192749) \| 0.393723 / 0.434364 (-0.040641) \| 0.448891 / 0.540337 (-0.091446) \| 0.533085 / 1.386936 (-0.853851) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n" ]
1,535,862,621	5,431	CI benchmarks are broken: Unknown arguments: runnerPath, path	closed	2023-01-17T06:49:57	2023-01-18T06:33:24	2023-01-17T08:51:18	https://github.com/huggingface/datasets/issues/5431	null	albertvillanova	false	[]
1,535,856,503	5,430	Support Apache Beam >= 2.44.0	closed	2023-01-17T06:42:12	2024-02-06T19:24:21	2024-02-06T19:24:21	https://github.com/huggingface/datasets/issues/5430	null	albertvillanova	false	[ "Some of the shard files now have 0 number of rows.\r\n\r\nWe have opened an issue in the Apache Beam repo:\r\n- https://github.com/apache/beam/issues/25041" ]
1,535,192,687	5,429	Fix CI by temporarily pinning apache-beam < 2.44.0	closed	2023-01-16T16:20:09	2023-01-16T16:51:42	2023-01-16T16:49:03	https://github.com/huggingface/datasets/pull/5429	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5429", "html_url": "https://github.com/huggingface/datasets/pull/5429", "diff_url": "https://github.com/huggingface/datasets/pull/5429.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5429.patch", "merged_at": "2023-01-16T16:49:03" }	albertvillanova	true	[ "_The documentation is not available anymore as the PR was closed or merged._" ]
1,535,166,139	5,428	Load/Save FAISS index using fsspec	closed	2023-01-16T16:08:12	2023-03-27T15:18:22	2023-03-27T15:18:22	https://github.com/huggingface/datasets/issues/5428	null	Dref360	false	[ "Hi! Sure, feel free to submit a PR. Maybe if we want to be consistent with the existing API, it would be cleaner to directly add support for `fsspec` paths in `Dataset.load_faiss_index`/`Dataset.save_faiss_index` in the same manner as it was done in `Dataset.load_from_disk`/`Dataset.save_to_disk`.", "That's a great idea! I'll do that instead. " ]
1,535,162,889	5,427	Unable to download dataset id_clickbait	closed	2023-01-16T16:05:36	2023-01-18T09:51:28	2023-01-18T09:25:19	https://github.com/huggingface/datasets/issues/5427	null	ilos-vigil	false	[ "Thanks for reporting, @ilos-vigil.\r\n\r\nWe have transferred this issue to the corresponding dataset on the Hugging Face Hub: https://huggingface.co/datasets/id_clickbait/discussions/1 " ]
1,535,158,555	5,426	CI tests are broken: SchemaInferenceError	closed	2023-01-16T16:02:07	2023-06-02T06:40:32	2023-01-16T16:49:04	https://github.com/huggingface/datasets/issues/5426	null	albertvillanova	false	[]
1,534,581,850	5,425	Sort on multiple keys with datasets.Dataset.sort()	closed	2023-01-16T09:22:26	2023-02-24T16:15:11	2023-02-24T16:15:11	https://github.com/huggingface/datasets/issues/5425	null	rocco-fortuna	false	[ "Hi! \r\n\r\n`Dataset.sort` calls `df.sort_values` internally, and `df.sort_values` brings all the \"sort\" columns in memory, so sorting on multiple keys could be very expensive. This makes me think that maybe we can replace `df.sort_values` with `pyarrow.compute.sort_indices` - the latter can also sort on multiple keys and currently loads the data into memory; however, there is a plan to eventually implement \"memory-map\" friendly kernels for the Arrow compute ops (using the Acero execution engine). \r\n\r\nSo to address this issue, you should replace `df.sort_values` with `pyarrow.compute.sort_indices` in `Dataset.sort` and adjust the signature of this function (deprecate the `kind` parameter, etc.).\r\n\r\nPS: Feel free to ping us if you need some additional help/pointers", "@mariosasko If I understand the code right, using `pyarrow.compute.sort_indices` would also require changes to the `select` method if it is meant to sort multiple keys. That's because `select` only accepts 1D input for `indices`, not an iterable or similar which would be required for multiple keys unless you want some looping over selects. Doesn't seem that straight-forward but I might be missing something here... ", "@MichlF No, it doesn't require modifying select because sorting on multiple keys also returns a 1D array.\r\n\r\nIt's easier to understand with an example:\r\n```python\r\n>>> import pyarrow as pa\r\n>>> import pyarrow.compute as pc\r\n>>> table = pa.table({\r\n... \"name\": [\"John\", \"Eve\", \"Peter\", \"John\"],\r\n... \"surname\": [\"Johnson\", \"Smith\", \"Smith\", \"Doe\"],\r\n... \"age\": [20, 40, 30, 50],\r\n... })\r\n>>> indices = pc.sort_indices(table, sort_keys=[(\"name\", \"ascending\"), (\"surname\", \"ascending\")])\r\n>>> print(indices)\r\n[\r\n 1,\r\n 3,\r\n 0,\r\n 2\r\n]\r\n```\r\n\r\n", "Thanks for clarifying.\r\nI can prepare a PR to address this issue. This would be my first PR here so I have a few maybe silly questions but:\r\n- What is the preferred input type of `sort_keys` for the sort method? A sequence with name, order tuples like pyarrow's `sort_indices` requires?\r\n- What about backwards compatability: is it supposed to also accept the old way of calling sort() or should both `column` and `kind` be deprecated?\r\n- If `sort_keys` is provided in the same format as for pyarrow's `sort_indices` - i.e. along with order for each column -, `reverse` doesn't make much sense either and should be deprecated as well I assume.", "I think we can have the following signature:\r\n```python\r\ndef sort(\r\n self,\r\n column_names: Union[str, Sequence[str]],\r\n reverse: Union[bool, Sequence[bool]] = False,\r\n kind=\"deprecated\",\r\n null_placement: str = \"last\",\r\n keep_in_memory: bool = False,\r\n load_from_cache_file: bool = True,\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\nSo we should:\r\n* rename`column` to `column_names`. `column` is a positional argument, so it's OK to rename it (not marked as positional-only with \"/\", but still should be fine)\r\n* deprecate `kind`\r\n* keep `reverse` instead of introducing `sort_keys`, but we should allow passing a list of booleans that defines the sort order of each column from `column_names` to it (`reverse = False` would be equal to `[False] * len(column_names)` and `reverse = True` to `[True] * len(column_names)`)", "I am pretty much done with the PR. Just one clarification: `Sequence` in `arrow_dataset.py` is a custom dataclass from `features.py` instead of the `type.hinting` class `Sequence` from Python. Do you suggest using that custom `Sequence` class somehow ? Otherwise signature currently reads instead:\r\n```Python\r\n def sort(\r\n self,\r\n column_names: Union[str, List[str]],\r\n reverse: Union[bool, List[bool]] = False,\r\n kind = \"deprecated\",\r\n null_placement: str = \"last\",\r\n keep_in_memory: bool = False,\r\n load_from_cache_file: bool = True,\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 )\r\n```\r\n\r\nAlso, to maintain backwards compatibility, I added conditionals for `null_placement`, because pyarrow's `null_placement` only accepts `at_start` and `at_end`, and not `last` and `first`.\r\nIf that is all good, I think I can open the PR.", "I meant `typing.Sequence` (`datasets.Sequence` is a feature type). \r\n\r\nRegarding `null_placement`, I think we can support both `at_start` and `at_end`, and `last` and `first` (for backward compatibility; convert internally to `at_end` and `at_start` respectively).", "> I meant typing.Sequence (datasets.Sequence is a feature type).\r\n\r\nSorry, I actually meant `typing.Sequence` and not `type.hinting`. However, the issue is still that `dataset.Sequence` is imported in `arrow_dataset.py` so I cannot import and use `typing.Sequence` for the `sort`'s signature without overwriting the `dataset.Sequence` import. The latter is used in the `align_labels_with_mapping` method so it's a necessary import for `arrow_dataset.py`. \r\nTo import `typing.Sequence` as something else than `Sequence` to avoid overwriting may only be confusing and doesn't seem good practice!? The other solution is to keep `List` type hinting as in the signature I posted in my previous post but this excludes other Sequence types and may cause problems further down the line.\r\nPlease advise,\r\nThanks for all the clarifications!", "You can avoid the name collision by renaming `typing.Sequence` to `Sequence_` when importing:\r\n```python\r\nfrom typing import Sequence as Sequence_\r\n```", "Resolved via #5502 " ]
1,534,394,756	5,424	When applying `ReadInstruction` to custom load it's not DatasetDict but list of Dataset?	closed	2023-01-16T06:54:28	2023-02-24T16:19:00	2023-02-24T16:19:00	https://github.com/huggingface/datasets/issues/5424	null	macabdul9	false	[ "Hi! You can get a `DatasetDict` if you pass a dictionary with read instructions as follows:\r\n```python\r\ninstructions = [\r\n ReadInstruction(split_name=\"train\", from_=0, to=10, unit='%', rounding='closest'),\r\n ReadInstruction(split_name=\"dev\", from_=0, to=10, unit='%', rounding='closest'),\r\n ReadInstruction(split_name=\"test\", from_=0, to=5, unit='%', rounding='closest')\r\n]\r\n\r\ndataset = load_dataset('csv', data_dir=\"data/\", data_files={\"train\":\"train.tsv\", \"dev\":\"dev.tsv\", \"test\":\"test.tsv\"}, delimiter=\"\\t\", split={inst.split_name: inst for inst in instructions})\r\n```\r\n" ]
1,533,385,239	5,422	Datasets load error for saved github issues	open	2023-01-14T17:29:38	2023-09-14T11:39:57	null	https://github.com/huggingface/datasets/issues/5422	null	folterj	false	[ "I can confirm that the error exists!\r\nI'm trying to read 3 parquet files locally:\r\n```python\r\nfrom datasets import load_dataset, Features, Value, ClassLabel\r\n\r\nreview_dataset = load_dataset(\r\n \"parquet\",\r\n data_files={\r\n \"train\": os.path.join(sentiment_analysis_data_path, \"train.parquet\"),\r\n \"validation\": os.path.join(sentiment_analysis_data_path, \"validation.parquet\"),\r\n \"test\": os.path.join(sentiment_analysis_data_path, \"test.parquet\"),\r\n },\r\n)\r\n```\r\n\r\nBut you can fix it, by specifying `features` for `load_dataset()` function like this:\r\n```python\r\nfrom datasets import load_dataset, Features, Value, ClassLabel\r\n\r\nfeatures = Features(\r\n {\r\n \"label\": ClassLabel(\r\n num_classes=3,\r\n names=[\"negative\", \"neutral\", \"positive\"],\r\n ),\r\n \"text\": Value(dtype=\"string\"),\r\n }\r\n)\r\n\r\nreview_dataset = load_dataset(\r\n \"parquet\",\r\n data_files={\r\n \"train\": os.path.join(sentiment_analysis_data_path, \"train.parquet\"),\r\n \"validation\": os.path.join(sentiment_analysis_data_path, \"validation.parquet\"),\r\n \"test\": os.path.join(sentiment_analysis_data_path, \"test.parquet\"),\r\n },\r\n features=features,\r\n)\r\n\r\nprint(review_dataset)\r\n```", "@Extremesarova I think this is a different issue, but understand using features could be a work-around.\r\nIt seems the field `closed_at` is `null` in many cases.\r\n\r\nI've not found a way to specify only a single feature without (succesfully) specifiying the full and quite detailed set of expected features. Using this features set gives an error the column names don't match.\r\n`features = Features({'closed_at': Value(dtype='timestamp[s]', id=None)})`\r\n\r\n", "Found this when searching for the same error, looks like based on #3965 it's just an issue with the data. I found that changing `df = pd.DataFrame.from_records(all_issues)` to `df = pd.DataFrame.from_records(all_issues).dropna(axis=1, how='all').drop(['milestone'], axis=1)` from the fetch_issues function fixed the issue. \r\nThe \"milestone\" column seemed to be problematic (only ~50 non null rows) and dropped any columns that were all null as well just in case.", "I have this same issue. I saved a dataset to disk and now I can't load it.", "Ok the solution was to use load_from_disk instead of load_dataset.", "Hi @folterj , I faced same issue while creating `issues_dataset` (https://huggingface.co/learn/nlp-course/chapter5/5?fw=pt). The fix which worked for me was loading the *.jsonl file as pd.read_json and then converting it into a Dataset using datasets API.\r\n```\r\nimport pandas as pd\r\ndf=pd.read_json(\"datasets-issues.jsonl\", lines=True)\r\ndf.head()\r\n\r\nfrom datasets import Dataset\r\nissues_dataset = Dataset.from_pandas(df)\r\nissues_dataset\r\nsample = issues_dataset.shuffle(seed=666).select(range(3))\r\nsample[0]\r\n```", "I understand some work-around suggestions would be to not use load_dataset(), and instead using a different API function. Another alternative would be using the same function using streaming, as I had already suggested in my original post.\r\nHowever, the fact remains that there is an issue in this function as reported." ]
1,532,278,307	5,421	Support case-insensitive Hub dataset name in load_dataset	closed	2023-01-13T13:07:07	2023-01-13T20:12:32	2023-01-13T20:12:32	https://github.com/huggingface/datasets/issues/5421	null	severo	false	[ "Closing as case-insensitivity should be only for URL redirection on the Hub. In the APIs, we will only support the canonical name (https://github.com/huggingface/moon-landing/pull/2399#issuecomment-1382085611)" ]
1,532,265,742	5,420	ci: 🎡 remove two obsolete issue templates	closed	2023-01-13T12:58:43	2023-01-13T13:36:00	2023-01-13T13:29:01	https://github.com/huggingface/datasets/pull/5420	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5420", "html_url": "https://github.com/huggingface/datasets/pull/5420", "diff_url": "https://github.com/huggingface/datasets/pull/5420.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5420.patch", "merged_at": "2023-01-13T13:29:01" }	severo	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008450 / 0.011353 (-0.002902) \| 0.004478 / 0.011008 (-0.006530) \| 0.100440 / 0.038508 (0.061931) \| 0.029568 / 0.023109 (0.006459) \| 0.296705 / 0.275898 (0.020807) \| 0.354565 / 0.323480 (0.031085) \| 0.006887 / 0.007986 (-0.001098) \| 0.003415 / 0.004328 (-0.000914) \| 0.078876 / 0.004250 (0.074626) \| 0.034927 / 0.037052 (-0.002125) \| 0.307695 / 0.258489 (0.049206) \| 0.340917 / 0.293841 (0.047076) \| 0.033630 / 0.128546 (-0.094916) \| 0.011626 / 0.075646 (-0.064020) \| 0.322644 / 0.419271 (-0.096627) \| 0.040254 / 0.043533 (-0.003279) \| 0.297419 / 0.255139 (0.042280) \| 0.321584 / 0.283200 (0.038384) \| 0.086202 / 0.141683 (-0.055481) \| 1.465579 / 1.452155 (0.013425) \| 1.521456 / 1.492716 (0.028740) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.200890 / 0.018006 (0.182884) \| 0.410300 / 0.000490 (0.409811) \| 0.001647 / 0.000200 (0.001447) \| 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) \| 0.022569 / 0.037411 (-0.014843) \| 0.096062 / 0.014526 (0.081536) \| 0.102474 / 0.176557 (-0.074082) \| 0.138596 / 0.737135 (-0.598539) \| 0.106262 / 0.296338 (-0.190077) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.415976 / 0.215209 (0.200766) \| 4.144322 / 2.077655 (2.066667) \| 1.871783 / 1.504120 (0.367663) \| 1.669478 / 1.541195 (0.128283) \| 1.718214 / 1.468490 (0.249724) \| 0.687870 / 4.584777 (-3.896907) \| 3.362084 / 3.745712 (-0.383628) \| 1.844127 / 5.269862 (-3.425735) \| 1.149611 / 4.565676 (-3.416066) \| 0.081410 / 0.424275 (-0.342865) \| 0.012278 / 0.007607 (0.004671) \| 0.518245 / 0.226044 (0.292200) \| 5.185164 / 2.268929 (2.916236) \| 2.299029 / 55.444624 (-53.145595) \| 1.960021 / 6.876477 (-4.916456) \| 2.009751 / 2.142072 (-0.132322) \| 0.803759 / 4.805227 (-4.001468) \| 0.147340 / 6.500664 (-6.353324) \| 0.063896 / 0.075469 (-0.011573) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.254142 / 1.841788 (-0.587646) \| 13.799683 / 8.074308 (5.725375) \| 13.940387 / 10.191392 (3.748995) \| 0.151246 / 0.680424 (-0.529178) \| 0.028709 / 0.534201 (-0.505491) \| 0.391600 / 0.579283 (-0.187683) \| 0.405750 / 0.434364 (-0.028614) \| 0.455479 / 0.540337 (-0.084858) \| 0.541022 / 1.386936 (-0.845914) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006462 / 0.011353 (-0.004891) \| 0.004462 / 0.011008 (-0.006547) \| 0.096588 / 0.038508 (0.058080) \| 0.026931 / 0.023109 (0.003822) \| 0.344595 / 0.275898 (0.068697) \| 0.378743 / 0.323480 (0.055264) \| 0.005672 / 0.007986 (-0.002314) \| 0.003345 / 0.004328 (-0.000984) \| 0.074363 / 0.004250 (0.070112) \| 0.037300 / 0.037052 (0.000248) \| 0.346895 / 0.258489 (0.088406) \| 0.388585 / 0.293841 (0.094744) \| 0.031459 / 0.128546 (-0.097088) \| 0.011522 / 0.075646 (-0.064124) \| 0.318507 / 0.419271 (-0.100764) \| 0.041145 / 0.043533 (-0.002388) \| 0.343866 / 0.255139 (0.088727) \| 0.366490 / 0.283200 (0.083291) \| 0.086793 / 0.141683 (-0.054890) \| 1.483859 / 1.452155 (0.031704) \| 1.574006 / 1.492716 (0.081290) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.220436 / 0.018006 (0.202430) \| 0.402988 / 0.000490 (0.402498) \| 0.000435 / 0.000200 (0.000235) \| 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) \| 0.024573 / 0.037411 (-0.012838) \| 0.099190 / 0.014526 (0.084664) \| 0.106796 / 0.176557 (-0.069761) \| 0.142387 / 0.737135 (-0.594748) \| 0.109991 / 0.296338 (-0.186347) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.473452 / 0.215209 (0.258243) \| 4.749554 / 2.077655 (2.671899) \| 2.433482 / 1.504120 (0.929362) \| 2.224276 / 1.541195 (0.683082) \| 2.261579 / 1.468490 (0.793088) \| 0.699876 / 4.584777 (-3.884901) \| 3.378366 / 3.745712 (-0.367346) \| 1.835062 / 5.269862 (-3.434799) \| 1.161249 / 4.565676 (-3.404427) \| 0.082967 / 0.424275 (-0.341308) \| 0.012745 / 0.007607 (0.005138) \| 0.580006 / 0.226044 (0.353962) \| 5.789868 / 2.268929 (3.520939) \| 2.909496 / 55.444624 (-52.535128) \| 2.539196 / 6.876477 (-4.337280) \| 2.617737 / 2.142072 (0.475665) \| 0.810320 / 4.805227 (-3.994907) \| 0.152501 / 6.500664 (-6.348163) \| 0.067201 / 0.075469 (-0.008268) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.257844 / 1.841788 (-0.583943) \| 13.865295 / 8.074308 (5.790987) \| 14.169073 / 10.191392 (3.977680) \| 0.135655 / 0.680424 (-0.544769) \| 0.016597 / 0.534201 (-0.517604) \| 0.374915 / 0.579283 (-0.204368) \| 0.382771 / 0.434364 (-0.051593) \| 0.431934 / 0.540337 (-0.108403) \| 0.524617 / 1.386936 (-0.862319) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008748 / 0.011353 (-0.002605) \| 0.004489 / 0.011008 (-0.006519) \| 0.100923 / 0.038508 (0.062415) \| 0.031436 / 0.023109 (0.008326) \| 0.306508 / 0.275898 (0.030610) \| 0.365110 / 0.323480 (0.041630) \| 0.007161 / 0.007986 (-0.000824) \| 0.005489 / 0.004328 (0.001160) \| 0.078909 / 0.004250 (0.074658) \| 0.036097 / 0.037052 (-0.000955) \| 0.307907 / 0.258489 (0.049418) \| 0.370277 / 0.293841 (0.076436) \| 0.034184 / 0.128546 (-0.094362) \| 0.011613 / 0.075646 (-0.064033) \| 0.322896 / 0.419271 (-0.096375) \| 0.041829 / 0.043533 (-0.001704) \| 0.299669 / 0.255139 (0.044530) \| 0.322217 / 0.283200 (0.039017) \| 0.087751 / 0.141683 (-0.053932) \| 1.476277 / 1.452155 (0.024122) \| 1.548196 / 1.492716 (0.055480) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.183002 / 0.018006 (0.164995) \| 0.415627 / 0.000490 (0.415138) \| 0.003272 / 0.000200 (0.003072) \| 0.000070 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024881 / 0.037411 (-0.012531) \| 0.103424 / 0.014526 (0.088898) \| 0.106446 / 0.176557 (-0.070110) \| 0.142806 / 0.737135 (-0.594330) \| 0.110938 / 0.296338 (-0.185401) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.421669 / 0.215209 (0.206460) \| 4.207457 / 2.077655 (2.129802) \| 1.882176 / 1.504120 (0.378056) \| 1.677609 / 1.541195 (0.136415) \| 1.734065 / 1.468490 (0.265575) \| 0.695915 / 4.584777 (-3.888862) \| 3.416731 / 3.745712 (-0.328981) \| 1.872575 / 5.269862 (-3.397286) \| 1.163612 / 4.565676 (-3.402064) \| 0.082710 / 0.424275 (-0.341565) \| 0.012659 / 0.007607 (0.005052) \| 0.528785 / 0.226044 (0.302741) \| 5.305328 / 2.268929 (3.036399) \| 2.299850 / 55.444624 (-53.144774) \| 1.968137 / 6.876477 (-4.908339) \| 2.028326 / 2.142072 (-0.113746) \| 0.813157 / 4.805227 (-3.992070) \| 0.149997 / 6.500664 (-6.350668) \| 0.066739 / 0.075469 (-0.008730) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.206332 / 1.841788 (-0.635456) \| 13.795510 / 8.074308 (5.721202) \| 14.367695 / 10.191392 (4.176303) \| 0.138106 / 0.680424 (-0.542318) \| 0.028760 / 0.534201 (-0.505441) \| 0.394822 / 0.579283 (-0.184461) \| 0.403291 / 0.434364 (-0.031073) \| 0.463273 / 0.540337 (-0.077065) \| 0.540881 / 1.386936 (-0.846055) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006830 / 0.011353 (-0.004523) \| 0.004606 / 0.011008 (-0.006402) \| 0.097763 / 0.038508 (0.059255) \| 0.027832 / 0.023109 (0.004723) \| 0.422970 / 0.275898 (0.147072) \| 0.460313 / 0.323480 (0.136833) \| 0.005110 / 0.007986 (-0.002876) \| 0.003428 / 0.004328 (-0.000901) \| 0.075047 / 0.004250 (0.070797) \| 0.038374 / 0.037052 (0.001322) \| 0.422762 / 0.258489 (0.164273) \| 0.469886 / 0.293841 (0.176045) \| 0.032391 / 0.128546 (-0.096155) \| 0.011804 / 0.075646 (-0.063843) \| 0.320439 / 0.419271 (-0.098832) \| 0.041939 / 0.043533 (-0.001594) \| 0.422521 / 0.255139 (0.167382) \| 0.446420 / 0.283200 (0.163220) \| 0.090715 / 0.141683 (-0.050968) \| 1.484578 / 1.452155 (0.032423) \| 1.556154 / 1.492716 (0.063438) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.260735 / 0.018006 (0.242728) \| 0.415586 / 0.000490 (0.415096) \| 0.026960 / 0.000200 (0.026760) \| 0.000296 / 0.000054 (0.000241) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024926 / 0.037411 (-0.012486) \| 0.099651 / 0.014526 (0.085125) \| 0.107810 / 0.176557 (-0.068747) \| 0.148685 / 0.737135 (-0.588451) \| 0.112725 / 0.296338 (-0.183614) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.472669 / 0.215209 (0.257460) \| 4.718827 / 2.077655 (2.641172) \| 2.475583 / 1.504120 (0.971463) \| 2.260862 / 1.541195 (0.719667) \| 2.307820 / 1.468490 (0.839330) \| 0.699464 / 4.584777 (-3.885313) \| 3.376282 / 3.745712 (-0.369431) \| 1.872650 / 5.269862 (-3.397211) \| 1.176399 / 4.565676 (-3.389277) \| 0.082854 / 0.424275 (-0.341421) \| 0.012845 / 0.007607 (0.005237) \| 0.582088 / 0.226044 (0.356044) \| 5.861609 / 2.268929 (3.592681) \| 2.930728 / 55.444624 (-52.513896) \| 2.624310 / 6.876477 (-4.252167) \| 2.762130 / 2.142072 (0.620058) \| 0.811902 / 4.805227 (-3.993325) \| 0.152516 / 6.500664 (-6.348149) \| 0.067670 / 0.075469 (-0.007799) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.289790 / 1.841788 (-0.551997) \| 14.267607 / 8.074308 (6.193299) \| 14.120655 / 10.191392 (3.929263) \| 0.128442 / 0.680424 (-0.551982) \| 0.017079 / 0.534201 (-0.517121) \| 0.381807 / 0.579283 (-0.197476) \| 0.400546 / 0.434364 (-0.033818) \| 0.447629 / 0.540337 (-0.092709) \| 0.532006 / 1.386936 (-0.854930) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n" ]
1,531,999,850	5,419	label_column='labels' in datasets.TextClassification and 'label' or 'label_ids' in transformers.DataColator	closed	2023-01-13T09:40:07	2023-07-21T14:27:08	2023-07-21T14:27:08	https://github.com/huggingface/datasets/issues/5419	null	CreatixEA	false	[ "Hi! Thanks for pointing out this inconsistency. Changing the default value at this point is probably not worth it, considering we've started discussing the state of the task API internally - we will most likely deprecate the current one and replace it with a more robust solution that relies on the `train_eval_index` field stored in the YAML section of the dataset cards.", "The task templates API has been deprecated (will be removed in version 3.0), so I'm closing this issue." ]
1,530,111,184	5,418	Add ProgressBar for `to_parquet`	closed	2023-01-12T05:06:20	2023-01-24T18:18:24	2023-01-24T18:18:24	https://github.com/huggingface/datasets/issues/5418	null	zanussbaum	false	[ "Thanks for your proposal, @zanussbaum. Yes, I agree that would definitely be a nice feature to have!", "@albertvillanova I’m happy to make a quick PR for the feature! let me know ", "That would be awesome ! You can comment `#self-assign` to assign you to this issue and open a PR :) Will be happy to review", "Closing as this has been merged @lhoestq " ]
1,526,988,113	5,416	Fix RuntimeError: Sharding is ambiguous for this dataset	closed	2023-01-10T08:43:19	2023-01-18T17:12:17	2023-01-18T14:09:02	https://github.com/huggingface/datasets/pull/5416	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5416", "html_url": "https://github.com/huggingface/datasets/pull/5416", "diff_url": "https://github.com/huggingface/datasets/pull/5416.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5416.patch", "merged_at": "2023-01-18T14:09:02" }	albertvillanova	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "By the way, do we know how many datasets are impacted by this issue?\r\n\r\nMaybe we should do a patch release with this fix.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009256 / 0.011353 (-0.002097) \| 0.005033 / 0.011008 (-0.005975) \| 0.099346 / 0.038508 (0.060838) \| 0.035204 / 0.023109 (0.012095) \| 0.303017 / 0.275898 (0.027119) \| 0.335632 / 0.323480 (0.012152) \| 0.007953 / 0.007986 (-0.000033) \| 0.005806 / 0.004328 (0.001477) \| 0.076121 / 0.004250 (0.071871) \| 0.041164 / 0.037052 (0.004112) \| 0.305536 / 0.258489 (0.047047) \| 0.348452 / 0.293841 (0.054611) \| 0.037704 / 0.128546 (-0.090842) \| 0.011982 / 0.075646 (-0.063664) \| 0.333264 / 0.419271 (-0.086008) \| 0.047738 / 0.043533 (0.004205) \| 0.310126 / 0.255139 (0.054987) \| 0.318719 / 0.283200 (0.035519) \| 0.098933 / 0.141683 (-0.042750) \| 1.421058 / 1.452155 (-0.031096) \| 1.554771 / 1.492716 (0.062054) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.258627 / 0.018006 (0.240620) \| 0.450814 / 0.000490 (0.450324) \| 0.011288 / 0.000200 (0.011088) \| 0.000136 / 0.000054 (0.000081) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027004 / 0.037411 (-0.010407) \| 0.109067 / 0.014526 (0.094541) \| 0.120401 / 0.176557 (-0.056155) \| 0.158336 / 0.737135 (-0.578799) \| 0.126244 / 0.296338 (-0.170094) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.401847 / 0.215209 (0.186638) \| 4.006003 / 2.077655 (1.928348) \| 1.806342 / 1.504120 (0.302223) \| 1.619751 / 1.541195 (0.078556) \| 1.709660 / 1.468490 (0.241170) \| 0.692444 / 4.584777 (-3.892333) \| 3.853691 / 3.745712 (0.107979) \| 2.143910 / 5.269862 (-3.125951) \| 1.471600 / 4.565676 (-3.094076) \| 0.084589 / 0.424275 (-0.339686) \| 0.012276 / 0.007607 (0.004669) \| 0.506529 / 0.226044 (0.280485) \| 5.028361 / 2.268929 (2.759432) \| 2.277660 / 55.444624 (-53.166964) \| 1.930365 / 6.876477 (-4.946112) \| 1.965494 / 2.142072 (-0.176579) \| 0.826752 / 4.805227 (-3.978475) \| 0.165050 / 6.500664 (-6.335614) \| 0.062702 / 0.075469 (-0.012767) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.234539 / 1.841788 (-0.607249) \| 15.067401 / 8.074308 (6.993093) \| 14.041920 / 10.191392 (3.850528) \| 0.162590 / 0.680424 (-0.517834) \| 0.028941 / 0.534201 (-0.505260) \| 0.438518 / 0.579283 (-0.140765) \| 0.443787 / 0.434364 (0.009423) \| 0.516671 / 0.540337 (-0.023666) \| 0.609036 / 1.386936 (-0.777900) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007535 / 0.011353 (-0.003818) \| 0.005283 / 0.011008 (-0.005725) \| 0.097116 / 0.038508 (0.058608) \| 0.033357 / 0.023109 (0.010247) \| 0.383398 / 0.275898 (0.107500) \| 0.425516 / 0.323480 (0.102037) \| 0.006039 / 0.007986 (-0.001947) \| 0.004074 / 0.004328 (-0.000255) \| 0.073207 / 0.004250 (0.068956) \| 0.052153 / 0.037052 (0.015101) \| 0.386385 / 0.258489 (0.127896) \| 0.429900 / 0.293841 (0.136059) \| 0.038341 / 0.128546 (-0.090205) \| 0.012417 / 0.075646 (-0.063230) \| 0.333859 / 0.419271 (-0.085413) \| 0.051157 / 0.043533 (0.007625) \| 0.395022 / 0.255139 (0.139883) \| 0.402462 / 0.283200 (0.119262) \| 0.105207 / 0.141683 (-0.036475) \| 1.510679 / 1.452155 (0.058524) \| 1.584205 / 1.492716 (0.091489) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.225805 / 0.018006 (0.207799) \| 0.452109 / 0.000490 (0.451619) \| 0.000429 / 0.000200 (0.000229) \| 0.000057 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029653 / 0.037411 (-0.007759) \| 0.112609 / 0.014526 (0.098083) \| 0.121828 / 0.176557 (-0.054728) \| 0.159003 / 0.737135 (-0.578133) \| 0.129306 / 0.296338 (-0.167033) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.453001 / 0.215209 (0.237792) \| 4.514882 / 2.077655 (2.437228) \| 2.277494 / 1.504120 (0.773374) \| 2.073870 / 1.541195 (0.532675) \| 2.153346 / 1.468490 (0.684856) \| 0.698363 / 4.584777 (-3.886414) \| 3.921763 / 3.745712 (0.176051) \| 2.123133 / 5.269862 (-3.146729) \| 1.347618 / 4.565676 (-3.218058) \| 0.085654 / 0.424275 (-0.338621) \| 0.012059 / 0.007607 (0.004452) \| 0.568183 / 0.226044 (0.342139) \| 5.720047 / 2.268929 (3.451119) \| 2.777973 / 55.444624 (-52.666651) \| 2.453426 / 6.876477 (-4.423051) \| 2.523977 / 2.142072 (0.381905) \| 0.841979 / 4.805227 (-3.963248) \| 0.167958 / 6.500664 (-6.332706) \| 0.064929 / 0.075469 (-0.010540) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.235297 / 1.841788 (-0.606491) \| 15.883598 / 8.074308 (7.809290) \| 14.395328 / 10.191392 (4.203936) \| 0.162401 / 0.680424 (-0.518022) \| 0.017806 / 0.534201 (-0.516394) \| 0.423853 / 0.579283 (-0.155430) \| 0.423266 / 0.434364 (-0.011098) \| 0.490351 / 0.540337 (-0.049986) \| 0.588116 / 1.386936 (-0.798820) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bb3fbfa162bb4700e23d084826b4b7f6d97284be \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010759 / 0.011353 (-0.000594) \| 0.005748 / 0.011008 (-0.005260) \| 0.119195 / 0.038508 (0.080687) \| 0.033476 / 0.023109 (0.010367) \| 0.364081 / 0.275898 (0.088183) \| 0.422456 / 0.323480 (0.098976) \| 0.009780 / 0.007986 (0.001795) \| 0.006170 / 0.004328 (0.001841) \| 0.093242 / 0.004250 (0.088991) \| 0.041049 / 0.037052 (0.003997) \| 0.372132 / 0.258489 (0.113643) \| 0.442501 / 0.293841 (0.148660) \| 0.054889 / 0.128546 (-0.073657) \| 0.018302 / 0.075646 (-0.057345) \| 0.378899 / 0.419271 (-0.040373) \| 0.058455 / 0.043533 (0.014922) \| 0.356141 / 0.255139 (0.101002) \| 0.400866 / 0.283200 (0.117666) \| 0.103384 / 0.141683 (-0.038299) \| 1.629867 / 1.452155 (0.177713) \| 1.693939 / 1.492716 (0.201222) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.240484 / 0.018006 (0.222478) \| 0.509137 / 0.000490 (0.508648) \| 0.000450 / 0.000200 (0.000250) \| 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) \| 0.025856 / 0.037411 (-0.011555) \| 0.113214 / 0.014526 (0.098689) \| 0.119420 / 0.176557 (-0.057136) \| 0.158663 / 0.737135 (-0.578473) \| 0.123542 / 0.296338 (-0.172797) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.555900 / 0.215209 (0.340691) \| 5.580295 / 2.077655 (3.502640) \| 2.216640 / 1.504120 (0.712520) \| 1.904944 / 1.541195 (0.363749) \| 1.865839 / 1.468490 (0.397349) \| 1.158325 / 4.584777 (-3.426452) \| 5.097420 / 3.745712 (1.351708) \| 2.881775 / 5.269862 (-2.388087) \| 2.068896 / 4.565676 (-2.496780) \| 0.129028 / 0.424275 (-0.295247) \| 0.014075 / 0.007607 (0.006468) \| 0.698874 / 0.226044 (0.472830) \| 7.131225 / 2.268929 (4.862296) \| 2.901686 / 55.444624 (-52.542939) \| 2.186146 / 6.876477 (-4.690330) \| 2.251172 / 2.142072 (0.109100) \| 1.342264 / 4.805227 (-3.462963) \| 0.232045 / 6.500664 (-6.268619) \| 0.073520 / 0.075469 (-0.001949) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.431314 / 1.841788 (-0.410474) \| 16.313055 / 8.074308 (8.238747) \| 18.451552 / 10.191392 (8.260160) \| 0.232875 / 0.680424 (-0.447549) \| 0.042170 / 0.534201 (-0.492031) \| 0.495261 / 0.579283 (-0.084022) \| 0.582901 / 0.434364 (0.148537) \| 0.582049 / 0.540337 (0.041712) \| 0.681122 / 1.386936 (-0.705814) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008131 / 0.011353 (-0.003222) \| 0.006162 / 0.011008 (-0.004847) \| 0.113721 / 0.038508 (0.075213) \| 0.030797 / 0.023109 (0.007688) \| 0.413108 / 0.275898 (0.137210) \| 0.449968 / 0.323480 (0.126488) \| 0.006126 / 0.007986 (-0.001860) \| 0.004848 / 0.004328 (0.000519) \| 0.085465 / 0.004250 (0.081214) \| 0.045817 / 0.037052 (0.008764) \| 0.419360 / 0.258489 (0.160871) \| 0.489077 / 0.293841 (0.195236) \| 0.050841 / 0.128546 (-0.077705) \| 0.020646 / 0.075646 (-0.055000) \| 0.379838 / 0.419271 (-0.039434) \| 0.068897 / 0.043533 (0.025365) \| 0.422182 / 0.255139 (0.167043) \| 0.435529 / 0.283200 (0.152330) \| 0.115299 / 0.141683 (-0.026384) \| 1.655134 / 1.452155 (0.202979) \| 1.835198 / 1.492716 (0.342481) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.207041 / 0.018006 (0.189034) \| 0.491263 / 0.000490 (0.490773) \| 0.003554 / 0.000200 (0.003354) \| 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) \| 0.030830 / 0.037411 (-0.006582) \| 0.127003 / 0.014526 (0.112477) \| 0.142901 / 0.176557 (-0.033656) \| 0.177570 / 0.737135 (-0.559565) \| 0.137758 / 0.296338 (-0.158580) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.632820 / 0.215209 (0.417611) \| 6.215535 / 2.077655 (4.137880) \| 2.615310 / 1.504120 (1.111190) \| 2.261431 / 1.541195 (0.720236) \| 2.220570 / 1.468490 (0.752080) \| 1.215820 / 4.584777 (-3.368957) \| 5.247680 / 3.745712 (1.501968) \| 3.120054 / 5.269862 (-2.149807) \| 1.950947 / 4.565676 (-2.614730) \| 0.149980 / 0.424275 (-0.274295) \| 0.015241 / 0.007607 (0.007634) \| 0.879714 / 0.226044 (0.653670) \| 7.941913 / 2.268929 (5.672984) \| 3.512456 / 55.444624 (-51.932168) \| 2.693833 / 6.876477 (-4.182644) \| 2.772780 / 2.142072 (0.630708) \| 1.459581 / 4.805227 (-3.345646) \| 0.264820 / 6.500664 (-6.235844) \| 0.076698 / 0.075469 (0.001228) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.437719 / 1.841788 (-0.404068) \| 16.750309 / 8.074308 (8.676001) \| 18.646776 / 10.191392 (8.455384) \| 0.227858 / 0.680424 (-0.452566) \| 0.024239 / 0.534201 (-0.509962) \| 0.486172 / 0.579283 (-0.093111) \| 0.574731 / 0.434364 (0.140367) \| 0.557776 / 0.540337 (0.017439) \| 0.672921 / 1.386936 (-0.714015) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bb3fbfa162bb4700e23d084826b4b7f6d97284be \"CML watermark\")\n" ]
1,526,904,861	5,415	RuntimeError: Sharding is ambiguous for this dataset	closed	2023-01-10T07:36:11	2023-01-18T14:09:04	2023-01-18T14:09:03	https://github.com/huggingface/datasets/issues/5415	null	albertvillanova	false	[]
1,525,733,818	5,414	Sharding error with Multilingual LibriSpeech	closed	2023-01-09T14:45:31	2023-01-18T14:09:04	2023-01-18T14:09:04	https://github.com/huggingface/datasets/issues/5414	null	Nithin-Holla	false	[ "Thanks for reporting, @Nithin-Holla.\r\n\r\nThis is a known issue for multiple datasets and we are investigating it:\r\n- See e.g.: https://huggingface.co/datasets/ami/discussions/3", "Main issue:\r\n- #5415", "@albertvillanova Thanks! As a workaround for now, can I use the dataset in streaming mode?", "Yes, @Nithin-Holla, in the meantime you can use this dataset in streaming mode." ]
1,524,591,837	5,413	concatenate_datasets fails when two dataset with shards > 1 and unequal shard numbers	closed	2023-01-08T17:01:52	2023-01-26T09:27:21	2023-01-26T09:27:21	https://github.com/huggingface/datasets/issues/5413	null	ZeguanXiao	false	[ "Hi ! Thanks for reporting :)\r\n\r\nI managed to reproduce the hub using\r\n```python\r\n\r\nfrom datasets import concatenate_datasets, Dataset, load_from_disk\r\n\r\nDataset.from_dict({\"a\": range(9)}).save_to_disk(\"tmp/ds1\")\r\nds1 = load_from_disk(\"tmp/ds1\")\r\nds1 = concatenate_datasets([ds1, ds1])\r\n\r\nDataset.from_dict({\"b\": range(6)}).save_to_disk(\"tmp/ds2\")\r\nds2 = load_from_disk(\"tmp/ds2\")\r\nds2 = concatenate_datasets([ds2, ds2, ds2])\r\n\r\nconcatenate_datasets([ds1, ds2], axis=1)\r\n```\r\nand I get\r\n```python\r\nTraceback (most recent call last): \r\n File \"test.py\", line 98, in <module>\r\n dds = concatenate_datasets([ds1, ds2], axis=1)\r\n File \"/Users/.../datasets/combine.py\", line 182, in concatenate_datasets\r\n return _concatenate_map_style_datasets(dsets, info=info, split=split, axis=axis)\r\n File \"/Users/.../datasets/arrow_dataset.py\", line 5499, in _concatenate_map_style_datasets\r\n table = concat_tables([dset._data for dset in dsets], axis=axis)\r\n File \"/Users/.../datasets/table.py\", line 1778, in concat_tables\r\n return ConcatenationTable.from_tables(tables, axis=axis)\r\n File \"/Users/.../datasets/table.py\", line 1483, in from_tables\r\n blocks = _extend_blocks(blocks, table_blocks, axis=axis)\r\n File \"/Users/.../datasets/table.py\", line 1477, in _extend_blocks\r\n result[i].extend(row_blocks)\r\nIndexError: list index out of range\r\n```\r\n\r\nIt appears to happen when the two datasets have a number of shards that is not the same" ]
1,524,250,269	5,412	load_dataset() cannot find dataset_info.json with multiple training runs in parallel	closed	2023-01-08T00:44:32	2023-01-19T20:28:43	2023-01-19T20:28:43	https://github.com/huggingface/datasets/issues/5412	null	mtoles	false	[ "Hi ! It fails because the dataset is already being prepared by your first run. I'd encourage you to prepare your dataset before using it for multiple trainings.\r\n\r\nYou can also specify another cache directory by passing `cache_dir=` to `load_dataset()`.", "Thank you! What do you mean by prepare it beforehand? I am unclear how to conduct dataset preparation outside of using the `load_dataset` function.", "You can have a separate script that does load_dataset + map + save_to_disk to save your prepared dataset somewhere. Then in your training script you can reload the dataset with load_from_disk", "Thank you! I believe I was running additional map steps after loading, resulting in the cache conflict. " ]
1,523,297,786	5,411	Update docs of S3 filesystem with async aiobotocore	closed	2023-01-06T23:19:17	2023-01-18T11:18:59	2023-01-18T11:12:04	https://github.com/huggingface/datasets/pull/5411	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5411", "html_url": "https://github.com/huggingface/datasets/pull/5411", "diff_url": "https://github.com/huggingface/datasets/pull/5411.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5411.patch", "merged_at": "2023-01-18T11:12:04" }	maheshpec	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008587 / 0.011353 (-0.002766) \| 0.004613 / 0.011008 (-0.006395) \| 0.100446 / 0.038508 (0.061938) \| 0.029606 / 0.023109 (0.006497) \| 0.302102 / 0.275898 (0.026204) \| 0.357364 / 0.323480 (0.033884) \| 0.007031 / 0.007986 (-0.000954) \| 0.003593 / 0.004328 (-0.000735) \| 0.078110 / 0.004250 (0.073860) \| 0.035495 / 0.037052 (-0.001557) \| 0.312522 / 0.258489 (0.054033) \| 0.349336 / 0.293841 (0.055495) \| 0.033719 / 0.128546 (-0.094827) \| 0.011449 / 0.075646 (-0.064197) \| 0.321760 / 0.419271 (-0.097512) \| 0.043697 / 0.043533 (0.000165) \| 0.304476 / 0.255139 (0.049337) \| 0.333126 / 0.283200 (0.049926) \| 0.092756 / 0.141683 (-0.048927) \| 1.506734 / 1.452155 (0.054579) \| 1.547381 / 1.492716 (0.054664) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.178177 / 0.018006 (0.160171) \| 0.427814 / 0.000490 (0.427324) \| 0.002505 / 0.000200 (0.002305) \| 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) \| 0.023039 / 0.037411 (-0.014372) \| 0.097113 / 0.014526 (0.082587) \| 0.105014 / 0.176557 (-0.071543) \| 0.141185 / 0.737135 (-0.595950) \| 0.108843 / 0.296338 (-0.187495) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.424148 / 0.215209 (0.208939) \| 4.247599 / 2.077655 (2.169944) \| 2.130720 / 1.504120 (0.626600) \| 1.916349 / 1.541195 (0.375154) \| 1.831515 / 1.468490 (0.363025) \| 0.688301 / 4.584777 (-3.896476) \| 3.381749 / 3.745712 (-0.363963) \| 2.900045 / 5.269862 (-2.369817) \| 1.576248 / 4.565676 (-2.989428) \| 0.082354 / 0.424275 (-0.341921) \| 0.012200 / 0.007607 (0.004593) \| 0.525753 / 0.226044 (0.299709) \| 5.277672 / 2.268929 (3.008743) \| 2.603870 / 55.444624 (-52.840754) \| 2.296203 / 6.876477 (-4.580273) \| 2.308014 / 2.142072 (0.165942) \| 0.809056 / 4.805227 (-3.996171) \| 0.148122 / 6.500664 (-6.352542) \| 0.066097 / 0.075469 (-0.009372) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.214059 / 1.841788 (-0.627728) \| 13.671332 / 8.074308 (5.597024) \| 13.694554 / 10.191392 (3.503162) \| 0.151454 / 0.680424 (-0.528970) \| 0.028514 / 0.534201 (-0.505687) \| 0.391480 / 0.579283 (-0.187804) \| 0.404499 / 0.434364 (-0.029865) \| 0.458111 / 0.540337 (-0.082226) \| 0.539454 / 1.386936 (-0.847482) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006795 / 0.011353 (-0.004558) \| 0.004463 / 0.011008 (-0.006545) \| 0.099542 / 0.038508 (0.061034) \| 0.027588 / 0.023109 (0.004479) \| 0.423023 / 0.275898 (0.147125) \| 0.458459 / 0.323480 (0.134979) \| 0.004981 / 0.007986 (-0.003005) \| 0.003321 / 0.004328 (-0.001008) \| 0.075727 / 0.004250 (0.071477) \| 0.040541 / 0.037052 (0.003489) \| 0.423724 / 0.258489 (0.165235) \| 0.468334 / 0.293841 (0.174493) \| 0.031732 / 0.128546 (-0.096814) \| 0.011478 / 0.075646 (-0.064168) \| 0.319807 / 0.419271 (-0.099465) \| 0.041215 / 0.043533 (-0.002318) \| 0.423060 / 0.255139 (0.167921) \| 0.446157 / 0.283200 (0.162957) \| 0.088884 / 0.141683 (-0.052799) \| 1.553404 / 1.452155 (0.101250) \| 1.607797 / 1.492716 (0.115080) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.208314 / 0.018006 (0.190307) \| 0.411627 / 0.000490 (0.411137) \| 0.002416 / 0.000200 (0.002216) \| 0.000078 / 0.000054 (0.000024) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024641 / 0.037411 (-0.012770) \| 0.101047 / 0.014526 (0.086521) \| 0.108410 / 0.176557 (-0.068147) \| 0.142860 / 0.737135 (-0.594276) \| 0.112486 / 0.296338 (-0.183852) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.485520 / 0.215209 (0.270311) \| 4.864009 / 2.077655 (2.786355) \| 2.541865 / 1.504120 (1.037745) \| 2.339569 / 1.541195 (0.798374) \| 2.378258 / 1.468490 (0.909768) \| 0.698000 / 4.584777 (-3.886777) \| 3.343137 / 3.745712 (-0.402575) \| 1.842264 / 5.269862 (-3.427597) \| 1.154707 / 4.565676 (-3.410969) \| 0.082826 / 0.424275 (-0.341449) \| 0.012379 / 0.007607 (0.004772) \| 0.583335 / 0.226044 (0.357291) \| 5.885934 / 2.268929 (3.617006) \| 2.997769 / 55.444624 (-52.446856) \| 2.653681 / 6.876477 (-4.222796) \| 2.761656 / 2.142072 (0.619583) \| 0.799883 / 4.805227 (-4.005344) \| 0.151398 / 6.500664 (-6.349266) \| 0.067445 / 0.075469 (-0.008024) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.292009 / 1.841788 (-0.549779) \| 13.976180 / 8.074308 (5.901872) \| 14.219469 / 10.191392 (4.028077) \| 0.127810 / 0.680424 (-0.552614) \| 0.016919 / 0.534201 (-0.517282) \| 0.376401 / 0.579283 (-0.202882) \| 0.388563 / 0.434364 (-0.045801) \| 0.444904 / 0.540337 (-0.095433) \| 0.532290 / 1.386936 (-0.854646) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#41d4378831cac1fe5fc624bf97a97b3cf81e0b8a \"CML watermark\")\n" ]
1,521,168,032	5,410	Map-style Dataset to IterableDataset	closed	2023-01-05T18:12:17	2023-02-01T18:11:45	2023-02-01T16:36:01	https://github.com/huggingface/datasets/pull/5410	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5410", "html_url": "https://github.com/huggingface/datasets/pull/5410", "diff_url": "https://github.com/huggingface/datasets/pull/5410.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5410.patch", "merged_at": "2023-02-01T16:36:01" }	lhoestq	true	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009812 / 0.011353 (-0.001540) \| 0.005290 / 0.011008 (-0.005719) \| 0.099728 / 0.038508 (0.061220) \| 0.036712 / 0.023109 (0.013602) \| 0.305924 / 0.275898 (0.030026) \| 0.349844 / 0.323480 (0.026365) \| 0.008353 / 0.007986 (0.000368) \| 0.004464 / 0.004328 (0.000135) \| 0.075329 / 0.004250 (0.071079) \| 0.046146 / 0.037052 (0.009094) \| 0.304197 / 0.258489 (0.045708) \| 0.354245 / 0.293841 (0.060404) \| 0.039270 / 0.128546 (-0.089276) \| 0.012496 / 0.075646 (-0.063151) \| 0.334390 / 0.419271 (-0.084882) \| 0.049428 / 0.043533 (0.005896) \| 0.297318 / 0.255139 (0.042179) \| 0.315646 / 0.283200 (0.032447) \| 0.106746 / 0.141683 (-0.034937) \| 1.443562 / 1.452155 (-0.008593) \| 1.546022 / 1.492716 (0.053305) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.303419 / 0.018006 (0.285413) \| 0.536971 / 0.000490 (0.536481) \| 0.001335 / 0.000200 (0.001135) \| 0.000088 / 0.000054 (0.000033) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030484 / 0.037411 (-0.006927) \| 0.110043 / 0.014526 (0.095518) \| 0.125265 / 0.176557 (-0.051291) \| 0.171410 / 0.737135 (-0.565725) \| 0.128978 / 0.296338 (-0.167361) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.398354 / 0.215209 (0.183145) \| 3.984180 / 2.077655 (1.906526) \| 1.781134 / 1.504120 (0.277014) \| 1.589656 / 1.541195 (0.048462) \| 1.704192 / 1.468490 (0.235702) \| 0.682271 / 4.584777 (-3.902506) \| 3.731504 / 3.745712 (-0.014208) \| 2.243520 / 5.269862 (-3.026342) \| 1.511334 / 4.565676 (-3.054343) \| 0.084243 / 0.424275 (-0.340032) \| 0.012261 / 0.007607 (0.004654) \| 0.507499 / 0.226044 (0.281454) \| 5.066037 / 2.268929 (2.797109) \| 2.246107 / 55.444624 (-53.198517) \| 1.921032 / 6.876477 (-4.955444) \| 2.144111 / 2.142072 (0.002039) \| 0.845233 / 4.805227 (-3.959995) \| 0.165392 / 6.500664 (-6.335272) \| 0.064201 / 0.075469 (-0.011268) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.217649 / 1.841788 (-0.624138) \| 15.890487 / 8.074308 (7.816179) \| 14.772039 / 10.191392 (4.580647) \| 0.192901 / 0.680424 (-0.487523) \| 0.029119 / 0.534201 (-0.505082) \| 0.442904 / 0.579283 (-0.136380) \| 0.451035 / 0.434364 (0.016671) \| 0.520788 / 0.540337 (-0.019550) \| 0.623588 / 1.386936 (-0.763348) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007452 / 0.011353 (-0.003901) \| 0.005426 / 0.011008 (-0.005582) \| 0.096488 / 0.038508 (0.057980) \| 0.033575 / 0.023109 (0.010465) \| 0.375688 / 0.275898 (0.099790) \| 0.412393 / 0.323480 (0.088913) \| 0.006050 / 0.007986 (-0.001936) \| 0.004424 / 0.004328 (0.000095) \| 0.073102 / 0.004250 (0.068852) \| 0.052672 / 0.037052 (0.015620) \| 0.379352 / 0.258489 (0.120862) \| 0.436065 / 0.293841 (0.142224) \| 0.036594 / 0.128546 (-0.091952) \| 0.012380 / 0.075646 (-0.063266) \| 0.332899 / 0.419271 (-0.086373) \| 0.048859 / 0.043533 (0.005326) \| 0.373215 / 0.255139 (0.118076) \| 0.386990 / 0.283200 (0.103791) \| 0.105166 / 0.141683 (-0.036517) \| 1.490762 / 1.452155 (0.038607) \| 1.611310 / 1.492716 (0.118593) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.333142 / 0.018006 (0.315136) \| 0.537137 / 0.000490 (0.536647) \| 0.000452 / 0.000200 (0.000252) \| 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) \| 0.030368 / 0.037411 (-0.007043) \| 0.109608 / 0.014526 (0.095083) \| 0.124220 / 0.176557 (-0.052336) \| 0.162834 / 0.737135 (-0.574301) \| 0.128037 / 0.296338 (-0.168302) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.440991 / 0.215209 (0.225782) \| 4.400825 / 2.077655 (2.323170) \| 2.158768 / 1.504120 (0.654648) \| 1.968158 / 1.541195 (0.426963) \| 2.085115 / 1.468490 (0.616625) \| 0.710757 / 4.584777 (-3.874020) \| 3.835441 / 3.745712 (0.089729) \| 2.204118 / 5.269862 (-3.065744) \| 1.378909 / 4.565676 (-3.186767) \| 0.089149 / 0.424275 (-0.335126) \| 0.013066 / 0.007607 (0.005459) \| 0.539165 / 0.226044 (0.313121) \| 5.414176 / 2.268929 (3.145248) \| 2.677020 / 55.444624 (-52.767604) \| 2.328334 / 6.876477 (-4.548143) \| 2.518933 / 2.142072 (0.376860) \| 0.840902 / 4.805227 (-3.964325) \| 0.170365 / 6.500664 (-6.330299) \| 0.063909 / 0.075469 (-0.011561) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.237205 / 1.841788 (-0.604583) \| 15.678776 / 8.074308 (7.604468) \| 14.118576 / 10.191392 (3.927184) \| 0.167236 / 0.680424 (-0.513188) \| 0.018177 / 0.534201 (-0.516024) \| 0.426680 / 0.579283 (-0.152603) \| 0.425126 / 0.434364 (-0.009238) \| 0.501755 / 0.540337 (-0.038582) \| 0.592754 / 1.386936 (-0.794182) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008708 / 0.011353 (-0.002645) \| 0.004462 / 0.011008 (-0.006546) \| 0.100159 / 0.038508 (0.061651) \| 0.029543 / 0.023109 (0.006434) \| 0.304056 / 0.275898 (0.028158) \| 0.367098 / 0.323480 (0.043618) \| 0.007049 / 0.007986 (-0.000937) \| 0.003294 / 0.004328 (-0.001034) \| 0.076954 / 0.004250 (0.072703) \| 0.036850 / 0.037052 (-0.000202) \| 0.307556 / 0.258489 (0.049067) \| 0.348327 / 0.293841 (0.054486) \| 0.033520 / 0.128546 (-0.095026) \| 0.011312 / 0.075646 (-0.064334) \| 0.317588 / 0.419271 (-0.101684) \| 0.040196 / 0.043533 (-0.003337) \| 0.298330 / 0.255139 (0.043191) \| 0.333821 / 0.283200 (0.050622) \| 0.086584 / 0.141683 (-0.055099) \| 1.480205 / 1.452155 (0.028050) \| 1.520975 / 1.492716 (0.028259) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.186641 / 0.018006 (0.168635) \| 0.414420 / 0.000490 (0.413930) \| 0.003021 / 0.000200 (0.002821) \| 0.000073 / 0.000054 (0.000018) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022953 / 0.037411 (-0.014458) \| 0.097338 / 0.014526 (0.082812) \| 0.104985 / 0.176557 (-0.071572) \| 0.139208 / 0.737135 (-0.597927) \| 0.108031 / 0.296338 (-0.188307) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.417969 / 0.215209 (0.202759) \| 4.173189 / 2.077655 (2.095534) \| 1.862813 / 1.504120 (0.358693) \| 1.653226 / 1.541195 (0.112031) \| 1.725917 / 1.468490 (0.257426) \| 0.701038 / 4.584777 (-3.883739) \| 3.350500 / 3.745712 (-0.395213) \| 1.913156 / 5.269862 (-3.356705) \| 1.267597 / 4.565676 (-3.298079) \| 0.082197 / 0.424275 (-0.342078) \| 0.012499 / 0.007607 (0.004892) \| 0.520173 / 0.226044 (0.294128) \| 5.219981 / 2.268929 (2.951053) \| 2.306029 / 55.444624 (-53.138595) \| 1.948169 / 6.876477 (-4.928307) \| 2.013160 / 2.142072 (-0.128912) \| 0.813325 / 4.805227 (-3.991902) \| 0.149729 / 6.500664 (-6.350935) \| 0.065492 / 0.075469 (-0.009977) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.194163 / 1.841788 (-0.647625) \| 13.739562 / 8.074308 (5.665254) \| 13.881988 / 10.191392 (3.690596) \| 0.138180 / 0.680424 (-0.542244) \| 0.029031 / 0.534201 (-0.505170) \| 0.387858 / 0.579283 (-0.191425) \| 0.395171 / 0.434364 (-0.039193) \| 0.446349 / 0.540337 (-0.093988) \| 0.527073 / 1.386936 (-0.859863) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006504 / 0.011353 (-0.004849) \| 0.004564 / 0.011008 (-0.006444) \| 0.099108 / 0.038508 (0.060599) \| 0.027420 / 0.023109 (0.004311) \| 0.340712 / 0.275898 (0.064814) \| 0.391613 / 0.323480 (0.068133) \| 0.004977 / 0.007986 (-0.003009) \| 0.003375 / 0.004328 (-0.000953) \| 0.076403 / 0.004250 (0.072152) \| 0.036650 / 0.037052 (-0.000402) \| 0.341948 / 0.258489 (0.083459) \| 0.392065 / 0.293841 (0.098224) \| 0.031802 / 0.128546 (-0.096745) \| 0.011659 / 0.075646 (-0.063987) \| 0.320099 / 0.419271 (-0.099173) \| 0.041615 / 0.043533 (-0.001918) \| 0.342125 / 0.255139 (0.086986) \| 0.372833 / 0.283200 (0.089633) \| 0.089032 / 0.141683 (-0.052650) \| 1.486691 / 1.452155 (0.034536) \| 1.567326 / 1.492716 (0.074610) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.193123 / 0.018006 (0.175117) \| 0.404062 / 0.000490 (0.403573) \| 0.003460 / 0.000200 (0.003260) \| 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) \| 0.024565 / 0.037411 (-0.012846) \| 0.098958 / 0.014526 (0.084432) \| 0.108701 / 0.176557 (-0.067855) \| 0.142567 / 0.737135 (-0.594569) \| 0.111048 / 0.296338 (-0.185290) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.474549 / 0.215209 (0.259340) \| 4.753776 / 2.077655 (2.676121) \| 2.435528 / 1.504120 (0.931409) \| 2.234491 / 1.541195 (0.693297) \| 2.269474 / 1.468490 (0.800984) \| 0.695636 / 4.584777 (-3.889141) \| 3.367816 / 3.745712 (-0.377896) \| 1.854828 / 5.269862 (-3.415034) \| 1.159729 / 4.565676 (-3.405948) \| 0.082267 / 0.424275 (-0.342008) \| 0.012483 / 0.007607 (0.004876) \| 0.578490 / 0.226044 (0.352446) \| 5.814490 / 2.268929 (3.545561) \| 2.893310 / 55.444624 (-52.551314) \| 2.540555 / 6.876477 (-4.335922) \| 2.573705 / 2.142072 (0.431633) \| 0.800545 / 4.805227 (-4.004682) \| 0.151306 / 6.500664 (-6.349358) \| 0.067925 / 0.075469 (-0.007544) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.294645 / 1.841788 (-0.547142) \| 13.641842 / 8.074308 (5.567534) \| 14.015200 / 10.191392 (3.823808) \| 0.128829 / 0.680424 (-0.551595) \| 0.016870 / 0.534201 (-0.517331) \| 0.389137 / 0.579283 (-0.190146) \| 0.388384 / 0.434364 (-0.045980) \| 0.447711 / 0.540337 (-0.092627) \| 0.540637 / 1.386936 (-0.846299) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#45ad185b9040a68285080b6099ed3af58442ccb2 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.012282 / 0.011353 (0.000929) \| 0.006328 / 0.011008 (-0.004680) \| 0.129666 / 0.038508 (0.091158) \| 0.039403 / 0.023109 (0.016294) \| 0.375464 / 0.275898 (0.099566) \| 0.463167 / 0.323480 (0.139687) \| 0.010329 / 0.007986 (0.002344) \| 0.005111 / 0.004328 (0.000782) \| 0.108727 / 0.004250 (0.104476) \| 0.047156 / 0.037052 (0.010103) \| 0.381869 / 0.258489 (0.123380) \| 0.441936 / 0.293841 (0.148095) \| 0.054750 / 0.128546 (-0.073796) \| 0.019809 / 0.075646 (-0.055837) \| 0.436389 / 0.419271 (0.017118) \| 0.066585 / 0.043533 (0.023052) \| 0.402108 / 0.255139 (0.146969) \| 0.424571 / 0.283200 (0.141371) \| 0.118326 / 0.141683 (-0.023357) \| 1.870175 / 1.452155 (0.418020) \| 1.878720 / 1.492716 (0.386004) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.012863 / 0.018006 (-0.005144) \| 0.528670 / 0.000490 (0.528181) \| 0.006057 / 0.000200 (0.005857) \| 0.000124 / 0.000054 (0.000069) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030091 / 0.037411 (-0.007320) \| 0.136143 / 0.014526 (0.121618) \| 0.148931 / 0.176557 (-0.027626) \| 0.179578 / 0.737135 (-0.557558) \| 0.144528 / 0.296338 (-0.151810) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.594080 / 0.215209 (0.378871) \| 6.029101 / 2.077655 (3.951446) \| 2.443084 / 1.504120 (0.938964) \| 2.123949 / 1.541195 (0.582754) \| 2.183021 / 1.468490 (0.714531) \| 1.235453 / 4.584777 (-3.349324) \| 5.585121 / 3.745712 (1.839408) \| 3.208510 / 5.269862 (-2.061351) \| 2.090334 / 4.565676 (-2.475342) \| 0.150353 / 0.424275 (-0.273922) \| 0.016787 / 0.007607 (0.009180) \| 0.797561 / 0.226044 (0.571516) \| 7.756291 / 2.268929 (5.487363) \| 3.283638 / 55.444624 (-52.160986) \| 2.527441 / 6.876477 (-4.349036) \| 2.590765 / 2.142072 (0.448692) \| 1.446818 / 4.805227 (-3.358409) \| 0.250563 / 6.500664 (-6.250101) \| 0.077919 / 0.075469 (0.002450) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.612022 / 1.841788 (-0.229765) \| 18.363316 / 8.074308 (10.289008) \| 22.578570 / 10.191392 (12.387178) \| 0.232801 / 0.680424 (-0.447623) \| 0.048232 / 0.534201 (-0.485969) \| 0.549518 / 0.579283 (-0.029766) \| 0.624663 / 0.434364 (0.190299) \| 0.674745 / 0.540337 (0.134408) \| 0.803489 / 1.386936 (-0.583447) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009872 / 0.011353 (-0.001481) \| 0.006593 / 0.011008 (-0.004415) \| 0.139248 / 0.038508 (0.100740) \| 0.035708 / 0.023109 (0.012598) \| 0.551335 / 0.275898 (0.275437) \| 0.544995 / 0.323480 (0.221515) \| 0.007085 / 0.007986 (-0.000900) \| 0.004742 / 0.004328 (0.000413) \| 0.095823 / 0.004250 (0.091572) \| 0.051674 / 0.037052 (0.014621) \| 0.463405 / 0.258489 (0.204916) \| 0.640392 / 0.293841 (0.346551) \| 0.055242 / 0.128546 (-0.073304) \| 0.022602 / 0.075646 (-0.053044) \| 0.419171 / 0.419271 (-0.000100) \| 0.062986 / 0.043533 (0.019453) \| 0.503683 / 0.255139 (0.248544) \| 0.568719 / 0.283200 (0.285519) \| 0.113906 / 0.141683 (-0.027777) \| 1.825248 / 1.452155 (0.373094) \| 1.985667 / 1.492716 (0.492951) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.237478 / 0.018006 (0.219472) \| 0.528861 / 0.000490 (0.528371) \| 0.008507 / 0.000200 (0.008307) \| 0.000158 / 0.000054 (0.000103) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033536 / 0.037411 (-0.003875) \| 0.144202 / 0.014526 (0.129677) \| 0.139472 / 0.176557 (-0.037084) \| 0.184540 / 0.737135 (-0.552596) \| 0.147818 / 0.296338 (-0.148520) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.671654 / 0.215209 (0.456445) \| 6.616368 / 2.077655 (4.538713) \| 2.805634 / 1.504120 (1.301514) \| 2.482890 / 1.541195 (0.941695) \| 2.547686 / 1.468490 (1.079195) \| 1.289169 / 4.584777 (-3.295608) \| 5.551436 / 3.745712 (1.805724) \| 5.228500 / 5.269862 (-0.041362) \| 2.456706 / 4.565676 (-2.108970) \| 0.148556 / 0.424275 (-0.275720) \| 0.015290 / 0.007607 (0.007683) \| 0.837090 / 0.226044 (0.611045) \| 8.373561 / 2.268929 (6.104632) \| 3.663910 / 55.444624 (-51.780714) \| 2.927117 / 6.876477 (-3.949360) \| 2.976785 / 2.142072 (0.834712) \| 1.501618 / 4.805227 (-3.303609) \| 0.263321 / 6.500664 (-6.237343) \| 0.082644 / 0.075469 (0.007175) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.707419 / 1.841788 (-0.134368) \| 18.371117 / 8.074308 (10.296809) \| 22.015154 / 10.191392 (11.823762) \| 0.232066 / 0.680424 (-0.448357) \| 0.027149 / 0.534201 (-0.507052) \| 0.544450 / 0.579283 (-0.034833) \| 0.605134 / 0.434364 (0.170770) \| 0.656063 / 0.540337 (0.115725) \| 0.788121 / 1.386936 (-0.598815) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f1e0ec31e07e4bc0469f4acfed601d9c71e9a459 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008952 / 0.011353 (-0.002401) \| 0.005592 / 0.011008 (-0.005416) \| 0.101138 / 0.038508 (0.062630) \| 0.035573 / 0.023109 (0.012464) \| 0.295959 / 0.275898 (0.020060) \| 0.365347 / 0.323480 (0.041867) \| 0.008136 / 0.007986 (0.000150) \| 0.004479 / 0.004328 (0.000150) \| 0.078806 / 0.004250 (0.074556) \| 0.045180 / 0.037052 (0.008127) \| 0.321687 / 0.258489 (0.063198) \| 0.345874 / 0.293841 (0.052033) \| 0.038720 / 0.128546 (-0.089826) \| 0.012534 / 0.075646 (-0.063112) \| 0.335571 / 0.419271 (-0.083700) \| 0.049048 / 0.043533 (0.005515) \| 0.294756 / 0.255139 (0.039617) \| 0.327496 / 0.283200 (0.044296) \| 0.109181 / 0.141683 (-0.032502) \| 1.417068 / 1.452155 (-0.035087) \| 1.455473 / 1.492716 (-0.037244) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.267774 / 0.018006 (0.249768) \| 0.538546 / 0.000490 (0.538056) \| 0.001755 / 0.000200 (0.001555) \| 0.000090 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026839 / 0.037411 (-0.010572) \| 0.105862 / 0.014526 (0.091336) \| 0.118278 / 0.176557 (-0.058279) \| 0.157926 / 0.737135 (-0.579209) \| 0.124700 / 0.296338 (-0.171638) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.399060 / 0.215209 (0.183851) \| 3.991409 / 2.077655 (1.913754) \| 1.763569 / 1.504120 (0.259449) \| 1.579602 / 1.541195 (0.038407) \| 1.652928 / 1.468490 (0.184438) \| 0.692962 / 4.584777 (-3.891815) \| 3.784635 / 3.745712 (0.038922) \| 3.249341 / 5.269862 (-2.020521) \| 1.815711 / 4.565676 (-2.749966) \| 0.084384 / 0.424275 (-0.339891) \| 0.012546 / 0.007607 (0.004939) \| 0.521397 / 0.226044 (0.295352) \| 5.075824 / 2.268929 (2.806895) \| 2.258353 / 55.444624 (-53.186272) \| 1.925220 / 6.876477 (-4.951256) \| 2.002821 / 2.142072 (-0.139252) \| 0.830507 / 4.805227 (-3.974720) \| 0.165845 / 6.500664 (-6.334819) \| 0.063905 / 0.075469 (-0.011565) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.198726 / 1.841788 (-0.643061) \| 14.804448 / 8.074308 (6.730139) \| 12.855167 / 10.191392 (2.663775) \| 0.167932 / 0.680424 (-0.512492) \| 0.028643 / 0.534201 (-0.505558) \| 0.441224 / 0.579283 (-0.138059) \| 0.434924 / 0.434364 (0.000560) \| 0.516188 / 0.540337 (-0.024150) \| 0.605017 / 1.386936 (-0.781919) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007031 / 0.011353 (-0.004322) \| 0.005157 / 0.011008 (-0.005851) \| 0.086943 / 0.038508 (0.048434) \| 0.031377 / 0.023109 (0.008268) \| 0.334810 / 0.275898 (0.058912) \| 0.368590 / 0.323480 (0.045110) \| 0.005973 / 0.007986 (-0.002013) \| 0.004173 / 0.004328 (-0.000155) \| 0.067033 / 0.004250 (0.062783) \| 0.054070 / 0.037052 (0.017018) \| 0.332232 / 0.258489 (0.073743) \| 0.384982 / 0.293841 (0.091141) \| 0.034023 / 0.128546 (-0.094524) \| 0.011301 / 0.075646 (-0.064345) \| 0.295644 / 0.419271 (-0.123628) \| 0.045589 / 0.043533 (0.002056) \| 0.330739 / 0.255139 (0.075600) \| 0.352841 / 0.283200 (0.069642) \| 0.104829 / 0.141683 (-0.036854) \| 1.329360 / 1.452155 (-0.122794) \| 1.437956 / 1.492716 (-0.054760) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.299187 / 0.018006 (0.281181) \| 0.563407 / 0.000490 (0.562917) \| 0.004179 / 0.000200 (0.003979) \| 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) \| 0.027405 / 0.037411 (-0.010006) \| 0.097498 / 0.014526 (0.082972) \| 0.114265 / 0.176557 (-0.062292) \| 0.146823 / 0.737135 (-0.590313) \| 0.117948 / 0.296338 (-0.178391) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.378756 / 0.215209 (0.163547) \| 3.774804 / 2.077655 (1.697150) \| 1.804149 / 1.504120 (0.300029) \| 1.626312 / 1.541195 (0.085117) \| 1.731111 / 1.468490 (0.262620) \| 0.633493 / 4.584777 (-3.951284) \| 3.488220 / 3.745712 (-0.257492) \| 3.064710 / 5.269862 (-2.205151) \| 1.690647 / 4.565676 (-2.875029) \| 0.076093 / 0.424275 (-0.348182) \| 0.010820 / 0.007607 (0.003213) \| 0.465091 / 0.226044 (0.239046) \| 4.676842 / 2.268929 (2.407913) \| 2.297381 / 55.444624 (-53.147244) \| 1.960355 / 6.876477 (-4.916122) \| 1.983742 / 2.142072 (-0.158330) \| 0.739525 / 4.805227 (-4.065702) \| 0.152663 / 6.500664 (-6.348001) \| 0.057316 / 0.075469 (-0.018153) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.104721 / 1.841788 (-0.737067) \| 14.577171 / 8.074308 (6.502863) \| 13.680402 / 10.191392 (3.489010) \| 0.182234 / 0.680424 (-0.498190) \| 0.018853 / 0.534201 (-0.515348) \| 0.426194 / 0.579283 (-0.153089) \| 0.429202 / 0.434364 (-0.005162) \| 0.543125 / 0.540337 (0.002788) \| 0.645887 / 1.386936 (-0.741049) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f830952573bdc59f8732b8f1a13f70d9187e0a65 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010055 / 0.011353 (-0.001298) \| 0.005576 / 0.011008 (-0.005432) \| 0.100059 / 0.038508 (0.061551) \| 0.038535 / 0.023109 (0.015425) \| 0.297538 / 0.275898 (0.021640) \| 0.368117 / 0.323480 (0.044637) \| 0.008540 / 0.007986 (0.000555) \| 0.004469 / 0.004328 (0.000141) \| 0.075801 / 0.004250 (0.071551) \| 0.046604 / 0.037052 (0.009552) \| 0.307242 / 0.258489 (0.048753) \| 0.343949 / 0.293841 (0.050108) \| 0.039353 / 0.128546 (-0.089194) \| 0.012446 / 0.075646 (-0.063200) \| 0.334628 / 0.419271 (-0.084643) \| 0.051628 / 0.043533 (0.008095) \| 0.298726 / 0.255139 (0.043587) \| 0.316010 / 0.283200 (0.032810) \| 0.120564 / 0.141683 (-0.021119) \| 1.459396 / 1.452155 (0.007241) \| 1.493682 / 1.492716 (0.000965) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.011702 / 0.018006 (-0.006304) \| 0.570261 / 0.000490 (0.569771) \| 0.003760 / 0.000200 (0.003560) \| 0.000091 / 0.000054 (0.000037) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028806 / 0.037411 (-0.008605) \| 0.112150 / 0.014526 (0.097625) \| 0.123140 / 0.176557 (-0.053417) \| 0.173055 / 0.737135 (-0.564080) \| 0.130060 / 0.296338 (-0.166279) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.398216 / 0.215209 (0.183007) \| 3.978677 / 2.077655 (1.901022) \| 1.754229 / 1.504120 (0.250109) \| 1.561892 / 1.541195 (0.020697) \| 1.679138 / 1.468490 (0.210648) \| 0.690254 / 4.584777 (-3.894523) \| 3.817698 / 3.745712 (0.071986) \| 2.177854 / 5.269862 (-3.092008) \| 1.361860 / 4.565676 (-3.203816) \| 0.084108 / 0.424275 (-0.340167) \| 0.012640 / 0.007607 (0.005033) \| 0.504385 / 0.226044 (0.278341) \| 5.034103 / 2.268929 (2.765174) \| 2.254032 / 55.444624 (-53.190593) \| 1.910439 / 6.876477 (-4.966038) \| 2.003515 / 2.142072 (-0.138558) \| 0.839747 / 4.805227 (-3.965480) \| 0.165654 / 6.500664 (-6.335010) \| 0.063483 / 0.075469 (-0.011986) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.187521 / 1.841788 (-0.654267) \| 15.381121 / 8.074308 (7.306812) \| 14.579418 / 10.191392 (4.388026) \| 0.199221 / 0.680424 (-0.481202) \| 0.029335 / 0.534201 (-0.504866) \| 0.443159 / 0.579283 (-0.136124) \| 0.447772 / 0.434364 (0.013408) \| 0.545071 / 0.540337 (0.004733) \| 0.650494 / 1.386936 (-0.736442) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007675 / 0.011353 (-0.003677) \| 0.005364 / 0.011008 (-0.005644) \| 0.097921 / 0.038508 (0.059413) \| 0.033645 / 0.023109 (0.010536) \| 0.404818 / 0.275898 (0.128920) \| 0.429983 / 0.323480 (0.106503) \| 0.006106 / 0.007986 (-0.001879) \| 0.005281 / 0.004328 (0.000953) \| 0.073762 / 0.004250 (0.069512) \| 0.053065 / 0.037052 (0.016012) \| 0.400657 / 0.258489 (0.142168) \| 0.447743 / 0.293841 (0.153902) \| 0.036782 / 0.128546 (-0.091765) \| 0.012593 / 0.075646 (-0.063054) \| 0.332825 / 0.419271 (-0.086446) \| 0.049424 / 0.043533 (0.005891) \| 0.400397 / 0.255139 (0.145258) \| 0.414794 / 0.283200 (0.131594) \| 0.106555 / 0.141683 (-0.035128) \| 1.466917 / 1.452155 (0.014762) \| 1.571351 / 1.492716 (0.078635) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.254337 / 0.018006 (0.236331) \| 0.568360 / 0.000490 (0.567870) \| 0.000445 / 0.000200 (0.000245) \| 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) \| 0.031044 / 0.037411 (-0.006367) \| 0.112282 / 0.014526 (0.097756) \| 0.127205 / 0.176557 (-0.049352) \| 0.166551 / 0.737135 (-0.570584) \| 0.130520 / 0.296338 (-0.165818) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.442906 / 0.215209 (0.227697) \| 4.430218 / 2.077655 (2.352563) \| 2.287251 / 1.504120 (0.783132) \| 2.112345 / 1.541195 (0.571150) \| 2.240952 / 1.468490 (0.772462) \| 0.713800 / 4.584777 (-3.870977) \| 3.884161 / 3.745712 (0.138449) \| 2.166901 / 5.269862 (-3.102960) \| 1.374490 / 4.565676 (-3.191187) \| 0.087548 / 0.424275 (-0.336727) \| 0.012369 / 0.007607 (0.004761) \| 0.540783 / 0.226044 (0.314739) \| 5.396187 / 2.268929 (3.127258) \| 2.779636 / 55.444624 (-52.664988) \| 2.434220 / 6.876477 (-4.442257) \| 2.508180 / 2.142072 (0.366107) \| 0.852470 / 4.805227 (-3.952757) \| 0.171266 / 6.500664 (-6.329398) \| 0.065463 / 0.075469 (-0.010006) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.241720 / 1.841788 (-0.600067) \| 15.332568 / 8.074308 (7.258260) \| 13.688723 / 10.191392 (3.497331) \| 0.145150 / 0.680424 (-0.535273) \| 0.017694 / 0.534201 (-0.516507) \| 0.426078 / 0.579283 (-0.153205) \| 0.441189 / 0.434364 (0.006825) \| 0.540284 / 0.540337 (-0.000054) \| 0.657548 / 1.386936 (-0.729388) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c47ecf71362f6b6290b6471b30e77184a5e1df31 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008604 / 0.011353 (-0.002749) \| 0.004566 / 0.011008 (-0.006442) \| 0.099607 / 0.038508 (0.061099) \| 0.029628 / 0.023109 (0.006519) \| 0.300481 / 0.275898 (0.024583) \| 0.342596 / 0.323480 (0.019116) \| 0.007003 / 0.007986 (-0.000982) \| 0.003408 / 0.004328 (-0.000920) \| 0.079076 / 0.004250 (0.074826) \| 0.034104 / 0.037052 (-0.002948) \| 0.303856 / 0.258489 (0.045367) \| 0.348729 / 0.293841 (0.054888) \| 0.033752 / 0.128546 (-0.094794) \| 0.011497 / 0.075646 (-0.064149) \| 0.321568 / 0.419271 (-0.097704) \| 0.041472 / 0.043533 (-0.002061) \| 0.303396 / 0.255139 (0.048257) \| 0.331121 / 0.283200 (0.047921) \| 0.086203 / 0.141683 (-0.055480) \| 1.476995 / 1.452155 (0.024840) \| 1.539428 / 1.492716 (0.046712) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.215810 / 0.018006 (0.197803) \| 0.414292 / 0.000490 (0.413802) \| 0.000388 / 0.000200 (0.000188) \| 0.000058 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023441 / 0.037411 (-0.013970) \| 0.098463 / 0.014526 (0.083938) \| 0.105435 / 0.176557 (-0.071121) \| 0.139736 / 0.737135 (-0.597399) \| 0.109467 / 0.296338 (-0.186872) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.418244 / 0.215209 (0.203035) \| 4.160693 / 2.077655 (2.083039) \| 1.878895 / 1.504120 (0.374775) \| 1.679338 / 1.541195 (0.138143) \| 1.730384 / 1.468490 (0.261894) \| 0.688603 / 4.584777 (-3.896174) \| 3.393542 / 3.745712 (-0.352170) \| 1.901337 / 5.269862 (-3.368525) \| 1.447269 / 4.565676 (-3.118408) \| 0.083003 / 0.424275 (-0.341272) \| 0.012574 / 0.007607 (0.004967) \| 0.526363 / 0.226044 (0.300318) \| 5.275159 / 2.268929 (3.006230) \| 2.323642 / 55.444624 (-53.120982) \| 1.982929 / 6.876477 (-4.893548) \| 2.014081 / 2.142072 (-0.127991) \| 0.809466 / 4.805227 (-3.995761) \| 0.149038 / 6.500664 (-6.351626) \| 0.064394 / 0.075469 (-0.011075) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.207439 / 1.841788 (-0.634349) \| 13.691048 / 8.074308 (5.616740) \| 13.880965 / 10.191392 (3.689573) \| 0.148553 / 0.680424 (-0.531871) \| 0.028397 / 0.534201 (-0.505804) \| 0.391818 / 0.579283 (-0.187465) \| 0.407181 / 0.434364 (-0.027183) \| 0.481163 / 0.540337 (-0.059175) \| 0.570689 / 1.386936 (-0.816247) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006361 / 0.011353 (-0.004992) \| 0.004520 / 0.011008 (-0.006488) \| 0.097679 / 0.038508 (0.059171) \| 0.027223 / 0.023109 (0.004113) \| 0.407966 / 0.275898 (0.132068) \| 0.439868 / 0.323480 (0.116388) \| 0.004625 / 0.007986 (-0.003360) \| 0.004039 / 0.004328 (-0.000289) \| 0.074548 / 0.004250 (0.070298) \| 0.034957 / 0.037052 (-0.002095) \| 0.412762 / 0.258489 (0.154273) \| 0.449716 / 0.293841 (0.155875) \| 0.031272 / 0.128546 (-0.097274) \| 0.011598 / 0.075646 (-0.064049) \| 0.320922 / 0.419271 (-0.098349) \| 0.041250 / 0.043533 (-0.002283) \| 0.411439 / 0.255139 (0.156300) \| 0.429722 / 0.283200 (0.146523) \| 0.087161 / 0.141683 (-0.054522) \| 1.512573 / 1.452155 (0.060418) \| 1.569385 / 1.492716 (0.076668) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.222612 / 0.018006 (0.204606) \| 0.409086 / 0.000490 (0.408596) \| 0.004246 / 0.000200 (0.004046) \| 0.000083 / 0.000054 (0.000028) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024324 / 0.037411 (-0.013087) \| 0.099055 / 0.014526 (0.084530) \| 0.106809 / 0.176557 (-0.069748) \| 0.141275 / 0.737135 (-0.595860) \| 0.109426 / 0.296338 (-0.186913) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.469736 / 0.215209 (0.254527) \| 4.686900 / 2.077655 (2.609246) \| 2.413392 / 1.504120 (0.909272) \| 2.217366 / 1.541195 (0.676171) \| 2.266957 / 1.468490 (0.798467) \| 0.698647 / 4.584777 (-3.886129) \| 3.389317 / 3.745712 (-0.356395) \| 1.862315 / 5.269862 (-3.407546) \| 1.160931 / 4.565676 (-3.404746) \| 0.082829 / 0.424275 (-0.341446) \| 0.012627 / 0.007607 (0.005020) \| 0.568027 / 0.226044 (0.341983) \| 5.683220 / 2.268929 (3.414291) \| 2.865701 / 55.444624 (-52.578924) \| 2.522401 / 6.876477 (-4.354076) \| 2.542395 / 2.142072 (0.400323) \| 0.801224 / 4.805227 (-4.004003) \| 0.149946 / 6.500664 (-6.350718) \| 0.065447 / 0.075469 (-0.010023) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.283756 / 1.841788 (-0.558032) \| 13.903662 / 8.074308 (5.829354) \| 13.238389 / 10.191392 (3.046997) \| 0.142304 / 0.680424 (-0.538120) \| 0.016922 / 0.534201 (-0.517279) \| 0.377797 / 0.579283 (-0.201487) \| 0.382460 / 0.434364 (-0.051904) \| 0.464645 / 0.540337 (-0.075692) \| 0.556270 / 1.386936 (-0.830666) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#675cf2910c5e6f083ed6664a7bffba9a58f78309 \"CML watermark\")\n", "> I think this would be more of a Conceptual Guide doc since this is more explanatory and compares the differences between a Dataset and an IterableDataset\r\n\r\nsounds good to me !\r\n\r\n> There are definitely places in the docs where we can add a nice and link to this doc though to build up the user's understanding of this topic. For example, in the Know your dataset [tutorial](https://huggingface.co/docs/datasets/access), we only introduce the regular Dataset object and not the IterableDataset. We can add a section there for IterableDataset and then link to this doc that explains the difference between the two 🙂\r\n\r\ngood idea, thanks :)", "I'll open a PR to add a section on `IterableDataset`'s in the tutorial, and once you're done editing this doc I can give it a final polish! 😄 ", "I moved the doc page to conceptual guides and took your suggestions into account :)\r\n\r\nI think this is ready for final review now", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009890 / 0.011353 (-0.001463) \| 0.005156 / 0.011008 (-0.005852) \| 0.099493 / 0.038508 (0.060984) \| 0.036671 / 0.023109 (0.013562) \| 0.304686 / 0.275898 (0.028788) \| 0.339070 / 0.323480 (0.015590) \| 0.008466 / 0.007986 (0.000481) \| 0.005863 / 0.004328 (0.001534) \| 0.075082 / 0.004250 (0.070832) \| 0.045926 / 0.037052 (0.008874) \| 0.303157 / 0.258489 (0.044668) \| 0.363710 / 0.293841 (0.069870) \| 0.038497 / 0.128546 (-0.090049) \| 0.012063 / 0.075646 (-0.063583) \| 0.334463 / 0.419271 (-0.084808) \| 0.048161 / 0.043533 (0.004628) \| 0.300431 / 0.255139 (0.045292) \| 0.330344 / 0.283200 (0.047145) \| 0.105509 / 0.141683 (-0.036174) \| 1.475242 / 1.452155 (0.023087) \| 1.550624 / 1.492716 (0.057908) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.245749 / 0.018006 (0.227743) \| 0.575091 / 0.000490 (0.574601) \| 0.001556 / 0.000200 (0.001357) \| 0.000089 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030447 / 0.037411 (-0.006964) \| 0.110982 / 0.014526 (0.096456) \| 0.126760 / 0.176557 (-0.049797) \| 0.173375 / 0.737135 (-0.563760) \| 0.128799 / 0.296338 (-0.167539) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.392861 / 0.215209 (0.177651) \| 3.911231 / 2.077655 (1.833576) \| 1.757413 / 1.504120 (0.253293) \| 1.563287 / 1.541195 (0.022093) \| 1.658678 / 1.468490 (0.190188) \| 0.677244 / 4.584777 (-3.907533) \| 3.754917 / 3.745712 (0.009205) \| 3.779417 / 5.269862 (-1.490444) \| 1.993159 / 4.565676 (-2.572517) \| 0.084425 / 0.424275 (-0.339850) \| 0.012500 / 0.007607 (0.004893) \| 0.501788 / 0.226044 (0.275743) \| 5.003173 / 2.268929 (2.734244) \| 2.273547 / 55.444624 (-53.171077) \| 1.909766 / 6.876477 (-4.966711) \| 1.968287 / 2.142072 (-0.173785) \| 0.834895 / 4.805227 (-3.970332) \| 0.165312 / 6.500664 (-6.335352) \| 0.062202 / 0.075469 (-0.013267) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.203080 / 1.841788 (-0.638708) \| 15.158284 / 8.074308 (7.083976) \| 14.174484 / 10.191392 (3.983092) \| 0.171540 / 0.680424 (-0.508883) \| 0.028604 / 0.534201 (-0.505597) \| 0.438379 / 0.579283 (-0.140904) \| 0.429447 / 0.434364 (-0.004917) \| 0.540979 / 0.540337 (0.000642) \| 0.630322 / 1.386936 (-0.756614) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007600 / 0.011353 (-0.003753) \| 0.005400 / 0.011008 (-0.005608) \| 0.097983 / 0.038508 (0.059475) \| 0.033407 / 0.023109 (0.010297) \| 0.384429 / 0.275898 (0.108531) \| 0.415880 / 0.323480 (0.092400) \| 0.006085 / 0.007986 (-0.001900) \| 0.004330 / 0.004328 (0.000002) \| 0.074654 / 0.004250 (0.070403) \| 0.053076 / 0.037052 (0.016024) \| 0.383958 / 0.258489 (0.125469) \| 0.427289 / 0.293841 (0.133448) \| 0.036710 / 0.128546 (-0.091836) \| 0.012400 / 0.075646 (-0.063246) \| 0.332712 / 0.419271 (-0.086560) \| 0.058390 / 0.043533 (0.014857) \| 0.377747 / 0.255139 (0.122608) \| 0.398997 / 0.283200 (0.115798) \| 0.117370 / 0.141683 (-0.024313) \| 1.464211 / 1.452155 (0.012057) \| 1.596465 / 1.492716 (0.103749) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.212989 / 0.018006 (0.194983) \| 0.554968 / 0.000490 (0.554479) \| 0.004305 / 0.000200 (0.004105) \| 0.000116 / 0.000054 (0.000061) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029167 / 0.037411 (-0.008244) \| 0.109156 / 0.014526 (0.094631) \| 0.122575 / 0.176557 (-0.053982) \| 0.163058 / 0.737135 (-0.574077) \| 0.127431 / 0.296338 (-0.168908) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.445395 / 0.215209 (0.230185) \| 4.447534 / 2.077655 (2.369879) \| 2.259186 / 1.504120 (0.755066) \| 2.082956 / 1.541195 (0.541761) \| 2.259126 / 1.468490 (0.790636) \| 0.692271 / 4.584777 (-3.892506) \| 3.795759 / 3.745712 (0.050047) \| 3.603000 / 5.269862 (-1.666862) \| 1.948556 / 4.565676 (-2.617120) \| 0.084589 / 0.424275 (-0.339687) \| 0.012751 / 0.007607 (0.005144) \| 0.544783 / 0.226044 (0.318738) \| 5.452278 / 2.268929 (3.183349) \| 2.809467 / 55.444624 (-52.635157) \| 2.479297 / 6.876477 (-4.397180) \| 2.587756 / 2.142072 (0.445683) \| 0.832258 / 4.805227 (-3.972970) \| 0.167424 / 6.500664 (-6.333240) \| 0.066064 / 0.075469 (-0.009405) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.262719 / 1.841788 (-0.579069) \| 15.917869 / 8.074308 (7.843561) \| 13.879301 / 10.191392 (3.687909) \| 0.187712 / 0.680424 (-0.492712) \| 0.018175 / 0.534201 (-0.516026) \| 0.425840 / 0.579283 (-0.153443) \| 0.426164 / 0.434364 (-0.008200) \| 0.527465 / 0.540337 (-0.012872) \| 0.629478 / 1.386936 (-0.757458) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5f7e178d6373e7d66a60662a22fd60af117f0885 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009064 / 0.011353 (-0.002289) \| 0.004824 / 0.011008 (-0.006184) \| 0.100869 / 0.038508 (0.062361) \| 0.030803 / 0.023109 (0.007694) \| 0.350880 / 0.275898 (0.074982) \| 0.423816 / 0.323480 (0.100336) \| 0.007581 / 0.007986 (-0.000405) \| 0.003642 / 0.004328 (-0.000686) \| 0.077682 / 0.004250 (0.073432) \| 0.039856 / 0.037052 (0.002803) \| 0.366097 / 0.258489 (0.107608) \| 0.409226 / 0.293841 (0.115385) \| 0.033698 / 0.128546 (-0.094848) \| 0.011730 / 0.075646 (-0.063916) \| 0.321683 / 0.419271 (-0.097588) \| 0.041794 / 0.043533 (-0.001739) \| 0.351175 / 0.255139 (0.096036) \| 0.374328 / 0.283200 (0.091128) \| 0.091833 / 0.141683 (-0.049850) \| 1.507082 / 1.452155 (0.054927) \| 1.543289 / 1.492716 (0.050572) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010670 / 0.018006 (-0.007337) \| 0.429674 / 0.000490 (0.429184) \| 0.003246 / 0.000200 (0.003046) \| 0.000081 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025015 / 0.037411 (-0.012397) \| 0.102155 / 0.014526 (0.087629) \| 0.107010 / 0.176557 (-0.069546) \| 0.144265 / 0.737135 (-0.592870) \| 0.110635 / 0.296338 (-0.185703) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.414211 / 0.215209 (0.199002) \| 4.125582 / 2.077655 (2.047928) \| 1.997856 / 1.504120 (0.493736) \| 1.847676 / 1.541195 (0.306481) \| 1.994100 / 1.468490 (0.525610) \| 0.694975 / 4.584777 (-3.889802) \| 3.373629 / 3.745712 (-0.372083) \| 2.863255 / 5.269862 (-2.406606) \| 1.565723 / 4.565676 (-2.999953) \| 0.082539 / 0.424275 (-0.341736) \| 0.012650 / 0.007607 (0.005043) \| 0.522989 / 0.226044 (0.296945) \| 5.205720 / 2.268929 (2.936792) \| 2.352292 / 55.444624 (-53.092332) \| 2.080467 / 6.876477 (-4.796010) \| 2.231014 / 2.142072 (0.088942) \| 0.811252 / 4.805227 (-3.993975) \| 0.149171 / 6.500664 (-6.351493) \| 0.065207 / 0.075469 (-0.010262) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.203137 / 1.841788 (-0.638651) \| 14.244903 / 8.074308 (6.170595) \| 14.454368 / 10.191392 (4.262976) \| 0.139090 / 0.680424 (-0.541334) \| 0.028738 / 0.534201 (-0.505463) \| 0.396394 / 0.579283 (-0.182889) \| 0.407207 / 0.434364 (-0.027156) \| 0.478036 / 0.540337 (-0.062302) \| 0.568488 / 1.386936 (-0.818448) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006878 / 0.011353 (-0.004475) \| 0.004636 / 0.011008 (-0.006372) \| 0.099118 / 0.038508 (0.060610) \| 0.028076 / 0.023109 (0.004967) \| 0.416097 / 0.275898 (0.140199) \| 0.451722 / 0.323480 (0.128242) \| 0.005364 / 0.007986 (-0.002622) \| 0.003506 / 0.004328 (-0.000822) \| 0.075791 / 0.004250 (0.071541) \| 0.041373 / 0.037052 (0.004321) \| 0.416358 / 0.258489 (0.157869) \| 0.458440 / 0.293841 (0.164599) \| 0.031870 / 0.128546 (-0.096676) \| 0.011751 / 0.075646 (-0.063896) \| 0.321748 / 0.419271 (-0.097524) \| 0.041780 / 0.043533 (-0.001752) \| 0.425037 / 0.255139 (0.169898) \| 0.444169 / 0.283200 (0.160969) \| 0.093145 / 0.141683 (-0.048538) \| 1.472151 / 1.452155 (0.019996) \| 1.542942 / 1.492716 (0.050226) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224287 / 0.018006 (0.206281) \| 0.415303 / 0.000490 (0.414813) \| 0.003180 / 0.000200 (0.002980) \| 0.000082 / 0.000054 (0.000027) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026377 / 0.037411 (-0.011035) \| 0.106222 / 0.014526 (0.091696) \| 0.113873 / 0.176557 (-0.062684) \| 0.143255 / 0.737135 (-0.593880) \| 0.112642 / 0.296338 (-0.183697) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.444149 / 0.215209 (0.228940) \| 4.421434 / 2.077655 (2.343779) \| 2.082198 / 1.504120 (0.578078) \| 1.879909 / 1.541195 (0.338715) \| 1.968526 / 1.468490 (0.500036) \| 0.697230 / 4.584777 (-3.887546) \| 3.430800 / 3.745712 (-0.314912) \| 1.893353 / 5.269862 (-3.376509) \| 1.173271 / 4.565676 (-3.392406) \| 0.082636 / 0.424275 (-0.341639) \| 0.012357 / 0.007607 (0.004750) \| 0.544008 / 0.226044 (0.317964) \| 5.465472 / 2.268929 (3.196543) \| 2.530017 / 55.444624 (-52.914608) \| 2.178462 / 6.876477 (-4.698014) \| 2.279570 / 2.142072 (0.137498) \| 0.804890 / 4.805227 (-4.000337) \| 0.152091 / 6.500664 (-6.348573) \| 0.069442 / 0.075469 (-0.006027) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.256722 / 1.841788 (-0.585065) \| 14.554131 / 8.074308 (6.479823) \| 13.499913 / 10.191392 (3.308521) \| 0.144350 / 0.680424 (-0.536074) \| 0.016977 / 0.534201 (-0.517224) \| 0.378836 / 0.579283 (-0.200447) \| 0.392004 / 0.434364 (-0.042360) \| 0.468423 / 0.540337 (-0.071914) \| 0.584711 / 1.386936 (-0.802225) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1e4894fcdf2a82b3355bb6a2dc5557c8e23f8144 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008542 / 0.011353 (-0.002811) \| 0.004552 / 0.011008 (-0.006456) \| 0.100543 / 0.038508 (0.062035) \| 0.029717 / 0.023109 (0.006608) \| 0.301948 / 0.275898 (0.026050) \| 0.360211 / 0.323480 (0.036731) \| 0.006881 / 0.007986 (-0.001105) \| 0.003433 / 0.004328 (-0.000896) \| 0.077760 / 0.004250 (0.073510) \| 0.037069 / 0.037052 (0.000017) \| 0.314084 / 0.258489 (0.055595) \| 0.347759 / 0.293841 (0.053918) \| 0.033255 / 0.128546 (-0.095291) \| 0.011487 / 0.075646 (-0.064160) \| 0.323873 / 0.419271 (-0.095399) \| 0.041203 / 0.043533 (-0.002330) \| 0.298397 / 0.255139 (0.043258) \| 0.327174 / 0.283200 (0.043974) \| 0.088892 / 0.141683 (-0.052791) \| 1.560114 / 1.452155 (0.107959) \| 1.532475 / 1.492716 (0.039759) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.226080 / 0.018006 (0.208074) \| 0.467492 / 0.000490 (0.467003) \| 0.002198 / 0.000200 (0.001998) \| 0.000074 / 0.000054 (0.000019) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023627 / 0.037411 (-0.013784) \| 0.096696 / 0.014526 (0.082170) \| 0.106196 / 0.176557 (-0.070360) \| 0.140496 / 0.737135 (-0.596639) \| 0.108859 / 0.296338 (-0.187480) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.422335 / 0.215209 (0.207126) \| 4.214879 / 2.077655 (2.137224) \| 1.865866 / 1.504120 (0.361747) \| 1.660914 / 1.541195 (0.119719) \| 1.691869 / 1.468490 (0.223379) \| 0.688164 / 4.584777 (-3.896613) \| 3.432708 / 3.745712 (-0.313004) \| 1.856852 / 5.269862 (-3.413010) \| 1.243685 / 4.565676 (-3.321991) \| 0.081552 / 0.424275 (-0.342723) \| 0.012491 / 0.007607 (0.004884) \| 0.524331 / 0.226044 (0.298287) \| 5.255090 / 2.268929 (2.986162) \| 2.269705 / 55.444624 (-53.174919) \| 1.936722 / 6.876477 (-4.939755) \| 2.018958 / 2.142072 (-0.123114) \| 0.800658 / 4.805227 (-4.004569) \| 0.148665 / 6.500664 (-6.351999) \| 0.064210 / 0.075469 (-0.011259) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.235422 / 1.841788 (-0.606365) \| 14.156755 / 8.074308 (6.082447) \| 14.005916 / 10.191392 (3.814524) \| 0.150983 / 0.680424 (-0.529441) \| 0.028500 / 0.534201 (-0.505701) \| 0.393013 / 0.579283 (-0.186270) \| 0.408191 / 0.434364 (-0.026173) \| 0.481017 / 0.540337 (-0.059320) \| 0.581711 / 1.386936 (-0.805225) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006950 / 0.011353 (-0.004403) \| 0.004575 / 0.011008 (-0.006434) \| 0.076702 / 0.038508 (0.038194) \| 0.028050 / 0.023109 (0.004941) \| 0.342916 / 0.275898 (0.067018) \| 0.378861 / 0.323480 (0.055381) \| 0.005315 / 0.007986 (-0.002671) \| 0.004822 / 0.004328 (0.000494) \| 0.075560 / 0.004250 (0.071310) \| 0.040441 / 0.037052 (0.003388) \| 0.344284 / 0.258489 (0.085795) \| 0.386519 / 0.293841 (0.092678) \| 0.032122 / 0.128546 (-0.096424) \| 0.011843 / 0.075646 (-0.063803) \| 0.085798 / 0.419271 (-0.333473) \| 0.043027 / 0.043533 (-0.000506) \| 0.342910 / 0.255139 (0.087771) \| 0.366618 / 0.283200 (0.083418) \| 0.094766 / 0.141683 (-0.046917) \| 1.492981 / 1.452155 (0.040827) \| 1.566994 / 1.492716 (0.074278) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.166083 / 0.018006 (0.148076) \| 0.409315 / 0.000490 (0.408826) \| 0.003189 / 0.000200 (0.002989) \| 0.000127 / 0.000054 (0.000072) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024753 / 0.037411 (-0.012658) \| 0.099112 / 0.014526 (0.084586) \| 0.106668 / 0.176557 (-0.069889) \| 0.142562 / 0.737135 (-0.594573) \| 0.110648 / 0.296338 (-0.185690) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.452668 / 0.215209 (0.237459) \| 4.501188 / 2.077655 (2.423534) \| 2.086197 / 1.504120 (0.582077) \| 1.873955 / 1.541195 (0.332761) \| 1.935610 / 1.468490 (0.467120) \| 0.708290 / 4.584777 (-3.876487) \| 3.426986 / 3.745712 (-0.318726) \| 2.805852 / 5.269862 (-2.464009) \| 1.516918 / 4.565676 (-3.048759) \| 0.084067 / 0.424275 (-0.340208) \| 0.012776 / 0.007607 (0.005169) \| 0.548853 / 0.226044 (0.322809) \| 5.488198 / 2.268929 (3.219270) \| 2.704464 / 55.444624 (-52.740161) \| 2.377817 / 6.876477 (-4.498660) \| 2.366152 / 2.142072 (0.224079) \| 0.818192 / 4.805227 (-3.987035) \| 0.152649 / 6.500664 (-6.348015) \| 0.066914 / 0.075469 (-0.008555) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.273803 / 1.841788 (-0.567985) \| 14.071633 / 8.074308 (5.997325) \| 13.655586 / 10.191392 (3.464194) \| 0.149471 / 0.680424 (-0.530953) \| 0.016745 / 0.534201 (-0.517456) \| 0.386850 / 0.579283 (-0.192434) \| 0.393595 / 0.434364 (-0.040769) \| 0.480396 / 0.540337 (-0.059942) \| 0.573708 / 1.386936 (-0.813228) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8b2c7de67b326a635c0dc39ea5dd1ae982c958d6 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008173 / 0.011353 (-0.003180) \| 0.004461 / 0.011008 (-0.006547) \| 0.100284 / 0.038508 (0.061776) \| 0.028900 / 0.023109 (0.005791) \| 0.293639 / 0.275898 (0.017741) \| 0.359450 / 0.323480 (0.035971) \| 0.007567 / 0.007986 (-0.000418) \| 0.003434 / 0.004328 (-0.000894) \| 0.077913 / 0.004250 (0.073663) \| 0.036313 / 0.037052 (-0.000740) \| 0.308484 / 0.258489 (0.049995) \| 0.347575 / 0.293841 (0.053734) \| 0.033367 / 0.128546 (-0.095179) \| 0.011508 / 0.075646 (-0.064138) \| 0.323490 / 0.419271 (-0.095782) \| 0.042285 / 0.043533 (-0.001248) \| 0.295696 / 0.255139 (0.040557) \| 0.332475 / 0.283200 (0.049276) \| 0.089980 / 0.141683 (-0.051703) \| 1.461851 / 1.452155 (0.009697) \| 1.493030 / 1.492716 (0.000314) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.191068 / 0.018006 (0.173062) \| 0.396768 / 0.000490 (0.396278) \| 0.002355 / 0.000200 (0.002155) \| 0.000080 / 0.000054 (0.000025) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023117 / 0.037411 (-0.014294) \| 0.096155 / 0.014526 (0.081630) \| 0.102424 / 0.176557 (-0.074132) \| 0.142148 / 0.737135 (-0.594987) \| 0.105954 / 0.296338 (-0.190384) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.421227 / 0.215209 (0.206018) \| 4.200403 / 2.077655 (2.122748) \| 1.899410 / 1.504120 (0.395290) \| 1.684091 / 1.541195 (0.142896) \| 1.698084 / 1.468490 (0.229594) \| 0.696195 / 4.584777 (-3.888582) \| 3.364116 / 3.745712 (-0.381596) \| 1.899133 / 5.269862 (-3.370728) \| 1.281405 / 4.565676 (-3.284272) \| 0.082958 / 0.424275 (-0.341317) \| 0.012433 / 0.007607 (0.004826) \| 0.521856 / 0.226044 (0.295812) \| 5.217626 / 2.268929 (2.948698) \| 2.309228 / 55.444624 (-53.135396) \| 1.956828 / 6.876477 (-4.919648) \| 2.018964 / 2.142072 (-0.123108) \| 0.816855 / 4.805227 (-3.988373) \| 0.152867 / 6.500664 (-6.347798) \| 0.064764 / 0.075469 (-0.010705) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.219020 / 1.841788 (-0.622768) \| 13.509058 / 8.074308 (5.434750) \| 13.637826 / 10.191392 (3.446434) \| 0.156620 / 0.680424 (-0.523804) \| 0.028518 / 0.534201 (-0.505683) \| 0.399138 / 0.579283 (-0.180146) \| 0.399931 / 0.434364 (-0.034433) \| 0.482902 / 0.540337 (-0.057435) \| 0.574089 / 1.386936 (-0.812847) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006232 / 0.011353 (-0.005121) \| 0.004467 / 0.011008 (-0.006542) \| 0.075494 / 0.038508 (0.036986) \| 0.026891 / 0.023109 (0.003782) \| 0.356603 / 0.275898 (0.080705) \| 0.371977 / 0.323480 (0.048497) \| 0.004709 / 0.007986 (-0.003276) \| 0.003230 / 0.004328 (-0.001099) \| 0.074338 / 0.004250 (0.070088) \| 0.035588 / 0.037052 (-0.001464) \| 0.349554 / 0.258489 (0.091065) \| 0.389672 / 0.293841 (0.095831) \| 0.031524 / 0.128546 (-0.097022) \| 0.011493 / 0.075646 (-0.064153) \| 0.084584 / 0.419271 (-0.334688) \| 0.041945 / 0.043533 (-0.001588) \| 0.341057 / 0.255139 (0.085918) \| 0.367876 / 0.283200 (0.084677) \| 0.090113 / 0.141683 (-0.051569) \| 1.507104 / 1.452155 (0.054949) \| 1.567810 / 1.492716 (0.075094) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.210939 / 0.018006 (0.192933) \| 0.392600 / 0.000490 (0.392110) \| 0.002188 / 0.000200 (0.001988) \| 0.000073 / 0.000054 (0.000018) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024294 / 0.037411 (-0.013118) \| 0.100325 / 0.014526 (0.085799) \| 0.104027 / 0.176557 (-0.072530) \| 0.141189 / 0.737135 (-0.595947) \| 0.107438 / 0.296338 (-0.188901) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.443314 / 0.215209 (0.228105) \| 4.429612 / 2.077655 (2.351957) \| 2.129275 / 1.504120 (0.625156) \| 1.940016 / 1.541195 (0.398821) \| 2.008975 / 1.468490 (0.540485) \| 0.695434 / 4.584777 (-3.889343) \| 3.355137 / 3.745712 (-0.390575) \| 2.606262 / 5.269862 (-2.663600) \| 1.451283 / 4.565676 (-3.114394) \| 0.082875 / 0.424275 (-0.341400) \| 0.012398 / 0.007607 (0.004791) \| 0.544262 / 0.226044 (0.318218) \| 5.450829 / 2.268929 (3.181900) \| 2.582074 / 55.444624 (-52.862550) \| 2.220037 / 6.876477 (-4.656439) \| 2.232473 / 2.142072 (0.090401) \| 0.802094 / 4.805227 (-4.003134) \| 0.150188 / 6.500664 (-6.350476) \| 0.066543 / 0.075469 (-0.008926) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.269098 / 1.841788 (-0.572690) \| 13.764780 / 8.074308 (5.690472) \| 13.461490 / 10.191392 (3.270098) \| 0.143841 / 0.680424 (-0.536583) \| 0.016687 / 0.534201 (-0.517514) \| 0.388548 / 0.579283 (-0.190736) \| 0.385229 / 0.434364 (-0.049135) \| 0.478966 / 0.540337 (-0.061371) \| 0.570355 / 1.386936 (-0.816581) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0ba81f5b299f0918cb0c0c071412feadd0ea3ef5 \"CML watermark\")\n", "I took your comments into account :)\r\n\r\n> Regarding the docs, I think it would be better to add this info as notes/tips/sections to the existing docs (Process/Stream; e.g. a tip under Dataset.shuffle that explains how to make this operation more performant by using to_iterable + shuffle, etc.) rather than introducing a new doc page.\r\n\r\nI added a paragraph in the Dataset.shuffle docstring, and a note in the Process doc page", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010906 / 0.011353 (-0.000447) \| 0.005995 / 0.011008 (-0.005014) \| 0.120183 / 0.038508 (0.081675) \| 0.042166 / 0.023109 (0.019057) \| 0.350945 / 0.275898 (0.075046) \| 0.433055 / 0.323480 (0.109575) \| 0.009093 / 0.007986 (0.001107) \| 0.004695 / 0.004328 (0.000366) \| 0.090362 / 0.004250 (0.086112) \| 0.051402 / 0.037052 (0.014350) \| 0.368677 / 0.258489 (0.110188) \| 0.410926 / 0.293841 (0.117086) \| 0.044471 / 0.128546 (-0.084075) \| 0.014051 / 0.075646 (-0.061595) \| 0.397765 / 0.419271 (-0.021507) \| 0.057227 / 0.043533 (0.013694) \| 0.357587 / 0.255139 (0.102448) \| 0.377470 / 0.283200 (0.094270) \| 0.119482 / 0.141683 (-0.022201) \| 1.719799 / 1.452155 (0.267645) \| 1.758228 / 1.492716 (0.265511) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224385 / 0.018006 (0.206379) \| 0.505070 / 0.000490 (0.504580) \| 0.004863 / 0.000200 (0.004663) \| 0.000379 / 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) \| 0.030366 / 0.037411 (-0.007046) \| 0.130481 / 0.014526 (0.115955) \| 0.136429 / 0.176557 (-0.040128) \| 0.182263 / 0.737135 (-0.554872) \| 0.142871 / 0.296338 (-0.153468) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.467623 / 0.215209 (0.252414) \| 4.665522 / 2.077655 (2.587868) \| 2.130885 / 1.504120 (0.626766) \| 1.903810 / 1.541195 (0.362615) \| 2.019077 / 1.468490 (0.550587) \| 0.820868 / 4.584777 (-3.763909) \| 4.543118 / 3.745712 (0.797406) \| 2.491541 / 5.269862 (-2.778321) \| 1.585377 / 4.565676 (-2.980299) \| 0.101850 / 0.424275 (-0.322426) \| 0.014737 / 0.007607 (0.007129) \| 0.597241 / 0.226044 (0.371197) \| 5.938445 / 2.268929 (3.669516) \| 2.695799 / 55.444624 (-52.748825) \| 2.286890 / 6.876477 (-4.589587) \| 2.363064 / 2.142072 (0.220991) \| 0.986670 / 4.805227 (-3.818557) \| 0.194407 / 6.500664 (-6.306257) \| 0.074767 / 0.075469 (-0.000702) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.420630 / 1.841788 (-0.421158) \| 17.537702 / 8.074308 (9.463394) \| 16.521804 / 10.191392 (6.330412) \| 0.173622 / 0.680424 (-0.506802) \| 0.033944 / 0.534201 (-0.500257) \| 0.520461 / 0.579283 (-0.058822) \| 0.541283 / 0.434364 (0.106919) \| 0.651906 / 0.540337 (0.111569) \| 0.771724 / 1.386936 (-0.615212) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008448 / 0.011353 (-0.002905) \| 0.005893 / 0.011008 (-0.005115) \| 0.087995 / 0.038508 (0.049487) \| 0.038602 / 0.023109 (0.015493) \| 0.400048 / 0.275898 (0.124150) \| 0.436998 / 0.323480 (0.113518) \| 0.006414 / 0.007986 (-0.001572) \| 0.004478 / 0.004328 (0.000149) \| 0.086444 / 0.004250 (0.082194) \| 0.056535 / 0.037052 (0.019483) \| 0.402066 / 0.258489 (0.143577) \| 0.458730 / 0.293841 (0.164889) \| 0.041622 / 0.128546 (-0.086924) \| 0.014014 / 0.075646 (-0.061632) \| 0.101382 / 0.419271 (-0.317889) \| 0.056986 / 0.043533 (0.013453) \| 0.404527 / 0.255139 (0.149388) \| 0.428105 / 0.283200 (0.144906) \| 0.118321 / 0.141683 (-0.023361) \| 1.716940 / 1.452155 (0.264785) \| 1.834683 / 1.492716 (0.341967) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.252917 / 0.018006 (0.234910) \| 0.485950 / 0.000490 (0.485461) \| 0.000489 / 0.000200 (0.000289) \| 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) \| 0.035023 / 0.037411 (-0.002388) \| 0.139055 / 0.014526 (0.124529) \| 0.144165 / 0.176557 (-0.032392) \| 0.189559 / 0.737135 (-0.547577) \| 0.153213 / 0.296338 (-0.143126) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.505069 / 0.215209 (0.289860) \| 5.024620 / 2.077655 (2.946965) \| 2.429469 / 1.504120 (0.925349) \| 2.186210 / 1.541195 (0.645015) \| 2.275971 / 1.468490 (0.807481) \| 0.829432 / 4.584777 (-3.755345) \| 4.518600 / 3.745712 (0.772888) \| 2.466418 / 5.269862 (-2.803443) \| 1.558910 / 4.565676 (-3.006767) \| 0.102017 / 0.424275 (-0.322258) \| 0.015191 / 0.007607 (0.007584) \| 0.619092 / 0.226044 (0.393048) \| 6.241105 / 2.268929 (3.972176) \| 3.044213 / 55.444624 (-52.400411) \| 2.630194 / 6.876477 (-4.246282) \| 2.723685 / 2.142072 (0.581613) \| 0.994018 / 4.805227 (-3.811210) \| 0.198722 / 6.500664 (-6.301942) \| 0.075812 / 0.075469 (0.000343) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.545497 / 1.841788 (-0.296291) \| 18.305250 / 8.074308 (10.230942) \| 16.035275 / 10.191392 (5.843883) \| 0.209339 / 0.680424 (-0.471085) \| 0.020903 / 0.534201 (-0.513298) \| 0.499909 / 0.579283 (-0.079374) \| 0.488775 / 0.434364 (0.054411) \| 0.581990 / 0.540337 (0.041653) \| 0.697786 / 1.386936 (-0.689150) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#78dca62e8aaddb9e0cf0212841f2c8d861fe74c8 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.011706 / 0.011353 (0.000353) \| 0.008406 / 0.011008 (-0.002602) \| 0.130887 / 0.038508 (0.092379) \| 0.037468 / 0.023109 (0.014359) \| 0.385043 / 0.275898 (0.109145) \| 0.458837 / 0.323480 (0.135357) \| 0.013400 / 0.007986 (0.005414) \| 0.004885 / 0.004328 (0.000557) \| 0.107156 / 0.004250 (0.102905) \| 0.046958 / 0.037052 (0.009906) \| 0.419314 / 0.258489 (0.160825) \| 0.456061 / 0.293841 (0.162220) \| 0.058859 / 0.128546 (-0.069687) \| 0.016682 / 0.075646 (-0.058965) \| 0.428401 / 0.419271 (0.009129) \| 0.062908 / 0.043533 (0.019376) \| 0.370902 / 0.255139 (0.115763) \| 0.433897 / 0.283200 (0.150697) \| 0.125672 / 0.141683 (-0.016011) \| 1.818279 / 1.452155 (0.366124) \| 1.935767 / 1.492716 (0.443050) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.011928 / 0.018006 (-0.006078) \| 0.591995 / 0.000490 (0.591506) \| 0.008416 / 0.000200 (0.008216) \| 0.000122 / 0.000054 (0.000067) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029640 / 0.037411 (-0.007772) \| 0.121044 / 0.014526 (0.106518) \| 0.141840 / 0.176557 (-0.034716) \| 0.195856 / 0.737135 (-0.541280) \| 0.146460 / 0.296338 (-0.149879) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.591838 / 0.215209 (0.376629) \| 5.817309 / 2.077655 (3.739654) \| 2.411864 / 1.504120 (0.907744) \| 2.098517 / 1.541195 (0.557323) \| 2.214609 / 1.468490 (0.746119) \| 1.217542 / 4.584777 (-3.367235) \| 5.658394 / 3.745712 (1.912682) \| 5.155807 / 5.269862 (-0.114055) \| 2.797313 / 4.565676 (-1.768363) \| 0.141309 / 0.424275 (-0.282967) \| 0.014462 / 0.007607 (0.006855) \| 0.772274 / 0.226044 (0.546230) \| 7.547357 / 2.268929 (5.278429) \| 3.150178 / 55.444624 (-52.294446) \| 2.500130 / 6.876477 (-4.376347) \| 2.572036 / 2.142072 (0.429964) \| 1.434498 / 4.805227 (-3.370729) \| 0.257355 / 6.500664 (-6.243309) \| 0.087491 / 0.075469 (0.012022) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.483899 / 1.841788 (-0.357889) \| 17.990741 / 8.074308 (9.916433) \| 20.398965 / 10.191392 (10.207573) \| 0.239529 / 0.680424 (-0.440895) \| 0.046118 / 0.534201 (-0.488083) \| 0.528349 / 0.579283 (-0.050934) \| 0.614333 / 0.434364 (0.179969) \| 0.653621 / 0.540337 (0.113284) \| 0.794654 / 1.386936 (-0.592282) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008732 / 0.011353 (-0.002621) \| 0.006432 / 0.011008 (-0.004576) \| 0.090811 / 0.038508 (0.052303) \| 0.030154 / 0.023109 (0.007045) \| 0.407885 / 0.275898 (0.131987) \| 0.452457 / 0.323480 (0.128977) \| 0.006966 / 0.007986 (-0.001020) \| 0.006449 / 0.004328 (0.002120) \| 0.094439 / 0.004250 (0.090188) \| 0.050628 / 0.037052 (0.013576) \| 0.401815 / 0.258489 (0.143326) \| 0.451814 / 0.293841 (0.157973) \| 0.047456 / 0.128546 (-0.081090) \| 0.019019 / 0.075646 (-0.056628) \| 0.112941 / 0.419271 (-0.306331) \| 0.057677 / 0.043533 (0.014145) \| 0.406160 / 0.255139 (0.151021) \| 0.434469 / 0.283200 (0.151269) \| 0.110515 / 0.141683 (-0.031167) \| 1.601393 / 1.452155 (0.149238) \| 1.745581 / 1.492716 (0.252865) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280264 / 0.018006 (0.262258) \| 0.630074 / 0.000490 (0.629585) \| 0.006900 / 0.000200 (0.006700) \| 0.000112 / 0.000054 (0.000058) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027338 / 0.037411 (-0.010073) \| 0.114772 / 0.014526 (0.100246) \| 0.130436 / 0.176557 (-0.046121) \| 0.168990 / 0.737135 (-0.568145) \| 0.135842 / 0.296338 (-0.160496) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.666739 / 0.215209 (0.451530) \| 6.212953 / 2.077655 (4.135298) \| 2.781716 / 1.504120 (1.277596) \| 2.369975 / 1.541195 (0.828781) \| 2.338807 / 1.468490 (0.870317) \| 1.174138 / 4.584777 (-3.410639) \| 5.420297 / 3.745712 (1.674585) \| 4.972669 / 5.269862 (-0.297192) \| 2.214294 / 4.565676 (-2.351382) \| 0.135429 / 0.424275 (-0.288846) \| 0.013877 / 0.007607 (0.006270) \| 0.750805 / 0.226044 (0.524761) \| 7.145429 / 2.268929 (4.876500) \| 3.215081 / 55.444624 (-52.229544) \| 2.598307 / 6.876477 (-4.278170) \| 2.690479 / 2.142072 (0.548406) \| 1.344673 / 4.805227 (-3.460554) \| 0.241536 / 6.500664 (-6.259128) \| 0.075544 / 0.075469 (0.000074) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.473595 / 1.841788 (-0.368192) \| 17.372237 / 8.074308 (9.297929) \| 18.586588 / 10.191392 (8.395196) \| 0.209300 / 0.680424 (-0.471124) \| 0.030878 / 0.534201 (-0.503323) \| 0.509131 / 0.579283 (-0.070152) \| 0.617884 / 0.434364 (0.183520) \| 0.633721 / 0.540337 (0.093383) \| 0.727624 / 1.386936 (-0.659312) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#87f2062d47fdbec3fadf5b39bab0801f59c0f4a3 \"CML watermark\")\n", "Took your last comments into account !\r\n\r\n> so maybe a better title for it would be \"Optimize processing\" (or \"Working with datasets at scale\" as I mentioned earlier on Slack)\r\n\r\nI think the content would be slightly different, e.g. focus more on multiprocessing/sharding or what data formats to use. This can be a complementary page IMO\r\n\r\n> PS: I think it would be a good idea to add links to the Guide pages for better discoverability and to somewhat \"justify their presence in the docs\" (from the tutorial/how-to pages to the guides; some guides are not referenced at all)\r\n\r\nAdded a link in the how-to stream page. We may want to include it in the tutorial at one point at well - right now none of the tutorials mention streaming", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009167 / 0.011353 (-0.002186) \| 0.005345 / 0.011008 (-0.005663) \| 0.098302 / 0.038508 (0.059794) \| 0.035649 / 0.023109 (0.012540) \| 0.295597 / 0.275898 (0.019699) \| 0.358843 / 0.323480 (0.035364) \| 0.008011 / 0.007986 (0.000025) \| 0.004229 / 0.004328 (-0.000100) \| 0.075123 / 0.004250 (0.070872) \| 0.046098 / 0.037052 (0.009046) \| 0.310581 / 0.258489 (0.052092) \| 0.343230 / 0.293841 (0.049389) \| 0.038318 / 0.128546 (-0.090229) \| 0.011954 / 0.075646 (-0.063693) \| 0.331056 / 0.419271 (-0.088216) \| 0.052875 / 0.043533 (0.009342) \| 0.302758 / 0.255139 (0.047619) \| 0.340596 / 0.283200 (0.057396) \| 0.113676 / 0.141683 (-0.028007) \| 1.448272 / 1.452155 (-0.003883) \| 1.498008 / 1.492716 (0.005291) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.240524 / 0.018006 (0.222518) \| 0.555823 / 0.000490 (0.555333) \| 0.003143 / 0.000200 (0.002943) \| 0.000098 / 0.000054 (0.000044) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027764 / 0.037411 (-0.009647) \| 0.105006 / 0.014526 (0.090480) \| 0.120550 / 0.176557 (-0.056007) \| 0.167052 / 0.737135 (-0.570084) \| 0.124521 / 0.296338 (-0.171818) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.401758 / 0.215209 (0.186549) \| 3.989629 / 2.077655 (1.911974) \| 1.767307 / 1.504120 (0.263187) \| 1.579451 / 1.541195 (0.038257) \| 1.637642 / 1.468490 (0.169152) \| 0.702524 / 4.584777 (-3.882253) \| 3.714326 / 3.745712 (-0.031386) \| 2.131829 / 5.269862 (-3.138033) \| 1.487410 / 4.565676 (-3.078267) \| 0.084901 / 0.424275 (-0.339374) \| 0.012292 / 0.007607 (0.004685) \| 0.505211 / 0.226044 (0.279166) \| 5.074479 / 2.268929 (2.805551) \| 2.243068 / 55.444624 (-53.201556) \| 1.880199 / 6.876477 (-4.996278) \| 2.003757 / 2.142072 (-0.138315) \| 0.870719 / 4.805227 (-3.934508) \| 0.167626 / 6.500664 (-6.333039) \| 0.062024 / 0.075469 (-0.013445) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.192969 / 1.841788 (-0.648819) \| 14.830812 / 8.074308 (6.756504) \| 14.331178 / 10.191392 (4.139786) \| 0.199222 / 0.680424 (-0.481202) \| 0.029292 / 0.534201 (-0.504909) \| 0.440427 / 0.579283 (-0.138857) \| 0.437893 / 0.434364 (0.003529) \| 0.547155 / 0.540337 (0.006818) \| 0.645255 / 1.386936 (-0.741681) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007465 / 0.011353 (-0.003888) \| 0.005386 / 0.011008 (-0.005622) \| 0.073609 / 0.038508 (0.035100) \| 0.033550 / 0.023109 (0.010440) \| 0.341730 / 0.275898 (0.065832) \| 0.371518 / 0.323480 (0.048038) \| 0.005986 / 0.007986 (-0.001999) \| 0.004264 / 0.004328 (-0.000065) \| 0.073749 / 0.004250 (0.069498) \| 0.051452 / 0.037052 (0.014399) \| 0.347385 / 0.258489 (0.088896) \| 0.392284 / 0.293841 (0.098444) \| 0.036981 / 0.128546 (-0.091566) \| 0.012431 / 0.075646 (-0.063216) \| 0.086421 / 0.419271 (-0.332850) \| 0.053014 / 0.043533 (0.009481) \| 0.336660 / 0.255139 (0.081521) \| 0.359155 / 0.283200 (0.075956) \| 0.107666 / 0.141683 (-0.034017) \| 1.424324 / 1.452155 (-0.027830) \| 1.543027 / 1.492716 (0.050310) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.260862 / 0.018006 (0.242855) \| 0.552057 / 0.000490 (0.551567) \| 0.000449 / 0.000200 (0.000249) \| 0.000059 / 0.000054 (0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029184 / 0.037411 (-0.008227) \| 0.108799 / 0.014526 (0.094274) \| 0.125136 / 0.176557 (-0.051421) \| 0.157436 / 0.737135 (-0.579699) \| 0.126333 / 0.296338 (-0.170005) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.424054 / 0.215209 (0.208845) \| 4.227847 / 2.077655 (2.150192) \| 2.051102 / 1.504120 (0.546983) \| 1.848651 / 1.541195 (0.307457) \| 1.922728 / 1.468490 (0.454238) \| 0.705903 / 4.584777 (-3.878874) \| 3.800977 / 3.745712 (0.055265) \| 2.099345 / 5.269862 (-3.170517) \| 1.342919 / 4.565676 (-3.222757) \| 0.086128 / 0.424275 (-0.338147) \| 0.012539 / 0.007607 (0.004932) \| 0.528767 / 0.226044 (0.302723) \| 5.299989 / 2.268929 (3.031061) \| 2.534280 / 55.444624 (-52.910345) \| 2.229532 / 6.876477 (-4.646945) \| 2.326704 / 2.142072 (0.184632) \| 0.838533 / 4.805227 (-3.966694) \| 0.168446 / 6.500664 (-6.332218) \| 0.065158 / 0.075469 (-0.010311) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.250091 / 1.841788 (-0.591697) \| 14.988651 / 8.074308 (6.914343) \| 13.655103 / 10.191392 (3.463711) \| 0.165079 / 0.680424 (-0.515345) \| 0.017829 / 0.534201 (-0.516372) \| 0.425903 / 0.579283 (-0.153381) \| 0.419771 / 0.434364 (-0.014593) \| 0.534309 / 0.540337 (-0.006028) \| 0.635563 / 1.386936 (-0.751373) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f7d17ccc9b9dde2d94803b1305226c5a58d916c5 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010569 / 0.011353 (-0.000784) \| 0.005790 / 0.011008 (-0.005218) \| 0.118626 / 0.038508 (0.080118) \| 0.040455 / 0.023109 (0.017346) \| 0.342309 / 0.275898 (0.066411) \| 0.411828 / 0.323480 (0.088349) \| 0.008824 / 0.007986 (0.000839) \| 0.005426 / 0.004328 (0.001098) \| 0.088740 / 0.004250 (0.084489) \| 0.050042 / 0.037052 (0.012990) \| 0.352350 / 0.258489 (0.093861) \| 0.396030 / 0.293841 (0.102189) \| 0.043385 / 0.128546 (-0.085162) \| 0.013805 / 0.075646 (-0.061841) \| 0.396489 / 0.419271 (-0.022783) \| 0.055667 / 0.043533 (0.012135) \| 0.336165 / 0.255139 (0.081026) \| 0.372912 / 0.283200 (0.089713) \| 0.115343 / 0.141683 (-0.026340) \| 1.656412 / 1.452155 (0.204257) \| 1.708993 / 1.492716 (0.216277) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.011650 / 0.018006 (-0.006357) \| 0.444415 / 0.000490 (0.443926) \| 0.003985 / 0.000200 (0.003785) \| 0.000136 / 0.000054 (0.000082) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031718 / 0.037411 (-0.005693) \| 0.119640 / 0.014526 (0.105114) \| 0.138519 / 0.176557 (-0.038037) \| 0.188847 / 0.737135 (-0.548288) \| 0.137891 / 0.296338 (-0.158448) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.447540 / 0.215209 (0.232331) \| 4.577189 / 2.077655 (2.499534) \| 2.106992 / 1.504120 (0.602872) \| 1.889631 / 1.541195 (0.348436) \| 1.972256 / 1.468490 (0.503766) \| 0.778209 / 4.584777 (-3.806568) \| 4.430279 / 3.745712 (0.684567) \| 2.401226 / 5.269862 (-2.868636) \| 1.481251 / 4.565676 (-3.084425) \| 0.094244 / 0.424275 (-0.330031) \| 0.013961 / 0.007607 (0.006354) \| 0.570962 / 0.226044 (0.344917) \| 5.809224 / 2.268929 (3.540295) \| 2.663290 / 55.444624 (-52.781334) \| 2.201228 / 6.876477 (-4.675249) \| 2.319240 / 2.142072 (0.177168) \| 0.938340 / 4.805227 (-3.866887) \| 0.185546 / 6.500664 (-6.315118) \| 0.069087 / 0.075469 (-0.006382) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.448597 / 1.841788 (-0.393191) \| 17.188573 / 8.074308 (9.114265) \| 16.197532 / 10.191392 (6.006140) \| 0.194064 / 0.680424 (-0.486360) \| 0.033694 / 0.534201 (-0.500507) \| 0.507585 / 0.579283 (-0.071699) \| 0.505470 / 0.434364 (0.071106) \| 0.623270 / 0.540337 (0.082932) \| 0.729964 / 1.386936 (-0.656972) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008529 / 0.011353 (-0.002824) \| 0.005705 / 0.011008 (-0.005304) \| 0.085594 / 0.038508 (0.047086) \| 0.038377 / 0.023109 (0.015268) \| 0.384221 / 0.275898 (0.108323) \| 0.414678 / 0.323480 (0.091199) \| 0.006195 / 0.007986 (-0.001791) \| 0.004549 / 0.004328 (0.000221) \| 0.082710 / 0.004250 (0.078460) \| 0.054899 / 0.037052 (0.017847) \| 0.404017 / 0.258489 (0.145528) \| 0.450309 / 0.293841 (0.156468) \| 0.040620 / 0.128546 (-0.087926) \| 0.013774 / 0.075646 (-0.061872) \| 0.099231 / 0.419271 (-0.320041) \| 0.057183 / 0.043533 (0.013650) \| 0.390806 / 0.255139 (0.135667) \| 0.419334 / 0.283200 (0.136134) \| 0.116449 / 0.141683 (-0.025234) \| 1.709124 / 1.452155 (0.256969) \| 1.812769 / 1.492716 (0.320052) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.225206 / 0.018006 (0.207199) \| 0.440530 / 0.000490 (0.440040) \| 0.002982 / 0.000200 (0.002782) \| 0.000102 / 0.000054 (0.000048) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032256 / 0.037411 (-0.005155) \| 0.127086 / 0.014526 (0.112560) \| 0.138133 / 0.176557 (-0.038424) \| 0.176168 / 0.737135 (-0.560968) \| 0.146072 / 0.296338 (-0.150267) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.474374 / 0.215209 (0.259165) \| 4.785106 / 2.077655 (2.707452) \| 2.319344 / 1.504120 (0.815225) \| 2.075239 / 1.541195 (0.534045) \| 2.179231 / 1.468490 (0.710741) \| 0.832124 / 4.584777 (-3.752653) \| 4.376302 / 3.745712 (0.630590) \| 3.966837 / 5.269862 (-1.303024) \| 1.820230 / 4.565676 (-2.745446) \| 0.100692 / 0.424275 (-0.323583) \| 0.014748 / 0.007607 (0.007141) \| 0.568702 / 0.226044 (0.342657) \| 5.771548 / 2.268929 (3.502619) \| 2.747431 / 55.444624 (-52.697193) \| 2.448482 / 6.876477 (-4.427994) \| 2.497206 / 2.142072 (0.355133) \| 0.960842 / 4.805227 (-3.844385) \| 0.192855 / 6.500664 (-6.307809) \| 0.072494 / 0.075469 (-0.002975) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.474542 / 1.841788 (-0.367245) \| 17.344804 / 8.074308 (9.270496) \| 15.336082 / 10.191392 (5.144690) \| 0.200134 / 0.680424 (-0.480290) \| 0.020728 / 0.534201 (-0.513473) \| 0.488854 / 0.579283 (-0.090429) \| 0.490781 / 0.434364 (0.056418) \| 0.626288 / 0.540337 (0.085950) \| 0.721130 / 1.386936 (-0.665806) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cd7877892aa48a2470b01f52013390c54aca8a49 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008542 / 0.011353 (-0.002811) \| 0.004624 / 0.011008 (-0.006384) \| 0.100749 / 0.038508 (0.062241) \| 0.029587 / 0.023109 (0.006478) \| 0.298680 / 0.275898 (0.022782) \| 0.359659 / 0.323480 (0.036180) \| 0.007001 / 0.007986 (-0.000984) \| 0.003398 / 0.004328 (-0.000930) \| 0.078654 / 0.004250 (0.074404) \| 0.036440 / 0.037052 (-0.000612) \| 0.313245 / 0.258489 (0.054756) \| 0.342776 / 0.293841 (0.048936) \| 0.033195 / 0.128546 (-0.095352) \| 0.011500 / 0.075646 (-0.064146) \| 0.323957 / 0.419271 (-0.095314) \| 0.039878 / 0.043533 (-0.003655) \| 0.298189 / 0.255139 (0.043050) \| 0.325488 / 0.283200 (0.042289) \| 0.087276 / 0.141683 (-0.054407) \| 1.480846 / 1.452155 (0.028691) \| 1.507016 / 1.492716 (0.014300) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.189570 / 0.018006 (0.171564) \| 0.406407 / 0.000490 (0.405917) \| 0.003062 / 0.000200 (0.002862) \| 0.000073 / 0.000054 (0.000019) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022865 / 0.037411 (-0.014546) \| 0.096103 / 0.014526 (0.081578) \| 0.106462 / 0.176557 (-0.070094) \| 0.140888 / 0.737135 (-0.596247) \| 0.108172 / 0.296338 (-0.188167) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.415951 / 0.215209 (0.200742) \| 4.172187 / 2.077655 (2.094532) \| 1.842210 / 1.504120 (0.338090) \| 1.636997 / 1.541195 (0.095802) \| 1.706078 / 1.468490 (0.237588) \| 0.695825 / 4.584777 (-3.888952) \| 3.337354 / 3.745712 (-0.408358) \| 1.877880 / 5.269862 (-3.391982) \| 1.153882 / 4.565676 (-3.411794) \| 0.082923 / 0.424275 (-0.341352) \| 0.012814 / 0.007607 (0.005207) \| 0.521793 / 0.226044 (0.295748) \| 5.275980 / 2.268929 (3.007051) \| 2.279230 / 55.444624 (-53.165394) \| 1.941777 / 6.876477 (-4.934700) \| 1.981297 / 2.142072 (-0.160775) \| 0.809669 / 4.805227 (-3.995558) \| 0.148753 / 6.500664 (-6.351911) \| 0.064909 / 0.075469 (-0.010560) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.226757 / 1.841788 (-0.615031) \| 13.717354 / 8.074308 (5.643046) \| 12.925885 / 10.191392 (2.734493) \| 0.137926 / 0.680424 (-0.542498) \| 0.028788 / 0.534201 (-0.505413) \| 0.396654 / 0.579283 (-0.182630) \| 0.401931 / 0.434364 (-0.032432) \| 0.460515 / 0.540337 (-0.079823) \| 0.537903 / 1.386936 (-0.849033) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006757 / 0.011353 (-0.004596) \| 0.004474 / 0.011008 (-0.006534) \| 0.076571 / 0.038508 (0.038063) \| 0.027580 / 0.023109 (0.004471) \| 0.348231 / 0.275898 (0.072333) \| 0.398403 / 0.323480 (0.074923) \| 0.005089 / 0.007986 (-0.002897) \| 0.004676 / 0.004328 (0.000347) \| 0.076444 / 0.004250 (0.072194) \| 0.038508 / 0.037052 (0.001456) \| 0.348515 / 0.258489 (0.090026) \| 0.401456 / 0.293841 (0.107615) \| 0.031630 / 0.128546 (-0.096916) \| 0.011698 / 0.075646 (-0.063949) \| 0.085805 / 0.419271 (-0.333467) \| 0.041962 / 0.043533 (-0.001570) \| 0.343415 / 0.255139 (0.088276) \| 0.383001 / 0.283200 (0.099801) \| 0.090231 / 0.141683 (-0.051452) \| 1.488114 / 1.452155 (0.035960) \| 1.569039 / 1.492716 (0.076323) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.261751 / 0.018006 (0.243745) \| 0.411354 / 0.000490 (0.410865) \| 0.015103 / 0.000200 (0.014903) \| 0.000262 / 0.000054 (0.000208) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025423 / 0.037411 (-0.011988) \| 0.101334 / 0.014526 (0.086808) \| 0.108835 / 0.176557 (-0.067722) \| 0.143995 / 0.737135 (-0.593140) \| 0.111751 / 0.296338 (-0.184588) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.446507 / 0.215209 (0.231298) \| 4.461543 / 2.077655 (2.383888) \| 2.104648 / 1.504120 (0.600528) \| 1.895900 / 1.541195 (0.354706) \| 1.985481 / 1.468490 (0.516991) \| 0.699029 / 4.584777 (-3.885748) \| 3.371064 / 3.745712 (-0.374648) \| 1.883445 / 5.269862 (-3.386416) \| 1.166150 / 4.565676 (-3.399527) \| 0.082639 / 0.424275 (-0.341636) \| 0.012605 / 0.007607 (0.004998) \| 0.544860 / 0.226044 (0.318815) \| 5.513223 / 2.268929 (3.244294) \| 2.570661 / 55.444624 (-52.873963) \| 2.206066 / 6.876477 (-4.670411) \| 2.256346 / 2.142072 (0.114273) \| 0.801142 / 4.805227 (-4.004085) \| 0.150412 / 6.500664 (-6.350252) \| 0.067742 / 0.075469 (-0.007727) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.303477 / 1.841788 (-0.538310) \| 14.287767 / 8.074308 (6.213458) \| 13.525563 / 10.191392 (3.334171) \| 0.148202 / 0.680424 (-0.532222) \| 0.016868 / 0.534201 (-0.517333) \| 0.380729 / 0.579283 (-0.198555) \| 0.388177 / 0.434364 (-0.046187) \| 0.477410 / 0.540337 (-0.062927) \| 0.569343 / 1.386936 (-0.817593) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79c18b77113da3f2e31af0570ec119877ca2a390 \"CML watermark\")\n", "> PS: I think it would be a good idea to add links to the Guide pages for better discoverability and to somewhat \"justify their presence in the docs\" (from the tutorial/how-to pages to the guides; some guides are not referenced at all)\r\n\r\nJust merged #5485, which references this new doc! Will look for other pages in the docs where it'd make sense to add them :)" ]
1,520,374,219	5,409	Fix deprecation warning when use_auth_token passed to download_and_prepare	closed	2023-01-05T09:10:58	2023-01-06T11:06:16	2023-01-06T10:59:13	https://github.com/huggingface/datasets/pull/5409	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5409", "html_url": "https://github.com/huggingface/datasets/pull/5409", "diff_url": "https://github.com/huggingface/datasets/pull/5409.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5409.patch", "merged_at": "2023-01-06T10:59:13" }	albertvillanova	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008627 / 0.011353 (-0.002726) \| 0.004572 / 0.011008 (-0.006436) \| 0.099653 / 0.038508 (0.061145) \| 0.030010 / 0.023109 (0.006901) \| 0.300492 / 0.275898 (0.024594) \| 0.360443 / 0.323480 (0.036963) \| 0.007125 / 0.007986 (-0.000860) \| 0.003431 / 0.004328 (-0.000897) \| 0.078103 / 0.004250 (0.073852) \| 0.036884 / 0.037052 (-0.000168) \| 0.312289 / 0.258489 (0.053800) \| 0.345795 / 0.293841 (0.051954) \| 0.034001 / 0.128546 (-0.094545) \| 0.011405 / 0.075646 (-0.064242) \| 0.321258 / 0.419271 (-0.098013) \| 0.040591 / 0.043533 (-0.002942) \| 0.301114 / 0.255139 (0.045975) \| 0.337226 / 0.283200 (0.054027) \| 0.088055 / 0.141683 (-0.053628) \| 1.451892 / 1.452155 (-0.000263) \| 1.494881 / 1.492716 (0.002164) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.186749 / 0.018006 (0.168743) \| 0.414089 / 0.000490 (0.413600) \| 0.002475 / 0.000200 (0.002275) \| 0.000070 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022413 / 0.037411 (-0.014999) \| 0.097547 / 0.014526 (0.083021) \| 0.104196 / 0.176557 (-0.072361) \| 0.139819 / 0.737135 (-0.597316) \| 0.108345 / 0.296338 (-0.187994) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.424750 / 0.215209 (0.209541) \| 4.261513 / 2.077655 (2.183859) \| 2.150888 / 1.504120 (0.646768) \| 1.935925 / 1.541195 (0.394730) \| 1.867456 / 1.468490 (0.398966) \| 0.694384 / 4.584777 (-3.890393) \| 3.370539 / 3.745712 (-0.375173) \| 1.886714 / 5.269862 (-3.383148) \| 1.256542 / 4.565676 (-3.309135) \| 0.082841 / 0.424275 (-0.341434) \| 0.012344 / 0.007607 (0.004737) \| 0.529801 / 0.226044 (0.303757) \| 5.315438 / 2.268929 (3.046509) \| 2.460517 / 55.444624 (-52.984107) \| 2.261840 / 6.876477 (-4.614637) \| 2.338710 / 2.142072 (0.196638) \| 0.818433 / 4.805227 (-3.986794) \| 0.150571 / 6.500664 (-6.350093) \| 0.066524 / 0.075469 (-0.008945) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.253086 / 1.841788 (-0.588702) \| 13.862614 / 8.074308 (5.788306) \| 14.145149 / 10.191392 (3.953757) \| 0.165867 / 0.680424 (-0.514557) \| 0.029269 / 0.534201 (-0.504932) \| 0.397579 / 0.579283 (-0.181704) \| 0.401113 / 0.434364 (-0.033251) \| 0.463269 / 0.540337 (-0.077068) \| 0.551494 / 1.386936 (-0.835442) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006610 / 0.011353 (-0.004743) \| 0.004583 / 0.011008 (-0.006425) \| 0.096680 / 0.038508 (0.058172) \| 0.027352 / 0.023109 (0.004242) \| 0.409292 / 0.275898 (0.133394) \| 0.445790 / 0.323480 (0.122310) \| 0.004987 / 0.007986 (-0.002999) \| 0.003462 / 0.004328 (-0.000866) \| 0.074472 / 0.004250 (0.070221) \| 0.037875 / 0.037052 (0.000822) \| 0.411496 / 0.258489 (0.153007) \| 0.454721 / 0.293841 (0.160880) \| 0.031884 / 0.128546 (-0.096662) \| 0.011682 / 0.075646 (-0.063964) \| 0.318831 / 0.419271 (-0.100441) \| 0.041781 / 0.043533 (-0.001752) \| 0.411247 / 0.255139 (0.156108) \| 0.436215 / 0.283200 (0.153016) \| 0.090021 / 0.141683 (-0.051662) \| 1.492385 / 1.452155 (0.040231) \| 1.565182 / 1.492716 (0.072465) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.221263 / 0.018006 (0.203257) \| 0.399074 / 0.000490 (0.398584) \| 0.000405 / 0.000200 (0.000205) \| 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) \| 0.025139 / 0.037411 (-0.012272) \| 0.097952 / 0.014526 (0.083426) \| 0.106078 / 0.176557 (-0.070479) \| 0.143231 / 0.737135 (-0.593904) \| 0.109177 / 0.296338 (-0.187161) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.441668 / 0.215209 (0.226459) \| 4.403247 / 2.077655 (2.325592) \| 2.072749 / 1.504120 (0.568629) \| 1.866248 / 1.541195 (0.325053) \| 1.906418 / 1.468490 (0.437927) \| 0.697234 / 4.584777 (-3.887543) \| 3.412016 / 3.745712 (-0.333696) \| 1.852572 / 5.269862 (-3.417289) \| 1.168270 / 4.565676 (-3.397407) \| 0.082132 / 0.424275 (-0.342144) \| 0.013191 / 0.007607 (0.005584) \| 0.548932 / 0.226044 (0.322888) \| 5.503891 / 2.268929 (3.234962) \| 2.539784 / 55.444624 (-52.904841) \| 2.181292 / 6.876477 (-4.695184) \| 2.242197 / 2.142072 (0.100125) \| 0.804027 / 4.805227 (-4.001200) \| 0.151649 / 6.500664 (-6.349015) \| 0.067088 / 0.075469 (-0.008381) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.296267 / 1.841788 (-0.545520) \| 13.986484 / 8.074308 (5.912176) \| 13.440705 / 10.191392 (3.249313) \| 0.140787 / 0.680424 (-0.539637) \| 0.017132 / 0.534201 (-0.517069) \| 0.381899 / 0.579283 (-0.197384) \| 0.385535 / 0.434364 (-0.048829) \| 0.439957 / 0.540337 (-0.100380) \| 0.532980 / 1.386936 (-0.853956) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n" ]
1,519,890,752	5,408	dataset map function could not be hash properly	closed	2023-01-05T01:59:59	2023-01-06T13:22:19	2023-01-06T13:22:18	https://github.com/huggingface/datasets/issues/5408	null	Tungway1990	false	[ "Hi ! On macos I tried with\r\n- py 3.9.11\r\n- datasets 2.8.0\r\n- transformers 4.25.1\r\n- dill 0.3.4\r\n\r\nand I was able to hash `prepare_dataset` correctly:\r\n```python\r\nfrom datasets.fingerprint import Hasher\r\nHasher.hash(prepare_dataset)\r\n```\r\n\r\nWhat version of transformers do you have ? Can you try to call `Hasher.hash` on the the tokenizer and the feature extractor to see which one can't be hashed ?", "Thanks for your prompt reply.\r\n\r\nI update datasets version to 2.8.0 and the warning is gong." ]
1,519,797,345	5,407	Datasets.from_sql() generates deprecation warning	closed	2023-01-05T00:43:17	2023-01-06T10:59:14	2023-01-06T10:59:14	https://github.com/huggingface/datasets/issues/5407	null	msummerfield	false	[ "Thanks for reporting @msummerfield. We are fixing it." ]
1,519,140,544	5,406	[2.6.1][2.7.0] Upgrade `datasets` to fix `TypeError: can only concatenate str (not "int") to str`	open	2023-01-04T15:10:04	2023-06-21T18:45:38	null	https://github.com/huggingface/datasets/issues/5406	null	lhoestq	false	[ "I still get this error on 2.9.0\r\n<img width=\"1925\" alt=\"image\" src=\"https://user-images.githubusercontent.com/7208470/215597359-2f253c76-c472-4612-8099-d3a74d16eb29.png\">\r\n", "Hi ! I just tested locally and or colab and it works fine for 2.9 on `sst2`.\r\n\r\nAlso the code that is shown in your stack trace is not present in the 2.9 source code - so I'm wondering how you installed `datasets` that could cause this ? (you can check by searching for `[0:{label_ids[-1] + 1}]` in the [2.9 codebase](https://github.dev/huggingface/datasets/tree/b5672a956d5de864e6f5550e493527d962d6ae55) - it doesn't find anything)\r\n\r\nAnyway you can try uninstalling `datasets` and install it again", "For what it's worth, I've also gotten this error on 2.9.0, and I've tried uninstalling an reinstalling\r\n![Screenshot 2023-02-01 at 11 06 55 AM](https://user-images.githubusercontent.com/22944438/216126466-6934e8f8-0be4-41f4-9822-8436dfafd61c.png)\r\n\r\nI'm very new to this package (I was following this tutorial: https://huggingface.co/docs/transformers/training), so there's a good chance I was doing something wrong 😅 but thought I'd pass along the feedback", "@ntrpnr @mtwichel Did you install `datasets` with conda ?\r\n\r\nI suspect that `datasets` 2.9 on conda still have this issue for some reason. When I install `datasets` with `pip` I don't have this error.", "> @ntrpnr @mtwichel Did you install datasets with conda ?\r\n\r\nI did yeah, I wonder if that's the issue", "I just checked on conda at https://anaconda.org/HuggingFace/datasets/files\r\n\r\nand everything looks fine, I got\r\n```python\r\n\r\nf\"ClassLabel expected a value for all label ids [0:{int(label_ids[-1]) + 1}] but some ids are missing.\"\r\n```\r\nas expected in features.py line 1760 (notice the \"int()\") to not have the TypeError.\r\n\r\nFrom where on conda did you install `datasets` ? You should use the `HuggingFace` official channel\r\n\r\nedit: the conda-forge one [here](https://anaconda.org/conda-forge/datasets/files) seems ok as well", "Could you also try this in your notebook ? In case your python kernel doesn't match the `pip` environment in your shell\r\n```python\r\nimport datasets; datasets.__version__\r\n```\r\nand\r\n```\r\n!which python\r\n```\r\n```python\r\nimport sys; sys.executable\r\n```", "Mmmm, just a potential clue:\r\n\r\nWhere are you running your Python code? Is it the Spyder IDE?\r\n\r\nI have recently seen some users reporting conflicting Python environments while using Spyder...\r\n\r\nMaybe related:\r\n- #5487", "Other potential clue:\r\n- Had you already imported `datasets` before pip-updating it? You should first update datasets, before importing it. Otherwise, you need to restart the kernel after updating it.", "I installed `datasets` with Conda using `conda install datasets` and got this issue.\r\n\r\nThen I tried to reinstall using\r\n`\r\nconda install -c huggingface -c conda-forge datasets\r\n`\r\nThe issue is now fixed.", "I'm still getting this error on 2.13.0" ]
1,517,879,386	5,405	size_in_bytes the same for all splits	open	2023-01-03T20:25:48	2023-01-04T09:22:59	null	https://github.com/huggingface/datasets/issues/5405	null	Breakend	false	[ "Hi @Breakend,\r\n\r\nIndeed, the attribute `size_in_bytes` refers to the size of the entire dataset configuration, for all splits (size of downloaded files + Arrow files), not the specific split.\r\nThis is also the case for `download_size` (downloaded files) and `dataset_size` (Arrow files).\r\n\r\nThe size of the Arrow files for a specific split can be accessed: e.g. size of the \"test\" split only\r\n```python\r\nds[\"train\"].info.splits[\"test\"].num_bytes\r\n```\r\n\r\nI agree this is confusing and maybe we should improve it." ]
1,517,566,331	5,404	Better integration of BIG-bench	open	2023-01-03T15:37:57	2023-02-09T20:30:26	null	https://github.com/huggingface/datasets/issues/5404	null	albertvillanova	false	[ "Hi, I made my version : https://huggingface.co/datasets/tasksource/bigbench" ]
1,517,466,492	5,403	Replace one letter import in docs	closed	2023-01-03T14:26:32	2023-01-03T15:06:18	2023-01-03T14:59:01	https://github.com/huggingface/datasets/pull/5403	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5403", "html_url": "https://github.com/huggingface/datasets/pull/5403", "diff_url": "https://github.com/huggingface/datasets/pull/5403.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5403.patch", "merged_at": "2023-01-03T14:59:01" }	MKhalusova	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "> Thanks for the docs fix for consistency.\r\n> \r\n> Again for consistency, it would be nice to make the same fix across all the docs, e.g.\r\n> \r\n> https://github.com/huggingface/datasets/blob/310cdddd1c43f9658de172b85b6509d07d5e31a1/docs/source/image_classification.mdx?plain=1#L41\r\n\r\nExcellent point!", "@albertvillanova Should be all of them now :)", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008776 / 0.011353 (-0.002576) \| 0.004534 / 0.011008 (-0.006474) \| 0.101921 / 0.038508 (0.063413) \| 0.029995 / 0.023109 (0.006886) \| 0.307180 / 0.275898 (0.031282) \| 0.371001 / 0.323480 (0.047521) \| 0.007089 / 0.007986 (-0.000896) \| 0.003474 / 0.004328 (-0.000855) \| 0.079498 / 0.004250 (0.075248) \| 0.036522 / 0.037052 (-0.000531) \| 0.311729 / 0.258489 (0.053240) \| 0.349861 / 0.293841 (0.056020) \| 0.033815 / 0.128546 (-0.094731) \| 0.011435 / 0.075646 (-0.064211) \| 0.322924 / 0.419271 (-0.096347) \| 0.040981 / 0.043533 (-0.002552) \| 0.306174 / 0.255139 (0.051035) \| 0.331979 / 0.283200 (0.048780) \| 0.091293 / 0.141683 (-0.050389) \| 1.480935 / 1.452155 (0.028780) \| 1.522022 / 1.492716 (0.029306) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.195053 / 0.018006 (0.177047) \| 0.424898 / 0.000490 (0.424408) \| 0.003869 / 0.000200 (0.003669) \| 0.000075 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024323 / 0.037411 (-0.013088) \| 0.098061 / 0.014526 (0.083535) \| 0.105770 / 0.176557 (-0.070787) \| 0.145799 / 0.737135 (-0.591336) \| 0.109109 / 0.296338 (-0.187230) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.420434 / 0.215209 (0.205225) \| 4.194781 / 2.077655 (2.117126) \| 2.030498 / 1.504120 (0.526378) \| 1.885314 / 1.541195 (0.344120) \| 1.996485 / 1.468490 (0.527995) \| 0.708540 / 4.584777 (-3.876237) \| 3.400694 / 3.745712 (-0.345018) \| 2.888704 / 5.269862 (-2.381157) \| 1.578100 / 4.565676 (-2.987577) \| 0.082150 / 0.424275 (-0.342125) \| 0.012277 / 0.007607 (0.004669) \| 0.527312 / 0.226044 (0.301268) \| 5.289566 / 2.268929 (3.020637) \| 2.369997 / 55.444624 (-53.074628) \| 2.040365 / 6.876477 (-4.836112) \| 2.298857 / 2.142072 (0.156785) \| 0.808446 / 4.805227 (-3.996781) \| 0.149355 / 6.500664 (-6.351309) \| 0.065993 / 0.075469 (-0.009477) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.231829 / 1.841788 (-0.609959) \| 13.874762 / 8.074308 (5.800454) \| 13.464379 / 10.191392 (3.272987) \| 0.151105 / 0.680424 (-0.529319) \| 0.028689 / 0.534201 (-0.505512) \| 0.398720 / 0.579283 (-0.180564) \| 0.402108 / 0.434364 (-0.032256) \| 0.463426 / 0.540337 (-0.076912) \| 0.541919 / 1.386936 (-0.845017) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006979 / 0.011353 (-0.004373) \| 0.004723 / 0.011008 (-0.006285) \| 0.099172 / 0.038508 (0.060664) \| 0.027970 / 0.023109 (0.004861) \| 0.415096 / 0.275898 (0.139198) \| 0.455916 / 0.323480 (0.132437) \| 0.005950 / 0.007986 (-0.002036) \| 0.003423 / 0.004328 (-0.000906) \| 0.075512 / 0.004250 (0.071262) \| 0.040894 / 0.037052 (0.003842) \| 0.419810 / 0.258489 (0.161321) \| 0.461913 / 0.293841 (0.168072) \| 0.033014 / 0.128546 (-0.095532) \| 0.011613 / 0.075646 (-0.064033) \| 0.320983 / 0.419271 (-0.098289) \| 0.049902 / 0.043533 (0.006369) \| 0.426378 / 0.255139 (0.171239) \| 0.445594 / 0.283200 (0.162394) \| 0.098978 / 0.141683 (-0.042705) \| 1.485724 / 1.452155 (0.033570) \| 1.563978 / 1.492716 (0.071262) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.232137 / 0.018006 (0.214131) \| 0.432785 / 0.000490 (0.432296) \| 0.006173 / 0.000200 (0.005973) \| 0.000085 / 0.000054 (0.000031) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024924 / 0.037411 (-0.012487) \| 0.102878 / 0.014526 (0.088352) \| 0.107976 / 0.176557 (-0.068581) \| 0.143581 / 0.737135 (-0.593554) \| 0.111644 / 0.296338 (-0.184694) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.490902 / 0.215209 (0.275693) \| 4.914060 / 2.077655 (2.836405) \| 2.569465 / 1.504120 (1.065345) \| 2.346872 / 1.541195 (0.805677) \| 2.412047 / 1.468490 (0.943557) \| 0.704975 / 4.584777 (-3.879802) \| 3.443669 / 3.745712 (-0.302043) \| 3.172055 / 5.269862 (-2.097807) \| 1.332152 / 4.565676 (-3.233525) \| 0.083023 / 0.424275 (-0.341252) \| 0.012699 / 0.007607 (0.005092) \| 0.592511 / 0.226044 (0.366466) \| 5.916376 / 2.268929 (3.647448) \| 3.028472 / 55.444624 (-52.416152) \| 2.691159 / 6.876477 (-4.185318) \| 2.786132 / 2.142072 (0.644060) \| 0.814045 / 4.805227 (-3.991182) \| 0.156630 / 6.500664 (-6.344034) \| 0.071330 / 0.075469 (-0.004139) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.277936 / 1.841788 (-0.563852) \| 14.331367 / 8.074308 (6.257059) \| 13.685694 / 10.191392 (3.494302) \| 0.138915 / 0.680424 (-0.541509) \| 0.016844 / 0.534201 (-0.517357) \| 0.390307 / 0.579283 (-0.188976) \| 0.385207 / 0.434364 (-0.049157) \| 0.448128 / 0.540337 (-0.092210) \| 0.532609 / 1.386936 (-0.854327) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n" ]
1,517,409,429	5,402	Missing state.json when creating a cloud dataset using a dataset_builder	open	2023-01-03T13:39:59	2023-01-04T17:23:57	null	https://github.com/huggingface/datasets/issues/5402	null	danielfleischer	false	[ "`load_from_disk` must be used on datasets saved using `save_to_disk`: they correspond to fully serialized datasets including their state.\r\n\r\nOn the other hand, `download_and_prepare` just downloads the raw data and convert them to arrow (or parquet if you want). We are working on allowing you to reload a dataset saved on S3 with `download_and_prepare` using `load_dataset` in #5281 \r\n\r\nFor now I'd encourage you to keep using `save_to_disk`", "Thanks, I'll follow that issue. \r\n\r\nI was following the [cloud storage](https://huggingface.co/docs/datasets/filesystems) docs section and perhaps I'm missing some part of the flow; start with `load_dataset_builder` + `download_and_prepare`. You say I need an explicit `save_to_disk` but what object needs to be saved? the builder? is that related to the other issue?", "Right now `load_dataset_builder` + `download_and_prepare` is to be used with tools like dask or spark, but `load_dataset` will support private cloud storage soon as well so you'll be able to reload the dataset with `datasets`.\r\n\r\nRight now the only function that can load a dataset from a cloud storage is `load_from_disk`, that must be used with a dataset serialized with `save_to_disk`." ]
1,517,160,935	5,401	Support Dataset conversion from/to Spark	open	2023-01-03T09:57:40	2023-01-05T14:21:33	null	https://github.com/huggingface/datasets/pull/5401	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5401", "html_url": "https://github.com/huggingface/datasets/pull/5401", "diff_url": "https://github.com/huggingface/datasets/pull/5401.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5401.patch", "merged_at": null }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5401). All of your documentation changes will be reflected on that endpoint.", "Cool thanks !\r\n\r\nSpark DataFrame are usually quite big, and I believe here `from_spark` would load everything in the driver node's RAM, which is quite limiting. Same for `to_spark` which would load everything in the driver node's RAM before sending the data to the executor. Maybe we can mention this in the docstring ?\r\n\r\nTo transfer big datasets from/into the HF ecosystem using Spark maybe we can just make sure that `pyspark` can read/write to the HF Hub, and that `datasets` can read from HDFS/S3/etc.", "Yes @lhoestq , consider this as a first integration of the Datasets library with Spark.\r\n- This PR implements the basic conversion between both.\r\n - And yes, we are using the Spark's `pandas` API (that uses `pyarrow` under the hood): everything is transferred to the driver.\r\n - Note that we are converting from/to a Datasets dataset: this is not distributed\r\n\r\nThe next step is to support the integration of the HF Hub with Spark, that I think should be done using `hffs`.", "Thinking more about it I don't really see how those two methods help in practice, since one can already do `datasets` <-> pandas <-> spark and those two methods don't add value over this.\r\n\r\nHowever I think it can be good documentation to explain that it's possible to do it and it's super simple" ]
1,517,032,972	5,400	Support streaming datasets with os.path.exists and Path.exists	closed	2023-01-03T07:42:37	2023-01-06T10:42:44	2023-01-06T10:35:44	https://github.com/huggingface/datasets/pull/5400	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5400", "html_url": "https://github.com/huggingface/datasets/pull/5400", "diff_url": "https://github.com/huggingface/datasets/pull/5400.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5400.patch", "merged_at": "2023-01-06T10:35:44" }	albertvillanova	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008638 / 0.011353 (-0.002715) \| 0.004565 / 0.011008 (-0.006444) \| 0.098984 / 0.038508 (0.060476) \| 0.030118 / 0.023109 (0.007009) \| 0.321779 / 0.275898 (0.045881) \| 0.366905 / 0.323480 (0.043426) \| 0.006931 / 0.007986 (-0.001055) \| 0.004728 / 0.004328 (0.000399) \| 0.078358 / 0.004250 (0.074108) \| 0.037755 / 0.037052 (0.000702) \| 0.312694 / 0.258489 (0.054205) \| 0.351781 / 0.293841 (0.057940) \| 0.033266 / 0.128546 (-0.095280) \| 0.011397 / 0.075646 (-0.064250) \| 0.323501 / 0.419271 (-0.095771) \| 0.040779 / 0.043533 (-0.002754) \| 0.303533 / 0.255139 (0.048394) \| 0.340940 / 0.283200 (0.057740) \| 0.088701 / 0.141683 (-0.052982) \| 1.472058 / 1.452155 (0.019904) \| 1.529535 / 1.492716 (0.036818) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.191803 / 0.018006 (0.173797) \| 0.409773 / 0.000490 (0.409283) \| 0.002704 / 0.000200 (0.002504) \| 0.000217 / 0.000054 (0.000163) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023520 / 0.037411 (-0.013891) \| 0.096967 / 0.014526 (0.082441) \| 0.107911 / 0.176557 (-0.068646) \| 0.146425 / 0.737135 (-0.590710) \| 0.109025 / 0.296338 (-0.187314) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.418565 / 0.215209 (0.203356) \| 4.183429 / 2.077655 (2.105774) \| 1.886534 / 1.504120 (0.382414) \| 1.689015 / 1.541195 (0.147820) \| 1.710757 / 1.468490 (0.242267) \| 0.693211 / 4.584777 (-3.891566) \| 3.380062 / 3.745712 (-0.365650) \| 2.619910 / 5.269862 (-2.649952) \| 1.457512 / 4.565676 (-3.108164) \| 0.082421 / 0.424275 (-0.341854) \| 0.012126 / 0.007607 (0.004519) \| 0.525249 / 0.226044 (0.299205) \| 5.244541 / 2.268929 (2.975613) \| 2.305908 / 55.444624 (-53.138717) \| 1.945298 / 6.876477 (-4.931178) \| 2.015618 / 2.142072 (-0.126455) \| 0.816746 / 4.805227 (-3.988481) \| 0.148325 / 6.500664 (-6.352339) \| 0.063939 / 0.075469 (-0.011530) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.255790 / 1.841788 (-0.585998) \| 13.433219 / 8.074308 (5.358911) \| 13.916957 / 10.191392 (3.725565) \| 0.153468 / 0.680424 (-0.526956) \| 0.028722 / 0.534201 (-0.505479) \| 0.398245 / 0.579283 (-0.181038) \| 0.399067 / 0.434364 (-0.035296) \| 0.457525 / 0.540337 (-0.082812) \| 0.542391 / 1.386936 (-0.844545) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006411 / 0.011353 (-0.004942) \| 0.004552 / 0.011008 (-0.006456) \| 0.098036 / 0.038508 (0.059527) \| 0.026532 / 0.023109 (0.003422) \| 0.412270 / 0.275898 (0.136372) \| 0.442771 / 0.323480 (0.119291) \| 0.004891 / 0.007986 (-0.003094) \| 0.003488 / 0.004328 (-0.000841) \| 0.075437 / 0.004250 (0.071186) \| 0.036228 / 0.037052 (-0.000824) \| 0.413246 / 0.258489 (0.154757) \| 0.453546 / 0.293841 (0.159705) \| 0.031054 / 0.128546 (-0.097492) \| 0.011589 / 0.075646 (-0.064058) \| 0.318477 / 0.419271 (-0.100794) \| 0.041075 / 0.043533 (-0.002457) \| 0.411182 / 0.255139 (0.156043) \| 0.436991 / 0.283200 (0.153792) \| 0.086563 / 0.141683 (-0.055120) \| 1.511948 / 1.452155 (0.059793) \| 1.570925 / 1.492716 (0.078208) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.200510 / 0.018006 (0.182504) \| 0.403450 / 0.000490 (0.402960) \| 0.000397 / 0.000200 (0.000197) \| 0.000058 / 0.000054 (0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023950 / 0.037411 (-0.013461) \| 0.097334 / 0.014526 (0.082808) \| 0.105228 / 0.176557 (-0.071328) \| 0.137699 / 0.737135 (-0.599436) \| 0.107063 / 0.296338 (-0.189275) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.474420 / 0.215209 (0.259211) \| 4.748212 / 2.077655 (2.670557) \| 2.407318 / 1.504120 (0.903198) \| 2.198949 / 1.541195 (0.657755) \| 2.220377 / 1.468490 (0.751887) \| 0.704022 / 4.584777 (-3.880755) \| 3.366128 / 3.745712 (-0.379584) \| 1.839454 / 5.269862 (-3.430408) \| 1.151183 / 4.565676 (-3.414493) \| 0.082818 / 0.424275 (-0.341457) \| 0.012765 / 0.007607 (0.005158) \| 0.571913 / 0.226044 (0.345868) \| 5.722544 / 2.268929 (3.453615) \| 2.858279 / 55.444624 (-52.586346) \| 2.513479 / 6.876477 (-4.362998) \| 2.574227 / 2.142072 (0.432154) \| 0.803282 / 4.805227 (-4.001945) \| 0.150603 / 6.500664 (-6.350061) \| 0.066594 / 0.075469 (-0.008875) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.301161 / 1.841788 (-0.540627) \| 13.580745 / 8.074308 (5.506436) \| 13.301551 / 10.191392 (3.110159) \| 0.141424 / 0.680424 (-0.539000) \| 0.016579 / 0.534201 (-0.517622) \| 0.380726 / 0.579283 (-0.198557) \| 0.383011 / 0.434364 (-0.051353) \| 0.438717 / 0.540337 (-0.101620) \| 0.527085 / 1.386936 (-0.859851) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n" ]
1,515,548,427	5,399	Got disconnected from remote data host. Retrying in 5sec [2/20]	closed	2023-01-01T13:00:11	2023-01-02T07:21:52	2023-01-02T07:21:52	https://github.com/huggingface/datasets/issues/5399	null	alhuri	false	[]
1,514,425,231	5,398	Unpin pydantic	closed	2022-12-30T10:37:31	2022-12-30T10:43:41	2022-12-30T10:43:41	https://github.com/huggingface/datasets/issues/5398	null	albertvillanova	false	[]
1,514,412,246	5,397	Unpin pydantic test dependency	closed	2022-12-30T10:22:09	2022-12-30T10:53:11	2022-12-30T10:43:40	https://github.com/huggingface/datasets/pull/5397	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5397", "html_url": "https://github.com/huggingface/datasets/pull/5397", "diff_url": "https://github.com/huggingface/datasets/pull/5397.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5397.patch", "merged_at": "2022-12-30T10:43:40" }	albertvillanova	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.012922 / 0.011353 (0.001569) \| 0.006568 / 0.011008 (-0.004440) \| 0.139567 / 0.038508 (0.101059) \| 0.039362 / 0.023109 (0.016253) \| 0.444238 / 0.275898 (0.168340) \| 0.529102 / 0.323480 (0.205622) \| 0.010275 / 0.007986 (0.002290) \| 0.006134 / 0.004328 (0.001805) \| 0.107506 / 0.004250 (0.103255) \| 0.047948 / 0.037052 (0.010896) \| 0.460469 / 0.258489 (0.201980) \| 0.516817 / 0.293841 (0.222976) \| 0.058637 / 0.128546 (-0.069909) \| 0.019516 / 0.075646 (-0.056130) \| 0.464111 / 0.419271 (0.044839) \| 0.062140 / 0.043533 (0.018607) \| 0.445004 / 0.255139 (0.189865) \| 0.460117 / 0.283200 (0.176917) \| 0.116591 / 0.141683 (-0.025092) \| 1.936834 / 1.452155 (0.484680) \| 1.941837 / 1.492716 (0.449120) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284130 / 0.018006 (0.266124) \| 0.588109 / 0.000490 (0.587619) \| 0.004383 / 0.000200 (0.004183) \| 0.000143 / 0.000054 (0.000089) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032984 / 0.037411 (-0.004427) \| 0.132811 / 0.014526 (0.118285) \| 0.150932 / 0.176557 (-0.025625) \| 0.203759 / 0.737135 (-0.533377) \| 0.149612 / 0.296338 (-0.146726) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.677666 / 0.215209 (0.462457) \| 6.627611 / 2.077655 (4.549956) \| 2.679526 / 1.504120 (1.175406) \| 2.272536 / 1.541195 (0.731342) \| 2.371179 / 1.468490 (0.902689) \| 1.205282 / 4.584777 (-3.379495) \| 5.733537 / 3.745712 (1.987825) \| 3.165279 / 5.269862 (-2.104583) \| 2.287918 / 4.565676 (-2.277759) \| 0.144581 / 0.424275 (-0.279695) \| 0.016812 / 0.007607 (0.009205) \| 0.841719 / 0.226044 (0.615675) \| 8.379119 / 2.268929 (6.110191) \| 3.507169 / 55.444624 (-51.937456) \| 2.756666 / 6.876477 (-4.119811) \| 2.814091 / 2.142072 (0.672018) \| 1.495835 / 4.805227 (-3.309392) \| 0.253651 / 6.500664 (-6.247013) \| 0.081258 / 0.075469 (0.005789) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.651586 / 1.841788 (-0.190202) \| 19.039628 / 8.074308 (10.965320) \| 21.269814 / 10.191392 (11.078421) \| 0.241024 / 0.680424 (-0.439400) \| 0.047975 / 0.534201 (-0.486225) \| 0.563727 / 0.579283 (-0.015556) \| 0.666808 / 0.434364 (0.232445) \| 0.661065 / 0.540337 (0.120728) \| 0.762884 / 1.386936 (-0.624052) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010141 / 0.011353 (-0.001212) \| 0.006216 / 0.011008 (-0.004792) \| 0.135491 / 0.038508 (0.096983) \| 0.035439 / 0.023109 (0.012330) \| 0.482789 / 0.275898 (0.206891) \| 0.520673 / 0.323480 (0.197193) \| 0.006358 / 0.007986 (-0.001627) \| 0.005432 / 0.004328 (0.001104) \| 0.094448 / 0.004250 (0.090197) \| 0.048379 / 0.037052 (0.011326) \| 0.509359 / 0.258489 (0.250870) \| 0.539583 / 0.293841 (0.245742) \| 0.054621 / 0.128546 (-0.073925) \| 0.021382 / 0.075646 (-0.054265) \| 0.435539 / 0.419271 (0.016267) \| 0.060630 / 0.043533 (0.017097) \| 0.469593 / 0.255139 (0.214454) \| 0.507838 / 0.283200 (0.224639) \| 0.112062 / 0.141683 (-0.029621) \| 1.829694 / 1.452155 (0.377539) \| 1.972266 / 1.492716 (0.479549) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291669 / 0.018006 (0.273663) \| 0.590104 / 0.000490 (0.589614) \| 0.000661 / 0.000200 (0.000461) \| 0.000082 / 0.000054 (0.000028) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034933 / 0.037411 (-0.002479) \| 0.134867 / 0.014526 (0.120341) \| 0.138892 / 0.176557 (-0.037665) \| 0.192619 / 0.737135 (-0.544516) \| 0.153787 / 0.296338 (-0.142551) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.666762 / 0.215209 (0.451553) \| 6.741736 / 2.077655 (4.664082) \| 2.988712 / 1.504120 (1.484592) \| 2.554823 / 1.541195 (1.013628) \| 2.655651 / 1.468490 (1.187161) \| 1.276603 / 4.584777 (-3.308174) \| 5.827960 / 3.745712 (2.082247) \| 5.046876 / 5.269862 (-0.222985) \| 2.829775 / 4.565676 (-1.735902) \| 0.151525 / 0.424275 (-0.272750) \| 0.016504 / 0.007607 (0.008897) \| 0.849749 / 0.226044 (0.623704) \| 8.331675 / 2.268929 (6.062747) \| 3.664529 / 55.444624 (-51.780096) \| 2.976495 / 6.876477 (-3.899982) \| 3.034737 / 2.142072 (0.892664) \| 1.499036 / 4.805227 (-3.306191) \| 0.261027 / 6.500664 (-6.239637) \| 0.088306 / 0.075469 (0.012837) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.693506 / 1.841788 (-0.148282) \| 18.939914 / 8.074308 (10.865605) \| 20.685460 / 10.191392 (10.494068) \| 0.218316 / 0.680424 (-0.462108) \| 0.029010 / 0.534201 (-0.505191) \| 0.565246 / 0.579283 (-0.014037) \| 0.633573 / 0.434364 (0.199209) \| 0.656895 / 0.540337 (0.116558) \| 0.781975 / 1.386936 (-0.604961) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n" ]
1,514,002,934	5,396	Fix checksum verification	closed	2022-12-29T19:45:17	2023-02-13T11:11:22	2023-02-13T11:11:22	https://github.com/huggingface/datasets/pull/5396	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5396", "html_url": "https://github.com/huggingface/datasets/pull/5396", "diff_url": "https://github.com/huggingface/datasets/pull/5396.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5396.patch", "merged_at": null }	daskol	true	[ "Hi ! If I'm not mistaken both `expected_checksums[url]` and `recorded_checksums[url]` are dictionaries with keys \"checksum\" and \"num_bytes\". So we need to check whether `expected_checksums[url] != recorded_checksums[url]` (or simply `expected_checksums[url][\"checksum\"] != recorded_checksums[url][\"checksum\"]`)\r\n\r\nBut in your fix you're checking `expected_checksums[url] != recorded_checksums[url]['checksum']`.\r\n\r\nSo I think it's fine to keep this as is", "No, the issue is that there is comparison of sclar value and dictionary.", "Acording to [`DatasetInfo`][1], we need specify a dictionary which maps a URL to a checksum as follows.\r\n\r\n```python\r\nCHECKSUMS = {\r\n URL: 'a5dc6bf63ea088ade6e98594bfa386f45211c38b2a3db3dd11b33bd530f3c481',\r\n}\r\n\r\nclass FancyDataset:\r\n def _info(self):\r\n return DatasetInfo(..., download_checksums=CHECKSUMS)\r\n```\r\n\r\nHowever, `load_dataset` fails with this checksum definition.\r\n\r\n[1]: https://github.com/huggingface/datasets/blob/main/src/datasets/info.py#L124-L125", "I think it has to be formatted like this right now. Maybe the DatasetInfo doc is unclear and we can improve it\r\n```python\r\nCHECKSUMS = {\r\n URL: {\"checksum\": checksum, \"num_bytes\": num_bytes},\r\n}\r\n```", "Right. I am not sure that this is a correct way to do it. People usually calculate sha256, md5, or whatever else but not size in bytes. Also, people use only some of checksum algorithms. This means that comparing dictionaries in `verify_checksums` is too strict (requires equality of all items) and raises compatibility issues in the future. Another issue is that a comparison of dictionaries assumes type constraints which imply type equality. \r\n\r\nSince almost noone uses checksums as far as I known, my PR suggests a minimal change to mitigate these issues except support of a specific checksum algorithm which is a separated feature and should be contributed in a separate PRs from my perspective.", "Applying this change will break the verification code, since the `expected_checksums` is a dict with those two keys.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5396). All of your documentation changes will be reflected on that endpoint." ]
1,513,997,335	5,395	Temporarily pin pydantic test dependency	closed	2022-12-29T19:34:19	2022-12-30T06:36:57	2022-12-29T21:00:26	https://github.com/huggingface/datasets/pull/5395	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5395", "html_url": "https://github.com/huggingface/datasets/pull/5395", "diff_url": "https://github.com/huggingface/datasets/pull/5395.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5395.patch", "merged_at": "2022-12-29T21:00:26" }	albertvillanova	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.012220 / 0.011353 (0.000867) \| 0.005943 / 0.011008 (-0.005065) \| 0.128223 / 0.038508 (0.089715) \| 0.037352 / 0.023109 (0.014242) \| 0.397143 / 0.275898 (0.121245) \| 0.483935 / 0.323480 (0.160455) \| 0.010279 / 0.007986 (0.002293) \| 0.004842 / 0.004328 (0.000513) \| 0.101403 / 0.004250 (0.097153) \| 0.042935 / 0.037052 (0.005883) \| 0.421642 / 0.258489 (0.163153) \| 0.456328 / 0.293841 (0.162487) \| 0.065639 / 0.128546 (-0.062907) \| 0.019820 / 0.075646 (-0.055826) \| 0.426090 / 0.419271 (0.006818) \| 0.069583 / 0.043533 (0.026051) \| 0.402662 / 0.255139 (0.147523) \| 0.428826 / 0.283200 (0.145626) \| 0.116760 / 0.141683 (-0.024923) \| 1.806216 / 1.452155 (0.354061) \| 1.852629 / 1.492716 (0.359913) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.226555 / 0.018006 (0.208548) \| 0.584693 / 0.000490 (0.584203) \| 0.008612 / 0.000200 (0.008412) \| 0.000205 / 0.000054 (0.000150) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028393 / 0.037411 (-0.009018) \| 0.123355 / 0.014526 (0.108829) \| 0.134423 / 0.176557 (-0.042133) \| 0.188536 / 0.737135 (-0.548600) \| 0.141595 / 0.296338 (-0.154743) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.589359 / 0.215209 (0.374150) \| 5.974655 / 2.077655 (3.897001) \| 2.465580 / 1.504120 (0.961460) \| 2.007618 / 1.541195 (0.466424) \| 2.078788 / 1.468490 (0.610298) \| 1.216646 / 4.584777 (-3.368131) \| 5.217516 / 3.745712 (1.471804) \| 3.107188 / 5.269862 (-2.162674) \| 2.251641 / 4.565676 (-2.314036) \| 0.138640 / 0.424275 (-0.285635) \| 0.015046 / 0.007607 (0.007439) \| 0.780092 / 0.226044 (0.554048) \| 7.749564 / 2.268929 (5.480635) \| 3.080708 / 55.444624 (-52.363917) \| 2.393897 / 6.876477 (-4.482579) \| 2.387738 / 2.142072 (0.245665) \| 1.458844 / 4.805227 (-3.346384) \| 0.252476 / 6.500664 (-6.248188) \| 0.076594 / 0.075469 (0.001125) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.540868 / 1.841788 (-0.300919) \| 17.295684 / 8.074308 (9.221376) \| 19.669300 / 10.191392 (9.477908) \| 0.250315 / 0.680424 (-0.430109) \| 0.045068 / 0.534201 (-0.489133) \| 0.538840 / 0.579283 (-0.040443) \| 0.584443 / 0.434364 (0.150079) \| 0.614476 / 0.540337 (0.074138) \| 0.729928 / 1.386936 (-0.657008) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009218 / 0.011353 (-0.002135) \| 0.006261 / 0.011008 (-0.004747) \| 0.125541 / 0.038508 (0.087033) \| 0.034405 / 0.023109 (0.011296) \| 0.468381 / 0.275898 (0.192483) \| 0.503336 / 0.323480 (0.179856) \| 0.006839 / 0.007986 (-0.001146) \| 0.004724 / 0.004328 (0.000396) \| 0.097875 / 0.004250 (0.093625) \| 0.051278 / 0.037052 (0.014225) \| 0.473323 / 0.258489 (0.214834) \| 0.537392 / 0.293841 (0.243551) \| 0.055588 / 0.128546 (-0.072958) \| 0.021041 / 0.075646 (-0.054605) \| 0.416952 / 0.419271 (-0.002320) \| 0.070128 / 0.043533 (0.026595) \| 0.465224 / 0.255139 (0.210085) \| 0.504678 / 0.283200 (0.221478) \| 0.112504 / 0.141683 (-0.029179) \| 1.865865 / 1.452155 (0.413710) \| 1.988296 / 1.492716 (0.495580) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.314170 / 0.018006 (0.296164) \| 0.526726 / 0.000490 (0.526236) \| 0.018691 / 0.000200 (0.018491) \| 0.000128 / 0.000054 (0.000073) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033772 / 0.037411 (-0.003639) \| 0.124796 / 0.014526 (0.110270) \| 0.134700 / 0.176557 (-0.041856) \| 0.190595 / 0.737135 (-0.546541) \| 0.143205 / 0.296338 (-0.153133) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.656708 / 0.215209 (0.441499) \| 6.470503 / 2.077655 (4.392848) \| 2.866430 / 1.504120 (1.362310) \| 2.506846 / 1.541195 (0.965651) \| 2.548669 / 1.468490 (1.080179) \| 1.226695 / 4.584777 (-3.358082) \| 5.117866 / 3.745712 (1.372153) \| 3.032822 / 5.269862 (-2.237040) \| 1.999152 / 4.565676 (-2.566524) \| 0.142974 / 0.424275 (-0.281301) \| 0.015011 / 0.007607 (0.007404) \| 0.799729 / 0.226044 (0.573684) \| 8.286313 / 2.268929 (6.017385) \| 3.636482 / 55.444624 (-51.808142) \| 2.888038 / 6.876477 (-3.988439) \| 2.924982 / 2.142072 (0.782910) \| 1.471996 / 4.805227 (-3.333231) \| 0.257119 / 6.500664 (-6.243545) \| 0.077294 / 0.075469 (0.001825) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.608290 / 1.841788 (-0.233497) \| 17.599119 / 8.074308 (9.524811) \| 18.917086 / 10.191392 (8.725694) \| 0.236237 / 0.680424 (-0.444187) \| 0.026061 / 0.534201 (-0.508140) \| 0.527359 / 0.579283 (-0.051925) \| 0.589176 / 0.434364 (0.154812) \| 0.602310 / 0.540337 (0.061973) \| 0.726756 / 1.386936 (-0.660180) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n", "Issue reported to `pydantic`: \r\n- https://github.com/pydantic/pydantic/issues/4885\r\n\r\nFixing PR at `pydantic`:\r\n- https://github.com/pydantic/pydantic/pull/4886" ]
1,513,976,229	5,394	CI error: TypeError: dataclass_transform() got an unexpected keyword argument 'field_specifiers'	closed	2022-12-29T18:58:44	2022-12-30T10:40:51	2022-12-29T21:00:27	https://github.com/huggingface/datasets/issues/5394	null	albertvillanova	false	[ "I still getting the same error :\r\n\r\n`python -m spacy download fr_core_news_lg\r\n`.\r\n`import spacy`", "@MFatnassi, this issue and the corresponding fix only affect our Continuous Integration testing environment.\r\n\r\nNote that `datasets` does not depend on `spacy`." ]
1,512,908,613	5,393	Finish deprecating the fs argument	closed	2022-12-28T15:33:17	2023-01-18T12:42:33	2023-01-18T12:35:32	https://github.com/huggingface/datasets/pull/5393	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5393", "html_url": "https://github.com/huggingface/datasets/pull/5393", "diff_url": "https://github.com/huggingface/datasets/pull/5393.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5393.patch", "merged_at": "2023-01-18T12:35:32" }	dconathan	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "> Thanks for the deprecation. Some minor suggested fixes below...\r\n> \r\n> Also note that the corresponding tests should be updated as well.\r\n\r\nThanks for the suggestions/typo fixes. I updated the failing test - passing locally now", "Nice thanks !\r\n\r\nI believe you also need to update `_load_info` and `_save_info` in `builder.py` - they're still passing `fs=self._fs` instead of `storage_options=self._fs.storage_options`\r\n\r\nThis should remove the remaining warnings in the CI such as \r\n\r\n```python\r\ntests/test_builder.py::test_builder_with_filesystem_download_and_prepare_reload\r\ntests/test_load.py::test_load_dataset_local[False]\r\ntests/test_load.py::test_load_dataset_local[True]\r\ntests/test_load.py::test_load_dataset_zip_csv[csv_path-False]\r\ntests/test_load.py::test_load_dataset_then_move_then_reload\r\n /opt/hostedtoolcache/Python/3.7.15/x64/lib/python3.7/site-packages/datasets/info.py:344: FutureWarning: 'fs' was deprecated in favor of 'storage_options' in version 2.9.0 and will be removed in 3.0.0.\r\n You can remove this warning by passing 'storage_options=fs.storage_options' instead.\r\n```", "re: docstring, I assume passing in `storage_options=s3.storage_options` is correct/necessary to pass the secrets?", "what about \r\nhttps://github.com/huggingface/datasets/blob/5b793dd8c43bf6e85f165238becb3c64f6cd3ed0/src/datasets/filesystems/__init__.py#L43-L54\r\nleave as is? Is this function no longer necessary?", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008877 / 0.011353 (-0.002475) \| 0.004725 / 0.011008 (-0.006283) \| 0.100738 / 0.038508 (0.062230) \| 0.030251 / 0.023109 (0.007141) \| 0.301483 / 0.275898 (0.025585) \| 0.374161 / 0.323480 (0.050681) \| 0.007225 / 0.007986 (-0.000761) \| 0.003654 / 0.004328 (-0.000674) \| 0.078400 / 0.004250 (0.074149) \| 0.035786 / 0.037052 (-0.001267) \| 0.309744 / 0.258489 (0.051255) \| 0.355834 / 0.293841 (0.061994) \| 0.034344 / 0.128546 (-0.094202) \| 0.011584 / 0.075646 (-0.064062) \| 0.321462 / 0.419271 (-0.097810) \| 0.041201 / 0.043533 (-0.002332) \| 0.298808 / 0.255139 (0.043669) \| 0.332626 / 0.283200 (0.049426) \| 0.089131 / 0.141683 (-0.052552) \| 1.477888 / 1.452155 (0.025734) \| 1.530365 / 1.492716 (0.037649) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.191647 / 0.018006 (0.173640) \| 0.424339 / 0.000490 (0.423849) \| 0.002941 / 0.000200 (0.002741) \| 0.000075 / 0.000054 (0.000020) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023442 / 0.037411 (-0.013969) \| 0.097264 / 0.014526 (0.082738) \| 0.105655 / 0.176557 (-0.070901) \| 0.145055 / 0.737135 (-0.592081) \| 0.108750 / 0.296338 (-0.187588) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.422925 / 0.215209 (0.207716) \| 4.216022 / 2.077655 (2.138367) \| 1.876441 / 1.504120 (0.372322) \| 1.665115 / 1.541195 (0.123920) \| 1.711105 / 1.468490 (0.242615) \| 0.701820 / 4.584777 (-3.882957) \| 3.389319 / 3.745712 (-0.356393) \| 1.909868 / 5.269862 (-3.359994) \| 1.270482 / 4.565676 (-3.295195) \| 0.083680 / 0.424275 (-0.340595) \| 0.012347 / 0.007607 (0.004740) \| 0.531076 / 0.226044 (0.305031) \| 5.344045 / 2.268929 (3.075117) \| 2.310897 / 55.444624 (-53.133728) \| 1.971953 / 6.876477 (-4.904524) \| 2.113748 / 2.142072 (-0.028325) \| 0.823766 / 4.805227 (-3.981462) \| 0.150864 / 6.500664 (-6.349800) \| 0.066263 / 0.075469 (-0.009206) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.253190 / 1.841788 (-0.588598) \| 13.757887 / 8.074308 (5.683579) \| 13.888195 / 10.191392 (3.696803) \| 0.137285 / 0.680424 (-0.543139) \| 0.029151 / 0.534201 (-0.505050) \| 0.387402 / 0.579283 (-0.191881) \| 0.401673 / 0.434364 (-0.032691) \| 0.450474 / 0.540337 (-0.089863) \| 0.533757 / 1.386936 (-0.853179) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006919 / 0.011353 (-0.004434) \| 0.004655 / 0.011008 (-0.006353) \| 0.096946 / 0.038508 (0.058438) \| 0.028697 / 0.023109 (0.005588) \| 0.420020 / 0.275898 (0.144122) \| 0.460193 / 0.323480 (0.136713) \| 0.005189 / 0.007986 (-0.002796) \| 0.003425 / 0.004328 (-0.000904) \| 0.074900 / 0.004250 (0.070649) \| 0.041844 / 0.037052 (0.004792) \| 0.421538 / 0.258489 (0.163049) \| 0.468497 / 0.293841 (0.174656) \| 0.032573 / 0.128546 (-0.095973) \| 0.011731 / 0.075646 (-0.063916) \| 0.320221 / 0.419271 (-0.099050) \| 0.042113 / 0.043533 (-0.001420) \| 0.422757 / 0.255139 (0.167618) \| 0.445372 / 0.283200 (0.162172) \| 0.090300 / 0.141683 (-0.051383) \| 1.458598 / 1.452155 (0.006443) \| 1.550060 / 1.492716 (0.057344) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.235489 / 0.018006 (0.217483) \| 0.418207 / 0.000490 (0.417718) \| 0.002511 / 0.000200 (0.002311) \| 0.000080 / 0.000054 (0.000025) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025603 / 0.037411 (-0.011808) \| 0.100237 / 0.014526 (0.085711) \| 0.108617 / 0.176557 (-0.067939) \| 0.148417 / 0.737135 (-0.588719) \| 0.110163 / 0.296338 (-0.186176) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.474804 / 0.215209 (0.259595) \| 4.745370 / 2.077655 (2.667715) \| 2.417819 / 1.504120 (0.913699) \| 2.209892 / 1.541195 (0.668697) \| 2.263296 / 1.468490 (0.794806) \| 0.695537 / 4.584777 (-3.889240) \| 3.381028 / 3.745712 (-0.364684) \| 2.952271 / 5.269862 (-2.317591) \| 1.507041 / 4.565676 (-3.058636) \| 0.083334 / 0.424275 (-0.340941) \| 0.012554 / 0.007607 (0.004947) \| 0.578861 / 0.226044 (0.352817) \| 5.795241 / 2.268929 (3.526313) \| 2.858544 / 55.444624 (-52.586080) \| 2.516270 / 6.876477 (-4.360207) \| 2.557350 / 2.142072 (0.415278) \| 0.801799 / 4.805227 (-4.003428) \| 0.151579 / 6.500664 (-6.349085) \| 0.068765 / 0.075469 (-0.006704) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.279935 / 1.841788 (-0.561853) \| 14.049065 / 8.074308 (5.974757) \| 13.972703 / 10.191392 (3.781311) \| 0.140551 / 0.680424 (-0.539873) \| 0.016831 / 0.534201 (-0.517370) \| 0.383886 / 0.579283 (-0.195397) \| 0.385661 / 0.434364 (-0.048703) \| 0.444525 / 0.540337 (-0.095813) \| 0.532197 / 1.386936 (-0.854739) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8d206848fb7afeafecf2a2581ca9a332bdedefa9 \"CML watermark\")\n" ]
1,512,712,529	5,392	Fix Colab notebook link	closed	2022-12-28T11:44:53	2023-01-03T15:36:14	2023-01-03T15:27:31	https://github.com/huggingface/datasets/pull/5392	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5392", "html_url": "https://github.com/huggingface/datasets/pull/5392", "diff_url": "https://github.com/huggingface/datasets/pull/5392.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5392.patch", "merged_at": "2023-01-03T15:27:31" }	albertvillanova	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.011196 / 0.011353 (-0.000157) \| 0.006039 / 0.011008 (-0.004969) \| 0.122497 / 0.038508 (0.083989) \| 0.043884 / 0.023109 (0.020774) \| 0.372982 / 0.275898 (0.097084) \| 0.444229 / 0.323480 (0.120749) \| 0.009489 / 0.007986 (0.001503) \| 0.004612 / 0.004328 (0.000284) \| 0.093921 / 0.004250 (0.089670) \| 0.052698 / 0.037052 (0.015646) \| 0.372327 / 0.258489 (0.113838) \| 0.426586 / 0.293841 (0.132745) \| 0.046755 / 0.128546 (-0.081792) \| 0.014848 / 0.075646 (-0.060799) \| 0.410474 / 0.419271 (-0.008798) \| 0.058206 / 0.043533 (0.014674) \| 0.367051 / 0.255139 (0.111912) \| 0.389950 / 0.283200 (0.106750) \| 0.120857 / 0.141683 (-0.020826) \| 1.795195 / 1.452155 (0.343040) \| 1.823938 / 1.492716 (0.331222) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.215199 / 0.018006 (0.197192) \| 0.482420 / 0.000490 (0.481930) \| 0.001834 / 0.000200 (0.001634) \| 0.000099 / 0.000054 (0.000044) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034483 / 0.037411 (-0.002928) \| 0.135503 / 0.014526 (0.120977) \| 0.149991 / 0.176557 (-0.026565) \| 0.198482 / 0.737135 (-0.538653) \| 0.153556 / 0.296338 (-0.142783) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.504492 / 0.215209 (0.289283) \| 4.950949 / 2.077655 (2.873294) \| 2.251186 / 1.504120 (0.747067) \| 2.049195 / 1.541195 (0.508000) \| 2.123325 / 1.468490 (0.654835) \| 0.865651 / 4.584777 (-3.719126) \| 4.652297 / 3.745712 (0.906585) \| 4.417260 / 5.269862 (-0.852602) \| 2.362390 / 4.565676 (-2.203287) \| 0.098845 / 0.424275 (-0.325430) \| 0.014675 / 0.007607 (0.007068) \| 0.608048 / 0.226044 (0.382003) \| 6.063863 / 2.268929 (3.794935) \| 2.753041 / 55.444624 (-52.691583) \| 2.340961 / 6.876477 (-4.535516) \| 2.511934 / 2.142072 (0.369862) \| 0.989297 / 4.805227 (-3.815930) \| 0.195770 / 6.500664 (-6.304894) \| 0.076027 / 0.075469 (0.000558) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.479617 / 1.841788 (-0.362170) \| 18.917860 / 8.074308 (10.843552) \| 18.219594 / 10.191392 (8.028202) \| 0.218494 / 0.680424 (-0.461930) \| 0.037207 / 0.534201 (-0.496994) \| 0.571543 / 0.579283 (-0.007741) \| 0.527884 / 0.434364 (0.093520) \| 0.658661 / 0.540337 (0.118324) \| 0.755449 / 1.386936 (-0.631487) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008762 / 0.011353 (-0.002591) \| 0.006019 / 0.011008 (-0.004989) \| 0.118756 / 0.038508 (0.080248) \| 0.039584 / 0.023109 (0.016474) \| 0.400127 / 0.275898 (0.124229) \| 0.468114 / 0.323480 (0.144634) \| 0.006771 / 0.007986 (-0.001215) \| 0.004689 / 0.004328 (0.000360) \| 0.087274 / 0.004250 (0.083023) \| 0.055548 / 0.037052 (0.018496) \| 0.419901 / 0.258489 (0.161412) \| 0.459516 / 0.293841 (0.165675) \| 0.044197 / 0.128546 (-0.084349) \| 0.014162 / 0.075646 (-0.061484) \| 0.409634 / 0.419271 (-0.009638) \| 0.058668 / 0.043533 (0.015135) \| 0.404758 / 0.255139 (0.149619) \| 0.431562 / 0.283200 (0.148363) \| 0.122361 / 0.141683 (-0.019322) \| 1.726597 / 1.452155 (0.274442) \| 1.798977 / 1.492716 (0.306260) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.250831 / 0.018006 (0.232825) \| 0.489811 / 0.000490 (0.489321) \| 0.000490 / 0.000200 (0.000290) \| 0.000071 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035666 / 0.037411 (-0.001745) \| 0.134899 / 0.014526 (0.120374) \| 0.153156 / 0.176557 (-0.023401) \| 0.202409 / 0.737135 (-0.534726) \| 0.157350 / 0.296338 (-0.138989) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.522464 / 0.215209 (0.307254) \| 5.204449 / 2.077655 (3.126794) \| 2.617410 / 1.504120 (1.113290) \| 2.406246 / 1.541195 (0.865052) \| 2.494487 / 1.468490 (1.025997) \| 0.834923 / 4.584777 (-3.749854) \| 4.794186 / 3.745712 (1.048474) \| 2.617939 / 5.269862 (-2.651922) \| 1.648310 / 4.565676 (-2.917367) \| 0.109785 / 0.424275 (-0.314490) \| 0.015217 / 0.007607 (0.007610) \| 0.682970 / 0.226044 (0.456926) \| 6.853894 / 2.268929 (4.584966) \| 3.277150 / 55.444624 (-52.167475) \| 2.832502 / 6.876477 (-4.043975) \| 2.984874 / 2.142072 (0.842802) \| 1.005307 / 4.805227 (-3.799921) \| 0.200623 / 6.500664 (-6.300041) \| 0.076852 / 0.075469 (0.001383) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.556656 / 1.841788 (-0.285131) \| 19.088978 / 8.074308 (11.014669) \| 16.946406 / 10.191392 (6.755014) \| 0.204419 / 0.680424 (-0.476004) \| 0.021456 / 0.534201 (-0.512745) \| 0.523603 / 0.579283 (-0.055680) \| 0.530067 / 0.434364 (0.095703) \| 0.604058 / 0.540337 (0.063721) \| 0.731531 / 1.386936 (-0.655405) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n" ]
1,510,350,400	5,391	Whisper Event - RuntimeError: The size of tensor a (504) must match the size of tensor b (448) at non-singleton dimension 1 100% 1000/1000 [2:52:21<00:00, 10.34s/it]	closed	2022-12-25T15:17:14	2023-07-21T14:29:47	2023-07-21T14:29:47	https://github.com/huggingface/datasets/issues/5391	null	catswithbats	false	[ "Hey @catswithbats! Super sorry for the late reply! This is happening because there is data with label length (504) that exceeds the model's max length (448). \r\n\r\nThere are two options here:\r\n1. Increase the model's `max_length` parameter: \r\n```python\r\nmodel.config.max_length = 512\r\n```\r\n2. Filter data with labels longer than max length: https://discuss.huggingface.co/t/open-to-the-community-whisper-fine-tuning-event/26681/21?u=sanchit-gandhi\r\n\r\nNote that the datasets repo is reserved for issues directly related to the HF datasets library. Issues related to custom fine-tuning implementations are more applicable to the HF Forum: https://discuss.huggingface.co. You're more likely to get a response by posting your issue in the most applicable place and boost the chance of someone sharing a working solution!", "@sanchit-gandhi Thank you for all your work on this topic.\r\n\r\nI'm finding that changing the `max_length` value does not make this error go away." ]
1,509,357,553	5,390	Error when pushing to the CI hub	closed	2022-12-23T13:36:37	2022-12-23T20:29:02	2022-12-23T20:29:02	https://github.com/huggingface/datasets/issues/5390	null	severo	false	[ "Hmmm, git bisect tells me that the behavior is the same since https://github.com/huggingface/datasets/commit/67e65c90e9490810b89ee140da11fdd13c356c9c (3 Oct), i.e. https://github.com/huggingface/datasets/pull/4926", "Maybe related to the discussions in https://github.com/huggingface/datasets/pull/5196", "Maybe the current version of moonlanding in Hub CI is the issue.\r\n\r\nI relaunched tests that were working two days ago: now they are failing. https://github.com/huggingface/datasets-server/commit/746414449cae4b311733f8a76e5b3b4ca73b38a9 for example\r\n\r\ncc @huggingface/moon-landing ", "Hi! I don't think this has anything to do with `datasets`. Hub CI seems to be the culprit - the identical failure can be found in [this](https://github.com/huggingface/datasets/pull/5389) PR (with unrelated changes) opened today.", "OK! Thanks for looking at it. Closing then." ]
1,509,348,626	5,389	Fix link in `load_dataset` docstring	closed	2022-12-23T13:26:31	2023-01-25T19:00:43	2023-01-24T16:33:38	https://github.com/huggingface/datasets/pull/5389	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5389", "html_url": "https://github.com/huggingface/datasets/pull/5389", "diff_url": "https://github.com/huggingface/datasets/pull/5389.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5389.patch", "merged_at": "2023-01-24T16:33:38" }	mariosasko	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008935 / 0.011353 (-0.002417) \| 0.004582 / 0.011008 (-0.006426) \| 0.100950 / 0.038508 (0.062442) \| 0.030305 / 0.023109 (0.007196) \| 0.299759 / 0.275898 (0.023861) \| 0.378577 / 0.323480 (0.055097) \| 0.007834 / 0.007986 (-0.000152) \| 0.003399 / 0.004328 (-0.000930) \| 0.078568 / 0.004250 (0.074318) \| 0.037990 / 0.037052 (0.000938) \| 0.313025 / 0.258489 (0.054536) \| 0.359543 / 0.293841 (0.065702) \| 0.033631 / 0.128546 (-0.094916) \| 0.011681 / 0.075646 (-0.063966) \| 0.324542 / 0.419271 (-0.094729) \| 0.041014 / 0.043533 (-0.002519) \| 0.302884 / 0.255139 (0.047745) \| 0.337059 / 0.283200 (0.053859) \| 0.089403 / 0.141683 (-0.052280) \| 1.491262 / 1.452155 (0.039108) \| 1.521626 / 1.492716 (0.028910) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.172627 / 0.018006 (0.154621) \| 0.419406 / 0.000490 (0.418917) \| 0.001974 / 0.000200 (0.001775) \| 0.000070 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023598 / 0.037411 (-0.013814) \| 0.098127 / 0.014526 (0.083601) \| 0.105611 / 0.176557 (-0.070946) \| 0.142612 / 0.737135 (-0.594523) \| 0.121687 / 0.296338 (-0.174651) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.418512 / 0.215209 (0.203303) \| 4.173099 / 2.077655 (2.095444) \| 1.865900 / 1.504120 (0.361780) \| 1.664053 / 1.541195 (0.122858) \| 1.726289 / 1.468490 (0.257799) \| 0.693214 / 4.584777 (-3.891563) \| 3.499982 / 3.745712 (-0.245730) \| 1.894278 / 5.269862 (-3.375583) \| 1.178214 / 4.565676 (-3.387463) \| 0.082391 / 0.424275 (-0.341884) \| 0.012486 / 0.007607 (0.004878) \| 0.532190 / 0.226044 (0.306145) \| 5.286612 / 2.268929 (3.017684) \| 2.316680 / 55.444624 (-53.127944) \| 1.964020 / 6.876477 (-4.912457) \| 2.016457 / 2.142072 (-0.125616) \| 0.812290 / 4.805227 (-3.992937) \| 0.149102 / 6.500664 (-6.351562) \| 0.064215 / 0.075469 (-0.011254) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.281919 / 1.841788 (-0.559869) \| 14.107509 / 8.074308 (6.033201) \| 13.892369 / 10.191392 (3.700977) \| 0.146164 / 0.680424 (-0.534260) \| 0.028740 / 0.534201 (-0.505460) \| 0.395218 / 0.579283 (-0.184066) \| 0.406321 / 0.434364 (-0.028043) \| 0.460880 / 0.540337 (-0.079458) \| 0.545975 / 1.386936 (-0.840961) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006797 / 0.011353 (-0.004556) \| 0.004522 / 0.011008 (-0.006486) \| 0.098440 / 0.038508 (0.059932) \| 0.027722 / 0.023109 (0.004613) \| 0.423995 / 0.275898 (0.148097) \| 0.456164 / 0.323480 (0.132684) \| 0.005156 / 0.007986 (-0.002830) \| 0.003439 / 0.004328 (-0.000889) \| 0.075307 / 0.004250 (0.071057) \| 0.039599 / 0.037052 (0.002547) \| 0.423671 / 0.258489 (0.165181) \| 0.463841 / 0.293841 (0.170001) \| 0.032473 / 0.128546 (-0.096073) \| 0.011674 / 0.075646 (-0.063972) \| 0.320548 / 0.419271 (-0.098723) \| 0.041618 / 0.043533 (-0.001915) \| 0.426133 / 0.255139 (0.170994) \| 0.443018 / 0.283200 (0.159819) \| 0.091103 / 0.141683 (-0.050579) \| 1.468758 / 1.452155 (0.016604) \| 1.532695 / 1.492716 (0.039978) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.255314 / 0.018006 (0.237308) \| 0.422982 / 0.000490 (0.422492) \| 0.015405 / 0.000200 (0.015205) \| 0.000103 / 0.000054 (0.000049) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025260 / 0.037411 (-0.012152) \| 0.102062 / 0.014526 (0.087537) \| 0.108161 / 0.176557 (-0.068395) \| 0.144205 / 0.737135 (-0.592930) \| 0.111686 / 0.296338 (-0.184653) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.482633 / 0.215209 (0.267424) \| 4.824777 / 2.077655 (2.747123) \| 2.488626 / 1.504120 (0.984506) \| 2.285410 / 1.541195 (0.744215) \| 2.336793 / 1.468490 (0.868303) \| 0.701894 / 4.584777 (-3.882883) \| 3.506908 / 3.745712 (-0.238804) \| 3.399789 / 5.269862 (-1.870072) \| 1.536359 / 4.565676 (-3.029317) \| 0.083621 / 0.424275 (-0.340655) \| 0.012702 / 0.007607 (0.005094) \| 0.581259 / 0.226044 (0.355215) \| 5.829640 / 2.268929 (3.560711) \| 2.932201 / 55.444624 (-52.512424) \| 2.577175 / 6.876477 (-4.299301) \| 2.621782 / 2.142072 (0.479710) \| 0.812074 / 4.805227 (-3.993153) \| 0.152840 / 6.500664 (-6.347824) \| 0.067982 / 0.075469 (-0.007487) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.274915 / 1.841788 (-0.566873) \| 14.345800 / 8.074308 (6.271492) \| 14.242475 / 10.191392 (4.051083) \| 0.143636 / 0.680424 (-0.536788) \| 0.016824 / 0.534201 (-0.517377) \| 0.376449 / 0.579283 (-0.202834) \| 0.394219 / 0.434364 (-0.040145) \| 0.435368 / 0.540337 (-0.104969) \| 0.518393 / 1.386936 (-0.868544) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#187e4faa978fef267a055f6988564f922e51eaa4 \"CML watermark\")\n", "I also fixed the rest of the links that point to the markdown files. \r\n\r\nPS: the CI failures are unrelated ", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008641 / 0.011353 (-0.002712) \| 0.004560 / 0.011008 (-0.006448) \| 0.100559 / 0.038508 (0.062051) \| 0.029744 / 0.023109 (0.006635) \| 0.300580 / 0.275898 (0.024682) \| 0.359100 / 0.323480 (0.035620) \| 0.007016 / 0.007986 (-0.000970) \| 0.003393 / 0.004328 (-0.000936) \| 0.078649 / 0.004250 (0.074399) \| 0.038138 / 0.037052 (0.001086) \| 0.307730 / 0.258489 (0.049241) \| 0.347678 / 0.293841 (0.053837) \| 0.033630 / 0.128546 (-0.094917) \| 0.011452 / 0.075646 (-0.064194) \| 0.320903 / 0.419271 (-0.098369) \| 0.042659 / 0.043533 (-0.000874) \| 0.298886 / 0.255139 (0.043747) \| 0.324371 / 0.283200 (0.041171) \| 0.092582 / 0.141683 (-0.049101) \| 1.490017 / 1.452155 (0.037863) \| 1.512825 / 1.492716 (0.020109) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.178965 / 0.018006 (0.160958) \| 0.420001 / 0.000490 (0.419512) \| 0.002686 / 0.000200 (0.002486) \| 0.000071 / 0.000054 (0.000017) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023568 / 0.037411 (-0.013843) \| 0.097027 / 0.014526 (0.082502) \| 0.104721 / 0.176557 (-0.071836) \| 0.148757 / 0.737135 (-0.588378) \| 0.110849 / 0.296338 (-0.185489) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.415034 / 0.215209 (0.199825) \| 4.155249 / 2.077655 (2.077594) \| 1.837027 / 1.504120 (0.332907) \| 1.627754 / 1.541195 (0.086559) \| 1.687958 / 1.468490 (0.219468) \| 0.699542 / 4.584777 (-3.885235) \| 3.376707 / 3.745712 (-0.369005) \| 2.900778 / 5.269862 (-2.369083) \| 1.556168 / 4.565676 (-3.009508) \| 0.082438 / 0.424275 (-0.341837) \| 0.012339 / 0.007607 (0.004732) \| 0.524952 / 0.226044 (0.298907) \| 5.269852 / 2.268929 (3.000924) \| 2.278770 / 55.444624 (-53.165854) \| 1.917987 / 6.876477 (-4.958490) \| 1.955000 / 2.142072 (-0.187072) \| 0.821169 / 4.805227 (-3.984058) \| 0.149019 / 6.500664 (-6.351645) \| 0.064604 / 0.075469 (-0.010865) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.199768 / 1.841788 (-0.642020) \| 13.760897 / 8.074308 (5.686589) \| 13.911550 / 10.191392 (3.720158) \| 0.161727 / 0.680424 (-0.518697) \| 0.028615 / 0.534201 (-0.505586) \| 0.393917 / 0.579283 (-0.185366) \| 0.392524 / 0.434364 (-0.041840) \| 0.451763 / 0.540337 (-0.088574) \| 0.536880 / 1.386936 (-0.850056) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006407 / 0.011353 (-0.004946) \| 0.004420 / 0.011008 (-0.006588) \| 0.097244 / 0.038508 (0.058736) \| 0.027114 / 0.023109 (0.004005) \| 0.412512 / 0.275898 (0.136614) \| 0.448189 / 0.323480 (0.124709) \| 0.005831 / 0.007986 (-0.002155) \| 0.005423 / 0.004328 (0.001095) \| 0.076051 / 0.004250 (0.071801) \| 0.038828 / 0.037052 (0.001776) \| 0.414586 / 0.258489 (0.156097) \| 0.457196 / 0.293841 (0.163355) \| 0.031615 / 0.128546 (-0.096931) \| 0.011542 / 0.075646 (-0.064104) \| 0.316967 / 0.419271 (-0.102304) \| 0.041278 / 0.043533 (-0.002254) \| 0.411371 / 0.255139 (0.156232) \| 0.436376 / 0.283200 (0.153177) \| 0.090212 / 0.141683 (-0.051471) \| 1.461831 / 1.452155 (0.009677) \| 1.606515 / 1.492716 (0.113799) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.221453 / 0.018006 (0.203447) \| 0.404140 / 0.000490 (0.403650) \| 0.000422 / 0.000200 (0.000222) \| 0.000060 / 0.000054 (0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024588 / 0.037411 (-0.012824) \| 0.098604 / 0.014526 (0.084078) \| 0.113682 / 0.176557 (-0.062874) \| 0.141141 / 0.737135 (-0.595994) \| 0.110069 / 0.296338 (-0.186270) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.477267 / 0.215209 (0.262058) \| 4.775086 / 2.077655 (2.697431) \| 2.445449 / 1.504120 (0.941329) \| 2.242220 / 1.541195 (0.701025) \| 2.303542 / 1.468490 (0.835051) \| 0.693448 / 4.584777 (-3.891329) \| 3.413319 / 3.745712 (-0.332393) \| 3.052734 / 5.269862 (-2.217127) \| 1.434075 / 4.565676 (-3.131602) \| 0.082429 / 0.424275 (-0.341846) \| 0.012594 / 0.007607 (0.004987) \| 0.584259 / 0.226044 (0.358214) \| 5.865098 / 2.268929 (3.596169) \| 2.926301 / 55.444624 (-52.518324) \| 2.572555 / 6.876477 (-4.303921) \| 2.608584 / 2.142072 (0.466512) \| 0.805029 / 4.805227 (-4.000198) \| 0.151247 / 6.500664 (-6.349417) \| 0.067142 / 0.075469 (-0.008327) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.285454 / 1.841788 (-0.556334) \| 14.296425 / 8.074308 (6.222117) \| 14.147278 / 10.191392 (3.955886) \| 0.151698 / 0.680424 (-0.528726) \| 0.016876 / 0.534201 (-0.517325) \| 0.383302 / 0.579283 (-0.195981) \| 0.388461 / 0.434364 (-0.045902) \| 0.438286 / 0.540337 (-0.102051) \| 0.525249 / 1.386936 (-0.861687) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2a3b2f04f1fd62249ac43c534761ce151ad5c269 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008677 / 0.011353 (-0.002676) \| 0.004863 / 0.011008 (-0.006145) \| 0.096606 / 0.038508 (0.058098) \| 0.034004 / 0.023109 (0.010895) \| 0.296362 / 0.275898 (0.020464) \| 0.323445 / 0.323480 (-0.000035) \| 0.007341 / 0.007986 (-0.000644) \| 0.005518 / 0.004328 (0.001189) \| 0.073584 / 0.004250 (0.069334) \| 0.041471 / 0.037052 (0.004419) \| 0.302183 / 0.258489 (0.043694) \| 0.339369 / 0.293841 (0.045528) \| 0.037375 / 0.128546 (-0.091171) \| 0.011827 / 0.075646 (-0.063819) \| 0.330723 / 0.419271 (-0.088549) \| 0.048751 / 0.043533 (0.005218) \| 0.298370 / 0.255139 (0.043231) \| 0.317781 / 0.283200 (0.034582) \| 0.097488 / 0.141683 (-0.044195) \| 1.456242 / 1.452155 (0.004088) \| 1.530149 / 1.492716 (0.037433) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.207053 / 0.018006 (0.189046) \| 0.438165 / 0.000490 (0.437675) \| 0.001161 / 0.000200 (0.000961) \| 0.000078 / 0.000054 (0.000024) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025353 / 0.037411 (-0.012059) \| 0.105536 / 0.014526 (0.091010) \| 0.116122 / 0.176557 (-0.060434) \| 0.151605 / 0.737135 (-0.585530) \| 0.121777 / 0.296338 (-0.174561) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.402780 / 0.215209 (0.187571) \| 4.017882 / 2.077655 (1.940227) \| 1.813111 / 1.504120 (0.308991) \| 1.620000 / 1.541195 (0.078805) \| 1.649186 / 1.468490 (0.180696) \| 0.687523 / 4.584777 (-3.897254) \| 3.712595 / 3.745712 (-0.033117) \| 2.038535 / 5.269862 (-3.231326) \| 1.414794 / 4.565676 (-3.150882) \| 0.083357 / 0.424275 (-0.340918) \| 0.012032 / 0.007607 (0.004425) \| 0.502899 / 0.226044 (0.276854) \| 5.038914 / 2.268929 (2.769985) \| 2.250476 / 55.444624 (-53.194148) \| 1.919954 / 6.876477 (-4.956523) \| 1.930928 / 2.142072 (-0.211144) \| 0.826634 / 4.805227 (-3.978593) \| 0.161599 / 6.500664 (-6.339066) \| 0.061356 / 0.075469 (-0.014113) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.228998 / 1.841788 (-0.612790) \| 14.587914 / 8.074308 (6.513606) \| 14.237514 / 10.191392 (4.046122) \| 0.190913 / 0.680424 (-0.489510) \| 0.029104 / 0.534201 (-0.505097) \| 0.436160 / 0.579283 (-0.143123) \| 0.431464 / 0.434364 (-0.002900) \| 0.511670 / 0.540337 (-0.028668) \| 0.609046 / 1.386936 (-0.777890) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006980 / 0.011353 (-0.004373) \| 0.005260 / 0.011008 (-0.005748) \| 0.095288 / 0.038508 (0.056780) \| 0.032465 / 0.023109 (0.009356) \| 0.410799 / 0.275898 (0.134901) \| 0.423814 / 0.323480 (0.100334) \| 0.005533 / 0.007986 (-0.002452) \| 0.005764 / 0.004328 (0.001436) \| 0.070713 / 0.004250 (0.066462) \| 0.048193 / 0.037052 (0.011141) \| 0.405742 / 0.258489 (0.147253) \| 0.458773 / 0.293841 (0.164932) \| 0.036415 / 0.128546 (-0.092131) \| 0.012192 / 0.075646 (-0.063454) \| 0.330655 / 0.419271 (-0.088617) \| 0.055945 / 0.043533 (0.012412) \| 0.407497 / 0.255139 (0.152358) \| 0.421496 / 0.283200 (0.138296) \| 0.106285 / 0.141683 (-0.035398) \| 1.459837 / 1.452155 (0.007683) \| 1.573147 / 1.492716 (0.080431) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.205776 / 0.018006 (0.187770) \| 0.441523 / 0.000490 (0.441033) \| 0.003073 / 0.000200 (0.002873) \| 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) \| 0.029207 / 0.037411 (-0.008205) \| 0.110295 / 0.014526 (0.095770) \| 0.130233 / 0.176557 (-0.046324) \| 0.157489 / 0.737135 (-0.579647) \| 0.125374 / 0.296338 (-0.170965) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.440942 / 0.215209 (0.225733) \| 4.389647 / 2.077655 (2.311992) \| 2.234883 / 1.504120 (0.730763) \| 2.029510 / 1.541195 (0.488315) \| 2.082503 / 1.468490 (0.614013) \| 0.698046 / 4.584777 (-3.886731) \| 3.769127 / 3.745712 (0.023415) \| 2.058511 / 5.269862 (-3.211351) \| 1.324302 / 4.565676 (-3.241375) \| 0.085695 / 0.424275 (-0.338580) \| 0.012122 / 0.007607 (0.004515) \| 0.552406 / 0.226044 (0.326362) \| 5.527073 / 2.268929 (3.258145) \| 2.711354 / 55.444624 (-52.733270) \| 2.328848 / 6.876477 (-4.547629) \| 2.340750 / 2.142072 (0.198678) \| 0.846300 / 4.805227 (-3.958927) \| 0.167465 / 6.500664 (-6.333199) \| 0.063419 / 0.075469 (-0.012050) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.262452 / 1.841788 (-0.579336) \| 15.043537 / 8.074308 (6.969229) \| 14.212563 / 10.191392 (4.021171) \| 0.170229 / 0.680424 (-0.510194) \| 0.017696 / 0.534201 (-0.516505) \| 0.423194 / 0.579283 (-0.156089) \| 0.430908 / 0.434364 (-0.003456) \| 0.491733 / 0.540337 (-0.048604) \| 0.599267 / 1.386936 (-0.787669) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2a3b2f04f1fd62249ac43c534761ce151ad5c269 \"CML watermark\")\n", "Program enthusiastic " ]
1,509,042,348	5,388	Getting Value Error while loading a dataset..	closed	2022-12-23T08:16:43	2022-12-29T08:36:33	2022-12-27T17:59:09	https://github.com/huggingface/datasets/issues/5388	null	valmetisrinivas	false	[ "Hi! I can't reproduce this error locally (Mac) or in Colab. What version of `datasets` are you using?", "Hi [mariosasko](https://github.com/mariosasko), the datasets version is '2.8.0'.", "@valmetisrinivas you get that error because you imported `datasets` (and thus `fsspec`) before installing `zstandard`.\r\n\r\nPlease, restart your Colab runtime and execute the install commands before importing `datasets`:\r\n```python\r\n!pip install datasets\r\n!pip install zstandard\r\n\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\r\n \"json\",\r\n data_files=\"https://the-eye.eu/public/AI/pile_preliminary_components/FreeLaw_Opinions.jsonl.zst\",\r\n split=\"train\",\r\n streaming=True,\r\n)\r\nnext(iter(ds))\r\n```", "> @valmetisrinivas you get that error because you imported `datasets` (and thus `fsspec`) before installing `zstandard`.\r\n> \r\n> Please, restart your Colab runtime and execute the install commands before importing `datasets`:\r\n> \r\n> ```python\r\n> !pip install datasets\r\n> !pip install zstandard\r\n> \r\n> from datasets import load_dataset\r\n> \r\n> ds = load_dataset(\r\n> \"json\",\r\n> data_files=\"https://the-eye.eu/public/AI/pile_preliminary_components/FreeLaw_Opinions.jsonl.zst\",\r\n> split=\"train\",\r\n> streaming=True,\r\n> )\r\n> next(iter(ds))\r\n> ```\r\n\r\nI guess that was the problem, importing datasets before the installation of zstandard. Thank you for the feedback. " ]
1,508,740,177	5,387	Missing documentation page : improve-performance	closed	2022-12-23T01:12:57	2023-01-24T16:33:40	2023-01-24T16:33:40	https://github.com/huggingface/datasets/issues/5387	null	astariul	false	[ "Hi! Our documentation builder does not support links to sections, hence the bug. This is the link it should point to https://huggingface.co/docs/datasets/v2.8.0/en/cache#improve-performance." ]
1,508,592,918	5,386	`max_shard_size` in `datasets.push_to_hub()` breaks with large files	closed	2022-12-22T21:50:58	2022-12-26T23:45:51	2022-12-26T23:45:51	https://github.com/huggingface/datasets/issues/5386	null	salieri	false	[ "Hi! \r\n\r\nThis behavior stems from the fact that we don't always embed image bytes in the underlying arrow table, which can lead to bad size estimation (we use the first 1000 table rows to [estimate](https://github.com/huggingface/datasets/blob/9a7272cd4222383a5b932b0083a4cc173fda44e8/src/datasets/arrow_dataset.py#L4627) the external file size). We plan to address this in the next major release by always embedding external bytes. In the meantime, you can either shuffle the dataset with `.shuffle().flatten_indices()` to make the estimation more precise or embed the bytes in the table like so:\r\n```python\r\nfrom datasets.table import embed_table_storage\r\nformat = ds.format\r\nds = ds.with_format(\"arrow\")\r\nds = ds.map(embed_table_storage, batched=True)\r\nds = ds.with_format(**format)\r\n...\r\nds.push_to_hub(...)\r\n```", "Embedding the bytes worked like charm. Thanks @mariosasko!" ]
1,508,535,532	5,385	Is `fs=` deprecated in `load_from_disk()` as well?	closed	2022-12-22T21:00:45	2023-01-23T10:50:05	2023-01-23T10:50:04	https://github.com/huggingface/datasets/issues/5385	null	dconathan	false	[ "Hi! Yes, we should deprecate the `fs` param here. Would you be interested in submitting a PR? ", "> Hi! Yes, we should deprecate the `fs` param here. Would you be interested in submitting a PR?\r\n\r\nYeah I can do that sometime next week. Should the storage_options be a new arg here? I’ll look around for anywhere else where fs is an arg.", "Closed by #5393." ]
1,508,152,598	5,384	Handle 0-dim tensors in `cast_to_python_objects`	closed	2022-12-22T16:15:30	2023-01-13T16:10:15	2023-01-13T16:00:52	https://github.com/huggingface/datasets/pull/5384	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5384", "html_url": "https://github.com/huggingface/datasets/pull/5384", "diff_url": "https://github.com/huggingface/datasets/pull/5384.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5384.patch", "merged_at": "2023-01-13T16:00:52" }	mariosasko	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010576 / 0.011353 (-0.000777) \| 0.006010 / 0.011008 (-0.004998) \| 0.109375 / 0.038508 (0.070867) \| 0.037780 / 0.023109 (0.014670) \| 0.381552 / 0.275898 (0.105654) \| 0.446039 / 0.323480 (0.122559) \| 0.009004 / 0.007986 (0.001019) \| 0.005653 / 0.004328 (0.001324) \| 0.087027 / 0.004250 (0.082776) \| 0.040346 / 0.037052 (0.003293) \| 0.398827 / 0.258489 (0.140338) \| 0.407281 / 0.293841 (0.113440) \| 0.051723 / 0.128546 (-0.076824) \| 0.020254 / 0.075646 (-0.055392) \| 0.376841 / 0.419271 (-0.042430) \| 0.055505 / 0.043533 (0.011972) \| 0.383464 / 0.255139 (0.128325) \| 0.436130 / 0.283200 (0.152930) \| 0.117403 / 0.141683 (-0.024280) \| 1.569016 / 1.452155 (0.116862) \| 1.889831 / 1.492716 (0.397115) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297962 / 0.018006 (0.279956) \| 0.683699 / 0.000490 (0.683210) \| 0.000918 / 0.000200 (0.000718) \| 0.000100 / 0.000054 (0.000045) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026742 / 0.037411 (-0.010669) \| 0.125293 / 0.014526 (0.110768) \| 0.128769 / 0.176557 (-0.047787) \| 0.179447 / 0.737135 (-0.557688) \| 0.142032 / 0.296338 (-0.154306) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.588389 / 0.215209 (0.373180) \| 5.943514 / 2.077655 (3.865859) \| 2.631163 / 1.504120 (1.127043) \| 1.865446 / 1.541195 (0.324252) \| 2.055610 / 1.468490 (0.587120) \| 1.090288 / 4.584777 (-3.494489) \| 5.457151 / 3.745712 (1.711439) \| 5.645614 / 5.269862 (0.375752) \| 2.849492 / 4.565676 (-1.716184) \| 0.140447 / 0.424275 (-0.283828) \| 0.015421 / 0.007607 (0.007813) \| 0.735528 / 0.226044 (0.509484) \| 7.394097 / 2.268929 (5.125169) \| 3.219714 / 55.444624 (-52.224911) \| 2.504134 / 6.876477 (-4.372342) \| 2.524291 / 2.142072 (0.382219) \| 1.452776 / 4.805227 (-3.352452) \| 0.256142 / 6.500664 (-6.244522) \| 0.093809 / 0.075469 (0.018340) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.570046 / 1.841788 (-0.271742) \| 17.360385 / 8.074308 (9.286077) \| 20.750595 / 10.191392 (10.559203) \| 0.218486 / 0.680424 (-0.461938) \| 0.048527 / 0.534201 (-0.485674) \| 0.549568 / 0.579283 (-0.029715) \| 0.633993 / 0.434364 (0.199629) \| 0.632585 / 0.540337 (0.092248) \| 0.712817 / 1.386936 (-0.674119) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010524 / 0.011353 (-0.000829) \| 0.006307 / 0.011008 (-0.004701) \| 0.129671 / 0.038508 (0.091162) \| 0.038952 / 0.023109 (0.015842) \| 0.421936 / 0.275898 (0.146038) \| 0.489911 / 0.323480 (0.166431) \| 0.007661 / 0.007986 (-0.000325) \| 0.005430 / 0.004328 (0.001102) \| 0.091851 / 0.004250 (0.087600) \| 0.059755 / 0.037052 (0.022703) \| 0.449810 / 0.258489 (0.191321) \| 0.519498 / 0.293841 (0.225657) \| 0.061644 / 0.128546 (-0.066902) \| 0.018950 / 0.075646 (-0.056696) \| 0.399149 / 0.419271 (-0.020122) \| 0.067670 / 0.043533 (0.024137) \| 0.441091 / 0.255139 (0.185952) \| 0.459327 / 0.283200 (0.176128) \| 0.122476 / 0.141683 (-0.019207) \| 1.760129 / 1.452155 (0.307974) \| 1.767945 / 1.492716 (0.275228) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.276675 / 0.018006 (0.258669) \| 0.606798 / 0.000490 (0.606308) \| 0.000449 / 0.000200 (0.000249) \| 0.000078 / 0.000054 (0.000023) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027762 / 0.037411 (-0.009649) \| 0.108330 / 0.014526 (0.093805) \| 0.134714 / 0.176557 (-0.041843) \| 0.175666 / 0.737135 (-0.561470) \| 0.134917 / 0.296338 (-0.161421) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.676756 / 0.215209 (0.461547) \| 6.746519 / 2.077655 (4.668864) \| 2.660869 / 1.504120 (1.156750) \| 2.273688 / 1.541195 (0.732494) \| 2.392580 / 1.468490 (0.924090) \| 1.127848 / 4.584777 (-3.456929) \| 5.356499 / 3.745712 (1.610787) \| 2.933006 / 5.269862 (-2.336855) \| 1.872877 / 4.565676 (-2.692799) \| 0.139504 / 0.424275 (-0.284771) \| 0.013501 / 0.007607 (0.005894) \| 0.749888 / 0.226044 (0.523843) \| 8.157031 / 2.268929 (5.888103) \| 3.627751 / 55.444624 (-51.816874) \| 2.713152 / 6.876477 (-4.163324) \| 2.934585 / 2.142072 (0.792512) \| 1.376398 / 4.805227 (-3.428829) \| 0.251537 / 6.500664 (-6.249127) \| 0.083995 / 0.075469 (0.008526) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.635446 / 1.841788 (-0.206342) \| 18.435807 / 8.074308 (10.361498) \| 21.395291 / 10.191392 (11.203899) \| 0.247238 / 0.680424 (-0.433186) \| 0.030503 / 0.534201 (-0.503698) \| 0.553096 / 0.579283 (-0.026187) \| 0.597583 / 0.434364 (0.163219) \| 0.594135 / 0.540337 (0.053797) \| 0.673815 / 1.386936 (-0.713122) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n" ]
1,507,293,968	5,383	IterableDataset missing column_names, differs from Dataset interface	closed	2022-12-22T05:27:02	2023-03-13T19:03:33	2023-03-13T19:03:33	https://github.com/huggingface/datasets/issues/5383	null	iceboundflame	false	[ "Another example is that `IterableDataset.map` does not have `fn_kwargs`, among other arguments. It makes it harder to convert code from Dataset to IterableDataset.", "Hi! `fn_kwargs` was added to `IterableDataset.map` in `datasets 2.5.0`, so please update your installation (`pip install -U datasets`) to use it.\r\n\r\nRegarding `column_names`, I agree we should add this property to `IterableDataset`. In the meantime, you can use `list(dataset.features.keys())` instead.", "Thanks! That's great news.\n\nOn Thu, Dec 22, 2022, 07:48 Mario Šaško *@.> wrote:\n\n> Hi! fn_kwargs was added to IterableDataset.map in datasets 2.5.0, so\n> please update your installation (pip install -U datasets) to use it.\n>\n> Regarding column_names, I agree we should add this property to\n> IterableDataset. In the meantime, you can use\n> list(dataset.features.keys()) instead.\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/5383#issuecomment-1362993633>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AAHD6N2EQUFEOUFDW3VHSILWORZ45ANCNFSM6AAAAAATGKWVGM>\n> .\n> You are receiving this because you authored the thread.Message ID:\n> @.*>\n>\n", "I'm marking this issue as a \"good first issue\", as it makes sense to have `IterableDataset.column_names` in the API. Besides the case when `features` are `None` (e.g., `features` are `None` after `map`), in which we can also return `column_names` as `None`, adding this property should be straightforward,", "Hi @mariosasko, I can work on this if that's ok?", "Yes! I've assigned you the issue." ]
1,504,788,691	5,382	Raise from disconnect error in xopen	closed	2022-12-20T15:52:44	2023-01-26T09:51:13	2023-01-26T09:42:45	https://github.com/huggingface/datasets/pull/5382	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5382", "html_url": "https://github.com/huggingface/datasets/pull/5382", "diff_url": "https://github.com/huggingface/datasets/pull/5382.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5382.patch", "merged_at": "2023-01-26T09:42:45" }	lhoestq	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "Could you review this small PR @albertvillanova ? :)", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.011200 / 0.011353 (-0.000153) \| 0.006156 / 0.011008 (-0.004852) \| 0.119072 / 0.038508 (0.080564) \| 0.042616 / 0.023109 (0.019507) \| 0.348329 / 0.275898 (0.072431) \| 0.418550 / 0.323480 (0.095070) \| 0.009302 / 0.007986 (0.001316) \| 0.004596 / 0.004328 (0.000267) \| 0.090111 / 0.004250 (0.085860) \| 0.053341 / 0.037052 (0.016289) \| 0.361234 / 0.258489 (0.102745) \| 0.400427 / 0.293841 (0.106586) \| 0.045601 / 0.128546 (-0.082945) \| 0.013806 / 0.075646 (-0.061841) \| 0.393178 / 0.419271 (-0.026094) \| 0.056809 / 0.043533 (0.013276) \| 0.344090 / 0.255139 (0.088951) \| 0.370610 / 0.283200 (0.087410) \| 0.125728 / 0.141683 (-0.015955) \| 1.671931 / 1.452155 (0.219776) \| 1.703143 / 1.492716 (0.210427) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.226534 / 0.018006 (0.208527) \| 0.496487 / 0.000490 (0.495998) \| 0.002235 / 0.000200 (0.002035) \| 0.000094 / 0.000054 (0.000039) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031298 / 0.037411 (-0.006113) \| 0.137740 / 0.014526 (0.123214) \| 0.153497 / 0.176557 (-0.023059) \| 0.204201 / 0.737135 (-0.532934) \| 0.162324 / 0.296338 (-0.134014) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.475922 / 0.215209 (0.260712) \| 4.682344 / 2.077655 (2.604689) \| 2.107387 / 1.504120 (0.603267) \| 1.884792 / 1.541195 (0.343597) \| 2.003180 / 1.468490 (0.534690) \| 0.810212 / 4.584777 (-3.774564) \| 4.631047 / 3.745712 (0.885334) \| 4.467606 / 5.269862 (-0.802256) \| 2.334196 / 4.565676 (-2.231480) \| 0.099713 / 0.424275 (-0.324562) \| 0.014732 / 0.007607 (0.007125) \| 0.604587 / 0.226044 (0.378543) \| 5.951679 / 2.268929 (3.682751) \| 2.704761 / 55.444624 (-52.739863) \| 2.280695 / 6.876477 (-4.595781) \| 2.279489 / 2.142072 (0.137417) \| 0.962474 / 4.805227 (-3.842753) \| 0.195279 / 6.500664 (-6.305385) \| 0.071503 / 0.075469 (-0.003966) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.558037 / 1.841788 (-0.283751) \| 17.722140 / 8.074308 (9.647832) \| 16.229016 / 10.191392 (6.037624) \| 0.177148 / 0.680424 (-0.503276) \| 0.034162 / 0.534201 (-0.500039) \| 0.513945 / 0.579283 (-0.065338) \| 0.533542 / 0.434364 (0.099178) \| 0.672457 / 0.540337 (0.132119) \| 0.762390 / 1.386936 (-0.624546) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009739 / 0.011353 (-0.001613) \| 0.006095 / 0.011008 (-0.004914) \| 0.105968 / 0.038508 (0.067460) \| 0.046229 / 0.023109 (0.023120) \| 0.449156 / 0.275898 (0.173258) \| 0.462182 / 0.323480 (0.138702) \| 0.006981 / 0.007986 (-0.001004) \| 0.004867 / 0.004328 (0.000539) \| 0.082142 / 0.004250 (0.077891) \| 0.058652 / 0.037052 (0.021600) \| 0.454542 / 0.258489 (0.196052) \| 0.494910 / 0.293841 (0.201069) \| 0.047159 / 0.128546 (-0.081387) \| 0.014677 / 0.075646 (-0.060969) \| 0.370819 / 0.419271 (-0.048452) \| 0.064603 / 0.043533 (0.021070) \| 0.441514 / 0.255139 (0.186375) \| 0.442802 / 0.283200 (0.159603) \| 0.138603 / 0.141683 (-0.003080) \| 1.692810 / 1.452155 (0.240655) \| 1.894596 / 1.492716 (0.401880) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281681 / 0.018006 (0.263675) \| 0.532693 / 0.000490 (0.532203) \| 0.005484 / 0.000200 (0.005284) \| 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) \| 0.032994 / 0.037411 (-0.004417) \| 0.134614 / 0.014526 (0.120088) \| 0.142286 / 0.176557 (-0.034270) \| 0.187220 / 0.737135 (-0.549916) \| 0.144897 / 0.296338 (-0.151441) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.519536 / 0.215209 (0.304327) \| 5.214429 / 2.077655 (3.136775) \| 2.612575 / 1.504120 (1.108455) \| 2.369085 / 1.541195 (0.827891) \| 2.503157 / 1.468490 (1.034667) \| 0.834827 / 4.584777 (-3.749950) \| 4.586789 / 3.745712 (0.841077) \| 4.472605 / 5.269862 (-0.797257) \| 2.314471 / 4.565676 (-2.251205) \| 0.095817 / 0.424275 (-0.328458) \| 0.014086 / 0.007607 (0.006478) \| 0.605875 / 0.226044 (0.379831) \| 6.153143 / 2.268929 (3.884214) \| 3.187456 / 55.444624 (-52.257169) \| 2.755377 / 6.876477 (-4.121100) \| 2.777118 / 2.142072 (0.635046) \| 0.967285 / 4.805227 (-3.837942) \| 0.199202 / 6.500664 (-6.301462) \| 0.075979 / 0.075469 (0.000510) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.481758 / 1.841788 (-0.360030) \| 18.053769 / 8.074308 (9.979461) \| 15.558780 / 10.191392 (5.367388) \| 0.226135 / 0.680424 (-0.454288) \| 0.021668 / 0.534201 (-0.512533) \| 0.562618 / 0.579283 (-0.016666) \| 0.518183 / 0.434364 (0.083819) \| 0.628580 / 0.540337 (0.088243) \| 0.740368 / 1.386936 (-0.646568) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4e4d46eec24c36799c0efcc1b7231f597039c497 \"CML watermark\")\n" ]
1,504,498,387	5,381	Wrong URL for the_pile dataset	closed	2022-12-20T12:40:14	2023-02-15T16:24:57	2023-02-15T16:24:57	https://github.com/huggingface/datasets/issues/5381	null	LeoGrin	false	[ "Hi! This error can happen if there is a local file/folder with the same name as the requested dataset. And to avoid it, rename the local file/folder.\r\n\r\nSoon, it will be possible to explicitly request a Hub dataset as follows:https://github.com/huggingface/datasets/issues/5228#issuecomment-1313494020" ]
1,504,404,043	5,380	Improve dataset `.skip()` speed in streaming mode	open	2022-12-20T11:25:23	2023-03-08T10:47:12	null	https://github.com/huggingface/datasets/issues/5380	null	versae	false	[ "Hi! I agree `skip` can be inefficient to use in the current state.\r\n\r\nTo make it fast, we could use \"statistics\" stored in Parquet metadata and read only the chunks needed to form a dataset. \r\n\r\nAnd thanks to the \"datasets-server\" project, which aims to store the Parquet versions of the Hub datasets (only the smaller datasets are covered currently), this solution can also be applied to datasets stored in formats other than Parquet. (cc @severo)", "@mariosasko do the current parquet files created by the datasets-server already have the required \"statistics\"? If not, please open an issue on https://github.com/huggingface/datasets-server with some details to make sure we implement it.", "Yes, nothing has to be changed on the datasets-server side. What I mean by \"statistics\" is that we can use the \"row_group\" metadata embedded in a Parquet file (by default) to fetch the requested rows more efficiently.", "Glad to see the feature could be of interest. \r\n\r\nI'm sure there are many possible ways to implement this feature. I don't know enough about the datasets-server, but I guess that it is not instantaneous, in the sense that user-owned private datasets might need hours or days until they are ported to the datasets-server (if at all), which could be cumbersome. Having optionally that information in the `dataset_infos.json` file would make it easier for users to control the skip process a bit.", "re: statistics:\r\n\r\n- https://arrow.apache.org/docs/python/generated/pyarrow.parquet.FileMetaData.html\r\n- https://arrow.apache.org/docs/python/generated/pyarrow.parquet.RowGroupMetaData.html\r\n\r\n```python\r\n>>> import pyarrow.parquet as pq\r\n>>> import hffs\r\n>>> fs = hffs.HfFileSystem(\"glue\", repo_type=\"dataset\", revision=\"refs/convert/parquet\")\r\n>>> metadata = pq.read_metadata(\"ax/glue-test.parquet\", filesystem=fs)\r\n>>> metadata\r\n<pyarrow._parquet.FileMetaData object at 0x7f4537cec400>\r\n created_by: parquet-cpp-arrow version 7.0.0\r\n num_columns: 4\r\n num_rows: 1104\r\n num_row_groups: 2\r\n format_version: 1.0\r\n serialized_size: 2902\r\n>>> metadata.row_group(0)\r\n<pyarrow._parquet.RowGroupMetaData object at 0x7f45564bcbd0>\r\n num_columns: 4\r\n num_rows: 1000\r\n total_byte_size: 164474\r\n>>> metadata.row_group(1)\r\n<pyarrow._parquet.RowGroupMetaData object at 0x7f455005c400>\r\n num_columns: 4\r\n num_rows: 104\r\n total_byte_size: 13064\r\n```", "> user-owned private datasets might need hours or days until they are ported to the datasets-server (if at all)\r\n\r\nprivate datasets are not supported yet (https://github.com/huggingface/datasets-server/issues/39)", "@versae `Dataset.push_to_hub` writes shards in Parquet, so this solution would also work for such datasets (immediately after the push). ", "@mariosasko that is right. However, there are still a good amount of datasets for which the shards are created manually. In our very specific case, we create medium-sized datasets (rarely over 100-200GB) of both text and audio, we prepare the shards by hand and then upload then. It would be great to have immediate access to this download skipping feature for them too.", "From looking at Arrow's source, it seems Parquet stores metadata at the end, which means one needs to iterate over a Parquet file's data before accessing its metadata. We could mimic Dask to address this \"limitation\" and write metadata in a `_metadata`/`_common_metadata` file in `to_parquet`/`push_to_hub`, which we could then use to optimize reads (if present). Plus, it's handy that PyArrow can also parse these metadata files.", "So if Parquet metadata needs to be in its own file anyway, why not implement this skipping feature by storing the example counts per shard in `dataset_infos.json`? That would allow:\r\n- Support both private and public datasets\r\n- Immediate access to the feature upon uploading of shards\r\n- Use any dataset, not only those uploaded using `.push_to_hub()`\r\n\r\nA proper Parquet metadata file could still be created and \"overwrite\" the `dataset_infos.json` info in the datasets-server." ]
1,504,010,639	5,379	feat: depth estimation dataset guide.	closed	2022-12-20T05:32:11	2023-01-13T12:30:31	2023-01-13T12:23:34	https://github.com/huggingface/datasets/pull/5379	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5379", "html_url": "https://github.com/huggingface/datasets/pull/5379", "diff_url": "https://github.com/huggingface/datasets/pull/5379.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5379.patch", "merged_at": "2023-01-13T12:23:34" }	sayakpaul	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "Thanks for the changes, looks good to me!", "@stevhliu I have pushed some quality improvements both in terms of code and content. Would you be able to re-review? ", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008325 / 0.011353 (-0.003028) \| 0.004432 / 0.011008 (-0.006576) \| 0.099794 / 0.038508 (0.061286) \| 0.029469 / 0.023109 (0.006360) \| 0.306554 / 0.275898 (0.030656) \| 0.367373 / 0.323480 (0.043893) \| 0.007532 / 0.007986 (-0.000454) \| 0.003310 / 0.004328 (-0.001018) \| 0.077453 / 0.004250 (0.073203) \| 0.034836 / 0.037052 (-0.002216) \| 0.311696 / 0.258489 (0.053207) \| 0.349683 / 0.293841 (0.055842) \| 0.033089 / 0.128546 (-0.095457) \| 0.011339 / 0.075646 (-0.064307) \| 0.321699 / 0.419271 (-0.097573) \| 0.040213 / 0.043533 (-0.003320) \| 0.304741 / 0.255139 (0.049602) \| 0.331569 / 0.283200 (0.048369) \| 0.090397 / 0.141683 (-0.051285) \| 1.526001 / 1.452155 (0.073847) \| 1.558863 / 1.492716 (0.066146) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.179446 / 0.018006 (0.161440) \| 0.416308 / 0.000490 (0.415818) \| 0.002390 / 0.000200 (0.002190) \| 0.000075 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023641 / 0.037411 (-0.013770) \| 0.096672 / 0.014526 (0.082147) \| 0.104330 / 0.176557 (-0.072227) \| 0.146338 / 0.737135 (-0.590797) \| 0.108278 / 0.296338 (-0.188060) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.420194 / 0.215209 (0.204985) \| 4.196981 / 2.077655 (2.119326) \| 1.861206 / 1.504120 (0.357086) \| 1.658748 / 1.541195 (0.117554) \| 1.704309 / 1.468490 (0.235819) \| 0.691639 / 4.584777 (-3.893138) \| 3.346303 / 3.745712 (-0.399409) \| 1.932962 / 5.269862 (-3.336900) \| 1.299395 / 4.565676 (-3.266281) \| 0.081869 / 0.424275 (-0.342406) \| 0.012415 / 0.007607 (0.004808) \| 0.530805 / 0.226044 (0.304761) \| 5.293486 / 2.268929 (3.024558) \| 2.328327 / 55.444624 (-53.116297) \| 1.964956 / 6.876477 (-4.911521) \| 2.002793 / 2.142072 (-0.139280) \| 0.813380 / 4.805227 (-3.991847) \| 0.150030 / 6.500664 (-6.350634) \| 0.065194 / 0.075469 (-0.010275) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.259421 / 1.841788 (-0.582367) \| 13.667796 / 8.074308 (5.593488) \| 13.819121 / 10.191392 (3.627729) \| 0.136718 / 0.680424 (-0.543706) \| 0.028510 / 0.534201 (-0.505691) \| 0.402246 / 0.579283 (-0.177037) \| 0.405279 / 0.434364 (-0.029085) \| 0.467185 / 0.540337 (-0.073153) \| 0.554213 / 1.386936 (-0.832723) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006738 / 0.011353 (-0.004615) \| 0.004616 / 0.011008 (-0.006393) \| 0.096978 / 0.038508 (0.058470) \| 0.027750 / 0.023109 (0.004640) \| 0.411505 / 0.275898 (0.135607) \| 0.441796 / 0.323480 (0.118316) \| 0.005073 / 0.007986 (-0.002913) \| 0.003360 / 0.004328 (-0.000968) \| 0.074445 / 0.004250 (0.070194) \| 0.040654 / 0.037052 (0.003602) \| 0.414277 / 0.258489 (0.155788) \| 0.448665 / 0.293841 (0.154824) \| 0.032346 / 0.128546 (-0.096200) \| 0.011533 / 0.075646 (-0.064114) \| 0.317349 / 0.419271 (-0.101923) \| 0.041934 / 0.043533 (-0.001599) \| 0.409102 / 0.255139 (0.153963) \| 0.429977 / 0.283200 (0.146777) \| 0.089459 / 0.141683 (-0.052224) \| 1.518127 / 1.452155 (0.065973) \| 1.569902 / 1.492716 (0.077186) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.232648 / 0.018006 (0.214642) \| 0.413751 / 0.000490 (0.413261) \| 0.000404 / 0.000200 (0.000204) \| 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) \| 0.025468 / 0.037411 (-0.011943) \| 0.098195 / 0.014526 (0.083669) \| 0.108882 / 0.176557 (-0.067674) \| 0.150059 / 0.737135 (-0.587076) \| 0.110742 / 0.296338 (-0.185597) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.445326 / 0.215209 (0.230117) \| 4.449200 / 2.077655 (2.371545) \| 2.098939 / 1.504120 (0.594819) \| 1.861207 / 1.541195 (0.320012) \| 1.901385 / 1.468490 (0.432894) \| 0.695287 / 4.584777 (-3.889490) \| 3.461775 / 3.745712 (-0.283938) \| 2.998566 / 5.269862 (-2.271296) \| 1.555036 / 4.565676 (-3.010641) \| 0.082789 / 0.424275 (-0.341486) \| 0.012772 / 0.007607 (0.005165) \| 0.564855 / 0.226044 (0.338811) \| 5.631049 / 2.268929 (3.362120) \| 2.543771 / 55.444624 (-52.900854) \| 2.194378 / 6.876477 (-4.682099) \| 2.267168 / 2.142072 (0.125095) \| 0.803330 / 4.805227 (-4.001898) \| 0.151336 / 6.500664 (-6.349328) \| 0.067015 / 0.075469 (-0.008454) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.298422 / 1.841788 (-0.543366) \| 13.933637 / 8.074308 (5.859329) \| 13.570848 / 10.191392 (3.379456) \| 0.150787 / 0.680424 (-0.529637) \| 0.016911 / 0.534201 (-0.517290) \| 0.384771 / 0.579283 (-0.194512) \| 0.397505 / 0.434364 (-0.036858) \| 0.450931 / 0.540337 (-0.089406) \| 0.534501 / 1.386936 (-0.852435) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n", "@lhoestq @nateraw made some changes as per the comments. PTAL and approve as necessary. ", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009037 / 0.011353 (-0.002316) \| 0.004970 / 0.011008 (-0.006038) \| 0.099223 / 0.038508 (0.060715) \| 0.034935 / 0.023109 (0.011826) \| 0.297027 / 0.275898 (0.021129) \| 0.352861 / 0.323480 (0.029382) \| 0.007558 / 0.007986 (-0.000427) \| 0.003903 / 0.004328 (-0.000425) \| 0.075663 / 0.004250 (0.071413) \| 0.042577 / 0.037052 (0.005524) \| 0.307182 / 0.258489 (0.048693) \| 0.344237 / 0.293841 (0.050396) \| 0.041438 / 0.128546 (-0.087108) \| 0.012159 / 0.075646 (-0.063487) \| 0.333771 / 0.419271 (-0.085501) \| 0.047847 / 0.043533 (0.004314) \| 0.290797 / 0.255139 (0.035658) \| 0.320517 / 0.283200 (0.037318) \| 0.098334 / 0.141683 (-0.043349) \| 1.446187 / 1.452155 (-0.005968) \| 1.495506 / 1.492716 (0.002789) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.203704 / 0.018006 (0.185698) \| 0.441325 / 0.000490 (0.440835) \| 0.001173 / 0.000200 (0.000973) \| 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) \| 0.026694 / 0.037411 (-0.010718) \| 0.103819 / 0.014526 (0.089294) \| 0.116377 / 0.176557 (-0.060179) \| 0.158280 / 0.737135 (-0.578856) \| 0.119797 / 0.296338 (-0.176541) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.405723 / 0.215209 (0.190514) \| 4.047633 / 2.077655 (1.969979) \| 1.805652 / 1.504120 (0.301532) \| 1.611382 / 1.541195 (0.070187) \| 1.663117 / 1.468490 (0.194627) \| 0.692589 / 4.584777 (-3.892188) \| 3.689970 / 3.745712 (-0.055742) \| 2.089760 / 5.269862 (-3.180101) \| 1.450576 / 4.565676 (-3.115101) \| 0.085276 / 0.424275 (-0.338999) \| 0.012042 / 0.007607 (0.004434) \| 0.513159 / 0.226044 (0.287115) \| 5.123235 / 2.268929 (2.854306) \| 2.281864 / 55.444624 (-53.162761) \| 1.926170 / 6.876477 (-4.950307) \| 2.035093 / 2.142072 (-0.106979) \| 0.857457 / 4.805227 (-3.947770) \| 0.166088 / 6.500664 (-6.334576) \| 0.062115 / 0.075469 (-0.013354) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.197776 / 1.841788 (-0.644012) \| 14.674452 / 8.074308 (6.600144) \| 14.275990 / 10.191392 (4.084598) \| 0.170848 / 0.680424 (-0.509576) \| 0.028613 / 0.534201 (-0.505588) \| 0.438650 / 0.579283 (-0.140633) \| 0.439323 / 0.434364 (0.004959) \| 0.515090 / 0.540337 (-0.025247) \| 0.614216 / 1.386936 (-0.772720) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007159 / 0.011353 (-0.004194) \| 0.005142 / 0.011008 (-0.005866) \| 0.096953 / 0.038508 (0.058445) \| 0.033036 / 0.023109 (0.009927) \| 0.391790 / 0.275898 (0.115892) \| 0.427120 / 0.323480 (0.103640) \| 0.005691 / 0.007986 (-0.002294) \| 0.004848 / 0.004328 (0.000519) \| 0.072258 / 0.004250 (0.068008) \| 0.049017 / 0.037052 (0.011965) \| 0.387267 / 0.258489 (0.128778) \| 0.437112 / 0.293841 (0.143272) \| 0.036360 / 0.128546 (-0.092186) \| 0.012249 / 0.075646 (-0.063397) \| 0.336246 / 0.419271 (-0.083025) \| 0.048777 / 0.043533 (0.005244) \| 0.397872 / 0.255139 (0.142733) \| 0.399768 / 0.283200 (0.116568) \| 0.101283 / 0.141683 (-0.040400) \| 1.443999 / 1.452155 (-0.008156) \| 1.575496 / 1.492716 (0.082779) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.220952 / 0.018006 (0.202946) \| 0.442220 / 0.000490 (0.441730) \| 0.000406 / 0.000200 (0.000206) \| 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) \| 0.028626 / 0.037411 (-0.008786) \| 0.109929 / 0.014526 (0.095403) \| 0.120989 / 0.176557 (-0.055568) \| 0.157377 / 0.737135 (-0.579758) \| 0.125522 / 0.296338 (-0.170816) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.436565 / 0.215209 (0.221356) \| 4.380771 / 2.077655 (2.303117) \| 2.200003 / 1.504120 (0.695883) \| 2.013289 / 1.541195 (0.472094) \| 2.052658 / 1.468490 (0.584168) \| 0.703706 / 4.584777 (-3.881071) \| 3.823289 / 3.745712 (0.077577) \| 2.064882 / 5.269862 (-3.204980) \| 1.330834 / 4.565676 (-3.234842) \| 0.085945 / 0.424275 (-0.338330) \| 0.012511 / 0.007607 (0.004904) \| 0.544171 / 0.226044 (0.318127) \| 5.476059 / 2.268929 (3.207130) \| 2.695586 / 55.444624 (-52.749039) \| 2.330239 / 6.876477 (-4.546238) \| 2.429290 / 2.142072 (0.287218) \| 0.843154 / 4.805227 (-3.962073) \| 0.169334 / 6.500664 (-6.331330) \| 0.064261 / 0.075469 (-0.011209) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.268344 / 1.841788 (-0.573444) \| 14.934342 / 8.074308 (6.860034) \| 13.555389 / 10.191392 (3.363997) \| 0.142725 / 0.680424 (-0.537699) \| 0.017891 / 0.534201 (-0.516310) \| 0.424833 / 0.579283 (-0.154450) \| 0.420035 / 0.434364 (-0.014329) \| 0.491009 / 0.540337 (-0.049329) \| 0.586953 / 1.386936 (-0.799983) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n", "Merging this PR with approvals from @stevhliu @lhoestq. ", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008586 / 0.011353 (-0.002767) \| 0.004659 / 0.011008 (-0.006350) \| 0.100343 / 0.038508 (0.061835) \| 0.029861 / 0.023109 (0.006751) \| 0.301090 / 0.275898 (0.025192) \| 0.369528 / 0.323480 (0.046048) \| 0.006920 / 0.007986 (-0.001065) \| 0.003513 / 0.004328 (-0.000815) \| 0.078514 / 0.004250 (0.074263) \| 0.035285 / 0.037052 (-0.001767) \| 0.311257 / 0.258489 (0.052768) \| 0.353995 / 0.293841 (0.060154) \| 0.033733 / 0.128546 (-0.094813) \| 0.011489 / 0.075646 (-0.064157) \| 0.323095 / 0.419271 (-0.096176) \| 0.040808 / 0.043533 (-0.002725) \| 0.301779 / 0.255139 (0.046640) \| 0.348517 / 0.283200 (0.065318) \| 0.086962 / 0.141683 (-0.054721) \| 1.496270 / 1.452155 (0.044115) \| 1.514260 / 1.492716 (0.021544) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.189502 / 0.018006 (0.171496) \| 0.419326 / 0.000490 (0.418837) \| 0.002160 / 0.000200 (0.001960) \| 0.000084 / 0.000054 (0.000029) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023669 / 0.037411 (-0.013742) \| 0.096574 / 0.014526 (0.082048) \| 0.105970 / 0.176557 (-0.070587) \| 0.148531 / 0.737135 (-0.588605) \| 0.109948 / 0.296338 (-0.186391) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.424968 / 0.215209 (0.209759) \| 4.246292 / 2.077655 (2.168637) \| 1.911062 / 1.504120 (0.406943) \| 1.700733 / 1.541195 (0.159538) \| 1.760756 / 1.468490 (0.292266) \| 0.696966 / 4.584777 (-3.887811) \| 3.372320 / 3.745712 (-0.373392) \| 2.886281 / 5.269862 (-2.383581) \| 1.553082 / 4.565676 (-3.012594) \| 0.082835 / 0.424275 (-0.341440) \| 0.012688 / 0.007607 (0.005081) \| 0.536352 / 0.226044 (0.310308) \| 5.382510 / 2.268929 (3.113582) \| 2.365664 / 55.444624 (-53.078960) \| 1.995631 / 6.876477 (-4.880845) \| 2.073865 / 2.142072 (-0.068207) \| 0.819109 / 4.805227 (-3.986118) \| 0.150278 / 6.500664 (-6.350386) \| 0.065201 / 0.075469 (-0.010268) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.239835 / 1.841788 (-0.601953) \| 13.911847 / 8.074308 (5.837539) \| 13.500433 / 10.191392 (3.309041) \| 0.137153 / 0.680424 (-0.543271) \| 0.028451 / 0.534201 (-0.505750) \| 0.394659 / 0.579283 (-0.184625) \| 0.404915 / 0.434364 (-0.029449) \| 0.458944 / 0.540337 (-0.081394) \| 0.542288 / 1.386936 (-0.844648) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006791 / 0.011353 (-0.004562) \| 0.004590 / 0.011008 (-0.006419) \| 0.098697 / 0.038508 (0.060189) \| 0.027634 / 0.023109 (0.004525) \| 0.344383 / 0.275898 (0.068485) \| 0.385607 / 0.323480 (0.062127) \| 0.005413 / 0.007986 (-0.002573) \| 0.003447 / 0.004328 (-0.000881) \| 0.077268 / 0.004250 (0.073018) \| 0.041823 / 0.037052 (0.004770) \| 0.342904 / 0.258489 (0.084414) \| 0.399371 / 0.293841 (0.105530) \| 0.032668 / 0.128546 (-0.095879) \| 0.011598 / 0.075646 (-0.064048) \| 0.319973 / 0.419271 (-0.099299) \| 0.041760 / 0.043533 (-0.001773) \| 0.340510 / 0.255139 (0.085371) \| 0.377929 / 0.283200 (0.094730) \| 0.090889 / 0.141683 (-0.050793) \| 1.496068 / 1.452155 (0.043913) \| 1.574884 / 1.492716 (0.082168) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230489 / 0.018006 (0.212483) \| 0.425234 / 0.000490 (0.424745) \| 0.000406 / 0.000200 (0.000206) \| 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) \| 0.024650 / 0.037411 (-0.012761) \| 0.102706 / 0.014526 (0.088180) \| 0.108017 / 0.176557 (-0.068539) \| 0.143645 / 0.737135 (-0.593490) \| 0.110556 / 0.296338 (-0.185782) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.468038 / 0.215209 (0.252829) \| 4.670514 / 2.077655 (2.592860) \| 2.446620 / 1.504120 (0.942500) \| 2.241255 / 1.541195 (0.700060) \| 2.286409 / 1.468490 (0.817919) \| 0.698923 / 4.584777 (-3.885854) \| 3.401121 / 3.745712 (-0.344592) \| 1.892399 / 5.269862 (-3.377462) \| 1.163101 / 4.565676 (-3.402575) \| 0.082567 / 0.424275 (-0.341708) \| 0.012662 / 0.007607 (0.005055) \| 0.571262 / 0.226044 (0.345218) \| 5.731740 / 2.268929 (3.462812) \| 2.879649 / 55.444624 (-52.564975) \| 2.533846 / 6.876477 (-4.342631) \| 2.654789 / 2.142072 (0.512717) \| 0.811345 / 4.805227 (-3.993882) \| 0.152495 / 6.500664 (-6.348169) \| 0.067748 / 0.075469 (-0.007721) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.267852 / 1.841788 (-0.573935) \| 14.114920 / 8.074308 (6.040612) \| 14.355403 / 10.191392 (4.164011) \| 0.150393 / 0.680424 (-0.530031) \| 0.016855 / 0.534201 (-0.517346) \| 0.378710 / 0.579283 (-0.200573) \| 0.385380 / 0.434364 (-0.048984) \| 0.439054 / 0.540337 (-0.101284) \| 0.524343 / 1.386936 (-0.862593) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n" ]
1,503,887,508	5,378	The dataset "the_pile", subset "enron_emails" , load_dataset() failure	closed	2022-12-20T02:19:13	2022-12-20T07:52:54	2022-12-20T07:52:54	https://github.com/huggingface/datasets/issues/5378	null	shaoyuta	false	[ "Thanks for reporting @shaoyuta. We are investigating it.\r\n\r\nWe are transferring the issue to \"the_pile\" Community tab on the Hub: https://huggingface.co/datasets/the_pile/discussions/4" ]
1,503,477,833	5,377	Add a parallel implementation of to_tf_dataset()	closed	2022-12-19T19:40:27	2023-01-25T16:28:44	2023-01-25T16:21:40	https://github.com/huggingface/datasets/pull/5377	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5377", "html_url": "https://github.com/huggingface/datasets/pull/5377", "diff_url": "https://github.com/huggingface/datasets/pull/5377.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5377.patch", "merged_at": "2023-01-25T16:21:40" }	Rocketknight1	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "Failing because the test server uses Py3.7 but the `SharedMemory` features require Py3.8! I forgot we still support 3.7 for another couple of months. I'm not sure exactly how to proceed, whether I should leave this PR until then, or just gate the feature behind a version check and skip the tests until the Python version catches up.", "I haven't played with `NumpyMultiprocessingGenerator` so I can't really help here, but this sounds promising :) Otherwise I think it's also fine to allow `num_workers` only for py>=3.8 for now. You can skip the test on 3.7 and make sure to raise an informative error if someone wants to use `num_workers` with 3.7", "Lots of comments here - I'll reply to the specific code comments underneath them, but in response to the general comments:\r\n\r\n@gante: I think this approach is much more performant than a `multiprocessing.Pool`. The reason is that when results are returned from a process `Pool`, the returned Python objects are pickled by the child processes, sent down a pipe and unpickled by the parent process. This creates a huge single-process bottleneck as the parent has to unpickle lots of large NumPy arrays, which is quite slow.\r\n\r\nWhen you use a `SharedMemory` approach, the data is just there for the parent process - the child and the parent are writing to exactly the same array in memory, and no pickling or unpickling occurs. This means the parent can just immediately copy the array (which is much faster than unpickling) and yield it to `tf.data`. We're taking advantage of the fact that we know the data is just big NumPy arrays and we don't need the full generality of `pickle`.\r\n\r\n@lhoestq: Sounds good! I'll add a clear error and skip the tests on Py<=3.7.", "Also, an extra technicality, just for information in case anyone looks at this PR later: Recent versions of Python allow [pickled objects to store out-of-band data](https://peps.python.org/pep-0574/). This allows for very efficient zero-copy unpickling of objects like NumPy arrays, with the unpickled object having a view on the same memory as the original. \r\n\r\nHowever, this explicitly does not work when the object is unpickled by a different process than the one that created it. For this to work you must explicitly allocate shared memory and create the array there, which pickle cannot handle for you. As a result, if you just benchmark unpickling vs copying of NumPy arrays it can seem like unpickling is very fast - but this is only true when the pickle was created in the unpickling process!", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008666 / 0.011353 (-0.002687) \| 0.004624 / 0.011008 (-0.006384) \| 0.099247 / 0.038508 (0.060739) \| 0.029766 / 0.023109 (0.006657) \| 0.303347 / 0.275898 (0.027449) \| 0.370022 / 0.323480 (0.046542) \| 0.007128 / 0.007986 (-0.000857) \| 0.003446 / 0.004328 (-0.000883) \| 0.076670 / 0.004250 (0.072420) \| 0.038892 / 0.037052 (0.001840) \| 0.313035 / 0.258489 (0.054546) \| 0.350503 / 0.293841 (0.056662) \| 0.033732 / 0.128546 (-0.094815) \| 0.011644 / 0.075646 (-0.064003) \| 0.323295 / 0.419271 (-0.095977) \| 0.040336 / 0.043533 (-0.003196) \| 0.302253 / 0.255139 (0.047114) \| 0.337199 / 0.283200 (0.053999) \| 0.089454 / 0.141683 (-0.052229) \| 1.624906 / 1.452155 (0.172752) \| 1.546187 / 1.492716 (0.053470) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.184614 / 0.018006 (0.166608) \| 0.427397 / 0.000490 (0.426907) \| 0.003342 / 0.000200 (0.003142) \| 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) \| 0.023684 / 0.037411 (-0.013727) \| 0.100095 / 0.014526 (0.085569) \| 0.104996 / 0.176557 (-0.071560) \| 0.144719 / 0.737135 (-0.592416) \| 0.110759 / 0.296338 (-0.185579) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.421108 / 0.215209 (0.205899) \| 4.214094 / 2.077655 (2.136440) \| 1.906231 / 1.504120 (0.402111) \| 1.698000 / 1.541195 (0.156806) \| 1.744856 / 1.468490 (0.276366) \| 0.693671 / 4.584777 (-3.891106) \| 3.362522 / 3.745712 (-0.383190) \| 1.878470 / 5.269862 (-3.391392) \| 1.167563 / 4.565676 (-3.398113) \| 0.082455 / 0.424275 (-0.341820) \| 0.012261 / 0.007607 (0.004654) \| 0.525196 / 0.226044 (0.299152) \| 5.257553 / 2.268929 (2.988624) \| 2.298286 / 55.444624 (-53.146339) \| 1.956106 / 6.876477 (-4.920371) \| 2.006308 / 2.142072 (-0.135764) \| 0.811069 / 4.805227 (-3.994158) \| 0.150368 / 6.500664 (-6.350296) \| 0.065699 / 0.075469 (-0.009771) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.224516 / 1.841788 (-0.617272) \| 13.619084 / 8.074308 (5.544776) \| 14.096666 / 10.191392 (3.905274) \| 0.151068 / 0.680424 (-0.529356) \| 0.028819 / 0.534201 (-0.505382) \| 0.402071 / 0.579283 (-0.177212) \| 0.408647 / 0.434364 (-0.025717) \| 0.466605 / 0.540337 (-0.073733) \| 0.547094 / 1.386936 (-0.839842) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006935 / 0.011353 (-0.004418) \| 0.004590 / 0.011008 (-0.006419) \| 0.099398 / 0.038508 (0.060890) \| 0.028145 / 0.023109 (0.005036) \| 0.426582 / 0.275898 (0.150684) \| 0.465712 / 0.323480 (0.142233) \| 0.005254 / 0.007986 (-0.002731) \| 0.004956 / 0.004328 (0.000627) \| 0.075616 / 0.004250 (0.071365) \| 0.039871 / 0.037052 (0.002819) \| 0.428859 / 0.258489 (0.170370) \| 0.470839 / 0.293841 (0.176998) \| 0.032150 / 0.128546 (-0.096396) \| 0.011778 / 0.075646 (-0.063868) \| 0.322358 / 0.419271 (-0.096913) \| 0.041974 / 0.043533 (-0.001559) \| 0.427459 / 0.255139 (0.172320) \| 0.446685 / 0.283200 (0.163485) \| 0.092000 / 0.141683 (-0.049683) \| 1.509231 / 1.452155 (0.057076) \| 1.578950 / 1.492716 (0.086234) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.168047 / 0.018006 (0.150041) \| 0.418993 / 0.000490 (0.418503) \| 0.002855 / 0.000200 (0.002655) \| 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) \| 0.025652 / 0.037411 (-0.011759) \| 0.100141 / 0.014526 (0.085616) \| 0.107293 / 0.176557 (-0.069264) \| 0.142857 / 0.737135 (-0.594278) \| 0.110933 / 0.296338 (-0.185406) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.477556 / 0.215209 (0.262347) \| 4.777951 / 2.077655 (2.700296) \| 2.461885 / 1.504120 (0.957765) \| 2.252307 / 1.541195 (0.711112) \| 2.307983 / 1.468490 (0.839493) \| 0.697570 / 4.584777 (-3.887207) \| 3.370323 / 3.745712 (-0.375389) \| 3.131333 / 5.269862 (-2.138529) \| 1.594839 / 4.565676 (-2.970838) \| 0.082333 / 0.424275 (-0.341942) \| 0.012574 / 0.007607 (0.004967) \| 0.583704 / 0.226044 (0.357660) \| 5.817675 / 2.268929 (3.548746) \| 2.927054 / 55.444624 (-52.517570) \| 2.582929 / 6.876477 (-4.293548) \| 2.634275 / 2.142072 (0.492202) \| 0.806407 / 4.805227 (-3.998821) \| 0.151438 / 6.500664 (-6.349226) \| 0.067429 / 0.075469 (-0.008040) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.267011 / 1.841788 (-0.574776) \| 13.989515 / 8.074308 (5.915207) \| 14.087968 / 10.191392 (3.896576) \| 0.142130 / 0.680424 (-0.538293) \| 0.017201 / 0.534201 (-0.517000) \| 0.383394 / 0.579283 (-0.195889) \| 0.381921 / 0.434364 (-0.052443) \| 0.439169 / 0.540337 (-0.101168) \| 0.524215 / 1.386936 (-0.862721) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#be2ebc8f3cfeb532c933be2443094603bafcab04 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008489 / 0.011353 (-0.002864) \| 0.004617 / 0.011008 (-0.006391) \| 0.102035 / 0.038508 (0.063527) \| 0.029850 / 0.023109 (0.006741) \| 0.296789 / 0.275898 (0.020891) \| 0.367270 / 0.323480 (0.043790) \| 0.006934 / 0.007986 (-0.001052) \| 0.004923 / 0.004328 (0.000595) \| 0.079150 / 0.004250 (0.074900) \| 0.036884 / 0.037052 (-0.000169) \| 0.305747 / 0.258489 (0.047258) \| 0.348510 / 0.293841 (0.054669) \| 0.034074 / 0.128546 (-0.094472) \| 0.011650 / 0.075646 (-0.063997) \| 0.324226 / 0.419271 (-0.095045) \| 0.041763 / 0.043533 (-0.001770) \| 0.300887 / 0.255139 (0.045748) \| 0.333393 / 0.283200 (0.050193) \| 0.093838 / 0.141683 (-0.047844) \| 1.499801 / 1.452155 (0.047646) \| 1.505988 / 1.492716 (0.013272) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.198610 / 0.018006 (0.180604) \| 0.407380 / 0.000490 (0.406891) \| 0.000367 / 0.000200 (0.000167) \| 0.000059 / 0.000054 (0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022858 / 0.037411 (-0.014554) \| 0.095727 / 0.014526 (0.081202) \| 0.104014 / 0.176557 (-0.072543) \| 0.138764 / 0.737135 (-0.598371) \| 0.105860 / 0.296338 (-0.190478) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.416352 / 0.215209 (0.201143) \| 4.150007 / 2.077655 (2.072352) \| 1.878727 / 1.504120 (0.374607) \| 1.678978 / 1.541195 (0.137783) \| 1.711990 / 1.468490 (0.243500) \| 0.691722 / 4.584777 (-3.893055) \| 3.386466 / 3.745712 (-0.359246) \| 1.835730 / 5.269862 (-3.434132) \| 1.149975 / 4.565676 (-3.415702) \| 0.081914 / 0.424275 (-0.342362) \| 0.012238 / 0.007607 (0.004631) \| 0.522945 / 0.226044 (0.296900) \| 5.251793 / 2.268929 (2.982864) \| 2.306907 / 55.444624 (-53.137717) \| 1.968400 / 6.876477 (-4.908076) \| 1.981154 / 2.142072 (-0.160919) \| 0.810126 / 4.805227 (-3.995101) \| 0.147876 / 6.500664 (-6.352788) \| 0.064042 / 0.075469 (-0.011428) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.199150 / 1.841788 (-0.642637) \| 13.913473 / 8.074308 (5.839165) \| 14.079132 / 10.191392 (3.887740) \| 0.137387 / 0.680424 (-0.543037) \| 0.028456 / 0.534201 (-0.505745) \| 0.394162 / 0.579283 (-0.185122) \| 0.402051 / 0.434364 (-0.032313) \| 0.461944 / 0.540337 (-0.078394) \| 0.542648 / 1.386936 (-0.844288) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006393 / 0.011353 (-0.004960) \| 0.004599 / 0.011008 (-0.006409) \| 0.097389 / 0.038508 (0.058881) \| 0.027719 / 0.023109 (0.004610) \| 0.341060 / 0.275898 (0.065162) \| 0.379604 / 0.323480 (0.056124) \| 0.004955 / 0.007986 (-0.003030) \| 0.003369 / 0.004328 (-0.000959) \| 0.075390 / 0.004250 (0.071139) \| 0.038518 / 0.037052 (0.001466) \| 0.347085 / 0.258489 (0.088596) \| 0.393468 / 0.293841 (0.099627) \| 0.031482 / 0.128546 (-0.097064) \| 0.011585 / 0.075646 (-0.064061) \| 0.317969 / 0.419271 (-0.101302) \| 0.041389 / 0.043533 (-0.002144) \| 0.343812 / 0.255139 (0.088673) \| 0.371047 / 0.283200 (0.087848) \| 0.090020 / 0.141683 (-0.051663) \| 1.461690 / 1.452155 (0.009536) \| 1.552458 / 1.492716 (0.059741) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.188691 / 0.018006 (0.170684) \| 0.415635 / 0.000490 (0.415145) \| 0.005285 / 0.000200 (0.005085) \| 0.000087 / 0.000054 (0.000033) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024695 / 0.037411 (-0.012716) \| 0.098939 / 0.014526 (0.084413) \| 0.108472 / 0.176557 (-0.068085) \| 0.152635 / 0.737135 (-0.584501) \| 0.109947 / 0.296338 (-0.186391) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.471975 / 0.215209 (0.256766) \| 4.716437 / 2.077655 (2.638782) \| 2.420148 / 1.504120 (0.916028) \| 2.219864 / 1.541195 (0.678669) \| 2.238647 / 1.468490 (0.770157) \| 0.697628 / 4.584777 (-3.887149) \| 3.530720 / 3.745712 (-0.214993) \| 3.327354 / 5.269862 (-1.942508) \| 1.665877 / 4.565676 (-2.899800) \| 0.082650 / 0.424275 (-0.341625) \| 0.012593 / 0.007607 (0.004986) \| 0.576109 / 0.226044 (0.350065) \| 5.744691 / 2.268929 (3.475762) \| 2.863473 / 55.444624 (-52.581152) \| 2.529616 / 6.876477 (-4.346861) \| 2.562802 / 2.142072 (0.420730) \| 0.805631 / 4.805227 (-3.999597) \| 0.150788 / 6.500664 (-6.349876) \| 0.065743 / 0.075469 (-0.009726) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.295134 / 1.841788 (-0.546654) \| 14.096046 / 8.074308 (6.021738) \| 13.901399 / 10.191392 (3.710007) \| 0.127481 / 0.680424 (-0.552943) \| 0.016666 / 0.534201 (-0.517535) \| 0.381819 / 0.579283 (-0.197464) \| 0.382629 / 0.434364 (-0.051735) \| 0.439354 / 0.540337 (-0.100984) \| 0.527662 / 1.386936 (-0.859274) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0fe2ad43f59e65d39f2f2ce7442c76990493deb7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008509 / 0.011353 (-0.002844) \| 0.004523 / 0.011008 (-0.006485) \| 0.100616 / 0.038508 (0.062108) \| 0.029573 / 0.023109 (0.006464) \| 0.306414 / 0.275898 (0.030516) \| 0.377034 / 0.323480 (0.053554) \| 0.007621 / 0.007986 (-0.000365) \| 0.003335 / 0.004328 (-0.000993) \| 0.078598 / 0.004250 (0.074348) \| 0.036902 / 0.037052 (-0.000150) \| 0.318146 / 0.258489 (0.059657) \| 0.355626 / 0.293841 (0.061785) \| 0.033441 / 0.128546 (-0.095105) \| 0.011552 / 0.075646 (-0.064094) \| 0.322973 / 0.419271 (-0.096299) \| 0.040564 / 0.043533 (-0.002968) \| 0.306451 / 0.255139 (0.051312) \| 0.337591 / 0.283200 (0.054392) \| 0.086822 / 0.141683 (-0.054861) \| 1.484601 / 1.452155 (0.032447) \| 1.542777 / 1.492716 (0.050061) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.201711 / 0.018006 (0.183705) \| 0.418387 / 0.000490 (0.417898) \| 0.002753 / 0.000200 (0.002553) \| 0.000263 / 0.000054 (0.000209) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023016 / 0.037411 (-0.014395) \| 0.097313 / 0.014526 (0.082787) \| 0.103435 / 0.176557 (-0.073122) \| 0.142665 / 0.737135 (-0.594470) \| 0.107397 / 0.296338 (-0.188942) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.422739 / 0.215209 (0.207530) \| 4.220126 / 2.077655 (2.142471) \| 1.865447 / 1.504120 (0.361327) \| 1.649647 / 1.541195 (0.108453) \| 1.711655 / 1.468490 (0.243165) \| 0.704269 / 4.584777 (-3.880508) \| 3.407390 / 3.745712 (-0.338322) \| 1.929224 / 5.269862 (-3.340638) \| 1.281225 / 4.565676 (-3.284452) \| 0.082924 / 0.424275 (-0.341351) \| 0.012588 / 0.007607 (0.004981) \| 0.531025 / 0.226044 (0.304980) \| 5.339441 / 2.268929 (3.070512) \| 2.298969 / 55.444624 (-53.145656) \| 1.952145 / 6.876477 (-4.924332) \| 2.034754 / 2.142072 (-0.107318) \| 0.823672 / 4.805227 (-3.981555) \| 0.151465 / 6.500664 (-6.349199) \| 0.066663 / 0.075469 (-0.008807) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.258981 / 1.841788 (-0.582807) \| 13.791640 / 8.074308 (5.717332) \| 14.001514 / 10.191392 (3.810122) \| 0.149805 / 0.680424 (-0.530619) \| 0.028614 / 0.534201 (-0.505587) \| 0.400266 / 0.579283 (-0.179017) \| 0.405891 / 0.434364 (-0.028473) \| 0.471903 / 0.540337 (-0.068435) \| 0.563656 / 1.386936 (-0.823280) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006751 / 0.011353 (-0.004601) \| 0.004665 / 0.011008 (-0.006343) \| 0.098362 / 0.038508 (0.059854) \| 0.027451 / 0.023109 (0.004342) \| 0.421859 / 0.275898 (0.145961) \| 0.458089 / 0.323480 (0.134609) \| 0.004885 / 0.007986 (-0.003101) \| 0.003459 / 0.004328 (-0.000870) \| 0.075871 / 0.004250 (0.071621) \| 0.036591 / 0.037052 (-0.000462) \| 0.423307 / 0.258489 (0.164818) \| 0.467040 / 0.293841 (0.173199) \| 0.031837 / 0.128546 (-0.096710) \| 0.011604 / 0.075646 (-0.064042) \| 0.321132 / 0.419271 (-0.098140) \| 0.041806 / 0.043533 (-0.001727) \| 0.421653 / 0.255139 (0.166514) \| 0.445896 / 0.283200 (0.162696) \| 0.087998 / 0.141683 (-0.053685) \| 1.475818 / 1.452155 (0.023664) \| 1.559487 / 1.492716 (0.066770) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.203096 / 0.018006 (0.185090) \| 0.401381 / 0.000490 (0.400892) \| 0.004037 / 0.000200 (0.003837) \| 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) \| 0.023757 / 0.037411 (-0.013654) \| 0.099919 / 0.014526 (0.085393) \| 0.108384 / 0.176557 (-0.068173) \| 0.143780 / 0.737135 (-0.593355) \| 0.111528 / 0.296338 (-0.184811) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.475896 / 0.215209 (0.260686) \| 4.754567 / 2.077655 (2.676912) \| 2.444986 / 1.504120 (0.940866) \| 2.231055 / 1.541195 (0.689860) \| 2.283646 / 1.468490 (0.815156) \| 0.701303 / 4.584777 (-3.883474) \| 3.381597 / 3.745712 (-0.364115) \| 1.878714 / 5.269862 (-3.391148) \| 1.171566 / 4.565676 (-3.394111) \| 0.083106 / 0.424275 (-0.341169) \| 0.012575 / 0.007607 (0.004967) \| 0.582570 / 0.226044 (0.356526) \| 5.813677 / 2.268929 (3.544748) \| 2.908578 / 55.444624 (-52.536046) \| 2.548459 / 6.876477 (-4.328017) \| 2.581211 / 2.142072 (0.439139) \| 0.807925 / 4.805227 (-3.997302) \| 0.153516 / 6.500664 (-6.347148) \| 0.068763 / 0.075469 (-0.006706) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.249595 / 1.841788 (-0.592193) \| 14.208573 / 8.074308 (6.134265) \| 14.179174 / 10.191392 (3.987781) \| 0.156005 / 0.680424 (-0.524419) \| 0.017045 / 0.534201 (-0.517156) \| 0.377414 / 0.579283 (-0.201869) \| 0.395291 / 0.434364 (-0.039073) \| 0.444642 / 0.540337 (-0.095695) \| 0.531626 / 1.386936 (-0.855311) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#52888645daa6854928474df6308bd997c8878ced \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008871 / 0.011353 (-0.002482) \| 0.004616 / 0.011008 (-0.006392) \| 0.100910 / 0.038508 (0.062402) \| 0.030381 / 0.023109 (0.007272) \| 0.304636 / 0.275898 (0.028737) \| 0.384258 / 0.323480 (0.060778) \| 0.007019 / 0.007986 (-0.000966) \| 0.004262 / 0.004328 (-0.000066) \| 0.077082 / 0.004250 (0.072832) \| 0.035235 / 0.037052 (-0.001817) \| 0.318293 / 0.258489 (0.059804) \| 0.356578 / 0.293841 (0.062737) \| 0.033568 / 0.128546 (-0.094978) \| 0.011583 / 0.075646 (-0.064063) \| 0.322442 / 0.419271 (-0.096830) \| 0.041941 / 0.043533 (-0.001592) \| 0.310469 / 0.255139 (0.055330) \| 0.335626 / 0.283200 (0.052427) \| 0.088195 / 0.141683 (-0.053487) \| 1.466778 / 1.452155 (0.014623) \| 1.512459 / 1.492716 (0.019743) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.184126 / 0.018006 (0.166120) \| 0.413392 / 0.000490 (0.412902) \| 0.002191 / 0.000200 (0.001992) \| 0.000072 / 0.000054 (0.000018) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023426 / 0.037411 (-0.013985) \| 0.096240 / 0.014526 (0.081715) \| 0.105908 / 0.176557 (-0.070648) \| 0.146331 / 0.737135 (-0.590804) \| 0.107441 / 0.296338 (-0.188898) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.420018 / 0.215209 (0.204809) \| 4.198129 / 2.077655 (2.120474) \| 1.998726 / 1.504120 (0.494606) \| 1.870410 / 1.541195 (0.329215) \| 1.925160 / 1.468490 (0.456670) \| 0.688790 / 4.584777 (-3.895987) \| 3.430629 / 3.745712 (-0.315083) \| 2.875616 / 5.269862 (-2.394246) \| 1.566269 / 4.565676 (-2.999408) \| 0.082431 / 0.424275 (-0.341844) \| 0.012409 / 0.007607 (0.004802) \| 0.536178 / 0.226044 (0.310134) \| 5.342918 / 2.268929 (3.073989) \| 2.410814 / 55.444624 (-53.033811) \| 2.056518 / 6.876477 (-4.819958) \| 2.240148 / 2.142072 (0.098075) \| 0.804848 / 4.805227 (-4.000379) \| 0.147325 / 6.500664 (-6.353340) \| 0.064217 / 0.075469 (-0.011252) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.285725 / 1.841788 (-0.556063) \| 13.909739 / 8.074308 (5.835431) \| 14.025774 / 10.191392 (3.834382) \| 0.142413 / 0.680424 (-0.538011) \| 0.028390 / 0.534201 (-0.505811) \| 0.402345 / 0.579283 (-0.176939) \| 0.404341 / 0.434364 (-0.030023) \| 0.463055 / 0.540337 (-0.077282) \| 0.556811 / 1.386936 (-0.830125) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006557 / 0.011353 (-0.004795) \| 0.004668 / 0.011008 (-0.006340) \| 0.098839 / 0.038508 (0.060331) \| 0.027618 / 0.023109 (0.004508) \| 0.409338 / 0.275898 (0.133440) \| 0.444048 / 0.323480 (0.120568) \| 0.004881 / 0.007986 (-0.003105) \| 0.003434 / 0.004328 (-0.000895) \| 0.076497 / 0.004250 (0.072247) \| 0.038932 / 0.037052 (0.001880) \| 0.411419 / 0.258489 (0.152930) \| 0.451167 / 0.293841 (0.157326) \| 0.031649 / 0.128546 (-0.096897) \| 0.011691 / 0.075646 (-0.063955) \| 0.321586 / 0.419271 (-0.097685) \| 0.041984 / 0.043533 (-0.001549) \| 0.407717 / 0.255139 (0.152578) \| 0.434687 / 0.283200 (0.151487) \| 0.086419 / 0.141683 (-0.055264) \| 1.491755 / 1.452155 (0.039601) \| 1.569081 / 1.492716 (0.076364) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.231746 / 0.018006 (0.213739) \| 0.412271 / 0.000490 (0.411781) \| 0.000403 / 0.000200 (0.000203) \| 0.000063 / 0.000054 (0.000008) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024264 / 0.037411 (-0.013147) \| 0.100478 / 0.014526 (0.085952) \| 0.107065 / 0.176557 (-0.069491) \| 0.140724 / 0.737135 (-0.596412) \| 0.110631 / 0.296338 (-0.185707) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.472476 / 0.215209 (0.257267) \| 4.738919 / 2.077655 (2.661265) \| 2.438049 / 1.504120 (0.933929) \| 2.237855 / 1.541195 (0.696660) \| 2.282885 / 1.468490 (0.814395) \| 0.690420 / 4.584777 (-3.894357) \| 3.426487 / 3.745712 (-0.319225) \| 1.842443 / 5.269862 (-3.427418) \| 1.154466 / 4.565676 (-3.411210) \| 0.082166 / 0.424275 (-0.342109) \| 0.012309 / 0.007607 (0.004701) \| 0.574730 / 0.226044 (0.348686) \| 5.737566 / 2.268929 (3.468638) \| 2.882405 / 55.444624 (-52.562220) \| 2.540276 / 6.876477 (-4.336201) \| 2.552356 / 2.142072 (0.410283) \| 0.796413 / 4.805227 (-4.008815) \| 0.152705 / 6.500664 (-6.347959) \| 0.068273 / 0.075469 (-0.007196) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.244423 / 1.841788 (-0.597365) \| 13.827750 / 8.074308 (5.753442) \| 14.074083 / 10.191392 (3.882691) \| 0.140291 / 0.680424 (-0.540133) \| 0.017337 / 0.534201 (-0.516864) \| 0.389314 / 0.579283 (-0.189969) \| 0.390914 / 0.434364 (-0.043450) \| 0.450333 / 0.540337 (-0.090004) \| 0.543860 / 1.386936 (-0.843076) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2cdcddc51d3cda24c2d79ad137af9e55d0a38044 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009490 / 0.011353 (-0.001863) \| 0.005211 / 0.011008 (-0.005798) \| 0.100884 / 0.038508 (0.062376) \| 0.035834 / 0.023109 (0.012725) \| 0.293623 / 0.275898 (0.017724) \| 0.378118 / 0.323480 (0.054638) \| 0.008106 / 0.007986 (0.000120) \| 0.005339 / 0.004328 (0.001010) \| 0.076311 / 0.004250 (0.072061) \| 0.045954 / 0.037052 (0.008902) \| 0.308163 / 0.258489 (0.049674) \| 0.353470 / 0.293841 (0.059629) \| 0.038539 / 0.128546 (-0.090008) \| 0.012174 / 0.075646 (-0.063472) \| 0.334875 / 0.419271 (-0.084396) \| 0.048602 / 0.043533 (0.005069) \| 0.295803 / 0.255139 (0.040664) \| 0.318894 / 0.283200 (0.035695) \| 0.105487 / 0.141683 (-0.036195) \| 1.433628 / 1.452155 (-0.018526) \| 1.466843 / 1.492716 (-0.025873) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.203426 / 0.018006 (0.185419) \| 0.456877 / 0.000490 (0.456387) \| 0.001452 / 0.000200 (0.001252) \| 0.000088 / 0.000054 (0.000033) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028308 / 0.037411 (-0.009103) \| 0.108965 / 0.014526 (0.094439) \| 0.119552 / 0.176557 (-0.057005) \| 0.156371 / 0.737135 (-0.580765) \| 0.124141 / 0.296338 (-0.172197) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.400183 / 0.215209 (0.184973) \| 3.990983 / 2.077655 (1.913329) \| 1.806729 / 1.504120 (0.302609) \| 1.611944 / 1.541195 (0.070750) \| 1.740019 / 1.468490 (0.271529) \| 0.699600 / 4.584777 (-3.885177) \| 3.868711 / 3.745712 (0.122999) \| 3.249758 / 5.269862 (-2.020103) \| 1.832213 / 4.565676 (-2.733463) \| 0.085282 / 0.424275 (-0.338993) \| 0.012726 / 0.007607 (0.005119) \| 0.509385 / 0.226044 (0.283341) \| 5.066913 / 2.268929 (2.797984) \| 2.325710 / 55.444624 (-53.118914) \| 1.962238 / 6.876477 (-4.914239) \| 2.017576 / 2.142072 (-0.124496) \| 0.839444 / 4.805227 (-3.965783) \| 0.166936 / 6.500664 (-6.333728) \| 0.064546 / 0.075469 (-0.010923) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.196396 / 1.841788 (-0.645392) \| 15.077063 / 8.074308 (7.002755) \| 14.268103 / 10.191392 (4.076711) \| 0.163782 / 0.680424 (-0.516642) \| 0.028794 / 0.534201 (-0.505407) \| 0.440564 / 0.579283 (-0.138719) \| 0.439826 / 0.434364 (0.005463) \| 0.514786 / 0.540337 (-0.025551) \| 0.603353 / 1.386936 (-0.783583) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007874 / 0.011353 (-0.003479) \| 0.005347 / 0.011008 (-0.005661) \| 0.099461 / 0.038508 (0.060953) \| 0.034010 / 0.023109 (0.010901) \| 0.384650 / 0.275898 (0.108752) \| 0.423827 / 0.323480 (0.100347) \| 0.006201 / 0.007986 (-0.001784) \| 0.004212 / 0.004328 (-0.000117) \| 0.074354 / 0.004250 (0.070104) \| 0.051675 / 0.037052 (0.014623) \| 0.392488 / 0.258489 (0.133999) \| 0.425828 / 0.293841 (0.131987) \| 0.037444 / 0.128546 (-0.091103) \| 0.012388 / 0.075646 (-0.063258) \| 0.334482 / 0.419271 (-0.084789) \| 0.050715 / 0.043533 (0.007182) \| 0.378323 / 0.255139 (0.123184) \| 0.395450 / 0.283200 (0.112250) \| 0.108403 / 0.141683 (-0.033280) \| 1.426803 / 1.452155 (-0.025352) \| 1.532417 / 1.492716 (0.039701) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.219989 / 0.018006 (0.201982) \| 0.454101 / 0.000490 (0.453611) \| 0.000407 / 0.000200 (0.000207) \| 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) \| 0.030590 / 0.037411 (-0.006822) \| 0.113483 / 0.014526 (0.098957) \| 0.122603 / 0.176557 (-0.053954) \| 0.161031 / 0.737135 (-0.576104) \| 0.128039 / 0.296338 (-0.168300) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.430458 / 0.215209 (0.215249) \| 4.286594 / 2.077655 (2.208940) \| 2.056666 / 1.504120 (0.552546) \| 1.861142 / 1.541195 (0.319948) \| 1.937185 / 1.468490 (0.468695) \| 0.701881 / 4.584777 (-3.882896) \| 3.970144 / 3.745712 (0.224432) \| 2.107118 / 5.269862 (-3.162744) \| 1.351561 / 4.565676 (-3.214115) \| 0.085470 / 0.424275 (-0.338805) \| 0.012366 / 0.007607 (0.004759) \| 0.525212 / 0.226044 (0.299168) \| 5.301553 / 2.268929 (3.032625) \| 2.593862 / 55.444624 (-52.850763) \| 2.287315 / 6.876477 (-4.589161) \| 2.368249 / 2.142072 (0.226176) \| 0.855656 / 4.805227 (-3.949571) \| 0.167846 / 6.500664 (-6.332818) \| 0.064521 / 0.075469 (-0.010948) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.237008 / 1.841788 (-0.604779) \| 15.784303 / 8.074308 (7.709995) \| 14.613081 / 10.191392 (4.421689) \| 0.161012 / 0.680424 (-0.519412) \| 0.017928 / 0.534201 (-0.516273) \| 0.423905 / 0.579283 (-0.155378) \| 0.428316 / 0.434364 (-0.006048) \| 0.500226 / 0.540337 (-0.040112) \| 0.606725 / 1.386936 (-0.780211) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#08473e2ee66acb7e6f82d3591bb9b03924a661ed \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008874 / 0.011353 (-0.002479) \| 0.004581 / 0.011008 (-0.006428) \| 0.100180 / 0.038508 (0.061672) \| 0.029990 / 0.023109 (0.006880) \| 0.301616 / 0.275898 (0.025718) \| 0.343662 / 0.323480 (0.020183) \| 0.007111 / 0.007986 (-0.000875) \| 0.003428 / 0.004328 (-0.000900) \| 0.078031 / 0.004250 (0.073780) \| 0.037332 / 0.037052 (0.000279) \| 0.301977 / 0.258489 (0.043488) \| 0.345581 / 0.293841 (0.051740) \| 0.034305 / 0.128546 (-0.094241) \| 0.011660 / 0.075646 (-0.063986) \| 0.322289 / 0.419271 (-0.096982) \| 0.041488 / 0.043533 (-0.002045) \| 0.301612 / 0.255139 (0.046473) \| 0.328174 / 0.283200 (0.044974) \| 0.085561 / 0.141683 (-0.056122) \| 1.482114 / 1.452155 (0.029959) \| 1.556194 / 1.492716 (0.063478) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.186989 / 0.018006 (0.168983) \| 0.421499 / 0.000490 (0.421009) \| 0.001193 / 0.000200 (0.000993) \| 0.000070 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023551 / 0.037411 (-0.013861) \| 0.099868 / 0.014526 (0.085343) \| 0.105233 / 0.176557 (-0.071324) \| 0.141628 / 0.737135 (-0.595507) \| 0.109004 / 0.296338 (-0.187335) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.415189 / 0.215209 (0.199979) \| 4.145716 / 2.077655 (2.068061) \| 1.837917 / 1.504120 (0.333797) \| 1.635043 / 1.541195 (0.093848) \| 1.683299 / 1.468490 (0.214809) \| 0.688538 / 4.584777 (-3.896239) \| 3.412628 / 3.745712 (-0.333084) \| 1.877456 / 5.269862 (-3.392405) \| 1.154129 / 4.565676 (-3.411547) \| 0.081850 / 0.424275 (-0.342425) \| 0.012309 / 0.007607 (0.004702) \| 0.522830 / 0.226044 (0.296785) \| 5.238685 / 2.268929 (2.969756) \| 2.277840 / 55.444624 (-53.166784) \| 1.941787 / 6.876477 (-4.934690) \| 1.999688 / 2.142072 (-0.142385) \| 0.807590 / 4.805227 (-3.997637) \| 0.148157 / 6.500664 (-6.352507) \| 0.064898 / 0.075469 (-0.010571) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.253859 / 1.841788 (-0.587929) \| 13.676097 / 8.074308 (5.601789) \| 14.237837 / 10.191392 (4.046444) \| 0.137178 / 0.680424 (-0.543246) \| 0.028971 / 0.534201 (-0.505230) \| 0.400380 / 0.579283 (-0.178903) \| 0.409990 / 0.434364 (-0.024374) \| 0.462552 / 0.540337 (-0.077786) \| 0.552153 / 1.386936 (-0.834783) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006831 / 0.011353 (-0.004522) \| 0.004627 / 0.011008 (-0.006381) \| 0.099883 / 0.038508 (0.061375) \| 0.028072 / 0.023109 (0.004962) \| 0.343556 / 0.275898 (0.067658) \| 0.386792 / 0.323480 (0.063312) \| 0.005080 / 0.007986 (-0.002906) \| 0.003508 / 0.004328 (-0.000820) \| 0.077803 / 0.004250 (0.073552) \| 0.040038 / 0.037052 (0.002985) \| 0.345089 / 0.258489 (0.086600) \| 0.396078 / 0.293841 (0.102238) \| 0.032241 / 0.128546 (-0.096305) \| 0.011711 / 0.075646 (-0.063935) \| 0.320531 / 0.419271 (-0.098740) \| 0.043658 / 0.043533 (0.000125) \| 0.344696 / 0.255139 (0.089557) \| 0.389847 / 0.283200 (0.106648) \| 0.092328 / 0.141683 (-0.049355) \| 1.477290 / 1.452155 (0.025136) \| 1.548698 / 1.492716 (0.055982) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.236073 / 0.018006 (0.218067) \| 0.422113 / 0.000490 (0.421624) \| 0.000431 / 0.000200 (0.000231) \| 0.000060 / 0.000054 (0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024738 / 0.037411 (-0.012673) \| 0.100546 / 0.014526 (0.086020) \| 0.107550 / 0.176557 (-0.069006) \| 0.146056 / 0.737135 (-0.591079) \| 0.112665 / 0.296338 (-0.183674) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.490259 / 0.215209 (0.275050) \| 4.907994 / 2.077655 (2.830339) \| 2.547175 / 1.504120 (1.043055) \| 2.344419 / 1.541195 (0.803224) \| 2.403985 / 1.468490 (0.935495) \| 0.696011 / 4.584777 (-3.888766) \| 3.442426 / 3.745712 (-0.303286) \| 1.878702 / 5.269862 (-3.391159) \| 1.158280 / 4.565676 (-3.407396) \| 0.082300 / 0.424275 (-0.341975) \| 0.012513 / 0.007607 (0.004906) \| 0.602696 / 0.226044 (0.376651) \| 6.014592 / 2.268929 (3.745663) \| 3.014466 / 55.444624 (-52.430159) \| 2.669376 / 6.876477 (-4.207101) \| 2.724485 / 2.142072 (0.582412) \| 0.799795 / 4.805227 (-4.005432) \| 0.151220 / 6.500664 (-6.349444) \| 0.067486 / 0.075469 (-0.007983) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.281265 / 1.841788 (-0.560523) \| 14.362284 / 8.074308 (6.287976) \| 14.313690 / 10.191392 (4.122298) \| 0.142870 / 0.680424 (-0.537554) \| 0.017206 / 0.534201 (-0.516995) \| 0.380084 / 0.579283 (-0.199199) \| 0.388161 / 0.434364 (-0.046203) \| 0.442617 / 0.540337 (-0.097721) \| 0.528487 / 1.386936 (-0.858449) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#452b7f8ae78967dc662f5436e751233d46c62e78 \"CML watermark\")\n", "@lhoestq @amyeroberts @gante I did a substantial rewrite and all tests are passing now (Windows seems to time out or something and I can't figure out why - not sure if that's related to this PR!). I also confirmed tests are passing locally with Py==3.10. \r\n\r\nAside from incorporating everyone's comments, I also made a context manager to create and handle shared memory - this ensures that shared memory is cleaned up even if execution is interrupted. Also, shared memory names include a UUID string now to avoid collisions. Finally, string arrays are now split up into fixed-width character arrays in the workers so that they can be passed through shared memory, and the parent process reconstructs them into string arrays.", "Update: `test_arrow_dataset.py` ran fine in this branch on my Windows machine (Py 3.10), so I have no idea what's up with those tests", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008852 / 0.011353 (-0.002500) \| 0.004545 / 0.011008 (-0.006464) \| 0.099814 / 0.038508 (0.061306) \| 0.030314 / 0.023109 (0.007205) \| 0.310426 / 0.275898 (0.034528) \| 0.366893 / 0.323480 (0.043413) \| 0.007183 / 0.007986 (-0.000802) \| 0.003476 / 0.004328 (-0.000853) \| 0.077566 / 0.004250 (0.073315) \| 0.038269 / 0.037052 (0.001217) \| 0.319133 / 0.258489 (0.060644) \| 0.352399 / 0.293841 (0.058558) \| 0.033847 / 0.128546 (-0.094700) \| 0.011568 / 0.075646 (-0.064078) \| 0.321355 / 0.419271 (-0.097917) \| 0.040719 / 0.043533 (-0.002814) \| 0.304812 / 0.255139 (0.049673) \| 0.329512 / 0.283200 (0.046312) \| 0.088045 / 0.141683 (-0.053638) \| 1.514182 / 1.452155 (0.062027) \| 1.529459 / 1.492716 (0.036742) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.216749 / 0.018006 (0.198743) \| 0.409909 / 0.000490 (0.409419) \| 0.002790 / 0.000200 (0.002590) \| 0.000081 / 0.000054 (0.000027) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023390 / 0.037411 (-0.014021) \| 0.095955 / 0.014526 (0.081430) \| 0.104749 / 0.176557 (-0.071807) \| 0.143414 / 0.737135 (-0.593721) \| 0.109011 / 0.296338 (-0.187328) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.420410 / 0.215209 (0.205201) \| 4.185745 / 2.077655 (2.108090) \| 1.910207 / 1.504120 (0.406087) \| 1.679330 / 1.541195 (0.138135) \| 1.727134 / 1.468490 (0.258644) \| 0.692379 / 4.584777 (-3.892398) \| 3.358731 / 3.745712 (-0.386982) \| 2.914657 / 5.269862 (-2.355205) \| 1.506083 / 4.565676 (-3.059594) \| 0.081922 / 0.424275 (-0.342353) \| 0.012691 / 0.007607 (0.005084) \| 0.530942 / 0.226044 (0.304897) \| 5.357642 / 2.268929 (3.088714) \| 2.387347 / 55.444624 (-53.057277) \| 2.030001 / 6.876477 (-4.846476) \| 2.026405 / 2.142072 (-0.115667) \| 0.809406 / 4.805227 (-3.995821) \| 0.149003 / 6.500664 (-6.351661) \| 0.066910 / 0.075469 (-0.008559) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.278160 / 1.841788 (-0.563627) \| 13.632742 / 8.074308 (5.558434) \| 13.995537 / 10.191392 (3.804145) \| 0.136507 / 0.680424 (-0.543917) \| 0.028817 / 0.534201 (-0.505384) \| 0.394842 / 0.579283 (-0.184441) \| 0.399526 / 0.434364 (-0.034838) \| 0.459174 / 0.540337 (-0.081163) \| 0.536877 / 1.386936 (-0.850059) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006814 / 0.011353 (-0.004539) \| 0.004456 / 0.011008 (-0.006552) \| 0.098386 / 0.038508 (0.059878) \| 0.028124 / 0.023109 (0.005015) \| 0.409004 / 0.275898 (0.133106) \| 0.446746 / 0.323480 (0.123266) \| 0.005108 / 0.007986 (-0.002877) \| 0.004807 / 0.004328 (0.000479) \| 0.075751 / 0.004250 (0.071500) \| 0.039297 / 0.037052 (0.002244) \| 0.413198 / 0.258489 (0.154709) \| 0.452124 / 0.293841 (0.158283) \| 0.032534 / 0.128546 (-0.096012) \| 0.011689 / 0.075646 (-0.063957) \| 0.325465 / 0.419271 (-0.093806) \| 0.041347 / 0.043533 (-0.002185) \| 0.411489 / 0.255139 (0.156350) \| 0.447120 / 0.283200 (0.163920) \| 0.093058 / 0.141683 (-0.048625) \| 1.489903 / 1.452155 (0.037748) \| 1.580771 / 1.492716 (0.088055) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.192619 / 0.018006 (0.174613) \| 0.399201 / 0.000490 (0.398711) \| 0.002894 / 0.000200 (0.002694) \| 0.000071 / 0.000054 (0.000017) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025120 / 0.037411 (-0.012292) \| 0.100126 / 0.014526 (0.085600) \| 0.108669 / 0.176557 (-0.067887) \| 0.148687 / 0.737135 (-0.588448) \| 0.112286 / 0.296338 (-0.184052) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.438866 / 0.215209 (0.223657) \| 4.382418 / 2.077655 (2.304764) \| 2.106450 / 1.504120 (0.602330) \| 1.885105 / 1.541195 (0.343910) \| 1.922948 / 1.468490 (0.454458) \| 0.693145 / 4.584777 (-3.891632) \| 3.378206 / 3.745712 (-0.367506) \| 1.867295 / 5.269862 (-3.402566) \| 1.164999 / 4.565676 (-3.400678) \| 0.081918 / 0.424275 (-0.342357) \| 0.012225 / 0.007607 (0.004618) \| 0.547114 / 0.226044 (0.321069) \| 5.454208 / 2.268929 (3.185279) \| 2.532112 / 55.444624 (-52.912512) \| 2.192573 / 6.876477 (-4.683904) \| 2.225364 / 2.142072 (0.083291) \| 0.797165 / 4.805227 (-4.008062) \| 0.151185 / 6.500664 (-6.349480) \| 0.067512 / 0.075469 (-0.007957) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.303905 / 1.841788 (-0.537883) \| 14.107678 / 8.074308 (6.033370) \| 14.147630 / 10.191392 (3.956238) \| 0.156597 / 0.680424 (-0.523827) \| 0.017037 / 0.534201 (-0.517164) \| 0.383202 / 0.579283 (-0.196081) \| 0.385340 / 0.434364 (-0.049024) \| 0.443338 / 0.540337 (-0.097000) \| 0.542345 / 1.386936 (-0.844591) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#38228533a03767aab713a3806aac0e8503668c68 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009982 / 0.011353 (-0.001371) \| 0.005327 / 0.011008 (-0.005681) \| 0.099092 / 0.038508 (0.060584) \| 0.035824 / 0.023109 (0.012715) \| 0.303258 / 0.275898 (0.027360) \| 0.335379 / 0.323480 (0.011899) \| 0.008192 / 0.007986 (0.000207) \| 0.004242 / 0.004328 (-0.000087) \| 0.076277 / 0.004250 (0.072026) \| 0.043851 / 0.037052 (0.006799) \| 0.307750 / 0.258489 (0.049261) \| 0.348459 / 0.293841 (0.054618) \| 0.038943 / 0.128546 (-0.089604) \| 0.012128 / 0.075646 (-0.063519) \| 0.334143 / 0.419271 (-0.085128) \| 0.047865 / 0.043533 (0.004332) \| 0.300909 / 0.255139 (0.045770) \| 0.320879 / 0.283200 (0.037680) \| 0.103812 / 0.141683 (-0.037871) \| 1.468646 / 1.452155 (0.016491) \| 1.557660 / 1.492716 (0.064944) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.244108 / 0.018006 (0.226102) \| 0.554895 / 0.000490 (0.554405) \| 0.005311 / 0.000200 (0.005111) \| 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) \| 0.028771 / 0.037411 (-0.008640) \| 0.108133 / 0.014526 (0.093608) \| 0.120098 / 0.176557 (-0.056458) \| 0.159815 / 0.737135 (-0.577320) \| 0.125437 / 0.296338 (-0.170901) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.397675 / 0.215209 (0.182466) \| 3.975839 / 2.077655 (1.898184) \| 1.797803 / 1.504120 (0.293683) \| 1.612517 / 1.541195 (0.071322) \| 1.659086 / 1.468490 (0.190596) \| 0.679822 / 4.584777 (-3.904955) \| 3.688321 / 3.745712 (-0.057391) \| 2.155285 / 5.269862 (-3.114576) \| 1.466453 / 4.565676 (-3.099223) \| 0.084102 / 0.424275 (-0.340173) \| 0.012074 / 0.007607 (0.004467) \| 0.503744 / 0.226044 (0.277699) \| 5.075599 / 2.268929 (2.806670) \| 2.312149 / 55.444624 (-53.132476) \| 1.975028 / 6.876477 (-4.901449) \| 2.069554 / 2.142072 (-0.072519) \| 0.828329 / 4.805227 (-3.976898) \| 0.162816 / 6.500664 (-6.337849) \| 0.063813 / 0.075469 (-0.011656) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.173327 / 1.841788 (-0.668461) \| 15.281584 / 8.074308 (7.207276) \| 14.450851 / 10.191392 (4.259459) \| 0.165621 / 0.680424 (-0.514802) \| 0.028779 / 0.534201 (-0.505422) \| 0.438483 / 0.579283 (-0.140800) \| 0.438477 / 0.434364 (0.004113) \| 0.517703 / 0.540337 (-0.022634) \| 0.615119 / 1.386936 (-0.771817) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007013 / 0.011353 (-0.004340) \| 0.005272 / 0.011008 (-0.005736) \| 0.097203 / 0.038508 (0.058695) \| 0.033103 / 0.023109 (0.009994) \| 0.380203 / 0.275898 (0.104305) \| 0.414868 / 0.323480 (0.091388) \| 0.006326 / 0.007986 (-0.001659) \| 0.005433 / 0.004328 (0.001104) \| 0.074299 / 0.004250 (0.070049) \| 0.049418 / 0.037052 (0.012366) \| 0.388771 / 0.258489 (0.130282) \| 0.435169 / 0.293841 (0.141328) \| 0.036170 / 0.128546 (-0.092377) \| 0.012452 / 0.075646 (-0.063195) \| 0.331215 / 0.419271 (-0.088056) \| 0.048577 / 0.043533 (0.005044) \| 0.381491 / 0.255139 (0.126352) \| 0.396731 / 0.283200 (0.113531) \| 0.106435 / 0.141683 (-0.035248) \| 1.446437 / 1.452155 (-0.005718) \| 1.542337 / 1.492716 (0.049621) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.216714 / 0.018006 (0.198707) \| 0.562460 / 0.000490 (0.561970) \| 0.003636 / 0.000200 (0.003436) \| 0.000100 / 0.000054 (0.000045) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028726 / 0.037411 (-0.008686) \| 0.111993 / 0.014526 (0.097467) \| 0.125325 / 0.176557 (-0.051232) \| 0.157779 / 0.737135 (-0.579356) \| 0.130633 / 0.296338 (-0.165705) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.440520 / 0.215209 (0.225311) \| 4.396283 / 2.077655 (2.318628) \| 2.204714 / 1.504120 (0.700594) \| 2.011667 / 1.541195 (0.470473) \| 2.050518 / 1.468490 (0.582028) \| 0.695204 / 4.584777 (-3.889573) \| 3.779699 / 3.745712 (0.033987) \| 2.096064 / 5.269862 (-3.173798) \| 1.325446 / 4.565676 (-3.240230) \| 0.085315 / 0.424275 (-0.338960) \| 0.012178 / 0.007607 (0.004570) \| 0.550478 / 0.226044 (0.324434) \| 5.471872 / 2.268929 (3.202943) \| 2.687147 / 55.444624 (-52.757478) \| 2.348465 / 6.876477 (-4.528011) \| 2.409700 / 2.142072 (0.267628) \| 0.839468 / 4.805227 (-3.965760) \| 0.167030 / 6.500664 (-6.333635) \| 0.063243 / 0.075469 (-0.012226) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.257347 / 1.841788 (-0.584441) \| 15.157821 / 8.074308 (7.083512) \| 14.646381 / 10.191392 (4.454989) \| 0.185550 / 0.680424 (-0.494874) \| 0.018441 / 0.534201 (-0.515760) \| 0.423330 / 0.579283 (-0.155954) \| 0.426204 / 0.434364 (-0.008160) \| 0.498985 / 0.540337 (-0.041352) \| 0.608432 / 1.386936 (-0.778504) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0f96e349ec5665e1e4135b5a108ba5db227bd3b1 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010856 / 0.011353 (-0.000497) \| 0.005897 / 0.011008 (-0.005111) \| 0.117826 / 0.038508 (0.079317) \| 0.041899 / 0.023109 (0.018790) \| 0.353804 / 0.275898 (0.077906) \| 0.431021 / 0.323480 (0.107541) \| 0.009288 / 0.007986 (0.001303) \| 0.004556 / 0.004328 (0.000227) \| 0.089344 / 0.004250 (0.085094) \| 0.052224 / 0.037052 (0.015172) \| 0.373242 / 0.258489 (0.114753) \| 0.420667 / 0.293841 (0.126826) \| 0.044191 / 0.128546 (-0.084355) \| 0.014083 / 0.075646 (-0.061564) \| 0.400373 / 0.419271 (-0.018898) \| 0.056119 / 0.043533 (0.012586) \| 0.363302 / 0.255139 (0.108163) \| 0.382073 / 0.283200 (0.098873) \| 0.118646 / 0.141683 (-0.023037) \| 1.696576 / 1.452155 (0.244422) \| 1.756518 / 1.492716 (0.263802) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.216388 / 0.018006 (0.198382) \| 0.485732 / 0.000490 (0.485242) \| 0.004012 / 0.000200 (0.003812) \| 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) \| 0.032095 / 0.037411 (-0.005316) \| 0.128954 / 0.014526 (0.114429) \| 0.137564 / 0.176557 (-0.038993) \| 0.184315 / 0.737135 (-0.552820) \| 0.144707 / 0.296338 (-0.151631) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.472792 / 0.215209 (0.257583) \| 4.723044 / 2.077655 (2.645390) \| 2.115075 / 1.504120 (0.610955) \| 1.898993 / 1.541195 (0.357798) \| 1.972894 / 1.468490 (0.504404) \| 0.807210 / 4.584777 (-3.777567) \| 4.493139 / 3.745712 (0.747427) \| 2.501053 / 5.269862 (-2.768808) \| 1.686121 / 4.565676 (-2.879556) \| 0.099545 / 0.424275 (-0.324730) \| 0.014360 / 0.007607 (0.006753) \| 0.596235 / 0.226044 (0.370191) \| 5.944285 / 2.268929 (3.675357) \| 2.654944 / 55.444624 (-52.789681) \| 2.281451 / 6.876477 (-4.595026) \| 2.448407 / 2.142072 (0.306334) \| 1.000512 / 4.805227 (-3.804716) \| 0.196413 / 6.500664 (-6.304251) \| 0.075810 / 0.075469 (0.000341) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.435707 / 1.841788 (-0.406081) \| 17.931070 / 8.074308 (9.856762) \| 16.635522 / 10.191392 (6.444130) \| 0.189119 / 0.680424 (-0.491304) \| 0.034392 / 0.534201 (-0.499809) \| 0.519041 / 0.579283 (-0.060242) \| 0.516159 / 0.434364 (0.081795) \| 0.601180 / 0.540337 (0.060843) \| 0.713180 / 1.386936 (-0.673756) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008741 / 0.011353 (-0.002612) \| 0.006102 / 0.011008 (-0.004906) \| 0.114787 / 0.038508 (0.076279) \| 0.039610 / 0.023109 (0.016501) \| 0.451730 / 0.275898 (0.175832) \| 0.488820 / 0.323480 (0.165340) \| 0.006979 / 0.007986 (-0.001006) \| 0.006458 / 0.004328 (0.002130) \| 0.086505 / 0.004250 (0.082254) \| 0.057684 / 0.037052 (0.020632) \| 0.451354 / 0.258489 (0.192865) \| 0.523143 / 0.293841 (0.229302) \| 0.043224 / 0.128546 (-0.085323) \| 0.014671 / 0.075646 (-0.060975) \| 0.398030 / 0.419271 (-0.021241) \| 0.063650 / 0.043533 (0.020117) \| 0.448324 / 0.255139 (0.193185) \| 0.476560 / 0.283200 (0.193361) \| 0.125772 / 0.141683 (-0.015911) \| 1.801051 / 1.452155 (0.348896) \| 1.872736 / 1.492716 (0.380020) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.256146 / 0.018006 (0.238139) \| 0.486915 / 0.000490 (0.486425) \| 0.000513 / 0.000200 (0.000313) \| 0.000067 / 0.000054 (0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035242 / 0.037411 (-0.002170) \| 0.134322 / 0.014526 (0.119797) \| 0.144786 / 0.176557 (-0.031770) \| 0.188786 / 0.737135 (-0.548349) \| 0.151737 / 0.296338 (-0.144602) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.506047 / 0.215209 (0.290838) \| 5.028253 / 2.077655 (2.950598) \| 2.393070 / 1.504120 (0.888950) \| 2.157847 / 1.541195 (0.616652) \| 2.229412 / 1.468490 (0.760922) \| 0.828973 / 4.584777 (-3.755804) \| 4.741470 / 3.745712 (0.995758) \| 4.048118 / 5.269862 (-1.221744) \| 2.573818 / 4.565676 (-1.991859) \| 0.101019 / 0.424275 (-0.323256) \| 0.014640 / 0.007607 (0.007033) \| 0.632591 / 0.226044 (0.406546) \| 6.289153 / 2.268929 (4.020224) \| 2.977261 / 55.444624 (-52.467363) \| 2.554396 / 6.876477 (-4.322081) \| 2.619446 / 2.142072 (0.477374) \| 0.988376 / 4.805227 (-3.816851) \| 0.196895 / 6.500664 (-6.303769) \| 0.076355 / 0.075469 (0.000886) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.493570 / 1.841788 (-0.348218) \| 18.422758 / 8.074308 (10.348449) \| 17.007352 / 10.191392 (6.815960) \| 0.191903 / 0.680424 (-0.488521) \| 0.020974 / 0.534201 (-0.513227) \| 0.500573 / 0.579283 (-0.078710) \| 0.489381 / 0.434364 (0.055017) \| 0.580765 / 0.540337 (0.040428) \| 0.698907 / 1.386936 (-0.688029) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fa9baa268a6d285ab0a61cc37413392c94cfe2e8 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008979 / 0.011353 (-0.002374) \| 0.004497 / 0.011008 (-0.006511) \| 0.102227 / 0.038508 (0.063719) \| 0.031302 / 0.023109 (0.008193) \| 0.298488 / 0.275898 (0.022590) \| 0.372589 / 0.323480 (0.049109) \| 0.007261 / 0.007986 (-0.000725) \| 0.003542 / 0.004328 (-0.000786) \| 0.078503 / 0.004250 (0.074253) \| 0.039474 / 0.037052 (0.002422) \| 0.310991 / 0.258489 (0.052502) \| 0.353245 / 0.293841 (0.059404) \| 0.033798 / 0.128546 (-0.094749) \| 0.011634 / 0.075646 (-0.064012) \| 0.321141 / 0.419271 (-0.098131) \| 0.041264 / 0.043533 (-0.002268) \| 0.300900 / 0.255139 (0.045761) \| 0.326255 / 0.283200 (0.043055) \| 0.092477 / 0.141683 (-0.049205) \| 1.478921 / 1.452155 (0.026766) \| 1.514915 / 1.492716 (0.022198) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.184415 / 0.018006 (0.166408) \| 0.428986 / 0.000490 (0.428497) \| 0.002590 / 0.000200 (0.002390) \| 0.000072 / 0.000054 (0.000018) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023730 / 0.037411 (-0.013681) \| 0.099846 / 0.014526 (0.085320) \| 0.107075 / 0.176557 (-0.069482) \| 0.147475 / 0.737135 (-0.589661) \| 0.111802 / 0.296338 (-0.184537) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.413704 / 0.215209 (0.198494) \| 4.144498 / 2.077655 (2.066843) \| 1.855900 / 1.504120 (0.351780) \| 1.647958 / 1.541195 (0.106763) \| 1.712437 / 1.468490 (0.243947) \| 0.688382 / 4.584777 (-3.896395) \| 3.432136 / 3.745712 (-0.313576) \| 2.837211 / 5.269862 (-2.432651) \| 1.519004 / 4.565676 (-3.046672) \| 0.082429 / 0.424275 (-0.341846) \| 0.012610 / 0.007607 (0.005003) \| 0.525078 / 0.226044 (0.299034) \| 5.272932 / 2.268929 (3.004003) \| 2.340482 / 55.444624 (-53.104143) \| 2.007372 / 6.876477 (-4.869104) \| 2.060567 / 2.142072 (-0.081506) \| 0.806476 / 4.805227 (-3.998752) \| 0.149421 / 6.500664 (-6.351243) \| 0.066252 / 0.075469 (-0.009218) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.235078 / 1.841788 (-0.606710) \| 13.870758 / 8.074308 (5.796450) \| 14.104582 / 10.191392 (3.913190) \| 0.159375 / 0.680424 (-0.521049) \| 0.029233 / 0.534201 (-0.504968) \| 0.392184 / 0.579283 (-0.187099) \| 0.407909 / 0.434364 (-0.026455) \| 0.458757 / 0.540337 (-0.081581) \| 0.547681 / 1.386936 (-0.839255) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007194 / 0.011353 (-0.004159) \| 0.004578 / 0.011008 (-0.006431) \| 0.098936 / 0.038508 (0.060428) \| 0.029639 / 0.023109 (0.006530) \| 0.347241 / 0.275898 (0.071343) \| 0.378838 / 0.323480 (0.055358) \| 0.005632 / 0.007986 (-0.002353) \| 0.003469 / 0.004328 (-0.000860) \| 0.075536 / 0.004250 (0.071285) \| 0.043301 / 0.037052 (0.006249) \| 0.348091 / 0.258489 (0.089602) \| 0.388595 / 0.293841 (0.094754) \| 0.033512 / 0.128546 (-0.095034) \| 0.011754 / 0.075646 (-0.063892) \| 0.321003 / 0.419271 (-0.098268) \| 0.044634 / 0.043533 (0.001101) \| 0.346688 / 0.255139 (0.091549) \| 0.366346 / 0.283200 (0.083147) \| 0.093650 / 0.141683 (-0.048033) \| 1.509913 / 1.452155 (0.057759) \| 1.596414 / 1.492716 (0.103698) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230466 / 0.018006 (0.212459) \| 0.417106 / 0.000490 (0.416617) \| 0.000959 / 0.000200 (0.000759) \| 0.000070 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025581 / 0.037411 (-0.011830) \| 0.105246 / 0.014526 (0.090720) \| 0.108997 / 0.176557 (-0.067560) \| 0.144342 / 0.737135 (-0.592794) \| 0.113911 / 0.296338 (-0.182427) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.479608 / 0.215209 (0.264399) \| 4.766081 / 2.077655 (2.688426) \| 2.446597 / 1.504120 (0.942477) \| 2.228278 / 1.541195 (0.687083) \| 2.289943 / 1.468490 (0.821453) \| 0.703146 / 4.584777 (-3.881631) \| 3.414150 / 3.745712 (-0.331562) \| 2.957730 / 5.269862 (-2.312132) \| 1.531524 / 4.565676 (-3.034152) \| 0.083449 / 0.424275 (-0.340826) \| 0.012684 / 0.007607 (0.005077) \| 0.587622 / 0.226044 (0.361578) \| 5.888791 / 2.268929 (3.619863) \| 2.884200 / 55.444624 (-52.560424) \| 2.543739 / 6.876477 (-4.332737) \| 2.596245 / 2.142072 (0.454173) \| 0.813070 / 4.805227 (-3.992157) \| 0.152706 / 6.500664 (-6.347958) \| 0.069257 / 0.075469 (-0.006212) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.302945 / 1.841788 (-0.538842) \| 14.484051 / 8.074308 (6.409743) \| 14.216143 / 10.191392 (4.024751) \| 0.154537 / 0.680424 (-0.525886) \| 0.016909 / 0.534201 (-0.517292) \| 0.389433 / 0.579283 (-0.189850) \| 0.393280 / 0.434364 (-0.041084) \| 0.446884 / 0.540337 (-0.093453) \| 0.534394 / 1.386936 (-0.852542) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2bcdeb952c57c5f22643061d49d16014a7b6426a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008822 / 0.011353 (-0.002530) \| 0.004826 / 0.011008 (-0.006182) \| 0.102710 / 0.038508 (0.064202) \| 0.030353 / 0.023109 (0.007244) \| 0.297224 / 0.275898 (0.021326) \| 0.371861 / 0.323480 (0.048381) \| 0.007266 / 0.007986 (-0.000720) \| 0.003632 / 0.004328 (-0.000696) \| 0.079960 / 0.004250 (0.075710) \| 0.036908 / 0.037052 (-0.000144) \| 0.309582 / 0.258489 (0.051093) \| 0.350108 / 0.293841 (0.056267) \| 0.034280 / 0.128546 (-0.094266) \| 0.011739 / 0.075646 (-0.063907) \| 0.323217 / 0.419271 (-0.096054) \| 0.043491 / 0.043533 (-0.000042) \| 0.298454 / 0.255139 (0.043315) \| 0.326735 / 0.283200 (0.043535) \| 0.093955 / 0.141683 (-0.047728) \| 1.494313 / 1.452155 (0.042159) \| 1.562104 / 1.492716 (0.069388) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.182796 / 0.018006 (0.164790) \| 0.420133 / 0.000490 (0.419643) \| 0.002537 / 0.000200 (0.002337) \| 0.000070 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023143 / 0.037411 (-0.014269) \| 0.098560 / 0.014526 (0.084034) \| 0.105060 / 0.176557 (-0.071496) \| 0.140269 / 0.737135 (-0.596866) \| 0.109120 / 0.296338 (-0.187219) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.419907 / 0.215209 (0.204698) \| 4.196179 / 2.077655 (2.118524) \| 1.887663 / 1.504120 (0.383543) \| 1.686232 / 1.541195 (0.145037) \| 1.741741 / 1.468490 (0.273251) \| 0.696222 / 4.584777 (-3.888555) \| 3.400250 / 3.745712 (-0.345462) \| 1.875058 / 5.269862 (-3.394803) \| 1.159466 / 4.565676 (-3.406211) \| 0.082520 / 0.424275 (-0.341755) \| 0.012408 / 0.007607 (0.004801) \| 0.525212 / 0.226044 (0.299168) \| 5.283691 / 2.268929 (3.014762) \| 2.314487 / 55.444624 (-53.130138) \| 1.966212 / 6.876477 (-4.910265) \| 2.023458 / 2.142072 (-0.118615) \| 0.808896 / 4.805227 (-3.996331) \| 0.148973 / 6.500664 (-6.351691) \| 0.065378 / 0.075469 (-0.010091) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.223833 / 1.841788 (-0.617955) \| 14.053651 / 8.074308 (5.979343) \| 14.072165 / 10.191392 (3.880773) \| 0.156006 / 0.680424 (-0.524418) \| 0.028665 / 0.534201 (-0.505536) \| 0.392099 / 0.579283 (-0.187184) \| 0.401460 / 0.434364 (-0.032904) \| 0.462184 / 0.540337 (-0.078153) \| 0.540459 / 1.386936 (-0.846477) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006907 / 0.011353 (-0.004446) \| 0.004585 / 0.011008 (-0.006423) \| 0.099027 / 0.038508 (0.060519) \| 0.028317 / 0.023109 (0.005208) \| 0.421068 / 0.275898 (0.145170) \| 0.450712 / 0.323480 (0.127233) \| 0.005229 / 0.007986 (-0.002756) \| 0.004873 / 0.004328 (0.000545) \| 0.077374 / 0.004250 (0.073124) \| 0.042530 / 0.037052 (0.005477) \| 0.417392 / 0.258489 (0.158903) \| 0.462605 / 0.293841 (0.168764) \| 0.032195 / 0.128546 (-0.096351) \| 0.011777 / 0.075646 (-0.063870) \| 0.321927 / 0.419271 (-0.097344) \| 0.041999 / 0.043533 (-0.001533) \| 0.419402 / 0.255139 (0.164263) \| 0.437179 / 0.283200 (0.153979) \| 0.089549 / 0.141683 (-0.052134) \| 1.469525 / 1.452155 (0.017370) \| 1.586407 / 1.492716 (0.093691) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.209533 / 0.018006 (0.191526) \| 0.413886 / 0.000490 (0.413396) \| 0.003357 / 0.000200 (0.003157) \| 0.000121 / 0.000054 (0.000067) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026133 / 0.037411 (-0.011278) \| 0.103128 / 0.014526 (0.088602) \| 0.110604 / 0.176557 (-0.065952) \| 0.153055 / 0.737135 (-0.584080) \| 0.112257 / 0.296338 (-0.184081) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.471281 / 0.215209 (0.256072) \| 4.708361 / 2.077655 (2.630706) \| 2.572681 / 1.504120 (1.068561) \| 2.370536 / 1.541195 (0.829341) \| 2.456010 / 1.468490 (0.987520) \| 0.694173 / 4.584777 (-3.890603) \| 3.434511 / 3.745712 (-0.311201) \| 1.877169 / 5.269862 (-3.392693) \| 1.158387 / 4.565676 (-3.407289) \| 0.081849 / 0.424275 (-0.342426) \| 0.012176 / 0.007607 (0.004569) \| 0.581736 / 0.226044 (0.355692) \| 5.803173 / 2.268929 (3.534245) \| 3.040003 / 55.444624 (-52.404621) \| 2.704698 / 6.876477 (-4.171779) \| 2.760138 / 2.142072 (0.618065) \| 0.802557 / 4.805227 (-4.002671) \| 0.151397 / 6.500664 (-6.349268) \| 0.068308 / 0.075469 (-0.007161) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.304062 / 1.841788 (-0.537725) \| 14.364809 / 8.074308 (6.290501) \| 14.192131 / 10.191392 (4.000739) \| 0.150025 / 0.680424 (-0.530399) \| 0.017020 / 0.534201 (-0.517181) \| 0.389235 / 0.579283 (-0.190048) \| 0.387557 / 0.434364 (-0.046807) \| 0.454636 / 0.540337 (-0.085702) \| 0.558182 / 1.386936 (-0.828754) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#663e5eddca188abbb37e2f803846f02fe4ca0d9b \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008519 / 0.011353 (-0.002834) \| 0.004538 / 0.011008 (-0.006470) \| 0.102066 / 0.038508 (0.063558) \| 0.029700 / 0.023109 (0.006591) \| 0.304573 / 0.275898 (0.028675) \| 0.366232 / 0.323480 (0.042752) \| 0.007154 / 0.007986 (-0.000832) \| 0.003497 / 0.004328 (-0.000831) \| 0.079119 / 0.004250 (0.074868) \| 0.036088 / 0.037052 (-0.000964) \| 0.311076 / 0.258489 (0.052587) \| 0.352205 / 0.293841 (0.058364) \| 0.033706 / 0.128546 (-0.094840) \| 0.011657 / 0.075646 (-0.063990) \| 0.324024 / 0.419271 (-0.095247) \| 0.040777 / 0.043533 (-0.002756) \| 0.302661 / 0.255139 (0.047522) \| 0.329091 / 0.283200 (0.045891) \| 0.086774 / 0.141683 (-0.054909) \| 1.485874 / 1.452155 (0.033720) \| 1.535726 / 1.492716 (0.043009) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.194284 / 0.018006 (0.176277) \| 0.412875 / 0.000490 (0.412385) \| 0.003348 / 0.000200 (0.003148) \| 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) \| 0.022432 / 0.037411 (-0.014979) \| 0.095008 / 0.014526 (0.080482) \| 0.103268 / 0.176557 (-0.073288) \| 0.140121 / 0.737135 (-0.597014) \| 0.106619 / 0.296338 (-0.189719) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.414786 / 0.215209 (0.199577) \| 4.146345 / 2.077655 (2.068690) \| 1.873703 / 1.504120 (0.369583) \| 1.673498 / 1.541195 (0.132303) \| 1.716993 / 1.468490 (0.248502) \| 0.692098 / 4.584777 (-3.892679) \| 3.380991 / 3.745712 (-0.364721) \| 1.846811 / 5.269862 (-3.423050) \| 1.159617 / 4.565676 (-3.406059) \| 0.081867 / 0.424275 (-0.342408) \| 0.012371 / 0.007607 (0.004764) \| 0.526228 / 0.226044 (0.300184) \| 5.273139 / 2.268929 (3.004211) \| 2.327147 / 55.444624 (-53.117477) \| 1.968366 / 6.876477 (-4.908111) \| 2.018053 / 2.142072 (-0.124019) \| 0.816098 / 4.805227 (-3.989130) \| 0.149438 / 6.500664 (-6.351226) \| 0.065000 / 0.075469 (-0.010469) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.244408 / 1.841788 (-0.597380) \| 13.774354 / 8.074308 (5.700046) \| 14.178923 / 10.191392 (3.987531) \| 0.150032 / 0.680424 (-0.530392) \| 0.029736 / 0.534201 (-0.504465) \| 0.399134 / 0.579283 (-0.180149) \| 0.404214 / 0.434364 (-0.030150) \| 0.462096 / 0.540337 (-0.078242) \| 0.542256 / 1.386936 (-0.844680) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006776 / 0.011353 (-0.004577) \| 0.004586 / 0.011008 (-0.006422) \| 0.097658 / 0.038508 (0.059150) \| 0.027627 / 0.023109 (0.004517) \| 0.423794 / 0.275898 (0.147896) \| 0.447443 / 0.323480 (0.123963) \| 0.005099 / 0.007986 (-0.002886) \| 0.004846 / 0.004328 (0.000517) \| 0.075135 / 0.004250 (0.070884) \| 0.038068 / 0.037052 (0.001016) \| 0.420999 / 0.258489 (0.162510) \| 0.460368 / 0.293841 (0.166527) \| 0.032107 / 0.128546 (-0.096439) \| 0.011775 / 0.075646 (-0.063871) \| 0.323854 / 0.419271 (-0.095418) \| 0.045538 / 0.043533 (0.002005) \| 0.420949 / 0.255139 (0.165810) \| 0.441906 / 0.283200 (0.158706) \| 0.091955 / 0.141683 (-0.049728) \| 1.523736 / 1.452155 (0.071581) \| 1.587865 / 1.492716 (0.095148) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.263297 / 0.018006 (0.245290) \| 0.416170 / 0.000490 (0.415680) \| 0.023161 / 0.000200 (0.022961) \| 0.000243 / 0.000054 (0.000188) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024000 / 0.037411 (-0.013412) \| 0.097787 / 0.014526 (0.083262) \| 0.106884 / 0.176557 (-0.069672) \| 0.140861 / 0.737135 (-0.596274) \| 0.108228 / 0.296338 (-0.188111) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.477222 / 0.215209 (0.262013) \| 4.774729 / 2.077655 (2.697074) \| 2.451575 / 1.504120 (0.947455) \| 2.251255 / 1.541195 (0.710060) \| 2.281154 / 1.468490 (0.812664) \| 0.699394 / 4.584777 (-3.885383) \| 3.421575 / 3.745712 (-0.324137) \| 2.704713 / 5.269862 (-2.565148) \| 1.508464 / 4.565676 (-3.057212) \| 0.082199 / 0.424275 (-0.342076) \| 0.012586 / 0.007607 (0.004979) \| 0.588783 / 0.226044 (0.362739) \| 5.878434 / 2.268929 (3.609505) \| 2.927422 / 55.444624 (-52.517202) \| 2.574357 / 6.876477 (-4.302120) \| 2.603626 / 2.142072 (0.461554) \| 0.804706 / 4.805227 (-4.000521) \| 0.152919 / 6.500664 (-6.347745) \| 0.069316 / 0.075469 (-0.006153) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.280025 / 1.841788 (-0.561763) \| 13.968407 / 8.074308 (5.894099) \| 13.874506 / 10.191392 (3.683114) \| 0.154711 / 0.680424 (-0.525713) \| 0.016827 / 0.534201 (-0.517374) \| 0.377775 / 0.579283 (-0.201508) \| 0.393035 / 0.434364 (-0.041329) \| 0.439405 / 0.540337 (-0.100932) \| 0.528135 / 1.386936 (-0.858801) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#00b27a59b8af9075967b800e3b0f1de8616aa0ce \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009035 / 0.011353 (-0.002318) \| 0.004518 / 0.011008 (-0.006490) \| 0.102077 / 0.038508 (0.063569) \| 0.030169 / 0.023109 (0.007060) \| 0.297713 / 0.275898 (0.021815) \| 0.364976 / 0.323480 (0.041496) \| 0.007079 / 0.007986 (-0.000906) \| 0.003438 / 0.004328 (-0.000890) \| 0.079667 / 0.004250 (0.075416) \| 0.035890 / 0.037052 (-0.001162) \| 0.306065 / 0.258489 (0.047576) \| 0.352133 / 0.293841 (0.058292) \| 0.033800 / 0.128546 (-0.094746) \| 0.011613 / 0.075646 (-0.064034) \| 0.322917 / 0.419271 (-0.096354) \| 0.040973 / 0.043533 (-0.002560) \| 0.300896 / 0.255139 (0.045757) \| 0.331540 / 0.283200 (0.048341) \| 0.089579 / 0.141683 (-0.052103) \| 1.466755 / 1.452155 (0.014600) \| 1.522120 / 1.492716 (0.029404) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.193172 / 0.018006 (0.175166) \| 0.408878 / 0.000490 (0.408389) \| 0.001586 / 0.000200 (0.001386) \| 0.000071 / 0.000054 (0.000017) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023496 / 0.037411 (-0.013915) \| 0.098046 / 0.014526 (0.083520) \| 0.104599 / 0.176557 (-0.071957) \| 0.139054 / 0.737135 (-0.598081) \| 0.111163 / 0.296338 (-0.185175) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.417374 / 0.215209 (0.202165) \| 4.145808 / 2.077655 (2.068153) \| 1.847101 / 1.504120 (0.342981) \| 1.637207 / 1.541195 (0.096012) \| 1.676906 / 1.468490 (0.208416) \| 0.689851 / 4.584777 (-3.894926) \| 3.402099 / 3.745712 (-0.343614) \| 1.896808 / 5.269862 (-3.373054) \| 1.257876 / 4.565676 (-3.307801) \| 0.081744 / 0.424275 (-0.342531) \| 0.012206 / 0.007607 (0.004599) \| 0.524830 / 0.226044 (0.298786) \| 5.251344 / 2.268929 (2.982416) \| 2.277907 / 55.444624 (-53.166717) \| 1.933985 / 6.876477 (-4.942491) \| 2.038500 / 2.142072 (-0.103573) \| 0.808696 / 4.805227 (-3.996532) \| 0.149488 / 6.500664 (-6.351176) \| 0.065323 / 0.075469 (-0.010146) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.204294 / 1.841788 (-0.637493) \| 13.696526 / 8.074308 (5.622218) \| 13.947195 / 10.191392 (3.755802) \| 0.136812 / 0.680424 (-0.543611) \| 0.028625 / 0.534201 (-0.505576) \| 0.397662 / 0.579283 (-0.181621) \| 0.403423 / 0.434364 (-0.030941) \| 0.465288 / 0.540337 (-0.075049) \| 0.551919 / 1.386936 (-0.835017) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006467 / 0.011353 (-0.004886) \| 0.004562 / 0.011008 (-0.006447) \| 0.097514 / 0.038508 (0.059006) \| 0.027471 / 0.023109 (0.004362) \| 0.425504 / 0.275898 (0.149606) \| 0.458856 / 0.323480 (0.135376) \| 0.004816 / 0.007986 (-0.003169) \| 0.003264 / 0.004328 (-0.001065) \| 0.074947 / 0.004250 (0.070697) \| 0.037147 / 0.037052 (0.000095) \| 0.429513 / 0.258489 (0.171024) \| 0.463971 / 0.293841 (0.170130) \| 0.031638 / 0.128546 (-0.096908) \| 0.011545 / 0.075646 (-0.064101) \| 0.320261 / 0.419271 (-0.099010) \| 0.041570 / 0.043533 (-0.001963) \| 0.424809 / 0.255139 (0.169670) \| 0.447158 / 0.283200 (0.163959) \| 0.088418 / 0.141683 (-0.053265) \| 1.492242 / 1.452155 (0.040087) \| 1.545523 / 1.492716 (0.052807) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.217865 / 0.018006 (0.199859) \| 0.399925 / 0.000490 (0.399436) \| 0.004853 / 0.000200 (0.004653) \| 0.000073 / 0.000054 (0.000019) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024275 / 0.037411 (-0.013137) \| 0.098249 / 0.014526 (0.083723) \| 0.107110 / 0.176557 (-0.069446) \| 0.143870 / 0.737135 (-0.593265) \| 0.108796 / 0.296338 (-0.187542) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.470856 / 0.215209 (0.255647) \| 4.687921 / 2.077655 (2.610266) \| 2.448631 / 1.504120 (0.944511) \| 2.247748 / 1.541195 (0.706553) \| 2.287713 / 1.468490 (0.819223) \| 0.687534 / 4.584777 (-3.897243) \| 3.421099 / 3.745712 (-0.324613) \| 2.977280 / 5.269862 (-2.292582) \| 1.274837 / 4.565676 (-3.290839) \| 0.081611 / 0.424275 (-0.342664) \| 0.012603 / 0.007607 (0.004996) \| 0.574600 / 0.226044 (0.348556) \| 5.802826 / 2.268929 (3.533898) \| 2.913178 / 55.444624 (-52.531446) \| 2.589486 / 6.876477 (-4.286991) \| 2.630004 / 2.142072 (0.487932) \| 0.790087 / 4.805227 (-4.015140) \| 0.150019 / 6.500664 (-6.350645) \| 0.067346 / 0.075469 (-0.008123) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.266521 / 1.841788 (-0.575267) \| 13.818770 / 8.074308 (5.744462) \| 13.872277 / 10.191392 (3.680885) \| 0.147375 / 0.680424 (-0.533049) \| 0.016837 / 0.534201 (-0.517363) \| 0.376421 / 0.579283 (-0.202862) \| 0.400236 / 0.434364 (-0.034128) \| 0.436623 / 0.540337 (-0.103714) \| 0.527173 / 1.386936 (-0.859763) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5f347cf8443aa35401ba6a4159600b92bc6a156b \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009341 / 0.011353 (-0.002012) \| 0.005188 / 0.011008 (-0.005820) \| 0.101831 / 0.038508 (0.063323) \| 0.035141 / 0.023109 (0.012032) \| 0.299324 / 0.275898 (0.023426) \| 0.334749 / 0.323480 (0.011269) \| 0.007958 / 0.007986 (-0.000027) \| 0.005482 / 0.004328 (0.001153) \| 0.077070 / 0.004250 (0.072820) \| 0.044733 / 0.037052 (0.007680) \| 0.310398 / 0.258489 (0.051909) \| 0.347925 / 0.293841 (0.054084) \| 0.038141 / 0.128546 (-0.090405) \| 0.012135 / 0.075646 (-0.063512) \| 0.333799 / 0.419271 (-0.085472) \| 0.048881 / 0.043533 (0.005348) \| 0.301336 / 0.255139 (0.046197) \| 0.314592 / 0.283200 (0.031393) \| 0.103635 / 0.141683 (-0.038048) \| 1.437321 / 1.452155 (-0.014833) \| 1.598781 / 1.492716 (0.106065) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.248911 / 0.018006 (0.230905) \| 0.528932 / 0.000490 (0.528442) \| 0.002495 / 0.000200 (0.002295) \| 0.000094 / 0.000054 (0.000040) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027903 / 0.037411 (-0.009509) \| 0.106716 / 0.014526 (0.092190) \| 0.122650 / 0.176557 (-0.053907) \| 0.162481 / 0.737135 (-0.574654) \| 0.126402 / 0.296338 (-0.169937) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.352819 / 0.215209 (0.137610) \| 3.522761 / 2.077655 (1.445106) \| 1.576761 / 1.504120 (0.072641) \| 1.411631 / 1.541195 (-0.129563) \| 1.449689 / 1.468490 (-0.018801) \| 0.608987 / 4.584777 (-3.975790) \| 3.705121 / 3.745712 (-0.040592) \| 2.085071 / 5.269862 (-3.184790) \| 1.308653 / 4.565676 (-3.257024) \| 0.083763 / 0.424275 (-0.340512) \| 0.011957 / 0.007607 (0.004350) \| 0.502182 / 0.226044 (0.276137) \| 5.008829 / 2.268929 (2.739900) \| 2.244687 / 55.444624 (-53.199937) \| 1.891411 / 6.876477 (-4.985065) \| 1.940789 / 2.142072 (-0.201284) \| 0.825966 / 4.805227 (-3.979261) \| 0.165267 / 6.500664 (-6.335397) \| 0.063020 / 0.075469 (-0.012449) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.196707 / 1.841788 (-0.645081) \| 14.236877 / 8.074308 (6.162569) \| 14.872954 / 10.191392 (4.681562) \| 0.168560 / 0.680424 (-0.511864) \| 0.029038 / 0.534201 (-0.505163) \| 0.440192 / 0.579283 (-0.139091) \| 0.437021 / 0.434364 (0.002657) \| 0.519612 / 0.540337 (-0.020725) \| 0.612013 / 1.386936 (-0.774923) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007170 / 0.011353 (-0.004183) \| 0.005303 / 0.011008 (-0.005705) \| 0.098503 / 0.038508 (0.059995) \| 0.032573 / 0.023109 (0.009463) \| 0.398203 / 0.275898 (0.122305) \| 0.446075 / 0.323480 (0.122595) \| 0.005712 / 0.007986 (-0.002274) \| 0.004165 / 0.004328 (-0.000164) \| 0.074273 / 0.004250 (0.070023) \| 0.049587 / 0.037052 (0.012534) \| 0.399458 / 0.258489 (0.140969) \| 0.459167 / 0.293841 (0.165327) \| 0.036063 / 0.128546 (-0.092483) \| 0.012394 / 0.075646 (-0.063253) \| 0.332559 / 0.419271 (-0.086713) \| 0.048499 / 0.043533 (0.004967) \| 0.404044 / 0.255139 (0.148905) \| 0.410462 / 0.283200 (0.127262) \| 0.104104 / 0.141683 (-0.037579) \| 1.488141 / 1.452155 (0.035986) \| 1.535517 / 1.492716 (0.042801) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292976 / 0.018006 (0.274970) \| 0.569139 / 0.000490 (0.568649) \| 0.000553 / 0.000200 (0.000353) \| 0.000063 / 0.000054 (0.000008) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030144 / 0.037411 (-0.007267) \| 0.098699 / 0.014526 (0.084173) \| 0.114437 / 0.176557 (-0.062120) \| 0.156657 / 0.737135 (-0.580478) \| 0.117449 / 0.296338 (-0.178890) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.441921 / 0.215209 (0.226712) \| 4.413090 / 2.077655 (2.335435) \| 2.190458 / 1.504120 (0.686338) \| 2.008919 / 1.541195 (0.467724) \| 2.049657 / 1.468490 (0.581167) \| 0.691751 / 4.584777 (-3.893026) \| 3.767524 / 3.745712 (0.021812) \| 3.395564 / 5.269862 (-1.874297) \| 1.633480 / 4.565676 (-2.932196) \| 0.084880 / 0.424275 (-0.339395) \| 0.012133 / 0.007607 (0.004526) \| 0.555372 / 0.226044 (0.329327) \| 5.522820 / 2.268929 (3.253892) \| 2.723331 / 55.444624 (-52.721293) \| 2.337583 / 6.876477 (-4.538894) \| 2.368746 / 2.142072 (0.226674) \| 0.830127 / 4.805227 (-3.975100) \| 0.166239 / 6.500664 (-6.334425) \| 0.064279 / 0.075469 (-0.011190) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.123421 / 1.841788 (-0.718367) \| 14.413392 / 8.074308 (6.339084) \| 12.865143 / 10.191392 (2.673751) \| 0.132198 / 0.680424 (-0.548226) \| 0.016138 / 0.534201 (-0.518063) \| 0.380760 / 0.579283 (-0.198523) \| 0.387223 / 0.434364 (-0.047141) \| 0.445574 / 0.540337 (-0.094764) \| 0.535658 / 1.386936 (-0.851278) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a89564d3d17b5960db2435662cb9c49f8ad7488a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008316 / 0.011353 (-0.003037) \| 0.004503 / 0.011008 (-0.006505) \| 0.100565 / 0.038508 (0.062057) \| 0.030388 / 0.023109 (0.007279) \| 0.304417 / 0.275898 (0.028519) \| 0.369655 / 0.323480 (0.046175) \| 0.007796 / 0.007986 (-0.000190) \| 0.003450 / 0.004328 (-0.000878) \| 0.078694 / 0.004250 (0.074443) \| 0.038068 / 0.037052 (0.001016) \| 0.316353 / 0.258489 (0.057864) \| 0.352344 / 0.293841 (0.058503) \| 0.033271 / 0.128546 (-0.095276) \| 0.011427 / 0.075646 (-0.064220) \| 0.322367 / 0.419271 (-0.096904) \| 0.041497 / 0.043533 (-0.002036) \| 0.305876 / 0.255139 (0.050737) \| 0.332279 / 0.283200 (0.049079) \| 0.086719 / 0.141683 (-0.054964) \| 1.488367 / 1.452155 (0.036212) \| 1.528943 / 1.492716 (0.036227) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.171072 / 0.018006 (0.153066) \| 0.421048 / 0.000490 (0.420558) \| 0.003622 / 0.000200 (0.003422) \| 0.000075 / 0.000054 (0.000020) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022632 / 0.037411 (-0.014779) \| 0.095304 / 0.014526 (0.080778) \| 0.106254 / 0.176557 (-0.070302) \| 0.138437 / 0.737135 (-0.598698) \| 0.107258 / 0.296338 (-0.189080) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.423201 / 0.215209 (0.207992) \| 4.208397 / 2.077655 (2.130742) \| 1.899800 / 1.504120 (0.395680) \| 1.682782 / 1.541195 (0.141587) \| 1.708840 / 1.468490 (0.240350) \| 0.694492 / 4.584777 (-3.890285) \| 3.380369 / 3.745712 (-0.365344) \| 1.851731 / 5.269862 (-3.418130) \| 1.151615 / 4.565676 (-3.414061) \| 0.082446 / 0.424275 (-0.341829) \| 0.012483 / 0.007607 (0.004876) \| 0.533688 / 0.226044 (0.307643) \| 5.373434 / 2.268929 (3.104505) \| 2.346403 / 55.444624 (-53.098221) \| 1.978505 / 6.876477 (-4.897971) \| 2.005875 / 2.142072 (-0.136198) \| 0.820785 / 4.805227 (-3.984442) \| 0.150728 / 6.500664 (-6.349936) \| 0.065761 / 0.075469 (-0.009708) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.244550 / 1.841788 (-0.597237) \| 13.219096 / 8.074308 (5.144788) \| 13.960463 / 10.191392 (3.769071) \| 0.135572 / 0.680424 (-0.544852) \| 0.028746 / 0.534201 (-0.505455) \| 0.393082 / 0.579283 (-0.186201) \| 0.402852 / 0.434364 (-0.031512) \| 0.461191 / 0.540337 (-0.079147) \| 0.543500 / 1.386936 (-0.843436) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006316 / 0.011353 (-0.005037) \| 0.004394 / 0.011008 (-0.006615) \| 0.096478 / 0.038508 (0.057970) \| 0.026965 / 0.023109 (0.003855) \| 0.340371 / 0.275898 (0.064473) \| 0.368334 / 0.323480 (0.044854) \| 0.004744 / 0.007986 (-0.003242) \| 0.004652 / 0.004328 (0.000324) \| 0.074479 / 0.004250 (0.070228) \| 0.036358 / 0.037052 (-0.000694) \| 0.342968 / 0.258489 (0.084479) \| 0.383675 / 0.293841 (0.089834) \| 0.031439 / 0.128546 (-0.097107) \| 0.011529 / 0.075646 (-0.064117) \| 0.319560 / 0.419271 (-0.099711) \| 0.041370 / 0.043533 (-0.002163) \| 0.342594 / 0.255139 (0.087455) \| 0.363237 / 0.283200 (0.080038) \| 0.087316 / 0.141683 (-0.054367) \| 1.468690 / 1.452155 (0.016535) \| 1.553974 / 1.492716 (0.061257) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.198366 / 0.018006 (0.180360) \| 0.401581 / 0.000490 (0.401091) \| 0.000400 / 0.000200 (0.000200) \| 0.000059 / 0.000054 (0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023150 / 0.037411 (-0.014261) \| 0.097797 / 0.014526 (0.083271) \| 0.106198 / 0.176557 (-0.070359) \| 0.139599 / 0.737135 (-0.597536) \| 0.108361 / 0.296338 (-0.187978) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.472962 / 0.215209 (0.257753) \| 4.702688 / 2.077655 (2.625033) \| 2.401002 / 1.504120 (0.896882) \| 2.193857 / 1.541195 (0.652663) \| 2.219188 / 1.468490 (0.750697) \| 0.689993 / 4.584777 (-3.894784) \| 3.369409 / 3.745712 (-0.376304) \| 1.824801 / 5.269862 (-3.445061) \| 1.150815 / 4.565676 (-3.414862) \| 0.082197 / 0.424275 (-0.342078) \| 0.012287 / 0.007607 (0.004679) \| 0.581963 / 0.226044 (0.355918) \| 5.786943 / 2.268929 (3.518015) \| 2.871235 / 55.444624 (-52.573389) \| 2.516009 / 6.876477 (-4.360468) \| 2.535669 / 2.142072 (0.393597) \| 0.804733 / 4.805227 (-4.000494) \| 0.150545 / 6.500664 (-6.350119) \| 0.066964 / 0.075469 (-0.008505) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.285431 / 1.841788 (-0.556356) \| 14.097108 / 8.074308 (6.022800) \| 13.821497 / 10.191392 (3.630105) \| 0.141922 / 0.680424 (-0.538502) \| 0.016964 / 0.534201 (-0.517237) \| 0.374784 / 0.579283 (-0.204500) \| 0.381034 / 0.434364 (-0.053330) \| 0.435487 / 0.540337 (-0.104850) \| 0.521894 / 1.386936 (-0.865042) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#462000c2b12a11f1fc26853e842d3f6e40287737 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009486 / 0.011353 (-0.001867) \| 0.005363 / 0.011008 (-0.005645) \| 0.101008 / 0.038508 (0.062500) \| 0.036355 / 0.023109 (0.013246) \| 0.290575 / 0.275898 (0.014677) \| 0.391634 / 0.323480 (0.068154) \| 0.009085 / 0.007986 (0.001099) \| 0.005780 / 0.004328 (0.001451) \| 0.077848 / 0.004250 (0.073598) \| 0.049062 / 0.037052 (0.012009) \| 0.310900 / 0.258489 (0.052411) \| 0.358224 / 0.293841 (0.064383) \| 0.038838 / 0.128546 (-0.089708) \| 0.012244 / 0.075646 (-0.063402) \| 0.333701 / 0.419271 (-0.085570) \| 0.048021 / 0.043533 (0.004488) \| 0.289584 / 0.255139 (0.034445) \| 0.317556 / 0.283200 (0.034356) \| 0.109807 / 0.141683 (-0.031876) \| 1.465966 / 1.452155 (0.013811) \| 1.526341 / 1.492716 (0.033625) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.246221 / 0.018006 (0.228215) \| 0.580659 / 0.000490 (0.580169) \| 0.000627 / 0.000200 (0.000427) \| 0.000089 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028352 / 0.037411 (-0.009059) \| 0.110569 / 0.014526 (0.096043) \| 0.126456 / 0.176557 (-0.050100) \| 0.163633 / 0.737135 (-0.573503) \| 0.128252 / 0.296338 (-0.168087) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.397271 / 0.215209 (0.182062) \| 3.975336 / 2.077655 (1.897682) \| 1.786957 / 1.504120 (0.282837) \| 1.598468 / 1.541195 (0.057273) \| 1.645299 / 1.468490 (0.176809) \| 0.686221 / 4.584777 (-3.898556) \| 3.753184 / 3.745712 (0.007472) \| 2.089505 / 5.269862 (-3.180356) \| 1.325799 / 4.565676 (-3.239878) \| 0.084608 / 0.424275 (-0.339667) \| 0.012343 / 0.007607 (0.004736) \| 0.509951 / 0.226044 (0.283907) \| 5.092102 / 2.268929 (2.823174) \| 2.297551 / 55.444624 (-53.147073) \| 1.938177 / 6.876477 (-4.938300) \| 2.012448 / 2.142072 (-0.129625) \| 0.835206 / 4.805227 (-3.970021) \| 0.166373 / 6.500664 (-6.334291) \| 0.063996 / 0.075469 (-0.011473) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.212936 / 1.841788 (-0.628851) \| 15.067370 / 8.074308 (6.993062) \| 14.165214 / 10.191392 (3.973822) \| 0.157041 / 0.680424 (-0.523383) \| 0.029612 / 0.534201 (-0.504589) \| 0.440006 / 0.579283 (-0.139277) \| 0.439165 / 0.434364 (0.004801) \| 0.524970 / 0.540337 (-0.015368) \| 0.608305 / 1.386936 (-0.778631) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007433 / 0.011353 (-0.003920) \| 0.005310 / 0.011008 (-0.005698) \| 0.097194 / 0.038508 (0.058686) \| 0.033265 / 0.023109 (0.010156) \| 0.369908 / 0.275898 (0.094010) \| 0.411508 / 0.323480 (0.088028) \| 0.006000 / 0.007986 (-0.001986) \| 0.005647 / 0.004328 (0.001319) \| 0.075597 / 0.004250 (0.071347) \| 0.051951 / 0.037052 (0.014899) \| 0.378469 / 0.258489 (0.119980) \| 0.424849 / 0.293841 (0.131008) \| 0.036700 / 0.128546 (-0.091846) \| 0.012535 / 0.075646 (-0.063111) \| 0.333197 / 0.419271 (-0.086074) \| 0.049046 / 0.043533 (0.005513) \| 0.381845 / 0.255139 (0.126706) \| 0.397846 / 0.283200 (0.114646) \| 0.109152 / 0.141683 (-0.032531) \| 1.432407 / 1.452155 (-0.019748) \| 1.555509 / 1.492716 (0.062793) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.265433 / 0.018006 (0.247427) \| 0.559590 / 0.000490 (0.559100) \| 0.000492 / 0.000200 (0.000292) \| 0.000060 / 0.000054 (0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029748 / 0.037411 (-0.007663) \| 0.110490 / 0.014526 (0.095964) \| 0.124125 / 0.176557 (-0.052431) \| 0.160089 / 0.737135 (-0.577046) \| 0.128755 / 0.296338 (-0.167583) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.443976 / 0.215209 (0.228767) \| 4.416960 / 2.077655 (2.339305) \| 2.239408 / 1.504120 (0.735288) \| 2.055341 / 1.541195 (0.514147) \| 2.093479 / 1.468490 (0.624988) \| 0.688846 / 4.584777 (-3.895930) \| 3.797526 / 3.745712 (0.051814) \| 3.578137 / 5.269862 (-1.691725) \| 2.015073 / 4.565676 (-2.550603) \| 0.084126 / 0.424275 (-0.340149) \| 0.012581 / 0.007607 (0.004974) \| 0.549774 / 0.226044 (0.323730) \| 5.492185 / 2.268929 (3.223256) \| 2.739851 / 55.444624 (-52.704773) \| 2.371091 / 6.876477 (-4.505386) \| 2.400178 / 2.142072 (0.258105) \| 0.831227 / 4.805227 (-3.974001) \| 0.166156 / 6.500664 (-6.334508) \| 0.063901 / 0.075469 (-0.011568) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.236127 / 1.841788 (-0.605660) \| 15.236884 / 8.074308 (7.162576) \| 14.434351 / 10.191392 (4.242959) \| 0.163725 / 0.680424 (-0.516699) \| 0.018009 / 0.534201 (-0.516192) \| 0.430612 / 0.579283 (-0.148671) \| 0.420426 / 0.434364 (-0.013938) \| 0.497062 / 0.540337 (-0.043275) \| 0.590924 / 1.386936 (-0.796012) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#63377dc53fc94f19bc2b0bbfb118a90d01a1d020 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010862 / 0.011353 (-0.000491) \| 0.005741 / 0.011008 (-0.005267) \| 0.111911 / 0.038508 (0.073403) \| 0.042316 / 0.023109 (0.019207) \| 0.347665 / 0.275898 (0.071767) \| 0.377335 / 0.323480 (0.053855) \| 0.009400 / 0.007986 (0.001414) \| 0.006814 / 0.004328 (0.002486) \| 0.087194 / 0.004250 (0.082943) \| 0.046878 / 0.037052 (0.009826) \| 0.348920 / 0.258489 (0.090430) \| 0.393347 / 0.293841 (0.099507) \| 0.044212 / 0.128546 (-0.084334) \| 0.013925 / 0.075646 (-0.061722) \| 0.386076 / 0.419271 (-0.033195) \| 0.054195 / 0.043533 (0.010662) \| 0.358486 / 0.255139 (0.103347) \| 0.360132 / 0.283200 (0.076932) \| 0.109783 / 0.141683 (-0.031900) \| 1.679875 / 1.452155 (0.227720) \| 1.794379 / 1.492716 (0.301663) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.221927 / 0.018006 (0.203921) \| 0.487352 / 0.000490 (0.486863) \| 0.003494 / 0.000200 (0.003294) \| 0.000091 / 0.000054 (0.000037) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032201 / 0.037411 (-0.005210) \| 0.125861 / 0.014526 (0.111335) \| 0.133905 / 0.176557 (-0.042652) \| 0.183319 / 0.737135 (-0.553817) \| 0.142646 / 0.296338 (-0.153693) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.442720 / 0.215209 (0.227511) \| 4.602619 / 2.077655 (2.524964) \| 2.050214 / 1.504120 (0.546094) \| 1.837968 / 1.541195 (0.296773) \| 1.961199 / 1.468490 (0.492709) \| 0.793426 / 4.584777 (-3.791351) \| 4.472078 / 3.745712 (0.726366) \| 2.364903 / 5.269862 (-2.904959) \| 1.515076 / 4.565676 (-3.050600) \| 0.103087 / 0.424275 (-0.321188) \| 0.014676 / 0.007607 (0.007068) \| 0.576887 / 0.226044 (0.350843) \| 5.785525 / 2.268929 (3.516596) \| 2.765231 / 55.444624 (-52.679393) \| 2.365364 / 6.876477 (-4.511113) \| 2.448335 / 2.142072 (0.306262) \| 0.978726 / 4.805227 (-3.826501) \| 0.191417 / 6.500664 (-6.309247) \| 0.073295 / 0.075469 (-0.002174) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.378995 / 1.841788 (-0.462792) \| 16.583655 / 8.074308 (8.509347) \| 14.944731 / 10.191392 (4.753339) \| 0.168916 / 0.680424 (-0.511508) \| 0.035272 / 0.534201 (-0.498928) \| 0.489729 / 0.579283 (-0.089554) \| 0.496231 / 0.434364 (0.061867) \| 0.576218 / 0.540337 (0.035880) \| 0.673558 / 1.386936 (-0.713378) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008104 / 0.011353 (-0.003249) \| 0.005179 / 0.011008 (-0.005829) \| 0.103908 / 0.038508 (0.065400) \| 0.034661 / 0.023109 (0.011552) \| 0.398119 / 0.275898 (0.122221) \| 0.411765 / 0.323480 (0.088286) \| 0.006016 / 0.007986 (-0.001970) \| 0.005637 / 0.004328 (0.001308) \| 0.073662 / 0.004250 (0.069412) \| 0.052411 / 0.037052 (0.015359) \| 0.391826 / 0.258489 (0.133337) \| 0.455217 / 0.293841 (0.161376) \| 0.039924 / 0.128546 (-0.088622) \| 0.013390 / 0.075646 (-0.062256) \| 0.390319 / 0.419271 (-0.028953) \| 0.054312 / 0.043533 (0.010779) \| 0.395492 / 0.255139 (0.140353) \| 0.446324 / 0.283200 (0.163124) \| 0.116461 / 0.141683 (-0.025222) \| 1.502163 / 1.452155 (0.050008) \| 1.731541 / 1.492716 (0.238825) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282612 / 0.018006 (0.264606) \| 0.503170 / 0.000490 (0.502680) \| 0.005307 / 0.000200 (0.005107) \| 0.000100 / 0.000054 (0.000046) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029071 / 0.037411 (-0.008340) \| 0.123831 / 0.014526 (0.109306) \| 0.133284 / 0.176557 (-0.043272) \| 0.172029 / 0.737135 (-0.565106) \| 0.140639 / 0.296338 (-0.155700) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.496812 / 0.215209 (0.281603) \| 4.958915 / 2.077655 (2.881260) \| 2.559188 / 1.504120 (1.055068) \| 2.262434 / 1.541195 (0.721240) \| 2.371126 / 1.468490 (0.902636) \| 0.780150 / 4.584777 (-3.804627) \| 4.417060 / 3.745712 (0.671348) \| 2.401909 / 5.269862 (-2.867953) \| 1.527943 / 4.565676 (-3.037733) \| 0.100074 / 0.424275 (-0.324201) \| 0.014853 / 0.007607 (0.007246) \| 0.630192 / 0.226044 (0.404147) \| 6.409685 / 2.268929 (4.140757) \| 3.224718 / 55.444624 (-52.219906) \| 2.795301 / 6.876477 (-4.081176) \| 2.927205 / 2.142072 (0.785132) \| 0.989537 / 4.805227 (-3.815690) \| 0.199775 / 6.500664 (-6.300889) \| 0.076725 / 0.075469 (0.001256) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.433504 / 1.841788 (-0.408284) \| 17.117134 / 8.074308 (9.042825) \| 16.606367 / 10.191392 (6.414975) \| 0.165653 / 0.680424 (-0.514771) \| 0.020818 / 0.534201 (-0.513383) \| 0.496782 / 0.579283 (-0.082501) \| 0.473895 / 0.434364 (0.039531) \| 0.576796 / 0.540337 (0.036459) \| 0.703272 / 1.386936 (-0.683664) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6627fb6f2639ac3b1435b3386545612db038a42e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.012501 / 0.011353 (0.001148) \| 0.006437 / 0.011008 (-0.004571) \| 0.129387 / 0.038508 (0.090878) \| 0.035847 / 0.023109 (0.012737) \| 0.339243 / 0.275898 (0.063345) \| 0.423274 / 0.323480 (0.099794) \| 0.008489 / 0.007986 (0.000503) \| 0.004596 / 0.004328 (0.000268) \| 0.103322 / 0.004250 (0.099071) \| 0.043570 / 0.037052 (0.006517) \| 0.357004 / 0.258489 (0.098515) \| 0.426511 / 0.293841 (0.132670) \| 0.062923 / 0.128546 (-0.065623) \| 0.021168 / 0.075646 (-0.054478) \| 0.387485 / 0.419271 (-0.031787) \| 0.059745 / 0.043533 (0.016213) \| 0.341101 / 0.255139 (0.085962) \| 0.365530 / 0.283200 (0.082331) \| 0.102110 / 0.141683 (-0.039573) \| 1.729408 / 1.452155 (0.277253) \| 1.759510 / 1.492716 (0.266794) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.187065 / 0.018006 (0.169059) \| 0.499685 / 0.000490 (0.499196) \| 0.004677 / 0.000200 (0.004478) \| 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) \| 0.025827 / 0.037411 (-0.011584) \| 0.113780 / 0.014526 (0.099255) \| 0.146060 / 0.176557 (-0.030496) \| 0.158169 / 0.737135 (-0.578966) \| 0.136133 / 0.296338 (-0.160206) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.608421 / 0.215209 (0.393211) \| 5.907395 / 2.077655 (3.829741) \| 2.193140 / 1.504120 (0.689021) \| 1.870315 / 1.541195 (0.329120) \| 1.885660 / 1.468490 (0.417170) \| 1.227637 / 4.584777 (-3.357140) \| 5.319242 / 3.745712 (1.573530) \| 2.991595 / 5.269862 (-2.278267) \| 2.043906 / 4.565676 (-2.521771) \| 0.151829 / 0.424275 (-0.272447) \| 0.018974 / 0.007607 (0.011367) \| 0.778035 / 0.226044 (0.551991) \| 7.705796 / 2.268929 (5.436868) \| 2.990156 / 55.444624 (-52.454468) \| 2.372643 / 6.876477 (-4.503834) \| 2.240847 / 2.142072 (0.098775) \| 1.407209 / 4.805227 (-3.398018) \| 0.242336 / 6.500664 (-6.258328) \| 0.069847 / 0.075469 (-0.005622) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.445817 / 1.841788 (-0.395970) \| 16.059632 / 8.074308 (7.985324) \| 18.541971 / 10.191392 (8.350579) \| 0.237830 / 0.680424 (-0.442594) \| 0.041060 / 0.534201 (-0.493141) \| 0.496765 / 0.579283 (-0.082518) \| 0.609666 / 0.434364 (0.175302) \| 0.584614 / 0.540337 (0.044277) \| 0.680858 / 1.386936 (-0.706078) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009037 / 0.011353 (-0.002315) \| 0.005961 / 0.011008 (-0.005047) \| 0.127204 / 0.038508 (0.088696) \| 0.030664 / 0.023109 (0.007555) \| 0.417968 / 0.275898 (0.142070) \| 0.515316 / 0.323480 (0.191836) \| 0.006549 / 0.007986 (-0.001436) \| 0.004456 / 0.004328 (0.000128) \| 0.083715 / 0.004250 (0.079464) \| 0.043701 / 0.037052 (0.006648) \| 0.521153 / 0.258489 (0.262664) \| 0.565456 / 0.293841 (0.271615) \| 0.055298 / 0.128546 (-0.073248) \| 0.018103 / 0.075646 (-0.057544) \| 0.403990 / 0.419271 (-0.015282) \| 0.060162 / 0.043533 (0.016629) \| 0.486383 / 0.255139 (0.231244) \| 0.470342 / 0.283200 (0.187142) \| 0.102269 / 0.141683 (-0.039414) \| 1.643241 / 1.452155 (0.191086) \| 1.763850 / 1.492716 (0.271133) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.185602 / 0.018006 (0.167596) \| 0.489163 / 0.000490 (0.488674) \| 0.000426 / 0.000200 (0.000226) \| 0.000086 / 0.000054 (0.000031) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026689 / 0.037411 (-0.010722) \| 0.111520 / 0.014526 (0.096994) \| 0.119838 / 0.176557 (-0.056719) \| 0.153698 / 0.737135 (-0.583437) \| 0.130969 / 0.296338 (-0.165370) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.616170 / 0.215209 (0.400961) \| 6.219702 / 2.077655 (4.142048) \| 2.533554 / 1.504120 (1.029434) \| 2.256009 / 1.541195 (0.714815) \| 2.217617 / 1.468490 (0.749127) \| 1.156920 / 4.584777 (-3.427857) \| 5.175759 / 3.745712 (1.430046) \| 2.848419 / 5.269862 (-2.421442) \| 1.943864 / 4.565676 (-2.621813) \| 0.138342 / 0.424275 (-0.285933) \| 0.013140 / 0.007607 (0.005533) \| 0.782105 / 0.226044 (0.556060) \| 7.602003 / 2.268929 (5.333075) \| 3.629577 / 55.444624 (-51.815047) \| 2.713849 / 6.876477 (-4.162628) \| 2.663888 / 2.142072 (0.521816) \| 1.418381 / 4.805227 (-3.386847) \| 0.250649 / 6.500664 (-6.250015) \| 0.073564 / 0.075469 (-0.001905) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.483739 / 1.841788 (-0.358049) \| 16.386204 / 8.074308 (8.311896) \| 20.685262 / 10.191392 (10.493870) \| 0.237084 / 0.680424 (-0.443340) \| 0.039097 / 0.534201 (-0.495104) \| 0.525399 / 0.579283 (-0.053884) \| 0.587541 / 0.434364 (0.153177) \| 0.566605 / 0.540337 (0.026268) \| 0.677384 / 1.386936 (-0.709552) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b3b67d42733dabb15ce4997c8324f8e047ce12bd \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.014050 / 0.011353 (0.002697) \| 0.005981 / 0.011008 (-0.005028) \| 0.126307 / 0.038508 (0.087799) \| 0.035400 / 0.023109 (0.012290) \| 0.387821 / 0.275898 (0.111923) \| 0.462785 / 0.323480 (0.139305) \| 0.009427 / 0.007986 (0.001441) \| 0.005081 / 0.004328 (0.000753) \| 0.097273 / 0.004250 (0.093023) \| 0.044699 / 0.037052 (0.007647) \| 0.396025 / 0.258489 (0.137536) \| 0.450137 / 0.293841 (0.156296) \| 0.055660 / 0.128546 (-0.072886) \| 0.022710 / 0.075646 (-0.052936) \| 0.443784 / 0.419271 (0.024513) \| 0.065756 / 0.043533 (0.022223) \| 0.379350 / 0.255139 (0.124211) \| 0.396783 / 0.283200 (0.113583) \| 0.114088 / 0.141683 (-0.027594) \| 1.856834 / 1.452155 (0.404679) \| 1.839292 / 1.492716 (0.346576) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.206748 / 0.018006 (0.188742) \| 0.517711 / 0.000490 (0.517222) \| 0.008302 / 0.000200 (0.008102) \| 0.000494 / 0.000054 (0.000440) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033987 / 0.037411 (-0.003424) \| 0.131067 / 0.014526 (0.116542) \| 0.155539 / 0.176557 (-0.021018) \| 0.188598 / 0.737135 (-0.548537) \| 0.156000 / 0.296338 (-0.140338) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.641413 / 0.215209 (0.426204) \| 6.156680 / 2.077655 (4.079025) \| 2.428858 / 1.504120 (0.924738) \| 2.086195 / 1.541195 (0.545000) \| 2.109604 / 1.468490 (0.641114) \| 1.209426 / 4.584777 (-3.375351) \| 5.139398 / 3.745712 (1.393686) \| 3.041337 / 5.269862 (-2.228524) \| 2.294809 / 4.565676 (-2.270868) \| 0.142206 / 0.424275 (-0.282069) \| 0.015167 / 0.007607 (0.007560) \| 0.816269 / 0.226044 (0.590224) \| 7.953931 / 2.268929 (5.685002) \| 3.201793 / 55.444624 (-52.242832) \| 2.448620 / 6.876477 (-4.427857) \| 2.521670 / 2.142072 (0.379597) \| 1.484094 / 4.805227 (-3.321133) \| 0.255069 / 6.500664 (-6.245595) \| 0.076031 / 0.075469 (0.000561) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.590951 / 1.841788 (-0.250836) \| 17.661353 / 8.074308 (9.587045) \| 21.097837 / 10.191392 (10.906445) \| 0.229265 / 0.680424 (-0.451159) \| 0.042618 / 0.534201 (-0.491583) \| 0.535942 / 0.579283 (-0.043342) \| 0.590195 / 0.434364 (0.155831) \| 0.623985 / 0.540337 (0.083648) \| 0.742637 / 1.386936 (-0.644299) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009264 / 0.011353 (-0.002088) \| 0.008798 / 0.011008 (-0.002210) \| 0.122208 / 0.038508 (0.083700) \| 0.034835 / 0.023109 (0.011726) \| 0.462618 / 0.275898 (0.186720) \| 0.505632 / 0.323480 (0.182152) \| 0.006320 / 0.007986 (-0.001665) \| 0.005383 / 0.004328 (0.001054) \| 0.091229 / 0.004250 (0.086979) \| 0.045828 / 0.037052 (0.008775) \| 0.477507 / 0.258489 (0.219018) \| 0.539616 / 0.293841 (0.245775) \| 0.061913 / 0.128546 (-0.066633) \| 0.019390 / 0.075646 (-0.056257) \| 0.420016 / 0.419271 (0.000745) \| 0.065958 / 0.043533 (0.022425) \| 0.468603 / 0.255139 (0.213464) \| 0.486246 / 0.283200 (0.203046) \| 0.107924 / 0.141683 (-0.033759) \| 1.843614 / 1.452155 (0.391459) \| 1.988159 / 1.492716 (0.495442) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.247043 / 0.018006 (0.229037) \| 0.515580 / 0.000490 (0.515090) \| 0.005630 / 0.000200 (0.005430) \| 0.000115 / 0.000054 (0.000060) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030674 / 0.037411 (-0.006737) \| 0.130783 / 0.014526 (0.116258) \| 0.147669 / 0.176557 (-0.028888) \| 0.175656 / 0.737135 (-0.561479) \| 0.138317 / 0.296338 (-0.158022) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.727119 / 0.215209 (0.511909) \| 6.848208 / 2.077655 (4.770553) \| 3.121418 / 1.504120 (1.617298) \| 2.701799 / 1.541195 (1.160604) \| 2.749179 / 1.468490 (1.280689) \| 1.312058 / 4.584777 (-3.272719) \| 5.400562 / 3.745712 (1.654850) \| 3.058142 / 5.269862 (-2.211719) \| 2.076361 / 4.565676 (-2.489316) \| 0.142169 / 0.424275 (-0.282106) \| 0.014340 / 0.007607 (0.006733) \| 0.853534 / 0.226044 (0.627490) \| 8.734484 / 2.268929 (6.465556) \| 3.968130 / 55.444624 (-51.476495) \| 3.118032 / 6.876477 (-3.758444) \| 3.078757 / 2.142072 (0.936684) \| 1.460694 / 4.805227 (-3.344533) \| 0.261858 / 6.500664 (-6.238806) \| 0.081089 / 0.075469 (0.005620) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.611473 / 1.841788 (-0.230315) \| 17.660545 / 8.074308 (9.586237) \| 20.526023 / 10.191392 (10.334631) \| 0.223320 / 0.680424 (-0.457103) \| 0.027939 / 0.534201 (-0.506261) \| 0.542704 / 0.579283 (-0.036579) \| 0.563826 / 0.434364 (0.129462) \| 0.639936 / 0.540337 (0.099599) \| 0.755974 / 1.386936 (-0.630962) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#942141e13ba2be853e2231d9edbfa38044e2632d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008776 / 0.011353 (-0.002577) \| 0.004532 / 0.011008 (-0.006476) \| 0.100373 / 0.038508 (0.061865) \| 0.029706 / 0.023109 (0.006597) \| 0.304374 / 0.275898 (0.028476) \| 0.337223 / 0.323480 (0.013743) \| 0.007021 / 0.007986 (-0.000965) \| 0.003420 / 0.004328 (-0.000908) \| 0.077754 / 0.004250 (0.073504) \| 0.034411 / 0.037052 (-0.002642) \| 0.302926 / 0.258489 (0.044437) \| 0.342654 / 0.293841 (0.048813) \| 0.034528 / 0.128546 (-0.094018) \| 0.011926 / 0.075646 (-0.063721) \| 0.322971 / 0.419271 (-0.096301) \| 0.041384 / 0.043533 (-0.002149) \| 0.306433 / 0.255139 (0.051294) \| 0.332293 / 0.283200 (0.049093) \| 0.084972 / 0.141683 (-0.056711) \| 1.493426 / 1.452155 (0.041271) \| 1.570446 / 1.492716 (0.077729) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.189090 / 0.018006 (0.171084) \| 0.433904 / 0.000490 (0.433414) \| 0.001323 / 0.000200 (0.001124) \| 0.000073 / 0.000054 (0.000019) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023531 / 0.037411 (-0.013880) \| 0.097774 / 0.014526 (0.083248) \| 0.106383 / 0.176557 (-0.070174) \| 0.139158 / 0.737135 (-0.597977) \| 0.109443 / 0.296338 (-0.186896) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.419078 / 0.215209 (0.203869) \| 4.182657 / 2.077655 (2.105002) \| 1.887276 / 1.504120 (0.383156) \| 1.679542 / 1.541195 (0.138347) \| 1.718035 / 1.468490 (0.249545) \| 0.692628 / 4.584777 (-3.892149) \| 3.361354 / 3.745712 (-0.384358) \| 1.928583 / 5.269862 (-3.341278) \| 1.317291 / 4.565676 (-3.248386) \| 0.081799 / 0.424275 (-0.342476) \| 0.012318 / 0.007607 (0.004711) \| 0.525927 / 0.226044 (0.299883) \| 5.285905 / 2.268929 (3.016977) \| 2.317524 / 55.444624 (-53.127100) \| 1.966478 / 6.876477 (-4.909998) \| 2.054869 / 2.142072 (-0.087204) \| 0.807579 / 4.805227 (-3.997649) \| 0.149854 / 6.500664 (-6.350810) \| 0.065285 / 0.075469 (-0.010184) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.180516 / 1.841788 (-0.661271) \| 13.889734 / 8.074308 (5.815426) \| 14.076163 / 10.191392 (3.884771) \| 0.156276 / 0.680424 (-0.524148) \| 0.029187 / 0.534201 (-0.505013) \| 0.403859 / 0.579283 (-0.175424) \| 0.404998 / 0.434364 (-0.029366) \| 0.471467 / 0.540337 (-0.068871) \| 0.564526 / 1.386936 (-0.822410) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006739 / 0.011353 (-0.004614) \| 0.004644 / 0.011008 (-0.006364) \| 0.097326 / 0.038508 (0.058818) \| 0.027728 / 0.023109 (0.004619) \| 0.413537 / 0.275898 (0.137639) \| 0.452012 / 0.323480 (0.128532) \| 0.005346 / 0.007986 (-0.002639) \| 0.003338 / 0.004328 (-0.000991) \| 0.075670 / 0.004250 (0.071420) \| 0.038825 / 0.037052 (0.001772) \| 0.415612 / 0.258489 (0.157123) \| 0.454680 / 0.293841 (0.160839) \| 0.031866 / 0.128546 (-0.096680) \| 0.011616 / 0.075646 (-0.064031) \| 0.319527 / 0.419271 (-0.099745) \| 0.041283 / 0.043533 (-0.002250) \| 0.412046 / 0.255139 (0.156907) \| 0.435244 / 0.283200 (0.152044) \| 0.088400 / 0.141683 (-0.053283) \| 1.478125 / 1.452155 (0.025970) \| 1.553677 / 1.492716 (0.060960) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.229919 / 0.018006 (0.211913) \| 0.415446 / 0.000490 (0.414956) \| 0.000386 / 0.000200 (0.000186) \| 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) \| 0.024365 / 0.037411 (-0.013046) \| 0.098225 / 0.014526 (0.083699) \| 0.106674 / 0.176557 (-0.069883) \| 0.144755 / 0.737135 (-0.592380) \| 0.109221 / 0.296338 (-0.187117) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.457665 / 0.215209 (0.242456) \| 4.597849 / 2.077655 (2.520195) \| 2.171275 / 1.504120 (0.667155) \| 1.945547 / 1.541195 (0.404352) \| 2.014043 / 1.468490 (0.545553) \| 0.699732 / 4.584777 (-3.885045) \| 3.420711 / 3.745712 (-0.325001) \| 3.298702 / 5.269862 (-1.971159) \| 1.390324 / 4.565676 (-3.175353) \| 0.082668 / 0.424275 (-0.341607) \| 0.012556 / 0.007607 (0.004949) \| 0.550406 / 0.226044 (0.324361) \| 5.501060 / 2.268929 (3.232132) \| 2.659841 / 55.444624 (-52.784783) \| 2.243443 / 6.876477 (-4.633034) \| 2.266006 / 2.142072 (0.123934) \| 0.806295 / 4.805227 (-3.998933) \| 0.151399 / 6.500664 (-6.349265) \| 0.067048 / 0.075469 (-0.008421) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.291404 / 1.841788 (-0.550384) \| 14.164728 / 8.074308 (6.090419) \| 13.980219 / 10.191392 (3.788827) \| 0.140599 / 0.680424 (-0.539824) \| 0.016880 / 0.534201 (-0.517321) \| 0.379073 / 0.579283 (-0.200210) \| 0.385770 / 0.434364 (-0.048594) \| 0.442516 / 0.540337 (-0.097822) \| 0.533569 / 1.386936 (-0.853367) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#29fa15df972353f51fc434cf8eceb574b60a415f \"CML watermark\")\n", "Tests seem to be failing for unrelated reasons.", "Tests are failing because of a bug on the Hub side - this is being fixed :)\r\n\r\nlmk once the TF documentation page is updated and we can merge !", "@lhoestq Docs updated!" ]
1,502,730,559	5,376	set dev version	closed	2022-12-19T10:56:56	2022-12-19T11:01:55	2022-12-19T10:57:16	https://github.com/huggingface/datasets/pull/5376	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5376", "html_url": "https://github.com/huggingface/datasets/pull/5376", "diff_url": "https://github.com/huggingface/datasets/pull/5376.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5376.patch", "merged_at": "2022-12-19T10:57:16" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5376). All of your documentation changes will be reflected on that endpoint." ]
1,502,720,404	5,375	Release: 2.8.0	closed	2022-12-19T10:48:26	2022-12-19T10:55:43	2022-12-19T10:53:15	https://github.com/huggingface/datasets/pull/5375	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5375", "html_url": "https://github.com/huggingface/datasets/pull/5375", "diff_url": "https://github.com/huggingface/datasets/pull/5375.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5375.patch", "merged_at": "2022-12-19T10:53:15" }	lhoestq	true	[ "_The documentation is not available anymore as the PR was closed or merged._" ]
1,501,872,945	5,374	Using too many threads results in: Got disconnected from remote data host. Retrying in 5sec	closed	2022-12-18T11:38:58	2023-07-24T15:23:07	2023-07-24T15:23:07	https://github.com/huggingface/datasets/issues/5374	null	Muennighoff	false	[ "The data files are hosted on HF at https://huggingface.co/datasets/allenai/c4/tree/main\r\n\r\nYou have 200 runs streaming the same files in parallel. So this is probably a Hub limitation. Maybe rate limiting ? cc @julien-c \r\n\r\nMaybe you can also try to reduce the number of HTTP requests by increasing the block size of each request. This can be done by increasing `DEFAULT_BLOCK_SIZE` in `fsspec.implementations.http`. Default is `5 * 220` (5MiB)\r\n\r\nAnyway maybe it's just better to save the dataset locally in that case ?", "you don't get an HTTP error code or something in your stack trace? Kinda hard to debug with this info", "You could try to re-run using this `datasets` branch: [raise-err-when-disconnect](https://github.com/huggingface/datasets/compare/raise-err-when-disconnect?expand=1)\r\nIt should raise the fsspec error", "The weird thing is that I already have it saved locally & it seems to indeed be using the cached one 🧐 ; I'm also using offline mode, so I don't think it has something to do with the Hub.\r\n```\r\nWARNING:datasets.load:Using the latest cached version of the module from /users/muennighoff/.cache/huggingface/modules/datasets_modules/datasets/c4/df532b158939272d032cc63ef19cd5b83e9b4d00c922b833e4cb18b2e9869b01 (last modified on Mon Dec 12 10:45:02 2022) since it couldn't be found locally at c4.\r\n```\r\n\r\n", "No, you passed `streaming=True` so it streams the data from the Hub.\r\nThis message just shows that you use the cached version of the `c4` module**, aka the python script that is run to generate the examples from the raw data files.\r\n\r\nMaybe the offline mode should also disable `fsspec`/`aiohttp` HTTP calls in `datasets` and not just the `requests` ones.", "> This message just shows that you use the cached version of the c4 module\r\n\r\nAh my bad you're right about the module, but it's also using the downloaded & cached c4 dataset. There's no internet during the runs so it wouldn't work otherwise", "You don't have internet, therefore you get an error while trying to stream ;)" ]
1,501,484,197	5,373	Simplify skipping	closed	2022-12-17T17:23:52	2022-12-18T21:43:31	2022-12-18T21:40:21	https://github.com/huggingface/datasets/pull/5373	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5373", "html_url": "https://github.com/huggingface/datasets/pull/5373", "diff_url": "https://github.com/huggingface/datasets/pull/5373.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5373.patch", "merged_at": "2022-12-18T21:40:21" }	Muennighoff	true	[ "_The documentation is not available anymore as the PR was closed or merged._" ]
1,501,377,802	5,372	Fix streaming pandas.read_excel	closed	2022-12-17T12:58:52	2023-01-06T11:50:58	2023-01-06T11:43:37	https://github.com/huggingface/datasets/pull/5372	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5372", "html_url": "https://github.com/huggingface/datasets/pull/5372", "diff_url": "https://github.com/huggingface/datasets/pull/5372.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5372.patch", "merged_at": "2023-01-06T11:43:37" }	albertvillanova	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009517 / 0.011353 (-0.001835) \| 0.005210 / 0.011008 (-0.005798) \| 0.098916 / 0.038508 (0.060408) \| 0.036123 / 0.023109 (0.013014) \| 0.301564 / 0.275898 (0.025666) \| 0.358086 / 0.323480 (0.034606) \| 0.008159 / 0.007986 (0.000174) \| 0.004122 / 0.004328 (-0.000206) \| 0.075899 / 0.004250 (0.071648) \| 0.046082 / 0.037052 (0.009030) \| 0.302871 / 0.258489 (0.044382) \| 0.351162 / 0.293841 (0.057321) \| 0.038215 / 0.128546 (-0.090331) \| 0.012026 / 0.075646 (-0.063620) \| 0.330988 / 0.419271 (-0.088284) \| 0.048351 / 0.043533 (0.004818) \| 0.291840 / 0.255139 (0.036701) \| 0.320387 / 0.283200 (0.037187) \| 0.105018 / 0.141683 (-0.036665) \| 1.447158 / 1.452155 (-0.004997) \| 1.491205 / 1.492716 (-0.001511) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.250870 / 0.018006 (0.232863) \| 0.562974 / 0.000490 (0.562484) \| 0.001789 / 0.000200 (0.001589) \| 0.000252 / 0.000054 (0.000197) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028208 / 0.037411 (-0.009203) \| 0.110897 / 0.014526 (0.096371) \| 0.120394 / 0.176557 (-0.056163) \| 0.164980 / 0.737135 (-0.572156) \| 0.126283 / 0.296338 (-0.170056) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.397922 / 0.215209 (0.182713) \| 3.969233 / 2.077655 (1.891578) \| 1.766422 / 1.504120 (0.262302) \| 1.577503 / 1.541195 (0.036308) \| 1.672344 / 1.468490 (0.203854) \| 0.695708 / 4.584777 (-3.889069) \| 3.770763 / 3.745712 (0.025051) \| 3.369592 / 5.269862 (-1.900269) \| 1.851122 / 4.565676 (-2.714554) \| 0.084063 / 0.424275 (-0.340212) \| 0.012156 / 0.007607 (0.004549) \| 0.534639 / 0.226044 (0.308594) \| 5.021955 / 2.268929 (2.753027) \| 2.215438 / 55.444624 (-53.229186) \| 1.890459 / 6.876477 (-4.986018) \| 2.071361 / 2.142072 (-0.070712) \| 0.834623 / 4.805227 (-3.970604) \| 0.165588 / 6.500664 (-6.335076) \| 0.064336 / 0.075469 (-0.011133) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.205651 / 1.841788 (-0.636136) \| 14.916871 / 8.074308 (6.842563) \| 14.559495 / 10.191392 (4.368103) \| 0.166889 / 0.680424 (-0.513535) \| 0.028645 / 0.534201 (-0.505556) \| 0.433634 / 0.579283 (-0.145649) \| 0.429849 / 0.434364 (-0.004515) \| 0.508617 / 0.540337 (-0.031720) \| 0.595261 / 1.386936 (-0.791675) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007696 / 0.011353 (-0.003657) \| 0.005434 / 0.011008 (-0.005574) \| 0.099234 / 0.038508 (0.060725) \| 0.033904 / 0.023109 (0.010795) \| 0.379181 / 0.275898 (0.103283) \| 0.401858 / 0.323480 (0.078379) \| 0.006257 / 0.007986 (-0.001729) \| 0.004406 / 0.004328 (0.000077) \| 0.073174 / 0.004250 (0.068923) \| 0.056033 / 0.037052 (0.018981) \| 0.379375 / 0.258489 (0.120886) \| 0.425928 / 0.293841 (0.132087) \| 0.037476 / 0.128546 (-0.091071) \| 0.012520 / 0.075646 (-0.063127) \| 0.364975 / 0.419271 (-0.054297) \| 0.049341 / 0.043533 (0.005808) \| 0.370519 / 0.255139 (0.115380) \| 0.390585 / 0.283200 (0.107385) \| 0.113339 / 0.141683 (-0.028344) \| 1.460575 / 1.452155 (0.008421) \| 1.564951 / 1.492716 (0.072235) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.246217 / 0.018006 (0.228210) \| 0.554358 / 0.000490 (0.553869) \| 0.000451 / 0.000200 (0.000251) \| 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) \| 0.029557 / 0.037411 (-0.007855) \| 0.110472 / 0.014526 (0.095946) \| 0.122652 / 0.176557 (-0.053904) \| 0.159396 / 0.737135 (-0.577739) \| 0.128852 / 0.296338 (-0.167486) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.447927 / 0.215209 (0.232718) \| 4.448292 / 2.077655 (2.370637) \| 2.228874 / 1.504120 (0.724754) \| 2.030231 / 1.541195 (0.489036) \| 2.116417 / 1.468490 (0.647927) \| 0.702713 / 4.584777 (-3.882064) \| 3.774063 / 3.745712 (0.028351) \| 3.521662 / 5.269862 (-1.748200) \| 1.476700 / 4.565676 (-3.088976) \| 0.084921 / 0.424275 (-0.339354) \| 0.012862 / 0.007607 (0.005255) \| 0.559142 / 0.226044 (0.333098) \| 5.512233 / 2.268929 (3.243305) \| 2.750024 / 55.444624 (-52.694600) \| 2.388845 / 6.876477 (-4.487632) \| 2.541786 / 2.142072 (0.399714) \| 0.842256 / 4.805227 (-3.962971) \| 0.168088 / 6.500664 (-6.332576) \| 0.064211 / 0.075469 (-0.011258) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.239001 / 1.841788 (-0.602787) \| 15.286345 / 8.074308 (7.212036) \| 13.883981 / 10.191392 (3.692589) \| 0.186212 / 0.680424 (-0.494212) \| 0.018305 / 0.534201 (-0.515896) \| 0.420459 / 0.579283 (-0.158824) \| 0.421039 / 0.434364 (-0.013325) \| 0.487348 / 0.540337 (-0.052989) \| 0.587730 / 1.386936 (-0.799206) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n" ]
1,501,369,036	5,371	Add a robustness benchmark dataset for vision	open	2022-12-17T12:35:13	2022-12-20T06:21:41	null	https://github.com/huggingface/datasets/issues/5371	null	sayakpaul	false	[ "Ccing @nazneenrajani @lvwerra @osanseviero " ]
1,500,622,276	5,369	Distributed support	closed	2022-12-16T17:43:47	2023-07-25T12:00:31	2023-01-16T13:33:32	https://github.com/huggingface/datasets/pull/5369	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5369", "html_url": "https://github.com/huggingface/datasets/pull/5369", "diff_url": "https://github.com/huggingface/datasets/pull/5369.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5369.patch", "merged_at": "2023-01-16T13:33:32" }	lhoestq	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "Alright all the tests are passing - this is ready for review", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.015146 / 0.011353 (0.003793) \| 0.006683 / 0.011008 (-0.004326) \| 0.125994 / 0.038508 (0.087486) \| 0.041345 / 0.023109 (0.018235) \| 0.378609 / 0.275898 (0.102711) \| 0.483139 / 0.323480 (0.159659) \| 0.009669 / 0.007986 (0.001684) \| 0.005143 / 0.004328 (0.000814) \| 0.092015 / 0.004250 (0.087765) \| 0.052728 / 0.037052 (0.015676) \| 0.397166 / 0.258489 (0.138677) \| 0.465820 / 0.293841 (0.171979) \| 0.051025 / 0.128546 (-0.077521) \| 0.018451 / 0.075646 (-0.057196) \| 0.397311 / 0.419271 (-0.021960) \| 0.054842 / 0.043533 (0.011309) \| 0.391203 / 0.255139 (0.136064) \| 0.412743 / 0.283200 (0.129543) \| 0.111356 / 0.141683 (-0.030327) \| 1.697526 / 1.452155 (0.245372) \| 1.795017 / 1.492716 (0.302301) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.253737 / 0.018006 (0.235731) \| 0.583071 / 0.000490 (0.582581) \| 0.005958 / 0.000200 (0.005758) \| 0.000110 / 0.000054 (0.000056) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030397 / 0.037411 (-0.007014) \| 0.112242 / 0.014526 (0.097716) \| 0.138807 / 0.176557 (-0.037749) \| 0.209820 / 0.737135 (-0.527316) \| 0.139530 / 0.296338 (-0.156808) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.574111 / 0.215209 (0.358902) \| 5.623713 / 2.077655 (3.546058) \| 2.416880 / 1.504120 (0.912760) \| 1.951013 / 1.541195 (0.409819) \| 2.124565 / 1.468490 (0.656075) \| 1.268854 / 4.584777 (-3.315923) \| 5.942368 / 3.745712 (2.196656) \| 5.413814 / 5.269862 (0.143952) \| 2.931638 / 4.565676 (-1.634038) \| 0.135070 / 0.424275 (-0.289205) \| 0.014290 / 0.007607 (0.006683) \| 0.708384 / 0.226044 (0.482340) \| 7.487994 / 2.268929 (5.219065) \| 3.074210 / 55.444624 (-52.370414) \| 2.380583 / 6.876477 (-4.495893) \| 2.522298 / 2.142072 (0.380226) \| 1.336741 / 4.805227 (-3.468486) \| 0.236761 / 6.500664 (-6.263903) \| 0.076592 / 0.075469 (0.001123) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.629415 / 1.841788 (-0.212373) \| 19.000640 / 8.074308 (10.926332) \| 21.474058 / 10.191392 (11.282666) \| 0.231227 / 0.680424 (-0.449197) \| 0.046213 / 0.534201 (-0.487988) \| 0.565703 / 0.579283 (-0.013580) \| 0.662956 / 0.434364 (0.228592) \| 0.656475 / 0.540337 (0.116137) \| 0.762534 / 1.386936 (-0.624402) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010952 / 0.011353 (-0.000400) \| 0.006259 / 0.011008 (-0.004749) \| 0.132430 / 0.038508 (0.093922) \| 0.037920 / 0.023109 (0.014811) \| 0.483565 / 0.275898 (0.207667) \| 0.528190 / 0.323480 (0.204710) \| 0.008116 / 0.007986 (0.000130) \| 0.006768 / 0.004328 (0.002440) \| 0.100520 / 0.004250 (0.096270) \| 0.055208 / 0.037052 (0.018155) \| 0.484672 / 0.258489 (0.226183) \| 0.556937 / 0.293841 (0.263096) \| 0.057938 / 0.128546 (-0.070609) \| 0.020821 / 0.075646 (-0.054826) \| 0.430735 / 0.419271 (0.011464) \| 0.066317 / 0.043533 (0.022785) \| 0.496652 / 0.255139 (0.241513) \| 0.502004 / 0.283200 (0.218804) \| 0.125403 / 0.141683 (-0.016280) \| 1.833396 / 1.452155 (0.381241) \| 1.974517 / 1.492716 (0.481800) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.269198 / 0.018006 (0.251191) \| 0.620314 / 0.000490 (0.619824) \| 0.000535 / 0.000200 (0.000335) \| 0.000083 / 0.000054 (0.000029) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032373 / 0.037411 (-0.005039) \| 0.130043 / 0.014526 (0.115517) \| 0.146217 / 0.176557 (-0.030339) \| 0.200187 / 0.737135 (-0.536948) \| 0.152839 / 0.296338 (-0.143499) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.677478 / 0.215209 (0.462268) \| 6.678856 / 2.077655 (4.601201) \| 3.025870 / 1.504120 (1.521750) \| 2.678196 / 1.541195 (1.137001) \| 2.740640 / 1.468490 (1.272150) \| 1.237163 / 4.584777 (-3.347614) \| 5.752621 / 3.745712 (2.006908) \| 3.170435 / 5.269862 (-2.099427) \| 2.049174 / 4.565676 (-2.516502) \| 0.147663 / 0.424275 (-0.276612) \| 0.016107 / 0.007607 (0.008500) \| 0.849666 / 0.226044 (0.623621) \| 8.395212 / 2.268929 (6.126283) \| 3.741120 / 55.444624 (-51.703505) \| 3.102926 / 6.876477 (-3.773550) \| 3.233655 / 2.142072 (1.091583) \| 1.520349 / 4.805227 (-3.284878) \| 0.267159 / 6.500664 (-6.233505) \| 0.083646 / 0.075469 (0.008177) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.640458 / 1.841788 (-0.201330) \| 19.043169 / 8.074308 (10.968861) \| 22.786126 / 10.191392 (12.594734) \| 0.218040 / 0.680424 (-0.462384) \| 0.032948 / 0.534201 (-0.501253) \| 0.569574 / 0.579283 (-0.009710) \| 0.658746 / 0.434364 (0.224382) \| 0.650501 / 0.540337 (0.110164) \| 0.730588 / 1.386936 (-0.656348) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n", "just added a note :)", "Hi @lhoestq ,\r\nCan you please throw some light on the following statement\r\n`If the dataset has a number of shards that is a factor of world_size (i.e. if dataset.n_shards % world_size == 0), then the shards are evenly assigned across the nodes, which is the most optimized. Otherwise, each node keeps 1 example out of world_size, skipping the other examples.`\r\n\r\nLet's assume I have 127 parquet files and world_size is 4. I was not able to fully comprehend the above statement\r\nWhat does this statement mean?\r\n`each node keeps 1 example out of world_size, skipping the other examples.`\r\nThank you!", "If you have 128 parquet files, then `dataset.n_shards % world_size == 0`. In this case each worker can take care of 32 parquet files.\r\n\r\nOn the other hand if you have `dataset.n_shards % world_size != 0` (in your case 127 files), then we can't assign the same number of files to each worker. This is an issue because it may under-utilize your GPU at the end of your training since some workers will take longer to iterate on the dataset than others.\r\n\r\nTherefore in this case, all the workers take care of the 127 parquet files but workers will skip examples to not end up with duplicates. That's what \"each node keeps 1 example out of world_size, skipping the other examples\" means, and in your case it implies:\r\n- rank=0 will read the samples with idx=0, 4, 8 etc.\r\n- rank=1 will read the samples with idx=1, 5, 9 etc.\r\n- rank=2 will read the samples with idx=2, 6, 10 etc.\r\n- rank=3 will read the samples with idx=3, 7, 11 etc.", "Thanks a lot @lhoestq , this helps!", "Hi, in the case above, if we use `keep_in_memory=True` for `Dataset`, then we still need to read in n times the dataset if we use DDP on n GPUs (1 node), right? That means we need n times the memory. Is there any way to only load the data once, to save memory?", "`Dataset` objects are memory mapped from disk so they use almost no RAM (only the current batch)\r\n\r\nAlso they are perfectly sharded using `split_dataset_by_node` so it's going to be read exactly once in total using DDP.\r\nYou can also achieve the same thing using a DistributedSampler in pytorch for DDP instead of using `split_dataset_by_node`.", "Hi, please correct if I mistake anything: \r\n1. `Dataset` with `keep_in_memory=True` would explicitly pre-load the data into memory, instead of reading from disk via the memory map for every batch. The former way should be faster than the latter.\r\n2. When using DDP, before sending the `Dataset` object into `split_dataset_by_node` or incorporate it with `DistributedSampler`, every process still needs to pre-load the entire data into memory (when `keep_in_memory=True`) and then select the chunked indices from the loaded data. \r\n\r\nGenerally, the dilemma I'm facing is:\r\nSuppose we have a data around 120GB, and we want to use `DistributedLengthGroupedSampler` to optimize batching. When using DDP and `keep_in_memory=True`, every process loads 120GB which is not acceptable. For now, I turned off `keep_in_memory` and try to increase the number of workers for `DataLoader` to get better pipelining. \r\n\r\n*But is it possible to load 120GB once into 4 A100 (which has around 4120GB memory) and make each process read from this shared data from memory? Theoretically, maybe it should be faster?* ", "Feel free to ask your questions on the [forum](https://discuss.huggingface.co/c/datasets/10) if you don't mind, this way the discussions may be useful to other people ;) " ]
1,500,322,973	5,368	Align remove columns behavior and input dict mutation in `map` with previous behavior	closed	2022-12-16T14:28:47	2022-12-16T16:28:08	2022-12-16T16:25:12	https://github.com/huggingface/datasets/pull/5368	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5368", "html_url": "https://github.com/huggingface/datasets/pull/5368", "diff_url": "https://github.com/huggingface/datasets/pull/5368.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5368.patch", "merged_at": "2022-12-16T16:25:12" }	mariosasko	true	[ "_The documentation is not available anymore as the PR was closed or merged._" ]
1,499,174,749	5,367	Fix remove columns from lazy dict	closed	2022-12-15T22:04:12	2022-12-15T22:27:53	2022-12-15T22:24:50	https://github.com/huggingface/datasets/pull/5367	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5367", "html_url": "https://github.com/huggingface/datasets/pull/5367", "diff_url": "https://github.com/huggingface/datasets/pull/5367.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5367.patch", "merged_at": "2022-12-15T22:24:50" }	lhoestq	true	[ "_The documentation is not available anymore as the PR was closed or merged._" ]
1,498,530,851	5,366	ExamplesIterable fixes	closed	2022-12-15T14:23:05	2022-12-15T14:44:47	2022-12-15T14:41:45	https://github.com/huggingface/datasets/pull/5366	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5366", "html_url": "https://github.com/huggingface/datasets/pull/5366", "diff_url": "https://github.com/huggingface/datasets/pull/5366.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5366.patch", "merged_at": "2022-12-15T14:41:45" }	lhoestq	true	[ "_The documentation is not available anymore as the PR was closed or merged._" ]
1,498,422,466	5,365	fix: image array should support other formats than uint8	closed	2022-12-15T13:17:50	2023-01-26T18:46:45	2023-01-26T18:39:36	https://github.com/huggingface/datasets/pull/5365	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5365", "html_url": "https://github.com/huggingface/datasets/pull/5365", "diff_url": "https://github.com/huggingface/datasets/pull/5365.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5365.patch", "merged_at": "2023-01-26T18:39:36" }	vigsterkr	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "Hi, thanks for working on this! \r\n\r\nI agree that the current type-casting (always cast to `np.uint8` as Tensorflow Datasets does) is a bit too harsh. However, not all dtypes are supported in `Image.fromarray` (e.g. np.int64), so we need to treat these with special care (e.g. downcast to the closest supported dtype, maybe with warnings to let the user know what's happening).\r\n\r\nPS: To avoid the CI failures, we need to handle two more instances of the cast to `np.uint8` (both are in the `image.py` file).", "I've made some changes to the PR.\r\n\r\nNow the encoding procedure behaves as follows:\r\n* for multi-channel arrays: if their dtype is `int`/`uint`, cast to np.uint8 (the only supported dtype for multi-channel arrays), throw an error otherwise\r\n* if the array dtype is of valid kind (\"u\", \"i\", \"f\", ...):\r\n * don't do anything if Pillow natively supports it\r\n * otherwise, downcast until it becomes compatible with Pillow\r\n* raise an error if nothing from above is true", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009537 / 0.011353 (-0.001816) \| 0.004946 / 0.011008 (-0.006062) \| 0.100552 / 0.038508 (0.062043) \| 0.035119 / 0.023109 (0.012009) \| 0.295989 / 0.275898 (0.020091) \| 0.361326 / 0.323480 (0.037846) \| 0.007608 / 0.007986 (-0.000378) \| 0.004151 / 0.004328 (-0.000177) \| 0.077301 / 0.004250 (0.073050) \| 0.042921 / 0.037052 (0.005869) \| 0.304804 / 0.258489 (0.046315) \| 0.345934 / 0.293841 (0.052093) \| 0.038987 / 0.128546 (-0.089559) \| 0.012055 / 0.075646 (-0.063591) \| 0.334035 / 0.419271 (-0.085236) \| 0.052679 / 0.043533 (0.009146) \| 0.291700 / 0.255139 (0.036561) \| 0.335423 / 0.283200 (0.052223) \| 0.107002 / 0.141683 (-0.034680) \| 1.516780 / 1.452155 (0.064625) \| 1.514137 / 1.492716 (0.021420) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.014719 / 0.018006 (-0.003287) \| 0.545251 / 0.000490 (0.544761) \| 0.004719 / 0.000200 (0.004519) \| 0.000275 / 0.000054 (0.000220) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026633 / 0.037411 (-0.010779) \| 0.106911 / 0.014526 (0.092385) \| 0.120258 / 0.176557 (-0.056299) \| 0.156196 / 0.737135 (-0.580940) \| 0.123132 / 0.296338 (-0.173207) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.398018 / 0.215209 (0.182809) \| 3.973992 / 2.077655 (1.896337) \| 1.776436 / 1.504120 (0.272316) \| 1.579036 / 1.541195 (0.037841) \| 1.643345 / 1.468490 (0.174855) \| 0.692408 / 4.584777 (-3.892369) \| 3.757243 / 3.745712 (0.011531) \| 3.226212 / 5.269862 (-2.043649) \| 1.797845 / 4.565676 (-2.767831) \| 0.085878 / 0.424275 (-0.338398) \| 0.012451 / 0.007607 (0.004844) \| 0.509755 / 0.226044 (0.283711) \| 5.029035 / 2.268929 (2.760107) \| 2.255507 / 55.444624 (-53.189117) \| 1.892868 / 6.876477 (-4.983609) \| 1.900017 / 2.142072 (-0.242055) \| 0.853965 / 4.805227 (-3.951263) \| 0.167268 / 6.500664 (-6.333396) \| 0.062796 / 0.075469 (-0.012673) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.183361 / 1.841788 (-0.658427) \| 15.103797 / 8.074308 (7.029489) \| 14.112931 / 10.191392 (3.921539) \| 0.167234 / 0.680424 (-0.513190) \| 0.029487 / 0.534201 (-0.504713) \| 0.444121 / 0.579283 (-0.135162) \| 0.437821 / 0.434364 (0.003457) \| 0.544900 / 0.540337 (0.004562) \| 0.642142 / 1.386936 (-0.744794) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007078 / 0.011353 (-0.004275) \| 0.004983 / 0.011008 (-0.006026) \| 0.097106 / 0.038508 (0.058598) \| 0.033747 / 0.023109 (0.010637) \| 0.382030 / 0.275898 (0.106132) \| 0.410193 / 0.323480 (0.086713) \| 0.006658 / 0.007986 (-0.001327) \| 0.005358 / 0.004328 (0.001029) \| 0.073878 / 0.004250 (0.069628) \| 0.049292 / 0.037052 (0.012240) \| 0.384053 / 0.258489 (0.125564) \| 0.427826 / 0.293841 (0.133985) \| 0.036780 / 0.128546 (-0.091766) \| 0.012469 / 0.075646 (-0.063178) \| 0.332989 / 0.419271 (-0.086283) \| 0.059531 / 0.043533 (0.015998) \| 0.378431 / 0.255139 (0.123292) \| 0.402672 / 0.283200 (0.119473) \| 0.110782 / 0.141683 (-0.030901) \| 1.484570 / 1.452155 (0.032416) \| 1.608081 / 1.492716 (0.115365) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.232356 / 0.018006 (0.214350) \| 0.545648 / 0.000490 (0.545158) \| 0.003113 / 0.000200 (0.002913) \| 0.000089 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028138 / 0.037411 (-0.009273) \| 0.110786 / 0.014526 (0.096260) \| 0.123615 / 0.176557 (-0.052941) \| 0.165773 / 0.737135 (-0.571362) \| 0.126401 / 0.296338 (-0.169937) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.440518 / 0.215209 (0.225309) \| 4.393821 / 2.077655 (2.316166) \| 2.295479 / 1.504120 (0.791359) \| 2.116679 / 1.541195 (0.575485) \| 2.215561 / 1.468490 (0.747071) \| 0.722343 / 4.584777 (-3.862434) \| 3.783360 / 3.745712 (0.037647) \| 3.302242 / 5.269862 (-1.967620) \| 1.681535 / 4.565676 (-2.884142) \| 0.085738 / 0.424275 (-0.338537) \| 0.012373 / 0.007607 (0.004766) \| 0.540499 / 0.226044 (0.314455) \| 5.384915 / 2.268929 (3.115986) \| 2.766346 / 55.444624 (-52.678279) \| 2.451994 / 6.876477 (-4.424483) \| 2.505720 / 2.142072 (0.363647) \| 0.833006 / 4.805227 (-3.972221) \| 0.168206 / 6.500664 (-6.332458) \| 0.064971 / 0.075469 (-0.010498) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.253499 / 1.841788 (-0.588289) \| 15.381840 / 8.074308 (7.307532) \| 13.519493 / 10.191392 (3.328101) \| 0.165559 / 0.680424 (-0.514865) \| 0.017682 / 0.534201 (-0.516519) \| 0.422248 / 0.579283 (-0.157035) \| 0.422750 / 0.434364 (-0.011614) \| 0.524546 / 0.540337 (-0.015792) \| 0.626956 / 1.386936 (-0.759980) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d9a8d8af0961c473103516dd018e2d34d23cea02 \"CML watermark\")\n" ]
1,498,360,628	5,364	Support for writing arrow files directly with BeamWriter	closed	2022-12-15T12:38:05	2024-01-11T14:52:33	2024-01-11T14:45:15	https://github.com/huggingface/datasets/pull/5364	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5364", "html_url": "https://github.com/huggingface/datasets/pull/5364", "diff_url": "https://github.com/huggingface/datasets/pull/5364.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5364.patch", "merged_at": null }	mariosasko	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5364). All of your documentation changes will be reflected on that endpoint.", "Deleting `BeamPipeline` and `upload_local_to_remote` would break the existing Beam scripts, so I reverted this change.\r\n\r\nFrom what I understand, we need these components in our scripts for the pattern:\r\n```python\r\nif not pipeline.is_local():\r\n dl_manager.ship_files_with_pipeline()\r\n```\r\n\r\nI plan to address this in a subsequent PR by (implicitly) downloading the files directly to the remote storage of the non-local runners.", "I got `AttributeError: 'Pipeline' object has no attribute 'is_local'` when running\r\n```python\r\nload_dataset(\"wikipedia\", language=\"af\", date=\"20230101\", beam_runner=\"DirectRunner\")\r\n```\r\n```python\r\n~/.cache/huggingface/modules/datasets_modules/datasets/wikipedia/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559/wikipedia.py in _split_generators(self, dl_manager, pipeline)\r\n 965 # Use dictionary since testing mock always returns the same result.\r\n 966 downloaded_files = dl_manager.download({\"xml\": xml_urls})\r\n--> 967 if not pipeline.is_local():\r\n 968 downloaded_files = dl_manager.ship_files_with_pipeline(downloaded_files, pipeline)\r\n 969 \r\n\r\nAttributeError: 'Pipeline' object has no attribute 'is_local'\r\n```", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010649 / 0.011353 (-0.000704) \| 0.006116 / 0.011008 (-0.004892) \| 0.115568 / 0.038508 (0.077060) \| 0.041704 / 0.023109 (0.018595) \| 0.360459 / 0.275898 (0.084561) \| 0.425679 / 0.323480 (0.102200) \| 0.008992 / 0.007986 (0.001006) \| 0.006321 / 0.004328 (0.001993) \| 0.090223 / 0.004250 (0.085973) \| 0.049877 / 0.037052 (0.012824) \| 0.382447 / 0.258489 (0.123958) \| 0.406567 / 0.293841 (0.112726) \| 0.045138 / 0.128546 (-0.083409) \| 0.014203 / 0.075646 (-0.061444) \| 0.388897 / 0.419271 (-0.030375) \| 0.057176 / 0.043533 (0.013644) \| 0.358729 / 0.255139 (0.103590) \| 0.386086 / 0.283200 (0.102887) \| 0.119221 / 0.141683 (-0.022462) \| 1.731574 / 1.452155 (0.279419) \| 1.744103 / 1.492716 (0.251386) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230380 / 0.018006 (0.212373) \| 0.493690 / 0.000490 (0.493201) \| 0.005150 / 0.000200 (0.004950) \| 0.000097 / 0.000054 (0.000042) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030771 / 0.037411 (-0.006641) \| 0.123196 / 0.014526 (0.108671) \| 0.134097 / 0.176557 (-0.042459) \| 0.190442 / 0.737135 (-0.546693) \| 0.138416 / 0.296338 (-0.157923) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.469763 / 0.215209 (0.254554) \| 4.682847 / 2.077655 (2.605192) \| 2.076717 / 1.504120 (0.572597) \| 1.843721 / 1.541195 (0.302527) \| 1.923486 / 1.468490 (0.454996) \| 0.817680 / 4.584777 (-3.767097) \| 4.482409 / 3.745712 (0.736697) \| 3.898695 / 5.269862 (-1.371167) \| 2.078291 / 4.565676 (-2.487386) \| 0.100285 / 0.424275 (-0.323990) \| 0.014761 / 0.007607 (0.007154) \| 0.611261 / 0.226044 (0.385217) \| 5.926919 / 2.268929 (3.657990) \| 2.685080 / 55.444624 (-52.759544) \| 2.232179 / 6.876477 (-4.644298) \| 2.305576 / 2.142072 (0.163504) \| 0.993729 / 4.805227 (-3.811498) \| 0.194491 / 6.500664 (-6.306173) \| 0.074176 / 0.075469 (-0.001293) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.388592 / 1.841788 (-0.453196) \| 17.146945 / 8.074308 (9.072636) \| 15.989570 / 10.191392 (5.798178) \| 0.200147 / 0.680424 (-0.480277) \| 0.034009 / 0.534201 (-0.500192) \| 0.517531 / 0.579283 (-0.061753) \| 0.533966 / 0.434364 (0.099602) \| 0.637024 / 0.540337 (0.096687) \| 0.749166 / 1.386936 (-0.637770) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008240 / 0.011353 (-0.003113) \| 0.006139 / 0.011008 (-0.004869) \| 0.112258 / 0.038508 (0.073750) \| 0.039001 / 0.023109 (0.015891) \| 0.449467 / 0.275898 (0.173569) \| 0.483422 / 0.323480 (0.159942) \| 0.006176 / 0.007986 (-0.001810) \| 0.006340 / 0.004328 (0.002012) \| 0.083105 / 0.004250 (0.078855) \| 0.047002 / 0.037052 (0.009950) \| 0.458564 / 0.258489 (0.200075) \| 0.513704 / 0.293841 (0.219863) \| 0.041359 / 0.128546 (-0.087188) \| 0.014515 / 0.075646 (-0.061131) \| 0.392599 / 0.419271 (-0.026673) \| 0.055222 / 0.043533 (0.011690) \| 0.446956 / 0.255139 (0.191817) \| 0.469194 / 0.283200 (0.185994) \| 0.118212 / 0.141683 (-0.023471) \| 1.682647 / 1.452155 (0.230492) \| 1.780076 / 1.492716 (0.287360) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.259124 / 0.018006 (0.241117) \| 0.507559 / 0.000490 (0.507069) \| 0.001080 / 0.000200 (0.000880) \| 0.000081 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031969 / 0.037411 (-0.005442) \| 0.126997 / 0.014526 (0.112471) \| 0.139593 / 0.176557 (-0.036963) \| 0.182735 / 0.737135 (-0.554400) \| 0.145871 / 0.296338 (-0.150468) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.530894 / 0.215209 (0.315685) \| 5.284979 / 2.077655 (3.207324) \| 2.592886 / 1.504120 (1.088766) \| 2.407202 / 1.541195 (0.866007) \| 2.434079 / 1.468490 (0.965589) \| 0.829382 / 4.584777 (-3.755395) \| 4.481710 / 3.745712 (0.735998) \| 3.912280 / 5.269862 (-1.357581) \| 1.962291 / 4.565676 (-2.603386) \| 0.101840 / 0.424275 (-0.322435) \| 0.014528 / 0.007607 (0.006921) \| 0.639956 / 0.226044 (0.413911) \| 6.414685 / 2.268929 (4.145756) \| 3.240290 / 55.444624 (-52.204334) \| 2.795208 / 6.876477 (-4.081269) \| 2.912122 / 2.142072 (0.770050) \| 0.992188 / 4.805227 (-3.813039) \| 0.200701 / 6.500664 (-6.299964) \| 0.074235 / 0.075469 (-0.001234) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.455075 / 1.841788 (-0.386712) \| 17.186669 / 8.074308 (9.112361) \| 15.404357 / 10.191392 (5.212965) \| 0.168267 / 0.680424 (-0.512157) \| 0.020774 / 0.534201 (-0.513427) \| 0.502603 / 0.579283 (-0.076680) \| 0.506500 / 0.434364 (0.072136) \| 0.624245 / 0.540337 (0.083907) \| 0.735529 / 1.386936 (-0.651407) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n", "I think we could close this PR.", "indeed, since Beam stuff are deprecated" ]
1,498,171,317	5,363	Dataset.from_generator() crashes on simple example	closed	2022-12-15T10:21:28	2022-12-15T11:51:33	2022-12-15T11:51:33	https://github.com/huggingface/datasets/issues/5363	null	villmow	false	[]

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