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3.35B
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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
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1,988,571,317
| 6,400
|
Safely load datasets by disabling execution of dataset loading script
|
closed
| 2023-11-10T23:48:29
| 2024-06-13T15:56:13
| 2024-06-13T15:56:13
|
https://github.com/huggingface/datasets/issues/6400
| null |
irenedea
| false
|
[
"great idea IMO\r\n\r\nthis could be a `trust_remote_code=True` flag like in transformers. We could also default to loading the Parquet conversion rather than executing code (for dataset repos that have both)",
"@julien-c that would be great!",
"We added the `trust_remote_code` argument to `load_dataset()` in `datasets` 2.16:\r\n- in the future users will have to pass trust_remote_code=True to use datasets with a script\r\n- for now we just show a warning when a dataset script is used\r\n- we fallback on the Hugging Face Parquet exports when possible (to keep compatibility with old datasets with scripts)\r\n\r\nSo feel free to use `trust_remote_code=False` in the meantime to disable loading from dataset loading scripts :)",
"Passing `trust_remote_code=True` explicitly is now mandatory to load a dataset with a script since https://github.com/huggingface/datasets/pull/6954"
] |
1,988,368,503
| 6,399
|
TypeError: Cannot convert pyarrow.lib.ChunkedArray to pyarrow.lib.Array
|
open
| 2023-11-10T20:48:46
| 2024-06-22T00:13:48
| null |
https://github.com/huggingface/datasets/issues/6399
| null |
y-hwang
| false
|
[
"Seconding encountering this issue."
] |
1,987,786,446
| 6,398
|
Remove redundant condition in builders
|
closed
| 2023-11-10T14:56:43
| 2023-11-14T10:49:15
| 2023-11-14T10:43:00
|
https://github.com/huggingface/datasets/pull/6398
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6398",
"html_url": "https://github.com/huggingface/datasets/pull/6398",
"diff_url": "https://github.com/huggingface/datasets/pull/6398.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6398.patch",
"merged_at": "2023-11-14T10:43:00"
}
|
albertvillanova
| true
|
[
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004475 / 0.011353 (-0.006878) | 0.002840 / 0.011008 (-0.008168) | 0.061544 / 0.038508 (0.023036) | 0.031237 / 0.023109 (0.008128) | 0.243270 / 0.275898 (-0.032628) | 0.271903 / 0.323480 (-0.051577) | 0.002906 / 0.007986 (-0.005080) | 0.003118 / 0.004328 (-0.001210) | 0.047362 / 0.004250 (0.043112) | 0.047840 / 0.037052 (0.010788) | 0.244044 / 0.258489 (-0.014445) | 0.279310 / 0.293841 (-0.014531) | 0.023408 / 0.128546 (-0.105138) | 0.007110 / 0.075646 (-0.068536) | 0.207328 / 0.419271 (-0.211943) | 0.058463 / 0.043533 (0.014930) | 0.245631 / 0.255139 (-0.009508) | 0.267755 / 0.283200 (-0.015445) | 0.018147 / 0.141683 (-0.123536) | 1.086877 / 1.452155 (-0.365278) | 1.155380 / 1.492716 (-0.337337) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091925 / 0.018006 (0.073919) | 0.299858 / 0.000490 (0.299368) | 0.000232 / 0.000200 (0.000032) | 0.000047 / 0.000054 (-0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018416 / 0.037411 (-0.018995) | 0.062608 / 0.014526 (0.048082) | 0.073897 / 0.176557 (-0.102660) | 0.120216 / 0.737135 (-0.616919) | 0.075788 / 0.296338 (-0.220550) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287823 / 0.215209 (0.072614) | 2.797546 / 2.077655 (0.719891) | 1.470878 / 1.504120 (-0.033242) | 1.347497 / 1.541195 (-0.193698) | 1.363837 / 1.468490 (-0.104653) | 0.400069 / 4.584777 (-4.184708) | 2.338870 / 3.745712 (-1.406842) | 2.564075 / 5.269862 (-2.705787) | 1.568454 / 4.565676 (-2.997222) | 0.047103 / 0.424275 (-0.377172) | 0.004783 / 0.007607 (-0.002824) | 0.345244 / 0.226044 (0.119200) | 3.407752 / 2.268929 (1.138823) | 1.826552 / 55.444624 (-53.618073) | 1.536714 / 6.876477 (-5.339763) | 1.543138 / 2.142072 (-0.598934) | 0.478996 / 4.805227 (-4.326232) | 0.099580 / 6.500664 (-6.401085) | 0.041994 / 0.075469 (-0.033475) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.947106 / 1.841788 (-0.894682) | 11.391262 / 8.074308 (3.316954) | 10.531141 / 10.191392 (0.339749) | 0.141497 / 0.680424 (-0.538927) | 0.014214 / 0.534201 (-0.519987) | 0.269346 / 0.579283 (-0.309937) | 0.268129 / 0.434364 (-0.166235) | 0.309496 / 0.540337 (-0.230841) | 0.429207 / 1.386936 (-0.957729) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004770 / 0.011353 (-0.006583) | 0.002878 / 0.011008 (-0.008130) | 0.048248 / 0.038508 (0.009740) | 0.051068 / 0.023109 (0.027959) | 0.272076 / 0.275898 (-0.003822) | 0.292423 / 0.323480 (-0.031057) | 0.004016 / 0.007986 (-0.003970) | 0.002522 / 0.004328 (-0.001807) | 0.047617 / 0.004250 (0.043367) | 0.038168 / 0.037052 (0.001115) | 0.275236 / 0.258489 (0.016746) | 0.303811 / 0.293841 (0.009970) | 0.023816 / 0.128546 (-0.104730) | 0.007177 / 0.075646 (-0.068469) | 0.053453 / 0.419271 (-0.365818) | 0.032425 / 0.043533 (-0.011108) | 0.271620 / 0.255139 (0.016481) | 0.289618 / 0.283200 (0.006418) | 0.017986 / 0.141683 (-0.123697) | 1.154225 / 1.452155 (-0.297930) | 1.224244 / 1.492716 (-0.268472) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090477 / 0.018006 (0.072471) | 0.299461 / 0.000490 (0.298971) | 0.000224 / 0.000200 (0.000024) | 0.000053 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022043 / 0.037411 (-0.015369) | 0.070327 / 0.014526 (0.055801) | 0.080132 / 0.176557 (-0.096425) | 0.120007 / 0.737135 (-0.617128) | 0.083037 / 0.296338 (-0.213301) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294538 / 0.215209 (0.079329) | 2.882791 / 2.077655 (0.805136) | 1.582923 / 1.504120 (0.078803) | 1.457091 / 1.541195 (-0.084104) | 1.536149 / 1.468490 (0.067659) | 0.401539 / 4.584777 (-4.183238) | 2.440919 / 3.745712 (-1.304793) | 2.503108 / 5.269862 (-2.766753) | 1.509216 / 4.565676 (-3.056460) | 0.046267 / 0.424275 (-0.378008) | 0.004790 / 0.007607 (-0.002817) | 0.336137 / 0.226044 (0.110093) | 3.331655 / 2.268929 (1.062726) | 1.954228 / 55.444624 (-53.490396) | 1.686637 / 6.876477 (-5.189840) | 1.650278 / 2.142072 (-0.491794) | 0.473895 / 4.805227 (-4.331333) | 0.096908 / 6.500664 (-6.403756) | 0.040387 / 0.075469 (-0.035082) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.972999 / 1.841788 (-0.868789) | 11.978367 / 8.074308 (3.904059) | 10.861092 / 10.191392 (0.669699) | 0.129054 / 0.680424 (-0.551369) | 0.015988 / 0.534201 (-0.518213) | 0.268827 / 0.579283 (-0.310456) | 0.271714 / 0.434364 (-0.162649) | 0.304045 / 0.540337 (-0.236293) | 0.413158 / 1.386936 (-0.973778) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9e4348a233a75907c305b3159ac9cb183cf30ea5 \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005286 / 0.011353 (-0.006067) | 0.002860 / 0.011008 (-0.008149) | 0.062449 / 0.038508 (0.023941) | 0.035346 / 0.023109 (0.012237) | 0.241685 / 0.275898 (-0.034213) | 0.268116 / 0.323480 (-0.055364) | 0.003050 / 0.007986 (-0.004935) | 0.003134 / 0.004328 (-0.001194) | 0.048818 / 0.004250 (0.044567) | 0.049187 / 0.037052 (0.012135) | 0.247395 / 0.258489 (-0.011094) | 0.280301 / 0.293841 (-0.013540) | 0.023801 / 0.128546 (-0.104745) | 0.007653 / 0.075646 (-0.067994) | 0.204185 / 0.419271 (-0.215087) | 0.071251 / 0.043533 (0.027718) | 0.244409 / 0.255139 (-0.010730) | 0.262363 / 0.283200 (-0.020836) | 0.018631 / 0.141683 (-0.123052) | 1.110152 / 1.452155 (-0.342003) | 1.165093 / 1.492716 (-0.327624) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.099536 / 0.018006 (0.081530) | 0.309598 / 0.000490 (0.309109) | 0.000207 / 0.000200 (0.000007) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019213 / 0.037411 (-0.018198) | 0.069296 / 0.014526 (0.054770) | 0.074752 / 0.176557 (-0.101804) | 0.121314 / 0.737135 (-0.615822) | 0.081274 / 0.296338 (-0.215065) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.281345 / 0.215209 (0.066136) | 2.755435 / 2.077655 (0.677780) | 1.453358 / 1.504120 (-0.050762) | 1.328222 / 1.541195 (-0.212973) | 1.392281 / 1.468490 (-0.076209) | 0.410539 / 4.584777 (-4.174238) | 2.452072 / 3.745712 (-1.293640) | 2.777757 / 5.269862 (-2.492105) | 1.656719 / 4.565676 (-2.908958) | 0.046844 / 0.424275 (-0.377431) | 0.004785 / 0.007607 (-0.002822) | 0.336567 / 0.226044 (0.110522) | 3.317564 / 2.268929 (1.048635) | 1.830737 / 55.444624 (-53.613888) | 1.528464 / 6.876477 (-5.348013) | 1.620527 / 2.142072 (-0.521545) | 0.480662 / 4.805227 (-4.324565) | 0.100819 / 6.500664 (-6.399845) | 0.042501 / 0.075469 (-0.032968) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.962593 / 1.841788 (-0.879195) | 12.508048 / 8.074308 (4.433740) | 11.117398 / 10.191392 (0.926006) | 0.131265 / 0.680424 (-0.549159) | 0.014469 / 0.534201 (-0.519732) | 0.271627 / 0.579283 (-0.307656) | 0.274966 / 0.434364 (-0.159398) | 0.313260 / 0.540337 (-0.227077) | 0.444741 / 1.386936 (-0.942195) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004974 / 0.011353 (-0.006379) | 0.003383 / 0.011008 (-0.007626) | 0.048792 / 0.038508 (0.010284) | 0.052821 / 0.023109 (0.029712) | 0.267123 / 0.275898 (-0.008775) | 0.293604 / 0.323480 (-0.029876) | 0.003968 / 0.007986 (-0.004018) | 0.002594 / 0.004328 (-0.001735) | 0.047690 / 0.004250 (0.043439) | 0.040236 / 0.037052 (0.003183) | 0.267805 / 0.258489 (0.009315) | 0.310543 / 0.293841 (0.016702) | 0.025707 / 0.128546 (-0.102839) | 0.008012 / 0.075646 (-0.067634) | 0.054460 / 0.419271 (-0.364812) | 0.033545 / 0.043533 (-0.009988) | 0.270166 / 0.255139 (0.015027) | 0.285965 / 0.283200 (0.002765) | 0.019391 / 0.141683 (-0.122292) | 1.144991 / 1.452155 (-0.307164) | 1.198491 / 1.492716 (-0.294225) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094757 / 0.018006 (0.076751) | 0.306712 / 0.000490 (0.306222) | 0.000218 / 0.000200 (0.000018) | 0.000055 / 0.000054 (0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020995 / 0.037411 (-0.016417) | 0.070293 / 0.014526 (0.055767) | 0.081441 / 0.176557 (-0.095116) | 0.119538 / 0.737135 (-0.617597) | 0.081454 / 0.296338 (-0.214885) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293451 / 0.215209 (0.078242) | 2.880378 / 2.077655 (0.802723) | 1.572547 / 1.504120 (0.068427) | 1.439172 / 1.541195 (-0.102023) | 1.506343 / 1.468490 (0.037853) | 0.402764 / 4.584777 (-4.182013) | 2.501341 / 3.745712 (-1.244371) | 2.538494 / 5.269862 (-2.731367) | 1.524306 / 4.565676 (-3.041371) | 0.046401 / 0.424275 (-0.377874) | 0.004781 / 0.007607 (-0.002826) | 0.349448 / 0.226044 (0.123404) | 3.416181 / 2.268929 (1.147252) | 1.964204 / 55.444624 (-53.480420) | 1.648564 / 6.876477 (-5.227912) | 1.675977 / 2.142072 (-0.466095) | 0.475717 / 4.805227 (-4.329511) | 0.098416 / 6.500664 (-6.402248) | 0.041212 / 0.075469 (-0.034257) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.975928 / 1.841788 (-0.865860) | 12.066648 / 8.074308 (3.992340) | 10.943181 / 10.191392 (0.751789) | 0.149687 / 0.680424 (-0.530736) | 0.015107 / 0.534201 (-0.519094) | 0.268950 / 0.579283 (-0.310333) | 0.280419 / 0.434364 (-0.153945) | 0.305263 / 0.540337 (-0.235074) | 0.408486 / 1.386936 (-0.978450) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#344086a7a1707ef20b57399f813ef64ce679e956 \"CML watermark\")\n"
] |
1,987,622,152
| 6,397
|
Raise a different exception for inexisting dataset vs files without known extension
|
closed
| 2023-11-10T13:22:14
| 2023-11-22T15:12:34
| 2023-11-22T15:12:34
|
https://github.com/huggingface/datasets/issues/6397
| null |
severo
| false
|
[] |
1,987,308,077
| 6,396
|
Issue with pyarrow 14.0.1
|
closed
| 2023-11-10T10:02:12
| 2025-08-19T18:13:30
| 2023-11-14T10:23:30
|
https://github.com/huggingface/datasets/issues/6396
| null |
severo
| false
|
[
"Looks like we should stop using `PyExtensionType` and use `ExtensionType` instead\r\n\r\nsee https://github.com/apache/arrow/commit/f14170976372436ec1d03a724d8d3f3925484ecf",
"https://github.com/huggingface/datasets-server/pull/2089#pullrequestreview-1724449532\r\n\r\n> Yes, I understand now: they have disabled their `PyExtensionType` and we use it in `datasets` for arrays... ",
"related?\r\n\r\nhttps://huggingface.co/datasets/ssbuild/tools_data/discussions/1#654e663b77c8ec680d10479c",
"> related?\r\n>\r\n> https://huggingface.co/datasets/ssbuild/tools_data/discussions/1#654e663b77c8ec680d10479c\r\n\r\nNo, related to https://github.com/huggingface/datasets/issues/5706",
"Running the following is a workaround for `pyarrow <= 20.0.0` :\n\n```\nimport pyarrow\npyarrow.PyExtensionType.set_auto_load(True)\n```"
] |
1,986,484,124
| 6,395
|
Add ability to set lock type
|
closed
| 2023-11-09T22:12:30
| 2023-11-23T18:50:00
| 2023-11-23T18:50:00
|
https://github.com/huggingface/datasets/issues/6395
| null |
leoleoasd
| false
|
[
"We've replaced our filelock implementation with the `filelock` package, so their repo is the right place to request this feature.\r\n\r\nIn the meantime, the following should work: \r\n```python\r\nimport filelock\r\nfilelock.FileLock = filelock.SoftFileLock\r\n\r\nimport datasets\r\n...\r\n```"
] |
1,985,947,116
| 6,394
|
TorchFormatter images (H, W, C) instead of (C, H, W) format
|
closed
| 2023-11-09T16:02:15
| 2024-04-11T12:40:16
| 2024-04-11T12:40:16
|
https://github.com/huggingface/datasets/issues/6394
| null |
Modexus
| false
|
[
"Here's a PR for that. https://github.com/huggingface/datasets/pull/6402\r\n\r\nIt's not backward compatible, unfortunately. ",
"Just ran into this working on data lib that's attempting to achieve common interfaces across hf datasets, webdataset, native torch style datasets. The defacto standards for image tensors are numpy == HWC, torch.Tensor == CHW. \r\n\r\nI had to drop use of 'torch' formatting because as is (H, W, C) makes it incompatible with pretty much all standard torch vision processing (torchvision, etc) including model inputs themselves... not sure what the breakage scope would be, but might be worth considering a breaking change since I'm not aware of many use cases where a torch.Tensor image is expected to be in HWC form. And if I set the format to 'torch', I'd expect to be able to apply torchvision transforms, etc directly to the output...\r\n\r\nEDIT: For 'torch' output to be compatible with torch conventions (namely torchvision for images), should follow this https://pytorch.org/vision/0.17/transforms.html#supported-input-types-and-conventions\r\n\r\nattn @lhoestq \r\n\r\n",
"We can define something like `.with_format(\"torch\", image_data_format=\"channels_first\")` and recommend using this in the docs maybe ? also cc @NielsRogge ",
"Sounds good to me. I guess it's not allowed to use the channels first format by default for backwards compatibility purposes?",
"This works, but am wondering how widespread the use of the function is for image datasets? My hunch would be that it's not used widely enough with image datasets to favour backwards compat (keeping default channels_last) over clumsiness of needing this to be 'correct' for typical use.. but don't have the data to back that up.",
"I see. I just checked in the HF libraries and it shouldn't break anything. And to be consistent with them we should actually use C H W. For example `transformers` image processors use C H W by default too.\r\n\r\nSo I'm ok with doing a breaking change to make it consistent with `transformers`, `torchvision`, etc.",
"Since it is quite connected, the proposed PR #6402 will not work for monochrome `PIL` images since they only have 2 dimensions as `numpy `arrays. [Torchvision ](https://pytorch.org/vision/stable/_modules/torchvision/transforms/functional.html#pil_to_tensor) adds a channel before permuting. Would that make sense here as well?",
"@Modexus yes, indeed that would make sense as torch expects 1, H, W for monochrome, not H,W as you'd often see in numpy (via PIL), OpenCV, etc.\r\n\r\nThe reference should be the torchvision fn https://pytorch.org/vision/main/_modules/torchvision/transforms/functional.html#pil_to_tensor",
"My PR now should handle monochrome PIL image. Thanks for the heads up :)"
] |
1,984,913,259
| 6,393
|
Filter occasionally hangs
|
closed
| 2023-11-09T06:18:30
| 2025-02-22T00:49:19
| 2025-02-22T00:49:19
|
https://github.com/huggingface/datasets/issues/6393
| null |
dakinggg
| false
|
[
"It looks like I may not be the first to encounter this: https://github.com/huggingface/datasets/issues/3172",
"Adding some more information, it seems to occur more frequently with large (millions of samples) datasets.",
"More information. My code is structured as (1) load (2) map (3) filter (4) filter. It was always the second filter that failed. Combining the two filters into one seems to reliably work.",
"@lhoestq it'd be great if someone had a chance to look at this. I suspect it is impacting many users given the other issue that I linked.",
"Hi ! Sorry for the late response. Was it happening after the first or the second filter ?\r\n\r\nIt looks like an issue with the garbage collector (which makes it random). Maybe datasets created with `filter` are not always handled properly ? cc @mariosasko",
"It was after the second filter (and combining the two filters into one seemingly resolved it). I obviously haven't tried all settings to know that these details are causal, but it did work for me.",
"Thanks, that's good to know.\r\n\r\nThe stacktrace suggests an issue when `del self._indices` is called, which happens when a filtered dataset falls out of scope. The indices are a PyArrow table memory mapped from disk, so I'm not quite sure how calling `del` on it can cause this issue. We do `del self._indices` to make sure the file on disk is not used anymore by the current process and avoid e.g. permission errors.\r\n\r\nHopefully we can find a way to reproduce this error, otherwise it will be quite hard to understand what happened",
"Yeah, I have a reliable repro, but it is not even close to minimal and uses a dataset I can't share. Perhaps you could try getting close to my setting.\r\n\r\n(1) make a large (~20GB) jsonl with prompt/response pairs\r\n(2) load it on a linux machine (`dataset = load_dataset(...)`)\r\n(3) map a tokenizer to it, with multiprocessing (`tokenized_dataset = dataset.map(...)`)\r\n(4) filter it once based on something, with multiprocessing (`filtered_1 = tokenized_dataset.filter(...)`)\r\n(5) filter it again based on something, with multiprocessing (`filtered_2 = filtered_1.filter(...)`)\r\n\r\nI included the variable names just in case it is relevant that I was creating new datasets each time, not overwriting the same variable.",
"@lhoestq I have another version of the repro that seems fairly reliably. I have lots of jsonl files, and I iteratively load each one with `load_dataset('json', data_files='path/to/my/file.jsonl', streaming=False, split='train')` and then `dataset.map(..., num_proc=<int>)`. This iteration hangs in a random place each time. So seems like there is a bug that hits with _some_ frequency.",
"With `num_proc=None` it works fine.",
"I am also having similar issue to #3172 when trying to tokenize the data. My dataset contains 10M samples. Is there anything that could be done without having to split up the processing into multiple datasets?",
"https://github.com/huggingface/datasets/pull/7411 seems to have fixed the issue for me, curious if it resolves others issues too."
] |
1,984,369,545
| 6,392
|
`push_to_hub` is not robust to hub closing connection
|
closed
| 2023-11-08T20:44:53
| 2023-12-20T07:28:24
| 2023-12-01T17:51:34
|
https://github.com/huggingface/datasets/issues/6392
| null |
msis
| false
|
[
"Hi! We made some improvements to `push_to_hub` to make it more robust a couple of weeks ago but haven't published a release in the meantime, so it would help if you could install `datasets` from `main` (`pip install https://github.com/huggingface/datasets`) and let us know if this improved version of `push_to_hub` resolves the issue (in case the `ConnectionError` happens, re-running `push_to_hub` should be faster now).\r\n\r\nAlso, note that the previous implementation retries the upload, but sometimes this is not enough, so re-running the op is the only option.",
"The update helped push more data.\r\nHowever it still crashed a little later:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 270, in hf_raise_for_status\r\n response.raise_for_status()\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/models.py\", line 1021, in raise_for_status\r\n raise HTTPError(http_error_msg, response=self)\r\nrequests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://hf-hub-lfs-us-east-1.s3.us-east-1.amazonaws.com/repos/6c/33/6c33b3be1463a656e43c7a4f2d43c4a1cdae6e9d81fff87f69167ef25ccb1b88/5f53cb57cf2a52ca0d4c2166a69a6714c64fcdbb7cb8936dfa5b11ac60058e5f?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA2JU7TKAQFN2FTF47%2F20231110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20231110T011254Z&X-Amz-Expires=86400&X-Amz-Signature=74e3e33c09ac4e7c6ac887aaee8d489f068869abbe1ee6d58a910fb18d0601d4&X-Amz-SignedHeaders=host&partNumber=13&uploadId=kQwunNkunfmT9D8GulQu_ufw1BTZtRA6wEUI4hnYOjytfdf.GKxDETgMr4wm8_0WNF2yGaNco_0h3JAGm4l9KV1N0nqr5XXyUCbs1ROmHP475fn9FIhc1umWQLEDc97V&x-id=UploadPart\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 391, in _wrapped_lfs_upload\r\n lfs_upload(operation=operation, lfs_batch_action=batch_action, token=token)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 223, in lfs_upload\r\n _upload_multi_part(operation=operation, header=header, chunk_size=chunk_size, upload_url=upload_action[\"href\"])\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 319, in _upload_multi_part\r\n else _upload_parts_iteratively(operation=operation, sorted_parts_urls=sorted_parts_urls, chunk_size=chunk_size)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 376, in _upload_parts_iteratively\r\n hf_raise_for_status(part_upload_res)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 330, in hf_raise_for_status\r\n raise HfHubHTTPError(str(e), response=response) from e\r\nhuggingface_hub.utils._errors.HfHubHTTPError: 500 Server Error: Internal Server Error for url: https://hf-hub-lfs-us-east-1.s3.us-east-1.amazonaws.com/repos/6c/33/6c33b3be1463a656e43c7a4f2d43c4a1cdae6e9d81fff87f69167ef25ccb1b88/5f53cb57cf2a52ca0d4c2166a69a6714c64fcdbb7cb8936dfa5b11ac60058e5f?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA2JU7TKAQFN2FTF47%2F20231110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20231110T011254Z&X-Amz-Expires=86400&X-Amz-Signature=74e3e33c09ac4e7c6ac887aaee8d489f068869abbe1ee6d58a910fb18d0601d4&X-Amz-SignedHeaders=host&partNumber=13&uploadId=kQwunNkunfmT9D8GulQu_ufw1BTZtRA6wEUI4hnYOjytfdf.GKxDETgMr4wm8_0WNF2yGaNco_0h3JAGm4l9KV1N0nqr5XXyUCbs1ROmHP475fn9FIhc1umWQLEDc97V&x-id=UploadPart\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"convert_to_hf.py\", line 121, in <module>\r\n main()\r\n File \"convert_to_hf.py\", line 109, in main\r\n audio_dataset.push_to_hub(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/dataset_dict.py\", line 1699, in push_to_hub\r\n split_additions, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 5215, in _push_parquet_shards_to_hub\r\n _retry(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 290, in _retry\r\n return func(*func_args, **func_kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 3665, in preupload_lfs_files\r\n _upload_lfs_files(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn\r\n return fn(*args, **kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 401, in _upload_lfs_files\r\n _wrapped_lfs_upload(filtered_actions[0])\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 393, in _wrapped_lfs_upload\r\n raise RuntimeError(f\"Error while uploading '{operation.path_in_repo}' to the Hub.\") from exc\r\nRuntimeError: Error while uploading 'batch_20/train-00206-of-00261.parquet' to the Hub.\r\n```",
"I think the previous implementation was actually better: it pushes to the hub every shard. So if it fails, as long as the shards have the same checksum, it will skip the ones that have been pushed.\r\n\r\nThe implementation in `main` pushes commits at the end, so when it fails, there are no commits and therefore restarts from the beginning every time.\r\n\r\nBelow is the another error log from another run with `main`. I've reverting back to the current release as it does the job for me.\r\n\r\n```\r\nUploading the dataset shards: 86%|████████▌ | 224/261 [21:46<03:35, 5.83s/it]s]\r\nTraceback (most recent call last):\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 270, in hf_raise_for_status\r\n response.raise_for_status()\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/models.py\", line 1021, in raise_for_status\r\n raise HTTPError(http_error_msg, response=self)\r\nrequests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://hf-hub-lfs-us-east-1.s3.us-east-1.amazonaws.com/repos/6c/33/6c33b3be1463a656e43c7a4f2d43c4a1cdae6e9d81fff87f69167ef25ccb1b88/97e68d7a5d4a747ffaa249fc09798e961d621fe4170599e6100197f7733f321d?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA2JU7TKAQFN2FTF47%2F20231110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20231110T145155Z&X-Amz-Expires=86400&X-Amz-Signature=5341e4b34dc325737f92dc9005c4a31e4d3f9a3d3d853b267e01915260acf629&X-Amz-SignedHeaders=host&partNumber=27&uploadId=NRD0izEWv7MPtC2bYrm5VJ4XgIbHctKNguR7zS1UhGOOrXwBJvigrOywBvQBnS9sxiy0J0ma9sNog8S13nIdTdE9p60MIITTstUFeKvLHSxpU.a527QED1JVYzJ.9xA0&x-id=UploadPart\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 391, in _wrapped_lfs_upload\r\n lfs_upload(operation=operation, lfs_batch_action=batch_action, token=token)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 223, in lfs_upload\r\n _upload_multi_part(operation=operation, header=header, chunk_size=chunk_size, upload_url=upload_action[\"href\"])\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 319, in _upload_multi_part\r\n else _upload_parts_iteratively(operation=operation, sorted_parts_urls=sorted_parts_urls, chunk_size=chunk_size)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 376, in _upload_parts_iteratively\r\n hf_raise_for_status(part_upload_res)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 330, in hf_raise_for_status\r\n raise HfHubHTTPError(str(e), response=response) from e\r\nhuggingface_hub.utils._errors.HfHubHTTPError: 500 Server Error: Internal Server Error for url: https://hf-hub-lfs-us-east-1.s3.us-east-1.amazonaws.com/repos/6c/33/6c33b3be1463a656e43c7a4f2d43c4a1cdae6e9d81fff87f69167ef25ccb1b88/97e68d7a5d4a747ffaa249fc09798e961d621fe4170599e6100197f7733f321d?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA2JU7TKAQFN2FTF47%2F20231110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20231110T145155Z&X-Amz-Expires=86400&X-Amz-Signature=5341e4b34dc325737f92dc9005c4a31e4d3f9a3d3d853b267e01915260acf629&X-Amz-SignedHeaders=host&partNumber=27&uploadId=NRD0izEWv7MPtC2bYrm5VJ4XgIbHctKNguR7zS1UhGOOrXwBJvigrOywBvQBnS9sxiy0J0ma9sNog8S13nIdTdE9p60MIITTstUFeKvLHSxpU.a527QED1JVYzJ.9xA0&x-id=UploadPart\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"convert_to_hf.py\", line 121, in <module>\r\n main()\r\n File \"convert_to_hf.py\", line 109, in main\r\n audio_dataset.push_to_hub(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/dataset_dict.py\", line 1699, in push_to_hub\r\n p, glob_pattern_to_regex(PUSH_TO_HUB_WITHOUT_METADATA_CONFIGS_SPLIT_PATTERN_SHARDED)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 5215, in _push_parquet_shards_to_hub\r\n token = token if token is not None else HfFolder.get_token()\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 290, in _retry\r\n return func(*func_args, **func_kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 3665, in preupload_lfs_files\r\n _upload_lfs_files(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn\r\n return fn(*args, **kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 401, in _upload_lfs_files\r\n _wrapped_lfs_upload(filtered_actions[0])\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 393, in _wrapped_lfs_upload\r\n raise RuntimeError(f\"Error while uploading '{operation.path_in_repo}' to the Hub.\") from exc\r\nRuntimeError: Error while uploading 'batch_20/train-00224-of-00261.parquet' to the Hub.\r\n```",
"There's a new error from the hub now:\r\n```\r\nPushing dataset shards to the dataset hub: 49%|████▉ | 128/261 [11:38<12:05, 5.45s/it]\r\nTraceback (most recent call last):\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 270, in hf_raise_for_status\r\n response.raise_for_status()\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/requests/models.py\", line 1021, in raise_for_status\r\n raise HTTPError(http_error_msg, response=self)\r\nrequests.exceptions.HTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/api/datasets/tarteel-ai/tawseem/commit/main\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"convert_to_hf.py\", line 121, in <module>\r\n main()\r\n File \"convert_to_hf.py\", line 109, in main\r\n audio_dataset.push_to_hub(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/dataset_dict.py\", line 1641, in push_to_hub\r\n repo_id, split, uploaded_size, dataset_nbytes, _, _ = self[split]._push_parquet_shards_to_hub(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 5308, in _push_parquet_shards_to_hub\r\n _retry(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 293, in _retry\r\n raise err\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 290, in _retry\r\n return func(*func_args, **func_kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn\r\n return fn(*args, **kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 1045, in _inner\r\n return fn(self, *args, **kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 3850, in upload_file\r\n commit_info = self.create_commit(\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn\r\n return fn(*args, **kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 1045, in _inner\r\n return fn(self, *args, **kwargs)\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 3237, in create_commit\r\n hf_raise_for_status(commit_resp, endpoint_name=\"commit\")\r\n File \"/admin/home-piraka9011/.virtualenvs/w2v2/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 330, in hf_raise_for_status\r\n raise HfHubHTTPError(str(e), response=response) from e\r\nhuggingface_hub.utils._errors.HfHubHTTPError: 429 Client Error: Too Many Requests for url: https://huggingface.co/api/datasets/tarteel-ai/tawseem/commit/main (Request ID: Root=1-654e48e6-598511b14413bb293fa67084;783522b4-66f9-4f8a-8a74-2accf7cabd17)\r\n\r\nYou have exceeded our hourly quotas for action: commit. We invite you to retry later.\r\n```\r\n\r\nAt least this is more explicit from the server side.",
"> think the previous implementation was actually better: it pushes to the hub every shard. So if it fails, as long as the shards have the same checksum, it will skip the ones that have been pushed.\r\n>\r\n>The implementation in main pushes commits at the end, so when it fails, there are no commits and therefore restarts from the beginning every time.\r\n>\r\n>Below is the another error log from another run with main. I've reverting back to the current release as it does the job for me.\r\n\r\nThe `preupload` step is instant for the already uploaded shards, so only the Parquet conversion is repeated without uploading the actual Parquet data (only to check the SHAs). The previous implementation manually checks the Parquet shard's fingerprint to resume uploading, so the current implementation is cleaner.\r\n\r\n> You have exceeded our hourly quotas for action: commit. We invite you to retry later.\r\n\r\nThis is the problem with the previous implementation. If the number of shards is large, it creates too many commits for the Hub in a short period.",
"But I agree that the `500 Server Error` returned by the Hub is annoying. Earlier today, I also got it on a small 5GB dataset (with 500 MB shards).\r\n\r\n@Wauplin @julien-c Is there something we can do about this?",
"@mariosasko can't do much if AWS raises a HTTP 500 unfortunately (we are simply pushing data to a S3 bucket).\r\nWhat we can do is to add a retry mechanism in the multi-part upload logic here: https://github.com/huggingface/huggingface_hub/blob/c972cba1fecb456a7b3325cdd1fdbcc425f21f94/src/huggingface_hub/lfs.py#L370 :confused: ",
"@Wauplin That code already retries the request using `http_backoff`, no?",
"> That code already retries the request using http_backoff, no?\r\n\r\nCurrently only on HTTP 503 by default. We should add 500 as well (and hope it is a transient error from AWS)",
"Opened a PR to retry in case S3 raises HTTP 500. Will also retry on any `ConnectionError` (connection reset by peer, connection lost,...). Hopefully this should make the upload process more robust to transient errors.",
"I still get the same error, using `push_to_hub`. Using `git lfs` and pushing the files solved it for me.",
"@BEpresent the fix has not been released yet. You can expect a release of `huggingface_hub` (with this fix) today or tomorrow :)"
] |
1,984,091,776
| 6,391
|
Webdataset dataset builder
|
closed
| 2023-11-08T17:31:59
| 2024-05-22T16:51:08
| 2023-11-28T16:33:10
|
https://github.com/huggingface/datasets/pull/6391
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6391",
"html_url": "https://github.com/huggingface/datasets/pull/6391",
"diff_url": "https://github.com/huggingface/datasets/pull/6391.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6391.patch",
"merged_at": "2023-11-28T16:33:10"
}
|
lhoestq
| true
|
[
"_The documentation is not available anymore as the PR was closed or merged._",
"I added an error message if the first examples don't appear to be in webdataset format\r\n```\r\n\"The TAR archives of the dataset should be in Webdataset format, \"\r\n\"but the files in the archive don't share the same prefix or the same types.\"\r\n```",
"@mariosasko could you review this ? I think it's fine to have webdataset as an optional dependency for now, then depending on usage and user feedbacks see if it makes sense to have our own implementation or not",
"I just removed the dependency on `webdataset` @mariosasko :)",
"took your comments into account, lmk if you see anything else"
] |
1,983,725,707
| 6,390
|
handle future deprecation argument
|
closed
| 2023-11-08T14:21:25
| 2023-11-21T02:10:24
| 2023-11-14T15:15:59
|
https://github.com/huggingface/datasets/pull/6390
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6390",
"html_url": "https://github.com/huggingface/datasets/pull/6390",
"diff_url": "https://github.com/huggingface/datasets/pull/6390.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6390.patch",
"merged_at": "2023-11-14T15:15:59"
}
|
winglian
| true
|
[
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004368 / 0.011353 (-0.006985) | 0.002613 / 0.011008 (-0.008396) | 0.061365 / 0.038508 (0.022856) | 0.029553 / 0.023109 (0.006444) | 0.240535 / 0.275898 (-0.035363) | 0.280634 / 0.323480 (-0.042845) | 0.002923 / 0.007986 (-0.005063) | 0.003696 / 0.004328 (-0.000632) | 0.049824 / 0.004250 (0.045573) | 0.044935 / 0.037052 (0.007882) | 0.246870 / 0.258489 (-0.011619) | 0.317248 / 0.293841 (0.023407) | 0.022717 / 0.128546 (-0.105829) | 0.006933 / 0.075646 (-0.068713) | 0.201118 / 0.419271 (-0.218154) | 0.053422 / 0.043533 (0.009890) | 0.266262 / 0.255139 (0.011123) | 0.269114 / 0.283200 (-0.014086) | 0.016908 / 0.141683 (-0.124775) | 1.154296 / 1.452155 (-0.297859) | 1.218825 / 1.492716 (-0.273892) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089908 / 0.018006 (0.071902) | 0.300029 / 0.000490 (0.299539) | 0.000209 / 0.000200 (0.000009) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018518 / 0.037411 (-0.018894) | 0.062246 / 0.014526 (0.047720) | 0.073542 / 0.176557 (-0.103014) | 0.119386 / 0.737135 (-0.617749) | 0.075256 / 0.296338 (-0.221082) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280812 / 0.215209 (0.065603) | 2.701282 / 2.077655 (0.623628) | 1.455146 / 1.504120 (-0.048974) | 1.310198 / 1.541195 (-0.230996) | 1.335287 / 1.468490 (-0.133203) | 0.388245 / 4.584777 (-4.196532) | 2.357770 / 3.745712 (-1.387942) | 2.534640 / 5.269862 (-2.735222) | 1.541382 / 4.565676 (-3.024295) | 0.045597 / 0.424275 (-0.378678) | 0.004842 / 0.007607 (-0.002765) | 0.325416 / 0.226044 (0.099371) | 3.221873 / 2.268929 (0.952944) | 1.791061 / 55.444624 (-53.653563) | 1.485094 / 6.876477 (-5.391382) | 1.512354 / 2.142072 (-0.629718) | 0.471241 / 4.805227 (-4.333986) | 0.098672 / 6.500664 (-6.401992) | 0.041668 / 0.075469 (-0.033801) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.953553 / 1.841788 (-0.888234) | 11.378394 / 8.074308 (3.304086) | 10.355970 / 10.191392 (0.164578) | 0.126891 / 0.680424 (-0.553533) | 0.013808 / 0.534201 (-0.520393) | 0.267800 / 0.579283 (-0.311484) | 0.266436 / 0.434364 (-0.167928) | 0.306668 / 0.540337 (-0.233670) | 0.427666 / 1.386936 (-0.959270) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004908 / 0.011353 (-0.006445) | 0.002698 / 0.011008 (-0.008310) | 0.047492 / 0.038508 (0.008984) | 0.049906 / 0.023109 (0.026797) | 0.271466 / 0.275898 (-0.004432) | 0.291030 / 0.323480 (-0.032449) | 0.003938 / 0.007986 (-0.004047) | 0.002457 / 0.004328 (-0.001871) | 0.047347 / 0.004250 (0.043096) | 0.038599 / 0.037052 (0.001547) | 0.269950 / 0.258489 (0.011461) | 0.303026 / 0.293841 (0.009185) | 0.024196 / 0.128546 (-0.104351) | 0.006889 / 0.075646 (-0.068757) | 0.053357 / 0.419271 (-0.365914) | 0.032249 / 0.043533 (-0.011284) | 0.271660 / 0.255139 (0.016521) | 0.286395 / 0.283200 (0.003196) | 0.017914 / 0.141683 (-0.123769) | 1.128762 / 1.452155 (-0.323393) | 1.206495 / 1.492716 (-0.286221) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093384 / 0.018006 (0.075378) | 0.305504 / 0.000490 (0.305014) | 0.000227 / 0.000200 (0.000027) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021183 / 0.037411 (-0.016229) | 0.070113 / 0.014526 (0.055587) | 0.080288 / 0.176557 (-0.096269) | 0.120798 / 0.737135 (-0.616337) | 0.082896 / 0.296338 (-0.213442) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292758 / 0.215209 (0.077549) | 2.893975 / 2.077655 (0.816320) | 1.584909 / 1.504120 (0.080789) | 1.455509 / 1.541195 (-0.085686) | 1.501625 / 1.468490 (0.033135) | 0.400772 / 4.584777 (-4.184005) | 2.446319 / 3.745712 (-1.299393) | 2.530690 / 5.269862 (-2.739172) | 1.525957 / 4.565676 (-3.039719) | 0.046070 / 0.424275 (-0.378205) | 0.004756 / 0.007607 (-0.002851) | 0.343039 / 0.226044 (0.116995) | 3.366772 / 2.268929 (1.097844) | 1.948895 / 55.444624 (-53.495729) | 1.666419 / 6.876477 (-5.210058) | 1.658258 / 2.142072 (-0.483814) | 0.470835 / 4.805227 (-4.334392) | 0.098008 / 6.500664 (-6.402656) | 0.040743 / 0.075469 (-0.034726) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.978025 / 1.841788 (-0.863763) | 11.945229 / 8.074308 (3.870920) | 11.025810 / 10.191392 (0.834418) | 0.129706 / 0.680424 (-0.550717) | 0.015148 / 0.534201 (-0.519053) | 0.269160 / 0.579283 (-0.310123) | 0.284306 / 0.434364 (-0.150058) | 0.307154 / 0.540337 (-0.233183) | 0.409153 / 1.386936 (-0.977783) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9c75c104fd79cbf53be25f0fbbeb001e535f7e9b \"CML watermark\")\n"
] |
1,983,545,744
| 6,389
|
Index 339 out of range for dataset of size 339 <-- save_to_file()
|
open
| 2023-11-08T12:52:09
| 2023-11-24T09:14:13
| null |
https://github.com/huggingface/datasets/issues/6389
| null |
jaggzh
| false
|
[
"Hi! Can you make the above reproducer self-contained by adding code that generates the data?",
"I managed a workaround eventually but I don't know what it was (I made a lot of changes to seq2seq). I'll try to include generating code in the future. (If I close, I don't know if you see it. Feel free to close; I'll re-open if I encounter it again (if I can))."
] |
1,981,136,093
| 6,388
|
How to create 3d medical imgae dataset?
|
open
| 2023-11-07T11:27:36
| 2023-11-07T11:28:53
| null |
https://github.com/huggingface/datasets/issues/6388
| null |
QingYunA
| false
|
[] |
1,980,224,020
| 6,387
|
How to load existing downloaded dataset ?
|
closed
| 2023-11-06T22:51:44
| 2023-11-16T18:07:01
| 2023-11-16T18:07:01
|
https://github.com/huggingface/datasets/issues/6387
| null |
liming-ai
| false
|
[
"Feel free to use `dataset.save_to_disk(...)`, then scp the directory containing the saved dataset and reload it on your other machine using `dataset = load_from_disk(...)`"
] |
1,979,878,014
| 6,386
|
Formatting overhead
|
closed
| 2023-11-06T19:06:38
| 2023-11-06T23:56:12
| 2023-11-06T23:56:12
|
https://github.com/huggingface/datasets/issues/6386
| null |
d-miketa
| false
|
[
"Ah I think the `line-profiler` log is off-by-one and it is in fact the `extract_batch` method that's taking forever. Will investigate further.",
"I tracked it down to a quirk of my setup. Apologies."
] |
1,979,308,338
| 6,385
|
Get an error when i try to concatenate the squad dataset with my own dataset
|
closed
| 2023-11-06T14:29:22
| 2023-11-06T16:50:45
| 2023-11-06T16:50:45
|
https://github.com/huggingface/datasets/issues/6385
| null |
CCDXDX
| false
|
[
"The `answers.text` field in the JSON dataset needs to be a list of strings, not a string.\r\n\r\nSo, here is the fixed code:\r\n```python\r\nfrom huggingface_hub import notebook_login\r\nfrom datasets import load_dataset\r\n\r\n\r\n\r\nnotebook_login(\"mymailadresse\", \"mypassword\")\r\nsquad = load_dataset(\"squad\", split=\"train[:5000]\")\r\nsquad = squad.train_test_split(test_size=0.2)\r\ndataset1 = squad[\"train\"]\r\n\r\n\r\n\r\n\r\nimport json\r\n\r\nmybase = [\r\n {\r\n \"id\": \"1\",\r\n \"context\": \"She lives in Nantes\",\r\n \"question\": \"Where does she live?\",\r\n \"answers\": {\r\n \"text\": [\"Nantes\"],\r\n \"answer_start\": [13],\r\n }\r\n }\r\n]\r\n\r\n\r\n\r\n\r\n# Save the data to a JSON file\r\njson_file_path = r\"data\"\r\nwith open(json_file_path, \"w\", encoding= \"utf-8\") as json_file:\r\n json.dump(mybase, json_file, indent=4)\r\n\r\n\r\n\r\n\r\n# Load the JSON file as a dataset\r\ncustom_dataset = load_dataset(\"json\", data_files=json_file_path, features=dataset1.features)\r\n\r\n\r\n# Access the train split\r\ntrain_dataset = custom_dataset[\"train\"]\r\n\r\n\r\nfrom datasets import concatenate_datasets\r\n\r\n\r\n# Concatenate the datasets\r\nconcatenated_dataset = concatenate_datasets([train_dataset, dataset1])\r\n```",
"Thank you @mariosasko for your help ! It works !"
] |
1,979,117,069
| 6,384
|
Load the local dataset folder from other place
|
closed
| 2023-11-06T13:07:04
| 2023-11-19T05:42:06
| 2023-11-19T05:42:05
|
https://github.com/huggingface/datasets/issues/6384
| null |
OrangeSodahub
| false
|
[
"Solved"
] |
1,978,189,389
| 6,383
|
imagenet-1k downloads over and over
|
closed
| 2023-11-06T02:58:58
| 2024-06-12T13:15:00
| 2023-11-06T06:02:39
|
https://github.com/huggingface/datasets/issues/6383
| null |
seann999
| false
|
[
"Have you solved this problem?"
] |
1,977,400,799
| 6,382
|
Add CheXpert dataset for vision
|
open
| 2023-11-04T15:36:11
| 2024-01-10T11:53:52
| null |
https://github.com/huggingface/datasets/issues/6382
| null |
SauravMaheshkar
| false
|
[
"Hey @SauravMaheshkar ! Just responded to your email.\r\n\r\n_For transparency, copying part of my response here:_\r\nI agree, it would be really great to have this and other BenchMD datasets easily accessible on the hub.\r\n\r\nI think the main limiting factor is that the ChexPert dataset is currently hosted on the Stanford AIMI Shared Datasets website, with a license that does not permit redistribution IIRC. Thus, I believe we would need to create a [dataset loading script](https://huggingface.co/docs/datasets/image_dataset#loading-script) that would check authentication with the Stanford AIMI site before downloading and extracting the data. \r\n\r\nI've started a HF dataset repo [here](https://huggingface.co/datasets/katielink/CheXpert), in case you want to collaborate on writing up this loading script! I'm also happy to take a stab when I have some more time next week.",
"Hey @katielink I would love to try this out. Please guide me.",
"Hi @katielink , I would also love to be on board and contribute to this loading script/project if it is still being developed. I'm interested because I personally would like to gain access to the CheXpert dataset and am facing some weird issues, so I'd like to sort it out for me, and potentially others. Please keep me updated and guide me on this as well!!!"
] |
1,975,028,470
| 6,381
|
Add my dataset
|
closed
| 2023-11-02T20:59:52
| 2023-11-08T14:37:46
| 2023-11-06T15:50:14
|
https://github.com/huggingface/datasets/pull/6381
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6381",
"html_url": "https://github.com/huggingface/datasets/pull/6381",
"diff_url": "https://github.com/huggingface/datasets/pull/6381.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6381.patch",
"merged_at": null
}
|
keyur536
| true
|
[
"Hi! We do not host datasets in this repo. Instead, you should use `dataset.push_to_hub` to upload the dataset to the HF Hub.",
"@mariosasko could you provide me proper guide to push data on HF hub ",
"You can find this info here: https://huggingface.co/docs/datasets/upload_dataset. Also, check https://huggingface.co/docs/datasets/loading for how to load a local dataset (before pushing it to the Hub)."
] |
1,974,741,221
| 6,380
|
Fix for continuation behaviour on broken dataset archives due to starving download connections via HTTP-GET
|
open
| 2023-11-02T17:28:23
| 2023-11-02T17:31:19
| null |
https://github.com/huggingface/datasets/pull/6380
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6380",
"html_url": "https://github.com/huggingface/datasets/pull/6380",
"diff_url": "https://github.com/huggingface/datasets/pull/6380.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6380.patch",
"merged_at": null
}
|
RuntimeRacer
| true
|
[] |
1,974,638,850
| 6,379
|
Avoid redundant warning when encoding NumPy array as `Image`
|
closed
| 2023-11-02T16:37:58
| 2023-11-06T17:53:27
| 2023-11-02T17:08:07
|
https://github.com/huggingface/datasets/pull/6379
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6379",
"html_url": "https://github.com/huggingface/datasets/pull/6379",
"diff_url": "https://github.com/huggingface/datasets/pull/6379.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6379.patch",
"merged_at": "2023-11-02T17:08:07"
}
|
mariosasko
| true
|
[
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008649 / 0.011353 (-0.002704) | 0.005754 / 0.011008 (-0.005254) | 0.101992 / 0.038508 (0.063484) | 0.084932 / 0.023109 (0.061823) | 0.393928 / 0.275898 (0.118030) | 0.414059 / 0.323480 (0.090579) | 0.006564 / 0.007986 (-0.001422) | 0.004746 / 0.004328 (0.000418) | 0.078624 / 0.004250 (0.074373) | 0.060465 / 0.037052 (0.023412) | 0.420767 / 0.258489 (0.162278) | 0.497797 / 0.293841 (0.203956) | 0.047031 / 0.128546 (-0.081516) | 0.014316 / 0.075646 (-0.061330) | 0.340347 / 0.419271 (-0.078925) | 0.067126 / 0.043533 (0.023593) | 0.390806 / 0.255139 (0.135667) | 0.413711 / 0.283200 (0.130512) | 0.037838 / 0.141683 (-0.103845) | 1.713547 / 1.452155 (0.261393) | 1.825591 / 1.492716 (0.332874) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.316357 / 0.018006 (0.298350) | 0.594279 / 0.000490 (0.593789) | 0.013659 / 0.000200 (0.013459) | 0.000547 / 0.000054 (0.000492) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031310 / 0.037411 (-0.006101) | 0.090410 / 0.014526 (0.075884) | 0.114620 / 0.176557 (-0.061936) | 0.183036 / 0.737135 (-0.554099) | 0.112700 / 0.296338 (-0.183638) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.582424 / 0.215209 (0.367215) | 5.670424 / 2.077655 (3.592769) | 2.444326 / 1.504120 (0.940206) | 2.108555 / 1.541195 (0.567360) | 2.091594 / 1.468490 (0.623104) | 0.839067 / 4.584777 (-3.745710) | 5.280942 / 3.745712 (1.535230) | 4.611059 / 5.269862 (-0.658803) | 2.911145 / 4.565676 (-1.654531) | 0.091929 / 0.424275 (-0.332346) | 0.008774 / 0.007607 (0.001167) | 0.657948 / 0.226044 (0.431904) | 6.816300 / 2.268929 (4.547371) | 3.232260 / 55.444624 (-52.212364) | 2.479626 / 6.876477 (-4.396851) | 2.497886 / 2.142072 (0.355813) | 0.959160 / 4.805227 (-3.846068) | 0.222306 / 6.500664 (-6.278358) | 0.072962 / 0.075469 (-0.002507) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.580415 / 1.841788 (-0.261372) | 23.689597 / 8.074308 (15.615289) | 20.430709 / 10.191392 (10.239317) | 0.237891 / 0.680424 (-0.442533) | 0.028194 / 0.534201 (-0.506007) | 0.464915 / 0.579283 (-0.114368) | 0.611512 / 0.434364 (0.177148) | 0.556564 / 0.540337 (0.016227) | 0.811075 / 1.386936 (-0.575861) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008703 / 0.011353 (-0.002649) | 0.005030 / 0.011008 (-0.005978) | 0.079251 / 0.038508 (0.040743) | 0.079054 / 0.023109 (0.055945) | 0.440220 / 0.275898 (0.164322) | 0.479824 / 0.323480 (0.156344) | 0.006312 / 0.007986 (-0.001673) | 0.004506 / 0.004328 (0.000177) | 0.078454 / 0.004250 (0.074203) | 0.061041 / 0.037052 (0.023989) | 0.490104 / 0.258489 (0.231615) | 0.480925 / 0.293841 (0.187084) | 0.049601 / 0.128546 (-0.078945) | 0.013114 / 0.075646 (-0.062532) | 0.092576 / 0.419271 (-0.326696) | 0.059516 / 0.043533 (0.015983) | 0.433728 / 0.255139 (0.178589) | 0.490039 / 0.283200 (0.206839) | 0.035359 / 0.141683 (-0.106324) | 1.823618 / 1.452155 (0.371463) | 1.980894 / 1.492716 (0.488178) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.284679 / 0.018006 (0.266673) | 0.606623 / 0.000490 (0.606133) | 0.007531 / 0.000200 (0.007331) | 0.000109 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033261 / 0.037411 (-0.004150) | 0.102908 / 0.014526 (0.088382) | 0.123912 / 0.176557 (-0.052644) | 0.169893 / 0.737135 (-0.567242) | 0.115366 / 0.296338 (-0.180973) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.598239 / 0.215209 (0.383030) | 6.003464 / 2.077655 (3.925809) | 2.828483 / 1.504120 (1.324363) | 2.485996 / 1.541195 (0.944802) | 2.434986 / 1.468490 (0.966496) | 0.832718 / 4.584777 (-3.752058) | 5.327407 / 3.745712 (1.581694) | 4.732271 / 5.269862 (-0.537590) | 3.047555 / 4.565676 (-1.518121) | 0.103576 / 0.424275 (-0.320699) | 0.009795 / 0.007607 (0.002188) | 0.755443 / 0.226044 (0.529399) | 7.465857 / 2.268929 (5.196928) | 3.564923 / 55.444624 (-51.879701) | 2.740483 / 6.876477 (-4.135994) | 3.044993 / 2.142072 (0.902920) | 1.012925 / 4.805227 (-3.792302) | 0.207498 / 6.500664 (-6.293167) | 0.073361 / 0.075469 (-0.002108) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.704988 / 1.841788 (-0.136800) | 24.669992 / 8.074308 (16.595684) | 21.103096 / 10.191392 (10.911704) | 0.253759 / 0.680424 (-0.426665) | 0.040109 / 0.534201 (-0.494092) | 0.465646 / 0.579283 (-0.113637) | 0.619696 / 0.434364 (0.185332) | 0.552228 / 0.540337 (0.011890) | 0.794907 / 1.386936 (-0.592029) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#85bba8991f6a2d9ed9fd4769d945eeaf318d3aa6 \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006347 / 0.011353 (-0.005006) | 0.003725 / 0.011008 (-0.007283) | 0.080233 / 0.038508 (0.041725) | 0.061013 / 0.023109 (0.037904) | 0.390046 / 0.275898 (0.114148) | 0.420526 / 0.323480 (0.097046) | 0.003579 / 0.007986 (-0.004407) | 0.002837 / 0.004328 (-0.001491) | 0.062929 / 0.004250 (0.058678) | 0.048781 / 0.037052 (0.011729) | 0.400722 / 0.258489 (0.142233) | 0.435022 / 0.293841 (0.141182) | 0.027560 / 0.128546 (-0.100986) | 0.007981 / 0.075646 (-0.067666) | 0.262838 / 0.419271 (-0.156433) | 0.045480 / 0.043533 (0.001947) | 0.394443 / 0.255139 (0.139304) | 0.413828 / 0.283200 (0.130628) | 0.023375 / 0.141683 (-0.118307) | 1.412865 / 1.452155 (-0.039290) | 1.495761 / 1.492716 (0.003044) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224876 / 0.018006 (0.206870) | 0.424234 / 0.000490 (0.423745) | 0.007502 / 0.000200 (0.007302) | 0.000220 / 0.000054 (0.000166) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024246 / 0.037411 (-0.013165) | 0.073982 / 0.014526 (0.059456) | 0.082704 / 0.176557 (-0.093852) | 0.143137 / 0.737135 (-0.593998) | 0.083398 / 0.296338 (-0.212941) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400220 / 0.215209 (0.185010) | 3.973037 / 2.077655 (1.895382) | 2.025903 / 1.504120 (0.521783) | 1.912888 / 1.541195 (0.371693) | 1.999578 / 1.468490 (0.531088) | 0.499378 / 4.584777 (-4.085399) | 3.025715 / 3.745712 (-0.719997) | 2.992338 / 5.269862 (-2.277524) | 1.851155 / 4.565676 (-2.714522) | 0.057528 / 0.424275 (-0.366747) | 0.006802 / 0.007607 (-0.000805) | 0.469516 / 0.226044 (0.243471) | 4.675630 / 2.268929 (2.406702) | 2.472166 / 55.444624 (-52.972458) | 2.238052 / 6.876477 (-4.638424) | 2.288255 / 2.142072 (0.146183) | 0.584906 / 4.805227 (-4.220321) | 0.125902 / 6.500664 (-6.374762) | 0.060681 / 0.075469 (-0.014788) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.236383 / 1.841788 (-0.605404) | 17.554238 / 8.074308 (9.479930) | 13.749298 / 10.191392 (3.557906) | 0.144715 / 0.680424 (-0.535708) | 0.017449 / 0.534201 (-0.516752) | 0.334831 / 0.579283 (-0.244452) | 0.362660 / 0.434364 (-0.071704) | 0.385295 / 0.540337 (-0.155043) | 0.541173 / 1.386936 (-0.845763) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006118 / 0.011353 (-0.005235) | 0.003660 / 0.011008 (-0.007348) | 0.062373 / 0.038508 (0.023865) | 0.063404 / 0.023109 (0.040295) | 0.354149 / 0.275898 (0.078251) | 0.410324 / 0.323480 (0.086844) | 0.004826 / 0.007986 (-0.003160) | 0.002881 / 0.004328 (-0.001448) | 0.061631 / 0.004250 (0.057381) | 0.048052 / 0.037052 (0.010999) | 0.352905 / 0.258489 (0.094416) | 0.400096 / 0.293841 (0.106255) | 0.028472 / 0.128546 (-0.100075) | 0.008076 / 0.075646 (-0.067571) | 0.067910 / 0.419271 (-0.351362) | 0.040671 / 0.043533 (-0.002862) | 0.352131 / 0.255139 (0.096992) | 0.402140 / 0.283200 (0.118940) | 0.020065 / 0.141683 (-0.121618) | 1.456938 / 1.452155 (0.004783) | 1.506484 / 1.492716 (0.013767) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222295 / 0.018006 (0.204288) | 0.416672 / 0.000490 (0.416183) | 0.003015 / 0.000200 (0.002815) | 0.000079 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026428 / 0.037411 (-0.010983) | 0.080072 / 0.014526 (0.065547) | 0.089992 / 0.176557 (-0.086564) | 0.141739 / 0.737135 (-0.595397) | 0.092281 / 0.296338 (-0.204058) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417758 / 0.215209 (0.202549) | 4.175673 / 2.077655 (2.098018) | 2.262369 / 1.504120 (0.758249) | 2.100440 / 1.541195 (0.559246) | 2.075827 / 1.468490 (0.607337) | 0.505673 / 4.584777 (-4.079104) | 3.129020 / 3.745712 (-0.616692) | 2.843255 / 5.269862 (-2.426607) | 1.853288 / 4.565676 (-2.712389) | 0.058337 / 0.424275 (-0.365938) | 0.006461 / 0.007607 (-0.001147) | 0.491797 / 0.226044 (0.265753) | 4.933327 / 2.268929 (2.664399) | 2.675374 / 55.444624 (-52.769250) | 2.358103 / 6.876477 (-4.518374) | 2.540436 / 2.142072 (0.398363) | 0.591550 / 4.805227 (-4.213677) | 0.121572 / 6.500664 (-6.379092) | 0.057311 / 0.075469 (-0.018158) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.365368 / 1.841788 (-0.476419) | 17.763413 / 8.074308 (9.689105) | 14.368754 / 10.191392 (4.177362) | 0.132979 / 0.680424 (-0.547445) | 0.017957 / 0.534201 (-0.516244) | 0.334035 / 0.579283 (-0.245248) | 0.385349 / 0.434364 (-0.049015) | 0.392636 / 0.540337 (-0.147702) | 0.537957 / 1.386936 (-0.848979) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#92503c94839b31125b4d5288d0a49d81b9b9b3cc \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008053 / 0.011353 (-0.003300) | 0.004966 / 0.011008 (-0.006043) | 0.102219 / 0.038508 (0.063711) | 0.099319 / 0.023109 (0.076210) | 0.418458 / 0.275898 (0.142559) | 0.459344 / 0.323480 (0.135864) | 0.004756 / 0.007986 (-0.003229) | 0.003940 / 0.004328 (-0.000388) | 0.076824 / 0.004250 (0.072573) | 0.068090 / 0.037052 (0.031038) | 0.428689 / 0.258489 (0.170200) | 0.476153 / 0.293841 (0.182312) | 0.036927 / 0.128546 (-0.091619) | 0.010232 / 0.075646 (-0.065414) | 0.345126 / 0.419271 (-0.074145) | 0.063182 / 0.043533 (0.019649) | 0.416633 / 0.255139 (0.161494) | 0.437418 / 0.283200 (0.154218) | 0.028192 / 0.141683 (-0.113491) | 1.768869 / 1.452155 (0.316715) | 1.847022 / 1.492716 (0.354306) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.269997 / 0.018006 (0.251991) | 0.544246 / 0.000490 (0.543756) | 0.012940 / 0.000200 (0.012740) | 0.000754 / 0.000054 (0.000699) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035570 / 0.037411 (-0.001842) | 0.104318 / 0.014526 (0.089792) | 0.115263 / 0.176557 (-0.061294) | 0.184693 / 0.737135 (-0.552442) | 0.116023 / 0.296338 (-0.180315) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.472361 / 0.215209 (0.257152) | 4.714327 / 2.077655 (2.636673) | 2.405434 / 1.504120 (0.901314) | 2.197871 / 1.541195 (0.656677) | 2.312901 / 1.468490 (0.844411) | 0.569736 / 4.584777 (-4.015041) | 4.600008 / 3.745712 (0.854296) | 4.127967 / 5.269862 (-1.141895) | 2.462232 / 4.565676 (-2.103445) | 0.067759 / 0.424275 (-0.356516) | 0.009277 / 0.007607 (0.001670) | 0.569658 / 0.226044 (0.343614) | 5.694050 / 2.268929 (3.425121) | 3.041495 / 55.444624 (-52.403129) | 2.688418 / 6.876477 (-4.188059) | 2.762175 / 2.142072 (0.620102) | 0.683250 / 4.805227 (-4.121977) | 0.158772 / 6.500664 (-6.341892) | 0.073364 / 0.075469 (-0.002105) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.627241 / 1.841788 (-0.214547) | 23.054465 / 8.074308 (14.980157) | 17.122451 / 10.191392 (6.931059) | 0.170272 / 0.680424 (-0.510152) | 0.021678 / 0.534201 (-0.512523) | 0.467301 / 0.579283 (-0.111982) | 0.509480 / 0.434364 (0.075116) | 0.555077 / 0.540337 (0.014740) | 0.816199 / 1.386936 (-0.570737) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008499 / 0.011353 (-0.002854) | 0.004724 / 0.011008 (-0.006284) | 0.077519 / 0.038508 (0.039011) | 0.103237 / 0.023109 (0.080127) | 0.447470 / 0.275898 (0.171572) | 0.484778 / 0.323480 (0.161298) | 0.006475 / 0.007986 (-0.001511) | 0.003946 / 0.004328 (-0.000383) | 0.075596 / 0.004250 (0.071346) | 0.069265 / 0.037052 (0.032213) | 0.454185 / 0.258489 (0.195696) | 0.491039 / 0.293841 (0.197198) | 0.038611 / 0.128546 (-0.089935) | 0.009889 / 0.075646 (-0.065758) | 0.084012 / 0.419271 (-0.335260) | 0.057265 / 0.043533 (0.013732) | 0.448622 / 0.255139 (0.193483) | 0.470961 / 0.283200 (0.187762) | 0.029220 / 0.141683 (-0.112463) | 1.773347 / 1.452155 (0.321192) | 1.872669 / 1.492716 (0.379953) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.272429 / 0.018006 (0.254423) | 0.569907 / 0.000490 (0.569418) | 0.013359 / 0.000200 (0.013159) | 0.000187 / 0.000054 (0.000133) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038784 / 0.037411 (0.001373) | 0.114958 / 0.014526 (0.100432) | 0.132745 / 0.176557 (-0.043811) | 0.186283 / 0.737135 (-0.550852) | 0.126652 / 0.296338 (-0.169686) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.482753 / 0.215209 (0.267544) | 4.827287 / 2.077655 (2.749633) | 2.539959 / 1.504120 (1.035839) | 2.348483 / 1.541195 (0.807288) | 2.421739 / 1.468490 (0.953249) | 0.586064 / 4.584777 (-3.998713) | 4.579865 / 3.745712 (0.834152) | 3.950617 / 5.269862 (-1.319244) | 2.528447 / 4.565676 (-2.037229) | 0.070280 / 0.424275 (-0.353995) | 0.008801 / 0.007607 (0.001194) | 0.568857 / 0.226044 (0.342812) | 5.692739 / 2.268929 (3.423810) | 3.192045 / 55.444624 (-52.252579) | 2.768092 / 6.876477 (-4.108384) | 3.002934 / 2.142072 (0.860862) | 0.701887 / 4.805227 (-4.103340) | 0.155563 / 6.500664 (-6.345102) | 0.069397 / 0.075469 (-0.006072) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.607991 / 1.841788 (-0.233796) | 24.658060 / 8.074308 (16.583752) | 17.616229 / 10.191392 (7.424837) | 0.209730 / 0.680424 (-0.470693) | 0.024052 / 0.534201 (-0.510149) | 0.476648 / 0.579283 (-0.102635) | 0.534452 / 0.434364 (0.100089) | 0.567702 / 0.540337 (0.027365) | 0.772933 / 1.386936 (-0.614003) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a49e78ede85c2a680adddacbb6b9638cba4062f3 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004684 / 0.011353 (-0.006669) | 0.002944 / 0.011008 (-0.008064) | 0.063065 / 0.038508 (0.024557) | 0.051627 / 0.023109 (0.028518) | 0.243485 / 0.275898 (-0.032413) | 0.275144 / 0.323480 (-0.048336) | 0.002934 / 0.007986 (-0.005052) | 0.002395 / 0.004328 (-0.001934) | 0.048579 / 0.004250 (0.044328) | 0.038940 / 0.037052 (0.001887) | 0.250244 / 0.258489 (-0.008245) | 0.287404 / 0.293841 (-0.006437) | 0.022958 / 0.128546 (-0.105588) | 0.007189 / 0.075646 (-0.068458) | 0.202483 / 0.419271 (-0.216788) | 0.035477 / 0.043533 (-0.008056) | 0.243793 / 0.255139 (-0.011346) | 0.265990 / 0.283200 (-0.017209) | 0.019675 / 0.141683 (-0.122008) | 1.119127 / 1.452155 (-0.333028) | 1.183230 / 1.492716 (-0.309486) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097090 / 0.018006 (0.079084) | 0.305815 / 0.000490 (0.305325) | 0.000228 / 0.000200 (0.000028) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019233 / 0.037411 (-0.018178) | 0.061743 / 0.014526 (0.047217) | 0.077033 / 0.176557 (-0.099524) | 0.119786 / 0.737135 (-0.617349) | 0.074740 / 0.296338 (-0.221598) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284361 / 0.215209 (0.069152) | 2.761501 / 2.077655 (0.683846) | 1.464980 / 1.504120 (-0.039140) | 1.348026 / 1.541195 (-0.193169) | 1.362690 / 1.468490 (-0.105800) | 0.392022 / 4.584777 (-4.192755) | 2.401330 / 3.745712 (-1.344382) | 2.618999 / 5.269862 (-2.650863) | 1.599526 / 4.565676 (-2.966150) | 0.045621 / 0.424275 (-0.378654) | 0.005153 / 0.007607 (-0.002454) | 0.337279 / 0.226044 (0.111234) | 3.330135 / 2.268929 (1.061206) | 1.803544 / 55.444624 (-53.641081) | 1.515545 / 6.876477 (-5.360932) | 1.561745 / 2.142072 (-0.580327) | 0.468735 / 4.805227 (-4.336492) | 0.098882 / 6.500664 (-6.401782) | 0.042923 / 0.075469 (-0.032546) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.961106 / 1.841788 (-0.880682) | 12.030489 / 8.074308 (3.956181) | 10.824166 / 10.191392 (0.632774) | 0.132135 / 0.680424 (-0.548289) | 0.015320 / 0.534201 (-0.518881) | 0.269691 / 0.579283 (-0.309592) | 0.270700 / 0.434364 (-0.163664) | 0.308317 / 0.540337 (-0.232020) | 0.397871 / 1.386936 (-0.989065) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004859 / 0.011353 (-0.006494) | 0.003400 / 0.011008 (-0.007609) | 0.048095 / 0.038508 (0.009587) | 0.054885 / 0.023109 (0.031776) | 0.276976 / 0.275898 (0.001078) | 0.302298 / 0.323480 (-0.021182) | 0.004084 / 0.007986 (-0.003902) | 0.002647 / 0.004328 (-0.001681) | 0.048570 / 0.004250 (0.044319) | 0.040683 / 0.037052 (0.003631) | 0.279828 / 0.258489 (0.021339) | 0.306037 / 0.293841 (0.012196) | 0.024263 / 0.128546 (-0.104283) | 0.007336 / 0.075646 (-0.068310) | 0.053768 / 0.419271 (-0.365503) | 0.032284 / 0.043533 (-0.011248) | 0.276706 / 0.255139 (0.021567) | 0.294706 / 0.283200 (0.011506) | 0.018092 / 0.141683 (-0.123591) | 1.153430 / 1.452155 (-0.298725) | 1.208783 / 1.492716 (-0.283933) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096946 / 0.018006 (0.078939) | 0.308118 / 0.000490 (0.307628) | 0.000234 / 0.000200 (0.000034) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021834 / 0.037411 (-0.015577) | 0.070934 / 0.014526 (0.056408) | 0.080310 / 0.176557 (-0.096247) | 0.123299 / 0.737135 (-0.613836) | 0.081591 / 0.296338 (-0.214748) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.302242 / 0.215209 (0.087033) | 2.934477 / 2.077655 (0.856822) | 1.623768 / 1.504120 (0.119648) | 1.493868 / 1.541195 (-0.047326) | 1.516553 / 1.468490 (0.048063) | 0.410319 / 4.584777 (-4.174458) | 2.471346 / 3.745712 (-1.274366) | 2.667371 / 5.269862 (-2.602491) | 1.625390 / 4.565676 (-2.940286) | 0.046465 / 0.424275 (-0.377810) | 0.004867 / 0.007607 (-0.002740) | 0.355516 / 0.226044 (0.129471) | 3.442294 / 2.268929 (1.173365) | 1.973859 / 55.444624 (-53.470765) | 1.682089 / 6.876477 (-5.194388) | 1.865253 / 2.142072 (-0.276819) | 0.475750 / 4.805227 (-4.329477) | 0.098298 / 6.500664 (-6.402366) | 0.041025 / 0.075469 (-0.034445) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.969864 / 1.841788 (-0.871924) | 12.437806 / 8.074308 (4.363498) | 10.461262 / 10.191392 (0.269870) | 0.131051 / 0.680424 (-0.549373) | 0.016232 / 0.534201 (-0.517969) | 0.273968 / 0.579283 (-0.305315) | 0.285369 / 0.434364 (-0.148995) | 0.309046 / 0.540337 (-0.231291) | 0.398776 / 1.386936 (-0.988160) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a49e78ede85c2a680adddacbb6b9638cba4062f3 \"CML watermark\")\n"
] |
1,973,942,770
| 6,378
|
Support pyarrow 14.0.0
|
closed
| 2023-11-02T10:25:10
| 2023-11-02T15:24:28
| 2023-11-02T15:15:44
|
https://github.com/huggingface/datasets/pull/6378
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6378",
"html_url": "https://github.com/huggingface/datasets/pull/6378",
"diff_url": "https://github.com/huggingface/datasets/pull/6378.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6378.patch",
"merged_at": "2023-11-02T15:15:44"
}
|
albertvillanova
| true
|
[
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007561 / 0.011353 (-0.003792) | 0.004824 / 0.011008 (-0.006184) | 0.110372 / 0.038508 (0.071864) | 0.076767 / 0.023109 (0.053657) | 0.357094 / 0.275898 (0.081196) | 0.420566 / 0.323480 (0.097086) | 0.004753 / 0.007986 (-0.003232) | 0.004734 / 0.004328 (0.000405) | 0.072926 / 0.004250 (0.068675) | 0.058045 / 0.037052 (0.020992) | 0.401109 / 0.258489 (0.142620) | 0.444585 / 0.293841 (0.150744) | 0.046492 / 0.128546 (-0.082055) | 0.013948 / 0.075646 (-0.061698) | 0.305188 / 0.419271 (-0.114083) | 0.063112 / 0.043533 (0.019579) | 0.384711 / 0.255139 (0.129572) | 0.411375 / 0.283200 (0.128175) | 0.048147 / 0.141683 (-0.093536) | 1.632357 / 1.452155 (0.180202) | 1.661021 / 1.492716 (0.168304) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.281104 / 0.018006 (0.263098) | 0.567152 / 0.000490 (0.566662) | 0.007178 / 0.000200 (0.006978) | 0.000121 / 0.000054 (0.000066) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029337 / 0.037411 (-0.008075) | 0.081644 / 0.014526 (0.067118) | 0.103326 / 0.176557 (-0.073230) | 0.155299 / 0.737135 (-0.581836) | 0.093518 / 0.296338 (-0.202821) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.517979 / 0.215209 (0.302769) | 5.250052 / 2.077655 (3.172397) | 2.220543 / 1.504120 (0.716424) | 1.901087 / 1.541195 (0.359892) | 1.920564 / 1.468490 (0.452073) | 0.766289 / 4.584777 (-3.818488) | 5.130968 / 3.745712 (1.385256) | 4.561874 / 5.269862 (-0.707988) | 2.702808 / 4.565676 (-1.862868) | 0.078929 / 0.424275 (-0.345346) | 0.007834 / 0.007607 (0.000226) | 0.636628 / 0.226044 (0.410583) | 6.309391 / 2.268929 (4.040463) | 2.942180 / 55.444624 (-52.502445) | 2.369557 / 6.876477 (-4.506920) | 2.347528 / 2.142072 (0.205456) | 0.911110 / 4.805227 (-3.894117) | 0.189102 / 6.500664 (-6.311562) | 0.068012 / 0.075469 (-0.007457) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.494431 / 1.841788 (-0.347356) | 22.161476 / 8.074308 (14.087168) | 19.426403 / 10.191392 (9.235011) | 0.211154 / 0.680424 (-0.469270) | 0.030655 / 0.534201 (-0.503546) | 0.440449 / 0.579283 (-0.138834) | 0.526522 / 0.434364 (0.092158) | 0.517494 / 0.540337 (-0.022844) | 0.727387 / 1.386936 (-0.659549) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008354 / 0.011353 (-0.002999) | 0.006108 / 0.011008 (-0.004900) | 0.069079 / 0.038508 (0.030571) | 0.080402 / 0.023109 (0.057292) | 0.452166 / 0.275898 (0.176268) | 0.440264 / 0.323480 (0.116784) | 0.005942 / 0.007986 (-0.002043) | 0.003397 / 0.004328 (-0.000932) | 0.079856 / 0.004250 (0.075606) | 0.056329 / 0.037052 (0.019276) | 0.424261 / 0.258489 (0.165772) | 0.464362 / 0.293841 (0.170521) | 0.051968 / 0.128546 (-0.076578) | 0.015204 / 0.075646 (-0.060442) | 0.085940 / 0.419271 (-0.333332) | 0.066673 / 0.043533 (0.023140) | 0.436481 / 0.255139 (0.181342) | 0.445285 / 0.283200 (0.162085) | 0.035188 / 0.141683 (-0.106495) | 1.579442 / 1.452155 (0.127288) | 1.686120 / 1.492716 (0.193404) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.319039 / 0.018006 (0.301032) | 0.655080 / 0.000490 (0.654591) | 0.005445 / 0.000200 (0.005245) | 0.000112 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028566 / 0.037411 (-0.008845) | 0.092131 / 0.014526 (0.077605) | 0.103654 / 0.176557 (-0.072902) | 0.158082 / 0.737135 (-0.579054) | 0.107520 / 0.296338 (-0.188819) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.573479 / 0.215209 (0.358270) | 5.629751 / 2.077655 (3.552096) | 2.501722 / 1.504120 (0.997602) | 2.156255 / 1.541195 (0.615061) | 2.251296 / 1.468490 (0.782805) | 0.767686 / 4.584777 (-3.817091) | 5.080866 / 3.745712 (1.335154) | 4.353351 / 5.269862 (-0.916510) | 2.818707 / 4.565676 (-1.746970) | 0.082617 / 0.424275 (-0.341658) | 0.008045 / 0.007607 (0.000438) | 0.665462 / 0.226044 (0.439417) | 6.961380 / 2.268929 (4.692452) | 3.308717 / 55.444624 (-52.135907) | 2.664239 / 6.876477 (-4.212238) | 2.782790 / 2.142072 (0.640718) | 0.919567 / 4.805227 (-3.885660) | 0.186731 / 6.500664 (-6.313933) | 0.063437 / 0.075469 (-0.012032) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.668076 / 1.841788 (-0.173712) | 22.720187 / 8.074308 (14.645879) | 19.803359 / 10.191392 (9.611967) | 0.237201 / 0.680424 (-0.443223) | 0.041156 / 0.534201 (-0.493045) | 0.458974 / 0.579283 (-0.120309) | 0.620276 / 0.434364 (0.185912) | 0.544079 / 0.540337 (0.003741) | 0.722715 / 1.386936 (-0.664221) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1ed9306b6c512befb721b681fba3222221c8468e \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006882 / 0.011353 (-0.004471) | 0.004238 / 0.011008 (-0.006770) | 0.084042 / 0.038508 (0.045534) | 0.074175 / 0.023109 (0.051065) | 0.308771 / 0.275898 (0.032873) | 0.346300 / 0.323480 (0.022820) | 0.005455 / 0.007986 (-0.002530) | 0.003638 / 0.004328 (-0.000690) | 0.065326 / 0.004250 (0.061076) | 0.056080 / 0.037052 (0.019028) | 0.326324 / 0.258489 (0.067834) | 0.360133 / 0.293841 (0.066292) | 0.031577 / 0.128546 (-0.096969) | 0.008675 / 0.075646 (-0.066971) | 0.288051 / 0.419271 (-0.131221) | 0.052769 / 0.043533 (0.009236) | 0.308689 / 0.255139 (0.053550) | 0.328270 / 0.283200 (0.045070) | 0.025028 / 0.141683 (-0.116655) | 1.520670 / 1.452155 (0.068515) | 1.585229 / 1.492716 (0.092513) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.284078 / 0.018006 (0.266072) | 0.558134 / 0.000490 (0.557644) | 0.015042 / 0.000200 (0.014842) | 0.000429 / 0.000054 (0.000375) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028747 / 0.037411 (-0.008664) | 0.083816 / 0.014526 (0.069290) | 0.207467 / 0.176557 (0.030911) | 0.163527 / 0.737135 (-0.573608) | 0.100148 / 0.296338 (-0.196190) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.376109 / 0.215209 (0.160900) | 3.749639 / 2.077655 (1.671984) | 1.827081 / 1.504120 (0.322961) | 1.662021 / 1.541195 (0.120827) | 1.734655 / 1.468490 (0.266165) | 0.483701 / 4.584777 (-4.101075) | 3.454772 / 3.745712 (-0.290941) | 3.465079 / 5.269862 (-1.804783) | 2.070874 / 4.565676 (-2.494802) | 0.056714 / 0.424275 (-0.367561) | 0.007786 / 0.007607 (0.000179) | 0.455980 / 0.226044 (0.229936) | 4.530612 / 2.268929 (2.261683) | 2.345757 / 55.444624 (-53.098867) | 2.030289 / 6.876477 (-4.846188) | 2.068440 / 2.142072 (-0.073632) | 0.576502 / 4.805227 (-4.228725) | 0.131787 / 6.500664 (-6.368878) | 0.060038 / 0.075469 (-0.015431) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.272225 / 1.841788 (-0.569563) | 19.373635 / 8.074308 (11.299327) | 14.167831 / 10.191392 (3.976439) | 0.166336 / 0.680424 (-0.514088) | 0.018420 / 0.534201 (-0.515781) | 0.387878 / 0.579283 (-0.191405) | 0.413105 / 0.434364 (-0.021259) | 0.458618 / 0.540337 (-0.081720) | 0.639031 / 1.386936 (-0.747905) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007122 / 0.011353 (-0.004230) | 0.004193 / 0.011008 (-0.006815) | 0.066194 / 0.038508 (0.027686) | 0.077775 / 0.023109 (0.054666) | 0.349780 / 0.275898 (0.073882) | 0.383417 / 0.323480 (0.059937) | 0.006416 / 0.007986 (-0.001570) | 0.003651 / 0.004328 (-0.000677) | 0.064837 / 0.004250 (0.060587) | 0.058012 / 0.037052 (0.020959) | 0.351085 / 0.258489 (0.092596) | 0.387302 / 0.293841 (0.093462) | 0.032447 / 0.128546 (-0.096099) | 0.008636 / 0.075646 (-0.067011) | 0.071962 / 0.419271 (-0.347309) | 0.047839 / 0.043533 (0.004306) | 0.349508 / 0.255139 (0.094369) | 0.361892 / 0.283200 (0.078693) | 0.024129 / 0.141683 (-0.117554) | 1.523828 / 1.452155 (0.071673) | 1.607371 / 1.492716 (0.114655) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.245928 / 0.018006 (0.227922) | 0.567708 / 0.000490 (0.567218) | 0.003789 / 0.000200 (0.003589) | 0.000092 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034107 / 0.037411 (-0.003304) | 0.092539 / 0.014526 (0.078014) | 0.110735 / 0.176557 (-0.065821) | 0.163251 / 0.737135 (-0.573884) | 0.110353 / 0.296338 (-0.185985) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.399992 / 0.215209 (0.184783) | 3.976526 / 2.077655 (1.898872) | 2.056182 / 1.504120 (0.552062) | 1.856624 / 1.541195 (0.315429) | 1.941540 / 1.468490 (0.473050) | 0.484662 / 4.584777 (-4.100115) | 3.548228 / 3.745712 (-0.197484) | 3.352900 / 5.269862 (-1.916962) | 2.056310 / 4.565676 (-2.509366) | 0.056952 / 0.424275 (-0.367323) | 0.007284 / 0.007607 (-0.000323) | 0.473749 / 0.226044 (0.247704) | 4.736510 / 2.268929 (2.467581) | 2.570711 / 55.444624 (-52.873913) | 2.204237 / 6.876477 (-4.672239) | 2.438512 / 2.142072 (0.296439) | 0.575542 / 4.805227 (-4.229685) | 0.129260 / 6.500664 (-6.371404) | 0.057704 / 0.075469 (-0.017765) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.316659 / 1.841788 (-0.525128) | 20.103340 / 8.074308 (12.029032) | 14.488385 / 10.191392 (4.296993) | 0.171841 / 0.680424 (-0.508583) | 0.020148 / 0.534201 (-0.514053) | 0.398456 / 0.579283 (-0.180828) | 0.443516 / 0.434364 (0.009152) | 0.479597 / 0.540337 (-0.060741) | 0.643665 / 1.386936 (-0.743271) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#370be814b0c18769ea8e699e3647fadcf431e6df \"CML watermark\")\n"
] |
1,973,937,612
| 6,377
|
Support pyarrow 14.0.0
|
closed
| 2023-11-02T10:22:08
| 2023-11-02T15:15:45
| 2023-11-02T15:15:45
|
https://github.com/huggingface/datasets/issues/6377
| null |
albertvillanova
| false
|
[] |
1,973,927,468
| 6,376
|
Caching problem when deleting a dataset
|
closed
| 2023-11-02T10:15:58
| 2023-12-04T16:53:34
| 2023-12-04T16:53:33
|
https://github.com/huggingface/datasets/issues/6376
| null |
clefourrier
| false
|
[
"Thanks for reporting! Can you also share the error message printed in step 5?",
"I did not store it at the time but I'll try to re-do a mwe next week to get it again",
"I haven't managed to reproduce this issue using a [notebook](https://colab.research.google.com/drive/1m6eduYun7pFTkigrCJAFgw0BghlbvXIL?usp=sharing) that follows the steps to reproduce the bug. So, I'm closing it.\r\n\r\nBut feel free to re-open it if you have a better reproducer."
] |
1,973,877,879
| 6,375
|
Temporarily pin pyarrow < 14.0.0
|
closed
| 2023-11-02T09:48:58
| 2023-11-02T10:22:33
| 2023-11-02T10:11:19
|
https://github.com/huggingface/datasets/pull/6375
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6375",
"html_url": "https://github.com/huggingface/datasets/pull/6375",
"diff_url": "https://github.com/huggingface/datasets/pull/6375.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6375.patch",
"merged_at": "2023-11-02T10:11:19"
}
|
albertvillanova
| true
|
[
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008947 / 0.011353 (-0.002406) | 0.005602 / 0.011008 (-0.005406) | 0.111208 / 0.038508 (0.072700) | 0.082750 / 0.023109 (0.059641) | 0.453277 / 0.275898 (0.177379) | 0.480072 / 0.323480 (0.156592) | 0.005254 / 0.007986 (-0.002731) | 0.005421 / 0.004328 (0.001092) | 0.082899 / 0.004250 (0.078648) | 0.062859 / 0.037052 (0.025807) | 0.466703 / 0.258489 (0.208214) | 0.478241 / 0.293841 (0.184400) | 0.050754 / 0.128546 (-0.077792) | 0.017726 / 0.075646 (-0.057920) | 0.374830 / 0.419271 (-0.044442) | 0.068577 / 0.043533 (0.025044) | 0.453643 / 0.255139 (0.198504) | 0.453736 / 0.283200 (0.170537) | 0.037313 / 0.141683 (-0.104369) | 1.741215 / 1.452155 (0.289060) | 1.862247 / 1.492716 (0.369531) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.314174 / 0.018006 (0.296168) | 0.644439 / 0.000490 (0.643949) | 0.013914 / 0.000200 (0.013715) | 0.000478 / 0.000054 (0.000424) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030462 / 0.037411 (-0.006949) | 0.096789 / 0.014526 (0.082263) | 0.109999 / 0.176557 (-0.066557) | 0.184610 / 0.737135 (-0.552525) | 0.113846 / 0.296338 (-0.182493) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.586508 / 0.215209 (0.371299) | 5.785138 / 2.077655 (3.707484) | 2.578512 / 1.504120 (1.074392) | 2.266981 / 1.541195 (0.725786) | 2.442463 / 1.468490 (0.973973) | 0.880973 / 4.584777 (-3.703804) | 5.410327 / 3.745712 (1.664615) | 4.976842 / 5.269862 (-0.293020) | 3.020535 / 4.565676 (-1.545142) | 0.089640 / 0.424275 (-0.334635) | 0.009126 / 0.007607 (0.001519) | 0.682364 / 0.226044 (0.456319) | 6.840507 / 2.268929 (4.571579) | 3.313314 / 55.444624 (-52.131310) | 2.815313 / 6.876477 (-4.061164) | 2.851787 / 2.142072 (0.709715) | 1.044916 / 4.805227 (-3.760312) | 0.218346 / 6.500664 (-6.282318) | 0.075655 / 0.075469 (0.000186) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.641767 / 1.841788 (-0.200020) | 24.618096 / 8.074308 (16.543788) | 21.557652 / 10.191392 (11.366260) | 0.211622 / 0.680424 (-0.468801) | 0.028775 / 0.534201 (-0.505426) | 0.480469 / 0.579283 (-0.098814) | 0.593311 / 0.434364 (0.158948) | 0.560620 / 0.540337 (0.020283) | 0.827026 / 1.386936 (-0.559910) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009347 / 0.011353 (-0.002006) | 0.005184 / 0.011008 (-0.005824) | 0.078878 / 0.038508 (0.040370) | 0.083067 / 0.023109 (0.059957) | 0.446591 / 0.275898 (0.170693) | 0.512934 / 0.323480 (0.189454) | 0.006614 / 0.007986 (-0.001372) | 0.004477 / 0.004328 (0.000148) | 0.087403 / 0.004250 (0.083153) | 0.060710 / 0.037052 (0.023658) | 0.451811 / 0.258489 (0.193322) | 0.482031 / 0.293841 (0.188190) | 0.051685 / 0.128546 (-0.076862) | 0.013436 / 0.075646 (-0.062210) | 0.109012 / 0.419271 (-0.310259) | 0.059654 / 0.043533 (0.016121) | 0.439041 / 0.255139 (0.183902) | 0.481708 / 0.283200 (0.198508) | 0.037393 / 0.141683 (-0.104290) | 1.761704 / 1.452155 (0.309549) | 1.946711 / 1.492716 (0.453995) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.287981 / 0.018006 (0.269975) | 0.610219 / 0.000490 (0.609729) | 0.006733 / 0.000200 (0.006533) | 0.000128 / 0.000054 (0.000074) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038999 / 0.037411 (0.001588) | 0.100613 / 0.014526 (0.086087) | 0.126445 / 0.176557 (-0.050111) | 0.187596 / 0.737135 (-0.549540) | 0.122130 / 0.296338 (-0.174208) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.647686 / 0.215209 (0.432477) | 6.176079 / 2.077655 (4.098424) | 2.800232 / 1.504120 (1.296112) | 2.434625 / 1.541195 (0.893430) | 2.460646 / 1.468490 (0.992155) | 0.923736 / 4.584777 (-3.661041) | 5.480197 / 3.745712 (1.734485) | 4.849250 / 5.269862 (-0.420612) | 3.031576 / 4.565676 (-1.534101) | 0.102525 / 0.424275 (-0.321750) | 0.008688 / 0.007607 (0.001081) | 0.766097 / 0.226044 (0.540052) | 7.626822 / 2.268929 (5.357893) | 3.719155 / 55.444624 (-51.725469) | 2.967121 / 6.876477 (-3.909356) | 3.182464 / 2.142072 (1.040392) | 1.018315 / 4.805227 (-3.786912) | 0.211300 / 6.500664 (-6.289364) | 0.083055 / 0.075469 (0.007586) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.731619 / 1.841788 (-0.110168) | 25.315978 / 8.074308 (17.241669) | 22.736306 / 10.191392 (12.544914) | 0.270330 / 0.680424 (-0.410094) | 0.034790 / 0.534201 (-0.499411) | 0.488675 / 0.579283 (-0.090608) | 0.603426 / 0.434364 (0.169062) | 0.572547 / 0.540337 (0.032210) | 0.825719 / 1.386936 (-0.561217) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1eaa85a4ad79aa0e411218d61a8894cc14a75fa0 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008992 / 0.011353 (-0.002360) | 0.005086 / 0.011008 (-0.005923) | 0.107400 / 0.038508 (0.068892) | 0.091894 / 0.023109 (0.068785) | 0.382347 / 0.275898 (0.106449) | 0.446581 / 0.323480 (0.123101) | 0.005179 / 0.007986 (-0.002807) | 0.006356 / 0.004328 (0.002028) | 0.084979 / 0.004250 (0.080729) | 0.060647 / 0.037052 (0.023594) | 0.385940 / 0.258489 (0.127451) | 0.444817 / 0.293841 (0.150976) | 0.049484 / 0.128546 (-0.079062) | 0.014173 / 0.075646 (-0.061473) | 0.345704 / 0.419271 (-0.073567) | 0.068082 / 0.043533 (0.024550) | 0.377170 / 0.255139 (0.122031) | 0.411816 / 0.283200 (0.128616) | 0.043049 / 0.141683 (-0.098633) | 1.681499 / 1.452155 (0.229344) | 1.805428 / 1.492716 (0.312712) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.323170 / 0.018006 (0.305164) | 0.693845 / 0.000490 (0.693355) | 0.015499 / 0.000200 (0.015299) | 0.000603 / 0.000054 (0.000548) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031629 / 0.037411 (-0.005783) | 0.093511 / 0.014526 (0.078985) | 0.112400 / 0.176557 (-0.064157) | 0.173731 / 0.737135 (-0.563405) | 0.116013 / 0.296338 (-0.180325) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.576724 / 0.215209 (0.361515) | 5.775055 / 2.077655 (3.697400) | 2.755869 / 1.504120 (1.251749) | 2.430253 / 1.541195 (0.889058) | 2.479629 / 1.468490 (1.011139) | 0.841472 / 4.584777 (-3.743305) | 5.120536 / 3.745712 (1.374824) | 4.813281 / 5.269862 (-0.456581) | 3.054617 / 4.565676 (-1.511059) | 0.091459 / 0.424275 (-0.332816) | 0.009072 / 0.007607 (0.001465) | 0.742674 / 0.226044 (0.516629) | 7.137861 / 2.268929 (4.868933) | 3.497568 / 55.444624 (-51.947056) | 2.814658 / 6.876477 (-4.061819) | 2.934415 / 2.142072 (0.792343) | 0.970855 / 4.805227 (-3.834372) | 0.213366 / 6.500664 (-6.287299) | 0.078763 / 0.075469 (0.003293) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.584716 / 1.841788 (-0.257072) | 24.098173 / 8.074308 (16.023865) | 20.746352 / 10.191392 (10.554960) | 0.215313 / 0.680424 (-0.465111) | 0.029538 / 0.534201 (-0.504663) | 0.448672 / 0.579283 (-0.130611) | 0.580023 / 0.434364 (0.145659) | 0.537867 / 0.540337 (-0.002471) | 0.804622 / 1.386936 (-0.582314) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008965 / 0.011353 (-0.002388) | 0.005544 / 0.011008 (-0.005464) | 0.076806 / 0.038508 (0.038298) | 0.085333 / 0.023109 (0.062224) | 0.509974 / 0.275898 (0.234076) | 0.511548 / 0.323480 (0.188068) | 0.007136 / 0.007986 (-0.000849) | 0.004491 / 0.004328 (0.000163) | 0.086687 / 0.004250 (0.082437) | 0.066539 / 0.037052 (0.029486) | 0.483663 / 0.258489 (0.225174) | 0.529480 / 0.293841 (0.235639) | 0.046296 / 0.128546 (-0.082250) | 0.014736 / 0.075646 (-0.060910) | 0.088261 / 0.419271 (-0.331010) | 0.056753 / 0.043533 (0.013220) | 0.511698 / 0.255139 (0.256559) | 0.497956 / 0.283200 (0.214756) | 0.034753 / 0.141683 (-0.106930) | 1.828354 / 1.452155 (0.376199) | 1.799211 / 1.492716 (0.306494) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.389652 / 0.018006 (0.371645) | 0.602522 / 0.000490 (0.602033) | 0.068363 / 0.000200 (0.068163) | 0.000493 / 0.000054 (0.000439) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036431 / 0.037411 (-0.000980) | 0.102162 / 0.014526 (0.087636) | 0.122466 / 0.176557 (-0.054091) | 0.181001 / 0.737135 (-0.556134) | 0.125743 / 0.296338 (-0.170596) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.583847 / 0.215209 (0.368638) | 5.913008 / 2.077655 (3.835354) | 2.716088 / 1.504120 (1.211968) | 2.328631 / 1.541195 (0.787437) | 2.459953 / 1.468490 (0.991463) | 0.792829 / 4.584777 (-3.791948) | 5.183965 / 3.745712 (1.438253) | 4.508264 / 5.269862 (-0.761598) | 2.855444 / 4.565676 (-1.710232) | 0.090704 / 0.424275 (-0.333571) | 0.009303 / 0.007607 (0.001696) | 0.694303 / 0.226044 (0.468258) | 6.951876 / 2.268929 (4.682947) | 3.418244 / 55.444624 (-52.026381) | 2.799743 / 6.876477 (-4.076734) | 3.043657 / 2.142072 (0.901584) | 0.921537 / 4.805227 (-3.883691) | 0.191774 / 6.500664 (-6.308890) | 0.068602 / 0.075469 (-0.006867) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.624842 / 1.841788 (-0.216946) | 24.570622 / 8.074308 (16.496314) | 21.207566 / 10.191392 (11.016174) | 0.217734 / 0.680424 (-0.462689) | 0.033109 / 0.534201 (-0.501091) | 0.451651 / 0.579283 (-0.127632) | 0.590890 / 0.434364 (0.156526) | 0.546195 / 0.540337 (0.005858) | 0.730298 / 1.386936 (-0.656638) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f6bdecff73303cf97f279a4e36622faf53133f9c \"CML watermark\")\n"
] |
1,973,857,428
| 6,374
|
CI is broken: TypeError: Couldn't cast array
|
closed
| 2023-11-02T09:37:06
| 2023-11-02T10:11:20
| 2023-11-02T10:11:20
|
https://github.com/huggingface/datasets/issues/6374
| null |
albertvillanova
| false
|
[] |
1,973,349,695
| 6,373
|
Fix typo in `Dataset.map` docstring
|
closed
| 2023-11-02T01:36:49
| 2023-11-02T15:18:22
| 2023-11-02T10:11:38
|
https://github.com/huggingface/datasets/pull/6373
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6373",
"html_url": "https://github.com/huggingface/datasets/pull/6373",
"diff_url": "https://github.com/huggingface/datasets/pull/6373.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6373.patch",
"merged_at": "2023-11-02T10:11:38"
}
|
bryant1410
| true
|
[
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006709 / 0.011353 (-0.004643) | 0.004102 / 0.011008 (-0.006906) | 0.084449 / 0.038508 (0.045941) | 0.076078 / 0.023109 (0.052969) | 0.319831 / 0.275898 (0.043933) | 0.359918 / 0.323480 (0.036438) | 0.006092 / 0.007986 (-0.001894) | 0.003402 / 0.004328 (-0.000926) | 0.064715 / 0.004250 (0.060465) | 0.054541 / 0.037052 (0.017488) | 0.330394 / 0.258489 (0.071905) | 0.366048 / 0.293841 (0.072207) | 0.031594 / 0.128546 (-0.096952) | 0.008591 / 0.075646 (-0.067056) | 0.292983 / 0.419271 (-0.126288) | 0.052986 / 0.043533 (0.009453) | 0.322253 / 0.255139 (0.067114) | 0.340082 / 0.283200 (0.056882) | 0.023390 / 0.141683 (-0.118293) | 1.459038 / 1.452155 (0.006883) | 1.536256 / 1.492716 (0.043540) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.233527 / 0.018006 (0.215521) | 0.459145 / 0.000490 (0.458655) | 0.007471 / 0.000200 (0.007271) | 0.000281 / 0.000054 (0.000227) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028158 / 0.037411 (-0.009253) | 0.083079 / 0.014526 (0.068553) | 0.097159 / 0.176557 (-0.079397) | 0.151927 / 0.737135 (-0.585208) | 0.098024 / 0.296338 (-0.198314) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.386882 / 0.215209 (0.171673) | 3.849635 / 2.077655 (1.771981) | 1.832885 / 1.504120 (0.328765) | 1.668356 / 1.541195 (0.127162) | 1.745066 / 1.468490 (0.276576) | 0.484476 / 4.584777 (-4.100301) | 3.547604 / 3.745712 (-0.198108) | 3.480338 / 5.269862 (-1.789523) | 2.066837 / 4.565676 (-2.498840) | 0.056755 / 0.424275 (-0.367520) | 0.007747 / 0.007607 (0.000140) | 0.467999 / 0.226044 (0.241955) | 4.678875 / 2.268929 (2.409946) | 2.341930 / 55.444624 (-53.102695) | 1.985632 / 6.876477 (-4.890844) | 2.046998 / 2.142072 (-0.095074) | 0.579860 / 4.805227 (-4.225367) | 0.131488 / 6.500664 (-6.369176) | 0.060193 / 0.075469 (-0.015276) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.249656 / 1.841788 (-0.592132) | 19.079517 / 8.074308 (11.005209) | 14.328827 / 10.191392 (4.137435) | 0.173707 / 0.680424 (-0.506717) | 0.018250 / 0.534201 (-0.515951) | 0.392225 / 0.579283 (-0.187058) | 0.413920 / 0.434364 (-0.020444) | 0.464124 / 0.540337 (-0.076214) | 0.640283 / 1.386936 (-0.746653) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006859 / 0.011353 (-0.004494) | 0.004068 / 0.011008 (-0.006940) | 0.063936 / 0.038508 (0.025428) | 0.077187 / 0.023109 (0.054078) | 0.365098 / 0.275898 (0.089200) | 0.391003 / 0.323480 (0.067523) | 0.005571 / 0.007986 (-0.002415) | 0.003425 / 0.004328 (-0.000904) | 0.063220 / 0.004250 (0.058970) | 0.056964 / 0.037052 (0.019912) | 0.367793 / 0.258489 (0.109304) | 0.398776 / 0.293841 (0.104935) | 0.033182 / 0.128546 (-0.095364) | 0.008601 / 0.075646 (-0.067045) | 0.070276 / 0.419271 (-0.348996) | 0.048383 / 0.043533 (0.004850) | 0.360414 / 0.255139 (0.105275) | 0.368171 / 0.283200 (0.084971) | 0.023114 / 0.141683 (-0.118569) | 1.503503 / 1.452155 (0.051349) | 1.567279 / 1.492716 (0.074562) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224296 / 0.018006 (0.206290) | 0.455138 / 0.000490 (0.454648) | 0.004014 / 0.000200 (0.003814) | 0.000104 / 0.000054 (0.000050) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032337 / 0.037411 (-0.005074) | 0.094385 / 0.014526 (0.079859) | 0.109870 / 0.176557 (-0.066687) | 0.156978 / 0.737135 (-0.580157) | 0.107559 / 0.296338 (-0.188780) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.427409 / 0.215209 (0.212200) | 4.261772 / 2.077655 (2.184117) | 2.276106 / 1.504120 (0.771986) | 2.115232 / 1.541195 (0.574038) | 2.192048 / 1.468490 (0.723558) | 0.488459 / 4.584777 (-4.096318) | 3.675463 / 3.745712 (-0.070249) | 3.322475 / 5.269862 (-1.947387) | 2.072253 / 4.565676 (-2.493424) | 0.058259 / 0.424275 (-0.366017) | 0.007319 / 0.007607 (-0.000288) | 0.499513 / 0.226044 (0.273469) | 4.994774 / 2.268929 (2.725845) | 2.760927 / 55.444624 (-52.683697) | 2.391947 / 6.876477 (-4.484530) | 2.600557 / 2.142072 (0.458484) | 0.587597 / 4.805227 (-4.217630) | 0.131444 / 6.500664 (-6.369220) | 0.057334 / 0.075469 (-0.018135) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.354636 / 1.841788 (-0.487152) | 19.685735 / 8.074308 (11.611427) | 14.295920 / 10.191392 (4.104528) | 0.171921 / 0.680424 (-0.508503) | 0.019926 / 0.534201 (-0.514274) | 0.395216 / 0.579283 (-0.184068) | 0.432791 / 0.434364 (-0.001573) | 0.473055 / 0.540337 (-0.067282) | 0.638633 / 1.386936 (-0.748303) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fad7c899ec9218a717311223aa6ef5c09a6c7885 \"CML watermark\")\n"
] |
1,972,837,794
| 6,372
|
do not try to download from HF GCS for generator
|
closed
| 2023-11-01T17:57:11
| 2023-11-02T16:02:52
| 2023-11-02T15:52:09
|
https://github.com/huggingface/datasets/pull/6372
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6372",
"html_url": "https://github.com/huggingface/datasets/pull/6372",
"diff_url": "https://github.com/huggingface/datasets/pull/6372.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6372.patch",
"merged_at": "2023-11-02T15:52:09"
}
|
yundai424
| true
|
[
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007617 / 0.011353 (-0.003735) | 0.005371 / 0.011008 (-0.005638) | 0.092110 / 0.038508 (0.053602) | 0.070654 / 0.023109 (0.047544) | 0.362501 / 0.275898 (0.086603) | 0.412835 / 0.323480 (0.089355) | 0.006752 / 0.007986 (-0.001234) | 0.003752 / 0.004328 (-0.000576) | 0.075644 / 0.004250 (0.071394) | 0.055666 / 0.037052 (0.018614) | 0.355906 / 0.258489 (0.097417) | 0.405078 / 0.293841 (0.111237) | 0.045767 / 0.128546 (-0.082779) | 0.013778 / 0.075646 (-0.061868) | 0.324696 / 0.419271 (-0.094575) | 0.062200 / 0.043533 (0.018667) | 0.359571 / 0.255139 (0.104432) | 0.387274 / 0.283200 (0.104075) | 0.035323 / 0.141683 (-0.106360) | 1.586294 / 1.452155 (0.134139) | 1.707564 / 1.492716 (0.214847) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.303940 / 0.018006 (0.285934) | 0.583349 / 0.000490 (0.582859) | 0.014845 / 0.000200 (0.014645) | 0.000698 / 0.000054 (0.000643) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028994 / 0.037411 (-0.008417) | 0.085555 / 0.014526 (0.071029) | 0.097856 / 0.176557 (-0.078701) | 0.161480 / 0.737135 (-0.575655) | 0.098573 / 0.296338 (-0.197766) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.591294 / 0.215209 (0.376085) | 5.751350 / 2.077655 (3.673695) | 2.241620 / 1.504120 (0.737500) | 1.991083 / 1.541195 (0.449888) | 2.006711 / 1.468490 (0.538221) | 0.832339 / 4.584777 (-3.752438) | 5.213808 / 3.745712 (1.468095) | 4.650355 / 5.269862 (-0.619506) | 2.860494 / 4.565676 (-1.705182) | 0.093090 / 0.424275 (-0.331185) | 0.009740 / 0.007607 (0.002133) | 0.693509 / 0.226044 (0.467464) | 6.828735 / 2.268929 (4.559807) | 2.967763 / 55.444624 (-52.476862) | 2.311461 / 6.876477 (-4.565016) | 2.400051 / 2.142072 (0.257979) | 0.914753 / 4.805227 (-3.890474) | 0.202804 / 6.500664 (-6.297860) | 0.076905 / 0.075469 (0.001436) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.576424 / 1.841788 (-0.265363) | 22.963472 / 8.074308 (14.889164) | 19.948105 / 10.191392 (9.756713) | 0.228982 / 0.680424 (-0.451442) | 0.029038 / 0.534201 (-0.505163) | 0.477715 / 0.579283 (-0.101568) | 0.554924 / 0.434364 (0.120560) | 0.532118 / 0.540337 (-0.008219) | 0.775096 / 1.386936 (-0.611840) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009127 / 0.011353 (-0.002226) | 0.004978 / 0.011008 (-0.006030) | 0.084166 / 0.038508 (0.045658) | 0.083391 / 0.023109 (0.060282) | 0.420760 / 0.275898 (0.144862) | 0.459072 / 0.323480 (0.135592) | 0.007102 / 0.007986 (-0.000883) | 0.004175 / 0.004328 (-0.000154) | 0.082922 / 0.004250 (0.078672) | 0.059010 / 0.037052 (0.021957) | 0.416959 / 0.258489 (0.158470) | 0.472220 / 0.293841 (0.178379) | 0.049999 / 0.128546 (-0.078547) | 0.014126 / 0.075646 (-0.061520) | 0.096894 / 0.419271 (-0.322378) | 0.057920 / 0.043533 (0.014387) | 0.405779 / 0.255139 (0.150640) | 0.464286 / 0.283200 (0.181087) | 0.034957 / 0.141683 (-0.106726) | 1.637921 / 1.452155 (0.185767) | 1.768231 / 1.492716 (0.275515) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.354875 / 0.018006 (0.336868) | 0.554667 / 0.000490 (0.554177) | 0.074127 / 0.000200 (0.073927) | 0.000411 / 0.000054 (0.000357) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027681 / 0.037411 (-0.009730) | 0.087746 / 0.014526 (0.073220) | 0.093714 / 0.176557 (-0.082843) | 0.145380 / 0.737135 (-0.591755) | 0.095686 / 0.296338 (-0.200652) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.522079 / 0.215209 (0.306870) | 5.197366 / 2.077655 (3.119711) | 2.300744 / 1.504120 (0.796624) | 2.056846 / 1.541195 (0.515652) | 2.009897 / 1.468490 (0.541407) | 0.813025 / 4.584777 (-3.771751) | 5.177732 / 3.745712 (1.432020) | 4.076749 / 5.269862 (-1.193112) | 2.545588 / 4.565676 (-2.020088) | 0.083507 / 0.424275 (-0.340769) | 0.007011 / 0.007607 (-0.000596) | 0.598820 / 0.226044 (0.372776) | 6.203730 / 2.268929 (3.934801) | 2.945385 / 55.444624 (-52.499239) | 2.304849 / 6.876477 (-4.571628) | 2.599035 / 2.142072 (0.456962) | 1.002721 / 4.805227 (-3.802506) | 0.191781 / 6.500664 (-6.308883) | 0.064178 / 0.075469 (-0.011292) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.549560 / 1.841788 (-0.292228) | 22.395727 / 8.074308 (14.321418) | 20.537895 / 10.191392 (10.346503) | 0.246542 / 0.680424 (-0.433882) | 0.031673 / 0.534201 (-0.502528) | 0.442490 / 0.579283 (-0.136793) | 0.589838 / 0.434364 (0.155474) | 0.535201 / 0.540337 (-0.005136) | 0.733660 / 1.386936 (-0.653276) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e742718e504a372cce0b1f87c2cac65eb8c35792 \"CML watermark\")\n"
] |
1,972,807,579
| 6,371
|
`Dataset.from_generator` should not try to download from HF GCS
|
closed
| 2023-11-01T17:36:17
| 2023-11-02T15:52:10
| 2023-11-02T15:52:10
|
https://github.com/huggingface/datasets/issues/6371
| null |
yundai424
| false
|
[
"Indeed, setting `try_from_gcs` to `False` makes sense for `from_generator`.\r\n\r\nWe plan to deprecate and remove `try_from_hf_gcs` soon, as we can use Hub for file hosting now, but this is a good temporary fix.\r\n"
] |
1,972,073,909
| 6,370
|
TensorDataset format does not work with Trainer from transformers
|
closed
| 2023-11-01T10:09:54
| 2023-11-29T16:31:08
| 2023-11-29T16:31:08
|
https://github.com/huggingface/datasets/issues/6370
| null |
jinzzasol
| false
|
[
"I figured it out. I found that `Trainer` does not work with TensorDataset even though the document says it uses it. Instead, I ended up creating a dictionary and converting it to a dataset using `dataset.Dataset.from_dict()`.\r\n\r\nI will leave this post open for a while. If someone knows a better approach, please leave a comment.",
"Only issues directly related to the HF datasets library should be reported here. ~So, I'm transferring this issue to the `transformers` repo.~ I'm not a `transformers` maintainer, so GitHub doesn't let me transfer it there :(. This means you need to do it manually."
] |
1,971,794,108
| 6,369
|
Multi process map did not load cache file correctly
|
closed
| 2023-11-01T06:36:54
| 2023-11-30T16:04:46
| 2023-11-30T16:04:45
|
https://github.com/huggingface/datasets/issues/6369
| null |
enze5088
| false
|
[
"The inconsistency may be caused by the usage of \"update_fingerprint\" and setting \"trust_remote_code\" to \"True.\"\r\nWhen the tokenizer employs \"trust_remote_code,\" the behavior of the map function varies with each code execution. Even if the remote code of the tokenizer remains the same, the result of \"asher.hexdigest()\" is found to be inconsistent each time.\r\nThis may result in different processes executing multiple maps\r\n\r\n\r\n\r\n",
"The issue may be related to problems previously discussed in GitHub issues [#3847](https://github.com/huggingface/datasets/issues/3847) and [#6318](https://github.com/huggingface/datasets/pull/6318). \r\nThis arises from the fact that tokenizer.tokens_trie._tokens is an unordered set, leading to varying hash results:\r\n`value = hash_bytes(dumps(tokenizer.tokens_trie._tokens))`\r\nConsequently, this results in different outcomes each time for:\r\n`new_fingerprint = update_fingerprint(datasets._fingerprint, transform, kwargs_for_fingerprint)`\r\n\r\nTo address this issue, it's essential to make `Trie._tokens` a deterministic set while ensuring a consistent order after the final update of `_tokens`.\r\n",
"We now sort `set` and `dict` items to make their hashes deterministic (install from `main` with `pip install git+https://github.com/huggingface/datasets` to test this). Consequently, this should also make the `tokenizer.tokens_trie`'s hash deterministic. Feel free to re-open the issue if this is not the case."
] |
1,971,193,692
| 6,368
|
Fix python formatting for complex types in `format_table`
|
closed
| 2023-10-31T19:48:08
| 2023-11-02T14:42:28
| 2023-11-02T14:21:16
|
https://github.com/huggingface/datasets/pull/6368
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6368",
"html_url": "https://github.com/huggingface/datasets/pull/6368",
"diff_url": "https://github.com/huggingface/datasets/pull/6368.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6368.patch",
"merged_at": "2023-11-02T14:21:16"
}
|
mariosasko
| true
|
[
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008047 / 0.011353 (-0.003305) | 0.004649 / 0.011008 (-0.006359) | 0.100275 / 0.038508 (0.061767) | 0.089551 / 0.023109 (0.066442) | 0.369831 / 0.275898 (0.093933) | 0.431023 / 0.323480 (0.107544) | 0.004721 / 0.007986 (-0.003265) | 0.004904 / 0.004328 (0.000575) | 0.076345 / 0.004250 (0.072095) | 0.066902 / 0.037052 (0.029849) | 0.377208 / 0.258489 (0.118718) | 0.430989 / 0.293841 (0.137148) | 0.036260 / 0.128546 (-0.092287) | 0.010158 / 0.075646 (-0.065488) | 0.344923 / 0.419271 (-0.074349) | 0.062504 / 0.043533 (0.018971) | 0.373038 / 0.255139 (0.117899) | 0.399918 / 0.283200 (0.116718) | 0.028257 / 0.141683 (-0.113425) | 1.782546 / 1.452155 (0.330391) | 1.920010 / 1.492716 (0.427293) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.277670 / 0.018006 (0.259664) | 0.500543 / 0.000490 (0.500053) | 0.018256 / 0.000200 (0.018056) | 0.000343 / 0.000054 (0.000289) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033337 / 0.037411 (-0.004074) | 0.100542 / 0.014526 (0.086017) | 0.114903 / 0.176557 (-0.061654) | 0.181267 / 0.737135 (-0.555868) | 0.115019 / 0.296338 (-0.181320) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.457333 / 0.215209 (0.242124) | 4.542082 / 2.077655 (2.464427) | 2.231817 / 1.504120 (0.727697) | 2.028523 / 1.541195 (0.487328) | 2.110715 / 1.468490 (0.642225) | 0.583162 / 4.584777 (-4.001615) | 4.179413 / 3.745712 (0.433701) | 4.145620 / 5.269862 (-1.124241) | 2.452458 / 4.565676 (-2.113218) | 0.068229 / 0.424275 (-0.356046) | 0.009027 / 0.007607 (0.001420) | 0.549002 / 0.226044 (0.322957) | 5.485707 / 2.268929 (3.216779) | 2.789467 / 55.444624 (-52.655157) | 2.397499 / 6.876477 (-4.478977) | 2.492083 / 2.142072 (0.350010) | 0.692445 / 4.805227 (-4.112782) | 0.160527 / 6.500664 (-6.340137) | 0.071597 / 0.075469 (-0.003872) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.486043 / 1.841788 (-0.355744) | 22.377207 / 8.074308 (14.302899) | 16.443719 / 10.191392 (6.252327) | 0.170740 / 0.680424 (-0.509684) | 0.021511 / 0.534201 (-0.512690) | 0.470798 / 0.579283 (-0.108485) | 0.511851 / 0.434364 (0.077487) | 0.551154 / 0.540337 (0.010817) | 0.768420 / 1.386936 (-0.618516) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008049 / 0.011353 (-0.003303) | 0.004676 / 0.011008 (-0.006332) | 0.076360 / 0.038508 (0.037852) | 0.093648 / 0.023109 (0.070539) | 0.480597 / 0.275898 (0.204699) | 0.524674 / 0.323480 (0.201194) | 0.006242 / 0.007986 (-0.001744) | 0.003827 / 0.004328 (-0.000501) | 0.077039 / 0.004250 (0.072788) | 0.067992 / 0.037052 (0.030940) | 0.480287 / 0.258489 (0.221798) | 0.528546 / 0.293841 (0.234706) | 0.038347 / 0.128546 (-0.090199) | 0.010036 / 0.075646 (-0.065611) | 0.084386 / 0.419271 (-0.334885) | 0.057211 / 0.043533 (0.013678) | 0.475993 / 0.255139 (0.220854) | 0.504881 / 0.283200 (0.221682) | 0.026658 / 0.141683 (-0.115025) | 1.777095 / 1.452155 (0.324940) | 1.896446 / 1.492716 (0.403730) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242450 / 0.018006 (0.224443) | 0.488864 / 0.000490 (0.488374) | 0.007329 / 0.000200 (0.007129) | 0.000108 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.039093 / 0.037411 (0.001682) | 0.114724 / 0.014526 (0.100198) | 0.124965 / 0.176557 (-0.051591) | 0.188165 / 0.737135 (-0.548971) | 0.125336 / 0.296338 (-0.171002) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.515718 / 0.215209 (0.300509) | 5.150865 / 2.077655 (3.073210) | 2.767866 / 1.504120 (1.263746) | 2.571003 / 1.541195 (1.029808) | 2.656224 / 1.468490 (1.187734) | 0.583771 / 4.584777 (-4.001006) | 4.268713 / 3.745712 (0.523001) | 3.938699 / 5.269862 (-1.331163) | 2.413569 / 4.565676 (-2.152108) | 0.068848 / 0.424275 (-0.355427) | 0.008758 / 0.007607 (0.001151) | 0.610831 / 0.226044 (0.384786) | 6.099965 / 2.268929 (3.831037) | 3.337530 / 55.444624 (-52.107095) | 2.910962 / 6.876477 (-3.965514) | 3.149813 / 2.142072 (1.007740) | 0.700576 / 4.805227 (-4.104651) | 0.157569 / 6.500664 (-6.343095) | 0.072237 / 0.075469 (-0.003232) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.655840 / 1.841788 (-0.185947) | 23.639061 / 8.074308 (15.564753) | 17.301593 / 10.191392 (7.110201) | 0.201717 / 0.680424 (-0.478707) | 0.023836 / 0.534201 (-0.510365) | 0.470941 / 0.579283 (-0.108342) | 0.498157 / 0.434364 (0.063794) | 0.581195 / 0.540337 (0.040857) | 0.788304 / 1.386936 (-0.598632) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f657900acfd8ea1afaf47267e552a7ad2c6ef28b \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004823 / 0.011353 (-0.006530) | 0.002976 / 0.011008 (-0.008032) | 0.062070 / 0.038508 (0.023562) | 0.051623 / 0.023109 (0.028513) | 0.242249 / 0.275898 (-0.033649) | 0.271223 / 0.323480 (-0.052257) | 0.003906 / 0.007986 (-0.004079) | 0.002709 / 0.004328 (-0.001620) | 0.047874 / 0.004250 (0.043624) | 0.038123 / 0.037052 (0.001071) | 0.253737 / 0.258489 (-0.004752) | 0.281942 / 0.293841 (-0.011899) | 0.023750 / 0.128546 (-0.104797) | 0.007227 / 0.075646 (-0.068420) | 0.203137 / 0.419271 (-0.216134) | 0.036254 / 0.043533 (-0.007278) | 0.243923 / 0.255139 (-0.011216) | 0.263908 / 0.283200 (-0.019291) | 0.017795 / 0.141683 (-0.123888) | 1.105680 / 1.452155 (-0.346475) | 1.166804 / 1.492716 (-0.325912) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097388 / 0.018006 (0.079381) | 0.305481 / 0.000490 (0.304991) | 0.000210 / 0.000200 (0.000010) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020096 / 0.037411 (-0.017315) | 0.063990 / 0.014526 (0.049464) | 0.073694 / 0.176557 (-0.102863) | 0.122909 / 0.737135 (-0.614227) | 0.076199 / 0.296338 (-0.220140) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285612 / 0.215209 (0.070403) | 2.770524 / 2.077655 (0.692869) | 1.451624 / 1.504120 (-0.052496) | 1.329223 / 1.541195 (-0.211972) | 1.369980 / 1.468490 (-0.098510) | 0.398269 / 4.584777 (-4.186507) | 2.418740 / 3.745712 (-1.326972) | 2.796384 / 5.269862 (-2.473478) | 1.686490 / 4.565676 (-2.879186) | 0.046417 / 0.424275 (-0.377858) | 0.005414 / 0.007607 (-0.002193) | 0.345505 / 0.226044 (0.119460) | 3.391857 / 2.268929 (1.122929) | 1.856696 / 55.444624 (-53.587929) | 1.538061 / 6.876477 (-5.338416) | 1.631489 / 2.142072 (-0.510584) | 0.479188 / 4.805227 (-4.326039) | 0.101549 / 6.500664 (-6.399116) | 0.042150 / 0.075469 (-0.033319) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.957961 / 1.841788 (-0.883827) | 12.349371 / 8.074308 (4.275063) | 10.778214 / 10.191392 (0.586822) | 0.141265 / 0.680424 (-0.539158) | 0.014559 / 0.534201 (-0.519642) | 0.272071 / 0.579283 (-0.307212) | 0.262493 / 0.434364 (-0.171871) | 0.310351 / 0.540337 (-0.229986) | 0.399220 / 1.386936 (-0.987716) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005127 / 0.011353 (-0.006226) | 0.002926 / 0.011008 (-0.008082) | 0.048320 / 0.038508 (0.009812) | 0.063082 / 0.023109 (0.039973) | 0.269846 / 0.275898 (-0.006052) | 0.294470 / 0.323480 (-0.029010) | 0.004201 / 0.007986 (-0.003784) | 0.002434 / 0.004328 (-0.001894) | 0.048020 / 0.004250 (0.043770) | 0.043909 / 0.037052 (0.006856) | 0.271328 / 0.258489 (0.012839) | 0.298820 / 0.293841 (0.004979) | 0.024565 / 0.128546 (-0.103981) | 0.007752 / 0.075646 (-0.067894) | 0.054171 / 0.419271 (-0.365101) | 0.033147 / 0.043533 (-0.010386) | 0.266628 / 0.255139 (0.011489) | 0.288651 / 0.283200 (0.005452) | 0.018910 / 0.141683 (-0.122773) | 1.153679 / 1.452155 (-0.298476) | 1.214979 / 1.492716 (-0.277737) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097064 / 0.018006 (0.079057) | 0.307504 / 0.000490 (0.307014) | 0.000230 / 0.000200 (0.000030) | 0.000051 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021848 / 0.037411 (-0.015563) | 0.071159 / 0.014526 (0.056633) | 0.081310 / 0.176557 (-0.095247) | 0.120175 / 0.737135 (-0.616961) | 0.082619 / 0.296338 (-0.213720) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296606 / 0.215209 (0.081397) | 2.908495 / 2.077655 (0.830840) | 1.606522 / 1.504120 (0.102402) | 1.528599 / 1.541195 (-0.012596) | 1.508332 / 1.468490 (0.039842) | 0.396336 / 4.584777 (-4.188441) | 2.449163 / 3.745712 (-1.296549) | 2.533372 / 5.269862 (-2.736490) | 1.623061 / 4.565676 (-2.942615) | 0.046723 / 0.424275 (-0.377552) | 0.005120 / 0.007607 (-0.002487) | 0.345763 / 0.226044 (0.119718) | 3.427382 / 2.268929 (1.158454) | 1.962806 / 55.444624 (-53.481819) | 1.678548 / 6.876477 (-5.197929) | 1.865773 / 2.142072 (-0.276300) | 0.477932 / 4.805227 (-4.327295) | 0.100994 / 6.500664 (-6.399670) | 0.042212 / 0.075469 (-0.033258) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.992766 / 1.841788 (-0.849022) | 12.764885 / 8.074308 (4.690577) | 10.892094 / 10.191392 (0.700702) | 0.143211 / 0.680424 (-0.537213) | 0.016347 / 0.534201 (-0.517853) | 0.270181 / 0.579283 (-0.309102) | 0.278658 / 0.434364 (-0.155706) | 0.307134 / 0.540337 (-0.233203) | 0.396792 / 1.386936 (-0.990144) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6d2f2a5e0fea3827eccfd1717d8021c15fc4292a \"CML watermark\")\n",
"Thanks for the fix ! It was probably my mistake (forgot to re-apply the features)"
] |
1,971,015,861
| 6,367
|
Fix time measuring snippet in docs
|
closed
| 2023-10-31T17:57:17
| 2023-10-31T18:35:53
| 2023-10-31T18:24:02
|
https://github.com/huggingface/datasets/pull/6367
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6367",
"html_url": "https://github.com/huggingface/datasets/pull/6367",
"diff_url": "https://github.com/huggingface/datasets/pull/6367.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6367.patch",
"merged_at": "2023-10-31T18:24:02"
}
|
mariosasko
| true
|
[
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007683 / 0.011353 (-0.003670) | 0.004159 / 0.011008 (-0.006849) | 0.097017 / 0.038508 (0.058509) | 0.074216 / 0.023109 (0.051107) | 0.323115 / 0.275898 (0.047217) | 0.412836 / 0.323480 (0.089356) | 0.005151 / 0.007986 (-0.002834) | 0.004037 / 0.004328 (-0.000292) | 0.067881 / 0.004250 (0.063631) | 0.051395 / 0.037052 (0.014342) | 0.356391 / 0.258489 (0.097901) | 0.386744 / 0.293841 (0.092903) | 0.043571 / 0.128546 (-0.084975) | 0.012844 / 0.075646 (-0.062803) | 0.369440 / 0.419271 (-0.049832) | 0.056944 / 0.043533 (0.013411) | 0.316159 / 0.255139 (0.061020) | 0.435530 / 0.283200 (0.152330) | 0.033622 / 0.141683 (-0.108061) | 1.379602 / 1.452155 (-0.072553) | 1.766400 / 1.492716 (0.273683) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.304151 / 0.018006 (0.286145) | 0.616365 / 0.000490 (0.615875) | 0.013588 / 0.000200 (0.013389) | 0.000441 / 0.000054 (0.000387) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032812 / 0.037411 (-0.004600) | 0.100914 / 0.014526 (0.086388) | 0.124004 / 0.176557 (-0.052552) | 0.195087 / 0.737135 (-0.542048) | 0.124388 / 0.296338 (-0.171951) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.575649 / 0.215209 (0.360440) | 5.665461 / 2.077655 (3.587806) | 2.474892 / 1.504120 (0.970773) | 2.142687 / 1.541195 (0.601492) | 2.254962 / 1.468490 (0.786472) | 0.816635 / 4.584777 (-3.768141) | 5.044279 / 3.745712 (1.298567) | 4.566728 / 5.269862 (-0.703134) | 2.867146 / 4.565676 (-1.698531) | 0.092994 / 0.424275 (-0.331281) | 0.008395 / 0.007607 (0.000788) | 0.680346 / 0.226044 (0.454302) | 6.909875 / 2.268929 (4.640946) | 3.275602 / 55.444624 (-52.169022) | 2.556000 / 6.876477 (-4.320477) | 2.581337 / 2.142072 (0.439264) | 0.997883 / 4.805227 (-3.807344) | 0.204109 / 6.500664 (-6.296555) | 0.069705 / 0.075469 (-0.005764) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.504573 / 1.841788 (-0.337215) | 22.219363 / 8.074308 (14.145055) | 19.078040 / 10.191392 (8.886648) | 0.234970 / 0.680424 (-0.445454) | 0.027324 / 0.534201 (-0.506877) | 0.427960 / 0.579283 (-0.151323) | 0.570258 / 0.434364 (0.135894) | 0.502335 / 0.540337 (-0.038003) | 0.788078 / 1.386936 (-0.598858) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008370 / 0.011353 (-0.002982) | 0.004573 / 0.011008 (-0.006435) | 0.073080 / 0.038508 (0.034572) | 0.068752 / 0.023109 (0.045643) | 0.439648 / 0.275898 (0.163750) | 0.499700 / 0.323480 (0.176220) | 0.006119 / 0.007986 (-0.001866) | 0.004300 / 0.004328 (-0.000028) | 0.073173 / 0.004250 (0.068923) | 0.055676 / 0.037052 (0.018624) | 0.464152 / 0.258489 (0.205663) | 0.476954 / 0.293841 (0.183113) | 0.046335 / 0.128546 (-0.082211) | 0.013373 / 0.075646 (-0.062274) | 0.092006 / 0.419271 (-0.327265) | 0.054802 / 0.043533 (0.011269) | 0.456594 / 0.255139 (0.201455) | 0.491931 / 0.283200 (0.208732) | 0.034021 / 0.141683 (-0.107662) | 1.575200 / 1.452155 (0.123045) | 1.689742 / 1.492716 (0.197026) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.299432 / 0.018006 (0.281426) | 0.605643 / 0.000490 (0.605153) | 0.006280 / 0.000200 (0.006080) | 0.000120 / 0.000054 (0.000066) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028414 / 0.037411 (-0.008997) | 0.085812 / 0.014526 (0.071286) | 0.109142 / 0.176557 (-0.067414) | 0.163458 / 0.737135 (-0.573677) | 0.100837 / 0.296338 (-0.195501) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.615557 / 0.215209 (0.400348) | 6.051599 / 2.077655 (3.973944) | 2.872353 / 1.504120 (1.368234) | 2.508322 / 1.541195 (0.967128) | 2.550073 / 1.468490 (1.081583) | 0.835793 / 4.584777 (-3.748983) | 5.208484 / 3.745712 (1.462772) | 4.361846 / 5.269862 (-0.908016) | 2.776164 / 4.565676 (-1.789513) | 0.090831 / 0.424275 (-0.333444) | 0.007320 / 0.007607 (-0.000287) | 0.725533 / 0.226044 (0.499488) | 7.051321 / 2.268929 (4.782393) | 3.515464 / 55.444624 (-51.929160) | 2.798193 / 6.876477 (-4.078284) | 3.022512 / 2.142072 (0.880440) | 0.986744 / 4.805227 (-3.818484) | 0.198050 / 6.500664 (-6.302615) | 0.069200 / 0.075469 (-0.006269) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.623759 / 1.841788 (-0.218029) | 22.269700 / 8.074308 (14.195392) | 19.577429 / 10.191392 (9.386037) | 0.215990 / 0.680424 (-0.464434) | 0.033005 / 0.534201 (-0.501196) | 0.436848 / 0.579283 (-0.142435) | 0.591442 / 0.434364 (0.157078) | 0.547701 / 0.540337 (0.007364) | 0.741695 / 1.386936 (-0.645241) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7e17e139b1323aca3321a5d2c2da40d82c458bae \"CML watermark\")\n",
"CI failures are unrelated",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009027 / 0.011353 (-0.002326) | 0.006118 / 0.011008 (-0.004890) | 0.118939 / 0.038508 (0.080431) | 0.089979 / 0.023109 (0.066869) | 0.412425 / 0.275898 (0.136527) | 0.455706 / 0.323480 (0.132227) | 0.006762 / 0.007986 (-0.001224) | 0.004409 / 0.004328 (0.000080) | 0.088002 / 0.004250 (0.083751) | 0.063708 / 0.037052 (0.026656) | 0.417373 / 0.258489 (0.158884) | 0.489582 / 0.293841 (0.195741) | 0.050222 / 0.128546 (-0.078324) | 0.014386 / 0.075646 (-0.061260) | 0.435363 / 0.419271 (0.016092) | 0.069375 / 0.043533 (0.025842) | 0.410242 / 0.255139 (0.155103) | 0.436439 / 0.283200 (0.153239) | 0.039318 / 0.141683 (-0.102365) | 1.857574 / 1.452155 (0.405419) | 1.919402 / 1.492716 (0.426686) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.343916 / 0.018006 (0.325910) | 0.633639 / 0.000490 (0.633150) | 0.014756 / 0.000200 (0.014557) | 0.000707 / 0.000054 (0.000652) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031983 / 0.037411 (-0.005429) | 0.097222 / 0.014526 (0.082697) | 0.114644 / 0.176557 (-0.061912) | 0.187787 / 0.737135 (-0.549348) | 0.120595 / 0.296338 (-0.175743) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.605861 / 0.215209 (0.390652) | 6.039318 / 2.077655 (3.961664) | 2.699251 / 1.504120 (1.195132) | 2.436398 / 1.541195 (0.895203) | 2.493653 / 1.468490 (1.025163) | 0.889423 / 4.584777 (-3.695354) | 5.384769 / 3.745712 (1.639056) | 5.033033 / 5.269862 (-0.236829) | 3.056894 / 4.565676 (-1.508783) | 0.100683 / 0.424275 (-0.323592) | 0.009103 / 0.007607 (0.001495) | 0.737066 / 0.226044 (0.511021) | 7.370485 / 2.268929 (5.101556) | 3.422670 / 55.444624 (-52.021954) | 2.830392 / 6.876477 (-4.046084) | 2.985789 / 2.142072 (0.843717) | 0.999239 / 4.805227 (-3.805989) | 0.203506 / 6.500664 (-6.297158) | 0.076135 / 0.075469 (0.000666) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.697001 / 1.841788 (-0.144787) | 24.653975 / 8.074308 (16.579667) | 22.241622 / 10.191392 (12.050230) | 0.257075 / 0.680424 (-0.423349) | 0.029159 / 0.534201 (-0.505041) | 0.493329 / 0.579283 (-0.085954) | 0.596661 / 0.434364 (0.162297) | 0.569431 / 0.540337 (0.029094) | 0.812231 / 1.386936 (-0.574705) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009815 / 0.011353 (-0.001538) | 0.005136 / 0.011008 (-0.005872) | 0.078224 / 0.038508 (0.039716) | 0.103276 / 0.023109 (0.080166) | 0.512742 / 0.275898 (0.236844) | 0.544010 / 0.323480 (0.220530) | 0.007957 / 0.007986 (-0.000029) | 0.004629 / 0.004328 (0.000300) | 0.074983 / 0.004250 (0.070733) | 0.071831 / 0.037052 (0.034778) | 0.542752 / 0.258489 (0.284262) | 0.573176 / 0.293841 (0.279335) | 0.053939 / 0.128546 (-0.074607) | 0.015007 / 0.075646 (-0.060640) | 0.085389 / 0.419271 (-0.333882) | 0.063587 / 0.043533 (0.020055) | 0.509580 / 0.255139 (0.254441) | 0.563374 / 0.283200 (0.280174) | 0.037575 / 0.141683 (-0.104108) | 1.840740 / 1.452155 (0.388585) | 1.836414 / 1.492716 (0.343698) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.310188 / 0.018006 (0.292182) | 0.641478 / 0.000490 (0.640988) | 0.011057 / 0.000200 (0.010857) | 0.000173 / 0.000054 (0.000119) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.043280 / 0.037411 (0.005869) | 0.109256 / 0.014526 (0.094730) | 0.126701 / 0.176557 (-0.049856) | 0.199172 / 0.737135 (-0.537963) | 0.123584 / 0.296338 (-0.172755) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.649272 / 0.215209 (0.434063) | 6.487501 / 2.077655 (4.409846) | 3.170330 / 1.504120 (1.666210) | 2.960912 / 1.541195 (1.419718) | 3.024531 / 1.468490 (1.556041) | 0.905112 / 4.584777 (-3.679665) | 5.560961 / 3.745712 (1.815249) | 4.920463 / 5.269862 (-0.349399) | 3.158989 / 4.565676 (-1.406687) | 0.095444 / 0.424275 (-0.328831) | 0.008264 / 0.007607 (0.000657) | 0.819292 / 0.226044 (0.593247) | 7.982695 / 2.268929 (5.713767) | 4.098704 / 55.444624 (-51.345921) | 3.442330 / 6.876477 (-3.434147) | 3.763426 / 2.142072 (1.621354) | 1.065464 / 4.805227 (-3.739763) | 0.215089 / 6.500664 (-6.285575) | 0.085280 / 0.075469 (0.009811) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.881770 / 1.841788 (0.039983) | 25.671479 / 8.074308 (17.597171) | 22.367019 / 10.191392 (12.175627) | 0.241377 / 0.680424 (-0.439047) | 0.033555 / 0.534201 (-0.500646) | 0.501786 / 0.579283 (-0.077497) | 0.596376 / 0.434364 (0.162012) | 0.579674 / 0.540337 (0.039337) | 0.855534 / 1.386936 (-0.531402) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c9c1166e1cf81d38534020f9c167b326585339e5 \"CML watermark\")\n"
] |
1,970,213,490
| 6,366
|
with_format() function returns bytes instead of PIL images even when image column is not part of "columns"
|
closed
| 2023-10-31T11:10:48
| 2023-11-02T14:21:17
| 2023-11-02T14:21:17
|
https://github.com/huggingface/datasets/issues/6366
| null |
leot13
| false
|
[
"Thanks for reporting! I've opened a PR with a fix."
] |
1,970,140,392
| 6,365
|
Parquet size grows exponential for categorical data
|
closed
| 2023-10-31T10:29:02
| 2023-10-31T10:49:17
| 2023-10-31T10:49:17
|
https://github.com/huggingface/datasets/issues/6365
| null |
aseganti
| false
|
[
"Wrong repo."
] |
1,969,136,106
| 6,364
|
ArrowNotImplementedError: Unsupported cast from string to list using function cast_list
|
closed
| 2023-10-30T20:14:01
| 2023-10-31T19:21:23
| 2023-10-31T19:21:23
|
https://github.com/huggingface/datasets/issues/6364
| null |
divyakrishna-devisetty
| false
|
[
"You can use the following code to load this CSV with the list values preserved:\r\n```python\r\nfrom datasets import load_dataset\r\nimport ast\r\n\r\nconverters = {\r\n \"contexts\" : ast.literal_eval,\r\n \"ground_truths\" : ast.literal_eval,\r\n}\r\n\r\nds = load_dataset(\"csv\", data_files=\"golden_dataset.csv\", converters=converters)\r\n```",
"Thank you! it worked :)"
] |
1,968,891,277
| 6,363
|
dataset.transform() hangs indefinitely while finetuning the stable diffusion XL
|
closed
| 2023-10-30T17:34:05
| 2023-11-22T00:29:21
| 2023-11-22T00:29:21
|
https://github.com/huggingface/datasets/issues/6363
| null |
bhosalems
| false
|
[
"I think the code hangs on the `accelerator.main_process_first()` context manager exit. To verify this, you can append a print statement to the end of the `accelerator.main_process_first()` block. \r\n\r\n\r\nIf the problem is in `with_transform`, it would help if you could share the error stack trace printed when you interrupt the process (while it hangs)",
"@bhosalems Were you able to fix that ? I face this issue as well",
"@matankley No I am not able to resolve this issue yet.",
"@mariosasko yes the problem seems to be to exit from accelerator.main_process_first(). Is there any known problem?",
"NCCL debug info I get below output, if it helps.\r\n```\r\n11/09/2023 13:36:44 - INFO - __main__ - Distributed environment: MULTI_GPU Backend: nccl\r\nNum processes: 2\r\nProcess index: 1\r\nLocal process index: 1\r\nDevice: cuda:1\r\n\r\nMixed precision type: fp16\r\n\r\nDetected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\r\n11/09/2023 13:36:44 - INFO - __main__ - Distributed environment: MULTI_GPU Backend: nccl\r\nNum processes: 2\r\nProcess index: 0\r\nLocal process index: 0\r\nDevice: cuda:0\r\n\r\nMixed precision type: fp16\r\n\r\n{'timestep_spacing', 'thresholding', 'variance_type', 'clip_sample_range', 'prediction_type', 'dynamic_thresholding_ratio', 'sample_max_value'} was not found in config. Values will be initialized to default values.\r\n{'norm_num_groups', 'force_upcast'} was not found in config. Values will be initialized to default values.\r\n{'num_attention_heads', 'projection_class_embeddings_input_dim', 'addition_embed_type_num_heads', 'mid_block_only_cross_attention', 'addition_embed_type', 'num_class_embeds', 'upcast_attention', 'cross_attention_norm', 'addition_time_embed_dim', 'time_embedding_dim', 'class_embeddings_concat', 'encoder_hid_dim', 'encoder_hid_dim_type', 'resnet_out_scale_factor', 'attention_type', 'conv_out_kernel', 'only_cross_attention', 'resnet_time_scale_shift', 'resnet_skip_time_act', 'reverse_transformer_layers_per_block', 'conv_in_kernel', 'time_cond_proj_dim', 'use_linear_projection', 'mid_block_type', 'time_embedding_act_fn', 'dropout', 'timestep_post_act', 'dual_cross_attention', 'class_embed_type', 'transformer_layers_per_block', 'time_embedding_type'} was not found in config. Values will be initialized to default values.\r\n{'num_attention_heads', 'projection_class_embeddings_input_dim', 'addition_embed_type_num_heads', 'mid_block_only_cross_attention', 'addition_embed_type', 'num_class_embeds', 'upcast_attention', 'cross_attention_norm', 'addition_time_embed_dim', 'time_embedding_dim', 'class_embeddings_concat', 'encoder_hid_dim', 'encoder_hid_dim_type', 'resnet_out_scale_factor', 'attention_type', 'conv_out_kernel', 'only_cross_attention', 'resnet_time_scale_shift', 'resnet_skip_time_act', 'reverse_transformer_layers_per_block', 'conv_in_kernel', 'time_cond_proj_dim', 'use_linear_projection', 'mid_block_type', 'time_embedding_act_fn', 'dropout', 'timestep_post_act', 'dual_cross_attention', 'class_embed_type', 'transformer_layers_per_block', 'time_embedding_type'} was not found in config. Values will be initialized to default values.\r\ndeepbull5:1311249:1311249 [0] NCCL INFO Bootstrap : Using enp194s0f0:128.205.43.171<0>\r\ndeepbull5:1311249:1311249 [0] NCCL INFO NET/Plugin : No plugin found (libnccl-net.so), using internal implementation\r\ndeepbull5:1311249:1311249 [0] NCCL INFO cudaDriverVersion 11070\r\nNCCL version 2.14.3+cuda11.7\r\ndeepbull5:1311250:1311250 [1] NCCL INFO cudaDriverVersion 11070\r\ndeepbull5:1311249:1311365 [0] NCCL INFO NET/IB : No device found.\r\ndeepbull5:1311249:1311365 [0] NCCL INFO NET/Socket : Using [0]enp194s0f0:128.205.43.171<0>\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Using network Socket\r\ndeepbull5:1311250:1311250 [1] NCCL INFO Bootstrap : Using enp194s0f0:128.205.43.171<0>\r\ndeepbull5:1311250:1311250 [1] NCCL INFO NET/Plugin : No plugin found (libnccl-net.so), using internal implementation\r\ndeepbull5:1311250:1311366 [1] NCCL INFO NET/IB : No device found.\r\ndeepbull5:1311250:1311366 [1] NCCL INFO NET/Socket : Using [0]enp194s0f0:128.205.43.171<0>\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Using network Socket\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Setting affinity for GPU 1 to ff,ffff0000,00ffffff\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Setting affinity for GPU 0 to ff,ffff0000,00ffffff\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 00/04 : 0 1\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Trees [0] -1/-1/-1->1->0 [1] 0/-1/-1->1->-1 [2] -1/-1/-1->1->0 [3] 0/-1/-1->1->-1\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 01/04 : 0 1\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 02/04 : 0 1\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 03/04 : 0 1\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] -1/-1/-1->0->1 [2] 1/-1/-1->0->-1 [3] -1/-1/-1->0->1\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 00/0 : 0[1000] -> 1[24000] via P2P/IPC\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Channel 00/0 : 1[24000] -> 0[1000] via P2P/IPC\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 01/0 : 0[1000] -> 1[24000] via P2P/IPC\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Channel 01/0 : 1[24000] -> 0[1000] via P2P/IPC\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Channel 02/0 : 1[24000] -> 0[1000] via P2P/IPC\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 02/0 : 0[1000] -> 1[24000] via P2P/IPC\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Channel 03/0 : 1[24000] -> 0[1000] via P2P/IPC\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 03/0 : 0[1000] -> 1[24000] via P2P/IPC\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Connected all rings\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Connected all trees\r\ndeepbull5:1311249:1311365 [0] NCCL INFO threadThresholds 8/8/64 | 16/8/64 | 512 | 512\r\ndeepbull5:1311249:1311365 [0] NCCL INFO 4 coll channels, 4 p2p channels, 2 p2p channels per peer\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Connected all rings\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Connected all trees\r\ndeepbull5:1311250:1311366 [1] NCCL INFO threadThresholds 8/8/64 | 16/8/64 | 512 | 512\r\ndeepbull5:1311250:1311366 [1] NCCL INFO 4 coll channels, 4 p2p channels, 2 p2p channels per peer\r\ndeepbull5:1311249:1311365 [0] NCCL INFO comm 0x88a84ee0 rank 0 nranks 2 cudaDev 0 busId 1000 - Init COMPLETE\r\ndeepbull5:1311250:1311366 [1] NCCL INFO comm 0x89a42f60 rank 1 nranks 2 cudaDev 1 busId 24000 - Init COMPLETE\r\n\r\n```",
"Maybe @muellerzr can help as an `accelerate` maintainer.",
"I don't know what the issue was, but after going through the thread here I loved the issue with https://github.com/huggingface/accelerate/issues/314#issuecomment-1565259831"
] |
1,965,794,569
| 6,362
|
Simplify filesystem logic
|
closed
| 2023-10-27T15:54:18
| 2023-11-15T14:08:29
| 2023-11-15T14:02:02
|
https://github.com/huggingface/datasets/pull/6362
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6362",
"html_url": "https://github.com/huggingface/datasets/pull/6362",
"diff_url": "https://github.com/huggingface/datasets/pull/6362.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6362.patch",
"merged_at": "2023-11-15T14:02:02"
}
|
mariosasko
| true
|
[
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008852 / 0.011353 (-0.002501) | 0.004613 / 0.011008 (-0.006396) | 0.096153 / 0.038508 (0.057645) | 0.074945 / 0.023109 (0.051836) | 0.365960 / 0.275898 (0.090062) | 0.385450 / 0.323480 (0.061970) | 0.004757 / 0.007986 (-0.003229) | 0.003453 / 0.004328 (-0.000876) | 0.069944 / 0.004250 (0.065693) | 0.057781 / 0.037052 (0.020729) | 0.361056 / 0.258489 (0.102567) | 0.409218 / 0.293841 (0.115377) | 0.045714 / 0.128546 (-0.082833) | 0.013776 / 0.075646 (-0.061871) | 0.328797 / 0.419271 (-0.090474) | 0.063431 / 0.043533 (0.019899) | 0.370799 / 0.255139 (0.115660) | 0.370701 / 0.283200 (0.087502) | 0.034894 / 0.141683 (-0.106789) | 1.730290 / 1.452155 (0.278136) | 1.863600 / 1.492716 (0.370883) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.245571 / 0.018006 (0.227565) | 0.509666 / 0.000490 (0.509176) | 0.008051 / 0.000200 (0.007851) | 0.000104 / 0.000054 (0.000050) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027854 / 0.037411 (-0.009557) | 0.090735 / 0.014526 (0.076209) | 0.100100 / 0.176557 (-0.076457) | 0.158267 / 0.737135 (-0.578868) | 0.107537 / 0.296338 (-0.188801) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.565455 / 0.215209 (0.350246) | 5.671436 / 2.077655 (3.593781) | 2.438078 / 1.504120 (0.933958) | 2.072403 / 1.541195 (0.531208) | 2.127830 / 1.468490 (0.659340) | 0.840101 / 4.584777 (-3.744675) | 4.945952 / 3.745712 (1.200240) | 4.840904 / 5.269862 (-0.428957) | 3.037936 / 4.565676 (-1.527740) | 0.099027 / 0.424275 (-0.325248) | 0.008448 / 0.007607 (0.000841) | 0.703315 / 0.226044 (0.477271) | 6.837550 / 2.268929 (4.568621) | 3.204232 / 55.444624 (-52.240393) | 2.492985 / 6.876477 (-4.383492) | 2.426792 / 2.142072 (0.284720) | 0.998430 / 4.805227 (-3.806797) | 0.203854 / 6.500664 (-6.296811) | 0.072386 / 0.075469 (-0.003083) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.606627 / 1.841788 (-0.235161) | 22.287391 / 8.074308 (14.213082) | 20.245654 / 10.191392 (10.054262) | 0.229377 / 0.680424 (-0.451046) | 0.028399 / 0.534201 (-0.505802) | 0.446567 / 0.579283 (-0.132716) | 0.565277 / 0.434364 (0.130913) | 0.502957 / 0.540337 (-0.037381) | 0.749268 / 1.386936 (-0.637668) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008253 / 0.011353 (-0.003100) | 0.004432 / 0.011008 (-0.006576) | 0.081995 / 0.038508 (0.043487) | 0.075443 / 0.023109 (0.052334) | 0.442139 / 0.275898 (0.166241) | 0.507308 / 0.323480 (0.183829) | 0.007343 / 0.007986 (-0.000643) | 0.003850 / 0.004328 (-0.000478) | 0.072656 / 0.004250 (0.068406) | 0.054585 / 0.037052 (0.017533) | 0.430057 / 0.258489 (0.171568) | 0.466953 / 0.293841 (0.173112) | 0.050350 / 0.128546 (-0.078196) | 0.013682 / 0.075646 (-0.061965) | 0.088164 / 0.419271 (-0.331107) | 0.061726 / 0.043533 (0.018193) | 0.444420 / 0.255139 (0.189281) | 0.470406 / 0.283200 (0.187206) | 0.033258 / 0.141683 (-0.108425) | 1.635977 / 1.452155 (0.183823) | 1.732767 / 1.492716 (0.240051) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227350 / 0.018006 (0.209344) | 0.500805 / 0.000490 (0.500316) | 0.006473 / 0.000200 (0.006273) | 0.000110 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034456 / 0.037411 (-0.002955) | 0.094832 / 0.014526 (0.080306) | 0.118549 / 0.176557 (-0.058008) | 0.177971 / 0.737135 (-0.559164) | 0.114165 / 0.296338 (-0.182174) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.664805 / 0.215209 (0.449596) | 6.509756 / 2.077655 (4.432101) | 2.936840 / 1.504120 (1.432720) | 2.662645 / 1.541195 (1.121450) | 2.659957 / 1.468490 (1.191467) | 0.903019 / 4.584777 (-3.681758) | 5.237191 / 3.745712 (1.491479) | 4.791917 / 5.269862 (-0.477945) | 3.130905 / 4.565676 (-1.434772) | 0.100953 / 0.424275 (-0.323322) | 0.008388 / 0.007607 (0.000781) | 0.776393 / 0.226044 (0.550348) | 7.726230 / 2.268929 (5.457301) | 3.669223 / 55.444624 (-51.775401) | 2.904556 / 6.876477 (-3.971921) | 3.205546 / 2.142072 (1.063473) | 1.058899 / 4.805227 (-3.746329) | 0.213733 / 6.500664 (-6.286931) | 0.071374 / 0.075469 (-0.004096) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.713384 / 1.841788 (-0.128403) | 23.325498 / 8.074308 (15.251190) | 20.140510 / 10.191392 (9.949118) | 0.211565 / 0.680424 (-0.468859) | 0.032916 / 0.534201 (-0.501285) | 0.460504 / 0.579283 (-0.118779) | 0.594352 / 0.434364 (0.159988) | 0.556384 / 0.540337 (0.016047) | 0.788586 / 1.386936 (-0.598350) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3c7249c11d8330ce49b1fe119c34fc6100f10774 \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008840 / 0.011353 (-0.002513) | 0.005045 / 0.011008 (-0.005963) | 0.110777 / 0.038508 (0.072269) | 0.100495 / 0.023109 (0.077386) | 0.420302 / 0.275898 (0.144404) | 0.456423 / 0.323480 (0.132943) | 0.006873 / 0.007986 (-0.001113) | 0.005230 / 0.004328 (0.000902) | 0.081316 / 0.004250 (0.077066) | 0.063047 / 0.037052 (0.025995) | 0.439469 / 0.258489 (0.180979) | 0.488477 / 0.293841 (0.194636) | 0.048553 / 0.128546 (-0.079994) | 0.014984 / 0.075646 (-0.060662) | 0.401317 / 0.419271 (-0.017955) | 0.074578 / 0.043533 (0.031045) | 0.435298 / 0.255139 (0.180159) | 0.464406 / 0.283200 (0.181206) | 0.048788 / 0.141683 (-0.092895) | 1.836166 / 1.452155 (0.384011) | 1.959808 / 1.492716 (0.467091) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.321419 / 0.018006 (0.303412) | 0.595736 / 0.000490 (0.595246) | 0.021144 / 0.000200 (0.020944) | 0.000626 / 0.000054 (0.000571) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033033 / 0.037411 (-0.004379) | 0.112621 / 0.014526 (0.098095) | 0.118736 / 0.176557 (-0.057821) | 0.195533 / 0.737135 (-0.541602) | 0.120807 / 0.296338 (-0.175531) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.616692 / 0.215209 (0.401483) | 6.033674 / 2.077655 (3.956019) | 2.630106 / 1.504120 (1.125986) | 2.316739 / 1.541195 (0.775544) | 2.387525 / 1.468490 (0.919035) | 0.863385 / 4.584777 (-3.721392) | 5.288193 / 3.745712 (1.542481) | 5.115766 / 5.269862 (-0.154096) | 3.083055 / 4.565676 (-1.482621) | 0.104885 / 0.424275 (-0.319391) | 0.012233 / 0.007607 (0.004626) | 0.739924 / 0.226044 (0.513880) | 7.422996 / 2.268929 (5.154067) | 3.403316 / 55.444624 (-52.041309) | 2.778740 / 6.876477 (-4.097736) | 2.836937 / 2.142072 (0.694864) | 1.059683 / 4.805227 (-3.745544) | 0.235838 / 6.500664 (-6.264826) | 0.083725 / 0.075469 (0.008256) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.755843 / 1.841788 (-0.085944) | 25.186642 / 8.074308 (17.112334) | 24.133582 / 10.191392 (13.942190) | 0.240511 / 0.680424 (-0.439913) | 0.029563 / 0.534201 (-0.504638) | 0.486049 / 0.579283 (-0.093234) | 0.610064 / 0.434364 (0.175700) | 0.559521 / 0.540337 (0.019184) | 0.828289 / 1.386936 (-0.558647) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.012134 / 0.011353 (0.000781) | 0.005133 / 0.011008 (-0.005875) | 0.084521 / 0.038508 (0.046013) | 0.095172 / 0.023109 (0.072063) | 0.527298 / 0.275898 (0.251400) | 0.558915 / 0.323480 (0.235435) | 0.006996 / 0.007986 (-0.000989) | 0.004283 / 0.004328 (-0.000045) | 0.082975 / 0.004250 (0.078725) | 0.067976 / 0.037052 (0.030924) | 0.534020 / 0.258489 (0.275531) | 0.560810 / 0.293841 (0.266969) | 0.051603 / 0.128546 (-0.076943) | 0.013330 / 0.075646 (-0.062316) | 0.094093 / 0.419271 (-0.325178) | 0.068967 / 0.043533 (0.025434) | 0.512527 / 0.255139 (0.257388) | 0.542182 / 0.283200 (0.258982) | 0.039159 / 0.141683 (-0.102524) | 1.858841 / 1.452155 (0.406686) | 1.915450 / 1.492716 (0.422734) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.269013 / 0.018006 (0.251007) | 0.601711 / 0.000490 (0.601222) | 0.013950 / 0.000200 (0.013750) | 0.000166 / 0.000054 (0.000112) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038817 / 0.037411 (0.001405) | 0.138528 / 0.014526 (0.124002) | 0.130691 / 0.176557 (-0.045865) | 0.192825 / 0.737135 (-0.544310) | 0.128337 / 0.296338 (-0.168002) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.678725 / 0.215209 (0.463516) | 6.869763 / 2.077655 (4.792108) | 3.416224 / 1.504120 (1.912104) | 3.106971 / 1.541195 (1.565776) | 3.117248 / 1.468490 (1.648757) | 0.895004 / 4.584777 (-3.689773) | 5.551618 / 3.745712 (1.805906) | 4.964811 / 5.269862 (-0.305051) | 3.239555 / 4.565676 (-1.326121) | 0.099776 / 0.424275 (-0.324500) | 0.008723 / 0.007607 (0.001116) | 0.818554 / 0.226044 (0.592510) | 8.015976 / 2.268929 (5.747047) | 4.200392 / 55.444624 (-51.244232) | 3.566942 / 6.876477 (-3.309535) | 3.766249 / 2.142072 (1.624177) | 1.083428 / 4.805227 (-3.721799) | 0.214614 / 6.500664 (-6.286050) | 0.081951 / 0.075469 (0.006482) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.854400 / 1.841788 (0.012612) | 26.002556 / 8.074308 (17.928248) | 24.315194 / 10.191392 (14.123802) | 0.249012 / 0.680424 (-0.431412) | 0.032681 / 0.534201 (-0.501520) | 0.502360 / 0.579283 (-0.076923) | 0.606014 / 0.434364 (0.171650) | 0.616852 / 0.540337 (0.076514) | 0.861785 / 1.386936 (-0.525151) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53c5c01583153cc112a507082aff4679433a1cce \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006723 / 0.011353 (-0.004630) | 0.004135 / 0.011008 (-0.006873) | 0.079241 / 0.038508 (0.040733) | 0.065484 / 0.023109 (0.042374) | 0.302831 / 0.275898 (0.026933) | 0.343747 / 0.323480 (0.020268) | 0.005910 / 0.007986 (-0.002076) | 0.006028 / 0.004328 (0.001699) | 0.064000 / 0.004250 (0.059750) | 0.047872 / 0.037052 (0.010820) | 0.336928 / 0.258489 (0.078439) | 0.357726 / 0.293841 (0.063885) | 0.039375 / 0.128546 (-0.089171) | 0.010439 / 0.075646 (-0.065207) | 0.310453 / 0.419271 (-0.108819) | 0.055320 / 0.043533 (0.011787) | 0.294722 / 0.255139 (0.039583) | 0.314649 / 0.283200 (0.031450) | 0.033223 / 0.141683 (-0.108460) | 1.386705 / 1.452155 (-0.065450) | 1.420546 / 1.492716 (-0.072170) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.262649 / 0.018006 (0.244643) | 0.536764 / 0.000490 (0.536274) | 0.011090 / 0.000200 (0.010891) | 0.000118 / 0.000054 (0.000063) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023822 / 0.037411 (-0.013590) | 0.074279 / 0.014526 (0.059753) | 0.081295 / 0.176557 (-0.095262) | 0.135853 / 0.737135 (-0.601282) | 0.080193 / 0.296338 (-0.216146) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.468577 / 0.215209 (0.253368) | 4.615975 / 2.077655 (2.538321) | 2.059232 / 1.504120 (0.555112) | 1.798578 / 1.541195 (0.257383) | 1.801436 / 1.468490 (0.332946) | 0.660489 / 4.584777 (-3.924288) | 4.394652 / 3.745712 (0.648940) | 3.956277 / 5.269862 (-1.313585) | 2.406700 / 4.565676 (-2.158976) | 0.077174 / 0.424275 (-0.347101) | 0.007121 / 0.007607 (-0.000486) | 0.568213 / 0.226044 (0.342168) | 5.721217 / 2.268929 (3.452289) | 2.662741 / 55.444624 (-52.781883) | 2.207333 / 6.876477 (-4.669144) | 2.165279 / 2.142072 (0.023206) | 0.772566 / 4.805227 (-4.032661) | 0.162845 / 6.500664 (-6.337819) | 0.057515 / 0.075469 (-0.017954) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.313565 / 1.841788 (-0.528223) | 19.298926 / 8.074308 (11.224618) | 17.194320 / 10.191392 (7.002928) | 0.223404 / 0.680424 (-0.457020) | 0.024735 / 0.534201 (-0.509466) | 0.388452 / 0.579283 (-0.190831) | 0.489354 / 0.434364 (0.054990) | 0.427962 / 0.540337 (-0.112375) | 0.629483 / 1.386936 (-0.757453) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007404 / 0.011353 (-0.003949) | 0.004434 / 0.011008 (-0.006574) | 0.061633 / 0.038508 (0.023125) | 0.058446 / 0.023109 (0.035336) | 0.386107 / 0.275898 (0.110209) | 0.397676 / 0.323480 (0.074197) | 0.005463 / 0.007986 (-0.002523) | 0.003797 / 0.004328 (-0.000531) | 0.067323 / 0.004250 (0.063072) | 0.053826 / 0.037052 (0.016774) | 0.387910 / 0.258489 (0.129421) | 0.409364 / 0.293841 (0.115523) | 0.039836 / 0.128546 (-0.088710) | 0.011940 / 0.075646 (-0.063706) | 0.071812 / 0.419271 (-0.347459) | 0.047952 / 0.043533 (0.004419) | 0.386826 / 0.255139 (0.131687) | 0.392845 / 0.283200 (0.109645) | 0.029430 / 0.141683 (-0.112253) | 1.390961 / 1.452155 (-0.061194) | 1.482744 / 1.492716 (-0.009972) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.258814 / 0.018006 (0.240807) | 0.535505 / 0.000490 (0.535015) | 0.006097 / 0.000200 (0.005897) | 0.000130 / 0.000054 (0.000075) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028046 / 0.037411 (-0.009365) | 0.078077 / 0.014526 (0.063552) | 0.087713 / 0.176557 (-0.088843) | 0.140856 / 0.737135 (-0.596279) | 0.090565 / 0.296338 (-0.205773) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.504375 / 0.215209 (0.289165) | 5.133472 / 2.077655 (3.055817) | 2.368968 / 1.504120 (0.864848) | 2.176939 / 1.541195 (0.635744) | 2.151976 / 1.468490 (0.683486) | 0.720566 / 4.584777 (-3.864211) | 5.050505 / 3.745712 (1.304793) | 3.993614 / 5.269862 (-1.276248) | 2.492234 / 4.565676 (-2.073443) | 0.089629 / 0.424275 (-0.334646) | 0.008074 / 0.007607 (0.000467) | 0.677706 / 0.226044 (0.451661) | 6.208332 / 2.268929 (3.939403) | 3.058299 / 55.444624 (-52.386325) | 2.461078 / 6.876477 (-4.415399) | 2.622681 / 2.142072 (0.480609) | 0.873573 / 4.805227 (-3.931654) | 0.176321 / 6.500664 (-6.324343) | 0.062410 / 0.075469 (-0.013059) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.454767 / 1.841788 (-0.387021) | 19.544225 / 8.074308 (11.469917) | 17.365997 / 10.191392 (7.174605) | 0.225461 / 0.680424 (-0.454963) | 0.027679 / 0.534201 (-0.506522) | 0.396419 / 0.579283 (-0.182864) | 0.513244 / 0.434364 (0.078880) | 0.469054 / 0.540337 (-0.071283) | 0.676458 / 1.386936 (-0.710478) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d44d8a649b541cd0b10ea99fbfe7a02c3ba50a63 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007606 / 0.011353 (-0.003747) | 0.004692 / 0.011008 (-0.006317) | 0.100525 / 0.038508 (0.062017) | 0.085426 / 0.023109 (0.062317) | 0.378568 / 0.275898 (0.102670) | 0.412268 / 0.323480 (0.088788) | 0.004756 / 0.007986 (-0.003230) | 0.003871 / 0.004328 (-0.000457) | 0.075244 / 0.004250 (0.070994) | 0.064969 / 0.037052 (0.027916) | 0.385569 / 0.258489 (0.127079) | 0.429117 / 0.293841 (0.135276) | 0.035798 / 0.128546 (-0.092749) | 0.009999 / 0.075646 (-0.065647) | 0.351380 / 0.419271 (-0.067891) | 0.060850 / 0.043533 (0.017317) | 0.381327 / 0.255139 (0.126188) | 0.403663 / 0.283200 (0.120464) | 0.028103 / 0.141683 (-0.113580) | 1.814143 / 1.452155 (0.361988) | 1.895062 / 1.492716 (0.402346) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.263581 / 0.018006 (0.245575) | 0.506988 / 0.000490 (0.506499) | 0.012775 / 0.000200 (0.012575) | 0.000456 / 0.000054 (0.000402) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033452 / 0.037411 (-0.003959) | 0.104950 / 0.014526 (0.090425) | 0.114803 / 0.176557 (-0.061754) | 0.182465 / 0.737135 (-0.554671) | 0.116156 / 0.296338 (-0.180183) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441574 / 0.215209 (0.226365) | 4.394601 / 2.077655 (2.316946) | 2.170797 / 1.504120 (0.666677) | 1.926675 / 1.541195 (0.385480) | 1.974867 / 1.468490 (0.506377) | 0.546777 / 4.584777 (-4.038000) | 4.053612 / 3.745712 (0.307900) | 3.934278 / 5.269862 (-1.335583) | 2.354660 / 4.565676 (-2.211017) | 0.067706 / 0.424275 (-0.356569) | 0.009217 / 0.007607 (0.001610) | 0.539261 / 0.226044 (0.313217) | 5.409552 / 2.268929 (3.140623) | 2.835739 / 55.444624 (-52.608886) | 2.282246 / 6.876477 (-4.594230) | 2.359930 / 2.142072 (0.217858) | 0.696363 / 4.805227 (-4.108864) | 0.155947 / 6.500664 (-6.344717) | 0.071293 / 0.075469 (-0.004176) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.495512 / 1.841788 (-0.346275) | 22.027128 / 8.074308 (13.952820) | 16.226068 / 10.191392 (6.034676) | 0.180281 / 0.680424 (-0.500142) | 0.021839 / 0.534201 (-0.512362) | 0.446151 / 0.579283 (-0.133132) | 0.476872 / 0.434364 (0.042508) | 0.515171 / 0.540337 (-0.025166) | 0.731372 / 1.386936 (-0.655564) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006843 / 0.011353 (-0.004510) | 0.004286 / 0.011008 (-0.006722) | 0.074104 / 0.038508 (0.035596) | 0.076789 / 0.023109 (0.053680) | 0.441506 / 0.275898 (0.165608) | 0.500999 / 0.323480 (0.177519) | 0.006041 / 0.007986 (-0.001945) | 0.003718 / 0.004328 (-0.000610) | 0.074189 / 0.004250 (0.069938) | 0.060513 / 0.037052 (0.023461) | 0.460812 / 0.258489 (0.202323) | 0.503631 / 0.293841 (0.209790) | 0.037026 / 0.128546 (-0.091520) | 0.009611 / 0.075646 (-0.066035) | 0.077037 / 0.419271 (-0.342234) | 0.052191 / 0.043533 (0.008658) | 0.444567 / 0.255139 (0.189428) | 0.486730 / 0.283200 (0.203530) | 0.023846 / 0.141683 (-0.117837) | 1.692422 / 1.452155 (0.240267) | 1.809648 / 1.492716 (0.316932) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.240007 / 0.018006 (0.222001) | 0.481980 / 0.000490 (0.481490) | 0.006945 / 0.000200 (0.006746) | 0.000120 / 0.000054 (0.000065) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037198 / 0.037411 (-0.000213) | 0.119413 / 0.014526 (0.104887) | 0.137409 / 0.176557 (-0.039148) | 0.199130 / 0.737135 (-0.538005) | 0.133137 / 0.296338 (-0.163202) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.521747 / 0.215209 (0.306538) | 4.955653 / 2.077655 (2.877999) | 2.694323 / 1.504120 (1.190203) | 2.496629 / 1.541195 (0.955434) | 2.661151 / 1.468490 (1.192660) | 0.576687 / 4.584777 (-4.008089) | 4.251437 / 3.745712 (0.505725) | 3.683020 / 5.269862 (-1.586842) | 2.363951 / 4.565676 (-2.201726) | 0.064631 / 0.424275 (-0.359644) | 0.007958 / 0.007607 (0.000351) | 0.616498 / 0.226044 (0.390454) | 5.919424 / 2.268929 (3.650496) | 3.255936 / 55.444624 (-52.188689) | 2.866167 / 6.876477 (-4.010309) | 3.007272 / 2.142072 (0.865199) | 0.660259 / 4.805227 (-4.144968) | 0.152469 / 6.500664 (-6.348195) | 0.065254 / 0.075469 (-0.010215) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.547912 / 1.841788 (-0.293876) | 22.494611 / 8.074308 (14.420303) | 16.400746 / 10.191392 (6.209354) | 0.184137 / 0.680424 (-0.496287) | 0.023615 / 0.534201 (-0.510586) | 0.473923 / 0.579283 (-0.105360) | 0.473030 / 0.434364 (0.038666) | 0.534264 / 0.540337 (-0.006073) | 0.770178 / 1.386936 (-0.616758) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#565efb7f43839072ef01247681645ca404ba0b94 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006812 / 0.011353 (-0.004541) | 0.004254 / 0.011008 (-0.006754) | 0.084271 / 0.038508 (0.045763) | 0.084299 / 0.023109 (0.061189) | 0.317437 / 0.275898 (0.041539) | 0.350855 / 0.323480 (0.027375) | 0.004296 / 0.007986 (-0.003690) | 0.003610 / 0.004328 (-0.000718) | 0.065205 / 0.004250 (0.060955) | 0.057734 / 0.037052 (0.020682) | 0.324049 / 0.258489 (0.065560) | 0.365042 / 0.293841 (0.071201) | 0.031454 / 0.128546 (-0.097092) | 0.008703 / 0.075646 (-0.066943) | 0.286603 / 0.419271 (-0.132668) | 0.052251 / 0.043533 (0.008719) | 0.312404 / 0.255139 (0.057265) | 0.335902 / 0.283200 (0.052703) | 0.025087 / 0.141683 (-0.116595) | 1.478573 / 1.452155 (0.026418) | 1.559548 / 1.492716 (0.066831) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.307637 / 0.018006 (0.289631) | 0.567169 / 0.000490 (0.566679) | 0.006782 / 0.000200 (0.006582) | 0.000235 / 0.000054 (0.000180) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030979 / 0.037411 (-0.006433) | 0.089972 / 0.014526 (0.075446) | 0.101689 / 0.176557 (-0.074868) | 0.162038 / 0.737135 (-0.575097) | 0.103107 / 0.296338 (-0.193232) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.382458 / 0.215209 (0.167248) | 3.813105 / 2.077655 (1.735450) | 1.855198 / 1.504120 (0.351078) | 1.699850 / 1.541195 (0.158656) | 1.902818 / 1.468490 (0.434328) | 0.478654 / 4.584777 (-4.106123) | 3.536926 / 3.745712 (-0.208786) | 3.558557 / 5.269862 (-1.711304) | 2.121098 / 4.565676 (-2.444579) | 0.056584 / 0.424275 (-0.367691) | 0.007693 / 0.007607 (0.000086) | 0.471157 / 0.226044 (0.245112) | 4.717742 / 2.268929 (2.448813) | 2.389033 / 55.444624 (-53.055591) | 2.102898 / 6.876477 (-4.773579) | 2.233404 / 2.142072 (0.091332) | 0.585829 / 4.805227 (-4.219398) | 0.133784 / 6.500664 (-6.366880) | 0.063963 / 0.075469 (-0.011506) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.272234 / 1.841788 (-0.569554) | 19.897647 / 8.074308 (11.823339) | 14.808090 / 10.191392 (4.616698) | 0.167199 / 0.680424 (-0.513224) | 0.018357 / 0.534201 (-0.515844) | 0.391635 / 0.579283 (-0.187648) | 0.409603 / 0.434364 (-0.024761) | 0.467670 / 0.540337 (-0.072668) | 0.639763 / 1.386936 (-0.747173) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006794 / 0.011353 (-0.004559) | 0.004317 / 0.011008 (-0.006692) | 0.065434 / 0.038508 (0.026926) | 0.079066 / 0.023109 (0.055957) | 0.415486 / 0.275898 (0.139588) | 0.448072 / 0.323480 (0.124593) | 0.005705 / 0.007986 (-0.002281) | 0.003589 / 0.004328 (-0.000739) | 0.065195 / 0.004250 (0.060945) | 0.058951 / 0.037052 (0.021899) | 0.414466 / 0.258489 (0.155977) | 0.453844 / 0.293841 (0.160003) | 0.032437 / 0.128546 (-0.096110) | 0.008805 / 0.075646 (-0.066841) | 0.071741 / 0.419271 (-0.347530) | 0.048051 / 0.043533 (0.004518) | 0.413197 / 0.255139 (0.158058) | 0.430071 / 0.283200 (0.146872) | 0.023144 / 0.141683 (-0.118539) | 1.507756 / 1.452155 (0.055601) | 1.572180 / 1.492716 (0.079464) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.326556 / 0.018006 (0.308550) | 0.533664 / 0.000490 (0.533174) | 0.007400 / 0.000200 (0.007200) | 0.000119 / 0.000054 (0.000065) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033397 / 0.037411 (-0.004014) | 0.092486 / 0.014526 (0.077960) | 0.108454 / 0.176557 (-0.068103) | 0.163885 / 0.737135 (-0.573250) | 0.109682 / 0.296338 (-0.186657) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429283 / 0.215209 (0.214074) | 4.285774 / 2.077655 (2.208119) | 2.245646 / 1.504120 (0.741526) | 2.088460 / 1.541195 (0.547265) | 2.217908 / 1.468490 (0.749418) | 0.500126 / 4.584777 (-4.084651) | 3.640253 / 3.745712 (-0.105459) | 3.435069 / 5.269862 (-1.834793) | 2.158015 / 4.565676 (-2.407662) | 0.059087 / 0.424275 (-0.365188) | 0.007479 / 0.007607 (-0.000128) | 0.518067 / 0.226044 (0.292023) | 5.181891 / 2.268929 (2.912963) | 2.759156 / 55.444624 (-52.685468) | 2.452164 / 6.876477 (-4.424313) | 2.712764 / 2.142072 (0.570692) | 0.604871 / 4.805227 (-4.200356) | 0.137810 / 6.500664 (-6.362854) | 0.061999 / 0.075469 (-0.013470) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.338081 / 1.841788 (-0.503706) | 19.934668 / 8.074308 (11.860360) | 14.482526 / 10.191392 (4.291134) | 0.167615 / 0.680424 (-0.512809) | 0.020257 / 0.534201 (-0.513944) | 0.399103 / 0.579283 (-0.180180) | 0.431785 / 0.434364 (-0.002579) | 0.475470 / 0.540337 (-0.064868) | 0.648003 / 1.386936 (-0.738933) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#153a6c0b0897fc30203ded5a6d6c358c53aa3a0e \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011916 / 0.011353 (0.000563) | 0.004696 / 0.011008 (-0.006313) | 0.101061 / 0.038508 (0.062553) | 0.093383 / 0.023109 (0.070274) | 0.391517 / 0.275898 (0.115619) | 0.434374 / 0.323480 (0.110894) | 0.006193 / 0.007986 (-0.001792) | 0.003840 / 0.004328 (-0.000489) | 0.077946 / 0.004250 (0.073696) | 0.066332 / 0.037052 (0.029280) | 0.413103 / 0.258489 (0.154614) | 0.452988 / 0.293841 (0.159148) | 0.044899 / 0.128546 (-0.083647) | 0.009969 / 0.075646 (-0.065677) | 0.344569 / 0.419271 (-0.074703) | 0.064688 / 0.043533 (0.021155) | 0.388042 / 0.255139 (0.132903) | 0.417615 / 0.283200 (0.134416) | 0.032899 / 0.141683 (-0.108784) | 1.738834 / 1.452155 (0.286679) | 1.837562 / 1.492716 (0.344845) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.255265 / 0.018006 (0.237259) | 0.547550 / 0.000490 (0.547061) | 0.009018 / 0.000200 (0.008818) | 0.001232 / 0.000054 (0.001178) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033171 / 0.037411 (-0.004241) | 0.102569 / 0.014526 (0.088043) | 0.113611 / 0.176557 (-0.062946) | 0.181805 / 0.737135 (-0.555330) | 0.115015 / 0.296338 (-0.181323) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.456430 / 0.215209 (0.241221) | 4.536000 / 2.077655 (2.458346) | 2.220554 / 1.504120 (0.716434) | 2.037965 / 1.541195 (0.496770) | 2.223780 / 1.468490 (0.755290) | 0.565732 / 4.584777 (-4.019045) | 4.574917 / 3.745712 (0.829205) | 4.085683 / 5.269862 (-1.184178) | 2.529052 / 4.565676 (-2.036624) | 0.067061 / 0.424275 (-0.357214) | 0.009161 / 0.007607 (0.001554) | 0.551377 / 0.226044 (0.325332) | 5.510422 / 2.268929 (3.241493) | 2.788264 / 55.444624 (-52.656360) | 2.432821 / 6.876477 (-4.443656) | 2.500835 / 2.142072 (0.358762) | 0.683645 / 4.805227 (-4.121582) | 0.155595 / 6.500664 (-6.345069) | 0.072265 / 0.075469 (-0.003204) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.512571 / 1.841788 (-0.329217) | 23.752582 / 8.074308 (15.678273) | 16.798834 / 10.191392 (6.607442) | 0.210325 / 0.680424 (-0.470099) | 0.023446 / 0.534201 (-0.510755) | 0.472964 / 0.579283 (-0.106319) | 0.518003 / 0.434364 (0.083639) | 0.588422 / 0.540337 (0.048085) | 0.830762 / 1.386936 (-0.556174) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008075 / 0.011353 (-0.003278) | 0.004569 / 0.011008 (-0.006439) | 0.079786 / 0.038508 (0.041278) | 0.092741 / 0.023109 (0.069632) | 0.500732 / 0.275898 (0.224834) | 0.544108 / 0.323480 (0.220628) | 0.006305 / 0.007986 (-0.001680) | 0.003843 / 0.004328 (-0.000486) | 0.078347 / 0.004250 (0.074096) | 0.066969 / 0.037052 (0.029916) | 0.504116 / 0.258489 (0.245627) | 0.548109 / 0.293841 (0.254268) | 0.038263 / 0.128546 (-0.090283) | 0.010006 / 0.075646 (-0.065640) | 0.085582 / 0.419271 (-0.333690) | 0.056937 / 0.043533 (0.013404) | 0.502861 / 0.255139 (0.247722) | 0.532002 / 0.283200 (0.248802) | 0.027003 / 0.141683 (-0.114679) | 1.811658 / 1.452155 (0.359503) | 1.878863 / 1.492716 (0.386147) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242297 / 0.018006 (0.224291) | 0.489060 / 0.000490 (0.488570) | 0.005770 / 0.000200 (0.005570) | 0.000129 / 0.000054 (0.000075) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.040368 / 0.037411 (0.002956) | 0.116221 / 0.014526 (0.101695) | 0.125195 / 0.176557 (-0.051361) | 0.188616 / 0.737135 (-0.548519) | 0.126473 / 0.296338 (-0.169866) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.513975 / 0.215209 (0.298766) | 5.122407 / 2.077655 (3.044752) | 2.854024 / 1.504120 (1.349904) | 2.611101 / 1.541195 (1.069906) | 2.704880 / 1.468490 (1.236390) | 0.581568 / 4.584777 (-4.003209) | 4.628965 / 3.745712 (0.883253) | 4.069359 / 5.269862 (-1.200503) | 2.433793 / 4.565676 (-2.131883) | 0.068624 / 0.424275 (-0.355651) | 0.008843 / 0.007607 (0.001235) | 0.609147 / 0.226044 (0.383102) | 6.096923 / 2.268929 (3.827995) | 3.411687 / 55.444624 (-52.032937) | 2.972037 / 6.876477 (-3.904440) | 3.210266 / 2.142072 (1.068194) | 0.697935 / 4.805227 (-4.107292) | 0.156855 / 6.500664 (-6.343809) | 0.072600 / 0.075469 (-0.002869) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.673126 / 1.841788 (-0.168661) | 24.782231 / 8.074308 (16.707923) | 17.945937 / 10.191392 (7.754545) | 0.229063 / 0.680424 (-0.451361) | 0.024264 / 0.534201 (-0.509937) | 0.474904 / 0.579283 (-0.104379) | 0.616602 / 0.434364 (0.182238) | 0.587687 / 0.540337 (0.047350) | 0.875600 / 1.386936 (-0.511336) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a46ae63ad72c0733c947fa0f2996fa739d80e1ef \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004866 / 0.011353 (-0.006487) | 0.002877 / 0.011008 (-0.008132) | 0.061786 / 0.038508 (0.023277) | 0.051555 / 0.023109 (0.028446) | 0.262182 / 0.275898 (-0.013716) | 0.288908 / 0.323480 (-0.034572) | 0.002929 / 0.007986 (-0.005057) | 0.002358 / 0.004328 (-0.001971) | 0.048246 / 0.004250 (0.043995) | 0.040391 / 0.037052 (0.003339) | 0.268165 / 0.258489 (0.009675) | 0.304844 / 0.293841 (0.011003) | 0.023280 / 0.128546 (-0.105266) | 0.007274 / 0.075646 (-0.068372) | 0.200698 / 0.419271 (-0.218574) | 0.036181 / 0.043533 (-0.007352) | 0.267292 / 0.255139 (0.012153) | 0.286981 / 0.283200 (0.003781) | 0.018686 / 0.141683 (-0.122996) | 1.131903 / 1.452155 (-0.320251) | 1.196631 / 1.492716 (-0.296086) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092158 / 0.018006 (0.074152) | 0.300621 / 0.000490 (0.300132) | 0.000205 / 0.000200 (0.000006) | 0.000041 / 0.000054 (-0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018101 / 0.037411 (-0.019310) | 0.062478 / 0.014526 (0.047952) | 0.073092 / 0.176557 (-0.103464) | 0.119397 / 0.737135 (-0.617738) | 0.073768 / 0.296338 (-0.222570) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286711 / 0.215209 (0.071502) | 2.766663 / 2.077655 (0.689008) | 1.431238 / 1.504120 (-0.072882) | 1.308312 / 1.541195 (-0.232883) | 1.344886 / 1.468490 (-0.123605) | 0.396719 / 4.584777 (-4.188058) | 2.371154 / 3.745712 (-1.374558) | 2.626471 / 5.269862 (-2.643391) | 1.574837 / 4.565676 (-2.990840) | 0.046344 / 0.424275 (-0.377931) | 0.005108 / 0.007607 (-0.002499) | 0.334200 / 0.226044 (0.108156) | 3.277034 / 2.268929 (1.008106) | 1.789338 / 55.444624 (-53.655286) | 1.527584 / 6.876477 (-5.348892) | 1.570417 / 2.142072 (-0.571656) | 0.472663 / 4.805227 (-4.332564) | 0.100825 / 6.500664 (-6.399839) | 0.042270 / 0.075469 (-0.033199) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.965416 / 1.841788 (-0.876372) | 11.827406 / 8.074308 (3.753098) | 10.820703 / 10.191392 (0.629311) | 0.128636 / 0.680424 (-0.551788) | 0.014696 / 0.534201 (-0.519505) | 0.271019 / 0.579283 (-0.308264) | 0.270077 / 0.434364 (-0.164287) | 0.313054 / 0.540337 (-0.227284) | 0.402941 / 1.386936 (-0.983995) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005204 / 0.011353 (-0.006149) | 0.002976 / 0.011008 (-0.008032) | 0.047723 / 0.038508 (0.009215) | 0.056180 / 0.023109 (0.033071) | 0.277751 / 0.275898 (0.001853) | 0.304109 / 0.323480 (-0.019371) | 0.004254 / 0.007986 (-0.003732) | 0.002386 / 0.004328 (-0.001943) | 0.047815 / 0.004250 (0.043564) | 0.041553 / 0.037052 (0.004501) | 0.280958 / 0.258489 (0.022469) | 0.308639 / 0.293841 (0.014799) | 0.023549 / 0.128546 (-0.104997) | 0.007846 / 0.075646 (-0.067800) | 0.053762 / 0.419271 (-0.365509) | 0.031763 / 0.043533 (-0.011770) | 0.278208 / 0.255139 (0.023069) | 0.294024 / 0.283200 (0.010825) | 0.018648 / 0.141683 (-0.123035) | 1.140664 / 1.452155 (-0.311490) | 1.206706 / 1.492716 (-0.286010) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093211 / 0.018006 (0.075205) | 0.303067 / 0.000490 (0.302577) | 0.000222 / 0.000200 (0.000022) | 0.000055 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021745 / 0.037411 (-0.015666) | 0.070400 / 0.014526 (0.055874) | 0.083250 / 0.176557 (-0.093307) | 0.119745 / 0.737135 (-0.617391) | 0.083004 / 0.296338 (-0.213335) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.305841 / 0.215209 (0.090632) | 2.958171 / 2.077655 (0.880516) | 1.596990 / 1.504120 (0.092870) | 1.466522 / 1.541195 (-0.074673) | 1.487050 / 1.468490 (0.018560) | 0.402866 / 4.584777 (-4.181911) | 2.425415 / 3.745712 (-1.320297) | 2.545245 / 5.269862 (-2.724617) | 1.569719 / 4.565676 (-2.995958) | 0.046344 / 0.424275 (-0.377931) | 0.005275 / 0.007607 (-0.002332) | 0.362024 / 0.226044 (0.135980) | 3.556721 / 2.268929 (1.287792) | 1.961359 / 55.444624 (-53.483266) | 1.672835 / 6.876477 (-5.203641) | 1.814036 / 2.142072 (-0.328036) | 0.482012 / 4.805227 (-4.323215) | 0.099275 / 6.500664 (-6.401389) | 0.040988 / 0.075469 (-0.034481) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.984368 / 1.841788 (-0.857420) | 12.251555 / 8.074308 (4.177247) | 10.645975 / 10.191392 (0.454583) | 0.128955 / 0.680424 (-0.551468) | 0.015355 / 0.534201 (-0.518846) | 0.272498 / 0.579283 (-0.306785) | 0.279342 / 0.434364 (-0.155022) | 0.303055 / 0.540337 (-0.237282) | 0.392437 / 1.386936 (-0.994499) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#56458fa98d2670ff6bf47a782b6f418785c017fd \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009502 / 0.011353 (-0.001851) | 0.004957 / 0.011008 (-0.006052) | 0.111062 / 0.038508 (0.072553) | 0.100012 / 0.023109 (0.076903) | 0.415747 / 0.275898 (0.139849) | 0.453910 / 0.323480 (0.130430) | 0.006030 / 0.007986 (-0.001956) | 0.004271 / 0.004328 (-0.000057) | 0.088694 / 0.004250 (0.084444) | 0.064529 / 0.037052 (0.027477) | 0.414999 / 0.258489 (0.156510) | 0.477115 / 0.293841 (0.183274) | 0.047565 / 0.128546 (-0.080982) | 0.013352 / 0.075646 (-0.062294) | 0.367948 / 0.419271 (-0.051324) | 0.067577 / 0.043533 (0.024044) | 0.405107 / 0.255139 (0.149968) | 0.430281 / 0.283200 (0.147081) | 0.041629 / 0.141683 (-0.100054) | 1.784746 / 1.452155 (0.332591) | 1.901539 / 1.492716 (0.408822) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.308456 / 0.018006 (0.290450) | 0.623253 / 0.000490 (0.622763) | 0.014966 / 0.000200 (0.014766) | 0.000393 / 0.000054 (0.000338) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031538 / 0.037411 (-0.005873) | 0.100321 / 0.014526 (0.085796) | 0.112788 / 0.176557 (-0.063769) | 0.180998 / 0.737135 (-0.556138) | 0.111589 / 0.296338 (-0.184750) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.603121 / 0.215209 (0.387912) | 5.769795 / 2.077655 (3.692140) | 2.501168 / 1.504120 (0.997048) | 2.240982 / 1.541195 (0.699787) | 2.333123 / 1.468490 (0.864633) | 0.799246 / 4.584777 (-3.785531) | 5.148529 / 3.745712 (1.402817) | 4.737782 / 5.269862 (-0.532080) | 3.003032 / 4.565676 (-1.562644) | 0.087457 / 0.424275 (-0.336818) | 0.008777 / 0.007607 (0.001170) | 0.692961 / 0.226044 (0.466916) | 7.235537 / 2.268929 (4.966608) | 3.464074 / 55.444624 (-51.980551) | 2.817360 / 6.876477 (-4.059116) | 2.903121 / 2.142072 (0.761049) | 1.026150 / 4.805227 (-3.779077) | 0.231814 / 6.500664 (-6.268850) | 0.088358 / 0.075469 (0.012888) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.527889 / 1.841788 (-0.313898) | 24.374770 / 8.074308 (16.300462) | 21.720415 / 10.191392 (11.529023) | 0.209357 / 0.680424 (-0.471067) | 0.027587 / 0.534201 (-0.506614) | 0.479136 / 0.579283 (-0.100147) | 0.573005 / 0.434364 (0.138641) | 0.537713 / 0.540337 (-0.002625) | 0.753628 / 1.386936 (-0.633308) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009724 / 0.011353 (-0.001629) | 0.004798 / 0.011008 (-0.006210) | 0.076423 / 0.038508 (0.037915) | 0.085693 / 0.023109 (0.062584) | 0.446864 / 0.275898 (0.170966) | 0.482700 / 0.323480 (0.159220) | 0.006448 / 0.007986 (-0.001537) | 0.004451 / 0.004328 (0.000122) | 0.078295 / 0.004250 (0.074045) | 0.061940 / 0.037052 (0.024888) | 0.446091 / 0.258489 (0.187601) | 0.478567 / 0.293841 (0.184726) | 0.047206 / 0.128546 (-0.081340) | 0.012608 / 0.075646 (-0.063038) | 0.089719 / 0.419271 (-0.329552) | 0.057791 / 0.043533 (0.014258) | 0.438357 / 0.255139 (0.183218) | 0.475060 / 0.283200 (0.191860) | 0.035466 / 0.141683 (-0.106216) | 1.691982 / 1.452155 (0.239827) | 1.773834 / 1.492716 (0.281118) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.290053 / 0.018006 (0.272047) | 0.595465 / 0.000490 (0.594976) | 0.007531 / 0.000200 (0.007331) | 0.000179 / 0.000054 (0.000124) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034625 / 0.037411 (-0.002786) | 0.098725 / 0.014526 (0.084200) | 0.111248 / 0.176557 (-0.065308) | 0.172113 / 0.737135 (-0.565022) | 0.111299 / 0.296338 (-0.185040) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.581773 / 0.215209 (0.366564) | 6.150993 / 2.077655 (4.073338) | 2.761099 / 1.504120 (1.256980) | 2.431459 / 1.541195 (0.890264) | 2.501471 / 1.468490 (1.032981) | 0.805751 / 4.584777 (-3.779026) | 5.375406 / 3.745712 (1.629693) | 4.829323 / 5.269862 (-0.440538) | 3.095235 / 4.565676 (-1.470442) | 0.103336 / 0.424275 (-0.320939) | 0.012678 / 0.007607 (0.005071) | 0.730121 / 0.226044 (0.504077) | 7.272025 / 2.268929 (5.003097) | 3.607889 / 55.444624 (-51.836735) | 2.904797 / 6.876477 (-3.971680) | 3.179139 / 2.142072 (1.037067) | 0.997510 / 4.805227 (-3.807717) | 0.219023 / 6.500664 (-6.281641) | 0.076680 / 0.075469 (0.001211) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.712838 / 1.841788 (-0.128950) | 24.242240 / 8.074308 (16.167932) | 19.746825 / 10.191392 (9.555433) | 0.234590 / 0.680424 (-0.445833) | 0.032015 / 0.534201 (-0.502186) | 0.462554 / 0.579283 (-0.116729) | 0.604529 / 0.434364 (0.170165) | 0.537779 / 0.540337 (-0.002558) | 0.777386 / 1.386936 (-0.609550) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#89c1b13d85b8971925440deb84f558e23c224a47 \"CML watermark\")\n",
"Cool ! Nice to simplify this",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004659 / 0.011353 (-0.006693) | 0.002672 / 0.011008 (-0.008337) | 0.062385 / 0.038508 (0.023877) | 0.030581 / 0.023109 (0.007471) | 0.243210 / 0.275898 (-0.032688) | 0.271441 / 0.323480 (-0.052039) | 0.002909 / 0.007986 (-0.005076) | 0.002371 / 0.004328 (-0.001957) | 0.049213 / 0.004250 (0.044962) | 0.043952 / 0.037052 (0.006900) | 0.250257 / 0.258489 (-0.008232) | 0.280470 / 0.293841 (-0.013371) | 0.023048 / 0.128546 (-0.105499) | 0.006893 / 0.075646 (-0.068754) | 0.204026 / 0.419271 (-0.215245) | 0.054067 / 0.043533 (0.010534) | 0.248730 / 0.255139 (-0.006409) | 0.272325 / 0.283200 (-0.010874) | 0.019028 / 0.141683 (-0.122655) | 1.103477 / 1.452155 (-0.348678) | 1.185775 / 1.492716 (-0.306942) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097295 / 0.018006 (0.079289) | 0.302997 / 0.000490 (0.302507) | 0.000216 / 0.000200 (0.000016) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018653 / 0.037411 (-0.018759) | 0.062604 / 0.014526 (0.048079) | 0.075652 / 0.176557 (-0.100904) | 0.121298 / 0.737135 (-0.615838) | 0.074129 / 0.296338 (-0.222209) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283315 / 0.215209 (0.068106) | 2.833975 / 2.077655 (0.756320) | 1.463877 / 1.504120 (-0.040243) | 1.352197 / 1.541195 (-0.188998) | 1.337623 / 1.468490 (-0.130867) | 0.405282 / 4.584777 (-4.179495) | 2.371381 / 3.745712 (-1.374331) | 2.584853 / 5.269862 (-2.685009) | 1.565902 / 4.565676 (-2.999775) | 0.046398 / 0.424275 (-0.377877) | 0.004795 / 0.007607 (-0.002812) | 0.345949 / 0.226044 (0.119905) | 3.326662 / 2.268929 (1.057733) | 1.778394 / 55.444624 (-53.666230) | 1.520788 / 6.876477 (-5.355688) | 1.526517 / 2.142072 (-0.615556) | 0.471788 / 4.805227 (-4.333439) | 0.099236 / 6.500664 (-6.401428) | 0.041886 / 0.075469 (-0.033583) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.958183 / 1.841788 (-0.883605) | 11.474476 / 8.074308 (3.400168) | 10.547550 / 10.191392 (0.356158) | 0.129316 / 0.680424 (-0.551108) | 0.013969 / 0.534201 (-0.520232) | 0.272028 / 0.579283 (-0.307255) | 0.271027 / 0.434364 (-0.163337) | 0.312124 / 0.540337 (-0.228214) | 0.423879 / 1.386936 (-0.963057) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004743 / 0.011353 (-0.006610) | 0.002724 / 0.011008 (-0.008284) | 0.049526 / 0.038508 (0.011018) | 0.051429 / 0.023109 (0.028319) | 0.265202 / 0.275898 (-0.010696) | 0.287498 / 0.323480 (-0.035981) | 0.004034 / 0.007986 (-0.003951) | 0.002460 / 0.004328 (-0.001868) | 0.049367 / 0.004250 (0.045116) | 0.038526 / 0.037052 (0.001474) | 0.271496 / 0.258489 (0.013007) | 0.300969 / 0.293841 (0.007128) | 0.024159 / 0.128546 (-0.104387) | 0.006959 / 0.075646 (-0.068687) | 0.055316 / 0.419271 (-0.363955) | 0.032409 / 0.043533 (-0.011124) | 0.267524 / 0.255139 (0.012385) | 0.284667 / 0.283200 (0.001467) | 0.017305 / 0.141683 (-0.124378) | 1.127560 / 1.452155 (-0.324595) | 1.188271 / 1.492716 (-0.304445) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093587 / 0.018006 (0.075581) | 0.301834 / 0.000490 (0.301344) | 0.000211 / 0.000200 (0.000011) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020899 / 0.037411 (-0.016512) | 0.069999 / 0.014526 (0.055473) | 0.081434 / 0.176557 (-0.095123) | 0.120538 / 0.737135 (-0.616598) | 0.082708 / 0.296338 (-0.213630) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291845 / 0.215209 (0.076636) | 2.872476 / 2.077655 (0.794822) | 1.579330 / 1.504120 (0.075210) | 1.453083 / 1.541195 (-0.088112) | 1.496675 / 1.468490 (0.028185) | 0.406178 / 4.584777 (-4.178599) | 2.434121 / 3.745712 (-1.311592) | 2.519760 / 5.269862 (-2.750101) | 1.535781 / 4.565676 (-3.029895) | 0.046331 / 0.424275 (-0.377944) | 0.004749 / 0.007607 (-0.002858) | 0.340862 / 0.226044 (0.114817) | 3.362750 / 2.268929 (1.093822) | 1.924707 / 55.444624 (-53.519917) | 1.646820 / 6.876477 (-5.229657) | 1.630885 / 2.142072 (-0.511188) | 0.478623 / 4.805227 (-4.326605) | 0.098235 / 6.500664 (-6.402429) | 0.040741 / 0.075469 (-0.034728) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.989858 / 1.841788 (-0.851929) | 12.111035 / 8.074308 (4.036727) | 11.065284 / 10.191392 (0.873892) | 0.143443 / 0.680424 (-0.536981) | 0.015873 / 0.534201 (-0.518328) | 0.271932 / 0.579283 (-0.307351) | 0.281440 / 0.434364 (-0.152924) | 0.309518 / 0.540337 (-0.230819) | 0.414701 / 1.386936 (-0.972235) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#322ee4bd7d460a5789f9991a45453b9fb5f5aed1 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005840 / 0.011353 (-0.005513) | 0.003580 / 0.011008 (-0.007428) | 0.079921 / 0.038508 (0.041413) | 0.036316 / 0.023109 (0.013206) | 0.321065 / 0.275898 (0.045167) | 0.348594 / 0.323480 (0.025115) | 0.004662 / 0.007986 (-0.003324) | 0.002884 / 0.004328 (-0.001444) | 0.062964 / 0.004250 (0.058714) | 0.052856 / 0.037052 (0.015804) | 0.322087 / 0.258489 (0.063598) | 0.355546 / 0.293841 (0.061705) | 0.027025 / 0.128546 (-0.101521) | 0.007969 / 0.075646 (-0.067678) | 0.261416 / 0.419271 (-0.157855) | 0.066612 / 0.043533 (0.023079) | 0.314631 / 0.255139 (0.059492) | 0.340939 / 0.283200 (0.057739) | 0.019710 / 0.141683 (-0.121972) | 1.446068 / 1.452155 (-0.006086) | 1.510342 / 1.492716 (0.017625) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.219742 / 0.018006 (0.201736) | 0.431794 / 0.000490 (0.431304) | 0.005717 / 0.000200 (0.005517) | 0.000195 / 0.000054 (0.000141) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024486 / 0.037411 (-0.012926) | 0.073231 / 0.014526 (0.058706) | 0.084053 / 0.176557 (-0.092503) | 0.145857 / 0.737135 (-0.591279) | 0.083050 / 0.296338 (-0.213289) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400532 / 0.215209 (0.185323) | 3.989293 / 2.077655 (1.911638) | 1.935520 / 1.504120 (0.431400) | 1.754146 / 1.541195 (0.212951) | 1.821060 / 1.468490 (0.352570) | 0.512603 / 4.584777 (-4.072173) | 3.070974 / 3.745712 (-0.674738) | 2.984617 / 5.269862 (-2.285245) | 1.875790 / 4.565676 (-2.689886) | 0.057881 / 0.424275 (-0.366394) | 0.006403 / 0.007607 (-0.001204) | 0.465542 / 0.226044 (0.239498) | 4.659589 / 2.268929 (2.390661) | 2.349637 / 55.444624 (-53.094987) | 2.011511 / 6.876477 (-4.864965) | 2.071893 / 2.142072 (-0.070179) | 0.591113 / 4.805227 (-4.214114) | 0.125000 / 6.500664 (-6.375664) | 0.061372 / 0.075469 (-0.014097) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.237068 / 1.841788 (-0.604720) | 17.493192 / 8.074308 (9.418884) | 13.600688 / 10.191392 (3.409296) | 0.142508 / 0.680424 (-0.537916) | 0.017305 / 0.534201 (-0.516896) | 0.333352 / 0.579283 (-0.245931) | 0.366699 / 0.434364 (-0.067665) | 0.381104 / 0.540337 (-0.159233) | 0.562645 / 1.386936 (-0.824291) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006337 / 0.011353 (-0.005016) | 0.003584 / 0.011008 (-0.007424) | 0.063351 / 0.038508 (0.024843) | 0.061351 / 0.023109 (0.038242) | 0.430690 / 0.275898 (0.154792) | 0.462158 / 0.323480 (0.138678) | 0.004922 / 0.007986 (-0.003064) | 0.002898 / 0.004328 (-0.001430) | 0.063722 / 0.004250 (0.059472) | 0.046970 / 0.037052 (0.009918) | 0.436340 / 0.258489 (0.177851) | 0.472842 / 0.293841 (0.179001) | 0.029238 / 0.128546 (-0.099309) | 0.008079 / 0.075646 (-0.067568) | 0.068425 / 0.419271 (-0.350846) | 0.041272 / 0.043533 (-0.002261) | 0.429150 / 0.255139 (0.174011) | 0.451859 / 0.283200 (0.168659) | 0.020135 / 0.141683 (-0.121547) | 1.440388 / 1.452155 (-0.011767) | 1.506784 / 1.492716 (0.014068) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225810 / 0.018006 (0.207804) | 0.408447 / 0.000490 (0.407957) | 0.002484 / 0.000200 (0.002284) | 0.000079 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026162 / 0.037411 (-0.011250) | 0.079292 / 0.014526 (0.064766) | 0.091126 / 0.176557 (-0.085431) | 0.141607 / 0.737135 (-0.595528) | 0.090073 / 0.296338 (-0.206266) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420689 / 0.215209 (0.205479) | 4.207631 / 2.077655 (2.129976) | 2.163469 / 1.504120 (0.659350) | 2.098208 / 1.541195 (0.557013) | 2.217340 / 1.468490 (0.748850) | 0.502599 / 4.584777 (-4.082178) | 3.128151 / 3.745712 (-0.617561) | 2.921041 / 5.269862 (-2.348820) | 1.808352 / 4.565676 (-2.757325) | 0.057724 / 0.424275 (-0.366551) | 0.006423 / 0.007607 (-0.001184) | 0.490631 / 0.226044 (0.264587) | 4.878761 / 2.268929 (2.609833) | 2.614831 / 55.444624 (-52.829793) | 2.214611 / 6.876477 (-4.661866) | 2.253313 / 2.142072 (0.111241) | 0.585643 / 4.805227 (-4.219584) | 0.122436 / 6.500664 (-6.378228) | 0.057974 / 0.075469 (-0.017495) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.334290 / 1.841788 (-0.507498) | 17.778981 / 8.074308 (9.704672) | 14.982837 / 10.191392 (4.791445) | 0.135731 / 0.680424 (-0.544693) | 0.018314 / 0.534201 (-0.515887) | 0.332318 / 0.579283 (-0.246966) | 0.380185 / 0.434364 (-0.054179) | 0.391430 / 0.540337 (-0.148907) | 0.554577 / 1.386936 (-0.832359) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1715b61a096cafb20caeb136111522432aba04f5 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005248 / 0.011353 (-0.006105) | 0.003188 / 0.011008 (-0.007820) | 0.063045 / 0.038508 (0.024537) | 0.033620 / 0.023109 (0.010511) | 0.244725 / 0.275898 (-0.031173) | 0.283259 / 0.323480 (-0.040220) | 0.003013 / 0.007986 (-0.004973) | 0.002486 / 0.004328 (-0.001842) | 0.048873 / 0.004250 (0.044623) | 0.049431 / 0.037052 (0.012379) | 0.245297 / 0.258489 (-0.013192) | 0.283127 / 0.293841 (-0.010714) | 0.024204 / 0.128546 (-0.104342) | 0.007542 / 0.075646 (-0.068104) | 0.204831 / 0.419271 (-0.214440) | 0.067487 / 0.043533 (0.023954) | 0.251477 / 0.255139 (-0.003662) | 0.273108 / 0.283200 (-0.010091) | 0.021035 / 0.141683 (-0.120648) | 1.108361 / 1.452155 (-0.343793) | 1.172923 / 1.492716 (-0.319793) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094729 / 0.018006 (0.076722) | 0.301877 / 0.000490 (0.301388) | 0.000223 / 0.000200 (0.000023) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019901 / 0.037411 (-0.017511) | 0.068059 / 0.014526 (0.053534) | 0.075333 / 0.176557 (-0.101224) | 0.123276 / 0.737135 (-0.613859) | 0.076810 / 0.296338 (-0.219528) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283421 / 0.215209 (0.068211) | 2.775511 / 2.077655 (0.697857) | 1.430927 / 1.504120 (-0.073193) | 1.317334 / 1.541195 (-0.223860) | 1.359483 / 1.468490 (-0.109007) | 0.403186 / 4.584777 (-4.181591) | 2.405789 / 3.745712 (-1.339923) | 2.773039 / 5.269862 (-2.496823) | 1.666722 / 4.565676 (-2.898954) | 0.047937 / 0.424275 (-0.376338) | 0.004879 / 0.007607 (-0.002728) | 0.347225 / 0.226044 (0.121180) | 3.380860 / 2.268929 (1.111931) | 1.838532 / 55.444624 (-53.606092) | 1.597681 / 6.876477 (-5.278796) | 1.600123 / 2.142072 (-0.541949) | 0.478836 / 4.805227 (-4.326391) | 0.100332 / 6.500664 (-6.400332) | 0.043334 / 0.075469 (-0.032135) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.942591 / 1.841788 (-0.899196) | 12.588886 / 8.074308 (4.514578) | 11.375666 / 10.191392 (1.184274) | 0.143460 / 0.680424 (-0.536964) | 0.014990 / 0.534201 (-0.519211) | 0.271068 / 0.579283 (-0.308216) | 0.265478 / 0.434364 (-0.168885) | 0.310914 / 0.540337 (-0.229423) | 0.428310 / 1.386936 (-0.958626) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004986 / 0.011353 (-0.006367) | 0.003263 / 0.011008 (-0.007745) | 0.049076 / 0.038508 (0.010567) | 0.063665 / 0.023109 (0.040556) | 0.270352 / 0.275898 (-0.005546) | 0.298849 / 0.323480 (-0.024631) | 0.004083 / 0.007986 (-0.003903) | 0.002503 / 0.004328 (-0.001826) | 0.048586 / 0.004250 (0.044335) | 0.040701 / 0.037052 (0.003648) | 0.274082 / 0.258489 (0.015593) | 0.308279 / 0.293841 (0.014438) | 0.024734 / 0.128546 (-0.103812) | 0.007535 / 0.075646 (-0.068111) | 0.054670 / 0.419271 (-0.364602) | 0.032828 / 0.043533 (-0.010705) | 0.276226 / 0.255139 (0.021087) | 0.289322 / 0.283200 (0.006122) | 0.018789 / 0.141683 (-0.122893) | 1.279837 / 1.452155 (-0.172318) | 1.203010 / 1.492716 (-0.289706) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095674 / 0.018006 (0.077667) | 0.309754 / 0.000490 (0.309265) | 0.000229 / 0.000200 (0.000029) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021733 / 0.037411 (-0.015678) | 0.074858 / 0.014526 (0.060332) | 0.081845 / 0.176557 (-0.094711) | 0.121991 / 0.737135 (-0.615145) | 0.084057 / 0.296338 (-0.212281) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298456 / 0.215209 (0.083246) | 2.884930 / 2.077655 (0.807276) | 1.574875 / 1.504120 (0.070755) | 1.451598 / 1.541195 (-0.089597) | 1.548106 / 1.468490 (0.079616) | 0.408662 / 4.584777 (-4.176115) | 2.444306 / 3.745712 (-1.301406) | 2.737027 / 5.269862 (-2.532835) | 1.633085 / 4.565676 (-2.932592) | 0.047349 / 0.424275 (-0.376926) | 0.004864 / 0.007607 (-0.002744) | 0.355434 / 0.226044 (0.129389) | 3.495531 / 2.268929 (1.226603) | 1.972737 / 55.444624 (-53.471888) | 1.706973 / 6.876477 (-5.169504) | 1.798985 / 2.142072 (-0.343087) | 0.490353 / 4.805227 (-4.314874) | 0.099533 / 6.500664 (-6.401131) | 0.042397 / 0.075469 (-0.033073) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.978092 / 1.841788 (-0.863696) | 13.166220 / 8.074308 (5.091912) | 11.673518 / 10.191392 (1.482126) | 0.134253 / 0.680424 (-0.546171) | 0.016478 / 0.534201 (-0.517723) | 0.271629 / 0.579283 (-0.307654) | 0.284082 / 0.434364 (-0.150282) | 0.313352 / 0.540337 (-0.226986) | 0.416913 / 1.386936 (-0.970023) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#01e7144a02825fea3418872c51a8ca93950f3080 \"CML watermark\")\n"
] |
1,965,672,950
| 6,360
|
Add support for `Sequence(Audio/Image)` feature in `push_to_hub`
|
closed
| 2023-10-27T14:39:57
| 2024-02-06T19:24:20
| 2024-02-06T19:24:20
|
https://github.com/huggingface/datasets/issues/6360
| null |
Laurent2916
| false
|
[
"This issue stems from https://github.com/huggingface/datasets/blob/6d2f2a5e0fea3827eccfd1717d8021c15fc4292a/src/datasets/table.py#L2203-L2205\r\n\r\nI'll address it as part of https://github.com/huggingface/datasets/pull/6283.\r\n\r\nIn the meantime, this should work\r\n\r\n```python\r\nimport pyarrow as pa\r\nfrom datasets import Image\r\n\r\ndataset = dataset.with_format(\"arrow\")\r\n\r\ndef embed_images(pa_table):\r\n images_arr = pa.chunked_array(\r\n [\r\n pa.ListArray.from_arrays(chunk.offsets, Image().embed_storage(chunk.values), mask=chunk.is_null())\r\n for chunk in pa_table[\"images\"].chunks\r\n ]\r\n )\r\n return pa_table.set_column(pa_table.schema.get_field_index(\"images\"), \"images\", images_arr)\r\n\r\ndataset = dataset.map(embed_images, batched=True)\r\n\r\ndataset = dataset.with_format(\"python\")\r\n\r\ndataset.push_to_hub(...)\r\n```"
] |
1,965,378,583
| 6,359
|
Stuck in "Resolving data files..."
|
open
| 2023-10-27T12:01:51
| 2025-03-09T02:18:19
| null |
https://github.com/huggingface/datasets/issues/6359
| null |
Luciennnnnnn
| false
|
[
"Most likely, the data file inference logic is the problem here.\r\n\r\nYou can run the following code to verify this:\r\n```python\r\nimport time\r\nfrom datasets.data_files import get_data_patterns\r\nstart_time = time.time()\r\nget_data_patterns(\"/path/to/img_dir\")\r\nend_time = time.time()\r\nprint(f\"Elapsed time: {end_time - start_time:.2f}s\")\r\n```\r\n \r\nWe plan to optimize this for the next version (or version after that). In the meantime, specifying the split patterns manually should give better performance:\r\n```python\r\nds = load_dataset(\"imagefolder\", data_files={\"train\": \"path/to/img_dir/train/**\", ...}, split=\"train\")\r\n```",
"Hi, @mariosasko, you are right; data file inference logic is extremely slow.\r\n\r\nI have done a similar test, that is I modify the source code of datasets/load.py to measure the cost of two suspicious operations:\r\n```python\r\ndef get_module(self) -> DatasetModule:\r\n base_path = Path(self.data_dir or \"\").expanduser().resolve().as_posix()\r\n start = time.time()\r\n patterns = sanitize_patterns(self.data_files) if self.data_files is not None else get_data_patterns(base_path)\r\n print(f\"patterns: {time.time() - start}\")\r\n start = time.time()\r\n data_files = DataFilesDict.from_patterns(\r\n patterns,\r\n download_config=self.download_config,\r\n base_path=base_path,\r\n )\r\n print(f\"data_files: {time.time() - start}\")\r\n```\r\nIt gaves:\r\npatterns: 3062.2050700187683\r\ndata_files: 413.9576675891876\r\n\r\nThus, these two operations contribute to almost all of load time. What's going on in them?",
"Furthermore, what's my current workaround about this problem? Should I save it by `save_to_disk()` and load dataset through `load_from_disk`?",
"were you able to solve this issue?, I am facing the same issue",
"Still suffering from this issue. For me, I cannot download Emilia dataset, and stucked at 'Resolving data files' forever.\n``` e = load_dataset('amphion/Emilia-Dataset', token=my_token)\nResolving data files: 0%| | 1/4343 [00:00<11:27, 6.32it/s]\nResolving data files: 100%|█████████████████████████████████████████████████████████████████████████████| 4343/4343 [00:01<00:00, 3844.15it/s]\n```"
] |
1,965,014,595
| 6,358
|
Mounting datasets cache fails due to absolute paths.
|
closed
| 2023-10-27T08:20:27
| 2024-04-10T08:50:06
| 2023-11-28T14:47:12
|
https://github.com/huggingface/datasets/issues/6358
| null |
charliebudd
| false
|
[
"You may be able to make it work by tweaking some environment variables, such as [`HF_HOME`](https://huggingface.co/docs/huggingface_hub/main/en/package_reference/environment_variables#hfhome) or [`HF_DATASETS_CACHE`](https://huggingface.co/docs/datasets/cache#cache-directory).",
"> You may be able to make it work by tweaking some environment variables, such as [`HF_HOME`](https://huggingface.co/docs/huggingface_hub/main/en/package_reference/environment_variables#hfhome) or [`HF_DATASETS_CACHE`](https://huggingface.co/docs/datasets/cache#cache-directory).\r\n\r\nI am already doing this. The problem is that, while this seemingly allows flexibility, the absolute paths written into the cache still have the old cache directory. The paths written into the cache should be relative to the cache location to allow this sort of flexibility. Sorry, I omitted this in the reproduction steps, I have now added it.",
"I'm unable to reproduce this with the cache\r\n```bash\r\nexport HF_CACHE=$PWD/hf_cache\r\npython -c \"import datasets; datasets.load_dataset('imdb')\"\r\n```\r\nimported inside a dummy container that is built from\r\n```bash\r\nFROM python:3.9\r\n\r\nWORKDIR /usr/src/app\r\n\r\nRUN pip install datasets\r\n\r\nCOPY ./hf_cache ./hf_cache\r\n\r\nENV HF_HOME=./hf_cache\r\nENV HF_DATASETS_OFFLINE=1\r\n\r\nCMD [\"python\"]\r\n```\r\nWhat do you mean by \"absolute paths written into the cache\"? Paths inside the HF cache paths are based on hash (hashed URL of the downloaded files, etc.)",
"@mariosasko Same problem: the absolute paths written into the cache still have the old cache directory. Like:\r\n\r\n{'bytes': None, 'path': 'E:\\\\work-20240321\\\\datasets\\\\downloads\\\\extracted\\\\9752883596854dc57e01c74cc3f494b2ba63754dadd9e77f9d1932deddbd2273\\\\58f33a03-026f-4adc-b69f-b89d16b9f35a.webp'}\r\n\r\nWhen I move this cached directory to another directory, these datasets cannot be used casue path changes. So, the paths written into the cache should be relative to the cache location to allow this sort of flexibility. ",
"Sorry, the reply on this thread escaped my attention. The problem with @mariosasko's attempted reproduction is the absolute path `./hf_cache` is the same in the host system and the docker container, so naturally the paths would be correct. Modifying the docker image as below should reproduce the error...\r\n\r\n```\r\nFROM python:3.9\r\n\r\nWORKDIR /usr/src/app\r\n\r\nRUN pip install datasets\r\n\r\nCOPY ./hf_cache ./my_cache/\r\n\r\nENV HF_HOME=./my_cache/\r\nENV HF_DATASETS_OFFLINE=1\r\n\r\nCMD [\"python\"]\r\n```\r\n\r\nThe paths written inside the cache will still have `./hf_cache` prefixing all the paths. If they were relative paths (relative to the top level of the cache) this would be avoided."
] |
1,964,653,995
| 6,357
|
Allow passing a multiprocessing context to functions that support `num_proc`
|
open
| 2023-10-27T02:31:16
| 2023-10-27T02:31:16
| null |
https://github.com/huggingface/datasets/issues/6357
| null |
bryant1410
| false
|
[] |
1,964,015,802
| 6,356
|
Add `fsspec` version to the `datasets-cli env` command output
|
closed
| 2023-10-26T17:19:25
| 2023-10-26T18:42:56
| 2023-10-26T18:32:21
|
https://github.com/huggingface/datasets/pull/6356
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6356",
"html_url": "https://github.com/huggingface/datasets/pull/6356",
"diff_url": "https://github.com/huggingface/datasets/pull/6356.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6356.patch",
"merged_at": "2023-10-26T18:32:21"
}
|
mariosasko
| true
|
[
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008775 / 0.011353 (-0.002578) | 0.005304 / 0.011008 (-0.005704) | 0.108912 / 0.038508 (0.070404) | 0.075589 / 0.023109 (0.052479) | 0.456612 / 0.275898 (0.180713) | 0.502303 / 0.323480 (0.178823) | 0.006695 / 0.007986 (-0.001291) | 0.004404 / 0.004328 (0.000076) | 0.084802 / 0.004250 (0.080552) | 0.062711 / 0.037052 (0.025659) | 0.465062 / 0.258489 (0.206573) | 0.505321 / 0.293841 (0.211480) | 0.049401 / 0.128546 (-0.079146) | 0.014784 / 0.075646 (-0.060862) | 0.378202 / 0.419271 (-0.041069) | 0.069826 / 0.043533 (0.026293) | 0.461161 / 0.255139 (0.206022) | 0.484616 / 0.283200 (0.201416) | 0.035998 / 0.141683 (-0.105685) | 1.846343 / 1.452155 (0.394189) | 1.999439 / 1.492716 (0.506723) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.317779 / 0.018006 (0.299773) | 0.605967 / 0.000490 (0.605477) | 0.011412 / 0.000200 (0.011212) | 0.000410 / 0.000054 (0.000356) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031118 / 0.037411 (-0.006293) | 0.095425 / 0.014526 (0.080900) | 0.108002 / 0.176557 (-0.068554) | 0.184625 / 0.737135 (-0.552511) | 0.108180 / 0.296338 (-0.188159) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.587497 / 0.215209 (0.372288) | 5.818632 / 2.077655 (3.740977) | 2.629776 / 1.504120 (1.125656) | 2.266129 / 1.541195 (0.724934) | 2.324618 / 1.468490 (0.856128) | 0.830049 / 4.584777 (-3.754728) | 5.380062 / 3.745712 (1.634350) | 4.808525 / 5.269862 (-0.461336) | 2.960368 / 4.565676 (-1.605309) | 0.093637 / 0.424275 (-0.330638) | 0.009187 / 0.007607 (0.001580) | 0.703468 / 0.226044 (0.477424) | 6.924509 / 2.268929 (4.655580) | 3.380582 / 55.444624 (-52.064043) | 2.689118 / 6.876477 (-4.187358) | 2.712418 / 2.142072 (0.570345) | 1.017144 / 4.805227 (-3.788084) | 0.212874 / 6.500664 (-6.287791) | 0.080053 / 0.075469 (0.004584) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.623663 / 1.841788 (-0.218125) | 23.668872 / 8.074308 (15.594564) | 20.245972 / 10.191392 (10.054580) | 0.236448 / 0.680424 (-0.443976) | 0.029730 / 0.534201 (-0.504470) | 0.491525 / 0.579283 (-0.087758) | 0.593780 / 0.434364 (0.159416) | 0.548776 / 0.540337 (0.008438) | 0.799370 / 1.386936 (-0.587566) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009714 / 0.011353 (-0.001639) | 0.005328 / 0.011008 (-0.005681) | 0.078460 / 0.038508 (0.039952) | 0.077791 / 0.023109 (0.054682) | 0.510124 / 0.275898 (0.234226) | 0.547769 / 0.323480 (0.224289) | 0.006868 / 0.007986 (-0.001118) | 0.004145 / 0.004328 (-0.000183) | 0.088696 / 0.004250 (0.084445) | 0.072387 / 0.037052 (0.035334) | 0.527373 / 0.258489 (0.268884) | 0.561948 / 0.293841 (0.268107) | 0.049769 / 0.128546 (-0.078777) | 0.014401 / 0.075646 (-0.061246) | 0.097541 / 0.419271 (-0.321731) | 0.062237 / 0.043533 (0.018705) | 0.531001 / 0.255139 (0.275862) | 0.561797 / 0.283200 (0.278597) | 0.038482 / 0.141683 (-0.103201) | 1.783558 / 1.452155 (0.331404) | 1.864339 / 1.492716 (0.371622) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.289389 / 0.018006 (0.271383) | 0.595326 / 0.000490 (0.594836) | 0.004583 / 0.000200 (0.004383) | 0.000114 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034492 / 0.037411 (-0.002919) | 0.102934 / 0.014526 (0.088409) | 0.121689 / 0.176557 (-0.054868) | 0.182121 / 0.737135 (-0.555015) | 0.127087 / 0.296338 (-0.169252) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.645726 / 0.215209 (0.430517) | 6.462235 / 2.077655 (4.384580) | 3.044176 / 1.504120 (1.540056) | 2.731181 / 1.541195 (1.189986) | 2.805508 / 1.468490 (1.337018) | 0.846324 / 4.584777 (-3.738453) | 5.341074 / 3.745712 (1.595362) | 4.687111 / 5.269862 (-0.582751) | 3.035472 / 4.565676 (-1.530205) | 0.099193 / 0.424275 (-0.325082) | 0.008825 / 0.007607 (0.001218) | 0.795102 / 0.226044 (0.569058) | 7.895770 / 2.268929 (5.626842) | 3.826752 / 55.444624 (-51.617873) | 3.112217 / 6.876477 (-3.764259) | 3.526878 / 2.142072 (1.384806) | 1.011352 / 4.805227 (-3.793875) | 0.213424 / 6.500664 (-6.287240) | 0.076228 / 0.075469 (0.000759) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.805232 / 1.841788 (-0.036556) | 24.049100 / 8.074308 (15.974792) | 23.056011 / 10.191392 (12.864619) | 0.261656 / 0.680424 (-0.418767) | 0.032021 / 0.534201 (-0.502179) | 0.483829 / 0.579283 (-0.095454) | 0.602208 / 0.434364 (0.167844) | 0.565848 / 0.540337 (0.025511) | 0.818678 / 1.386936 (-0.568258) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#71fc5e2ca41f5f725b9117f4cf99f348534902f3 \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008043 / 0.011353 (-0.003310) | 0.004642 / 0.011008 (-0.006366) | 0.102592 / 0.038508 (0.064084) | 0.099508 / 0.023109 (0.076399) | 0.377692 / 0.275898 (0.101794) | 0.409929 / 0.323480 (0.086450) | 0.006363 / 0.007986 (-0.001622) | 0.003881 / 0.004328 (-0.000447) | 0.076636 / 0.004250 (0.072386) | 0.067021 / 0.037052 (0.029969) | 0.371454 / 0.258489 (0.112964) | 0.423637 / 0.293841 (0.129796) | 0.038632 / 0.128546 (-0.089914) | 0.010055 / 0.075646 (-0.065591) | 0.352021 / 0.419271 (-0.067251) | 0.064988 / 0.043533 (0.021456) | 0.369614 / 0.255139 (0.114475) | 0.396972 / 0.283200 (0.113773) | 0.028866 / 0.141683 (-0.112817) | 1.757620 / 1.452155 (0.305465) | 1.886283 / 1.492716 (0.393567) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.257579 / 0.018006 (0.239572) | 0.529859 / 0.000490 (0.529369) | 0.011720 / 0.000200 (0.011520) | 0.000455 / 0.000054 (0.000401) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034163 / 0.037411 (-0.003248) | 0.101422 / 0.014526 (0.086896) | 0.114858 / 0.176557 (-0.061698) | 0.180265 / 0.737135 (-0.556870) | 0.116034 / 0.296338 (-0.180305) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.477609 / 0.215209 (0.262400) | 4.830116 / 2.077655 (2.752461) | 2.323844 / 1.504120 (0.819724) | 2.174496 / 1.541195 (0.633301) | 2.268594 / 1.468490 (0.800104) | 0.612429 / 4.584777 (-3.972348) | 4.265277 / 3.745712 (0.519565) | 4.095741 / 5.269862 (-1.174121) | 2.561532 / 4.565676 (-2.004144) | 0.068043 / 0.424275 (-0.356233) | 0.009139 / 0.007607 (0.001532) | 0.545512 / 0.226044 (0.319467) | 5.456403 / 2.268929 (3.187475) | 2.778937 / 55.444624 (-52.665688) | 2.428560 / 6.876477 (-4.447917) | 2.557483 / 2.142072 (0.415411) | 0.696721 / 4.805227 (-4.108506) | 0.157217 / 6.500664 (-6.343447) | 0.071334 / 0.075469 (-0.004135) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.617755 / 1.841788 (-0.224032) | 23.368508 / 8.074308 (15.294200) | 17.028591 / 10.191392 (6.837199) | 0.195881 / 0.680424 (-0.484542) | 0.021788 / 0.534201 (-0.512413) | 0.468484 / 0.579283 (-0.110799) | 0.474604 / 0.434364 (0.040240) | 0.544738 / 0.540337 (0.004400) | 0.771722 / 1.386936 (-0.615214) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007939 / 0.011353 (-0.003414) | 0.004684 / 0.011008 (-0.006324) | 0.077273 / 0.038508 (0.038765) | 0.088763 / 0.023109 (0.065654) | 0.489178 / 0.275898 (0.213280) | 0.531547 / 0.323480 (0.208067) | 0.006214 / 0.007986 (-0.001772) | 0.003988 / 0.004328 (-0.000340) | 0.076685 / 0.004250 (0.072434) | 0.066628 / 0.037052 (0.029576) | 0.497153 / 0.258489 (0.238664) | 0.538301 / 0.293841 (0.244460) | 0.037939 / 0.128546 (-0.090607) | 0.010054 / 0.075646 (-0.065592) | 0.084642 / 0.419271 (-0.334629) | 0.057140 / 0.043533 (0.013608) | 0.487701 / 0.255139 (0.232562) | 0.519676 / 0.283200 (0.236477) | 0.026560 / 0.141683 (-0.115123) | 1.809676 / 1.452155 (0.357521) | 1.864884 / 1.492716 (0.372168) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.259005 / 0.018006 (0.240998) | 0.522900 / 0.000490 (0.522410) | 0.006885 / 0.000200 (0.006685) | 0.000156 / 0.000054 (0.000102) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.039838 / 0.037411 (0.002426) | 0.117777 / 0.014526 (0.103251) | 0.129189 / 0.176557 (-0.047368) | 0.198584 / 0.737135 (-0.538552) | 0.129753 / 0.296338 (-0.166586) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.543366 / 0.215209 (0.328157) | 5.241502 / 2.077655 (3.163847) | 2.719079 / 1.504120 (1.214959) | 2.525337 / 1.541195 (0.984142) | 2.648908 / 1.468490 (1.180418) | 0.589239 / 4.584777 (-3.995538) | 4.379856 / 3.745712 (0.634144) | 4.139919 / 5.269862 (-1.129943) | 2.633412 / 4.565676 (-1.932264) | 0.074582 / 0.424275 (-0.349693) | 0.009106 / 0.007607 (0.001499) | 0.635540 / 0.226044 (0.409495) | 6.072965 / 2.268929 (3.804037) | 3.327233 / 55.444624 (-52.117391) | 3.012637 / 6.876477 (-3.863840) | 3.113226 / 2.142072 (0.971154) | 0.712705 / 4.805227 (-4.092523) | 0.159550 / 6.500664 (-6.341114) | 0.073446 / 0.075469 (-0.002023) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.718732 / 1.841788 (-0.123055) | 23.249445 / 8.074308 (15.175137) | 17.630643 / 10.191392 (7.439251) | 0.201017 / 0.680424 (-0.479407) | 0.024162 / 0.534201 (-0.510039) | 0.475054 / 0.579283 (-0.104229) | 0.492348 / 0.434364 (0.057985) | 0.587118 / 0.540337 (0.046781) | 0.777462 / 1.386936 (-0.609474) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#feb036956a592b9a9ecdf048cc801549f233dbef \"CML watermark\")\n"
] |
1,963,979,896
| 6,355
|
More hub centric docs
|
closed
| 2023-10-26T16:54:46
| 2024-01-11T06:34:16
| 2023-10-30T17:32:57
|
https://github.com/huggingface/datasets/pull/6355
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6355",
"html_url": "https://github.com/huggingface/datasets/pull/6355",
"diff_url": "https://github.com/huggingface/datasets/pull/6355.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6355.patch",
"merged_at": null
}
|
lhoestq
| true
|
[
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006941 / 0.011353 (-0.004412) | 0.004255 / 0.011008 (-0.006753) | 0.085237 / 0.038508 (0.046729) | 0.080962 / 0.023109 (0.057853) | 0.312016 / 0.275898 (0.036118) | 0.353161 / 0.323480 (0.029681) | 0.005756 / 0.007986 (-0.002230) | 0.003591 / 0.004328 (-0.000738) | 0.065416 / 0.004250 (0.061166) | 0.057837 / 0.037052 (0.020785) | 0.316169 / 0.258489 (0.057680) | 0.372345 / 0.293841 (0.078504) | 0.031958 / 0.128546 (-0.096588) | 0.008798 / 0.075646 (-0.066848) | 0.294764 / 0.419271 (-0.124507) | 0.053954 / 0.043533 (0.010421) | 0.310961 / 0.255139 (0.055822) | 0.330063 / 0.283200 (0.046864) | 0.025298 / 0.141683 (-0.116385) | 1.454715 / 1.452155 (0.002560) | 1.557915 / 1.492716 (0.065198) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.274830 / 0.018006 (0.256824) | 0.565890 / 0.000490 (0.565400) | 0.009242 / 0.000200 (0.009042) | 0.000321 / 0.000054 (0.000266) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031092 / 0.037411 (-0.006320) | 0.087558 / 0.014526 (0.073033) | 0.103395 / 0.176557 (-0.073162) | 0.160078 / 0.737135 (-0.577057) | 0.102356 / 0.296338 (-0.193983) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.402912 / 0.215209 (0.187703) | 4.029374 / 2.077655 (1.951719) | 2.048237 / 1.504120 (0.544117) | 1.887470 / 1.541195 (0.346276) | 1.994807 / 1.468490 (0.526316) | 0.491109 / 4.584777 (-4.093668) | 3.645059 / 3.745712 (-0.100653) | 3.516376 / 5.269862 (-1.753486) | 2.103267 / 4.565676 (-2.462409) | 0.058072 / 0.424275 (-0.366203) | 0.007796 / 0.007607 (0.000189) | 0.480544 / 0.226044 (0.254499) | 4.795422 / 2.268929 (2.526494) | 2.507770 / 55.444624 (-52.936854) | 2.187106 / 6.876477 (-4.689371) | 2.271005 / 2.142072 (0.128933) | 0.585376 / 4.805227 (-4.219851) | 0.134741 / 6.500664 (-6.365923) | 0.060684 / 0.075469 (-0.014785) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.264349 / 1.841788 (-0.577439) | 19.448735 / 8.074308 (11.374427) | 14.521197 / 10.191392 (4.329805) | 0.167295 / 0.680424 (-0.513129) | 0.018352 / 0.534201 (-0.515849) | 0.396345 / 0.579283 (-0.182938) | 0.418690 / 0.434364 (-0.015674) | 0.469703 / 0.540337 (-0.070635) | 0.637852 / 1.386936 (-0.749084) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006939 / 0.011353 (-0.004414) | 0.004196 / 0.011008 (-0.006812) | 0.064719 / 0.038508 (0.026211) | 0.077517 / 0.023109 (0.054407) | 0.401977 / 0.275898 (0.126079) | 0.431089 / 0.323480 (0.107609) | 0.005624 / 0.007986 (-0.002362) | 0.003680 / 0.004328 (-0.000649) | 0.065817 / 0.004250 (0.061567) | 0.058297 / 0.037052 (0.021245) | 0.399614 / 0.258489 (0.141125) | 0.440089 / 0.293841 (0.146248) | 0.032492 / 0.128546 (-0.096054) | 0.008974 / 0.075646 (-0.066672) | 0.071311 / 0.419271 (-0.347961) | 0.048001 / 0.043533 (0.004468) | 0.394763 / 0.255139 (0.139624) | 0.416754 / 0.283200 (0.133554) | 0.023730 / 0.141683 (-0.117953) | 1.509677 / 1.452155 (0.057522) | 1.605711 / 1.492716 (0.112994) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.265490 / 0.018006 (0.247483) | 0.561745 / 0.000490 (0.561255) | 0.004616 / 0.000200 (0.004417) | 0.000105 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033371 / 0.037411 (-0.004040) | 0.092763 / 0.014526 (0.078238) | 0.108905 / 0.176557 (-0.067652) | 0.160380 / 0.737135 (-0.576756) | 0.106968 / 0.296338 (-0.189370) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430268 / 0.215209 (0.215059) | 4.299313 / 2.077655 (2.221658) | 2.308971 / 1.504120 (0.804851) | 2.155855 / 1.541195 (0.614661) | 2.392698 / 1.468490 (0.924208) | 0.498464 / 4.584777 (-4.086313) | 3.694473 / 3.745712 (-0.051239) | 3.409625 / 5.269862 (-1.860236) | 2.106144 / 4.565676 (-2.459532) | 0.058992 / 0.424275 (-0.365283) | 0.007395 / 0.007607 (-0.000212) | 0.511291 / 0.226044 (0.285247) | 5.101806 / 2.268929 (2.832877) | 2.853100 / 55.444624 (-52.591524) | 2.527216 / 6.876477 (-4.349260) | 2.819380 / 2.142072 (0.677308) | 0.635155 / 4.805227 (-4.170072) | 0.135816 / 6.500664 (-6.364848) | 0.062056 / 0.075469 (-0.013413) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.353479 / 1.841788 (-0.488308) | 20.318513 / 8.074308 (12.244205) | 15.105336 / 10.191392 (4.913944) | 0.166186 / 0.680424 (-0.514238) | 0.020742 / 0.534201 (-0.513459) | 0.399286 / 0.579283 (-0.179997) | 0.431785 / 0.434364 (-0.002579) | 0.478667 / 0.540337 (-0.061671) | 0.654683 / 1.386936 (-0.732253) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b39d1ce0b8f231649752f28cb724971f4df1c7ae \"CML watermark\")\n",
"Yea I think some of it should be in the Hub docs indeed, let me open a new PR there.\r\n\r\nThen I'll update the `datasets` docs anyway to avoid redundant stuff and add redirects instead"
] |
1,963,483,324
| 6,354
|
`IterableDataset.from_spark` does not support multiple workers in pytorch `Dataloader`
|
open
| 2023-10-26T12:43:36
| 2024-12-10T14:06:06
| null |
https://github.com/huggingface/datasets/issues/6354
| null |
NazyS
| false
|
[
"I am having issues as well with this. \r\n\r\nHowever, the error I am getting is :\r\n`RuntimeError: It appears that you are attempting to reference SparkContext from a broadcast variable, action, or transformation. SparkContext can only be used on the driver, not in code that it run on workers. For more information, see SPARK-5063.`\r\n\r\nAlso did not work with pyspark==3.3.0 and py4j==0.10.9.5",
"Hi, may you have some solution of this bug now?",
"cc @maddiedawson if you have an idea ?"
] |
1,962,646,450
| 6,353
|
load_dataset save_to_disk load_from_disk error
|
closed
| 2023-10-26T03:47:06
| 2024-04-03T05:31:01
| 2023-10-26T10:18:04
|
https://github.com/huggingface/datasets/issues/6353
| null |
brisker
| false
|
[
"solved.\r\nfsspec version problem",
"I'm using the latest datasets and fsspec , but still got this error!\r\n\r\ndatasets : Version: 2.13.0\r\n\r\nfsspec Version: 2023.10.0\r\n\r\n```\r\nFile \"/home/guoby/app/Anaconda3-2021.05/envs/news/lib/python3.8/site-packages/datasets/load.py\", line 1892, in load_from_disk\r\n return DatasetDict.load_from_disk(dataset_path, keep_in_memory=keep_in_memory, storage_options=storage_options)\r\n File \"/home/guoby/app/Anaconda3-2021.05/envs/news/lib/python3.8/site-packages/datasets/dataset_dict.py\", line 1371, in load_from_disk\r\n dataset_dict[k] = Dataset.load_from_disk(\r\n File \"/home/guoby/app/Anaconda3-2021.05/envs/news/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 1639, in load_from_disk\r\n fs_token_paths = fsspec.get_fs_token_paths(dataset_path, storage_options=storage_options)\r\n File \"/home/guoby/app/Anaconda3-2021.05/envs/news/lib/python3.8/site-packages/fsspec/core.py\", line 610, in get_fs_token_paths\r\n chain = _un_chain(urlpath0, storage_options or {})\r\n File \"/home/guoby/app/Anaconda3-2021.05/envs/news/lib/python3.8/site-packages/fsspec/core.py\", line 325, in _un_chain\r\n cls = get_filesystem_class(protocol)\r\n File \"/home/guoby/app/Anaconda3-2021.05/envs/news/lib/python3.8/site-packages/fsspec/registry.py\", line 232, in get_filesystem_class\r\n raise ValueError(f\"Protocol not known: {protocol}\")\r\n```",
"These two versions work.\r\n<img width=\"807\" alt=\"截圖 2023-11-22 下午5 55 28\" src=\"https://github.com/huggingface/datasets/assets/77866896/faa8333f-0519-4d69-b243-a8880cd7fc1f\">\r\n",
"datasets==2.10.1 and fsspec==2023.6.0 also works for me.",
"确实"
] |
1,962,296,057
| 6,352
|
Error loading wikitext data raise NotImplementedError(f"Loading a dataset cached in a {type(self._fs).__name__} is not supported.")
|
closed
| 2023-10-25T21:55:31
| 2024-03-19T16:46:22
| 2023-11-07T07:26:54
|
https://github.com/huggingface/datasets/issues/6352
| null |
Ahmed-Roushdy
| false
|
[
"+1 \r\n```\r\nFound cached dataset csv (file:///home/ubuntu/.cache/huggingface/datasets/theSquarePond___csv/theSquarePond--XXXXX-bbf0a8365d693d2c/0.0.0/eea64c71ca8b46dd3f537ed218fc9bf495d5707789152eb2764f5c78fa66d59d)\r\n---------------------------------------------------------------------------\r\nNotImplementedError Traceback (most recent call last)\r\nCell In[14], line 4\r\n 1 get_ipython().system('pip install -U datasets')\r\n 3 # Load dataset from the hub\r\n----> 4 dataset = load_dataset(dataset_name)\r\n\r\nFile ~/anaconda3/envs/python38-env/lib/python3.8/site-packages/datasets/load.py:1810, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)\r\n 1806 # Build dataset for splits\r\n 1807 keep_in_memory = (\r\n 1808 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)\r\n 1809 )\r\n-> 1810 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)\r\n 1811 # Rename and cast features to match task schema\r\n 1812 if task is not None:\r\n\r\nFile ~/anaconda3/envs/python38-env/lib/python3.8/site-packages/datasets/builder.py:1128, in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory)\r\n 1126 is_local = not is_remote_filesystem(self._fs)\r\n 1127 if not is_local:\r\n-> 1128 raise NotImplementedError(f\"Loading a dataset cached in a {type(self._fs).__name__} is not supported.\")\r\n 1129 if not os.path.exists(self._output_dir):\r\n 1130 raise FileNotFoundError(\r\n 1131 f\"Dataset {self.name}: could not find data in {self._output_dir}. Please make sure to call \"\r\n 1132 \"builder.download_and_prepare(), or use \"\r\n 1133 \"datasets.load_dataset() before trying to access the Dataset object.\"\r\n 1134 )\r\n\r\nNotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported.\r\n```",
"+1\r\n\r\n```\r\nFound cached dataset csv ([file://C:/Users/Shady/.cache/huggingface/datasets/knkarthick___csv/knkarthick--dialogsum-cd36827d3490488d/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1](file:///C:/Users/Shady/.cache/huggingface/datasets/knkarthick___csv/knkarthick--dialogsum-cd36827d3490488d/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1))\r\n---------------------------------------------------------------------------\r\nNotImplementedError Traceback (most recent call last)\r\nCell In[38], line 3\r\n 1 huggingface_dataset_name = \"knkarthick/dialogsum\"\r\n----> 3 dataset = load_dataset(huggingface_dataset_name)\r\n\r\nFile D:\\Desktop\\Workspace\\GenAI\\genai\\lib\\site-packages\\datasets\\load.py:1804, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)\r\n 1800 # Build dataset for splits\r\n 1801 keep_in_memory = (\r\n 1802 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)\r\n 1803 )\r\n-> 1804 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)\r\n 1805 # Rename and cast features to match task schema\r\n 1806 if task is not None:\r\n\r\nFile D:\\Desktop\\Workspace\\GenAI\\genai\\lib\\site-packages\\datasets\\builder.py:1108, in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory)\r\n 1106 is_local = not is_remote_filesystem(self._fs)\r\n 1107 if not is_local:\r\n-> 1108 raise NotImplementedError(f\"Loading a dataset cached in a {type(self._fs).__name__} is not supported.\")\r\n 1109 if not os.path.exists(self._output_dir):\r\n 1110 raise FileNotFoundError(\r\n 1111 f\"Dataset {self.name}: could not find data in {self._output_dir}. Please make sure to call \"\r\n 1112 \"builder.download_and_prepare(), or use \"\r\n 1113 \"datasets.load_dataset() before trying to access the Dataset object.\"\r\n 1114 )\r\n\r\nNotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported.\r\n```",
"This error stems from a breaking change in `fsspec`. It has been fixed in the latest `datasets` release (`2.14.6`). Updating the installation with `pip install -U datasets` should fix the issue.\r\n",
"> 此错误源于 中的重大更改。此问题已在最新版本 （） 中修复。更新安装应该可以解决此问题。`fsspec``datasets``2.14.6``pip install -U datasets`\r\n\r\nthanks , 太好啦，刚好解决了我的问题，GPT都没解决了，终于被你搞定了",
"https://stackoverflow.com/questions/77433096/notimplementederror-loading-a-dataset-cached-in-a-localfilesystem-is-not-suppor/77433141#77433141",
"Fixed by:\r\n- https://github.com/huggingface/datasets/pull/6334\r\n\r\nThe fix was released in `datasets-2.14.6`.",
"this is fixed in 2.15.0, but broken again in 2.17.0. Can someone verify?",
"I'm on `2.17.1` and can confirm it's broken again. Downgrading to `2.16` helped.",
"> 2.14.6\r\n\r\ni update the version but the error still exist \r\n",
"The issue seems to persist in 2.18.0",
"same problem in 2.18.0",
"Which version of `fsspec` and OS are you using ?",
"> Which version of `fsspec` and OS are you using ?\r\n\r\n`fsspec-2023.10.0` and Windows 10, guess fsspec version too old..."
] |
1,961,982,988
| 6,351
|
Fix use_dataset.mdx
|
closed
| 2023-10-25T18:21:08
| 2023-10-26T17:19:49
| 2023-10-26T17:10:27
|
https://github.com/huggingface/datasets/pull/6351
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6351",
"html_url": "https://github.com/huggingface/datasets/pull/6351",
"diff_url": "https://github.com/huggingface/datasets/pull/6351.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6351.patch",
"merged_at": "2023-10-26T17:10:27"
}
|
angel-luis
| true
|
[
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007718 / 0.011353 (-0.003635) | 0.004730 / 0.011008 (-0.006278) | 0.097262 / 0.038508 (0.058754) | 0.077880 / 0.023109 (0.054771) | 0.363855 / 0.275898 (0.087957) | 0.394470 / 0.323480 (0.070990) | 0.006416 / 0.007986 (-0.001570) | 0.003596 / 0.004328 (-0.000732) | 0.076494 / 0.004250 (0.072243) | 0.062656 / 0.037052 (0.025603) | 0.366160 / 0.258489 (0.107671) | 0.421383 / 0.293841 (0.127542) | 0.035756 / 0.128546 (-0.092791) | 0.009430 / 0.075646 (-0.066217) | 0.327722 / 0.419271 (-0.091550) | 0.061252 / 0.043533 (0.017719) | 0.352167 / 0.255139 (0.097028) | 0.385166 / 0.283200 (0.101966) | 0.026656 / 0.141683 (-0.115027) | 1.718533 / 1.452155 (0.266378) | 1.886646 / 1.492716 (0.393930) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.254564 / 0.018006 (0.236558) | 0.490942 / 0.000490 (0.490452) | 0.011656 / 0.000200 (0.011456) | 0.000313 / 0.000054 (0.000259) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028753 / 0.037411 (-0.008659) | 0.093076 / 0.014526 (0.078550) | 0.096441 / 0.176557 (-0.080116) | 0.154848 / 0.737135 (-0.582287) | 0.092903 / 0.296338 (-0.203435) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.395611 / 0.215209 (0.180402) | 3.860736 / 2.077655 (1.783082) | 1.908808 / 1.504120 (0.404688) | 1.708975 / 1.541195 (0.167781) | 1.848173 / 1.468490 (0.379683) | 0.527022 / 4.584777 (-4.057755) | 3.815171 / 3.745712 (0.069459) | 3.621132 / 5.269862 (-1.648730) | 2.220238 / 4.565676 (-2.345439) | 0.063169 / 0.424275 (-0.361106) | 0.008906 / 0.007607 (0.001299) | 0.510478 / 0.226044 (0.284433) | 4.828116 / 2.268929 (2.559187) | 2.340801 / 55.444624 (-53.103824) | 2.040834 / 6.876477 (-4.835642) | 2.092316 / 2.142072 (-0.049757) | 0.579194 / 4.805227 (-4.226033) | 0.135525 / 6.500664 (-6.365139) | 0.062720 / 0.075469 (-0.012749) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.393091 / 1.841788 (-0.448697) | 19.751526 / 8.074308 (11.677218) | 14.161795 / 10.191392 (3.970403) | 0.163340 / 0.680424 (-0.517084) | 0.021504 / 0.534201 (-0.512697) | 0.393183 / 0.579283 (-0.186100) | 0.448407 / 0.434364 (0.014043) | 0.504169 / 0.540337 (-0.036169) | 0.663698 / 1.386936 (-0.723238) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007390 / 0.011353 (-0.003962) | 0.004381 / 0.011008 (-0.006628) | 0.074501 / 0.038508 (0.035993) | 0.078242 / 0.023109 (0.055133) | 0.481108 / 0.275898 (0.205210) | 0.512111 / 0.323480 (0.188631) | 0.006280 / 0.007986 (-0.001705) | 0.003820 / 0.004328 (-0.000509) | 0.071602 / 0.004250 (0.067351) | 0.068359 / 0.037052 (0.031307) | 0.478484 / 0.258489 (0.219995) | 0.519543 / 0.293841 (0.225702) | 0.036211 / 0.128546 (-0.092335) | 0.009433 / 0.075646 (-0.066213) | 0.086140 / 0.419271 (-0.333132) | 0.054177 / 0.043533 (0.010644) | 0.466726 / 0.255139 (0.211587) | 0.514085 / 0.283200 (0.230885) | 0.026729 / 0.141683 (-0.114954) | 1.743770 / 1.452155 (0.291615) | 1.833469 / 1.492716 (0.340753) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.251339 / 0.018006 (0.233333) | 0.472294 / 0.000490 (0.471804) | 0.013381 / 0.000200 (0.013181) | 0.000117 / 0.000054 (0.000062) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037845 / 0.037411 (0.000433) | 0.105977 / 0.014526 (0.091451) | 0.124446 / 0.176557 (-0.052111) | 0.180432 / 0.737135 (-0.556703) | 0.120844 / 0.296338 (-0.175495) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.470928 / 0.215209 (0.255719) | 4.738154 / 2.077655 (2.660499) | 2.558618 / 1.504120 (1.054498) | 2.359745 / 1.541195 (0.818550) | 2.458438 / 1.468490 (0.989948) | 0.548580 / 4.584777 (-4.036197) | 3.912145 / 3.745712 (0.166433) | 3.764174 / 5.269862 (-1.505687) | 2.325265 / 4.565676 (-2.240411) | 0.078022 / 0.424275 (-0.346254) | 0.008279 / 0.007607 (0.000672) | 0.571635 / 0.226044 (0.345590) | 5.672445 / 2.268929 (3.403517) | 2.760577 / 55.444624 (-52.684047) | 2.544229 / 6.876477 (-4.332248) | 2.537509 / 2.142072 (0.395436) | 0.609858 / 4.805227 (-4.195369) | 0.131053 / 6.500664 (-6.369611) | 0.056433 / 0.075469 (-0.019036) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.567231 / 1.841788 (-0.274556) | 21.415586 / 8.074308 (13.341278) | 15.982328 / 10.191392 (5.790936) | 0.167648 / 0.680424 (-0.512776) | 0.023562 / 0.534201 (-0.510639) | 0.477307 / 0.579283 (-0.101976) | 0.471929 / 0.434364 (0.037566) | 0.549996 / 0.540337 (0.009659) | 0.753927 / 1.386936 (-0.633009) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1fb2785be9198997e8b9006225b0e231f4d8ed31 \"CML watermark\")\n"
] |
1,961,869,203
| 6,350
|
Different objects are returned from calls that should be returning the same kind of object.
|
open
| 2023-10-25T17:08:39
| 2023-10-26T21:03:06
| null |
https://github.com/huggingface/datasets/issues/6350
| null |
phalexo
| false
|
[
"`load_dataset` returns a `DatasetDict` object unless `split` is defined, in which case it returns a `Dataset` (or a list of datasets if `split` is a list). We've discussed dropping `DatasetDict` from the API in https://github.com/huggingface/datasets/issues/5189 to always return the same type in `load_dataset` and support datasets without (explicit) splits. IIRC the main discussion point is deciding what to return when loading a dataset with multiple splits, but `split` is not specified. What would you expect as a return value in that scenario?",
"> `load_dataset` returns a `DatasetDict` object unless `split` is defined, in which case it returns a `Dataset` (or a list of datasets if `split` is a list). We've discussed dropping `DatasetDict` from the API in #5189 to always return the same type in `load_dataset` and support datasets without (explicit) splits. IIRC the main discussion point is deciding what to return when loading a dataset with multiple splits, but `split` is not specified. What would you expect as a return value in that scenario?\r\n\r\nWouldn't a dataset with multiple splits already have keys and their related data arrays?\r\n\r\nLets say the dataset has \"train\" : trainset, \"valid\": validset and \"test\": testset\r\n\r\nSo a dictionary can be returned,, i.e.\r\n\r\n{ \r\n\"train\": trainset,\r\n\"valid\": validset,\r\n\"test\": testset\r\n}\r\n\r\nif a split is provided split=['train[:80%]', 'valid[80%:90%]', 'test[90%:100%]']\r\n\r\nwould also return the same dictionary as above.\r\n\r\nsplit='train[:10%]' should return the same value as split=['train[:10%]']\r\n\r\n{\r\n\"train\": trainset\r\n}\r\n "
] |
1,961,435,673
| 6,349
|
Can't load ds = load_dataset("imdb")
|
closed
| 2023-10-25T13:29:51
| 2024-03-20T15:09:53
| 2023-10-31T19:59:35
|
https://github.com/huggingface/datasets/issues/6349
| null |
vivianc2
| false
|
[
"I'm unable to reproduce this error. The server hosting the files may have been down temporarily, so try again.",
"getting the same error",
"I am getting the following error:\r\nEnv: Python3.10\r\ndatasets: 2.10.1\r\nLinux: Amazon Linux2\r\n\r\n`Traceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/ec2-user/anaconda3/envs/JupyterSystemEnv/lib/python3.10/site-packages/datasets/load.py\", line 1759, in load_dataset\r\n builder_instance = load_dataset_builder(\r\n File \"/home/ec2-user/anaconda3/envs/JupyterSystemEnv/lib/python3.10/site-packages/datasets/load.py\", line 1496, in load_dataset_builder\r\n dataset_module = dataset_module_factory(\r\n File \"/home/ec2-user/anaconda3/envs/JupyterSystemEnv/lib/python3.10/site-packages/datasets/load.py\", line 1218, in dataset_module_factory\r\n raise e1 from None\r\n File \"/home/ec2-user/anaconda3/envs/JupyterSystemEnv/lib/python3.10/site-packages/datasets/load.py\", line 1202, in dataset_module_factory\r\n ).get_module()\r\n File \"/home/ec2-user/anaconda3/envs/JupyterSystemEnv/lib/python3.10/site-packages/datasets/load.py\", line 767, in get_module\r\n else get_data_patterns_in_dataset_repository(hfh_dataset_info, self.data_dir)\r\n File \"/home/ec2-user/anaconda3/envs/JupyterSystemEnv/lib/python3.10/site-packages/datasets/data_files.py\", line 675, in get_data_patterns_in_dataset_repository\r\n return _get_data_files_patterns(resolver)\r\n File \"/home/ec2-user/anaconda3/envs/JupyterSystemEnv/lib/python3.10/site-packages/datasets/data_files.py\", line 236, in _get_data_files_patterns\r\n data_files = pattern_resolver(pattern)\r\n File \"/home/ec2-user/anaconda3/envs/JupyterSystemEnv/lib/python3.10/site-packages/datasets/data_files.py\", line 486, in _resolve_single_pattern_in_dataset_repository\r\n glob_iter = [PurePath(filepath) for filepath in fs.glob(PurePath(pattern).as_posix()) if fs.isfile(filepath)]\r\n File \"/home/ec2-user/anaconda3/envs/JupyterSystemEnv/lib/python3.10/site-packages/fsspec/spec.py\", line 606, in glob\r\n pattern = glob_translate(path + (\"/\" if ends_with_sep else \"\"))\r\n File \"/home/ec2-user/anaconda3/envs/JupyterSystemEnv/lib/python3.10/site-packages/fsspec/utils.py\", line 734, in glob_translate\r\n raise ValueError(\r\nValueError: Invalid pattern: '**' can only be an entire path component`",
"Resolved by upgrading datasets version to 2.18.0"
] |
1,961,268,504
| 6,348
|
Parquet stream-conversion fails to embed images/audio files from gated repos
|
open
| 2023-10-25T12:12:44
| 2025-04-17T12:21:43
| null |
https://github.com/huggingface/datasets/issues/6348
| null |
severo
| false
|
[
"I have created a project to stream audio in the datasets viewer on Hugging Face using Parquet.\n\nhttps://github.com/pr0mila/ParquetToHuggingFace"
] |
1,959,004,835
| 6,347
|
Incorrect example code in 'Create a dataset' docs
|
closed
| 2023-10-24T11:01:21
| 2023-10-25T13:05:21
| 2023-10-25T13:05:21
|
https://github.com/huggingface/datasets/issues/6347
| null |
rwood-97
| false
|
[
"This was fixed in https://github.com/huggingface/datasets/pull/6247. You can find the fix in the `main` version of the docs",
"Ah great, thanks :)"
] |
1,958,777,076
| 6,346
|
Fix UnboundLocalError if preprocessing returns an empty list
|
closed
| 2023-10-24T08:38:43
| 2023-10-25T17:39:17
| 2023-10-25T16:36:38
|
https://github.com/huggingface/datasets/pull/6346
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6346",
"html_url": "https://github.com/huggingface/datasets/pull/6346",
"diff_url": "https://github.com/huggingface/datasets/pull/6346.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6346.patch",
"merged_at": "2023-10-25T16:36:38"
}
|
cwallenwein
| true
|
[
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009286 / 0.011353 (-0.002067) | 0.005478 / 0.011008 (-0.005530) | 0.109768 / 0.038508 (0.071260) | 0.088460 / 0.023109 (0.065351) | 0.387664 / 0.275898 (0.111766) | 0.457379 / 0.323480 (0.133899) | 0.006517 / 0.007986 (-0.001469) | 0.004037 / 0.004328 (-0.000292) | 0.083911 / 0.004250 (0.079661) | 0.071658 / 0.037052 (0.034605) | 0.385065 / 0.258489 (0.126576) | 0.460928 / 0.293841 (0.167087) | 0.048062 / 0.128546 (-0.080484) | 0.016343 / 0.075646 (-0.059303) | 0.373675 / 0.419271 (-0.045597) | 0.067640 / 0.043533 (0.024108) | 0.391730 / 0.255139 (0.136591) | 0.432908 / 0.283200 (0.149708) | 0.035748 / 0.141683 (-0.105935) | 1.767625 / 1.452155 (0.315471) | 1.965606 / 1.492716 (0.472889) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.277405 / 0.018006 (0.259399) | 0.538448 / 0.000490 (0.537958) | 0.013795 / 0.000200 (0.013595) | 0.000518 / 0.000054 (0.000464) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.043962 / 0.037411 (0.006550) | 0.115305 / 0.014526 (0.100780) | 0.117572 / 0.176557 (-0.058985) | 0.182168 / 0.737135 (-0.554968) | 0.114833 / 0.296338 (-0.181505) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.604209 / 0.215209 (0.389000) | 6.186113 / 2.077655 (4.108458) | 2.771067 / 1.504120 (1.266947) | 2.425420 / 1.541195 (0.884226) | 2.475200 / 1.468490 (1.006710) | 0.887096 / 4.584777 (-3.697681) | 5.214349 / 3.745712 (1.468637) | 4.989606 / 5.269862 (-0.280256) | 3.092135 / 4.565676 (-1.473541) | 0.104464 / 0.424275 (-0.319811) | 0.008994 / 0.007607 (0.001387) | 0.732819 / 0.226044 (0.506775) | 7.396007 / 2.268929 (5.127078) | 3.371167 / 55.444624 (-52.073457) | 2.645475 / 6.876477 (-4.231001) | 2.704215 / 2.142072 (0.562143) | 1.034724 / 4.805227 (-3.770504) | 0.219063 / 6.500664 (-6.281601) | 0.073863 / 0.075469 (-0.001606) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.625020 / 1.841788 (-0.216768) | 23.369980 / 8.074308 (15.295671) | 22.480951 / 10.191392 (12.289559) | 0.228219 / 0.680424 (-0.452204) | 0.026981 / 0.534201 (-0.507220) | 0.487670 / 0.579283 (-0.091613) | 0.582310 / 0.434364 (0.147946) | 0.539182 / 0.540337 (-0.001156) | 0.791962 / 1.386936 (-0.594974) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008657 / 0.011353 (-0.002696) | 0.004971 / 0.011008 (-0.006037) | 0.089499 / 0.038508 (0.050991) | 0.075963 / 0.023109 (0.052854) | 0.497719 / 0.275898 (0.221821) | 0.507912 / 0.323480 (0.184432) | 0.006067 / 0.007986 (-0.001919) | 0.004118 / 0.004328 (-0.000210) | 0.079397 / 0.004250 (0.075146) | 0.059181 / 0.037052 (0.022129) | 0.501108 / 0.258489 (0.242619) | 0.565792 / 0.293841 (0.271951) | 0.048818 / 0.128546 (-0.079729) | 0.014813 / 0.075646 (-0.060833) | 0.093863 / 0.419271 (-0.325409) | 0.060824 / 0.043533 (0.017292) | 0.489289 / 0.255139 (0.234150) | 0.533624 / 0.283200 (0.250425) | 0.034997 / 0.141683 (-0.106685) | 1.770574 / 1.452155 (0.318419) | 1.837213 / 1.492716 (0.344496) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237319 / 0.018006 (0.219313) | 0.594976 / 0.000490 (0.594486) | 0.008888 / 0.000200 (0.008688) | 0.000124 / 0.000054 (0.000070) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036955 / 0.037411 (-0.000456) | 0.097825 / 0.014526 (0.083299) | 0.111139 / 0.176557 (-0.065418) | 0.174776 / 0.737135 (-0.562359) | 0.117755 / 0.296338 (-0.178584) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.606498 / 0.215209 (0.391289) | 6.089874 / 2.077655 (4.012219) | 2.811135 / 1.504120 (1.307015) | 2.428486 / 1.541195 (0.887292) | 2.399512 / 1.468490 (0.931022) | 0.823492 / 4.584777 (-3.761285) | 4.897107 / 3.745712 (1.151395) | 4.407589 / 5.269862 (-0.862272) | 2.868442 / 4.565676 (-1.697235) | 0.098774 / 0.424275 (-0.325502) | 0.007998 / 0.007607 (0.000391) | 0.699489 / 0.226044 (0.473445) | 7.139214 / 2.268929 (4.870285) | 3.511158 / 55.444624 (-51.933466) | 2.775459 / 6.876477 (-4.101018) | 2.951549 / 2.142072 (0.809477) | 1.006921 / 4.805227 (-3.798306) | 0.200105 / 6.500664 (-6.300559) | 0.071064 / 0.075469 (-0.004405) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.680599 / 1.841788 (-0.161189) | 23.399777 / 8.074308 (15.325469) | 21.776357 / 10.191392 (11.584965) | 0.264697 / 0.680424 (-0.415726) | 0.034272 / 0.534201 (-0.499929) | 0.506984 / 0.579283 (-0.072299) | 0.609556 / 0.434364 (0.175192) | 0.599014 / 0.540337 (0.058677) | 0.824068 / 1.386936 (-0.562868) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3ab9de69420de8bd5d057579d71d07187b3a2c60 \"CML watermark\")\n"
] |
1,957,707,870
| 6,345
|
support squad structure datasets using a YAML parameter
|
open
| 2023-10-23T17:55:37
| 2023-10-23T17:55:37
| null |
https://github.com/huggingface/datasets/issues/6345
| null |
MajdTannous1
| false
|
[] |
1,957,412,169
| 6,344
|
set dev version
|
closed
| 2023-10-23T15:13:28
| 2023-10-23T15:24:31
| 2023-10-23T15:13:38
|
https://github.com/huggingface/datasets/pull/6344
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6344",
"html_url": "https://github.com/huggingface/datasets/pull/6344",
"diff_url": "https://github.com/huggingface/datasets/pull/6344.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6344.patch",
"merged_at": "2023-10-23T15:13:38"
}
|
lhoestq
| true
|
[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6344). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008237 / 0.011353 (-0.003116) | 0.004658 / 0.011008 (-0.006351) | 0.105902 / 0.038508 (0.067394) | 0.082690 / 0.023109 (0.059581) | 0.471745 / 0.275898 (0.195847) | 0.464772 / 0.323480 (0.141292) | 0.006373 / 0.007986 (-0.001613) | 0.003823 / 0.004328 (-0.000505) | 0.077721 / 0.004250 (0.073471) | 0.068371 / 0.037052 (0.031318) | 0.457004 / 0.258489 (0.198515) | 0.500989 / 0.293841 (0.207148) | 0.036688 / 0.128546 (-0.091858) | 0.010004 / 0.075646 (-0.065643) | 0.363398 / 0.419271 (-0.055874) | 0.065354 / 0.043533 (0.021821) | 0.440326 / 0.255139 (0.185187) | 0.475314 / 0.283200 (0.192115) | 0.029024 / 0.141683 (-0.112659) | 1.851005 / 1.452155 (0.398851) | 1.939997 / 1.492716 (0.447281) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.269739 / 0.018006 (0.251732) | 0.510411 / 0.000490 (0.509922) | 0.013423 / 0.000200 (0.013223) | 0.000513 / 0.000054 (0.000458) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032912 / 0.037411 (-0.004499) | 0.097497 / 0.014526 (0.082971) | 0.111945 / 0.176557 (-0.064612) | 0.179264 / 0.737135 (-0.557871) | 0.111901 / 0.296338 (-0.184437) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.480994 / 0.215209 (0.265785) | 4.800969 / 2.077655 (2.723314) | 2.467390 / 1.504120 (0.963270) | 2.283219 / 1.541195 (0.742024) | 2.407735 / 1.468490 (0.939245) | 0.573862 / 4.584777 (-4.010915) | 4.213394 / 3.745712 (0.467682) | 4.120092 / 5.269862 (-1.149770) | 2.479549 / 4.565676 (-2.086128) | 0.077204 / 0.424275 (-0.347071) | 0.009165 / 0.007607 (0.001558) | 0.583887 / 0.226044 (0.357842) | 5.760759 / 2.268929 (3.491830) | 3.089220 / 55.444624 (-52.355404) | 2.652330 / 6.876477 (-4.224146) | 2.746255 / 2.142072 (0.604182) | 0.689010 / 4.805227 (-4.116217) | 0.158042 / 6.500664 (-6.342622) | 0.072789 / 0.075469 (-0.002680) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.658877 / 1.841788 (-0.182911) | 22.928756 / 8.074308 (14.854448) | 17.231823 / 10.191392 (7.040431) | 0.201475 / 0.680424 (-0.478949) | 0.025533 / 0.534201 (-0.508668) | 0.467023 / 0.579283 (-0.112260) | 0.470779 / 0.434364 (0.036415) | 0.643192 / 0.540337 (0.102855) | 0.822006 / 1.386936 (-0.564930) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008096 / 0.011353 (-0.003257) | 0.004708 / 0.011008 (-0.006300) | 0.076607 / 0.038508 (0.038099) | 0.086278 / 0.023109 (0.063168) | 0.478027 / 0.275898 (0.202129) | 0.533121 / 0.323480 (0.209641) | 0.006331 / 0.007986 (-0.001654) | 0.004005 / 0.004328 (-0.000324) | 0.076018 / 0.004250 (0.071767) | 0.067240 / 0.037052 (0.030188) | 0.484882 / 0.258489 (0.226393) | 0.536924 / 0.293841 (0.243083) | 0.045064 / 0.128546 (-0.083482) | 0.010071 / 0.075646 (-0.065575) | 0.084319 / 0.419271 (-0.334953) | 0.066267 / 0.043533 (0.022734) | 0.479283 / 0.255139 (0.224144) | 0.507832 / 0.283200 (0.224633) | 0.026436 / 0.141683 (-0.115247) | 1.820043 / 1.452155 (0.367889) | 1.954663 / 1.492716 (0.461947) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.292672 / 0.018006 (0.274666) | 0.495523 / 0.000490 (0.495033) | 0.020836 / 0.000200 (0.020636) | 0.000143 / 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) | 0.038326 / 0.037411 (0.000915) | 0.114629 / 0.014526 (0.100103) | 0.126036 / 0.176557 (-0.050521) | 0.191498 / 0.737135 (-0.545638) | 0.128763 / 0.296338 (-0.167575) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.507657 / 0.215209 (0.292448) | 5.062056 / 2.077655 (2.984401) | 2.765895 / 1.504120 (1.261775) | 2.590335 / 1.541195 (1.049141) | 2.790912 / 1.468490 (1.322422) | 0.582819 / 4.584777 (-4.001958) | 4.350034 / 3.745712 (0.604322) | 3.899466 / 5.269862 (-1.370396) | 2.499655 / 4.565676 (-2.066021) | 0.068909 / 0.424275 (-0.355366) | 0.008633 / 0.007607 (0.001026) | 0.593597 / 0.226044 (0.367553) | 5.934398 / 2.268929 (3.665470) | 3.358549 / 55.444624 (-52.086075) | 3.145686 / 6.876477 (-3.730791) | 3.232153 / 2.142072 (1.090080) | 0.753039 / 4.805227 (-4.052188) | 0.164043 / 6.500664 (-6.336621) | 0.072084 / 0.075469 (-0.003385) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.632702 / 1.841788 (-0.209086) | 23.411084 / 8.074308 (15.336776) | 17.035726 / 10.191392 (6.844334) | 0.223460 / 0.680424 (-0.456964) | 0.023723 / 0.534201 (-0.510478) | 0.474160 / 0.579283 (-0.105124) | 0.538638 / 0.434364 (0.104274) | 0.595591 / 0.540337 (0.055254) | 0.803324 / 1.386936 (-0.583612) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#84855c8ddc8d3e33b516f04b687e01d498d0906e \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008300 / 0.011353 (-0.003053) | 0.004667 / 0.011008 (-0.006341) | 0.101028 / 0.038508 (0.062520) | 0.100269 / 0.023109 (0.077160) | 0.418651 / 0.275898 (0.142752) | 0.459061 / 0.323480 (0.135581) | 0.006786 / 0.007986 (-0.001199) | 0.003926 / 0.004328 (-0.000403) | 0.076682 / 0.004250 (0.072432) | 0.066173 / 0.037052 (0.029120) | 0.430644 / 0.258489 (0.172155) | 0.466244 / 0.293841 (0.172403) | 0.040601 / 0.128546 (-0.087946) | 0.009856 / 0.075646 (-0.065790) | 0.351467 / 0.419271 (-0.067805) | 0.068727 / 0.043533 (0.025194) | 0.419527 / 0.255139 (0.164388) | 0.431245 / 0.283200 (0.148045) | 0.028933 / 0.141683 (-0.112750) | 1.749540 / 1.452155 (0.297386) | 1.829076 / 1.492716 (0.336360) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.282248 / 0.018006 (0.264242) | 0.587293 / 0.000490 (0.586803) | 0.014497 / 0.000200 (0.014297) | 0.000383 / 0.000054 (0.000329) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031861 / 0.037411 (-0.005550) | 0.097395 / 0.014526 (0.082869) | 0.113610 / 0.176557 (-0.062946) | 0.181208 / 0.737135 (-0.555927) | 0.115340 / 0.296338 (-0.180999) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.459746 / 0.215209 (0.244537) | 4.582387 / 2.077655 (2.504733) | 2.247968 / 1.504120 (0.743848) | 2.032340 / 1.541195 (0.491145) | 2.151766 / 1.468490 (0.683276) | 0.567664 / 4.584777 (-4.017113) | 4.491732 / 3.745712 (0.746020) | 4.000651 / 5.269862 (-1.269211) | 2.429113 / 4.565676 (-2.136564) | 0.067052 / 0.424275 (-0.357223) | 0.009095 / 0.007607 (0.001488) | 0.546461 / 0.226044 (0.320417) | 5.473524 / 2.268929 (3.204595) | 2.902091 / 55.444624 (-52.542533) | 2.517510 / 6.876477 (-4.358966) | 2.572537 / 2.142072 (0.430464) | 0.683499 / 4.805227 (-4.121728) | 0.154863 / 6.500664 (-6.345801) | 0.071298 / 0.075469 (-0.004171) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.625236 / 1.841788 (-0.216552) | 23.531541 / 8.074308 (15.457233) | 16.762514 / 10.191392 (6.571122) | 0.215922 / 0.680424 (-0.464502) | 0.021928 / 0.534201 (-0.512273) | 0.466055 / 0.579283 (-0.113228) | 0.553036 / 0.434364 (0.118672) | 0.590063 / 0.540337 (0.049725) | 0.789959 / 1.386936 (-0.596977) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated 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) | 0.004151 / 0.011008 (-0.006858) | 0.077988 / 0.038508 (0.039479) | 0.092865 / 0.023109 (0.069756) | 0.468238 / 0.275898 (0.192340) | 0.512882 / 0.323480 (0.189402) | 0.006632 / 0.007986 (-0.001354) | 0.003879 / 0.004328 (-0.000450) | 0.076238 / 0.004250 (0.071988) | 0.069372 / 0.037052 (0.032319) | 0.481040 / 0.258489 (0.222550) | 0.526332 / 0.293841 (0.232491) | 0.036768 / 0.128546 (-0.091778) | 0.009891 / 0.075646 (-0.065756) | 0.084426 / 0.419271 (-0.334846) | 0.062382 / 0.043533 (0.018849) | 0.480667 / 0.255139 (0.225528) | 0.509001 / 0.283200 (0.225802) | 0.029215 / 0.141683 (-0.112468) | 1.776075 / 1.452155 (0.323920) | 1.948558 / 1.492716 (0.455841) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.257879 / 0.018006 (0.239873) | 0.471038 / 0.000490 (0.470548) | 0.009273 / 0.000200 (0.009073) | 0.000208 / 0.000054 (0.000154) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.039249 / 0.037411 (0.001838) | 0.133281 / 0.014526 (0.118755) | 0.138261 / 0.176557 (-0.038296) | 0.191051 / 0.737135 (-0.546084) | 0.134493 / 0.296338 (-0.161845) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.507165 / 0.215209 (0.291955) | 5.081018 / 2.077655 (3.003364) | 2.747633 / 1.504120 (1.243513) | 2.558265 / 1.541195 (1.017070) | 2.710839 / 1.468490 (1.242348) | 0.579913 / 4.584777 (-4.004864) | 4.843657 / 3.745712 (1.097945) | 3.942503 / 5.269862 (-1.327358) | 2.529641 / 4.565676 (-2.036036) | 0.068826 / 0.424275 (-0.355449) | 0.008847 / 0.007607 (0.001240) | 0.605332 / 0.226044 (0.379287) | 6.039574 / 2.268929 (3.770646) | 3.437291 / 55.444624 (-52.007333) | 3.086631 / 6.876477 (-3.789846) | 3.189340 / 2.142072 (1.047267) | 0.702650 / 4.805227 (-4.102578) | 0.157403 / 6.500664 (-6.343261) | 0.074637 / 0.075469 (-0.000832) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.816532 / 1.841788 (-0.025256) | 24.526675 / 8.074308 (16.452367) | 17.371691 / 10.191392 (7.180299) | 0.236044 / 0.680424 (-0.444380) | 0.024759 / 0.534201 (-0.509442) | 0.530578 / 0.579283 (-0.048705) | 0.527424 / 0.434364 (0.093060) | 0.620267 / 0.540337 (0.079929) | 0.791159 / 1.386936 (-0.595777) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#78cfce823b98b6cce79a9297fe6fa9e8f80a869c \"CML watermark\")\n"
] |
1,957,370,711
| 6,343
|
Remove unused argument in `_get_data_files_patterns`
|
closed
| 2023-10-23T14:54:18
| 2023-11-16T09:09:42
| 2023-11-16T09:03:39
|
https://github.com/huggingface/datasets/pull/6343
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6343",
"html_url": "https://github.com/huggingface/datasets/pull/6343",
"diff_url": "https://github.com/huggingface/datasets/pull/6343.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6343.patch",
"merged_at": "2023-11-16T09:03:39"
}
|
lhoestq
| true
|
[
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006584 / 0.011353 (-0.004769) | 0.004197 / 0.011008 (-0.006812) | 0.083598 / 0.038508 (0.045090) | 0.075502 / 0.023109 (0.052392) | 0.312986 / 0.275898 (0.037088) | 0.344630 / 0.323480 (0.021150) | 0.005394 / 0.007986 (-0.002591) | 0.003485 / 0.004328 (-0.000843) | 0.064529 / 0.004250 (0.060279) | 0.055003 / 0.037052 (0.017950) | 0.320522 / 0.258489 (0.062033) | 0.362623 / 0.293841 (0.068782) | 0.030900 / 0.128546 (-0.097646) | 0.008459 / 0.075646 (-0.067187) | 0.286986 / 0.419271 (-0.132285) | 0.052310 / 0.043533 (0.008777) | 0.315873 / 0.255139 (0.060734) | 0.333962 / 0.283200 (0.050762) | 0.023836 / 0.141683 (-0.117847) | 1.481806 / 1.452155 (0.029651) | 1.567926 / 1.492716 (0.075209) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.268188 / 0.018006 (0.250182) | 0.520542 / 0.000490 (0.520052) | 0.017617 / 0.000200 (0.017417) | 0.000631 / 0.000054 (0.000577) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028828 / 0.037411 (-0.008584) | 0.083028 / 0.014526 (0.068502) | 0.099808 / 0.176557 (-0.076748) | 0.154282 / 0.737135 (-0.582853) | 0.098590 / 0.296338 (-0.197748) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.407548 / 0.215209 (0.192339) | 4.066128 / 2.077655 (1.988474) | 2.036757 / 1.504120 (0.532637) | 1.870130 / 1.541195 (0.328935) | 1.949031 / 1.468490 (0.480541) | 0.489263 / 4.584777 (-4.095514) | 3.506269 / 3.745712 (-0.239443) | 3.457232 / 5.269862 (-1.812629) | 2.060097 / 4.565676 (-2.505580) | 0.057252 / 0.424275 (-0.367024) | 0.007727 / 0.007607 (0.000120) | 0.480229 / 0.226044 (0.254185) | 4.807064 / 2.268929 (2.538135) | 2.495438 / 55.444624 (-52.949186) | 2.186194 / 6.876477 (-4.690283) | 2.243372 / 2.142072 (0.101300) | 0.580550 / 4.805227 (-4.224678) | 0.135398 / 6.500664 (-6.365266) | 0.061878 / 0.075469 (-0.013591) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.305635 / 1.841788 (-0.536152) | 19.194421 / 8.074308 (11.120113) | 14.531699 / 10.191392 (4.340307) | 0.167144 / 0.680424 (-0.513280) | 0.018270 / 0.534201 (-0.515931) | 0.393702 / 0.579283 (-0.185581) | 0.406518 / 0.434364 (-0.027846) | 0.458126 / 0.540337 (-0.082211) | 0.639839 / 1.386936 (-0.747097) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006742 / 0.011353 (-0.004611) | 0.004092 / 0.011008 (-0.006916) | 0.065547 / 0.038508 (0.027039) | 0.076293 / 0.023109 (0.053184) | 0.389701 / 0.275898 (0.113803) | 0.429158 / 0.323480 (0.105678) | 0.005606 / 0.007986 (-0.002380) | 0.003491 / 0.004328 (-0.000837) | 0.065903 / 0.004250 (0.061653) | 0.057346 / 0.037052 (0.020293) | 0.393233 / 0.258489 (0.134744) | 0.433106 / 0.293841 (0.139265) | 0.032612 / 0.128546 (-0.095934) | 0.008777 / 0.075646 (-0.066869) | 0.073135 / 0.419271 (-0.346137) | 0.048167 / 0.043533 (0.004635) | 0.389309 / 0.255139 (0.134170) | 0.416442 / 0.283200 (0.133242) | 0.022839 / 0.141683 (-0.118844) | 1.531607 / 1.452155 (0.079453) | 1.598950 / 1.492716 (0.106234) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.254856 / 0.018006 (0.236850) | 0.528186 / 0.000490 (0.527697) | 0.006975 / 0.000200 (0.006775) | 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) | 0.032377 / 0.037411 (-0.005034) | 0.092706 / 0.014526 (0.078180) | 0.107618 / 0.176557 (-0.068939) | 0.160103 / 0.737135 (-0.577032) | 0.107226 / 0.296338 (-0.189112) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430922 / 0.215209 (0.215713) | 4.312556 / 2.077655 (2.234901) | 2.287686 / 1.504120 (0.783567) | 2.111103 / 1.541195 (0.569908) | 2.284105 / 1.468490 (0.815614) | 0.485987 / 4.584777 (-4.098790) | 3.557320 / 3.745712 (-0.188392) | 3.341150 / 5.269862 (-1.928711) | 2.056705 / 4.565676 (-2.508972) | 0.057265 / 0.424275 (-0.367010) | 0.007264 / 0.007607 (-0.000344) | 0.505191 / 0.226044 (0.279146) | 5.045379 / 2.268929 (2.776450) | 2.732357 / 55.444624 (-52.712267) | 2.390256 / 6.876477 (-4.486220) | 2.643676 / 2.142072 (0.501604) | 0.584630 / 4.805227 (-4.220597) | 0.132402 / 6.500664 (-6.368262) | 0.061387 / 0.075469 (-0.014082) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.340721 / 1.841788 (-0.501066) | 19.744145 / 8.074308 (11.669837) | 14.694482 / 10.191392 (4.503090) | 0.166294 / 0.680424 (-0.514129) | 0.020691 / 0.534201 (-0.513510) | 0.398359 / 0.579283 (-0.180924) | 0.423831 / 0.434364 (-0.010533) | 0.474365 / 0.540337 (-0.065972) | 0.649410 / 1.386936 (-0.737526) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b29bc9cef6237eb0d18f77c56686705f468bed25 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004369 / 0.011353 (-0.006984) | 0.002728 / 0.011008 (-0.008280) | 0.063754 / 0.038508 (0.025246) | 0.029396 / 0.023109 (0.006287) | 0.269409 / 0.275898 (-0.006489) | 0.287654 / 0.323480 (-0.035826) | 0.003926 / 0.007986 (-0.004060) | 0.002366 / 0.004328 (-0.001963) | 0.048910 / 0.004250 (0.044660) | 0.043126 / 0.037052 (0.006074) | 0.260774 / 0.258489 (0.002285) | 0.299996 / 0.293841 (0.006155) | 0.023359 / 0.128546 (-0.105187) | 0.007259 / 0.075646 (-0.068388) | 0.211412 / 0.419271 (-0.207860) | 0.053883 / 0.043533 (0.010350) | 0.268946 / 0.255139 (0.013807) | 0.287664 / 0.283200 (0.004465) | 0.017600 / 0.141683 (-0.124083) | 1.096478 / 1.452155 (-0.355676) | 1.193063 / 1.492716 (-0.299653) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090985 / 0.018006 (0.072979) | 0.287168 / 0.000490 (0.286678) | 0.000208 / 0.000200 (0.000009) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019238 / 0.037411 (-0.018173) | 0.062660 / 0.014526 (0.048134) | 0.073414 / 0.176557 (-0.103143) | 0.120842 / 0.737135 (-0.616294) | 0.077658 / 0.296338 (-0.218681) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280285 / 0.215209 (0.065076) | 2.729807 / 2.077655 (0.652152) | 1.430686 / 1.504120 (-0.073434) | 1.307260 / 1.541195 (-0.233935) | 1.321013 / 1.468490 (-0.147477) | 0.387253 / 4.584777 (-4.197524) | 2.415635 / 3.745712 (-1.330077) | 2.557206 / 5.269862 (-2.712656) | 1.553224 / 4.565676 (-3.012453) | 0.045402 / 0.424275 (-0.378873) | 0.004798 / 0.007607 (-0.002809) | 0.330493 / 0.226044 (0.104449) | 3.226835 / 2.268929 (0.957906) | 1.739068 / 55.444624 (-53.705557) | 1.494841 / 6.876477 (-5.381636) | 1.528253 / 2.142072 (-0.613820) | 0.451525 / 4.805227 (-4.353702) | 0.096620 / 6.500664 (-6.404044) | 0.041176 / 0.075469 (-0.034293) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.930892 / 1.841788 (-0.910896) | 11.343351 / 8.074308 (3.269043) | 10.420327 / 10.191392 (0.228935) | 0.137629 / 0.680424 (-0.542795) | 0.013907 / 0.534201 (-0.520293) | 0.267778 / 0.579283 (-0.311505) | 0.260774 / 0.434364 (-0.173590) | 0.308213 / 0.540337 (-0.232124) | 0.419659 / 1.386936 (-0.967277) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004867 / 0.011353 (-0.006486) | 0.002830 / 0.011008 (-0.008178) | 0.048506 / 0.038508 (0.009998) | 0.048190 / 0.023109 (0.025080) | 0.279995 / 0.275898 (0.004097) | 0.296396 / 0.323480 (-0.027083) | 0.004700 / 0.007986 (-0.003285) | 0.003546 / 0.004328 (-0.000782) | 0.048237 / 0.004250 (0.043987) | 0.037102 / 0.037052 (0.000050) | 0.284582 / 0.258489 (0.026093) | 0.315896 / 0.293841 (0.022055) | 0.024699 / 0.128546 (-0.103848) | 0.007077 / 0.075646 (-0.068569) | 0.054471 / 0.419271 (-0.364800) | 0.032537 / 0.043533 (-0.010996) | 0.276761 / 0.255139 (0.021622) | 0.294741 / 0.283200 (0.011542) | 0.017766 / 0.141683 (-0.123917) | 1.118377 / 1.452155 (-0.333778) | 1.186617 / 1.492716 (-0.306100) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088981 / 0.018006 (0.070975) | 0.297793 / 0.000490 (0.297303) | 0.000220 / 0.000200 (0.000020) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021300 / 0.037411 (-0.016111) | 0.070059 / 0.014526 (0.055533) | 0.080452 / 0.176557 (-0.096104) | 0.118461 / 0.737135 (-0.618674) | 0.081099 / 0.296338 (-0.215240) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300560 / 0.215209 (0.085351) | 2.951461 / 2.077655 (0.873806) | 1.621978 / 1.504120 (0.117858) | 1.478871 / 1.541195 (-0.062324) | 1.520732 / 1.468490 (0.052242) | 0.408625 / 4.584777 (-4.176152) | 2.407253 / 3.745712 (-1.338459) | 2.546000 / 5.269862 (-2.723861) | 1.525920 / 4.565676 (-3.039757) | 0.046817 / 0.424275 (-0.377458) | 0.004880 / 0.007607 (-0.002727) | 0.350866 / 0.226044 (0.124821) | 3.489379 / 2.268929 (1.220451) | 1.967197 / 55.444624 (-53.477427) | 1.686083 / 6.876477 (-5.190394) | 1.699307 / 2.142072 (-0.442766) | 0.479659 / 4.805227 (-4.325568) | 0.098853 / 6.500664 (-6.401811) | 0.040718 / 0.075469 (-0.034751) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.018352 / 1.841788 (-0.823436) | 12.022551 / 8.074308 (3.948243) | 10.841890 / 10.191392 (0.650498) | 0.130732 / 0.680424 (-0.549692) | 0.016334 / 0.534201 (-0.517867) | 0.271984 / 0.579283 (-0.307299) | 0.276733 / 0.434364 (-0.157631) | 0.308049 / 0.540337 (-0.232289) | 0.415428 / 1.386936 (-0.971508) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#31d95717e4e5fc6dd7699878720f063d51f1d595 \"CML watermark\")\n"
] |
1,957,344,445
| 6,342
|
Release: 2.14.6
|
closed
| 2023-10-23T14:43:26
| 2023-10-23T15:21:54
| 2023-10-23T15:07:25
|
https://github.com/huggingface/datasets/pull/6342
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6342",
"html_url": "https://github.com/huggingface/datasets/pull/6342",
"diff_url": "https://github.com/huggingface/datasets/pull/6342.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6342.patch",
"merged_at": "2023-10-23T15:07:25"
}
|
lhoestq
| true
|
[
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007051 / 0.011353 (-0.004302) | 0.004291 / 0.011008 (-0.006717) | 0.085557 / 0.038508 (0.047048) | 0.087919 / 0.023109 (0.064810) | 0.356912 / 0.275898 (0.081014) | 0.394835 / 0.323480 (0.071355) | 0.004464 / 0.007986 (-0.003522) | 0.003688 / 0.004328 (-0.000640) | 0.065437 / 0.004250 (0.061186) | 0.060156 / 0.037052 (0.023103) | 0.361807 / 0.258489 (0.103318) | 0.420917 / 0.293841 (0.127076) | 0.031704 / 0.128546 (-0.096842) | 0.008921 / 0.075646 (-0.066726) | 0.287828 / 0.419271 (-0.131443) | 0.053600 / 0.043533 (0.010067) | 0.361833 / 0.255139 (0.106694) | 0.396732 / 0.283200 (0.113532) | 0.025874 / 0.141683 (-0.115809) | 1.474926 / 1.452155 (0.022771) | 1.563186 / 1.492716 (0.070469) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.316823 / 0.018006 (0.298817) | 0.604085 / 0.000490 (0.603595) | 0.020828 / 0.000200 (0.020628) | 0.000351 / 0.000054 (0.000297) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030468 / 0.037411 (-0.006943) | 0.083904 / 0.014526 (0.069378) | 0.103019 / 0.176557 (-0.073537) | 0.159018 / 0.737135 (-0.578117) | 0.102737 / 0.296338 (-0.193602) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.405311 / 0.215209 (0.190102) | 4.029060 / 2.077655 (1.951406) | 2.046590 / 1.504120 (0.542470) | 1.919335 / 1.541195 (0.378140) | 2.030371 / 1.468490 (0.561881) | 0.484209 / 4.584777 (-4.100568) | 3.486888 / 3.745712 (-0.258824) | 3.390777 / 5.269862 (-1.879084) | 2.110744 / 4.565676 (-2.454933) | 0.056587 / 0.424275 (-0.367688) | 0.007766 / 0.007607 (0.000159) | 0.488217 / 0.226044 (0.262173) | 4.853904 / 2.268929 (2.584976) | 2.595122 / 55.444624 (-52.849502) | 2.217712 / 6.876477 (-4.658765) | 2.500368 / 2.142072 (0.358296) | 0.580843 / 4.805227 (-4.224384) | 0.132719 / 6.500664 (-6.367945) | 0.060202 / 0.075469 (-0.015267) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.260748 / 1.841788 (-0.581040) | 20.148848 / 8.074308 (12.074540) | 14.738779 / 10.191392 (4.547387) | 0.167562 / 0.680424 (-0.512862) | 0.018944 / 0.534201 (-0.515257) | 0.394314 / 0.579283 (-0.184969) | 0.409345 / 0.434364 (-0.025019) | 0.458743 / 0.540337 (-0.081594) | 0.638175 / 1.386936 (-0.748761) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007097 / 0.011353 (-0.004256) | 0.004304 / 0.011008 (-0.006705) | 0.065539 / 0.038508 (0.027030) | 0.094078 / 0.023109 (0.070969) | 0.412411 / 0.275898 (0.136513) | 0.441900 / 0.323480 (0.118420) | 0.006038 / 0.007986 (-0.001948) | 0.003647 / 0.004328 (-0.000682) | 0.065298 / 0.004250 (0.061048) | 0.062571 / 0.037052 (0.025518) | 0.405156 / 0.258489 (0.146667) | 0.443779 / 0.293841 (0.149938) | 0.034470 / 0.128546 (-0.094077) | 0.008858 / 0.075646 (-0.066789) | 0.071840 / 0.419271 (-0.347431) | 0.050468 / 0.043533 (0.006935) | 0.404198 / 0.255139 (0.149059) | 0.430196 / 0.283200 (0.146997) | 0.025710 / 0.141683 (-0.115973) | 1.525374 / 1.452155 (0.073219) | 1.591830 / 1.492716 (0.099114) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.294330 / 0.018006 (0.276324) | 0.516943 / 0.000490 (0.516453) | 0.004807 / 0.000200 (0.004607) | 0.000103 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034505 / 0.037411 (-0.002907) | 0.096645 / 0.014526 (0.082119) | 0.111926 / 0.176557 (-0.064630) | 0.165241 / 0.737135 (-0.571894) | 0.111834 / 0.296338 (-0.184504) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436370 / 0.215209 (0.221161) | 4.357568 / 2.077655 (2.279913) | 2.360529 / 1.504120 (0.856409) | 2.196375 / 1.541195 (0.655180) | 2.307481 / 1.468490 (0.838991) | 0.494072 / 4.584777 (-4.090705) | 3.565078 / 3.745712 (-0.180634) | 3.405174 / 5.269862 (-1.864688) | 2.203307 / 4.565676 (-2.362369) | 0.058582 / 0.424275 (-0.365693) | 0.007410 / 0.007607 (-0.000197) | 0.514323 / 0.226044 (0.288279) | 5.139834 / 2.268929 (2.870905) | 2.884111 / 55.444624 (-52.560513) | 2.589021 / 6.876477 (-4.287456) | 2.787577 / 2.142072 (0.645504) | 0.590765 / 4.805227 (-4.214462) | 0.135237 / 6.500664 (-6.365427) | 0.061078 / 0.075469 (-0.014391) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.346938 / 1.841788 (-0.494850) | 21.009948 / 8.074308 (12.935640) | 15.203281 / 10.191392 (5.011889) | 0.166208 / 0.680424 (-0.514216) | 0.020634 / 0.534201 (-0.513567) | 0.413825 / 0.579283 (-0.165458) | 0.416477 / 0.434364 (-0.017887) | 0.485888 / 0.540337 (-0.054449) | 0.664941 / 1.386936 (-0.721995) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#395b30ee2c0f6088e28fe78a3e61b591e40a4668 \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005927 / 0.011353 (-0.005425) | 0.003622 / 0.011008 (-0.007386) | 0.081414 / 0.038508 (0.042906) | 0.061031 / 0.023109 (0.037922) | 0.358323 / 0.275898 (0.082425) | 0.394192 / 0.323480 (0.070712) | 0.003471 / 0.007986 (-0.004515) | 0.002930 / 0.004328 (-0.001399) | 0.064215 / 0.004250 (0.059964) | 0.048678 / 0.037052 (0.011625) | 0.367966 / 0.258489 (0.109477) | 0.412618 / 0.293841 (0.118777) | 0.027192 / 0.128546 (-0.101355) | 0.007921 / 0.075646 (-0.067725) | 0.262213 / 0.419271 (-0.157059) | 0.044750 / 0.043533 (0.001217) | 0.351573 / 0.255139 (0.096434) | 0.389000 / 0.283200 (0.105800) | 0.020842 / 0.141683 (-0.120840) | 1.448925 / 1.452155 (-0.003229) | 1.530478 / 1.492716 (0.037761) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227787 / 0.018006 (0.209780) | 0.423161 / 0.000490 (0.422671) | 0.007557 / 0.000200 (0.007357) | 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) | 0.024703 / 0.037411 (-0.012709) | 0.074044 / 0.014526 (0.059518) | 0.085520 / 0.176557 (-0.091037) | 0.146132 / 0.737135 (-0.591003) | 0.085637 / 0.296338 (-0.210701) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.393177 / 0.215209 (0.177968) | 3.926740 / 2.077655 (1.849085) | 1.892420 / 1.504120 (0.388300) | 1.716844 / 1.541195 (0.175650) | 1.784040 / 1.468490 (0.315550) | 0.499570 / 4.584777 (-4.085207) | 3.057764 / 3.745712 (-0.687948) | 2.885463 / 5.269862 (-2.384399) | 1.905206 / 4.565676 (-2.660471) | 0.058216 / 0.424275 (-0.366059) | 0.006805 / 0.007607 (-0.000802) | 0.465406 / 0.226044 (0.239361) | 4.658569 / 2.268929 (2.389641) | 2.461737 / 55.444624 (-52.982887) | 2.170620 / 6.876477 (-4.705856) | 2.373715 / 2.142072 (0.231643) | 0.592818 / 4.805227 (-4.212409) | 0.127960 / 6.500664 (-6.372704) | 0.061696 / 0.075469 (-0.013773) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.229073 / 1.841788 (-0.612715) | 17.832087 / 8.074308 (9.757778) | 13.889485 / 10.191392 (3.698093) | 0.142237 / 0.680424 (-0.538187) | 0.016752 / 0.534201 (-0.517449) | 0.338342 / 0.579283 (-0.240941) | 0.383933 / 0.434364 (-0.050431) | 0.393017 / 0.540337 (-0.147320) | 0.557621 / 1.386936 (-0.829315) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006218 / 0.011353 (-0.005135) | 0.003679 / 0.011008 (-0.007329) | 0.062934 / 0.038508 (0.024426) | 0.066764 / 0.023109 (0.043655) | 0.482737 / 0.275898 (0.206839) | 0.483241 / 0.323480 (0.159761) | 0.004828 / 0.007986 (-0.003158) | 0.002880 / 0.004328 (-0.001448) | 0.063111 / 0.004250 (0.058861) | 0.049500 / 0.037052 (0.012448) | 0.453155 / 0.258489 (0.194666) | 0.488776 / 0.293841 (0.194935) | 0.028568 / 0.128546 (-0.099978) | 0.008490 / 0.075646 (-0.067157) | 0.068202 / 0.419271 (-0.351069) | 0.040695 / 0.043533 (-0.002838) | 0.457473 / 0.255139 (0.202334) | 0.471968 / 0.283200 (0.188768) | 0.021261 / 0.141683 (-0.120422) | 1.476304 / 1.452155 (0.024150) | 1.503433 / 1.492716 (0.010716) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227108 / 0.018006 (0.209102) | 0.428330 / 0.000490 (0.427840) | 0.004637 / 0.000200 (0.004437) | 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) | 0.027253 / 0.037411 (-0.010158) | 0.081990 / 0.014526 (0.067464) | 0.092763 / 0.176557 (-0.083794) | 0.146155 / 0.737135 (-0.590981) | 0.093175 / 0.296338 (-0.203164) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.464585 / 0.215209 (0.249376) | 4.630704 / 2.077655 (2.553050) | 2.583272 / 1.504120 (1.079152) | 2.393810 / 1.541195 (0.852615) | 2.463255 / 1.468490 (0.994765) | 0.507045 / 4.584777 (-4.077732) | 3.181972 / 3.745712 (-0.563740) | 2.902321 / 5.269862 (-2.367541) | 1.905431 / 4.565676 (-2.660246) | 0.059427 / 0.424275 (-0.364848) | 0.006387 / 0.007607 (-0.001220) | 0.542247 / 0.226044 (0.316203) | 5.426868 / 2.268929 (3.157939) | 3.073489 / 55.444624 (-52.371136) | 2.719620 / 6.876477 (-4.156857) | 2.861865 / 2.142072 (0.719793) | 0.593757 / 4.805227 (-4.211471) | 0.125439 / 6.500664 (-6.375225) | 0.060901 / 0.075469 (-0.014568) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.359938 / 1.841788 (-0.481850) | 18.484867 / 8.074308 (10.410559) | 14.685645 / 10.191392 (4.494253) | 0.164098 / 0.680424 (-0.516325) | 0.018090 / 0.534201 (-0.516111) | 0.339760 / 0.579283 (-0.239523) | 0.376668 / 0.434364 (-0.057696) | 0.396963 / 0.540337 (-0.143374) | 0.549305 / 1.386936 (-0.837631) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0c896f4195ec8a91e09f8bb9a57950bcec8b8450 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006052 / 0.011353 (-0.005301) | 0.003715 / 0.011008 (-0.007293) | 0.079646 / 0.038508 (0.041138) | 0.059053 / 0.023109 (0.035944) | 0.393016 / 0.275898 (0.117118) | 0.424758 / 0.323480 (0.101278) | 0.005407 / 0.007986 (-0.002578) | 0.002920 / 0.004328 (-0.001408) | 0.062145 / 0.004250 (0.057894) | 0.047289 / 0.037052 (0.010237) | 0.399848 / 0.258489 (0.141359) | 0.434239 / 0.293841 (0.140398) | 0.027388 / 0.128546 (-0.101158) | 0.007967 / 0.075646 (-0.067680) | 0.262546 / 0.419271 (-0.156725) | 0.045014 / 0.043533 (0.001482) | 0.398086 / 0.255139 (0.142947) | 0.414615 / 0.283200 (0.131415) | 0.020410 / 0.141683 (-0.121272) | 1.447276 / 1.452155 (-0.004879) | 1.512390 / 1.492716 (0.019673) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224854 / 0.018006 (0.206847) | 0.434173 / 0.000490 (0.433683) | 0.010091 / 0.000200 (0.009891) | 0.000259 / 0.000054 (0.000205) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025316 / 0.037411 (-0.012095) | 0.073284 / 0.014526 (0.058758) | 0.085177 / 0.176557 (-0.091379) | 0.148905 / 0.737135 (-0.588230) | 0.084696 / 0.296338 (-0.211642) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.438259 / 0.215209 (0.223050) | 4.380679 / 2.077655 (2.303025) | 2.310329 / 1.504120 (0.806209) | 2.144002 / 1.541195 (0.602807) | 2.203761 / 1.468490 (0.735270) | 0.500559 / 4.584777 (-4.084218) | 3.031172 / 3.745712 (-0.714540) | 2.839425 / 5.269862 (-2.430436) | 1.878391 / 4.565676 (-2.687285) | 0.057325 / 0.424275 (-0.366950) | 0.006719 / 0.007607 (-0.000888) | 0.510122 / 0.226044 (0.284078) | 5.108632 / 2.268929 (2.839704) | 2.805716 / 55.444624 (-52.638909) | 2.422183 / 6.876477 (-4.454293) | 2.635280 / 2.142072 (0.493207) | 0.589351 / 4.805227 (-4.215876) | 0.125416 / 6.500664 (-6.375248) | 0.061142 / 0.075469 (-0.014327) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.234997 / 1.841788 (-0.606791) | 17.731828 / 8.074308 (9.657520) | 13.858081 / 10.191392 (3.666689) | 0.145975 / 0.680424 (-0.534449) | 0.016827 / 0.534201 (-0.517374) | 0.335701 / 0.579283 (-0.243582) | 0.361867 / 0.434364 (-0.072497) | 0.394620 / 0.540337 (-0.145718) | 0.532146 / 1.386936 (-0.854790) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006091 / 0.011353 (-0.005262) | 0.003663 / 0.011008 (-0.007345) | 0.062596 / 0.038508 (0.024088) | 0.061649 / 0.023109 (0.038539) | 0.440647 / 0.275898 (0.164749) | 0.472974 / 0.323480 (0.149494) | 0.005009 / 0.007986 (-0.002976) | 0.002879 / 0.004328 (-0.001449) | 0.062815 / 0.004250 (0.058565) | 0.049000 / 0.037052 (0.011947) | 0.442990 / 0.258489 (0.184501) | 0.477622 / 0.293841 (0.183781) | 0.028512 / 0.128546 (-0.100034) | 0.008031 / 0.075646 (-0.067615) | 0.067853 / 0.419271 (-0.351418) | 0.040823 / 0.043533 (-0.002710) | 0.437811 / 0.255139 (0.182672) | 0.464615 / 0.283200 (0.181416) | 0.021348 / 0.141683 (-0.120334) | 1.479230 / 1.452155 (0.027075) | 1.544053 / 1.492716 (0.051337) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210697 / 0.018006 (0.192691) | 0.436450 / 0.000490 (0.435960) | 0.003413 / 0.000200 (0.003213) | 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) | 0.027190 / 0.037411 (-0.010222) | 0.083254 / 0.014526 (0.068728) | 0.092936 / 0.176557 (-0.083620) | 0.147261 / 0.737135 (-0.589874) | 0.092910 / 0.296338 (-0.203429) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.454195 / 0.215209 (0.238986) | 4.569122 / 2.077655 (2.491468) | 2.497198 / 1.504120 (0.993079) | 2.314337 / 1.541195 (0.773142) | 2.378471 / 1.468490 (0.909981) | 0.515402 / 4.584777 (-4.069375) | 3.199374 / 3.745712 (-0.546338) | 2.899300 / 5.269862 (-2.370562) | 1.873314 / 4.565676 (-2.692362) | 0.058820 / 0.424275 (-0.365455) | 0.006651 / 0.007607 (-0.000957) | 0.526681 / 0.226044 (0.300636) | 5.275232 / 2.268929 (3.006303) | 2.969107 / 55.444624 (-52.475517) | 2.600959 / 6.876477 (-4.275518) | 2.762930 / 2.142072 (0.620858) | 0.605726 / 4.805227 (-4.199501) | 0.127618 / 6.500664 (-6.373046) | 0.062840 / 0.075469 (-0.012629) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.367276 / 1.841788 (-0.474512) | 18.069385 / 8.074308 (9.995077) | 14.691945 / 10.191392 (4.500553) | 0.147203 / 0.680424 (-0.533221) | 0.018484 / 0.534201 (-0.515717) | 0.333759 / 0.579283 (-0.245524) | 0.395503 / 0.434364 (-0.038861) | 0.387031 / 0.540337 (-0.153306) | 0.550428 / 1.386936 (-0.836508) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4c8f7eb79dff66dd03211321dcb55f7a7a05ef38 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007675 / 0.011353 (-0.003678) | 0.004532 / 0.011008 (-0.006476) | 0.088176 / 0.038508 (0.049668) | 0.103257 / 0.023109 (0.080148) | 0.314785 / 0.275898 (0.038887) | 0.354280 / 0.323480 (0.030800) | 0.004638 / 0.007986 (-0.003348) | 0.003736 / 0.004328 (-0.000592) | 0.066744 / 0.004250 (0.062493) | 0.064647 / 0.037052 (0.027595) | 0.320227 / 0.258489 (0.061738) | 0.369581 / 0.293841 (0.075740) | 0.032347 / 0.128546 (-0.096199) | 0.009226 / 0.075646 (-0.066421) | 0.292966 / 0.419271 (-0.126306) | 0.055738 / 0.043533 (0.012206) | 0.316537 / 0.255139 (0.061398) | 0.334699 / 0.283200 (0.051499) | 0.027401 / 0.141683 (-0.114282) | 1.482390 / 1.452155 (0.030236) | 1.594771 / 1.492716 (0.102055) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.322181 / 0.018006 (0.304175) | 0.577701 / 0.000490 (0.577212) | 0.014565 / 0.000200 (0.014365) | 0.000393 / 0.000054 (0.000338) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033255 / 0.037411 (-0.004156) | 0.094271 / 0.014526 (0.079745) | 0.105360 / 0.176557 (-0.071197) | 0.163699 / 0.737135 (-0.573436) | 0.105620 / 0.296338 (-0.190719) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.383449 / 0.215209 (0.168240) | 3.824292 / 2.077655 (1.746637) | 1.861809 / 1.504120 (0.357689) | 1.698153 / 1.541195 (0.156958) | 1.819460 / 1.468490 (0.350970) | 0.488277 / 4.584777 (-4.096500) | 3.622772 / 3.745712 (-0.122940) | 3.486041 / 5.269862 (-1.783821) | 2.211679 / 4.565676 (-2.353998) | 0.057637 / 0.424275 (-0.366638) | 0.008028 / 0.007607 (0.000421) | 0.461917 / 0.226044 (0.235873) | 4.626493 / 2.268929 (2.357565) | 2.374846 / 55.444624 (-53.069779) | 1.976003 / 6.876477 (-4.900473) | 2.325342 / 2.142072 (0.183269) | 0.582538 / 4.805227 (-4.222689) | 0.133575 / 6.500664 (-6.367089) | 0.061696 / 0.075469 (-0.013773) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.271846 / 1.841788 (-0.569941) | 20.944702 / 8.074308 (12.870394) | 15.438119 / 10.191392 (5.246727) | 0.167334 / 0.680424 (-0.513090) | 0.019538 / 0.534201 (-0.514663) | 0.401467 / 0.579283 (-0.177816) | 0.428222 / 0.434364 (-0.006142) | 0.466108 / 0.540337 (-0.074229) | 0.645326 / 1.386936 (-0.741610) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007096 / 0.011353 (-0.004257) | 0.004398 / 0.011008 (-0.006610) | 0.066253 / 0.038508 (0.027745) | 0.089415 / 0.023109 (0.066306) | 0.395760 / 0.275898 (0.119862) | 0.436058 / 0.323480 (0.112579) | 0.005944 / 0.007986 (-0.002042) | 0.003821 / 0.004328 (-0.000507) | 0.065286 / 0.004250 (0.061036) | 0.060990 / 0.037052 (0.023937) | 0.394674 / 0.258489 (0.136185) | 0.437672 / 0.293841 (0.143831) | 0.032370 / 0.128546 (-0.096177) | 0.009025 / 0.075646 (-0.066622) | 0.071365 / 0.419271 (-0.347906) | 0.048232 / 0.043533 (0.004699) | 0.395677 / 0.255139 (0.140538) | 0.415869 / 0.283200 (0.132669) | 0.024632 / 0.141683 (-0.117051) | 1.511386 / 1.452155 (0.059231) | 1.604475 / 1.492716 (0.111759) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.312864 / 0.018006 (0.294858) | 0.535432 / 0.000490 (0.534943) | 0.005195 / 0.000200 (0.004995) | 0.000101 / 0.000054 (0.000047) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035827 / 0.037411 (-0.001584) | 0.099353 / 0.014526 (0.084827) | 0.110796 / 0.176557 (-0.065761) | 0.165224 / 0.737135 (-0.571911) | 0.112111 / 0.296338 (-0.184228) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428873 / 0.215209 (0.213664) | 4.284264 / 2.077655 (2.206609) | 2.303966 / 1.504120 (0.799847) | 2.153868 / 1.541195 (0.612674) | 2.275669 / 1.468490 (0.807179) | 0.495452 / 4.584777 (-4.089325) | 3.706773 / 3.745712 (-0.038939) | 3.471988 / 5.269862 (-1.797874) | 2.194851 / 4.565676 (-2.370825) | 0.058998 / 0.424275 (-0.365277) | 0.007522 / 0.007607 (-0.000085) | 0.511222 / 0.226044 (0.285177) | 5.097058 / 2.268929 (2.828130) | 2.856793 / 55.444624 (-52.587832) | 2.521907 / 6.876477 (-4.354569) | 2.783133 / 2.142072 (0.641060) | 0.600511 / 4.805227 (-4.204717) | 0.134130 / 6.500664 (-6.366534) | 0.061726 / 0.075469 (-0.013743) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.385272 / 1.841788 (-0.456516) | 21.149260 / 8.074308 (13.074952) | 15.548746 / 10.191392 (5.357354) | 0.167506 / 0.680424 (-0.512918) | 0.020494 / 0.534201 (-0.513707) | 0.400697 / 0.579283 (-0.178586) | 0.427386 / 0.434364 (-0.006978) | 0.478514 / 0.540337 (-0.061824) | 0.655753 / 1.386936 (-0.731183) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4c8f7eb79dff66dd03211321dcb55f7a7a05ef38 \"CML watermark\")\n"
] |
1,956,917,893
| 6,340
|
Release 2.14.5
|
closed
| 2023-10-23T11:10:22
| 2023-10-23T14:20:46
| 2023-10-23T11:12:40
|
https://github.com/huggingface/datasets/pull/6340
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6340",
"html_url": "https://github.com/huggingface/datasets/pull/6340",
"diff_url": "https://github.com/huggingface/datasets/pull/6340.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6340.patch",
"merged_at": null
}
|
lhoestq
| true
|
[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6340). All of your documentation changes will be reflected on that endpoint."
] |
1,956,912,627
| 6,339
|
minor release step improvement
|
closed
| 2023-10-23T11:07:04
| 2023-11-07T10:38:54
| 2023-11-07T10:32:41
|
https://github.com/huggingface/datasets/pull/6339
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6339",
"html_url": "https://github.com/huggingface/datasets/pull/6339",
"diff_url": "https://github.com/huggingface/datasets/pull/6339.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6339.patch",
"merged_at": "2023-11-07T10:32:41"
}
|
lhoestq
| true
|
[
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006572 / 0.011353 (-0.004780) | 0.004019 / 0.011008 (-0.006989) | 0.084080 / 0.038508 (0.045572) | 0.070111 / 0.023109 (0.047002) | 0.340440 / 0.275898 (0.064542) | 0.358839 / 0.323480 (0.035359) | 0.005254 / 0.007986 (-0.002732) | 0.003296 / 0.004328 (-0.001032) | 0.064368 / 0.004250 (0.060117) | 0.054549 / 0.037052 (0.017497) | 0.343817 / 0.258489 (0.085328) | 0.369871 / 0.293841 (0.076030) | 0.030621 / 0.128546 (-0.097925) | 0.008457 / 0.075646 (-0.067189) | 0.287839 / 0.419271 (-0.131432) | 0.051700 / 0.043533 (0.008167) | 0.331602 / 0.255139 (0.076463) | 0.339836 / 0.283200 (0.056636) | 0.023224 / 0.141683 (-0.118459) | 1.494597 / 1.452155 (0.042443) | 1.578640 / 1.492716 (0.085924) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236985 / 0.018006 (0.218979) | 0.506153 / 0.000490 (0.505664) | 0.009753 / 0.000200 (0.009553) | 0.000345 / 0.000054 (0.000291) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028355 / 0.037411 (-0.009056) | 0.082104 / 0.014526 (0.067578) | 0.095141 / 0.176557 (-0.081415) | 0.151054 / 0.737135 (-0.586081) | 0.095139 / 0.296338 (-0.201200) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.403773 / 0.215209 (0.188564) | 4.025567 / 2.077655 (1.947912) | 2.024641 / 1.504120 (0.520521) | 1.857039 / 1.541195 (0.315845) | 1.957346 / 1.468490 (0.488856) | 0.481486 / 4.584777 (-4.103291) | 3.574463 / 3.745712 (-0.171249) | 3.399311 / 5.269862 (-1.870551) | 1.996806 / 4.565676 (-2.568870) | 0.056644 / 0.424275 (-0.367631) | 0.007503 / 0.007607 (-0.000104) | 0.479480 / 0.226044 (0.253435) | 4.793686 / 2.268929 (2.524757) | 2.481011 / 55.444624 (-52.963613) | 2.176473 / 6.876477 (-4.700004) | 2.203192 / 2.142072 (0.061120) | 0.574071 / 4.805227 (-4.231156) | 0.131852 / 6.500664 (-6.368812) | 0.058883 / 0.075469 (-0.016586) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.249945 / 1.841788 (-0.591842) | 18.439267 / 8.074308 (10.364959) | 14.100934 / 10.191392 (3.909542) | 0.164191 / 0.680424 (-0.516233) | 0.018086 / 0.534201 (-0.516115) | 0.390821 / 0.579283 (-0.188462) | 0.414166 / 0.434364 (-0.020198) | 0.460073 / 0.540337 (-0.080265) | 0.636299 / 1.386936 (-0.750637) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006606 / 0.011353 (-0.004747) | 0.003987 / 0.011008 (-0.007021) | 0.064616 / 0.038508 (0.026108) | 0.070830 / 0.023109 (0.047721) | 0.397340 / 0.275898 (0.121442) | 0.426823 / 0.323480 (0.103343) | 0.005345 / 0.007986 (-0.002641) | 0.003264 / 0.004328 (-0.001065) | 0.064728 / 0.004250 (0.060477) | 0.055763 / 0.037052 (0.018711) | 0.405347 / 0.258489 (0.146858) | 0.433163 / 0.293841 (0.139322) | 0.032394 / 0.128546 (-0.096153) | 0.008474 / 0.075646 (-0.067172) | 0.071583 / 0.419271 (-0.347689) | 0.048424 / 0.043533 (0.004892) | 0.400582 / 0.255139 (0.145443) | 0.418111 / 0.283200 (0.134911) | 0.022257 / 0.141683 (-0.119426) | 1.495521 / 1.452155 (0.043366) | 1.554626 / 1.492716 (0.061910) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218249 / 0.018006 (0.200242) | 0.438527 / 0.000490 (0.438037) | 0.005406 / 0.000200 (0.005206) | 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) | 0.031600 / 0.037411 (-0.005812) | 0.090836 / 0.014526 (0.076310) | 0.105000 / 0.176557 (-0.071556) | 0.157648 / 0.737135 (-0.579487) | 0.103827 / 0.296338 (-0.192512) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426428 / 0.215209 (0.211219) | 4.259435 / 2.077655 (2.181780) | 2.300795 / 1.504120 (0.796675) | 2.121302 / 1.541195 (0.580108) | 2.145602 / 1.468490 (0.677112) | 0.486856 / 4.584777 (-4.097921) | 3.673568 / 3.745712 (-0.072144) | 3.278619 / 5.269862 (-1.991243) | 2.037760 / 4.565676 (-2.527917) | 0.057699 / 0.424275 (-0.366576) | 0.007269 / 0.007607 (-0.000338) | 0.499549 / 0.226044 (0.273505) | 4.996214 / 2.268929 (2.727285) | 2.766480 / 55.444624 (-52.678144) | 2.417308 / 6.876477 (-4.459168) | 2.581026 / 2.142072 (0.438953) | 0.589463 / 4.805227 (-4.215765) | 0.134820 / 6.500664 (-6.365844) | 0.061699 / 0.075469 (-0.013770) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.353704 / 1.841788 (-0.488084) | 19.104167 / 8.074308 (11.029859) | 14.652166 / 10.191392 (4.460774) | 0.171885 / 0.680424 (-0.508539) | 0.020222 / 0.534201 (-0.513978) | 0.396777 / 0.579283 (-0.182506) | 0.426304 / 0.434364 (-0.008060) | 0.471347 / 0.540337 (-0.068991) | 0.635887 / 1.386936 (-0.751049) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ce19ec527c581eddec306a03ad1db554223cc94a \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004686 / 0.011353 (-0.006667) | 0.002998 / 0.011008 (-0.008010) | 0.063604 / 0.038508 (0.025096) | 0.048927 / 0.023109 (0.025818) | 0.247238 / 0.275898 (-0.028660) | 0.272409 / 0.323480 (-0.051071) | 0.003909 / 0.007986 (-0.004077) | 0.002469 / 0.004328 (-0.001859) | 0.048473 / 0.004250 (0.044223) | 0.037514 / 0.037052 (0.000462) | 0.257292 / 0.258489 (-0.001197) | 0.285203 / 0.293841 (-0.008638) | 0.023131 / 0.128546 (-0.105415) | 0.006803 / 0.075646 (-0.068843) | 0.202920 / 0.419271 (-0.216351) | 0.035653 / 0.043533 (-0.007880) | 0.254791 / 0.255139 (-0.000348) | 0.272973 / 0.283200 (-0.010226) | 0.017707 / 0.141683 (-0.123976) | 1.091606 / 1.452155 (-0.360549) | 1.151453 / 1.492716 (-0.341263) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093701 / 0.018006 (0.075695) | 0.304199 / 0.000490 (0.303709) | 0.000223 / 0.000200 (0.000023) | 0.000051 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019291 / 0.037411 (-0.018120) | 0.062168 / 0.014526 (0.047642) | 0.073273 / 0.176557 (-0.103284) | 0.119497 / 0.737135 (-0.617638) | 0.075008 / 0.296338 (-0.221331) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279983 / 0.215209 (0.064774) | 2.774413 / 2.077655 (0.696758) | 1.476678 / 1.504120 (-0.027441) | 1.336273 / 1.541195 (-0.204922) | 1.332349 / 1.468490 (-0.136142) | 0.403150 / 4.584777 (-4.181627) | 2.390026 / 3.745712 (-1.355686) | 2.619151 / 5.269862 (-2.650711) | 1.578607 / 4.565676 (-2.987069) | 0.046632 / 0.424275 (-0.377643) | 0.007352 / 0.007607 (-0.000255) | 0.333419 / 0.226044 (0.107375) | 3.288734 / 2.268929 (1.019805) | 1.843677 / 55.444624 (-53.600947) | 1.536746 / 6.876477 (-5.339731) | 1.573005 / 2.142072 (-0.569067) | 0.475699 / 4.805227 (-4.329529) | 0.104742 / 6.500664 (-6.395922) | 0.042450 / 0.075469 (-0.033019) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.949039 / 1.841788 (-0.892749) | 11.895928 / 8.074308 (3.821620) | 10.650521 / 10.191392 (0.459129) | 0.142308 / 0.680424 (-0.538116) | 0.014207 / 0.534201 (-0.519994) | 0.274011 / 0.579283 (-0.305272) | 0.288259 / 0.434364 (-0.146105) | 0.327729 / 0.540337 (-0.212609) | 0.395728 / 1.386936 (-0.991208) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004830 / 0.011353 (-0.006523) | 0.002978 / 0.011008 (-0.008030) | 0.048623 / 0.038508 (0.010114) | 0.055040 / 0.023109 (0.031930) | 0.276436 / 0.275898 (0.000538) | 0.302403 / 0.323480 (-0.021076) | 0.004080 / 0.007986 (-0.003905) | 0.002479 / 0.004328 (-0.001849) | 0.048078 / 0.004250 (0.043827) | 0.039680 / 0.037052 (0.002627) | 0.279095 / 0.258489 (0.020606) | 0.307399 / 0.293841 (0.013558) | 0.024533 / 0.128546 (-0.104013) | 0.007196 / 0.075646 (-0.068450) | 0.053879 / 0.419271 (-0.365393) | 0.032545 / 0.043533 (-0.010988) | 0.275501 / 0.255139 (0.020362) | 0.298530 / 0.283200 (0.015330) | 0.017992 / 0.141683 (-0.123691) | 1.144191 / 1.452155 (-0.307963) | 1.208309 / 1.492716 (-0.284408) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095690 / 0.018006 (0.077684) | 0.304932 / 0.000490 (0.304442) | 0.000223 / 0.000200 (0.000023) | 0.000055 / 0.000054 (0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021409 / 0.037411 (-0.016003) | 0.069861 / 0.014526 (0.055335) | 0.080959 / 0.176557 (-0.095597) | 0.119432 / 0.737135 (-0.617703) | 0.083649 / 0.296338 (-0.212690) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297243 / 0.215209 (0.082034) | 2.909288 / 2.077655 (0.831634) | 1.571512 / 1.504120 (0.067392) | 1.452403 / 1.541195 (-0.088792) | 1.481290 / 1.468490 (0.012800) | 0.405795 / 4.584777 (-4.178982) | 2.452923 / 3.745712 (-1.292789) | 2.513371 / 5.269862 (-2.756490) | 1.593216 / 4.565676 (-2.972460) | 0.048073 / 0.424275 (-0.376202) | 0.005312 / 0.007607 (-0.002296) | 0.355783 / 0.226044 (0.129738) | 3.494062 / 2.268929 (1.225133) | 1.947388 / 55.444624 (-53.497236) | 1.651724 / 6.876477 (-5.224753) | 1.789007 / 2.142072 (-0.353065) | 0.487073 / 4.805227 (-4.318154) | 0.100271 / 6.500664 (-6.400393) | 0.041571 / 0.075469 (-0.033898) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.983766 / 1.841788 (-0.858021) | 12.384778 / 8.074308 (4.310469) | 10.669519 / 10.191392 (0.478127) | 0.133105 / 0.680424 (-0.547318) | 0.016665 / 0.534201 (-0.517536) | 0.269479 / 0.579283 (-0.309804) | 0.276498 / 0.434364 (-0.157866) | 0.302105 / 0.540337 (-0.238233) | 0.391204 / 1.386936 (-0.995732) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#12ebe695b4748c5a26e08b44ed51955f74f5801d \"CML watermark\")\n"
] |
1,956,886,072
| 6,338
|
pin fsspec before it switches to glob.glob
|
closed
| 2023-10-23T10:50:54
| 2024-01-11T06:32:56
| 2023-10-23T10:51:52
|
https://github.com/huggingface/datasets/pull/6338
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6338",
"html_url": "https://github.com/huggingface/datasets/pull/6338",
"diff_url": "https://github.com/huggingface/datasets/pull/6338.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6338.patch",
"merged_at": null
}
|
lhoestq
| true
|
[
"closing in favor of https://github.com/huggingface/datasets/pull/6337",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6338). All of your documentation changes will be reflected on that endpoint."
] |
1,956,875,259
| 6,337
|
Pin supported upper version of fsspec
|
closed
| 2023-10-23T10:44:16
| 2023-10-23T12:13:20
| 2023-10-23T12:04:36
|
https://github.com/huggingface/datasets/pull/6337
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6337",
"html_url": "https://github.com/huggingface/datasets/pull/6337",
"diff_url": "https://github.com/huggingface/datasets/pull/6337.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6337.patch",
"merged_at": "2023-10-23T12:04:36"
}
|
albertvillanova
| true
|
[
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006915 / 0.011353 (-0.004438) | 0.004110 / 0.011008 (-0.006898) | 0.084392 / 0.038508 (0.045884) | 0.079649 / 0.023109 (0.056540) | 0.305760 / 0.275898 (0.029862) | 0.343968 / 0.323480 (0.020488) | 0.005402 / 0.007986 (-0.002584) | 0.003342 / 0.004328 (-0.000986) | 0.064774 / 0.004250 (0.060523) | 0.055919 / 0.037052 (0.018866) | 0.315194 / 0.258489 (0.056705) | 0.355014 / 0.293841 (0.061173) | 0.032140 / 0.128546 (-0.096406) | 0.008865 / 0.075646 (-0.066781) | 0.287684 / 0.419271 (-0.131588) | 0.053504 / 0.043533 (0.009971) | 0.306852 / 0.255139 (0.051713) | 0.331125 / 0.283200 (0.047925) | 0.023476 / 0.141683 (-0.118207) | 1.506590 / 1.452155 (0.054435) | 1.574508 / 1.492716 (0.081792) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.239987 / 0.018006 (0.221981) | 0.459144 / 0.000490 (0.458654) | 0.008509 / 0.000200 (0.008309) | 0.000335 / 0.000054 (0.000280) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028353 / 0.037411 (-0.009058) | 0.082345 / 0.014526 (0.067819) | 0.499524 / 0.176557 (0.322967) | 0.152896 / 0.737135 (-0.584239) | 0.096978 / 0.296338 (-0.199360) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.404855 / 0.215209 (0.189646) | 4.053103 / 2.077655 (1.975448) | 2.069638 / 1.504120 (0.565518) | 1.917354 / 1.541195 (0.376159) | 2.035816 / 1.468490 (0.567326) | 0.480358 / 4.584777 (-4.104419) | 3.594316 / 3.745712 (-0.151396) | 3.582952 / 5.269862 (-1.686910) | 2.101142 / 4.565676 (-2.464535) | 0.057004 / 0.424275 (-0.367271) | 0.007715 / 0.007607 (0.000108) | 0.487417 / 0.226044 (0.261372) | 4.863100 / 2.268929 (2.594172) | 2.569038 / 55.444624 (-52.875587) | 2.187167 / 6.876477 (-4.689310) | 2.270034 / 2.142072 (0.127962) | 0.578095 / 4.805227 (-4.227132) | 0.133283 / 6.500664 (-6.367381) | 0.060164 / 0.075469 (-0.015305) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.269120 / 1.841788 (-0.572667) | 19.493072 / 8.074308 (11.418764) | 14.560576 / 10.191392 (4.369184) | 0.167440 / 0.680424 (-0.512984) | 0.018493 / 0.534201 (-0.515708) | 0.392774 / 0.579283 (-0.186509) | 0.420903 / 0.434364 (-0.013461) | 0.461904 / 0.540337 (-0.078433) | 0.643104 / 1.386936 (-0.743832) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006985 / 0.011353 (-0.004368) | 0.004246 / 0.011008 (-0.006762) | 0.066246 / 0.038508 (0.027738) | 0.080757 / 0.023109 (0.057648) | 0.391774 / 0.275898 (0.115876) | 0.424957 / 0.323480 (0.101478) | 0.005575 / 0.007986 (-0.002411) | 0.003447 / 0.004328 (-0.000881) | 0.066565 / 0.004250 (0.062315) | 0.057597 / 0.037052 (0.020544) | 0.394663 / 0.258489 (0.136174) | 0.430310 / 0.293841 (0.136469) | 0.032746 / 0.128546 (-0.095800) | 0.008783 / 0.075646 (-0.066863) | 0.071940 / 0.419271 (-0.347331) | 0.048877 / 0.043533 (0.005344) | 0.390269 / 0.255139 (0.135130) | 0.411867 / 0.283200 (0.128668) | 0.024101 / 0.141683 (-0.117582) | 1.507370 / 1.452155 (0.055215) | 1.585810 / 1.492716 (0.093093) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222796 / 0.018006 (0.204790) | 0.459035 / 0.000490 (0.458546) | 0.005322 / 0.000200 (0.005122) | 0.000099 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033237 / 0.037411 (-0.004174) | 0.098244 / 0.014526 (0.083718) | 0.106654 / 0.176557 (-0.069903) | 0.159675 / 0.737135 (-0.577460) | 0.108470 / 0.296338 (-0.187869) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429085 / 0.215209 (0.213876) | 4.281206 / 2.077655 (2.203551) | 2.320492 / 1.504120 (0.816372) | 2.153218 / 1.541195 (0.612024) | 2.287122 / 1.468490 (0.818632) | 0.497307 / 4.584777 (-4.087470) | 3.799541 / 3.745712 (0.053828) | 3.380053 / 5.269862 (-1.889809) | 2.100009 / 4.565676 (-2.465667) | 0.057988 / 0.424275 (-0.366287) | 0.007381 / 0.007607 (-0.000226) | 0.506843 / 0.226044 (0.280798) | 5.071286 / 2.268929 (2.802357) | 2.750487 / 55.444624 (-52.694137) | 2.415613 / 6.876477 (-4.460864) | 2.667144 / 2.142072 (0.525072) | 0.624889 / 4.805227 (-4.180338) | 0.134191 / 6.500664 (-6.366473) | 0.060704 / 0.075469 (-0.014765) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.353074 / 1.841788 (-0.488714) | 20.507074 / 8.074308 (12.432766) | 14.911788 / 10.191392 (4.720396) | 0.149248 / 0.680424 (-0.531176) | 0.020593 / 0.534201 (-0.513608) | 0.398458 / 0.579283 (-0.180825) | 0.434846 / 0.434364 (0.000482) | 0.478853 / 0.540337 (-0.061484) | 0.648072 / 1.386936 (-0.738864) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b30c72a2d3d9c191a590e0f0a6b3a6363ab15e8f \"CML watermark\")\n",
"In particular I expect fsspec to do another breaking change in the next release (switch to glob.glob)",
"_The documentation is not available anymore as the PR was closed or merged._",
"see https://github.com/huggingface/datasets/pull/6338",
"Yes, unfortunately breaking changes are quite usual from their part.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006099 / 0.011353 (-0.005253) | 0.003672 / 0.011008 (-0.007336) | 0.083095 / 0.038508 (0.044587) | 0.059607 / 0.023109 (0.036498) | 0.319591 / 0.275898 (0.043693) | 0.351945 / 0.323480 (0.028465) | 0.004785 / 0.007986 (-0.003201) | 0.002965 / 0.004328 (-0.001364) | 0.062907 / 0.004250 (0.058657) | 0.049122 / 0.037052 (0.012070) | 0.344641 / 0.258489 (0.086152) | 0.361519 / 0.293841 (0.067678) | 0.027254 / 0.128546 (-0.101292) | 0.008081 / 0.075646 (-0.067565) | 0.261569 / 0.419271 (-0.157702) | 0.045101 / 0.043533 (0.001568) | 0.313645 / 0.255139 (0.058506) | 0.337843 / 0.283200 (0.054644) | 0.020968 / 0.141683 (-0.120715) | 1.438450 / 1.452155 (-0.013705) | 1.507567 / 1.492716 (0.014850) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230826 / 0.018006 (0.212820) | 0.434363 / 0.000490 (0.433873) | 0.008210 / 0.000200 (0.008010) | 0.000212 / 0.000054 (0.000157) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025278 / 0.037411 (-0.012133) | 0.073659 / 0.014526 (0.059133) | 0.085147 / 0.176557 (-0.091409) | 0.145451 / 0.737135 (-0.591684) | 0.086400 / 0.296338 (-0.209939) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429887 / 0.215209 (0.214678) | 4.292626 / 2.077655 (2.214971) | 2.266824 / 1.504120 (0.762704) | 2.090472 / 1.541195 (0.549277) | 2.186477 / 1.468490 (0.717987) | 0.503684 / 4.584777 (-4.081093) | 3.100791 / 3.745712 (-0.644921) | 3.008938 / 5.269862 (-2.260923) | 1.885559 / 4.565676 (-2.680118) | 0.057434 / 0.424275 (-0.366841) | 0.006639 / 0.007607 (-0.000969) | 0.506579 / 0.226044 (0.280535) | 5.058905 / 2.268929 (2.789977) | 2.708321 / 55.444624 (-52.736304) | 2.367388 / 6.876477 (-4.509089) | 2.422660 / 2.142072 (0.280587) | 0.587562 / 4.805227 (-4.217665) | 0.125260 / 6.500664 (-6.375404) | 0.061856 / 0.075469 (-0.013613) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.280495 / 1.841788 (-0.561292) | 17.968873 / 8.074308 (9.894565) | 13.922838 / 10.191392 (3.731446) | 0.149907 / 0.680424 (-0.530517) | 0.016736 / 0.534201 (-0.517465) | 0.333417 / 0.579283 (-0.245866) | 0.367710 / 0.434364 (-0.066654) | 0.389648 / 0.540337 (-0.150690) | 0.535625 / 1.386936 (-0.851311) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006237 / 0.011353 (-0.005116) | 0.003787 / 0.011008 (-0.007221) | 0.062536 / 0.038508 (0.024028) | 0.062335 / 0.023109 (0.039226) | 0.455209 / 0.275898 (0.179311) | 0.488961 / 0.323480 (0.165482) | 0.004875 / 0.007986 (-0.003111) | 0.002961 / 0.004328 (-0.001368) | 0.063045 / 0.004250 (0.058795) | 0.048624 / 0.037052 (0.011571) | 0.455743 / 0.258489 (0.197254) | 0.494024 / 0.293841 (0.200183) | 0.028690 / 0.128546 (-0.099856) | 0.008147 / 0.075646 (-0.067499) | 0.069479 / 0.419271 (-0.349792) | 0.041613 / 0.043533 (-0.001919) | 0.460472 / 0.255139 (0.205333) | 0.475606 / 0.283200 (0.192406) | 0.020600 / 0.141683 (-0.121083) | 1.464960 / 1.452155 (0.012805) | 1.540942 / 1.492716 (0.048226) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.214558 / 0.018006 (0.196552) | 0.410482 / 0.000490 (0.409992) | 0.005539 / 0.000200 (0.005339) | 0.000076 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027044 / 0.037411 (-0.010367) | 0.081512 / 0.014526 (0.066986) | 0.101963 / 0.176557 (-0.074593) | 0.146686 / 0.737135 (-0.590449) | 0.092676 / 0.296338 (-0.203663) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.468766 / 0.215209 (0.253557) | 4.680514 / 2.077655 (2.602859) | 2.562454 / 1.504120 (1.058334) | 2.383692 / 1.541195 (0.842497) | 2.481820 / 1.468490 (1.013330) | 0.509122 / 4.584777 (-4.075655) | 3.201597 / 3.745712 (-0.544115) | 2.853539 / 5.269862 (-2.416323) | 1.891535 / 4.565676 (-2.674141) | 0.058594 / 0.424275 (-0.365681) | 0.006448 / 0.007607 (-0.001159) | 0.535950 / 0.226044 (0.309906) | 5.388239 / 2.268929 (3.119311) | 2.999986 / 55.444624 (-52.444638) | 2.733291 / 6.876477 (-4.143186) | 2.841548 / 2.142072 (0.699475) | 0.602388 / 4.805227 (-4.202840) | 0.126369 / 6.500664 (-6.374295) | 0.061519 / 0.075469 (-0.013951) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.322746 / 1.841788 (-0.519042) | 17.940825 / 8.074308 (9.866517) | 14.679559 / 10.191392 (4.488167) | 0.146481 / 0.680424 (-0.533943) | 0.018060 / 0.534201 (-0.516141) | 0.334924 / 0.579283 (-0.244359) | 0.384735 / 0.434364 (-0.049629) | 0.391834 / 0.540337 (-0.148503) | 0.540011 / 1.386936 (-0.846925) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d82f3c2264436ef60fac8c397fb11c80175c5132 \"CML watermark\")\n"
] |
1,956,827,232
| 6,336
|
unpin-fsspec
|
closed
| 2023-10-23T10:16:46
| 2024-02-07T12:41:35
| 2023-10-23T10:17:48
|
https://github.com/huggingface/datasets/pull/6336
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6336",
"html_url": "https://github.com/huggingface/datasets/pull/6336",
"diff_url": "https://github.com/huggingface/datasets/pull/6336.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6336.patch",
"merged_at": "2023-10-23T10:17:48"
}
|
lhoestq
| true
|
[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6336). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006202 / 0.011353 (-0.005151) | 0.003627 / 0.011008 (-0.007381) | 0.080643 / 0.038508 (0.042135) | 0.057135 / 0.023109 (0.034026) | 0.315853 / 0.275898 (0.039955) | 0.348503 / 0.323480 (0.025023) | 0.004762 / 0.007986 (-0.003224) | 0.002884 / 0.004328 (-0.001445) | 0.063208 / 0.004250 (0.058958) | 0.046777 / 0.037052 (0.009725) | 0.321426 / 0.258489 (0.062937) | 0.362128 / 0.293841 (0.068287) | 0.027494 / 0.128546 (-0.101052) | 0.007931 / 0.075646 (-0.067715) | 0.262262 / 0.419271 (-0.157009) | 0.044330 / 0.043533 (0.000797) | 0.310504 / 0.255139 (0.055366) | 0.339409 / 0.283200 (0.056209) | 0.021030 / 0.141683 (-0.120652) | 1.405333 / 1.452155 (-0.046822) | 1.493497 / 1.492716 (0.000781) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225431 / 0.018006 (0.207425) | 0.451723 / 0.000490 (0.451233) | 0.007763 / 0.000200 (0.007563) | 0.000310 / 0.000054 (0.000256) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023381 / 0.037411 (-0.014031) | 0.074183 / 0.014526 (0.059657) | 0.084003 / 0.176557 (-0.092553) | 0.143628 / 0.737135 (-0.593507) | 0.084543 / 0.296338 (-0.211796) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.393062 / 0.215209 (0.177853) | 3.905649 / 2.077655 (1.827994) | 1.923155 / 1.504120 (0.419035) | 1.751554 / 1.541195 (0.210359) | 1.816141 / 1.468490 (0.347651) | 0.502789 / 4.584777 (-4.081988) | 3.006149 / 3.745712 (-0.739564) | 2.979645 / 5.269862 (-2.290216) | 1.877408 / 4.565676 (-2.688269) | 0.057544 / 0.424275 (-0.366731) | 0.006733 / 0.007607 (-0.000874) | 0.468469 / 0.226044 (0.242425) | 4.695595 / 2.268929 (2.426667) | 2.367238 / 55.444624 (-53.077387) | 2.041035 / 6.876477 (-4.835442) | 2.087396 / 2.142072 (-0.054676) | 0.586866 / 4.805227 (-4.218361) | 0.125616 / 6.500664 (-6.375049) | 0.060535 / 0.075469 (-0.014934) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.244753 / 1.841788 (-0.597035) | 17.652902 / 8.074308 (9.578594) | 13.733195 / 10.191392 (3.541803) | 0.143741 / 0.680424 (-0.536683) | 0.016775 / 0.534201 (-0.517426) | 0.335487 / 0.579283 (-0.243797) | 0.350292 / 0.434364 (-0.084072) | 0.388744 / 0.540337 (-0.151594) | 0.536630 / 1.386936 (-0.850306) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006008 / 0.011353 (-0.005345) | 0.003708 / 0.011008 (-0.007301) | 0.062504 / 0.038508 (0.023996) | 0.058570 / 0.023109 (0.035461) | 0.450549 / 0.275898 (0.174651) | 0.467768 / 0.323480 (0.144288) | 0.004955 / 0.007986 (-0.003031) | 0.002903 / 0.004328 (-0.001426) | 0.062778 / 0.004250 (0.058528) | 0.048750 / 0.037052 (0.011698) | 0.439848 / 0.258489 (0.181359) | 0.471780 / 0.293841 (0.177939) | 0.028472 / 0.128546 (-0.100074) | 0.008221 / 0.075646 (-0.067425) | 0.068325 / 0.419271 (-0.350946) | 0.040612 / 0.043533 (-0.002921) | 0.435530 / 0.255139 (0.180391) | 0.458992 / 0.283200 (0.175792) | 0.020143 / 0.141683 (-0.121539) | 1.479101 / 1.452155 (0.026947) | 1.507408 / 1.492716 (0.014692) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.207723 / 0.018006 (0.189717) | 0.406596 / 0.000490 (0.406106) | 0.004431 / 0.000200 (0.004231) | 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) | 0.027037 / 0.037411 (-0.010374) | 0.081576 / 0.014526 (0.067050) | 0.091177 / 0.176557 (-0.085379) | 0.146191 / 0.737135 (-0.590944) | 0.092485 / 0.296338 (-0.203854) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.456676 / 0.215209 (0.241467) | 4.556214 / 2.077655 (2.478559) | 2.500146 / 1.504120 (0.996026) | 2.325175 / 1.541195 (0.783981) | 2.421023 / 1.468490 (0.952533) | 0.512135 / 4.584777 (-4.072641) | 3.167070 / 3.745712 (-0.578642) | 2.897697 / 5.269862 (-2.372165) | 1.881974 / 4.565676 (-2.683702) | 0.058453 / 0.424275 (-0.365823) | 0.006515 / 0.007607 (-0.001092) | 0.530742 / 0.226044 (0.304698) | 5.304943 / 2.268929 (3.036014) | 2.928824 / 55.444624 (-52.515800) | 2.598023 / 6.876477 (-4.278454) | 2.758496 / 2.142072 (0.616423) | 0.601777 / 4.805227 (-4.203450) | 0.126701 / 6.500664 (-6.373964) | 0.061808 / 0.075469 (-0.013661) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.357844 / 1.841788 (-0.483943) | 17.887666 / 8.074308 (9.813358) | 14.561904 / 10.191392 (4.370512) | 0.146788 / 0.680424 (-0.533636) | 0.018277 / 0.534201 (-0.515924) | 0.343168 / 0.579283 (-0.236115) | 0.382220 / 0.434364 (-0.052144) | 0.401234 / 0.540337 (-0.139104) | 0.546246 / 1.386936 (-0.840690) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b0980a74d58098b8b1738e2411f1212161a211b8 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008919 / 0.011353 (-0.002434) | 0.006110 / 0.011008 (-0.004898) | 0.110554 / 0.038508 (0.072046) | 0.075705 / 0.023109 (0.052596) | 0.391235 / 0.275898 (0.115336) | 0.458331 / 0.323480 (0.134851) | 0.007489 / 0.007986 (-0.000497) | 0.003744 / 0.004328 (-0.000585) | 0.078124 / 0.004250 (0.073874) | 0.057244 / 0.037052 (0.020192) | 0.393251 / 0.258489 (0.134762) | 0.460153 / 0.293841 (0.166312) | 0.047245 / 0.128546 (-0.081301) | 0.014086 / 0.075646 (-0.061560) | 0.421272 / 0.419271 (0.002001) | 0.067668 / 0.043533 (0.024135) | 0.397325 / 0.255139 (0.142186) | 0.432683 / 0.283200 (0.149483) | 0.039086 / 0.141683 (-0.102596) | 1.764898 / 1.452155 (0.312744) | 1.848820 / 1.492716 (0.356104) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.258163 / 0.018006 (0.240156) | 0.498655 / 0.000490 (0.498165) | 0.014959 / 0.000200 (0.014759) | 0.000465 / 0.000054 (0.000410) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028889 / 0.037411 (-0.008522) | 0.091568 / 0.014526 (0.077042) | 0.102700 / 0.176557 (-0.073857) | 0.173580 / 0.737135 (-0.563555) | 0.108763 / 0.296338 (-0.187576) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.610147 / 0.215209 (0.394938) | 5.851239 / 2.077655 (3.773584) | 2.467471 / 1.504120 (0.963351) | 2.117189 / 1.541195 (0.575995) | 2.197947 / 1.468490 (0.729457) | 0.851736 / 4.584777 (-3.733041) | 5.163183 / 3.745712 (1.417471) | 5.039564 / 5.269862 (-0.230297) | 3.067215 / 4.565676 (-1.498462) | 0.098593 / 0.424275 (-0.325682) | 0.008646 / 0.007607 (0.001038) | 0.788397 / 0.226044 (0.562352) | 7.340837 / 2.268929 (5.071909) | 3.511611 / 55.444624 (-51.933013) | 2.767479 / 6.876477 (-4.108998) | 2.687368 / 2.142072 (0.545296) | 1.046387 / 4.805227 (-3.758841) | 0.215902 / 6.500664 (-6.284763) | 0.072939 / 0.075469 (-0.002530) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.512795 / 1.841788 (-0.328992) | 22.086131 / 8.074308 (14.011823) | 20.235550 / 10.191392 (10.044158) | 0.240381 / 0.680424 (-0.440043) | 0.029171 / 0.534201 (-0.505030) | 0.465123 / 0.579283 (-0.114160) | 0.569260 / 0.434364 (0.134896) | 0.540967 / 0.540337 (0.000629) | 0.764006 / 1.386936 (-0.622930) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011024 / 0.011353 (-0.000329) | 0.005915 / 0.011008 (-0.005094) | 0.076455 / 0.038508 (0.037947) | 0.087842 / 0.023109 (0.064733) | 0.471732 / 0.275898 (0.195834) | 0.513666 / 0.323480 (0.190186) | 0.007062 / 0.007986 (-0.000924) | 0.004013 / 0.004328 (-0.000315) | 0.076016 / 0.004250 (0.071766) | 0.061296 / 0.037052 (0.024244) | 0.487277 / 0.258489 (0.228788) | 0.508185 / 0.293841 (0.214344) | 0.049963 / 0.128546 (-0.078583) | 0.013774 / 0.075646 (-0.061873) | 0.089376 / 0.419271 (-0.329895) | 0.067502 / 0.043533 (0.023969) | 0.471283 / 0.255139 (0.216144) | 0.507365 / 0.283200 (0.224165) | 0.033638 / 0.141683 (-0.108045) | 1.785544 / 1.452155 (0.333390) | 1.878765 / 1.492716 (0.386048) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230462 / 0.018006 (0.212456) | 0.502458 / 0.000490 (0.501968) | 0.005987 / 0.000200 (0.005787) | 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) | 0.031588 / 0.037411 (-0.005824) | 0.113566 / 0.014526 (0.099040) | 0.115734 / 0.176557 (-0.060822) | 0.174162 / 0.737135 (-0.562974) | 0.121574 / 0.296338 (-0.174764) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.662837 / 0.215209 (0.447628) | 6.420327 / 2.077655 (4.342672) | 3.033522 / 1.504120 (1.529402) | 2.728294 / 1.541195 (1.187099) | 2.790621 / 1.468490 (1.322131) | 0.852478 / 4.584777 (-3.732299) | 5.033637 / 3.745712 (1.287925) | 4.543152 / 5.269862 (-0.726709) | 2.980261 / 4.565676 (-1.585415) | 0.102444 / 0.424275 (-0.321831) | 0.008362 / 0.007607 (0.000755) | 0.786868 / 0.226044 (0.560823) | 7.887665 / 2.268929 (5.618737) | 4.010614 / 55.444624 (-51.434010) | 3.220715 / 6.876477 (-3.655762) | 3.317316 / 2.142072 (1.175244) | 1.098137 / 4.805227 (-3.707090) | 0.218309 / 6.500664 (-6.282355) | 0.078182 / 0.075469 (0.002713) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.696740 / 1.841788 (-0.145047) | 23.762454 / 8.074308 (15.688146) | 21.802645 / 10.191392 (11.611253) | 0.233654 / 0.680424 (-0.446770) | 0.032911 / 0.534201 (-0.501290) | 0.511760 / 0.579283 (-0.067524) | 0.586299 / 0.434364 (0.151935) | 0.583704 / 0.540337 (0.043367) | 0.780762 / 1.386936 (-0.606174) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0a94aa2f738075bbc08291583f1b153220d5e6e7 \"CML watermark\")\n"
] |
1,956,740,818
| 6,335
|
Support fsspec 2023.10.0
|
closed
| 2023-10-23T09:29:17
| 2024-01-11T06:33:35
| 2023-11-14T14:17:40
|
https://github.com/huggingface/datasets/pull/6335
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6335",
"html_url": "https://github.com/huggingface/datasets/pull/6335",
"diff_url": "https://github.com/huggingface/datasets/pull/6335.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6335.patch",
"merged_at": null
}
|
albertvillanova
| true
|
[
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006013 / 0.011353 (-0.005340) | 0.003647 / 0.011008 (-0.007362) | 0.081781 / 0.038508 (0.043273) | 0.059020 / 0.023109 (0.035911) | 0.321823 / 0.275898 (0.045925) | 0.350159 / 0.323480 (0.026679) | 0.003599 / 0.007986 (-0.004386) | 0.002877 / 0.004328 (-0.001452) | 0.063941 / 0.004250 (0.059690) | 0.049460 / 0.037052 (0.012408) | 0.330185 / 0.258489 (0.071696) | 0.362220 / 0.293841 (0.068379) | 0.027613 / 0.128546 (-0.100934) | 0.007976 / 0.075646 (-0.067670) | 0.263386 / 0.419271 (-0.155885) | 0.045504 / 0.043533 (0.001971) | 0.321172 / 0.255139 (0.066033) | 0.345291 / 0.283200 (0.062091) | 0.023133 / 0.141683 (-0.118550) | 1.435816 / 1.452155 (-0.016339) | 1.557241 / 1.492716 (0.064524) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222228 / 0.018006 (0.204222) | 0.420008 / 0.000490 (0.419518) | 0.008598 / 0.000200 (0.008398) | 0.000343 / 0.000054 (0.000288) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023725 / 0.037411 (-0.013686) | 0.073023 / 0.014526 (0.058497) | 0.814888 / 0.176557 (0.638332) | 0.294122 / 0.737135 (-0.443013) | 0.088945 / 0.296338 (-0.207393) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.393561 / 0.215209 (0.178352) | 3.946544 / 2.077655 (1.868890) | 1.916476 / 1.504120 (0.412356) | 1.721544 / 1.541195 (0.180349) | 1.768583 / 1.468490 (0.300093) | 0.508067 / 4.584777 (-4.076710) | 3.047832 / 3.745712 (-0.697880) | 2.952842 / 5.269862 (-2.317020) | 1.869337 / 4.565676 (-2.696339) | 0.057812 / 0.424275 (-0.366463) | 0.006694 / 0.007607 (-0.000913) | 0.463007 / 0.226044 (0.236963) | 4.635087 / 2.268929 (2.366158) | 2.419833 / 55.444624 (-53.024792) | 2.018519 / 6.876477 (-4.857958) | 2.043430 / 2.142072 (-0.098643) | 0.590895 / 4.805227 (-4.214333) | 0.126113 / 6.500664 (-6.374552) | 0.061045 / 0.075469 (-0.014424) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.226850 / 1.841788 (-0.614937) | 17.336630 / 8.074308 (9.262322) | 13.651049 / 10.191392 (3.459656) | 0.143308 / 0.680424 (-0.537116) | 0.016938 / 0.534201 (-0.517263) | 0.332829 / 0.579283 (-0.246454) | 0.368684 / 0.434364 (-0.065680) | 0.385848 / 0.540337 (-0.154489) | 0.546391 / 1.386936 (-0.840545) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006149 / 0.011353 (-0.005204) | 0.003818 / 0.011008 (-0.007191) | 0.064012 / 0.038508 (0.025504) | 0.059846 / 0.023109 (0.036737) | 0.455928 / 0.275898 (0.180030) | 0.480736 / 0.323480 (0.157256) | 0.004874 / 0.007986 (-0.003111) | 0.002877 / 0.004328 (-0.001451) | 0.064195 / 0.004250 (0.059944) | 0.048146 / 0.037052 (0.011094) | 0.452638 / 0.258489 (0.194149) | 0.484339 / 0.293841 (0.190499) | 0.028832 / 0.128546 (-0.099715) | 0.008162 / 0.075646 (-0.067485) | 0.069855 / 0.419271 (-0.349417) | 0.041429 / 0.043533 (-0.002104) | 0.453282 / 0.255139 (0.198143) | 0.473812 / 0.283200 (0.190613) | 0.021186 / 0.141683 (-0.120497) | 1.465207 / 1.452155 (0.013052) | 1.508216 / 1.492716 (0.015500) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242491 / 0.018006 (0.224485) | 0.421219 / 0.000490 (0.420730) | 0.011201 / 0.000200 (0.011001) | 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) | 0.027015 / 0.037411 (-0.010396) | 0.080465 / 0.014526 (0.065939) | 0.092622 / 0.176557 (-0.083934) | 0.146111 / 0.737135 (-0.591024) | 0.091546 / 0.296338 (-0.204793) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.458351 / 0.215209 (0.243142) | 4.591454 / 2.077655 (2.513799) | 2.508156 / 1.504120 (1.004037) | 2.328771 / 1.541195 (0.787576) | 2.423251 / 1.468490 (0.954761) | 0.508504 / 4.584777 (-4.076273) | 3.133789 / 3.745712 (-0.611923) | 2.862777 / 5.269862 (-2.407084) | 1.886327 / 4.565676 (-2.679350) | 0.058017 / 0.424275 (-0.366258) | 0.006496 / 0.007607 (-0.001111) | 0.529629 / 0.226044 (0.303585) | 5.310338 / 2.268929 (3.041409) | 2.973075 / 55.444624 (-52.471549) | 2.601313 / 6.876477 (-4.275163) | 2.777348 / 2.142072 (0.635275) | 0.593711 / 4.805227 (-4.211516) | 0.125453 / 6.500664 (-6.375211) | 0.061034 / 0.075469 (-0.014435) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.374391 / 1.841788 (-0.467397) | 18.768026 / 8.074308 (10.693718) | 15.053637 / 10.191392 (4.862245) | 0.158253 / 0.680424 (-0.522171) | 0.018126 / 0.534201 (-0.516075) | 0.337427 / 0.579283 (-0.241856) | 0.391678 / 0.434364 (-0.042686) | 0.398524 / 0.540337 (-0.141813) | 0.558629 / 1.386936 (-0.828307) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e0b79660f180c88517884f831eca620bc46a0957 \"CML watermark\")\n",
"I think https://github.com/huggingface/datasets/pull/6334 fixes it already no ?",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006432 / 0.011353 (-0.004921) | 0.003861 / 0.011008 (-0.007147) | 0.084132 / 0.038508 (0.045624) | 0.069391 / 0.023109 (0.046282) | 0.341081 / 0.275898 (0.065183) | 0.375975 / 0.323480 (0.052495) | 0.003962 / 0.007986 (-0.004024) | 0.003235 / 0.004328 (-0.001094) | 0.064927 / 0.004250 (0.060677) | 0.054190 / 0.037052 (0.017137) | 0.350719 / 0.258489 (0.092230) | 0.393216 / 0.293841 (0.099375) | 0.031002 / 0.128546 (-0.097544) | 0.008416 / 0.075646 (-0.067230) | 0.289268 / 0.419271 (-0.130003) | 0.052167 / 0.043533 (0.008634) | 0.347559 / 0.255139 (0.092420) | 0.370908 / 0.283200 (0.087709) | 0.022540 / 0.141683 (-0.119142) | 1.486297 / 1.452155 (0.034143) | 1.576968 / 1.492716 (0.084252) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237048 / 0.018006 (0.219042) | 0.452065 / 0.000490 (0.451575) | 0.013963 / 0.000200 (0.013763) | 0.000242 / 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) | 0.028084 / 0.037411 (-0.009327) | 0.081271 / 0.014526 (0.066745) | 0.096490 / 0.176557 (-0.080067) | 0.152106 / 0.737135 (-0.585030) | 0.096174 / 0.296338 (-0.200164) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.386585 / 0.215209 (0.171375) | 3.854996 / 2.077655 (1.777342) | 1.832898 / 1.504120 (0.328778) | 1.662832 / 1.541195 (0.121638) | 1.730753 / 1.468490 (0.262263) | 0.485286 / 4.584777 (-4.099491) | 3.571410 / 3.745712 (-0.174302) | 3.373035 / 5.269862 (-1.896826) | 1.995570 / 4.565676 (-2.570107) | 0.056711 / 0.424275 (-0.367564) | 0.007447 / 0.007607 (-0.000160) | 0.462985 / 0.226044 (0.236941) | 4.617186 / 2.268929 (2.348257) | 2.313915 / 55.444624 (-53.130709) | 1.961697 / 6.876477 (-4.914780) | 1.990410 / 2.142072 (-0.151662) | 0.580536 / 4.805227 (-4.224692) | 0.146275 / 6.500664 (-6.354389) | 0.059458 / 0.075469 (-0.016011) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.274841 / 1.841788 (-0.566947) | 18.641853 / 8.074308 (10.567545) | 13.977525 / 10.191392 (3.786133) | 0.151469 / 0.680424 (-0.528955) | 0.018111 / 0.534201 (-0.516090) | 0.393243 / 0.579283 (-0.186040) | 0.412310 / 0.434364 (-0.022054) | 0.461646 / 0.540337 (-0.078692) | 0.633016 / 1.386936 (-0.753920) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006496 / 0.011353 (-0.004857) | 0.003973 / 0.011008 (-0.007035) | 0.064527 / 0.038508 (0.026019) | 0.069390 / 0.023109 (0.046281) | 0.401162 / 0.275898 (0.125264) | 0.431031 / 0.323480 (0.107551) | 0.005244 / 0.007986 (-0.002741) | 0.003283 / 0.004328 (-0.001046) | 0.064931 / 0.004250 (0.060680) | 0.054402 / 0.037052 (0.017350) | 0.397917 / 0.258489 (0.139428) | 0.436728 / 0.293841 (0.142887) | 0.031932 / 0.128546 (-0.096614) | 0.008557 / 0.075646 (-0.067089) | 0.073336 / 0.419271 (-0.345935) | 0.047559 / 0.043533 (0.004026) | 0.395825 / 0.255139 (0.140686) | 0.423002 / 0.283200 (0.139802) | 0.021708 / 0.141683 (-0.119975) | 1.501140 / 1.452155 (0.048985) | 1.558376 / 1.492716 (0.065660) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.289522 / 0.018006 (0.271516) | 0.449078 / 0.000490 (0.448589) | 0.034174 / 0.000200 (0.033974) | 0.000396 / 0.000054 (0.000342) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032533 / 0.037411 (-0.004878) | 0.093398 / 0.014526 (0.078872) | 0.106930 / 0.176557 (-0.069626) | 0.158743 / 0.737135 (-0.578393) | 0.106904 / 0.296338 (-0.189435) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.427479 / 0.215209 (0.212270) | 4.271758 / 2.077655 (2.194103) | 2.298770 / 1.504120 (0.794650) | 2.134906 / 1.541195 (0.593712) | 2.220487 / 1.468490 (0.751996) | 0.490506 / 4.584777 (-4.094270) | 3.593876 / 3.745712 (-0.151836) | 3.225656 / 5.269862 (-2.044205) | 2.004434 / 4.565676 (-2.561243) | 0.058015 / 0.424275 (-0.366260) | 0.007221 / 0.007607 (-0.000387) | 0.504928 / 0.226044 (0.278884) | 5.049547 / 2.268929 (2.780618) | 2.743843 / 55.444624 (-52.700781) | 2.398399 / 6.876477 (-4.478078) | 2.562939 / 2.142072 (0.420867) | 0.597229 / 4.805227 (-4.207998) | 0.134664 / 6.500664 (-6.366001) | 0.059612 / 0.075469 (-0.015857) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.369692 / 1.841788 (-0.472095) | 19.065326 / 8.074308 (10.991018) | 14.404508 / 10.191392 (4.213116) | 0.175809 / 0.680424 (-0.504615) | 0.020137 / 0.534201 (-0.514064) | 0.394043 / 0.579283 (-0.185240) | 0.424772 / 0.434364 (-0.009592) | 0.475587 / 0.540337 (-0.064751) | 0.644275 / 1.386936 (-0.742661) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#224977971accd63d97ba0a90cc108c4754055ebb \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007259 / 0.011353 (-0.004094) | 0.004396 / 0.011008 (-0.006612) | 0.096456 / 0.038508 (0.057948) | 0.078752 / 0.023109 (0.055643) | 0.359215 / 0.275898 (0.083317) | 0.396927 / 0.323480 (0.073448) | 0.005611 / 0.007986 (-0.002375) | 0.003687 / 0.004328 (-0.000641) | 0.072794 / 0.004250 (0.068544) | 0.059794 / 0.037052 (0.022741) | 0.372352 / 0.258489 (0.113863) | 0.414038 / 0.293841 (0.120197) | 0.034490 / 0.128546 (-0.094056) | 0.009790 / 0.075646 (-0.065857) | 0.326338 / 0.419271 (-0.092934) | 0.058582 / 0.043533 (0.015049) | 0.354221 / 0.255139 (0.099082) | 0.386669 / 0.283200 (0.103469) | 0.025356 / 0.141683 (-0.116327) | 1.664104 / 1.452155 (0.211950) | 1.766825 / 1.492716 (0.274108) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.251107 / 0.018006 (0.233101) | 0.478833 / 0.000490 (0.478344) | 0.010776 / 0.000200 (0.010577) | 0.000292 / 0.000054 (0.000238) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032869 / 0.037411 (-0.004543) | 0.098449 / 0.014526 (0.083923) | 0.109954 / 0.176557 (-0.066602) | 0.176786 / 0.737135 (-0.560350) | 0.113477 / 0.296338 (-0.182862) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431169 / 0.215209 (0.215960) | 4.303239 / 2.077655 (2.225585) | 2.088885 / 1.504120 (0.584765) | 1.895900 / 1.541195 (0.354706) | 1.997442 / 1.468490 (0.528952) | 0.541840 / 4.584777 (-4.042937) | 3.991982 / 3.745712 (0.246270) | 3.842421 / 5.269862 (-1.427440) | 2.281150 / 4.565676 (-2.284526) | 0.063851 / 0.424275 (-0.360425) | 0.008470 / 0.007607 (0.000863) | 0.515886 / 0.226044 (0.289841) | 5.202908 / 2.268929 (2.933980) | 2.662789 / 55.444624 (-52.781835) | 2.266731 / 6.876477 (-4.609746) | 2.343760 / 2.142072 (0.201688) | 0.641050 / 4.805227 (-4.164177) | 0.148236 / 6.500664 (-6.352428) | 0.067422 / 0.075469 (-0.008047) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.475729 / 1.841788 (-0.366059) | 22.401583 / 8.074308 (14.327274) | 15.886237 / 10.191392 (5.694845) | 0.171828 / 0.680424 (-0.508595) | 0.022161 / 0.534201 (-0.512040) | 0.465873 / 0.579283 (-0.113411) | 0.476386 / 0.434364 (0.042022) | 0.538317 / 0.540337 (-0.002020) | 0.754375 / 1.386936 (-0.632561) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007429 / 0.011353 (-0.003924) | 0.004592 / 0.011008 (-0.006416) | 0.072315 / 0.038508 (0.033807) | 0.080806 / 0.023109 (0.057697) | 0.444607 / 0.275898 (0.168709) | 0.476970 / 0.323480 (0.153490) | 0.006030 / 0.007986 (-0.001956) | 0.003755 / 0.004328 (-0.000573) | 0.074602 / 0.004250 (0.070352) | 0.061846 / 0.037052 (0.024794) | 0.450928 / 0.258489 (0.192439) | 0.493932 / 0.293841 (0.200091) | 0.037398 / 0.128546 (-0.091148) | 0.009807 / 0.075646 (-0.065840) | 0.080531 / 0.419271 (-0.338741) | 0.054052 / 0.043533 (0.010519) | 0.453034 / 0.255139 (0.197895) | 0.464959 / 0.283200 (0.181760) | 0.024718 / 0.141683 (-0.116965) | 1.687552 / 1.452155 (0.235397) | 1.765746 / 1.492716 (0.273029) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.266998 / 0.018006 (0.248992) | 0.479832 / 0.000490 (0.479342) | 0.005429 / 0.000200 (0.005229) | 0.000117 / 0.000054 (0.000062) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038885 / 0.037411 (0.001474) | 0.105931 / 0.014526 (0.091405) | 0.120880 / 0.176557 (-0.055677) | 0.184006 / 0.737135 (-0.553130) | 0.120750 / 0.296338 (-0.175589) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.478626 / 0.215209 (0.263417) | 4.797355 / 2.077655 (2.719700) | 2.582758 / 1.504120 (1.078638) | 2.396488 / 1.541195 (0.855293) | 2.515597 / 1.468490 (1.047107) | 0.544541 / 4.584777 (-4.040236) | 4.150702 / 3.745712 (0.404990) | 3.676837 / 5.269862 (-1.593024) | 2.287275 / 4.565676 (-2.278402) | 0.064602 / 0.424275 (-0.359673) | 0.008253 / 0.007607 (0.000646) | 0.576201 / 0.226044 (0.350157) | 5.859839 / 2.268929 (3.590910) | 3.248603 / 55.444624 (-52.196021) | 2.841959 / 6.876477 (-4.034518) | 2.991120 / 2.142072 (0.849047) | 0.667755 / 4.805227 (-4.137472) | 0.151219 / 6.500664 (-6.349445) | 0.068990 / 0.075469 (-0.006479) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.572359 / 1.841788 (-0.269429) | 21.890279 / 8.074308 (13.815971) | 15.927473 / 10.191392 (5.736081) | 0.170388 / 0.680424 (-0.510036) | 0.023282 / 0.534201 (-0.510919) | 0.459371 / 0.579283 (-0.119912) | 0.468838 / 0.434364 (0.034475) | 0.546438 / 0.540337 (0.006101) | 0.746912 / 1.386936 (-0.640024) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8197ce872d2e24bd1ffbb07213faea25078f1386 \"CML watermark\")\n",
"Yes, @lhoestq, you are right. I think we cross-send fixing PRs in a 15 minute interval... :sweat_smile: \r\n\r\nI would say the code in this PR is simpler and easier to understand, but feel free to ignore it.",
"I think the correct way it to check if \"file\" in in the tuple if it's a tuple (in case someone adds another protocol name for the local filesystem)"
] |
1,956,719,774
| 6,334
|
datasets.filesystems: fix is_remote_filesystems
|
closed
| 2023-10-23T09:17:54
| 2024-02-07T12:41:15
| 2023-10-23T10:14:10
|
https://github.com/huggingface/datasets/pull/6334
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6334",
"html_url": "https://github.com/huggingface/datasets/pull/6334",
"diff_url": "https://github.com/huggingface/datasets/pull/6334.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6334.patch",
"merged_at": "2023-10-23T10:14:10"
}
|
ap--
| true
|
[
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006648 / 0.011353 (-0.004705) | 0.004104 / 0.011008 (-0.006904) | 0.084718 / 0.038508 (0.046210) | 0.075342 / 0.023109 (0.052232) | 0.332624 / 0.275898 (0.056726) | 0.376758 / 0.323480 (0.053278) | 0.005371 / 0.007986 (-0.002614) | 0.003317 / 0.004328 (-0.001011) | 0.065153 / 0.004250 (0.060902) | 0.055270 / 0.037052 (0.018218) | 0.342410 / 0.258489 (0.083920) | 0.397484 / 0.293841 (0.103643) | 0.031168 / 0.128546 (-0.097379) | 0.008545 / 0.075646 (-0.067101) | 0.297641 / 0.419271 (-0.121631) | 0.052404 / 0.043533 (0.008871) | 0.327633 / 0.255139 (0.072494) | 0.362177 / 0.283200 (0.078977) | 0.025056 / 0.141683 (-0.116627) | 1.459023 / 1.452155 (0.006868) | 1.529651 / 1.492716 (0.036935) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242838 / 0.018006 (0.224832) | 0.451007 / 0.000490 (0.450517) | 0.013732 / 0.000200 (0.013532) | 0.000345 / 0.000054 (0.000290) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028068 / 0.037411 (-0.009343) | 0.081970 / 0.014526 (0.067444) | 0.096148 / 0.176557 (-0.080409) | 0.151758 / 0.737135 (-0.585377) | 0.095617 / 0.296338 (-0.200721) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.389188 / 0.215209 (0.173979) | 3.867506 / 2.077655 (1.789852) | 1.941912 / 1.504120 (0.437792) | 1.759270 / 1.541195 (0.218076) | 1.774714 / 1.468490 (0.306224) | 0.476587 / 4.584777 (-4.108190) | 3.539342 / 3.745712 (-0.206370) | 3.434389 / 5.269862 (-1.835472) | 2.047581 / 4.565676 (-2.518096) | 0.056322 / 0.424275 (-0.367954) | 0.007286 / 0.007607 (-0.000321) | 0.461826 / 0.226044 (0.235781) | 4.604179 / 2.268929 (2.335251) | 2.405267 / 55.444624 (-53.039357) | 2.133998 / 6.876477 (-4.742479) | 2.187724 / 2.142072 (0.045652) | 0.566578 / 4.805227 (-4.238650) | 0.130007 / 6.500664 (-6.370657) | 0.059685 / 0.075469 (-0.015784) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.256204 / 1.841788 (-0.585584) | 18.829475 / 8.074308 (10.755167) | 13.937879 / 10.191392 (3.746487) | 0.163948 / 0.680424 (-0.516475) | 0.018118 / 0.534201 (-0.516083) | 0.389369 / 0.579283 (-0.189914) | 0.399988 / 0.434364 (-0.034376) | 0.459504 / 0.540337 (-0.080834) | 0.674696 / 1.386936 (-0.712240) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006806 / 0.011353 (-0.004547) | 0.004103 / 0.011008 (-0.006905) | 0.064477 / 0.038508 (0.025969) | 0.079514 / 0.023109 (0.056405) | 0.391657 / 0.275898 (0.115759) | 0.422997 / 0.323480 (0.099517) | 0.005485 / 0.007986 (-0.002501) | 0.003461 / 0.004328 (-0.000868) | 0.064621 / 0.004250 (0.060371) | 0.057686 / 0.037052 (0.020633) | 0.396885 / 0.258489 (0.138396) | 0.431508 / 0.293841 (0.137667) | 0.032305 / 0.128546 (-0.096241) | 0.008617 / 0.075646 (-0.067030) | 0.071577 / 0.419271 (-0.347694) | 0.047769 / 0.043533 (0.004236) | 0.394037 / 0.255139 (0.138898) | 0.412593 / 0.283200 (0.129393) | 0.023800 / 0.141683 (-0.117883) | 1.479114 / 1.452155 (0.026959) | 1.562422 / 1.492716 (0.069706) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229822 / 0.018006 (0.211816) | 0.452465 / 0.000490 (0.451975) | 0.005877 / 0.000200 (0.005677) | 0.000097 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033528 / 0.037411 (-0.003884) | 0.091819 / 0.014526 (0.077294) | 0.106188 / 0.176557 (-0.070368) | 0.159480 / 0.737135 (-0.577655) | 0.106326 / 0.296338 (-0.190013) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.427396 / 0.215209 (0.212187) | 4.275196 / 2.077655 (2.197541) | 2.287446 / 1.504120 (0.783326) | 2.137089 / 1.541195 (0.595894) | 2.198439 / 1.468490 (0.729949) | 0.491006 / 4.584777 (-4.093771) | 3.531067 / 3.745712 (-0.214645) | 3.264357 / 5.269862 (-2.005505) | 2.047760 / 4.565676 (-2.517916) | 0.057982 / 0.424275 (-0.366293) | 0.007278 / 0.007607 (-0.000329) | 0.507471 / 0.226044 (0.281426) | 5.073901 / 2.268929 (2.804973) | 2.781799 / 55.444624 (-52.662825) | 2.410759 / 6.876477 (-4.465718) | 2.623331 / 2.142072 (0.481258) | 0.601601 / 4.805227 (-4.203626) | 0.131461 / 6.500664 (-6.369204) | 0.060045 / 0.075469 (-0.015424) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.372946 / 1.841788 (-0.468842) | 19.560818 / 8.074308 (11.486509) | 14.388468 / 10.191392 (4.197076) | 0.177310 / 0.680424 (-0.503114) | 0.020233 / 0.534201 (-0.513967) | 0.395938 / 0.579283 (-0.183345) | 0.418336 / 0.434364 (-0.016028) | 0.471731 / 0.540337 (-0.068607) | 0.684679 / 1.386936 (-0.702257) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4bedb7dcbaedd292ae5764f0fe6d44c16e1c2c10 \"CML watermark\")\n",
"We did a patch release containing your fix @ap-- !"
] |
1,956,714,423
| 6,333
|
Support fsspec 2023.10.0
|
closed
| 2023-10-23T09:14:53
| 2024-02-07T12:39:58
| 2024-02-07T12:39:58
|
https://github.com/huggingface/datasets/issues/6333
| null |
albertvillanova
| false
|
[
"Hi @albertvillanova @lhoestq \r\n\r\nI believe the pull request that pins the fsspec version (https://github.com/huggingface/datasets/pull/6331) was merged by mistake. Another fix for the issue was merged on the same day an hour apart. See https://github.com/huggingface/datasets/pull/6334\r\n\r\nI'm now having an issue in my project where I can't use newer versions of fsspec.\r\n\r\nCan we remove the pin?\r\n\r\nHave a nice day! :)",
"Hi @tomscholz,\r\n\r\nThanks for pointing this out. I think you are right.\r\n\r\nI am doing some cross-checks and fixing it. ",
"Hi again, @tomscholz.\r\n\r\nAfter a more cautious investigation, I think the pin is OK because there are other reasons for it. Chronologically:\r\n- #6331 \r\n- #6334\r\n- #6336 \r\n- #6337 \r\n\r\nThe reason is that after version 2023.10.0, they changed again the behavior of their `glob` function. See: https://github.com/huggingface/datasets/pull/6337#issuecomment-1774930135\r\nWe are working on our side to support both previous and new glob behavior.\r\n\r\nNote:\r\n- First pin was < 2023.10.0\r\n- Last pin is <= 2023.10.0",
"Fixed by #6334 and #6336."
] |
1,956,697,328
| 6,332
|
Replace deprecated license_file in setup.cfg
|
closed
| 2023-10-23T09:05:26
| 2023-11-07T08:23:10
| 2023-11-07T08:09:06
|
https://github.com/huggingface/datasets/pull/6332
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6332",
"html_url": "https://github.com/huggingface/datasets/pull/6332",
"diff_url": "https://github.com/huggingface/datasets/pull/6332.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6332.patch",
"merged_at": "2023-11-07T08:09:06"
}
|
albertvillanova
| true
|
[
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006884 / 0.011353 (-0.004469) | 0.004132 / 0.011008 (-0.006877) | 0.085993 / 0.038508 (0.047485) | 0.084049 / 0.023109 (0.060940) | 0.346194 / 0.275898 (0.070296) | 0.386999 / 0.323480 (0.063519) | 0.004185 / 0.007986 (-0.003801) | 0.004354 / 0.004328 (0.000026) | 0.065137 / 0.004250 (0.060886) | 0.057629 / 0.037052 (0.020577) | 0.353639 / 0.258489 (0.095150) | 0.400815 / 0.293841 (0.106974) | 0.031370 / 0.128546 (-0.097176) | 0.008719 / 0.075646 (-0.066927) | 0.289579 / 0.419271 (-0.129693) | 0.052826 / 0.043533 (0.009293) | 0.351110 / 0.255139 (0.095971) | 0.375663 / 0.283200 (0.092464) | 0.025892 / 0.141683 (-0.115791) | 1.481943 / 1.452155 (0.029789) | 1.541494 / 1.492716 (0.048778) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.240007 / 0.018006 (0.222000) | 0.456216 / 0.000490 (0.455726) | 0.009348 / 0.000200 (0.009148) | 0.000370 / 0.000054 (0.000315) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029541 / 0.037411 (-0.007870) | 0.088394 / 0.014526 (0.073868) | 0.098460 / 0.176557 (-0.078096) | 0.154053 / 0.737135 (-0.583083) | 0.098821 / 0.296338 (-0.197518) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.386751 / 0.215209 (0.171542) | 3.809818 / 2.077655 (1.732164) | 1.833439 / 1.504120 (0.329319) | 1.686924 / 1.541195 (0.145729) | 1.796882 / 1.468490 (0.328392) | 0.488853 / 4.584777 (-4.095924) | 3.606369 / 3.745712 (-0.139343) | 3.460003 / 5.269862 (-1.809858) | 2.087493 / 4.565676 (-2.478184) | 0.056838 / 0.424275 (-0.367437) | 0.007679 / 0.007607 (0.000072) | 0.455080 / 0.226044 (0.229036) | 4.539227 / 2.268929 (2.270299) | 2.337245 / 55.444624 (-53.107379) | 1.988195 / 6.876477 (-4.888281) | 2.067473 / 2.142072 (-0.074600) | 0.576640 / 4.805227 (-4.228587) | 0.132140 / 6.500664 (-6.368525) | 0.060737 / 0.075469 (-0.014732) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.268866 / 1.841788 (-0.572922) | 19.695296 / 8.074308 (11.620988) | 14.431254 / 10.191392 (4.239862) | 0.166779 / 0.680424 (-0.513645) | 0.018262 / 0.534201 (-0.515939) | 0.390406 / 0.579283 (-0.188877) | 0.411284 / 0.434364 (-0.023080) | 0.456696 / 0.540337 (-0.083642) | 0.629660 / 1.386936 (-0.757276) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007210 / 0.011353 (-0.004143) | 0.004124 / 0.011008 (-0.006884) | 0.065877 / 0.038508 (0.027368) | 0.086242 / 0.023109 (0.063133) | 0.420087 / 0.275898 (0.144189) | 0.454327 / 0.323480 (0.130847) | 0.005586 / 0.007986 (-0.002399) | 0.003465 / 0.004328 (-0.000863) | 0.065153 / 0.004250 (0.060902) | 0.059337 / 0.037052 (0.022285) | 0.420913 / 0.258489 (0.162424) | 0.458552 / 0.293841 (0.164711) | 0.032335 / 0.128546 (-0.096211) | 0.008672 / 0.075646 (-0.066974) | 0.072029 / 0.419271 (-0.347242) | 0.048148 / 0.043533 (0.004615) | 0.423334 / 0.255139 (0.168196) | 0.440616 / 0.283200 (0.157416) | 0.023761 / 0.141683 (-0.117922) | 1.487022 / 1.452155 (0.034868) | 1.554028 / 1.492716 (0.061312) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216693 / 0.018006 (0.198687) | 0.446359 / 0.000490 (0.445869) | 0.005294 / 0.000200 (0.005094) | 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) | 0.034655 / 0.037411 (-0.002756) | 0.099479 / 0.014526 (0.084953) | 0.111822 / 0.176557 (-0.064735) | 0.160675 / 0.737135 (-0.576461) | 0.108718 / 0.296338 (-0.187621) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440270 / 0.215209 (0.225061) | 4.389013 / 2.077655 (2.311358) | 2.408007 / 1.504120 (0.903887) | 2.237233 / 1.541195 (0.696038) | 2.344131 / 1.468490 (0.875641) | 0.493143 / 4.584777 (-4.091634) | 3.620024 / 3.745712 (-0.125688) | 3.335810 / 5.269862 (-1.934052) | 2.079256 / 4.565676 (-2.486420) | 0.058324 / 0.424275 (-0.365951) | 0.007410 / 0.007607 (-0.000197) | 0.512057 / 0.226044 (0.286013) | 5.120629 / 2.268929 (2.851701) | 2.913268 / 55.444624 (-52.531356) | 2.558214 / 6.876477 (-4.318262) | 2.784146 / 2.142072 (0.642074) | 0.593308 / 4.805227 (-4.211920) | 0.134941 / 6.500664 (-6.365723) | 0.062292 / 0.075469 (-0.013177) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.351795 / 1.841788 (-0.489993) | 20.489559 / 8.074308 (12.415251) | 15.046116 / 10.191392 (4.854724) | 0.166339 / 0.680424 (-0.514085) | 0.020449 / 0.534201 (-0.513752) | 0.406570 / 0.579283 (-0.172713) | 0.423405 / 0.434364 (-0.010959) | 0.474541 / 0.540337 (-0.065796) | 0.653280 / 1.386936 (-0.733656) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3bde0f0f0e556e55b95c72b0f83bdcf7145c813c \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006362 / 0.011353 (-0.004991) | 0.003990 / 0.011008 (-0.007018) | 0.084020 / 0.038508 (0.045512) | 0.072198 / 0.023109 (0.049089) | 0.335992 / 0.275898 (0.060094) | 0.362056 / 0.323480 (0.038576) | 0.005298 / 0.007986 (-0.002688) | 0.003421 / 0.004328 (-0.000908) | 0.065343 / 0.004250 (0.061092) | 0.053310 / 0.037052 (0.016258) | 0.344855 / 0.258489 (0.086366) | 0.385524 / 0.293841 (0.091683) | 0.030209 / 0.128546 (-0.098337) | 0.008465 / 0.075646 (-0.067181) | 0.287359 / 0.419271 (-0.131912) | 0.051371 / 0.043533 (0.007838) | 0.338716 / 0.255139 (0.083577) | 0.351730 / 0.283200 (0.068530) | 0.023581 / 0.141683 (-0.118102) | 1.473772 / 1.452155 (0.021617) | 1.560594 / 1.492716 (0.067878) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.309019 / 0.018006 (0.291013) | 0.561428 / 0.000490 (0.560939) | 0.007237 / 0.000200 (0.007038) | 0.000266 / 0.000054 (0.000212) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028172 / 0.037411 (-0.009239) | 0.081050 / 0.014526 (0.066524) | 0.095952 / 0.176557 (-0.080604) | 0.151796 / 0.737135 (-0.585340) | 0.096132 / 0.296338 (-0.200206) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.384287 / 0.215209 (0.169078) | 3.840797 / 2.077655 (1.763142) | 1.891120 / 1.504120 (0.387000) | 1.743498 / 1.541195 (0.202303) | 1.821037 / 1.468490 (0.352547) | 0.484946 / 4.584777 (-4.099831) | 3.586053 / 3.745712 (-0.159659) | 3.446215 / 5.269862 (-1.823647) | 2.054352 / 4.565676 (-2.511325) | 0.057315 / 0.424275 (-0.366960) | 0.007541 / 0.007607 (-0.000066) | 0.464088 / 0.226044 (0.238044) | 4.634005 / 2.268929 (2.365076) | 2.355818 / 55.444624 (-53.088806) | 2.045584 / 6.876477 (-4.830893) | 2.039455 / 2.142072 (-0.102617) | 0.576137 / 4.805227 (-4.229090) | 0.132071 / 6.500664 (-6.368593) | 0.059611 / 0.075469 (-0.015858) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.280078 / 1.841788 (-0.561710) | 19.054079 / 8.074308 (10.979771) | 14.291090 / 10.191392 (4.099698) | 0.170607 / 0.680424 (-0.509817) | 0.018489 / 0.534201 (-0.515712) | 0.391802 / 0.579283 (-0.187481) | 0.418945 / 0.434364 (-0.015419) | 0.464084 / 0.540337 (-0.076254) | 0.638099 / 1.386936 (-0.748837) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006735 / 0.011353 (-0.004618) | 0.004133 / 0.011008 (-0.006876) | 0.064620 / 0.038508 (0.026112) | 0.076395 / 0.023109 (0.053286) | 0.399659 / 0.275898 (0.123761) | 0.426821 / 0.323480 (0.103341) | 0.006407 / 0.007986 (-0.001578) | 0.003472 / 0.004328 (-0.000857) | 0.064922 / 0.004250 (0.060671) | 0.058312 / 0.037052 (0.021260) | 0.403286 / 0.258489 (0.144797) | 0.437772 / 0.293841 (0.143931) | 0.032323 / 0.128546 (-0.096223) | 0.008727 / 0.075646 (-0.066919) | 0.071344 / 0.419271 (-0.347927) | 0.048673 / 0.043533 (0.005141) | 0.400693 / 0.255139 (0.145554) | 0.418668 / 0.283200 (0.135468) | 0.022871 / 0.141683 (-0.118812) | 1.517691 / 1.452155 (0.065536) | 1.552021 / 1.492716 (0.059305) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.305279 / 0.018006 (0.287272) | 0.520054 / 0.000490 (0.519564) | 0.007247 / 0.000200 (0.007047) | 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) | 0.032001 / 0.037411 (-0.005410) | 0.091273 / 0.014526 (0.076747) | 0.106480 / 0.176557 (-0.070077) | 0.163122 / 0.737135 (-0.574014) | 0.105244 / 0.296338 (-0.191094) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.432207 / 0.215209 (0.216998) | 4.304856 / 2.077655 (2.227202) | 2.326790 / 1.504120 (0.822670) | 2.150081 / 1.541195 (0.608886) | 2.150558 / 1.468490 (0.682068) | 0.488808 / 4.584777 (-4.095969) | 3.690435 / 3.745712 (-0.055277) | 3.302625 / 5.269862 (-1.967236) | 2.044193 / 4.565676 (-2.521483) | 0.057520 / 0.424275 (-0.366755) | 0.007281 / 0.007607 (-0.000326) | 0.521078 / 0.226044 (0.295034) | 5.162620 / 2.268929 (2.893691) | 2.744041 / 55.444624 (-52.700583) | 2.407211 / 6.876477 (-4.469266) | 2.606290 / 2.142072 (0.464217) | 0.586412 / 4.805227 (-4.218815) | 0.132152 / 6.500664 (-6.368512) | 0.059424 / 0.075469 (-0.016045) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.351879 / 1.841788 (-0.489908) | 19.460608 / 8.074308 (11.386299) | 14.643413 / 10.191392 (4.452021) | 0.168062 / 0.680424 (-0.512362) | 0.020396 / 0.534201 (-0.513805) | 0.395885 / 0.579283 (-0.183398) | 0.439551 / 0.434364 (0.005187) | 0.473051 / 0.540337 (-0.067286) | 0.644614 / 1.386936 (-0.742322) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#732b2ed47728fffc8d74f92691c21de8ac7423fe \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.014708 / 0.011353 (0.003355) | 0.008309 / 0.011008 (-0.002699) | 0.138986 / 0.038508 (0.100478) | 0.121781 / 0.023109 (0.098671) | 0.495536 / 0.275898 (0.219637) | 0.565195 / 0.323480 (0.241715) | 0.008018 / 0.007986 (0.000032) | 0.004904 / 0.004328 (0.000575) | 0.080622 / 0.004250 (0.076371) | 0.078917 / 0.037052 (0.041865) | 0.489424 / 0.258489 (0.230935) | 0.540496 / 0.293841 (0.246656) | 0.061110 / 0.128546 (-0.067437) | 0.021443 / 0.075646 (-0.054203) | 0.395789 / 0.419271 (-0.023482) | 0.076727 / 0.043533 (0.033194) | 0.427808 / 0.255139 (0.172669) | 0.519672 / 0.283200 (0.236473) | 0.041607 / 0.141683 (-0.100076) | 2.098675 / 1.452155 (0.646520) | 2.175123 / 1.492716 (0.682407) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.275784 / 0.018006 (0.257777) | 0.707103 / 0.000490 (0.706613) | 0.011524 / 0.000200 (0.011324) | 0.000390 / 0.000054 (0.000336) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032897 / 0.037411 (-0.004514) | 0.123239 / 0.014526 (0.108713) | 0.151815 / 0.176557 (-0.024741) | 0.214790 / 0.737135 (-0.522345) | 0.139166 / 0.296338 (-0.157173) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.740662 / 0.215209 (0.525453) | 7.540376 / 2.077655 (5.462721) | 3.168207 / 1.504120 (1.664087) | 2.745663 / 1.541195 (1.204468) | 2.714020 / 1.468490 (1.245530) | 1.182632 / 4.584777 (-3.402145) | 6.365807 / 3.745712 (2.620095) | 6.317228 / 5.269862 (1.047366) | 4.061107 / 4.565676 (-0.504569) | 0.146939 / 0.424275 (-0.277336) | 0.011765 / 0.007607 (0.004158) | 0.910564 / 0.226044 (0.684519) | 9.020618 / 2.268929 (6.751689) | 4.180748 / 55.444624 (-51.263876) | 3.290257 / 6.876477 (-3.586220) | 3.363172 / 2.142072 (1.221099) | 1.239142 / 4.805227 (-3.566086) | 0.294965 / 6.500664 (-6.205699) | 0.088520 / 0.075469 (0.013051) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.867528 / 1.841788 (0.025741) | 29.494058 / 8.074308 (21.419750) | 31.386703 / 10.191392 (21.195311) | 0.302488 / 0.680424 (-0.377936) | 0.036116 / 0.534201 (-0.498085) | 0.622112 / 0.579283 (0.042829) | 0.775658 / 0.434364 (0.341294) | 0.632452 / 0.540337 (0.092115) | 0.909424 / 1.386936 (-0.477512) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.016002 / 0.011353 (0.004649) | 0.007007 / 0.011008 (-0.004002) | 0.100463 / 0.038508 (0.061955) | 0.124423 / 0.023109 (0.101314) | 0.556014 / 0.275898 (0.280116) | 0.600909 / 0.323480 (0.277429) | 0.007272 / 0.007986 (-0.000714) | 0.006743 / 0.004328 (0.002415) | 0.088575 / 0.004250 (0.084324) | 0.066003 / 0.037052 (0.028951) | 0.580080 / 0.258489 (0.321591) | 0.655567 / 0.293841 (0.361726) | 0.065295 / 0.128546 (-0.063252) | 0.021105 / 0.075646 (-0.054541) | 0.120044 / 0.419271 (-0.299227) | 0.081133 / 0.043533 (0.037600) | 0.570322 / 0.255139 (0.315183) | 0.581134 / 0.283200 (0.297934) | 0.046298 / 0.141683 (-0.095385) | 2.113200 / 1.452155 (0.661045) | 2.344187 / 1.492716 (0.851471) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.284517 / 0.018006 (0.266511) | 0.611834 / 0.000490 (0.611345) | 0.005581 / 0.000200 (0.005381) | 0.000153 / 0.000054 (0.000098) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.042162 / 0.037411 (0.004750) | 0.114496 / 0.014526 (0.099970) | 0.134034 / 0.176557 (-0.042523) | 0.201649 / 0.737135 (-0.535486) | 0.143235 / 0.296338 (-0.153103) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.764863 / 0.215209 (0.549654) | 7.603076 / 2.077655 (5.525421) | 3.318911 / 1.504120 (1.814791) | 2.939815 / 1.541195 (1.398620) | 2.870911 / 1.468490 (1.402421) | 1.171978 / 4.584777 (-3.412799) | 6.479933 / 3.745712 (2.734221) | 5.944387 / 5.269862 (0.674526) | 4.282625 / 4.565676 (-0.283051) | 0.123672 / 0.424275 (-0.300603) | 0.009666 / 0.007607 (0.002059) | 0.870683 / 0.226044 (0.644638) | 9.187788 / 2.268929 (6.918859) | 4.431818 / 55.444624 (-51.012807) | 3.460457 / 6.876477 (-3.416020) | 3.708198 / 2.142072 (1.566126) | 1.353673 / 4.805227 (-3.451554) | 0.264274 / 6.500664 (-6.236390) | 0.074943 / 0.075469 (-0.000526) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 2.073810 / 1.841788 (0.232023) | 29.182464 / 8.074308 (21.108156) | 30.527040 / 10.191392 (20.335648) | 0.307561 / 0.680424 (-0.372863) | 0.047384 / 0.534201 (-0.486817) | 0.662760 / 0.579283 (0.083477) | 0.768321 / 0.434364 (0.333957) | 0.692296 / 0.540337 (0.151959) | 0.955197 / 1.386936 (-0.431739) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1e82d6f017c7fc0ab6b65847c1e34772c880d3b7 \"CML watermark\")\n"
] |
1,956,671,256
| 6,331
|
Temporarily pin fsspec < 2023.10.0
|
closed
| 2023-10-23T08:51:50
| 2023-10-23T09:26:42
| 2023-10-23T09:17:55
|
https://github.com/huggingface/datasets/pull/6331
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6331",
"html_url": "https://github.com/huggingface/datasets/pull/6331",
"diff_url": "https://github.com/huggingface/datasets/pull/6331.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6331.patch",
"merged_at": "2023-10-23T09:17:55"
}
|
albertvillanova
| true
|
[
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009605 / 0.011353 (-0.001747) | 0.004864 / 0.011008 (-0.006144) | 0.114605 / 0.038508 (0.076097) | 0.090874 / 0.023109 (0.067765) | 0.429203 / 0.275898 (0.153305) | 0.489888 / 0.323480 (0.166408) | 0.006542 / 0.007986 (-0.001443) | 0.004585 / 0.004328 (0.000257) | 0.090251 / 0.004250 (0.086001) | 0.066612 / 0.037052 (0.029560) | 0.437491 / 0.258489 (0.179002) | 0.515196 / 0.293841 (0.221355) | 0.047756 / 0.128546 (-0.080791) | 0.013587 / 0.075646 (-0.062059) | 0.376960 / 0.419271 (-0.042311) | 0.069701 / 0.043533 (0.026168) | 0.430850 / 0.255139 (0.175711) | 0.475061 / 0.283200 (0.191861) | 0.034800 / 0.141683 (-0.106883) | 1.799947 / 1.452155 (0.347793) | 1.941863 / 1.492716 (0.449147) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.316685 / 0.018006 (0.298679) | 0.595098 / 0.000490 (0.594608) | 0.015447 / 0.000200 (0.015247) | 0.000463 / 0.000054 (0.000409) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.039364 / 0.037411 (0.001953) | 0.091295 / 0.014526 (0.076769) | 0.109380 / 0.176557 (-0.067177) | 0.185454 / 0.737135 (-0.551681) | 0.104476 / 0.296338 (-0.191862) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.626291 / 0.215209 (0.411082) | 5.869948 / 2.077655 (3.792293) | 2.466267 / 1.504120 (0.962147) | 2.183572 / 1.541195 (0.642377) | 2.208286 / 1.468490 (0.739796) | 0.817175 / 4.584777 (-3.767602) | 5.255141 / 3.745712 (1.509429) | 4.878668 / 5.269862 (-0.391193) | 2.917020 / 4.565676 (-1.648657) | 0.104995 / 0.424275 (-0.319280) | 0.008687 / 0.007607 (0.001080) | 0.678993 / 0.226044 (0.452948) | 7.004983 / 2.268929 (4.736054) | 3.444040 / 55.444624 (-52.000584) | 2.745075 / 6.876477 (-4.131402) | 2.720151 / 2.142072 (0.578078) | 0.995803 / 4.805227 (-3.809424) | 0.205928 / 6.500664 (-6.294736) | 0.077053 / 0.075469 (0.001584) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.587354 / 1.841788 (-0.254434) | 23.843227 / 8.074308 (15.768919) | 21.355771 / 10.191392 (11.164379) | 0.225593 / 0.680424 (-0.454831) | 0.029054 / 0.534201 (-0.505147) | 0.469676 / 0.579283 (-0.109607) | 0.582619 / 0.434364 (0.148255) | 0.576932 / 0.540337 (0.036594) | 0.946182 / 1.386936 (-0.440754) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009819 / 0.011353 (-0.001534) | 0.005562 / 0.011008 (-0.005446) | 0.075512 / 0.038508 (0.037004) | 0.084294 / 0.023109 (0.061185) | 0.549516 / 0.275898 (0.273618) | 0.550364 / 0.323480 (0.226884) | 0.006603 / 0.007986 (-0.001383) | 0.004587 / 0.004328 (0.000259) | 0.084040 / 0.004250 (0.079789) | 0.066815 / 0.037052 (0.029762) | 0.549224 / 0.258489 (0.290735) | 0.556213 / 0.293841 (0.262372) | 0.048538 / 0.128546 (-0.080008) | 0.014050 / 0.075646 (-0.061596) | 0.088955 / 0.419271 (-0.330317) | 0.062393 / 0.043533 (0.018860) | 0.528770 / 0.255139 (0.273631) | 0.564854 / 0.283200 (0.281655) | 0.033976 / 0.141683 (-0.107707) | 1.858558 / 1.452155 (0.406403) | 1.894616 / 1.492716 (0.401899) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.378597 / 0.018006 (0.360591) | 0.650586 / 0.000490 (0.650097) | 0.033179 / 0.000200 (0.032979) | 0.000477 / 0.000054 (0.000423) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031779 / 0.037411 (-0.005632) | 0.103393 / 0.014526 (0.088867) | 0.119810 / 0.176557 (-0.056747) | 0.192188 / 0.737135 (-0.544948) | 0.114545 / 0.296338 (-0.181794) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.623571 / 0.215209 (0.408362) | 6.350249 / 2.077655 (4.272594) | 3.207773 / 1.504120 (1.703653) | 2.861118 / 1.541195 (1.319923) | 2.864445 / 1.468490 (1.395955) | 0.827451 / 4.584777 (-3.757326) | 5.323860 / 3.745712 (1.578148) | 4.569197 / 5.269862 (-0.700665) | 2.967595 / 4.565676 (-1.598081) | 0.090926 / 0.424275 (-0.333349) | 0.007820 / 0.007607 (0.000213) | 0.731610 / 0.226044 (0.505565) | 7.342651 / 2.268929 (5.073723) | 3.781727 / 55.444624 (-51.662897) | 3.222100 / 6.876477 (-3.654377) | 3.546145 / 2.142072 (1.404073) | 1.030500 / 4.805227 (-3.774728) | 0.226563 / 6.500664 (-6.274101) | 0.078633 / 0.075469 (0.003164) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.733677 / 1.841788 (-0.108111) | 24.650616 / 8.074308 (16.576308) | 22.033745 / 10.191392 (11.842353) | 0.211055 / 0.680424 (-0.469369) | 0.031658 / 0.534201 (-0.502543) | 0.467190 / 0.579283 (-0.112094) | 0.598303 / 0.434364 (0.163939) | 0.569318 / 0.540337 (0.028981) | 0.825984 / 1.386936 (-0.560952) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7eef7749031232d0b29f7ca10e3fa9f997b19ef7 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006873 / 0.011353 (-0.004479) | 0.004174 / 0.011008 (-0.006835) | 0.085874 / 0.038508 (0.047366) | 0.074207 / 0.023109 (0.051098) | 0.307342 / 0.275898 (0.031444) | 0.339972 / 0.323480 (0.016493) | 0.005522 / 0.007986 (-0.002463) | 0.003576 / 0.004328 (-0.000753) | 0.065680 / 0.004250 (0.061430) | 0.056274 / 0.037052 (0.019222) | 0.313121 / 0.258489 (0.054632) | 0.364699 / 0.293841 (0.070858) | 0.031297 / 0.128546 (-0.097249) | 0.008652 / 0.075646 (-0.066994) | 0.288431 / 0.419271 (-0.130840) | 0.053081 / 0.043533 (0.009548) | 0.309076 / 0.255139 (0.053937) | 0.329251 / 0.283200 (0.046052) | 0.024840 / 0.141683 (-0.116843) | 1.484155 / 1.452155 (0.032001) | 1.598665 / 1.492716 (0.105949) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.270933 / 0.018006 (0.252927) | 0.565867 / 0.000490 (0.565377) | 0.006964 / 0.000200 (0.006764) | 0.000298 / 0.000054 (0.000244) |\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) | 0.081756 / 0.014526 (0.067230) | 0.095733 / 0.176557 (-0.080823) | 0.152426 / 0.737135 (-0.584710) | 0.096655 / 0.296338 (-0.199683) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.403992 / 0.215209 (0.188783) | 4.027230 / 2.077655 (1.949576) | 2.031102 / 1.504120 (0.526982) | 1.843727 / 1.541195 (0.302532) | 1.898342 / 1.468490 (0.429852) | 0.479186 / 4.584777 (-4.105591) | 3.488153 / 3.745712 (-0.257559) | 3.523953 / 5.269862 (-1.745909) | 2.078392 / 4.565676 (-2.487284) | 0.056104 / 0.424275 (-0.368171) | 0.007368 / 0.007607 (-0.000239) | 0.479630 / 0.226044 (0.253585) | 4.787400 / 2.268929 (2.518471) | 2.488268 / 55.444624 (-52.956356) | 2.229955 / 6.876477 (-4.646522) | 2.260468 / 2.142072 (0.118396) | 0.587934 / 4.805227 (-4.217294) | 0.147124 / 6.500664 (-6.353540) | 0.059954 / 0.075469 (-0.015515) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.283155 / 1.841788 (-0.558632) | 19.013574 / 8.074308 (10.939266) | 13.915188 / 10.191392 (3.723796) | 0.174101 / 0.680424 (-0.506323) | 0.018172 / 0.534201 (-0.516029) | 0.390322 / 0.579283 (-0.188961) | 0.405493 / 0.434364 (-0.028871) | 0.456914 / 0.540337 (-0.083424) | 0.635213 / 1.386936 (-0.751723) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006622 / 0.011353 (-0.004731) | 0.003997 / 0.011008 (-0.007011) | 0.064542 / 0.038508 (0.026034) | 0.074165 / 0.023109 (0.051056) | 0.392285 / 0.275898 (0.116387) | 0.423522 / 0.323480 (0.100042) | 0.006361 / 0.007986 (-0.001625) | 0.003463 / 0.004328 (-0.000866) | 0.064891 / 0.004250 (0.060641) | 0.058485 / 0.037052 (0.021433) | 0.425217 / 0.258489 (0.166728) | 0.435907 / 0.293841 (0.142066) | 0.031501 / 0.128546 (-0.097045) | 0.008575 / 0.075646 (-0.067071) | 0.072094 / 0.419271 (-0.347178) | 0.047904 / 0.043533 (0.004371) | 0.397174 / 0.255139 (0.142035) | 0.417940 / 0.283200 (0.134741) | 0.023324 / 0.141683 (-0.118358) | 1.517245 / 1.452155 (0.065090) | 1.586497 / 1.492716 (0.093781) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.268311 / 0.018006 (0.250305) | 0.561118 / 0.000490 (0.560628) | 0.004352 / 0.000200 (0.004152) | 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) | 0.033039 / 0.037411 (-0.004373) | 0.091596 / 0.014526 (0.077071) | 0.111520 / 0.176557 (-0.065036) | 0.161755 / 0.737135 (-0.575381) | 0.107681 / 0.296338 (-0.188657) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.427170 / 0.215209 (0.211961) | 4.252648 / 2.077655 (2.174994) | 2.257623 / 1.504120 (0.753503) | 2.098446 / 1.541195 (0.557251) | 2.128544 / 1.468490 (0.660054) | 0.496639 / 4.584777 (-4.088138) | 3.593385 / 3.745712 (-0.152328) | 3.396367 / 5.269862 (-1.873494) | 2.073369 / 4.565676 (-2.492308) | 0.058386 / 0.424275 (-0.365889) | 0.007515 / 0.007607 (-0.000093) | 0.502358 / 0.226044 (0.276313) | 5.015224 / 2.268929 (2.746296) | 2.735740 / 55.444624 (-52.708885) | 2.388368 / 6.876477 (-4.488109) | 2.682857 / 2.142072 (0.540785) | 0.595003 / 4.805227 (-4.210225) | 0.135419 / 6.500664 (-6.365245) | 0.062824 / 0.075469 (-0.012645) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.367507 / 1.841788 (-0.474281) | 19.569288 / 8.074308 (11.494979) | 14.748693 / 10.191392 (4.557301) | 0.198659 / 0.680424 (-0.481765) | 0.020954 / 0.534201 (-0.513247) | 0.414858 / 0.579283 (-0.164426) | 0.421226 / 0.434364 (-0.013138) | 0.477774 / 0.540337 (-0.062563) | 0.676173 / 1.386936 (-0.710763) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#87ed467af77d87ad7e38279cf9a24c341545cbac \"CML watermark\")\n"
] |
1,956,053,294
| 6,330
|
Latest fsspec==2023.10.0 issue with streaming datasets
|
closed
| 2023-10-22T20:57:10
| 2025-06-09T22:00:16
| 2023-10-23T09:17:56
|
https://github.com/huggingface/datasets/issues/6330
| null |
ZachNagengast
| false
|
[
"I also encountered a similar error below.\r\nAppreciate the team could shed some light on this issue.\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nNotImplementedError Traceback (most recent call last)\r\n[/home/ubuntu/work/EveryDream2trainer/prepare_dataset.ipynb](https://vscode-remote+ssh-002dremote-002braspberry-002dg5-002e4x.vscode-resource.vscode-cdn.net/home/ubuntu/work/EveryDream2trainer/prepare_dataset.ipynb) Cell 1 line 4\r\n [1](vscode-notebook-cell://ssh-remote%2Braspberry-g5.4x/home/ubuntu/work/EveryDream2trainer/prepare_dataset.ipynb#W0sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0) from datasets import load_dataset, load_dataset\r\n [3](vscode-notebook-cell://ssh-remote%2Braspberry-g5.4x/home/ubuntu/work/EveryDream2trainer/prepare_dataset.ipynb#W0sdnNjb2RlLXJlbW90ZQ%3D%3D?line=2) # ds = load_dataset(\"parquet\", data_dir=\"/home/ubuntu/work/EveryDream2trainer/datasets/monse_v1/data\")\r\n----> [4](vscode-notebook-cell://ssh-remote%2Braspberry-g5.4x/home/ubuntu/work/EveryDream2trainer/prepare_dataset.ipynb#W0sdnNjb2RlLXJlbW90ZQ%3D%3D?line=3) ds = load_dataset(\"Raspberry-ai/monse-v1\")\r\n\r\nFile [/opt/conda/envs/everydream/lib/python3.10/site-packages/datasets/load.py:1804](https://vscode-remote+ssh-002dremote-002braspberry-002dg5-002e4x.vscode-resource.vscode-cdn.net/opt/conda/envs/everydream/lib/python3.10/site-packages/datasets/load.py:1804), in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)\r\n 1800 # Build dataset for splits\r\n 1801 keep_in_memory = (\r\n 1802 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)\r\n 1803 )\r\n-> 1804 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)\r\n 1805 # Rename and cast features to match task schema\r\n 1806 if task is not None:\r\n\r\nFile [/opt/conda/envs/everydream/lib/python3.10/site-packages/datasets/builder.py:1108](https://vscode-remote+ssh-002dremote-002braspberry-002dg5-002e4x.vscode-resource.vscode-cdn.net/opt/conda/envs/everydream/lib/python3.10/site-packages/datasets/builder.py:1108), in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory)\r\n 1106 is_local = not is_remote_filesystem(self._fs)\r\n 1107 if not is_local:\r\n-> 1108 raise NotImplementedError(f\"Loading a dataset cached in a {type(self._fs).__name__} is not supported.\")\r\n 1109 if not os.path.exists(self._output_dir):\r\n 1110 raise FileNotFoundError(\r\n 1111 f\"Dataset {self.name}: could not find data in {self._output_dir}. Please make sure to call \"\r\n 1112 \"builder.download_and_prepare(), or use \"\r\n 1113 \"datasets.load_dataset() before trying to access the Dataset object.\"\r\n 1114 )\r\n\r\nNotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported.\r\n```\r\n\r\nCode to reproduce the issue:\r\n\r\n```\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"Raspberry-ai/monse-v1\")\r\n```\r\n\r\n\r\nDependencies:\r\n```\r\nPackage Version\r\n------------------------- ------------\r\nabsl-py 2.0.0\r\naccelerate 0.23.0\r\naiohttp 3.8.4\r\naiosignal 1.3.1\r\nantlr4-python3-runtime 4.9.3\r\nanyio 4.0.0\r\nappdirs 1.4.4\r\nargon2-cffi 23.1.0\r\nargon2-cffi-bindings 21.2.0\r\narrow 1.3.0\r\nasttokens 2.4.0\r\nasync-lru 2.0.4\r\nasync-timeout 4.0.3\r\nattrs 23.1.0\r\nBabel 2.13.0\r\nbackcall 0.2.0\r\nbeautifulsoup4 4.12.2\r\nbitsandbytes 0.41.1\r\nbleach 6.1.0\r\nbraceexpand 0.1.7\r\ncachetools 5.3.1\r\ncertifi 2023.7.22\r\ncffi 1.16.0\r\ncharset-normalizer 3.3.1\r\nclick 8.1.7\r\ncmake 3.27.7\r\ncolorama 0.4.6\r\ncomm 0.1.4\r\ncompel 1.1.6\r\ndatasets 2.11.0\r\ndebugpy 1.8.0\r\ndecorator 5.1.1\r\ndefusedxml 0.7.1\r\ndiffusers 0.18.0\r\ndill 0.3.6\r\ndocker-pycreds 0.4.0\r\ndowg 0.3.1\r\neinops 0.7.0\r\neinops-exts 0.0.4\r\nexceptiongroup 1.1.3\r\nexecuting 2.0.0\r\nfastjsonschema 2.18.1\r\nfilelock 3.12.4\r\nfqdn 1.5.1\r\nfrozenlist 1.4.0\r\nfsspec 2023.10.0\r\nftfy 6.1.1\r\ngitdb 4.0.11\r\nGitPython 3.1.40\r\ngoogle-auth 2.23.3\r\ngoogle-auth-oauthlib 1.1.0\r\ngrpcio 1.59.0\r\nhuggingface-hub 0.18.0\r\nidna 3.4\r\nimportlib-metadata 6.8.0\r\ninflection 0.5.1\r\nipykernel 6.25.2\r\nipython 8.16.1\r\nisoduration 20.11.0\r\njedi 0.19.1\r\nJinja2 3.1.2\r\njoblib 1.3.2\r\njson5 0.9.14\r\njsonpointer 2.4\r\njsonschema 4.19.1\r\njsonschema-specifications 2023.7.1\r\njupyter_client 8.4.0\r\njupyter_core 5.4.0\r\njupyter-events 0.8.0\r\njupyter-lsp 2.2.0\r\njupyter_server 2.8.0\r\njupyter_server_terminals 0.4.4\r\njupyterlab 4.0.7\r\njupyterlab-pygments 0.2.2\r\njupyterlab_server 2.25.0\r\nlightning-utilities 0.9.0\r\nlion-pytorch 0.1.2\r\nlit 17.0.3\r\nMarkdown 3.5\r\nMarkupSafe 2.1.3\r\nmatplotlib-inline 0.1.6\r\nmistune 3.0.2\r\nmore-itertools 10.1.0\r\nmpmath 1.3.0\r\nmultidict 6.0.4\r\nmultiprocess 0.70.14\r\nmypy-extensions 1.0.0\r\nnbclient 0.8.0\r\nnbconvert 7.9.2\r\nnbformat 5.9.2\r\nnest-asyncio 1.5.8\r\nnetworkx 3.2\r\nnltk 3.8.1\r\nnotebook_shim 0.2.3\r\nnumpy 1.23.5\r\noauthlib 3.2.2\r\nomegaconf 2.2.3\r\nopen-clip-torch 2.22.0\r\nopen-flamingo 2.0.0\r\noverrides 7.4.0\r\npackaging 23.2\r\npandas 2.1.1\r\npandocfilters 1.5.0\r\nparso 0.8.3\r\npathtools 0.1.2\r\npexpect 4.8.0\r\npickleshare 0.7.5\r\nPillow 10.1.0\r\npip 23.3.1\r\nplatformdirs 3.11.0\r\nprometheus-client 0.17.1\r\nprompt-toolkit 3.0.39\r\nprotobuf 3.20.1\r\npsutil 5.9.6\r\nptyprocess 0.7.0\r\npure-eval 0.2.2\r\npyarrow 13.0.0\r\npyasn1 0.5.0\r\npyasn1-modules 0.3.0\r\npycparser 2.21\r\npyDeprecate 0.3.2\r\nPygments 2.16.1\r\npynvml 11.4.1\r\npyparsing 3.1.1\r\npyre-extensions 0.0.29\r\npython-dateutil 2.8.2\r\npython-json-logger 2.0.7\r\npytorch-lightning 1.6.5\r\npytz 2023.3.post1\r\nPyYAML 6.0.1\r\npyzmq 25.1.1\r\nreferencing 0.30.2\r\nregex 2023.10.3\r\nrequests 2.31.0\r\nrequests-oauthlib 1.3.1\r\nresponses 0.18.0\r\nrfc3339-validator 0.1.4\r\nrfc3986-validator 0.1.1\r\nrpds-py 0.10.6\r\nrsa 4.9\r\nsafetensors 0.4.0\r\nscipy 1.11.3\r\nSend2Trash 1.8.2\r\nsentencepiece 0.1.98\r\nsentry-sdk 1.32.0\r\nsetproctitle 1.3.3\r\nsetuptools 68.2.2\r\nsix 1.16.0\r\nsmmap 5.0.1\r\nsniffio 1.3.0\r\nsoupsieve 2.5\r\nstack-data 0.6.3\r\nsympy 1.12\r\ntensorboard 2.15.0\r\ntensorboard-data-server 0.7.1\r\nterminado 0.17.1\r\ntimm 0.9.8\r\ntinycss2 1.2.1\r\ntokenizers 0.13.3\r\ntomli 2.0.1\r\ntorch 2.0.1+cu118\r\ntorchmetrics 1.2.0\r\ntorchvision 0.15.2+cu118\r\ntornado 6.3.3\r\ntqdm 4.66.1\r\ntraitlets 5.11.2\r\ntransformers 4.29.2\r\ntriton 2.0.0\r\ntypes-python-dateutil 2.8.19.14\r\ntyping_extensions 4.8.0\r\ntyping-inspect 0.9.0\r\ntzdata 2023.3\r\nuri-template 1.3.0\r\nurllib3 2.0.7\r\nwandb 0.15.12\r\nwcwidth 0.2.8\r\nwebcolors 1.13\r\nwebdataset 0.2.62\r\nwebencodings 0.5.1\r\nwebsocket-client 1.6.4\r\nWerkzeug 3.0.0\r\nwheel 0.41.2\r\nxformers 0.0.20\r\nxxhash 3.4.1\r\nyarl 1.9.2\r\nzipp 3.17.0\r\n```",
"@humpydonkey FWIW setting fsspec down to 2023.9.2 fixed the issue\r\n\r\n`pip install fsspec==2023.9.2`",
"got it, thanks @ZachNagengast ",
"Thanks for reporting and for the investigation, @ZachNagengast! :hugs: \r\n\r\nWe are investigating the root cause of the issue. In the meantime, we are going to pin fsspec < 2023.10.0. ",
"https://stackoverflow.com/questions/77433096/notimplementederror-loading-a-dataset-cached-in-a-localfilesystem-is-not-suppor/77433141#77433141",
"You can also update `datasets`:\r\n\r\n```\r\npip install -U datasets\r\n```\r\n\r\nIt will also update `fsspec` to use the right version",
"This seems to work fine in 2.19.0. Hopefully it will not break again",
"not working for 2.19.1 !",
"Hi, just wished to follow up on this issue. I am recently using datasets==2.12.0 and fsspec==2023.9.2 and the same error: \"NotImplementedError: Loading a streaming dataset cached in a LocalFileSystem is not supported yet.\" popped up. I tried doing upgrading datasets to the latest version with `pip install -U datasets` and the same issue occurred. "
] |
1,955,858,020
| 6,329
|
شبکه های متن به گفتار ابتدا متن داده شده را به بازنمایی میانی
|
closed
| 2023-10-22T11:07:46
| 2023-10-23T09:22:58
| 2023-10-23T09:22:58
|
https://github.com/huggingface/datasets/issues/6329
| null |
shabnam706
| false
|
[] |
1,955,857,904
| 6,328
|
شبکه های متن به گفتار ابتدا متن داده شده را به بازنمایی میانی
|
closed
| 2023-10-22T11:07:21
| 2023-10-23T09:22:38
| 2023-10-23T09:22:38
|
https://github.com/huggingface/datasets/issues/6328
| null |
shabnam706
| false
|
[
"شبکه های متن به گفتار ابتدا متن داده شده را به بازنمایی میانی"
] |
1,955,470,755
| 6,327
|
FileNotFoundError when trying to load the downloaded dataset with `load_dataset(..., streaming=True)`
|
closed
| 2023-10-21T12:27:03
| 2023-10-23T18:50:07
| 2023-10-23T18:50:07
|
https://github.com/huggingface/datasets/issues/6327
| null |
yzhangcs
| false
|
[
"You can clone the `togethercomputer/RedPajama-Data-1T-Sample` repo and load the dataset with `load_dataset(\"path/to/cloned_repo\")` to use it offline.",
"@mariosasko Thank you for your kind reply! I'll try it as a workaround.\r\nDoes that mean that currently it's not supported to simply load with a short name?",
"It is, but manually downloading repo files to the cache can easily lead to failure (the HF cache is not meant to be modified by a user besides deleting the files 🙂), as in your case. Hence, the clone + `load_dataset(\"path/to/cloned_repo\")` workflow should be used instead."
] |
1,955,420,536
| 6,326
|
Create battery_analysis.py
|
closed
| 2023-10-21T10:07:48
| 2023-10-23T14:56:20
| 2023-10-23T14:56:20
|
https://github.com/huggingface/datasets/pull/6326
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6326",
"html_url": "https://github.com/huggingface/datasets/pull/6326",
"diff_url": "https://github.com/huggingface/datasets/pull/6326.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6326.patch",
"merged_at": null
}
|
vinitkm
| true
|
[] |
1,955,420,178
| 6,325
|
Create battery_analysis.py
|
closed
| 2023-10-21T10:06:37
| 2023-10-23T14:55:58
| 2023-10-23T14:55:58
|
https://github.com/huggingface/datasets/pull/6325
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6325",
"html_url": "https://github.com/huggingface/datasets/pull/6325",
"diff_url": "https://github.com/huggingface/datasets/pull/6325.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6325.patch",
"merged_at": null
}
|
vinitkm
| true
|
[] |
1,955,126,687
| 6,324
|
Conversion to Arrow fails due to wrong type heuristic
|
closed
| 2023-10-20T23:20:58
| 2023-10-23T20:52:57
| 2023-10-23T20:52:57
|
https://github.com/huggingface/datasets/issues/6324
| null |
jphme
| false
|
[
"Unlike Pandas, Arrow is strict with types, so converting the problematic strings to ints (or ints to strings) to ensure all the values have the same type is the only fix. \r\n\r\nJSON support has been requested in Arrow [here](https://github.com/apache/arrow/issues/32538), but I don't expect this to be implemented soon. \r\n\r\nAlso, this type could be represented with the Arrow Union type. However, due to low usage, the Union type has limited support in the Arrow ecosystem (e.g., IIRC Parquet still does not support it). So, we should probably wait a bit more before adding support for it in `datasets`",
"> Unlike Pandas, Arrow is strict with types, so converting the problematic strings to ints (or ints to strings) to ensure all the values have the same type is the only fix.\r\n> \r\n> JSON support has been requested in Arrow [here](https://github.com/apache/arrow/issues/32538), but I don't expect this to be implemented soon.\r\n> \r\n> Also, this type could be represented with the Arrow Union type. However, due to low usage, the Union type has limited support in the Arrow ecosystem (e.g., IIRC Parquet still does not support it). So, we should probably wait a bit more before adding support for it in `datasets`\r\n\r\nOk many thanks, I was able to mitigate the problem by manually checking and converting all problematic fields now."
] |
1,954,245,980
| 6,323
|
Loading dataset from large GCS bucket very slow since 2.14
|
open
| 2023-10-20T12:59:55
| 2024-09-03T18:42:33
| null |
https://github.com/huggingface/datasets/issues/6323
| null |
jbcdnr
| false
|
[
"I've also encountered this issue recently and want to ask if this has been seen.\r\n\r\n@albertvillanova for visibility - I'm not sure who the right person is to tag, but I noticed you were active recently so perhaps you can direct this to the right person.\r\n\r\nThanks!"
] |
1,952,947,461
| 6,322
|
Fix regex `get_data_files` formatting for base paths
|
closed
| 2023-10-19T19:45:10
| 2023-10-23T14:40:45
| 2023-10-23T14:31:21
|
https://github.com/huggingface/datasets/pull/6322
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6322",
"html_url": "https://github.com/huggingface/datasets/pull/6322",
"diff_url": "https://github.com/huggingface/datasets/pull/6322.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6322.patch",
"merged_at": "2023-10-23T14:31:21"
}
|
ZachNagengast
| true
|
[
"_The documentation is not available anymore as the PR was closed or merged._",
"> The reason why I used the the glob_pattern_to_regex in the entire pattern is because otherwise I got an error for Windows local paths: a base_path like 'C:\\\\Users\\\\runneradmin... made the function string_to_dict raise re.error: incomplete escape \\U at position 2\r\n\r\nWhat is the expected inputs and outputs for the windows `base_path`\r\n\r\n> That issue was fixed once we pass the base_path as POSIX.\r\n\r\nI'm not sure what you meant by that, are there still changes needed?\r\n",
"We took the liberty of continuing this PR to include it in today's patch release :)\r\nI hope you don't mind",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007109 / 0.011353 (-0.004244) | 0.004209 / 0.011008 (-0.006799) | 0.097401 / 0.038508 (0.058892) | 0.079532 / 0.023109 (0.056423) | 0.341300 / 0.275898 (0.065402) | 0.402165 / 0.323480 (0.078685) | 0.005838 / 0.007986 (-0.002148) | 0.003310 / 0.004328 (-0.001018) | 0.072804 / 0.004250 (0.068553) | 0.059418 / 0.037052 (0.022366) | 0.339277 / 0.258489 (0.080788) | 0.418495 / 0.293841 (0.124654) | 0.035975 / 0.128546 (-0.092571) | 0.008101 / 0.075646 (-0.067546) | 0.339236 / 0.419271 (-0.080035) | 0.059326 / 0.043533 (0.015794) | 0.326880 / 0.255139 (0.071741) | 0.393614 / 0.283200 (0.110414) | 0.025830 / 0.141683 (-0.115852) | 1.657726 / 1.452155 (0.205571) | 1.817250 / 1.492716 (0.324534) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.256015 / 0.018006 (0.238008) | 0.482447 / 0.000490 (0.481957) | 0.012166 / 0.000200 (0.011966) | 0.000343 / 0.000054 (0.000288) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029898 / 0.037411 (-0.007514) | 0.088218 / 0.014526 (0.073692) | 0.102353 / 0.176557 (-0.074203) | 0.165863 / 0.737135 (-0.571272) | 0.100342 / 0.296338 (-0.195996) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429362 / 0.215209 (0.214153) | 4.147327 / 2.077655 (2.069672) | 2.014653 / 1.504120 (0.510533) | 1.824394 / 1.541195 (0.283199) | 1.936408 / 1.468490 (0.467917) | 0.542960 / 4.584777 (-4.041817) | 3.917215 / 3.745712 (0.171503) | 3.714825 / 5.269862 (-1.555036) | 2.180279 / 4.565676 (-2.385398) | 0.057808 / 0.424275 (-0.366467) | 0.008426 / 0.007607 (0.000819) | 0.472372 / 0.226044 (0.246327) | 4.879656 / 2.268929 (2.610728) | 2.602729 / 55.444624 (-52.841896) | 2.142593 / 6.876477 (-4.733884) | 2.206070 / 2.142072 (0.063997) | 0.635591 / 4.805227 (-4.169636) | 0.140928 / 6.500664 (-6.359736) | 0.065119 / 0.075469 (-0.010350) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.455909 / 1.841788 (-0.385879) | 20.803592 / 8.074308 (12.729284) | 14.788713 / 10.191392 (4.597321) | 0.170546 / 0.680424 (-0.509878) | 0.021189 / 0.534201 (-0.513012) | 0.432368 / 0.579283 (-0.146915) | 0.444664 / 0.434364 (0.010300) | 0.517744 / 0.540337 (-0.022593) | 0.699265 / 1.386936 (-0.687671) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007592 / 0.011353 (-0.003760) | 0.004045 / 0.011008 (-0.006964) | 0.073434 / 0.038508 (0.034926) | 0.076962 / 0.023109 (0.053853) | 0.468873 / 0.275898 (0.192975) | 0.479968 / 0.323480 (0.156488) | 0.006270 / 0.007986 (-0.001716) | 0.003652 / 0.004328 (-0.000677) | 0.069893 / 0.004250 (0.065643) | 0.061902 / 0.037052 (0.024850) | 0.443379 / 0.258489 (0.184890) | 0.492627 / 0.293841 (0.198786) | 0.035967 / 0.128546 (-0.092579) | 0.009276 / 0.075646 (-0.066370) | 0.083060 / 0.419271 (-0.336212) | 0.050870 / 0.043533 (0.007337) | 0.438246 / 0.255139 (0.183107) | 0.472074 / 0.283200 (0.188874) | 0.023724 / 0.141683 (-0.117959) | 1.677178 / 1.452155 (0.225023) | 1.732273 / 1.492716 (0.239557) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.244693 / 0.018006 (0.226687) | 0.470067 / 0.000490 (0.469577) | 0.005574 / 0.000200 (0.005374) | 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) | 0.036242 / 0.037411 (-0.001169) | 0.099166 / 0.014526 (0.084641) | 0.116785 / 0.176557 (-0.059772) | 0.174986 / 0.737135 (-0.562149) | 0.118130 / 0.296338 (-0.178209) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.475907 / 0.215209 (0.260698) | 4.708125 / 2.077655 (2.630470) | 2.600855 / 1.504120 (1.096735) | 2.446498 / 1.541195 (0.905303) | 2.538786 / 1.468490 (1.070296) | 0.566787 / 4.584777 (-4.017990) | 4.066187 / 3.745712 (0.320475) | 3.743632 / 5.269862 (-1.526229) | 2.337737 / 4.565676 (-2.227939) | 0.068402 / 0.424275 (-0.355873) | 0.008674 / 0.007607 (0.001067) | 0.593428 / 0.226044 (0.367384) | 5.840687 / 2.268929 (3.571759) | 3.194937 / 55.444624 (-52.249688) | 2.899033 / 6.876477 (-3.977444) | 2.977870 / 2.142072 (0.835797) | 0.683673 / 4.805227 (-4.121554) | 0.154933 / 6.500664 (-6.345731) | 0.071619 / 0.075469 (-0.003850) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.501895 / 1.841788 (-0.339893) | 21.709792 / 8.074308 (13.635484) | 15.679556 / 10.191392 (5.488164) | 0.188028 / 0.680424 (-0.492396) | 0.022555 / 0.534201 (-0.511646) | 0.439840 / 0.579283 (-0.139443) | 0.452140 / 0.434364 (0.017776) | 0.526421 / 0.540337 (-0.013916) | 0.731692 / 1.386936 (-0.655244) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#02ecc84a2e2ed664f574ccbcab0e525a7377a01d \"CML watermark\")\n"
] |
1,952,643,483
| 6,321
|
Fix typos
|
closed
| 2023-10-19T16:24:35
| 2023-10-19T17:18:00
| 2023-10-19T17:07:35
|
https://github.com/huggingface/datasets/pull/6321
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6321",
"html_url": "https://github.com/huggingface/datasets/pull/6321",
"diff_url": "https://github.com/huggingface/datasets/pull/6321.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6321.patch",
"merged_at": "2023-10-19T17:07:35"
}
|
python273
| true
|
[
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007809 / 0.011353 (-0.003544) | 0.004573 / 0.011008 (-0.006435) | 0.101201 / 0.038508 (0.062693) | 0.089703 / 0.023109 (0.066594) | 0.416502 / 0.275898 (0.140604) | 0.463352 / 0.323480 (0.139872) | 0.006101 / 0.007986 (-0.001885) | 0.003783 / 0.004328 (-0.000545) | 0.076531 / 0.004250 (0.072281) | 0.064017 / 0.037052 (0.026964) | 0.422453 / 0.258489 (0.163964) | 0.485926 / 0.293841 (0.192085) | 0.036797 / 0.128546 (-0.091749) | 0.010172 / 0.075646 (-0.065474) | 0.344442 / 0.419271 (-0.074829) | 0.062240 / 0.043533 (0.018707) | 0.422685 / 0.255139 (0.167546) | 0.451457 / 0.283200 (0.168257) | 0.027831 / 0.141683 (-0.113852) | 1.737187 / 1.452155 (0.285033) | 1.847631 / 1.492716 (0.354915) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.270336 / 0.018006 (0.252330) | 0.500540 / 0.000490 (0.500050) | 0.017042 / 0.000200 (0.016842) | 0.000704 / 0.000054 (0.000650) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033450 / 0.037411 (-0.003962) | 0.100314 / 0.014526 (0.085788) | 0.117216 / 0.176557 (-0.059340) | 0.182352 / 0.737135 (-0.554784) | 0.114903 / 0.296338 (-0.181436) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.458562 / 0.215209 (0.243353) | 4.570492 / 2.077655 (2.492837) | 2.230286 / 1.504120 (0.726167) | 2.032229 / 1.541195 (0.491034) | 2.130431 / 1.468490 (0.661941) | 0.563254 / 4.584777 (-4.021523) | 4.108455 / 3.745712 (0.362743) | 3.994059 / 5.269862 (-1.275802) | 2.424589 / 4.565676 (-2.141087) | 0.067534 / 0.424275 (-0.356741) | 0.008774 / 0.007607 (0.001167) | 0.546356 / 0.226044 (0.320312) | 5.527772 / 2.268929 (3.258843) | 2.934410 / 55.444624 (-52.510215) | 2.536871 / 6.876477 (-4.339605) | 2.598704 / 2.142072 (0.456632) | 0.676721 / 4.805227 (-4.128506) | 0.155904 / 6.500664 (-6.344760) | 0.073274 / 0.075469 (-0.002195) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.559170 / 1.841788 (-0.282618) | 23.228524 / 8.074308 (15.154216) | 16.743246 / 10.191392 (6.551854) | 0.184113 / 0.680424 (-0.496310) | 0.021804 / 0.534201 (-0.512397) | 0.466158 / 0.579283 (-0.113125) | 0.539911 / 0.434364 (0.105547) | 0.544377 / 0.540337 (0.004040) | 0.765779 / 1.386936 (-0.621157) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008249 / 0.011353 (-0.003104) | 0.004734 / 0.011008 (-0.006275) | 0.077083 / 0.038508 (0.038575) | 0.096959 / 0.023109 (0.073850) | 0.497501 / 0.275898 (0.221603) | 0.530687 / 0.323480 (0.207207) | 0.006379 / 0.007986 (-0.001607) | 0.003899 / 0.004328 (-0.000430) | 0.076165 / 0.004250 (0.071915) | 0.069406 / 0.037052 (0.032354) | 0.515847 / 0.258489 (0.257358) | 0.540639 / 0.293841 (0.246798) | 0.038334 / 0.128546 (-0.090213) | 0.010112 / 0.075646 (-0.065534) | 0.084918 / 0.419271 (-0.334353) | 0.056866 / 0.043533 (0.013333) | 0.495555 / 0.255139 (0.240416) | 0.518988 / 0.283200 (0.235789) | 0.028556 / 0.141683 (-0.113127) | 1.799320 / 1.452155 (0.347165) | 1.874647 / 1.492716 (0.381931) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.264283 / 0.018006 (0.246277) | 0.510278 / 0.000490 (0.509788) | 0.015219 / 0.000200 (0.015019) | 0.000160 / 0.000054 (0.000105) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038462 / 0.037411 (0.001051) | 0.115420 / 0.014526 (0.100894) | 0.124250 / 0.176557 (-0.052306) | 0.187724 / 0.737135 (-0.549411) | 0.126674 / 0.296338 (-0.169664) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.499345 / 0.215209 (0.284136) | 4.983924 / 2.077655 (2.906269) | 2.705099 / 1.504120 (1.200980) | 2.516344 / 1.541195 (0.975149) | 2.621103 / 1.468490 (1.152613) | 0.583254 / 4.584777 (-4.001523) | 4.231215 / 3.745712 (0.485503) | 4.028326 / 5.269862 (-1.241536) | 2.459171 / 4.565676 (-2.106505) | 0.069194 / 0.424275 (-0.355081) | 0.008850 / 0.007607 (0.001243) | 0.593878 / 0.226044 (0.367834) | 5.926478 / 2.268929 (3.657549) | 3.287435 / 55.444624 (-52.157189) | 2.902104 / 6.876477 (-3.974372) | 3.151307 / 2.142072 (1.009234) | 0.696922 / 4.805227 (-4.108306) | 0.161140 / 6.500664 (-6.339524) | 0.073728 / 0.075469 (-0.001741) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.636456 / 1.841788 (-0.205331) | 23.884606 / 8.074308 (15.810298) | 17.180875 / 10.191392 (6.989483) | 0.176782 / 0.680424 (-0.503642) | 0.023731 / 0.534201 (-0.510470) | 0.475191 / 0.579283 (-0.104092) | 0.506603 / 0.434364 (0.072239) | 0.571976 / 0.540337 (0.031638) | 0.826935 / 1.386936 (-0.560002) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2b19f6b30f49e09b0d1f0c4a38b10d76f35ac483 \"CML watermark\")\n"
] |
1,952,618,316
| 6,320
|
Dataset slice splits can't load training and validation at the same time
|
closed
| 2023-10-19T16:09:22
| 2023-11-30T16:21:15
| 2023-11-30T16:21:15
|
https://github.com/huggingface/datasets/issues/6320
| null |
timlac
| false
|
[
"The expression \"train+test\" concatenates the splits.\r\n\r\nThe individual splits as separate datasets can be obtained as follows:\r\n```python\r\ntrain_ds, test_ds = load_dataset(\"<dataset_name>\", split=[\"train\", \"test\"])\r\ntrain_10pct_ds, test_10pct_ds = load_dataset(\"<dataset_name>\", split=[\"train[:10%]\", \"test[:%10]\"])\r\n```"
] |
1,952,101,717
| 6,319
|
Datasets.map is severely broken
|
open
| 2023-10-19T12:19:33
| 2024-08-08T17:05:08
| null |
https://github.com/huggingface/datasets/issues/6319
| null |
phalexo
| false
|
[
"Hi! Instead of processing a single example at a time, you should use the batched `map` for the best performance (with `num_proc=1`) - the fast tokenizers can process a batch's samples in parallel in that scenario.\r\n\r\nE.g., the following code in Colab takes an hour to complete:\r\n```python\r\n# !pip install datasets transformers\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\ntokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"]), batched=True, remove_columns=[\"text\", \"meta\"])\r\n```",
"Batched is far worse. A single batch of 1000 took hours and that was only 1%\r\n\r\n\r\nOn Thu, Oct 19, 2023, 2:26 PM Mario Šaško ***@***.***> wrote:\r\n\r\n> Hi! You should use the batched map for the best performance (with\r\n> num_proc=1) - the fast tokenizers can process a batch's samples in\r\n> parallel.\r\n>\r\n> E.g., the following code in Colab takes an hour to complete:\r\n>\r\n> # !pip install datasets transformersfrom datasets import load_datasetfrom transformers import AutoTokenizertokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"]), batched=True, remove_columns=[\"text\", \"meta\"])\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/6319#issuecomment-1771503757>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/ABDD3ZJHPSRVDEXFNMXR2N3YAFWFZAVCNFSM6AAAAAA6HDKPSCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTONZRGUYDGNZVG4>\r\n> .\r\n> You are receiving this because you authored the thread.Message ID:\r\n> ***@***.***>\r\n>\r\n",
"Can you please provide a self-contained reproducer?",
"Which specific version of datasets are you using?\r\n\r\nWhat is the architecture of your colab setup? Ram? Cores? OS?\r\n\r\n\r\nOn Thu, Oct 19, 2023, 2:27 PM pensive introvert ***@***.***>\r\nwrote:\r\n\r\n> Batched is far worse. A single batch of 1000 took hours and that was only\r\n> 1%\r\n>\r\n>\r\n> On Thu, Oct 19, 2023, 2:26 PM Mario Šaško ***@***.***>\r\n> wrote:\r\n>\r\n>> Hi! You should use the batched map for the best performance (with\r\n>> num_proc=1) - the fast tokenizers can process a batch's samples in\r\n>> parallel.\r\n>>\r\n>> E.g., the following code in Colab takes an hour to complete:\r\n>>\r\n>> # !pip install datasets transformersfrom datasets import load_datasetfrom transformers import AutoTokenizertokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"]), batched=True, remove_columns=[\"text\", \"meta\"])\r\n>>\r\n>> —\r\n>> Reply to this email directly, view it on GitHub\r\n>> <https://github.com/huggingface/datasets/issues/6319#issuecomment-1771503757>,\r\n>> or unsubscribe\r\n>> <https://github.com/notifications/unsubscribe-auth/ABDD3ZJHPSRVDEXFNMXR2N3YAFWFZAVCNFSM6AAAAAA6HDKPSCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTONZRGUYDGNZVG4>\r\n>> .\r\n>> You are receiving this because you authored the thread.Message ID:\r\n>> ***@***.***>\r\n>>\r\n>\r\n",
"from functools import partial\r\nimport transformers\r\nfrom datasets import load_dataset, concatenate_datasets, load_from_disk\r\n\r\nmodel_name_or_path=\"/opt/data/data/daryl149/llama-2-7b-chat-hf\"\r\noutput_dir=\"/opt/data/data/LongLoRA/checkpoints\"\r\ncache_dir=\"/opt/data/data/LongLoRA/cache\"\r\nmodel_max_length=16384\r\n\r\nIGNORE_INDEX = -100\r\nDEFAULT_PAD_TOKEN = \"[PAD]\"\r\nDEFAULT_EOS_TOKEN = \"</s>\"\r\nDEFAULT_BOS_TOKEN = \"<s>\"\r\nDEFAULT_UNK_TOKEN = \"<unk>\"\r\n\r\n\r\ntokenizer = transformers.LlamaTokenizerFast.from_pretrained(\r\n model_name_or_path,\r\n cache_dir=cache_dir,\r\n model_max_length=model_max_length,\r\n padding_side=\"right\",\r\n use_fast=True,\r\n #use_fast=False\r\n)\r\n\r\nspecial_tokens_dict = dict()\r\nif tokenizer.pad_token is None:\r\n special_tokens_dict[\"pad_token\"] = DEFAULT_PAD_TOKEN\r\nif tokenizer.eos_token is None:\r\n special_tokens_dict[\"eos_token\"] = DEFAULT_EOS_TOKEN\r\nif tokenizer.bos_token is None:\r\n special_tokens_dict[\"bos_token\"] = DEFAULT_BOS_TOKEN\r\nif tokenizer.unk_token is None:\r\n special_tokens_dict[\"unk_token\"] = DEFAULT_UNK_TOKEN\r\n\r\ntokenizer.add_special_tokens(special_tokens_dict)\r\n\r\ndef tokenize_fn(tokenizer, example):\r\n context_length = tokenizer.model_max_length\r\n outputs = tokenizer(\r\n tokenizer.eos_token.join(example[\"text\"]),\r\n #truncation=False,\r\n truncation=True,\r\n return_tensors=\"pt\",\r\n #return_tensors=\"np\",\r\n pad_to_multiple_of=context_length,\r\n padding=True,\r\n )\r\n return {\"input_ids\": outputs[\"input_ids\"].view(-1, context_length)}\r\n\r\nfor idx in range(100):\r\n dataset = load_dataset(\"togethercomputer/RedPajama-Data-1T-Sample\",\r\ncache_dir=cache_dir, split=f'train[{idx}%:{idx+1}%]')\r\n dataset = dataset.map(partial(tokenize_fn, tokenizer), batched=False,\r\nnum_proc=16, remove_columns=[\"text\", \"meta\"])\r\n dataset.save_to_disk(training_args.cache_dir + f\"/training_data_{idx}\")\r\n\r\n\r\nOn Thu, Oct 19, 2023 at 2:30 PM Mario Šaško ***@***.***>\r\nwrote:\r\n\r\n> Can you please provide a self-contained reproducer?\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/6319#issuecomment-1771509229>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/ABDD3ZNBZ3BE7Q4EQZZK6MLYAFWURAVCNFSM6AAAAAA6HDKPSCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTONZRGUYDSMRSHE>\r\n> .\r\n> You are receiving this because you authored the thread.Message ID:\r\n> ***@***.***>\r\n>\r\n",
"I changed the tokenizer to one without \"Fast suffix, and something changed.\r\nThe fraction, although still slowed a lot at 80% was able to get over the\r\nfinish line of 100%\r\n\r\nI have to do more testng, see if the whole set can be processed\r\n\r\n\r\n\r\nOn Thu, Oct 19, 2023 at 3:03 PM pensive introvert <\r\n***@***.***> wrote:\r\n\r\n> from functools import partial\r\n> import transformers\r\n> from datasets import load_dataset, concatenate_datasets, load_from_disk\r\n>\r\n> model_name_or_path=\"/opt/data/data/daryl149/llama-2-7b-chat-hf\"\r\n> output_dir=\"/opt/data/data/LongLoRA/checkpoints\"\r\n> cache_dir=\"/opt/data/data/LongLoRA/cache\"\r\n> model_max_length=16384\r\n>\r\n> IGNORE_INDEX = -100\r\n> DEFAULT_PAD_TOKEN = \"[PAD]\"\r\n> DEFAULT_EOS_TOKEN = \"</s>\"\r\n> DEFAULT_BOS_TOKEN = \"<s>\"\r\n> DEFAULT_UNK_TOKEN = \"<unk>\"\r\n>\r\n>\r\n> tokenizer = transformers.LlamaTokenizerFast.from_pretrained(\r\n> model_name_or_path,\r\n> cache_dir=cache_dir,\r\n> model_max_length=model_max_length,\r\n> padding_side=\"right\",\r\n> use_fast=True,\r\n> #use_fast=False\r\n> )\r\n>\r\n> special_tokens_dict = dict()\r\n> if tokenizer.pad_token is None:\r\n> special_tokens_dict[\"pad_token\"] = DEFAULT_PAD_TOKEN\r\n> if tokenizer.eos_token is None:\r\n> special_tokens_dict[\"eos_token\"] = DEFAULT_EOS_TOKEN\r\n> if tokenizer.bos_token is None:\r\n> special_tokens_dict[\"bos_token\"] = DEFAULT_BOS_TOKEN\r\n> if tokenizer.unk_token is None:\r\n> special_tokens_dict[\"unk_token\"] = DEFAULT_UNK_TOKEN\r\n>\r\n> tokenizer.add_special_tokens(special_tokens_dict)\r\n>\r\n> def tokenize_fn(tokenizer, example):\r\n> context_length = tokenizer.model_max_length\r\n> outputs = tokenizer(\r\n> tokenizer.eos_token.join(example[\"text\"]),\r\n> #truncation=False,\r\n> truncation=True,\r\n> return_tensors=\"pt\",\r\n> #return_tensors=\"np\",\r\n> pad_to_multiple_of=context_length,\r\n> padding=True,\r\n> )\r\n> return {\"input_ids\": outputs[\"input_ids\"].view(-1, context_length)}\r\n>\r\n> for idx in range(100):\r\n> dataset = load_dataset(\"togethercomputer/RedPajama-Data-1T-Sample\",\r\n> cache_dir=cache_dir, split=f'train[{idx}%:{idx+1}%]')\r\n> dataset = dataset.map(partial(tokenize_fn, tokenizer), batched=False,\r\n> num_proc=16, remove_columns=[\"text\", \"meta\"])\r\n> dataset.save_to_disk(training_args.cache_dir + f\"/training_data_{idx}\")\r\n>\r\n>\r\n> On Thu, Oct 19, 2023 at 2:30 PM Mario Šaško ***@***.***>\r\n> wrote:\r\n>\r\n>> Can you please provide a self-contained reproducer?\r\n>>\r\n>> —\r\n>> Reply to this email directly, view it on GitHub\r\n>> <https://github.com/huggingface/datasets/issues/6319#issuecomment-1771509229>,\r\n>> or unsubscribe\r\n>> <https://github.com/notifications/unsubscribe-auth/ABDD3ZNBZ3BE7Q4EQZZK6MLYAFWURAVCNFSM6AAAAAA6HDKPSCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTONZRGUYDSMRSHE>\r\n>> .\r\n>> You are receiving this because you authored the thread.Message ID:\r\n>> ***@***.***>\r\n>>\r\n>\r\n",
"So, using LlamaTokenizerFast was the problem. Changing it to LlamaTokenizer\r\nfixed things,\r\n\r\nOn Thu, Oct 19, 2023 at 4:04 PM pensive introvert <\r\n***@***.***> wrote:\r\n\r\n> I changed the tokenizer to one without \"Fast suffix, and something\r\n> changed. The fraction, although still slowed a lot at 80% was able to get\r\n> over the finish line of 100%\r\n>\r\n> I have to do more testng, see if the whole set can be processed\r\n>\r\n>\r\n>\r\n> On Thu, Oct 19, 2023 at 3:03 PM pensive introvert <\r\n> ***@***.***> wrote:\r\n>\r\n>> from functools import partial\r\n>> import transformers\r\n>> from datasets import load_dataset, concatenate_datasets, load_from_disk\r\n>>\r\n>> model_name_or_path=\"/opt/data/data/daryl149/llama-2-7b-chat-hf\"\r\n>> output_dir=\"/opt/data/data/LongLoRA/checkpoints\"\r\n>> cache_dir=\"/opt/data/data/LongLoRA/cache\"\r\n>> model_max_length=16384\r\n>>\r\n>> IGNORE_INDEX = -100\r\n>> DEFAULT_PAD_TOKEN = \"[PAD]\"\r\n>> DEFAULT_EOS_TOKEN = \"</s>\"\r\n>> DEFAULT_BOS_TOKEN = \"<s>\"\r\n>> DEFAULT_UNK_TOKEN = \"<unk>\"\r\n>>\r\n>>\r\n>> tokenizer = transformers.LlamaTokenizerFast.from_pretrained(\r\n>> model_name_or_path,\r\n>> cache_dir=cache_dir,\r\n>> model_max_length=model_max_length,\r\n>> padding_side=\"right\",\r\n>> use_fast=True,\r\n>> #use_fast=False\r\n>> )\r\n>>\r\n>> special_tokens_dict = dict()\r\n>> if tokenizer.pad_token is None:\r\n>> special_tokens_dict[\"pad_token\"] = DEFAULT_PAD_TOKEN\r\n>> if tokenizer.eos_token is None:\r\n>> special_tokens_dict[\"eos_token\"] = DEFAULT_EOS_TOKEN\r\n>> if tokenizer.bos_token is None:\r\n>> special_tokens_dict[\"bos_token\"] = DEFAULT_BOS_TOKEN\r\n>> if tokenizer.unk_token is None:\r\n>> special_tokens_dict[\"unk_token\"] = DEFAULT_UNK_TOKEN\r\n>>\r\n>> tokenizer.add_special_tokens(special_tokens_dict)\r\n>>\r\n>> def tokenize_fn(tokenizer, example):\r\n>> context_length = tokenizer.model_max_length\r\n>> outputs = tokenizer(\r\n>> tokenizer.eos_token.join(example[\"text\"]),\r\n>> #truncation=False,\r\n>> truncation=True,\r\n>> return_tensors=\"pt\",\r\n>> #return_tensors=\"np\",\r\n>> pad_to_multiple_of=context_length,\r\n>> padding=True,\r\n>> )\r\n>> return {\"input_ids\": outputs[\"input_ids\"].view(-1, context_length)}\r\n>>\r\n>> for idx in range(100):\r\n>> dataset = load_dataset(\"togethercomputer/RedPajama-Data-1T-Sample\",\r\n>> cache_dir=cache_dir, split=f'train[{idx}%:{idx+1}%]')\r\n>> dataset = dataset.map(partial(tokenize_fn, tokenizer), batched=False,\r\n>> num_proc=16, remove_columns=[\"text\", \"meta\"])\r\n>> dataset.save_to_disk(training_args.cache_dir +\r\n>> f\"/training_data_{idx}\")\r\n>>\r\n>>\r\n>> On Thu, Oct 19, 2023 at 2:30 PM Mario Šaško ***@***.***>\r\n>> wrote:\r\n>>\r\n>>> Can you please provide a self-contained reproducer?\r\n>>>\r\n>>> —\r\n>>> Reply to this email directly, view it on GitHub\r\n>>> <https://github.com/huggingface/datasets/issues/6319#issuecomment-1771509229>,\r\n>>> or unsubscribe\r\n>>> <https://github.com/notifications/unsubscribe-auth/ABDD3ZNBZ3BE7Q4EQZZK6MLYAFWURAVCNFSM6AAAAAA6HDKPSCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTONZRGUYDSMRSHE>\r\n>>> .\r\n>>> You are receiving this because you authored the thread.Message ID:\r\n>>> ***@***.***>\r\n>>>\r\n>>\r\n",
"Indeed, the tokenizer is super slow. Perhaps @ArthurZucker knows the reason why.\r\n\r\n([This](https://colab.research.google.com/drive/1VgeurX-4Fl2X6aBQTwh_X4kuQKZ6K9L1?usp=sharing) simplified Colab can be used to reproduce the behavior)",
"same issue here\r\nsample to reproduce: https://github.com/philschmid/document-ai-transformers/blob/main/training/donut_sroie.ipynb\r\nwith following map line\r\nhttps://github.com/philschmid/document-ai-transformers/blob/main/training/donut_sroie.ipynb\r\n\r\nIf I directly iterate over the dataset and call the mapping method, it is very fast\r\n```py\r\nfor sample in dataset:\r\n def preprocess_documents_for_donut(sample):\r\n```\r\n\r\nif i removed `.convert('RGB')` It can run to completion without getting stuck. I suspect it has something to do with the Image.\r\n\r\nIf I use batch, it's even slower.",
"@ewfian \r\n\r\n> If I directly iterate over the dataset and call the mapping method, it is very fast\r\n\r\n`Dataset.map` must also convert the images into bytes to write them to an Arrow file (the write itself takes some time, too). \r\n\r\nYou can make the `map` faster by manually converting the images into an \"arrow-compatible\" representation. Otherwise, the Pillow defaults are used when saving an image, which seems particularly slow for the notebook's case.\r\n\r\n```python\r\ndef preprocess_documents_for_donut(sample):\r\n text = json.loads(sample[\"text\"])\r\n d_doc = task_start_token + json2token(text) + eos_token\r\n image = sample[\"image\"].convert('RGB')\r\n # convert image to bytes\r\n buffer = io.BytesIO()\r\n image.save(buffer, format=\"PNG\", compress_level=1)\r\n return {\"image\": {\"bytes\": buffer.getvalue()}, \"text\": d_doc}\r\n\r\nproc_dataset = dataset.map(preprocess_documents_for_donut, writer_batch_size=50)\r\n```",
"The problem I had was to do with map using fork and copying locks from the\r\nparent process in acquired state. I ended up changing the context to use\r\nforkserver instead.\r\n\r\n\r\nOn Wed, Nov 29, 2023, 10:04 PM Mario Šaško ***@***.***> wrote:\r\n\r\n> @ewfian <https://github.com/ewfian>\r\n>\r\n> If I directly iterate over the dataset and call the mapping method, it is\r\n> very fast\r\n>\r\n> Dataset.map must also convert the images into bytes to write them to an\r\n> Arrow file (the write itself takes some time, too).\r\n>\r\n> You can make the map faster by manually converting the images into an\r\n> \"arrow-compatible\" representation. Otherwise, the Pillow defaults are used\r\n> when saving an image, which seems particularly slow for the notebook's case.\r\n>\r\n> def preprocess_documents_for_donut(sample):\r\n> text = json.loads(sample[\"text\"])\r\n> d_doc = task_start_token + json2token(text) + eos_token\r\n> image = sample[\"image\"].convert('RGB')\r\n> # convert image to bytes\r\n> buffer = io.BytesIO()\r\n> image.save(buffer, format=\"PNG\", compress_level=1)\r\n> return {\"image\": {\"bytes\": buffer.getvalue()}, \"text\": d_doc}\r\n> proc_dataset = dataset.map(preprocess_documents_for_donut, writer_batch_size=50)\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/6319#issuecomment-1833033973>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/ABDD3ZKKEKJVWBFH7QHLRJ3YG7ZUJAVCNFSM6AAAAAA6HDKPSCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTQMZTGAZTGOJXGM>\r\n> .\r\n> You are receiving this because you authored the thread.Message ID:\r\n> ***@***.***>\r\n>\r\n",
"I face the same issue many times.\r\n\r\nNot only when using the transformers' tokenizer, but also when applying nltk's [pos_tag](https://www.nltk.org/api/nltk.tag.pos_tag.html) to the entire English Wikipedia. So I suspect the cause is not in the tokenizer but in the Dataset.map\r\n\r\nMy case:\r\nAt the beginning of the run, the speed was 600 samples/s, but it slowed down to 20 samples/s at around 90% (after 3 hours). I am concerned that the CPU usage was only about 5% at the end of the run, even though there was still lots of data left.",
"It is the interaction of fork() inside the map and tokenizer mutexes/locks.\r\n\r\nYou have to set up your own process pool and use fork server instead of\r\nfork.\r\n\r\n\r\nOn Tue, Aug 6, 2024, 11:44 AM yuji96 ***@***.***> wrote:\r\n\r\n> I face the same issue many times.\r\n>\r\n> Not only when using the transformers' tokenizer, but also when applying\r\n> nltk's pos_tag <https://www.nltk.org/api/nltk.tag.pos_tag.html> to the\r\n> entire English Wikipedia. So I suspect the cause is not in the tokenizer\r\n> but in the Dataset.map\r\n>\r\n> My case:\r\n> At the beginning of the run, the speed was 600 samples/s, but it slowed\r\n> down to 20 samples/s at around 90% (after 3 hours). I am concerned that the\r\n> CPU usage was only about 5% at the end of the run, even though there was\r\n> still lots of data left.\r\n>\r\n> #6319 (comment)\r\n> <https://github.com/huggingface/datasets/issues/6319#issuecomment-1771629160>\r\n> It's very nice to hear that the run is complete, but the original issue\r\n> has not been solved, which is that it gets slower and slower. As it is now,\r\n> Dataset.map will not be able to handle the large datasets that are getting\r\n> larger day by day.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/6319#issuecomment-2271603976>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/ABDD3ZLFHSIGNNXAEJJIXWLZQDVPTAVCNFSM6AAAAABMCTVK2SVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDENZRGYYDGOJXGY>\r\n> .\r\n> You are receiving this because you authored the thread.Message ID:\r\n> ***@***.***>\r\n>\r\n",
"Thank you for your advice!\r\n\r\nI added `multiprocess.set_start_method(\"forkserver\")` but the result seemed to be the same. In my case, it may be due to the very simple fact that about 10% of the process, which includes long text, never ends. I'll try shard by data size.\r\n\r\n",
"Would recommend using `LlamaTokenizerFast` not `LlamaTokenizer` ! "
] |
1,952,100,706
| 6,318
|
Deterministic set hash
|
closed
| 2023-10-19T12:19:13
| 2023-10-19T16:27:20
| 2023-10-19T16:16:31
|
https://github.com/huggingface/datasets/pull/6318
|
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"html_url": "https://github.com/huggingface/datasets/pull/6318",
"diff_url": "https://github.com/huggingface/datasets/pull/6318.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6318.patch",
"merged_at": "2023-10-19T16:16:31"
}
|
lhoestq
| true
|
[
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006827 / 0.011353 (-0.004526) | 0.004468 / 0.011008 (-0.006540) | 0.088687 / 0.038508 (0.050179) | 0.072560 / 0.023109 (0.049451) | 0.333421 / 0.275898 (0.057523) | 0.374977 / 0.323480 (0.051497) | 0.005829 / 0.007986 (-0.002156) | 0.003284 / 0.004328 (-0.001045) | 0.068929 / 0.004250 (0.064678) | 0.057212 / 0.037052 (0.020160) | 0.328911 / 0.258489 (0.070422) | 0.389107 / 0.293841 (0.095266) | 0.033518 / 0.128546 (-0.095029) | 0.009919 / 0.075646 (-0.065728) | 0.308100 / 0.419271 (-0.111171) | 0.059380 / 0.043533 (0.015847) | 0.345587 / 0.255139 (0.090448) | 0.353703 / 0.283200 (0.070503) | 0.026454 / 0.141683 (-0.115229) | 1.573309 / 1.452155 (0.121155) | 1.663812 / 1.492716 (0.171095) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.255081 / 0.018006 (0.237075) | 0.472613 / 0.000490 (0.472123) | 0.016120 / 0.000200 (0.015920) | 0.000383 / 0.000054 (0.000328) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028219 / 0.037411 (-0.009192) | 0.086600 / 0.014526 (0.072074) | 0.099484 / 0.176557 (-0.077073) | 0.154604 / 0.737135 (-0.582531) | 0.099168 / 0.296338 (-0.197171) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.421703 / 0.215209 (0.206494) | 4.188600 / 2.077655 (2.110945) | 2.037575 / 1.504120 (0.533456) | 1.843389 / 1.541195 (0.302194) | 1.912554 / 1.468490 (0.444064) | 0.517452 / 4.584777 (-4.067325) | 3.838002 / 3.745712 (0.092290) | 3.698899 / 5.269862 (-1.570963) | 2.175393 / 4.565676 (-2.390283) | 0.066059 / 0.424275 (-0.358216) | 0.008455 / 0.007607 (0.000848) | 0.506813 / 0.226044 (0.280768) | 4.826994 / 2.268929 (2.558066) | 2.544437 / 55.444624 (-52.900187) | 2.164938 / 6.876477 (-4.711539) | 2.171725 / 2.142072 (0.029652) | 0.603757 / 4.805227 (-4.201470) | 0.149113 / 6.500664 (-6.351551) | 0.065093 / 0.075469 (-0.010376) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.366887 / 1.841788 (-0.474901) | 20.508089 / 8.074308 (12.433780) | 14.836531 / 10.191392 (4.645139) | 0.167418 / 0.680424 (-0.513006) | 0.019707 / 0.534201 (-0.514494) | 0.409897 / 0.579283 (-0.169387) | 0.439412 / 0.434364 (0.005048) | 0.495784 / 0.540337 (-0.044553) | 0.685367 / 1.386936 (-0.701569) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007604 / 0.011353 (-0.003749) | 0.004368 / 0.011008 (-0.006640) | 0.072628 / 0.038508 (0.034120) | 0.084187 / 0.023109 (0.061077) | 0.461396 / 0.275898 (0.185498) | 0.481429 / 0.323480 (0.157949) | 0.005894 / 0.007986 (-0.002092) | 0.003472 / 0.004328 (-0.000857) | 0.068717 / 0.004250 (0.064466) | 0.061066 / 0.037052 (0.024014) | 0.464217 / 0.258489 (0.205728) | 0.498061 / 0.293841 (0.204220) | 0.035458 / 0.128546 (-0.093089) | 0.009474 / 0.075646 (-0.066173) | 0.079633 / 0.419271 (-0.339639) | 0.053966 / 0.043533 (0.010433) | 0.454911 / 0.255139 (0.199772) | 0.470837 / 0.283200 (0.187637) | 0.026358 / 0.141683 (-0.115325) | 1.665131 / 1.452155 (0.212976) | 1.730365 / 1.492716 (0.237648) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.234810 / 0.018006 (0.216804) | 0.453672 / 0.000490 (0.453183) | 0.004620 / 0.000200 (0.004420) | 0.000119 / 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) | 0.035310 / 0.037411 (-0.002101) | 0.100379 / 0.014526 (0.085853) | 0.118802 / 0.176557 (-0.057754) | 0.173853 / 0.737135 (-0.563282) | 0.115714 / 0.296338 (-0.180624) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.466797 / 0.215209 (0.251588) | 4.698324 / 2.077655 (2.620670) | 2.446897 / 1.504120 (0.942777) | 2.277346 / 1.541195 (0.736151) | 2.347211 / 1.468490 (0.878721) | 0.514377 / 4.584777 (-4.070400) | 3.931269 / 3.745712 (0.185557) | 3.573575 / 5.269862 (-1.696286) | 2.208122 / 4.565676 (-2.357554) | 0.061081 / 0.424275 (-0.363194) | 0.007803 / 0.007607 (0.000196) | 0.544376 / 0.226044 (0.318332) | 5.440003 / 2.268929 (3.171074) | 3.012559 / 55.444624 (-52.432065) | 2.617286 / 6.876477 (-4.259191) | 2.863978 / 2.142072 (0.721906) | 0.610024 / 4.805227 (-4.195203) | 0.133643 / 6.500664 (-6.367021) | 0.064766 / 0.075469 (-0.010703) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.465225 / 1.841788 (-0.376563) | 21.308351 / 8.074308 (13.234043) | 15.176634 / 10.191392 (4.985242) | 0.172701 / 0.680424 (-0.507723) | 0.020345 / 0.534201 (-0.513855) | 0.433923 / 0.579283 (-0.145360) | 0.450183 / 0.434364 (0.015819) | 0.514048 / 0.540337 (-0.026289) | 0.736302 / 1.386936 (-0.650634) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7f1a7d621fff3b08ace02643466097654a5e010f \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008305 / 0.011353 (-0.003048) | 0.006007 / 0.011008 (-0.005001) | 0.103521 / 0.038508 (0.065013) | 0.075776 / 0.023109 (0.052666) | 0.378888 / 0.275898 (0.102990) | 0.405245 / 0.323480 (0.081765) | 0.004596 / 0.007986 (-0.003390) | 0.003687 / 0.004328 (-0.000641) | 0.079043 / 0.004250 (0.074792) | 0.055895 / 0.037052 (0.018843) | 0.406565 / 0.258489 (0.148076) | 0.433869 / 0.293841 (0.140028) | 0.045321 / 0.128546 (-0.083226) | 0.014317 / 0.075646 (-0.061329) | 0.345312 / 0.419271 (-0.073960) | 0.064485 / 0.043533 (0.020953) | 0.381744 / 0.255139 (0.126605) | 0.401162 / 0.283200 (0.117962) | 0.035973 / 0.141683 (-0.105709) | 1.829616 / 1.452155 (0.377461) | 1.868487 / 1.492716 (0.375771) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.245432 / 0.018006 (0.227426) | 0.494249 / 0.000490 (0.493759) | 0.010878 / 0.000200 (0.010678) | 0.000492 / 0.000054 (0.000437) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032778 / 0.037411 (-0.004633) | 0.103418 / 0.014526 (0.088892) | 0.108010 / 0.176557 (-0.068547) | 0.176477 / 0.737135 (-0.560658) | 0.107732 / 0.296338 (-0.188606) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.572471 / 0.215209 (0.357262) | 5.647039 / 2.077655 (3.569384) | 2.385069 / 1.504120 (0.880949) | 2.048928 / 1.541195 (0.507733) | 2.108538 / 1.468490 (0.640048) | 0.861436 / 4.584777 (-3.723341) | 4.933452 / 3.745712 (1.187739) | 4.735219 / 5.269862 (-0.534642) | 2.926971 / 4.565676 (-1.638705) | 0.097687 / 0.424275 (-0.326588) | 0.008346 / 0.007607 (0.000739) | 0.677754 / 0.226044 (0.451709) | 6.798433 / 2.268929 (4.529504) | 3.129862 / 55.444624 (-52.314762) | 2.454033 / 6.876477 (-4.422444) | 2.464590 / 2.142072 (0.322517) | 1.034497 / 4.805227 (-3.770730) | 0.205753 / 6.500664 (-6.294911) | 0.076618 / 0.075469 (0.001149) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.617569 / 1.841788 (-0.224219) | 22.091489 / 8.074308 (14.017181) | 20.406312 / 10.191392 (10.214920) | 0.222012 / 0.680424 (-0.458411) | 0.027787 / 0.534201 (-0.506414) | 0.441669 / 0.579283 (-0.137615) | 0.564773 / 0.434364 (0.130409) | 0.510389 / 0.540337 (-0.029948) | 0.753672 / 1.386936 (-0.633264) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011107 / 0.011353 (-0.000246) | 0.004973 / 0.011008 (-0.006035) | 0.078331 / 0.038508 (0.039823) | 0.083964 / 0.023109 (0.060855) | 0.518980 / 0.275898 (0.243082) | 0.528264 / 0.323480 (0.204784) | 0.007452 / 0.007986 (-0.000534) | 0.003931 / 0.004328 (-0.000397) | 0.079724 / 0.004250 (0.075474) | 0.061739 / 0.037052 (0.024686) | 0.517804 / 0.258489 (0.259315) | 0.582764 / 0.293841 (0.288923) | 0.049674 / 0.128546 (-0.078873) | 0.014540 / 0.075646 (-0.061106) | 0.093130 / 0.419271 (-0.326141) | 0.060647 / 0.043533 (0.017114) | 0.492628 / 0.255139 (0.237489) | 0.549761 / 0.283200 (0.266562) | 0.034313 / 0.141683 (-0.107369) | 1.824574 / 1.452155 (0.372419) | 2.013664 / 1.492716 (0.520947) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231335 / 0.018006 (0.213329) | 0.521477 / 0.000490 (0.520987) | 0.011314 / 0.000200 (0.011114) | 0.000397 / 0.000054 (0.000343) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033303 / 0.037411 (-0.004108) | 0.098238 / 0.014526 (0.083712) | 0.119527 / 0.176557 (-0.057030) | 0.169163 / 0.737135 (-0.567972) | 0.114536 / 0.296338 (-0.181803) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.578401 / 0.215209 (0.363191) | 5.966438 / 2.077655 (3.888783) | 2.646370 / 1.504120 (1.142250) | 2.361833 / 1.541195 (0.820638) | 2.476573 / 1.468490 (1.008083) | 0.777411 / 4.584777 (-3.807366) | 4.811070 / 3.745712 (1.065357) | 4.314221 / 5.269862 (-0.955641) | 2.743317 / 4.565676 (-1.822359) | 0.110394 / 0.424275 (-0.313881) | 0.008333 / 0.007607 (0.000726) | 0.729588 / 0.226044 (0.503543) | 7.743226 / 2.268929 (5.474298) | 3.606294 / 55.444624 (-51.838330) | 2.838069 / 6.876477 (-4.038408) | 3.087494 / 2.142072 (0.945421) | 1.053341 / 4.805227 (-3.751886) | 0.205105 / 6.500664 (-6.295559) | 0.075204 / 0.075469 (-0.000265) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.561959 / 1.841788 (-0.279829) | 21.407849 / 8.074308 (13.333541) | 19.084263 / 10.191392 (8.892871) | 0.226129 / 0.680424 (-0.454295) | 0.029695 / 0.534201 (-0.504506) | 0.427035 / 0.579283 (-0.152248) | 0.565353 / 0.434364 (0.130989) | 0.526789 / 0.540337 (-0.013548) | 0.734820 / 1.386936 (-0.652116) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5b52536f4e39df3b98f7e0b03ee71b24c4fff49a \"CML watermark\")\n"
] |
1,951,965,668
| 6,317
|
sentiment140 dataset unavailable
|
closed
| 2023-10-19T11:25:21
| 2023-10-19T13:04:56
| 2023-10-19T13:04:56
|
https://github.com/huggingface/datasets/issues/6317
| null |
AndreasKarasenko
| false
|
[
"Thanks for reporting. We are investigating the issue.",
"We have opened an issue in the corresponding Hub dataset: https://huggingface.co/datasets/sentiment140/discussions/3\r\n\r\nLet's continue the discussion there."
] |
1,951,819,869
| 6,316
|
Fix loading Hub datasets with CSV metadata file
|
closed
| 2023-10-19T10:21:34
| 2023-10-20T06:23:21
| 2023-10-20T06:14:09
|
https://github.com/huggingface/datasets/pull/6316
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6316",
"html_url": "https://github.com/huggingface/datasets/pull/6316",
"diff_url": "https://github.com/huggingface/datasets/pull/6316.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6316.patch",
"merged_at": "2023-10-20T06:14:09"
}
|
albertvillanova
| true
|
[
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008896 / 0.011353 (-0.002456) | 0.005811 / 0.011008 (-0.005197) | 0.108582 / 0.038508 (0.070074) | 0.096509 / 0.023109 (0.073399) | 0.481725 / 0.275898 (0.205827) | 0.534743 / 0.323480 (0.211263) | 0.005517 / 0.007986 (-0.002468) | 0.006479 / 0.004328 (0.002151) | 0.081313 / 0.004250 (0.077062) | 0.063578 / 0.037052 (0.026525) | 0.493977 / 0.258489 (0.235488) | 0.551897 / 0.293841 (0.258056) | 0.051835 / 0.128546 (-0.076711) | 0.014105 / 0.075646 (-0.061541) | 0.385866 / 0.419271 (-0.033405) | 0.069131 / 0.043533 (0.025598) | 0.484780 / 0.255139 (0.229641) | 0.493221 / 0.283200 (0.210021) | 0.039560 / 0.141683 (-0.102123) | 1.782331 / 1.452155 (0.330176) | 1.899193 / 1.492716 (0.406477) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.329978 / 0.018006 (0.311972) | 0.600839 / 0.000490 (0.600349) | 0.013187 / 0.000200 (0.012987) | 0.000499 / 0.000054 (0.000444) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031835 / 0.037411 (-0.005576) | 0.103740 / 0.014526 (0.089214) | 0.115875 / 0.176557 (-0.060681) | 0.189880 / 0.737135 (-0.547255) | 0.132614 / 0.296338 (-0.163725) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.596255 / 0.215209 (0.381046) | 5.967993 / 2.077655 (3.890339) | 2.612675 / 1.504120 (1.108555) | 2.251461 / 1.541195 (0.710266) | 2.308585 / 1.468490 (0.840095) | 0.816516 / 4.584777 (-3.768261) | 5.241791 / 3.745712 (1.496079) | 4.680745 / 5.269862 (-0.589117) | 2.997370 / 4.565676 (-1.568307) | 0.098632 / 0.424275 (-0.325643) | 0.010912 / 0.007607 (0.003305) | 0.659092 / 0.226044 (0.433047) | 6.825562 / 2.268929 (4.556634) | 3.323844 / 55.444624 (-52.120780) | 2.796203 / 6.876477 (-4.080274) | 2.946994 / 2.142072 (0.804922) | 1.002814 / 4.805227 (-3.802413) | 0.202613 / 6.500664 (-6.298051) | 0.072011 / 0.075469 (-0.003459) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.613873 / 1.841788 (-0.227914) | 24.500990 / 8.074308 (16.426682) | 21.941599 / 10.191392 (11.750207) | 0.214450 / 0.680424 (-0.465974) | 0.031227 / 0.534201 (-0.502974) | 0.498297 / 0.579283 (-0.080986) | 0.597460 / 0.434364 (0.163096) | 0.558152 / 0.540337 (0.017815) | 0.789693 / 1.386936 (-0.597243) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011299 / 0.011353 (-0.000053) | 0.005103 / 0.011008 (-0.005905) | 0.083161 / 0.038508 (0.044653) | 0.094201 / 0.023109 (0.071092) | 0.560457 / 0.275898 (0.284559) | 0.590459 / 0.323480 (0.266980) | 0.007059 / 0.007986 (-0.000926) | 0.004418 / 0.004328 (0.000090) | 0.081343 / 0.004250 (0.077093) | 0.067069 / 0.037052 (0.030016) | 0.538137 / 0.258489 (0.279648) | 0.600416 / 0.293841 (0.306575) | 0.049046 / 0.128546 (-0.079500) | 0.014299 / 0.075646 (-0.061347) | 0.093631 / 0.419271 (-0.325641) | 0.062536 / 0.043533 (0.019003) | 0.557238 / 0.255139 (0.302099) | 0.571050 / 0.283200 (0.287850) | 0.035881 / 0.141683 (-0.105802) | 1.918487 / 1.452155 (0.466332) | 2.013979 / 1.492716 (0.521263) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.400995 / 0.018006 (0.382989) | 0.634898 / 0.000490 (0.634408) | 0.041809 / 0.000200 (0.041609) | 0.000279 / 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) | 0.034160 / 0.037411 (-0.003251) | 0.109996 / 0.014526 (0.095470) | 0.124335 / 0.176557 (-0.052222) | 0.188100 / 0.737135 (-0.549035) | 0.135897 / 0.296338 (-0.160442) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.639751 / 0.215209 (0.424542) | 6.403312 / 2.077655 (4.325657) | 3.146453 / 1.504120 (1.642333) | 2.840358 / 1.541195 (1.299164) | 2.908667 / 1.468490 (1.440177) | 0.818767 / 4.584777 (-3.766010) | 5.416939 / 3.745712 (1.671227) | 4.853498 / 5.269862 (-0.416364) | 3.023526 / 4.565676 (-1.542150) | 0.110850 / 0.424275 (-0.313425) | 0.013103 / 0.007607 (0.005496) | 0.799720 / 0.226044 (0.573676) | 7.837704 / 2.268929 (5.568775) | 4.016526 / 55.444624 (-51.428099) | 3.338965 / 6.876477 (-3.537512) | 3.715721 / 2.142072 (1.573648) | 1.088340 / 4.805227 (-3.716887) | 0.213610 / 6.500664 (-6.287054) | 0.079244 / 0.075469 (0.003775) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.833175 / 1.841788 (-0.008612) | 25.307218 / 8.074308 (17.232910) | 23.716075 / 10.191392 (13.524683) | 0.259114 / 0.680424 (-0.421310) | 0.035171 / 0.534201 (-0.499029) | 0.530128 / 0.579283 (-0.049155) | 0.651484 / 0.434364 (0.217120) | 0.589414 / 0.540337 (0.049077) | 0.862691 / 1.386936 (-0.524245) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1bdfba93b8a739b9d885b8fb1909d47ff689bbc2 \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"Me too, I thought the same... quite surprised... :open_mouth: ",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006929 / 0.011353 (-0.004423) | 0.004345 / 0.011008 (-0.006663) | 0.085522 / 0.038508 (0.047014) | 0.083380 / 0.023109 (0.060271) | 0.310332 / 0.275898 (0.034434) | 0.350525 / 0.323480 (0.027045) | 0.004367 / 0.007986 (-0.003618) | 0.005503 / 0.004328 (0.001175) | 0.066311 / 0.004250 (0.062061) | 0.059545 / 0.037052 (0.022492) | 0.314090 / 0.258489 (0.055601) | 0.366661 / 0.293841 (0.072821) | 0.031581 / 0.128546 (-0.096965) | 0.008852 / 0.075646 (-0.066794) | 0.289312 / 0.419271 (-0.129960) | 0.052960 / 0.043533 (0.009427) | 0.308134 / 0.255139 (0.052995) | 0.330342 / 0.283200 (0.047142) | 0.026157 / 0.141683 (-0.115526) | 1.488463 / 1.452155 (0.036308) | 1.561441 / 1.492716 (0.068725) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.327735 / 0.018006 (0.309729) | 0.568162 / 0.000490 (0.567672) | 0.012097 / 0.000200 (0.011897) | 0.000438 / 0.000054 (0.000383) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029503 / 0.037411 (-0.007909) | 0.084327 / 0.014526 (0.069801) | 0.102065 / 0.176557 (-0.074492) | 0.157392 / 0.737135 (-0.579744) | 0.101428 / 0.296338 (-0.194910) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.386767 / 0.215209 (0.171558) | 3.870757 / 2.077655 (1.793102) | 1.870048 / 1.504120 (0.365928) | 1.678221 / 1.541195 (0.137026) | 1.799423 / 1.468490 (0.330933) | 0.477718 / 4.584777 (-4.107059) | 3.618351 / 3.745712 (-0.127361) | 3.577921 / 5.269862 (-1.691941) | 2.146217 / 4.565676 (-2.419459) | 0.056290 / 0.424275 (-0.367985) | 0.007378 / 0.007607 (-0.000229) | 0.460678 / 0.226044 (0.234633) | 4.606243 / 2.268929 (2.337314) | 2.303460 / 55.444624 (-53.141164) | 1.982662 / 6.876477 (-4.893814) | 2.103891 / 2.142072 (-0.038182) | 0.570700 / 4.805227 (-4.234527) | 0.131747 / 6.500664 (-6.368918) | 0.060915 / 0.075469 (-0.014554) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.286364 / 1.841788 (-0.555424) | 20.106330 / 8.074308 (12.032022) | 14.780833 / 10.191392 (4.589441) | 0.164301 / 0.680424 (-0.516123) | 0.018730 / 0.534201 (-0.515471) | 0.398530 / 0.579283 (-0.180754) | 0.418084 / 0.434364 (-0.016280) | 0.468735 / 0.540337 (-0.071602) | 0.690122 / 1.386936 (-0.696814) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007262 / 0.011353 (-0.004091) | 0.004228 / 0.011008 (-0.006780) | 0.065866 / 0.038508 (0.027358) | 0.096151 / 0.023109 (0.073042) | 0.409352 / 0.275898 (0.133454) | 0.441234 / 0.323480 (0.117754) | 0.005946 / 0.007986 (-0.002039) | 0.003630 / 0.004328 (-0.000698) | 0.066271 / 0.004250 (0.062020) | 0.061567 / 0.037052 (0.024515) | 0.409097 / 0.258489 (0.150608) | 0.447675 / 0.293841 (0.153834) | 0.032804 / 0.128546 (-0.095743) | 0.008793 / 0.075646 (-0.066853) | 0.070790 / 0.419271 (-0.348482) | 0.048650 / 0.043533 (0.005117) | 0.411021 / 0.255139 (0.155882) | 0.421398 / 0.283200 (0.138198) | 0.025305 / 0.141683 (-0.116378) | 1.494826 / 1.452155 (0.042671) | 1.580441 / 1.492716 (0.087724) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.321871 / 0.018006 (0.303865) | 0.526471 / 0.000490 (0.525982) | 0.006913 / 0.000200 (0.006713) | 0.000108 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034889 / 0.037411 (-0.002522) | 0.096096 / 0.014526 (0.081570) | 0.111920 / 0.176557 (-0.064636) | 0.166103 / 0.737135 (-0.571032) | 0.111162 / 0.296338 (-0.185176) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428037 / 0.215209 (0.212828) | 4.294150 / 2.077655 (2.216495) | 2.270331 / 1.504120 (0.766211) | 2.108235 / 1.541195 (0.567041) | 2.242560 / 1.468490 (0.774070) | 0.489941 / 4.584777 (-4.094836) | 3.688111 / 3.745712 (-0.057601) | 3.450180 / 5.269862 (-1.819681) | 2.175106 / 4.565676 (-2.390570) | 0.057657 / 0.424275 (-0.366619) | 0.007478 / 0.007607 (-0.000130) | 0.505242 / 0.226044 (0.279198) | 5.047817 / 2.268929 (2.778888) | 2.724125 / 55.444624 (-52.720500) | 2.419765 / 6.876477 (-4.456711) | 2.723231 / 2.142072 (0.581159) | 0.602382 / 4.805227 (-4.202846) | 0.132362 / 6.500664 (-6.368302) | 0.060600 / 0.075469 (-0.014869) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.363356 / 1.841788 (-0.478431) | 21.446474 / 8.074308 (13.372165) | 15.074732 / 10.191392 (4.883340) | 0.191837 / 0.680424 (-0.488587) | 0.020565 / 0.534201 (-0.513636) | 0.396692 / 0.579283 (-0.182591) | 0.432390 / 0.434364 (-0.001974) | 0.491747 / 0.540337 (-0.048591) | 0.699203 / 1.386936 (-0.687733) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c3a8a87c841426495d3a7ed1863c26660a6a551f \"CML watermark\")\n"
] |
1,951,800,819
| 6,315
|
Hub datasets with CSV metadata raise ArrowInvalid: JSON parse error: Invalid value. in row 0
|
closed
| 2023-10-19T10:11:29
| 2023-10-20T06:14:10
| 2023-10-20T06:14:10
|
https://github.com/huggingface/datasets/issues/6315
| null |
albertvillanova
| false
|
[] |
1,951,684,763
| 6,314
|
Support creating new branch in push_to_hub
|
closed
| 2023-10-19T09:12:39
| 2023-10-19T09:20:06
| 2023-10-19T09:19:48
|
https://github.com/huggingface/datasets/pull/6314
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6314",
"html_url": "https://github.com/huggingface/datasets/pull/6314",
"diff_url": "https://github.com/huggingface/datasets/pull/6314.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6314.patch",
"merged_at": null
}
|
jmif
| true
|
[] |
1,951,527,712
| 6,313
|
Fix commit message formatting in multi-commit uploads
|
closed
| 2023-10-19T07:53:56
| 2023-10-20T14:06:13
| 2023-10-20T13:57:39
|
https://github.com/huggingface/datasets/pull/6313
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6313",
"html_url": "https://github.com/huggingface/datasets/pull/6313",
"diff_url": "https://github.com/huggingface/datasets/pull/6313.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6313.patch",
"merged_at": "2023-10-20T13:57:38"
}
|
qgallouedec
| true
|
[
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006760 / 0.011353 (-0.004593) | 0.003918 / 0.011008 (-0.007091) | 0.084016 / 0.038508 (0.045508) | 0.069927 / 0.023109 (0.046818) | 0.307898 / 0.275898 (0.032000) | 0.337453 / 0.323480 (0.013973) | 0.004132 / 0.007986 (-0.003854) | 0.003248 / 0.004328 (-0.001081) | 0.064526 / 0.004250 (0.060275) | 0.056424 / 0.037052 (0.019371) | 0.316313 / 0.258489 (0.057824) | 0.356302 / 0.293841 (0.062461) | 0.030634 / 0.128546 (-0.097912) | 0.008467 / 0.075646 (-0.067180) | 0.286676 / 0.419271 (-0.132595) | 0.051813 / 0.043533 (0.008280) | 0.309874 / 0.255139 (0.054735) | 0.332513 / 0.283200 (0.049313) | 0.023919 / 0.141683 (-0.117764) | 1.509033 / 1.452155 (0.056878) | 1.549636 / 1.492716 (0.056920) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221464 / 0.018006 (0.203458) | 0.447873 / 0.000490 (0.447384) | 0.002408 / 0.000200 (0.002208) | 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) | 0.027634 / 0.037411 (-0.009777) | 0.081802 / 0.014526 (0.067276) | 0.781489 / 0.176557 (0.604933) | 0.165184 / 0.737135 (-0.571951) | 0.121526 / 0.296338 (-0.174813) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.408215 / 0.215209 (0.193006) | 4.091192 / 2.077655 (2.013538) | 2.062608 / 1.504120 (0.558488) | 1.895747 / 1.541195 (0.354552) | 1.873682 / 1.468490 (0.405192) | 0.484184 / 4.584777 (-4.100593) | 3.469096 / 3.745712 (-0.276616) | 3.365325 / 5.269862 (-1.904537) | 2.000333 / 4.565676 (-2.565343) | 0.056661 / 0.424275 (-0.367614) | 0.007100 / 0.007607 (-0.000507) | 0.478587 / 0.226044 (0.252542) | 4.768703 / 2.268929 (2.499774) | 2.472432 / 55.444624 (-52.972192) | 2.133611 / 6.876477 (-4.742865) | 2.154296 / 2.142072 (0.012223) | 0.582293 / 4.805227 (-4.222934) | 0.131932 / 6.500664 (-6.368732) | 0.060259 / 0.075469 (-0.015211) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.259167 / 1.841788 (-0.582620) | 18.465604 / 8.074308 (10.391296) | 14.024528 / 10.191392 (3.833136) | 0.162320 / 0.680424 (-0.518104) | 0.018144 / 0.534201 (-0.516057) | 0.389931 / 0.579283 (-0.189352) | 0.396456 / 0.434364 (-0.037908) | 0.454734 / 0.540337 (-0.085603) | 0.636406 / 1.386936 (-0.750530) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006565 / 0.011353 (-0.004788) | 0.004008 / 0.011008 (-0.007000) | 0.064526 / 0.038508 (0.026018) | 0.071963 / 0.023109 (0.048854) | 0.415456 / 0.275898 (0.139557) | 0.441199 / 0.323480 (0.117719) | 0.005619 / 0.007986 (-0.002366) | 0.003261 / 0.004328 (-0.001067) | 0.064817 / 0.004250 (0.060567) | 0.055349 / 0.037052 (0.018296) | 0.425172 / 0.258489 (0.166683) | 0.452629 / 0.293841 (0.158788) | 0.031676 / 0.128546 (-0.096870) | 0.008432 / 0.075646 (-0.067214) | 0.071752 / 0.419271 (-0.347519) | 0.047176 / 0.043533 (0.003643) | 0.408641 / 0.255139 (0.153502) | 0.428579 / 0.283200 (0.145380) | 0.021548 / 0.141683 (-0.120135) | 1.495153 / 1.452155 (0.042999) | 1.557933 / 1.492716 (0.065217) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212749 / 0.018006 (0.194743) | 0.441263 / 0.000490 (0.440773) | 0.005831 / 0.000200 (0.005631) | 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) | 0.031844 / 0.037411 (-0.005567) | 0.091590 / 0.014526 (0.077064) | 0.102859 / 0.176557 (-0.073697) | 0.155859 / 0.737135 (-0.581276) | 0.104717 / 0.296338 (-0.191622) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425924 / 0.215209 (0.210715) | 4.292829 / 2.077655 (2.215174) | 2.314350 / 1.504120 (0.810230) | 2.163087 / 1.541195 (0.621892) | 2.217310 / 1.468490 (0.748820) | 0.490889 / 4.584777 (-4.093887) | 3.498287 / 3.745712 (-0.247425) | 3.224980 / 5.269862 (-2.044881) | 1.987739 / 4.565676 (-2.577938) | 0.057486 / 0.424275 (-0.366790) | 0.007199 / 0.007607 (-0.000408) | 0.501194 / 0.226044 (0.275149) | 5.015202 / 2.268929 (2.746273) | 2.816307 / 55.444624 (-52.628318) | 2.474593 / 6.876477 (-4.401884) | 2.649510 / 2.142072 (0.507437) | 0.597167 / 4.805227 (-4.208060) | 0.131199 / 6.500664 (-6.369465) | 0.059532 / 0.075469 (-0.015938) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.384053 / 1.841788 (-0.457734) | 18.964201 / 8.074308 (10.889893) | 14.336209 / 10.191392 (4.144817) | 0.187522 / 0.680424 (-0.492902) | 0.020201 / 0.534201 (-0.514000) | 0.394778 / 0.579283 (-0.184505) | 0.408393 / 0.434364 (-0.025971) | 0.470965 / 0.540337 (-0.069373) | 0.667974 / 1.386936 (-0.718962) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3b3333d790800ddaa3bf386ee71dc800258c921c \"CML watermark\")\n"
] |
1,950,128,416
| 6,312
|
docs: resolving namespace conflict, refactored variable
|
closed
| 2023-10-18T16:10:59
| 2023-10-19T16:31:59
| 2023-10-19T16:23:07
|
https://github.com/huggingface/datasets/pull/6312
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6312",
"html_url": "https://github.com/huggingface/datasets/pull/6312",
"diff_url": "https://github.com/huggingface/datasets/pull/6312.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6312.patch",
"merged_at": "2023-10-19T16:23:07"
}
|
smty2018
| true
|
[
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006209 / 0.011353 (-0.005144) | 0.003708 / 0.011008 (-0.007300) | 0.080435 / 0.038508 (0.041926) | 0.060105 / 0.023109 (0.036995) | 0.392962 / 0.275898 (0.117064) | 0.429381 / 0.323480 (0.105902) | 0.003596 / 0.007986 (-0.004390) | 0.003849 / 0.004328 (-0.000480) | 0.062377 / 0.004250 (0.058127) | 0.048718 / 0.037052 (0.011666) | 0.400906 / 0.258489 (0.142417) | 0.440335 / 0.293841 (0.146494) | 0.027807 / 0.128546 (-0.100739) | 0.008066 / 0.075646 (-0.067580) | 0.262542 / 0.419271 (-0.156730) | 0.045513 / 0.043533 (0.001980) | 0.399608 / 0.255139 (0.144469) | 0.418007 / 0.283200 (0.134807) | 0.023475 / 0.141683 (-0.118208) | 1.476563 / 1.452155 (0.024409) | 1.528898 / 1.492716 (0.036182) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223798 / 0.018006 (0.205792) | 0.430526 / 0.000490 (0.430036) | 0.009232 / 0.000200 (0.009032) | 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) | 0.024921 / 0.037411 (-0.012490) | 0.077692 / 0.014526 (0.063166) | 0.085382 / 0.176557 (-0.091174) | 0.146220 / 0.737135 (-0.590915) | 0.086396 / 0.296338 (-0.209943) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.439986 / 0.215209 (0.224777) | 4.384552 / 2.077655 (2.306897) | 2.373697 / 1.504120 (0.869577) | 2.176138 / 1.541195 (0.634943) | 2.225914 / 1.468490 (0.757424) | 0.505776 / 4.584777 (-4.079001) | 3.053744 / 3.745712 (-0.691968) | 3.080443 / 5.269862 (-2.189419) | 1.904392 / 4.565676 (-2.661285) | 0.058112 / 0.424275 (-0.366163) | 0.006631 / 0.007607 (-0.000976) | 0.503409 / 0.226044 (0.277365) | 5.053375 / 2.268929 (2.784447) | 2.789963 / 55.444624 (-52.654661) | 2.452659 / 6.876477 (-4.423818) | 2.512353 / 2.142072 (0.370280) | 0.590095 / 4.805227 (-4.215132) | 0.126267 / 6.500664 (-6.374397) | 0.061246 / 0.075469 (-0.014223) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.249884 / 1.841788 (-0.591903) | 17.684730 / 8.074308 (9.610422) | 13.967467 / 10.191392 (3.776075) | 0.144202 / 0.680424 (-0.536222) | 0.017004 / 0.534201 (-0.517197) | 0.333634 / 0.579283 (-0.245649) | 0.387251 / 0.434364 (-0.047113) | 0.390189 / 0.540337 (-0.150148) | 0.535662 / 1.386936 (-0.851274) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006379 / 0.011353 (-0.004974) | 0.003681 / 0.011008 (-0.007327) | 0.063005 / 0.038508 (0.024497) | 0.064221 / 0.023109 (0.041112) | 0.446074 / 0.275898 (0.170176) | 0.471997 / 0.323480 (0.148517) | 0.005074 / 0.007986 (-0.002911) | 0.002945 / 0.004328 (-0.001383) | 0.063305 / 0.004250 (0.059054) | 0.050608 / 0.037052 (0.013556) | 0.443260 / 0.258489 (0.184771) | 0.478497 / 0.293841 (0.184656) | 0.028980 / 0.128546 (-0.099566) | 0.008145 / 0.075646 (-0.067502) | 0.068412 / 0.419271 (-0.350859) | 0.041552 / 0.043533 (-0.001980) | 0.436649 / 0.255139 (0.181510) | 0.462397 / 0.283200 (0.179198) | 0.019929 / 0.141683 (-0.121753) | 1.530248 / 1.452155 (0.078093) | 1.611117 / 1.492716 (0.118401) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.232894 / 0.018006 (0.214888) | 0.421451 / 0.000490 (0.420961) | 0.003984 / 0.000200 (0.003784) | 0.000084 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027776 / 0.037411 (-0.009635) | 0.081632 / 0.014526 (0.067106) | 0.094031 / 0.176557 (-0.082526) | 0.147930 / 0.737135 (-0.589206) | 0.094226 / 0.296338 (-0.202112) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.471722 / 0.215209 (0.256513) | 4.713241 / 2.077655 (2.635587) | 2.662660 / 1.504120 (1.158540) | 2.490778 / 1.541195 (0.949583) | 2.555786 / 1.468490 (1.087296) | 0.512209 / 4.584777 (-4.072568) | 3.210612 / 3.745712 (-0.535100) | 2.863346 / 5.269862 (-2.406516) | 1.884664 / 4.565676 (-2.681012) | 0.058514 / 0.424275 (-0.365761) | 0.006473 / 0.007607 (-0.001134) | 0.543279 / 0.226044 (0.317235) | 5.441485 / 2.268929 (3.172556) | 3.145398 / 55.444624 (-52.299226) | 2.749603 / 6.876477 (-4.126874) | 2.925738 / 2.142072 (0.783666) | 0.598725 / 4.805227 (-4.206502) | 0.125616 / 6.500664 (-6.375048) | 0.061314 / 0.075469 (-0.014155) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.384270 / 1.841788 (-0.457518) | 18.307618 / 8.074308 (10.233310) | 14.635768 / 10.191392 (4.444376) | 0.148787 / 0.680424 (-0.531637) | 0.018191 / 0.534201 (-0.516010) | 0.333166 / 0.579283 (-0.246117) | 0.405116 / 0.434364 (-0.029247) | 0.392798 / 0.540337 (-0.147540) | 0.582299 / 1.386936 (-0.804637) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7004f0f2ec59832fe53af033efdca10d00377760 \"CML watermark\")\n"
] |
1,949,304,993
| 6,311
|
cast_column to Sequence with length=4 occur exception raise in datasets/table.py:2146
|
closed
| 2023-10-18T09:38:05
| 2024-02-06T19:24:20
| 2024-02-06T19:24:20
|
https://github.com/huggingface/datasets/issues/6311
| null |
neiblegy
| false
|
[
"Thanks for reporting! We've spotted the bugs with the `array.values` handling and are fixing them in https://github.com/huggingface/datasets/pull/6283 (should be part of the next release).",
"> Thanks for reporting! We've spotted the bugs with the `array.values` handling and are fixing them in #6283 (should be part of the next release).\r\n\r\ni encounter another exception while cast_column to type `Sequence(feature={\"points\": Array2D(shape=(-1, 2), dtype=\"int64\"), \"label\": ClassLabel(num_classes=num_classes, names=names)})`\r\n\r\nwhile my data like this: '{\"points\": [[0.6,0.6], [0.7,0.7], [0.8,0.8]], \"label\": \"A1\"}'\r\n\r\nhere is the backtrace info:\r\n\r\n```\r\n out = func(dataset, *args, **kwargs)\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 2110, in cast_column\r\n return self.cast(features)\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 2055, in cast\r\n dataset = dataset.map(\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 592, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 557, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 3097, in map\r\n for rank, done, content in Dataset._map_single(**dataset_kwargs):\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 3474, in _map_single\r\n batch = apply_function_on_filtered_inputs(\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 3353, in apply_function_on_filtered_inputs\r\n processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py\", line 2329, in table_cast\r\n return cast_table_to_schema(table, schema)\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py\", line 2288, in cast_table_to_schema\r\n arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py\", line 2288, in <listcomp>\r\n arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py\", line 1831, in wrapper\r\n return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py\", line 1831, in <listcomp>\r\n return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py\", line 2073, in cast_array_to_feature\r\n arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py\", line 2073, in <listcomp>\r\n arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py\", line 1833, in wrapper\r\n return func(array, *args, **kwargs)\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py\", line 2095, in cast_array_to_feature\r\n casted_values = _c(array.values, feature.feature)\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py\", line 1833, in wrapper\r\n return func(array, *args, **kwargs)\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py\", line 2144, in cast_array_to_feature\r\n return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py\", line 1833, in wrapper\r\n return func(array, *args, **kwargs)\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py\", line 1967, in array_cast\r\n return pa_type.wrap_array(array)\r\n File \"pyarrow/types.pxi\", line 1369, in pyarrow.lib.BaseExtensionType.wrap_array\r\nTypeError: Incompatible storage type for extension<arrow.py_extension_type<Array2DExtensionType>>: expected list<item: list<item: double>>, got list<item: double>\r\n```\r\nand i print(array) in datasets/table.py:1967 indeed get 2D list. is that same issue in #6283 ?\r\n\r\nbesides this, hugging face datasets seems don't naturally support multi-labels which means `Sequence(ClassLabel)` illegal if data is [\"label1\", \"label2\"]. so i have to define a class derived from `ClassLabel`, like this:\r\n\r\n```\r\nclass AisClassLabels(ClassLabel):\r\n def encode_example(self, example_data):\r\n if self.num_classes is None:\r\n raise ValueError(\r\n \"Trying to use ClassLabel feature with undefined number of class. \"\r\n \"Please set ClassLabel.names or num_classes.\"\r\n )\r\n if not isinstance(example_data, list):\r\n example_data = [example_data]\r\n\r\n for i in range(len(example_data)):\r\n if isinstance(example_data[i], str):\r\n example_data[i] = self.str2int(example_data[i])\r\n if not -1 <= example_data[i] < self.num_classes:\r\n raise ValueError(f\"Class label {example_data:d} greater than configured num_classes {self.num_classes}\")\r\n return example_data\r\n```\r\nand it works well in my case. but is there any recommend way to implement multi-labels?",
"`Incompatible storage type for extension<arrow.py_extension_type<Array2DExtensionType>>: expected list<item: list<item: double>>, got list<item: double>`\r\nif i change `Array2D(shape=(-1, 2), dtype=\"int64\")` to `Sequence(Value(\"int64\"))` , every thing goes well. but my data is 2D int list",
"i test Sequence(ClassLabel) is ok if one column is label list. but it is not ok in nested column such as `Sequence(feature= {\"points\": Sequence(Value(\"int32\")), \"label\": Sequence(ClassLabel(num_classes....)))`. in this case i need override ClassLabels. encode_example as i given above."
] |
1,947,457,988
| 6,310
|
Add return_file_name in load_dataset
|
closed
| 2023-10-17T13:36:57
| 2024-08-09T11:51:55
| 2024-07-31T13:56:50
|
https://github.com/huggingface/datasets/pull/6310
|
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"html_url": "https://github.com/huggingface/datasets/pull/6310",
"diff_url": "https://github.com/huggingface/datasets/pull/6310.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6310.patch",
"merged_at": null
}
|
juliendenize
| true
|
[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6310). All of your documentation changes will be reflected on that endpoint.",
"> Thanks for the change !\r\n> \r\n> Since `return` in python often refers to what is actually returned by the function (here `load_dataset`), I think we can use another word for the parameter. Maybe name it `with_file_names`?\r\n> \r\n> cc @mariosasko in case you have an opinion\r\n\r\nI changed the argument name to your suggestion, I agree that it should be less confusing :)",
"> Thanks! I've left some comments.\r\n> \r\n> @lhoestq WDYT about returning a data file's name (the last part) instead of the full path? This way we could have the same values in the streaming and the non-streaming mode. (In the non-streaming mode, we would also have to iterate over remote files to not output the files' hash (from the HF cache))\r\n\r\nConcerning the last part of the file name, do you have suggestions on how to do that? Because it can happen that the files are located in different folders with the same name so I am wondering what would be the way to go.",
"Hi @lhoestq @mariosasko, I just pushed a version rebased on main I've seen some activity on the issue associated to the PR so I wonder if you are interested in keeping up the process of merging this PR. I still have the same issues as indicated in the previous comment.",
"The original issue https://github.com/huggingface/datasets/issues/5806 is about returning the file names in order to be able to resume data loading from a checkpoint.\r\n\r\nData loading checkpointing has been implemented recently actually, using `torchdata` `StatefulDataloader`\r\n\r\nSee the discussion at https://github.com/huggingface/datasets/issues/5454\r\n\r\nI'm not sure if it's worth continuing this PR then ?",
"> The original issue #5806 is about returning the file names in order to be able to resume data loading from a checkpoint.\r\n> \r\n> Data loading checkpointing has been implemented recently actually, using `torchdata` `StatefulDataloader`\r\n> \r\n> See the discussion at #5454\r\n> \r\n> I'm not sure if it's worth continuing this PR then ?\r\n\r\nThanks for your answer, I'll close the PR if now the feature already exists :).",
"I need this for downstream filter. People always put important information in the file name."
] |
1,946,916,969
| 6,309
|
Fix get_data_patterns for directories with the word data twice
|
closed
| 2023-10-17T09:00:39
| 2023-10-18T14:01:52
| 2023-10-18T13:50:35
|
https://github.com/huggingface/datasets/pull/6309
|
{
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"diff_url": "https://github.com/huggingface/datasets/pull/6309.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6309.patch",
"merged_at": "2023-10-18T13:50:35"
}
|
albertvillanova
| true
|
[
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006461 / 0.011353 (-0.004891) | 0.004035 / 0.011008 (-0.006973) | 0.085037 / 0.038508 (0.046529) | 0.072434 / 0.023109 (0.049325) | 0.308565 / 0.275898 (0.032667) | 0.330455 / 0.323480 (0.006975) | 0.003782 / 0.007986 (-0.004204) | 0.004363 / 0.004328 (0.000034) | 0.065242 / 0.004250 (0.060991) | 0.056111 / 0.037052 (0.019058) | 0.318008 / 0.258489 (0.059519) | 0.357904 / 0.293841 (0.064063) | 0.030702 / 0.128546 (-0.097844) | 0.008741 / 0.075646 (-0.066905) | 0.287666 / 0.419271 (-0.131605) | 0.052281 / 0.043533 (0.008748) | 0.306894 / 0.255139 (0.051755) | 0.335739 / 0.283200 (0.052540) | 0.023712 / 0.141683 (-0.117971) | 1.492304 / 1.452155 (0.040149) | 1.544540 / 1.492716 (0.051823) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.299419 / 0.018006 (0.281413) | 0.547195 / 0.000490 (0.546705) | 0.011571 / 0.000200 (0.011371) | 0.000223 / 0.000054 (0.000168) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028364 / 0.037411 (-0.009048) | 0.081445 / 0.014526 (0.066919) | 0.626670 / 0.176557 (0.450114) | 0.159964 / 0.737135 (-0.577171) | 0.100528 / 0.296338 (-0.195811) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.409915 / 0.215209 (0.194705) | 4.108689 / 2.077655 (2.031034) | 2.046247 / 1.504120 (0.542127) | 1.851081 / 1.541195 (0.309887) | 1.857857 / 1.468490 (0.389367) | 0.493246 / 4.584777 (-4.091531) | 3.581557 / 3.745712 (-0.164155) | 3.456708 / 5.269862 (-1.813153) | 2.051054 / 4.565676 (-2.514623) | 0.057553 / 0.424275 (-0.366722) | 0.007287 / 0.007607 (-0.000320) | 0.493094 / 0.226044 (0.267050) | 4.873051 / 2.268929 (2.604122) | 2.515266 / 55.444624 (-52.929358) | 2.144743 / 6.876477 (-4.731733) | 2.159412 / 2.142072 (0.017340) | 0.595627 / 4.805227 (-4.209601) | 0.133773 / 6.500664 (-6.366891) | 0.059965 / 0.075469 (-0.015504) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.259625 / 1.841788 (-0.582163) | 19.030742 / 8.074308 (10.956434) | 14.039246 / 10.191392 (3.847854) | 0.168116 / 0.680424 (-0.512308) | 0.018168 / 0.534201 (-0.516033) | 0.391187 / 0.579283 (-0.188096) | 0.420901 / 0.434364 (-0.013463) | 0.465827 / 0.540337 (-0.074511) | 0.718373 / 1.386936 (-0.668563) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006616 / 0.011353 (-0.004737) | 0.004048 / 0.011008 (-0.006960) | 0.064568 / 0.038508 (0.026060) | 0.075933 / 0.023109 (0.052824) | 0.396353 / 0.275898 (0.120455) | 0.424159 / 0.323480 (0.100679) | 0.005446 / 0.007986 (-0.002540) | 0.003393 / 0.004328 (-0.000935) | 0.064673 / 0.004250 (0.060422) | 0.056983 / 0.037052 (0.019930) | 0.402478 / 0.258489 (0.143989) | 0.433240 / 0.293841 (0.139399) | 0.032100 / 0.128546 (-0.096446) | 0.008664 / 0.075646 (-0.066983) | 0.070502 / 0.419271 (-0.348770) | 0.047800 / 0.043533 (0.004267) | 0.399506 / 0.255139 (0.144367) | 0.418376 / 0.283200 (0.135176) | 0.022654 / 0.141683 (-0.119029) | 1.487280 / 1.452155 (0.035125) | 1.543733 / 1.492716 (0.051017) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.317660 / 0.018006 (0.299654) | 0.523922 / 0.000490 (0.523432) | 0.007086 / 0.000200 (0.006886) | 0.000109 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032381 / 0.037411 (-0.005030) | 0.091636 / 0.014526 (0.077110) | 0.104743 / 0.176557 (-0.071814) | 0.158793 / 0.737135 (-0.578342) | 0.103164 / 0.296338 (-0.193175) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434081 / 0.215209 (0.218872) | 4.329448 / 2.077655 (2.251794) | 2.335855 / 1.504120 (0.831735) | 2.177513 / 1.541195 (0.636319) | 2.205406 / 1.468490 (0.736916) | 0.500117 / 4.584777 (-4.084660) | 3.693715 / 3.745712 (-0.051997) | 3.305803 / 5.269862 (-1.964059) | 2.048283 / 4.565676 (-2.517394) | 0.058301 / 0.424275 (-0.365974) | 0.007196 / 0.007607 (-0.000411) | 0.512917 / 0.226044 (0.286873) | 5.129283 / 2.268929 (2.860355) | 2.836200 / 55.444624 (-52.608425) | 2.499022 / 6.876477 (-4.377455) | 2.652305 / 2.142072 (0.510232) | 0.604219 / 4.805227 (-4.201008) | 0.137310 / 6.500664 (-6.363354) | 0.060880 / 0.075469 (-0.014589) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.346948 / 1.841788 (-0.494839) | 19.499516 / 8.074308 (11.425208) | 14.701500 / 10.191392 (4.510108) | 0.168626 / 0.680424 (-0.511798) | 0.020002 / 0.534201 (-0.514199) | 0.394729 / 0.579283 (-0.184554) | 0.428323 / 0.434364 (-0.006040) | 0.481202 / 0.540337 (-0.059136) | 0.684768 / 1.386936 (-0.702169) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fed9c07458afc73870e8ec9846bf1fc5cac0b378 \"CML watermark\")\n",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6309). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007033 / 0.011353 (-0.004320) | 0.004411 / 0.011008 (-0.006597) | 0.086146 / 0.038508 (0.047638) | 0.086669 / 0.023109 (0.063560) | 0.329145 / 0.275898 (0.053247) | 0.348728 / 0.323480 (0.025248) | 0.004404 / 0.007986 (-0.003582) | 0.003656 / 0.004328 (-0.000673) | 0.066120 / 0.004250 (0.061869) | 0.059157 / 0.037052 (0.022105) | 0.316537 / 0.258489 (0.058048) | 0.369065 / 0.293841 (0.075224) | 0.031921 / 0.128546 (-0.096625) | 0.008877 / 0.075646 (-0.066770) | 0.290068 / 0.419271 (-0.129204) | 0.054007 / 0.043533 (0.010475) | 0.308823 / 0.255139 (0.053684) | 0.331189 / 0.283200 (0.047989) | 0.027313 / 0.141683 (-0.114370) | 1.486772 / 1.452155 (0.034617) | 1.570359 / 1.492716 (0.077643) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.315991 / 0.018006 (0.297985) | 0.577876 / 0.000490 (0.577386) | 0.011207 / 0.000200 (0.011007) | 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) | 0.031753 / 0.037411 (-0.005658) | 0.089270 / 0.014526 (0.074744) | 0.102518 / 0.176557 (-0.074038) | 0.160260 / 0.737135 (-0.576875) | 0.103365 / 0.296338 (-0.192973) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.405789 / 0.215209 (0.190580) | 4.052740 / 2.077655 (1.975085) | 2.052076 / 1.504120 (0.547956) | 1.873966 / 1.541195 (0.332771) | 1.997156 / 1.468490 (0.528665) | 0.494975 / 4.584777 (-4.089802) | 3.600007 / 3.745712 (-0.145705) | 3.626459 / 5.269862 (-1.643403) | 2.176927 / 4.565676 (-2.388750) | 0.057894 / 0.424275 (-0.366381) | 0.007469 / 0.007607 (-0.000138) | 0.487422 / 0.226044 (0.261377) | 4.868744 / 2.268929 (2.599815) | 2.528707 / 55.444624 (-52.915918) | 2.149520 / 6.876477 (-4.726956) | 2.275491 / 2.142072 (0.133419) | 0.589112 / 4.805227 (-4.216115) | 0.136644 / 6.500664 (-6.364020) | 0.062144 / 0.075469 (-0.013325) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.286625 / 1.841788 (-0.555163) | 20.528128 / 8.074308 (12.453819) | 15.290866 / 10.191392 (5.099474) | 0.168380 / 0.680424 (-0.512044) | 0.018908 / 0.534201 (-0.515293) | 0.397210 / 0.579283 (-0.182073) | 0.426133 / 0.434364 (-0.008231) | 0.471754 / 0.540337 (-0.068584) | 0.653343 / 1.386936 (-0.733593) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007599 / 0.011353 (-0.003754) | 0.004499 / 0.011008 (-0.006509) | 0.066248 / 0.038508 (0.027740) | 0.097704 / 0.023109 (0.074595) | 0.414558 / 0.275898 (0.138660) | 0.451088 / 0.323480 (0.127609) | 0.005932 / 0.007986 (-0.002054) | 0.003698 / 0.004328 (-0.000630) | 0.065784 / 0.004250 (0.061534) | 0.064777 / 0.037052 (0.027725) | 0.443318 / 0.258489 (0.184829) | 0.456896 / 0.293841 (0.163055) | 0.033436 / 0.128546 (-0.095111) | 0.008977 / 0.075646 (-0.066669) | 0.072067 / 0.419271 (-0.347205) | 0.049571 / 0.043533 (0.006038) | 0.420325 / 0.255139 (0.165186) | 0.443588 / 0.283200 (0.160388) | 0.026723 / 0.141683 (-0.114960) | 1.512566 / 1.452155 (0.060411) | 1.647591 / 1.492716 (0.154875) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.326410 / 0.018006 (0.308404) | 0.532878 / 0.000490 (0.532388) | 0.006257 / 0.000200 (0.006057) | 0.000104 / 0.000054 (0.000049) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037289 / 0.037411 (-0.000122) | 0.104940 / 0.014526 (0.090414) | 0.113597 / 0.176557 (-0.062960) | 0.170562 / 0.737135 (-0.566573) | 0.114583 / 0.296338 (-0.181755) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.435530 / 0.215209 (0.220321) | 4.332659 / 2.077655 (2.255005) | 2.343576 / 1.504120 (0.839456) | 2.190517 / 1.541195 (0.649322) | 2.323101 / 1.468490 (0.854611) | 0.493019 / 4.584777 (-4.091758) | 3.686726 / 3.745712 (-0.058986) | 3.437143 / 5.269862 (-1.832719) | 2.167193 / 4.565676 (-2.398483) | 0.059636 / 0.424275 (-0.364639) | 0.007696 / 0.007607 (0.000089) | 0.511159 / 0.226044 (0.285115) | 5.119358 / 2.268929 (2.850429) | 2.814934 / 55.444624 (-52.629690) | 2.477871 / 6.876477 (-4.398606) | 2.774473 / 2.142072 (0.632401) | 0.590258 / 4.805227 (-4.214969) | 0.135923 / 6.500664 (-6.364741) | 0.062793 / 0.075469 (-0.012676) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.350192 / 1.841788 (-0.491596) | 21.382135 / 8.074308 (13.307827) | 16.024198 / 10.191392 (5.832806) | 0.163623 / 0.680424 (-0.516801) | 0.020749 / 0.534201 (-0.513452) | 0.402578 / 0.579283 (-0.176705) | 0.436569 / 0.434364 (0.002205) | 0.477217 / 0.540337 (-0.063121) | 0.682929 / 1.386936 (-0.704007) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fa36173f2e8c6f266efd236933eff3a95af0382c \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006671 / 0.011353 (-0.004681) | 0.004176 / 0.011008 (-0.006832) | 0.084095 / 0.038508 (0.045587) | 0.076345 / 0.023109 (0.053236) | 0.341201 / 0.275898 (0.065303) | 0.381920 / 0.323480 (0.058440) | 0.005578 / 0.007986 (-0.002408) | 0.003535 / 0.004328 (-0.000794) | 0.065227 / 0.004250 (0.060976) | 0.054983 / 0.037052 (0.017931) | 0.345938 / 0.258489 (0.087449) | 0.398708 / 0.293841 (0.104867) | 0.031029 / 0.128546 (-0.097518) | 0.008643 / 0.075646 (-0.067004) | 0.287286 / 0.419271 (-0.131985) | 0.052424 / 0.043533 (0.008892) | 0.342914 / 0.255139 (0.087775) | 0.366982 / 0.283200 (0.083782) | 0.024511 / 0.141683 (-0.117172) | 1.510575 / 1.452155 (0.058421) | 1.593214 / 1.492716 (0.100497) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.272703 / 0.018006 (0.254697) | 0.583235 / 0.000490 (0.582746) | 0.008467 / 0.000200 (0.008267) | 0.000295 / 0.000054 (0.000240) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029654 / 0.037411 (-0.007757) | 0.085078 / 0.014526 (0.070552) | 0.106391 / 0.176557 (-0.070165) | 0.155790 / 0.737135 (-0.581345) | 0.104835 / 0.296338 (-0.191503) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.408584 / 0.215209 (0.193375) | 4.082557 / 2.077655 (2.004902) | 2.054001 / 1.504120 (0.549881) | 1.868470 / 1.541195 (0.327275) | 1.950600 / 1.468490 (0.482110) | 0.492572 / 4.584777 (-4.092205) | 3.497105 / 3.745712 (-0.248607) | 3.464596 / 5.269862 (-1.805265) | 2.106399 / 4.565676 (-2.459278) | 0.057413 / 0.424275 (-0.366862) | 0.007449 / 0.007607 (-0.000158) | 0.482900 / 0.226044 (0.256856) | 4.844152 / 2.268929 (2.575223) | 2.499930 / 55.444624 (-52.944695) | 2.180396 / 6.876477 (-4.696081) | 2.282830 / 2.142072 (0.140758) | 0.581371 / 4.805227 (-4.223857) | 0.134641 / 6.500664 (-6.366023) | 0.063137 / 0.075469 (-0.012332) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.274291 / 1.841788 (-0.567496) | 19.426189 / 8.074308 (11.351881) | 14.292833 / 10.191392 (4.101441) | 0.166321 / 0.680424 (-0.514102) | 0.018419 / 0.534201 (-0.515782) | 0.392433 / 0.579283 (-0.186850) | 0.415128 / 0.434364 (-0.019236) | 0.459274 / 0.540337 (-0.081063) | 0.714668 / 1.386936 (-0.672268) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006740 / 0.011353 (-0.004613) | 0.004283 / 0.011008 (-0.006725) | 0.063845 / 0.038508 (0.025337) | 0.077037 / 0.023109 (0.053927) | 0.425103 / 0.275898 (0.149205) | 0.445525 / 0.323480 (0.122046) | 0.005755 / 0.007986 (-0.002230) | 0.003589 / 0.004328 (-0.000739) | 0.064515 / 0.004250 (0.060265) | 0.057398 / 0.037052 (0.020346) | 0.424781 / 0.258489 (0.166292) | 0.452162 / 0.293841 (0.158321) | 0.032164 / 0.128546 (-0.096382) | 0.008660 / 0.075646 (-0.066986) | 0.069873 / 0.419271 (-0.349399) | 0.048100 / 0.043533 (0.004567) | 0.409097 / 0.255139 (0.153958) | 0.441533 / 0.283200 (0.158333) | 0.024122 / 0.141683 (-0.117560) | 1.503431 / 1.452155 (0.051277) | 1.577518 / 1.492716 (0.084802) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.264433 / 0.018006 (0.246426) | 0.553631 / 0.000490 (0.553141) | 0.006354 / 0.000200 (0.006154) | 0.000106 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033259 / 0.037411 (-0.004152) | 0.094908 / 0.014526 (0.080382) | 0.108238 / 0.176557 (-0.068318) | 0.161354 / 0.737135 (-0.575781) | 0.109073 / 0.296338 (-0.187265) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434450 / 0.215209 (0.219241) | 4.347501 / 2.077655 (2.269847) | 2.362225 / 1.504120 (0.858105) | 2.189285 / 1.541195 (0.648090) | 2.288797 / 1.468490 (0.820307) | 0.487782 / 4.584777 (-4.096995) | 3.598732 / 3.745712 (-0.146980) | 3.343263 / 5.269862 (-1.926599) | 2.086256 / 4.565676 (-2.479420) | 0.057838 / 0.424275 (-0.366437) | 0.007412 / 0.007607 (-0.000195) | 0.510098 / 0.226044 (0.284054) | 5.088743 / 2.268929 (2.819814) | 2.809105 / 55.444624 (-52.635519) | 2.476005 / 6.876477 (-4.400471) | 2.753785 / 2.142072 (0.611712) | 0.585045 / 4.805227 (-4.220182) | 0.131162 / 6.500664 (-6.369502) | 0.060431 / 0.075469 (-0.015038) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.342149 / 1.841788 (-0.499639) | 20.602369 / 8.074308 (12.528061) | 14.973301 / 10.191392 (4.781909) | 0.151655 / 0.680424 (-0.528769) | 0.020793 / 0.534201 (-0.513408) | 0.401657 / 0.579283 (-0.177626) | 0.419845 / 0.434364 (-0.014519) | 0.467225 / 0.540337 (-0.073113) | 0.672469 / 1.386936 (-0.714467) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#474beafbc1c2735ff4747f5675855583be2ede06 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007006 / 0.011353 (-0.004346) | 0.004282 / 0.011008 (-0.006726) | 0.085413 / 0.038508 (0.046905) | 0.085148 / 0.023109 (0.062038) | 0.336543 / 0.275898 (0.060645) | 0.367959 / 0.323480 (0.044479) | 0.004337 / 0.007986 (-0.003648) | 0.004535 / 0.004328 (0.000207) | 0.065379 / 0.004250 (0.061128) | 0.059993 / 0.037052 (0.022941) | 0.343162 / 0.258489 (0.084673) | 0.383766 / 0.293841 (0.089925) | 0.031520 / 0.128546 (-0.097026) | 0.008605 / 0.075646 (-0.067042) | 0.288620 / 0.419271 (-0.130651) | 0.053617 / 0.043533 (0.010084) | 0.339389 / 0.255139 (0.084250) | 0.350842 / 0.283200 (0.067642) | 0.027816 / 0.141683 (-0.113867) | 1.505500 / 1.452155 (0.053346) | 1.566511 / 1.492716 (0.073795) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.272203 / 0.018006 (0.254197) | 0.569729 / 0.000490 (0.569240) | 0.010061 / 0.000200 (0.009861) | 0.000328 / 0.000054 (0.000273) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030015 / 0.037411 (-0.007396) | 0.083991 / 0.014526 (0.069465) | 0.099796 / 0.176557 (-0.076761) | 0.159131 / 0.737135 (-0.578004) | 0.099102 / 0.296338 (-0.197237) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.390076 / 0.215209 (0.174867) | 3.897157 / 2.077655 (1.819502) | 1.935912 / 1.504120 (0.431793) | 1.815109 / 1.541195 (0.273915) | 1.875041 / 1.468490 (0.406551) | 0.482168 / 4.584777 (-4.102609) | 3.556140 / 3.745712 (-0.189572) | 3.528889 / 5.269862 (-1.740972) | 2.132767 / 4.565676 (-2.432909) | 0.057761 / 0.424275 (-0.366514) | 0.007353 / 0.007607 (-0.000254) | 0.464801 / 0.226044 (0.238757) | 4.637301 / 2.268929 (2.368372) | 2.362239 / 55.444624 (-53.082386) | 2.049811 / 6.876477 (-4.826665) | 2.143485 / 2.142072 (0.001412) | 0.580929 / 4.805227 (-4.224299) | 0.140252 / 6.500664 (-6.360412) | 0.061352 / 0.075469 (-0.014117) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.257487 / 1.841788 (-0.584301) | 19.453319 / 8.074308 (11.379011) | 14.276332 / 10.191392 (4.084940) | 0.166772 / 0.680424 (-0.513652) | 0.018339 / 0.534201 (-0.515862) | 0.393008 / 0.579283 (-0.186275) | 0.420960 / 0.434364 (-0.013404) | 0.464331 / 0.540337 (-0.076007) | 0.717973 / 1.386936 (-0.668963) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007255 / 0.011353 (-0.004098) | 0.004230 / 0.011008 (-0.006778) | 0.065191 / 0.038508 (0.026683) | 0.085765 / 0.023109 (0.062655) | 0.412464 / 0.275898 (0.136566) | 0.446067 / 0.323480 (0.122587) | 0.005875 / 0.007986 (-0.002110) | 0.003700 / 0.004328 (-0.000628) | 0.065430 / 0.004250 (0.061179) | 0.060284 / 0.037052 (0.023231) | 0.419984 / 0.258489 (0.161495) | 0.453779 / 0.293841 (0.159938) | 0.032595 / 0.128546 (-0.095952) | 0.008873 / 0.075646 (-0.066773) | 0.072124 / 0.419271 (-0.347148) | 0.048072 / 0.043533 (0.004539) | 0.408725 / 0.255139 (0.153586) | 0.432485 / 0.283200 (0.149285) | 0.024662 / 0.141683 (-0.117021) | 1.540434 / 1.452155 (0.088279) | 1.624768 / 1.492716 (0.132051) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.253220 / 0.018006 (0.235214) | 0.555469 / 0.000490 (0.554980) | 0.007765 / 0.000200 (0.007565) | 0.000101 / 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) | 0.032666 / 0.037411 (-0.004745) | 0.094786 / 0.014526 (0.080260) | 0.108219 / 0.176557 (-0.068337) | 0.161546 / 0.737135 (-0.575589) | 0.109828 / 0.296338 (-0.186510) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437024 / 0.215209 (0.221815) | 4.354065 / 2.077655 (2.276411) | 2.336832 / 1.504120 (0.832713) | 2.161959 / 1.541195 (0.620764) | 2.257214 / 1.468490 (0.788724) | 0.501576 / 4.584777 (-4.083201) | 3.654292 / 3.745712 (-0.091420) | 3.349504 / 5.269862 (-1.920357) | 2.092998 / 4.565676 (-2.472679) | 0.058740 / 0.424275 (-0.365535) | 0.007420 / 0.007607 (-0.000187) | 0.513443 / 0.226044 (0.287399) | 5.151247 / 2.268929 (2.882319) | 2.816036 / 55.444624 (-52.628589) | 2.451863 / 6.876477 (-4.424613) | 2.709908 / 2.142072 (0.567836) | 0.597834 / 4.805227 (-4.207394) | 0.136547 / 6.500664 (-6.364117) | 0.062030 / 0.075469 (-0.013439) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.371412 / 1.841788 (-0.470375) | 20.398981 / 8.074308 (12.324673) | 14.932307 / 10.191392 (4.740915) | 0.167796 / 0.680424 (-0.512628) | 0.020740 / 0.534201 (-0.513461) | 0.397162 / 0.579283 (-0.182121) | 0.435493 / 0.434364 (0.001129) | 0.477074 / 0.540337 (-0.063264) | 0.697546 / 1.386936 (-0.689390) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#017cefbc832bfe662afd87d9d1241104bf67c53e \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007388 / 0.011353 (-0.003964) | 0.004408 / 0.011008 (-0.006600) | 0.098225 / 0.038508 (0.059717) | 0.079368 / 0.023109 (0.056259) | 0.381866 / 0.275898 (0.105968) | 0.425942 / 0.323480 (0.102462) | 0.005978 / 0.007986 (-0.002007) | 0.003677 / 0.004328 (-0.000651) | 0.075488 / 0.004250 (0.071238) | 0.061725 / 0.037052 (0.024672) | 0.389126 / 0.258489 (0.130637) | 0.444099 / 0.293841 (0.150258) | 0.036222 / 0.128546 (-0.092324) | 0.009926 / 0.075646 (-0.065720) | 0.336632 / 0.419271 (-0.082640) | 0.060867 / 0.043533 (0.017335) | 0.385437 / 0.255139 (0.130298) | 0.416599 / 0.283200 (0.133399) | 0.025118 / 0.141683 (-0.116565) | 1.728073 / 1.452155 (0.275919) | 1.847750 / 1.492716 (0.355033) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.263774 / 0.018006 (0.245768) | 0.491242 / 0.000490 (0.490752) | 0.013621 / 0.000200 (0.013421) | 0.000333 / 0.000054 (0.000279) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032911 / 0.037411 (-0.004500) | 0.095738 / 0.014526 (0.081212) | 0.110482 / 0.176557 (-0.066075) | 0.175533 / 0.737135 (-0.561603) | 0.109240 / 0.296338 (-0.187098) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.453967 / 0.215209 (0.238758) | 4.489384 / 2.077655 (2.411730) | 2.185496 / 1.504120 (0.681376) | 1.979126 / 1.541195 (0.437931) | 2.016364 / 1.468490 (0.547874) | 0.565539 / 4.584777 (-4.019238) | 4.106561 / 3.745712 (0.360849) | 3.906402 / 5.269862 (-1.363460) | 2.342186 / 4.565676 (-2.223491) | 0.067815 / 0.424275 (-0.356460) | 0.008663 / 0.007607 (0.001056) | 0.543841 / 0.226044 (0.317796) | 5.433491 / 2.268929 (3.164563) | 2.785723 / 55.444624 (-52.658901) | 2.355716 / 6.876477 (-4.520760) | 2.397563 / 2.142072 (0.255491) | 0.682587 / 4.805227 (-4.122641) | 0.156548 / 6.500664 (-6.344116) | 0.070654 / 0.075469 (-0.004815) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.475183 / 1.841788 (-0.366605) | 21.353030 / 8.074308 (13.278722) | 15.938324 / 10.191392 (5.746932) | 0.167010 / 0.680424 (-0.513413) | 0.020931 / 0.534201 (-0.513270) | 0.464376 / 0.579283 (-0.114907) | 0.472546 / 0.434364 (0.038182) | 0.544645 / 0.540337 (0.004308) | 0.752940 / 1.386936 (-0.633996) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007359 / 0.011353 (-0.003994) | 0.004276 / 0.011008 (-0.006732) | 0.075345 / 0.038508 (0.036837) | 0.080105 / 0.023109 (0.056995) | 0.480456 / 0.275898 (0.204558) | 0.514974 / 0.323480 (0.191494) | 0.006087 / 0.007986 (-0.001899) | 0.003717 / 0.004328 (-0.000611) | 0.075067 / 0.004250 (0.070816) | 0.063739 / 0.037052 (0.026686) | 0.487569 / 0.258489 (0.229080) | 0.530198 / 0.293841 (0.236357) | 0.036056 / 0.128546 (-0.092491) | 0.009606 / 0.075646 (-0.066041) | 0.082343 / 0.419271 (-0.336929) | 0.055488 / 0.043533 (0.011956) | 0.484789 / 0.255139 (0.229650) | 0.501918 / 0.283200 (0.218718) | 0.025340 / 0.141683 (-0.116342) | 1.784417 / 1.452155 (0.332262) | 1.854202 / 1.492716 (0.361486) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.252476 / 0.018006 (0.234470) | 0.484967 / 0.000490 (0.484478) | 0.005471 / 0.000200 (0.005271) | 0.000111 / 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) | 0.037084 / 0.037411 (-0.000327) | 0.106648 / 0.014526 (0.092122) | 0.123393 / 0.176557 (-0.053164) | 0.183088 / 0.737135 (-0.554047) | 0.122572 / 0.296338 (-0.173767) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.516003 / 0.215209 (0.300793) | 5.107748 / 2.077655 (3.030093) | 2.778044 / 1.504120 (1.273924) | 2.589944 / 1.541195 (1.048749) | 2.649921 / 1.468490 (1.181431) | 0.572783 / 4.584777 (-4.011994) | 4.211331 / 3.745712 (0.465619) | 3.738859 / 5.269862 (-1.531003) | 2.331628 / 4.565676 (-2.234048) | 0.067347 / 0.424275 (-0.356928) | 0.008513 / 0.007607 (0.000905) | 0.601056 / 0.226044 (0.375012) | 5.990921 / 2.268929 (3.721992) | 3.311544 / 55.444624 (-52.133081) | 2.929850 / 6.876477 (-3.946627) | 3.118741 / 2.142072 (0.976669) | 0.685975 / 4.805227 (-4.119253) | 0.155105 / 6.500664 (-6.345559) | 0.069629 / 0.075469 (-0.005840) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.602367 / 1.841788 (-0.239421) | 22.577072 / 8.074308 (14.502764) | 17.049655 / 10.191392 (6.858263) | 0.182412 / 0.680424 (-0.498011) | 0.023137 / 0.534201 (-0.511064) | 0.466988 / 0.579283 (-0.112295) | 0.483887 / 0.434364 (0.049523) | 0.556099 / 0.540337 (0.015761) | 0.798332 / 1.386936 (-0.588604) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3e6d8318bd73a91852c22d14f1d788ac6dc8ae90 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009086 / 0.011353 (-0.002267) | 0.004755 / 0.011008 (-0.006253) | 0.128866 / 0.038508 (0.090358) | 0.086099 / 0.023109 (0.062990) | 0.378079 / 0.275898 (0.102181) | 0.487431 / 0.323480 (0.163951) | 0.004712 / 0.007986 (-0.003274) | 0.003622 / 0.004328 (-0.000706) | 0.081214 / 0.004250 (0.076963) | 0.057226 / 0.037052 (0.020174) | 0.407655 / 0.258489 (0.149166) | 0.448630 / 0.293841 (0.154789) | 0.049051 / 0.128546 (-0.079495) | 0.014537 / 0.075646 (-0.061110) | 0.467343 / 0.419271 (0.048071) | 0.070482 / 0.043533 (0.026949) | 0.379664 / 0.255139 (0.124525) | 0.464181 / 0.283200 (0.180981) | 0.039973 / 0.141683 (-0.101710) | 1.731164 / 1.452155 (0.279010) | 1.886895 / 1.492716 (0.394178) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.251327 / 0.018006 (0.233321) | 0.502670 / 0.000490 (0.502180) | 0.012183 / 0.000200 (0.011984) | 0.000111 / 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) | 0.028892 / 0.037411 (-0.008519) | 0.093789 / 0.014526 (0.079263) | 0.104255 / 0.176557 (-0.072301) | 0.170257 / 0.737135 (-0.566879) | 0.115430 / 0.296338 (-0.180909) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.573745 / 0.215209 (0.358536) | 5.873732 / 2.077655 (3.796077) | 2.485188 / 1.504120 (0.981068) | 2.018476 / 1.541195 (0.477282) | 2.062765 / 1.468490 (0.594275) | 0.913816 / 4.584777 (-3.670961) | 5.362338 / 3.745712 (1.616626) | 4.698758 / 5.269862 (-0.571103) | 3.132973 / 4.565676 (-1.432703) | 0.093594 / 0.424275 (-0.330681) | 0.008359 / 0.007607 (0.000751) | 0.693997 / 0.226044 (0.467953) | 7.042645 / 2.268929 (4.773717) | 3.196180 / 55.444624 (-52.248445) | 2.384585 / 6.876477 (-4.491892) | 2.301256 / 2.142072 (0.159183) | 1.048025 / 4.805227 (-3.757202) | 0.206931 / 6.500664 (-6.293733) | 0.069401 / 0.075469 (-0.006068) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.598898 / 1.841788 (-0.242889) | 22.963667 / 8.074308 (14.889359) | 20.373688 / 10.191392 (10.182296) | 0.239716 / 0.680424 (-0.440707) | 0.040213 / 0.534201 (-0.493988) | 0.503268 / 0.579283 (-0.076015) | 0.630750 / 0.434364 (0.196386) | 0.578007 / 0.540337 (0.037669) | 0.789564 / 1.386936 (-0.597372) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009129 / 0.011353 (-0.002224) | 0.005453 / 0.011008 (-0.005555) | 0.101040 / 0.038508 (0.062532) | 0.099172 / 0.023109 (0.076062) | 0.508453 / 0.275898 (0.232555) | 0.570858 / 0.323480 (0.247378) | 0.006584 / 0.007986 (-0.001401) | 0.003800 / 0.004328 (-0.000528) | 0.094349 / 0.004250 (0.090098) | 0.064642 / 0.037052 (0.027590) | 0.563008 / 0.258489 (0.304518) | 0.625560 / 0.293841 (0.331719) | 0.050121 / 0.128546 (-0.078426) | 0.014183 / 0.075646 (-0.061463) | 0.106564 / 0.419271 (-0.312707) | 0.061030 / 0.043533 (0.017498) | 0.522311 / 0.255139 (0.267172) | 0.598356 / 0.283200 (0.315156) | 0.042008 / 0.141683 (-0.099675) | 1.879999 / 1.452155 (0.427844) | 1.963879 / 1.492716 (0.471162) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.270573 / 0.018006 (0.252567) | 0.554356 / 0.000490 (0.553866) | 0.008145 / 0.000200 (0.007945) | 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) | 0.031089 / 0.037411 (-0.006322) | 0.099568 / 0.014526 (0.085043) | 0.118304 / 0.176557 (-0.058253) | 0.182991 / 0.737135 (-0.554144) | 0.115874 / 0.296338 (-0.180465) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.615020 / 0.215209 (0.399811) | 6.279740 / 2.077655 (4.202085) | 2.882094 / 1.504120 (1.377974) | 2.559265 / 1.541195 (1.018070) | 2.639259 / 1.468490 (1.170769) | 0.903727 / 4.584777 (-3.681050) | 5.248555 / 3.745712 (1.502843) | 4.817340 / 5.269862 (-0.452522) | 3.056880 / 4.565676 (-1.508797) | 0.096602 / 0.424275 (-0.327673) | 0.008660 / 0.007607 (0.001053) | 0.794347 / 0.226044 (0.568303) | 7.625127 / 2.268929 (5.356198) | 3.766826 / 55.444624 (-51.677798) | 2.968254 / 6.876477 (-3.908223) | 3.260595 / 2.142072 (1.118523) | 1.066228 / 4.805227 (-3.739000) | 0.207158 / 6.500664 (-6.293506) | 0.076920 / 0.075469 (0.001451) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.741442 / 1.841788 (-0.100345) | 23.499552 / 8.074308 (15.425244) | 22.064966 / 10.191392 (11.873574) | 0.239173 / 0.680424 (-0.441251) | 0.032105 / 0.534201 (-0.502096) | 0.484709 / 0.579283 (-0.094574) | 0.583632 / 0.434364 (0.149268) | 0.569018 / 0.540337 (0.028681) | 0.815764 / 1.386936 (-0.571172) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3aeb078ba1afd713e901df43343c160877403d07 \"CML watermark\")\n"
] |
1,946,810,625
| 6,308
|
module 'resource' has no attribute 'error'
|
closed
| 2023-10-17T08:08:54
| 2023-10-25T17:09:22
| 2023-10-25T17:09:22
|
https://github.com/huggingface/datasets/issues/6308
| null |
NeoWang9999
| false
|
[
"This (Windows) issue was fixed in `fsspec` in https://github.com/fsspec/filesystem_spec/pull/1275. So, to avoid the error, update the `fsspec` installation with `pip install -U fsspec`.",
"> This (Windows) issue was fixed in `fsspec` in [fsspec/filesystem_spec#1275](https://github.com/fsspec/filesystem_spec/pull/1275). So, to avoid the error, update the `fsspec` installation with `pip install -U fsspec`.\r\n\r\nafter I run `pip install -U fsspec`\r\n\r\nit occurs a new error:\r\n```\r\nERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflict\r\ns.\r\ndatasets 2.14.5 requires fsspec[http]<2023.9.0,>=2023.1.0, but you have fsspec 2023.9.2 which is incompatible.\r\n\r\n```",
"The `fsspec<2023.9.0` upper bound will be removed in the next release. The `ResourceError` fix is also present in version 2023.6.0, so use that version in the meantime (`pip install fsspec==2023.6.0`).",
"> The `fsspec<2023.9.0` upper bound will be removed in the next release. The `ResourceError` fix is also present in version 2023.6.0, so use that version in the meantime (`pip install fsspec==2023.6.0`).\r\n\r\nthanks for reply！"
] |
1,946,414,808
| 6,307
|
Fix typo in code example in docs
|
closed
| 2023-10-17T02:28:50
| 2023-10-17T12:59:26
| 2023-10-17T06:36:19
|
https://github.com/huggingface/datasets/pull/6307
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6307",
"html_url": "https://github.com/huggingface/datasets/pull/6307",
"diff_url": "https://github.com/huggingface/datasets/pull/6307.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6307.patch",
"merged_at": "2023-10-17T06:36:18"
}
|
bryant1410
| true
|
[
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011548 / 0.011353 (0.000196) | 0.004630 / 0.011008 (-0.006378) | 0.105349 / 0.038508 (0.066841) | 0.110557 / 0.023109 (0.087448) | 0.395463 / 0.275898 (0.119565) | 0.448391 / 0.323480 (0.124912) | 0.005112 / 0.007986 (-0.002873) | 0.003854 / 0.004328 (-0.000474) | 0.088513 / 0.004250 (0.084263) | 0.073081 / 0.037052 (0.036028) | 0.391572 / 0.258489 (0.133083) | 0.459543 / 0.293841 (0.165702) | 0.040424 / 0.128546 (-0.088122) | 0.010306 / 0.075646 (-0.065340) | 0.365493 / 0.419271 (-0.053778) | 0.068154 / 0.043533 (0.024622) | 0.397675 / 0.255139 (0.142536) | 0.447147 / 0.283200 (0.163947) | 0.033482 / 0.141683 (-0.108201) | 1.857087 / 1.452155 (0.404932) | 1.973311 / 1.492716 (0.480595) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.257938 / 0.018006 (0.239932) | 0.569572 / 0.000490 (0.569083) | 0.012155 / 0.000200 (0.011955) | 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) | 0.033094 / 0.037411 (-0.004318) | 0.102370 / 0.014526 (0.087844) | 0.122421 / 0.176557 (-0.054136) | 0.189983 / 0.737135 (-0.547152) | 0.117902 / 0.296338 (-0.178437) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.468419 / 0.215209 (0.253210) | 4.671410 / 2.077655 (2.593755) | 2.371136 / 1.504120 (0.867016) | 2.191877 / 1.541195 (0.650682) | 2.301894 / 1.468490 (0.833404) | 0.572260 / 4.584777 (-4.012517) | 4.302031 / 3.745712 (0.556319) | 4.128431 / 5.269862 (-1.141431) | 2.464543 / 4.565676 (-2.101133) | 0.067663 / 0.424275 (-0.356612) | 0.008947 / 0.007607 (0.001340) | 0.570063 / 0.226044 (0.344018) | 5.684460 / 2.268929 (3.415531) | 2.969708 / 55.444624 (-52.474916) | 2.573568 / 6.876477 (-4.302909) | 2.666074 / 2.142072 (0.524001) | 0.710098 / 4.805227 (-4.095129) | 0.158413 / 6.500664 (-6.342251) | 0.072776 / 0.075469 (-0.002693) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.564166 / 1.841788 (-0.277622) | 23.612774 / 8.074308 (15.538465) | 17.725070 / 10.191392 (7.533678) | 0.178982 / 0.680424 (-0.501442) | 0.021615 / 0.534201 (-0.512586) | 0.467090 / 0.579283 (-0.112193) | 0.472648 / 0.434364 (0.038284) | 0.578820 / 0.540337 (0.038483) | 0.783533 / 1.386936 (-0.603403) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008895 / 0.011353 (-0.002458) | 0.004617 / 0.011008 (-0.006392) | 0.077677 / 0.038508 (0.039169) | 0.090283 / 0.023109 (0.067174) | 0.491115 / 0.275898 (0.215217) | 0.525189 / 0.323480 (0.201709) | 0.007845 / 0.007986 (-0.000141) | 0.003742 / 0.004328 (-0.000586) | 0.077856 / 0.004250 (0.073606) | 0.067447 / 0.037052 (0.030394) | 0.488423 / 0.258489 (0.229933) | 0.532938 / 0.293841 (0.239097) | 0.041035 / 0.128546 (-0.087511) | 0.009917 / 0.075646 (-0.065730) | 0.085313 / 0.419271 (-0.333958) | 0.063374 / 0.043533 (0.019841) | 0.472287 / 0.255139 (0.217148) | 0.509773 / 0.283200 (0.226573) | 0.028706 / 0.141683 (-0.112977) | 1.775558 / 1.452155 (0.323403) | 1.967778 / 1.492716 (0.475061) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.249834 / 0.018006 (0.231828) | 0.467266 / 0.000490 (0.466776) | 0.005837 / 0.000200 (0.005637) | 0.000128 / 0.000054 (0.000074) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038759 / 0.037411 (0.001347) | 0.113156 / 0.014526 (0.098630) | 0.123936 / 0.176557 (-0.052621) | 0.186831 / 0.737135 (-0.550304) | 0.125195 / 0.296338 (-0.171143) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.545666 / 0.215209 (0.330457) | 5.465713 / 2.077655 (3.388058) | 2.941279 / 1.504120 (1.437159) | 2.688377 / 1.541195 (1.147182) | 2.619501 / 1.468490 (1.151010) | 0.577974 / 4.584777 (-4.006803) | 4.300966 / 3.745712 (0.555254) | 3.879552 / 5.269862 (-1.390310) | 2.454932 / 4.565676 (-2.110745) | 0.069233 / 0.424275 (-0.355043) | 0.009729 / 0.007607 (0.002122) | 0.595290 / 0.226044 (0.369245) | 5.945445 / 2.268929 (3.676516) | 3.314607 / 55.444624 (-52.130017) | 2.894474 / 6.876477 (-3.982002) | 3.140790 / 2.142072 (0.998718) | 0.695808 / 4.805227 (-4.109419) | 0.158087 / 6.500664 (-6.342577) | 0.071374 / 0.075469 (-0.004095) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.706482 / 1.841788 (-0.135306) | 24.022666 / 8.074308 (15.948358) | 17.658003 / 10.191392 (7.466611) | 0.196771 / 0.680424 (-0.483653) | 0.023928 / 0.534201 (-0.510273) | 0.471992 / 0.579283 (-0.107291) | 0.510463 / 0.434364 (0.076099) | 0.621250 / 0.540337 (0.080912) | 0.807670 / 1.386936 (-0.579266) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f77539cbd88d00ec1ab2b9d4edfd01d5a58ef88a \"CML watermark\")\n"
] |
1,946,363,452
| 6,306
|
pyinstaller : OSError: could not get source code
|
closed
| 2023-10-17T01:41:51
| 2023-11-02T07:24:51
| 2023-10-18T14:03:42
|
https://github.com/huggingface/datasets/issues/6306
| null |
dusk877647949
| false
|
[
"more information：\r\n``` \r\nFile \"text2vec\\__init__.py\", line 8, in <module>\r\nFile \"<frozen importlib._bootstrap>\", line 1027, in _find_and_load\r\nFile \"<frozen importlib._bootstrap>\", line 1006, in _find_and_load_unlocked\r\nFile \"<frozen importlib._bootstrap>\", line 688, in _load_unlocked\r\nFile \"PyInstaller\\loader\\pyimod02_importers.py\", line 499, in exec_module\r\nFile \"text2vec\\bertmatching_model.py\", line 19, in <module>\r\nFile \"<frozen importlib._bootstrap>\", line 1027, in _find_and_load\r\nFile \"<frozen importlib._bootstrap>\", line 1006, in _find_and_load_unlocked\r\nFile \"<frozen importlib._bootstrap>\", line 688, in _load_unlocked\r\nFile \"PyInstaller\\loader\\pyimod02_importers.py\", line 499, in exec_module\r\nFile \"text2vec\\bertmatching_dataset.py\", line 7, in <module>\r\nFile \"<frozen importlib._bootstrap>\", line 1027, in _find_and_load\r\nFile \"<frozen importlib._bootstrap>\", line 1006, in _find_and_load_unlocked\r\nFile \"<frozen importlib._bootstrap>\", line 688, in _load_unlocked\r\nFile \"PyInstaller\\loader\\pyimod02_importers.py\", line 499, in exec_module\r\nFile \"datasets\\__init__.py\", line 52, in <module>\r\nFile \"<frozen importlib._bootstrap>\", line 1027, in _find_and_load\r\nFile \"<frozen importlib._bootstrap>\", line 1006, in _find_and_load_unlocked\r\nFile \"<frozen importlib._bootstrap>\", line 688, in _load_unlocked\r\nFile \"PyInstaller\\loader\\pyimod02_importers.py\", line 499, in exec_module\r\nFile \"datasets\\inspect.py\", line 30, in <module>\r\nFile \"<frozen importlib._bootstrap>\", line 1027, in _find_and_load\r\nFile \"<frozen importlib._bootstrap>\", line 1006, in _find_and_load_unlocked\r\nFile \"<frozen importlib._bootstrap>\", line 688, in _load_unlocked\r\nFile \"PyInstaller\\loader\\pyimod02_importers.py\", line 499, in exec_module\r\nFile \"datasets\\load.py\", line 58, in <module>\r\nFile \"<frozen importlib._bootstrap>\", line 1027, in _find_and_load\r\nFile \"<frozen importlib._bootstrap>\", line 1006, in _find_and_load_unlocked\r\nFile \"<frozen importlib._bootstrap>\", line 688, in _load_unlocked\r\nFile \"PyInstaller\\loader\\pyimod02_importers.py\", line 499, in exec_module\r\nFile \"datasets\\packaged_modules\\__init__.py\", line 31, in <module>\r\nFile \"inspect.py\", line 1147, in getsource\r\nFile \"inspect.py\", line 1129, in getsourcelines\r\nFile \"inspect.py\", line 958, in findsource\r\nOSError: could not get source code\r\n```\r\n",
"Can you share a reproducer? I haven't been able to reproduce the error myself.",
"> '\r\n\r\nthanks,I solve it.it's about pyinstaller.",
"1",
"> > '\r\n> \r\n> thanks,I solve it.it's about pyinstaller.\r\n\r\nI encountered the same error, how to solve it?"
] |
1,946,010,912
| 6,305
|
Cannot load dataset with `2.14.5`: `FileNotFound` error
|
closed
| 2023-10-16T20:11:27
| 2023-10-18T13:50:36
| 2023-10-18T13:50:36
|
https://github.com/huggingface/datasets/issues/6305
| null |
finiteautomata
| false
|
[
"Thanks for reporting, @finiteautomata.\r\n\r\nWe are investigating it. ",
"There is a bug in `datasets`. You can see our proposed fix:\r\n- #6309 "
] |
1,945,913,521
| 6,304
|
Update README.md
|
closed
| 2023-10-16T19:10:39
| 2023-10-17T15:13:37
| 2023-10-17T15:04:52
|
https://github.com/huggingface/datasets/pull/6304
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6304",
"html_url": "https://github.com/huggingface/datasets/pull/6304",
"diff_url": "https://github.com/huggingface/datasets/pull/6304.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6304.patch",
"merged_at": "2023-10-17T15:04:52"
}
|
smty2018
| true
|
[
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006678 / 0.011353 (-0.004675) | 0.004013 / 0.011008 (-0.006995) | 0.083372 / 0.038508 (0.044864) | 0.070339 / 0.023109 (0.047230) | 0.339026 / 0.275898 (0.063128) | 0.370945 / 0.323480 (0.047465) | 0.004050 / 0.007986 (-0.003935) | 0.003283 / 0.004328 (-0.001046) | 0.064956 / 0.004250 (0.060705) | 0.055427 / 0.037052 (0.018374) | 0.341787 / 0.258489 (0.083297) | 0.385030 / 0.293841 (0.091189) | 0.031791 / 0.128546 (-0.096755) | 0.008511 / 0.075646 (-0.067135) | 0.286538 / 0.419271 (-0.132734) | 0.052893 / 0.043533 (0.009360) | 0.338522 / 0.255139 (0.083383) | 0.371821 / 0.283200 (0.088622) | 0.023731 / 0.141683 (-0.117951) | 1.485857 / 1.452155 (0.033702) | 1.515218 / 1.492716 (0.022502) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.232798 / 0.018006 (0.214792) | 0.446783 / 0.000490 (0.446293) | 0.007395 / 0.000200 (0.007195) | 0.000385 / 0.000054 (0.000330) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028866 / 0.037411 (-0.008545) | 0.081653 / 0.014526 (0.067127) | 0.094457 / 0.176557 (-0.082099) | 0.151761 / 0.737135 (-0.585375) | 0.095579 / 0.296338 (-0.200760) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.379926 / 0.215209 (0.164717) | 3.801839 / 2.077655 (1.724184) | 1.830302 / 1.504120 (0.326182) | 1.686912 / 1.541195 (0.145717) | 1.803418 / 1.468490 (0.334928) | 0.484431 / 4.584777 (-4.100346) | 3.592748 / 3.745712 (-0.152964) | 3.402578 / 5.269862 (-1.867284) | 2.043434 / 4.565676 (-2.522242) | 0.057274 / 0.424275 (-0.367001) | 0.007211 / 0.007607 (-0.000396) | 0.462611 / 0.226044 (0.236567) | 4.610703 / 2.268929 (2.341775) | 2.397668 / 55.444624 (-53.046956) | 2.149983 / 6.876477 (-4.726494) | 2.199100 / 2.142072 (0.057028) | 0.575883 / 4.805227 (-4.229344) | 0.133421 / 6.500664 (-6.367243) | 0.061168 / 0.075469 (-0.014301) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.246792 / 1.841788 (-0.594995) | 18.974385 / 8.074308 (10.900077) | 14.268859 / 10.191392 (4.077467) | 0.166340 / 0.680424 (-0.514084) | 0.018227 / 0.534201 (-0.515974) | 0.389646 / 0.579283 (-0.189637) | 0.418780 / 0.434364 (-0.015584) | 0.458063 / 0.540337 (-0.082275) | 0.635156 / 1.386936 (-0.751780) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006613 / 0.011353 (-0.004740) | 0.003977 / 0.011008 (-0.007031) | 0.064609 / 0.038508 (0.026101) | 0.070418 / 0.023109 (0.047308) | 0.395814 / 0.275898 (0.119916) | 0.424803 / 0.323480 (0.101323) | 0.005342 / 0.007986 (-0.002644) | 0.003252 / 0.004328 (-0.001076) | 0.065177 / 0.004250 (0.060927) | 0.055299 / 0.037052 (0.018247) | 0.403983 / 0.258489 (0.145494) | 0.438522 / 0.293841 (0.144681) | 0.032336 / 0.128546 (-0.096210) | 0.008524 / 0.075646 (-0.067122) | 0.071645 / 0.419271 (-0.347627) | 0.048137 / 0.043533 (0.004604) | 0.395170 / 0.255139 (0.140031) | 0.421727 / 0.283200 (0.138528) | 0.023028 / 0.141683 (-0.118655) | 1.500739 / 1.452155 (0.048584) | 1.568887 / 1.492716 (0.076170) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227542 / 0.018006 (0.209536) | 0.447882 / 0.000490 (0.447393) | 0.005416 / 0.000200 (0.005216) | 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) | 0.032954 / 0.037411 (-0.004457) | 0.091994 / 0.014526 (0.077468) | 0.105957 / 0.176557 (-0.070600) | 0.158728 / 0.737135 (-0.578407) | 0.104734 / 0.296338 (-0.191605) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436275 / 0.215209 (0.221066) | 4.344864 / 2.077655 (2.267209) | 2.304949 / 1.504120 (0.800829) | 2.123963 / 1.541195 (0.582768) | 2.189099 / 1.468490 (0.720609) | 0.492662 / 4.584777 (-4.092115) | 3.633662 / 3.745712 (-0.112051) | 3.251338 / 5.269862 (-2.018524) | 2.061378 / 4.565676 (-2.504299) | 0.058100 / 0.424275 (-0.366175) | 0.007311 / 0.007607 (-0.000297) | 0.516227 / 0.226044 (0.290183) | 5.184228 / 2.268929 (2.915300) | 2.780343 / 55.444624 (-52.664281) | 2.423428 / 6.876477 (-4.453048) | 2.617371 / 2.142072 (0.475298) | 0.590455 / 4.805227 (-4.214772) | 0.131728 / 6.500664 (-6.368936) | 0.059994 / 0.075469 (-0.015475) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.354920 / 1.841788 (-0.486868) | 19.427822 / 8.074308 (11.353514) | 15.289037 / 10.191392 (5.097645) | 0.170437 / 0.680424 (-0.509987) | 0.020242 / 0.534201 (-0.513959) | 0.394921 / 0.579283 (-0.184362) | 0.426447 / 0.434364 (-0.007917) | 0.468321 / 0.540337 (-0.072017) | 0.671052 / 1.386936 (-0.715884) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bade7af74437347a760830466eb74f7a8ce0d799 \"CML watermark\")\n"
] |
1,943,466,532
| 6,303
|
Parquet uploads off-by-one naming scheme
|
open
| 2023-10-14T18:31:03
| 2023-10-16T16:33:21
| null |
https://github.com/huggingface/datasets/issues/6303
| null |
ZachNagengast
| false
|
[
"You can find the reasoning behind this naming scheme [here](https://github.com/huggingface/transformers/pull/16343#discussion_r931182168).\r\n\r\nThis point has been raised several times, so I'd be okay with starting with `00001-` (also to be consistent with the `transformers` sharding), but I'm not sure @lhoestq agrees.",
"We start at 0 in `datasets` for consistency with Apache Spark, Apache Beam, Dask and others.\r\n\r\nAlso note `transformers` isn't a good reference on this topic. I talked with the maintainers when they added shards but it was already released this way. Though we found that there is a backward-compatible way in `transformers` to start at 0, but no request from `transformers` users to changes this AFAIK.",
"not sure it would be a good idea to break the consistency now, IMO",
"Makes sense to start at 0 for plenty of good reasons so I'm on board.\r\n\r\nWhat about the second part `-of-0000X`? With single commit PR #6269 just getting merged, there was a note about issues with 100+ file edits https://github.com/huggingface/datasets/pull/6269#issuecomment-1755428581.\r\n\r\nThat would be my last remaining concern in the context of the `push_to_hub(..., append=True)` work to be done, where appending a single file to the full dataset will require renaming every other existing file in the dataset. If it doesn't seem like a big issue for this work then all the better 👍"
] |
1,942,096,078
| 6,302
|
ArrowWriter/ParquetWriter `write` method does not increase `_num_bytes` and hence datasets not sharding at `max_shard_size`
|
closed
| 2023-10-13T14:43:36
| 2023-10-17T06:52:12
| 2023-10-17T06:52:11
|
https://github.com/huggingface/datasets/issues/6302
| null |
Rassibassi
| false
|
[
"`writer._num_bytes` is updated every `writer_batch_size`-th call to the `write` method (default `writer_batch_size` is 1000 (examples)). You should be able to see the update by passing a smaller `writer_batch_size` to the `load_dataset_builder`.\r\n\r\nWe could improve this by supporting the string `writer_batch_size` version as we do with `max_shard_size`, and capping `writer_batch_size` to `max_shard_size` in scenarios where the default `writer_batch_size` > `max_shard_size`. ",
"Thanks, reducing `writer_batch_size` solved my problem :)"
] |
1,940,183,999
| 6,301
|
Unpin `tensorflow` maximum version
|
closed
| 2023-10-12T14:58:07
| 2023-10-12T15:58:20
| 2023-10-12T15:49:54
|
https://github.com/huggingface/datasets/pull/6301
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6301",
"html_url": "https://github.com/huggingface/datasets/pull/6301",
"diff_url": "https://github.com/huggingface/datasets/pull/6301.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6301.patch",
"merged_at": "2023-10-12T15:49:54"
}
|
mariosasko
| true
|
[
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006663 / 0.011353 (-0.004690) | 0.004091 / 0.011008 (-0.006918) | 0.084954 / 0.038508 (0.046445) | 0.071869 / 0.023109 (0.048760) | 0.314706 / 0.275898 (0.038808) | 0.352794 / 0.323480 (0.029314) | 0.004027 / 0.007986 (-0.003959) | 0.003371 / 0.004328 (-0.000957) | 0.065456 / 0.004250 (0.061205) | 0.055828 / 0.037052 (0.018775) | 0.316502 / 0.258489 (0.058013) | 0.377979 / 0.293841 (0.084138) | 0.030870 / 0.128546 (-0.097676) | 0.008616 / 0.075646 (-0.067030) | 0.288625 / 0.419271 (-0.130646) | 0.052314 / 0.043533 (0.008781) | 0.322725 / 0.255139 (0.067586) | 0.351810 / 0.283200 (0.068611) | 0.025726 / 0.141683 (-0.115957) | 1.439308 / 1.452155 (-0.012847) | 1.524484 / 1.492716 (0.031768) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235212 / 0.018006 (0.217206) | 0.444926 / 0.000490 (0.444437) | 0.009887 / 0.000200 (0.009687) | 0.000402 / 0.000054 (0.000347) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028956 / 0.037411 (-0.008455) | 0.084401 / 0.014526 (0.069875) | 0.339686 / 0.176557 (0.163130) | 0.186785 / 0.737135 (-0.550350) | 0.195017 / 0.296338 (-0.101322) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.405480 / 0.215209 (0.190271) | 4.024315 / 2.077655 (1.946661) | 2.056398 / 1.504120 (0.552278) | 1.912099 / 1.541195 (0.370904) | 1.950119 / 1.468490 (0.481629) | 0.486071 / 4.584777 (-4.098706) | 3.578501 / 3.745712 (-0.167211) | 3.268980 / 5.269862 (-2.000881) | 2.018114 / 4.565676 (-2.547563) | 0.057440 / 0.424275 (-0.366835) | 0.007281 / 0.007607 (-0.000326) | 0.474760 / 0.226044 (0.248716) | 4.746908 / 2.268929 (2.477979) | 2.550111 / 55.444624 (-52.894513) | 2.171932 / 6.876477 (-4.704544) | 2.392235 / 2.142072 (0.250162) | 0.585940 / 4.805227 (-4.219287) | 0.136445 / 6.500664 (-6.364219) | 0.062125 / 0.075469 (-0.013344) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.270763 / 1.841788 (-0.571025) | 19.213516 / 8.074308 (11.139208) | 13.992620 / 10.191392 (3.801228) | 0.167356 / 0.680424 (-0.513068) | 0.018261 / 0.534201 (-0.515940) | 0.392489 / 0.579283 (-0.186794) | 0.418845 / 0.434364 (-0.015519) | 0.461824 / 0.540337 (-0.078513) | 0.649661 / 1.386936 (-0.737275) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006675 / 0.011353 (-0.004678) | 0.003913 / 0.011008 (-0.007096) | 0.064943 / 0.038508 (0.026435) | 0.072426 / 0.023109 (0.049317) | 0.400785 / 0.275898 (0.124887) | 0.434359 / 0.323480 (0.110879) | 0.005370 / 0.007986 (-0.002616) | 0.003290 / 0.004328 (-0.001038) | 0.065035 / 0.004250 (0.060785) | 0.054924 / 0.037052 (0.017872) | 0.404442 / 0.258489 (0.145953) | 0.439027 / 0.293841 (0.145186) | 0.032467 / 0.128546 (-0.096080) | 0.008565 / 0.075646 (-0.067081) | 0.070653 / 0.419271 (-0.348619) | 0.048034 / 0.043533 (0.004501) | 0.400869 / 0.255139 (0.145730) | 0.423048 / 0.283200 (0.139848) | 0.022757 / 0.141683 (-0.118926) | 1.516956 / 1.452155 (0.064801) | 1.581599 / 1.492716 (0.088883) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.214761 / 0.018006 (0.196755) | 0.440921 / 0.000490 (0.440431) | 0.007538 / 0.000200 (0.007338) | 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) | 0.032313 / 0.037411 (-0.005099) | 0.091365 / 0.014526 (0.076839) | 0.106665 / 0.176557 (-0.069891) | 0.158637 / 0.737135 (-0.578498) | 0.104894 / 0.296338 (-0.191445) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.432995 / 0.215209 (0.217786) | 4.339911 / 2.077655 (2.262256) | 2.313139 / 1.504120 (0.809019) | 2.142552 / 1.541195 (0.601357) | 2.279275 / 1.468490 (0.810785) | 0.501133 / 4.584777 (-4.083644) | 3.696160 / 3.745712 (-0.049552) | 3.341886 / 5.269862 (-1.927976) | 2.105972 / 4.565676 (-2.459705) | 0.059268 / 0.424275 (-0.365008) | 0.007568 / 0.007607 (-0.000039) | 0.512546 / 0.226044 (0.286502) | 5.130219 / 2.268929 (2.861290) | 2.808292 / 55.444624 (-52.636332) | 2.478721 / 6.876477 (-4.397755) | 2.679341 / 2.142072 (0.537269) | 0.599022 / 4.805227 (-4.206206) | 0.143761 / 6.500664 (-6.356903) | 0.062061 / 0.075469 (-0.013409) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.430507 / 1.841788 (-0.411281) | 20.458085 / 8.074308 (12.383777) | 15.268356 / 10.191392 (5.076964) | 0.163359 / 0.680424 (-0.517065) | 0.020908 / 0.534201 (-0.513293) | 0.396870 / 0.579283 (-0.182413) | 0.432630 / 0.434364 (-0.001733) | 0.475909 / 0.540337 (-0.064429) | 0.681031 / 1.386936 (-0.705905) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fd1dd6aa4c7fa7744c1c1f877573ff59f1529292 \"CML watermark\")\n",
"CI failures are unrelated",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005815 / 0.011353 (-0.005538) | 0.003419 / 0.011008 (-0.007589) | 0.080286 / 0.038508 (0.041778) | 0.056487 / 0.023109 (0.033377) | 0.304414 / 0.275898 (0.028516) | 0.341039 / 0.323480 (0.017559) | 0.004392 / 0.007986 (-0.003594) | 0.002852 / 0.004328 (-0.001477) | 0.062339 / 0.004250 (0.058089) | 0.044683 / 0.037052 (0.007630) | 0.311651 / 0.258489 (0.053162) | 0.357249 / 0.293841 (0.063409) | 0.027300 / 0.128546 (-0.101246) | 0.007963 / 0.075646 (-0.067683) | 0.261948 / 0.419271 (-0.157323) | 0.044952 / 0.043533 (0.001419) | 0.309990 / 0.255139 (0.054851) | 0.340735 / 0.283200 (0.057536) | 0.020786 / 0.141683 (-0.120897) | 1.471378 / 1.452155 (0.019224) | 1.517260 / 1.492716 (0.024543) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.245447 / 0.018006 (0.227441) | 0.418967 / 0.000490 (0.418477) | 0.007039 / 0.000200 (0.006840) | 0.000196 / 0.000054 (0.000142) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022880 / 0.037411 (-0.014532) | 0.071862 / 0.014526 (0.057337) | 0.083009 / 0.176557 (-0.093547) | 0.143414 / 0.737135 (-0.593722) | 0.082896 / 0.296338 (-0.213442) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.390645 / 0.215209 (0.175436) | 3.888104 / 2.077655 (1.810450) | 1.859572 / 1.504120 (0.355452) | 1.683803 / 1.541195 (0.142608) | 1.697902 / 1.468490 (0.229412) | 0.499537 / 4.584777 (-4.085239) | 3.015832 / 3.745712 (-0.729881) | 2.805696 / 5.269862 (-2.464166) | 1.830408 / 4.565676 (-2.735268) | 0.058191 / 0.424275 (-0.366085) | 0.006357 / 0.007607 (-0.001250) | 0.462486 / 0.226044 (0.236442) | 4.634951 / 2.268929 (2.366022) | 2.309364 / 55.444624 (-53.135260) | 1.979521 / 6.876477 (-4.896956) | 2.080011 / 2.142072 (-0.062062) | 0.593086 / 4.805227 (-4.212141) | 0.124856 / 6.500664 (-6.375808) | 0.060172 / 0.075469 (-0.015297) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.251439 / 1.841788 (-0.590349) | 17.068999 / 8.074308 (8.994691) | 13.527209 / 10.191392 (3.335817) | 0.146636 / 0.680424 (-0.533788) | 0.016866 / 0.534201 (-0.517335) | 0.333202 / 0.579283 (-0.246081) | 0.360444 / 0.434364 (-0.073920) | 0.388378 / 0.540337 (-0.151959) | 0.530519 / 1.386936 (-0.856417) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006043 / 0.011353 (-0.005310) | 0.003612 / 0.011008 (-0.007396) | 0.062644 / 0.038508 (0.024135) | 0.056104 / 0.023109 (0.032995) | 0.446328 / 0.275898 (0.170430) | 0.478044 / 0.323480 (0.154564) | 0.004641 / 0.007986 (-0.003345) | 0.002896 / 0.004328 (-0.001432) | 0.062344 / 0.004250 (0.058093) | 0.046339 / 0.037052 (0.009287) | 0.454866 / 0.258489 (0.196377) | 0.484242 / 0.293841 (0.190401) | 0.028602 / 0.128546 (-0.099944) | 0.008075 / 0.075646 (-0.067571) | 0.067980 / 0.419271 (-0.351291) | 0.041339 / 0.043533 (-0.002194) | 0.452911 / 0.255139 (0.197772) | 0.474180 / 0.283200 (0.190981) | 0.019395 / 0.141683 (-0.122288) | 1.432161 / 1.452155 (-0.019993) | 1.505800 / 1.492716 (0.013083) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216983 / 0.018006 (0.198977) | 0.406232 / 0.000490 (0.405743) | 0.005101 / 0.000200 (0.004902) | 0.000077 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026295 / 0.037411 (-0.011116) | 0.080490 / 0.014526 (0.065964) | 0.088105 / 0.176557 (-0.088451) | 0.143294 / 0.737135 (-0.593841) | 0.089125 / 0.296338 (-0.207213) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.465512 / 0.215209 (0.250302) | 4.648656 / 2.077655 (2.571002) | 2.598225 / 1.504120 (1.094105) | 2.409588 / 1.541195 (0.868393) | 2.513745 / 1.468490 (1.045255) | 0.507425 / 4.584777 (-4.077352) | 3.130164 / 3.745712 (-0.615548) | 2.836817 / 5.269862 (-2.433045) | 1.836029 / 4.565676 (-2.729647) | 0.058829 / 0.424275 (-0.365446) | 0.006551 / 0.007607 (-0.001056) | 0.537892 / 0.226044 (0.311848) | 5.401079 / 2.268929 (3.132150) | 3.019817 / 55.444624 (-52.424807) | 2.695131 / 6.876477 (-4.181346) | 2.805321 / 2.142072 (0.663248) | 0.595681 / 4.805227 (-4.209546) | 0.124368 / 6.500664 (-6.376296) | 0.060712 / 0.075469 (-0.014757) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.361508 / 1.841788 (-0.480279) | 17.811373 / 8.074308 (9.737065) | 14.482705 / 10.191392 (4.291313) | 0.153193 / 0.680424 (-0.527231) | 0.018347 / 0.534201 (-0.515854) | 0.330900 / 0.579283 (-0.248383) | 0.374948 / 0.434364 (-0.059416) | 0.385615 / 0.540337 (-0.154722) | 0.568077 / 1.386936 (-0.818859) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#18ef408c21f8efbb2142f050a691b5c916455af3 \"CML watermark\")\n"
] |
1,940,153,432
| 6,300
|
Unpin `jax` maximum version
|
closed
| 2023-10-12T14:42:40
| 2023-10-12T16:37:55
| 2023-10-12T16:28:57
|
https://github.com/huggingface/datasets/pull/6300
|
{
"url": "https://api.github.com/repos/huggingface/datasets/pulls/6300",
"html_url": "https://github.com/huggingface/datasets/pull/6300",
"diff_url": "https://github.com/huggingface/datasets/pull/6300.diff",
"patch_url": "https://github.com/huggingface/datasets/pull/6300.patch",
"merged_at": "2023-10-12T16:28:57"
}
|
mariosasko
| true
|
[
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008410 / 0.011353 (-0.002943) | 0.004888 / 0.011008 (-0.006120) | 0.103342 / 0.038508 (0.064834) | 0.103697 / 0.023109 (0.080587) | 0.416445 / 0.275898 (0.140547) | 0.454604 / 0.323480 (0.131124) | 0.004976 / 0.007986 (-0.003010) | 0.003957 / 0.004328 (-0.000371) | 0.077398 / 0.004250 (0.073148) | 0.069026 / 0.037052 (0.031973) | 0.420484 / 0.258489 (0.161995) | 0.471828 / 0.293841 (0.177987) | 0.037133 / 0.128546 (-0.091413) | 0.010009 / 0.075646 (-0.065637) | 0.349573 / 0.419271 (-0.069698) | 0.063240 / 0.043533 (0.019708) | 0.421554 / 0.255139 (0.166415) | 0.433548 / 0.283200 (0.150348) | 0.029397 / 0.141683 (-0.112286) | 1.716860 / 1.452155 (0.264705) | 1.851264 / 1.492716 (0.358547) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.269733 / 0.018006 (0.251727) | 0.493313 / 0.000490 (0.492823) | 0.010438 / 0.000200 (0.010238) | 0.000401 / 0.000054 (0.000347) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034690 / 0.037411 (-0.002722) | 0.105304 / 0.014526 (0.090778) | 0.115831 / 0.176557 (-0.060726) | 0.185017 / 0.737135 (-0.552118) | 0.117480 / 0.296338 (-0.178859) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.479414 / 0.215209 (0.264205) | 4.785526 / 2.077655 (2.707871) | 2.388412 / 1.504120 (0.884292) | 2.178222 / 1.541195 (0.637027) | 2.248214 / 1.468490 (0.779723) | 0.571723 / 4.584777 (-4.013054) | 4.721250 / 3.745712 (0.975538) | 4.073893 / 5.269862 (-1.195969) | 2.618131 / 4.565676 (-1.947546) | 0.068406 / 0.424275 (-0.355869) | 0.008890 / 0.007607 (0.001283) | 0.564224 / 0.226044 (0.338180) | 5.631412 / 2.268929 (3.362483) | 3.072212 / 55.444624 (-52.372412) | 2.760574 / 6.876477 (-4.115903) | 2.963060 / 2.142072 (0.820987) | 0.708150 / 4.805227 (-4.097077) | 0.160324 / 6.500664 (-6.340340) | 0.075402 / 0.075469 (-0.000067) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.649965 / 1.841788 (-0.191823) | 24.297517 / 8.074308 (16.223209) | 17.658675 / 10.191392 (7.467283) | 0.171399 / 0.680424 (-0.509025) | 0.021172 / 0.534201 (-0.513029) | 0.477196 / 0.579283 (-0.102087) | 0.503900 / 0.434364 (0.069536) | 0.555858 / 0.540337 (0.015520) | 0.824302 / 1.386936 (-0.562634) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008613 / 0.011353 (-0.002740) | 0.004848 / 0.011008 (-0.006160) | 0.078344 / 0.038508 (0.039836) | 0.098976 / 0.023109 (0.075867) | 0.520713 / 0.275898 (0.244815) | 0.566350 / 0.323480 (0.242870) | 0.006658 / 0.007986 (-0.001327) | 0.004043 / 0.004328 (-0.000285) | 0.077881 / 0.004250 (0.073631) | 0.070731 / 0.037052 (0.033678) | 0.519717 / 0.258489 (0.261228) | 0.575623 / 0.293841 (0.281782) | 0.038542 / 0.128546 (-0.090004) | 0.010277 / 0.075646 (-0.065369) | 0.084269 / 0.419271 (-0.335002) | 0.058088 / 0.043533 (0.014555) | 0.541790 / 0.255139 (0.286651) | 0.534915 / 0.283200 (0.251715) | 0.027851 / 0.141683 (-0.113831) | 1.814827 / 1.452155 (0.362672) | 1.898208 / 1.492716 (0.405492) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.244162 / 0.018006 (0.226156) | 0.482895 / 0.000490 (0.482405) | 0.005734 / 0.000200 (0.005534) | 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) | 0.039328 / 0.037411 (0.001917) | 0.119795 / 0.014526 (0.105269) | 0.128570 / 0.176557 (-0.047986) | 0.191207 / 0.737135 (-0.545929) | 0.127147 / 0.296338 (-0.169192) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.533545 / 0.215209 (0.318336) | 5.320135 / 2.077655 (3.242480) | 2.924573 / 1.504120 (1.420453) | 2.741351 / 1.541195 (1.200156) | 2.824217 / 1.468490 (1.355727) | 0.595842 / 4.584777 (-3.988935) | 4.343499 / 3.745712 (0.597787) | 3.976546 / 5.269862 (-1.293316) | 2.532541 / 4.565676 (-2.033135) | 0.070480 / 0.424275 (-0.353795) | 0.008868 / 0.007607 (0.001260) | 0.634297 / 0.226044 (0.408253) | 6.327314 / 2.268929 (4.058386) | 3.530741 / 55.444624 (-51.913883) | 3.121435 / 6.876477 (-3.755042) | 3.344473 / 2.142072 (1.202401) | 0.719413 / 4.805227 (-4.085814) | 0.162348 / 6.500664 (-6.338316) | 0.074964 / 0.075469 (-0.000505) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.679095 / 1.841788 (-0.162693) | 25.071620 / 8.074308 (16.997312) | 18.422398 / 10.191392 (8.231006) | 0.223981 / 0.680424 (-0.456443) | 0.026537 / 0.534201 (-0.507664) | 0.513867 / 0.579283 (-0.065416) | 0.535874 / 0.434364 (0.101510) | 0.567971 / 0.540337 (0.027634) | 0.842545 / 1.386936 (-0.544391) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d8b871016c25cb3b90ac1ff65a4e54f0454f525e \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006445 / 0.011353 (-0.004908) | 0.003978 / 0.011008 (-0.007030) | 0.084542 / 0.038508 (0.046034) | 0.069231 / 0.023109 (0.046122) | 0.308794 / 0.275898 (0.032896) | 0.339246 / 0.323480 (0.015766) | 0.005269 / 0.007986 (-0.002716) | 0.003285 / 0.004328 (-0.001043) | 0.065336 / 0.004250 (0.061086) | 0.053480 / 0.037052 (0.016428) | 0.316775 / 0.258489 (0.058286) | 0.357885 / 0.293841 (0.064044) | 0.031309 / 0.128546 (-0.097237) | 0.008450 / 0.075646 (-0.067196) | 0.287911 / 0.419271 (-0.131361) | 0.052756 / 0.043533 (0.009223) | 0.321516 / 0.255139 (0.066377) | 0.331998 / 0.283200 (0.048799) | 0.024129 / 0.141683 (-0.117553) | 1.507718 / 1.452155 (0.055563) | 1.571400 / 1.492716 (0.078683) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237536 / 0.018006 (0.219530) | 0.499691 / 0.000490 (0.499201) | 0.007644 / 0.000200 (0.007444) | 0.000284 / 0.000054 (0.000230) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028243 / 0.037411 (-0.009168) | 0.081556 / 0.014526 (0.067030) | 0.096877 / 0.176557 (-0.079680) | 0.149985 / 0.737135 (-0.587150) | 0.095556 / 0.296338 (-0.200783) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.383215 / 0.215209 (0.168006) | 3.815800 / 2.077655 (1.738145) | 1.832227 / 1.504120 (0.328107) | 1.664001 / 1.541195 (0.122806) | 1.698786 / 1.468490 (0.230296) | 0.487594 / 4.584777 (-4.097183) | 3.569767 / 3.745712 (-0.175945) | 3.262387 / 5.269862 (-2.007475) | 2.017105 / 4.565676 (-2.548572) | 0.057555 / 0.424275 (-0.366720) | 0.007170 / 0.007607 (-0.000437) | 0.460134 / 0.226044 (0.234090) | 4.629800 / 2.268929 (2.360871) | 2.357126 / 55.444624 (-53.087499) | 1.970144 / 6.876477 (-4.906332) | 2.123520 / 2.142072 (-0.018552) | 0.613058 / 4.805227 (-4.192169) | 0.135869 / 6.500664 (-6.364795) | 0.061292 / 0.075469 (-0.014177) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.311294 / 1.841788 (-0.530494) | 18.640807 / 8.074308 (10.566499) | 13.946834 / 10.191392 (3.755442) | 0.163976 / 0.680424 (-0.516448) | 0.018527 / 0.534201 (-0.515674) | 0.390530 / 0.579283 (-0.188753) | 0.412661 / 0.434364 (-0.021703) | 0.459514 / 0.540337 (-0.080823) | 0.635026 / 1.386936 (-0.751910) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006645 / 0.011353 (-0.004708) | 0.003943 / 0.011008 (-0.007066) | 0.064470 / 0.038508 (0.025962) | 0.069895 / 0.023109 (0.046786) | 0.411091 / 0.275898 (0.135193) | 0.437628 / 0.323480 (0.114148) | 0.005214 / 0.007986 (-0.002772) | 0.003281 / 0.004328 (-0.001047) | 0.064434 / 0.004250 (0.060183) | 0.054294 / 0.037052 (0.017241) | 0.413576 / 0.258489 (0.155087) | 0.448793 / 0.293841 (0.154952) | 0.031754 / 0.128546 (-0.096793) | 0.008530 / 0.075646 (-0.067117) | 0.069950 / 0.419271 (-0.349322) | 0.047747 / 0.043533 (0.004214) | 0.411241 / 0.255139 (0.156102) | 0.430076 / 0.283200 (0.146876) | 0.023462 / 0.141683 (-0.118220) | 1.519501 / 1.452155 (0.067346) | 1.575782 / 1.492716 (0.083066) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231816 / 0.018006 (0.213810) | 0.442802 / 0.000490 (0.442312) | 0.005738 / 0.000200 (0.005539) | 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) | 0.031426 / 0.037411 (-0.005985) | 0.090758 / 0.014526 (0.076233) | 0.103414 / 0.176557 (-0.073142) | 0.156409 / 0.737135 (-0.580726) | 0.103900 / 0.296338 (-0.192439) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.438897 / 0.215209 (0.223688) | 4.385318 / 2.077655 (2.307663) | 2.352042 / 1.504120 (0.847923) | 2.182228 / 1.541195 (0.641033) | 2.266256 / 1.468490 (0.797766) | 0.492780 / 4.584777 (-4.091997) | 3.665787 / 3.745712 (-0.079925) | 3.315329 / 5.269862 (-1.954533) | 2.027993 / 4.565676 (-2.537684) | 0.058220 / 0.424275 (-0.366055) | 0.007429 / 0.007607 (-0.000178) | 0.508790 / 0.226044 (0.282746) | 5.107093 / 2.268929 (2.838164) | 2.799789 / 55.444624 (-52.644836) | 2.462828 / 6.876477 (-4.413649) | 2.610193 / 2.142072 (0.468120) | 0.588133 / 4.805227 (-4.217094) | 0.133418 / 6.500664 (-6.367246) | 0.059793 / 0.075469 (-0.015676) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.363358 / 1.841788 (-0.478430) | 19.258372 / 8.074308 (11.184064) | 14.730977 / 10.191392 (4.539584) | 0.169493 / 0.680424 (-0.510931) | 0.020462 / 0.534201 (-0.513739) | 0.397980 / 0.579283 (-0.181303) | 0.426638 / 0.434364 (-0.007726) | 0.474249 / 0.540337 (-0.066088) | 0.677640 / 1.386936 (-0.709296) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#90b3d2619ecb8f01dd12283c30f04dfe6e443795 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006536 / 0.011353 (-0.004817) | 0.003827 / 0.011008 (-0.007181) | 0.084394 / 0.038508 (0.045886) | 0.073166 / 0.023109 (0.050056) | 0.309380 / 0.275898 (0.033482) | 0.338501 / 0.323480 (0.015021) | 0.005346 / 0.007986 (-0.002640) | 0.003273 / 0.004328 (-0.001056) | 0.064606 / 0.004250 (0.060356) | 0.053500 / 0.037052 (0.016447) | 0.313143 / 0.258489 (0.054654) | 0.354364 / 0.293841 (0.060523) | 0.030919 / 0.128546 (-0.097627) | 0.008512 / 0.075646 (-0.067134) | 0.292774 / 0.419271 (-0.126498) | 0.052441 / 0.043533 (0.008908) | 0.310503 / 0.255139 (0.055364) | 0.341211 / 0.283200 (0.058011) | 0.023608 / 0.141683 (-0.118074) | 1.456220 / 1.452155 (0.004065) | 1.540189 / 1.492716 (0.047473) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.234321 / 0.018006 (0.216315) | 0.451809 / 0.000490 (0.451319) | 0.008560 / 0.000200 (0.008360) | 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) | 0.028165 / 0.037411 (-0.009246) | 0.082548 / 0.014526 (0.068023) | 0.752621 / 0.176557 (0.576065) | 0.263949 / 0.737135 (-0.473187) | 0.097635 / 0.296338 (-0.198704) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.386611 / 0.215209 (0.171402) | 3.847528 / 2.077655 (1.769873) | 1.859173 / 1.504120 (0.355053) | 1.685269 / 1.541195 (0.144074) | 1.715823 / 1.468490 (0.247333) | 0.485272 / 4.584777 (-4.099505) | 3.500724 / 3.745712 (-0.244988) | 3.252149 / 5.269862 (-2.017713) | 2.052914 / 4.565676 (-2.512762) | 0.056794 / 0.424275 (-0.367481) | 0.007317 / 0.007607 (-0.000291) | 0.457924 / 0.226044 (0.231879) | 4.570092 / 2.268929 (2.301163) | 2.328829 / 55.444624 (-53.115796) | 1.986502 / 6.876477 (-4.889975) | 2.164645 / 2.142072 (0.022573) | 0.580455 / 4.805227 (-4.224772) | 0.134415 / 6.500664 (-6.366249) | 0.060506 / 0.075469 (-0.014963) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.267423 / 1.841788 (-0.574364) | 18.653450 / 8.074308 (10.579142) | 13.919682 / 10.191392 (3.728290) | 0.144001 / 0.680424 (-0.536423) | 0.018218 / 0.534201 (-0.515983) | 0.389933 / 0.579283 (-0.189350) | 0.418366 / 0.434364 (-0.015998) | 0.456341 / 0.540337 (-0.083997) | 0.631401 / 1.386936 (-0.755535) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006838 / 0.011353 (-0.004515) | 0.003973 / 0.011008 (-0.007036) | 0.065217 / 0.038508 (0.026709) | 0.068357 / 0.023109 (0.045248) | 0.407960 / 0.275898 (0.132062) | 0.437794 / 0.323480 (0.114314) | 0.005398 / 0.007986 (-0.002587) | 0.003360 / 0.004328 (-0.000969) | 0.065503 / 0.004250 (0.061253) | 0.055676 / 0.037052 (0.018623) | 0.411381 / 0.258489 (0.152892) | 0.446902 / 0.293841 (0.153061) | 0.032156 / 0.128546 (-0.096390) | 0.008702 / 0.075646 (-0.066944) | 0.072295 / 0.419271 (-0.346976) | 0.047722 / 0.043533 (0.004189) | 0.406125 / 0.255139 (0.150986) | 0.428359 / 0.283200 (0.145160) | 0.021901 / 0.141683 (-0.119782) | 1.464186 / 1.452155 (0.012032) | 1.532809 / 1.492716 (0.040093) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218505 / 0.018006 (0.200499) | 0.447450 / 0.000490 (0.446961) | 0.006509 / 0.000200 (0.006309) | 0.000099 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031789 / 0.037411 (-0.005622) | 0.091100 / 0.014526 (0.076574) | 0.102812 / 0.176557 (-0.073745) | 0.155988 / 0.737135 (-0.581147) | 0.103983 / 0.296338 (-0.192355) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436431 / 0.215209 (0.221222) | 4.336072 / 2.077655 (2.258417) | 2.344613 / 1.504120 (0.840493) | 2.173513 / 1.541195 (0.632319) | 2.313134 / 1.468490 (0.844644) | 0.493651 / 4.584777 (-4.091126) | 3.657541 / 3.745712 (-0.088171) | 3.289933 / 5.269862 (-1.979928) | 2.040271 / 4.565676 (-2.525406) | 0.058092 / 0.424275 (-0.366183) | 0.007348 / 0.007607 (-0.000259) | 0.507506 / 0.226044 (0.281462) | 5.093477 / 2.268929 (2.824548) | 2.770579 / 55.444624 (-52.674046) | 2.449507 / 6.876477 (-4.426970) | 2.645470 / 2.142072 (0.503397) | 0.590799 / 4.805227 (-4.214429) | 0.133411 / 6.500664 (-6.367253) | 0.059507 / 0.075469 (-0.015962) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.381148 / 1.841788 (-0.460639) | 19.188716 / 8.074308 (11.114408) | 14.709111 / 10.191392 (4.517719) | 0.191104 / 0.680424 (-0.489320) | 0.019862 / 0.534201 (-0.514339) | 0.395380 / 0.579283 (-0.183903) | 0.424757 / 0.434364 (-0.009607) | 0.468810 / 0.540337 (-0.071527) | 0.687058 / 1.386936 (-0.699878) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#407169e1ea91ae31f79ff29c4115b04a461279ab \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008872 / 0.011353 (-0.002481) | 0.004824 / 0.011008 (-0.006184) | 0.097012 / 0.038508 (0.058504) | 0.074728 / 0.023109 (0.051619) | 0.400604 / 0.275898 (0.124706) | 0.434316 / 0.323480 (0.110836) | 0.006025 / 0.007986 (-0.001961) | 0.004153 / 0.004328 (-0.000176) | 0.074093 / 0.004250 (0.069842) | 0.057239 / 0.037052 (0.020187) | 0.420611 / 0.258489 (0.162122) | 0.457779 / 0.293841 (0.163938) | 0.047610 / 0.128546 (-0.080936) | 0.014577 / 0.075646 (-0.061069) | 0.414351 / 0.419271 (-0.004921) | 0.063072 / 0.043533 (0.019539) | 0.426141 / 0.255139 (0.171002) | 0.429844 / 0.283200 (0.146644) | 0.034754 / 0.141683 (-0.106929) | 1.620946 / 1.452155 (0.168792) | 1.725831 / 1.492716 (0.233115) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.304712 / 0.018006 (0.286706) | 0.646924 / 0.000490 (0.646434) | 0.014486 / 0.000200 (0.014286) | 0.000626 / 0.000054 (0.000572) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034935 / 0.037411 (-0.002477) | 0.085788 / 0.014526 (0.071262) | 0.107749 / 0.176557 (-0.068807) | 0.170924 / 0.737135 (-0.566211) | 0.134985 / 0.296338 (-0.161354) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.602913 / 0.215209 (0.387704) | 6.041700 / 2.077655 (3.964045) | 2.539970 / 1.504120 (1.035850) | 2.184166 / 1.541195 (0.642972) | 2.241783 / 1.468490 (0.773293) | 0.864601 / 4.584777 (-3.720176) | 5.246955 / 3.745712 (1.501243) | 4.850458 / 5.269862 (-0.419404) | 3.101497 / 4.565676 (-1.464179) | 0.098591 / 0.424275 (-0.325684) | 0.008902 / 0.007607 (0.001295) | 0.732278 / 0.226044 (0.506234) | 7.163557 / 2.268929 (4.894629) | 3.226444 / 55.444624 (-52.218180) | 2.578737 / 6.876477 (-4.297740) | 2.850212 / 2.142072 (0.708140) | 1.026390 / 4.805227 (-3.778837) | 0.217077 / 6.500664 (-6.283587) | 0.080344 / 0.075469 (0.004875) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.687488 / 1.841788 (-0.154300) | 24.686337 / 8.074308 (16.612029) | 21.315989 / 10.191392 (11.124597) | 0.226176 / 0.680424 (-0.454248) | 0.035774 / 0.534201 (-0.498427) | 0.477807 / 0.579283 (-0.101476) | 0.636305 / 0.434364 (0.201941) | 0.553341 / 0.540337 (0.013003) | 0.797267 / 1.386936 (-0.589669) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008955 / 0.011353 (-0.002398) | 0.006099 / 0.011008 (-0.004909) | 0.086306 / 0.038508 (0.047798) | 0.090783 / 0.023109 (0.067674) | 0.554802 / 0.275898 (0.278904) | 0.598778 / 0.323480 (0.275299) | 0.008656 / 0.007986 (0.000670) | 0.004487 / 0.004328 (0.000159) | 0.084194 / 0.004250 (0.079943) | 0.076048 / 0.037052 (0.038996) | 0.533212 / 0.258489 (0.274723) | 0.584029 / 0.293841 (0.290188) | 0.051913 / 0.128546 (-0.076634) | 0.014253 / 0.075646 (-0.061393) | 0.100500 / 0.419271 (-0.318772) | 0.061092 / 0.043533 (0.017560) | 0.516955 / 0.255139 (0.261816) | 0.562754 / 0.283200 (0.279554) | 0.036673 / 0.141683 (-0.105010) | 1.853655 / 1.452155 (0.401501) | 1.968358 / 1.492716 (0.475642) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.308258 / 0.018006 (0.290252) | 0.630492 / 0.000490 (0.630002) | 0.010575 / 0.000200 (0.010375) | 0.000271 / 0.000054 (0.000217) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034762 / 0.037411 (-0.002649) | 0.107314 / 0.014526 (0.092788) | 0.132160 / 0.176557 (-0.044396) | 0.178737 / 0.737135 (-0.558398) | 0.125988 / 0.296338 (-0.170351) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.730738 / 0.215209 (0.515528) | 7.240393 / 2.077655 (5.162738) | 3.557665 / 1.504120 (2.053545) | 3.541425 / 1.541195 (2.000230) | 3.103849 / 1.468490 (1.635359) | 0.926843 / 4.584777 (-3.657934) | 5.818264 / 3.745712 (2.072552) | 5.012984 / 5.269862 (-0.256878) | 3.286085 / 4.565676 (-1.279591) | 0.104879 / 0.424275 (-0.319396) | 0.009010 / 0.007607 (0.001403) | 0.806145 / 0.226044 (0.580101) | 8.263655 / 2.268929 (5.994727) | 4.108932 / 55.444624 (-51.335693) | 3.454613 / 6.876477 (-3.421864) | 3.629045 / 2.142072 (1.486973) | 1.062325 / 4.805227 (-3.742902) | 0.220482 / 6.500664 (-6.280182) | 0.081440 / 0.075469 (0.005970) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.665587 / 1.841788 (-0.176201) | 23.695299 / 8.074308 (15.620991) | 22.917493 / 10.191392 (12.726101) | 0.259033 / 0.680424 (-0.421391) | 0.040118 / 0.534201 (-0.494083) | 0.487329 / 0.579283 (-0.091954) | 0.607482 / 0.434364 (0.173118) | 0.568383 / 0.540337 (0.028045) | 0.824486 / 1.386936 (-0.562450) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53592bb8f635a1d6ea3e77acc290efdfb28fcbd7 \"CML watermark\")\n",
"CI failures are unrelated",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007095 / 0.011353 (-0.004258) | 0.004260 / 0.011008 (-0.006748) | 0.084729 / 0.038508 (0.046221) | 0.076498 / 0.023109 (0.053389) | 0.325981 / 0.275898 (0.050083) | 0.357140 / 0.323480 (0.033661) | 0.004325 / 0.007986 (-0.003660) | 0.003632 / 0.004328 (-0.000696) | 0.065075 / 0.004250 (0.060824) | 0.059058 / 0.037052 (0.022006) | 0.331895 / 0.258489 (0.073406) | 0.370782 / 0.293841 (0.076941) | 0.031886 / 0.128546 (-0.096660) | 0.008782 / 0.075646 (-0.066864) | 0.288159 / 0.419271 (-0.131113) | 0.053012 / 0.043533 (0.009479) | 0.319992 / 0.255139 (0.064853) | 0.347061 / 0.283200 (0.063861) | 0.026365 / 0.141683 (-0.115317) | 1.486112 / 1.452155 (0.033958) | 1.570150 / 1.492716 (0.077434) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.277155 / 0.018006 (0.259149) | 0.573507 / 0.000490 (0.573017) | 0.010122 / 0.000200 (0.009922) | 0.000322 / 0.000054 (0.000268) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029076 / 0.037411 (-0.008335) | 0.082517 / 0.014526 (0.067991) | 0.100710 / 0.176557 (-0.075847) | 0.154529 / 0.737135 (-0.582606) | 0.099531 / 0.296338 (-0.196807) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.382058 / 0.215209 (0.166849) | 3.803307 / 2.077655 (1.725652) | 1.834107 / 1.504120 (0.329987) | 1.665703 / 1.541195 (0.124508) | 1.739520 / 1.468490 (0.271030) | 0.490544 / 4.584777 (-4.094233) | 3.577874 / 3.745712 (-0.167838) | 3.327631 / 5.269862 (-1.942231) | 2.056634 / 4.565676 (-2.509043) | 0.057871 / 0.424275 (-0.366404) | 0.007326 / 0.007607 (-0.000281) | 0.453993 / 0.226044 (0.227949) | 4.549179 / 2.268929 (2.280250) | 2.320304 / 55.444624 (-53.124321) | 1.966082 / 6.876477 (-4.910395) | 2.189979 / 2.142072 (0.047907) | 0.586678 / 4.805227 (-4.218549) | 0.134919 / 6.500664 (-6.365745) | 0.061649 / 0.075469 (-0.013820) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.286228 / 1.841788 (-0.555560) | 19.409674 / 8.074308 (11.335366) | 14.290463 / 10.191392 (4.099071) | 0.165766 / 0.680424 (-0.514658) | 0.018200 / 0.534201 (-0.516001) | 0.390526 / 0.579283 (-0.188757) | 0.410953 / 0.434364 (-0.023411) | 0.455921 / 0.540337 (-0.084416) | 0.642271 / 1.386936 (-0.744665) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007288 / 0.011353 (-0.004064) | 0.004348 / 0.011008 (-0.006660) | 0.065935 / 0.038508 (0.027427) | 0.087327 / 0.023109 (0.064218) | 0.413461 / 0.275898 (0.137563) | 0.458904 / 0.323480 (0.135424) | 0.005996 / 0.007986 (-0.001990) | 0.003648 / 0.004328 (-0.000680) | 0.066578 / 0.004250 (0.062328) | 0.062072 / 0.037052 (0.025020) | 0.418469 / 0.258489 (0.159980) | 0.468960 / 0.293841 (0.175119) | 0.032616 / 0.128546 (-0.095930) | 0.008961 / 0.075646 (-0.066686) | 0.072537 / 0.419271 (-0.346734) | 0.048302 / 0.043533 (0.004769) | 0.411845 / 0.255139 (0.156706) | 0.441730 / 0.283200 (0.158530) | 0.025038 / 0.141683 (-0.116645) | 1.519402 / 1.452155 (0.067248) | 1.601791 / 1.492716 (0.109074) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.322494 / 0.018006 (0.304488) | 0.570210 / 0.000490 (0.569720) | 0.025815 / 0.000200 (0.025615) | 0.000166 / 0.000054 (0.000111) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034657 / 0.037411 (-0.002754) | 0.096024 / 0.014526 (0.081498) | 0.109134 / 0.176557 (-0.067422) | 0.162170 / 0.737135 (-0.574965) | 0.110472 / 0.296338 (-0.185866) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.439032 / 0.215209 (0.223823) | 4.385768 / 2.077655 (2.308113) | 2.343261 / 1.504120 (0.839142) | 2.157926 / 1.541195 (0.616731) | 2.299193 / 1.468490 (0.830703) | 0.498961 / 4.584777 (-4.085816) | 3.651909 / 3.745712 (-0.093803) | 3.387587 / 5.269862 (-1.882275) | 2.144553 / 4.565676 (-2.421123) | 0.058242 / 0.424275 (-0.366033) | 0.007416 / 0.007607 (-0.000191) | 0.512714 / 0.226044 (0.286670) | 5.138569 / 2.268929 (2.869641) | 2.778683 / 55.444624 (-52.665941) | 2.532990 / 6.876477 (-4.343487) | 2.782211 / 2.142072 (0.640139) | 0.591881 / 4.805227 (-4.213346) | 0.135005 / 6.500664 (-6.365660) | 0.060965 / 0.075469 (-0.014504) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.356311 / 1.841788 (-0.485477) | 20.029994 / 8.074308 (11.955686) | 14.666570 / 10.191392 (4.475178) | 0.164363 / 0.680424 (-0.516061) | 0.020685 / 0.534201 (-0.513516) | 0.396020 / 0.579283 (-0.183263) | 0.429407 / 0.434364 (-0.004957) | 0.476924 / 0.540337 (-0.063413) | 0.693389 / 1.386936 (-0.693547) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#292d627e398e30a538a616395f3b5ce4e89bb1e8 \"CML watermark\")\n"
] |
1,939,649,238
| 6,299
|
Support for newer versions of JAX
|
closed
| 2023-10-12T10:03:46
| 2023-10-12T16:28:59
| 2023-10-12T16:28:59
|
https://github.com/huggingface/datasets/issues/6299
| null |
ddrous
| false
|
[] |
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