Split (1)

train · 5k rows

id int64 953M 3.35B	number int64 2.72k 7.75k	title stringlengths 1 290	state stringclasses 2 values	created_at timestamp[s]date 2021-07-26 12:21:17 2025-08-23 00:18:43	updated_at timestamp[s]date 2021-07-26 13:27:59 2025-08-23 12:34:39	closed_at timestamp[s]date 2021-07-26 13:27:59 2025-08-20 16:35:55 ⌀	html_url stringlengths 49 51	pull_request dict	user_login stringlengths 3 26	is_pull_request bool 2 classes	comments listlengths 0 30
1,871,582,175	6,190	`Invalid user token` even when correct user token is passed!	closed	2023-08-29T12:37:03	2023-08-29T13:01:10	2023-08-29T13:01:09	https://github.com/huggingface/datasets/issues/6190	null	Vaibhavs10	false	[ "This is because `download_config.use_auth_token` is deprecated - you should use `download_config.token` instead", "Works! Thanks for the quick fix! <3" ]
1,871,569,855	6,189	Don't alter input in Features.from_dict	closed	2023-08-29T12:29:47	2023-08-29T13:04:59	2023-08-29T12:52:48	https://github.com/huggingface/datasets/pull/6189	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6189", "html_url": "https://github.com/huggingface/datasets/pull/6189", "diff_url": "https://github.com/huggingface/datasets/pull/6189.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6189.patch", "merged_at": "2023-08-29T12:52:48" }	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) \| 0.006166 / 0.011353 (-0.005187) \| 0.003643 / 0.011008 (-0.007365) \| 0.080966 / 0.038508 (0.042458) \| 0.060538 / 0.023109 (0.037429) \| 0.309205 / 0.275898 (0.033307) \| 0.351007 / 0.323480 (0.027527) \| 0.003592 / 0.007986 (-0.004393) \| 0.002880 / 0.004328 (-0.001448) \| 0.062957 / 0.004250 (0.058707) \| 0.049015 / 0.037052 (0.011963) \| 0.309436 / 0.258489 (0.050947) \| 0.362695 / 0.293841 (0.068854) \| 0.027818 / 0.128546 (-0.100728) \| 0.008030 / 0.075646 (-0.067616) \| 0.262678 / 0.419271 (-0.156594) \| 0.046024 / 0.043533 (0.002491) \| 0.316246 / 0.255139 (0.061107) \| 0.337454 / 0.283200 (0.054254) \| 0.022529 / 0.141683 (-0.119154) \| 1.432492 / 1.452155 (-0.019662) \| 1.499646 / 1.492716 (0.006929) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.190931 / 0.018006 (0.172925) \| 0.428053 / 0.000490 (0.427564) \| 0.002839 / 0.000200 (0.002639) \| 0.000069 / 0.000054 (0.000014) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024042 / 0.037411 (-0.013370) \| 0.073952 / 0.014526 (0.059426) \| 0.905973 / 0.176557 (0.729417) \| 0.177767 / 0.737135 (-0.559368) \| 0.125779 / 0.296338 (-0.170559) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.398997 / 0.215209 (0.183788) \| 3.959575 / 2.077655 (1.881920) \| 1.907038 / 1.504120 (0.402918) \| 1.732908 / 1.541195 (0.191713) \| 1.757038 / 1.468490 (0.288548) \| 0.495917 / 4.584777 (-4.088860) \| 3.021437 / 3.745712 (-0.724275) \| 2.793960 / 5.269862 (-2.475901) \| 1.827753 / 4.565676 (-2.737923) \| 0.057143 / 0.424275 (-0.367132) \| 0.006583 / 0.007607 (-0.001024) \| 0.469402 / 0.226044 (0.243357) \| 4.685623 / 2.268929 (2.416695) \| 2.325200 / 55.444624 (-53.119424) \| 1.985559 / 6.876477 (-4.890918) \| 2.151208 / 2.142072 (0.009136) \| 0.589498 / 4.805227 (-4.215730) \| 0.125433 / 6.500664 (-6.375231) \| 0.060834 / 0.075469 (-0.014636) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.228217 / 1.841788 (-0.613571) \| 18.076089 / 8.074308 (10.001780) \| 13.814460 / 10.191392 (3.623068) \| 0.144674 / 0.680424 (-0.535750) \| 0.016749 / 0.534201 (-0.517452) \| 0.332839 / 0.579283 (-0.246444) \| 0.357211 / 0.434364 (-0.077153) \| 0.380367 / 0.540337 (-0.159971) \| 0.531177 / 1.386936 (-0.855759) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006006 / 0.011353 (-0.005347) \| 0.003552 / 0.011008 (-0.007456) \| 0.061822 / 0.038508 (0.023313) \| 0.057724 / 0.023109 (0.034615) \| 0.462326 / 0.275898 (0.186428) \| 0.492842 / 0.323480 (0.169362) \| 0.004833 / 0.007986 (-0.003152) \| 0.002847 / 0.004328 (-0.001481) \| 0.062278 / 0.004250 (0.058028) \| 0.046754 / 0.037052 (0.009702) \| 0.464185 / 0.258489 (0.205696) \| 0.496416 / 0.293841 (0.202576) \| 0.028949 / 0.128546 (-0.099597) \| 0.008038 / 0.075646 (-0.067608) \| 0.067572 / 0.419271 (-0.351700) \| 0.041176 / 0.043533 (-0.002356) \| 0.460047 / 0.255139 (0.204908) \| 0.482728 / 0.283200 (0.199528) \| 0.020047 / 0.141683 (-0.121635) \| 1.455958 / 1.452155 (0.003804) \| 1.525730 / 1.492716 (0.033014) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283643 / 0.018006 (0.265637) \| 0.443046 / 0.000490 (0.442556) \| 0.041019 / 0.000200 (0.040819) \| 0.000340 / 0.000054 (0.000286) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026229 / 0.037411 (-0.011182) \| 0.081498 / 0.014526 (0.066972) \| 0.091412 / 0.176557 (-0.085145) \| 0.146621 / 0.737135 (-0.590514) \| 0.092113 / 0.296338 (-0.204225) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.463525 / 0.215209 (0.248315) \| 4.629852 / 2.077655 (2.552198) \| 2.564831 / 1.504120 (1.060711) \| 2.386976 / 1.541195 (0.845781) \| 2.457757 / 1.468490 (0.989266) \| 0.507317 / 4.584777 (-4.077460) \| 3.142418 / 3.745712 (-0.603294) \| 2.851642 / 5.269862 (-2.418219) \| 1.894444 / 4.565676 (-2.671233) \| 0.058495 / 0.424275 (-0.365780) \| 0.006453 / 0.007607 (-0.001154) \| 0.545363 / 0.226044 (0.319319) \| 5.448092 / 2.268929 (3.179164) \| 2.996328 / 55.444624 (-52.448296) \| 2.664666 / 6.876477 (-4.211811) \| 2.832247 / 2.142072 (0.690174) \| 0.597631 / 4.805227 (-4.207596) \| 0.126101 / 6.500664 (-6.374563) \| 0.062573 / 0.075469 (-0.012896) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.366502 / 1.841788 (-0.475286) \| 18.872990 / 8.074308 (10.798682) \| 14.892114 / 10.191392 (4.700722) \| 0.146668 / 0.680424 (-0.533756) \| 0.017876 / 0.534201 (-0.516325) \| 0.338490 / 0.579283 (-0.240793) \| 0.357471 / 0.434364 (-0.076893) \| 0.398730 / 0.540337 (-0.141608) \| 0.542464 / 1.386936 (-0.844472) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a6ff3e846d86814fa6962326e9346a4f1f1e8a80 \"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) \| 0.009132 / 0.011353 (-0.002221) \| 0.005796 / 0.011008 (-0.005212) \| 0.119495 / 0.038508 (0.080987) \| 0.081708 / 0.023109 (0.058599) \| 0.432940 / 0.275898 (0.157042) \| 0.466793 / 0.323480 (0.143313) \| 0.006464 / 0.007986 (-0.001521) \| 0.004308 / 0.004328 (-0.000021) \| 0.086344 / 0.004250 (0.082093) \| 0.065987 / 0.037052 (0.028935) \| 0.445213 / 0.258489 (0.186724) \| 0.482405 / 0.293841 (0.188564) \| 0.053553 / 0.128546 (-0.074993) \| 0.015320 / 0.075646 (-0.060326) \| 0.455669 / 0.419271 (0.036397) \| 0.071619 / 0.043533 (0.028086) \| 0.434843 / 0.255139 (0.179704) \| 0.503224 / 0.283200 (0.220025) \| 0.038280 / 0.141683 (-0.103403) \| 1.901877 / 1.452155 (0.449722) \| 2.040406 / 1.492716 (0.547690) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.268275 / 0.018006 (0.250269) \| 0.622795 / 0.000490 (0.622305) \| 0.004572 / 0.000200 (0.004372) \| 0.000107 / 0.000054 (0.000053) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032514 / 0.037411 (-0.004898) \| 0.100619 / 0.014526 (0.086093) \| 0.118407 / 0.176557 (-0.058149) \| 0.190311 / 0.737135 (-0.546824) \| 0.117160 / 0.296338 (-0.179178) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.629836 / 0.215209 (0.414627) \| 6.236124 / 2.077655 (4.158470) \| 2.750775 / 1.504120 (1.246655) \| 2.380111 / 1.541195 (0.838916) \| 2.487279 / 1.468490 (1.018789) \| 0.849568 / 4.584777 (-3.735209) \| 5.571308 / 3.745712 (1.825596) \| 4.934114 / 5.269862 (-0.335747) \| 3.205478 / 4.565676 (-1.360198) \| 0.104804 / 0.424275 (-0.319471) \| 0.009856 / 0.007607 (0.002248) \| 0.753352 / 0.226044 (0.527308) \| 7.523482 / 2.268929 (5.254554) \| 3.660088 / 55.444624 (-51.784537) \| 2.726493 / 6.876477 (-4.149984) \| 3.011344 / 2.142072 (0.869271) \| 1.093410 / 4.805227 (-3.711817) \| 0.229758 / 6.500664 (-6.270906) \| 0.081516 / 0.075469 (0.006047) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.700199 / 1.841788 (-0.141588) \| 25.238736 / 8.074308 (17.164428) \| 23.188131 / 10.191392 (12.996739) \| 0.257862 / 0.680424 (-0.422562) \| 0.028885 / 0.534201 (-0.505316) \| 0.510693 / 0.579283 (-0.068590) \| 0.648474 / 0.434364 (0.214110) \| 0.576314 / 0.540337 (0.035976) \| 0.800606 / 1.386936 (-0.586330) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009426 / 0.011353 (-0.001927) \| 0.006205 / 0.011008 (-0.004803) \| 0.083947 / 0.038508 (0.045438) \| 0.089164 / 0.023109 (0.066055) \| 0.540500 / 0.275898 (0.264602) \| 0.578825 / 0.323480 (0.255345) \| 0.006792 / 0.007986 (-0.001194) \| 0.005125 / 0.004328 (0.000797) \| 0.083284 / 0.004250 (0.079034) \| 0.067539 / 0.037052 (0.030487) \| 0.544330 / 0.258489 (0.285841) \| 0.593836 / 0.293841 (0.299995) \| 0.050647 / 0.128546 (-0.077899) \| 0.014688 / 0.075646 (-0.060959) \| 0.095977 / 0.419271 (-0.323295) \| 0.062326 / 0.043533 (0.018793) \| 0.536096 / 0.255139 (0.280957) \| 0.578691 / 0.283200 (0.295492) \| 0.035488 / 0.141683 (-0.106194) \| 1.911145 / 1.452155 (0.458990) \| 1.977647 / 1.492716 (0.484931) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.368365 / 0.018006 (0.350359) \| 0.609836 / 0.000490 (0.609346) \| 0.054720 / 0.000200 (0.054520) \| 0.000465 / 0.000054 (0.000411) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036057 / 0.037411 (-0.001355) \| 0.126434 / 0.014526 (0.111908) \| 0.124740 / 0.176557 (-0.051817) \| 0.198907 / 0.737135 (-0.538228) \| 0.138201 / 0.296338 (-0.158137) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.684814 / 0.215209 (0.469605) \| 6.738182 / 2.077655 (4.660527) \| 3.231054 / 1.504120 (1.726934) \| 2.889550 / 1.541195 (1.348355) \| 2.933985 / 1.468490 (1.465495) \| 0.867176 / 4.584777 (-3.717601) \| 5.465475 / 3.745712 (1.719763) \| 4.928370 / 5.269862 (-0.341492) \| 3.126382 / 4.565676 (-1.439294) \| 0.129673 / 0.424275 (-0.294603) \| 0.009755 / 0.007607 (0.002148) \| 0.797860 / 0.226044 (0.571816) \| 8.003178 / 2.268929 (5.734250) \| 4.081658 / 55.444624 (-51.362966) \| 3.303837 / 6.876477 (-3.572640) \| 3.574577 / 2.142072 (1.432505) \| 1.064674 / 4.805227 (-3.740554) \| 0.232894 / 6.500664 (-6.267770) \| 0.082298 / 0.075469 (0.006829) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.858701 / 1.841788 (0.016913) \| 25.839794 / 8.074308 (17.765485) \| 24.291425 / 10.191392 (14.100033) \| 0.250181 / 0.680424 (-0.430243) \| 0.034479 / 0.534201 (-0.499722) \| 0.540754 / 0.579283 (-0.038529) \| 0.615996 / 0.434364 (0.181632) \| 0.631499 / 0.540337 (0.091161) \| 0.838719 / 1.386936 (-0.548217) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0b6bb2f0e7a460d4ed04855eafe1184a7ce7c09c \"CML watermark\")\n" ]
1,870,987,640	6,188	[Feature Request] Check the length of batch before writing so that empty batch is allowed	closed	2023-08-29T06:37:34	2023-09-19T21:55:38	2023-09-19T21:55:37	https://github.com/huggingface/datasets/issues/6188	null	namespace-Pt	false	[ "I think this error means you filter all examples within an (input) batch by deleting its columns. In that case, to avoid the error, you can set the column value to an empty list (`input_batch[\"col\"] = []`) instead." ]
1,870,936,143	6,187	Couldn't find a dataset script at /content/tsv/tsv.py or any data file in the same directory	open	2023-08-29T05:49:56	2023-08-29T16:21:45	null	https://github.com/huggingface/datasets/issues/6187	null	andysingal	false	[ "Hi! You can load this dataset with:\r\n```python\r\ndata_files = {\r\n \"train\": \"/content/PUBHEALTH/train.tsv\",\r\n \"validation\": \"/content/PUBHEALTH/dev.tsv\",\r\n \"test\": \"/content/PUBHEALTH/test.tsv\",\r\n}\r\n\r\ntsv_datasets_reloaded = load_dataset(\"csv\", data_files=data_files, sep=\"\\t\")\r\n```\r\n\r\nTo support your `load_dataset` call, defining aliases for the packaged builders, as suggested in https://github.com/huggingface/datasets/issues/5625, must be implemented. We can consider adding this feature if more people request it.\r\n \r\n(Also answered on the Discord [here](https://discord.com/channels/879548962464493619/1145956791134470224/1146071491260186744))" ]
1,869,431,457	6,186	Feature request: add code example of multi-GPU processing	closed	2023-08-28T10:00:59	2024-10-07T09:39:51	2023-11-22T15:42:20	https://github.com/huggingface/datasets/issues/6186	null	NielsRogge	false	[ "That'd be a great idea! @mariosasko or @lhoestq, would it be possible to fix the code snippet or do you have another suggested way for doing this?", "Indeed `if __name__ == \"__main__\"` is important in this case.\r\n\r\nNot sure about the imbalanced GPU usage though, but maybe you can try using the `torch.cuda.device` context manager ?\r\n\r\n> also, should I do it like this or use nn.DataParallel?\r\n\r\nIn this case you wouldn't need a multiprocessed map no ? Since nn.DataParallel would take care of parallelism", "Adding this Tweet for reference: https://twitter.com/jxmnop/status/1716834517909119019.", "I think the issue is that we set `CUDA_VISIBLE_DEVICES` after pytorch is imported ?\r\n\r\nWe should use `torch.cuda.set_device(...)` instead", "@lhoestq \r\n> In this case you wouldn't need a multiprocessed map no ?\r\n\r\nYes. But how to load a model to 2 GPU simultaneously without something like accelerate?", "> @lhoestq\r\n> \r\n> > In this case you wouldn't need a multiprocessed map no ?\r\n> \r\n> Yes. But how to load a model to 2 GPU simultaneously without something like accelerate?\r\n\r\nTake a look at this fix #6550 . Basically, you move the model to each GPU inside of the function to be mapped. \r\n\r\n", "In case someone also runs into this issue, I wrote a [blog post](https://forrestbao.github.io/2024/01/30/datasets_map_with_rank_multiple_GPUs.html) with a complete working example by compiling information from several PRs and issues here. Hope it can help. This issue cost me a few hours. I hope my blog post can save you time before the official document gets fixed. ", "Thanks ! I updated the docs in https://github.com/huggingface/datasets/pull/6550", "hey @forrestbao , i was too struggling with the same issue for weeks hence i checked out your blog. great work on the blog. \r\nhowever i wanted to ask you could we scale up the process by reinitializing the same model on the same GPU multiple times for even more speedups ? \r\n\r\ni mean to say given that on a multi GPU setup where GPU vram is above 40GB each, after intializing the translation model which is barely 1-2GB in VRAM size, the rest of VRAM sits idle, how could i keep creating multiple instances of the same model on the same GPU for all GPUs to maxmize flops ? ", "You can use one single instance on your GPU and increase the batch size until you fill the VRAM", "@lhoestq i tried that, but i noticed that after a certain number of batch_size, using a larger batch_size makes the overall process really slow than using a lower batch_size.", "Hi @lhoestq , could you help with my two questions: \r\n1. You mentioned `if __name__ == \"__main__\"`, why is that? I tried with a toy dataset and didn't put this line, my two GPU usage looks balanced. \r\n2. Is there any difference between \r\n`from multiprocess import set_start_method` and `from multiprocessing import set_start_method`? The latter is Python's built-in library. In [the official doc](https://huggingface.co/docs/datasets/en/process), it uses `from multiprocess import set_start_method`, but it gives me error like \r\n```\r\n[jobuser@f6e2419a0a63d45638da-n0-0 ~]$ python test.py\r\nTraceback (most recent call last):\r\n File \"/home/jobuser/test.py\", line 33, in <module>\r\n updated_dataset = dataset.map(\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 593, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, args, kwargs)\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 558, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, args, **kwargs)\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3189, in map\r\n with Pool(len(kwargs_per_job)) as pool:\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/multiprocess/context.py\", line 119, in Pool\r\n return Pool(processes, initializer, initargs, maxtasksperchild,\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/multiprocess/pool.py\", line 191, in __init__\r\n self._setup_queues()\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/multiprocess/pool.py\", line 343, in _setup_queues\r\n self._inqueue = self._ctx.SimpleQueue()\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/multiprocess/context.py\", line 113, in SimpleQueue\r\n return SimpleQueue(ctx=self.get_context())\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/multiprocess/queues.py\", line 339, in __init__\r\n self._rlock = ctx.Lock()\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/multiprocess/context.py\", line 68, in Lock\r\n return Lock(ctx=self.get_context())\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/multiprocess/synchronize.py\", line 168, in __init__\r\n SemLock.__init__(self, SEMAPHORE, 1, 1, ctx=ctx)\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/multiprocess/synchronize.py\", line 86, in __init__\r\n register(self._semlock.name, \"semaphore\")\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/multiprocess/resource_tracker.py\", line 150, in register\r\n self._send('REGISTER', name, rtype)\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/multiprocess/resource_tracker.py\", line 157, in _send\r\n self.ensure_running()\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/multiprocess/resource_tracker.py\", line 124, in ensure_running\r\n pid = util.spawnv_passfds(exe, args, fds_to_pass)\r\n File \"/home/jobuser/.local/lib/python3.10/site-packages/multiprocess/util.py\", line 452, in spawnv_passfds\r\n return _posixsubprocess.fork_exec(\r\nTypeError: fork_exec() takes exactly 21 arguments (17 given)\r\n```\r\nwhich seems caused by python version. I am using Python 3.10.2. ", "Hi ! \r\n\r\n> You mentioned if __name__ == \"__main__\", why is that? I tried with a toy dataset and didn't put this line, my two GPU usage looks balanced.\r\n\r\nIt's a good practice when doing multiprocessing in python. Depending on the multiprocessing method and your python version, python could re-run the code in your main.py in subprocesses that you don't want to re-run (e.g. recursively spawning processes and failing). Though some multiprocessing methods don't re-run main.py and it appears to be your case ;)\r\n\r\n> Is there any difference between\r\nfrom multiprocess import set_start_method and from multiprocessing import set_start_method? The latter is Python's built-in library. In [the official doc](https://huggingface.co/docs/datasets/en/process), it uses from multiprocess import set_start_method, but it gives me error like\r\n\r\nYes, `datasets` uses `multiprocess` which is a separate library from the built-in `multiprocessing`.\r\n\r\n`multiprocess` is an extended version of `multiprocessing` which allows e.g. to pass `lambda` functions to subprocesses", "Thanks @lhoestq for explanation. Is it okay we use `multiprocessing` for set_start_method given the above-mentioned issue for multiprocess? From my run with toy example, it's fine. Just want to check if you foresee any problems. ", "Not sure whether `multiprocessing.set_start_method` has any effect actually since we use `dill` for multiprocessed `map()`", "I'm running the [code example of multi-GPU processing](https://huggingface.co/docs/datasets/en/process#multiprocessing) on a Linux 8x A100 instance. The entire python code run time is 30 seconds faster if I add one line to set torch number of threads immediately after the `import torch` statement. It loads faster to the eight GPUs (however the map() progress bars take similar amount of time without/with this additional line).\r\n```\r\nimport torch\r\ntorch.set_num_threads(1) # I added this line.\r\n\r\nfrom multiprocess import set_start_method\r\n```\r\nFWIW: my instance has these versions.\r\n```\r\nCUDA 12.2 driver 535.161.08\r\nPython 3.10.12\r\ntorch '2.2.2'\r\nmultiprocess '0.70.16'\r\ntransformers '4.39.2'\r\ndatasets '2.18.0'\r\n```", "@lhoestq Thanks for the updated GPU multiprocessing documentation! When I tried to add `updated_dataset.save_to_disk()` after the map function with multiple GPUs, I get an error during saving: \r\n```\r\nSaving the dataset (0/20 shards): 0%\| \| 78000/84761821 [01:07<20:22:25, 1154.59 examples/s]Exception ignored in: <generator object Dataset._save_to_disk_single at 0x7f2498f15070>\r\nTraceback (most recent call last):\r\n File \"/home/ubuntu/lib/python3.10/site-packages/datasets/utils/py_utils.py\", line 679, in _write_generator_to_queue\r\n queue.put(result)\r\nRuntimeError: generator ignored GeneratorExit\r\n```\r\nDo you have any thoughts?", "Hmm first time I see this, and it's even more surprising given there is no generator in `_write_generator_to_queue`. Could you open a new issue ?" ]
1,868,077,748	6,185	Error in saving the PIL image into *.arrow files using datasets.arrow_writer	open	2023-08-26T12:15:57	2023-08-29T14:49:58	null	https://github.com/huggingface/datasets/issues/6185	null	HaozheZhao	false	[ "You can cast the `input_image` column to the `Image` type to fix the issue:\r\n```python\r\nds.cast_column(\"input_image\", datasets.Image())\r\n```" ]
1,867,766,143	6,184	Map cache does not detect function changes in another module	closed	2023-08-25T22:59:14	2023-08-29T20:57:07	2023-08-29T20:56:49	https://github.com/huggingface/datasets/issues/6184	null	jonathanasdf	false	[ "This issue is a duplicate of https://github.com/huggingface/datasets/issues/3297. This is a limitation of `dill`, a package we use for caching (non-`__main__` module objects are serialized by reference). You can find more info about it here: https://github.com/uqfoundation/dill/issues/424.\r\n\r\nIn your case, moving \r\n```\r\ndata = datasets.load_dataset('json', data_files=['/tmp/test.json'], split='train')\r\ndata = data.map(transform)\r\n``` \r\nto `test.py` and setting `transform.__module__ = None` at the end of `dataset.py` should fix the issue.", "I understand this may be a limitation of an upstream tool, but for a user for datasets this is very annoying, as when you have dozens of different datasets with different preprocessing functions you can't really move them all into the same file. It may be worth seeing if there is a way to specialize the dependency (eg. subclass it) and enforce behaviors that makes sense for your product.\r\n\r\nI was able to work around this for now by setting `__module__ = None`. If such workarounds are required for now it may be better to document it somewhere than a single obscure issue from a long time ago.\r\n\r\nAs this is a duplicate issue I'm closing it.\r\n\r\nI have another issue with the cache https://github.com/huggingface/datasets/issues/6179 can you take a look?" ]
1,867,743,276	6,183	Load dataset with non-existent file	closed	2023-08-25T22:21:22	2023-08-29T13:26:22	2023-08-29T13:26:22	https://github.com/huggingface/datasets/issues/6183	null	freQuensy23-coder	false	[ "Same problem", "This was fixed in https://github.com/huggingface/datasets/pull/6155, which will be included in the next release (or you can install `datasets` from source to use it immediately)." ]
1,867,203,131	6,182	Loading Meteor metric in HF evaluate module crashes due to datasets import issue	closed	2023-08-25T14:54:06	2023-09-04T16:41:11	2023-08-31T14:38:23	https://github.com/huggingface/datasets/issues/6182	null	dsashulya	false	[ "Our minimal Python version requirement is 3.8, so we dropped `importlib_metadata`. \r\n\r\nFeel free to open a PR in the `evaluate` repo to replace the problematic import with\r\n```python\r\nif PY_VERSION < version.parse(\"3.8\"):\r\n import importlib_metadata\r\nelse:\r\n import importlib.metadata as importlib_metadata\r\n```", "Any idea when you guys will release the next version which deals with this problem?\r\nI'm still having the same issue with py 3.10 when I install the lib with pip.\r\nI'm assuming that it has not yet been updated since the merge was 3 days ago.", "Yes, this requires a new `evaluate` release (cc @lvwerra for this). \r\n\r\nIn the meantime, you can get the fixed version by installing `evaluate` from `main`: `pip install git+https://github.com/huggingface/evaluate.git`", "I'll aim for a release this week!" ]
1,867,035,522	6,181	Fix import in `image_load` doc	closed	2023-08-25T13:12:19	2023-08-25T16:12:46	2023-08-25T16:02:24	https://github.com/huggingface/datasets/pull/6181	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6181", "html_url": "https://github.com/huggingface/datasets/pull/6181", "diff_url": "https://github.com/huggingface/datasets/pull/6181.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6181.patch", "merged_at": "2023-08-25T16:02:24" }	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) \| 0.009072 / 0.011353 (-0.002281) \| 0.006088 / 0.011008 (-0.004920) \| 0.134520 / 0.038508 (0.096011) \| 0.074935 / 0.023109 (0.051826) \| 0.480364 / 0.275898 (0.204466) \| 0.568943 / 0.323480 (0.245464) \| 0.006821 / 0.007986 (-0.001164) \| 0.004941 / 0.004328 (0.000612) \| 0.083274 / 0.004250 (0.079023) \| 0.061080 / 0.037052 (0.024028) \| 0.478960 / 0.258489 (0.220471) \| 0.542720 / 0.293841 (0.248879) \| 0.058023 / 0.128546 (-0.070524) \| 0.020120 / 0.075646 (-0.055526) \| 0.492680 / 0.419271 (0.073409) \| 0.079118 / 0.043533 (0.035585) \| 0.425087 / 0.255139 (0.169948) \| 0.603228 / 0.283200 (0.320028) \| 0.044102 / 0.141683 (-0.097581) \| 2.138848 / 1.452155 (0.686693) \| 2.454418 / 1.492716 (0.961702) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.255745 / 0.018006 (0.237738) \| 0.587559 / 0.000490 (0.587069) \| 0.006872 / 0.000200 (0.006672) \| 0.000111 / 0.000054 (0.000056) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.038480 / 0.037411 (0.001069) \| 0.115479 / 0.014526 (0.100953) \| 0.138395 / 0.176557 (-0.038161) \| 0.218007 / 0.737135 (-0.519129) \| 0.128866 / 0.296338 (-0.167472) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.756089 / 0.215209 (0.540880) \| 7.754631 / 2.077655 (5.676976) \| 3.615716 / 1.504120 (2.111596) \| 2.994327 / 1.541195 (1.453132) \| 3.196169 / 1.468490 (1.727679) \| 1.066937 / 4.584777 (-3.517840) \| 6.079595 / 3.745712 (2.333883) \| 5.455523 / 5.269862 (0.185661) \| 3.559036 / 4.565676 (-1.006640) \| 0.113044 / 0.424275 (-0.311231) \| 0.011401 / 0.007607 (0.003794) \| 0.961475 / 0.226044 (0.735430) \| 8.664226 / 2.268929 (6.395298) \| 4.203804 / 55.444624 (-51.240821) \| 3.122437 / 6.876477 (-3.754039) \| 3.549168 / 2.142072 (1.407095) \| 1.213035 / 4.805227 (-3.592193) \| 0.274725 / 6.500664 (-6.225939) \| 0.094499 / 0.075469 (0.019030) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.770299 / 1.841788 (-0.071489) \| 27.644591 / 8.074308 (19.570283) \| 23.239529 / 10.191392 (13.048137) \| 0.270185 / 0.680424 (-0.410238) \| 0.033563 / 0.534201 (-0.500638) \| 0.588301 / 0.579283 (0.009018) \| 0.658746 / 0.434364 (0.224382) \| 0.644476 / 0.540337 (0.104139) \| 0.834314 / 1.386936 (-0.552622) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.011021 / 0.011353 (-0.000332) \| 0.006719 / 0.011008 (-0.004289) \| 0.087669 / 0.038508 (0.049161) \| 0.088905 / 0.023109 (0.065796) \| 0.594230 / 0.275898 (0.318332) \| 0.620929 / 0.323480 (0.297449) \| 0.006776 / 0.007986 (-0.001210) \| 0.004725 / 0.004328 (0.000396) \| 0.082006 / 0.004250 (0.077756) \| 0.072164 / 0.037052 (0.035111) \| 0.604489 / 0.258489 (0.346000) \| 0.598520 / 0.293841 (0.304679) \| 0.057534 / 0.128546 (-0.071013) \| 0.016799 / 0.075646 (-0.058847) \| 0.115029 / 0.419271 (-0.304243) \| 0.070013 / 0.043533 (0.026481) \| 0.561773 / 0.255139 (0.306634) \| 0.624097 / 0.283200 (0.340897) \| 0.043518 / 0.141683 (-0.098164) \| 2.017089 / 1.452155 (0.564934) \| 2.188159 / 1.492716 (0.695443) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.386476 / 0.018006 (0.368469) \| 0.633195 / 0.000490 (0.632705) \| 0.028469 / 0.000200 (0.028269) \| 0.000159 / 0.000054 (0.000104) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.040020 / 0.037411 (0.002609) \| 0.112927 / 0.014526 (0.098402) \| 0.143663 / 0.176557 (-0.032894) \| 0.205931 / 0.737135 (-0.531204) \| 0.177814 / 0.296338 (-0.118524) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.711542 / 0.215209 (0.496333) \| 7.518535 / 2.077655 (5.440880) \| 3.714930 / 1.504120 (2.210810) \| 3.031999 / 1.541195 (1.490804) \| 3.328497 / 1.468490 (1.860006) \| 0.858912 / 4.584777 (-3.725865) \| 6.108384 / 3.745712 (2.362672) \| 5.184329 / 5.269862 (-0.085532) \| 3.622589 / 4.565676 (-0.943087) \| 0.096933 / 0.424275 (-0.327342) \| 0.008727 / 0.007607 (0.001120) \| 0.830102 / 0.226044 (0.604057) \| 8.331959 / 2.268929 (6.063030) \| 4.165106 / 55.444624 (-51.279519) \| 3.477003 / 6.876477 (-3.399474) \| 3.794225 / 2.142072 (1.652153) \| 1.237667 / 4.805227 (-3.567561) \| 0.233731 / 6.500664 (-6.266933) \| 0.076682 / 0.075469 (0.001213) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.944813 / 1.841788 (0.103026) \| 27.666997 / 8.074308 (19.592689) \| 24.562677 / 10.191392 (14.371285) \| 0.279320 / 0.680424 (-0.401104) \| 0.037802 / 0.534201 (-0.496399) \| 0.553579 / 0.579283 (-0.025704) \| 0.718229 / 0.434364 (0.283865) \| 0.623456 / 0.540337 (0.083118) \| 0.856777 / 1.386936 (-0.530159) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4c2a9d31d5e720e85976af8b457d45755a7e6911 \"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) \| 0.007716 / 0.011353 (-0.003637) \| 0.004624 / 0.011008 (-0.006384) \| 0.099987 / 0.038508 (0.061479) \| 0.082651 / 0.023109 (0.059542) \| 0.376277 / 0.275898 (0.100379) \| 0.401210 / 0.323480 (0.077730) \| 0.004528 / 0.007986 (-0.003458) \| 0.003763 / 0.004328 (-0.000566) \| 0.076274 / 0.004250 (0.072024) \| 0.062933 / 0.037052 (0.025881) \| 0.393881 / 0.258489 (0.135392) \| 0.431695 / 0.293841 (0.137854) \| 0.036795 / 0.128546 (-0.091752) \| 0.009935 / 0.075646 (-0.065712) \| 0.343638 / 0.419271 (-0.075634) \| 0.061456 / 0.043533 (0.017923) \| 0.372235 / 0.255139 (0.117096) \| 0.412994 / 0.283200 (0.129794) \| 0.027993 / 0.141683 (-0.113690) \| 1.798018 / 1.452155 (0.345863) \| 1.898502 / 1.492716 (0.405786) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.237330 / 0.018006 (0.219324) \| 0.494956 / 0.000490 (0.494467) \| 0.003543 / 0.000200 (0.003343) \| 0.000113 / 0.000054 (0.000059) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034084 / 0.037411 (-0.003327) \| 0.093407 / 0.014526 (0.078881) \| 0.108378 / 0.176557 (-0.068179) \| 0.177016 / 0.737135 (-0.560119) \| 0.108622 / 0.296338 (-0.187716) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.456449 / 0.215209 (0.241240) \| 4.522405 / 2.077655 (2.444750) \| 2.206564 / 1.504120 (0.702444) \| 1.994185 / 1.541195 (0.452990) \| 2.083785 / 1.468490 (0.615295) \| 0.563352 / 4.584777 (-4.021425) \| 4.207295 / 3.745712 (0.461583) \| 3.783061 / 5.269862 (-1.486800) \| 2.372874 / 4.565676 (-2.192802) \| 0.066907 / 0.424275 (-0.357368) \| 0.009013 / 0.007607 (0.001406) \| 0.537852 / 0.226044 (0.311808) \| 5.349928 / 2.268929 (3.081000) \| 2.759409 / 55.444624 (-52.685215) \| 2.345972 / 6.876477 (-4.530505) \| 2.630559 / 2.142072 (0.488486) \| 0.681134 / 4.805227 (-4.124093) \| 0.157898 / 6.500664 (-6.342766) \| 0.071638 / 0.075469 (-0.003831) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.470730 / 1.841788 (-0.371058) \| 22.479252 / 8.074308 (14.404944) \| 16.543080 / 10.191392 (6.351688) \| 0.191943 / 0.680424 (-0.488481) \| 0.021641 / 0.534201 (-0.512560) \| 0.467571 / 0.579283 (-0.111712) \| 0.486728 / 0.434364 (0.052364) \| 0.543359 / 0.540337 (0.003021) \| 0.733968 / 1.386936 (-0.652968) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008135 / 0.011353 (-0.003218) \| 0.004662 / 0.011008 (-0.006347) \| 0.077218 / 0.038508 (0.038710) \| 0.092220 / 0.023109 (0.069111) \| 0.481219 / 0.275898 (0.205321) \| 0.530373 / 0.323480 (0.206893) \| 0.006418 / 0.007986 (-0.001568) \| 0.003924 / 0.004328 (-0.000404) \| 0.076681 / 0.004250 (0.072431) \| 0.068693 / 0.037052 (0.031641) \| 0.491938 / 0.258489 (0.233449) \| 0.540501 / 0.293841 (0.246660) \| 0.038106 / 0.128546 (-0.090441) \| 0.010035 / 0.075646 (-0.065611) \| 0.084502 / 0.419271 (-0.334769) \| 0.057234 / 0.043533 (0.013701) \| 0.483239 / 0.255139 (0.228100) \| 0.510026 / 0.283200 (0.226826) \| 0.028770 / 0.141683 (-0.112913) \| 1.854937 / 1.452155 (0.402783) \| 1.948268 / 1.492716 (0.455552) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.380192 / 0.018006 (0.362186) \| 0.523318 / 0.000490 (0.522828) \| 0.051153 / 0.000200 (0.050953) \| 0.000691 / 0.000054 (0.000637) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036838 / 0.037411 (-0.000573) \| 0.109202 / 0.014526 (0.094676) \| 0.124110 / 0.176557 (-0.052446) \| 0.186717 / 0.737135 (-0.550419) \| 0.124088 / 0.296338 (-0.172250) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.506411 / 0.215209 (0.291202) \| 5.045421 / 2.077655 (2.967766) \| 2.711911 / 1.504120 (1.207791) \| 2.531668 / 1.541195 (0.990474) \| 2.635680 / 1.468490 (1.167190) \| 0.578395 / 4.584777 (-4.006382) \| 4.206891 / 3.745712 (0.461178) \| 3.851063 / 5.269862 (-1.418799) \| 2.388327 / 4.565676 (-2.177350) \| 0.068041 / 0.424275 (-0.356234) \| 0.008769 / 0.007607 (0.001162) \| 0.594170 / 0.226044 (0.368125) \| 5.953138 / 2.268929 (3.684210) \| 3.290586 / 55.444624 (-52.154038) \| 2.877086 / 6.876477 (-3.999390) \| 3.138600 / 2.142072 (0.996528) \| 0.686393 / 4.805227 (-4.118834) \| 0.156541 / 6.500664 (-6.344123) \| 0.071514 / 0.075469 (-0.003955) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.613514 / 1.841788 (-0.228274) \| 23.593185 / 8.074308 (15.518877) \| 17.146647 / 10.191392 (6.955255) \| 0.177230 / 0.680424 (-0.503193) \| 0.023661 / 0.534201 (-0.510540) \| 0.472367 / 0.579283 (-0.106916) \| 0.484614 / 0.434364 (0.050250) \| 0.547150 / 0.540337 (0.006813) \| 0.843726 / 1.386936 (-0.543210) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dba64cd381bfe384cb64ab9826f6054a0f1df1ff \"CML watermark\")\n" ]
1,867,032,578	6,180	Use `hf-internal-testing` repos for hosting test dataset repos	closed	2023-08-25T13:10:26	2023-08-25T16:58:02	2023-08-25T16:46:22	https://github.com/huggingface/datasets/pull/6180	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6180", "html_url": "https://github.com/huggingface/datasets/pull/6180", "diff_url": "https://github.com/huggingface/datasets/pull/6180.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6180.patch", "merged_at": "2023-08-25T16:46:22" }	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) \| 0.006505 / 0.011353 (-0.004847) \| 0.003950 / 0.011008 (-0.007058) \| 0.084554 / 0.038508 (0.046046) \| 0.074376 / 0.023109 (0.051267) \| 0.350184 / 0.275898 (0.074286) \| 0.380704 / 0.323480 (0.057224) \| 0.004011 / 0.007986 (-0.003975) \| 0.003890 / 0.004328 (-0.000438) \| 0.065483 / 0.004250 (0.061232) \| 0.054912 / 0.037052 (0.017860) \| 0.359586 / 0.258489 (0.101097) \| 0.403360 / 0.293841 (0.109519) \| 0.030614 / 0.128546 (-0.097932) \| 0.008530 / 0.075646 (-0.067117) \| 0.288220 / 0.419271 (-0.131052) \| 0.052270 / 0.043533 (0.008737) \| 0.352557 / 0.255139 (0.097418) \| 0.380509 / 0.283200 (0.097309) \| 0.025513 / 0.141683 (-0.116170) \| 1.488469 / 1.452155 (0.036315) \| 1.559182 / 1.492716 (0.066466) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.266163 / 0.018006 (0.248157) \| 0.596345 / 0.000490 (0.595855) \| 0.004368 / 0.000200 (0.004168) \| 0.000211 / 0.000054 (0.000156) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027137 / 0.037411 (-0.010274) \| 0.082251 / 0.014526 (0.067725) \| 0.094745 / 0.176557 (-0.081812) \| 0.148756 / 0.737135 (-0.588379) \| 0.094580 / 0.296338 (-0.201758) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.383506 / 0.215209 (0.168297) \| 3.823147 / 2.077655 (1.745493) \| 1.859627 / 1.504120 (0.355507) \| 1.687969 / 1.541195 (0.146775) \| 1.720786 / 1.468490 (0.252296) \| 0.476552 / 4.584777 (-4.108225) \| 3.539558 / 3.745712 (-0.206154) \| 3.209032 / 5.269862 (-2.060830) \| 1.999643 / 4.565676 (-2.566034) \| 0.056484 / 0.424275 (-0.367791) \| 0.007443 / 0.007607 (-0.000164) \| 0.456089 / 0.226044 (0.230044) \| 4.562522 / 2.268929 (2.293593) \| 2.348286 / 55.444624 (-53.096338) \| 1.984323 / 6.876477 (-4.892154) \| 2.148988 / 2.142072 (0.006915) \| 0.570761 / 4.805227 (-4.234466) \| 0.131439 / 6.500664 (-6.369225) \| 0.059752 / 0.075469 (-0.015717) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.276803 / 1.841788 (-0.564985) \| 19.406812 / 8.074308 (11.332504) \| 13.979088 / 10.191392 (3.787696) \| 0.157418 / 0.680424 (-0.523006) \| 0.018051 / 0.534201 (-0.516150) \| 0.392307 / 0.579283 (-0.186976) \| 0.406603 / 0.434364 (-0.027760) \| 0.458450 / 0.540337 (-0.081888) \| 0.622569 / 1.386936 (-0.764367) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006552 / 0.011353 (-0.004800) \| 0.004060 / 0.011008 (-0.006948) \| 0.063522 / 0.038508 (0.025014) \| 0.072537 / 0.023109 (0.049428) \| 0.398452 / 0.275898 (0.122554) \| 0.422059 / 0.323480 (0.098579) \| 0.005577 / 0.007986 (-0.002409) \| 0.003413 / 0.004328 (-0.000916) \| 0.064095 / 0.004250 (0.059845) \| 0.056883 / 0.037052 (0.019831) \| 0.407119 / 0.258489 (0.148630) \| 0.435889 / 0.293841 (0.142048) \| 0.031549 / 0.128546 (-0.096998) \| 0.008418 / 0.075646 (-0.067228) \| 0.070315 / 0.419271 (-0.348957) \| 0.047828 / 0.043533 (0.004295) \| 0.398705 / 0.255139 (0.143566) \| 0.416986 / 0.283200 (0.133786) \| 0.022304 / 0.141683 (-0.119379) \| 1.512597 / 1.452155 (0.060442) \| 1.570588 / 1.492716 (0.077871) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295100 / 0.018006 (0.277094) \| 0.541883 / 0.000490 (0.541393) \| 0.007375 / 0.000200 (0.007175) \| 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) \| 0.030877 / 0.037411 (-0.006534) \| 0.090807 / 0.014526 (0.076281) \| 0.106155 / 0.176557 (-0.070402) \| 0.155546 / 0.737135 (-0.581589) \| 0.103847 / 0.296338 (-0.192492) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.441176 / 0.215209 (0.225967) \| 4.401025 / 2.077655 (2.323371) \| 2.394764 / 1.504120 (0.890644) \| 2.226434 / 1.541195 (0.685239) \| 2.247248 / 1.468490 (0.778758) \| 0.489149 / 4.584777 (-4.095628) \| 3.642468 / 3.745712 (-0.103244) \| 3.235597 / 5.269862 (-2.034265) \| 1.992660 / 4.565676 (-2.573016) \| 0.057457 / 0.424275 (-0.366818) \| 0.007192 / 0.007607 (-0.000415) \| 0.515978 / 0.226044 (0.289934) \| 5.147728 / 2.268929 (2.878800) \| 2.837394 / 55.444624 (-52.607230) \| 2.505753 / 6.876477 (-4.370723) \| 2.653090 / 2.142072 (0.511018) \| 0.583274 / 4.805227 (-4.221954) \| 0.132116 / 6.500664 (-6.368548) \| 0.058794 / 0.075469 (-0.016675) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.331630 / 1.841788 (-0.510158) \| 20.056890 / 8.074308 (11.982582) \| 14.950561 / 10.191392 (4.759169) \| 0.165449 / 0.680424 (-0.514975) \| 0.020161 / 0.534201 (-0.514040) \| 0.395791 / 0.579283 (-0.183492) \| 0.415631 / 0.434364 (-0.018733) \| 0.474440 / 0.540337 (-0.065898) \| 0.643060 / 1.386936 (-0.743876) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#712185ed5e9cb3ff6d6528b4528882d51935f334 \"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) \| 0.007440 / 0.011353 (-0.003913) \| 0.004456 / 0.011008 (-0.006552) \| 0.099498 / 0.038508 (0.060990) \| 0.077579 / 0.023109 (0.054470) \| 0.374934 / 0.275898 (0.099036) \| 0.409590 / 0.323480 (0.086110) \| 0.005876 / 0.007986 (-0.002110) \| 0.003642 / 0.004328 (-0.000687) \| 0.076781 / 0.004250 (0.072531) \| 0.060185 / 0.037052 (0.023133) \| 0.374762 / 0.258489 (0.116273) \| 0.445608 / 0.293841 (0.151767) \| 0.036557 / 0.128546 (-0.091990) \| 0.009941 / 0.075646 (-0.065706) \| 0.345214 / 0.419271 (-0.074058) \| 0.061912 / 0.043533 (0.018379) \| 0.378346 / 0.255139 (0.123207) \| 0.415275 / 0.283200 (0.132076) \| 0.027396 / 0.141683 (-0.114287) \| 1.776602 / 1.452155 (0.324447) \| 1.827683 / 1.492716 (0.334967) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.235227 / 0.018006 (0.217221) \| 0.491846 / 0.000490 (0.491356) \| 0.004987 / 0.000200 (0.004787) \| 0.000127 / 0.000054 (0.000073) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032517 / 0.037411 (-0.004894) \| 0.099217 / 0.014526 (0.084691) \| 0.109749 / 0.176557 (-0.066807) \| 0.176190 / 0.737135 (-0.560946) \| 0.109868 / 0.296338 (-0.186471) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.455188 / 0.215209 (0.239979) \| 4.560143 / 2.077655 (2.482489) \| 2.249928 / 1.504120 (0.745809) \| 2.032808 / 1.541195 (0.491614) \| 2.090096 / 1.468490 (0.621605) \| 0.567813 / 4.584777 (-4.016964) \| 4.338299 / 3.745712 (0.592587) \| 3.701589 / 5.269862 (-1.568273) \| 2.404805 / 4.565676 (-2.160871) \| 0.067931 / 0.424275 (-0.356344) \| 0.009011 / 0.007607 (0.001404) \| 0.542565 / 0.226044 (0.316521) \| 5.406578 / 2.268929 (3.137650) \| 2.773508 / 55.444624 (-52.671116) \| 2.402926 / 6.876477 (-4.473550) \| 2.679318 / 2.142072 (0.537246) \| 0.683781 / 4.805227 (-4.121446) \| 0.155970 / 6.500664 (-6.344694) \| 0.070108 / 0.075469 (-0.005361) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.541583 / 1.841788 (-0.300205) \| 21.592562 / 8.074308 (13.518254) \| 16.426868 / 10.191392 (6.235476) \| 0.168618 / 0.680424 (-0.511806) \| 0.021560 / 0.534201 (-0.512641) \| 0.467062 / 0.579283 (-0.112221) \| 0.479968 / 0.434364 (0.045604) \| 0.540747 / 0.540337 (0.000410) \| 0.775502 / 1.386936 (-0.611434) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008632 / 0.011353 (-0.002721) \| 0.004523 / 0.011008 (-0.006485) \| 0.075814 / 0.038508 (0.037306) \| 0.087096 / 0.023109 (0.063987) \| 0.482136 / 0.275898 (0.206238) \| 0.529969 / 0.323480 (0.206489) \| 0.006882 / 0.007986 (-0.001103) \| 0.003720 / 0.004328 (-0.000609) \| 0.076232 / 0.004250 (0.071981) \| 0.069307 / 0.037052 (0.032254) \| 0.491554 / 0.258489 (0.233065) \| 0.528989 / 0.293841 (0.235148) \| 0.042219 / 0.128546 (-0.086327) \| 0.009717 / 0.075646 (-0.065929) \| 0.103006 / 0.419271 (-0.316266) \| 0.060377 / 0.043533 (0.016844) \| 0.484454 / 0.255139 (0.229315) \| 0.536072 / 0.283200 (0.252872) \| 0.027482 / 0.141683 (-0.114201) \| 1.844677 / 1.452155 (0.392522) \| 2.001800 / 1.492716 (0.509083) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.252367 / 0.018006 (0.234361) \| 0.483601 / 0.000490 (0.483111) \| 0.007445 / 0.000200 (0.007245) \| 0.000163 / 0.000054 (0.000108) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036463 / 0.037411 (-0.000948) \| 0.108837 / 0.014526 (0.094311) \| 0.122256 / 0.176557 (-0.054300) \| 0.186455 / 0.737135 (-0.550681) \| 0.122270 / 0.296338 (-0.174069) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.506291 / 0.215209 (0.291082) \| 5.038044 / 2.077655 (2.960389) \| 2.751017 / 1.504120 (1.246897) \| 2.553655 / 1.541195 (1.012460) \| 2.612724 / 1.468490 (1.144234) \| 0.581755 / 4.584777 (-4.003022) \| 4.376012 / 3.745712 (0.630300) \| 3.749755 / 5.269862 (-1.520107) \| 2.394059 / 4.565676 (-2.171618) \| 0.068727 / 0.424275 (-0.355548) \| 0.008714 / 0.007607 (0.001107) \| 0.607371 / 0.226044 (0.381326) \| 6.062053 / 2.268929 (3.793125) \| 3.278378 / 55.444624 (-52.166247) \| 2.866417 / 6.876477 (-4.010060) \| 3.056150 / 2.142072 (0.914077) \| 0.695090 / 4.805227 (-4.110137) \| 0.155274 / 6.500664 (-6.345390) \| 0.071106 / 0.075469 (-0.004363) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.584552 / 1.841788 (-0.257236) \| 23.092569 / 8.074308 (15.018260) \| 17.381905 / 10.191392 (7.190513) \| 0.206535 / 0.680424 (-0.473888) \| 0.025401 / 0.534201 (-0.508800) \| 0.514297 / 0.579283 (-0.064986) \| 0.507487 / 0.434364 (0.073123) \| 0.566883 / 0.540337 (0.026545) \| 0.811074 / 1.386936 (-0.575862) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5fb01295bff860f09a4c466e745f3840f851efdc \"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) \| 0.008400 / 0.011353 (-0.002953) \| 0.004872 / 0.011008 (-0.006136) \| 0.104434 / 0.038508 (0.065926) \| 0.074411 / 0.023109 (0.051302) \| 0.395970 / 0.275898 (0.120072) \| 0.431661 / 0.323480 (0.108181) \| 0.005365 / 0.007986 (-0.002621) \| 0.005495 / 0.004328 (0.001167) \| 0.081255 / 0.004250 (0.077004) \| 0.057141 / 0.037052 (0.020089) \| 0.397242 / 0.258489 (0.138753) \| 0.456052 / 0.293841 (0.162211) \| 0.048362 / 0.128546 (-0.080184) \| 0.014077 / 0.075646 (-0.061569) \| 0.351128 / 0.419271 (-0.068143) \| 0.067842 / 0.043533 (0.024309) \| 0.372820 / 0.255139 (0.117681) \| 0.407917 / 0.283200 (0.124717) \| 0.037707 / 0.141683 (-0.103976) \| 1.677136 / 1.452155 (0.224981) \| 1.764614 / 1.492716 (0.271897) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.269850 / 0.018006 (0.251844) \| 0.601458 / 0.000490 (0.600969) \| 0.006500 / 0.000200 (0.006300) \| 0.000107 / 0.000054 (0.000053) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030340 / 0.037411 (-0.007072) \| 0.098041 / 0.014526 (0.083515) \| 0.107270 / 0.176557 (-0.069287) \| 0.173502 / 0.737135 (-0.563633) \| 0.113296 / 0.296338 (-0.183043) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.575788 / 0.215209 (0.360579) \| 5.723878 / 2.077655 (3.646223) \| 2.326339 / 1.504120 (0.822219) \| 2.130667 / 1.541195 (0.589472) \| 2.080885 / 1.468490 (0.612395) \| 0.800936 / 4.584777 (-3.783841) \| 5.227888 / 3.745712 (1.482176) \| 4.592647 / 5.269862 (-0.677214) \| 2.935765 / 4.565676 (-1.629911) \| 0.095909 / 0.424275 (-0.328367) \| 0.008763 / 0.007607 (0.001156) \| 0.697362 / 0.226044 (0.471318) \| 6.968105 / 2.268929 (4.699176) \| 3.129070 / 55.444624 (-52.315554) \| 2.554818 / 6.876477 (-4.321658) \| 2.776005 / 2.142072 (0.633933) \| 1.017064 / 4.805227 (-3.788163) \| 0.211552 / 6.500664 (-6.289112) \| 0.072132 / 0.075469 (-0.003338) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.517072 / 1.841788 (-0.324716) \| 23.737742 / 8.074308 (15.663433) \| 22.236447 / 10.191392 (12.045055) \| 0.235408 / 0.680424 (-0.445016) \| 0.031889 / 0.534201 (-0.502312) \| 0.458997 / 0.579283 (-0.120286) \| 0.610513 / 0.434364 (0.176149) \| 0.536508 / 0.540337 (-0.003830) \| 0.750137 / 1.386936 (-0.636799) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008696 / 0.011353 (-0.002657) \| 0.005374 / 0.011008 (-0.005634) \| 0.077974 / 0.038508 (0.039466) \| 0.083471 / 0.023109 (0.060362) \| 0.498890 / 0.275898 (0.222992) \| 0.517570 / 0.323480 (0.194090) \| 0.006523 / 0.007986 (-0.001462) \| 0.004315 / 0.004328 (-0.000013) \| 0.082262 / 0.004250 (0.078012) \| 0.064828 / 0.037052 (0.027776) \| 0.473101 / 0.258489 (0.214612) \| 0.534172 / 0.293841 (0.240331) \| 0.051884 / 0.128546 (-0.076662) \| 0.015191 / 0.075646 (-0.060455) \| 0.084307 / 0.419271 (-0.334965) \| 0.066050 / 0.043533 (0.022517) \| 0.518007 / 0.255139 (0.262868) \| 0.511145 / 0.283200 (0.227946) \| 0.045302 / 0.141683 (-0.096381) \| 1.670973 / 1.452155 (0.218818) \| 1.829225 / 1.492716 (0.336509) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.436537 / 0.018006 (0.418531) \| 0.608380 / 0.000490 (0.607890) \| 0.075211 / 0.000200 (0.075011) \| 0.000733 / 0.000054 (0.000679) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.039117 / 0.037411 (0.001706) \| 0.103525 / 0.014526 (0.088999) \| 0.124413 / 0.176557 (-0.052144) \| 0.192352 / 0.737135 (-0.544783) \| 0.120379 / 0.296338 (-0.175959) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.673338 / 0.215209 (0.458129) \| 6.799435 / 2.077655 (4.721780) \| 3.600913 / 1.504120 (2.096793) \| 2.881008 / 1.541195 (1.339814) \| 2.667154 / 1.468490 (1.198664) \| 0.868775 / 4.584777 (-3.716002) \| 5.517063 / 3.745712 (1.771351) \| 4.646706 / 5.269862 (-0.623156) \| 2.914825 / 4.565676 (-1.650852) \| 0.098784 / 0.424275 (-0.325491) \| 0.011504 / 0.007607 (0.003897) \| 0.724233 / 0.226044 (0.498188) \| 7.311045 / 2.268929 (5.042117) \| 3.685490 / 55.444624 (-51.759135) \| 2.892360 / 6.876477 (-3.984117) \| 3.253189 / 2.142072 (1.111117) \| 0.983065 / 4.805227 (-3.822162) \| 0.201097 / 6.500664 (-6.299567) \| 0.068020 / 0.075469 (-0.007450) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.793904 / 1.841788 (-0.047884) \| 24.451356 / 8.074308 (16.377048) \| 21.697191 / 10.191392 (11.505799) \| 0.228545 / 0.680424 (-0.451879) \| 0.034600 / 0.534201 (-0.499601) \| 0.483253 / 0.579283 (-0.096030) \| 0.615103 / 0.434364 (0.180739) \| 0.564600 / 0.540337 (0.024262) \| 0.799688 / 1.386936 (-0.587248) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#74d60213dcbd7c99484c62ce1d3dfd90a1df0770 \"CML watermark\")\n" ]
1,867,009,016	6,179	Map cache with tokenizer	open	2023-08-25T12:55:18	2023-08-31T15:17:24	null	https://github.com/huggingface/datasets/issues/6179	null	jonathanasdf	false	[ "https://github.com/huggingface/datasets/issues/5147 may be a solution, by passing in the tokenizer in a fn_kwargs and ignoring it in the fingerprint calculations", "I have a similar issue. I was using a Jupyter Notebook and every time I call the map function it performs tokenization from scratch again although the cache files of last run still exists. \r\n\r\nI ran with 20 processes and now in the cache folder there are two groups of cached results of tokenized dataset:\r\n\r\n```\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:56:46 2023 cache-1982fea76aa54a13_00001_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 13:02:08 2023 cache-1982fea76aa54a13_00004_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:56:40 2023 cache-1982fea76aa54a13_00005_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Sat Aug 26 12:50:59 2023 cache-1982fea76aa54a13_00006_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:57:37 2023 cache-1982fea76aa54a13_00007_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:57:31 2023 cache-1982fea76aa54a13_00008_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:59:47 2023 cache-1982fea76aa54a13_00010_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Sat Aug 26 12:59:44 2023 cache-1982fea76aa54a13_00011_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Sat Aug 26 12:55:24 2023 cache-1982fea76aa54a13_00012_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Sat Aug 26 12:56:21 2023 cache-1982fea76aa54a13_00013_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:57:24 2023 cache-1982fea76aa54a13_00014_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 13:00:48 2023 cache-1982fea76aa54a13_00015_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:56:56 2023 cache-1982fea76aa54a13_00017_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:56:54 2023 cache-1982fea76aa54a13_00018_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Sat Aug 26 12:57:27 2023 cache-1982fea76aa54a13_00019_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:15:40 2023 cache-454431f643cdc5e8_00000_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:16:46 2023 cache-454431f643cdc5e8_00001_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:14:53 2023 cache-454431f643cdc5e8_00002_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:13:10 2023 cache-454431f643cdc5e8_00003_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:13:04 2023 cache-454431f643cdc5e8_00004_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:16:42 2023 cache-454431f643cdc5e8_00005_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Wed Aug 23 19:01:29 2023 cache-454431f643cdc5e8_00006_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:16:41 2023 cache-454431f643cdc5e8_00007_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:14:04 2023 cache-454431f643cdc5e8_00008_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:17:41 2023 cache-454431f643cdc5e8_00009_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:17:06 2023 cache-454431f643cdc5e8_00010_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Wed Aug 23 19:17:16 2023 cache-454431f643cdc5e8_00011_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Wed Aug 23 19:15:13 2023 cache-454431f643cdc5e8_00012_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 241 MB Wed Aug 23 19:16:01 2023 cache-454431f643cdc5e8_00013_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:16:35 2023 cache-454431f643cdc5e8_00014_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:16:20 2023 cache-454431f643cdc5e8_00015_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:14:48 2023 cache-454431f643cdc5e8_00016_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 18:59:32 2023 cache-454431f643cdc5e8_00017_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:17:58 2023 cache-454431f643cdc5e8_00018_of_00020.arrow\r\n.rw-r--r-- fad3ew bii_dsc_community 240 MB Wed Aug 23 19:15:25 2023 cache-454431f643cdc5e8_00019_of_00020.arrow\r\n```\r\n\r\ncan we specify the cache file for map so that it won't redo everything again?", "@Luosuu [map](https://huggingface.co/docs/datasets/v2.14.4/en/package_reference/main_classes#datasets.Dataset.map) has cache_file_name parameter\r\n\r\nIn my case, I do want the cache to detect when the map function changes, so I can't pass a constant cache file name.", "Implementing a proper hashing function for the (fast) tokenizers is currently impossible for the reasons mentioned in the referenced issues. So the only alternative to the `cache_file_name` (or `new_fingerprint`) parameter is a custom serializer (e.g., that deserializes the tokenizer from a local save path) defined using `copyreg` or a class that wraps the tokenizer object and has `__reduce__`(`__setstate__`/`__getstate__`) " ]
1,866,610,102	6,178	'import datasets' throws "invalid syntax error"	closed	2023-08-25T08:35:14	2023-09-27T17:33:39	2023-09-27T17:33:39	https://github.com/huggingface/datasets/issues/6178	null	elia-ashraf	false	[ "This seems to be related to your environment and not the `datasets` code (e.g., this could happen when exposing the Python 3.9 site packages to a lower Python version (interpreter))" ]
1,865,490,962	6,177	Use object detection images from `huggingface/documentation-images`	closed	2023-08-24T16:16:09	2023-08-25T16:30:00	2023-08-25T16:21:17	https://github.com/huggingface/datasets/pull/6177	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6177", "html_url": "https://github.com/huggingface/datasets/pull/6177", "diff_url": "https://github.com/huggingface/datasets/pull/6177.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6177.patch", "merged_at": "2023-08-25T16:21:17" }	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) \| 0.005847 / 0.011353 (-0.005506) \| 0.003488 / 0.011008 (-0.007521) \| 0.079545 / 0.038508 (0.041037) \| 0.055114 / 0.023109 (0.032005) \| 0.312694 / 0.275898 (0.036796) \| 0.338808 / 0.323480 (0.015329) \| 0.004573 / 0.007986 (-0.003413) \| 0.002818 / 0.004328 (-0.001510) \| 0.062102 / 0.004250 (0.057852) \| 0.044072 / 0.037052 (0.007019) \| 0.317682 / 0.258489 (0.059192) \| 0.354139 / 0.293841 (0.060298) \| 0.026905 / 0.128546 (-0.101641) \| 0.007990 / 0.075646 (-0.067656) \| 0.260071 / 0.419271 (-0.159201) \| 0.043658 / 0.043533 (0.000125) \| 0.313828 / 0.255139 (0.058689) \| 0.339678 / 0.283200 (0.056478) \| 0.020076 / 0.141683 (-0.121607) \| 1.446321 / 1.452155 (-0.005834) \| 1.527046 / 1.492716 (0.034330) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.197801 / 0.018006 (0.179795) \| 0.432874 / 0.000490 (0.432385) \| 0.004093 / 0.000200 (0.003893) \| 0.000069 / 0.000054 (0.000014) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023505 / 0.037411 (-0.013906) \| 0.072377 / 0.014526 (0.057852) \| 0.081058 / 0.176557 (-0.095498) \| 0.141628 / 0.737135 (-0.595507) \| 0.081622 / 0.296338 (-0.214716) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.395005 / 0.215209 (0.179795) \| 3.949006 / 2.077655 (1.871352) \| 1.934028 / 1.504120 (0.429908) \| 1.756065 / 1.541195 (0.214871) \| 1.778719 / 1.468490 (0.310229) \| 0.501279 / 4.584777 (-4.083498) \| 3.032120 / 3.745712 (-0.713592) \| 2.859751 / 5.269862 (-2.410110) \| 1.885924 / 4.565676 (-2.679753) \| 0.057236 / 0.424275 (-0.367039) \| 0.006704 / 0.007607 (-0.000903) \| 0.465794 / 0.226044 (0.239750) \| 4.648622 / 2.268929 (2.379694) \| 2.345649 / 55.444624 (-53.098975) \| 1.981122 / 6.876477 (-4.895355) \| 2.148235 / 2.142072 (0.006163) \| 0.591466 / 4.805227 (-4.213761) \| 0.125262 / 6.500664 (-6.375402) \| 0.061305 / 0.075469 (-0.014164) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.243932 / 1.841788 (-0.597856) \| 17.912110 / 8.074308 (9.837802) \| 13.662097 / 10.191392 (3.470705) \| 0.148051 / 0.680424 (-0.532373) \| 0.016778 / 0.534201 (-0.517423) \| 0.340342 / 0.579283 (-0.238941) \| 0.351720 / 0.434364 (-0.082644) \| 0.377837 / 0.540337 (-0.162501) \| 0.521163 / 1.386936 (-0.865774) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006011 / 0.011353 (-0.005342) \| 0.003549 / 0.011008 (-0.007459) \| 0.063579 / 0.038508 (0.025071) \| 0.056196 / 0.023109 (0.033087) \| 0.448879 / 0.275898 (0.172981) \| 0.491542 / 0.323480 (0.168062) \| 0.004597 / 0.007986 (-0.003389) \| 0.002790 / 0.004328 (-0.001539) \| 0.063257 / 0.004250 (0.059006) \| 0.045653 / 0.037052 (0.008600) \| 0.459714 / 0.258489 (0.201225) \| 0.491371 / 0.293841 (0.197530) \| 0.028124 / 0.128546 (-0.100422) \| 0.008016 / 0.075646 (-0.067630) \| 0.069418 / 0.419271 (-0.349853) \| 0.040393 / 0.043533 (-0.003140) \| 0.450978 / 0.255139 (0.195839) \| 0.472075 / 0.283200 (0.188875) \| 0.020006 / 0.141683 (-0.121677) \| 1.451946 / 1.452155 (-0.000209) \| 1.513557 / 1.492716 (0.020840) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.225416 / 0.018006 (0.207410) \| 0.412287 / 0.000490 (0.411797) \| 0.004075 / 0.000200 (0.003875) \| 0.000073 / 0.000054 (0.000019) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025949 / 0.037411 (-0.011463) \| 0.080633 / 0.014526 (0.066108) \| 0.089960 / 0.176557 (-0.086597) \| 0.144530 / 0.737135 (-0.592606) \| 0.091427 / 0.296338 (-0.204911) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.462311 / 0.215209 (0.247102) \| 4.605063 / 2.077655 (2.527408) \| 2.541083 / 1.504120 (1.036963) \| 2.356341 / 1.541195 (0.815147) \| 2.389824 / 1.468490 (0.921334) \| 0.507397 / 4.584777 (-4.077380) \| 3.079023 / 3.745712 (-0.666689) \| 2.792025 / 5.269862 (-2.477837) \| 1.846931 / 4.565676 (-2.718746) \| 0.058422 / 0.424275 (-0.365853) \| 0.006409 / 0.007607 (-0.001199) \| 0.530648 / 0.226044 (0.304604) \| 5.321030 / 2.268929 (3.052101) \| 2.978335 / 55.444624 (-52.466289) \| 2.641188 / 6.876477 (-4.235288) \| 2.780450 / 2.142072 (0.638378) \| 0.593864 / 4.805227 (-4.211363) \| 0.125394 / 6.500664 (-6.375270) \| 0.061432 / 0.075469 (-0.014037) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.337142 / 1.841788 (-0.504646) \| 18.841575 / 8.074308 (10.767267) \| 14.678622 / 10.191392 (4.487230) \| 0.144491 / 0.680424 (-0.535933) \| 0.018145 / 0.534201 (-0.516056) \| 0.339376 / 0.579283 (-0.239907) \| 0.339129 / 0.434364 (-0.095235) \| 0.394842 / 0.540337 (-0.145495) \| 0.547924 / 1.386936 (-0.839012) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#57af0ab30796df59d28bf933e756ffbe5f34db1e \"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) \| 0.006478 / 0.011353 (-0.004875) \| 0.003845 / 0.011008 (-0.007163) \| 0.084179 / 0.038508 (0.045671) \| 0.071327 / 0.023109 (0.048217) \| 0.315206 / 0.275898 (0.039308) \| 0.353477 / 0.323480 (0.029997) \| 0.005267 / 0.007986 (-0.002719) \| 0.003282 / 0.004328 (-0.001046) \| 0.064062 / 0.004250 (0.059811) \| 0.051940 / 0.037052 (0.014888) \| 0.332004 / 0.258489 (0.073515) \| 0.363199 / 0.293841 (0.069358) \| 0.030546 / 0.128546 (-0.098000) \| 0.008453 / 0.075646 (-0.067193) \| 0.287636 / 0.419271 (-0.131636) \| 0.051999 / 0.043533 (0.008466) \| 0.325220 / 0.255139 (0.070081) \| 0.355324 / 0.283200 (0.072125) \| 0.023417 / 0.141683 (-0.118266) \| 1.473370 / 1.452155 (0.021215) \| 1.596903 / 1.492716 (0.104186) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.212645 / 0.018006 (0.194638) \| 0.463766 / 0.000490 (0.463276) \| 0.002834 / 0.000200 (0.002634) \| 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) \| 0.028424 / 0.037411 (-0.008987) \| 0.082188 / 0.014526 (0.067662) \| 0.777186 / 0.176557 (0.600629) \| 0.218290 / 0.737135 (-0.518845) \| 0.099098 / 0.296338 (-0.197240) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.387138 / 0.215209 (0.171929) \| 3.845655 / 2.077655 (1.768000) \| 1.929812 / 1.504120 (0.425692) \| 1.718263 / 1.541195 (0.177069) \| 1.760933 / 1.468490 (0.292443) \| 0.475171 / 4.584777 (-4.109606) \| 3.523366 / 3.745712 (-0.222346) \| 3.167322 / 5.269862 (-2.102540) \| 1.975164 / 4.565676 (-2.590513) \| 0.056106 / 0.424275 (-0.368169) \| 0.007448 / 0.007607 (-0.000159) \| 0.459824 / 0.226044 (0.233779) \| 4.590566 / 2.268929 (2.321638) \| 2.377968 / 55.444624 (-53.066656) \| 2.034052 / 6.876477 (-4.842425) \| 2.224976 / 2.142072 (0.082904) \| 0.575901 / 4.805227 (-4.229326) \| 0.131546 / 6.500664 (-6.369118) \| 0.059266 / 0.075469 (-0.016203) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.254783 / 1.841788 (-0.587005) \| 19.497795 / 8.074308 (11.423487) \| 13.937672 / 10.191392 (3.746280) \| 0.164092 / 0.680424 (-0.516332) \| 0.017915 / 0.534201 (-0.516286) \| 0.391430 / 0.579283 (-0.187853) \| 0.403681 / 0.434364 (-0.030683) \| 0.457711 / 0.540337 (-0.082626) \| 0.620395 / 1.386936 (-0.766541) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006793 / 0.011353 (-0.004560) \| 0.004101 / 0.011008 (-0.006907) \| 0.064780 / 0.038508 (0.026272) \| 0.071087 / 0.023109 (0.047977) \| 0.401963 / 0.275898 (0.126065) \| 0.433085 / 0.323480 (0.109605) \| 0.005348 / 0.007986 (-0.002638) \| 0.003289 / 0.004328 (-0.001039) \| 0.065209 / 0.004250 (0.060958) \| 0.054202 / 0.037052 (0.017150) \| 0.405629 / 0.258489 (0.147140) \| 0.440326 / 0.293841 (0.146485) \| 0.032283 / 0.128546 (-0.096263) \| 0.008510 / 0.075646 (-0.067137) \| 0.071144 / 0.419271 (-0.348127) \| 0.047414 / 0.043533 (0.003881) \| 0.402065 / 0.255139 (0.146926) \| 0.421217 / 0.283200 (0.138017) \| 0.021924 / 0.141683 (-0.119759) \| 1.490067 / 1.452155 (0.037913) \| 1.539134 / 1.492716 (0.046417) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280072 / 0.018006 (0.262066) \| 0.456130 / 0.000490 (0.455641) \| 0.020926 / 0.000200 (0.020726) \| 0.000107 / 0.000054 (0.000053) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032040 / 0.037411 (-0.005371) \| 0.092343 / 0.014526 (0.077817) \| 0.104866 / 0.176557 (-0.071690) \| 0.156631 / 0.737135 (-0.580505) \| 0.107203 / 0.296338 (-0.189136) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.426268 / 0.215209 (0.211059) \| 4.255539 / 2.077655 (2.177884) \| 2.285077 / 1.504120 (0.780957) \| 2.114277 / 1.541195 (0.573083) \| 2.159242 / 1.468490 (0.690752) \| 0.489421 / 4.584777 (-4.095356) \| 3.630797 / 3.745712 (-0.114915) \| 3.205238 / 5.269862 (-2.064624) \| 1.985846 / 4.565676 (-2.579830) \| 0.057436 / 0.424275 (-0.366839) \| 0.007154 / 0.007607 (-0.000454) \| 0.507294 / 0.226044 (0.281250) \| 5.050105 / 2.268929 (2.781176) \| 2.750474 / 55.444624 (-52.694151) \| 2.404116 / 6.876477 (-4.472360) \| 2.576483 / 2.142072 (0.434411) \| 0.584909 / 4.805227 (-4.220318) \| 0.130695 / 6.500664 (-6.369969) \| 0.059743 / 0.075469 (-0.015726) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.352702 / 1.841788 (-0.489086) \| 19.687944 / 8.074308 (11.613636) \| 14.991847 / 10.191392 (4.800455) \| 0.185164 / 0.680424 (-0.495260) \| 0.020314 / 0.534201 (-0.513887) \| 0.395162 / 0.579283 (-0.184121) \| 0.408917 / 0.434364 (-0.025447) \| 0.467049 / 0.540337 (-0.073288) \| 0.649209 / 1.386936 (-0.737727) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#885518608ceab83b7ed8ceba7a0b72bc68096026 \"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) \| 0.006142 / 0.011353 (-0.005211) \| 0.003621 / 0.011008 (-0.007387) \| 0.079880 / 0.038508 (0.041372) \| 0.059283 / 0.023109 (0.036173) \| 0.310971 / 0.275898 (0.035072) \| 0.351620 / 0.323480 (0.028140) \| 0.003453 / 0.007986 (-0.004532) \| 0.003785 / 0.004328 (-0.000543) \| 0.062395 / 0.004250 (0.058145) \| 0.047614 / 0.037052 (0.010562) \| 0.312688 / 0.258489 (0.054199) \| 0.363762 / 0.293841 (0.069921) \| 0.027051 / 0.128546 (-0.101495) \| 0.007920 / 0.075646 (-0.067726) \| 0.261080 / 0.419271 (-0.158192) \| 0.044476 / 0.043533 (0.000943) \| 0.312615 / 0.255139 (0.057476) \| 0.343672 / 0.283200 (0.060472) \| 0.022723 / 0.141683 (-0.118960) \| 1.441449 / 1.452155 (-0.010706) \| 1.509253 / 1.492716 (0.016536) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.193171 / 0.018006 (0.175165) \| 0.434771 / 0.000490 (0.434281) \| 0.003114 / 0.000200 (0.002914) \| 0.000065 / 0.000054 (0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024209 / 0.037411 (-0.013203) \| 0.073891 / 0.014526 (0.059365) \| 0.083497 / 0.176557 (-0.093060) \| 0.144962 / 0.737135 (-0.592173) \| 0.084594 / 0.296338 (-0.211745) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.392512 / 0.215209 (0.177303) \| 3.912692 / 2.077655 (1.835037) \| 1.914010 / 1.504120 (0.409890) \| 1.743827 / 1.541195 (0.202632) \| 1.829244 / 1.468490 (0.360753) \| 0.497740 / 4.584777 (-4.087037) \| 2.979222 / 3.745712 (-0.766490) \| 2.849786 / 5.269862 (-2.420076) \| 1.874411 / 4.565676 (-2.691265) \| 0.057270 / 0.424275 (-0.367005) \| 0.006673 / 0.007607 (-0.000934) \| 0.460724 / 0.226044 (0.234679) \| 4.600617 / 2.268929 (2.331689) \| 2.333178 / 55.444624 (-53.111446) \| 1.999902 / 6.876477 (-4.876575) \| 2.170600 / 2.142072 (0.028528) \| 0.587716 / 4.805227 (-4.217511) \| 0.126374 / 6.500664 (-6.374290) \| 0.061926 / 0.075469 (-0.013543) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.229767 / 1.841788 (-0.612021) \| 18.494462 / 8.074308 (10.420154) \| 13.799801 / 10.191392 (3.608409) \| 0.137952 / 0.680424 (-0.542472) \| 0.017037 / 0.534201 (-0.517164) \| 0.333252 / 0.579283 (-0.246031) \| 0.357276 / 0.434364 (-0.077088) \| 0.380069 / 0.540337 (-0.160268) \| 0.526968 / 1.386936 (-0.859968) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006185 / 0.011353 (-0.005168) \| 0.003595 / 0.011008 (-0.007413) \| 0.063371 / 0.038508 (0.024863) \| 0.060461 / 0.023109 (0.037351) \| 0.455016 / 0.275898 (0.179118) \| 0.490505 / 0.323480 (0.167026) \| 0.004738 / 0.007986 (-0.003247) \| 0.002852 / 0.004328 (-0.001477) \| 0.064161 / 0.004250 (0.059910) \| 0.047411 / 0.037052 (0.010359) \| 0.453815 / 0.258489 (0.195326) \| 0.485354 / 0.293841 (0.191513) \| 0.028358 / 0.128546 (-0.100188) \| 0.008101 / 0.075646 (-0.067545) \| 0.068399 / 0.419271 (-0.350873) \| 0.040928 / 0.043533 (-0.002605) \| 0.462263 / 0.255139 (0.207124) \| 0.478773 / 0.283200 (0.195574) \| 0.019787 / 0.141683 (-0.121896) \| 1.475798 / 1.452155 (0.023643) \| 1.563890 / 1.492716 (0.071174) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.239701 / 0.018006 (0.221695) \| 0.417442 / 0.000490 (0.416953) \| 0.005895 / 0.000200 (0.005695) \| 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) \| 0.026155 / 0.037411 (-0.011256) \| 0.081264 / 0.014526 (0.066738) \| 0.089734 / 0.176557 (-0.086822) \| 0.143965 / 0.737135 (-0.593171) \| 0.092156 / 0.296338 (-0.204182) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.456420 / 0.215209 (0.241211) \| 4.545675 / 2.077655 (2.468020) \| 2.477141 / 1.504120 (0.973022) \| 2.295142 / 1.541195 (0.753947) \| 2.349525 / 1.468490 (0.881035) \| 0.502485 / 4.584777 (-4.082292) \| 3.072347 / 3.745712 (-0.673365) \| 2.798565 / 5.269862 (-2.471296) \| 1.849030 / 4.565676 (-2.716647) \| 0.057789 / 0.424275 (-0.366487) \| 0.006436 / 0.007607 (-0.001172) \| 0.529648 / 0.226044 (0.303604) \| 5.285670 / 2.268929 (3.016741) \| 2.954964 / 55.444624 (-52.489660) \| 2.593161 / 6.876477 (-4.283316) \| 2.735254 / 2.142072 (0.593181) \| 0.587635 / 4.805227 (-4.217592) \| 0.124732 / 6.500664 (-6.375932) \| 0.060999 / 0.075469 (-0.014470) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.354957 / 1.841788 (-0.486831) \| 18.803998 / 8.074308 (10.729690) \| 14.902712 / 10.191392 (4.711320) \| 0.146729 / 0.680424 (-0.533695) \| 0.017989 / 0.534201 (-0.516212) \| 0.333633 / 0.579283 (-0.245650) \| 0.347685 / 0.434364 (-0.086679) \| 0.386497 / 0.540337 (-0.153840) \| 0.590885 / 1.386936 (-0.796051) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#392d8a46f4da066408785281d9b87760f7273254 \"CML watermark\")\n" ]
1,864,436,408	6,176	how to limit the size of memory mapped file?	open	2023-08-24T05:33:45	2023-10-11T06:00:10	null	https://github.com/huggingface/datasets/issues/6176	null	williamium3000	false	[ "Hi! Can you share the error this reproducer throws in your environment? `streaming=True` streams the dataset as it's iterated over without creating a memory-map file.", "The trace of the error. Streaming works but is slower.\r\n```\r\nRoot Cause (first observed failure):\r\n[0]:\r\n time : 2023-08-24_06:06:01\r\n host : compute-126.cm.cluster\r\n rank : 0 (local_rank: 0)\r\n exitcode : 1 (pid: 48442)\r\n error_file: /tmp/torchelastic_4fqzcuuz/none_rx2470jl/attempt_0/0/error.json\r\n traceback : Traceback (most recent call last):\r\n File \"/users/yli7/.conda/envs/pytorch2.0/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py\", line 346, in wrapper\r\n return f(args, *kwargs)\r\n File \"Pretrain.py\", line 214, in main\r\n pair_dataset, c4_dataset = create_dataset('pretrain', config)\r\n File \"/dcs05/qiao/data/william/project/DaVinci/dataset/__init__.py\", line 109, in create_dataset\r\n c4_dataset = load_dataset(\"c4\", \"en\", split=\"train\").to_iterable_dataset(num_shards=1024).map(pre_caption_huggingface)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/load.py\", line 1810, in load_dataset\r\n ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py\", line 1145, in as_dataset\r\n datasets = map_nested(\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 436, in map_nested\r\n return function(data_struct)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py\", line 1175, in _build_single_dataset\r\n ds = self._as_dataset(\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py\", line 1246, in _as_dataset\r\n dataset_kwargs = ArrowReader(cache_dir, self.info).read(\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 244, in read\r\n return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 265, in read_files\r\n pa_table = self._read_files(files, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 200, in _read_files\r\n pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 336, in _get_table_from_filename\r\n table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 357, in read_table\r\n return table_cls.from_file(filename)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py\", line 1059, in from_file\r\n table = _memory_mapped_arrow_table_from_file(filename)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py\", line 65, in _memory_mapped_arrow_table_from_file\r\n opened_stream = _memory_mapped_record_batch_reader_from_file(filename)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py\", line 50, in _memory_mapped_record_batch_reader_from_file\r\n memory_mapped_stream = pa.memory_map(filename)\r\n File \"pyarrow/io.pxi\", line 1009, in pyarrow.lib.memory_map\r\n File \"pyarrow/io.pxi\", line 956, in pyarrow.lib.MemoryMappedFile._open\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 115, in pyarrow.lib.check_status\r\n OSError: Memory mapping file failed: Cannot allocate memory\r\n```", "This issue has previously been reported here: https://github.com/huggingface/datasets/issues/5710. Reporting it in the Arrow repo makes more sense as they have control over memory mapping.\r\n\r\nPS: this is the API to reduce the size of the generated Arrow file:\r\n```python\r\nfrom datasets import load_dataset\r\nbuilder = load_dataset_builder(\"c4\", \"en\")\r\nbuilder.download_and_prepare(max_shard_size=\"5GB\")\r\ndataset = builder.as_dataset()\r\n```\r\n\r\nIf this resolves the issue, we can consider exposing `max_shard_size` in `load_dataset`.", "Thanks for the response. The problem seems not resolved. The memory I allocated to the environment is 64G and the following error still occurs\r\n`Python 3.8.16 (default, Jun 12 2023, 18:09:05) \r\n[GCC 11.2.0] :: Anaconda, Inc. on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> from datasets import load_dataset_builder\r\n>>> builder = load_dataset_builder(\"c4\", \"en\")\r\n>>> builder.download_and_prepare(max_shard_size=\"5GB\")\r\nFound cached dataset c4 (/users/yli7/.cache/huggingface/datasets/c4/en/0.0.0/df532b158939272d032cc63ef19cd5b83e9b4d00c922b833e4cb18b2e9869b01)\r\n>>> dataset = builder.as_dataset()\r\n 0%\| \| 0/2 [00:00<?, ?it/s]Traceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py\", line 1145, in as_dataset\r\n datasets = map_nested(\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 444, in map_nested\r\n mapped = [\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 445, in <listcomp>\r\n _single_map_nested((function, obj, types, None, True, None))\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 347, in _single_map_nested\r\n return function(data_struct)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py\", line 1175, in _build_single_dataset\r\n ds = self._as_dataset(\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/builder.py\", line 1246, in _as_dataset\r\n dataset_kwargs = ArrowReader(cache_dir, self.info).read(\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 244, in read\r\n return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 265, in read_files\r\n pa_table = self._read_files(files, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 200, in _read_files\r\n pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 336, in _get_table_from_filename\r\n table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 357, in read_table\r\n return table_cls.from_file(filename)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py\", line 1059, in from_file\r\n table = _memory_mapped_arrow_table_from_file(filename)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py\", line 65, in _memory_mapped_arrow_table_from_file\r\n opened_stream = _memory_mapped_record_batch_reader_from_file(filename)\r\n File \"/users/yli7/.local/lib/python3.8/site-packages/datasets/table.py\", line 50, in _memory_mapped_record_batch_reader_from_file\r\n memory_mapped_stream = pa.memory_map(filename)\r\n File \"pyarrow/io.pxi\", line 1009, in pyarrow.lib.memory_map\r\n File \"pyarrow/io.pxi\", line 956, in pyarrow.lib.MemoryMappedFile._open\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 115, in pyarrow.lib.check_status\r\nOSError: Memory mapping file failed: Cannot allocate memory`", "Have you solved the problem?", "Nope. Streaming works but is slower." ]
1,863,592,678	6,175	PyArrow 13 CI fixes	closed	2023-08-23T15:45:53	2023-08-25T13:15:59	2023-08-25T13:06:52	https://github.com/huggingface/datasets/pull/6175	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6175", "html_url": "https://github.com/huggingface/datasets/pull/6175", "diff_url": "https://github.com/huggingface/datasets/pull/6175.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6175.patch", "merged_at": "2023-08-25T13:06:52" }	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) \| 0.006095 / 0.011353 (-0.005258) \| 0.003580 / 0.011008 (-0.007429) \| 0.080146 / 0.038508 (0.041638) \| 0.063445 / 0.023109 (0.040336) \| 0.321930 / 0.275898 (0.046032) \| 0.397933 / 0.323480 (0.074453) \| 0.003455 / 0.007986 (-0.004531) \| 0.002856 / 0.004328 (-0.001472) \| 0.062938 / 0.004250 (0.058687) \| 0.048896 / 0.037052 (0.011843) \| 0.333070 / 0.258489 (0.074581) \| 0.404485 / 0.293841 (0.110644) \| 0.027156 / 0.128546 (-0.101390) \| 0.007974 / 0.075646 (-0.067672) \| 0.261505 / 0.419271 (-0.157766) \| 0.045328 / 0.043533 (0.001795) \| 0.311203 / 0.255139 (0.056064) \| 0.390006 / 0.283200 (0.106806) \| 0.023650 / 0.141683 (-0.118033) \| 1.468856 / 1.452155 (0.016701) \| 1.503867 / 1.492716 (0.011151) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.202110 / 0.018006 (0.184103) \| 0.436433 / 0.000490 (0.435944) \| 0.002278 / 0.000200 (0.002078) \| 0.000070 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024575 / 0.037411 (-0.012836) \| 0.073005 / 0.014526 (0.058479) \| 0.083609 / 0.176557 (-0.092947) \| 0.144881 / 0.737135 (-0.592254) \| 0.083495 / 0.296338 (-0.212844) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.398911 / 0.215209 (0.183702) \| 3.994035 / 2.077655 (1.916381) \| 2.056768 / 1.504120 (0.552649) \| 1.913242 / 1.541195 (0.372047) \| 1.932934 / 1.468490 (0.464444) \| 0.498953 / 4.584777 (-4.085824) \| 3.031107 / 3.745712 (-0.714605) \| 2.817165 / 5.269862 (-2.452696) \| 1.858886 / 4.565676 (-2.706790) \| 0.056977 / 0.424275 (-0.367299) \| 0.006634 / 0.007607 (-0.000973) \| 0.472580 / 0.226044 (0.246536) \| 4.738301 / 2.268929 (2.469372) \| 2.373938 / 55.444624 (-53.070686) \| 2.021057 / 6.876477 (-4.855420) \| 2.195419 / 2.142072 (0.053346) \| 0.585182 / 4.805227 (-4.220045) \| 0.124260 / 6.500664 (-6.376405) \| 0.060250 / 0.075469 (-0.015219) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.227350 / 1.841788 (-0.614438) \| 18.496525 / 8.074308 (10.422216) \| 13.946658 / 10.191392 (3.755266) \| 0.140024 / 0.680424 (-0.540399) \| 0.017077 / 0.534201 (-0.517124) \| 0.334415 / 0.579283 (-0.244868) \| 0.351118 / 0.434364 (-0.083246) \| 0.379556 / 0.540337 (-0.160782) \| 0.525064 / 1.386936 (-0.861872) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006176 / 0.011353 (-0.005177) \| 0.003648 / 0.011008 (-0.007360) \| 0.063461 / 0.038508 (0.024953) \| 0.062770 / 0.023109 (0.039660) \| 0.448786 / 0.275898 (0.172888) \| 0.486490 / 0.323480 (0.163010) \| 0.005527 / 0.007986 (-0.002458) \| 0.002860 / 0.004328 (-0.001469) \| 0.063803 / 0.004250 (0.059553) \| 0.049657 / 0.037052 (0.012604) \| 0.449625 / 0.258489 (0.191136) \| 0.489378 / 0.293841 (0.195537) \| 0.028406 / 0.128546 (-0.100140) \| 0.008062 / 0.075646 (-0.067584) \| 0.068417 / 0.419271 (-0.350854) \| 0.040854 / 0.043533 (-0.002678) \| 0.461670 / 0.255139 (0.206531) \| 0.481622 / 0.283200 (0.198423) \| 0.021018 / 0.141683 (-0.120665) \| 1.450328 / 1.452155 (-0.001826) \| 1.501283 / 1.492716 (0.008567) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.269824 / 0.018006 (0.251817) \| 0.412296 / 0.000490 (0.411807) \| 0.039582 / 0.000200 (0.039382) \| 0.000266 / 0.000054 (0.000211) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026436 / 0.037411 (-0.010976) \| 0.080633 / 0.014526 (0.066107) \| 0.089786 / 0.176557 (-0.086770) \| 0.145020 / 0.737135 (-0.592115) \| 0.092327 / 0.296338 (-0.204012) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.464349 / 0.215209 (0.249140) \| 4.630631 / 2.077655 (2.552976) \| 2.560527 / 1.504120 (1.056407) \| 2.374195 / 1.541195 (0.833000) \| 2.424774 / 1.468490 (0.956284) \| 0.510428 / 4.584777 (-4.074349) \| 3.099805 / 3.745712 (-0.645907) \| 2.781096 / 5.269862 (-2.488765) \| 1.854276 / 4.565676 (-2.711400) \| 0.058102 / 0.424275 (-0.366173) \| 0.006365 / 0.007607 (-0.001242) \| 0.534082 / 0.226044 (0.308038) \| 5.355003 / 2.268929 (3.086074) \| 3.012546 / 55.444624 (-52.432078) \| 2.665222 / 6.876477 (-4.211255) \| 2.821014 / 2.142072 (0.678942) \| 0.597733 / 4.805227 (-4.207494) \| 0.125433 / 6.500664 (-6.375231) \| 0.060802 / 0.075469 (-0.014667) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.345699 / 1.841788 (-0.496088) \| 18.836083 / 8.074308 (10.761774) \| 14.895458 / 10.191392 (4.704066) \| 0.146843 / 0.680424 (-0.533581) \| 0.018082 / 0.534201 (-0.516119) \| 0.335729 / 0.579283 (-0.243554) \| 0.351013 / 0.434364 (-0.083351) \| 0.388435 / 0.540337 (-0.151902) \| 0.543826 / 1.386936 (-0.843110) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d0c7e8c4808a1fb6ee7234b4caa25aa9fcfdc88f \"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) \| 0.006593 / 0.011353 (-0.004760) \| 0.004089 / 0.011008 (-0.006919) \| 0.084753 / 0.038508 (0.046245) \| 0.079899 / 0.023109 (0.056790) \| 0.311528 / 0.275898 (0.035630) \| 0.349722 / 0.323480 (0.026243) \| 0.004288 / 0.007986 (-0.003698) \| 0.004552 / 0.004328 (0.000224) \| 0.065896 / 0.004250 (0.061646) \| 0.053813 / 0.037052 (0.016760) \| 0.316958 / 0.258489 (0.058469) \| 0.367011 / 0.293841 (0.073170) \| 0.031082 / 0.128546 (-0.097464) \| 0.008684 / 0.075646 (-0.066963) \| 0.288003 / 0.419271 (-0.131268) \| 0.052560 / 0.043533 (0.009027) \| 0.305589 / 0.255139 (0.050450) \| 0.349656 / 0.283200 (0.066457) \| 0.023857 / 0.141683 (-0.117826) \| 1.462360 / 1.452155 (0.010205) \| 1.568170 / 1.492716 (0.075454) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.272342 / 0.018006 (0.254336) \| 0.585108 / 0.000490 (0.584618) \| 0.003427 / 0.000200 (0.003227) \| 0.000078 / 0.000054 (0.000023) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030347 / 0.037411 (-0.007064) \| 0.086325 / 0.014526 (0.071799) \| 0.100958 / 0.176557 (-0.075598) \| 0.156534 / 0.737135 (-0.580601) \| 0.102506 / 0.296338 (-0.193832) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.406625 / 0.215209 (0.191416) \| 4.065957 / 2.077655 (1.988302) \| 2.075867 / 1.504120 (0.571747) \| 1.914390 / 1.541195 (0.373196) \| 2.013321 / 1.468490 (0.544831) \| 0.486832 / 4.584777 (-4.097945) \| 3.545940 / 3.745712 (-0.199772) \| 3.323226 / 5.269862 (-1.946635) \| 2.067742 / 4.565676 (-2.497934) \| 0.057884 / 0.424275 (-0.366391) \| 0.007751 / 0.007607 (0.000144) \| 0.484923 / 0.226044 (0.258878) \| 4.844885 / 2.268929 (2.575956) \| 2.569828 / 55.444624 (-52.874796) \| 2.224058 / 6.876477 (-4.652419) \| 2.485587 / 2.142072 (0.343515) \| 0.584311 / 4.805227 (-4.220916) \| 0.134984 / 6.500664 (-6.365680) \| 0.062164 / 0.075469 (-0.013305) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.247182 / 1.841788 (-0.594605) \| 20.107500 / 8.074308 (12.033192) \| 14.194444 / 10.191392 (4.003052) \| 0.147134 / 0.680424 (-0.533290) \| 0.018062 / 0.534201 (-0.516138) \| 0.392029 / 0.579283 (-0.187254) \| 0.402991 / 0.434364 (-0.031373) \| 0.457600 / 0.540337 (-0.082737) \| 0.632553 / 1.386936 (-0.754383) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006920 / 0.011353 (-0.004433) \| 0.004257 / 0.011008 (-0.006751) \| 0.065233 / 0.038508 (0.026725) \| 0.078151 / 0.023109 (0.055042) \| 0.389141 / 0.275898 (0.113243) \| 0.431518 / 0.323480 (0.108038) \| 0.005752 / 0.007986 (-0.002234) \| 0.003584 / 0.004328 (-0.000745) \| 0.065173 / 0.004250 (0.060922) \| 0.059113 / 0.037052 (0.022060) \| 0.398225 / 0.258489 (0.139736) \| 0.430980 / 0.293841 (0.137139) \| 0.032802 / 0.128546 (-0.095744) \| 0.008702 / 0.075646 (-0.066945) \| 0.071345 / 0.419271 (-0.347926) \| 0.048269 / 0.043533 (0.004736) \| 0.389264 / 0.255139 (0.134125) \| 0.416008 / 0.283200 (0.132809) \| 0.024845 / 0.141683 (-0.116838) \| 1.499100 / 1.452155 (0.046945) \| 1.576397 / 1.492716 (0.083681) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296674 / 0.018006 (0.278668) \| 0.540108 / 0.000490 (0.539619) \| 0.004293 / 0.000200 (0.004093) \| 0.000151 / 0.000054 (0.000096) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034108 / 0.037411 (-0.003303) \| 0.092747 / 0.014526 (0.078221) \| 0.112203 / 0.176557 (-0.064354) \| 0.162728 / 0.737135 (-0.574407) \| 0.109955 / 0.296338 (-0.186383) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.432006 / 0.215209 (0.216797) \| 4.297591 / 2.077655 (2.219937) \| 2.379645 / 1.504120 (0.875525) \| 2.218680 / 1.541195 (0.677485) \| 2.314608 / 1.468490 (0.846117) \| 0.495562 / 4.584777 (-4.089215) \| 3.589787 / 3.745712 (-0.155925) \| 3.349593 / 5.269862 (-1.920268) \| 2.119893 / 4.565676 (-2.445783) \| 0.057976 / 0.424275 (-0.366299) \| 0.007612 / 0.007607 (0.000005) \| 0.509422 / 0.226044 (0.283378) \| 5.101444 / 2.268929 (2.832515) \| 2.794532 / 55.444624 (-52.650092) \| 2.459033 / 6.876477 (-4.417444) \| 2.714424 / 2.142072 (0.572352) \| 0.588444 / 4.805227 (-4.216784) \| 0.135763 / 6.500664 (-6.364901) \| 0.062593 / 0.075469 (-0.012876) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.361415 / 1.841788 (-0.480372) \| 20.940684 / 8.074308 (12.866376) \| 15.161364 / 10.191392 (4.969972) \| 0.154243 / 0.680424 (-0.526181) \| 0.020305 / 0.534201 (-0.513896) \| 0.397438 / 0.579283 (-0.181845) \| 0.415047 / 0.434364 (-0.019317) \| 0.473250 / 0.540337 (-0.067088) \| 0.740681 / 1.386936 (-0.646255) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6e84937af4f24194bf61f09244ebef6528fb7c4c \"CML watermark\")\n" ]
1,863,422,065	6,173	Fix CI for pyarrow 13.0.0	closed	2023-08-23T14:11:20	2023-08-25T13:06:53	2023-08-25T13:06:53	https://github.com/huggingface/datasets/issues/6173	null	lhoestq	false	[]
1,863,318,027	6,172	Make Dataset streaming queries retryable	open	2023-08-23T13:15:38	2023-11-06T13:54:16	null	https://github.com/huggingface/datasets/issues/6172	null	rojagtap	false	[ "Hi! The streaming mode also retries requests - `datasets.config.STREAMING_READ_MAX_RETRIES` (20 sec by default) controls the number of retries and `datasets.config.STREAMING_READ_RETRY_INTERVAL` (5 sec) the sleep time between retries.\r\n\r\n> At step 1800 I got a 504 HTTP status code error from Huggingface hub for my pytorch dataloader\r\n\r\nA minor Hub outage that we experienced yesterday could be the cause.", "I wanted something similar. I have a huge dataset I want to process (laion-2b), but after processing several batches, it sometimes fails with this error: `HTTP 502 Bad Gateway for url`. I had the following code to handle it but this way I believe it restarts processing the data from the first batch? How can I set the attribute values you mention above?\r\n\r\n```\r\niterable_dataset = load_dataset(\"laion/laion2B-multi\", streaming=True, split='train')\r\ndataloader = DataLoader(iterable_dataset, batch_size=131072, collate_fn=custom_collate_fn, num_workers=8)\r\n\r\nMAX_RETRIES = 5\r\nRETRY_WAIT = 10 # wait 10 seconds before retry\r\n\r\n for retry in range(MAX_RETRIES):\r\n try:\r\n for j, batch in enumerate(dataloader):\r\n < process batch>\r\n\r\n except HfHubHTTPError as e:\r\n if \"502\" in str(e) and retry < MAX_RETRIES - 1:\r\n logging.warning(f\"Encountered a 502 error on batch {j}. Waiting for {RETRY_WAIT} seconds before retrying.\")\r\n time.sleep(RETRY_WAIT)\r\n continue\r\n else:\r\n raise", "Hey all! Wondering if there's a way of making Datasets streaming mode somewhat robust to Hub outages? Over the weekend, I got two quite cryptic errors, which I reckon were probably from Hub issues:\r\n\r\n<details>\r\n<summary> Stack Trace 1 </summary>\r\n\r\n```\r\n File \"/home/sanchitgandhi/small-12-4-tpu-timestamped-prob-0.2/run_distillation.py\", line 2119, in <module>\r\n main()\r\n File \"/home/sanchitgandhi/small-12-4-tpu-timestamped-prob-0.2/run_distillation.py\", line 1954, in main\r\n for batch in train_loader:\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 630, in __next__\r\n data = self._next_data()\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 1325, in _next_data\r\n return self._process_data(data)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 1371, in _process_data\r\n data.reraise()\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/_utils.py\", line 694, in reraise\r\n raise exception\r\nConnectionError: Caught ConnectionError in DataLoader worker process 8.\r\nOriginal Traceback (most recent call last):\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/aiohttp/connector.py\", line 1155, in _create_direct_connection\r\n hosts = await asyncio.shield(host_resolved)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/aiohttp/connector.py\", line 874, in _resolve_host\r\n addrs = await self._resolver.resolve(host, port, family=self._family)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/aiohttp/resolver.py\", line 33, in resolve\r\n infos = await self._loop.getaddrinfo(\r\n File \"/usr/lib/python3.10/asyncio/base_events.py\", line 863, in getaddrinfo\r\n return await self.run_in_executor(\r\n File \"/usr/lib/python3.10/concurrent/futures/thread.py\", line 58, in run\r\n result = self.fn(self.args, self.kwargs)\r\n File \"/usr/lib/python3.10/socket.py\", line 955, in getaddrinfo\r\n for res in _socket.getaddrinfo(host, port, family, type, proto, flags):\r\nsocket.gaierror: [Errno -3] Temporary failure in name resolution\r\nThe above exception was the direct cause of the following exception:\r\nTraceback (most recent call last):\r\n File \"/home/sanchitgandhi/datasets/src/datasets/download/streaming_download_manager.py\", line 333, in read_with_retries\r\n out = read(args, *kwargs)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 612, in read\r\n return super().read(length)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/spec.py\", line 1856, in read\r\n out = self.cache._fetch(self.loc, self.loc + length)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/caching.py\", line 439, in _fetch\r\n new = self.fetcher(self.end, bend)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/asyn.py\", line 118, in wrapper\r\n return sync(self.loop, func, args, kwargs)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/asyn.py\", line 103, in sync\r\n raise return_result\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/asyn.py\", line 56, in _runner\r\n result[0] = await coro\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 660, in async_fetch_range\r\n r = await self.session.get(\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/aiohttp/client.py\", line 562, in _request\r\n conn = await self._connector.connect(\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/aiohttp/connector.py\", line 540, in connect\r\n proto = await self._create_connection(req, traces, timeout)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/aiohttp/connector.py\", line 901, in _create_connection\r\n _, proto = await self._create_direct_connection(req, traces, timeout)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/aiohttp/connector.py\", line 1169, in _create_direct_connection\r\n raise ClientConnectorError(req.connection_key, exc) from exc\r\naiohttp.client_exceptions.ClientConnectorError: Cannot connect to host huggingface.co:443 ssl:default [Temporary failure in name resolution]\r\nThe above exception was the direct cause of the following exception:\r\nTraceback (most recent call last):\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py\", line 308, in _worker_loop\r\n data = fetcher.fetch(index)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py\", line 32, in fetch\r\n data.append(next(self.dataset_iter))\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1358, in __iter__\r\n yield from self._iter_pytorch()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1293, in _iter_pytorch\r\n for key, example in ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 982, in __iter__\r\n for x in self.ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 862, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 899, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 862, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 899, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 678, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 740, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 862, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 899, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1114, in __iter__\r\n for key, example in self.ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 429, in __iter__\r\n if not iterators[i].hasnext():\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 106, in hasnext\r\n self._thenext = next(self.it)\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 678, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 740, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1114, in __iter__\r\n for key, example in self.ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 281, in __iter__\r\n for key, pa_table in self.generate_tables_fn(self.kwargs):\r\n File \"/home/sanchitgandhi/.cache/huggingface/modules/datasets_modules/datasets/distil-whisper--switchboard-data/9472ee64cca0e1a7e11909c7033c2354511fa62805f81a2e07616980c765abfe/switchboard-data.py\", line 247, in _generate_tables\r\n for record_batch in pf.iter_batches():\r\n File \"pyarrow/_parquet.pyx\", line 1327, in iter_batches\r\n File \"/home/sanchitgandhi/datasets/src/datasets/download/streaming_download_manager.py\", line 342, in read_with_retries\r\n raise ConnectionError(\"Server Disconnected\") from disconnect_err\r\nConnectionError: Server Disconnected\r\n```\r\n\r\n</details>\r\n\r\n<details>\r\n<summary> Stack Trace 2 </summary>\r\n\r\n```\r\n File \"/home/sanchitgandhi/small-12-2-tpu-v3-timestamped-prob-0.2-bs-512/run_distillation.py\", line 2119, in <module>\r\n main()\r\n File \"/home/sanchitgandhi/small-12-2-tpu-v3-timestamped-prob-0.2-bs-512/run_distillation.py\", line 1954, in main\r\n for batch in train_loader:\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 630, in __next__\r\n data = self._next_data()\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 1325, in _next_data\r\n return self._process_data(data)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 1371, in _process_data\r\n data.reraise()\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/_utils.py\", line 694, in reraise\r\n raise exception\r\nrequests.exceptions.ConnectionError: Caught ConnectionError in DataLoader worker process 13.\r\nOriginal Traceback (most recent call last):\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/connectionpool.py\", line 791, in urlopen\r\n response = self._make_request(\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/connectionpool.py\", line 537, in _make_request\r\n response = conn.getresponse()\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/connection.py\", line 461, in getresponse\r\n httplib_response = super().getresponse()\r\n File \"/usr/lib/python3.10/http/client.py\", line 1375, in getresponse\r\n response.begin()\r\n File \"/usr/lib/python3.10/http/client.py\", line 318, in begin\r\n version, status, reason = self._read_status()\r\n File \"/usr/lib/python3.10/http/client.py\", line 287, in _read_status\r\n raise RemoteDisconnected(\"Remote end closed connection without\"\r\nhttp.client.RemoteDisconnected: Remote end closed connection without response\r\nDuring handling of the above exception, another exception occurred:\r\nTraceback (most recent call last):\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/requests/adapters.py\", line 486, in send\r\n resp = conn.urlopen(\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/connectionpool.py\", line 845, in urlopen\r\n retries = retries.increment(\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/util/retry.py\", line 470, in increment\r\n raise reraise(type(error), error, _stacktrace)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/util/util.py\", line 38, in reraise\r\n raise value.with_traceback(tb)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/connectionpool.py\", line 791, in urlopen\r\n response = self._make_request(\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/connectionpool.py\", line 537, in _make_request\r\n response = conn.getresponse()\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/urllib3/connection.py\", line 461, in getresponse\r\n httplib_response = super().getresponse()\r\n File \"/usr/lib/python3.10/http/client.py\", line 1375, in getresponse\r\n response.begin()\r\n File \"/usr/lib/python3.10/http/client.py\", line 318, in begin\r\n version, status, reason = self._read_status()\r\n File \"/usr/lib/python3.10/http/client.py\", line 287, in _read_status\r\n raise RemoteDisconnected(\"Remote end closed connection without\"\r\nurllib3.exceptions.ProtocolError: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))\r\nDuring handling of the above exception, another exception occurred:\r\nTraceback (most recent call last):\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py\", line 308, in _worker_loop\r\n data = fetcher.fetch(index)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py\", line 32, in fetch\r\n data.append(next(self.dataset_iter))\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1358, in __iter__\r\n yield from self._iter_pytorch()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1293, in _iter_pytorch\r\n for key, example in ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 982, in __iter__\r\n for x in self.ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 862, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 899, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 862, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 899, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 678, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 740, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 862, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 899, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1114, in __iter__\r\n for key, example in self.ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 429, in __iter__\r\n if not iterators[i].hasnext():\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 106, in hasnext\r\n self._thenext = next(self.it)\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 678, in __iter__\r\n yield from self._iter()\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 740, in _iter\r\n for key, example in iterator:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1114, in __iter__\r\n for key, example in self.ex_iterable:\r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 281, in __iter__\r\n for key, pa_table in self.generate_tables_fn(*self.kwargs):\r\n File \"/home/sanchitgandhi/datasets/src/datasets/packaged_modules/parquet/parquet.py\", line 87, in _generate_tables\r\n for batch_idx, record_batch in enumerate(\r\n File \"pyarrow/_parquet.pyx\", line 1327, in iter_batches\r\n File \"/home/sanchitgandhi/datasets/src/datasets/download/streaming_download_manager.py\", line 333, in read_with_retries\r\n out = read(args, kwargs)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/spec.py\", line 1856, in read\r\n out = self.cache._fetch(self.loc, self.loc + length)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/caching.py\", line 189, in _fetch\r\n self.cache = self.fetcher(start, end) # new block replaces old\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py\", line 410, in _fetch_range\r\n r = http_backoff(\"GET\", url, headers=headers)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/huggingface_hub/utils/_http.py\", line 258, in http_backoff\r\n response = session.request(method=method, url=url, kwargs)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/requests/sessions.py\", line 589, in request\r\n resp = self.send(prep, send_kwargs)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/requests/sessions.py\", line 703, in send\r\n r = adapter.send(request, kwargs)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/huggingface_hub/utils/_http.py\", line 63, in send\r\n return super().send(request, args, *kwargs)\r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/requests/adapters.py\", line 501, in send\r\n raise ConnectionError(err, request=request)\r\nrequests.exceptions.ConnectionError: (ProtocolError('Connection aborted.', RemoteDisconnected('Remote end closed connection without response')), '(Request ID: 5fce9fc2-e22f-41c2-91af-529f13f1d611)')\r\n```\r\n\r\n</details>\r\n\r\nHaving streaming mode fail when the Hub goes down makes using it problematic for long training runs where large amounts of data is involved. However, this is the precise situation for which streaming mode is so appealing!\r\n\r\nWondering if there were a 'common' set of Hub errors that we could catch in `iterable_datasets` and prevent from crashing the script?\r\n\r\ncc @lhoestq @mariosasko ", "Errors are already caught and requests are already retried.\r\n\r\nWhat you can do is increase the number of retries before an error is raised.\r\n\r\n```python\r\nimport datasets\r\n\r\ndatasets.config.STREAMING_READ_MAX_RETRIES = 20 # default\r\ndatasets.config.STREAMING_READ_RETRY_INTERVAL = 5 # default\r\n```" ]
1,862,922,767	6,171	Fix typo in about_mapstyle_vs_iterable.mdx	closed	2023-08-23T09:21:11	2023-08-23T09:32:59	2023-08-23T09:21:19	https://github.com/huggingface/datasets/pull/6171	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6171", "html_url": "https://github.com/huggingface/datasets/pull/6171", "diff_url": "https://github.com/huggingface/datasets/pull/6171.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6171.patch", "merged_at": "2023-08-23T09:21:19" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6171). 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) \| 0.009315 / 0.011353 (-0.002038) \| 0.004931 / 0.011008 (-0.006077) \| 0.100534 / 0.038508 (0.062026) \| 0.089270 / 0.023109 (0.066161) \| 0.394995 / 0.275898 (0.119097) \| 0.440244 / 0.323480 (0.116764) \| 0.006026 / 0.007986 (-0.001959) \| 0.004252 / 0.004328 (-0.000077) \| 0.078828 / 0.004250 (0.074577) \| 0.066770 / 0.037052 (0.029718) \| 0.411152 / 0.258489 (0.152663) \| 0.445616 / 0.293841 (0.151775) \| 0.048344 / 0.128546 (-0.080203) \| 0.013700 / 0.075646 (-0.061946) \| 0.361205 / 0.419271 (-0.058066) \| 0.072085 / 0.043533 (0.028552) \| 0.399173 / 0.255139 (0.144034) \| 0.439334 / 0.283200 (0.156134) \| 0.035815 / 0.141683 (-0.105868) \| 1.779023 / 1.452155 (0.326868) \| 1.865099 / 1.492716 (0.372383) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275978 / 0.018006 (0.257972) \| 0.588850 / 0.000490 (0.588360) \| 0.004953 / 0.000200 (0.004754) \| 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) \| 0.031329 / 0.037411 (-0.006082) \| 0.095435 / 0.014526 (0.080910) \| 0.111182 / 0.176557 (-0.065375) \| 0.177692 / 0.737135 (-0.559444) \| 0.113345 / 0.296338 (-0.182993) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.577882 / 0.215209 (0.362673) \| 5.865872 / 2.077655 (3.788217) \| 2.664218 / 1.504120 (1.160098) \| 2.383354 / 1.541195 (0.842159) \| 2.336821 / 1.468490 (0.868331) \| 0.834585 / 4.584777 (-3.750192) \| 5.418720 / 3.745712 (1.673008) \| 4.551790 / 5.269862 (-0.718072) \| 2.921874 / 4.565676 (-1.643803) \| 0.095738 / 0.424275 (-0.328537) \| 0.009625 / 0.007607 (0.002018) \| 0.688317 / 0.226044 (0.462273) \| 6.831826 / 2.268929 (4.562897) \| 3.482607 / 55.444624 (-51.962017) \| 2.633482 / 6.876477 (-4.242995) \| 2.878786 / 2.142072 (0.736714) \| 0.971615 / 4.805227 (-3.833613) \| 0.208661 / 6.500664 (-6.292003) \| 0.080271 / 0.075469 (0.004802) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.661193 / 1.841788 (-0.180594) \| 24.223041 / 8.074308 (16.148733) \| 21.621791 / 10.191392 (11.430399) \| 0.243809 / 0.680424 (-0.436614) \| 0.031630 / 0.534201 (-0.502571) \| 0.501408 / 0.579283 (-0.077875) \| 0.600002 / 0.434364 (0.165638) \| 0.572066 / 0.540337 (0.031728) \| 0.791992 / 1.386936 (-0.594944) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009410 / 0.011353 (-0.001943) \| 0.005255 / 0.011008 (-0.005753) \| 0.079202 / 0.038508 (0.040693) \| 0.078973 / 0.023109 (0.055863) \| 0.557416 / 0.275898 (0.281518) \| 0.560417 / 0.323480 (0.236937) \| 0.007066 / 0.007986 (-0.000920) \| 0.004560 / 0.004328 (0.000232) \| 0.080359 / 0.004250 (0.076109) \| 0.060071 / 0.037052 (0.023019) \| 0.538441 / 0.258489 (0.279952) \| 0.592486 / 0.293841 (0.298645) \| 0.053221 / 0.128546 (-0.075325) \| 0.014056 / 0.075646 (-0.061591) \| 0.094084 / 0.419271 (-0.325188) \| 0.066721 / 0.043533 (0.023188) \| 0.521873 / 0.255139 (0.266734) \| 0.579637 / 0.283200 (0.296437) \| 0.041476 / 0.141683 (-0.100206) \| 1.829681 / 1.452155 (0.377527) \| 1.948418 / 1.492716 (0.455702) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.347594 / 0.018006 (0.329588) \| 0.606906 / 0.000490 (0.606417) \| 0.035413 / 0.000200 (0.035213) \| 0.000371 / 0.000054 (0.000317) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031987 / 0.037411 (-0.005425) \| 0.096985 / 0.014526 (0.082459) \| 0.109275 / 0.176557 (-0.067282) \| 0.175340 / 0.737135 (-0.561795) \| 0.110763 / 0.296338 (-0.185575) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.634823 / 0.215209 (0.419614) \| 6.527172 / 2.077655 (4.449517) \| 3.135709 / 1.504120 (1.631589) \| 2.634357 / 1.541195 (1.093162) \| 2.670583 / 1.468490 (1.202093) \| 0.888686 / 4.584777 (-3.696091) \| 5.382289 / 3.745712 (1.636577) \| 4.701189 / 5.269862 (-0.568673) \| 3.161290 / 4.565676 (-1.404386) \| 0.112414 / 0.424275 (-0.311861) \| 0.009443 / 0.007607 (0.001836) \| 0.774703 / 0.226044 (0.548658) \| 7.905334 / 2.268929 (5.636405) \| 3.689548 / 55.444624 (-51.755076) \| 3.087263 / 6.876477 (-3.789214) \| 3.366568 / 2.142072 (1.224496) \| 1.185951 / 4.805227 (-3.619277) \| 0.248638 / 6.500664 (-6.252026) \| 0.104598 / 0.075469 (0.029129) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.820667 / 1.841788 (-0.021120) \| 24.536703 / 8.074308 (16.462395) \| 23.083964 / 10.191392 (12.892572) \| 0.252897 / 0.680424 (-0.427527) \| 0.032954 / 0.534201 (-0.501247) \| 0.482467 / 0.579283 (-0.096816) \| 0.602247 / 0.434364 (0.167883) \| 0.600563 / 0.540337 (0.060225) \| 0.824013 / 1.386936 (-0.562923) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c07a54ed4d570c5842d7bbe467025805be16ef51 \"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) \| 0.009242 / 0.011353 (-0.002111) \| 0.005244 / 0.011008 (-0.005764) \| 0.112678 / 0.038508 (0.074170) \| 0.089176 / 0.023109 (0.066067) \| 0.405823 / 0.275898 (0.129925) \| 0.465703 / 0.323480 (0.142223) \| 0.005227 / 0.007986 (-0.002758) \| 0.004296 / 0.004328 (-0.000032) \| 0.082961 / 0.004250 (0.078711) \| 0.063144 / 0.037052 (0.026092) \| 0.422369 / 0.258489 (0.163880) \| 0.478185 / 0.293841 (0.184344) \| 0.049770 / 0.128546 (-0.078776) \| 0.016561 / 0.075646 (-0.059086) \| 0.380172 / 0.419271 (-0.039100) \| 0.068698 / 0.043533 (0.025165) \| 0.397773 / 0.255139 (0.142634) \| 0.461284 / 0.283200 (0.178084) \| 0.036907 / 0.141683 (-0.104775) \| 1.828017 / 1.452155 (0.375862) \| 2.028385 / 1.492716 (0.535669) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291245 / 0.018006 (0.273239) \| 0.605519 / 0.000490 (0.605030) \| 0.003790 / 0.000200 (0.003590) \| 0.000094 / 0.000054 (0.000040) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029269 / 0.037411 (-0.008142) \| 0.087014 / 0.014526 (0.072488) \| 0.116984 / 0.176557 (-0.059573) \| 0.170644 / 0.737135 (-0.566491) \| 0.109011 / 0.296338 (-0.187328) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.603045 / 0.215209 (0.387836) \| 6.125308 / 2.077655 (4.047653) \| 2.637127 / 1.504120 (1.133007) \| 2.468636 / 1.541195 (0.927441) \| 2.383773 / 1.468490 (0.915283) \| 0.838139 / 4.584777 (-3.746638) \| 5.355777 / 3.745712 (1.610065) \| 4.753015 / 5.269862 (-0.516846) \| 3.097486 / 4.565676 (-1.468191) \| 0.094749 / 0.424275 (-0.329526) \| 0.009040 / 0.007607 (0.001433) \| 0.699987 / 0.226044 (0.473942) \| 7.111671 / 2.268929 (4.842742) \| 3.297798 / 55.444624 (-52.146827) \| 2.614578 / 6.876477 (-4.261898) \| 2.927717 / 2.142072 (0.785645) \| 1.037292 / 4.805227 (-3.767935) \| 0.218025 / 6.500664 (-6.282639) \| 0.086306 / 0.075469 (0.010836) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.645146 / 1.841788 (-0.196642) \| 24.191875 / 8.074308 (16.117567) \| 21.844371 / 10.191392 (11.652979) \| 0.245369 / 0.680424 (-0.435055) \| 0.031776 / 0.534201 (-0.502425) \| 0.465634 / 0.579283 (-0.113649) \| 0.565498 / 0.434364 (0.131134) \| 0.497409 / 0.540337 (-0.042929) \| 0.748048 / 1.386936 (-0.638889) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009239 / 0.011353 (-0.002114) \| 0.005345 / 0.011008 (-0.005663) \| 0.072732 / 0.038508 (0.034224) \| 0.099880 / 0.023109 (0.076770) \| 0.466933 / 0.275898 (0.191035) \| 0.471730 / 0.323480 (0.148250) \| 0.006164 / 0.007986 (-0.001821) \| 0.004486 / 0.004328 (0.000158) \| 0.075475 / 0.004250 (0.071224) \| 0.068291 / 0.037052 (0.031238) \| 0.465925 / 0.258489 (0.207436) \| 0.469198 / 0.293841 (0.175357) \| 0.047304 / 0.128546 (-0.081242) \| 0.013368 / 0.075646 (-0.062278) \| 0.083563 / 0.419271 (-0.335708) \| 0.063204 / 0.043533 (0.019671) \| 0.457422 / 0.255139 (0.202283) \| 0.478793 / 0.283200 (0.195593) \| 0.036120 / 0.141683 (-0.105563) \| 1.841209 / 1.452155 (0.389054) \| 1.955984 / 1.492716 (0.463267) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.369160 / 0.018006 (0.351154) \| 0.607140 / 0.000490 (0.606650) \| 0.047253 / 0.000200 (0.047054) \| 0.000475 / 0.000054 (0.000420) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.040226 / 0.037411 (0.002815) \| 0.107361 / 0.014526 (0.092835) \| 0.122424 / 0.176557 (-0.054133) \| 0.186447 / 0.737135 (-0.550688) \| 0.127060 / 0.296338 (-0.169279) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.706737 / 0.215209 (0.491528) \| 6.791287 / 2.077655 (4.713632) \| 3.194471 / 1.504120 (1.690352) \| 2.928145 / 1.541195 (1.386950) \| 2.829078 / 1.468490 (1.360588) \| 0.929797 / 4.584777 (-3.654980) \| 5.484638 / 3.745712 (1.738926) \| 4.841570 / 5.269862 (-0.428292) \| 2.995247 / 4.565676 (-1.570430) \| 0.104709 / 0.424275 (-0.319566) \| 0.009543 / 0.007607 (0.001936) \| 0.817605 / 0.226044 (0.591561) \| 7.879234 / 2.268929 (5.610305) \| 3.838073 / 55.444624 (-51.606551) \| 3.189728 / 6.876477 (-3.686749) \| 3.483775 / 2.142072 (1.341703) \| 1.092823 / 4.805227 (-3.712404) \| 0.227660 / 6.500664 (-6.273004) \| 0.082452 / 0.075469 (0.006983) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.750413 / 1.841788 (-0.091374) \| 27.078082 / 8.074308 (19.003774) \| 23.968038 / 10.191392 (13.776646) \| 0.248065 / 0.680424 (-0.432359) \| 0.029961 / 0.534201 (-0.504240) \| 0.508630 / 0.579283 (-0.070653) \| 0.608707 / 0.434364 (0.174343) \| 0.611062 / 0.540337 (0.070725) \| 0.830797 / 1.386936 (-0.556139) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9d793220dd8cbaa099a3928c2132c94c9f7453bc \"CML watermark\")\n" ]
1,862,705,731	6,170	feat: Return the name of the currently loaded file	open	2023-08-23T07:08:17	2023-08-29T12:41:05	null	https://github.com/huggingface/datasets/pull/6170	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6170", "html_url": "https://github.com/huggingface/datasets/pull/6170", "diff_url": "https://github.com/huggingface/datasets/pull/6170.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6170.patch", "merged_at": null }	Amitesh-Patel	true	[ "Your change adds a new element in the key used to avoid duplicates when generating the examples of a dataset. I don't think it fixes the issue you're trying to solve." ]
1,862,360,199	6,169	Configurations in yaml not working	open	2023-08-23T00:13:22	2023-08-23T15:35:31	null	https://github.com/huggingface/datasets/issues/6169	null	tsor13	false	[ "Unfortunately, I cannot reproduce this behavior on my machine or Colab - the reproducer returns `['main_data', 'additional_data']` as expected.", "Thank you for looking into this, Mario. Is this on [my repository](https://huggingface.co/datasets/tsor13/test), or on another one that you have reproduced? Would you mind pointing me to it if so?", "Whoa, in colab I received the correct behavior using my dataset. It must have something to do with my local copy of `datasets` (which again just failed).\r\n\r\nI've tried uninstalling/reinstnalling to no avail", "hi @tsor13 , I haven't been able to reproduce your issue on `tsor13/test` dataset locally either. reinstalling doesn't help?" ]
1,861,867,274	6,168	Fix ArrayXD YAML conversion	closed	2023-08-22T17:02:54	2023-12-12T15:06:59	2023-12-12T15:00:43	https://github.com/huggingface/datasets/pull/6168	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6168", "html_url": "https://github.com/huggingface/datasets/pull/6168", "diff_url": "https://github.com/huggingface/datasets/pull/6168.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6168.patch", "merged_at": "2023-12-12T15:00:43" }	mariosasko	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6168). 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) \| 0.009350 / 0.011353 (-0.002003) \| 0.005658 / 0.011008 (-0.005350) \| 0.123173 / 0.038508 (0.084664) \| 0.096354 / 0.023109 (0.073244) \| 0.464398 / 0.275898 (0.188500) \| 0.544455 / 0.323480 (0.220975) \| 0.007337 / 0.007986 (-0.000648) \| 0.004424 / 0.004328 (0.000096) \| 0.089715 / 0.004250 (0.085465) \| 0.072462 / 0.037052 (0.035410) \| 0.460601 / 0.258489 (0.202112) \| 0.544384 / 0.293841 (0.250543) \| 0.052994 / 0.128546 (-0.075552) \| 0.014459 / 0.075646 (-0.061187) \| 0.464368 / 0.419271 (0.045096) \| 0.072889 / 0.043533 (0.029356) \| 0.471387 / 0.255139 (0.216248) \| 0.560982 / 0.283200 (0.277783) \| 0.041398 / 0.141683 (-0.100285) \| 1.964688 / 1.452155 (0.512533) \| 2.240727 / 1.492716 (0.748011) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.308524 / 0.018006 (0.290518) \| 0.669306 / 0.000490 (0.668816) \| 0.006644 / 0.000200 (0.006444) \| 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) \| 0.037395 / 0.037411 (-0.000016) \| 0.111303 / 0.014526 (0.096777) \| 0.158988 / 0.176557 (-0.017569) \| 0.236155 / 0.737135 (-0.500980) \| 0.134775 / 0.296338 (-0.161564) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.648830 / 0.215209 (0.433621) \| 6.614794 / 2.077655 (4.537139) \| 2.867526 / 1.504120 (1.363407) \| 2.472967 / 1.541195 (0.931772) \| 2.488419 / 1.468490 (1.019929) \| 0.915785 / 4.584777 (-3.668992) \| 6.010754 / 3.745712 (2.265042) \| 5.468873 / 5.269862 (0.199011) \| 3.446535 / 4.565676 (-1.119141) \| 0.118592 / 0.424275 (-0.305684) \| 0.012005 / 0.007607 (0.004398) \| 0.808467 / 0.226044 (0.582423) \| 8.152122 / 2.268929 (5.883193) \| 3.751282 / 55.444624 (-51.693342) \| 3.009569 / 6.876477 (-3.866908) \| 3.282613 / 2.142072 (1.140540) \| 1.152727 / 4.805227 (-3.652500) \| 0.240224 / 6.500664 (-6.260440) \| 0.097871 / 0.075469 (0.022402) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.824944 / 1.841788 (-0.016843) \| 27.840842 / 8.074308 (19.766533) \| 24.368669 / 10.191392 (14.177277) \| 0.260621 / 0.680424 (-0.419803) \| 0.033730 / 0.534201 (-0.500471) \| 0.552494 / 0.579283 (-0.026789) \| 0.666921 / 0.434364 (0.232557) \| 0.648812 / 0.540337 (0.108475) \| 0.912602 / 1.386936 (-0.474334) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.011688 / 0.011353 (0.000335) \| 0.005794 / 0.011008 (-0.005215) \| 0.093466 / 0.038508 (0.054958) \| 0.102583 / 0.023109 (0.079474) \| 0.593572 / 0.275898 (0.317674) \| 0.614351 / 0.323480 (0.290871) \| 0.007006 / 0.007986 (-0.000980) \| 0.005557 / 0.004328 (0.001229) \| 0.087779 / 0.004250 (0.083529) \| 0.072639 / 0.037052 (0.035586) \| 0.577464 / 0.258489 (0.318975) \| 0.628240 / 0.293841 (0.334399) \| 0.053876 / 0.128546 (-0.074670) \| 0.015383 / 0.075646 (-0.060263) \| 0.110633 / 0.419271 (-0.308639) \| 0.067467 / 0.043533 (0.023934) \| 0.613457 / 0.255139 (0.358318) \| 0.604939 / 0.283200 (0.321739) \| 0.041738 / 0.141683 (-0.099945) \| 1.967167 / 1.452155 (0.515012) \| 2.121009 / 1.492716 (0.628293) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.449937 / 0.018006 (0.431930) \| 0.694410 / 0.000490 (0.693921) \| 0.064051 / 0.000200 (0.063851) \| 0.000810 / 0.000054 (0.000756) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.045138 / 0.037411 (0.007727) \| 0.116831 / 0.014526 (0.102306) \| 0.131906 / 0.176557 (-0.044651) \| 0.202421 / 0.737135 (-0.534714) \| 0.132568 / 0.296338 (-0.163770) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.698046 / 0.215209 (0.482837) \| 7.112591 / 2.077655 (5.034936) \| 3.332679 / 1.504120 (1.828559) \| 2.946384 / 1.541195 (1.405189) \| 3.074484 / 1.468490 (1.605994) \| 0.970917 / 4.584777 (-3.613859) \| 6.143506 / 3.745712 (2.397794) \| 5.572496 / 5.269862 (0.302634) \| 3.602673 / 4.565676 (-0.963004) \| 0.115068 / 0.424275 (-0.309207) \| 0.009971 / 0.007607 (0.002364) \| 0.891090 / 0.226044 (0.665046) \| 8.761788 / 2.268929 (6.492859) \| 4.362685 / 55.444624 (-51.081939) \| 3.612893 / 6.876477 (-3.263583) \| 3.797948 / 2.142072 (1.655876) \| 1.202890 / 4.805227 (-3.602337) \| 0.238120 / 6.500664 (-6.262544) \| 0.095612 / 0.075469 (0.020143) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.958880 / 1.841788 (0.117092) \| 28.216454 / 8.074308 (20.142146) \| 25.361424 / 10.191392 (15.170032) \| 0.308203 / 0.680424 (-0.372221) \| 0.032903 / 0.534201 (-0.501298) \| 0.539714 / 0.579283 (-0.039569) \| 0.688278 / 0.434364 (0.253914) \| 0.644818 / 0.540337 (0.104481) \| 0.905694 / 1.386936 (-0.481242) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a5289345e5b23548fee680a0bbc047c0b9a5ee8c \"CML watermark\")\n", "Maybe convert all the tuples by default instead of hardcoding a logic specific to ArrayXD ?", "@mariosasko Have you been able to fix this issue ? we're having quite a rough time updating our dataset lately", "@lhoestq Does it look good now?", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005519 / 0.011353 (-0.005834) \| 0.003482 / 0.011008 (-0.007527) \| 0.064904 / 0.038508 (0.026396) \| 0.052399 / 0.023109 (0.029289) \| 0.247238 / 0.275898 (-0.028660) \| 0.273426 / 0.323480 (-0.050054) \| 0.003102 / 0.007986 (-0.004884) \| 0.003420 / 0.004328 (-0.000908) \| 0.048029 / 0.004250 (0.043779) \| 0.039378 / 0.037052 (0.002326) \| 0.253809 / 0.258489 (-0.004680) \| 0.287483 / 0.293841 (-0.006358) \| 0.028096 / 0.128546 (-0.100450) \| 0.010806 / 0.075646 (-0.064841) \| 0.207799 / 0.419271 (-0.211472) \| 0.035861 / 0.043533 (-0.007672) \| 0.251912 / 0.255139 (-0.003227) \| 0.278877 / 0.283200 (-0.004323) \| 0.019498 / 0.141683 (-0.122185) \| 1.104916 / 1.452155 (-0.347238) \| 1.157376 / 1.492716 (-0.335340) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093051 / 0.018006 (0.075045) \| 0.303331 / 0.000490 (0.302841) \| 0.000209 / 0.000200 (0.000009) \| 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) \| 0.018052 / 0.037411 (-0.019359) \| 0.060597 / 0.014526 (0.046071) \| 0.074033 / 0.176557 (-0.102524) \| 0.120966 / 0.737135 (-0.616169) \| 0.075012 / 0.296338 (-0.221326) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283922 / 0.215209 (0.068713) \| 2.805445 / 2.077655 (0.727791) \| 1.498292 / 1.504120 (-0.005828) \| 1.371686 / 1.541195 (-0.169509) \| 1.402074 / 1.468490 (-0.066416) \| 0.567231 / 4.584777 (-4.017546) \| 2.393291 / 3.745712 (-1.352422) \| 2.800329 / 5.269862 (-2.469533) \| 1.789197 / 4.565676 (-2.776479) \| 0.063620 / 0.424275 (-0.360655) \| 0.005008 / 0.007607 (-0.002600) \| 0.338929 / 0.226044 (0.112884) \| 3.292122 / 2.268929 (1.023193) \| 1.813313 / 55.444624 (-53.631311) \| 1.557122 / 6.876477 (-5.319354) \| 1.576395 / 2.142072 (-0.565677) \| 0.666714 / 4.805227 (-4.138513) \| 0.118253 / 6.500664 (-6.382411) \| 0.042633 / 0.075469 (-0.032836) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.950678 / 1.841788 (-0.891110) \| 11.589806 / 8.074308 (3.515498) \| 10.436701 / 10.191392 (0.245309) \| 0.141048 / 0.680424 (-0.539376) \| 0.014766 / 0.534201 (-0.519435) \| 0.298359 / 0.579283 (-0.280924) \| 0.268850 / 0.434364 (-0.165514) \| 0.340242 / 0.540337 (-0.200095) \| 0.451447 / 1.386936 (-0.935489) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005405 / 0.011353 (-0.005948) \| 0.003545 / 0.011008 (-0.007464) \| 0.048959 / 0.038508 (0.010451) \| 0.056565 / 0.023109 (0.033455) \| 0.274289 / 0.275898 (-0.001609) \| 0.296565 / 0.323480 (-0.026915) \| 0.004790 / 0.007986 (-0.003196) \| 0.002772 / 0.004328 (-0.001557) \| 0.048605 / 0.004250 (0.044354) \| 0.040676 / 0.037052 (0.003624) \| 0.279949 / 0.258489 (0.021460) \| 0.312816 / 0.293841 (0.018976) \| 0.029605 / 0.128546 (-0.098941) \| 0.010799 / 0.075646 (-0.064848) \| 0.056941 / 0.419271 (-0.362331) \| 0.034518 / 0.043533 (-0.009014) \| 0.277193 / 0.255139 (0.022054) \| 0.292334 / 0.283200 (0.009134) \| 0.018836 / 0.141683 (-0.122847) \| 1.145228 / 1.452155 (-0.306927) \| 1.198958 / 1.492716 (-0.293758) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093618 / 0.018006 (0.075612) \| 0.303687 / 0.000490 (0.303197) \| 0.000234 / 0.000200 (0.000034) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021347 / 0.037411 (-0.016065) \| 0.067811 / 0.014526 (0.053286) \| 0.080631 / 0.176557 (-0.095926) \| 0.119289 / 0.737135 (-0.617846) \| 0.082085 / 0.296338 (-0.214254) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293374 / 0.215209 (0.078165) \| 2.864516 / 2.077655 (0.786861) \| 1.611042 / 1.504120 (0.106922) \| 1.466124 / 1.541195 (-0.075071) \| 1.480509 / 1.468490 (0.012019) \| 0.569463 / 4.584777 (-4.015314) \| 2.448181 / 3.745712 (-1.297531) \| 2.841732 / 5.269862 (-2.428130) \| 1.754458 / 4.565676 (-2.811219) \| 0.063771 / 0.424275 (-0.360505) \| 0.004976 / 0.007607 (-0.002631) \| 0.346094 / 0.226044 (0.120050) \| 3.440090 / 2.268929 (1.171162) \| 1.961862 / 55.444624 (-53.482763) \| 1.675780 / 6.876477 (-5.200697) \| 1.679676 / 2.142072 (-0.462396) \| 0.641063 / 4.805227 (-4.164164) \| 0.116268 / 6.500664 (-6.384396) \| 0.041767 / 0.075469 (-0.033702) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.980286 / 1.841788 (-0.861502) \| 12.055227 / 8.074308 (3.980919) \| 10.685417 / 10.191392 (0.494025) \| 0.140842 / 0.680424 (-0.539582) \| 0.015413 / 0.534201 (-0.518788) \| 0.286939 / 0.579283 (-0.292344) \| 0.278796 / 0.434364 (-0.155568) \| 0.326740 / 0.540337 (-0.213597) \| 0.574516 / 1.386936 (-0.812421) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53b8c2d9d3714f4a56d74637c0840a90cbfb7aa0 \"CML watermark\")\n" ]
1,861,474,327	6,167	Allow hyphen in split name	closed	2023-08-22T13:30:59	2024-01-11T06:31:31	2023-08-22T15:38:53	https://github.com/huggingface/datasets/pull/6167	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6167", "html_url": "https://github.com/huggingface/datasets/pull/6167", "diff_url": "https://github.com/huggingface/datasets/pull/6167.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6167.patch", "merged_at": null }	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) \| 0.007342 / 0.011353 (-0.004011) \| 0.004586 / 0.011008 (-0.006422) \| 0.100430 / 0.038508 (0.061922) \| 0.081053 / 0.023109 (0.057944) \| 0.368130 / 0.275898 (0.092232) \| 0.402852 / 0.323480 (0.079372) \| 0.004504 / 0.007986 (-0.003482) \| 0.003824 / 0.004328 (-0.000505) \| 0.075326 / 0.004250 (0.071076) \| 0.063329 / 0.037052 (0.026277) \| 0.372837 / 0.258489 (0.114348) \| 0.437857 / 0.293841 (0.144017) \| 0.035512 / 0.128546 (-0.093034) \| 0.009756 / 0.075646 (-0.065890) \| 0.341035 / 0.419271 (-0.078236) \| 0.060503 / 0.043533 (0.016970) \| 0.362555 / 0.255139 (0.107416) \| 0.409216 / 0.283200 (0.126017) \| 0.030093 / 0.141683 (-0.111590) \| 1.751550 / 1.452155 (0.299395) \| 1.848676 / 1.492716 (0.355959) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.229448 / 0.018006 (0.211442) \| 0.500300 / 0.000490 (0.499811) \| 0.005195 / 0.000200 (0.004995) \| 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) \| 0.031753 / 0.037411 (-0.005658) \| 0.096075 / 0.014526 (0.081549) \| 0.111476 / 0.176557 (-0.065081) \| 0.179236 / 0.737135 (-0.557899) \| 0.113599 / 0.296338 (-0.182739) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.472817 / 0.215209 (0.257608) \| 4.715029 / 2.077655 (2.637374) \| 2.417934 / 1.504120 (0.913814) \| 2.235014 / 1.541195 (0.693819) \| 2.323588 / 1.468490 (0.855098) \| 0.553751 / 4.584777 (-4.031026) \| 4.153467 / 3.745712 (0.407755) \| 3.858836 / 5.269862 (-1.411025) \| 2.377499 / 4.565676 (-2.188178) \| 0.066528 / 0.424275 (-0.357747) \| 0.008979 / 0.007607 (0.001372) \| 0.561076 / 0.226044 (0.335032) \| 5.609817 / 2.268929 (3.340888) \| 3.011098 / 55.444624 (-52.433526) \| 2.594162 / 6.876477 (-4.282314) \| 2.863597 / 2.142072 (0.721525) \| 0.681135 / 4.805227 (-4.124092) \| 0.158863 / 6.500664 (-6.341801) \| 0.072551 / 0.075469 (-0.002918) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.492230 / 1.841788 (-0.349558) \| 23.028828 / 8.074308 (14.954519) \| 16.663265 / 10.191392 (6.471873) \| 0.173146 / 0.680424 (-0.507278) \| 0.021635 / 0.534201 (-0.512566) \| 0.478919 / 0.579283 (-0.100364) \| 0.472908 / 0.434364 (0.038544) \| 0.547248 / 0.540337 (0.006910) \| 0.770288 / 1.386936 (-0.616648) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007728 / 0.011353 (-0.003625) \| 0.004477 / 0.011008 (-0.006531) \| 0.074858 / 0.038508 (0.036350) \| 0.084266 / 0.023109 (0.061157) \| 0.420280 / 0.275898 (0.144382) \| 0.466835 / 0.323480 (0.143356) \| 0.005980 / 0.007986 (-0.002006) \| 0.003600 / 0.004328 (-0.000729) \| 0.074941 / 0.004250 (0.070691) \| 0.066414 / 0.037052 (0.029361) \| 0.425949 / 0.258489 (0.167460) \| 0.473236 / 0.293841 (0.179395) \| 0.037213 / 0.128546 (-0.091333) \| 0.009743 / 0.075646 (-0.065903) \| 0.083758 / 0.419271 (-0.335513) \| 0.057916 / 0.043533 (0.014383) \| 0.423031 / 0.255139 (0.167892) \| 0.451107 / 0.283200 (0.167907) \| 0.028577 / 0.141683 (-0.113106) \| 1.810509 / 1.452155 (0.358354) \| 1.875579 / 1.492716 (0.382863) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296052 / 0.018006 (0.278046) \| 0.496618 / 0.000490 (0.496128) \| 0.028667 / 0.000200 (0.028467) \| 0.000140 / 0.000054 (0.000086) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036694 / 0.037411 (-0.000717) \| 0.110873 / 0.014526 (0.096347) \| 0.126550 / 0.176557 (-0.050007) \| 0.182924 / 0.737135 (-0.554212) \| 0.123793 / 0.296338 (-0.172545) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.509881 / 0.215209 (0.294672) \| 5.067402 / 2.077655 (2.989747) \| 2.696028 / 1.504120 (1.191908) \| 2.489861 / 1.541195 (0.948666) \| 2.563400 / 1.468490 (1.094910) \| 0.571184 / 4.584777 (-4.013593) \| 4.154231 / 3.745712 (0.408519) \| 3.891004 / 5.269862 (-1.378858) \| 2.435290 / 4.565676 (-2.130387) \| 0.065825 / 0.424275 (-0.358450) \| 0.008460 / 0.007607 (0.000853) \| 0.597579 / 0.226044 (0.371534) \| 5.914954 / 2.268929 (3.646025) \| 3.219305 / 55.444624 (-52.225319) \| 2.843548 / 6.876477 (-4.032929) \| 3.070300 / 2.142072 (0.928228) \| 0.686018 / 4.805227 (-4.119209) \| 0.160077 / 6.500664 (-6.340587) \| 0.074058 / 0.075469 (-0.001411) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.598748 / 1.841788 (-0.243039) \| 23.475685 / 8.074308 (15.401377) \| 17.257831 / 10.191392 (7.066439) \| 0.176539 / 0.680424 (-0.503885) \| 0.021969 / 0.534201 (-0.512232) \| 0.473565 / 0.579283 (-0.105718) \| 0.465471 / 0.434364 (0.031107) \| 0.567107 / 0.540337 (0.026769) \| 0.783757 / 1.386936 (-0.603179) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2f6bb450b4a3065a7d5fc50ea67711082749a337 \"CML watermark\")\n", "Note that the https://github.com/huggingface/datasets-server/ explicitly relies on the fact that a split cannot contain a hyphen. cc @lhoestq ", "We can't enable this that easily unfortunately because it could make arrow file names ambiguous in the cache.\r\n\r\ne.g. dataset_name-train-0000-of-0008.arrow", "Oh, this would indeed make the caching for the multi-proc case ambiguous. Implementing this is only worth it if we get more requests, so I'm closing this PR for now." ]
1,861,259,055	6,166	Document BUILDER_CONFIG_CLASS	closed	2023-08-22T11:27:41	2023-08-23T14:01:25	2023-08-23T13:52:36	https://github.com/huggingface/datasets/pull/6166	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6166", "html_url": "https://github.com/huggingface/datasets/pull/6166", "diff_url": "https://github.com/huggingface/datasets/pull/6166.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6166.patch", "merged_at": "2023-08-23T13:52:36" }	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) \| 0.009036 / 0.011353 (-0.002317) \| 0.004564 / 0.011008 (-0.006444) \| 0.114958 / 0.038508 (0.076449) \| 0.087329 / 0.023109 (0.064220) \| 0.440111 / 0.275898 (0.164213) \| 0.486056 / 0.323480 (0.162576) \| 0.006580 / 0.007986 (-0.001406) \| 0.004257 / 0.004328 (-0.000072) \| 0.093458 / 0.004250 (0.089208) \| 0.063380 / 0.037052 (0.026328) \| 0.469455 / 0.258489 (0.210966) \| 0.521630 / 0.293841 (0.227790) \| 0.053496 / 0.128546 (-0.075050) \| 0.013466 / 0.075646 (-0.062181) \| 0.361629 / 0.419271 (-0.057642) \| 0.068095 / 0.043533 (0.024562) \| 0.472440 / 0.255139 (0.217301) \| 0.508682 / 0.283200 (0.225483) \| 0.034648 / 0.141683 (-0.107035) \| 1.820117 / 1.452155 (0.367962) \| 1.933448 / 1.492716 (0.440732) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.276543 / 0.018006 (0.258537) \| 0.563380 / 0.000490 (0.562890) \| 0.005345 / 0.000200 (0.005146) \| 0.000107 / 0.000054 (0.000053) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029230 / 0.037411 (-0.008181) \| 0.095613 / 0.014526 (0.081087) \| 0.106178 / 0.176557 (-0.070378) \| 0.181095 / 0.737135 (-0.556040) \| 0.107789 / 0.296338 (-0.188550) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.612051 / 0.215209 (0.396842) \| 6.065008 / 2.077655 (3.987353) \| 2.720911 / 1.504120 (1.216791) \| 2.495218 / 1.541195 (0.954023) \| 2.423351 / 1.468490 (0.954860) \| 0.835571 / 4.584777 (-3.749205) \| 5.438230 / 3.745712 (1.692518) \| 4.550301 / 5.269862 (-0.719561) \| 2.919889 / 4.565676 (-1.645788) \| 0.097748 / 0.424275 (-0.326527) \| 0.009285 / 0.007607 (0.001678) \| 0.741968 / 0.226044 (0.515923) \| 7.285394 / 2.268929 (5.016466) \| 3.433634 / 55.444624 (-52.010991) \| 2.680823 / 6.876477 (-4.195654) \| 2.931149 / 2.142072 (0.789076) \| 1.012852 / 4.805227 (-3.792375) \| 0.224899 / 6.500664 (-6.275765) \| 0.089411 / 0.075469 (0.013942) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.622759 / 1.841788 (-0.219029) \| 23.690030 / 8.074308 (15.615721) \| 21.034451 / 10.191392 (10.843059) \| 0.241504 / 0.680424 (-0.438920) \| 0.030109 / 0.534201 (-0.504092) \| 0.472536 / 0.579283 (-0.106747) \| 0.631396 / 0.434364 (0.197032) \| 0.598997 / 0.540337 (0.058659) \| 0.798680 / 1.386936 (-0.588256) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008696 / 0.011353 (-0.002657) \| 0.005032 / 0.011008 (-0.005977) \| 0.087369 / 0.038508 (0.048861) \| 0.078105 / 0.023109 (0.054996) \| 0.464861 / 0.275898 (0.188963) \| 0.509620 / 0.323480 (0.186140) \| 0.006399 / 0.007986 (-0.001587) \| 0.004276 / 0.004328 (-0.000052) \| 0.081643 / 0.004250 (0.077393) \| 0.062560 / 0.037052 (0.025508) \| 0.495377 / 0.258489 (0.236888) \| 0.484885 / 0.293841 (0.191044) \| 0.054354 / 0.128546 (-0.074193) \| 0.013851 / 0.075646 (-0.061795) \| 0.089531 / 0.419271 (-0.329740) \| 0.068732 / 0.043533 (0.025199) \| 0.455842 / 0.255139 (0.200703) \| 0.528775 / 0.283200 (0.245575) \| 0.039646 / 0.141683 (-0.102037) \| 1.733600 / 1.452155 (0.281445) \| 1.879074 / 1.492716 (0.386358) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.369616 / 0.018006 (0.351610) \| 0.607426 / 0.000490 (0.606936) \| 0.055540 / 0.000200 (0.055341) \| 0.000543 / 0.000054 (0.000488) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036026 / 0.037411 (-0.001385) \| 0.103968 / 0.014526 (0.089442) \| 0.114852 / 0.176557 (-0.061705) \| 0.187313 / 0.737135 (-0.549822) \| 0.116839 / 0.296338 (-0.179500) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.614018 / 0.215209 (0.398809) \| 6.139914 / 2.077655 (4.062259) \| 2.826246 / 1.504120 (1.322126) \| 2.524133 / 1.541195 (0.982938) \| 2.606981 / 1.468490 (1.138491) \| 0.844604 / 4.584777 (-3.740173) \| 5.537178 / 3.745712 (1.791465) \| 4.594624 / 5.269862 (-0.675237) \| 3.032145 / 4.565676 (-1.533532) \| 0.094771 / 0.424275 (-0.329504) \| 0.008132 / 0.007607 (0.000525) \| 0.714287 / 0.226044 (0.488242) \| 7.296733 / 2.268929 (5.027804) \| 3.698066 / 55.444624 (-51.746558) \| 2.862781 / 6.876477 (-4.013696) \| 3.114502 / 2.142072 (0.972429) \| 0.986612 / 4.805227 (-3.818616) \| 0.214438 / 6.500664 (-6.286226) \| 0.076201 / 0.075469 (0.000732) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.747728 / 1.841788 (-0.094060) \| 24.159845 / 8.074308 (16.085537) \| 23.553485 / 10.191392 (13.362093) \| 0.248387 / 0.680424 (-0.432037) \| 0.029850 / 0.534201 (-0.504351) \| 0.526416 / 0.579283 (-0.052867) \| 0.625681 / 0.434364 (0.191317) \| 0.619690 / 0.540337 (0.079352) \| 0.827485 / 1.386936 (-0.559451) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#75639f9064dab9549add79fd5ee7de2a4429992c \"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) \| 0.006728 / 0.011353 (-0.004625) \| 0.003960 / 0.011008 (-0.007048) \| 0.085569 / 0.038508 (0.047061) \| 0.077463 / 0.023109 (0.054354) \| 0.343112 / 0.275898 (0.067214) \| 0.379128 / 0.323480 (0.055648) \| 0.004087 / 0.007986 (-0.003899) \| 0.003357 / 0.004328 (-0.000972) \| 0.065570 / 0.004250 (0.061320) \| 0.056259 / 0.037052 (0.019207) \| 0.368595 / 0.258489 (0.110106) \| 0.402672 / 0.293841 (0.108831) \| 0.030946 / 0.128546 (-0.097600) \| 0.008509 / 0.075646 (-0.067137) \| 0.288552 / 0.419271 (-0.130719) \| 0.052134 / 0.043533 (0.008601) \| 0.344653 / 0.255139 (0.089514) \| 0.374199 / 0.283200 (0.090999) \| 0.026251 / 0.141683 (-0.115432) \| 1.488258 / 1.452155 (0.036103) \| 1.567119 / 1.492716 (0.074402) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.218740 / 0.018006 (0.200734) \| 0.465483 / 0.000490 (0.464994) \| 0.003959 / 0.000200 (0.003759) \| 0.000083 / 0.000054 (0.000029) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029860 / 0.037411 (-0.007551) \| 0.087968 / 0.014526 (0.073442) \| 0.098257 / 0.176557 (-0.078299) \| 0.155478 / 0.737135 (-0.581657) \| 0.100696 / 0.296338 (-0.195642) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.384642 / 0.215209 (0.169432) \| 3.821692 / 2.077655 (1.744038) \| 1.838012 / 1.504120 (0.333892) \| 1.677554 / 1.541195 (0.136360) \| 1.764284 / 1.468490 (0.295794) \| 0.487512 / 4.584777 (-4.097265) \| 3.614572 / 3.745712 (-0.131141) \| 3.300740 / 5.269862 (-1.969122) \| 2.079044 / 4.565676 (-2.486632) \| 0.057392 / 0.424275 (-0.366883) \| 0.007642 / 0.007607 (0.000035) \| 0.456161 / 0.226044 (0.230117) \| 4.554124 / 2.268929 (2.285196) \| 2.319288 / 55.444624 (-53.125336) \| 1.972024 / 6.876477 (-4.904452) \| 2.210598 / 2.142072 (0.068526) \| 0.588442 / 4.805227 (-4.216785) \| 0.134474 / 6.500664 (-6.366191) \| 0.062682 / 0.075469 (-0.012787) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.243548 / 1.841788 (-0.598239) \| 20.267230 / 8.074308 (12.192922) \| 14.872096 / 10.191392 (4.680704) \| 0.165164 / 0.680424 (-0.515260) \| 0.018985 / 0.534201 (-0.515216) \| 0.394526 / 0.579283 (-0.184757) \| 0.413918 / 0.434364 (-0.020446) \| 0.467130 / 0.540337 (-0.073208) \| 0.627055 / 1.386936 (-0.759881) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006940 / 0.011353 (-0.004412) \| 0.004203 / 0.011008 (-0.006805) \| 0.065828 / 0.038508 (0.027320) \| 0.076604 / 0.023109 (0.053495) \| 0.401781 / 0.275898 (0.125883) \| 0.434838 / 0.323480 (0.111358) \| 0.005626 / 0.007986 (-0.002359) \| 0.003409 / 0.004328 (-0.000920) \| 0.064702 / 0.004250 (0.060452) \| 0.057525 / 0.037052 (0.020473) \| 0.405032 / 0.258489 (0.146543) \| 0.440906 / 0.293841 (0.147065) \| 0.032713 / 0.128546 (-0.095833) \| 0.008723 / 0.075646 (-0.066923) \| 0.071448 / 0.419271 (-0.347823) \| 0.048186 / 0.043533 (0.004653) \| 0.403950 / 0.255139 (0.148811) \| 0.419506 / 0.283200 (0.136307) \| 0.023532 / 0.141683 (-0.118150) \| 1.496435 / 1.452155 (0.044280) \| 1.567236 / 1.492716 (0.074519) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.229194 / 0.018006 (0.211188) \| 0.451363 / 0.000490 (0.450873) \| 0.003651 / 0.000200 (0.003451) \| 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) \| 0.033674 / 0.037411 (-0.003737) \| 0.097521 / 0.014526 (0.082995) \| 0.108806 / 0.176557 (-0.067751) \| 0.161002 / 0.737135 (-0.576133) \| 0.108594 / 0.296338 (-0.187745) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.436638 / 0.215209 (0.221429) \| 4.348844 / 2.077655 (2.271189) \| 2.341737 / 1.504120 (0.837617) \| 2.195850 / 1.541195 (0.654656) \| 2.332147 / 1.468490 (0.863657) \| 0.496180 / 4.584777 (-4.088597) \| 3.680987 / 3.745712 (-0.064725) \| 3.332203 / 5.269862 (-1.937659) \| 2.099541 / 4.565676 (-2.466136) \| 0.058629 / 0.424275 (-0.365646) \| 0.007363 / 0.007607 (-0.000245) \| 0.517658 / 0.226044 (0.291614) \| 5.175321 / 2.268929 (2.906392) \| 2.858660 / 55.444624 (-52.585964) \| 2.540557 / 6.876477 (-4.335920) \| 2.755360 / 2.142072 (0.613288) \| 0.595488 / 4.805227 (-4.209739) \| 0.134265 / 6.500664 (-6.366399) \| 0.062033 / 0.075469 (-0.013436) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.389950 / 1.841788 (-0.451838) \| 20.800274 / 8.074308 (12.725966) \| 15.314531 / 10.191392 (5.123139) \| 0.166822 / 0.680424 (-0.513602) \| 0.021099 / 0.534201 (-0.513102) \| 0.400388 / 0.579283 (-0.178895) \| 0.419981 / 0.434364 (-0.014383) \| 0.474259 / 0.540337 (-0.066078) \| 0.731678 / 1.386936 (-0.655258) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4566827557acbeba0d4cb66449bb70367e341b05 \"CML watermark\")\n" ]
1,861,124,284	6,165	Fix multiprocessing with spawn in iterable datasets	closed	2023-08-22T10:07:23	2023-08-29T13:27:14	2023-08-29T13:18:11	https://github.com/huggingface/datasets/pull/6165	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6165", "html_url": "https://github.com/huggingface/datasets/pull/6165", "diff_url": "https://github.com/huggingface/datasets/pull/6165.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6165.patch", "merged_at": "2023-08-29T13:18:11" }	bruno-hays	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq \r\nA test is failing, but I don't think it is due to my changes", "Good catch ! Could you add a test to make sure transformed IterableDataset objects are still picklable ?\r\n\r\nSomething like `test_pickle_after_many_transforms` in in `test_iterable_dataset.py` that does a bunch or rename, map, take on a dataset and checks that the dataset can be pickled at the end and the reloaded dataset returns the same elements", "@lhoestq \r\nI added the test and fixed one last method", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006537 / 0.011353 (-0.004816) \| 0.003960 / 0.011008 (-0.007048) \| 0.085135 / 0.038508 (0.046627) \| 0.079271 / 0.023109 (0.056162) \| 0.383743 / 0.275898 (0.107845) \| 0.414622 / 0.323480 (0.091143) \| 0.004202 / 0.007986 (-0.003784) \| 0.003537 / 0.004328 (-0.000791) \| 0.065758 / 0.004250 (0.061508) \| 0.054225 / 0.037052 (0.017173) \| 0.395715 / 0.258489 (0.137226) \| 0.438985 / 0.293841 (0.145144) \| 0.030590 / 0.128546 (-0.097956) \| 0.008754 / 0.075646 (-0.066892) \| 0.288415 / 0.419271 (-0.130857) \| 0.051863 / 0.043533 (0.008330) \| 0.382501 / 0.255139 (0.127363) \| 0.414428 / 0.283200 (0.131228) \| 0.024084 / 0.141683 (-0.117599) \| 1.478726 / 1.452155 (0.026572) \| 1.544763 / 1.492716 (0.052047) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285143 / 0.018006 (0.267136) \| 0.603859 / 0.000490 (0.603369) \| 0.004330 / 0.000200 (0.004131) \| 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) \| 0.027856 / 0.037411 (-0.009555) \| 0.081963 / 0.014526 (0.067437) \| 0.104106 / 0.176557 (-0.072451) \| 0.151378 / 0.737135 (-0.585757) \| 0.096476 / 0.296338 (-0.199862) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.402938 / 0.215209 (0.187729) \| 4.042312 / 2.077655 (1.964657) \| 2.068421 / 1.504120 (0.564301) \| 1.877870 / 1.541195 (0.336675) \| 1.947643 / 1.468490 (0.479153) \| 0.482031 / 4.584777 (-4.102746) \| 3.554747 / 3.745712 (-0.190965) \| 3.307811 / 5.269862 (-1.962050) \| 2.082886 / 4.565676 (-2.482791) \| 0.056853 / 0.424275 (-0.367422) \| 0.007535 / 0.007607 (-0.000072) \| 0.483694 / 0.226044 (0.257649) \| 4.827906 / 2.268929 (2.558978) \| 2.567572 / 55.444624 (-52.877052) \| 2.167206 / 6.876477 (-4.709271) \| 2.414442 / 2.142072 (0.272369) \| 0.579472 / 4.805227 (-4.225755) \| 0.132976 / 6.500664 (-6.367688) \| 0.059315 / 0.075469 (-0.016154) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.260086 / 1.841788 (-0.581702) \| 19.438297 / 8.074308 (11.363989) \| 14.188161 / 10.191392 (3.996769) \| 0.168534 / 0.680424 (-0.511890) \| 0.018070 / 0.534201 (-0.516131) \| 0.394241 / 0.579283 (-0.185043) \| 0.411057 / 0.434364 (-0.023307) \| 0.461123 / 0.540337 (-0.079215) \| 0.626844 / 1.386936 (-0.760092) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006896 / 0.011353 (-0.004457) \| 0.004207 / 0.011008 (-0.006801) \| 0.064981 / 0.038508 (0.026473) \| 0.080261 / 0.023109 (0.057152) \| 0.399403 / 0.275898 (0.123505) \| 0.433099 / 0.323480 (0.109619) \| 0.005697 / 0.007986 (-0.002288) \| 0.003601 / 0.004328 (-0.000728) \| 0.065924 / 0.004250 (0.061673) \| 0.058868 / 0.037052 (0.021815) \| 0.403705 / 0.258489 (0.145216) \| 0.439218 / 0.293841 (0.145377) \| 0.032789 / 0.128546 (-0.095757) \| 0.008675 / 0.075646 (-0.066971) \| 0.071217 / 0.419271 (-0.348055) \| 0.048487 / 0.043533 (0.004954) \| 0.399878 / 0.255139 (0.144739) \| 0.412816 / 0.283200 (0.129616) \| 0.023905 / 0.141683 (-0.117778) \| 1.541402 / 1.452155 (0.089247) \| 1.588080 / 1.492716 (0.095364) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.322863 / 0.018006 (0.304856) \| 0.530291 / 0.000490 (0.529802) \| 0.004862 / 0.000200 (0.004662) \| 0.000097 / 0.000054 (0.000042) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032697 / 0.037411 (-0.004715) \| 0.092416 / 0.014526 (0.077891) \| 0.107355 / 0.176557 (-0.069201) \| 0.160217 / 0.737135 (-0.576918) \| 0.109286 / 0.296338 (-0.187052) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.437375 / 0.215209 (0.222166) \| 4.362644 / 2.077655 (2.284990) \| 2.335404 / 1.504120 (0.831284) \| 2.173215 / 1.541195 (0.632020) \| 2.254061 / 1.468490 (0.785571) \| 0.493906 / 4.584777 (-4.090871) \| 3.609025 / 3.745712 (-0.136687) \| 3.352380 / 5.269862 (-1.917481) \| 2.074185 / 4.565676 (-2.491492) \| 0.057863 / 0.424275 (-0.366412) \| 0.007297 / 0.007607 (-0.000310) \| 0.512464 / 0.226044 (0.286420) \| 5.135921 / 2.268929 (2.866993) \| 2.788889 / 55.444624 (-52.655736) \| 2.479097 / 6.876477 (-4.397379) \| 2.717848 / 2.142072 (0.575776) \| 0.590442 / 4.805227 (-4.214785) \| 0.133721 / 6.500664 (-6.366943) \| 0.061491 / 0.075469 (-0.013978) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.429564 / 1.841788 (-0.412224) \| 20.628733 / 8.074308 (12.554425) \| 15.299571 / 10.191392 (5.108179) \| 0.171032 / 0.680424 (-0.509392) \| 0.019995 / 0.534201 (-0.514206) \| 0.401283 / 0.579283 (-0.178000) \| 0.416504 / 0.434364 (-0.017860) \| 0.471219 / 0.540337 (-0.069118) \| 0.641299 / 1.386936 (-0.745637) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5503e7beb5a31926aec03c6c9b24813f9f83cd7b \"CML watermark\")\n" ]
1,859,560,007	6,164	Fix: Missing a MetadataConfigs init when the repo has a `datasets_info.json` but no README	closed	2023-08-21T14:57:54	2023-08-21T16:27:05	2023-08-21T16:18:26	https://github.com/huggingface/datasets/pull/6164	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6164", "html_url": "https://github.com/huggingface/datasets/pull/6164", "diff_url": "https://github.com/huggingface/datasets/pull/6164.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6164.patch", "merged_at": "2023-08-21T16:18:26" }	clefourrier	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) \| 0.006874 / 0.011353 (-0.004479) \| 0.004276 / 0.011008 (-0.006732) \| 0.085198 / 0.038508 (0.046690) \| 0.084281 / 0.023109 (0.061171) \| 0.344767 / 0.275898 (0.068869) \| 0.377798 / 0.323480 (0.054318) \| 0.005656 / 0.007986 (-0.002330) \| 0.003601 / 0.004328 (-0.000727) \| 0.065486 / 0.004250 (0.061235) \| 0.056191 / 0.037052 (0.019139) \| 0.351412 / 0.258489 (0.092923) \| 0.398591 / 0.293841 (0.104750) \| 0.031662 / 0.128546 (-0.096884) \| 0.008901 / 0.075646 (-0.066745) \| 0.290423 / 0.419271 (-0.128849) \| 0.053793 / 0.043533 (0.010260) \| 0.347968 / 0.255139 (0.092829) \| 0.376978 / 0.283200 (0.093778) \| 0.026745 / 0.141683 (-0.114938) \| 1.514119 / 1.452155 (0.061964) \| 1.580920 / 1.492716 (0.088203) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.273648 / 0.018006 (0.255642) \| 0.575176 / 0.000490 (0.574686) \| 0.003557 / 0.000200 (0.003357) \| 0.000093 / 0.000054 (0.000038) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031714 / 0.037411 (-0.005697) \| 0.089166 / 0.014526 (0.074640) \| 0.101525 / 0.176557 (-0.075032) \| 0.161855 / 0.737135 (-0.575281) \| 0.101391 / 0.296338 (-0.194947) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.380947 / 0.215209 (0.165738) \| 3.800527 / 2.077655 (1.722873) \| 1.820789 / 1.504120 (0.316669) \| 1.657327 / 1.541195 (0.116132) \| 1.776242 / 1.468490 (0.307752) \| 0.486954 / 4.584777 (-4.097823) \| 3.688340 / 3.745712 (-0.057372) \| 3.354453 / 5.269862 (-1.915409) \| 2.119995 / 4.565676 (-2.445682) \| 0.057446 / 0.424275 (-0.366829) \| 0.007752 / 0.007607 (0.000145) \| 0.461907 / 0.226044 (0.235862) \| 4.617870 / 2.268929 (2.348942) \| 2.337025 / 55.444624 (-53.107599) \| 1.964770 / 6.876477 (-4.911707) \| 2.252066 / 2.142072 (0.109993) \| 0.591585 / 4.805227 (-4.213642) \| 0.134655 / 6.500664 (-6.366009) \| 0.060646 / 0.075469 (-0.014823) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.263271 / 1.841788 (-0.578517) \| 20.822286 / 8.074308 (12.747978) \| 14.710256 / 10.191392 (4.518864) \| 0.167285 / 0.680424 (-0.513139) \| 0.018302 / 0.534201 (-0.515899) \| 0.401023 / 0.579283 (-0.178260) \| 0.428956 / 0.434364 (-0.005407) \| 0.466120 / 0.540337 (-0.074218) \| 0.637868 / 1.386936 (-0.749069) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007174 / 0.011353 (-0.004179) \| 0.004418 / 0.011008 (-0.006590) \| 0.065731 / 0.038508 (0.027223) \| 0.090457 / 0.023109 (0.067348) \| 0.387306 / 0.275898 (0.111408) \| 0.427178 / 0.323480 (0.103698) \| 0.005699 / 0.007986 (-0.002286) \| 0.003662 / 0.004328 (-0.000666) \| 0.066190 / 0.004250 (0.061940) \| 0.062860 / 0.037052 (0.025808) \| 0.388855 / 0.258489 (0.130366) \| 0.427853 / 0.293841 (0.134012) \| 0.032770 / 0.128546 (-0.095776) \| 0.008780 / 0.075646 (-0.066866) \| 0.071156 / 0.419271 (-0.348116) \| 0.050174 / 0.043533 (0.006641) \| 0.385254 / 0.255139 (0.130115) \| 0.405069 / 0.283200 (0.121869) \| 0.025561 / 0.141683 (-0.116122) \| 1.506907 / 1.452155 (0.054752) \| 1.543270 / 1.492716 (0.050554) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.304651 / 0.018006 (0.286645) \| 0.577269 / 0.000490 (0.576780) \| 0.004479 / 0.000200 (0.004279) \| 0.000127 / 0.000054 (0.000073) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034070 / 0.037411 (-0.003341) \| 0.097664 / 0.014526 (0.083138) \| 0.106969 / 0.176557 (-0.069588) \| 0.163093 / 0.737135 (-0.574043) \| 0.109384 / 0.296338 (-0.186955) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.414823 / 0.215209 (0.199614) \| 4.148390 / 2.077655 (2.070735) \| 2.114038 / 1.504120 (0.609918) \| 1.959316 / 1.541195 (0.418121) \| 2.098138 / 1.468490 (0.629648) \| 0.486338 / 4.584777 (-4.098439) \| 3.642850 / 3.745712 (-0.102863) \| 3.458311 / 5.269862 (-1.811551) \| 2.185662 / 4.565676 (-2.380014) \| 0.057555 / 0.424275 (-0.366720) \| 0.007522 / 0.007607 (-0.000085) \| 0.497975 / 0.226044 (0.271931) \| 4.971528 / 2.268929 (2.702600) \| 2.614087 / 55.444624 (-52.830537) \| 2.288406 / 6.876477 (-4.588070) \| 2.564067 / 2.142072 (0.421995) \| 0.582248 / 4.805227 (-4.222979) \| 0.134931 / 6.500664 (-6.365733) \| 0.062689 / 0.075469 (-0.012780) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.343331 / 1.841788 (-0.498457) \| 21.398950 / 8.074308 (13.324642) \| 14.620971 / 10.191392 (4.429579) \| 0.169779 / 0.680424 (-0.510644) \| 0.018683 / 0.534201 (-0.515518) \| 0.396152 / 0.579283 (-0.183131) \| 0.409596 / 0.434364 (-0.024768) \| 0.482875 / 0.540337 (-0.057463) \| 0.659977 / 1.386936 (-0.726959) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1fd2234b8c802d47db5a5aa939148f98c9c49350 \"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) \| 0.006662 / 0.011353 (-0.004691) \| 0.003959 / 0.011008 (-0.007049) \| 0.084447 / 0.038508 (0.045939) \| 0.070267 / 0.023109 (0.047158) \| 0.310301 / 0.275898 (0.034403) \| 0.339866 / 0.323480 (0.016386) \| 0.004008 / 0.007986 (-0.003977) \| 0.003270 / 0.004328 (-0.001058) \| 0.064997 / 0.004250 (0.060746) \| 0.053151 / 0.037052 (0.016099) \| 0.327867 / 0.258489 (0.069378) \| 0.368560 / 0.293841 (0.074719) \| 0.031436 / 0.128546 (-0.097111) \| 0.008547 / 0.075646 (-0.067099) \| 0.288513 / 0.419271 (-0.130758) \| 0.051833 / 0.043533 (0.008300) \| 0.312660 / 0.255139 (0.057521) \| 0.347180 / 0.283200 (0.063980) \| 0.024982 / 0.141683 (-0.116701) \| 1.472487 / 1.452155 (0.020333) \| 1.550138 / 1.492716 (0.057422) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.208443 / 0.018006 (0.190437) \| 0.451927 / 0.000490 (0.451437) \| 0.004452 / 0.000200 (0.004252) \| 0.000082 / 0.000054 (0.000027) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029164 / 0.037411 (-0.008247) \| 0.085801 / 0.014526 (0.071275) \| 0.096229 / 0.176557 (-0.080327) \| 0.153063 / 0.737135 (-0.584072) \| 0.097712 / 0.296338 (-0.198626) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.383969 / 0.215209 (0.168760) \| 3.829216 / 2.077655 (1.751561) \| 1.854466 / 1.504120 (0.350346) \| 1.684149 / 1.541195 (0.142954) \| 1.759422 / 1.468490 (0.290932) \| 0.480229 / 4.584777 (-4.104548) \| 3.653363 / 3.745712 (-0.092349) \| 3.264456 / 5.269862 (-2.005406) \| 2.020579 / 4.565676 (-2.545097) \| 0.056920 / 0.424275 (-0.367355) \| 0.007625 / 0.007607 (0.000018) \| 0.458559 / 0.226044 (0.232515) \| 4.580288 / 2.268929 (2.311359) \| 2.353783 / 55.444624 (-53.090841) \| 1.967223 / 6.876477 (-4.909253) \| 2.182707 / 2.142072 (0.040634) \| 0.631341 / 4.805227 (-4.173886) \| 0.141656 / 6.500664 (-6.359008) \| 0.059918 / 0.075469 (-0.015551) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.279635 / 1.841788 (-0.562153) \| 19.725763 / 8.074308 (11.651455) \| 14.477946 / 10.191392 (4.286554) \| 0.164360 / 0.680424 (-0.516064) \| 0.018286 / 0.534201 (-0.515915) \| 0.394935 / 0.579283 (-0.184348) \| 0.419638 / 0.434364 (-0.014726) \| 0.460366 / 0.540337 (-0.079972) \| 0.636876 / 1.386936 (-0.750060) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006568 / 0.011353 (-0.004785) \| 0.004270 / 0.011008 (-0.006738) \| 0.065522 / 0.038508 (0.027014) \| 0.071597 / 0.023109 (0.048487) \| 0.394929 / 0.275898 (0.119031) \| 0.427548 / 0.323480 (0.104068) \| 0.005320 / 0.007986 (-0.002665) \| 0.003366 / 0.004328 (-0.000962) \| 0.065780 / 0.004250 (0.061530) \| 0.055390 / 0.037052 (0.018338) \| 0.397950 / 0.258489 (0.139461) \| 0.435800 / 0.293841 (0.141959) \| 0.031816 / 0.128546 (-0.096730) \| 0.008555 / 0.075646 (-0.067091) \| 0.072110 / 0.419271 (-0.347161) \| 0.049077 / 0.043533 (0.005544) \| 0.390065 / 0.255139 (0.134926) \| 0.410294 / 0.283200 (0.127094) \| 0.023389 / 0.141683 (-0.118294) \| 1.491491 / 1.452155 (0.039336) \| 1.551057 / 1.492716 (0.058341) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.243869 / 0.018006 (0.225862) \| 0.451961 / 0.000490 (0.451471) \| 0.019834 / 0.000200 (0.019634) \| 0.000114 / 0.000054 (0.000059) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031031 / 0.037411 (-0.006380) \| 0.088189 / 0.014526 (0.073663) \| 0.101743 / 0.176557 (-0.074814) \| 0.155236 / 0.737135 (-0.581899) \| 0.101245 / 0.296338 (-0.195094) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.422178 / 0.215209 (0.206969) \| 4.199989 / 2.077655 (2.122334) \| 2.228816 / 1.504120 (0.724696) \| 2.057172 / 1.541195 (0.515978) \| 2.162651 / 1.468490 (0.694161) \| 0.491186 / 4.584777 (-4.093591) \| 3.666221 / 3.745712 (-0.079491) \| 3.289531 / 5.269862 (-1.980331) \| 2.050027 / 4.565676 (-2.515650) \| 0.057464 / 0.424275 (-0.366811) \| 0.007379 / 0.007607 (-0.000228) \| 0.506532 / 0.226044 (0.280487) \| 5.066385 / 2.268929 (2.797456) \| 2.694405 / 55.444624 (-52.750219) \| 2.372200 / 6.876477 (-4.504277) \| 2.562724 / 2.142072 (0.420652) \| 0.615474 / 4.805227 (-4.189753) \| 0.148284 / 6.500664 (-6.352380) \| 0.061380 / 0.075469 (-0.014089) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.332649 / 1.841788 (-0.509139) \| 20.591063 / 8.074308 (12.516755) \| 14.105253 / 10.191392 (3.913861) \| 0.151886 / 0.680424 (-0.528537) \| 0.018200 / 0.534201 (-0.516001) \| 0.395278 / 0.579283 (-0.184005) \| 0.407113 / 0.434364 (-0.027251) \| 0.473168 / 0.540337 (-0.067170) \| 0.660766 / 1.386936 (-0.726170) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8b8e6ee067eb74e7965ca2a6768f15f9398cb7c8 \"CML watermark\")\n" ]
1,857,682,241	6,163	Error type: ArrowInvalid Details: Failed to parse string: '[254,254]' as a scalar of type int32	open	2023-08-19T11:34:40	2025-07-22T12:04:46	null	https://github.com/huggingface/datasets/issues/6163	null	shishirCTC	false	[ "Answered on the forum [here](https://discuss.huggingface.co/t/error-type-arrowinvalid-details-failed-to-parse-string-254-254-as-a-scalar-of-type-int32/51323).", "it seems that no answer in this link" ]
1,856,198,342	6,162	load_dataset('json',...) from togethercomputer/RedPajama-Data-1T errors when jsonl rows contains different data fields	open	2023-08-18T07:19:39	2023-08-18T17:00:35	null	https://github.com/huggingface/datasets/issues/6162	null	rbrugaro	false	[ "Hi ! Feel free to open a discussion at https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T/discussions to ask the file to be fixed (or directly open a PR with the fixed file)\r\n\r\n`datasets` expects all the examples to have the same fields", "@lhoestq I think the problem is caused by the fact that hugging face datasets writes a copy of data to the local cache using pyarrow. And the data scheme is inferred from the first few data blocks as can be seen [here](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_writer.py#L570). Maybe setting `streaming=True` can workaround this problem. Would you agree with my statement? ", "> @lhoestq I think the problem is caused by the fact that hugging face datasets writes a copy of data to the local cache using pyarrow. And the data scheme is inferred from the first few data blocks as can be seen [here](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_writer.py#L570).\r\n\r\nCorrect. Therefore any example that doesn't follow the inferred schema will make the code fail.\r\n\r\n> Maybe setting streaming=True can workaround this problem. Would you agree with my statement?\r\n\r\nYou'll meet the same problem but later - when streaming and arriving at the problematic example", "@lhoestq I just run below test with streaming=True and is not failing at the problematic example\r\n```python\r\nds = load_dataset('json', data_files='/path_to_local_RedPajamaData/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl', streaming=True)\r\ncount = 0\r\nfor i in ds['train']:\r\n count += 1\r\n print(count)\r\n```\r\n\r\nand completes the 262241 samples successfully. It does error our when streaming is not used " ]
1,855,794,354	6,161	Fix protocol prefix for Beam	closed	2023-08-17T22:40:37	2024-03-18T17:01:21	2024-03-18T17:01:21	https://github.com/huggingface/datasets/pull/6161	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6161", "html_url": "https://github.com/huggingface/datasets/pull/6161", "diff_url": "https://github.com/huggingface/datasets/pull/6161.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6161.patch", "merged_at": null }	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) \| 0.006736 / 0.011353 (-0.004617) \| 0.004099 / 0.011008 (-0.006909) \| 0.084339 / 0.038508 (0.045831) \| 0.073715 / 0.023109 (0.050605) \| 0.311962 / 0.275898 (0.036064) \| 0.356108 / 0.323480 (0.032628) \| 0.005321 / 0.007986 (-0.002665) \| 0.003390 / 0.004328 (-0.000939) \| 0.064622 / 0.004250 (0.060372) \| 0.053978 / 0.037052 (0.016926) \| 0.328967 / 0.258489 (0.070478) \| 0.370506 / 0.293841 (0.076665) \| 0.031123 / 0.128546 (-0.097423) \| 0.008465 / 0.075646 (-0.067181) \| 0.288136 / 0.419271 (-0.131136) \| 0.052909 / 0.043533 (0.009376) \| 0.325189 / 0.255139 (0.070050) \| 0.360112 / 0.283200 (0.076912) \| 0.023389 / 0.141683 (-0.118294) \| 1.492899 / 1.452155 (0.040744) \| 1.586449 / 1.492716 (0.093733) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.219708 / 0.018006 (0.201702) \| 0.469550 / 0.000490 (0.469060) \| 0.002776 / 0.000200 (0.002576) \| 0.000084 / 0.000054 (0.000029) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028985 / 0.037411 (-0.008427) \| 0.083487 / 0.014526 (0.068961) \| 0.096938 / 0.176557 (-0.079619) \| 0.152886 / 0.737135 (-0.584249) \| 0.096242 / 0.296338 (-0.200096) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.381959 / 0.215209 (0.166750) \| 3.800033 / 2.077655 (1.722378) \| 1.831903 / 1.504120 (0.327783) \| 1.663207 / 1.541195 (0.122012) \| 1.747282 / 1.468490 (0.278792) \| 0.481671 / 4.584777 (-4.103106) \| 3.653725 / 3.745712 (-0.091987) \| 3.253058 / 5.269862 (-2.016804) \| 2.022014 / 4.565676 (-2.543663) \| 0.056651 / 0.424275 (-0.367624) \| 0.007640 / 0.007607 (0.000033) \| 0.461795 / 0.226044 (0.235750) \| 4.625535 / 2.268929 (2.356606) \| 2.356341 / 55.444624 (-53.088283) \| 1.977437 / 6.876477 (-4.899040) \| 2.179672 / 2.142072 (0.037599) \| 0.582875 / 4.805227 (-4.222353) \| 0.132964 / 6.500664 (-6.367700) \| 0.060398 / 0.075469 (-0.015071) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.309567 / 1.841788 (-0.532220) \| 19.856306 / 8.074308 (11.781997) \| 14.074350 / 10.191392 (3.882958) \| 0.149615 / 0.680424 (-0.530809) \| 0.018487 / 0.534201 (-0.515714) \| 0.393995 / 0.579283 (-0.185288) \| 0.409057 / 0.434364 (-0.025307) \| 0.459551 / 0.540337 (-0.080787) \| 0.644594 / 1.386936 (-0.742342) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006824 / 0.011353 (-0.004529) \| 0.004099 / 0.011008 (-0.006909) \| 0.064415 / 0.038508 (0.025907) \| 0.077983 / 0.023109 (0.054874) \| 0.359351 / 0.275898 (0.083453) \| 0.395168 / 0.323480 (0.071688) \| 0.005384 / 0.007986 (-0.002602) \| 0.003298 / 0.004328 (-0.001030) \| 0.065041 / 0.004250 (0.060791) \| 0.056717 / 0.037052 (0.019664) \| 0.366882 / 0.258489 (0.108393) \| 0.401337 / 0.293841 (0.107496) \| 0.032273 / 0.128546 (-0.096273) \| 0.008666 / 0.075646 (-0.066981) \| 0.071442 / 0.419271 (-0.347829) \| 0.049999 / 0.043533 (0.006466) \| 0.365001 / 0.255139 (0.109862) \| 0.379579 / 0.283200 (0.096379) \| 0.023357 / 0.141683 (-0.118326) \| 1.476839 / 1.452155 (0.024684) \| 1.541703 / 1.492716 (0.048987) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.239014 / 0.018006 (0.221008) \| 0.460678 / 0.000490 (0.460188) \| 0.003368 / 0.000200 (0.003168) \| 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) \| 0.030981 / 0.037411 (-0.006430) \| 0.088287 / 0.014526 (0.073761) \| 0.102459 / 0.176557 (-0.074098) \| 0.154695 / 0.737135 (-0.582441) \| 0.103479 / 0.296338 (-0.192860) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.416084 / 0.215209 (0.200874) \| 4.128365 / 2.077655 (2.050710) \| 2.113053 / 1.504120 (0.608934) \| 1.948993 / 1.541195 (0.407798) \| 2.035609 / 1.468490 (0.567119) \| 0.481705 / 4.584777 (-4.103072) \| 3.630366 / 3.745712 (-0.115346) \| 3.340837 / 5.269862 (-1.929024) \| 2.052573 / 4.565676 (-2.513104) \| 0.056805 / 0.424275 (-0.367470) \| 0.007294 / 0.007607 (-0.000313) \| 0.489597 / 0.226044 (0.263553) \| 4.892728 / 2.268929 (2.623799) \| 2.564692 / 55.444624 (-52.879932) \| 2.251964 / 6.876477 (-4.624513) \| 2.457912 / 2.142072 (0.315839) \| 0.588433 / 4.805227 (-4.216794) \| 0.133588 / 6.500664 (-6.367076) \| 0.062298 / 0.075469 (-0.013171) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.328566 / 1.841788 (-0.513222) \| 20.145568 / 8.074308 (12.071260) \| 14.231306 / 10.191392 (4.039914) \| 0.168356 / 0.680424 (-0.512067) \| 0.018333 / 0.534201 (-0.515868) \| 0.390901 / 0.579283 (-0.188382) \| 0.415005 / 0.434364 (-0.019359) \| 0.477282 / 0.540337 (-0.063055) \| 0.652085 / 1.386936 (-0.734851) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#341a41880a70b29f030caa0d36f1e297535ba5f9 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6161). 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) \| 0.006388 / 0.011353 (-0.004965) \| 0.003917 / 0.011008 (-0.007092) \| 0.087397 / 0.038508 (0.048889) \| 0.068522 / 0.023109 (0.045412) \| 0.313299 / 0.275898 (0.037401) \| 0.342884 / 0.323480 (0.019405) \| 0.005216 / 0.007986 (-0.002770) \| 0.003293 / 0.004328 (-0.001035) \| 0.067474 / 0.004250 (0.063224) \| 0.051122 / 0.037052 (0.014070) \| 0.326443 / 0.258489 (0.067954) \| 0.355744 / 0.293841 (0.061903) \| 0.031130 / 0.128546 (-0.097416) \| 0.008617 / 0.075646 (-0.067029) \| 0.291201 / 0.419271 (-0.128070) \| 0.052050 / 0.043533 (0.008517) \| 0.312135 / 0.255139 (0.056996) \| 0.347233 / 0.283200 (0.064034) \| 0.023775 / 0.141683 (-0.117907) \| 1.478807 / 1.452155 (0.026652) \| 1.581239 / 1.492716 (0.088522) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.208252 / 0.018006 (0.190246) \| 0.466314 / 0.000490 (0.465824) \| 0.004439 / 0.000200 (0.004239) \| 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) \| 0.027918 / 0.037411 (-0.009494) \| 0.082410 / 0.014526 (0.067884) \| 0.094231 / 0.176557 (-0.082326) \| 0.150189 / 0.737135 (-0.586946) \| 0.095404 / 0.296338 (-0.200935) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.382026 / 0.215209 (0.166817) \| 3.822213 / 2.077655 (1.744559) \| 1.833716 / 1.504120 (0.329596) \| 1.666250 / 1.541195 (0.125055) \| 1.703350 / 1.468490 (0.234860) \| 0.477918 / 4.584777 (-4.106859) \| 3.629304 / 3.745712 (-0.116408) \| 3.199672 / 5.269862 (-2.070190) \| 1.977855 / 4.565676 (-2.587821) \| 0.056275 / 0.424275 (-0.368000) \| 0.007538 / 0.007607 (-0.000070) \| 0.455995 / 0.226044 (0.229950) \| 4.559234 / 2.268929 (2.290305) \| 2.333819 / 55.444624 (-53.110805) \| 2.006851 / 6.876477 (-4.869625) \| 2.150683 / 2.142072 (0.008611) \| 0.576786 / 4.805227 (-4.228441) \| 0.132352 / 6.500664 (-6.368312) \| 0.059359 / 0.075469 (-0.016110) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.261525 / 1.841788 (-0.580262) \| 19.174957 / 8.074308 (11.100649) \| 14.286796 / 10.191392 (4.095404) \| 0.144610 / 0.680424 (-0.535813) \| 0.018213 / 0.534201 (-0.515988) \| 0.390404 / 0.579283 (-0.188879) \| 0.404678 / 0.434364 (-0.029686) \| 0.455636 / 0.540337 (-0.084701) \| 0.620801 / 1.386936 (-0.766135) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006383 / 0.011353 (-0.004970) \| 0.003852 / 0.011008 (-0.007156) \| 0.064116 / 0.038508 (0.025607) \| 0.068920 / 0.023109 (0.045810) \| 0.359439 / 0.275898 (0.083541) \| 0.388904 / 0.323480 (0.065425) \| 0.005192 / 0.007986 (-0.002794) \| 0.003233 / 0.004328 (-0.001095) \| 0.064589 / 0.004250 (0.060339) \| 0.054496 / 0.037052 (0.017444) \| 0.368699 / 0.258489 (0.110210) \| 0.400420 / 0.293841 (0.106579) \| 0.030869 / 0.128546 (-0.097677) \| 0.008424 / 0.075646 (-0.067222) \| 0.071015 / 0.419271 (-0.348257) \| 0.048333 / 0.043533 (0.004801) \| 0.360652 / 0.255139 (0.105513) \| 0.393534 / 0.283200 (0.110334) \| 0.022685 / 0.141683 (-0.118998) \| 1.495565 / 1.452155 (0.043410) \| 1.537947 / 1.492716 (0.045230) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.232911 / 0.018006 (0.214905) \| 0.454191 / 0.000490 (0.453702) \| 0.005711 / 0.000200 (0.005511) \| 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) \| 0.029486 / 0.037411 (-0.007925) \| 0.087249 / 0.014526 (0.072724) \| 0.100104 / 0.176557 (-0.076453) \| 0.151556 / 0.737135 (-0.585580) \| 0.100853 / 0.296338 (-0.195485) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.415134 / 0.215209 (0.199925) \| 4.139068 / 2.077655 (2.061413) \| 2.121079 / 1.504120 (0.616959) \| 1.945616 / 1.541195 (0.404421) \| 1.988188 / 1.468490 (0.519698) \| 0.483994 / 4.584777 (-4.100783) \| 3.640366 / 3.745712 (-0.105347) \| 3.218896 / 5.269862 (-2.050966) \| 2.015527 / 4.565676 (-2.550149) \| 0.056946 / 0.424275 (-0.367329) \| 0.007262 / 0.007607 (-0.000345) \| 0.486075 / 0.226044 (0.260031) \| 4.864191 / 2.268929 (2.595262) \| 2.590853 / 55.444624 (-52.853772) \| 2.315359 / 6.876477 (-4.561118) \| 2.418733 / 2.142072 (0.276661) \| 0.582378 / 4.805227 (-4.222849) \| 0.134097 / 6.500664 (-6.366568) \| 0.060797 / 0.075469 (-0.014672) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.337021 / 1.841788 (-0.504766) \| 19.468907 / 8.074308 (11.394599) \| 14.348874 / 10.191392 (4.157482) \| 0.170408 / 0.680424 (-0.510016) \| 0.018414 / 0.534201 (-0.515787) \| 0.394551 / 0.579283 (-0.184732) \| 0.404750 / 0.434364 (-0.029613) \| 0.471972 / 0.540337 (-0.068365) \| 0.650607 / 1.386936 (-0.736329) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ab4d978e2d5c246dc91e2fed041b06a38190be3b \"CML watermark\")\n", "The CI errors are unrelated to the changes", "The Beam API is now deprecated, so I think closing this PR is also fine :)" ]
1,855,760,543	6,160	Fix Parquet loading with `columns`	closed	2023-08-17T21:58:24	2023-08-17T22:44:59	2023-08-17T22:36:04	https://github.com/huggingface/datasets/pull/6160	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6160", "html_url": "https://github.com/huggingface/datasets/pull/6160", "diff_url": "https://github.com/huggingface/datasets/pull/6160.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6160.patch", "merged_at": "2023-08-17T22:36:04" }	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) \| 0.008368 / 0.011353 (-0.002985) \| 0.004754 / 0.011008 (-0.006254) \| 0.096646 / 0.038508 (0.058138) \| 0.088980 / 0.023109 (0.065871) \| 0.374532 / 0.275898 (0.098633) \| 0.404840 / 0.323480 (0.081360) \| 0.006026 / 0.007986 (-0.001960) \| 0.005716 / 0.004328 (0.001387) \| 0.076297 / 0.004250 (0.072047) \| 0.072335 / 0.037052 (0.035283) \| 0.379435 / 0.258489 (0.120946) \| 0.423449 / 0.293841 (0.129608) \| 0.041344 / 0.128546 (-0.087202) \| 0.009758 / 0.075646 (-0.065889) \| 0.341550 / 0.419271 (-0.077721) \| 0.068559 / 0.043533 (0.025026) \| 0.368313 / 0.255139 (0.113174) \| 0.415147 / 0.283200 (0.131947) \| 0.028692 / 0.141683 (-0.112990) \| 1.816198 / 1.452155 (0.364044) \| 1.983351 / 1.492716 (0.490635) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.222712 / 0.018006 (0.204706) \| 0.517850 / 0.000490 (0.517360) \| 0.004436 / 0.000200 (0.004236) \| 0.000094 / 0.000054 (0.000040) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033168 / 0.037411 (-0.004243) \| 0.101353 / 0.014526 (0.086827) \| 0.113235 / 0.176557 (-0.063322) \| 0.180308 / 0.737135 (-0.556827) \| 0.114604 / 0.296338 (-0.181734) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.454415 / 0.215209 (0.239206) \| 4.500355 / 2.077655 (2.422701) \| 2.188223 / 1.504120 (0.684103) \| 1.974256 / 1.541195 (0.433061) \| 2.067331 / 1.468490 (0.598841) \| 0.572982 / 4.584777 (-4.011795) \| 4.239160 / 3.745712 (0.493448) \| 3.836812 / 5.269862 (-1.433049) \| 2.367022 / 4.565676 (-2.198655) \| 0.066886 / 0.424275 (-0.357389) \| 0.009111 / 0.007607 (0.001504) \| 0.539881 / 0.226044 (0.313837) \| 5.362247 / 2.268929 (3.093319) \| 2.784044 / 55.444624 (-52.660580) \| 2.320975 / 6.876477 (-4.555502) \| 2.543108 / 2.142072 (0.401036) \| 0.685751 / 4.805227 (-4.119477) \| 0.156840 / 6.500664 (-6.343824) \| 0.071764 / 0.075469 (-0.003705) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.549830 / 1.841788 (-0.291958) \| 22.799622 / 8.074308 (14.725314) \| 16.750692 / 10.191392 (6.559300) \| 0.196192 / 0.680424 (-0.484232) \| 0.024518 / 0.534201 (-0.509683) \| 0.479302 / 0.579283 (-0.099981) \| 0.522256 / 0.434364 (0.087892) \| 0.545809 / 0.540337 (0.005471) \| 0.748437 / 1.386936 (-0.638499) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007789 / 0.011353 (-0.003564) \| 0.004563 / 0.011008 (-0.006445) \| 0.074631 / 0.038508 (0.036123) \| 0.086892 / 0.023109 (0.063783) \| 0.427014 / 0.275898 (0.151116) \| 0.463257 / 0.323480 (0.139777) \| 0.005987 / 0.007986 (-0.001999) \| 0.003803 / 0.004328 (-0.000526) \| 0.074799 / 0.004250 (0.070549) \| 0.063473 / 0.037052 (0.026420) \| 0.429905 / 0.258489 (0.171416) \| 0.468967 / 0.293841 (0.175127) \| 0.036768 / 0.128546 (-0.091778) \| 0.009675 / 0.075646 (-0.065971) \| 0.082546 / 0.419271 (-0.336725) \| 0.058027 / 0.043533 (0.014494) \| 0.429813 / 0.255139 (0.174674) \| 0.449200 / 0.283200 (0.166001) \| 0.026713 / 0.141683 (-0.114969) \| 1.812022 / 1.452155 (0.359867) \| 1.847305 / 1.492716 (0.354589) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.320383 / 0.018006 (0.302377) \| 0.485995 / 0.000490 (0.485505) \| 0.024365 / 0.000200 (0.024165) \| 0.000156 / 0.000054 (0.000101) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036341 / 0.037411 (-0.001071) \| 0.104635 / 0.014526 (0.090110) \| 0.119456 / 0.176557 (-0.057101) \| 0.182042 / 0.737135 (-0.555093) \| 0.118944 / 0.296338 (-0.177395) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.506410 / 0.215209 (0.291201) \| 5.061119 / 2.077655 (2.983465) \| 2.756557 / 1.504120 (1.252437) \| 2.546504 / 1.541195 (1.005309) \| 2.585509 / 1.468490 (1.117019) \| 0.564291 / 4.584777 (-4.020486) \| 4.281219 / 3.745712 (0.535507) \| 3.919439 / 5.269862 (-1.350423) \| 2.588788 / 4.565676 (-1.976889) \| 0.066900 / 0.424275 (-0.357375) \| 0.008680 / 0.007607 (0.001073) \| 0.598435 / 0.226044 (0.372390) \| 5.976054 / 2.268929 (3.707125) \| 3.260211 / 55.444624 (-52.184414) \| 2.874597 / 6.876477 (-4.001880) \| 3.105769 / 2.142072 (0.963697) \| 0.692938 / 4.805227 (-4.112289) \| 0.157777 / 6.500664 (-6.342887) \| 0.073128 / 0.075469 (-0.002341) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.559380 / 1.841788 (-0.282408) \| 22.986540 / 8.074308 (14.912232) \| 16.305564 / 10.191392 (6.114172) \| 0.174939 / 0.680424 (-0.505485) \| 0.021932 / 0.534201 (-0.512269) \| 0.468162 / 0.579283 (-0.111121) \| 0.472610 / 0.434364 (0.038246) \| 0.574574 / 0.540337 (0.034237) \| 0.783505 / 1.386936 (-0.603431) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#550923b5d6ae64eb20b8f66da843395e9fa404ac \"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) \| 0.012553 / 0.011353 (0.001201) \| 0.005358 / 0.011008 (-0.005650) \| 0.108338 / 0.038508 (0.069830) \| 0.101105 / 0.023109 (0.077995) \| 0.416808 / 0.275898 (0.140910) \| 0.454599 / 0.323480 (0.131119) \| 0.006665 / 0.007986 (-0.001321) \| 0.004186 / 0.004328 (-0.000143) \| 0.084900 / 0.004250 (0.080649) \| 0.062881 / 0.037052 (0.025829) \| 0.424423 / 0.258489 (0.165934) \| 0.482651 / 0.293841 (0.188810) \| 0.055740 / 0.128546 (-0.072807) \| 0.014469 / 0.075646 (-0.061177) \| 0.383267 / 0.419271 (-0.036005) \| 0.067487 / 0.043533 (0.023955) \| 0.414983 / 0.255139 (0.159844) \| 0.459437 / 0.283200 (0.176237) \| 0.038679 / 0.141683 (-0.103004) \| 1.828002 / 1.452155 (0.375847) \| 1.951946 / 1.492716 (0.459230) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288033 / 0.018006 (0.270027) \| 0.603536 / 0.000490 (0.603046) \| 0.004874 / 0.000200 (0.004674) \| 0.000138 / 0.000054 (0.000084) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031988 / 0.037411 (-0.005423) \| 0.095807 / 0.014526 (0.081281) \| 0.113459 / 0.176557 (-0.063098) \| 0.182012 / 0.737135 (-0.555123) \| 0.113121 / 0.296338 (-0.183217) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.620709 / 0.215209 (0.405500) \| 6.096569 / 2.077655 (4.018915) \| 2.754612 / 1.504120 (1.250492) \| 2.449786 / 1.541195 (0.908591) \| 2.470694 / 1.468490 (1.002204) \| 0.837016 / 4.584777 (-3.747761) \| 5.237290 / 3.745712 (1.491578) \| 4.713220 / 5.269862 (-0.556642) \| 3.020934 / 4.565676 (-1.544743) \| 0.096892 / 0.424275 (-0.327383) \| 0.009423 / 0.007607 (0.001816) \| 0.720313 / 0.226044 (0.494269) \| 7.369673 / 2.268929 (5.100744) \| 3.550384 / 55.444624 (-51.894241) \| 2.868868 / 6.876477 (-4.007609) \| 3.081469 / 2.142072 (0.939397) \| 1.042968 / 4.805227 (-3.762259) \| 0.232530 / 6.500664 (-6.268134) \| 0.080805 / 0.075469 (0.005336) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.645777 / 1.841788 (-0.196011) \| 24.590862 / 8.074308 (16.516554) \| 21.315496 / 10.191392 (11.124104) \| 0.228796 / 0.680424 (-0.451628) \| 0.028479 / 0.534201 (-0.505722) \| 0.494413 / 0.579283 (-0.084870) \| 0.582773 / 0.434364 (0.148409) \| 0.552575 / 0.540337 (0.012238) \| 0.787217 / 1.386936 (-0.599719) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008743 / 0.011353 (-0.002609) \| 0.005253 / 0.011008 (-0.005755) \| 0.083766 / 0.038508 (0.045257) \| 0.086305 / 0.023109 (0.063195) \| 0.520171 / 0.275898 (0.244273) \| 0.565812 / 0.323480 (0.242332) \| 0.006465 / 0.007986 (-0.001520) \| 0.004585 / 0.004328 (0.000257) \| 0.085344 / 0.004250 (0.081094) \| 0.063418 / 0.037052 (0.026366) \| 0.519759 / 0.258489 (0.261270) \| 0.552770 / 0.293841 (0.258929) \| 0.049439 / 0.128546 (-0.079107) \| 0.017564 / 0.075646 (-0.058082) \| 0.092713 / 0.419271 (-0.326559) \| 0.065837 / 0.043533 (0.022305) \| 0.516133 / 0.255139 (0.260994) \| 0.539813 / 0.283200 (0.256613) \| 0.036531 / 0.141683 (-0.105152) \| 1.919275 / 1.452155 (0.467121) \| 2.039987 / 1.492716 (0.547271) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297978 / 0.018006 (0.279972) \| 0.608243 / 0.000490 (0.607753) \| 0.006611 / 0.000200 (0.006411) \| 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) \| 0.033909 / 0.037411 (-0.003503) \| 0.106370 / 0.014526 (0.091844) \| 0.119032 / 0.176557 (-0.057524) \| 0.180319 / 0.737135 (-0.556816) \| 0.122826 / 0.296338 (-0.173513) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.639265 / 0.215209 (0.424056) \| 6.248430 / 2.077655 (4.170775) \| 2.944760 / 1.504120 (1.440640) \| 2.654005 / 1.541195 (1.112811) \| 2.733625 / 1.468490 (1.265134) \| 0.837172 / 4.584777 (-3.747605) \| 5.245084 / 3.745712 (1.499372) \| 4.722614 / 5.269862 (-0.547248) \| 3.008286 / 4.565676 (-1.557391) \| 0.102340 / 0.424275 (-0.321935) \| 0.009433 / 0.007607 (0.001826) \| 0.762991 / 0.226044 (0.536946) \| 7.385020 / 2.268929 (5.116092) \| 3.787648 / 55.444624 (-51.656977) \| 3.234345 / 6.876477 (-3.642132) \| 3.394444 / 2.142072 (1.252371) \| 1.023472 / 4.805227 (-3.781756) \| 0.208199 / 6.500664 (-6.292465) \| 0.081513 / 0.075469 (0.006043) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.795864 / 1.841788 (-0.045923) \| 25.270852 / 8.074308 (17.196544) \| 23.356413 / 10.191392 (13.165021) \| 0.228002 / 0.680424 (-0.452422) \| 0.031851 / 0.534201 (-0.502350) \| 0.499424 / 0.579283 (-0.079859) \| 0.588027 / 0.434364 (0.153664) \| 0.581746 / 0.540337 (0.041408) \| 0.814183 / 1.386936 (-0.572753) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33ee536876a667403ee44574bd685073261c4903 \"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) \| 0.006477 / 0.011353 (-0.004876) \| 0.003878 / 0.011008 (-0.007130) \| 0.084085 / 0.038508 (0.045577) \| 0.071297 / 0.023109 (0.048188) \| 0.309176 / 0.275898 (0.033278) \| 0.342830 / 0.323480 (0.019350) \| 0.005189 / 0.007986 (-0.002796) \| 0.003263 / 0.004328 (-0.001065) \| 0.063920 / 0.004250 (0.059670) \| 0.052233 / 0.037052 (0.015180) \| 0.324830 / 0.258489 (0.066341) \| 0.357956 / 0.293841 (0.064115) \| 0.030459 / 0.128546 (-0.098087) \| 0.008350 / 0.075646 (-0.067297) \| 0.287330 / 0.419271 (-0.131942) \| 0.051005 / 0.043533 (0.007473) \| 0.309227 / 0.255139 (0.054088) \| 0.346184 / 0.283200 (0.062984) \| 0.023961 / 0.141683 (-0.117722) \| 1.463983 / 1.452155 (0.011829) \| 1.573036 / 1.492716 (0.080319) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.205653 / 0.018006 (0.187647) \| 0.457336 / 0.000490 (0.456846) \| 0.005347 / 0.000200 (0.005147) \| 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) \| 0.028080 / 0.037411 (-0.009332) \| 0.081755 / 0.014526 (0.067229) \| 0.095716 / 0.176557 (-0.080841) \| 0.151340 / 0.737135 (-0.585795) \| 0.097174 / 0.296338 (-0.199164) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.390725 / 0.215209 (0.175516) \| 3.899114 / 2.077655 (1.821459) \| 1.895352 / 1.504120 (0.391232) \| 1.716072 / 1.541195 (0.174877) \| 1.784952 / 1.468490 (0.316462) \| 0.477247 / 4.584777 (-4.107530) \| 3.606641 / 3.745712 (-0.139071) \| 3.203337 / 5.269862 (-2.066524) \| 2.017003 / 4.565676 (-2.548674) \| 0.056182 / 0.424275 (-0.368094) \| 0.007508 / 0.007607 (-0.000099) \| 0.461965 / 0.226044 (0.235921) \| 4.605926 / 2.268929 (2.336997) \| 2.466695 / 55.444624 (-52.977929) \| 2.136376 / 6.876477 (-4.740100) \| 2.277334 / 2.142072 (0.135261) \| 0.576119 / 4.805227 (-4.229109) \| 0.131497 / 6.500664 (-6.369167) \| 0.060068 / 0.075469 (-0.015401) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.262681 / 1.841788 (-0.579107) \| 19.411572 / 8.074308 (11.337264) \| 14.383421 / 10.191392 (4.192029) \| 0.166115 / 0.680424 (-0.514308) \| 0.018366 / 0.534201 (-0.515835) \| 0.393903 / 0.579283 (-0.185380) \| 0.408788 / 0.434364 (-0.025576) \| 0.461796 / 0.540337 (-0.078541) \| 0.628460 / 1.386936 (-0.758476) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006501 / 0.011353 (-0.004852) \| 0.003915 / 0.011008 (-0.007093) \| 0.065245 / 0.038508 (0.026737) \| 0.073146 / 0.023109 (0.050037) \| 0.363537 / 0.275898 (0.087639) \| 0.391571 / 0.323480 (0.068092) \| 0.005181 / 0.007986 (-0.002805) \| 0.003272 / 0.004328 (-0.001056) \| 0.065060 / 0.004250 (0.060810) \| 0.054302 / 0.037052 (0.017249) \| 0.361571 / 0.258489 (0.103082) \| 0.400221 / 0.293841 (0.106380) \| 0.030762 / 0.128546 (-0.097784) \| 0.008449 / 0.075646 (-0.067197) \| 0.071148 / 0.419271 (-0.348123) \| 0.048111 / 0.043533 (0.004578) \| 0.360327 / 0.255139 (0.105188) \| 0.379073 / 0.283200 (0.095874) \| 0.024367 / 0.141683 (-0.117316) \| 1.451080 / 1.452155 (-0.001074) \| 1.510818 / 1.492716 (0.018102) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.267078 / 0.018006 (0.249072) \| 0.454074 / 0.000490 (0.453584) \| 0.015055 / 0.000200 (0.014855) \| 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) \| 0.030916 / 0.037411 (-0.006496) \| 0.089212 / 0.014526 (0.074686) \| 0.100005 / 0.176557 (-0.076552) \| 0.155100 / 0.737135 (-0.582035) \| 0.101759 / 0.296338 (-0.194580) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.412826 / 0.215209 (0.197616) \| 4.122520 / 2.077655 (2.044865) \| 2.107870 / 1.504120 (0.603750) \| 1.911936 / 1.541195 (0.370741) \| 1.984936 / 1.468490 (0.516446) \| 0.483835 / 4.584777 (-4.100942) \| 3.641860 / 3.745712 (-0.103852) \| 3.220540 / 5.269862 (-2.049322) \| 2.015521 / 4.565676 (-2.550155) \| 0.056913 / 0.424275 (-0.367362) \| 0.007285 / 0.007607 (-0.000322) \| 0.484886 / 0.226044 (0.258842) \| 4.854734 / 2.268929 (2.585805) \| 2.593550 / 55.444624 (-52.851074) \| 2.233904 / 6.876477 (-4.642572) \| 2.438858 / 2.142072 (0.296785) \| 0.580880 / 4.805227 (-4.224347) \| 0.133891 / 6.500664 (-6.366773) \| 0.061678 / 0.075469 (-0.013791) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.336843 / 1.841788 (-0.504944) \| 19.731571 / 8.074308 (11.657263) \| 14.290228 / 10.191392 (4.098836) \| 0.167635 / 0.680424 (-0.512789) \| 0.018767 / 0.534201 (-0.515434) \| 0.394953 / 0.579283 (-0.184330) \| 0.407711 / 0.434364 (-0.026653) \| 0.472371 / 0.540337 (-0.067966) \| 0.655278 / 1.386936 (-0.731658) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#528b15f775a4724836bdefdc38d932c06484d702 \"CML watermark\")\n" ]
1,855,691,512	6,159	Add `BoundingBox` feature	open	2023-08-17T20:49:51	2024-11-18T17:58:43	null	https://github.com/huggingface/datasets/issues/6159	null	mariosasko	false	[ "My proposal would look like this:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nfrom datasets.features import Sequence, BboxFeature\r\n\r\n# load images along with boxes\r\ndataset = load_dataset(\"imagefolder\", data_dir=\"/path/to/folder\", split=\"train\")\r\n\r\n# map the boxes column to the appropriate format\r\ndataset = dataset.map(lambda example: map_to_ymin_xmin_ymax_xmax(example))\r\n\r\n# cast to feature\r\ndataset = dataset.cast_column('boxes', Sequence(BboxFeature))\r\n````\r\nWe could for instance make [BboxFeature](https://www.tensorflow.org/datasets/api_docs/python/tfds/features/BBoxFeature) default to normalized coordinates in the ymin, xmin, ymax, xmax format. This is just one of the many different formats of bounding boxes, so we would for instance default to the format used by Tensorflow datasets." ]
1,855,374,220	6,158	[docs] Complete `to_iterable_dataset`	closed	2023-08-17T17:02:11	2023-08-17T19:24:20	2023-08-17T19:13:15	https://github.com/huggingface/datasets/pull/6158	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6158", "html_url": "https://github.com/huggingface/datasets/pull/6158", "diff_url": "https://github.com/huggingface/datasets/pull/6158.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6158.patch", "merged_at": "2023-08-17T19:13:15" }	stevhliu	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) \| 0.008219 / 0.011353 (-0.003134) \| 0.005201 / 0.011008 (-0.005807) \| 0.108542 / 0.038508 (0.070034) \| 0.076427 / 0.023109 (0.053318) \| 0.441257 / 0.275898 (0.165358) \| 0.436477 / 0.323480 (0.112997) \| 0.006915 / 0.007986 (-0.001071) \| 0.004215 / 0.004328 (-0.000113) \| 0.072517 / 0.004250 (0.068267) \| 0.066906 / 0.037052 (0.029853) \| 0.431153 / 0.258489 (0.172664) \| 0.413359 / 0.293841 (0.119518) \| 0.051112 / 0.128546 (-0.077435) \| 0.014664 / 0.075646 (-0.060982) \| 0.358385 / 0.419271 (-0.060887) \| 0.069682 / 0.043533 (0.026149) \| 0.434810 / 0.255139 (0.179671) \| 0.484372 / 0.283200 (0.201172) \| 0.035731 / 0.141683 (-0.105952) \| 1.827648 / 1.452155 (0.375494) \| 2.039761 / 1.492716 (0.547045) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.277386 / 0.018006 (0.259379) \| 0.599771 / 0.000490 (0.599282) \| 0.005033 / 0.000200 (0.004833) \| 0.000091 / 0.000054 (0.000037) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030652 / 0.037411 (-0.006759) \| 0.103435 / 0.014526 (0.088909) \| 0.120072 / 0.176557 (-0.056485) \| 0.177886 / 0.737135 (-0.559249) \| 0.140636 / 0.296338 (-0.155702) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.603729 / 0.215209 (0.388520) \| 6.144213 / 2.077655 (4.066558) \| 2.785080 / 1.504120 (1.280960) \| 2.368958 / 1.541195 (0.827763) \| 2.409806 / 1.468490 (0.941316) \| 0.836531 / 4.584777 (-3.748246) \| 5.154035 / 3.745712 (1.408323) \| 4.620224 / 5.269862 (-0.649638) \| 2.879441 / 4.565676 (-1.686235) \| 0.087322 / 0.424275 (-0.336953) \| 0.007698 / 0.007607 (0.000090) \| 0.678443 / 0.226044 (0.452399) \| 7.431798 / 2.268929 (5.162869) \| 3.589905 / 55.444624 (-51.854719) \| 2.679349 / 6.876477 (-4.197127) \| 3.100569 / 2.142072 (0.958496) \| 1.021501 / 4.805227 (-3.783726) \| 0.203150 / 6.500664 (-6.297514) \| 0.073545 / 0.075469 (-0.001924) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.669981 / 1.841788 (-0.171806) \| 23.379274 / 8.074308 (15.304966) \| 19.811451 / 10.191392 (9.620059) \| 0.197705 / 0.680424 (-0.482719) \| 0.030112 / 0.534201 (-0.504089) \| 0.501720 / 0.579283 (-0.077563) \| 0.582413 / 0.434364 (0.148049) \| 0.513261 / 0.540337 (-0.027076) \| 0.729710 / 1.386936 (-0.657226) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.011493 / 0.011353 (0.000140) \| 0.005478 / 0.011008 (-0.005530) \| 0.070955 / 0.038508 (0.032447) \| 0.073877 / 0.023109 (0.050768) \| 0.425765 / 0.275898 (0.149867) \| 0.440869 / 0.323480 (0.117389) \| 0.008322 / 0.007986 (0.000337) \| 0.004004 / 0.004328 (-0.000325) \| 0.071968 / 0.004250 (0.067718) \| 0.060576 / 0.037052 (0.023524) \| 0.448731 / 0.258489 (0.190242) \| 0.517038 / 0.293841 (0.223197) \| 0.051542 / 0.128546 (-0.077005) \| 0.013219 / 0.075646 (-0.062427) \| 0.077933 / 0.419271 (-0.341339) \| 0.072879 / 0.043533 (0.029346) \| 0.436553 / 0.255139 (0.181414) \| 0.510050 / 0.283200 (0.226850) \| 0.037136 / 0.141683 (-0.104547) \| 1.535706 / 1.452155 (0.083552) \| 1.611909 / 1.492716 (0.119192) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.335648 / 0.018006 (0.317642) \| 0.612787 / 0.000490 (0.612297) \| 0.021934 / 0.000200 (0.021734) \| 0.000113 / 0.000054 (0.000059) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028164 / 0.037411 (-0.009247) \| 0.097686 / 0.014526 (0.083160) \| 0.093343 / 0.176557 (-0.083214) \| 0.156871 / 0.737135 (-0.580264) \| 0.102694 / 0.296338 (-0.193645) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.609348 / 0.215209 (0.394139) \| 5.835798 / 2.077655 (3.758144) \| 2.792700 / 1.504120 (1.288580) \| 2.539597 / 1.541195 (0.998403) \| 2.413003 / 1.468490 (0.944513) \| 0.882404 / 4.584777 (-3.702372) \| 5.170564 / 3.745712 (1.424852) \| 4.621663 / 5.269862 (-0.648199) \| 3.029683 / 4.565676 (-1.535993) \| 0.097061 / 0.424275 (-0.327214) \| 0.008940 / 0.007607 (0.001333) \| 0.723052 / 0.226044 (0.497007) \| 7.484947 / 2.268929 (5.216018) \| 3.833049 / 55.444624 (-51.611575) \| 3.019606 / 6.876477 (-3.856871) \| 3.270503 / 2.142072 (1.128430) \| 0.977870 / 4.805227 (-3.827357) \| 0.210090 / 6.500664 (-6.290574) \| 0.094723 / 0.075469 (0.019254) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.585278 / 1.841788 (-0.256510) \| 22.769727 / 8.074308 (14.695419) \| 19.503640 / 10.191392 (9.312248) \| 0.231996 / 0.680424 (-0.448428) \| 0.032641 / 0.534201 (-0.501560) \| 0.429833 / 0.579283 (-0.149451) \| 0.549606 / 0.434364 (0.115242) \| 0.527405 / 0.540337 (-0.012933) \| 0.713302 / 1.386936 (-0.673634) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#546c7bb5cbeff0f8673cf60c4432ea167283cc42 \"CML watermark\")\n" ]
1,855,265,663	6,157	DatasetInfo.__init__() got an unexpected keyword argument '_column_requires_decoding'	closed	2023-08-17T15:48:11	2023-09-27T17:36:14	2023-09-27T17:36:14	https://github.com/huggingface/datasets/issues/6157	null	aihao2000	false	[ "Thanks for reporting, but we can only fix this issue if you can provide a reproducer that consistently reproduces it.", "@mariosasko Ok. What exactly does it mean to provide a reproducer", "To provide a code that reproduces the issue :)", "@mariosasko I complete the above code, is it enough?", "@mariosasko That's all the code, I'm using locally stored data", "Does this error occur even if you change the cache directory (the `cache_dir` parameter in `load_dataset`)?", "@mariosasko I didn't add any parameters for catch. Nor did any cache configuration change.", "@mariosasko And I changed the data file, but executing load_dataset is always the previous result. I had to change something in images.py to use the new results. Using 'cleanup_cache_files' is invalid! Help me.", "@mariosasko I added a comprehensive error message. Check that _column_requires_decoding is being passed where it shouldn't be. DatasetInfo.__init__() Whether this parameter is required", "I can see the issue now... \r\n\r\nYou can fix it by returning a `DatasetInfo` object in the `_info` method as follows:\r\n```python\r\n def _info(self):\r\n if self.config.name == \"similar_pairs\":\r\n features = datasets.Features(\r\n {\r\n \"image1\": datasets.features.Image(),\r\n \"prompt1\": datasets.Value(\"string\"),\r\n \"image2\": datasets.features.Image(),\r\n \"prompt2\": datasets.Value(\"string\"),\r\n \"similarity\": datasets.Value(\"float32\"),\r\n }\r\n )\r\n elif self.config.name == \"image_prompt_pairs\":\r\n features = datasets.Features(\r\n {\"image\": datasets.features.Image(), \"prompt\": datasets.Value(\"string\")}\r\n )\r\n return datasets.DatasetInfo(features=features)\r\n```", "@mariosasko Oh, that's the problem. Thank you very much. Returned the wrong object and it actually works? I've been training with it for a long time", "@mariosasko The original code can still see progress. emmm, I can't see how many examples is generated so far, so I don't know if we should wait", "The original issue has been addressed, so I'm closing it. \r\n\r\nPlease open a new issue if you encounter more errors." ]
1,854,768,618	6,156	Why not use self._epoch as seed to shuffle in distributed training with IterableDataset	closed	2023-08-17T10:58:20	2023-08-17T14:33:15	2023-08-17T14:33:14	https://github.com/huggingface/datasets/issues/6156	null	npuichigo	false	[ "@lhoestq ", "`_effective_generator` returns a RNG that takes into account `self._epoch` and the current dataset's base shuffling RNG (which can be set by specifying `seed=` in `.shuffle() for example`).\r\n\r\nTo fix your error you can pass `seed=` to `.shuffle()`. And the shuffling will depend on both this seed and `self._epoch`", "Thanks for the reply" ]
1,854,661,682	6,155	Raise FileNotFoundError when passing data_files that don't exist	closed	2023-08-17T09:49:48	2023-08-18T13:45:58	2023-08-18T13:35:13	https://github.com/huggingface/datasets/pull/6155	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6155", "html_url": "https://github.com/huggingface/datasets/pull/6155", "diff_url": "https://github.com/huggingface/datasets/pull/6155.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6155.patch", "merged_at": "2023-08-18T13:35:13" }	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) \| 0.009288 / 0.011353 (-0.002065) \| 0.005950 / 0.011008 (-0.005058) \| 0.122376 / 0.038508 (0.083868) \| 0.093177 / 0.023109 (0.070068) \| 0.448517 / 0.275898 (0.172619) \| 0.474999 / 0.323480 (0.151520) \| 0.005133 / 0.007986 (-0.002853) \| 0.005123 / 0.004328 (0.000795) \| 0.085479 / 0.004250 (0.081229) \| 0.065613 / 0.037052 (0.028561) \| 0.451179 / 0.258489 (0.192690) \| 0.516876 / 0.293841 (0.223036) \| 0.047536 / 0.128546 (-0.081010) \| 0.013894 / 0.075646 (-0.061752) \| 0.382149 / 0.419271 (-0.037122) \| 0.067380 / 0.043533 (0.023848) \| 0.419282 / 0.255139 (0.164143) \| 0.482042 / 0.283200 (0.198842) \| 0.041230 / 0.141683 (-0.100452) \| 1.818127 / 1.452155 (0.365972) \| 1.938123 / 1.492716 (0.445406) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.271824 / 0.018006 (0.253817) \| 0.604933 / 0.000490 (0.604443) \| 0.004953 / 0.000200 (0.004753) \| 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) \| 0.036682 / 0.037411 (-0.000729) \| 0.095604 / 0.014526 (0.081078) \| 0.116862 / 0.176557 (-0.059695) \| 0.191335 / 0.737135 (-0.545800) \| 0.116620 / 0.296338 (-0.179718) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.620735 / 0.215209 (0.405526) \| 6.157119 / 2.077655 (4.079465) \| 2.848548 / 1.504120 (1.344428) \| 2.493731 / 1.541195 (0.952536) \| 2.505801 / 1.468490 (1.037311) \| 0.837315 / 4.584777 (-3.747462) \| 5.360653 / 3.745712 (1.614941) \| 4.908863 / 5.269862 (-0.360999) \| 3.184672 / 4.565676 (-1.381004) \| 0.105687 / 0.424275 (-0.318588) \| 0.011350 / 0.007607 (0.003743) \| 0.745729 / 0.226044 (0.519684) \| 7.431584 / 2.268929 (5.162655) \| 3.644670 / 55.444624 (-51.799954) \| 2.910159 / 6.876477 (-3.966317) \| 3.257137 / 2.142072 (1.115065) \| 1.041377 / 4.805227 (-3.763851) \| 0.213289 / 6.500664 (-6.287375) \| 0.089208 / 0.075469 (0.013739) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.727274 / 1.841788 (-0.114513) \| 25.448436 / 8.074308 (17.374128) \| 23.016108 / 10.191392 (12.824716) \| 0.219454 / 0.680424 (-0.460970) \| 0.028531 / 0.534201 (-0.505670) \| 0.500231 / 0.579283 (-0.079052) \| 0.614631 / 0.434364 (0.180267) \| 0.557926 / 0.540337 (0.017588) \| 0.786261 / 1.386936 (-0.600675) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008608 / 0.011353 (-0.002745) \| 0.006185 / 0.011008 (-0.004823) \| 0.089258 / 0.038508 (0.050750) \| 0.090109 / 0.023109 (0.067000) \| 0.522200 / 0.275898 (0.246302) \| 0.559218 / 0.323480 (0.235738) \| 0.008983 / 0.007986 (0.000997) \| 0.004488 / 0.004328 (0.000159) \| 0.083658 / 0.004250 (0.079408) \| 0.064962 / 0.037052 (0.027909) \| 0.519477 / 0.258489 (0.260988) \| 0.573842 / 0.293841 (0.280001) \| 0.053984 / 0.128546 (-0.074562) \| 0.014665 / 0.075646 (-0.060982) \| 0.089438 / 0.419271 (-0.329834) \| 0.065756 / 0.043533 (0.022223) \| 0.525131 / 0.255139 (0.269992) \| 0.568934 / 0.283200 (0.285734) \| 0.037308 / 0.141683 (-0.104375) \| 1.928790 / 1.452155 (0.476635) \| 2.027926 / 1.492716 (0.535209) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.309595 / 0.018006 (0.291588) \| 0.615675 / 0.000490 (0.615186) \| 0.004869 / 0.000200 (0.004669) \| 0.000116 / 0.000054 (0.000061) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033306 / 0.037411 (-0.004105) \| 0.104429 / 0.014526 (0.089904) \| 0.116989 / 0.176557 (-0.059568) \| 0.183638 / 0.737135 (-0.553497) \| 0.132624 / 0.296338 (-0.163714) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.644511 / 0.215209 (0.429302) \| 6.425544 / 2.077655 (4.347889) \| 3.079071 / 1.504120 (1.574951) \| 2.720963 / 1.541195 (1.179769) \| 2.835607 / 1.468490 (1.367117) \| 0.863561 / 4.584777 (-3.721216) \| 5.333462 / 3.745712 (1.587750) \| 4.843183 / 5.269862 (-0.426678) \| 3.106858 / 4.565676 (-1.458819) \| 0.106790 / 0.424275 (-0.317485) \| 0.008829 / 0.007607 (0.001222) \| 0.759003 / 0.226044 (0.532958) \| 7.771247 / 2.268929 (5.502318) \| 3.896844 / 55.444624 (-51.547780) \| 3.246671 / 6.876477 (-3.629806) \| 3.486167 / 2.142072 (1.344094) \| 1.071290 / 4.805227 (-3.733937) \| 0.217972 / 6.500664 (-6.282692) \| 0.089848 / 0.075469 (0.014379) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.816048 / 1.841788 (-0.025739) \| 25.625084 / 8.074308 (17.550776) \| 24.490882 / 10.191392 (14.299490) \| 0.242356 / 0.680424 (-0.438067) \| 0.027886 / 0.534201 (-0.506315) \| 0.496997 / 0.579283 (-0.082286) \| 0.613815 / 0.434364 (0.179451) \| 0.607132 / 0.540337 (0.066795) \| 0.833051 / 1.386936 (-0.553885) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0adfa9ada14c38fce5973b5e3f196a2c46dc9170 \"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) \| 0.011580 / 0.011353 (0.000227) \| 0.004199 / 0.011008 (-0.006809) \| 0.084055 / 0.038508 (0.045547) \| 0.096824 / 0.023109 (0.073715) \| 0.308755 / 0.275898 (0.032857) \| 0.341717 / 0.323480 (0.018237) \| 0.006018 / 0.007986 (-0.001968) \| 0.003597 / 0.004328 (-0.000731) \| 0.064953 / 0.004250 (0.060702) \| 0.059577 / 0.037052 (0.022525) \| 0.316292 / 0.258489 (0.057803) \| 0.358991 / 0.293841 (0.065150) \| 0.033925 / 0.128546 (-0.094621) \| 0.008828 / 0.075646 (-0.066818) \| 0.288673 / 0.419271 (-0.130599) \| 0.055494 / 0.043533 (0.011961) \| 0.311181 / 0.255139 (0.056042) \| 0.345220 / 0.283200 (0.062021) \| 0.024033 / 0.141683 (-0.117649) \| 1.504709 / 1.452155 (0.052554) \| 1.587920 / 1.492716 (0.095204) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.301099 / 0.018006 (0.283093) \| 0.594497 / 0.000490 (0.594007) \| 0.006244 / 0.000200 (0.006044) \| 0.000228 / 0.000054 (0.000174) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027663 / 0.037411 (-0.009748) \| 0.081767 / 0.014526 (0.067241) \| 0.097342 / 0.176557 (-0.079215) \| 0.153200 / 0.737135 (-0.583935) \| 0.097474 / 0.296338 (-0.198864) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.405929 / 0.215209 (0.190719) \| 4.045398 / 2.077655 (1.967743) \| 2.044669 / 1.504120 (0.540549) \| 1.872889 / 1.541195 (0.331694) \| 1.911901 / 1.468490 (0.443411) \| 0.480939 / 4.584777 (-4.103838) \| 3.652833 / 3.745712 (-0.092879) \| 3.281659 / 5.269862 (-1.988202) \| 2.038023 / 4.565676 (-2.527654) \| 0.056501 / 0.424275 (-0.367775) \| 0.007571 / 0.007607 (-0.000036) \| 0.481053 / 0.226044 (0.255009) \| 4.802048 / 2.268929 (2.533119) \| 2.560479 / 55.444624 (-52.884145) \| 2.164852 / 6.876477 (-4.711625) \| 2.374595 / 2.142072 (0.232523) \| 0.576309 / 4.805227 (-4.228918) \| 0.134831 / 6.500664 (-6.365833) \| 0.060649 / 0.075469 (-0.014820) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.254210 / 1.841788 (-0.587578) \| 19.826143 / 8.074308 (11.751835) \| 14.446391 / 10.191392 (4.254999) \| 0.165707 / 0.680424 (-0.514717) \| 0.018221 / 0.534201 (-0.515980) \| 0.395996 / 0.579283 (-0.183287) \| 0.424567 / 0.434364 (-0.009796) \| 0.459836 / 0.540337 (-0.080501) \| 0.635969 / 1.386936 (-0.750967) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006696 / 0.011353 (-0.004657) \| 0.004131 / 0.011008 (-0.006877) \| 0.064587 / 0.038508 (0.026079) \| 0.079189 / 0.023109 (0.056080) \| 0.359977 / 0.275898 (0.084079) \| 0.389331 / 0.323480 (0.065851) \| 0.005502 / 0.007986 (-0.002483) \| 0.003492 / 0.004328 (-0.000837) \| 0.064967 / 0.004250 (0.060716) \| 0.055953 / 0.037052 (0.018901) \| 0.363997 / 0.258489 (0.105508) \| 0.398405 / 0.293841 (0.104564) \| 0.031292 / 0.128546 (-0.097254) \| 0.008693 / 0.075646 (-0.066953) \| 0.070451 / 0.419271 (-0.348820) \| 0.048965 / 0.043533 (0.005432) \| 0.358288 / 0.255139 (0.103149) \| 0.379136 / 0.283200 (0.095936) \| 0.024364 / 0.141683 (-0.117319) \| 1.478998 / 1.452155 (0.026843) \| 1.547282 / 1.492716 (0.054566) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.328188 / 0.018006 (0.310182) \| 0.525968 / 0.000490 (0.525478) \| 0.003782 / 0.000200 (0.003582) \| 0.000089 / 0.000054 (0.000034) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032528 / 0.037411 (-0.004883) \| 0.087685 / 0.014526 (0.073159) \| 0.100684 / 0.176557 (-0.075872) \| 0.155944 / 0.737135 (-0.581192) \| 0.101949 / 0.296338 (-0.194389) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.418591 / 0.215209 (0.203382) \| 4.199235 / 2.077655 (2.121580) \| 2.183880 / 1.504120 (0.679760) \| 2.024502 / 1.541195 (0.483307) \| 2.017435 / 1.468490 (0.548945) \| 0.488881 / 4.584777 (-4.095896) \| 3.635002 / 3.745712 (-0.110710) \| 3.359992 / 5.269862 (-1.909870) \| 2.089686 / 4.565676 (-2.475991) \| 0.057813 / 0.424275 (-0.366462) \| 0.007349 / 0.007607 (-0.000258) \| 0.490719 / 0.226044 (0.264674) \| 4.859950 / 2.268929 (2.591022) \| 2.616711 / 55.444624 (-52.827914) \| 2.238671 / 6.876477 (-4.637806) \| 2.442262 / 2.142072 (0.300190) \| 0.598368 / 4.805227 (-4.206859) \| 0.135281 / 6.500664 (-6.365383) \| 0.063072 / 0.075469 (-0.012397) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.356396 / 1.841788 (-0.485392) \| 20.075123 / 8.074308 (12.000815) \| 14.191317 / 10.191392 (3.999925) \| 0.167691 / 0.680424 (-0.512732) \| 0.018290 / 0.534201 (-0.515911) \| 0.392881 / 0.579283 (-0.186402) \| 0.413665 / 0.434364 (-0.020699) \| 0.480766 / 0.540337 (-0.059571) \| 0.655625 / 1.386936 (-0.731311) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a46ca9cc138754629be261522301e725c7d14152 \"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) \| 0.007834 / 0.011353 (-0.003519) \| 0.004744 / 0.011008 (-0.006264) \| 0.102061 / 0.038508 (0.063553) \| 0.089246 / 0.023109 (0.066137) \| 0.399936 / 0.275898 (0.124038) \| 0.436974 / 0.323480 (0.113494) \| 0.004791 / 0.007986 (-0.003195) \| 0.005976 / 0.004328 (0.001647) \| 0.079336 / 0.004250 (0.075086) \| 0.065947 / 0.037052 (0.028894) \| 0.403747 / 0.258489 (0.145258) \| 0.460249 / 0.293841 (0.166408) \| 0.038065 / 0.128546 (-0.090482) \| 0.010179 / 0.075646 (-0.065467) \| 0.403620 / 0.419271 (-0.015652) \| 0.066439 / 0.043533 (0.022906) \| 0.412123 / 0.255139 (0.156984) \| 0.452121 / 0.283200 (0.168921) \| 0.033533 / 0.141683 (-0.108150) \| 1.858650 / 1.452155 (0.406495) \| 1.916248 / 1.492716 (0.423532) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.237180 / 0.018006 (0.219174) \| 0.526844 / 0.000490 (0.526354) \| 0.004220 / 0.000200 (0.004020) \| 0.000123 / 0.000054 (0.000069) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033860 / 0.037411 (-0.003552) \| 0.105054 / 0.014526 (0.090528) \| 0.116494 / 0.176557 (-0.060063) \| 0.185990 / 0.737135 (-0.551145) \| 0.119072 / 0.296338 (-0.177266) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.488549 / 0.215209 (0.273340) \| 4.884950 / 2.077655 (2.807295) \| 2.521819 / 1.504120 (1.017699) \| 2.329382 / 1.541195 (0.788188) \| 2.413710 / 1.468490 (0.945220) \| 0.568325 / 4.584777 (-4.016452) \| 4.243505 / 3.745712 (0.497793) \| 3.785983 / 5.269862 (-1.483879) \| 2.387146 / 4.565676 (-2.178531) \| 0.067176 / 0.424275 (-0.357099) \| 0.009145 / 0.007607 (0.001538) \| 0.571482 / 0.226044 (0.345437) \| 5.688822 / 2.268929 (3.419894) \| 3.067346 / 55.444624 (-52.377278) \| 2.688723 / 6.876477 (-4.187754) \| 2.883785 / 2.142072 (0.741713) \| 0.679326 / 4.805227 (-4.125901) \| 0.156018 / 6.500664 (-6.344646) \| 0.070947 / 0.075469 (-0.004522) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.556611 / 1.841788 (-0.285177) \| 23.545074 / 8.074308 (15.470766) \| 17.125108 / 10.191392 (6.933716) \| 0.180180 / 0.680424 (-0.500244) \| 0.021420 / 0.534201 (-0.512781) \| 0.466888 / 0.579283 (-0.112395) \| 0.485746 / 0.434364 (0.051383) \| 0.606181 / 0.540337 (0.065843) \| 0.776691 / 1.386936 (-0.610245) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007820 / 0.011353 (-0.003533) \| 0.004531 / 0.011008 (-0.006478) \| 0.076142 / 0.038508 (0.037634) \| 0.086367 / 0.023109 (0.063258) \| 0.456150 / 0.275898 (0.180252) \| 0.499712 / 0.323480 (0.176232) \| 0.006545 / 0.007986 (-0.001441) \| 0.003760 / 0.004328 (-0.000568) \| 0.076400 / 0.004250 (0.072150) \| 0.069689 / 0.037052 (0.032637) \| 0.459732 / 0.258489 (0.201243) \| 0.504217 / 0.293841 (0.210376) \| 0.037838 / 0.128546 (-0.090709) \| 0.009804 / 0.075646 (-0.065843) \| 0.084654 / 0.419271 (-0.334617) \| 0.060301 / 0.043533 (0.016768) \| 0.452984 / 0.255139 (0.197845) \| 0.479956 / 0.283200 (0.196757) \| 0.029674 / 0.141683 (-0.112009) \| 1.814059 / 1.452155 (0.361904) \| 1.878886 / 1.492716 (0.386170) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.326174 / 0.018006 (0.308168) \| 0.539722 / 0.000490 (0.539232) \| 0.025637 / 0.000200 (0.025437) \| 0.000209 / 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) \| 0.036328 / 0.037411 (-0.001084) \| 0.106369 / 0.014526 (0.091843) \| 0.118598 / 0.176557 (-0.057958) \| 0.182760 / 0.737135 (-0.554376) \| 0.120013 / 0.296338 (-0.176326) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.507328 / 0.215209 (0.292119) \| 5.092689 / 2.077655 (3.015034) \| 2.962334 / 1.504120 (1.458214) \| 2.507699 / 1.541195 (0.966504) \| 2.612245 / 1.468490 (1.143755) \| 0.568625 / 4.584777 (-4.016152) \| 4.296484 / 3.745712 (0.550772) \| 4.037788 / 5.269862 (-1.232073) \| 2.579826 / 4.565676 (-1.985850) \| 0.068558 / 0.424275 (-0.355717) \| 0.008916 / 0.007607 (0.001309) \| 0.601054 / 0.226044 (0.375010) \| 6.016061 / 2.268929 (3.747133) \| 3.311880 / 55.444624 (-52.132744) \| 2.912926 / 6.876477 (-3.963551) \| 3.101465 / 2.142072 (0.959393) \| 0.686848 / 4.805227 (-4.118380) \| 0.160243 / 6.500664 (-6.340421) \| 0.074084 / 0.075469 (-0.001385) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.754343 / 1.841788 (-0.087444) \| 24.215302 / 8.074308 (16.140994) \| 17.211007 / 10.191392 (7.019615) \| 0.188370 / 0.680424 (-0.492054) \| 0.028157 / 0.534201 (-0.506044) \| 0.490879 / 0.579283 (-0.088404) \| 0.501508 / 0.434364 (0.067144) \| 0.599719 / 0.540337 (0.059381) \| 0.852438 / 1.386936 (-0.534498) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d84cd1d6f51ca75ec5f5c3db3f372f093758cac9 \"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) \| 0.009736 / 0.011353 (-0.001617) \| 0.004761 / 0.011008 (-0.006247) \| 0.100069 / 0.038508 (0.061561) \| 0.077944 / 0.023109 (0.054835) \| 0.419944 / 0.275898 (0.144046) \| 0.459803 / 0.323480 (0.136323) \| 0.006296 / 0.007986 (-0.001689) \| 0.005375 / 0.004328 (0.001047) \| 0.089457 / 0.004250 (0.085207) \| 0.060585 / 0.037052 (0.023532) \| 0.437988 / 0.258489 (0.179499) \| 0.482676 / 0.293841 (0.188835) \| 0.049126 / 0.128546 (-0.079420) \| 0.015043 / 0.075646 (-0.060603) \| 0.342500 / 0.419271 (-0.076771) \| 0.067088 / 0.043533 (0.023555) \| 0.418364 / 0.255139 (0.163225) \| 0.458259 / 0.283200 (0.175059) \| 0.034091 / 0.141683 (-0.107592) \| 1.721589 / 1.452155 (0.269434) \| 1.823142 / 1.492716 (0.330426) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.212110 / 0.018006 (0.194103) \| 0.530957 / 0.000490 (0.530467) \| 0.003581 / 0.000200 (0.003382) \| 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) \| 0.030202 / 0.037411 (-0.007210) \| 0.100552 / 0.014526 (0.086026) \| 0.108150 / 0.176557 (-0.068407) \| 0.173203 / 0.737135 (-0.563932) \| 0.108624 / 0.296338 (-0.187715) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.577340 / 0.215209 (0.362131) \| 5.794197 / 2.077655 (3.716543) \| 2.396285 / 1.504120 (0.892165) \| 2.151972 / 1.541195 (0.610777) \| 2.109485 / 1.468490 (0.640995) \| 0.873906 / 4.584777 (-3.710871) \| 5.083302 / 3.745712 (1.337589) \| 4.600756 / 5.269862 (-0.669105) \| 2.891731 / 4.565676 (-1.673945) \| 0.096293 / 0.424275 (-0.327982) \| 0.008651 / 0.007607 (0.001044) \| 0.719095 / 0.226044 (0.493051) \| 7.193225 / 2.268929 (4.924297) \| 3.220145 / 55.444624 (-52.224479) \| 2.496715 / 6.876477 (-4.379762) \| 2.672972 / 2.142072 (0.530900) \| 1.031656 / 4.805227 (-3.773571) \| 0.207854 / 6.500664 (-6.292810) \| 0.074507 / 0.075469 (-0.000962) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.552821 / 1.841788 (-0.288967) \| 22.573015 / 8.074308 (14.498707) \| 21.074321 / 10.191392 (10.882929) \| 0.231911 / 0.680424 (-0.448513) \| 0.027761 / 0.534201 (-0.506440) \| 0.474644 / 0.579283 (-0.104639) \| 0.563780 / 0.434364 (0.129416) \| 0.527593 / 0.540337 (-0.012745) \| 0.732299 / 1.386936 (-0.654637) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008675 / 0.011353 (-0.002678) \| 0.005268 / 0.011008 (-0.005741) \| 0.079078 / 0.038508 (0.040570) \| 0.073505 / 0.023109 (0.050395) \| 0.453982 / 0.275898 (0.178083) \| 0.487839 / 0.323480 (0.164359) \| 0.005950 / 0.007986 (-0.002035) \| 0.003848 / 0.004328 (-0.000481) \| 0.076004 / 0.004250 (0.071754) \| 0.058410 / 0.037052 (0.021358) \| 0.460099 / 0.258489 (0.201610) \| 0.514860 / 0.293841 (0.221019) \| 0.048843 / 0.128546 (-0.079703) \| 0.014275 / 0.075646 (-0.061371) \| 0.090243 / 0.419271 (-0.329029) \| 0.060092 / 0.043533 (0.016559) \| 0.455669 / 0.255139 (0.200530) \| 0.484738 / 0.283200 (0.201538) \| 0.033012 / 0.141683 (-0.108671) \| 1.738854 / 1.452155 (0.286699) \| 1.852552 / 1.492716 (0.359835) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.245453 / 0.018006 (0.227447) \| 0.519929 / 0.000490 (0.519439) \| 0.007262 / 0.000200 (0.007062) \| 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) \| 0.031446 / 0.037411 (-0.005965) \| 0.094236 / 0.014526 (0.079710) \| 0.114457 / 0.176557 (-0.062100) \| 0.167448 / 0.737135 (-0.569687) \| 0.108791 / 0.296338 (-0.187548) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.603331 / 0.215209 (0.388122) \| 6.051556 / 2.077655 (3.973902) \| 2.797110 / 1.504120 (1.292990) \| 2.500517 / 1.541195 (0.959322) \| 2.531421 / 1.468490 (1.062931) \| 0.852075 / 4.584777 (-3.732702) \| 5.034140 / 3.745712 (1.288427) \| 4.576573 / 5.269862 (-0.693289) \| 2.973541 / 4.565676 (-1.592135) \| 0.101303 / 0.424275 (-0.322972) \| 0.008467 / 0.007607 (0.000860) \| 0.707143 / 0.226044 (0.481098) \| 7.262803 / 2.268929 (4.993874) \| 3.548841 / 55.444624 (-51.895783) \| 2.895975 / 6.876477 (-3.980502) \| 3.063521 / 2.142072 (0.921449) \| 1.014961 / 4.805227 (-3.790266) \| 0.208527 / 6.500664 (-6.292137) \| 0.074939 / 0.075469 (-0.000530) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.670708 / 1.841788 (-0.171080) \| 22.685227 / 8.074308 (14.610919) \| 20.393017 / 10.191392 (10.201625) \| 0.239303 / 0.680424 (-0.441121) \| 0.027742 / 0.534201 (-0.506459) \| 0.467230 / 0.579283 (-0.112053) \| 0.564169 / 0.434364 (0.129805) \| 0.554859 / 0.540337 (0.014522) \| 0.767471 / 1.386936 (-0.619465) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#72a57356a46ded67f4d7a02741141a96061246a8 \"CML watermark\")\n" ]
1,854,595,943	6,154	Use yaml instead of get data patterns when possible	closed	2023-08-17T09:17:05	2023-08-17T20:46:25	2023-08-17T20:37:19	https://github.com/huggingface/datasets/pull/6154	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6154", "html_url": "https://github.com/huggingface/datasets/pull/6154", "diff_url": "https://github.com/huggingface/datasets/pull/6154.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6154.patch", "merged_at": "2023-08-17T20:37:19" }	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) \| 0.006829 / 0.011353 (-0.004524) \| 0.004535 / 0.011008 (-0.006473) \| 0.085255 / 0.038508 (0.046747) \| 0.080861 / 0.023109 (0.057752) \| 0.366023 / 0.275898 (0.090125) \| 0.403095 / 0.323480 (0.079615) \| 0.005615 / 0.007986 (-0.002370) \| 0.003830 / 0.004328 (-0.000498) \| 0.064502 / 0.004250 (0.060251) \| 0.053916 / 0.037052 (0.016863) \| 0.366010 / 0.258489 (0.107521) \| 0.414565 / 0.293841 (0.120724) \| 0.031500 / 0.128546 (-0.097046) \| 0.009252 / 0.075646 (-0.066394) \| 0.289584 / 0.419271 (-0.129688) \| 0.052984 / 0.043533 (0.009451) \| 0.352626 / 0.255139 (0.097487) \| 0.390964 / 0.283200 (0.107764) \| 0.025118 / 0.141683 (-0.116565) \| 1.462316 / 1.452155 (0.010161) \| 1.565682 / 1.492716 (0.072966) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294432 / 0.018006 (0.276426) \| 0.618366 / 0.000490 (0.617876) \| 0.003270 / 0.000200 (0.003071) \| 0.000081 / 0.000054 (0.000027) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031194 / 0.037411 (-0.006217) \| 0.088892 / 0.014526 (0.074366) \| 0.102580 / 0.176557 (-0.073977) \| 0.159449 / 0.737135 (-0.577686) \| 0.104434 / 0.296338 (-0.191905) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.385690 / 0.215209 (0.170481) \| 3.832782 / 2.077655 (1.755128) \| 1.862521 / 1.504120 (0.358401) \| 1.685674 / 1.541195 (0.144479) \| 1.724984 / 1.468490 (0.256494) \| 0.483700 / 4.584777 (-4.101077) \| 3.664154 / 3.745712 (-0.081558) \| 3.323023 / 5.269862 (-1.946839) \| 2.055958 / 4.565676 (-2.509718) \| 0.056990 / 0.424275 (-0.367285) \| 0.007674 / 0.007607 (0.000067) \| 0.460642 / 0.226044 (0.234598) \| 4.609964 / 2.268929 (2.341036) \| 2.434868 / 55.444624 (-53.009756) \| 2.003347 / 6.876477 (-4.873130) \| 2.209520 / 2.142072 (0.067448) \| 0.629363 / 4.805227 (-4.175864) \| 0.135434 / 6.500664 (-6.365230) \| 0.060498 / 0.075469 (-0.014971) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.253917 / 1.841788 (-0.587870) \| 19.988953 / 8.074308 (11.914645) \| 14.353739 / 10.191392 (4.162347) \| 0.165987 / 0.680424 (-0.514437) \| 0.018299 / 0.534201 (-0.515902) \| 0.395532 / 0.579283 (-0.183751) \| 0.418708 / 0.434364 (-0.015656) \| 0.460865 / 0.540337 (-0.079472) \| 0.633925 / 1.386936 (-0.753011) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006631 / 0.011353 (-0.004722) \| 0.004109 / 0.011008 (-0.006899) \| 0.065003 / 0.038508 (0.026495) \| 0.080407 / 0.023109 (0.057297) \| 0.362966 / 0.275898 (0.087068) \| 0.389727 / 0.323480 (0.066247) \| 0.005588 / 0.007986 (-0.002397) \| 0.003517 / 0.004328 (-0.000812) \| 0.065821 / 0.004250 (0.061570) \| 0.057614 / 0.037052 (0.020561) \| 0.367422 / 0.258489 (0.108932) \| 0.400706 / 0.293841 (0.106865) \| 0.031560 / 0.128546 (-0.096986) \| 0.008659 / 0.075646 (-0.066987) \| 0.070756 / 0.419271 (-0.348516) \| 0.049821 / 0.043533 (0.006288) \| 0.360836 / 0.255139 (0.105697) \| 0.383981 / 0.283200 (0.100781) \| 0.023719 / 0.141683 (-0.117963) \| 1.485197 / 1.452155 (0.033043) \| 1.544899 / 1.492716 (0.052182) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.336480 / 0.018006 (0.318474) \| 0.532839 / 0.000490 (0.532349) \| 0.003767 / 0.000200 (0.003567) \| 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) \| 0.034132 / 0.037411 (-0.003280) \| 0.090131 / 0.014526 (0.075605) \| 0.104086 / 0.176557 (-0.072471) \| 0.158385 / 0.737135 (-0.578751) \| 0.106417 / 0.296338 (-0.189922) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.416462 / 0.215209 (0.201253) \| 4.160409 / 2.077655 (2.082755) \| 2.195355 / 1.504120 (0.691235) \| 2.051234 / 1.541195 (0.510040) \| 2.012116 / 1.468490 (0.543626) \| 0.477414 / 4.584777 (-4.107363) \| 3.590326 / 3.745712 (-0.155386) \| 3.318490 / 5.269862 (-1.951371) \| 2.064124 / 4.565676 (-2.501553) \| 0.057040 / 0.424275 (-0.367235) \| 0.007283 / 0.007607 (-0.000324) \| 0.480490 / 0.226044 (0.254445) \| 4.804013 / 2.268929 (2.535084) \| 2.625940 / 55.444624 (-52.818685) \| 2.231537 / 6.876477 (-4.644939) \| 2.441649 / 2.142072 (0.299576) \| 0.573207 / 4.805227 (-4.232020) \| 0.131685 / 6.500664 (-6.368979) \| 0.060112 / 0.075469 (-0.015357) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.358587 / 1.841788 (-0.483200) \| 20.457562 / 8.074308 (12.383254) \| 14.236304 / 10.191392 (4.044912) \| 0.152860 / 0.680424 (-0.527563) \| 0.018466 / 0.534201 (-0.515735) \| 0.401391 / 0.579283 (-0.177893) \| 0.410252 / 0.434364 (-0.024111) \| 0.484335 / 0.540337 (-0.056002) \| 0.663818 / 1.386936 (-0.723118) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#acac88873abcb585892dc361eb9f6a70a1fd9a59 \"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) \| 0.007725 / 0.011353 (-0.003628) \| 0.004448 / 0.011008 (-0.006560) \| 0.098689 / 0.038508 (0.060180) \| 0.082919 / 0.023109 (0.059809) \| 0.380707 / 0.275898 (0.104809) \| 0.452977 / 0.323480 (0.129497) \| 0.004430 / 0.007986 (-0.003555) \| 0.003712 / 0.004328 (-0.000616) \| 0.076675 / 0.004250 (0.072425) \| 0.062281 / 0.037052 (0.025228) \| 0.403370 / 0.258489 (0.144881) \| 0.464557 / 0.293841 (0.170716) \| 0.035646 / 0.128546 (-0.092900) \| 0.009776 / 0.075646 (-0.065870) \| 0.341955 / 0.419271 (-0.077316) \| 0.059515 / 0.043533 (0.015983) \| 0.388421 / 0.255139 (0.133282) \| 0.439496 / 0.283200 (0.156296) \| 0.029090 / 0.141683 (-0.112593) \| 1.727473 / 1.452155 (0.275319) \| 1.810448 / 1.492716 (0.317732) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.221215 / 0.018006 (0.203208) \| 0.486660 / 0.000490 (0.486171) \| 0.005467 / 0.000200 (0.005267) \| 0.000110 / 0.000054 (0.000056) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032491 / 0.037411 (-0.004920) \| 0.094446 / 0.014526 (0.079920) \| 0.110339 / 0.176557 (-0.066217) \| 0.175004 / 0.737135 (-0.562131) \| 0.109209 / 0.296338 (-0.187129) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.453966 / 0.215209 (0.238757) \| 4.515842 / 2.077655 (2.438187) \| 2.240512 / 1.504120 (0.736392) \| 2.059911 / 1.541195 (0.518717) \| 2.150635 / 1.468490 (0.682145) \| 0.564509 / 4.584777 (-4.020268) \| 4.055208 / 3.745712 (0.309496) \| 3.614084 / 5.269862 (-1.655778) \| 2.295760 / 4.565676 (-2.269917) \| 0.066507 / 0.424275 (-0.357768) \| 0.008909 / 0.007607 (0.001302) \| 0.542604 / 0.226044 (0.316560) \| 5.412162 / 2.268929 (3.143233) \| 2.758757 / 55.444624 (-52.685867) \| 2.430693 / 6.876477 (-4.445784) \| 2.669866 / 2.142072 (0.527793) \| 0.681756 / 4.805227 (-4.123471) \| 0.156524 / 6.500664 (-6.344140) \| 0.069499 / 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) \| 1.571591 / 1.841788 (-0.270197) \| 22.543437 / 8.074308 (14.469129) \| 16.068426 / 10.191392 (5.877034) \| 0.169860 / 0.680424 (-0.510564) \| 0.021216 / 0.534201 (-0.512985) \| 0.468745 / 0.579283 (-0.110538) \| 0.475924 / 0.434364 (0.041560) \| 0.535574 / 0.540337 (-0.004763) \| 0.733823 / 1.386936 (-0.653113) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008038 / 0.011353 (-0.003315) \| 0.004565 / 0.011008 (-0.006443) \| 0.076892 / 0.038508 (0.038384) \| 0.089559 / 0.023109 (0.066450) \| 0.456752 / 0.275898 (0.180854) \| 0.497282 / 0.323480 (0.173802) \| 0.005991 / 0.007986 (-0.001995) \| 0.003784 / 0.004328 (-0.000545) \| 0.076339 / 0.004250 (0.072089) \| 0.066050 / 0.037052 (0.028998) \| 0.462708 / 0.258489 (0.204219) \| 0.503711 / 0.293841 (0.209870) \| 0.037098 / 0.128546 (-0.091448) \| 0.009869 / 0.075646 (-0.065777) \| 0.083678 / 0.419271 (-0.335594) \| 0.058166 / 0.043533 (0.014633) \| 0.461839 / 0.255139 (0.206700) \| 0.481546 / 0.283200 (0.198347) \| 0.027755 / 0.141683 (-0.113928) \| 1.738490 / 1.452155 (0.286335) \| 1.832276 / 1.492716 (0.339560) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.329935 / 0.018006 (0.311929) \| 0.497438 / 0.000490 (0.496949) \| 0.034644 / 0.000200 (0.034444) \| 0.000199 / 0.000054 (0.000145) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035427 / 0.037411 (-0.001984) \| 0.105689 / 0.014526 (0.091163) \| 0.117706 / 0.176557 (-0.058850) \| 0.177862 / 0.737135 (-0.559273) \| 0.116791 / 0.296338 (-0.179547) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.484851 / 0.215209 (0.269642) \| 4.804346 / 2.077655 (2.726691) \| 2.494801 / 1.504120 (0.990681) \| 2.320185 / 1.541195 (0.778990) \| 2.374090 / 1.468490 (0.905600) \| 0.567397 / 4.584777 (-4.017380) \| 4.087402 / 3.745712 (0.341690) \| 3.794245 / 5.269862 (-1.475616) \| 2.378481 / 4.565676 (-2.187195) \| 0.068228 / 0.424275 (-0.356047) \| 0.008740 / 0.007607 (0.001133) \| 0.574876 / 0.226044 (0.348832) \| 5.742644 / 2.268929 (3.473716) \| 3.047661 / 55.444624 (-52.396963) \| 2.729742 / 6.876477 (-4.146735) \| 2.852510 / 2.142072 (0.710438) \| 0.679450 / 4.805227 (-4.125777) \| 0.156162 / 6.500664 (-6.344502) \| 0.074051 / 0.075469 (-0.001418) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.576182 / 1.841788 (-0.265605) \| 23.298147 / 8.074308 (15.223839) \| 16.344621 / 10.191392 (6.153229) \| 0.167571 / 0.680424 (-0.512852) \| 0.021423 / 0.534201 (-0.512778) \| 0.464511 / 0.579283 (-0.114772) \| 0.453257 / 0.434364 (0.018893) \| 0.563439 / 0.540337 (0.023102) \| 0.764759 / 1.386936 (-0.622177) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e8dc4b32b0d91bdb0971f8203ee37e6588c7770e \"CML watermark\")\n", "This should also fix https://github.com/huggingface/datasets/issues/6140, so please link it with this PR before merging.", "Done !", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006719 / 0.011353 (-0.004634) \| 0.004299 / 0.011008 (-0.006709) \| 0.085296 / 0.038508 (0.046788) \| 0.085144 / 0.023109 (0.062035) \| 0.361703 / 0.275898 (0.085805) \| 0.397721 / 0.323480 (0.074241) \| 0.005920 / 0.007986 (-0.002065) \| 0.003853 / 0.004328 (-0.000476) \| 0.065633 / 0.004250 (0.061383) \| 0.057000 / 0.037052 (0.019947) \| 0.379981 / 0.258489 (0.121492) \| 0.419041 / 0.293841 (0.125200) \| 0.031225 / 0.128546 (-0.097322) \| 0.008868 / 0.075646 (-0.066779) \| 0.288808 / 0.419271 (-0.130463) \| 0.052391 / 0.043533 (0.008859) \| 0.362349 / 0.255139 (0.107210) \| 0.399858 / 0.283200 (0.116658) \| 0.025843 / 0.141683 (-0.115840) \| 1.498988 / 1.452155 (0.046834) \| 1.547290 / 1.492716 (0.054574) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278091 / 0.018006 (0.260085) \| 0.621794 / 0.000490 (0.621305) \| 0.003770 / 0.000200 (0.003570) \| 0.000084 / 0.000054 (0.000029) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029128 / 0.037411 (-0.008283) \| 0.082061 / 0.014526 (0.067536) \| 0.101758 / 0.176557 (-0.074799) \| 0.155724 / 0.737135 (-0.581411) \| 0.102173 / 0.296338 (-0.194165) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.387145 / 0.215209 (0.171935) \| 3.868262 / 2.077655 (1.790607) \| 1.886440 / 1.504120 (0.382320) \| 1.723305 / 1.541195 (0.182111) \| 1.805411 / 1.468490 (0.336921) \| 0.485024 / 4.584777 (-4.099753) \| 3.637859 / 3.745712 (-0.107853) \| 3.319593 / 5.269862 (-1.950269) \| 2.087860 / 4.565676 (-2.477817) \| 0.056992 / 0.424275 (-0.367283) \| 0.007623 / 0.007607 (0.000016) \| 0.468182 / 0.226044 (0.242138) \| 4.681112 / 2.268929 (2.412183) \| 2.407010 / 55.444624 (-53.037614) \| 2.026604 / 6.876477 (-4.849872) \| 2.298158 / 2.142072 (0.156086) \| 0.581839 / 4.805227 (-4.223388) \| 0.132101 / 6.500664 (-6.368563) \| 0.060472 / 0.075469 (-0.014997) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.236422 / 1.841788 (-0.605365) \| 20.505168 / 8.074308 (12.430860) \| 14.356081 / 10.191392 (4.164689) \| 0.148808 / 0.680424 (-0.531616) \| 0.018433 / 0.534201 (-0.515768) \| 0.391323 / 0.579283 (-0.187960) \| 0.413142 / 0.434364 (-0.021222) \| 0.453484 / 0.540337 (-0.086853) \| 0.620771 / 1.386936 (-0.766165) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007030 / 0.011353 (-0.004323) \| 0.004430 / 0.011008 (-0.006578) \| 0.065578 / 0.038508 (0.027070) \| 0.090751 / 0.023109 (0.067642) \| 0.389121 / 0.275898 (0.113223) \| 0.424657 / 0.323480 (0.101177) \| 0.006575 / 0.007986 (-0.001410) \| 0.003855 / 0.004328 (-0.000473) \| 0.066175 / 0.004250 (0.061925) \| 0.063255 / 0.037052 (0.026202) \| 0.397161 / 0.258489 (0.138672) \| 0.435291 / 0.293841 (0.141450) \| 0.031622 / 0.128546 (-0.096925) \| 0.008900 / 0.075646 (-0.066747) \| 0.071694 / 0.419271 (-0.347577) \| 0.049161 / 0.043533 (0.005628) \| 0.386214 / 0.255139 (0.131075) \| 0.404571 / 0.283200 (0.121372) \| 0.024821 / 0.141683 (-0.116862) \| 1.489514 / 1.452155 (0.037359) \| 1.576139 / 1.492716 (0.083423) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289884 / 0.018006 (0.271878) \| 0.629342 / 0.000490 (0.628852) \| 0.004799 / 0.000200 (0.004599) \| 0.000160 / 0.000054 (0.000106) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032081 / 0.037411 (-0.005331) \| 0.088152 / 0.014526 (0.073626) \| 0.107289 / 0.176557 (-0.069267) \| 0.164598 / 0.737135 (-0.572537) \| 0.108395 / 0.296338 (-0.187944) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.426723 / 0.215209 (0.211514) \| 4.267719 / 2.077655 (2.190064) \| 2.289657 / 1.504120 (0.785537) \| 2.117435 / 1.541195 (0.576240) \| 2.187292 / 1.468490 (0.718802) \| 0.478387 / 4.584777 (-4.106390) \| 3.625096 / 3.745712 (-0.120616) \| 3.408036 / 5.269862 (-1.861826) \| 2.124117 / 4.565676 (-2.441559) \| 0.056537 / 0.424275 (-0.367738) \| 0.007489 / 0.007607 (-0.000118) \| 0.502434 / 0.226044 (0.276389) \| 5.025357 / 2.268929 (2.756428) \| 2.740554 / 55.444624 (-52.704070) \| 2.418841 / 6.876477 (-4.457635) \| 2.730764 / 2.142072 (0.588691) \| 0.600013 / 4.805227 (-4.205214) \| 0.133039 / 6.500664 (-6.367625) \| 0.061466 / 0.075469 (-0.014003) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.330211 / 1.841788 (-0.511577) \| 21.092100 / 8.074308 (13.017792) \| 14.463054 / 10.191392 (4.271662) \| 0.154149 / 0.680424 (-0.526274) \| 0.018891 / 0.534201 (-0.515310) \| 0.393078 / 0.579283 (-0.186205) \| 0.415279 / 0.434364 (-0.019085) \| 0.479469 / 0.540337 (-0.060868) \| 0.659953 / 1.386936 (-0.726983) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5ca2ba050340829b4dd44791afc15db0d82a3276 \"CML watermark\")\n" ]
1,852,494,646	6,152	FolderBase Dataset automatically resolves under current directory when data_dir is not specified	closed	2023-08-16T04:38:09	2025-06-18T14:18:42	2025-06-18T14:18:42	https://github.com/huggingface/datasets/issues/6152	null	npuichigo	false	[ "@lhoestq ", "Makes sense, I guess this can be fixed in the load_dataset_builder method.\r\nIt concerns every packaged builder I think (see values in `_PACKAGED_DATASETS_MODULES`)", "I think the behavior is related to these lines, which short circuited the error handling.\r\nhttps://github.com/huggingface/datasets/blob/664a1cb72ea1e6ef7c47e671e2686ca4a35e8d63/src/datasets/load.py#L946-L952\r\n\r\nSo should data_dir be checked here or still delegating to actual `DatasetModule`? In that case, how to properly set `data_files` here.", "This is location in PackagedDatasetModuleFactory.get_module seems the be the right place to check if at least data_dir or data_files are passed", "@mariosasko can you please assign this issue to me,I want to work on this", "#self-assign", "@mariosasko is this issue still open? i would love to kickstart my journey to open source with this issue!\r\nRegards\r\nzutarich", "@zutarich It is unless @debrupf2946 is working on it.", "#self-assign", "I am working and will open a pull request soon @Etelis \r\n", "@mariosasko can i take this up? ", "#self-assign", "Yes, feel free to work on this :)", "i think its working as expected . Heres the log i get for the same line -\r\n\r\n![image](https://github.com/huggingface/datasets/assets/63234112/8a857ec5-8dd0-4b01-b3a7-7c93444b9558)\r\n", "#self-assign", "Hey! I saw that PR #7314 fixed the issue when no matching data files are found, which is great. But I think the builder still defaults to the current directory when `data_dir` isn't specified, which could lead to unexpected behavior.\n\nIf no one’s actively working on that part, I’d be happy to pick it up and propose a fix. Let me know if that works!\n", "Cool, feel free to ping me if you open a PR @aniyagnik :)", "Hey! I noticed this is still open and unresolved. I’d like to take this up and submit a PR to prevent resolving to the current directory when data_dir is not specified!!!", "Hi @aniyagnik @npuichigo @sahillihas @lhoestq @mariosasko,\nI've opened a PR to address the issue discussed in #6152 by adding an early validation check for folder-based datasets. Would appreciate your review whenever convenient. Thanks!\n\nhttps://github.com/huggingface/datasets/pull/7618" ]
1,851,497,818	6,151	Faster sorting for single key items	closed	2023-08-15T14:02:31	2023-08-21T14:38:26	2023-08-21T14:38:25	https://github.com/huggingface/datasets/issues/6151	null	jackapbutler	false	[ "`Dataset.sort` essentially does the same thing except it uses `pyarrow.compute.sort_indices` which doesn't involve copying the data into python objects (saving memory)\r\n\r\n```python\r\nsort_keys = [(col, \"ascending\") for col in column_names]\r\nindices = pc.sort_indices(self.data, sort_keys=sort_keys)\r\nreturn self.select(indices)\r\n```", "Ok interesting, I'll continue debugging to see what is going wrong on my end." ]
1,850,740,456	6,150	Allow dataset implement .take	open	2023-08-15T00:17:51	2023-08-17T13:49:37	null	https://github.com/huggingface/datasets/issues/6150	null	brando90	false	[ "```\r\n dataset = IterableDataset(dataset) if type(dataset) != IterableDataset else dataset # to force dataset.take(batch_size) to work in non-streaming mode\r\n ```\r\n", "hf discuss: https://discuss.huggingface.co/t/how-does-one-make-dataset-take-512-work-with-streaming-false-with-hugging-face-data-set/50770", "so: https://stackoverflow.com/questions/76902824/how-does-one-make-dataset-take512-work-with-streaming-false-with-hugging-fac", "Feel free to work on this. In addition, `IterableDataset` supports `skip`, so we should also add this method to `Dataset`." ]
1,850,700,624	6,149	Dataset.from_parquet cannot load subset of columns	closed	2023-08-14T23:28:22	2023-08-17T22:36:05	2023-08-17T22:36:05	https://github.com/huggingface/datasets/issues/6149	null	dwyatte	false	[ "Looks like this regression was introduced in `datasets==2.13.0` (`2.12.0` could load a subset of columns)\r\n\r\nThis does not appear to be fixed by https://github.com/huggingface/datasets/pull/6045 (bug still exists on `main`)" ]
1,849,524,683	6,148	Ignore parallel warning in map_nested	closed	2023-08-14T10:43:41	2023-08-17T08:54:06	2023-08-17T08:43:58	https://github.com/huggingface/datasets/pull/6148	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6148", "html_url": "https://github.com/huggingface/datasets/pull/6148", "diff_url": "https://github.com/huggingface/datasets/pull/6148.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6148.patch", "merged_at": "2023-08-17T08:43:58" }	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) \| 0.006818 / 0.011353 (-0.004534) \| 0.004166 / 0.011008 (-0.006842) \| 0.086756 / 0.038508 (0.048248) \| 0.084444 / 0.023109 (0.061335) \| 0.319249 / 0.275898 (0.043351) \| 0.358689 / 0.323480 (0.035209) \| 0.004344 / 0.007986 (-0.003641) \| 0.003564 / 0.004328 (-0.000765) \| 0.065021 / 0.004250 (0.060771) \| 0.055991 / 0.037052 (0.018939) \| 0.319573 / 0.258489 (0.061084) \| 0.373239 / 0.293841 (0.079398) \| 0.031431 / 0.128546 (-0.097115) \| 0.008671 / 0.075646 (-0.066975) \| 0.288484 / 0.419271 (-0.130788) \| 0.053501 / 0.043533 (0.009968) \| 0.316934 / 0.255139 (0.061795) \| 0.354233 / 0.283200 (0.071034) \| 0.028088 / 0.141683 (-0.113595) \| 1.510905 / 1.452155 (0.058750) \| 1.568614 / 1.492716 (0.075898) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292343 / 0.018006 (0.274337) \| 0.592309 / 0.000490 (0.591819) \| 0.003850 / 0.000200 (0.003650) \| 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) \| 0.033510 / 0.037411 (-0.003901) \| 0.089546 / 0.014526 (0.075020) \| 0.104909 / 0.176557 (-0.071648) \| 0.162219 / 0.737135 (-0.574916) \| 0.104137 / 0.296338 (-0.192202) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.407993 / 0.215209 (0.192784) \| 4.063423 / 2.077655 (1.985768) \| 2.050237 / 1.504120 (0.546117) \| 1.888939 / 1.541195 (0.347744) \| 2.015195 / 1.468490 (0.546704) \| 0.492617 / 4.584777 (-4.092160) \| 3.595871 / 3.745712 (-0.149841) \| 3.320467 / 5.269862 (-1.949395) \| 2.099987 / 4.565676 (-2.465690) \| 0.058513 / 0.424275 (-0.365762) \| 0.007709 / 0.007607 (0.000102) \| 0.479277 / 0.226044 (0.253233) \| 4.790712 / 2.268929 (2.521783) \| 2.517292 / 55.444624 (-52.927332) \| 2.167461 / 6.876477 (-4.709016) \| 2.432011 / 2.142072 (0.289939) \| 0.600537 / 4.805227 (-4.204690) \| 0.133538 / 6.500664 (-6.367126) \| 0.059621 / 0.075469 (-0.015848) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.280375 / 1.841788 (-0.561413) \| 20.777971 / 8.074308 (12.703663) \| 14.869539 / 10.191392 (4.678147) \| 0.159372 / 0.680424 (-0.521052) \| 0.018096 / 0.534201 (-0.516105) \| 0.393945 / 0.579283 (-0.185338) \| 0.409598 / 0.434364 (-0.024766) \| 0.459202 / 0.540337 (-0.081136) \| 0.632298 / 1.386936 (-0.754638) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006694 / 0.011353 (-0.004659) \| 0.004299 / 0.011008 (-0.006709) \| 0.064880 / 0.038508 (0.026372) \| 0.083233 / 0.023109 (0.060124) \| 0.366488 / 0.275898 (0.090590) \| 0.405049 / 0.323480 (0.081569) \| 0.005602 / 0.007986 (-0.002384) \| 0.003623 / 0.004328 (-0.000705) \| 0.064410 / 0.004250 (0.060160) \| 0.057962 / 0.037052 (0.020910) \| 0.365318 / 0.258489 (0.106829) \| 0.403151 / 0.293841 (0.109310) \| 0.031285 / 0.128546 (-0.097261) \| 0.008867 / 0.075646 (-0.066780) \| 0.071137 / 0.419271 (-0.348135) \| 0.048398 / 0.043533 (0.004865) \| 0.360187 / 0.255139 (0.105048) \| 0.383872 / 0.283200 (0.100673) \| 0.023232 / 0.141683 (-0.118451) \| 1.526980 / 1.452155 (0.074826) \| 1.587265 / 1.492716 (0.094549) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.362603 / 0.018006 (0.344596) \| 0.557034 / 0.000490 (0.556544) \| 0.025303 / 0.000200 (0.025103) \| 0.000562 / 0.000054 (0.000508) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030636 / 0.037411 (-0.006775) \| 0.088085 / 0.014526 (0.073559) \| 0.103238 / 0.176557 (-0.073318) \| 0.155208 / 0.737135 (-0.581928) \| 0.106661 / 0.296338 (-0.189678) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.413660 / 0.215209 (0.198451) \| 4.122717 / 2.077655 (2.045063) \| 2.097656 / 1.504120 (0.593536) \| 1.931995 / 1.541195 (0.390801) \| 2.071497 / 1.468490 (0.603007) \| 0.490257 / 4.584777 (-4.094520) \| 3.588076 / 3.745712 (-0.157636) \| 3.423087 / 5.269862 (-1.846774) \| 2.147974 / 4.565676 (-2.417703) \| 0.058783 / 0.424275 (-0.365492) \| 0.007456 / 0.007607 (-0.000151) \| 0.492350 / 0.226044 (0.266305) \| 4.935935 / 2.268929 (2.667006) \| 2.604217 / 55.444624 (-52.840407) \| 2.333723 / 6.876477 (-4.542754) \| 2.585293 / 2.142072 (0.443220) \| 0.608800 / 4.805227 (-4.196427) \| 0.135806 / 6.500664 (-6.364858) \| 0.062716 / 0.075469 (-0.012753) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.347359 / 1.841788 (-0.494429) \| 21.420505 / 8.074308 (13.346197) \| 14.325914 / 10.191392 (4.134522) \| 0.159617 / 0.680424 (-0.520806) \| 0.018769 / 0.534201 (-0.515432) \| 0.399677 / 0.579283 (-0.179606) \| 0.402992 / 0.434364 (-0.031372) \| 0.484629 / 0.540337 (-0.055709) \| 0.656007 / 1.386936 (-0.730929) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ac94bb10d5c00ce8fdaf461eb1ff4b8572cfe956 \"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) \| 0.007291 / 0.011353 (-0.004062) \| 0.004501 / 0.011008 (-0.006508) \| 0.097529 / 0.038508 (0.059021) \| 0.079257 / 0.023109 (0.056147) \| 0.356390 / 0.275898 (0.080492) \| 0.390065 / 0.323480 (0.066585) \| 0.006071 / 0.007986 (-0.001914) \| 0.003783 / 0.004328 (-0.000546) \| 0.074598 / 0.004250 (0.070348) \| 0.059626 / 0.037052 (0.022574) \| 0.395344 / 0.258489 (0.136855) \| 0.418564 / 0.293841 (0.124723) \| 0.041843 / 0.128546 (-0.086704) \| 0.009293 / 0.075646 (-0.066354) \| 0.332668 / 0.419271 (-0.086604) \| 0.065753 / 0.043533 (0.022220) \| 0.357285 / 0.255139 (0.102146) \| 0.402974 / 0.283200 (0.119775) \| 0.028714 / 0.141683 (-0.112968) \| 1.733913 / 1.452155 (0.281759) \| 1.802574 / 1.492716 (0.309858) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.253114 / 0.018006 (0.235108) \| 0.606338 / 0.000490 (0.605848) \| 0.006871 / 0.000200 (0.006671) \| 0.000126 / 0.000054 (0.000072) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031850 / 0.037411 (-0.005562) \| 0.095148 / 0.014526 (0.080622) \| 0.111499 / 0.176557 (-0.065057) \| 0.174653 / 0.737135 (-0.562483) \| 0.109396 / 0.296338 (-0.186943) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.440442 / 0.215209 (0.225233) \| 4.408792 / 2.077655 (2.331137) \| 2.149778 / 1.504120 (0.645658) \| 1.922430 / 1.541195 (0.381235) \| 2.029281 / 1.468490 (0.560791) \| 0.611586 / 4.584777 (-3.973191) \| 4.204571 / 3.745712 (0.458859) \| 3.638194 / 5.269862 (-1.631668) \| 2.336146 / 4.565676 (-2.229531) \| 0.065383 / 0.424275 (-0.358892) \| 0.008441 / 0.007607 (0.000834) \| 0.527357 / 0.226044 (0.301313) \| 5.247892 / 2.268929 (2.978963) \| 2.654005 / 55.444624 (-52.790620) \| 2.256596 / 6.876477 (-4.619881) \| 2.432191 / 2.142072 (0.290119) \| 0.672759 / 4.805227 (-4.132469) \| 0.148494 / 6.500664 (-6.352170) \| 0.068248 / 0.075469 (-0.007221) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.544250 / 1.841788 (-0.297538) \| 21.882016 / 8.074308 (13.807708) \| 16.470182 / 10.191392 (6.278790) \| 0.166107 / 0.680424 (-0.514317) \| 0.021305 / 0.534201 (-0.512896) \| 0.445069 / 0.579283 (-0.134214) \| 0.500631 / 0.434364 (0.066267) \| 0.525801 / 0.540337 (-0.014536) \| 0.806534 / 1.386936 (-0.580402) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007322 / 0.011353 (-0.004030) \| 0.004206 / 0.011008 (-0.006802) \| 0.074827 / 0.038508 (0.036319) \| 0.084759 / 0.023109 (0.061650) \| 0.421204 / 0.275898 (0.145306) \| 0.464442 / 0.323480 (0.140962) \| 0.006523 / 0.007986 (-0.001463) \| 0.003613 / 0.004328 (-0.000716) \| 0.073796 / 0.004250 (0.069545) \| 0.066609 / 0.037052 (0.029557) \| 0.430108 / 0.258489 (0.171619) \| 0.463165 / 0.293841 (0.169324) \| 0.036015 / 0.128546 (-0.092532) \| 0.009696 / 0.075646 (-0.065951) \| 0.083326 / 0.419271 (-0.335946) \| 0.056804 / 0.043533 (0.013271) \| 0.423333 / 0.255139 (0.168194) \| 0.450538 / 0.283200 (0.167338) \| 0.027067 / 0.141683 (-0.114616) \| 1.700563 / 1.452155 (0.248408) \| 1.748738 / 1.492716 (0.256021) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.395682 / 0.018006 (0.377675) \| 0.540192 / 0.000490 (0.539702) \| 0.140049 / 0.000200 (0.139849) \| 0.000694 / 0.000054 (0.000639) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036643 / 0.037411 (-0.000769) \| 0.104422 / 0.014526 (0.089896) \| 0.113072 / 0.176557 (-0.063484) \| 0.179561 / 0.737135 (-0.557575) \| 0.118620 / 0.296338 (-0.177718) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.476547 / 0.215209 (0.261338) \| 4.716009 / 2.077655 (2.638354) \| 2.412111 / 1.504120 (0.907991) \| 2.246389 / 1.541195 (0.705194) \| 2.307058 / 1.468490 (0.838568) \| 0.552759 / 4.584777 (-4.032018) \| 4.172484 / 3.745712 (0.426771) \| 3.848419 / 5.269862 (-1.421443) \| 2.310338 / 4.565676 (-2.255339) \| 0.071757 / 0.424275 (-0.352518) \| 0.011206 / 0.007607 (0.003599) \| 0.609526 / 0.226044 (0.383482) \| 5.583065 / 2.268929 (3.314136) \| 3.081227 / 55.444624 (-52.363397) \| 2.637782 / 6.876477 (-4.238695) \| 2.887561 / 2.142072 (0.745489) \| 0.667227 / 4.805227 (-4.138000) \| 0.154421 / 6.500664 (-6.346243) \| 0.070772 / 0.075469 (-0.004697) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.605500 / 1.841788 (-0.236288) \| 22.872717 / 8.074308 (14.798409) \| 15.865333 / 10.191392 (5.673941) \| 0.170353 / 0.680424 (-0.510071) \| 0.021854 / 0.534201 (-0.512347) \| 0.461467 / 0.579283 (-0.117816) \| 0.477743 / 0.434364 (0.043379) \| 0.597234 / 0.540337 (0.056896) \| 0.800416 / 1.386936 (-0.586520) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a \"CML watermark\")\n" ]
1,848,914,830	6,147	ValueError when running BeamBasedBuilder with GCS path in cache_dir	closed	2023-08-14T03:11:34	2024-03-18T16:59:15	2024-03-18T16:59:14	https://github.com/huggingface/datasets/issues/6147	null	ktrk115	false	[ "The cause of the error seems to be that `datasets` adds \"gcs://\" as a schema, while `beam` checks only \"gs://\".\r\n\r\ndatasets: https://github.com/huggingface/datasets/blob/c02a44715c036b5261686669727394b1308a3a4b/src/datasets/builder.py#L822\r\n\r\nbeam: [link](https://github.com/apache/beam/blob/25e1a64641b1c8a3c0a6c75c6e86031b87307f22/sdks/python/apache_beam/io/filesystems.py#L98-L101)\r\n```\r\n systems = [\r\n fs for fs in FileSystem.get_all_subclasses()\r\n if fs.scheme() == path_scheme\r\n ]\r\n```", "Hi! We've deprecated the Beam API, as we don't have the bandwidth to support it properly..." ]
1,848,417,366	6,146	DatasetGenerationError when load glue benchmark datasets from `load_dataset`	closed	2023-08-13T05:17:56	2023-08-26T22:09:09	2023-08-26T22:09:09	https://github.com/huggingface/datasets/issues/6146	null	yusx-swapp	false	[ "I've tried clear the .cache file, doesn't work.", "This issue happens on AWS sagemaker", "This issue can happen if there is a directory named \"glue\" relative to the Python script with the `load_dataset` call (similar issue to this one: https://github.com/huggingface/datasets/issues/5228). Is this the case?", "> This issue can happen if there is a directory named \"glue\" relative to the Python script with the `load_dataset` call (similar issue to this one: #5228). Is this the case?\r\n\r\nThats correct!\r\nSorry for my late response." ]
1,852,630,074	6,153	custom load dataset to hub	closed	2023-08-13T04:42:22	2023-11-21T11:50:28	2023-10-08T17:04:16	https://github.com/huggingface/datasets/issues/6153	null	andysingal	false	[ "This is an issue for the [Datasets repo](https://github.com/huggingface/datasets).", "> This is an issue for the [Datasets repo](https://github.com/huggingface/datasets).\r\n\r\nThanks @sgugger , I guess I will wait for them to address the issue . Looking forward to hearing from them ", "You can use `.push_to_hub(\"<username>/<repo>\")` to push a `Dataset` to the Hub.", "> You can use `.push_to_hub(\"<username>/<repo>\")` to push a `Dataset` to the Hub.\r\n\r\nhow about subset? like `.load_dataset(\"<username>/<repo>\", \"<subset>\")`, how can I upload multi-dataset in one repo? thanks a lot ! ", "> > You can use `.push_to_hub(\"<username>/<repo>\")` to push a `Dataset` to the Hub.\r\n> \r\n> how about subset? like `.load_dataset(\"<username>/<repo>\", \"<subset>\")`, how can I upload multi-dataset in one repo? thanks a lot !\r\n\r\nI solved it by upgrading `Datasets` version to 2.15.0" ]
1,847,811,310	6,145	Export to_iterable_dataset to document	closed	2023-08-12T07:00:14	2023-08-15T17:04:01	2023-08-15T16:55:24	https://github.com/huggingface/datasets/pull/6145	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6145", "html_url": "https://github.com/huggingface/datasets/pull/6145", "diff_url": "https://github.com/huggingface/datasets/pull/6145.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6145.patch", "merged_at": "2023-08-15T16:55:24" }	npuichigo	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) \| 0.006076 / 0.011353 (-0.005277) \| 0.003730 / 0.011008 (-0.007279) \| 0.080778 / 0.038508 (0.042270) \| 0.062970 / 0.023109 (0.039860) \| 0.395864 / 0.275898 (0.119966) \| 0.430024 / 0.323480 (0.106544) \| 0.004823 / 0.007986 (-0.003162) \| 0.002949 / 0.004328 (-0.001379) \| 0.062423 / 0.004250 (0.058172) \| 0.047343 / 0.037052 (0.010291) \| 0.403153 / 0.258489 (0.144664) \| 0.443666 / 0.293841 (0.149825) \| 0.027798 / 0.128546 (-0.100748) \| 0.008056 / 0.075646 (-0.067590) \| 0.262260 / 0.419271 (-0.157011) \| 0.045958 / 0.043533 (0.002425) \| 0.391349 / 0.255139 (0.136210) \| 0.421831 / 0.283200 (0.138632) \| 0.021837 / 0.141683 (-0.119846) \| 1.485509 / 1.452155 (0.033355) \| 1.542940 / 1.492716 (0.050224) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.196831 / 0.018006 (0.178825) \| 0.435774 / 0.000490 (0.435285) \| 0.003647 / 0.000200 (0.003447) \| 0.000065 / 0.000054 (0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023756 / 0.037411 (-0.013655) \| 0.075737 / 0.014526 (0.061211) \| 0.303703 / 0.176557 (0.127146) \| 0.164862 / 0.737135 (-0.572273) \| 0.198483 / 0.296338 (-0.097855) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.405220 / 0.215209 (0.190011) \| 4.065983 / 2.077655 (1.988328) \| 2.043001 / 1.504120 (0.538881) \| 1.853318 / 1.541195 (0.312123) \| 1.977452 / 1.468490 (0.508962) \| 0.500897 / 4.584777 (-4.083880) \| 3.065756 / 3.745712 (-0.679956) \| 2.924096 / 5.269862 (-2.345765) \| 1.876194 / 4.565676 (-2.689482) \| 0.057774 / 0.424275 (-0.366501) \| 0.006809 / 0.007607 (-0.000798) \| 0.470979 / 0.226044 (0.244934) \| 4.719546 / 2.268929 (2.450618) \| 2.449651 / 55.444624 (-52.994973) \| 2.211817 / 6.876477 (-4.664660) \| 2.398760 / 2.142072 (0.256687) \| 0.590608 / 4.805227 (-4.214619) \| 0.125836 / 6.500664 (-6.374829) \| 0.060759 / 0.075469 (-0.014710) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.243609 / 1.841788 (-0.598179) \| 18.836193 / 8.074308 (10.761885) \| 13.835053 / 10.191392 (3.643661) \| 0.129708 / 0.680424 (-0.550716) \| 0.016708 / 0.534201 (-0.517493) \| 0.337219 / 0.579283 (-0.242065) \| 0.359045 / 0.434364 (-0.075319) \| 0.383329 / 0.540337 (-0.157009) \| 0.539629 / 1.386936 (-0.847307) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006073 / 0.011353 (-0.005280) \| 0.003713 / 0.011008 (-0.007295) \| 0.062642 / 0.038508 (0.024134) \| 0.062618 / 0.023109 (0.039508) \| 0.362029 / 0.275898 (0.086130) \| 0.401924 / 0.323480 (0.078445) \| 0.004689 / 0.007986 (-0.003297) \| 0.002945 / 0.004328 (-0.001384) \| 0.062720 / 0.004250 (0.058470) \| 0.048901 / 0.037052 (0.011848) \| 0.363780 / 0.258489 (0.105291) \| 0.405111 / 0.293841 (0.111270) \| 0.027738 / 0.128546 (-0.100808) \| 0.008046 / 0.075646 (-0.067600) \| 0.067752 / 0.419271 (-0.351519) \| 0.041955 / 0.043533 (-0.001577) \| 0.361615 / 0.255139 (0.106476) \| 0.388762 / 0.283200 (0.105562) \| 0.021302 / 0.141683 (-0.120380) \| 1.473527 / 1.452155 (0.021372) \| 1.529753 / 1.492716 (0.037037) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300446 / 0.018006 (0.282440) \| 0.425844 / 0.000490 (0.425354) \| 0.054507 / 0.000200 (0.054307) \| 0.000282 / 0.000054 (0.000228) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025478 / 0.037411 (-0.011933) \| 0.078298 / 0.014526 (0.063772) \| 0.087647 / 0.176557 (-0.088909) \| 0.138978 / 0.737135 (-0.598157) \| 0.088396 / 0.296338 (-0.207942) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.421345 / 0.215209 (0.206136) \| 4.209188 / 2.077655 (2.131533) \| 2.260731 / 1.504120 (0.756611) \| 2.072329 / 1.541195 (0.531134) \| 2.086778 / 1.468490 (0.618288) \| 0.495425 / 4.584777 (-4.089352) \| 2.987519 / 3.745712 (-0.758194) \| 2.895106 / 5.269862 (-2.374756) \| 1.874637 / 4.565676 (-2.691039) \| 0.057080 / 0.424275 (-0.367195) \| 0.006402 / 0.007607 (-0.001205) \| 0.498233 / 0.226044 (0.272188) \| 4.974385 / 2.268929 (2.705457) \| 2.671755 / 55.444624 (-52.772870) \| 2.356120 / 6.876477 (-4.520357) \| 2.531374 / 2.142072 (0.389301) \| 0.581955 / 4.805227 (-4.223272) \| 0.125491 / 6.500664 (-6.375173) \| 0.062267 / 0.075469 (-0.013202) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.307233 / 1.841788 (-0.534555) \| 18.929740 / 8.074308 (10.855431) \| 14.029693 / 10.191392 (3.838301) \| 0.161992 / 0.680424 (-0.518431) \| 0.017127 / 0.534201 (-0.517074) \| 0.336644 / 0.579283 (-0.242639) \| 0.336550 / 0.434364 (-0.097814) \| 0.400554 / 0.540337 (-0.139783) \| 0.560725 / 1.386936 (-0.826211) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cb8c5de5145c7e7eee65391cb7f4d92f0d565d62 \"CML watermark\")\n" ]
1,847,296,711	6,144	NIH exporter file not found	open	2023-08-11T19:05:25	2023-08-14T23:28:38	null	https://github.com/huggingface/datasets/issues/6144	null	brando90	false	[ "related: https://github.com/huggingface/datasets/issues/3504", "another file not found:\r\n```\r\nTraceback (most recent call last):\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 417, in _info\r\n await _file_info(\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 837, in _file_info\r\n r.raise_for_status()\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/aiohttp/client_reqrep.py\", line 1005, in raise_for_status\r\n raise ClientResponseError(\r\naiohttp.client_exceptions.ClientResponseError: 404, message='Not Found', url=URL('https://the-eye.eu/public/AI/pile_preliminary_components/pile_uspto.tar')\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 \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/runpy.py\", line 196, in _run_module_as_main\r\n return _run_code(code, main_globals, None,\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/runpy.py\", line 86, in _run_code\r\n exec(code, run_globals)\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/__main__.py\", line 39, in <module>\r\n cli.main()\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py\", line 430, in main\r\n run()\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py\", line 284, in run_file\r\n runpy.run_path(target, run_name=\"__main__\")\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 321, in run_path\r\n return _run_module_code(code, init_globals, run_name,\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 135, in _run_module_code\r\n _run_code(code, mod_globals, init_globals,\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 124, in _run_code\r\n exec(code, run_globals)\r\n File \"/lfs/ampere1/0/brando9/beyond-scale-language-data-diversity/src/diversity/div_coeff.py\", line 526, in <module>\r\n experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights()\r\n File \"/lfs/ampere1/0/brando9/beyond-scale-language-data-diversity/src/diversity/div_coeff.py\", line 475, in experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights\r\n column_names = next(iter(dataset)).keys()\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py\", line 1353, in __iter__\r\n for key, example in ex_iterable:\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py\", line 207, in __iter__\r\n yield from self.generate_examples_fn(*self.kwargs)\r\n File \"/lfs/ampere1/0/brando9/.cache/huggingface/modules/datasets_modules/datasets/EleutherAI--pile/ebea56d358e91cf4d37b0fde361d563bed1472fbd8221a21b38fc8bb4ba554fb/pile.py\", line 257, in _generate_examples\r\n for path, file in files[subset]:\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py\", line 840, in __iter__\r\n yield from self.generator(self.args, *self.kwargs)\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py\", line 891, in _iter_from_urlpath\r\n with xopen(urlpath, \"rb\", download_config=download_config) as f:\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py\", line 496, in xopen\r\n file_obj = fsspec.open(file, mode=mode, args, kwargs).open()\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py\", line 134, in open\r\n return self.__enter__()\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py\", line 102, in __enter__\r\n f = self.fs.open(self.path, mode=mode)\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/spec.py\", line 1241, in open\r\n f = self._open(\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 356, in _open\r\n size = size or self.info(path, kwargs)[\"size\"]\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py\", line 121, in wrapper\r\n return sync(self.loop, func, args, *kwargs)\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py\", line 106, in sync\r\n raise return_result\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py\", line 61, in _runner\r\n result[0] = await coro\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 430, in _info\r\n raise FileNotFoundError(url) from exc\r\nFileNotFoundError: https://the-eye.eu/public/AI/pile_preliminary_components/pile_uspto.tar\r\n```", "```\r\nFileNotFoundError: https://the-eye.eu/public/AI/pile_preliminary_components/pile_uspto.tar\r\n```\r\nmost relevant line I think.", "link to tweet: https://twitter.com/BrandoHablando/status/1690081313519489024?s=20 about issue", "so: https://stackoverflow.com/questions/76891189/how-to-download-data-from-hugging-face-that-is-visible-on-the-data-viewer-but-th", "this seems to work but it's rather annoying.\r\n\r\nSummary of how to make it work:\r\n1. get urls to parquet files into a list\r\n2. load list to load_dataset via `load_dataset('parquet', data_files=urls)` (note api names to hf are really confusing sometimes)\r\n3. then it should work, print a batch of text.\r\n\r\npresudo code\r\n```python\r\nurls_hacker_news = [\r\n \"https://huggingface.co/datasets/EleutherAI/pile/resolve/refs%2Fconvert%2Fparquet/hacker_news/pile-train-00000-of-00004.parquet\",\r\n \"https://huggingface.co/datasets/EleutherAI/pile/resolve/refs%2Fconvert%2Fparquet/hacker_news/pile-train-00001-of-00004.parquet\",\r\n \"https://huggingface.co/datasets/EleutherAI/pile/resolve/refs%2Fconvert%2Fparquet/hacker_news/pile-train-00002-of-00004.parquet\",\r\n \"https://huggingface.co/datasets/EleutherAI/pile/resolve/refs%2Fconvert%2Fparquet/hacker_news/pile-train-00003-of-00004.parquet\"\r\n]\r\n\r\n...\r\n\r\n\r\n # streaming = False\r\n from diversity.pile_subset_urls import urls_hacker_news\r\n path, name, data_files = 'parquet', 'hacker_news', urls_hacker_news\r\n # not changing\r\n batch_size = 512\r\n today = datetime.datetime.now().strftime('%Y-m%m-d%d-t%Hh_%Mm_%Ss')\r\n run_name = f'{path} div_coeff_{num_batches=} ({today=} ({name=}) {data_mixture_name=} {probabilities=})'\r\n print(f'{run_name=}')\r\n\r\n # - Init wandb\r\n debug: bool = mode == 'dryrun'\r\n run = wandb.init(mode=mode, project=\"beyond-scale\", name=run_name, save_code=True)\r\n wandb.config.update({\"num_batches\": num_batches, \"path\": path, \"name\": name, \"today\": today, 'probabilities': probabilities, 'batch_size': batch_size, 'debug': debug, 'data_mixture_name': data_mixture_name, 'streaming': streaming, 'data_files': data_files})\r\n # run.notify_on_failure() # https://community.wandb.ai/t/how-do-i-set-the-wandb-alert-programatically-for-my-current-run/4891\r\n print(f'{debug=}')\r\n print(f'{wandb.config=}')\r\n\r\n # -- Get probe network\r\n from datasets import load_dataset\r\n import torch\r\n from transformers import GPT2Tokenizer, GPT2LMHeadModel\r\n\r\n tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\r\n if tokenizer.pad_token_id is None:\r\n tokenizer.pad_token = tokenizer.eos_token\r\n probe_network = GPT2LMHeadModel.from_pretrained(\"gpt2\")\r\n device = torch.device(f\"cuda:{0}\" if torch.cuda.is_available() else \"cpu\")\r\n probe_network = probe_network.to(device)\r\n\r\n # -- Get data set\r\n def my_load_dataset(path, name):\r\n print(f'{path=} {name=} {streaming=}')\r\n if path == 'json' or path == 'bin' or path == 'csv':\r\n print(f'{data_files_prefix+name=}')\r\n return load_dataset(path, data_files=data_files_prefix+name, streaming=streaming, split=\"train\").with_format(\"torch\")\r\n elif path == 'parquet':\r\n print(f'{data_files=}')\r\n return load_dataset(path, data_files=data_files, streaming=streaming, split=\"train\").with_format(\"torch\")\r\n else:\r\n return load_dataset(path, name, streaming=streaming, split=\"train\").with_format(\"torch\")\r\n # - get data set for real now\r\n if isinstance(path, str):\r\n dataset = my_load_dataset(path, name)\r\n else:\r\n print('-- interleaving datasets')\r\n datasets = [my_load_dataset(path, name).with_format(\"torch\") for path, name in zip(path, name)]\r\n [print(f'{dataset.description=}') for dataset in datasets]\r\n dataset = interleave_datasets(datasets, probabilities)\r\n print(f'{dataset=}')\r\n batch = dataset.take(batch_size)\r\n print(f'{next(iter(batch))=}')\r\n column_names = next(iter(batch)).keys()\r\n print(f'{column_names=}')\r\n\r\n # - Prepare functions to tokenize batch\r\n def preprocess(examples):\r\n return tokenizer(examples[\"text\"], padding=\"max_length\", max_length=128, truncation=True, return_tensors=\"pt\")\r\n remove_columns = column_names # remove all keys that are not tensors to avoid bugs in collate function in task2vec's pytorch data loader\r\n def map(batch):\r\n return batch.map(preprocess, batched=True, remove_columns=remove_columns)\r\n tokenized_batch = map(batch)\r\n print(f'{next(iter(tokenized_batch))=}')\r\n```\r\n\r\nhttps://stackoverflow.com/questions/76891189/how-to-download-data-from-hugging-face-that-is-visible-on-the-data-viewer-but-th/76902681#76902681\r\n\r\nhttps://discuss.huggingface.co/t/how-to-download-data-from-hugging-face-that-is-visible-on-the-data-viewer-but-the-files-are-not-available/50555/5?u=severo" ]
1,846,205,216	6,142	the-stack-dedup fails to generate	closed	2023-08-11T05:10:49	2023-08-17T09:26:13	2023-08-17T09:26:13	https://github.com/huggingface/datasets/issues/6142	null	michaelroyzen	false	[ "@severo ", "It seems that some parquet files have additional columns.\r\n\r\nI ran a scan and found that two files have the additional `__id__` column:\r\n\r\n1. `hf://datasets/bigcode/the-stack-dedup/data/numpy/data-00000-of-00001.parquet`\r\n2. `hf://datasets/bigcode/the-stack-dedup/data/omgrofl/data-00000-of-00001.parquet`\r\n\r\nWe should open a PR to fix those two files", "I opened https://huggingface.co/datasets/bigcode/the-stack-dedup/discussions/31", "The files have been fixed ! I'm closing this one but feel free to re-open if you still have the issue" ]
1,846,117,729	6,141	TypeError: ClientSession._request() got an unexpected keyword argument 'https'	closed	2023-08-11T02:40:32	2023-08-30T13:51:33	2023-08-30T13:51:33	https://github.com/huggingface/datasets/issues/6141	null	q935970314	false	[ "Hi! I cannot reproduce this error on my machine or in Colab. Which version of `fsspec` do you have installed?" ]
1,845,384,712	6,140	Misalignment between file format specified in configs metadata YAML and the inferred builder	closed	2023-08-10T15:07:34	2023-08-17T20:37:20	2023-08-17T20:37:20	https://github.com/huggingface/datasets/issues/6140	null	albertvillanova	false	[]
1,844,991,583	6,139	Offline dataset viewer	closed	2023-08-10T11:30:00	2024-09-24T18:36:35	2023-09-29T13:10:22	https://github.com/huggingface/datasets/issues/6139	null	yuvalkirstain	false	[ "Hi, thanks for the suggestion. It's not possible at the moment. The viewer is part of the Hub codebase and only works on public datasets. Also, it relies on [Datasets Server](https://github.com/huggingface/datasets-server/), which prepares the data and provides an API to access the rows, size, etc.\r\n\r\nIf you're interested in hosting your data as a private dataset on the Hub, you might want to look at https://github.com/huggingface/datasets-server/issues/39.", "Hi, we are building an offline dataset viewer: https://github.com/Renumics/spotlight\r\nIt supports many HF datasets, but currently you have to use it via Pandas:\r\ndf=ds.to_pandas()\r\nspotlight.show(df)\r\n\r\nWould love to hear from you if that works for your use case. If not, feel free to open an issue on the repo: https://github.com/Renumics/spotlight/issues", "@ssuwelack thank you! I will definitely try it out.", "Related issues:\r\n- https://github.com/huggingface/datasets-server/issues/213\r\n- https://github.com/huggingface/datasets-server/issues/441\r\n- https://github.com/huggingface/datasets/issues/6014", "Closing for now, as developing and maintaining an offline viewer is not planned.", "@yuvalkirstain the dataset viewer is now available on private datasets for [PRO users](https://huggingface.co/pricing#pro) and [Enterprise Hub orgs](https://huggingface.co/enterprise). Would it fit your needs?", "Hi @ssuwelack I tried loading a HF dataset with your viewer but got this error https://github.com/Renumics/spotlight/issues/461 hope the team can help me on this. Thanks!" ]
1,844,952,496	6,138	Ignore CI lint rule violation in Pickler.memoize	closed	2023-08-10T11:03:15	2023-08-10T11:31:45	2023-08-10T11:22:56	https://github.com/huggingface/datasets/pull/6138	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6138", "html_url": "https://github.com/huggingface/datasets/pull/6138", "diff_url": "https://github.com/huggingface/datasets/pull/6138.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6138.patch", "merged_at": "2023-08-10T11:22:56" }	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) \| 0.006536 / 0.011353 (-0.004817) \| 0.003890 / 0.011008 (-0.007118) \| 0.084044 / 0.038508 (0.045536) \| 0.071893 / 0.023109 (0.048784) \| 0.346926 / 0.275898 (0.071028) \| 0.397487 / 0.323480 (0.074007) \| 0.004065 / 0.007986 (-0.003921) \| 0.003218 / 0.004328 (-0.001111) \| 0.064670 / 0.004250 (0.060420) \| 0.052414 / 0.037052 (0.015362) \| 0.355413 / 0.258489 (0.096924) \| 0.398894 / 0.293841 (0.105053) \| 0.030763 / 0.128546 (-0.097783) \| 0.008590 / 0.075646 (-0.067056) \| 0.286857 / 0.419271 (-0.132415) \| 0.051126 / 0.043533 (0.007593) \| 0.346125 / 0.255139 (0.090986) \| 0.395673 / 0.283200 (0.112474) \| 0.025766 / 0.141683 (-0.115917) \| 1.466238 / 1.452155 (0.014084) \| 1.543117 / 1.492716 (0.050400) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.213210 / 0.018006 (0.195204) \| 0.451981 / 0.000490 (0.451491) \| 0.003784 / 0.000200 (0.003585) \| 0.000096 / 0.000054 (0.000041) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027756 / 0.037411 (-0.009655) \| 0.082446 / 0.014526 (0.067920) \| 0.095414 / 0.176557 (-0.081142) \| 0.151812 / 0.737135 (-0.585323) \| 0.096296 / 0.296338 (-0.200042) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.383729 / 0.215209 (0.168520) \| 3.835126 / 2.077655 (1.757471) \| 1.891972 / 1.504120 (0.387852) \| 1.719934 / 1.541195 (0.178739) \| 1.899980 / 1.468490 (0.431490) \| 0.488741 / 4.584777 (-4.096036) \| 3.634120 / 3.745712 (-0.111592) \| 3.243314 / 5.269862 (-2.026547) \| 2.028382 / 4.565676 (-2.537294) \| 0.057355 / 0.424275 (-0.366920) \| 0.007717 / 0.007607 (0.000110) \| 0.459835 / 0.226044 (0.233790) \| 4.591793 / 2.268929 (2.322864) \| 2.346861 / 55.444624 (-53.097764) \| 2.067357 / 6.876477 (-4.809120) \| 2.254954 / 2.142072 (0.112882) \| 0.587016 / 4.805227 (-4.218211) \| 0.133918 / 6.500664 (-6.366746) \| 0.060311 / 0.075469 (-0.015158) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.250016 / 1.841788 (-0.591772) \| 19.674333 / 8.074308 (11.600025) \| 14.522764 / 10.191392 (4.331372) \| 0.145741 / 0.680424 (-0.534683) \| 0.018593 / 0.534201 (-0.515608) \| 0.392833 / 0.579283 (-0.186450) \| 0.408194 / 0.434364 (-0.026170) \| 0.455164 / 0.540337 (-0.085174) \| 0.622722 / 1.386936 (-0.764214) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006583 / 0.011353 (-0.004770) \| 0.004008 / 0.011008 (-0.007000) \| 0.064688 / 0.038508 (0.026180) \| 0.074969 / 0.023109 (0.051860) \| 0.360504 / 0.275898 (0.084606) \| 0.396926 / 0.323480 (0.073446) \| 0.005190 / 0.007986 (-0.002796) \| 0.003363 / 0.004328 (-0.000966) \| 0.064372 / 0.004250 (0.060122) \| 0.054428 / 0.037052 (0.017376) \| 0.361204 / 0.258489 (0.102715) \| 0.400917 / 0.293841 (0.107077) \| 0.031117 / 0.128546 (-0.097429) \| 0.008406 / 0.075646 (-0.067241) \| 0.069655 / 0.419271 (-0.349617) \| 0.048582 / 0.043533 (0.005049) \| 0.365396 / 0.255139 (0.110257) \| 0.381344 / 0.283200 (0.098145) \| 0.023809 / 0.141683 (-0.117874) \| 1.472926 / 1.452155 (0.020772) \| 1.547298 / 1.492716 (0.054582) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.276912 / 0.018006 (0.258906) \| 0.449096 / 0.000490 (0.448607) \| 0.018921 / 0.000200 (0.018721) \| 0.000111 / 0.000054 (0.000056) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030237 / 0.037411 (-0.007174) \| 0.088610 / 0.014526 (0.074084) \| 0.101529 / 0.176557 (-0.075027) \| 0.154070 / 0.737135 (-0.583065) \| 0.103471 / 0.296338 (-0.192867) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.416047 / 0.215209 (0.200838) \| 4.152374 / 2.077655 (2.074719) \| 2.111181 / 1.504120 (0.607061) \| 1.943582 / 1.541195 (0.402387) \| 2.031729 / 1.468490 (0.563239) \| 0.486740 / 4.584777 (-4.098037) \| 3.631547 / 3.745712 (-0.114165) \| 3.251202 / 5.269862 (-2.018660) \| 2.041272 / 4.565676 (-2.524405) \| 0.057287 / 0.424275 (-0.366988) \| 0.007303 / 0.007607 (-0.000304) \| 0.491027 / 0.226044 (0.264982) \| 4.906757 / 2.268929 (2.637829) \| 2.581694 / 55.444624 (-52.862931) \| 2.250996 / 6.876477 (-4.625481) \| 2.441771 / 2.142072 (0.299698) \| 0.600714 / 4.805227 (-4.204514) \| 0.133233 / 6.500664 (-6.367431) \| 0.060856 / 0.075469 (-0.014613) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.340062 / 1.841788 (-0.501725) \| 19.973899 / 8.074308 (11.899591) \| 14.347381 / 10.191392 (4.155989) \| 0.166651 / 0.680424 (-0.513773) \| 0.018691 / 0.534201 (-0.515510) \| 0.393580 / 0.579283 (-0.185703) \| 0.409425 / 0.434364 (-0.024939) \| 0.474409 / 0.540337 (-0.065929) \| 0.649423 / 1.386936 (-0.737514) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c5da68102297c3639207a7901952d2765a4cdb8b \"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) \| 0.006593 / 0.011353 (-0.004760) \| 0.004123 / 0.011008 (-0.006885) \| 0.084424 / 0.038508 (0.045916) \| 0.076867 / 0.023109 (0.053758) \| 0.309149 / 0.275898 (0.033251) \| 0.348572 / 0.323480 (0.025092) \| 0.005463 / 0.007986 (-0.002523) \| 0.003440 / 0.004328 (-0.000889) \| 0.064604 / 0.004250 (0.060353) \| 0.053920 / 0.037052 (0.016868) \| 0.345221 / 0.258489 (0.086732) \| 0.363209 / 0.293841 (0.069368) \| 0.031209 / 0.128546 (-0.097337) \| 0.008690 / 0.075646 (-0.066956) \| 0.288851 / 0.419271 (-0.130421) \| 0.052239 / 0.043533 (0.008707) \| 0.308643 / 0.255139 (0.053504) \| 0.346407 / 0.283200 (0.063207) \| 0.023935 / 0.141683 (-0.117748) \| 1.469207 / 1.452155 (0.017052) \| 1.532855 / 1.492716 (0.040138) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290885 / 0.018006 (0.272879) \| 0.580561 / 0.000490 (0.580071) \| 0.004698 / 0.000200 (0.004498) \| 0.000286 / 0.000054 (0.000231) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028015 / 0.037411 (-0.009396) \| 0.081172 / 0.014526 (0.066646) \| 0.096822 / 0.176557 (-0.079735) \| 0.151355 / 0.737135 (-0.585781) \| 0.098017 / 0.296338 (-0.198321) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.384069 / 0.215209 (0.168859) \| 3.828635 / 2.077655 (1.750980) \| 1.829311 / 1.504120 (0.325192) \| 1.672520 / 1.541195 (0.131325) \| 1.743944 / 1.468490 (0.275453) \| 0.481594 / 4.584777 (-4.103183) \| 3.556204 / 3.745712 (-0.189509) \| 3.279499 / 5.269862 (-1.990363) \| 2.033243 / 4.565676 (-2.532434) \| 0.056525 / 0.424275 (-0.367750) \| 0.007717 / 0.007607 (0.000109) \| 0.466815 / 0.226044 (0.240771) \| 4.657022 / 2.268929 (2.388094) \| 2.438600 / 55.444624 (-53.006024) \| 2.097999 / 6.876477 (-4.778478) \| 2.263122 / 2.142072 (0.121049) \| 0.636001 / 4.805227 (-4.169226) \| 0.147727 / 6.500664 (-6.352937) \| 0.059293 / 0.075469 (-0.016176) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.243111 / 1.841788 (-0.598677) \| 19.558379 / 8.074308 (11.484071) \| 14.141017 / 10.191392 (3.949625) \| 0.169840 / 0.680424 (-0.510583) \| 0.017912 / 0.534201 (-0.516289) \| 0.391325 / 0.579283 (-0.187958) \| 0.417169 / 0.434364 (-0.017195) \| 0.457129 / 0.540337 (-0.083209) \| 0.629907 / 1.386936 (-0.757029) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006687 / 0.011353 (-0.004666) \| 0.004165 / 0.011008 (-0.006844) \| 0.064738 / 0.038508 (0.026230) \| 0.077286 / 0.023109 (0.054177) \| 0.364236 / 0.275898 (0.088338) \| 0.393228 / 0.323480 (0.069748) \| 0.005451 / 0.007986 (-0.002535) \| 0.003547 / 0.004328 (-0.000781) \| 0.065761 / 0.004250 (0.061510) \| 0.056526 / 0.037052 (0.019474) \| 0.365523 / 0.258489 (0.107034) \| 0.403331 / 0.293841 (0.109490) \| 0.030900 / 0.128546 (-0.097646) \| 0.008757 / 0.075646 (-0.066889) \| 0.070961 / 0.419271 (-0.348311) \| 0.048394 / 0.043533 (0.004861) \| 0.365908 / 0.255139 (0.110769) \| 0.381197 / 0.283200 (0.097998) \| 0.022940 / 0.141683 (-0.118743) \| 1.487909 / 1.452155 (0.035754) \| 1.532931 / 1.492716 (0.040215) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.317506 / 0.018006 (0.299500) \| 0.513391 / 0.000490 (0.512902) \| 0.005464 / 0.000200 (0.005264) \| 0.000214 / 0.000054 (0.000159) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032289 / 0.037411 (-0.005122) \| 0.090157 / 0.014526 (0.075631) \| 0.103514 / 0.176557 (-0.073043) \| 0.158236 / 0.737135 (-0.578899) \| 0.106554 / 0.296338 (-0.189784) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.406455 / 0.215209 (0.191246) \| 4.061563 / 2.077655 (1.983908) \| 2.082201 / 1.504120 (0.578081) \| 1.914433 / 1.541195 (0.373238) \| 2.039342 / 1.468490 (0.570852) \| 0.478444 / 4.584777 (-4.106333) \| 3.599755 / 3.745712 (-0.145957) \| 3.294453 / 5.269862 (-1.975409) \| 2.028519 / 4.565676 (-2.537158) \| 0.056118 / 0.424275 (-0.368157) \| 0.007325 / 0.007607 (-0.000282) \| 0.493177 / 0.226044 (0.267132) \| 4.926218 / 2.268929 (2.657289) \| 2.605033 / 55.444624 (-52.839591) \| 2.239933 / 6.876477 (-4.636544) \| 2.454210 / 2.142072 (0.312137) \| 0.571905 / 4.805227 (-4.233322) \| 0.133251 / 6.500664 (-6.367413) \| 0.062422 / 0.075469 (-0.013047) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.352752 / 1.841788 (-0.489036) \| 20.265109 / 8.074308 (12.190801) \| 14.293064 / 10.191392 (4.101672) \| 0.169267 / 0.680424 (-0.511157) \| 0.018607 / 0.534201 (-0.515594) \| 0.393655 / 0.579283 (-0.185628) \| 0.402132 / 0.434364 (-0.032232) \| 0.477566 / 0.540337 (-0.062772) \| 0.651773 / 1.386936 (-0.735163) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#80023f36b2b6678347979421ef973d8969d31306 \"CML watermark\")\n" ]
1,844,952,312	6,137	(`from_spark()`) Unable to connect HDFS in pyspark YARN setting	open	2023-08-10T11:03:08	2023-08-10T11:03:08	null	https://github.com/huggingface/datasets/issues/6137	null	kyoungrok0517	false	[]
1,844,887,866	6,136	CI check_code_quality error: E721 Do not compare types, use `isinstance()`	closed	2023-08-10T10:19:50	2023-08-10T11:22:58	2023-08-10T11:22:58	https://github.com/huggingface/datasets/issues/6136	null	albertvillanova	false	[]
1,844,870,943	6,135	Remove unused allowed_extensions param	closed	2023-08-10T10:09:54	2023-08-10T12:08:38	2023-08-10T12:00:02	https://github.com/huggingface/datasets/pull/6135	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6135", "html_url": "https://github.com/huggingface/datasets/pull/6135", "diff_url": "https://github.com/huggingface/datasets/pull/6135.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6135.patch", "merged_at": "2023-08-10T12:00:01" }	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) \| 0.009055 / 0.011353 (-0.002298) \| 0.008835 / 0.011008 (-0.002173) \| 0.117048 / 0.038508 (0.078540) \| 0.096268 / 0.023109 (0.073159) \| 0.474678 / 0.275898 (0.198780) \| 0.550509 / 0.323480 (0.227029) \| 0.005552 / 0.007986 (-0.002434) \| 0.004315 / 0.004328 (-0.000013) \| 0.094336 / 0.004250 (0.090086) \| 0.061945 / 0.037052 (0.024892) \| 0.461422 / 0.258489 (0.202933) \| 0.521271 / 0.293841 (0.227430) \| 0.049116 / 0.128546 (-0.079430) \| 0.015007 / 0.075646 (-0.060639) \| 0.414351 / 0.419271 (-0.004920) \| 0.137520 / 0.043533 (0.093987) \| 0.465627 / 0.255139 (0.210488) \| 0.537244 / 0.283200 (0.254044) \| 0.068577 / 0.141683 (-0.073106) \| 1.921373 / 1.452155 (0.469219) \| 2.506653 / 1.492716 (1.013937) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.273970 / 0.018006 (0.255963) \| 0.750295 / 0.000490 (0.749805) \| 0.004241 / 0.000200 (0.004041) \| 0.000128 / 0.000054 (0.000073) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033793 / 0.037411 (-0.003618) \| 0.105562 / 0.014526 (0.091037) \| 0.131771 / 0.176557 (-0.044786) \| 0.196890 / 0.737135 (-0.540245) \| 0.119842 / 0.296338 (-0.176496) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.634881 / 0.215209 (0.419672) \| 6.069221 / 2.077655 (3.991566) \| 2.678765 / 1.504120 (1.174646) \| 2.460309 / 1.541195 (0.919114) \| 2.517579 / 1.468490 (1.049089) \| 0.869558 / 4.584777 (-3.715219) \| 5.407686 / 3.745712 (1.661974) \| 4.920687 / 5.269862 (-0.349175) \| 3.130066 / 4.565676 (-1.435611) \| 0.100337 / 0.424275 (-0.323938) \| 0.009615 / 0.007607 (0.002008) \| 0.745275 / 0.226044 (0.519231) \| 7.577890 / 2.268929 (5.308962) \| 3.607887 / 55.444624 (-51.836738) \| 2.922211 / 6.876477 (-3.954266) \| 3.205592 / 2.142072 (1.063519) \| 1.052298 / 4.805227 (-3.752929) \| 0.218798 / 6.500664 (-6.281866) \| 0.082137 / 0.075469 (0.006667) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.696551 / 1.841788 (-0.145237) \| 24.946074 / 8.074308 (16.871766) \| 23.114202 / 10.191392 (12.922810) \| 0.220498 / 0.680424 (-0.459925) \| 0.029388 / 0.534201 (-0.504813) \| 0.494721 / 0.579283 (-0.084562) \| 0.603085 / 0.434364 (0.168722) \| 0.573093 / 0.540337 (0.032756) \| 0.784937 / 1.386936 (-0.601999) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009642 / 0.011353 (-0.001711) \| 0.007551 / 0.011008 (-0.003457) \| 0.085224 / 0.038508 (0.046716) \| 0.099493 / 0.023109 (0.076384) \| 0.503824 / 0.275898 (0.227926) \| 0.546583 / 0.323480 (0.223103) \| 0.006385 / 0.007986 (-0.001601) \| 0.004751 / 0.004328 (0.000423) \| 0.084699 / 0.004250 (0.080449) \| 0.067875 / 0.037052 (0.030823) \| 0.485313 / 0.258489 (0.226824) \| 0.535808 / 0.293841 (0.241967) \| 0.049935 / 0.128546 (-0.078611) \| 0.014427 / 0.075646 (-0.061219) \| 0.095531 / 0.419271 (-0.323741) \| 0.068487 / 0.043533 (0.024954) \| 0.502204 / 0.255139 (0.247065) \| 0.514393 / 0.283200 (0.231193) \| 0.037350 / 0.141683 (-0.104333) \| 1.849380 / 1.452155 (0.397226) \| 1.920151 / 1.492716 (0.427434) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298363 / 0.018006 (0.280357) \| 0.651555 / 0.000490 (0.651065) \| 0.005910 / 0.000200 (0.005710) \| 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) \| 0.039170 / 0.037411 (0.001758) \| 0.106436 / 0.014526 (0.091910) \| 0.129880 / 0.176557 (-0.046677) \| 0.185401 / 0.737135 (-0.551734) \| 0.125732 / 0.296338 (-0.170607) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.643248 / 0.215209 (0.428039) \| 6.374807 / 2.077655 (4.297152) \| 3.057296 / 1.504120 (1.553176) \| 2.779534 / 1.541195 (1.238340) \| 2.790165 / 1.468490 (1.321675) \| 0.841580 / 4.584777 (-3.743197) \| 5.371478 / 3.745712 (1.625766) \| 4.973251 / 5.269862 (-0.296610) \| 3.235817 / 4.565676 (-1.329860) \| 0.097276 / 0.424275 (-0.326999) \| 0.008840 / 0.007607 (0.001233) \| 0.728678 / 0.226044 (0.502634) \| 7.526382 / 2.268929 (5.257454) \| 3.792550 / 55.444624 (-51.652074) \| 3.439134 / 6.876477 (-3.437342) \| 3.466626 / 2.142072 (1.324553) \| 1.035894 / 4.805227 (-3.769333) \| 0.211670 / 6.500664 (-6.288994) \| 0.087596 / 0.075469 (0.012127) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.782755 / 1.841788 (-0.059033) \| 25.704407 / 8.074308 (17.630099) \| 23.799672 / 10.191392 (13.608280) \| 0.233952 / 0.680424 (-0.446472) \| 0.030810 / 0.534201 (-0.503391) \| 0.505857 / 0.579283 (-0.073426) \| 0.629331 / 0.434364 (0.194967) \| 0.608530 / 0.540337 (0.068192) \| 0.813688 / 1.386936 (-0.573248) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ed4d6bb5f1331576c41b04acd9872a5349a0915c \"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) \| 0.006401 / 0.011353 (-0.004952) \| 0.003916 / 0.011008 (-0.007092) \| 0.083976 / 0.038508 (0.045468) \| 0.072583 / 0.023109 (0.049474) \| 0.322747 / 0.275898 (0.046849) \| 0.345159 / 0.323480 (0.021679) \| 0.005366 / 0.007986 (-0.002620) \| 0.003399 / 0.004328 (-0.000930) \| 0.064232 / 0.004250 (0.059982) \| 0.053313 / 0.037052 (0.016261) \| 0.353127 / 0.258489 (0.094638) \| 0.361398 / 0.293841 (0.067557) \| 0.030604 / 0.128546 (-0.097942) \| 0.008615 / 0.075646 (-0.067031) \| 0.285806 / 0.419271 (-0.133466) \| 0.050887 / 0.043533 (0.007354) \| 0.312293 / 0.255139 (0.057154) \| 0.349716 / 0.283200 (0.066516) \| 0.024546 / 0.141683 (-0.117137) \| 1.472318 / 1.452155 (0.020163) \| 1.536063 / 1.492716 (0.043347) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280012 / 0.018006 (0.262006) \| 0.593574 / 0.000490 (0.593085) \| 0.004083 / 0.000200 (0.003883) \| 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) \| 0.027715 / 0.037411 (-0.009696) \| 0.081392 / 0.014526 (0.066866) \| 0.096445 / 0.176557 (-0.080112) \| 0.152131 / 0.737135 (-0.585004) \| 0.094825 / 0.296338 (-0.201514) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.380749 / 0.215209 (0.165540) \| 3.806994 / 2.077655 (1.729339) \| 1.842544 / 1.504120 (0.338424) \| 1.682829 / 1.541195 (0.141635) \| 1.701679 / 1.468490 (0.233189) \| 0.484830 / 4.584777 (-4.099947) \| 3.517359 / 3.745712 (-0.228353) \| 3.231211 / 5.269862 (-2.038651) \| 2.029371 / 4.565676 (-2.536306) \| 0.057199 / 0.424275 (-0.367077) \| 0.007653 / 0.007607 (0.000046) \| 0.458572 / 0.226044 (0.232528) \| 4.579835 / 2.268929 (2.310907) \| 2.326467 / 55.444624 (-53.118157) \| 1.939646 / 6.876477 (-4.936831) \| 2.133150 / 2.142072 (-0.008922) \| 0.596251 / 4.805227 (-4.208976) \| 0.131979 / 6.500664 (-6.368686) \| 0.059226 / 0.075469 (-0.016243) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.234833 / 1.841788 (-0.606955) \| 19.475522 / 8.074308 (11.401214) \| 14.102760 / 10.191392 (3.911368) \| 0.159657 / 0.680424 (-0.520767) \| 0.018292 / 0.534201 (-0.515909) \| 0.391079 / 0.579283 (-0.188204) \| 0.406736 / 0.434364 (-0.027628) \| 0.459159 / 0.540337 (-0.081178) \| 0.618159 / 1.386936 (-0.768777) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006592 / 0.011353 (-0.004761) \| 0.004052 / 0.011008 (-0.006957) \| 0.064536 / 0.038508 (0.026028) \| 0.075051 / 0.023109 (0.051942) \| 0.379596 / 0.275898 (0.103698) \| 0.412413 / 0.323480 (0.088933) \| 0.005377 / 0.007986 (-0.002608) \| 0.003466 / 0.004328 (-0.000863) \| 0.064958 / 0.004250 (0.060708) \| 0.055265 / 0.037052 (0.018213) \| 0.391505 / 0.258489 (0.133016) \| 0.425345 / 0.293841 (0.131504) \| 0.030750 / 0.128546 (-0.097796) \| 0.008652 / 0.075646 (-0.066994) \| 0.072107 / 0.419271 (-0.347165) \| 0.048340 / 0.043533 (0.004807) \| 0.387714 / 0.255139 (0.132575) \| 0.402602 / 0.283200 (0.119402) \| 0.023492 / 0.141683 (-0.118191) \| 1.528377 / 1.452155 (0.076222) \| 1.574827 / 1.492716 (0.082110) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.316999 / 0.018006 (0.298993) \| 0.528391 / 0.000490 (0.527901) \| 0.005183 / 0.000200 (0.004983) \| 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) \| 0.029670 / 0.037411 (-0.007741) \| 0.087130 / 0.014526 (0.072604) \| 0.099897 / 0.176557 (-0.076660) \| 0.154074 / 0.737135 (-0.583062) \| 0.104309 / 0.296338 (-0.192030) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.408804 / 0.215209 (0.193595) \| 4.072248 / 2.077655 (1.994593) \| 2.103333 / 1.504120 (0.599213) \| 1.931972 / 1.541195 (0.390777) \| 1.980132 / 1.468490 (0.511642) \| 0.482623 / 4.584777 (-4.102154) \| 3.532789 / 3.745712 (-0.212923) \| 3.304962 / 5.269862 (-1.964899) \| 2.036672 / 4.565676 (-2.529004) \| 0.056944 / 0.424275 (-0.367331) \| 0.007190 / 0.007607 (-0.000417) \| 0.490650 / 0.226044 (0.264606) \| 4.903604 / 2.268929 (2.634675) \| 2.586247 / 55.444624 (-52.858377) \| 2.227631 / 6.876477 (-4.648846) \| 2.397286 / 2.142072 (0.255214) \| 0.579167 / 4.805227 (-4.226060) \| 0.132037 / 6.500664 (-6.368627) \| 0.059971 / 0.075469 (-0.015498) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.336430 / 1.841788 (-0.505358) \| 19.915846 / 8.074308 (11.841538) \| 14.102781 / 10.191392 (3.911389) \| 0.147956 / 0.680424 (-0.532468) \| 0.018192 / 0.534201 (-0.516009) \| 0.397949 / 0.579283 (-0.181334) \| 0.408529 / 0.434364 (-0.025835) \| 0.479382 / 0.540337 (-0.060955) \| 0.659735 / 1.386936 (-0.727201) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#98074122449bc031f7269f298f1c55f20e39b975 \"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) \| 0.005880 / 0.011353 (-0.005473) \| 0.003677 / 0.011008 (-0.007332) \| 0.080022 / 0.038508 (0.041514) \| 0.055554 / 0.023109 (0.032445) \| 0.397449 / 0.275898 (0.121551) \| 0.428346 / 0.323480 (0.104867) \| 0.004613 / 0.007986 (-0.003373) \| 0.002873 / 0.004328 (-0.001455) \| 0.062226 / 0.004250 (0.057976) \| 0.044721 / 0.037052 (0.007669) \| 0.404792 / 0.258489 (0.146303) \| 0.437467 / 0.293841 (0.143626) \| 0.027166 / 0.128546 (-0.101381) \| 0.008077 / 0.075646 (-0.067569) \| 0.260469 / 0.419271 (-0.158803) \| 0.043551 / 0.043533 (0.000018) \| 0.401712 / 0.255139 (0.146573) \| 0.427294 / 0.283200 (0.144094) \| 0.021243 / 0.141683 (-0.120440) \| 1.464553 / 1.452155 (0.012398) \| 1.507112 / 1.492716 (0.014396) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.198415 / 0.018006 (0.180408) \| 0.427940 / 0.000490 (0.427450) \| 0.004236 / 0.000200 (0.004036) \| 0.000067 / 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) \| 0.023759 / 0.037411 (-0.013652) \| 0.073262 / 0.014526 (0.058736) \| 0.677113 / 0.176557 (0.500557) \| 0.194964 / 0.737135 (-0.542172) \| 0.086121 / 0.296338 (-0.210217) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.401176 / 0.215209 (0.185967) \| 4.028688 / 2.077655 (1.951034) \| 2.026804 / 1.504120 (0.522685) \| 1.887964 / 1.541195 (0.346770) \| 2.008991 / 1.468490 (0.540501) \| 0.498847 / 4.584777 (-4.085930) \| 3.015920 / 3.745712 (-0.729792) \| 2.837019 / 5.269862 (-2.432843) \| 1.849976 / 4.565676 (-2.715701) \| 0.057545 / 0.424275 (-0.366730) \| 0.006645 / 0.007607 (-0.000962) \| 0.470225 / 0.226044 (0.244180) \| 4.720910 / 2.268929 (2.451982) \| 2.473693 / 55.444624 (-52.970931) \| 2.177525 / 6.876477 (-4.698952) \| 2.374702 / 2.142072 (0.232630) \| 0.588253 / 4.805227 (-4.216974) \| 0.125512 / 6.500664 (-6.375152) \| 0.061247 / 0.075469 (-0.014222) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.255829 / 1.841788 (-0.585959) \| 18.251689 / 8.074308 (10.177381) \| 13.690373 / 10.191392 (3.498981) \| 0.146928 / 0.680424 (-0.533496) \| 0.016534 / 0.534201 (-0.517667) \| 0.335249 / 0.579283 (-0.244034) \| 0.338940 / 0.434364 (-0.095424) \| 0.382170 / 0.540337 (-0.158168) \| 0.529570 / 1.386936 (-0.857366) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005920 / 0.011353 (-0.005433) \| 0.003557 / 0.011008 (-0.007451) \| 0.062776 / 0.038508 (0.024267) \| 0.058473 / 0.023109 (0.035364) \| 0.358780 / 0.275898 (0.082882) \| 0.394161 / 0.323480 (0.070682) \| 0.004636 / 0.007986 (-0.003349) \| 0.002865 / 0.004328 (-0.001463) \| 0.062033 / 0.004250 (0.057782) \| 0.047154 / 0.037052 (0.010101) \| 0.367718 / 0.258489 (0.109229) \| 0.400814 / 0.293841 (0.106973) \| 0.026919 / 0.128546 (-0.101628) \| 0.008071 / 0.075646 (-0.067575) \| 0.067802 / 0.419271 (-0.351469) \| 0.040894 / 0.043533 (-0.002638) \| 0.358757 / 0.255139 (0.103618) \| 0.384971 / 0.283200 (0.101771) \| 0.020019 / 0.141683 (-0.121664) \| 1.458578 / 1.452155 (0.006423) \| 1.525059 / 1.492716 (0.032342) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.207795 / 0.018006 (0.189789) \| 0.413201 / 0.000490 (0.412712) \| 0.005199 / 0.000200 (0.004999) \| 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) \| 0.025716 / 0.037411 (-0.011696) \| 0.078434 / 0.014526 (0.063908) \| 0.086920 / 0.176557 (-0.089637) \| 0.138327 / 0.737135 (-0.598808) \| 0.088120 / 0.296338 (-0.208219) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.434344 / 0.215209 (0.219135) \| 4.343114 / 2.077655 (2.265459) \| 2.384439 / 1.504120 (0.880319) \| 2.253929 / 1.541195 (0.712735) \| 2.306811 / 1.468490 (0.838321) \| 0.497572 / 4.584777 (-4.087205) \| 3.028794 / 3.745712 (-0.716919) \| 2.833484 / 5.269862 (-2.436377) \| 1.878918 / 4.565676 (-2.686759) \| 0.057133 / 0.424275 (-0.367143) \| 0.006357 / 0.007607 (-0.001251) \| 0.508019 / 0.226044 (0.281975) \| 5.076935 / 2.268929 (2.808007) \| 2.745784 / 55.444624 (-52.698841) \| 2.476291 / 6.876477 (-4.400186) \| 2.677264 / 2.142072 (0.535191) \| 0.587173 / 4.805227 (-4.218054) \| 0.126373 / 6.500664 (-6.374291) \| 0.062815 / 0.075469 (-0.012654) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.355482 / 1.841788 (-0.486305) \| 18.818227 / 8.074308 (10.743919) \| 13.954289 / 10.191392 (3.762896) \| 0.143413 / 0.680424 (-0.537011) \| 0.016844 / 0.534201 (-0.517357) \| 0.338334 / 0.579283 (-0.240949) \| 0.344559 / 0.434364 (-0.089805) \| 0.400669 / 0.540337 (-0.139669) \| 0.563835 / 1.386936 (-0.823101) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c02a44715c036b5261686669727394b1308a3a4b \"CML watermark\")\n" ]
1,844,535,142	6,134	`datasets` cannot be installed alongside `apache-beam`	closed	2023-08-10T06:54:32	2023-09-01T03:19:49	2023-08-10T15:22:10	https://github.com/huggingface/datasets/issues/6134	null	boyleconnor	false	[ "I noticed that this is actually covered by issue #5613, which for some reason I didn't see when I searched the issues in this repo the first time." ]
1,844,511,519	6,133	Dataset is slower after calling `to_iterable_dataset`	open	2023-08-10T06:36:23	2023-08-16T09:18:54	null	https://github.com/huggingface/datasets/issues/6133	null	npuichigo	false	[ "@lhoestq ", "It's roughly the same code between the two so we can expected roughly the same speed, could you share a benchmark ?" ]
1,843,491,020	6,132	to_iterable_dataset is missing in document	closed	2023-08-09T15:15:03	2023-08-16T04:43:36	2023-08-16T04:43:29	https://github.com/huggingface/datasets/issues/6132	null	npuichigo	false	[ "Fixed with PR" ]
1,843,158,846	6,130	default config name doesn't work when config kwargs are specified.	closed	2023-08-09T12:43:15	2023-11-22T11:50:49	2023-11-22T11:50:48	https://github.com/huggingface/datasets/issues/6130	null	npuichigo	false	[ "@lhoestq ", "What should be the behavior in this case ? Should it override the default config with the added parameter ?", "I know why it should be treated as a new config if overriding parameters are passed. But in some case, I just pass in some common fields like `data_dir`.\r\n\r\nFor example, I want to extend the FolderBasedBuilder as a multi-config version, the `data_dir` or `data_files` are always passed by user and should not be considered as overriding the default config. In current state, I cannot leverage the feature of default config since passing `data_dir` will disable the default config.", "Thinking more about it I think the current behavior is the right one.\r\n\r\nProvided parameters should be passed to instantiate a new BuilderConfig.\r\n\r\nWhat's the error you're getting ?", "For example, this works to use default config with name '_all_':\r\n```python\r\ndatasets.load_dataset(\"indonesian-nlp/librivox-indonesia\", split=\"train\")\r\n```\r\nwhile this failed to use default config\r\n```python\r\ndatasets.load_dataset(\"indonesian-nlp/librivox-indonesia\", split=\"train\", data_dir='.')\r\n```\r\nAfter manually specifying it, it works again.\r\n```python\r\ndatasets.load_dataset(\"indonesian-nlp/librivox-indonesia\", \"_all_\", split=\"train\", data_dir='.')\r\n```", "@lhoestq ", "It should work if you explicitly ask for the config you want to override\r\n\r\n```python\r\nload_dataset('/dataset/with/multiple/config', 'name_of_the_default_config', some_field_in_config='some')\r\n```\r\n\r\nAlternatively you can have a BuilderConfig class that when instantiated returns a config with the right default values. In this case this code would instantiate this config with the default values except for the parameter to override:\r\n\r\n```python\r\nload_dataset('/dataset/with/multiple/config', some_field_in_config='some')\r\n```", "@lhoestq Yes. But it doesn't work for me.\r\n\r\nHere's my dataset for example.\r\n```\r\nlass MyDatasetConfig(datasets.BuilderConfig):\r\n def __init__(self, name: str, version: str, kwargs):\r\n self.option1 = kwargs.pop(\"option1\", False)\r\n self.option2 = kwargs.pop(\"option2\", 5)\r\n\r\n super().__init__(\r\n name=name,\r\n version=datasets.Version(version),\r\n kwargs)\r\n\r\n\r\nclass MyDataset(datasets.GeneratorBasedBuilder):\r\n DEFAULT_CONFIG_NAME = \"v1\"\r\n\r\n BUILDER_CONFIGS = [\r\n UnifiedTtsDatasetConfig(\r\n name=\"v1\",\r\n version=\"1.0.0\",\r\n description=\"Initial version of the dataset\"\r\n ),\r\n ]\r\n\r\n def _info(self) -> DatasetInfo:\r\n _ = self.option1\r\n ....\r\n```\r\n\r\nHere it's okay to use `load_dataset('my_dataset.py')` for loading the default config `v1`.\r\n\r\nBut if I want to override the default values in config with `load_dataset('my_dataset.py', option2=3)`, it failed to find my default config `v1.\r\n\r\nUnless I use `load_dataset('my_dataset.py', 'v1', option2=3)`\r\n\r\nSo according to your advice, how can I modify my dataset to be able to override default config without manually specifying it.", "What's the error ? It should try to instantiate `MyDatasetConfig` with `option2=3`", "@lhoestq The error is\r\n```\r\ndef _info(self) -> DatasetInfo:\r\n _ = self.option1 <-\r\n ....\r\nAttributeError: 'BuilderConfig' object has no attribute 'option1'\r\n```\r\nwhich seems to find another unknown config.\r\n\r\nYou can try this line `datasets.load_dataset(\"indonesian-nlp/librivox-indonesia\", split=\"train\", data_dir='.')`, it's a multi-config dataset on HF hub and the error is the same.\r\n\r\nMy insights:\r\nhttps://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518\r\nif `config_kwargs` is provided here, the if branch is skipped.", "I see, you just have to set this class attribute to your builder class :)\r\n\r\n```python\r\nBUILDER_CONFIG_CLASS = MyDatasetConfig\r\n```", "So what does this attribute do? In most cases it's not used and the [documents for multi-config dataset](https://huggingface.co/docs/datasets/main/en/image_dataset#multiple-configurations) never mentioned that.", "It tells which builder config class to instantiate if additional config parameters are passed to load_dataset", "@lhoestq maybe we can enhance the document to say something about the common attributes of `DatasetBuilder`", "Ah indeed it's missing in the docs, thanks for reporting. I'm opening a PR" ]
1,841,563,517	6,129	Release 2.14.4	closed	2023-08-08T15:43:56	2023-08-08T16:08:22	2023-08-08T15:49:06	https://github.com/huggingface/datasets/pull/6129	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6129", "html_url": "https://github.com/huggingface/datasets/pull/6129", "diff_url": "https://github.com/huggingface/datasets/pull/6129.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6129.patch", "merged_at": "2023-08-08T15:49:06" }	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) \| 0.006053 / 0.011353 (-0.005299) \| 0.003532 / 0.011008 (-0.007476) \| 0.081930 / 0.038508 (0.043422) \| 0.059043 / 0.023109 (0.035934) \| 0.322785 / 0.275898 (0.046887) \| 0.378158 / 0.323480 (0.054678) \| 0.004709 / 0.007986 (-0.003277) \| 0.002907 / 0.004328 (-0.001421) \| 0.061516 / 0.004250 (0.057266) \| 0.047209 / 0.037052 (0.010157) \| 0.346885 / 0.258489 (0.088396) \| 0.381011 / 0.293841 (0.087170) \| 0.027491 / 0.128546 (-0.101055) \| 0.008014 / 0.075646 (-0.067632) \| 0.260663 / 0.419271 (-0.158608) \| 0.045427 / 0.043533 (0.001894) \| 0.315277 / 0.255139 (0.060138) \| 0.377902 / 0.283200 (0.094703) \| 0.021371 / 0.141683 (-0.120311) \| 1.416350 / 1.452155 (-0.035804) \| 1.483345 / 1.492716 (-0.009372) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.203660 / 0.018006 (0.185654) \| 0.569081 / 0.000490 (0.568591) \| 0.002742 / 0.000200 (0.002542) \| 0.000074 / 0.000054 (0.000019) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023456 / 0.037411 (-0.013955) \| 0.073954 / 0.014526 (0.059428) \| 0.082991 / 0.176557 (-0.093566) \| 0.144781 / 0.737135 (-0.592354) \| 0.083346 / 0.296338 (-0.212992) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.391542 / 0.215209 (0.176333) \| 3.909505 / 2.077655 (1.831850) \| 1.862234 / 1.504120 (0.358114) \| 1.676076 / 1.541195 (0.134881) \| 1.727595 / 1.468490 (0.259105) \| 0.501769 / 4.584777 (-4.083008) \| 3.083697 / 3.745712 (-0.662016) \| 2.819751 / 5.269862 (-2.450111) \| 1.867265 / 4.565676 (-2.698411) \| 0.057575 / 0.424275 (-0.366700) \| 0.006478 / 0.007607 (-0.001129) \| 0.466684 / 0.226044 (0.240640) \| 4.657982 / 2.268929 (2.389054) \| 2.347052 / 55.444624 (-53.097573) \| 1.964688 / 6.876477 (-4.911789) \| 2.077821 / 2.142072 (-0.064252) \| 0.590591 / 4.805227 (-4.214636) \| 0.124585 / 6.500664 (-6.376079) \| 0.059468 / 0.075469 (-0.016001) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.223484 / 1.841788 (-0.618304) \| 18.104638 / 8.074308 (10.030330) \| 13.755126 / 10.191392 (3.563734) \| 0.143158 / 0.680424 (-0.537266) \| 0.017147 / 0.534201 (-0.517054) \| 0.337427 / 0.579283 (-0.241856) \| 0.352270 / 0.434364 (-0.082094) \| 0.383718 / 0.540337 (-0.156619) \| 0.534973 / 1.386936 (-0.851963) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006039 / 0.011353 (-0.005314) \| 0.003735 / 0.011008 (-0.007274) \| 0.061954 / 0.038508 (0.023446) \| 0.061786 / 0.023109 (0.038677) \| 0.429420 / 0.275898 (0.153522) \| 0.457629 / 0.323480 (0.134149) \| 0.004748 / 0.007986 (-0.003237) \| 0.002843 / 0.004328 (-0.001485) \| 0.061811 / 0.004250 (0.057560) \| 0.048740 / 0.037052 (0.011687) \| 0.430066 / 0.258489 (0.171577) \| 0.465971 / 0.293841 (0.172130) \| 0.027577 / 0.128546 (-0.100969) \| 0.007981 / 0.075646 (-0.067665) \| 0.067580 / 0.419271 (-0.351692) \| 0.042058 / 0.043533 (-0.001475) \| 0.428412 / 0.255139 (0.173273) \| 0.451054 / 0.283200 (0.167855) \| 0.020850 / 0.141683 (-0.120833) \| 1.453907 / 1.452155 (0.001752) \| 1.509914 / 1.492716 (0.017197) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.237713 / 0.018006 (0.219707) \| 0.418064 / 0.000490 (0.417575) \| 0.006411 / 0.000200 (0.006211) \| 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) \| 0.024950 / 0.037411 (-0.012462) \| 0.076806 / 0.014526 (0.062281) \| 0.085237 / 0.176557 (-0.091320) \| 0.137940 / 0.737135 (-0.599196) \| 0.086266 / 0.296338 (-0.210072) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.418666 / 0.215209 (0.203457) \| 4.160547 / 2.077655 (2.082893) \| 2.135671 / 1.504120 (0.631551) \| 1.964985 / 1.541195 (0.423790) \| 2.009447 / 1.468490 (0.540957) \| 0.501377 / 4.584777 (-4.083400) \| 3.064293 / 3.745712 (-0.681419) \| 2.827153 / 5.269862 (-2.442709) \| 1.854698 / 4.565676 (-2.710978) \| 0.057662 / 0.424275 (-0.366613) \| 0.006829 / 0.007607 (-0.000778) \| 0.496730 / 0.226044 (0.270686) \| 4.964663 / 2.268929 (2.695735) \| 2.583133 / 55.444624 (-52.861491) \| 2.329700 / 6.876477 (-4.546776) \| 2.415521 / 2.142072 (0.273449) \| 0.591973 / 4.805227 (-4.213255) \| 0.126801 / 6.500664 (-6.373863) \| 0.062811 / 0.075469 (-0.012659) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.348575 / 1.841788 (-0.493212) \| 18.282861 / 8.074308 (10.208553) \| 13.734056 / 10.191392 (3.542664) \| 0.154987 / 0.680424 (-0.525437) \| 0.016996 / 0.534201 (-0.517205) \| 0.335264 / 0.579283 (-0.244019) \| 0.356907 / 0.434364 (-0.077456) \| 0.399185 / 0.540337 (-0.141152) \| 0.540209 / 1.386936 (-0.846727) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#887bef1217e0f4441d57bf0f4d1e806df12f2c50 \"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) \| 0.006768 / 0.011353 (-0.004585) \| 0.004250 / 0.011008 (-0.006758) \| 0.086780 / 0.038508 (0.048272) \| 0.080872 / 0.023109 (0.057762) \| 0.309281 / 0.275898 (0.033383) \| 0.352293 / 0.323480 (0.028814) \| 0.005604 / 0.007986 (-0.002382) \| 0.003544 / 0.004328 (-0.000784) \| 0.066910 / 0.004250 (0.062659) \| 0.055568 / 0.037052 (0.018516) \| 0.314931 / 0.258489 (0.056442) \| 0.366026 / 0.293841 (0.072185) \| 0.031247 / 0.128546 (-0.097300) \| 0.008860 / 0.075646 (-0.066786) \| 0.293210 / 0.419271 (-0.126061) \| 0.052868 / 0.043533 (0.009335) \| 0.316769 / 0.255139 (0.061630) \| 0.352128 / 0.283200 (0.068929) \| 0.025492 / 0.141683 (-0.116190) \| 1.478379 / 1.452155 (0.026224) \| 1.573652 / 1.492716 (0.080936) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294975 / 0.018006 (0.276968) \| 0.615093 / 0.000490 (0.614603) \| 0.004279 / 0.000200 (0.004079) \| 0.000102 / 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) \| 0.031557 / 0.037411 (-0.005855) \| 0.085026 / 0.014526 (0.070500) \| 0.101221 / 0.176557 (-0.075336) \| 0.157432 / 0.737135 (-0.579703) \| 0.102350 / 0.296338 (-0.193988) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.384158 / 0.215209 (0.168949) \| 3.826656 / 2.077655 (1.749001) \| 1.873510 / 1.504120 (0.369390) \| 1.721913 / 1.541195 (0.180718) \| 1.848779 / 1.468490 (0.380289) \| 0.485128 / 4.584777 (-4.099649) \| 3.656660 / 3.745712 (-0.089052) \| 3.441964 / 5.269862 (-1.827898) \| 2.150611 / 4.565676 (-2.415066) \| 0.056869 / 0.424275 (-0.367406) \| 0.007382 / 0.007607 (-0.000225) \| 0.458751 / 0.226044 (0.232707) \| 4.585028 / 2.268929 (2.316099) \| 2.439538 / 55.444624 (-53.005086) \| 2.116959 / 6.876477 (-4.759518) \| 2.459220 / 2.142072 (0.317147) \| 0.580907 / 4.805227 (-4.224321) \| 0.134502 / 6.500664 (-6.366162) \| 0.062528 / 0.075469 (-0.012941) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.251006 / 1.841788 (-0.590782) \| 20.755849 / 8.074308 (12.681541) \| 14.456950 / 10.191392 (4.265558) \| 0.167074 / 0.680424 (-0.513350) \| 0.018482 / 0.534201 (-0.515719) \| 0.395867 / 0.579283 (-0.183416) \| 0.415620 / 0.434364 (-0.018744) \| 0.462247 / 0.540337 (-0.078090) \| 0.645762 / 1.386936 (-0.741174) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007050 / 0.011353 (-0.004303) \| 0.004421 / 0.011008 (-0.006587) \| 0.065312 / 0.038508 (0.026804) \| 0.089790 / 0.023109 (0.066681) \| 0.366318 / 0.275898 (0.090420) \| 0.403542 / 0.323480 (0.080062) \| 0.005695 / 0.007986 (-0.002290) \| 0.003642 / 0.004328 (-0.000687) \| 0.064540 / 0.004250 (0.060289) \| 0.060933 / 0.037052 (0.023881) \| 0.369004 / 0.258489 (0.110515) \| 0.408056 / 0.293841 (0.114215) \| 0.032124 / 0.128546 (-0.096422) \| 0.008960 / 0.075646 (-0.066686) \| 0.071267 / 0.419271 (-0.348005) \| 0.049745 / 0.043533 (0.006212) \| 0.367203 / 0.255139 (0.112064) \| 0.383009 / 0.283200 (0.099809) \| 0.025330 / 0.141683 (-0.116353) \| 1.518290 / 1.452155 (0.066135) \| 1.581738 / 1.492716 (0.089022) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.338281 / 0.018006 (0.320275) \| 0.538195 / 0.000490 (0.537706) \| 0.008498 / 0.000200 (0.008298) \| 0.000121 / 0.000054 (0.000067) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033279 / 0.037411 (-0.004133) \| 0.093233 / 0.014526 (0.078707) \| 0.106019 / 0.176557 (-0.070538) \| 0.161262 / 0.737135 (-0.575874) \| 0.109935 / 0.296338 (-0.186404) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.411563 / 0.215209 (0.196354) \| 4.102149 / 2.077655 (2.024495) \| 2.108513 / 1.504120 (0.604393) \| 1.945344 / 1.541195 (0.404150) \| 2.066964 / 1.468490 (0.598474) \| 0.482771 / 4.584777 (-4.102006) \| 3.659160 / 3.745712 (-0.086552) \| 3.420833 / 5.269862 (-1.849029) \| 2.147276 / 4.565676 (-2.418400) \| 0.056957 / 0.424275 (-0.367318) \| 0.007898 / 0.007607 (0.000290) \| 0.482401 / 0.226044 (0.256357) \| 4.821044 / 2.268929 (2.552115) \| 2.567993 / 55.444624 (-52.876631) \| 2.336165 / 6.876477 (-4.540312) \| 2.545066 / 2.142072 (0.402994) \| 0.580888 / 4.805227 (-4.224339) \| 0.134092 / 6.500664 (-6.366572) \| 0.062681 / 0.075469 (-0.012788) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.379124 / 1.841788 (-0.462664) \| 21.627949 / 8.074308 (13.553641) \| 15.064818 / 10.191392 (4.873426) \| 0.169707 / 0.680424 (-0.510716) \| 0.018671 / 0.534201 (-0.515530) \| 0.400496 / 0.579283 (-0.178787) \| 0.415542 / 0.434364 (-0.018822) \| 0.484351 / 0.540337 (-0.055986) \| 0.646046 / 1.386936 (-0.740890) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53d55f33bfac9febb0c355e136f2847e5f3e3b53 \"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) \| 0.007113 / 0.011353 (-0.004240) \| 0.004436 / 0.011008 (-0.006572) \| 0.087422 / 0.038508 (0.048914) \| 0.085996 / 0.023109 (0.062887) \| 0.311772 / 0.275898 (0.035873) \| 0.353281 / 0.323480 (0.029801) \| 0.004562 / 0.007986 (-0.003423) \| 0.003840 / 0.004328 (-0.000488) \| 0.066500 / 0.004250 (0.062250) \| 0.061293 / 0.037052 (0.024241) \| 0.328840 / 0.258489 (0.070351) \| 0.365587 / 0.293841 (0.071746) \| 0.031802 / 0.128546 (-0.096744) \| 0.008881 / 0.075646 (-0.066765) \| 0.289671 / 0.419271 (-0.129601) \| 0.053348 / 0.043533 (0.009816) \| 0.307822 / 0.255139 (0.052683) \| 0.342559 / 0.283200 (0.059360) \| 0.025760 / 0.141683 (-0.115923) \| 1.509944 / 1.452155 (0.057789) \| 1.556634 / 1.492716 (0.063918) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282036 / 0.018006 (0.264029) \| 0.608350 / 0.000490 (0.607860) \| 0.004843 / 0.000200 (0.004643) \| 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) \| 0.029810 / 0.037411 (-0.007601) \| 0.086215 / 0.014526 (0.071689) \| 0.102200 / 0.176557 (-0.074356) \| 0.158051 / 0.737135 (-0.579084) \| 0.103083 / 0.296338 (-0.193255) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.392119 / 0.215209 (0.176910) \| 3.895796 / 2.077655 (1.818141) \| 1.921118 / 1.504120 (0.416998) \| 1.754271 / 1.541195 (0.213076) \| 1.880991 / 1.468490 (0.412501) \| 0.481158 / 4.584777 (-4.103618) \| 3.609210 / 3.745712 (-0.136502) \| 3.412018 / 5.269862 (-1.857843) \| 2.131710 / 4.565676 (-2.433967) \| 0.057122 / 0.424275 (-0.367153) \| 0.007444 / 0.007607 (-0.000163) \| 0.468880 / 0.226044 (0.242835) \| 4.682441 / 2.268929 (2.413512) \| 2.505613 / 55.444624 (-52.939012) \| 2.149655 / 6.876477 (-4.726822) \| 2.465904 / 2.142072 (0.323832) \| 0.578877 / 4.805227 (-4.226350) \| 0.133504 / 6.500664 (-6.367160) \| 0.061422 / 0.075469 (-0.014047) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.269395 / 1.841788 (-0.572393) \| 21.107558 / 8.074308 (13.033250) \| 15.318502 / 10.191392 (5.127110) \| 0.165273 / 0.680424 (-0.515151) \| 0.018783 / 0.534201 (-0.515418) \| 0.396259 / 0.579283 (-0.183024) \| 0.412907 / 0.434364 (-0.021457) \| 0.465723 / 0.540337 (-0.074615) \| 0.638414 / 1.386936 (-0.748522) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007083 / 0.011353 (-0.004270) \| 0.004216 / 0.011008 (-0.006793) \| 0.065362 / 0.038508 (0.026854) \| 0.095454 / 0.023109 (0.072345) \| 0.364220 / 0.275898 (0.088322) \| 0.417650 / 0.323480 (0.094170) \| 0.006114 / 0.007986 (-0.001872) \| 0.003577 / 0.004328 (-0.000751) \| 0.064830 / 0.004250 (0.060579) \| 0.062535 / 0.037052 (0.025483) \| 0.381844 / 0.258489 (0.123355) \| 0.418996 / 0.293841 (0.125155) \| 0.031386 / 0.128546 (-0.097160) \| 0.008913 / 0.075646 (-0.066733) \| 0.070860 / 0.419271 (-0.348411) \| 0.049132 / 0.043533 (0.005599) \| 0.360406 / 0.255139 (0.105267) \| 0.392407 / 0.283200 (0.109207) \| 0.024611 / 0.141683 (-0.117072) \| 1.509051 / 1.452155 (0.056896) \| 1.570288 / 1.492716 (0.077572) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.368611 / 0.018006 (0.350605) \| 0.537587 / 0.000490 (0.537098) \| 0.028056 / 0.000200 (0.027856) \| 0.000317 / 0.000054 (0.000262) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031570 / 0.037411 (-0.005841) \| 0.088985 / 0.014526 (0.074460) \| 0.105268 / 0.176557 (-0.071288) \| 0.156724 / 0.737135 (-0.580412) \| 0.105266 / 0.296338 (-0.191073) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.413861 / 0.215209 (0.198652) \| 4.127001 / 2.077655 (2.049347) \| 2.112114 / 1.504120 (0.607994) \| 1.945200 / 1.541195 (0.404005) \| 2.083031 / 1.468490 (0.614540) \| 0.488086 / 4.584777 (-4.096691) \| 3.565584 / 3.745712 (-0.180128) \| 3.380782 / 5.269862 (-1.889079) \| 2.103481 / 4.565676 (-2.462195) \| 0.058203 / 0.424275 (-0.366072) \| 0.007996 / 0.007607 (0.000389) \| 0.487986 / 0.226044 (0.261941) \| 4.871023 / 2.268929 (2.602095) \| 2.584632 / 55.444624 (-52.859992) \| 2.240103 / 6.876477 (-4.636374) \| 2.555165 / 2.142072 (0.413092) \| 0.591950 / 4.805227 (-4.213278) \| 0.134919 / 6.500664 (-6.365745) \| 0.062868 / 0.075469 (-0.012601) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.369731 / 1.841788 (-0.472057) \| 21.497888 / 8.074308 (13.423580) \| 14.555054 / 10.191392 (4.363662) \| 0.168768 / 0.680424 (-0.511656) \| 0.018837 / 0.534201 (-0.515364) \| 0.394512 / 0.579283 (-0.184771) \| 0.405459 / 0.434364 (-0.028905) \| 0.475479 / 0.540337 (-0.064858) \| 0.631994 / 1.386936 (-0.754942) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53d55f33bfac9febb0c355e136f2847e5f3e3b53 \"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) \| 0.009072 / 0.011353 (-0.002280) \| 0.004894 / 0.011008 (-0.006114) \| 0.108790 / 0.038508 (0.070282) \| 0.081783 / 0.023109 (0.058674) \| 0.381963 / 0.275898 (0.106064) \| 0.450700 / 0.323480 (0.127220) \| 0.006961 / 0.007986 (-0.001025) \| 0.004035 / 0.004328 (-0.000293) \| 0.081420 / 0.004250 (0.077169) \| 0.058029 / 0.037052 (0.020976) \| 0.437453 / 0.258489 (0.178964) \| 0.472607 / 0.293841 (0.178766) \| 0.048663 / 0.128546 (-0.079884) \| 0.013512 / 0.075646 (-0.062134) \| 0.406009 / 0.419271 (-0.013262) \| 0.067616 / 0.043533 (0.024084) \| 0.383641 / 0.255139 (0.128502) \| 0.456734 / 0.283200 (0.173534) \| 0.033391 / 0.141683 (-0.108292) \| 1.753529 / 1.452155 (0.301375) \| 1.859831 / 1.492716 (0.367115) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.215128 / 0.018006 (0.197122) \| 0.538261 / 0.000490 (0.537771) \| 0.005430 / 0.000200 (0.005230) \| 0.000124 / 0.000054 (0.000069) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032664 / 0.037411 (-0.004748) \| 0.093465 / 0.014526 (0.078939) \| 0.106637 / 0.176557 (-0.069919) \| 0.173642 / 0.737135 (-0.563494) \| 0.113944 / 0.296338 (-0.182394) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.629212 / 0.215209 (0.414003) \| 6.116729 / 2.077655 (4.039075) \| 2.818000 / 1.504120 (1.313880) \| 2.515317 / 1.541195 (0.974122) \| 2.466588 / 1.468490 (0.998098) \| 0.850815 / 4.584777 (-3.733962) \| 5.051292 / 3.745712 (1.305579) \| 4.472138 / 5.269862 (-0.797724) \| 2.968317 / 4.565676 (-1.597360) \| 0.100173 / 0.424275 (-0.324102) \| 0.008407 / 0.007607 (0.000800) \| 0.743972 / 0.226044 (0.517928) \| 7.397619 / 2.268929 (5.128690) \| 3.596681 / 55.444624 (-51.847943) \| 2.854674 / 6.876477 (-4.021803) \| 3.114274 / 2.142072 (0.972201) \| 1.064879 / 4.805227 (-3.740348) \| 0.215981 / 6.500664 (-6.284683) \| 0.078159 / 0.075469 (0.002690) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.543291 / 1.841788 (-0.298497) \| 23.244641 / 8.074308 (15.170333) \| 20.784610 / 10.191392 (10.593218) \| 0.222002 / 0.680424 (-0.458422) \| 0.028584 / 0.534201 (-0.505617) \| 0.478563 / 0.579283 (-0.100720) \| 0.556101 / 0.434364 (0.121737) \| 0.547446 / 0.540337 (0.007109) \| 0.764318 / 1.386936 (-0.622618) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008651 / 0.011353 (-0.002702) \| 0.004925 / 0.011008 (-0.006083) \| 0.078995 / 0.038508 (0.040487) \| 0.092878 / 0.023109 (0.069769) \| 0.485615 / 0.275898 (0.209717) \| 0.532157 / 0.323480 (0.208677) \| 0.008228 / 0.007986 (0.000243) \| 0.004777 / 0.004328 (0.000449) \| 0.076892 / 0.004250 (0.072642) \| 0.066905 / 0.037052 (0.029853) \| 0.465497 / 0.258489 (0.207008) \| 0.520153 / 0.293841 (0.226312) \| 0.047357 / 0.128546 (-0.081189) \| 0.016870 / 0.075646 (-0.058776) \| 0.090481 / 0.419271 (-0.328791) \| 0.060774 / 0.043533 (0.017241) \| 0.474368 / 0.255139 (0.219229) \| 0.503981 / 0.283200 (0.220781) \| 0.036025 / 0.141683 (-0.105658) \| 1.769939 / 1.452155 (0.317784) \| 1.851518 / 1.492716 (0.358802) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.265947 / 0.018006 (0.247941) \| 0.532317 / 0.000490 (0.531828) \| 0.004997 / 0.000200 (0.004797) \| 0.000130 / 0.000054 (0.000076) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034112 / 0.037411 (-0.003299) \| 0.102290 / 0.014526 (0.087764) \| 0.109989 / 0.176557 (-0.066567) \| 0.182813 / 0.737135 (-0.554323) \| 0.111774 / 0.296338 (-0.184565) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.584893 / 0.215209 (0.369684) \| 6.138505 / 2.077655 (4.060850) \| 2.925761 / 1.504120 (1.421641) \| 2.607320 / 1.541195 (1.066125) \| 2.655827 / 1.468490 (1.187337) \| 0.871140 / 4.584777 (-3.713637) \| 5.051171 / 3.745712 (1.305459) \| 4.708008 / 5.269862 (-0.561854) \| 3.027485 / 4.565676 (-1.538191) \| 0.100970 / 0.424275 (-0.323305) \| 0.009640 / 0.007607 (0.002033) \| 0.747818 / 0.226044 (0.521774) \| 7.539930 / 2.268929 (5.271001) \| 3.611693 / 55.444624 (-51.832931) \| 2.924087 / 6.876477 (-3.952390) \| 3.141993 / 2.142072 (0.999920) \| 1.062921 / 4.805227 (-3.742306) \| 0.213185 / 6.500664 (-6.287479) \| 0.077146 / 0.075469 (0.001677) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.669182 / 1.841788 (-0.172606) \| 23.810242 / 8.074308 (15.735934) \| 21.220649 / 10.191392 (11.029257) \| 0.212639 / 0.680424 (-0.467785) \| 0.026705 / 0.534201 (-0.507496) \| 0.469231 / 0.579283 (-0.110053) \| 0.551672 / 0.434364 (0.117308) \| 0.575043 / 0.540337 (0.034706) \| 0.767511 / 1.386936 (-0.619425) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53d55f33bfac9febb0c355e136f2847e5f3e3b53 \"CML watermark\")\n" ]
1,841,545,493	6,128	IndexError: Invalid key: 88 is out of bounds for size 0	closed	2023-08-08T15:32:08	2023-12-26T07:51:57	2023-08-11T13:35:09	https://github.com/huggingface/datasets/issues/6128	null	TomasAndersonFang	false	[ "Hi @TomasAndersonFang,\r\n\r\nHave you tried instead to use `torch_compile` in `transformers.TrainingArguments`? https://huggingface.co/docs/transformers/v4.31.0/en/main_classes/trainer#transformers.TrainingArguments.torch_compile", "> \r\n\r\nI tried this and got the following error:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 324, in _compile\r\n out_code = transform_code_object(code, transform)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/bytecode_transformation.py\", line 445, in transform_code_object\r\n transformations(instructions, code_options)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 311, in transform\r\n tracer.run()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py\", line 1726, in run\r\n super().run()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py\", line 576, in run\r\n and self.step()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py\", line 540, in step\r\n getattr(self, inst.opname)(inst)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py\", line 1030, in LOAD_ATTR\r\n result = BuiltinVariable(getattr).call_function(\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/variables/builtin.py\", line 566, in call_function\r\n result = handler(tx, args, kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/variables/builtin.py\", line 931, in call_getattr\r\n return obj.var_getattr(tx, name).add_options(options)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/variables/nn_module.py\", line 124, in var_getattr\r\n subobj = inspect.getattr_static(base, name)\r\n File \"/apps/Arch/software/Python/3.10.8-GCCcore-12.2.0/lib/python3.10/inspect.py\", line 1777, in getattr_static\r\n raise AttributeError(attr)\r\nAttributeError: config\r\n\r\nfrom user code:\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/peft/peft_model.py\", line 909, in forward\r\n if self.base_model.config.model_type == \"mpt\":\r\n\r\nSet torch._dynamo.config.verbose=True for more information\r\n\r\n\r\nYou can suppress this exception and fall back to eager by setting:\r\n torch._dynamo.config.suppress_errors = True\r\n\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 \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/llm-copt/fine-tune/falcon/falcon_sft.py\", line 228, in <module>\r\n main()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/llm-copt/fine-tune/falcon/falcon_sft.py\", line 221, in main\r\n trainer.train()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py\", line 1539, in train\r\n return inner_training_loop(\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py\", line 1809, in _inner_training_loop\r\n tr_loss_step = self.training_step(model, inputs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py\", line 2654, in training_step\r\n loss = self.compute_loss(model, inputs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py\", line 2679, in compute_loss\r\n outputs = model(inputs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\r\n return forward_call(args, *kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py\", line 82, in forward\r\n return self.dynamo_ctx(self._orig_mod.forward)(args, *kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py\", line 209, in _fn\r\n return fn(args, *kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/accelerate/utils/operations.py\", line 581, in forward\r\n return model_forward(args, *kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/accelerate/utils/operations.py\", line 569, in __call__\r\n return convert_to_fp32(self.model_forward(args, *kwargs))\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/amp/autocast_mode.py\", line 14, in decorate_autocast\r\n return func(args, *kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py\", line 337, in catch_errors\r\n return callback(frame, cache_size, hooks)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 404, in _convert_frame\r\n result = inner_convert(frame, cache_size, hooks)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 104, in _fn\r\n return fn(args, *kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 262, in _convert_frame_assert\r\n return _compile(\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/utils.py\", line 163, in time_wrapper\r\n r = func(args, **kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 394, in _compile\r\n raise InternalTorchDynamoError() from e\r\ntorch._dynamo.exc.InternalTorchDynamoError\r\n```", "Hi @TomasAndersonFang,\r\n\r\nI guess in this case it may be an issue with `transformers` (or `PyTorch`). I would recommend you open an issue on their repo.", "@albertvillanova Thanks for your recommendation. I'll do it", "@TomasAndersonFang were you able to find a solution to this issue? I would highly appreciate any help. \r\n\r\nThanks!" ]
1,839,746,721	6,127	Fix authentication issues	closed	2023-08-07T15:41:25	2023-08-08T15:24:59	2023-08-08T15:16:22	https://github.com/huggingface/datasets/pull/6127	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6127", "html_url": "https://github.com/huggingface/datasets/pull/6127", "diff_url": "https://github.com/huggingface/datasets/pull/6127.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6127.patch", "merged_at": "2023-08-08T15:16:22" }	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) \| 0.006103 / 0.011353 (-0.005250) \| 0.003588 / 0.011008 (-0.007420) \| 0.080335 / 0.038508 (0.041827) \| 0.059634 / 0.023109 (0.036525) \| 0.356093 / 0.275898 (0.080195) \| 0.407376 / 0.323480 (0.083896) \| 0.005343 / 0.007986 (-0.002643) \| 0.002928 / 0.004328 (-0.001400) \| 0.062580 / 0.004250 (0.058330) \| 0.047544 / 0.037052 (0.010491) \| 0.364305 / 0.258489 (0.105816) \| 0.421463 / 0.293841 (0.127623) \| 0.027249 / 0.128546 (-0.101298) \| 0.008010 / 0.075646 (-0.067636) \| 0.262543 / 0.419271 (-0.156728) \| 0.044978 / 0.043533 (0.001445) \| 0.339344 / 0.255139 (0.084205) \| 0.395288 / 0.283200 (0.112088) \| 0.021425 / 0.141683 (-0.120258) \| 1.439767 / 1.452155 (-0.012387) \| 1.498081 / 1.492716 (0.005365) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.196976 / 0.018006 (0.178970) \| 0.435383 / 0.000490 (0.434893) \| 0.004559 / 0.000200 (0.004359) \| 0.000071 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023653 / 0.037411 (-0.013759) \| 0.072944 / 0.014526 (0.058418) \| 0.083651 / 0.176557 (-0.092906) \| 0.144590 / 0.737135 (-0.592545) \| 0.084844 / 0.296338 (-0.211494) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.398752 / 0.215209 (0.183543) \| 3.959539 / 2.077655 (1.881884) \| 1.935277 / 1.504120 (0.431157) \| 1.751994 / 1.541195 (0.210799) \| 1.828386 / 1.468490 (0.359896) \| 0.500492 / 4.584777 (-4.084284) \| 3.086630 / 3.745712 (-0.659082) \| 2.851664 / 5.269862 (-2.418198) \| 1.869792 / 4.565676 (-2.695885) \| 0.058509 / 0.424275 (-0.365766) \| 0.006500 / 0.007607 (-0.001107) \| 0.467468 / 0.226044 (0.241424) \| 4.686168 / 2.268929 (2.417240) \| 2.427632 / 55.444624 (-53.016993) \| 2.193194 / 6.876477 (-4.683283) \| 2.408574 / 2.142072 (0.266501) \| 0.592173 / 4.805227 (-4.213054) \| 0.125381 / 6.500664 (-6.375283) \| 0.060679 / 0.075469 (-0.014790) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.236066 / 1.841788 (-0.605722) \| 18.591689 / 8.074308 (10.517381) \| 14.138774 / 10.191392 (3.947382) \| 0.147455 / 0.680424 (-0.532968) \| 0.016921 / 0.534201 (-0.517280) \| 0.328129 / 0.579283 (-0.251154) \| 0.348872 / 0.434364 (-0.085491) \| 0.380311 / 0.540337 (-0.160026) \| 0.532901 / 1.386936 (-0.854035) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005914 / 0.011353 (-0.005438) \| 0.003614 / 0.011008 (-0.007394) \| 0.062857 / 0.038508 (0.024349) \| 0.060633 / 0.023109 (0.037524) \| 0.419684 / 0.275898 (0.143786) \| 0.449025 / 0.323480 (0.125546) \| 0.004595 / 0.007986 (-0.003391) \| 0.002861 / 0.004328 (-0.001467) \| 0.063253 / 0.004250 (0.059003) \| 0.048770 / 0.037052 (0.011718) \| 0.419838 / 0.258489 (0.161349) \| 0.465183 / 0.293841 (0.171342) \| 0.027350 / 0.128546 (-0.101196) \| 0.008065 / 0.075646 (-0.067582) \| 0.068321 / 0.419271 (-0.350950) \| 0.041083 / 0.043533 (-0.002449) \| 0.400831 / 0.255139 (0.145692) \| 0.449286 / 0.283200 (0.166086) \| 0.020472 / 0.141683 (-0.121210) \| 1.437215 / 1.452155 (-0.014940) \| 1.503679 / 1.492716 (0.010963) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230764 / 0.018006 (0.212758) \| 0.420774 / 0.000490 (0.420285) \| 0.004012 / 0.000200 (0.003812) \| 0.000069 / 0.000054 (0.000014) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026009 / 0.037411 (-0.011402) \| 0.077943 / 0.014526 (0.063417) \| 0.087281 / 0.176557 (-0.089276) \| 0.139422 / 0.737135 (-0.597713) \| 0.089090 / 0.296338 (-0.207248) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.417298 / 0.215209 (0.202088) \| 4.152303 / 2.077655 (2.074648) \| 2.179996 / 1.504120 (0.675877) \| 2.020619 / 1.541195 (0.479424) \| 2.085241 / 1.468490 (0.616751) \| 0.501111 / 4.584777 (-4.083666) \| 3.079849 / 3.745712 (-0.665863) \| 2.820607 / 5.269862 (-2.449255) \| 1.863988 / 4.565676 (-2.701688) \| 0.057662 / 0.424275 (-0.366613) \| 0.006778 / 0.007607 (-0.000830) \| 0.498661 / 0.226044 (0.272616) \| 4.986503 / 2.268929 (2.717574) \| 2.620676 / 55.444624 (-52.823949) \| 2.297546 / 6.876477 (-4.578931) \| 2.458148 / 2.142072 (0.316075) \| 0.599490 / 4.805227 (-4.205738) \| 0.125102 / 6.500664 (-6.375562) \| 0.061411 / 0.075469 (-0.014059) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.323816 / 1.841788 (-0.517971) \| 18.462614 / 8.074308 (10.388306) \| 13.845826 / 10.191392 (3.654434) \| 0.146115 / 0.680424 (-0.534309) \| 0.016862 / 0.534201 (-0.517339) \| 0.335449 / 0.579283 (-0.243834) \| 0.343792 / 0.434364 (-0.090572) \| 0.394068 / 0.540337 (-0.146269) \| 0.536378 / 1.386936 (-0.850558) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#de3f00368c9236e9410821f5fddb95d6069883c1 \"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) \| 0.006825 / 0.011353 (-0.004527) \| 0.004005 / 0.011008 (-0.007003) \| 0.085504 / 0.038508 (0.046996) \| 0.077252 / 0.023109 (0.054143) \| 0.351891 / 0.275898 (0.075993) \| 0.383404 / 0.323480 (0.059924) \| 0.004153 / 0.007986 (-0.003833) \| 0.003344 / 0.004328 (-0.000985) \| 0.064936 / 0.004250 (0.060685) \| 0.057653 / 0.037052 (0.020601) \| 0.368155 / 0.258489 (0.109666) \| 0.406122 / 0.293841 (0.112282) \| 0.032049 / 0.128546 (-0.096497) \| 0.008698 / 0.075646 (-0.066949) \| 0.292394 / 0.419271 (-0.126878) \| 0.053634 / 0.043533 (0.010101) \| 0.358273 / 0.255139 (0.103134) \| 0.378441 / 0.283200 (0.095242) \| 0.026928 / 0.141683 (-0.114755) \| 1.458718 / 1.452155 (0.006563) \| 1.536231 / 1.492716 (0.043515) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.213956 / 0.018006 (0.195950) \| 0.458620 / 0.000490 (0.458130) \| 0.002718 / 0.000200 (0.002519) \| 0.000078 / 0.000054 (0.000023) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027870 / 0.037411 (-0.009541) \| 0.083922 / 0.014526 (0.069396) \| 0.152056 / 0.176557 (-0.024501) \| 0.151584 / 0.737135 (-0.585552) \| 0.095698 / 0.296338 (-0.200641) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.407762 / 0.215209 (0.192553) \| 4.074324 / 2.077655 (1.996669) \| 2.089929 / 1.504120 (0.585809) \| 1.920024 / 1.541195 (0.378829) \| 2.013410 / 1.468490 (0.544920) \| 0.486056 / 4.584777 (-4.098721) \| 3.656869 / 3.745712 (-0.088843) \| 3.304008 / 5.269862 (-1.965854) \| 2.074363 / 4.565676 (-2.491313) \| 0.057293 / 0.424275 (-0.366982) \| 0.007240 / 0.007607 (-0.000367) \| 0.482696 / 0.226044 (0.256652) \| 4.833251 / 2.268929 (2.564322) \| 2.570391 / 55.444624 (-52.874233) \| 2.220619 / 6.876477 (-4.655857) \| 2.426316 / 2.142072 (0.284243) \| 0.584811 / 4.805227 (-4.220416) \| 0.134907 / 6.500664 (-6.365757) \| 0.061115 / 0.075469 (-0.014354) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.251969 / 1.841788 (-0.589818) \| 19.601611 / 8.074308 (11.527303) \| 14.190217 / 10.191392 (3.998825) \| 0.166296 / 0.680424 (-0.514128) \| 0.018334 / 0.534201 (-0.515867) \| 0.395172 / 0.579283 (-0.184111) \| 0.410440 / 0.434364 (-0.023924) \| 0.462263 / 0.540337 (-0.078074) \| 0.645504 / 1.386936 (-0.741432) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006991 / 0.011353 (-0.004362) \| 0.004084 / 0.011008 (-0.006924) \| 0.065208 / 0.038508 (0.026700) \| 0.077809 / 0.023109 (0.054699) \| 0.386472 / 0.275898 (0.110574) \| 0.418686 / 0.323480 (0.095206) \| 0.005346 / 0.007986 (-0.002640) \| 0.003416 / 0.004328 (-0.000912) \| 0.066209 / 0.004250 (0.061958) \| 0.057517 / 0.037052 (0.020465) \| 0.407684 / 0.258489 (0.149195) \| 0.425438 / 0.293841 (0.131597) \| 0.032166 / 0.128546 (-0.096380) \| 0.008662 / 0.075646 (-0.066985) \| 0.071712 / 0.419271 (-0.347560) \| 0.049764 / 0.043533 (0.006231) \| 0.394882 / 0.255139 (0.139743) \| 0.403589 / 0.283200 (0.120389) \| 0.023688 / 0.141683 (-0.117995) \| 1.468488 / 1.452155 (0.016334) \| 1.533118 / 1.492716 (0.040401) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.252949 / 0.018006 (0.234943) \| 0.447355 / 0.000490 (0.446865) \| 0.011721 / 0.000200 (0.011521) \| 0.000107 / 0.000054 (0.000052) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031444 / 0.037411 (-0.005968) \| 0.089390 / 0.014526 (0.074864) \| 0.100103 / 0.176557 (-0.076454) \| 0.153301 / 0.737135 (-0.583835) \| 0.101336 / 0.296338 (-0.195003) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.408574 / 0.215209 (0.193365) \| 4.073135 / 2.077655 (1.995480) \| 2.086550 / 1.504120 (0.582430) \| 1.930651 / 1.541195 (0.389457) \| 2.013548 / 1.468490 (0.545058) \| 0.477235 / 4.584777 (-4.107542) \| 3.547545 / 3.745712 (-0.198167) \| 3.321957 / 5.269862 (-1.947905) \| 2.057705 / 4.565676 (-2.507971) \| 0.056730 / 0.424275 (-0.367545) \| 0.007882 / 0.007607 (0.000275) \| 0.487297 / 0.226044 (0.261253) \| 4.874184 / 2.268929 (2.605255) \| 2.631129 / 55.444624 (-52.813496) \| 2.235755 / 6.876477 (-4.640722) \| 2.463329 / 2.142072 (0.321257) \| 0.578308 / 4.805227 (-4.226919) \| 0.132726 / 6.500664 (-6.367938) \| 0.064883 / 0.075469 (-0.010586) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.347564 / 1.841788 (-0.494223) \| 20.192973 / 8.074308 (12.118665) \| 14.563553 / 10.191392 (4.372161) \| 0.168244 / 0.680424 (-0.512180) \| 0.018638 / 0.534201 (-0.515563) \| 0.394789 / 0.579283 (-0.184494) \| 0.419677 / 0.434364 (-0.014687) \| 0.480274 / 0.540337 (-0.060063) \| 0.641204 / 1.386936 (-0.745732) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9c7a0d56b60bf700d6a491fa30eaf66500969315 \"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) \| 0.005939 / 0.011353 (-0.005413) \| 0.003457 / 0.011008 (-0.007551) \| 0.079985 / 0.038508 (0.041477) \| 0.056492 / 0.023109 (0.033383) \| 0.312356 / 0.275898 (0.036458) \| 0.354038 / 0.323480 (0.030558) \| 0.004551 / 0.007986 (-0.003435) \| 0.002828 / 0.004328 (-0.001501) \| 0.062369 / 0.004250 (0.058119) \| 0.044712 / 0.037052 (0.007660) \| 0.318244 / 0.258489 (0.059755) \| 0.361977 / 0.293841 (0.068136) \| 0.026460 / 0.128546 (-0.102086) \| 0.007928 / 0.075646 (-0.067719) \| 0.261378 / 0.419271 (-0.157894) \| 0.044209 / 0.043533 (0.000676) \| 0.313931 / 0.255139 (0.058792) \| 0.339553 / 0.283200 (0.056354) \| 0.019776 / 0.141683 (-0.121907) \| 1.443126 / 1.452155 (-0.009029) \| 1.508149 / 1.492716 (0.015432) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.183801 / 0.018006 (0.165795) \| 0.427967 / 0.000490 (0.427477) \| 0.002028 / 0.000200 (0.001828) \| 0.000062 / 0.000054 (0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023697 / 0.037411 (-0.013715) \| 0.072128 / 0.014526 (0.057602) \| 0.083701 / 0.176557 (-0.092855) \| 0.142821 / 0.737135 (-0.594315) \| 0.082276 / 0.296338 (-0.214063) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.434427 / 0.215209 (0.219218) \| 4.325962 / 2.077655 (2.248308) \| 2.277115 / 1.504120 (0.772995) \| 2.093736 / 1.541195 (0.552541) \| 2.127984 / 1.468490 (0.659494) \| 0.502336 / 4.584777 (-4.082441) \| 3.023243 / 3.745712 (-0.722469) \| 2.805154 / 5.269862 (-2.464708) \| 1.821273 / 4.565676 (-2.744403) \| 0.057480 / 0.424275 (-0.366795) \| 0.006365 / 0.007607 (-0.001242) \| 0.508258 / 0.226044 (0.282213) \| 5.087950 / 2.268929 (2.819022) \| 2.705029 / 55.444624 (-52.739596) \| 2.378392 / 6.876477 (-4.498085) \| 2.515380 / 2.142072 (0.373307) \| 0.589283 / 4.805227 (-4.215944) \| 0.125719 / 6.500664 (-6.374945) \| 0.061074 / 0.075469 (-0.014395) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.221895 / 1.841788 (-0.619893) \| 18.025917 / 8.074308 (9.951609) \| 13.556901 / 10.191392 (3.365509) \| 0.142614 / 0.680424 (-0.537809) \| 0.016731 / 0.534201 (-0.517469) \| 0.328374 / 0.579283 (-0.250910) \| 0.342553 / 0.434364 (-0.091811) \| 0.374502 / 0.540337 (-0.165836) \| 0.534173 / 1.386936 (-0.852763) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005817 / 0.011353 (-0.005536) \| 0.003500 / 0.011008 (-0.007509) \| 0.062240 / 0.038508 (0.023732) \| 0.058128 / 0.023109 (0.035019) \| 0.424014 / 0.275898 (0.148116) \| 0.468453 / 0.323480 (0.144973) \| 0.004641 / 0.007986 (-0.003345) \| 0.002821 / 0.004328 (-0.001508) \| 0.062180 / 0.004250 (0.057930) \| 0.047578 / 0.037052 (0.010526) \| 0.427367 / 0.258489 (0.168878) \| 0.467889 / 0.293841 (0.174048) \| 0.027144 / 0.128546 (-0.101403) \| 0.007969 / 0.075646 (-0.067678) \| 0.067764 / 0.419271 (-0.351508) \| 0.040719 / 0.043533 (-0.002814) \| 0.423663 / 0.255139 (0.168524) \| 0.458556 / 0.283200 (0.175356) \| 0.019196 / 0.141683 (-0.122487) \| 1.471546 / 1.452155 (0.019392) \| 1.547541 / 1.492716 (0.054825) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.228777 / 0.018006 (0.210770) \| 0.406663 / 0.000490 (0.406173) \| 0.003688 / 0.000200 (0.003488) \| 0.000075 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025494 / 0.037411 (-0.011917) \| 0.076339 / 0.014526 (0.061814) \| 0.084233 / 0.176557 (-0.092324) \| 0.136995 / 0.737135 (-0.600140) \| 0.085443 / 0.296338 (-0.210895) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.420441 / 0.215209 (0.205232) \| 4.187018 / 2.077655 (2.109363) \| 2.142139 / 1.504120 (0.638019) \| 1.974530 / 1.541195 (0.433335) \| 2.027321 / 1.468490 (0.558831) \| 0.498116 / 4.584777 (-4.086661) \| 2.988514 / 3.745712 (-0.757198) \| 2.782046 / 5.269862 (-2.487816) \| 1.821725 / 4.565676 (-2.743951) \| 0.057711 / 0.424275 (-0.366564) \| 0.006664 / 0.007607 (-0.000944) \| 0.491015 / 0.226044 (0.264971) \| 4.921037 / 2.268929 (2.652108) \| 2.574964 / 55.444624 (-52.869661) \| 2.251703 / 6.876477 (-4.624774) \| 2.361154 / 2.142072 (0.219082) \| 0.593362 / 4.805227 (-4.211865) \| 0.126107 / 6.500664 (-6.374557) \| 0.061840 / 0.075469 (-0.013630) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.327459 / 1.841788 (-0.514328) \| 18.062960 / 8.074308 (9.988652) \| 13.669253 / 10.191392 (3.477861) \| 0.130719 / 0.680424 (-0.549705) \| 0.016564 / 0.534201 (-0.517637) \| 0.335821 / 0.579283 (-0.243462) \| 0.341691 / 0.434364 (-0.092673) \| 0.392651 / 0.540337 (-0.147686) \| 0.529650 / 1.386936 (-0.857286) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c65806b0542996e56825ab46a3ce8f9c07ab0df3 \"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) \| 0.009625 / 0.011353 (-0.001728) \| 0.005354 / 0.011008 (-0.005654) \| 0.114350 / 0.038508 (0.075842) \| 0.086637 / 0.023109 (0.063528) \| 0.465381 / 0.275898 (0.189483) \| 0.490411 / 0.323480 (0.166931) \| 0.006575 / 0.007986 (-0.001411) \| 0.004287 / 0.004328 (-0.000041) \| 0.093134 / 0.004250 (0.088884) \| 0.060209 / 0.037052 (0.023156) \| 0.459570 / 0.258489 (0.201080) \| 0.523320 / 0.293841 (0.229479) \| 0.047943 / 0.128546 (-0.080603) \| 0.014764 / 0.075646 (-0.060882) \| 0.383887 / 0.419271 (-0.035384) \| 0.069864 / 0.043533 (0.026331) \| 0.469122 / 0.255139 (0.213983) \| 0.509953 / 0.283200 (0.226753) \| 0.037800 / 0.141683 (-0.103883) \| 1.877589 / 1.452155 (0.425434) \| 2.014913 / 1.492716 (0.522197) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.309146 / 0.018006 (0.291140) \| 0.644390 / 0.000490 (0.643900) \| 0.005017 / 0.000200 (0.004817) \| 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) \| 0.032964 / 0.037411 (-0.004447) \| 0.103236 / 0.014526 (0.088711) \| 0.119950 / 0.176557 (-0.056607) \| 0.207674 / 0.737135 (-0.529461) \| 0.117278 / 0.296338 (-0.179060) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.605464 / 0.215209 (0.390255) \| 6.027805 / 2.077655 (3.950150) \| 2.719725 / 1.504120 (1.215605) \| 2.262752 / 1.541195 (0.721558) \| 2.330310 / 1.468490 (0.861820) \| 0.862537 / 4.584777 (-3.722240) \| 5.347080 / 3.745712 (1.601368) \| 4.792170 / 5.269862 (-0.477691) \| 3.103694 / 4.565676 (-1.461983) \| 0.103646 / 0.424275 (-0.320629) \| 0.009411 / 0.007607 (0.001804) \| 0.743052 / 0.226044 (0.517008) \| 7.289684 / 2.268929 (5.020755) \| 3.436530 / 55.444624 (-52.008094) \| 2.722440 / 6.876477 (-4.154036) \| 2.952380 / 2.142072 (0.810308) \| 1.047688 / 4.805227 (-3.757539) \| 0.212724 / 6.500664 (-6.287940) \| 0.081473 / 0.075469 (0.006004) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.714437 / 1.841788 (-0.127351) \| 24.384330 / 8.074308 (16.310022) \| 22.444162 / 10.191392 (12.252770) \| 0.226264 / 0.680424 (-0.454160) \| 0.030530 / 0.534201 (-0.503671) \| 0.473999 / 0.579283 (-0.105284) \| 0.575005 / 0.434364 (0.140641) \| 0.542789 / 0.540337 (0.002451) \| 0.776079 / 1.386936 (-0.610857) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009366 / 0.011353 (-0.001987) \| 0.005239 / 0.011008 (-0.005769) \| 0.085116 / 0.038508 (0.046608) \| 0.089600 / 0.023109 (0.066491) \| 0.485778 / 0.275898 (0.209880) \| 0.540054 / 0.323480 (0.216574) \| 0.006290 / 0.007986 (-0.001695) \| 0.004054 / 0.004328 (-0.000274) \| 0.083535 / 0.004250 (0.079284) \| 0.067200 / 0.037052 (0.030148) \| 0.519520 / 0.258489 (0.261031) \| 0.544049 / 0.293841 (0.250208) \| 0.054300 / 0.128546 (-0.074246) \| 0.013650 / 0.075646 (-0.061996) \| 0.102515 / 0.419271 (-0.316757) \| 0.063054 / 0.043533 (0.019522) \| 0.491724 / 0.255139 (0.236585) \| 0.547498 / 0.283200 (0.264298) \| 0.039266 / 0.141683 (-0.102416) \| 1.801226 / 1.452155 (0.349071) \| 1.861778 / 1.492716 (0.369061) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.313009 / 0.018006 (0.295003) \| 0.587695 / 0.000490 (0.587205) \| 0.004972 / 0.000200 (0.004772) \| 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) \| 0.029230 / 0.037411 (-0.008181) \| 0.091154 / 0.014526 (0.076628) \| 0.110505 / 0.176557 (-0.066052) \| 0.164204 / 0.737135 (-0.572932) \| 0.107812 / 0.296338 (-0.188526) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.610535 / 0.215209 (0.395326) \| 6.162517 / 2.077655 (4.084862) \| 2.866718 / 1.504120 (1.362598) \| 2.542412 / 1.541195 (1.001218) \| 2.584136 / 1.468490 (1.115645) \| 0.874319 / 4.584777 (-3.710458) \| 5.257184 / 3.745712 (1.511472) \| 4.705840 / 5.269862 (-0.564022) \| 2.971708 / 4.565676 (-1.593969) \| 0.099026 / 0.424275 (-0.325249) \| 0.009142 / 0.007607 (0.001535) \| 0.728660 / 0.226044 (0.502615) \| 7.560922 / 2.268929 (5.291994) \| 3.439521 / 55.444624 (-52.005103) \| 2.854730 / 6.876477 (-4.021746) \| 3.088951 / 2.142072 (0.946879) \| 0.973621 / 4.805227 (-3.831606) \| 0.209792 / 6.500664 (-6.290872) \| 0.081107 / 0.075469 (0.005638) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.716809 / 1.841788 (-0.124978) \| 24.386927 / 8.074308 (16.312619) \| 20.715524 / 10.191392 (10.524131) \| 0.260831 / 0.680424 (-0.419592) \| 0.030701 / 0.534201 (-0.503500) \| 0.490018 / 0.579283 (-0.089265) \| 0.590424 / 0.434364 (0.156060) \| 0.589942 / 0.540337 (0.049604) \| 0.798094 / 1.386936 (-0.588842) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c0a77dc943de68a17f23f141517028c734c78623 \"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) \| 0.006592 / 0.011353 (-0.004761) \| 0.003880 / 0.011008 (-0.007128) \| 0.083761 / 0.038508 (0.045253) \| 0.075966 / 0.023109 (0.052857) \| 0.315291 / 0.275898 (0.039393) \| 0.355920 / 0.323480 (0.032440) \| 0.004972 / 0.007986 (-0.003014) \| 0.003053 / 0.004328 (-0.001275) \| 0.063553 / 0.004250 (0.059302) \| 0.050794 / 0.037052 (0.013742) \| 0.317681 / 0.258489 (0.059192) \| 0.361991 / 0.293841 (0.068150) \| 0.028119 / 0.128546 (-0.100427) \| 0.008203 / 0.075646 (-0.067443) \| 0.271756 / 0.419271 (-0.147516) \| 0.046701 / 0.043533 (0.003168) \| 0.316520 / 0.255139 (0.061381) \| 0.350499 / 0.283200 (0.067300) \| 0.022399 / 0.141683 (-0.119284) \| 1.416017 / 1.452155 (-0.036138) \| 1.503087 / 1.492716 (0.010371) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.208250 / 0.018006 (0.190244) \| 0.470345 / 0.000490 (0.469856) \| 0.003687 / 0.000200 (0.003487) \| 0.000073 / 0.000054 (0.000019) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026163 / 0.037411 (-0.011248) \| 0.083315 / 0.014526 (0.068789) \| 0.088541 / 0.176557 (-0.088015) \| 0.150078 / 0.737135 (-0.587057) \| 0.088862 / 0.296338 (-0.207476) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.404911 / 0.215209 (0.189702) \| 4.059257 / 2.077655 (1.981602) \| 1.890987 / 1.504120 (0.386867) \| 1.726608 / 1.541195 (0.185413) \| 1.767479 / 1.468490 (0.298989) \| 0.518826 / 4.584777 (-4.065951) \| 3.212145 / 3.745712 (-0.533567) \| 3.029933 / 5.269862 (-2.239929) \| 2.000203 / 4.565676 (-2.565474) \| 0.059631 / 0.424275 (-0.364644) \| 0.006707 / 0.007607 (-0.000900) \| 0.485741 / 0.226044 (0.259697) \| 4.871938 / 2.268929 (2.603010) \| 2.418856 / 55.444624 (-53.025769) \| 2.084847 / 6.876477 (-4.791630) \| 2.207992 / 2.142072 (0.065920) \| 0.614354 / 4.805227 (-4.190873) \| 0.128932 / 6.500664 (-6.371732) \| 0.062342 / 0.075469 (-0.013127) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.325792 / 1.841788 (-0.515995) \| 19.718995 / 8.074308 (11.644687) \| 15.278535 / 10.191392 (5.087143) \| 0.146719 / 0.680424 (-0.533705) \| 0.017718 / 0.534201 (-0.516483) \| 0.335709 / 0.579283 (-0.243574) \| 0.378060 / 0.434364 (-0.056304) \| 0.391135 / 0.540337 (-0.149202) \| 0.548045 / 1.386936 (-0.838891) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006504 / 0.011353 (-0.004849) \| 0.003742 / 0.011008 (-0.007266) \| 0.064405 / 0.038508 (0.025897) \| 0.077618 / 0.023109 (0.054509) \| 0.365325 / 0.275898 (0.089427) \| 0.408109 / 0.323480 (0.084629) \| 0.004909 / 0.007986 (-0.003076) \| 0.002972 / 0.004328 (-0.001356) \| 0.063933 / 0.004250 (0.059682) \| 0.052916 / 0.037052 (0.015863) \| 0.370891 / 0.258489 (0.112402) \| 0.412134 / 0.293841 (0.118293) \| 0.028171 / 0.128546 (-0.100375) \| 0.008150 / 0.075646 (-0.067497) \| 0.069248 / 0.419271 (-0.350024) \| 0.042353 / 0.043533 (-0.001180) \| 0.368117 / 0.255139 (0.112978) \| 0.397548 / 0.283200 (0.114348) \| 0.022967 / 0.141683 (-0.118716) \| 1.472740 / 1.452155 (0.020586) \| 1.524028 / 1.492716 (0.031311) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.256854 / 0.018006 (0.238848) \| 0.471499 / 0.000490 (0.471009) \| 0.009609 / 0.000200 (0.009409) \| 0.000109 / 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) \| 0.027978 / 0.037411 (-0.009433) \| 0.086741 / 0.014526 (0.072215) \| 0.091189 / 0.176557 (-0.085368) \| 0.146117 / 0.737135 (-0.591018) \| 0.092358 / 0.296338 (-0.203980) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.426356 / 0.215209 (0.211147) \| 4.263782 / 2.077655 (2.186127) \| 2.178198 / 1.504120 (0.674078) \| 2.015405 / 1.541195 (0.474211) \| 2.055966 / 1.468490 (0.587476) \| 0.507531 / 4.584777 (-4.077246) \| 3.175967 / 3.745712 (-0.569745) \| 3.055697 / 5.269862 (-2.214165) \| 1.987663 / 4.565676 (-2.578014) \| 0.058452 / 0.424275 (-0.365823) \| 0.006944 / 0.007607 (-0.000663) \| 0.502534 / 0.226044 (0.276489) \| 5.024693 / 2.268929 (2.755765) \| 2.754971 / 55.444624 (-52.689653) \| 2.470845 / 6.876477 (-4.405632) \| 2.698675 / 2.142072 (0.556602) \| 0.602357 / 4.805227 (-4.202871) \| 0.129490 / 6.500664 (-6.371174) \| 0.065127 / 0.075469 (-0.010342) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.398487 / 1.841788 (-0.443301) \| 19.692279 / 8.074308 (11.617971) \| 15.124064 / 10.191392 (4.932672) \| 0.148938 / 0.680424 (-0.531486) \| 0.017418 / 0.534201 (-0.516783) \| 0.340480 / 0.579283 (-0.238803) \| 0.377223 / 0.434364 (-0.057141) \| 0.405303 / 0.540337 (-0.135034) \| 0.548923 / 1.386936 (-0.838013) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#58e62af004b6b8b84dcfd897a4bc71637cfa6c3f \"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) \| 0.006433 / 0.011353 (-0.004920) \| 0.004002 / 0.011008 (-0.007006) \| 0.084130 / 0.038508 (0.045622) \| 0.070628 / 0.023109 (0.047519) \| 0.312372 / 0.275898 (0.036474) \| 0.343993 / 0.323480 (0.020513) \| 0.003936 / 0.007986 (-0.004050) \| 0.003336 / 0.004328 (-0.000993) \| 0.064715 / 0.004250 (0.060465) \| 0.052511 / 0.037052 (0.015458) \| 0.314092 / 0.258489 (0.055603) \| 0.363152 / 0.293841 (0.069311) \| 0.030898 / 0.128546 (-0.097648) \| 0.008396 / 0.075646 (-0.067250) \| 0.288083 / 0.419271 (-0.131188) \| 0.051654 / 0.043533 (0.008122) \| 0.315252 / 0.255139 (0.060113) \| 0.346756 / 0.283200 (0.063556) \| 0.025167 / 0.141683 (-0.116515) \| 1.487265 / 1.452155 (0.035110) \| 1.557528 / 1.492716 (0.064812) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.206517 / 0.018006 (0.188510) \| 0.458359 / 0.000490 (0.457869) \| 0.003719 / 0.000200 (0.003519) \| 0.000070 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029631 / 0.037411 (-0.007780) \| 0.083856 / 0.014526 (0.069330) \| 0.340431 / 0.176557 (0.163875) \| 0.153864 / 0.737135 (-0.583271) \| 0.095951 / 0.296338 (-0.200388) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.379182 / 0.215209 (0.163973) \| 3.783396 / 2.077655 (1.705741) \| 1.835932 / 1.504120 (0.331813) \| 1.667563 / 1.541195 (0.126369) \| 1.739309 / 1.468490 (0.270818) \| 0.478957 / 4.584777 (-4.105820) \| 3.521974 / 3.745712 (-0.223738) \| 3.237635 / 5.269862 (-2.032227) \| 2.000300 / 4.565676 (-2.565377) \| 0.056389 / 0.424275 (-0.367887) \| 0.007242 / 0.007607 (-0.000365) \| 0.452642 / 0.226044 (0.226598) \| 4.524339 / 2.268929 (2.255411) \| 2.346210 / 55.444624 (-53.098414) \| 1.957196 / 6.876477 (-4.919281) \| 2.180051 / 2.142072 (0.037979) \| 0.570205 / 4.805227 (-4.235022) \| 0.131346 / 6.500664 (-6.369318) \| 0.059327 / 0.075469 (-0.016142) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.244709 / 1.841788 (-0.597079) \| 19.566277 / 8.074308 (11.491969) \| 14.172598 / 10.191392 (3.981206) \| 0.166493 / 0.680424 (-0.513931) \| 0.018281 / 0.534201 (-0.515920) \| 0.391608 / 0.579283 (-0.187675) \| 0.402642 / 0.434364 (-0.031722) \| 0.464974 / 0.540337 (-0.075364) \| 0.637565 / 1.386936 (-0.749371) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated 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 (-0.004424) \| 0.004114 / 0.011008 (-0.006894) \| 0.064589 / 0.038508 (0.026081) \| 0.083334 / 0.023109 (0.060225) \| 0.391280 / 0.275898 (0.115382) \| 0.426157 / 0.323480 (0.102678) \| 0.005336 / 0.007986 (-0.002650) \| 0.003395 / 0.004328 (-0.000934) \| 0.064560 / 0.004250 (0.060310) \| 0.057094 / 0.037052 (0.020042) \| 0.398959 / 0.258489 (0.140470) \| 0.432470 / 0.293841 (0.138629) \| 0.031412 / 0.128546 (-0.097134) \| 0.008670 / 0.075646 (-0.066976) \| 0.071249 / 0.419271 (-0.348022) \| 0.048934 / 0.043533 (0.005401) \| 0.384207 / 0.255139 (0.129068) \| 0.407992 / 0.283200 (0.124792) \| 0.024492 / 0.141683 (-0.117191) \| 1.467788 / 1.452155 (0.015634) \| 1.541011 / 1.492716 (0.048295) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279607 / 0.018006 (0.261600) \| 0.448899 / 0.000490 (0.448410) \| 0.020990 / 0.000200 (0.020790) \| 0.000132 / 0.000054 (0.000078) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030313 / 0.037411 (-0.007099) \| 0.089209 / 0.014526 (0.074684) \| 0.101024 / 0.176557 (-0.075532) \| 0.153468 / 0.737135 (-0.583667) \| 0.103219 / 0.296338 (-0.193120) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.429176 / 0.215209 (0.213967) \| 4.302234 / 2.077655 (2.224580) \| 2.291103 / 1.504120 (0.786983) \| 2.126257 / 1.541195 (0.585062) \| 2.207090 / 1.468490 (0.738600) \| 0.484643 / 4.584777 (-4.100134) \| 3.557429 / 3.745712 (-0.188283) \| 3.253804 / 5.269862 (-2.016058) \| 2.026087 / 4.565676 (-2.539589) \| 0.057793 / 0.424275 (-0.366482) \| 0.007761 / 0.007607 (0.000154) \| 0.504819 / 0.226044 (0.278775) \| 5.046868 / 2.268929 (2.777940) \| 2.773149 / 55.444624 (-52.671475) \| 2.398036 / 6.876477 (-4.478440) \| 2.608094 / 2.142072 (0.466021) \| 0.630499 / 4.805227 (-4.174729) \| 0.135496 / 6.500664 (-6.365168) \| 0.061329 / 0.075469 (-0.014140) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.327124 / 1.841788 (-0.514664) \| 19.889796 / 8.074308 (11.815488) \| 14.196100 / 10.191392 (4.004708) \| 0.161963 / 0.680424 (-0.518461) \| 0.018529 / 0.534201 (-0.515672) \| 0.392325 / 0.579283 (-0.186958) \| 0.404836 / 0.434364 (-0.029528) \| 0.475898 / 0.540337 (-0.064439) \| 0.633563 / 1.386936 (-0.753373) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e4684fc1032321abf0d494b0c130ea7c82ebda80 \"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) \| 0.006390 / 0.011353 (-0.004963) \| 0.003683 / 0.011008 (-0.007325) \| 0.081274 / 0.038508 (0.042766) \| 0.062193 / 0.023109 (0.039083) \| 0.355360 / 0.275898 (0.079462) \| 0.396471 / 0.323480 (0.072992) \| 0.003569 / 0.007986 (-0.004416) \| 0.003928 / 0.004328 (-0.000400) \| 0.062292 / 0.004250 (0.058041) \| 0.049700 / 0.037052 (0.012648) \| 0.354604 / 0.258489 (0.096115) \| 0.419436 / 0.293841 (0.125595) \| 0.027151 / 0.128546 (-0.101395) \| 0.007954 / 0.075646 (-0.067692) \| 0.262231 / 0.419271 (-0.157041) \| 0.045483 / 0.043533 (0.001950) \| 0.354285 / 0.255139 (0.099146) \| 0.385178 / 0.283200 (0.101978) \| 0.021183 / 0.141683 (-0.120500) \| 1.420785 / 1.452155 (-0.031370) \| 1.531545 / 1.492716 (0.038829) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.202298 / 0.018006 (0.184292) \| 0.442172 / 0.000490 (0.441683) \| 0.003565 / 0.000200 (0.003366) \| 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) \| 0.024229 / 0.037411 (-0.013183) \| 0.074352 / 0.014526 (0.059826) \| 0.087530 / 0.176557 (-0.089026) \| 0.146478 / 0.737135 (-0.590658) \| 0.085145 / 0.296338 (-0.211194) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.388395 / 0.215209 (0.173186) \| 3.877623 / 2.077655 (1.799968) \| 1.882444 / 1.504120 (0.378324) \| 1.707871 / 1.541195 (0.166676) \| 1.772132 / 1.468490 (0.303642) \| 0.491937 / 4.584777 (-4.092840) \| 3.057947 / 3.745712 (-0.687765) \| 2.822390 / 5.269862 (-2.447471) \| 1.879719 / 4.565676 (-2.685957) \| 0.056830 / 0.424275 (-0.367445) \| 0.006415 / 0.007607 (-0.001192) \| 0.458945 / 0.226044 (0.232900) \| 4.594502 / 2.268929 (2.325574) \| 2.339677 / 55.444624 (-53.104948) \| 1.983750 / 6.876477 (-4.892727) \| 2.173792 / 2.142072 (0.031719) \| 0.580390 / 4.805227 (-4.224838) \| 0.124568 / 6.500664 (-6.376096) \| 0.061694 / 0.075469 (-0.013775) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.265108 / 1.841788 (-0.576680) \| 18.415254 / 8.074308 (10.340946) \| 13.963829 / 10.191392 (3.772437) \| 0.148926 / 0.680424 (-0.531498) \| 0.016919 / 0.534201 (-0.517282) \| 0.331082 / 0.579283 (-0.248201) \| 0.345777 / 0.434364 (-0.088587) \| 0.381123 / 0.540337 (-0.159214) \| 0.543297 / 1.386936 (-0.843639) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006121 / 0.011353 (-0.005232) \| 0.003717 / 0.011008 (-0.007291) \| 0.063653 / 0.038508 (0.025144) \| 0.063723 / 0.023109 (0.040613) \| 0.360233 / 0.275898 (0.084335) \| 0.398353 / 0.323480 (0.074873) \| 0.004696 / 0.007986 (-0.003290) \| 0.002876 / 0.004328 (-0.001452) \| 0.063057 / 0.004250 (0.058806) \| 0.050258 / 0.037052 (0.013206) \| 0.362946 / 0.258489 (0.104457) \| 0.403260 / 0.293841 (0.109419) \| 0.027738 / 0.128546 (-0.100809) \| 0.008025 / 0.075646 (-0.067621) \| 0.068781 / 0.419271 (-0.350491) \| 0.042114 / 0.043533 (-0.001419) \| 0.363546 / 0.255139 (0.108407) \| 0.385640 / 0.283200 (0.102440) \| 0.021757 / 0.141683 (-0.119926) \| 1.482364 / 1.452155 (0.030209) \| 1.571859 / 1.492716 (0.079143) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.235628 / 0.018006 (0.217622) \| 0.439909 / 0.000490 (0.439419) \| 0.003070 / 0.000200 (0.002870) \| 0.000075 / 0.000054 (0.000020) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027045 / 0.037411 (-0.010366) \| 0.080413 / 0.014526 (0.065887) \| 0.088953 / 0.176557 (-0.087603) \| 0.141907 / 0.737135 (-0.595228) \| 0.090604 / 0.296338 (-0.205735) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.423250 / 0.215209 (0.208041) \| 4.216510 / 2.077655 (2.138855) \| 2.162946 / 1.504120 (0.658826) \| 2.014561 / 1.541195 (0.473366) \| 2.086347 / 1.468490 (0.617857) \| 0.496591 / 4.584777 (-4.088186) \| 3.089594 / 3.745712 (-0.656118) \| 2.853640 / 5.269862 (-2.416221) \| 1.878149 / 4.565676 (-2.687527) \| 0.056914 / 0.424275 (-0.367361) \| 0.006762 / 0.007607 (-0.000845) \| 0.493470 / 0.226044 (0.267426) \| 4.929966 / 2.268929 (2.661037) \| 2.640885 / 55.444624 (-52.803739) \| 2.335950 / 6.876477 (-4.540527) \| 2.565866 / 2.142072 (0.423793) \| 0.585433 / 4.805227 (-4.219794) \| 0.124969 / 6.500664 (-6.375695) \| 0.062361 / 0.075469 (-0.013108) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.369144 / 1.841788 (-0.472644) \| 19.037582 / 8.074308 (10.963274) \| 14.069141 / 10.191392 (3.877749) \| 0.146469 / 0.680424 (-0.533954) \| 0.016911 / 0.534201 (-0.517290) \| 0.336802 / 0.579283 (-0.242482) \| 0.336411 / 0.434364 (-0.097953) \| 0.392360 / 0.540337 (-0.147977) \| 0.536078 / 1.386936 (-0.850858) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#12cfc1196e62847e2e8239fbd727a02cbc86ddec \"CML watermark\")\n" ]
1,839,675,320	6,126	Private datasets do not load when passing token	closed	2023-08-07T15:06:47	2023-08-08T15:16:23	2023-08-08T15:16:23	https://github.com/huggingface/datasets/issues/6126	null	albertvillanova	false	[ "Our CI did not catch this issue because with current implementation, stored token in `HfFolder` (which always exists) is used by default.", "I can confirm this and have the same problem (and just went almost crazy because I couldn't figure out the source of this problem because on another computer everything worked well even with `DownloadMode.FORCE_REDOWNLOAD`).", "We are planning to do a patch release today, after the merge of the fix:\r\n- #6127\r\n\r\nIn the meantime, the problem can be circumvented by passing `download_config` instead:\r\n```python\r\nfrom datasets import DownloadConfig, load_dataset\r\n\r\nload_dataset(\"<DATASET-NAME>\", split=\"train\", download_config=DownloadConfig(token=\"<TOKEN>\"))\r\n``` ", "> We are planning to do a patch release today, after the merge of the fix:\r\n> \r\n> * [Fix authentication issues #6127](https://github.com/huggingface/datasets/pull/6127)\r\n> \r\n> \r\n> In the meantime, the problem can be circumvented by passing `download_config` instead:\r\n> \r\n> ```python\r\n> from datasets import DownloadConfig, load_dataset\r\n> \r\n> load_dataset(\"<DATASET-NAME>\", split=\"train\", download_config=DownloadConfig(token=\"<TOKEN>\"))\r\n> ```\r\n\r\nThis did not work for me (there was some other error with the split being an unexpected size 0). Downgrading to 2.13 fixed it...." ]
1,837,980,986	6,125	Reinforcement Learning and Robotics are not task categories in HF datasets metadata	closed	2023-08-05T23:59:42	2023-08-18T12:28:42	2023-08-18T12:28:42	https://github.com/huggingface/datasets/issues/6125	null	StoneT2000	false	[]
1,837,868,112	6,124	Datasets crashing runs due to KeyError	closed	2023-08-05T17:48:56	2023-11-30T16:28:57	2023-11-30T16:28:57	https://github.com/huggingface/datasets/issues/6124	null	conceptofmind	false	[ "i once had the same error and I could fix that by pushing a fake or a dummy commit on my hugging face dataset repo", "Hi! We need a reproducer to fix this. Can you provide a link to the dataset (if it's public)?", "> Hi! We need a reproducer to fix this. Can you provide a link to the dataset (if it's public)?\r\n\r\nHi Mario,\r\n\r\nUnfortunately, the dataset in question is currently private until the model is trained and released.\r\n\r\nThis is not happening with one dataset but numerous hosted private datasets.\r\n\r\nI am only loading the dataset and doing nothing else currently. It seems to happen completely sporadically.\r\n\r\nThank you,\r\n\r\nEnrico", "Hi,\r\n\r\nI have the same error in the dataset viewer with my dataset\r\nhttps://huggingface.co/datasets/elsaEU/ELSA10M_track1\r\n\r\nHas anyone solved this issue?\r\n\r\nEdit: After a dummy commit the error changed in ConfigNamesError", "@rs9000 The problem seems to be the (large) number of commits, as explained in https://huggingface.co/docs/hub/repositories-recommendations. This can be fixed by running:\r\n```python\r\nimport huggingface_hub\r\nhuggingface_hub.super_squash_history(repo_id=\"elsaEU/ELSA10M_track1\")\r\n``` \r\n\r\nThe issue stems from `push_to_hub` creating one commit per shard - https://github.com/huggingface/datasets/pull/6269 should fix this issue (will create one commit per 50 uploaded shards by default). The linked PR will be included in the next `datasets` release.\r\n\r\n\r\ncc @lhoestq @severo for visibility", "Thank you @mariosasko it works.", "#6269 has been merged, so I'm closing this issue" ]
1,837,789,294	6,123	Inaccurate Bounding Boxes in "wildreceipt" Dataset	closed	2023-08-05T14:34:13	2023-08-17T14:25:27	2023-08-17T14:25:26	https://github.com/huggingface/datasets/issues/6123	null	HamzaGbada	false	[ "Hi! Thanks for the investigation, but we are not the authors of these datasets, so please report this on the Hub instead so that the actual authors can fix it." ]
1,837,335,721	6,122	Upload README via `push_to_hub`	closed	2023-08-04T21:00:27	2023-08-21T18:18:54	2023-08-21T18:18:54	https://github.com/huggingface/datasets/issues/6122	null	liyucheng09	false	[ "You can use `huggingface_hub`'s [Card API](https://huggingface.co/docs/huggingface_hub/package_reference/cards) to programmatically push a dataset card to the Hub." ]
1,836,761,712	6,121	Small typo in the code example of create imagefolder dataset	closed	2023-08-04T13:36:59	2023-08-04T13:45:32	2023-08-04T13:41:43	https://github.com/huggingface/datasets/pull/6121	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6121", "html_url": "https://github.com/huggingface/datasets/pull/6121", "diff_url": "https://github.com/huggingface/datasets/pull/6121.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6121.patch", "merged_at": null }	WangXin93	true	[ "Hi,\r\n\r\nI found a small typo in the code example of create imagefolder dataset. It confused me a little when I first saw it.\r\n\r\nBest Regards.\r\n\r\nXin" ]
1,836,026,938	6,120	Lookahead streaming support?	open	2023-08-04T04:01:52	2023-08-17T17:48:42	null	https://github.com/huggingface/datasets/issues/6120	null	PicoCreator	false	[ "In which format is your dataset? We could expose the `pre_buffer` flag for Parquet to use PyArrow's background thread pool to speed up loading. " ]
1,835,996,350	6,119	[Docs] Add description of `select_columns` to guide	closed	2023-08-04T03:13:30	2023-08-16T10:13:02	2023-08-16T10:02:52	https://github.com/huggingface/datasets/pull/6119	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6119", "html_url": "https://github.com/huggingface/datasets/pull/6119", "diff_url": "https://github.com/huggingface/datasets/pull/6119.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6119.patch", "merged_at": "2023-08-16T10:02:52" }	unifyh	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) \| 0.007755 / 0.011353 (-0.003598) \| 0.004618 / 0.011008 (-0.006391) \| 0.098132 / 0.038508 (0.059624) \| 0.086759 / 0.023109 (0.063650) \| 0.374668 / 0.275898 (0.098770) \| 0.417131 / 0.323480 (0.093651) \| 0.004604 / 0.007986 (-0.003382) \| 0.005461 / 0.004328 (0.001132) \| 0.077249 / 0.004250 (0.072999) \| 0.063247 / 0.037052 (0.026195) \| 0.391801 / 0.258489 (0.133312) \| 0.432139 / 0.293841 (0.138298) \| 0.036755 / 0.128546 (-0.091791) \| 0.010011 / 0.075646 (-0.065636) \| 0.346175 / 0.419271 (-0.073097) \| 0.061503 / 0.043533 (0.017971) \| 0.374063 / 0.255139 (0.118924) \| 0.435873 / 0.283200 (0.152673) \| 0.029476 / 0.141683 (-0.112207) \| 1.786945 / 1.452155 (0.334790) \| 1.857190 / 1.492716 (0.364474) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.253939 / 0.018006 (0.235933) \| 0.506847 / 0.000490 (0.506358) \| 0.007278 / 0.000200 (0.007079) \| 0.000451 / 0.000054 (0.000397) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032938 / 0.037411 (-0.004474) \| 0.097493 / 0.014526 (0.082967) \| 0.112090 / 0.176557 (-0.064467) \| 0.177986 / 0.737135 (-0.559149) \| 0.112060 / 0.296338 (-0.184278) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.481858 / 0.215209 (0.266649) \| 4.814894 / 2.077655 (2.737239) \| 2.496428 / 1.504120 (0.992308) \| 2.309965 / 1.541195 (0.768770) \| 2.393819 / 1.468490 (0.925329) \| 0.564670 / 4.584777 (-4.020107) \| 4.151222 / 3.745712 (0.405510) \| 3.676115 / 5.269862 (-1.593747) \| 2.346165 / 4.565676 (-2.219512) \| 0.066344 / 0.424275 (-0.357931) \| 0.009006 / 0.007607 (0.001399) \| 0.567699 / 0.226044 (0.341654) \| 5.686799 / 2.268929 (3.417871) \| 3.031044 / 55.444624 (-52.413580) \| 2.606259 / 6.876477 (-4.270217) \| 2.864876 / 2.142072 (0.722804) \| 0.681730 / 4.805227 (-4.123498) \| 0.155405 / 6.500664 (-6.345259) \| 0.071492 / 0.075469 (-0.003977) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.514446 / 1.841788 (-0.327341) \| 22.624912 / 8.074308 (14.550604) \| 16.754145 / 10.191392 (6.562753) \| 0.193113 / 0.680424 (-0.487311) \| 0.021808 / 0.534201 (-0.512393) \| 0.468241 / 0.579283 (-0.111042) \| 0.499647 / 0.434364 (0.065283) \| 0.539571 / 0.540337 (-0.000766) \| 0.771268 / 1.386936 (-0.615668) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007562 / 0.011353 (-0.003791) \| 0.004548 / 0.011008 (-0.006460) \| 0.075998 / 0.038508 (0.037490) \| 0.081648 / 0.023109 (0.058539) \| 0.462876 / 0.275898 (0.186978) \| 0.499366 / 0.323480 (0.175886) \| 0.005839 / 0.007986 (-0.002147) \| 0.003753 / 0.004328 (-0.000576) \| 0.075918 / 0.004250 (0.071668) \| 0.063233 / 0.037052 (0.026181) \| 0.459024 / 0.258489 (0.200535) \| 0.506388 / 0.293841 (0.212547) \| 0.036179 / 0.128546 (-0.092367) \| 0.009961 / 0.075646 (-0.065685) \| 0.082061 / 0.419271 (-0.337211) \| 0.056469 / 0.043533 (0.012936) \| 0.459567 / 0.255139 (0.204428) \| 0.482578 / 0.283200 (0.199378) \| 0.026363 / 0.141683 (-0.115320) \| 1.742247 / 1.452155 (0.290092) \| 1.807166 / 1.492716 (0.314450) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.330526 / 0.018006 (0.312520) \| 0.511674 / 0.000490 (0.511184) \| 0.040969 / 0.000200 (0.040769) \| 0.000176 / 0.000054 (0.000121) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035492 / 0.037411 (-0.001920) \| 0.104338 / 0.014526 (0.089813) \| 0.116973 / 0.176557 (-0.059583) \| 0.180218 / 0.737135 (-0.556917) \| 0.118801 / 0.296338 (-0.177538) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.492196 / 0.215209 (0.276987) \| 4.910271 / 2.077655 (2.832616) \| 2.542562 / 1.504120 (1.038442) \| 2.333516 / 1.541195 (0.792321) \| 2.439682 / 1.468490 (0.971192) \| 0.571966 / 4.584777 (-4.012811) \| 4.089801 / 3.745712 (0.344089) \| 3.732129 / 5.269862 (-1.537733) \| 2.375887 / 4.565676 (-2.189789) \| 0.067376 / 0.424275 (-0.356900) \| 0.008350 / 0.007607 (0.000743) \| 0.583942 / 0.226044 (0.357897) \| 5.840002 / 2.268929 (3.571074) \| 3.062520 / 55.444624 (-52.382104) \| 2.722512 / 6.876477 (-4.153965) \| 2.938307 / 2.142072 (0.796234) \| 0.689459 / 4.805227 (-4.115769) \| 0.155632 / 6.500664 (-6.345032) \| 0.072387 / 0.075469 (-0.003082) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.595587 / 1.841788 (-0.246201) \| 23.035478 / 8.074308 (14.961170) \| 16.457675 / 10.191392 (6.266283) \| 0.170819 / 0.680424 (-0.509605) \| 0.022042 / 0.534201 (-0.512159) \| 0.466824 / 0.579283 (-0.112459) \| 0.486350 / 0.434364 (0.051986) \| 0.574330 / 0.540337 (0.033993) \| 0.764913 / 1.386936 (-0.622023) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#664a1cb72ea1e6ef7c47e671e2686ca4a35e8d63 \"CML watermark\")\n" ]
1,835,940,417	6,118	IterableDataset.from_generator() fails with pickle error when provided a generator or iterator	open	2023-08-04T01:45:04	2024-12-18T18:30:57	null	https://github.com/huggingface/datasets/issues/6118	null	finkga	false	[ "Hi! `IterableDataset.from_generator` expects a generator function, not the object (to be consistent with `Dataset.from_generator`).\r\n\r\nYou can fix the above snippet as follows:\r\n```python\r\ntrain_dataset = IterableDataset.from_generator(line_generator, fn_kwargs={\"files\": model_training_files})\r\n```", "to anyone reaching this issue, the argument is `gen_kwargs`:\r\n```py\r\ntrain_dataset = IterableDataset.from_generator(line_generator, gen_kwargs={\"files\": model_training_files})\r\n```", "This still fails, for both Dataset and IterableDataset\r\n\r\n```python\r\n records = [1, 2, 3]\r\n\r\n gen = ({\"row\": str(x)} for x in records)\r\n\r\n dataset = IterableDataset.from_generator(generator=gen)\r\n ```\r\n\r\nEdit: gen_kwargs must be picklable, it can't be an iterator even if you are not doing multiprocessing, the same goes for included namespace variables." ]
1,835,213,848	6,117	Set dev version	closed	2023-08-03T14:46:04	2023-08-03T14:56:59	2023-08-03T14:46:18	https://github.com/huggingface/datasets/pull/6117	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6117", "html_url": "https://github.com/huggingface/datasets/pull/6117", "diff_url": "https://github.com/huggingface/datasets/pull/6117.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6117.patch", "merged_at": "2023-08-03T14:46:18" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6117). 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) \| 0.012516 / 0.011353 (0.001163) \| 0.004725 / 0.011008 (-0.006283) \| 0.112245 / 0.038508 (0.073736) \| 0.079146 / 0.023109 (0.056037) \| 0.386415 / 0.275898 (0.110517) \| 0.420441 / 0.323480 (0.096961) \| 0.005682 / 0.007986 (-0.002304) \| 0.004169 / 0.004328 (-0.000160) \| 0.077847 / 0.004250 (0.073597) \| 0.055763 / 0.037052 (0.018711) \| 0.385529 / 0.258489 (0.127040) \| 0.422711 / 0.293841 (0.128870) \| 0.047212 / 0.128546 (-0.081334) \| 0.013711 / 0.075646 (-0.061935) \| 0.342856 / 0.419271 (-0.076416) \| 0.066788 / 0.043533 (0.023255) \| 0.380728 / 0.255139 (0.125589) \| 0.416241 / 0.283200 (0.133041) \| 0.034676 / 0.141683 (-0.107007) \| 1.679661 / 1.452155 (0.227506) \| 1.838014 / 1.492716 (0.345297) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.219556 / 0.018006 (0.201550) \| 0.524728 / 0.000490 (0.524238) \| 0.005045 / 0.000200 (0.004845) \| 0.000124 / 0.000054 (0.000069) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025475 / 0.037411 (-0.011936) \| 0.085937 / 0.014526 (0.071412) \| 0.099245 / 0.176557 (-0.077311) \| 0.158995 / 0.737135 (-0.578141) \| 0.101504 / 0.296338 (-0.194835) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.582200 / 0.215209 (0.366991) \| 5.794340 / 2.077655 (3.716685) \| 2.473635 / 1.504120 (0.969515) \| 2.168135 / 1.541195 (0.626941) \| 2.215886 / 1.468490 (0.747396) \| 0.855599 / 4.584777 (-3.729178) \| 5.003067 / 3.745712 (1.257354) \| 4.503566 / 5.269862 (-0.766295) \| 2.912248 / 4.565676 (-1.653428) \| 0.103267 / 0.424275 (-0.321008) \| 0.012114 / 0.007607 (0.004507) \| 0.712240 / 0.226044 (0.486196) \| 7.131946 / 2.268929 (4.863017) \| 3.280052 / 55.444624 (-52.164573) \| 2.583472 / 6.876477 (-4.293004) \| 2.820758 / 2.142072 (0.678686) \| 1.132097 / 4.805227 (-3.673131) \| 0.232191 / 6.500664 (-6.268473) \| 0.082966 / 0.075469 (0.007497) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.581125 / 1.841788 (-0.260662) \| 22.723878 / 8.074308 (14.649570) \| 19.969347 / 10.191392 (9.777955) \| 0.234365 / 0.680424 (-0.446059) \| 0.030245 / 0.534201 (-0.503956) \| 0.470843 / 0.579283 (-0.108440) \| 0.558069 / 0.434364 (0.123705) \| 0.534878 / 0.540337 (-0.005460) \| 0.801025 / 1.386936 (-0.585911) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008524 / 0.011353 (-0.002829) \| 0.005083 / 0.011008 (-0.005925) \| 0.078054 / 0.038508 (0.039546) \| 0.082025 / 0.023109 (0.058915) \| 0.458027 / 0.275898 (0.182129) \| 0.498232 / 0.323480 (0.174752) \| 0.005938 / 0.007986 (-0.002048) \| 0.003776 / 0.004328 (-0.000553) \| 0.080413 / 0.004250 (0.076163) \| 0.060485 / 0.037052 (0.023433) \| 0.462816 / 0.258489 (0.204327) \| 0.513970 / 0.293841 (0.220129) \| 0.047574 / 0.128546 (-0.080973) \| 0.013424 / 0.075646 (-0.062222) \| 0.087707 / 0.419271 (-0.331565) \| 0.065007 / 0.043533 (0.021474) \| 0.465844 / 0.255139 (0.210705) \| 0.498474 / 0.283200 (0.215274) \| 0.033518 / 0.141683 (-0.108164) \| 1.737507 / 1.452155 (0.285352) \| 1.848291 / 1.492716 (0.355574) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.316710 / 0.018006 (0.298703) \| 0.504415 / 0.000490 (0.503925) \| 0.042128 / 0.000200 (0.041928) \| 0.000171 / 0.000054 (0.000117) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032097 / 0.037411 (-0.005314) \| 0.099371 / 0.014526 (0.084845) \| 0.109311 / 0.176557 (-0.067246) \| 0.177373 / 0.737135 (-0.559762) \| 0.110753 / 0.296338 (-0.185585) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.688060 / 0.215209 (0.472851) \| 6.255219 / 2.077655 (4.177564) \| 2.696845 / 1.504120 (1.192725) \| 2.395424 / 1.541195 (0.854230) \| 2.414870 / 1.468490 (0.946380) \| 0.865704 / 4.584777 (-3.719073) \| 5.086828 / 3.745712 (1.341116) \| 4.648107 / 5.269862 (-0.621754) \| 3.091119 / 4.565676 (-1.474558) \| 0.101787 / 0.424275 (-0.322489) \| 0.008829 / 0.007607 (0.001222) \| 0.772398 / 0.226044 (0.546354) \| 7.700366 / 2.268929 (5.431438) \| 3.608632 / 55.444624 (-51.835992) \| 2.923309 / 6.876477 (-3.953168) \| 2.952141 / 2.142072 (0.810069) \| 1.093006 / 4.805227 (-3.712221) \| 0.224363 / 6.500664 (-6.276301) \| 0.074927 / 0.075469 (-0.000542) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.638414 / 1.841788 (-0.203374) \| 23.486781 / 8.074308 (15.412473) \| 21.129104 / 10.191392 (10.937712) \| 0.259955 / 0.680424 (-0.420469) \| 0.027305 / 0.534201 (-0.506895) \| 0.464448 / 0.579283 (-0.114835) \| 0.553737 / 0.434364 (0.119373) \| 0.571318 / 0.540337 (0.030981) \| 0.772917 / 1.386936 (-0.614019) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3ec5ee9e78b464364796651d995823c7ecb0f951 \"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) \| 0.009093 / 0.011353 (-0.002260) \| 0.005283 / 0.011008 (-0.005725) \| 0.112299 / 0.038508 (0.073791) \| 0.081341 / 0.023109 (0.058232) \| 0.363799 / 0.275898 (0.087901) \| 0.409261 / 0.323480 (0.085781) \| 0.006400 / 0.007986 (-0.001586) \| 0.003965 / 0.004328 (-0.000363) \| 0.074389 / 0.004250 (0.070139) \| 0.060654 / 0.037052 (0.023602) \| 0.391046 / 0.258489 (0.132557) \| 0.430514 / 0.293841 (0.136673) \| 0.054900 / 0.128546 (-0.073646) \| 0.017972 / 0.075646 (-0.057675) \| 0.410875 / 0.419271 (-0.008396) \| 0.067405 / 0.043533 (0.023873) \| 0.371468 / 0.255139 (0.116329) \| 0.435061 / 0.283200 (0.151861) \| 0.038063 / 0.141683 (-0.103620) \| 1.733509 / 1.452155 (0.281354) \| 1.833899 / 1.492716 (0.341182) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.243230 / 0.018006 (0.225224) \| 0.605636 / 0.000490 (0.605146) \| 0.004890 / 0.000200 (0.004690) \| 0.000098 / 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) \| 0.027624 / 0.037411 (-0.009787) \| 0.084799 / 0.014526 (0.070273) \| 0.104405 / 0.176557 (-0.072152) \| 0.165383 / 0.737135 (-0.571752) \| 0.102083 / 0.296338 (-0.194255) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.578334 / 0.215209 (0.363125) \| 5.369520 / 2.077655 (3.291866) \| 2.294174 / 1.504120 (0.790055) \| 2.054195 / 1.541195 (0.513000) \| 2.007304 / 1.468490 (0.538814) \| 0.839283 / 4.584777 (-3.745494) \| 5.262288 / 3.745712 (1.516576) \| 4.363346 / 5.269862 (-0.906516) \| 2.854903 / 4.565676 (-1.710773) \| 0.096975 / 0.424275 (-0.327300) \| 0.008237 / 0.007607 (0.000630) \| 0.646746 / 0.226044 (0.420702) \| 6.250621 / 2.268929 (3.981693) \| 2.900377 / 55.444624 (-52.544247) \| 2.283238 / 6.876477 (-4.593239) \| 2.443785 / 2.142072 (0.301713) \| 0.991719 / 4.805227 (-3.813508) \| 0.189755 / 6.500664 (-6.310909) \| 0.067906 / 0.075469 (-0.007563) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.515563 / 1.841788 (-0.326225) \| 21.956499 / 8.074308 (13.882191) \| 19.161750 / 10.191392 (8.970358) \| 0.238199 / 0.680424 (-0.442225) \| 0.026771 / 0.534201 (-0.507430) \| 0.450195 / 0.579283 (-0.129088) \| 0.585168 / 0.434364 (0.150804) \| 0.522945 / 0.540337 (-0.017393) \| 0.776244 / 1.386936 (-0.610693) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007997 / 0.011353 (-0.003356) \| 0.005021 / 0.011008 (-0.005988) \| 0.087308 / 0.038508 (0.048800) \| 0.077760 / 0.023109 (0.054650) \| 0.425313 / 0.275898 (0.149415) \| 0.451470 / 0.323480 (0.127990) \| 0.006848 / 0.007986 (-0.001137) \| 0.004812 / 0.004328 (0.000484) \| 0.071198 / 0.004250 (0.066947) \| 0.058325 / 0.037052 (0.021273) \| 0.427411 / 0.258489 (0.168922) \| 0.466069 / 0.293841 (0.172228) \| 0.048686 / 0.128546 (-0.079861) \| 0.011841 / 0.075646 (-0.063806) \| 0.086225 / 0.419271 (-0.333047) \| 0.060500 / 0.043533 (0.016967) \| 0.435580 / 0.255139 (0.180441) \| 0.456919 / 0.283200 (0.173719) \| 0.035094 / 0.141683 (-0.106588) \| 1.582805 / 1.452155 (0.130650) \| 1.717838 / 1.492716 (0.225122) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283967 / 0.018006 (0.265960) \| 0.517496 / 0.000490 (0.517006) \| 0.014747 / 0.000200 (0.014547) \| 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) \| 0.027870 / 0.037411 (-0.009541) \| 0.083835 / 0.014526 (0.069309) \| 0.099157 / 0.176557 (-0.077400) \| 0.173210 / 0.737135 (-0.563925) \| 0.094212 / 0.296338 (-0.202127) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.535720 / 0.215209 (0.320511) \| 5.273730 / 2.077655 (3.196075) \| 2.422560 / 1.504120 (0.918440) \| 2.131416 / 1.541195 (0.590222) \| 2.192000 / 1.468490 (0.723510) \| 0.708469 / 4.584777 (-3.876308) \| 4.758092 / 3.745712 (1.012380) \| 3.940729 / 5.269862 (-1.329133) \| 2.553093 / 4.565676 (-2.012583) \| 0.084895 / 0.424275 (-0.339380) \| 0.008730 / 0.007607 (0.001123) \| 0.646975 / 0.226044 (0.420930) \| 6.294811 / 2.268929 (4.025883) \| 3.293964 / 55.444624 (-52.150660) \| 2.568985 / 6.876477 (-4.307492) \| 2.743786 / 2.142072 (0.601713) \| 0.899733 / 4.805227 (-3.905494) \| 0.193484 / 6.500664 (-6.307181) \| 0.070012 / 0.075469 (-0.005457) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.502255 / 1.841788 (-0.339532) \| 20.690234 / 8.074308 (12.615926) \| 18.375791 / 10.191392 (8.184399) \| 0.200135 / 0.680424 (-0.480289) \| 0.029434 / 0.534201 (-0.504767) \| 0.477267 / 0.579283 (-0.102016) \| 0.566869 / 0.434364 (0.132505) \| 0.543756 / 0.540337 (0.003418) \| 0.700476 / 1.386936 (-0.686460) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ef17d9fd6c648bb41d43ba301c3de4d7b6f833d8 \"CML watermark\")\n" ]
1,835,098,484	6,116	[Docs] The "Process" how-to guide lacks description of `select_columns` function	closed	2023-08-03T13:45:10	2023-08-16T10:02:53	2023-08-16T10:02:53	https://github.com/huggingface/datasets/issues/6116	null	unifyh	false	[ "Great idea, feel free to open a PR! :)" ]
1,834,765,485	6,115	Release: 2.14.3	closed	2023-08-03T10:18:32	2023-08-03T15:08:02	2023-08-03T10:24:57	https://github.com/huggingface/datasets/pull/6115	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6115", "html_url": "https://github.com/huggingface/datasets/pull/6115", "diff_url": "https://github.com/huggingface/datasets/pull/6115.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6115.patch", "merged_at": "2023-08-03T10:24:57" }	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) \| 0.007578 / 0.011353 (-0.003775) \| 0.004271 / 0.011008 (-0.006738) \| 0.086607 / 0.038508 (0.048098) \| 0.063209 / 0.023109 (0.040099) \| 0.351724 / 0.275898 (0.075826) \| 0.399261 / 0.323480 (0.075781) \| 0.004767 / 0.007986 (-0.003219) \| 0.003487 / 0.004328 (-0.000842) \| 0.071483 / 0.004250 (0.067233) \| 0.051281 / 0.037052 (0.014229) \| 0.387726 / 0.258489 (0.129237) \| 0.408446 / 0.293841 (0.114605) \| 0.041189 / 0.128546 (-0.087357) \| 0.012446 / 0.075646 (-0.063200) \| 0.331147 / 0.419271 (-0.088124) \| 0.056721 / 0.043533 (0.013188) \| 0.361306 / 0.255139 (0.106167) \| 0.409651 / 0.283200 (0.126451) \| 0.035485 / 0.141683 (-0.106198) \| 1.461391 / 1.452155 (0.009236) \| 1.554820 / 1.492716 (0.062104) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.237119 / 0.018006 (0.219113) \| 0.518731 / 0.000490 (0.518241) \| 0.004192 / 0.000200 (0.003992) \| 0.000114 / 0.000054 (0.000059) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024912 / 0.037411 (-0.012499) \| 0.089420 / 0.014526 (0.074894) \| 0.091209 / 0.176557 (-0.085347) \| 0.152580 / 0.737135 (-0.584555) \| 0.089660 / 0.296338 (-0.206678) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.515223 / 0.215209 (0.300014) \| 5.328359 / 2.077655 (3.250705) \| 1.974326 / 1.504120 (0.470206) \| 1.665216 / 1.541195 (0.124021) \| 1.736040 / 1.468490 (0.267550) \| 0.734746 / 4.584777 (-3.850031) \| 4.186613 / 3.745712 (0.440901) \| 3.535760 / 5.269862 (-1.734102) \| 2.333247 / 4.565676 (-2.232429) \| 0.071845 / 0.424275 (-0.352430) \| 0.006147 / 0.007607 (-0.001460) \| 0.546649 / 0.226044 (0.320605) \| 5.452281 / 2.268929 (3.183353) \| 2.512984 / 55.444624 (-52.931640) \| 2.104210 / 6.876477 (-4.772267) \| 2.409251 / 2.142072 (0.267178) \| 0.822797 / 4.805227 (-3.982430) \| 0.166648 / 6.500664 (-6.334016) \| 0.056350 / 0.075469 (-0.019119) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.397798 / 1.841788 (-0.443989) \| 20.549399 / 8.074308 (12.475091) \| 19.118168 / 10.191392 (8.926776) \| 0.216361 / 0.680424 (-0.464063) \| 0.027064 / 0.534201 (-0.507136) \| 0.410762 / 0.579283 (-0.168521) \| 0.559225 / 0.434364 (0.124861) \| 0.468028 / 0.540337 (-0.072309) \| 0.691520 / 1.386936 (-0.695416) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006463 / 0.011353 (-0.004890) \| 0.003879 / 0.011008 (-0.007130) \| 0.058723 / 0.038508 (0.020215) \| 0.057202 / 0.023109 (0.034092) \| 0.344397 / 0.275898 (0.068499) \| 0.360388 / 0.323480 (0.036908) \| 0.005502 / 0.007986 (-0.002483) \| 0.004101 / 0.004328 (-0.000227) \| 0.058168 / 0.004250 (0.053917) \| 0.059112 / 0.037052 (0.022060) \| 0.362206 / 0.258489 (0.103717) \| 0.386444 / 0.293841 (0.092603) \| 0.036613 / 0.128546 (-0.091934) \| 0.010482 / 0.075646 (-0.065165) \| 0.065850 / 0.419271 (-0.353421) \| 0.046528 / 0.043533 (0.002995) \| 0.349568 / 0.255139 (0.094429) \| 0.360181 / 0.283200 (0.076981) \| 0.029030 / 0.141683 (-0.112653) \| 1.314569 / 1.452155 (-0.137586) \| 1.422393 / 1.492716 (-0.070324) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281554 / 0.018006 (0.263548) \| 0.608018 / 0.000490 (0.607528) \| 0.004568 / 0.000200 (0.004368) \| 0.000182 / 0.000054 (0.000127) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023515 / 0.037411 (-0.013896) \| 0.072994 / 0.014526 (0.058468) \| 0.080688 / 0.176557 (-0.095868) \| 0.125904 / 0.737135 (-0.611232) \| 0.085457 / 0.296338 (-0.210882) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.471530 / 0.215209 (0.256321) \| 4.796197 / 2.077655 (2.718542) \| 2.189181 / 1.504120 (0.685061) \| 1.886649 / 1.541195 (0.345454) \| 1.871067 / 1.468490 (0.402577) \| 0.661043 / 4.584777 (-3.923734) \| 4.344027 / 3.745712 (0.598315) \| 3.656967 / 5.269862 (-1.612895) \| 2.286033 / 4.565676 (-2.279644) \| 0.079146 / 0.424275 (-0.345129) \| 0.006840 / 0.007607 (-0.000767) \| 0.588750 / 0.226044 (0.362706) \| 6.301286 / 2.268929 (4.032357) \| 3.074702 / 55.444624 (-52.369923) \| 2.398739 / 6.876477 (-4.477738) \| 2.555057 / 2.142072 (0.412985) \| 0.874189 / 4.805227 (-3.931038) \| 0.191423 / 6.500664 (-6.309241) \| 0.061227 / 0.075469 (-0.014242) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.472763 / 1.841788 (-0.369024) \| 19.441304 / 8.074308 (11.366996) \| 15.974276 / 10.191392 (5.782884) \| 0.172503 / 0.680424 (-0.507921) \| 0.027016 / 0.534201 (-0.507185) \| 0.356085 / 0.579283 (-0.223198) \| 0.473251 / 0.434364 (0.038887) \| 0.427949 / 0.540337 (-0.112388) \| 0.588924 / 1.386936 (-0.798013) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0973da6e60ac7c1d24229ba6aa6881747b21858a \"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) \| 0.006166 / 0.011353 (-0.005187) \| 0.003558 / 0.011008 (-0.007450) \| 0.080576 / 0.038508 (0.042068) \| 0.066542 / 0.023109 (0.043432) \| 0.323997 / 0.275898 (0.048099) \| 0.369828 / 0.323480 (0.046348) \| 0.004896 / 0.007986 (-0.003090) \| 0.002909 / 0.004328 (-0.001419) \| 0.062553 / 0.004250 (0.058302) \| 0.049795 / 0.037052 (0.012742) \| 0.321369 / 0.258489 (0.062880) \| 0.422860 / 0.293841 (0.129019) \| 0.027394 / 0.128546 (-0.101152) \| 0.007954 / 0.075646 (-0.067693) \| 0.264122 / 0.419271 (-0.155149) \| 0.044881 / 0.043533 (0.001349) \| 0.316702 / 0.255139 (0.061563) \| 0.374718 / 0.283200 (0.091518) \| 0.021728 / 0.141683 (-0.119955) \| 1.394456 / 1.452155 (-0.057699) \| 1.474936 / 1.492716 (-0.017780) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.191902 / 0.018006 (0.173896) \| 0.430468 / 0.000490 (0.429979) \| 0.003790 / 0.000200 (0.003590) \| 0.000069 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024974 / 0.037411 (-0.012438) \| 0.073053 / 0.014526 (0.058527) \| 0.083801 / 0.176557 (-0.092756) \| 0.143457 / 0.737135 (-0.593678) \| 0.085099 / 0.296338 (-0.211240) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.428411 / 0.215209 (0.213202) \| 4.278077 / 2.077655 (2.200422) \| 2.230039 / 1.504120 (0.725919) \| 2.057191 / 1.541195 (0.515996) \| 2.120109 / 1.468490 (0.651619) \| 0.495242 / 4.584777 (-4.089535) \| 3.031299 / 3.745712 (-0.714413) \| 2.802685 / 5.269862 (-2.467176) \| 1.839828 / 4.565676 (-2.725849) \| 0.056875 / 0.424275 (-0.367401) \| 0.006446 / 0.007607 (-0.001161) \| 0.498958 / 0.226044 (0.272913) \| 4.980440 / 2.268929 (2.711511) \| 2.659659 / 55.444624 (-52.784965) \| 2.315174 / 6.876477 (-4.561303) \| 2.475920 / 2.142072 (0.333848) \| 0.586946 / 4.805227 (-4.218282) \| 0.124291 / 6.500664 (-6.376373) \| 0.060701 / 0.075469 (-0.014768) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.245062 / 1.841788 (-0.596725) \| 18.201444 / 8.074308 (10.127136) \| 13.723271 / 10.191392 (3.531879) \| 0.130203 / 0.680424 (-0.550221) \| 0.016773 / 0.534201 (-0.517428) \| 0.332909 / 0.579283 (-0.246374) \| 0.347469 / 0.434364 (-0.086895) \| 0.381364 / 0.540337 (-0.158973) \| 0.541723 / 1.386936 (-0.845213) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005934 / 0.011353 (-0.005419) \| 0.003573 / 0.011008 (-0.007435) \| 0.062195 / 0.038508 (0.023687) \| 0.059026 / 0.023109 (0.035917) \| 0.413993 / 0.275898 (0.138095) \| 0.459552 / 0.323480 (0.136072) \| 0.004610 / 0.007986 (-0.003376) \| 0.002907 / 0.004328 (-0.001421) \| 0.062983 / 0.004250 (0.058733) \| 0.047797 / 0.037052 (0.010745) \| 0.415461 / 0.258489 (0.156972) \| 0.417424 / 0.293841 (0.123583) \| 0.027098 / 0.128546 (-0.101449) \| 0.008106 / 0.075646 (-0.067540) \| 0.067600 / 0.419271 (-0.351672) \| 0.041432 / 0.043533 (-0.002101) \| 0.407861 / 0.255139 (0.152722) \| 0.430774 / 0.283200 (0.147575) \| 0.020738 / 0.141683 (-0.120945) \| 1.435127 / 1.452155 (-0.017028) \| 1.486961 / 1.492716 (-0.005755) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.231174 / 0.018006 (0.213168) \| 0.421208 / 0.000490 (0.420718) \| 0.005411 / 0.000200 (0.005211) \| 0.000078 / 0.000054 (0.000023) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025362 / 0.037411 (-0.012049) \| 0.078534 / 0.014526 (0.064008) \| 0.085304 / 0.176557 (-0.091252) \| 0.139048 / 0.737135 (-0.598087) \| 0.087015 / 0.296338 (-0.209323) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.448506 / 0.215209 (0.233297) \| 4.486694 / 2.077655 (2.409039) \| 2.488022 / 1.504120 (0.983902) \| 2.325321 / 1.541195 (0.784126) \| 2.381311 / 1.468490 (0.912821) \| 0.502102 / 4.584777 (-4.082675) \| 3.018326 / 3.745712 (-0.727386) \| 2.824922 / 5.269862 (-2.444940) \| 1.857414 / 4.565676 (-2.708263) \| 0.057514 / 0.424275 (-0.366761) \| 0.006829 / 0.007607 (-0.000779) \| 0.521939 / 0.226044 (0.295895) \| 5.224393 / 2.268929 (2.955465) \| 2.933132 / 55.444624 (-52.511492) \| 2.661187 / 6.876477 (-4.215290) \| 2.781950 / 2.142072 (0.639878) \| 0.592927 / 4.805227 (-4.212300) \| 0.126685 / 6.500664 (-6.373979) \| 0.064188 / 0.075469 (-0.011281) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.351107 / 1.841788 (-0.490681) \| 18.344453 / 8.074308 (10.270145) \| 13.838788 / 10.191392 (3.647396) \| 0.157881 / 0.680424 (-0.522543) \| 0.016636 / 0.534201 (-0.517565) \| 0.331597 / 0.579283 (-0.247686) \| 0.345573 / 0.434364 (-0.088791) \| 0.397361 / 0.540337 (-0.142976) \| 0.534289 / 1.386936 (-0.852647) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#582e722a76534904c0f3038d32ebb8db88ce9128 \"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) \| 0.006399 / 0.011353 (-0.004954) \| 0.003872 / 0.011008 (-0.007136) \| 0.083722 / 0.038508 (0.045214) \| 0.068845 / 0.023109 (0.045736) \| 0.329112 / 0.275898 (0.053214) \| 0.343295 / 0.323480 (0.019815) \| 0.005137 / 0.007986 (-0.002849) \| 0.003303 / 0.004328 (-0.001026) \| 0.064495 / 0.004250 (0.060245) \| 0.051448 / 0.037052 (0.014395) \| 0.322554 / 0.258489 (0.064065) \| 0.361934 / 0.293841 (0.068093) \| 0.030821 / 0.128546 (-0.097726) \| 0.008482 / 0.075646 (-0.067164) \| 0.288136 / 0.419271 (-0.131135) \| 0.051935 / 0.043533 (0.008402) \| 0.308283 / 0.255139 (0.053144) \| 0.343421 / 0.283200 (0.060221) \| 0.023639 / 0.141683 (-0.118044) \| 1.485442 / 1.452155 (0.033288) \| 1.533282 / 1.492716 (0.040565) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.218163 / 0.018006 (0.200157) \| 0.464473 / 0.000490 (0.463983) \| 0.003097 / 0.000200 (0.002897) \| 0.000081 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028650 / 0.037411 (-0.008761) \| 0.083295 / 0.014526 (0.068769) \| 0.096468 / 0.176557 (-0.080088) \| 0.152086 / 0.737135 (-0.585050) \| 0.102586 / 0.296338 (-0.193752) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.393038 / 0.215209 (0.177829) \| 3.925514 / 2.077655 (1.847859) \| 1.938419 / 1.504120 (0.434300) \| 1.760265 / 1.541195 (0.219071) \| 1.810024 / 1.468490 (0.341534) \| 0.486232 / 4.584777 (-4.098545) \| 3.618747 / 3.745712 (-0.126965) \| 3.206950 / 5.269862 (-2.062912) \| 1.999240 / 4.565676 (-2.566436) \| 0.056986 / 0.424275 (-0.367289) \| 0.007193 / 0.007607 (-0.000415) \| 0.469313 / 0.226044 (0.243269) \| 4.688670 / 2.268929 (2.419741) \| 2.400332 / 55.444624 (-53.044292) \| 2.074197 / 6.876477 (-4.802279) \| 2.290823 / 2.142072 (0.148751) \| 0.582339 / 4.805227 (-4.222888) \| 0.134127 / 6.500664 (-6.366537) \| 0.061061 / 0.075469 (-0.014408) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.272782 / 1.841788 (-0.569006) \| 19.463375 / 8.074308 (11.389067) \| 14.306819 / 10.191392 (4.115427) \| 0.164608 / 0.680424 (-0.515816) \| 0.018626 / 0.534201 (-0.515575) \| 0.395225 / 0.579283 (-0.184058) \| 0.408984 / 0.434364 (-0.025380) \| 0.463364 / 0.540337 (-0.076974) \| 0.630425 / 1.386936 (-0.756511) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006465 / 0.011353 (-0.004888) \| 0.003975 / 0.011008 (-0.007033) \| 0.063643 / 0.038508 (0.025134) \| 0.075214 / 0.023109 (0.052105) \| 0.361734 / 0.275898 (0.085836) \| 0.396664 / 0.323480 (0.073184) \| 0.005251 / 0.007986 (-0.002735) \| 0.003249 / 0.004328 (-0.001080) \| 0.063841 / 0.004250 (0.059591) \| 0.054504 / 0.037052 (0.017451) \| 0.374791 / 0.258489 (0.116302) \| 0.399205 / 0.293841 (0.105364) \| 0.031355 / 0.128546 (-0.097192) \| 0.008483 / 0.075646 (-0.067163) \| 0.070234 / 0.419271 (-0.349037) \| 0.048336 / 0.043533 (0.004803) \| 0.373484 / 0.255139 (0.118345) \| 0.382174 / 0.283200 (0.098974) \| 0.022560 / 0.141683 (-0.119123) \| 1.449799 / 1.452155 (-0.002355) \| 1.525255 / 1.492716 (0.032539) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.228350 / 0.018006 (0.210343) \| 0.444344 / 0.000490 (0.443855) \| 0.003699 / 0.000200 (0.003499) \| 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) \| 0.030681 / 0.037411 (-0.006731) \| 0.087340 / 0.014526 (0.072814) \| 0.098636 / 0.176557 (-0.077920) \| 0.151665 / 0.737135 (-0.585471) \| 0.100840 / 0.296338 (-0.195498) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.417857 / 0.215209 (0.202648) \| 4.168407 / 2.077655 (2.090752) \| 2.201758 / 1.504120 (0.697638) \| 1.997834 / 1.541195 (0.456639) \| 2.127693 / 1.468490 (0.659202) \| 0.486429 / 4.584777 (-4.098348) \| 3.676335 / 3.745712 (-0.069378) \| 3.226268 / 5.269862 (-2.043594) \| 2.027255 / 4.565676 (-2.538422) \| 0.056759 / 0.424275 (-0.367516) \| 0.007628 / 0.007607 (0.000021) \| 0.500482 / 0.226044 (0.274438) \| 4.996236 / 2.268929 (2.727307) \| 2.628884 / 55.444624 (-52.815740) \| 2.347611 / 6.876477 (-4.528866) \| 2.551328 / 2.142072 (0.409255) \| 0.582449 / 4.805227 (-4.222778) \| 0.132844 / 6.500664 (-6.367821) \| 0.061791 / 0.075469 (-0.013678) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.373718 / 1.841788 (-0.468070) \| 19.921217 / 8.074308 (11.846909) \| 14.209642 / 10.191392 (4.018250) \| 0.185334 / 0.680424 (-0.495090) \| 0.018228 / 0.534201 (-0.515973) \| 0.395549 / 0.579283 (-0.183734) \| 0.404446 / 0.434364 (-0.029918) \| 0.472456 / 0.540337 (-0.067882) \| 0.622739 / 1.386936 (-0.764197) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33f736eafa0f77de03aa6894ea4a6c923702e5d1 \"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) \| 0.006007 / 0.011353 (-0.005346) \| 0.003588 / 0.011008 (-0.007420) \| 0.080334 / 0.038508 (0.041826) \| 0.058932 / 0.023109 (0.035823) \| 0.404613 / 0.275898 (0.128715) \| 0.438377 / 0.323480 (0.114897) \| 0.003468 / 0.007986 (-0.004518) \| 0.003702 / 0.004328 (-0.000627) \| 0.062936 / 0.004250 (0.058686) \| 0.047987 / 0.037052 (0.010934) \| 0.411409 / 0.258489 (0.152920) \| 0.450244 / 0.293841 (0.156403) \| 0.027007 / 0.128546 (-0.101539) \| 0.007932 / 0.075646 (-0.067714) \| 0.261390 / 0.419271 (-0.157882) \| 0.044992 / 0.043533 (0.001459) \| 0.409730 / 0.255139 (0.154591) \| 0.433331 / 0.283200 (0.150131) \| 0.020446 / 0.141683 (-0.121237) \| 1.425418 / 1.452155 (-0.026736) \| 1.479242 / 1.492716 (-0.013475) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.187375 / 0.018006 (0.169368) \| 0.428532 / 0.000490 (0.428043) \| 0.003406 / 0.000200 (0.003206) \| 0.000072 / 0.000054 (0.000018) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024390 / 0.037411 (-0.013022) \| 0.072571 / 0.014526 (0.058045) \| 0.083513 / 0.176557 (-0.093044) \| 0.144395 / 0.737135 (-0.592741) \| 0.084813 / 0.296338 (-0.211526) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.409176 / 0.215209 (0.193967) \| 4.078082 / 2.077655 (2.000428) \| 1.913596 / 1.504120 (0.409476) \| 1.718470 / 1.541195 (0.177275) \| 1.753106 / 1.468490 (0.284616) \| 0.494167 / 4.584777 (-4.090610) \| 3.029531 / 3.745712 (-0.716181) \| 2.807331 / 5.269862 (-2.462531) \| 1.839471 / 4.565676 (-2.726206) \| 0.057169 / 0.424275 (-0.367106) \| 0.006433 / 0.007607 (-0.001175) \| 0.482666 / 0.226044 (0.256621) \| 4.817601 / 2.268929 (2.548673) \| 2.449967 / 55.444624 (-52.994658) \| 2.113891 / 6.876477 (-4.762586) \| 2.399293 / 2.142072 (0.257221) \| 0.578903 / 4.805227 (-4.226324) \| 0.124306 / 6.500664 (-6.376358) \| 0.061572 / 0.075469 (-0.013897) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.254692 / 1.841788 (-0.587096) \| 18.414049 / 8.074308 (10.339741) \| 13.992059 / 10.191392 (3.800667) \| 0.146671 / 0.680424 (-0.533753) \| 0.016925 / 0.534201 (-0.517275) \| 0.333124 / 0.579283 (-0.246159) \| 0.348007 / 0.434364 (-0.086357) \| 0.378519 / 0.540337 (-0.161819) \| 0.532540 / 1.386936 (-0.854396) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006050 / 0.011353 (-0.005303) \| 0.003614 / 0.011008 (-0.007394) \| 0.061707 / 0.038508 (0.023199) \| 0.062874 / 0.023109 (0.039765) \| 0.364760 / 0.275898 (0.088862) \| 0.398136 / 0.323480 (0.074656) \| 0.005598 / 0.007986 (-0.002388) \| 0.002836 / 0.004328 (-0.001493) \| 0.061880 / 0.004250 (0.057630) \| 0.048165 / 0.037052 (0.011113) \| 0.372656 / 0.258489 (0.114167) \| 0.403967 / 0.293841 (0.110126) \| 0.027046 / 0.128546 (-0.101501) \| 0.008091 / 0.075646 (-0.067555) \| 0.066783 / 0.419271 (-0.352489) \| 0.041186 / 0.043533 (-0.002347) \| 0.376009 / 0.255139 (0.120870) \| 0.391769 / 0.283200 (0.108569) \| 0.021020 / 0.141683 (-0.120663) \| 1.514593 / 1.452155 (0.062438) \| 1.548506 / 1.492716 (0.055790) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.237610 / 0.018006 (0.219604) \| 0.434274 / 0.000490 (0.433784) \| 0.009720 / 0.000200 (0.009520) \| 0.000098 / 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) \| 0.025605 / 0.037411 (-0.011807) \| 0.078971 / 0.014526 (0.064445) \| 0.088154 / 0.176557 (-0.088403) \| 0.139112 / 0.737135 (-0.598023) \| 0.088890 / 0.296338 (-0.207449) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.420027 / 0.215209 (0.204818) \| 4.189493 / 2.077655 (2.111838) \| 2.143907 / 1.504120 (0.639787) \| 1.967032 / 1.541195 (0.425837) \| 2.011845 / 1.468490 (0.543355) \| 0.496692 / 4.584777 (-4.088085) \| 3.025456 / 3.745712 (-0.720256) \| 2.828436 / 5.269862 (-2.441426) \| 1.860673 / 4.565676 (-2.705003) \| 0.057199 / 0.424275 (-0.367076) \| 0.006770 / 0.007607 (-0.000838) \| 0.491281 / 0.226044 (0.265236) \| 4.918065 / 2.268929 (2.649136) \| 2.593172 / 55.444624 (-52.851452) \| 2.250750 / 6.876477 (-4.625727) \| 2.406235 / 2.142072 (0.264162) \| 0.588648 / 4.805227 (-4.216579) \| 0.125635 / 6.500664 (-6.375029) \| 0.061697 / 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) \| 1.374065 / 1.841788 (-0.467722) \| 18.439315 / 8.074308 (10.365007) \| 14.031660 / 10.191392 (3.840268) \| 0.153665 / 0.680424 (-0.526759) \| 0.016980 / 0.534201 (-0.517221) \| 0.331799 / 0.579283 (-0.247484) \| 0.343201 / 0.434364 (-0.091163) \| 0.392445 / 0.540337 (-0.147892) \| 0.530387 / 1.386936 (-0.856549) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33f736eafa0f77de03aa6894ea4a6c923702e5d1 \"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) \| 0.008189 / 0.011353 (-0.003164) \| 0.004598 / 0.011008 (-0.006410) \| 0.102199 / 0.038508 (0.063691) \| 0.077961 / 0.023109 (0.054852) \| 0.364936 / 0.275898 (0.089038) \| 0.402606 / 0.323480 (0.079126) \| 0.005522 / 0.007986 (-0.002464) \| 0.004007 / 0.004328 (-0.000322) \| 0.071560 / 0.004250 (0.067310) \| 0.055818 / 0.037052 (0.018765) \| 0.378394 / 0.258489 (0.119905) \| 0.428990 / 0.293841 (0.135149) \| 0.043142 / 0.128546 (-0.085404) \| 0.013254 / 0.075646 (-0.062392) \| 0.331102 / 0.419271 (-0.088170) \| 0.061407 / 0.043533 (0.017875) \| 0.387397 / 0.255139 (0.132258) \| 0.416062 / 0.283200 (0.132862) \| 0.036330 / 0.141683 (-0.105353) \| 1.735352 / 1.452155 (0.283198) \| 1.773329 / 1.492716 (0.280613) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.188587 / 0.018006 (0.170581) \| 0.519506 / 0.000490 (0.519016) \| 0.004702 / 0.000200 (0.004502) \| 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) \| 0.027152 / 0.037411 (-0.010260) \| 0.094296 / 0.014526 (0.079770) \| 0.098155 / 0.176557 (-0.078402) \| 0.162541 / 0.737135 (-0.574595) \| 0.112092 / 0.296338 (-0.184246) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.537555 / 0.215209 (0.322346) \| 5.486821 / 2.077655 (3.409166) \| 2.377127 / 1.504120 (0.873008) \| 2.073205 / 1.541195 (0.532011) \| 2.075130 / 1.468490 (0.606640) \| 0.783779 / 4.584777 (-3.800998) \| 5.029524 / 3.745712 (1.283812) \| 4.382724 / 5.269862 (-0.887138) \| 2.836180 / 4.565676 (-1.729496) \| 0.108840 / 0.424275 (-0.315435) \| 0.008123 / 0.007607 (0.000516) \| 0.673460 / 0.226044 (0.447416) \| 6.674030 / 2.268929 (4.405102) \| 3.208922 / 55.444624 (-52.235702) \| 2.464908 / 6.876477 (-4.411568) \| 2.661929 / 2.142072 (0.519856) \| 0.962529 / 4.805227 (-3.842698) \| 0.197974 / 6.500664 (-6.302690) \| 0.066656 / 0.075469 (-0.008813) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.430373 / 1.841788 (-0.411415) \| 21.180540 / 8.074308 (13.106232) \| 19.027491 / 10.191392 (8.836099) \| 0.217520 / 0.680424 (-0.462904) \| 0.028038 / 0.534201 (-0.506163) \| 0.435266 / 0.579283 (-0.144017) \| 0.529510 / 0.434364 (0.095147) \| 0.511011 / 0.540337 (-0.029327) \| 0.728940 / 1.386936 (-0.657996) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007883 / 0.011353 (-0.003470) \| 0.004448 / 0.011008 (-0.006560) \| 0.071350 / 0.038508 (0.032842) \| 0.075269 / 0.023109 (0.052160) \| 0.396705 / 0.275898 (0.120807) \| 0.457809 / 0.323480 (0.134329) \| 0.005193 / 0.007986 (-0.002792) \| 0.003695 / 0.004328 (-0.000633) \| 0.078087 / 0.004250 (0.073836) \| 0.054276 / 0.037052 (0.017224) \| 0.412184 / 0.258489 (0.153695) \| 0.452400 / 0.293841 (0.158559) \| 0.049762 / 0.128546 (-0.078784) \| 0.013206 / 0.075646 (-0.062440) \| 0.085985 / 0.419271 (-0.333287) \| 0.058837 / 0.043533 (0.015304) \| 0.432481 / 0.255139 (0.177342) \| 0.433260 / 0.283200 (0.150060) \| 0.031190 / 0.141683 (-0.110493) \| 1.582707 / 1.452155 (0.130552) \| 1.664457 / 1.492716 (0.171741) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223639 / 0.018006 (0.205633) \| 0.524388 / 0.000490 (0.523899) \| 0.005489 / 0.000200 (0.005289) \| 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) \| 0.030182 / 0.037411 (-0.007230) \| 0.089309 / 0.014526 (0.074783) \| 0.103306 / 0.176557 (-0.073250) \| 0.162624 / 0.737135 (-0.574511) \| 0.108957 / 0.296338 (-0.187381) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.577423 / 0.215209 (0.362214) \| 5.900154 / 2.077655 (3.822500) \| 2.687369 / 1.504120 (1.183249) \| 2.513061 / 1.541195 (0.971866) \| 2.506453 / 1.468490 (1.037963) \| 0.830838 / 4.584777 (-3.753939) \| 5.032195 / 3.745712 (1.286483) \| 4.396827 / 5.269862 (-0.873035) \| 2.884230 / 4.565676 (-1.681447) \| 0.102239 / 0.424275 (-0.322036) \| 0.008178 / 0.007607 (0.000571) \| 0.710027 / 0.226044 (0.483983) \| 7.149626 / 2.268929 (4.880698) \| 3.403605 / 55.444624 (-52.041019) \| 2.661970 / 6.876477 (-4.214506) \| 2.760227 / 2.142072 (0.618154) \| 1.043981 / 4.805227 (-3.761246) \| 0.195028 / 6.500664 (-6.305636) \| 0.065211 / 0.075469 (-0.010258) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.581265 / 1.841788 (-0.260522) \| 21.640230 / 8.074308 (13.565922) \| 19.031860 / 10.191392 (8.840468) \| 0.196903 / 0.680424 (-0.483520) \| 0.027061 / 0.534201 (-0.507140) \| 0.444995 / 0.579283 (-0.134288) \| 0.528195 / 0.434364 (0.093831) \| 0.521540 / 0.540337 (-0.018797) \| 0.730204 / 1.386936 (-0.656732) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33f736eafa0f77de03aa6894ea4a6c923702e5d1 \"CML watermark\")\n" ]
1,834,015,584	6,114	Cache not being used when loading commonvoice 8.0.0	closed	2023-08-02T23:18:11	2023-08-18T23:59:00	2023-08-18T23:59:00	https://github.com/huggingface/datasets/issues/6114	null	clabornd	false	[ "You can avoid this by using the `revision` parameter in `load_dataset` to always force downloading a specific commit (if not specified it defaults to HEAD, hence the redownload).", "Thanks @mariosasko this works well, looks like I should have read the documentation a bit more carefully. \r\n\r\nIt is still a bit confusing which hash I should provide: passing `revision = c8fd66e85f086e3abb11eeee55b1737a3d1e8487` from https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0/commits/main caused the cached version at `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/b2f8b72f8f30b2e98c41ccf855954d9e35a5fa498c43332df198534ff9797a4a` to be loaded, so I had to know that it was the previous commit unless I've missed something else." ]
1,833,854,030	6,113	load_dataset() fails with streamlit caching inside docker	closed	2023-08-02T20:20:26	2023-08-21T18:18:27	2023-08-21T18:18:27	https://github.com/huggingface/datasets/issues/6113	null	fierval	false	[ "Hi! This should be fixed in the latest (patch) release (run `pip install -U datasets` to install it). This behavior was due to a bug in our authentication logic." ]
1,833,693,299	6,112	yaml error using push_to_hub with generated README.md	closed	2023-08-02T18:21:21	2023-12-12T15:00:44	2023-12-12T15:00:44	https://github.com/huggingface/datasets/issues/6112	null	kevintee	false	[ "Thanks for reporting! This is a bug in converting the `ArrayXD` types to YAML. It will be fixed soon." ]
1,832,781,654	6,111	raise FileNotFoundError("Directory {dataset_path} is neither a `Dataset` directory nor a `DatasetDict` directory." )	closed	2023-08-02T09:17:29	2023-08-29T02:00:28	2023-08-29T02:00:28	https://github.com/huggingface/datasets/issues/6111	null	2catycm	false	[ "any idea?", "This should work: `load_dataset(\"path/to/downloaded_repo\")`\r\n\r\n`load_from_disk` is intended to be used on directories created with `Dataset.save_to_disk` or `DatasetDict.save_to_disk`", "> This should work: `load_dataset(\"path/to/downloaded_repo\")`\r\n> \r\n> `load_from_disk` is intended to be used on directories created with `Dataset.save_to_disk` or `DatasetDict.save_to_disk`\r\n\r\nThanks for your help. This works." ]
1,831,110,633	6,110	[BUG] Dataset initialized from in-memory data does not create cache.	closed	2023-08-01T11:58:58	2023-08-17T14:03:01	2023-08-17T14:03:00	https://github.com/huggingface/datasets/issues/6110	null	MattYoon	false	[ "This is expected behavior. You must provide `cache_file_name` when performing `.map` on an in-memory dataset for the result to be cached." ]
1,830,753,793	6,109	Problems in downloading Amazon reviews from HF	closed	2023-08-01T08:38:29	2025-07-18T17:47:30	2023-08-02T07:12:07	https://github.com/huggingface/datasets/issues/6109	null	610v4nn1	false	[ "Thanks for reporting, @610v4nn1.\r\n\r\nIndeed, the source data files are no longer available. We have contacted the authors of the dataset and they report that Amazon has decided to stop distributing the multilingual reviews dataset.\r\n\r\nWe are adding a notification about this issue to the dataset card.\r\n\r\nSee: https://huggingface.co/datasets/amazon_reviews_multi/discussions/4#64c3898db63057f1fd3ce1a0 ", "The dataset can be accessed from https://www.kaggle.com/datasets/mexwell/amazon-reviews-multi.", "For those willing to transform the csv files from Kaggle into Huggingface datasets for their NLP course (exercise on summarisation), you can use this code on Google Collab:\r\n\r\n`from datasets import load_dataset\r\n\r\nimport pandas as pd\r\nfrom datasets import Dataset, DatasetDict\r\n\r\n# Load your CSV previously downloaded files from Kaggle on Google Collab\r\ntrain_csv_path = \"/content/train.csv\"\r\nvalidation_csv_path = \"/content/validation.csv\"\r\ntest_csv_path = '/content/test.csv'\r\n\r\n# Read CSV files into pandas DataFrames\r\ntrain_df = pd.read_csv(train_csv_path, engine='python')\r\nvalidation_df = pd.read_csv(validation_csv_path, engine='python')\r\ntest_df = pd.read_csv(test_csv_path, engine='python')\r\n\r\n# Filter by language ('es' for Spanish and 'en' for English)\r\nspanish_train_df = train_df[train_df['language'] == 'es']\r\nspanish_validation_df = validation_df[validation_df['language'] == 'es']\r\nspanish_test_df = test_df[test_df['language'] == 'es']\r\n\r\nenglish_train_df = train_df[train_df['language'] == 'en']\r\nenglish_validation_df = validation_df[validation_df['language'] == 'en']\r\nenglish_test_df = test_df[test_df['language'] == 'en']\r\n\r\n# Create Hugging Face datasets\r\nspanish_dataset = DatasetDict({\r\n 'train': Dataset.from_pandas(spanish_train_df),\r\n 'validation': Dataset.from_pandas(spanish_validation_df),\r\n 'test': Dataset.from_pandas(spanish_test_df)\r\n})\r\n\r\nenglish_dataset = DatasetDict({\r\n 'train': Dataset.from_pandas(english_train_df),\r\n 'validation': Dataset.from_pandas(english_validation_df),\r\n 'test': Dataset.from_pandas(english_test_df)\r\n})\r\nenglish_dataset = english_dataset.remove_columns(['Unnamed: 0', '__index_level_0__'])\r\nspanish_dataset = spanish_dataset.remove_columns(['Unnamed: 0', '__index_level_0__'])`" ]
1,830,347,187	6,108	Loading local datasets got strangely stuck	open	2023-08-01T02:28:06	2024-12-31T16:01:00	null	https://github.com/huggingface/datasets/issues/6108	null	LoveCatc	false	[ "Yesterday I waited for more than 12 hours to make sure it was really stuck instead of proceeding too slow.", "I've had similar weird issues with `load_dataset` as well. Not multiple files, but dataset is quite big, about 50G.", "We use a generic multiprocessing code, so there is little we can do about this - unfortunately, turning off multiprocessing seems to be the only solution. Multithreading would make our code easier to maintain and (most likely) avoid issues such as this one, but we cannot use it until the GIL is dropped (no-GIL Python should be released in 2024, so we can start exploring this then)", "The problem seems to be the `Generating train split`. Is it possible to avoid that? I have a dataset saved, just want to load it but somehow running into issues with that again.", "Hey guys, recently I ran into this problem again and I spent one whole day trying to locate the problem. Finally I found the problem seems to be with `pyarrow`'s json parser, and it seems a long-existing problem. Similar issue can be found in #2181. Anyway, my solution is to adjust the `load_dataset`'s parameter `chunksize`. You can inspect the parameter set in `datasets/packaged_modules/json/json.py`, now the actual chunksize should be very small, and you can increase the value. For me, `chunksize=10<<23` could solve the stuck problem. But I also find that too big `chunksize`, like `10 << 30`, would also cause a stuck, which is rather weird. I think I may explore this when I am free. And hope this can help those who also encounter the same problem. ", "Experiencing the same issue with the `kaist-ai/Feedback-Collection` dataset, which is comparatively small i.e. 100k rows.\r\nCode to reproduce\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"kaist-ai/Feedback-Collection\")\r\n```\r\n\r\nI have tried setting `num_proc=1` as well as `chunksize=1024, 64` but problem persists. Any pointers?", "sorry to disturb, at datasets==2.21.0, I add `chunksize` parameter but got error \"doesn't have a 'chunksize' key\". Is it got removed?" ]
1,829,625,320	6,107	Fix deprecation of use_auth_token in file_utils	closed	2023-07-31T16:32:01	2023-08-03T10:13:32	2023-08-03T10:04:18	https://github.com/huggingface/datasets/pull/6107	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6107", "html_url": "https://github.com/huggingface/datasets/pull/6107", "diff_url": "https://github.com/huggingface/datasets/pull/6107.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6107.patch", "merged_at": "2023-08-03T10:04:18" }	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) \| 0.007678 / 0.011353 (-0.003675) \| 0.004233 / 0.011008 (-0.006776) \| 0.095934 / 0.038508 (0.057426) \| 0.064201 / 0.023109 (0.041092) \| 0.345765 / 0.275898 (0.069867) \| 0.383089 / 0.323480 (0.059609) \| 0.004084 / 0.007986 (-0.003902) \| 0.003311 / 0.004328 (-0.001017) \| 0.072367 / 0.004250 (0.068117) \| 0.048252 / 0.037052 (0.011200) \| 0.338340 / 0.258489 (0.079851) \| 0.391627 / 0.293841 (0.097786) \| 0.045203 / 0.128546 (-0.083343) \| 0.013494 / 0.075646 (-0.062153) \| 0.314097 / 0.419271 (-0.105174) \| 0.058183 / 0.043533 (0.014650) \| 0.353946 / 0.255139 (0.098807) \| 0.385181 / 0.283200 (0.101981) \| 0.033111 / 0.141683 (-0.108572) \| 1.578489 / 1.452155 (0.126335) \| 1.631660 / 1.492716 (0.138944) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.202592 / 0.018006 (0.184586) \| 0.506450 / 0.000490 (0.505961) \| 0.004630 / 0.000200 (0.004430) \| 0.000105 / 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) \| 0.024761 / 0.037411 (-0.012651) \| 0.086295 / 0.014526 (0.071769) \| 0.094063 / 0.176557 (-0.082494) \| 0.154189 / 0.737135 (-0.582947) \| 0.096273 / 0.296338 (-0.200065) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.581731 / 0.215209 (0.366522) \| 5.552020 / 2.077655 (3.474365) \| 2.430800 / 1.504120 (0.926680) \| 2.130864 / 1.541195 (0.589669) \| 2.092802 / 1.468490 (0.624312) \| 0.833956 / 4.584777 (-3.750821) \| 4.840859 / 3.745712 (1.095147) \| 4.267812 / 5.269862 (-1.002050) \| 2.663245 / 4.565676 (-1.902432) \| 0.093195 / 0.424275 (-0.331080) \| 0.007942 / 0.007607 (0.000335) \| 0.651457 / 0.226044 (0.425413) \| 6.782986 / 2.268929 (4.514058) \| 3.103307 / 55.444624 (-52.341318) \| 2.373933 / 6.876477 (-4.502544) \| 2.571613 / 2.142072 (0.429540) \| 0.981389 / 4.805227 (-3.823839) \| 0.199019 / 6.500664 (-6.301645) \| 0.065828 / 0.075469 (-0.009641) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.429778 / 1.841788 (-0.412009) \| 20.967563 / 8.074308 (12.893255) \| 19.329723 / 10.191392 (9.138331) \| 0.222048 / 0.680424 (-0.458376) \| 0.033507 / 0.534201 (-0.500694) \| 0.436801 / 0.579283 (-0.142482) \| 0.530197 / 0.434364 (0.095833) \| 0.491532 / 0.540337 (-0.048805) \| 0.718216 / 1.386936 (-0.668720) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007798 / 0.011353 (-0.003555) \| 0.004748 / 0.011008 (-0.006260) \| 0.070847 / 0.038508 (0.032339) \| 0.069338 / 0.023109 (0.046229) \| 0.400890 / 0.275898 (0.124992) \| 0.429482 / 0.323480 (0.106002) \| 0.006469 / 0.007986 (-0.001517) \| 0.003514 / 0.004328 (-0.000814) \| 0.069049 / 0.004250 (0.064798) \| 0.059800 / 0.037052 (0.022748) \| 0.415644 / 0.258489 (0.157155) \| 0.432562 / 0.293841 (0.138721) \| 0.043778 / 0.128546 (-0.084768) \| 0.015141 / 0.075646 (-0.060506) \| 0.081521 / 0.419271 (-0.337750) \| 0.054692 / 0.043533 (0.011160) \| 0.404497 / 0.255139 (0.149358) \| 0.419783 / 0.283200 (0.136583) \| 0.029588 / 0.141683 (-0.112094) \| 1.593506 / 1.452155 (0.141351) \| 1.615977 / 1.492716 (0.123261) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.270981 / 0.018006 (0.252975) \| 0.522074 / 0.000490 (0.521584) \| 0.026568 / 0.000200 (0.026368) \| 0.000126 / 0.000054 (0.000072) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031551 / 0.037411 (-0.005861) \| 0.086723 / 0.014526 (0.072197) \| 0.103315 / 0.176557 (-0.073242) \| 0.154692 / 0.737135 (-0.582443) \| 0.099472 / 0.296338 (-0.196866) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.570238 / 0.215209 (0.355029) \| 5.655963 / 2.077655 (3.578308) \| 2.662670 / 1.504120 (1.158550) \| 2.380903 / 1.541195 (0.839709) \| 2.409467 / 1.468490 (0.940977) \| 0.828055 / 4.584777 (-3.756722) \| 4.964698 / 3.745712 (1.218986) \| 4.299995 / 5.269862 (-0.969867) \| 2.824162 / 4.565676 (-1.741514) \| 0.095872 / 0.424275 (-0.328403) \| 0.007907 / 0.007607 (0.000300) \| 0.701595 / 0.226044 (0.475551) \| 7.131965 / 2.268929 (4.863036) \| 3.250554 / 55.444624 (-52.194070) \| 2.531916 / 6.876477 (-4.344561) \| 2.717908 / 2.142072 (0.575835) \| 1.014479 / 4.805227 (-3.790748) \| 0.223804 / 6.500664 (-6.276861) \| 0.071893 / 0.075469 (-0.003576) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.541702 / 1.841788 (-0.300086) \| 21.668219 / 8.074308 (13.593911) \| 18.916032 / 10.191392 (8.724640) \| 0.205915 / 0.680424 (-0.474508) \| 0.026356 / 0.534201 (-0.507845) \| 0.429122 / 0.579283 (-0.150161) \| 0.506110 / 0.434364 (0.071746) \| 0.510148 / 0.540337 (-0.030190) \| 0.724699 / 1.386936 (-0.662237) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c4ca93ff86551b398c979862e7be7305725a240b \"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) \| 0.006884 / 0.011353 (-0.004469) \| 0.004492 / 0.011008 (-0.006516) \| 0.085439 / 0.038508 (0.046931) \| 0.083905 / 0.023109 (0.060796) \| 0.313604 / 0.275898 (0.037706) \| 0.354683 / 0.323480 (0.031203) \| 0.006535 / 0.007986 (-0.001451) \| 0.004318 / 0.004328 (-0.000011) \| 0.066129 / 0.004250 (0.061879) \| 0.057568 / 0.037052 (0.020516) \| 0.317162 / 0.258489 (0.058672) \| 0.372501 / 0.293841 (0.078660) \| 0.031059 / 0.128546 (-0.097488) \| 0.009013 / 0.075646 (-0.066634) \| 0.288794 / 0.419271 (-0.130478) \| 0.053326 / 0.043533 (0.009793) \| 0.314318 / 0.255139 (0.059179) \| 0.357505 / 0.283200 (0.074305) \| 0.027020 / 0.141683 (-0.114663) \| 1.530653 / 1.452155 (0.078498) \| 1.599782 / 1.492716 (0.107066) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278788 / 0.018006 (0.260782) \| 0.626822 / 0.000490 (0.626333) \| 0.003780 / 0.000200 (0.003580) \| 0.000086 / 0.000054 (0.000032) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031703 / 0.037411 (-0.005708) \| 0.085654 / 0.014526 (0.071128) \| 0.754858 / 0.176557 (0.578301) \| 0.212251 / 0.737135 (-0.524885) \| 0.171344 / 0.296338 (-0.124994) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.382291 / 0.215209 (0.167082) \| 3.825612 / 2.077655 (1.747958) \| 1.874553 / 1.504120 (0.370433) \| 1.712574 / 1.541195 (0.171379) \| 1.791479 / 1.468490 (0.322989) \| 0.481005 / 4.584777 (-4.103772) \| 3.530559 / 3.745712 (-0.215153) \| 3.395305 / 5.269862 (-1.874557) \| 2.133747 / 4.565676 (-2.431930) \| 0.056139 / 0.424275 (-0.368136) \| 0.007424 / 0.007607 (-0.000183) \| 0.458321 / 0.226044 (0.232277) \| 4.577665 / 2.268929 (2.308736) \| 2.380233 / 55.444624 (-53.064392) \| 2.004060 / 6.876477 (-4.872417) \| 2.290712 / 2.142072 (0.148639) \| 0.570157 / 4.805227 (-4.235070) \| 0.131670 / 6.500664 (-6.368994) \| 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) \| 1.294929 / 1.841788 (-0.546858) \| 21.386663 / 8.074308 (13.312355) \| 14.389440 / 10.191392 (4.198048) \| 0.171177 / 0.680424 (-0.509247) \| 0.018660 / 0.534201 (-0.515541) \| 0.394385 / 0.579283 (-0.184898) \| 0.424942 / 0.434364 (-0.009422) \| 0.463618 / 0.540337 (-0.076719) \| 0.651499 / 1.386936 (-0.735437) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007079 / 0.011353 (-0.004274) \| 0.004615 / 0.011008 (-0.006393) \| 0.066300 / 0.038508 (0.027792) \| 0.092636 / 0.023109 (0.069527) \| 0.399080 / 0.275898 (0.123182) \| 0.429873 / 0.323480 (0.106393) \| 0.006689 / 0.007986 (-0.001297) \| 0.004358 / 0.004328 (0.000029) \| 0.067155 / 0.004250 (0.062905) \| 0.064040 / 0.037052 (0.026988) \| 0.399905 / 0.258489 (0.141416) \| 0.448237 / 0.293841 (0.154397) \| 0.031985 / 0.128546 (-0.096561) \| 0.009053 / 0.075646 (-0.066593) \| 0.071904 / 0.419271 (-0.347368) \| 0.048759 / 0.043533 (0.005227) \| 0.386797 / 0.255139 (0.131658) \| 0.411240 / 0.283200 (0.128040) \| 0.028568 / 0.141683 (-0.113115) \| 1.501037 / 1.452155 (0.048882) \| 1.594560 / 1.492716 (0.101844) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300756 / 0.018006 (0.282750) \| 0.631220 / 0.000490 (0.630730) \| 0.010163 / 0.000200 (0.009963) \| 0.000144 / 0.000054 (0.000089) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033716 / 0.037411 (-0.003695) \| 0.093562 / 0.014526 (0.079037) \| 0.106975 / 0.176557 (-0.069582) \| 0.161919 / 0.737135 (-0.575216) \| 0.113397 / 0.296338 (-0.182942) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.410392 / 0.215209 (0.195183) \| 4.094411 / 2.077655 (2.016756) \| 2.085868 / 1.504120 (0.581748) \| 1.959589 / 1.541195 (0.418394) \| 2.096683 / 1.468490 (0.628193) \| 0.494593 / 4.584777 (-4.090184) \| 3.854302 / 3.745712 (0.108590) \| 3.742303 / 5.269862 (-1.527558) \| 2.379983 / 4.565676 (-2.185693) \| 0.058640 / 0.424275 (-0.365635) \| 0.008092 / 0.007607 (0.000484) \| 0.486957 / 0.226044 (0.260912) \| 4.855784 / 2.268929 (2.586855) \| 2.654029 / 55.444624 (-52.790595) \| 2.237627 / 6.876477 (-4.638850) \| 2.536955 / 2.142072 (0.394882) \| 0.622398 / 4.805227 (-4.182829) \| 0.139212 / 6.500664 (-6.361452) \| 0.062805 / 0.075469 (-0.012664) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.374862 / 1.841788 (-0.466926) \| 22.797015 / 8.074308 (14.722707) \| 14.393995 / 10.191392 (4.202603) \| 0.196603 / 0.680424 (-0.483821) \| 0.018602 / 0.534201 (-0.515599) \| 0.394568 / 0.579283 (-0.184715) \| 0.408792 / 0.434364 (-0.025572) \| 0.486706 / 0.540337 (-0.053631) \| 0.652365 / 1.386936 (-0.734571) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5713299a88f527ea162a099c2bf2cbceada8fb86 \"CML watermark\")\n" ]
1,829,131,223	6,106	load local json_file as dataset	closed	2023-07-31T12:53:49	2023-08-18T01:46:35	2023-08-18T01:46:35	https://github.com/huggingface/datasets/issues/6106	null	CiaoHe	false	[ "Hi! We use PyArrow to read JSON files, and PyArrow doesn't allow different value types in the same column. #5776 should address this.\r\n\r\nIn the meantime, you can combine `Dataset.from_generator` with the above code to cast the values to the same type. ", "Thanks for your help!" ]
1,829,008,430	6,105	Fix error when loading from GCP bucket	closed	2023-07-31T11:44:46	2023-08-01T10:48:52	2023-08-01T10:38:54	https://github.com/huggingface/datasets/pull/6105	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6105", "html_url": "https://github.com/huggingface/datasets/pull/6105", "diff_url": "https://github.com/huggingface/datasets/pull/6105.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6105.patch", "merged_at": "2023-08-01T10:38:54" }	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) \| 0.006706 / 0.011353 (-0.004647) \| 0.004016 / 0.011008 (-0.006992) \| 0.083696 / 0.038508 (0.045188) \| 0.074340 / 0.023109 (0.051230) \| 0.327338 / 0.275898 (0.051440) \| 0.366663 / 0.323480 (0.043183) \| 0.004052 / 0.007986 (-0.003934) \| 0.003423 / 0.004328 (-0.000906) \| 0.064576 / 0.004250 (0.060326) \| 0.055037 / 0.037052 (0.017985) \| 0.325089 / 0.258489 (0.066600) \| 0.379986 / 0.293841 (0.086145) \| 0.031614 / 0.128546 (-0.096932) \| 0.008553 / 0.075646 (-0.067094) \| 0.287430 / 0.419271 (-0.131841) \| 0.053032 / 0.043533 (0.009499) \| 0.318990 / 0.255139 (0.063851) \| 0.364426 / 0.283200 (0.081226) \| 0.024926 / 0.141683 (-0.116757) \| 1.461835 / 1.452155 (0.009680) \| 1.557172 / 1.492716 (0.064456) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.212430 / 0.018006 (0.194424) \| 0.512891 / 0.000490 (0.512402) \| 0.004772 / 0.000200 (0.004572) \| 0.000132 / 0.000054 (0.000078) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027873 / 0.037411 (-0.009538) \| 0.085598 / 0.014526 (0.071072) \| 0.097330 / 0.176557 (-0.079226) \| 0.152235 / 0.737135 (-0.584900) \| 0.097787 / 0.296338 (-0.198552) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.384645 / 0.215209 (0.169436) \| 3.841161 / 2.077655 (1.763506) \| 1.863696 / 1.504120 (0.359577) \| 1.685082 / 1.541195 (0.143887) \| 1.772904 / 1.468490 (0.304414) \| 0.480177 / 4.584777 (-4.104599) \| 3.601537 / 3.745712 (-0.144175) \| 3.273647 / 5.269862 (-1.996214) \| 2.014415 / 4.565676 (-2.551261) \| 0.056668 / 0.424275 (-0.367607) \| 0.007257 / 0.007607 (-0.000350) \| 0.458194 / 0.226044 (0.232150) \| 4.577311 / 2.268929 (2.308382) \| 2.333983 / 55.444624 (-53.110641) \| 1.964508 / 6.876477 (-4.911969) \| 2.193379 / 2.142072 (0.051307) \| 0.577557 / 4.805227 (-4.227670) \| 0.133899 / 6.500664 (-6.366765) \| 0.060804 / 0.075469 (-0.014665) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.249490 / 1.841788 (-0.592298) \| 19.791875 / 8.074308 (11.717567) \| 14.418728 / 10.191392 (4.227336) \| 0.167788 / 0.680424 (-0.512636) \| 0.018993 / 0.534201 (-0.515208) \| 0.396141 / 0.579283 (-0.183142) \| 0.412427 / 0.434364 (-0.021937) \| 0.456718 / 0.540337 (-0.083619) \| 0.641383 / 1.386936 (-0.745553) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006546 / 0.011353 (-0.004807) \| 0.004059 / 0.011008 (-0.006949) \| 0.064523 / 0.038508 (0.026015) \| 0.074988 / 0.023109 (0.051878) \| 0.388932 / 0.275898 (0.113034) \| 0.424496 / 0.323480 (0.101016) \| 0.005226 / 0.007986 (-0.002760) \| 0.003409 / 0.004328 (-0.000920) \| 0.064284 / 0.004250 (0.060034) \| 0.056829 / 0.037052 (0.019777) \| 0.386457 / 0.258489 (0.127968) \| 0.428063 / 0.293841 (0.134222) \| 0.031411 / 0.128546 (-0.097136) \| 0.008577 / 0.075646 (-0.067070) \| 0.070357 / 0.419271 (-0.348915) \| 0.048920 / 0.043533 (0.005388) \| 0.385197 / 0.255139 (0.130058) \| 0.407167 / 0.283200 (0.123967) \| 0.024469 / 0.141683 (-0.117214) \| 1.482733 / 1.452155 (0.030578) \| 1.539027 / 1.492716 (0.046311) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227532 / 0.018006 (0.209526) \| 0.448792 / 0.000490 (0.448302) \| 0.004139 / 0.000200 (0.003939) \| 0.000085 / 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) \| 0.031004 / 0.037411 (-0.006408) \| 0.088163 / 0.014526 (0.073637) \| 0.101452 / 0.176557 (-0.075105) \| 0.152907 / 0.737135 (-0.584229) \| 0.102325 / 0.296338 (-0.194014) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.418092 / 0.215209 (0.202883) \| 4.162277 / 2.077655 (2.084623) \| 2.232987 / 1.504120 (0.728867) \| 2.143583 / 1.541195 (0.602388) \| 2.246142 / 1.468490 (0.777652) \| 0.490181 / 4.584777 (-4.094596) \| 3.631514 / 3.745712 (-0.114198) \| 3.315025 / 5.269862 (-1.954837) \| 2.101853 / 4.565676 (-2.463823) \| 0.057905 / 0.424275 (-0.366370) \| 0.007686 / 0.007607 (0.000079) \| 0.489965 / 0.226044 (0.263921) \| 4.894375 / 2.268929 (2.625447) \| 2.655459 / 55.444624 (-52.789165) \| 2.262211 / 6.876477 (-4.614266) \| 2.505335 / 2.142072 (0.363263) \| 0.591329 / 4.805227 (-4.213898) \| 0.133554 / 6.500664 (-6.367110) \| 0.061922 / 0.075469 (-0.013547) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.347483 / 1.841788 (-0.494304) \| 20.027011 / 8.074308 (11.952703) \| 14.430737 / 10.191392 (4.239345) \| 0.165767 / 0.680424 (-0.514657) \| 0.018460 / 0.534201 (-0.515741) \| 0.393790 / 0.579283 (-0.185494) \| 0.407213 / 0.434364 (-0.027151) \| 0.474459 / 0.540337 (-0.065879) \| 0.635054 / 1.386936 (-0.751882) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7f575111481e2e2f4d4fc9180771797f69ebcc44 \"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) \| 0.007652 / 0.011353 (-0.003701) \| 0.004581 / 0.011008 (-0.006427) \| 0.101629 / 0.038508 (0.063121) \| 0.090233 / 0.023109 (0.067124) \| 0.392789 / 0.275898 (0.116891) \| 0.432163 / 0.323480 (0.108683) \| 0.004694 / 0.007986 (-0.003292) \| 0.003927 / 0.004328 (-0.000401) \| 0.076533 / 0.004250 (0.072282) \| 0.064442 / 0.037052 (0.027390) \| 0.397539 / 0.258489 (0.139050) \| 0.441323 / 0.293841 (0.147482) \| 0.036278 / 0.128546 (-0.092268) \| 0.009810 / 0.075646 (-0.065836) \| 0.343537 / 0.419271 (-0.075734) \| 0.060273 / 0.043533 (0.016740) \| 0.395023 / 0.255139 (0.139884) \| 0.427210 / 0.283200 (0.144011) \| 0.031717 / 0.141683 (-0.109966) \| 1.771221 / 1.452155 (0.319066) \| 1.896336 / 1.492716 (0.403620) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.235081 / 0.018006 (0.217075) \| 0.512781 / 0.000490 (0.512292) \| 0.004920 / 0.000200 (0.004721) \| 0.000097 / 0.000054 (0.000042) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033525 / 0.037411 (-0.003887) \| 0.104416 / 0.014526 (0.089890) \| 0.115695 / 0.176557 (-0.060861) \| 0.182216 / 0.737135 (-0.554919) \| 0.116259 / 0.296338 (-0.180079) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.454817 / 0.215209 (0.239608) \| 4.527753 / 2.077655 (2.450098) \| 2.222273 / 1.504120 (0.718153) \| 2.038448 / 1.541195 (0.497253) \| 2.179444 / 1.468490 (0.710953) \| 0.573665 / 4.584777 (-4.011112) \| 4.504943 / 3.745712 (0.759231) \| 3.848435 / 5.269862 (-1.421427) \| 2.455185 / 4.565676 (-2.110491) \| 0.067985 / 0.424275 (-0.356290) \| 0.008719 / 0.007607 (0.001112) \| 0.552405 / 0.226044 (0.326360) \| 5.515251 / 2.268929 (3.246322) \| 2.851557 / 55.444624 (-52.593067) \| 2.463070 / 6.876477 (-4.413407) \| 2.761596 / 2.142072 (0.619524) \| 0.688561 / 4.805227 (-4.116667) \| 0.159946 / 6.500664 (-6.340718) \| 0.075435 / 0.075469 (-0.000034) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.505178 / 1.841788 (-0.336610) \| 23.555236 / 8.074308 (15.480928) \| 17.272759 / 10.191392 (7.081367) \| 0.206495 / 0.680424 (-0.473928) \| 0.021869 / 0.534201 (-0.512332) \| 0.469271 / 0.579283 (-0.110012) \| 0.469200 / 0.434364 (0.034837) \| 0.542437 / 0.540337 (0.002100) \| 0.792864 / 1.386936 (-0.594072) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008151 / 0.011353 (-0.003202) \| 0.004992 / 0.011008 (-0.006016) \| 0.079545 / 0.038508 (0.041037) \| 0.100234 / 0.023109 (0.077125) \| 0.492791 / 0.275898 (0.216893) \| 0.511315 / 0.323480 (0.187835) \| 0.006878 / 0.007986 (-0.001108) \| 0.003807 / 0.004328 (-0.000522) \| 0.080876 / 0.004250 (0.076625) \| 0.076734 / 0.037052 (0.039681) \| 0.518247 / 0.258489 (0.259758) \| 0.524202 / 0.293841 (0.230361) \| 0.039896 / 0.128546 (-0.088650) \| 0.016581 / 0.075646 (-0.059065) \| 0.101228 / 0.419271 (-0.318043) \| 0.061990 / 0.043533 (0.018457) \| 0.490611 / 0.255139 (0.235472) \| 0.514930 / 0.283200 (0.231730) \| 0.028680 / 0.141683 (-0.113002) \| 1.966215 / 1.452155 (0.514061) \| 2.047757 / 1.492716 (0.555040) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286807 / 0.018006 (0.268801) \| 0.506448 / 0.000490 (0.505959) \| 0.005867 / 0.000200 (0.005667) \| 0.000110 / 0.000054 (0.000056) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037141 / 0.037411 (-0.000270) \| 0.113232 / 0.014526 (0.098706) \| 0.121201 / 0.176557 (-0.055356) \| 0.185472 / 0.737135 (-0.551663) \| 0.122896 / 0.296338 (-0.173442) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.514491 / 0.215209 (0.299282) \| 4.942457 / 2.077655 (2.864802) \| 2.533519 / 1.504120 (1.029399) \| 2.371011 / 1.541195 (0.829817) \| 2.495604 / 1.468490 (1.027114) \| 0.576224 / 4.584777 (-4.008553) \| 4.368584 / 3.745712 (0.622872) \| 3.885598 / 5.269862 (-1.384263) \| 2.443596 / 4.565676 (-2.122080) \| 0.068905 / 0.424275 (-0.355371) \| 0.009171 / 0.007607 (0.001564) \| 0.584977 / 0.226044 (0.358932) \| 5.835220 / 2.268929 (3.566291) \| 3.189037 / 55.444624 (-52.255588) \| 2.753228 / 6.876477 (-4.123249) \| 3.009062 / 2.142072 (0.866990) \| 0.690179 / 4.805227 (-4.115048) \| 0.157981 / 6.500664 (-6.342683) \| 0.074518 / 0.075469 (-0.000951) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.599907 / 1.841788 (-0.241880) \| 23.853903 / 8.074308 (15.779595) \| 17.419796 / 10.191392 (7.228404) \| 0.204974 / 0.680424 (-0.475450) \| 0.022014 / 0.534201 (-0.512187) \| 0.473379 / 0.579283 (-0.105905) \| 0.461346 / 0.434364 (0.026982) \| 0.564881 / 0.540337 (0.024543) \| 0.752933 / 1.386936 (-0.634003) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f49c9ca993fa600fae0e327636d52657328e7ffb \"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) \| 0.006547 / 0.011353 (-0.004805) \| 0.004020 / 0.011008 (-0.006988) \| 0.086828 / 0.038508 (0.048320) \| 0.072924 / 0.023109 (0.049815) \| 0.312847 / 0.275898 (0.036949) \| 0.344605 / 0.323480 (0.021125) \| 0.004117 / 0.007986 (-0.003868) \| 0.004365 / 0.004328 (0.000037) \| 0.066755 / 0.004250 (0.062505) \| 0.053248 / 0.037052 (0.016195) \| 0.315744 / 0.258489 (0.057255) \| 0.362426 / 0.293841 (0.068585) \| 0.030732 / 0.128546 (-0.097814) \| 0.008516 / 0.075646 (-0.067130) \| 0.289927 / 0.419271 (-0.129345) \| 0.052115 / 0.043533 (0.008582) \| 0.308026 / 0.255139 (0.052887) \| 0.343115 / 0.283200 (0.059915) \| 0.024131 / 0.141683 (-0.117551) \| 1.464290 / 1.452155 (0.012135) \| 1.559359 / 1.492716 (0.066642) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.216744 / 0.018006 (0.198738) \| 0.473156 / 0.000490 (0.472666) \| 0.004176 / 0.000200 (0.003977) \| 0.000093 / 0.000054 (0.000039) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028500 / 0.037411 (-0.008911) \| 0.083892 / 0.014526 (0.069366) \| 0.131851 / 0.176557 (-0.044705) \| 0.162202 / 0.737135 (-0.574933) \| 0.127989 / 0.296338 (-0.168349) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.404555 / 0.215209 (0.189346) \| 4.035989 / 2.077655 (1.958334) \| 2.025174 / 1.504120 (0.521054) \| 1.835785 / 1.541195 (0.294590) \| 1.909819 / 1.468490 (0.441329) \| 0.475352 / 4.584777 (-4.109425) \| 3.548055 / 3.745712 (-0.197657) \| 3.234782 / 5.269862 (-2.035080) \| 2.010305 / 4.565676 (-2.555371) \| 0.056507 / 0.424275 (-0.367768) \| 0.007259 / 0.007607 (-0.000348) \| 0.482021 / 0.226044 (0.255977) \| 4.818559 / 2.268929 (2.549631) \| 2.528765 / 55.444624 (-52.915860) \| 2.159804 / 6.876477 (-4.716673) \| 2.380640 / 2.142072 (0.238567) \| 0.585005 / 4.805227 (-4.220222) \| 0.133811 / 6.500664 (-6.366853) \| 0.060686 / 0.075469 (-0.014783) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.260902 / 1.841788 (-0.580886) \| 19.500215 / 8.074308 (11.425907) \| 14.164698 / 10.191392 (3.973306) \| 0.172492 / 0.680424 (-0.507932) \| 0.018221 / 0.534201 (-0.515980) \| 0.392609 / 0.579283 (-0.186674) \| 0.423265 / 0.434364 (-0.011099) \| 0.454705 / 0.540337 (-0.085633) \| 0.639856 / 1.386936 (-0.747080) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006656 / 0.011353 (-0.004697) \| 0.003903 / 0.011008 (-0.007106) \| 0.063780 / 0.038508 (0.025272) \| 0.076848 / 0.023109 (0.053739) \| 0.379429 / 0.275898 (0.103531) \| 0.442554 / 0.323480 (0.119074) \| 0.005327 / 0.007986 (-0.002658) \| 0.003318 / 0.004328 (-0.001010) \| 0.064307 / 0.004250 (0.060056) \| 0.057183 / 0.037052 (0.020131) \| 0.398163 / 0.258489 (0.139674) \| 0.448532 / 0.293841 (0.154691) \| 0.031322 / 0.128546 (-0.097224) \| 0.008462 / 0.075646 (-0.067184) \| 0.070354 / 0.419271 (-0.348917) \| 0.048420 / 0.043533 (0.004887) \| 0.368304 / 0.255139 (0.113165) \| 0.428786 / 0.283200 (0.145587) \| 0.023921 / 0.141683 (-0.117762) \| 1.499281 / 1.452155 (0.047126) \| 1.554448 / 1.492716 (0.061731) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.238830 / 0.018006 (0.220824) \| 0.464196 / 0.000490 (0.463706) \| 0.004812 / 0.000200 (0.004613) \| 0.000098 / 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) \| 0.031642 / 0.037411 (-0.005770) \| 0.089205 / 0.014526 (0.074679) \| 0.101577 / 0.176557 (-0.074980) \| 0.154993 / 0.737135 (-0.582142) \| 0.102935 / 0.296338 (-0.193403) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.415218 / 0.215209 (0.200009) \| 4.137711 / 2.077655 (2.060056) \| 2.128757 / 1.504120 (0.624637) \| 1.961086 / 1.541195 (0.419891) \| 2.047552 / 1.468490 (0.579061) \| 0.486953 / 4.584777 (-4.097824) \| 3.587851 / 3.745712 (-0.157861) \| 3.280771 / 5.269862 (-1.989090) \| 2.016980 / 4.565676 (-2.548697) \| 0.057284 / 0.424275 (-0.366991) \| 0.007705 / 0.007607 (0.000097) \| 0.492242 / 0.226044 (0.266197) \| 4.923213 / 2.268929 (2.654285) \| 2.672528 / 55.444624 (-52.772097) \| 2.292862 / 6.876477 (-4.583614) \| 2.517410 / 2.142072 (0.375337) \| 0.614798 / 4.805227 (-4.190429) \| 0.149642 / 6.500664 (-6.351023) \| 0.062898 / 0.075469 (-0.012571) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.323266 / 1.841788 (-0.518522) \| 19.891504 / 8.074308 (11.817196) \| 14.115069 / 10.191392 (3.923677) \| 0.169859 / 0.680424 (-0.510564) \| 0.018538 / 0.534201 (-0.515663) \| 0.398456 / 0.579283 (-0.180827) \| 0.410111 / 0.434364 (-0.024253) \| 0.483198 / 0.540337 (-0.057139) \| 0.639283 / 1.386936 (-0.747653) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#01e2194f2aab6aa98686a2069ee5201b69a53c14 \"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) \| 0.007731 / 0.011353 (-0.003622) \| 0.004064 / 0.011008 (-0.006944) \| 0.095261 / 0.038508 (0.056753) \| 0.081594 / 0.023109 (0.058485) \| 0.390413 / 0.275898 (0.114515) \| 0.415542 / 0.323480 (0.092063) \| 0.006031 / 0.007986 (-0.001954) \| 0.003817 / 0.004328 (-0.000512) \| 0.066381 / 0.004250 (0.062131) \| 0.058262 / 0.037052 (0.021210) \| 0.383626 / 0.258489 (0.125137) \| 0.443237 / 0.293841 (0.149396) \| 0.034358 / 0.128546 (-0.094188) \| 0.010002 / 0.075646 (-0.065644) \| 0.317472 / 0.419271 (-0.101800) \| 0.057428 / 0.043533 (0.013895) \| 0.393929 / 0.255139 (0.138790) \| 0.444572 / 0.283200 (0.161373) \| 0.026295 / 0.141683 (-0.115388) \| 1.603639 / 1.452155 (0.151484) \| 1.707750 / 1.492716 (0.215034) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.222171 / 0.018006 (0.204165) \| 0.491762 / 0.000490 (0.491272) \| 0.003389 / 0.000200 (0.003189) \| 0.000090 / 0.000054 (0.000036) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029420 / 0.037411 (-0.007991) \| 0.086201 / 0.014526 (0.071676) \| 0.100150 / 0.176557 (-0.076406) \| 0.162338 / 0.737135 (-0.574797) \| 0.099349 / 0.296338 (-0.196989) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.445976 / 0.215209 (0.230767) \| 4.460197 / 2.077655 (2.382542) \| 2.211767 / 1.504120 (0.707647) \| 1.988740 / 1.541195 (0.447545) \| 2.052289 / 1.468490 (0.583799) \| 0.570321 / 4.584777 (-4.014456) \| 4.148777 / 3.745712 (0.403065) \| 3.750977 / 5.269862 (-1.518885) \| 2.309443 / 4.565676 (-2.256234) \| 0.064552 / 0.424275 (-0.359724) \| 0.008167 / 0.007607 (0.000560) \| 0.523283 / 0.226044 (0.297238) \| 5.349347 / 2.268929 (3.080419) \| 2.710292 / 55.444624 (-52.734332) \| 2.344252 / 6.876477 (-4.532225) \| 2.549903 / 2.142072 (0.407831) \| 0.665942 / 4.805227 (-4.139285) \| 0.154108 / 6.500664 (-6.346556) \| 0.070181 / 0.075469 (-0.005289) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.455733 / 1.841788 (-0.386054) \| 21.846958 / 8.074308 (13.772650) \| 15.133865 / 10.191392 (4.942473) \| 0.199009 / 0.680424 (-0.481415) \| 0.021299 / 0.534201 (-0.512902) \| 0.421555 / 0.579283 (-0.157729) \| 0.437639 / 0.434364 (0.003275) \| 0.498568 / 0.540337 (-0.041769) \| 0.719649 / 1.386936 (-0.667287) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007858 / 0.011353 (-0.003495) \| 0.004629 / 0.011008 (-0.006380) \| 0.075701 / 0.038508 (0.037193) \| 0.084425 / 0.023109 (0.061316) \| 0.436650 / 0.275898 (0.160752) \| 0.466046 / 0.323480 (0.142566) \| 0.006042 / 0.007986 (-0.001944) \| 0.003834 / 0.004328 (-0.000495) \| 0.074729 / 0.004250 (0.070478) \| 0.065983 / 0.037052 (0.028931) \| 0.447239 / 0.258489 (0.188750) \| 0.466728 / 0.293841 (0.172887) \| 0.035814 / 0.128546 (-0.092733) \| 0.009919 / 0.075646 (-0.065727) \| 0.081151 / 0.419271 (-0.338120) \| 0.057256 / 0.043533 (0.013723) \| 0.435609 / 0.255139 (0.180470) \| 0.448901 / 0.283200 (0.165701) \| 0.026325 / 0.141683 (-0.115357) \| 1.745658 / 1.452155 (0.293503) \| 1.804137 / 1.492716 (0.311421) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302551 / 0.018006 (0.284544) \| 0.498438 / 0.000490 (0.497948) \| 0.038562 / 0.000200 (0.038362) \| 0.000411 / 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) \| 0.035573 / 0.037411 (-0.001839) \| 0.104957 / 0.014526 (0.090431) \| 0.117208 / 0.176557 (-0.059349) \| 0.178935 / 0.737135 (-0.558200) \| 0.124577 / 0.296338 (-0.171761) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.467076 / 0.215209 (0.251867) \| 4.698852 / 2.077655 (2.621197) \| 2.453389 / 1.504120 (0.949269) \| 2.257378 / 1.541195 (0.716183) \| 2.338615 / 1.468490 (0.870125) \| 0.542379 / 4.584777 (-4.042398) \| 4.066895 / 3.745712 (0.321183) \| 3.689540 / 5.269862 (-1.580321) \| 2.268997 / 4.565676 (-2.296679) \| 0.064754 / 0.424275 (-0.359521) \| 0.008866 / 0.007607 (0.001259) \| 0.546732 / 0.226044 (0.320687) \| 5.487765 / 2.268929 (3.218836) \| 2.974126 / 55.444624 (-52.470498) \| 2.585492 / 6.876477 (-4.290985) \| 2.754417 / 2.142072 (0.612345) \| 0.652045 / 4.805227 (-4.153183) \| 0.145597 / 6.500664 (-6.355067) \| 0.065415 / 0.075469 (-0.010054) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.553970 / 1.841788 (-0.287818) \| 22.300954 / 8.074308 (14.226646) \| 15.640990 / 10.191392 (5.449598) \| 0.170903 / 0.680424 (-0.509521) \| 0.021750 / 0.534201 (-0.512451) \| 0.455316 / 0.579283 (-0.123967) \| 0.455051 / 0.434364 (0.020687) \| 0.536174 / 0.540337 (-0.004164) \| 0.735930 / 1.386936 (-0.651006) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f68139846c26b43631bd235114854f4bf6cb9954 \"CML watermark\")\n" ]
1,828,959,107	6,104	HF Datasets data access is extremely slow even when in memory	open	2023-07-31T11:12:19	2023-08-01T11:22:43	null	https://github.com/huggingface/datasets/issues/6104	null	NightMachinery	false	[ "Possibly related:\r\n- https://github.com/pytorch/pytorch/issues/22462" ]
1,828,515,165	6,103	Set dev version	closed	2023-07-31T06:44:05	2023-07-31T06:55:58	2023-07-31T06:45:41	https://github.com/huggingface/datasets/pull/6103	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6103", "html_url": "https://github.com/huggingface/datasets/pull/6103", "diff_url": "https://github.com/huggingface/datasets/pull/6103.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6103.patch", "merged_at": "2023-07-31T06:45:41" }	albertvillanova	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6103). 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) \| 0.006528 / 0.011353 (-0.004825) \| 0.003909 / 0.011008 (-0.007099) \| 0.083954 / 0.038508 (0.045446) \| 0.070513 / 0.023109 (0.047404) \| 0.344362 / 0.275898 (0.068464) \| 0.370278 / 0.323480 (0.046798) \| 0.005395 / 0.007986 (-0.002591) \| 0.003323 / 0.004328 (-0.001005) \| 0.064538 / 0.004250 (0.060288) \| 0.055616 / 0.037052 (0.018564) \| 0.353590 / 0.258489 (0.095101) \| 0.382159 / 0.293841 (0.088318) \| 0.031133 / 0.128546 (-0.097414) \| 0.008429 / 0.075646 (-0.067217) \| 0.288665 / 0.419271 (-0.130606) \| 0.052626 / 0.043533 (0.009093) \| 0.347676 / 0.255139 (0.092537) \| 0.363726 / 0.283200 (0.080526) \| 0.021956 / 0.141683 (-0.119727) \| 1.506091 / 1.452155 (0.053936) \| 1.563940 / 1.492716 (0.071223) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.207658 / 0.018006 (0.189652) \| 0.473411 / 0.000490 (0.472922) \| 0.005437 / 0.000200 (0.005237) \| 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) \| 0.027769 / 0.037411 (-0.009643) \| 0.082566 / 0.014526 (0.068040) \| 0.092700 / 0.176557 (-0.083857) \| 0.152589 / 0.737135 (-0.584546) \| 0.093772 / 0.296338 (-0.202566) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.401072 / 0.215209 (0.185863) \| 3.997922 / 2.077655 (1.920267) \| 2.028223 / 1.504120 (0.524103) \| 1.845229 / 1.541195 (0.304035) \| 1.883980 / 1.468490 (0.415489) \| 0.485112 / 4.584777 (-4.099665) \| 3.657048 / 3.745712 (-0.088664) \| 4.998475 / 5.269862 (-0.271386) \| 3.007417 / 4.565676 (-1.558259) \| 0.057003 / 0.424275 (-0.367272) \| 0.007270 / 0.007607 (-0.000338) \| 0.482220 / 0.226044 (0.256176) \| 4.817560 / 2.268929 (2.548631) \| 2.484285 / 55.444624 (-52.960340) \| 2.163327 / 6.876477 (-4.713149) \| 2.326412 / 2.142072 (0.184339) \| 0.600349 / 4.805227 (-4.204878) \| 0.134245 / 6.500664 (-6.366419) \| 0.060705 / 0.075469 (-0.014764) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.281440 / 1.841788 (-0.560347) \| 19.165591 / 8.074308 (11.091283) \| 14.007728 / 10.191392 (3.816336) \| 0.168367 / 0.680424 (-0.512057) \| 0.018149 / 0.534201 (-0.516052) \| 0.391688 / 0.579283 (-0.187595) \| 0.414528 / 0.434364 (-0.019836) \| 0.456964 / 0.540337 (-0.083373) \| 0.613807 / 1.386936 (-0.773129) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006502 / 0.011353 (-0.004851) \| 0.003956 / 0.011008 (-0.007052) \| 0.064297 / 0.038508 (0.025789) \| 0.073430 / 0.023109 (0.050321) \| 0.364113 / 0.275898 (0.088215) \| 0.389021 / 0.323480 (0.065541) \| 0.005375 / 0.007986 (-0.002611) \| 0.003363 / 0.004328 (-0.000966) \| 0.064404 / 0.004250 (0.060153) \| 0.056664 / 0.037052 (0.019612) \| 0.365504 / 0.258489 (0.107015) \| 0.398477 / 0.293841 (0.104636) \| 0.031739 / 0.128546 (-0.096807) \| 0.008663 / 0.075646 (-0.066984) \| 0.070757 / 0.419271 (-0.348515) \| 0.051014 / 0.043533 (0.007481) \| 0.368287 / 0.255139 (0.113148) \| 0.382941 / 0.283200 (0.099742) \| 0.024642 / 0.141683 (-0.117041) \| 1.516721 / 1.452155 (0.064567) \| 1.557625 / 1.492716 (0.064908) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.208248 / 0.018006 (0.190242) \| 0.443560 / 0.000490 (0.443070) \| 0.004004 / 0.000200 (0.003805) \| 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) \| 0.031116 / 0.037411 (-0.006295) \| 0.086814 / 0.014526 (0.072288) \| 0.099111 / 0.176557 (-0.077445) \| 0.155032 / 0.737135 (-0.582104) \| 0.098938 / 0.296338 (-0.197401) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.413080 / 0.215209 (0.197871) \| 4.115546 / 2.077655 (2.037891) \| 2.162073 / 1.504120 (0.657953) \| 2.008107 / 1.541195 (0.466912) \| 2.052317 / 1.468490 (0.583827) \| 0.485158 / 4.584777 (-4.099619) \| 3.617478 / 3.745712 (-0.128234) \| 5.030564 / 5.269862 (-0.239298) \| 2.787812 / 4.565676 (-1.777865) \| 0.057466 / 0.424275 (-0.366809) \| 0.007656 / 0.007607 (0.000049) \| 0.490037 / 0.226044 (0.263993) \| 4.887896 / 2.268929 (2.618968) \| 2.639644 / 55.444624 (-52.804981) \| 2.258051 / 6.876477 (-4.618426) \| 2.417573 / 2.142072 (0.275500) \| 0.604473 / 4.805227 (-4.200754) \| 0.134770 / 6.500664 (-6.365894) \| 0.061709 / 0.075469 (-0.013760) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.342500 / 1.841788 (-0.499288) \| 19.354990 / 8.074308 (11.280682) \| 14.161975 / 10.191392 (3.970583) \| 0.157084 / 0.680424 (-0.523339) \| 0.018227 / 0.534201 (-0.515974) \| 0.391819 / 0.579283 (-0.187464) \| 0.399157 / 0.434364 (-0.035207) \| 0.460582 / 0.540337 (-0.079756) \| 0.612183 / 1.386936 (-0.774753) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b20f6a82410dd47e89585bb932616a22e0eaf2e6 \"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) \| 0.009318 / 0.011353 (-0.002035) \| 0.005515 / 0.011008 (-0.005493) \| 0.108532 / 0.038508 (0.070024) \| 0.103583 / 0.023109 (0.080473) \| 0.419249 / 0.275898 (0.143351) \| 0.453573 / 0.323480 (0.130093) \| 0.006601 / 0.007986 (-0.001384) \| 0.005297 / 0.004328 (0.000968) \| 0.082737 / 0.004250 (0.078487) \| 0.064708 / 0.037052 (0.027656) \| 0.425679 / 0.258489 (0.167190) \| 0.462028 / 0.293841 (0.168187) \| 0.048104 / 0.128546 (-0.080442) \| 0.014069 / 0.075646 (-0.061577) \| 0.377780 / 0.419271 (-0.041491) \| 0.067510 / 0.043533 (0.023977) \| 0.422421 / 0.255139 (0.167282) \| 0.447127 / 0.283200 (0.163927) \| 0.037745 / 0.141683 (-0.103938) \| 1.855306 / 1.452155 (0.403152) \| 1.943876 / 1.492716 (0.451160) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280161 / 0.018006 (0.262155) \| 0.598001 / 0.000490 (0.597512) \| 0.001130 / 0.000200 (0.000930) \| 0.000091 / 0.000054 (0.000036) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036064 / 0.037411 (-0.001347) \| 0.113256 / 0.014526 (0.098730) \| 0.120598 / 0.176557 (-0.055959) \| 0.191386 / 0.737135 (-0.545750) \| 0.118125 / 0.296338 (-0.178214) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.616887 / 0.215209 (0.401678) \| 6.085498 / 2.077655 (4.007844) \| 2.639428 / 1.504120 (1.135308) \| 2.215444 / 1.541195 (0.674249) \| 2.311990 / 1.468490 (0.843500) \| 0.820539 / 4.584777 (-3.764238) \| 5.306010 / 3.745712 (1.560298) \| 4.731726 / 5.269862 (-0.538136) \| 3.053933 / 4.565676 (-1.511744) \| 0.098862 / 0.424275 (-0.325413) \| 0.009456 / 0.007607 (0.001849) \| 0.725455 / 0.226044 (0.499411) \| 7.367385 / 2.268929 (5.098457) \| 3.464921 / 55.444624 (-51.979703) \| 2.833868 / 6.876477 (-4.042608) \| 3.033008 / 2.142072 (0.890935) \| 1.036751 / 4.805227 (-3.768476) \| 0.243646 / 6.500664 (-6.257018) \| 0.081079 / 0.075469 (0.005610) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.584695 / 1.841788 (-0.257093) \| 25.150355 / 8.074308 (17.076047) \| 21.826622 / 10.191392 (11.635230) \| 0.212502 / 0.680424 (-0.467921) \| 0.029865 / 0.534201 (-0.504335) \| 0.496814 / 0.579283 (-0.082470) \| 0.611959 / 0.434364 (0.177595) \| 0.550434 / 0.540337 (0.010097) \| 0.800897 / 1.386936 (-0.586039) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009117 / 0.011353 (-0.002236) \| 0.005236 / 0.011008 (-0.005772) \| 0.082402 / 0.038508 (0.043894) \| 0.090578 / 0.023109 (0.067468) \| 0.487302 / 0.275898 (0.211404) \| 0.523639 / 0.323480 (0.200159) \| 0.006684 / 0.007986 (-0.001302) \| 0.004306 / 0.004328 (-0.000023) \| 0.083273 / 0.004250 (0.079023) \| 0.068585 / 0.037052 (0.031532) \| 0.487751 / 0.258489 (0.229262) \| 0.538972 / 0.293841 (0.245131) \| 0.048915 / 0.128546 (-0.079632) \| 0.014312 / 0.075646 (-0.061335) \| 0.091863 / 0.419271 (-0.327409) \| 0.066114 / 0.043533 (0.022581) \| 0.483552 / 0.255139 (0.228413) \| 0.522250 / 0.283200 (0.239050) \| 0.038533 / 0.141683 (-0.103150) \| 1.803834 / 1.452155 (0.351680) \| 1.891927 / 1.492716 (0.399211) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.336662 / 0.018006 (0.318656) \| 0.611408 / 0.000490 (0.610918) \| 0.014310 / 0.000200 (0.014110) \| 0.000152 / 0.000054 (0.000097) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034755 / 0.037411 (-0.002656) \| 0.101008 / 0.014526 (0.086483) \| 0.124530 / 0.176557 (-0.052026) \| 0.179844 / 0.737135 (-0.557292) \| 0.125027 / 0.296338 (-0.171312) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.618341 / 0.215209 (0.403132) \| 6.146848 / 2.077655 (4.069193) \| 2.893305 / 1.504120 (1.389185) \| 2.608722 / 1.541195 (1.067528) \| 2.671276 / 1.468490 (1.202786) \| 0.860096 / 4.584777 (-3.724681) \| 5.440671 / 3.745712 (1.694959) \| 4.776958 / 5.269862 (-0.492903) \| 3.098300 / 4.565676 (-1.467376) \| 0.098664 / 0.424275 (-0.325611) \| 0.009270 / 0.007607 (0.001663) \| 0.712780 / 0.226044 (0.486735) \| 7.199721 / 2.268929 (4.930793) \| 3.620723 / 55.444624 (-51.823902) \| 3.052218 / 6.876477 (-3.824259) \| 3.321093 / 2.142072 (1.179021) \| 1.070992 / 4.805227 (-3.734235) \| 0.224091 / 6.500664 (-6.276573) \| 0.083395 / 0.075469 (0.007926) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.716867 / 1.841788 (-0.124921) \| 25.534617 / 8.074308 (17.460309) \| 25.221014 / 10.191392 (15.029621) \| 0.248098 / 0.680424 (-0.432326) \| 0.029659 / 0.534201 (-0.504542) \| 0.492929 / 0.579283 (-0.086355) \| 0.618253 / 0.434364 (0.183889) \| 0.577108 / 0.540337 (0.036771) \| 0.803188 / 1.386936 (-0.583748) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#584db360eed9155e173b199ba5fc037562b7b862 \"CML watermark\")\n" ]
1,828,494,896	6,102	Release 2.14.2	closed	2023-07-31T06:27:47	2023-07-31T06:48:09	2023-07-31T06:32:58	https://github.com/huggingface/datasets/pull/6102	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6102", "html_url": "https://github.com/huggingface/datasets/pull/6102", "diff_url": "https://github.com/huggingface/datasets/pull/6102.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6102.patch", "merged_at": "2023-07-31T06:32:58" }	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) \| 0.006517 / 0.011353 (-0.004836) \| 0.004217 / 0.011008 (-0.006792) \| 0.083162 / 0.038508 (0.044654) \| 0.074476 / 0.023109 (0.051367) \| 0.321193 / 0.275898 (0.045295) \| 0.358348 / 0.323480 (0.034868) \| 0.005531 / 0.007986 (-0.002455) \| 0.003621 / 0.004328 (-0.000707) \| 0.063819 / 0.004250 (0.059568) \| 0.056524 / 0.037052 (0.019471) \| 0.322145 / 0.258489 (0.063656) \| 0.371415 / 0.293841 (0.077574) \| 0.030612 / 0.128546 (-0.097934) \| 0.008907 / 0.075646 (-0.066739) \| 0.289451 / 0.419271 (-0.129821) \| 0.051959 / 0.043533 (0.008426) \| 0.317729 / 0.255139 (0.062590) \| 0.339750 / 0.283200 (0.056550) \| 0.022430 / 0.141683 (-0.119253) \| 1.487661 / 1.452155 (0.035506) \| 1.554916 / 1.492716 (0.062199) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296673 / 0.018006 (0.278667) \| 0.599183 / 0.000490 (0.598694) \| 0.002524 / 0.000200 (0.002324) \| 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) \| 0.027898 / 0.037411 (-0.009514) \| 0.080870 / 0.014526 (0.066344) \| 0.094894 / 0.176557 (-0.081662) \| 0.152350 / 0.737135 (-0.584785) \| 0.095765 / 0.296338 (-0.200573) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.415442 / 0.215209 (0.200233) \| 4.161155 / 2.077655 (2.083500) \| 2.117061 / 1.504120 (0.612941) \| 1.937846 / 1.541195 (0.396651) \| 1.979635 / 1.468490 (0.511145) \| 0.488381 / 4.584777 (-4.096396) \| 3.509836 / 3.745712 (-0.235876) \| 3.833074 / 5.269862 (-1.436788) \| 2.307536 / 4.565676 (-2.258141) \| 0.057059 / 0.424275 (-0.367216) \| 0.007366 / 0.007607 (-0.000241) \| 0.487752 / 0.226044 (0.261708) \| 4.869406 / 2.268929 (2.600478) \| 2.594775 / 55.444624 (-52.849849) \| 2.191712 / 6.876477 (-4.684765) \| 2.413220 / 2.142072 (0.271147) \| 0.584513 / 4.805227 (-4.220714) \| 0.132162 / 6.500664 (-6.368502) \| 0.061059 / 0.075469 (-0.014410) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.245178 / 1.841788 (-0.596610) \| 20.624563 / 8.074308 (12.550255) \| 14.675545 / 10.191392 (4.484153) \| 0.165838 / 0.680424 (-0.514586) \| 0.018700 / 0.534201 (-0.515501) \| 0.392475 / 0.579283 (-0.186808) \| 0.399884 / 0.434364 (-0.034480) \| 0.457478 / 0.540337 (-0.082859) \| 0.624553 / 1.386936 (-0.762383) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006716 / 0.011353 (-0.004637) \| 0.004308 / 0.011008 (-0.006700) \| 0.064495 / 0.038508 (0.025987) \| 0.083194 / 0.023109 (0.060085) \| 0.371994 / 0.275898 (0.096096) \| 0.433045 / 0.323480 (0.109566) \| 0.005535 / 0.007986 (-0.002450) \| 0.003469 / 0.004328 (-0.000859) \| 0.064342 / 0.004250 (0.060092) \| 0.059362 / 0.037052 (0.022309) \| 0.393819 / 0.258489 (0.135330) \| 0.442591 / 0.293841 (0.148750) \| 0.031594 / 0.128546 (-0.096952) \| 0.008943 / 0.075646 (-0.066703) \| 0.070689 / 0.419271 (-0.348582) \| 0.049219 / 0.043533 (0.005686) \| 0.361568 / 0.255139 (0.106429) \| 0.417085 / 0.283200 (0.133886) \| 0.025112 / 0.141683 (-0.116571) \| 1.497204 / 1.452155 (0.045049) \| 1.552781 / 1.492716 (0.060064) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.325254 / 0.018006 (0.307248) \| 0.528399 / 0.000490 (0.527909) \| 0.007429 / 0.000200 (0.007229) \| 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) \| 0.029908 / 0.037411 (-0.007504) \| 0.087114 / 0.014526 (0.072588) \| 0.103366 / 0.176557 (-0.073191) \| 0.155145 / 0.737135 (-0.581990) \| 0.103458 / 0.296338 (-0.192880) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.409432 / 0.215209 (0.194223) \| 4.093327 / 2.077655 (2.015673) \| 2.154115 / 1.504120 (0.649995) \| 1.953492 / 1.541195 (0.412297) \| 2.021532 / 1.468490 (0.553042) \| 0.478928 / 4.584777 (-4.105849) \| 3.515287 / 3.745712 (-0.230426) \| 4.976239 / 5.269862 (-0.293623) \| 2.832803 / 4.565676 (-1.732873) \| 0.057239 / 0.424275 (-0.367036) \| 0.007718 / 0.007607 (0.000111) \| 0.484102 / 0.226044 (0.258057) \| 4.833020 / 2.268929 (2.564092) \| 2.564550 / 55.444624 (-52.880074) \| 2.268969 / 6.876477 (-4.607508) \| 2.513308 / 2.142072 (0.371235) \| 0.582822 / 4.805227 (-4.222406) \| 0.133989 / 6.500664 (-6.366675) \| 0.062078 / 0.075469 (-0.013391) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.393766 / 1.841788 (-0.448021) \| 20.224546 / 8.074308 (12.150238) \| 14.359438 / 10.191392 (4.168046) \| 0.166358 / 0.680424 (-0.514066) \| 0.018840 / 0.534201 (-0.515361) \| 0.393206 / 0.579283 (-0.186077) \| 0.404220 / 0.434364 (-0.030144) \| 0.462346 / 0.540337 (-0.077992) \| 0.603078 / 1.386936 (-0.783858) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53e8007baeff133aaad8cbb366196be18a5e57fd \"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) \| 0.006835 / 0.011353 (-0.004518) \| 0.004530 / 0.011008 (-0.006478) \| 0.087506 / 0.038508 (0.048997) \| 0.088289 / 0.023109 (0.065180) \| 0.351575 / 0.275898 (0.075677) \| 0.391873 / 0.323480 (0.068393) \| 0.005627 / 0.007986 (-0.002359) \| 0.003735 / 0.004328 (-0.000594) \| 0.065747 / 0.004250 (0.061497) \| 0.058779 / 0.037052 (0.021726) \| 0.358076 / 0.258489 (0.099587) \| 0.408466 / 0.293841 (0.114626) \| 0.031369 / 0.128546 (-0.097178) \| 0.008807 / 0.075646 (-0.066839) \| 0.293253 / 0.419271 (-0.126019) \| 0.052950 / 0.043533 (0.009417) \| 0.350411 / 0.255139 (0.095272) \| 0.384827 / 0.283200 (0.101627) \| 0.026219 / 0.141683 (-0.115464) \| 1.464290 / 1.452155 (0.012136) \| 1.549688 / 1.492716 (0.056972) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.270354 / 0.018006 (0.252348) \| 0.593436 / 0.000490 (0.592946) \| 0.003872 / 0.000200 (0.003673) \| 0.000091 / 0.000054 (0.000036) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031625 / 0.037411 (-0.005787) \| 0.092599 / 0.014526 (0.078073) \| 0.104619 / 0.176557 (-0.071938) \| 0.163183 / 0.737135 (-0.573952) \| 0.103245 / 0.296338 (-0.193094) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.390213 / 0.215209 (0.175004) \| 3.894519 / 2.077655 (1.816864) \| 1.905739 / 1.504120 (0.401619) \| 1.728873 / 1.541195 (0.187678) \| 1.838692 / 1.468490 (0.370202) \| 0.484730 / 4.584777 (-4.100047) \| 3.706749 / 3.745712 (-0.038963) \| 5.572311 / 5.269862 (0.302449) \| 3.389949 / 4.565676 (-1.175727) \| 0.057315 / 0.424275 (-0.366960) \| 0.007475 / 0.007607 (-0.000132) \| 0.464690 / 0.226044 (0.238645) \| 4.622242 / 2.268929 (2.353314) \| 2.380957 / 55.444624 (-53.063667) \| 2.038225 / 6.876477 (-4.838251) \| 2.358881 / 2.142072 (0.216809) \| 0.606358 / 4.805227 (-4.198869) \| 0.133584 / 6.500664 (-6.367080) \| 0.061894 / 0.075469 (-0.013575) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.259575 / 1.841788 (-0.582213) \| 20.915216 / 8.074308 (12.840908) \| 14.971952 / 10.191392 (4.780560) \| 0.160206 / 0.680424 (-0.520218) \| 0.018675 / 0.534201 (-0.515526) \| 0.396821 / 0.579283 (-0.182462) \| 0.430982 / 0.434364 (-0.003382) \| 0.452895 / 0.540337 (-0.087443) \| 0.647869 / 1.386936 (-0.739067) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007194 / 0.011353 (-0.004158) \| 0.004340 / 0.011008 (-0.006669) \| 0.065125 / 0.038508 (0.026617) \| 0.096243 / 0.023109 (0.073134) \| 0.374361 / 0.275898 (0.098463) \| 0.411863 / 0.323480 (0.088383) \| 0.005813 / 0.007986 (-0.002172) \| 0.003615 / 0.004328 (-0.000713) \| 0.064953 / 0.004250 (0.060703) \| 0.063171 / 0.037052 (0.026119) \| 0.376238 / 0.258489 (0.117749) \| 0.415826 / 0.293841 (0.121985) \| 0.031926 / 0.128546 (-0.096620) \| 0.008821 / 0.075646 (-0.066825) \| 0.072150 / 0.419271 (-0.347122) \| 0.049484 / 0.043533 (0.005951) \| 0.369691 / 0.255139 (0.114552) \| 0.390669 / 0.283200 (0.107470) \| 0.025732 / 0.141683 (-0.115950) \| 1.493833 / 1.452155 (0.041679) \| 1.601786 / 1.492716 (0.109070) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284279 / 0.018006 (0.266272) \| 0.585909 / 0.000490 (0.585419) \| 0.000411 / 0.000200 (0.000211) \| 0.000057 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033642 / 0.037411 (-0.003769) \| 0.095328 / 0.014526 (0.080802) \| 0.105810 / 0.176557 (-0.070746) \| 0.159779 / 0.737135 (-0.577357) \| 0.108938 / 0.296338 (-0.187400) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.408112 / 0.215209 (0.192902) \| 4.067035 / 2.077655 (1.989380) \| 2.114504 / 1.504120 (0.610384) \| 1.944027 / 1.541195 (0.402832) \| 2.066117 / 1.468490 (0.597627) \| 0.486441 / 4.584777 (-4.098336) \| 3.622659 / 3.745712 (-0.123053) \| 3.399310 / 5.269862 (-1.870552) \| 2.183151 / 4.565676 (-2.382525) \| 0.057490 / 0.424275 (-0.366785) \| 0.007955 / 0.007607 (0.000347) \| 0.490221 / 0.226044 (0.264177) \| 4.887301 / 2.268929 (2.618373) \| 2.679806 / 55.444624 (-52.764819) \| 2.258992 / 6.876477 (-4.617484) \| 2.592493 / 2.142072 (0.450420) \| 0.606515 / 4.805227 (-4.198712) \| 0.135645 / 6.500664 (-6.365019) \| 0.063956 / 0.075469 (-0.011513) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.331304 / 1.841788 (-0.510483) \| 21.458611 / 8.074308 (13.384303) \| 14.898964 / 10.191392 (4.707572) \| 0.172110 / 0.680424 (-0.508314) \| 0.018791 / 0.534201 (-0.515409) \| 0.395944 / 0.579283 (-0.183339) \| 0.424526 / 0.434364 (-0.009838) \| 0.462517 / 0.540337 (-0.077821) \| 0.610139 / 1.386936 (-0.776797) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#09492ba523518289a84175ddb7ab3bc555e742ee \"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) \| 0.005957 / 0.011353 (-0.005396) \| 0.003581 / 0.011008 (-0.007427) \| 0.079624 / 0.038508 (0.041116) \| 0.058004 / 0.023109 (0.034895) \| 0.309345 / 0.275898 (0.033447) \| 0.346653 / 0.323480 (0.023173) \| 0.005420 / 0.007986 (-0.002566) \| 0.002906 / 0.004328 (-0.001423) \| 0.061970 / 0.004250 (0.057720) \| 0.047627 / 0.037052 (0.010575) \| 0.314096 / 0.258489 (0.055607) \| 0.361368 / 0.293841 (0.067527) \| 0.027211 / 0.128546 (-0.101335) \| 0.007853 / 0.075646 (-0.067793) \| 0.260202 / 0.419271 (-0.159070) \| 0.045308 / 0.043533 (0.001775) \| 0.312150 / 0.255139 (0.057011) \| 0.341085 / 0.283200 (0.057886) \| 0.021302 / 0.141683 (-0.120381) \| 1.430315 / 1.452155 (-0.021840) \| 1.608989 / 1.492716 (0.116273) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.185289 / 0.018006 (0.167283) \| 0.423318 / 0.000490 (0.422828) \| 0.005741 / 0.000200 (0.005541) \| 0.000070 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023777 / 0.037411 (-0.013634) \| 0.071937 / 0.014526 (0.057412) \| 0.079406 / 0.176557 (-0.097151) \| 0.143815 / 0.737135 (-0.593320) \| 0.081648 / 0.296338 (-0.214690) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.431514 / 0.215209 (0.216305) \| 4.314471 / 2.077655 (2.236817) \| 2.305167 / 1.504120 (0.801047) \| 2.137894 / 1.541195 (0.596699) \| 2.161034 / 1.468490 (0.692544) \| 0.511701 / 4.584777 (-4.073076) \| 3.098213 / 3.745712 (-0.647499) \| 4.086837 / 5.269862 (-1.183024) \| 2.517184 / 4.565676 (-2.048492) \| 0.058272 / 0.424275 (-0.366003) \| 0.006415 / 0.007607 (-0.001192) \| 0.504792 / 0.226044 (0.278747) \| 5.046758 / 2.268929 (2.777829) \| 2.752049 / 55.444624 (-52.692576) \| 2.407707 / 6.876477 (-4.468770) \| 2.532162 / 2.142072 (0.390090) \| 0.597562 / 4.805227 (-4.207666) \| 0.125935 / 6.500664 (-6.374729) \| 0.060837 / 0.075469 (-0.014632) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.257048 / 1.841788 (-0.584740) \| 17.877849 / 8.074308 (9.803541) \| 13.904805 / 10.191392 (3.713413) \| 0.131647 / 0.680424 (-0.548776) \| 0.016975 / 0.534201 (-0.517226) \| 0.329651 / 0.579283 (-0.249633) \| 0.354358 / 0.434364 (-0.080006) \| 0.377545 / 0.540337 (-0.162792) \| 0.545593 / 1.386936 (-0.841343) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005839 / 0.011353 (-0.005514) \| 0.003580 / 0.011008 (-0.007428) \| 0.062204 / 0.038508 (0.023696) \| 0.057943 / 0.023109 (0.034834) \| 0.400165 / 0.275898 (0.124267) \| 0.427911 / 0.323480 (0.104431) \| 0.004412 / 0.007986 (-0.003574) \| 0.002794 / 0.004328 (-0.001534) \| 0.062933 / 0.004250 (0.058683) \| 0.046243 / 0.037052 (0.009191) \| 0.413640 / 0.258489 (0.155151) \| 0.418592 / 0.293841 (0.124751) \| 0.027020 / 0.128546 (-0.101526) \| 0.007927 / 0.075646 (-0.067720) \| 0.067581 / 0.419271 (-0.351691) \| 0.041927 / 0.043533 (-0.001606) \| 0.381863 / 0.255139 (0.126724) \| 0.415711 / 0.283200 (0.132511) \| 0.019827 / 0.141683 (-0.121856) \| 1.464049 / 1.452155 (0.011894) \| 1.528387 / 1.492716 (0.035671) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224999 / 0.018006 (0.206993) \| 0.419167 / 0.000490 (0.418678) \| 0.000363 / 0.000200 (0.000163) \| 0.000054 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024827 / 0.037411 (-0.012585) \| 0.077134 / 0.014526 (0.062608) \| 0.085142 / 0.176557 (-0.091414) \| 0.137400 / 0.737135 (-0.599735) \| 0.086434 / 0.296338 (-0.209905) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.452716 / 0.215209 (0.237507) \| 4.530610 / 2.077655 (2.452955) \| 2.467309 / 1.504120 (0.963189) \| 2.300441 / 1.541195 (0.759246) \| 2.323475 / 1.468490 (0.854985) \| 0.501847 / 4.584777 (-4.082930) \| 3.079432 / 3.745712 (-0.666280) \| 2.793107 / 5.269862 (-2.476755) \| 1.835010 / 4.565676 (-2.730666) \| 0.057698 / 0.424275 (-0.366577) \| 0.006756 / 0.007607 (-0.000851) \| 0.529062 / 0.226044 (0.303017) \| 5.287822 / 2.268929 (3.018894) \| 2.908411 / 55.444624 (-52.536214) \| 2.571627 / 6.876477 (-4.304850) \| 2.691188 / 2.142072 (0.549116) \| 0.592289 / 4.805227 (-4.212938) \| 0.126091 / 6.500664 (-6.374573) \| 0.062312 / 0.075469 (-0.013157) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.328854 / 1.841788 (-0.512933) \| 18.185628 / 8.074308 (10.111320) \| 13.858781 / 10.191392 (3.667389) \| 0.142421 / 0.680424 (-0.538003) \| 0.016535 / 0.534201 (-0.517666) \| 0.330839 / 0.579283 (-0.248444) \| 0.346559 / 0.434364 (-0.087805) \| 0.389153 / 0.540337 (-0.151185) \| 0.516897 / 1.386936 (-0.870039) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#09492ba523518289a84175ddb7ab3bc555e742ee \"CML watermark\")\n" ]
1,828,469,648	6,101	Release 2.14.2	closed	2023-07-31T06:05:36	2023-07-31T06:33:00	2023-07-31T06:18:17	https://github.com/huggingface/datasets/pull/6101	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6101", "html_url": "https://github.com/huggingface/datasets/pull/6101", "diff_url": "https://github.com/huggingface/datasets/pull/6101.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6101.patch", "merged_at": "2023-07-31T06:18:17" }	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) \| 0.006543 / 0.011353 (-0.004810) \| 0.003894 / 0.011008 (-0.007115) \| 0.084742 / 0.038508 (0.046234) \| 0.072942 / 0.023109 (0.049833) \| 0.310722 / 0.275898 (0.034824) \| 0.346806 / 0.323480 (0.023326) \| 0.005373 / 0.007986 (-0.002613) \| 0.003270 / 0.004328 (-0.001059) \| 0.064379 / 0.004250 (0.060128) \| 0.054876 / 0.037052 (0.017824) \| 0.316794 / 0.258489 (0.058305) \| 0.350353 / 0.293841 (0.056512) \| 0.030683 / 0.128546 (-0.097863) \| 0.008275 / 0.075646 (-0.067371) \| 0.288747 / 0.419271 (-0.130525) \| 0.051892 / 0.043533 (0.008359) \| 0.315060 / 0.255139 (0.059921) \| 0.331664 / 0.283200 (0.048464) \| 0.023334 / 0.141683 (-0.118349) \| 1.499734 / 1.452155 (0.047579) \| 1.542006 / 1.492716 (0.049290) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.210488 / 0.018006 (0.192482) \| 0.462187 / 0.000490 (0.461697) \| 0.001280 / 0.000200 (0.001080) \| 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) \| 0.027812 / 0.037411 (-0.009599) \| 0.082492 / 0.014526 (0.067966) \| 0.096504 / 0.176557 (-0.080053) \| 0.158164 / 0.737135 (-0.578972) \| 0.096678 / 0.296338 (-0.199661) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.403317 / 0.215209 (0.188108) \| 4.008367 / 2.077655 (1.930713) \| 2.033067 / 1.504120 (0.528947) \| 1.869484 / 1.541195 (0.328290) \| 1.947450 / 1.468490 (0.478960) \| 0.494048 / 4.584777 (-4.090729) \| 3.631673 / 3.745712 (-0.114039) \| 5.322167 / 5.269862 (0.052306) \| 3.125570 / 4.565676 (-1.440107) \| 0.057341 / 0.424275 (-0.366934) \| 0.007318 / 0.007607 (-0.000289) \| 0.483990 / 0.226044 (0.257945) \| 4.830573 / 2.268929 (2.561645) \| 2.543267 / 55.444624 (-52.901358) \| 2.217890 / 6.876477 (-4.658587) \| 2.435111 / 2.142072 (0.293038) \| 0.597920 / 4.805227 (-4.207307) \| 0.132690 / 6.500664 (-6.367974) \| 0.060160 / 0.075469 (-0.015309) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.247656 / 1.841788 (-0.594131) \| 19.436984 / 8.074308 (11.362675) \| 14.504249 / 10.191392 (4.312857) \| 0.167444 / 0.680424 (-0.512980) \| 0.018214 / 0.534201 (-0.515987) \| 0.394790 / 0.579283 (-0.184493) \| 0.413770 / 0.434364 (-0.020594) \| 0.474290 / 0.540337 (-0.066048) \| 0.646782 / 1.386936 (-0.740154) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006575 / 0.011353 (-0.004778) \| 0.003924 / 0.011008 (-0.007084) \| 0.064402 / 0.038508 (0.025893) \| 0.072569 / 0.023109 (0.049460) \| 0.361981 / 0.275898 (0.086083) \| 0.398660 / 0.323480 (0.075180) \| 0.005380 / 0.007986 (-0.002605) \| 0.003355 / 0.004328 (-0.000974) \| 0.065173 / 0.004250 (0.060923) \| 0.057120 / 0.037052 (0.020067) \| 0.366347 / 0.258489 (0.107858) \| 0.402723 / 0.293841 (0.108882) \| 0.031258 / 0.128546 (-0.097288) \| 0.008499 / 0.075646 (-0.067147) \| 0.070558 / 0.419271 (-0.348714) \| 0.050089 / 0.043533 (0.006556) \| 0.361280 / 0.255139 (0.106141) \| 0.384497 / 0.283200 (0.101297) \| 0.024789 / 0.141683 (-0.116893) \| 1.492577 / 1.452155 (0.040422) \| 1.572242 / 1.492716 (0.079525) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.228054 / 0.018006 (0.210048) \| 0.448317 / 0.000490 (0.447828) \| 0.000368 / 0.000200 (0.000168) \| 0.000056 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030575 / 0.037411 (-0.006836) \| 0.088604 / 0.014526 (0.074078) \| 0.099317 / 0.176557 (-0.077239) \| 0.152455 / 0.737135 (-0.584680) \| 0.100444 / 0.296338 (-0.195894) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.411876 / 0.215209 (0.196667) \| 4.108187 / 2.077655 (2.030532) \| 2.096371 / 1.504120 (0.592251) \| 1.923532 / 1.541195 (0.382337) \| 1.998345 / 1.468490 (0.529855) \| 0.483853 / 4.584777 (-4.100924) \| 3.622433 / 3.745712 (-0.123279) \| 3.254430 / 5.269862 (-2.015431) \| 2.044342 / 4.565676 (-2.521334) \| 0.056756 / 0.424275 (-0.367519) \| 0.007720 / 0.007607 (0.000113) \| 0.487656 / 0.226044 (0.261612) \| 4.882024 / 2.268929 (2.613096) \| 2.585008 / 55.444624 (-52.859616) \| 2.229251 / 6.876477 (-4.647225) \| 2.408318 / 2.142072 (0.266246) \| 0.617537 / 4.805227 (-4.187691) \| 0.132102 / 6.500664 (-6.368562) \| 0.061694 / 0.075469 (-0.013775) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.362077 / 1.841788 (-0.479711) \| 19.750714 / 8.074308 (11.676406) \| 14.545299 / 10.191392 (4.353907) \| 0.168666 / 0.680424 (-0.511758) \| 0.018606 / 0.534201 (-0.515595) \| 0.394760 / 0.579283 (-0.184523) \| 0.410030 / 0.434364 (-0.024334) \| 0.464742 / 0.540337 (-0.075596) \| 0.610881 / 1.386936 (-0.776055) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53e8007baeff133aaad8cbb366196be18a5e57fd \"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) \| 0.005836 / 0.011353 (-0.005517) \| 0.003493 / 0.011008 (-0.007515) \| 0.079877 / 0.038508 (0.041369) \| 0.057299 / 0.023109 (0.034190) \| 0.332945 / 0.275898 (0.057047) \| 0.386615 / 0.323480 (0.063135) \| 0.004437 / 0.007986 (-0.003548) \| 0.002758 / 0.004328 (-0.001571) \| 0.062668 / 0.004250 (0.058418) \| 0.046135 / 0.037052 (0.009083) \| 0.346160 / 0.258489 (0.087671) \| 0.416720 / 0.293841 (0.122879) \| 0.026678 / 0.128546 (-0.101868) \| 0.007893 / 0.075646 (-0.067753) \| 0.260427 / 0.419271 (-0.158845) \| 0.044240 / 0.043533 (0.000707) \| 0.328101 / 0.255139 (0.072963) \| 0.380072 / 0.283200 (0.096872) \| 0.020813 / 0.141683 (-0.120870) \| 1.400202 / 1.452155 (-0.051952) \| 1.475627 / 1.492716 (-0.017089) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.174479 / 0.018006 (0.156473) \| 0.413810 / 0.000490 (0.413320) \| 0.003059 / 0.000200 (0.002860) \| 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) \| 0.023422 / 0.037411 (-0.013990) \| 0.071519 / 0.014526 (0.056993) \| 0.080555 / 0.176557 (-0.096001) \| 0.143825 / 0.737135 (-0.593311) \| 0.081182 / 0.296338 (-0.215157) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.406858 / 0.215209 (0.191648) \| 4.161475 / 2.077655 (2.083820) \| 1.991800 / 1.504120 (0.487680) \| 1.811224 / 1.541195 (0.270030) \| 1.828809 / 1.468490 (0.360318) \| 0.504882 / 4.584777 (-4.079895) \| 2.985010 / 3.745712 (-0.760703) \| 3.984856 / 5.269862 (-1.285006) \| 2.477936 / 4.565676 (-2.087740) \| 0.057553 / 0.424275 (-0.366722) \| 0.006436 / 0.007607 (-0.001172) \| 0.488061 / 0.226044 (0.262016) \| 4.805501 / 2.268929 (2.536573) \| 2.446508 / 55.444624 (-52.998116) \| 2.051406 / 6.876477 (-4.825071) \| 2.177696 / 2.142072 (0.035623) \| 0.588021 / 4.805227 (-4.217207) \| 0.125118 / 6.500664 (-6.375546) \| 0.060885 / 0.075469 (-0.014584) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.197130 / 1.841788 (-0.644658) \| 17.867450 / 8.074308 (9.793142) \| 13.536895 / 10.191392 (3.345503) \| 0.137603 / 0.680424 (-0.542821) \| 0.016706 / 0.534201 (-0.517495) \| 0.327642 / 0.579283 (-0.251641) \| 0.347201 / 0.434364 (-0.087163) \| 0.379570 / 0.540337 (-0.160768) \| 0.517825 / 1.386936 (-0.869111) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005769 / 0.011353 (-0.005584) \| 0.003414 / 0.011008 (-0.007594) \| 0.063198 / 0.038508 (0.024690) \| 0.056020 / 0.023109 (0.032911) \| 0.393333 / 0.275898 (0.117435) \| 0.421166 / 0.323480 (0.097686) \| 0.004360 / 0.007986 (-0.003626) \| 0.002860 / 0.004328 (-0.001469) \| 0.062712 / 0.004250 (0.058461) \| 0.045363 / 0.037052 (0.008311) \| 0.413156 / 0.258489 (0.154667) \| 0.422897 / 0.293841 (0.129056) \| 0.027092 / 0.128546 (-0.101455) \| 0.007960 / 0.075646 (-0.067687) \| 0.068531 / 0.419271 (-0.350740) \| 0.041402 / 0.043533 (-0.002131) \| 0.377008 / 0.255139 (0.121869) \| 0.409142 / 0.283200 (0.125942) \| 0.019707 / 0.141683 (-0.121976) \| 1.440556 / 1.452155 (-0.011599) \| 1.487403 / 1.492716 (-0.005314) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224355 / 0.018006 (0.206349) \| 0.397855 / 0.000490 (0.397365) \| 0.000363 / 0.000200 (0.000163) \| 0.000056 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025107 / 0.037411 (-0.012305) \| 0.076404 / 0.014526 (0.061878) \| 0.083194 / 0.176557 (-0.093362) \| 0.135347 / 0.737135 (-0.601789) \| 0.084786 / 0.296338 (-0.211553) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.433024 / 0.215209 (0.217815) \| 4.323879 / 2.077655 (2.246224) \| 2.263004 / 1.504120 (0.758884) \| 2.072053 / 1.541195 (0.530858) \| 2.113916 / 1.468490 (0.645426) \| 0.502742 / 4.584777 (-4.082035) \| 3.001716 / 3.745712 (-0.743996) \| 2.777960 / 5.269862 (-2.491901) \| 1.826514 / 4.565676 (-2.739162) \| 0.057735 / 0.424275 (-0.366540) \| 0.006671 / 0.007607 (-0.000937) \| 0.503347 / 0.226044 (0.277303) \| 5.037308 / 2.268929 (2.768380) \| 2.679146 / 55.444624 (-52.765478) \| 2.410899 / 6.876477 (-4.465577) \| 2.467341 / 2.142072 (0.325268) \| 0.589824 / 4.805227 (-4.215403) \| 0.125529 / 6.500664 (-6.375135) \| 0.061950 / 0.075469 (-0.013520) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.304128 / 1.841788 (-0.537659) \| 17.950215 / 8.074308 (9.875907) \| 13.673768 / 10.191392 (3.482376) \| 0.129863 / 0.680424 (-0.550561) \| 0.016720 / 0.534201 (-0.517481) \| 0.329795 / 0.579283 (-0.249488) \| 0.339057 / 0.434364 (-0.095307) \| 0.382279 / 0.540337 (-0.158059) \| 0.507337 / 1.386936 (-0.879599) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ef05b6f99a2b19990c6f5e4e28d95d28781570db \"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) \| 0.006199 / 0.011353 (-0.005154) \| 0.003749 / 0.011008 (-0.007259) \| 0.080600 / 0.038508 (0.042092) \| 0.061017 / 0.023109 (0.037908) \| 0.319966 / 0.275898 (0.044067) \| 0.354937 / 0.323480 (0.031457) \| 0.004854 / 0.007986 (-0.003131) \| 0.002996 / 0.004328 (-0.001333) \| 0.063100 / 0.004250 (0.058849) \| 0.050063 / 0.037052 (0.013011) \| 0.316744 / 0.258489 (0.058255) \| 0.358001 / 0.293841 (0.064160) \| 0.027503 / 0.128546 (-0.101043) \| 0.007876 / 0.075646 (-0.067771) \| 0.262211 / 0.419271 (-0.157060) \| 0.045717 / 0.043533 (0.002184) \| 0.317188 / 0.255139 (0.062049) \| 0.342404 / 0.283200 (0.059205) \| 0.020194 / 0.141683 (-0.121489) \| 1.498672 / 1.452155 (0.046517) \| 1.545479 / 1.492716 (0.052762) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.210985 / 0.018006 (0.192979) \| 0.433592 / 0.000490 (0.433102) \| 0.002864 / 0.000200 (0.002664) \| 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) \| 0.023463 / 0.037411 (-0.013948) \| 0.073375 / 0.014526 (0.058850) \| 0.083082 / 0.176557 (-0.093475) \| 0.142583 / 0.737135 (-0.594552) \| 0.084267 / 0.296338 (-0.212071) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.412890 / 0.215209 (0.197681) \| 4.131421 / 2.077655 (2.053766) \| 1.969164 / 1.504120 (0.465044) \| 1.772379 / 1.541195 (0.231185) \| 1.834154 / 1.468490 (0.365664) \| 0.496290 / 4.584777 (-4.088487) \| 3.056504 / 3.745712 (-0.689208) \| 3.400962 / 5.269862 (-1.868900) \| 2.120575 / 4.565676 (-2.445101) \| 0.056932 / 0.424275 (-0.367343) \| 0.006412 / 0.007607 (-0.001195) \| 0.484521 / 0.226044 (0.258477) \| 4.817474 / 2.268929 (2.548545) \| 2.464075 / 55.444624 (-52.980549) \| 2.085056 / 6.876477 (-4.791421) \| 2.324516 / 2.142072 (0.182444) \| 0.592013 / 4.805227 (-4.213214) \| 0.132232 / 6.500664 (-6.368432) \| 0.062825 / 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) \| 1.228080 / 1.841788 (-0.613708) \| 18.555385 / 8.074308 (10.481077) \| 13.939565 / 10.191392 (3.748173) \| 0.145979 / 0.680424 (-0.534445) \| 0.016823 / 0.534201 (-0.517377) \| 0.330569 / 0.579283 (-0.248714) \| 0.358094 / 0.434364 (-0.076270) \| 0.384642 / 0.540337 (-0.155696) \| 0.518347 / 1.386936 (-0.868589) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006198 / 0.011353 (-0.005155) \| 0.003670 / 0.011008 (-0.007338) \| 0.062502 / 0.038508 (0.023994) \| 0.064339 / 0.023109 (0.041229) \| 0.428414 / 0.275898 (0.152516) \| 0.463899 / 0.323480 (0.140420) \| 0.005524 / 0.007986 (-0.002462) \| 0.002915 / 0.004328 (-0.001413) \| 0.062521 / 0.004250 (0.058270) \| 0.051182 / 0.037052 (0.014130) \| 0.431144 / 0.258489 (0.172655) \| 0.469465 / 0.293841 (0.175624) \| 0.027463 / 0.128546 (-0.101083) \| 0.007974 / 0.075646 (-0.067673) \| 0.068029 / 0.419271 (-0.351242) \| 0.042123 / 0.043533 (-0.001409) \| 0.428667 / 0.255139 (0.173528) \| 0.455917 / 0.283200 (0.172717) \| 0.023264 / 0.141683 (-0.118419) \| 1.426986 / 1.452155 (-0.025168) \| 1.500049 / 1.492716 (0.007332) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.207264 / 0.018006 (0.189258) \| 0.440738 / 0.000490 (0.440248) \| 0.000802 / 0.000200 (0.000602) \| 0.000062 / 0.000054 (0.000008) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026245 / 0.037411 (-0.011166) \| 0.078749 / 0.014526 (0.064223) \| 0.087873 / 0.176557 (-0.088684) \| 0.141518 / 0.737135 (-0.595617) \| 0.089811 / 0.296338 (-0.206527) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.418955 / 0.215209 (0.203746) \| 4.177881 / 2.077655 (2.100226) \| 2.162678 / 1.504120 (0.658558) \| 1.998969 / 1.541195 (0.457775) \| 2.066720 / 1.468490 (0.598230) \| 0.496850 / 4.584777 (-4.087927) \| 3.041179 / 3.745712 (-0.704534) \| 4.126039 / 5.269862 (-1.143823) \| 2.740507 / 4.565676 (-1.825169) \| 0.058025 / 0.424275 (-0.366250) \| 0.006846 / 0.007607 (-0.000761) \| 0.493281 / 0.226044 (0.267237) \| 4.930196 / 2.268929 (2.661268) \| 2.685152 / 55.444624 (-52.759472) \| 2.378247 / 6.876477 (-4.498230) \| 2.469103 / 2.142072 (0.327031) \| 0.585346 / 4.805227 (-4.219882) \| 0.126099 / 6.500664 (-6.374565) \| 0.062946 / 0.075469 (-0.012523) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.313892 / 1.841788 (-0.527896) \| 19.177117 / 8.074308 (11.102809) \| 14.081321 / 10.191392 (3.889929) \| 0.133948 / 0.680424 (-0.546476) \| 0.017128 / 0.534201 (-0.517073) \| 0.332241 / 0.579283 (-0.247042) \| 0.373218 / 0.434364 (-0.061145) \| 0.395308 / 0.540337 (-0.145030) \| 0.529883 / 1.386936 (-0.857053) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#16f7c7677942083436062b904b74643accb9bcac \"CML watermark\")\n" ]
1,828,118,930	6,100	TypeError when loading from GCP bucket	closed	2023-07-30T23:03:00	2023-08-03T10:00:48	2023-08-01T10:38:55	https://github.com/huggingface/datasets/issues/6100	null	bilelomrani1	false	[ "Thanks for reporting, @bilelomrani1.\r\n\r\nWe are fixing it. ", "We have fixed it. We are planning to do a patch release today." ]
1,827,893,576	6,099	How do i get "amazon_us_reviews	closed	2023-07-30T11:02:17	2023-08-21T05:08:08	2023-08-10T05:02:35	https://github.com/huggingface/datasets/issues/6099	null	IqraBaluch	false	[ "Seems like the problem isn't with the library, but the dataset itself hosted on AWS S3.\r\n\r\nIts [homepage](https://s3.amazonaws.com/amazon-reviews-pds/readme.html) returns an `AccessDenied` XML response, which is the same thing you get if you try to log the `record` that triggers the exception\r\n\r\n```python\r\ntry:\r\n example = self.info.features.encode_example(record) if self.info.features is not None else record\r\nexcept Exception as e:\r\n print(record)\r\n```\r\n\r\n⬇️\r\n\r\n```\r\n{'<?xml version=\"1.0\" encoding=\"UTF-8\"?>': '<Error><Code>AccessDenied</Code><Message>Access Denied</Message><RequestId>N2HFJ82ZV8SZW9BV</RequestId><HostId>Zw2DQ0V2GdRmvH5qWEpumK4uj5+W8YPcilQbN9fLBr3VqQOcKPHOhUZLG3LcM9X5fkOetxp48Os=</HostId></Error>'}\r\n```", "I'm getting same errors when loading this dataset", "I have figured it out. there was an option of parquet formated files i downloaded some from there. ", "this dataset is unfortunately no longer public", "Thanks for reporting, @IqraBaluch.\r\n\r\nWe contacted the authors and unfortunately they reported that Amazon has decided to stop distributing this dataset.", "If anyone still needs this dataset, you could find it on kaggle here : https://www.kaggle.com/datasets/cynthiarempel/amazon-us-customer-reviews-dataset", "Thanks @Maryam-Mostafa ", "@albertvillanova don't tell 'em, we have figured it out. XD", "I noticed that some book data is missing, we can only get Books_v1_02 data. \r\nIs there any way we can get the Books_v1_00 and Books_v1_01? \r\nReally appreciate !!!", "@albertvillanova will this dataset be retired given the data are no longer hosted on S3? What is done in cases such as these?" ]
1,827,655,071	6,098	Expanduser in save_to_disk()	closed	2023-07-29T20:50:45	2023-10-27T14:14:11	2023-10-27T14:04:36	https://github.com/huggingface/datasets/pull/6098	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6098", "html_url": "https://github.com/huggingface/datasets/pull/6098", "diff_url": "https://github.com/huggingface/datasets/pull/6098.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6098.patch", "merged_at": "2023-10-27T14:04:36" }	Unknown3141592	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "> I am not sure why the case distinction between local and remote filesystems is even necessary for DatasetDict when saving to disk. Imo this could be removed (leaving only fs.makedirs(dataset_dict_path, exist_ok=True)).\r\n\r\nIndeed. But it's better to address this in a separate PR.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007696 / 0.011353 (-0.003656) \| 0.004497 / 0.011008 (-0.006511) \| 0.099302 / 0.038508 (0.060794) \| 0.083360 / 0.023109 (0.060251) \| 0.393483 / 0.275898 (0.117585) \| 0.450505 / 0.323480 (0.127025) \| 0.004610 / 0.007986 (-0.003376) \| 0.003637 / 0.004328 (-0.000692) \| 0.075752 / 0.004250 (0.071501) \| 0.064034 / 0.037052 (0.026982) \| 0.397785 / 0.258489 (0.139296) \| 0.462948 / 0.293841 (0.169107) \| 0.035902 / 0.128546 (-0.092644) \| 0.009640 / 0.075646 (-0.066007) \| 0.342299 / 0.419271 (-0.076973) \| 0.059586 / 0.043533 (0.016053) \| 0.404918 / 0.255139 (0.149779) \| 0.440889 / 0.283200 (0.157690) \| 0.028981 / 0.141683 (-0.112702) \| 1.775380 / 1.452155 (0.323226) \| 1.866663 / 1.492716 (0.373946) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.249080 / 0.018006 (0.231074) \| 0.456460 / 0.000490 (0.455970) \| 0.028145 / 0.000200 (0.027945) \| 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) \| 0.030373 / 0.037411 (-0.007038) \| 0.088562 / 0.014526 (0.074036) \| 0.122837 / 0.176557 (-0.053720) \| 0.167122 / 0.737135 (-0.570014) \| 0.103953 / 0.296338 (-0.192385) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.431714 / 0.215209 (0.216505) \| 4.182224 / 2.077655 (2.104570) \| 2.025650 / 1.504120 (0.521530) \| 1.838905 / 1.541195 (0.297710) \| 1.868710 / 1.468490 (0.400219) \| 0.538422 / 4.584777 (-4.046355) \| 4.038941 / 3.745712 (0.293228) \| 3.717695 / 5.269862 (-1.552166) \| 2.313197 / 4.565676 (-2.252479) \| 0.061060 / 0.424275 (-0.363215) \| 0.008248 / 0.007607 (0.000641) \| 0.497438 / 0.226044 (0.271394) \| 4.946663 / 2.268929 (2.677734) \| 2.571841 / 55.444624 (-52.872784) \| 2.155894 / 6.876477 (-4.720583) \| 2.183180 / 2.142072 (0.041107) \| 0.639810 / 4.805227 (-4.165417) \| 0.153273 / 6.500664 (-6.347391) \| 0.068606 / 0.075469 (-0.006863) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.376152 / 1.841788 (-0.465635) \| 20.747088 / 8.074308 (12.672780) \| 15.200311 / 10.191392 (5.008919) \| 0.166380 / 0.680424 (-0.514043) \| 0.021417 / 0.534201 (-0.512784) \| 0.435677 / 0.579283 (-0.143606) \| 0.460412 / 0.434364 (0.026048) \| 0.509978 / 0.540337 (-0.030359) \| 0.702506 / 1.386936 (-0.684430) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007378 / 0.011353 (-0.003975) \| 0.003938 / 0.011008 (-0.007070) \| 0.067095 / 0.038508 (0.028587) \| 0.082252 / 0.023109 (0.059143) \| 0.420317 / 0.275898 (0.144419) \| 0.477496 / 0.323480 (0.154017) \| 0.006259 / 0.007986 (-0.001727) \| 0.003513 / 0.004328 (-0.000816) \| 0.072107 / 0.004250 (0.067856) \| 0.061737 / 0.037052 (0.024684) \| 0.444142 / 0.258489 (0.185653) \| 0.488926 / 0.293841 (0.195085) \| 0.033623 / 0.128546 (-0.094923) \| 0.008091 / 0.075646 (-0.067555) \| 0.073997 / 0.419271 (-0.345274) \| 0.051295 / 0.043533 (0.007762) \| 0.442551 / 0.255139 (0.187412) \| 0.462713 / 0.283200 (0.179513) \| 0.023115 / 0.141683 (-0.118568) \| 1.645759 / 1.452155 (0.193604) \| 1.758121 / 1.492716 (0.265405) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.233450 / 0.018006 (0.215444) \| 0.445384 / 0.000490 (0.444894) \| 0.006412 / 0.000200 (0.006212) \| 0.000111 / 0.000054 (0.000056) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032446 / 0.037411 (-0.004965) \| 0.098515 / 0.014526 (0.083989) \| 0.109095 / 0.176557 (-0.067462) \| 0.167645 / 0.737135 (-0.569490) \| 0.110403 / 0.296338 (-0.185936) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.470189 / 0.215209 (0.254980) \| 4.663224 / 2.077655 (2.585569) \| 2.504474 / 1.504120 (1.000354) \| 2.282867 / 1.541195 (0.741673) \| 2.331598 / 1.468490 (0.863108) \| 0.554421 / 4.584777 (-4.030356) \| 4.078657 / 3.745712 (0.332945) \| 3.516339 / 5.269862 (-1.753523) \| 2.239134 / 4.565676 (-2.326542) \| 0.062690 / 0.424275 (-0.361585) \| 0.008406 / 0.007607 (0.000799) \| 0.533827 / 0.226044 (0.307782) \| 5.423984 / 2.268929 (3.155055) \| 2.972784 / 55.444624 (-52.471840) \| 2.699056 / 6.876477 (-4.177421) \| 2.844403 / 2.142072 (0.702331) \| 0.639194 / 4.805227 (-4.166033) \| 0.142097 / 6.500664 (-6.358567) \| 0.064646 / 0.075469 (-0.010823) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.544640 / 1.841788 (-0.297148) \| 21.453429 / 8.074308 (13.379121) \| 15.610723 / 10.191392 (5.419331) \| 0.207796 / 0.680424 (-0.472628) \| 0.021912 / 0.534201 (-0.512289) \| 0.430472 / 0.579283 (-0.148811) \| 0.467530 / 0.434364 (0.033166) \| 0.541339 / 0.540337 (0.001002) \| 0.721976 / 1.386936 (-0.664960) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#691c4bc65888005f3aadea5c104fdbc87694882d \"CML watermark\")\n" ]
1,827,054,143	6,097	Dataset.get_nearest_examples does not return all feature values for the k most similar datapoints - side effect of Dataset.set_format	closed	2023-07-28T20:31:59	2023-07-28T20:49:58	2023-07-28T20:49:58	https://github.com/huggingface/datasets/issues/6097	null	aschoenauer-sebag	false	[ "Actually, my bad -- specifying\r\n```python\r\nfoo.set_format('numpy', ['vectors'], output_all_columns=True)\r\n```\r\nfixes it." ]
1,826,731,091	6,096	Add `fsspec` support for `to_json`, `to_csv`, and `to_parquet`	closed	2023-07-28T16:36:59	2024-05-28T07:40:30	2024-03-06T11:12:42	https://github.com/huggingface/datasets/pull/6096	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6096", "html_url": "https://github.com/huggingface/datasets/pull/6096", "diff_url": "https://github.com/huggingface/datasets/pull/6096.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6096.patch", "merged_at": "2024-03-06T11:12:42" }	alvarobartt	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6096). All of your documentation changes will be reflected on that endpoint.", "Hi here @lhoestq @mariosasko I just realised this PR is still open, should we close it in case this is something not to include within `datasets`, or should we merge? Let me know whatever you decide 🤗", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005011 / 0.011353 (-0.006342) \| 0.003203 / 0.011008 (-0.007806) \| 0.064033 / 0.038508 (0.025524) \| 0.029152 / 0.023109 (0.006043) \| 0.242884 / 0.275898 (-0.033014) \| 0.263517 / 0.323480 (-0.059963) \| 0.004088 / 0.007986 (-0.003898) \| 0.002570 / 0.004328 (-0.001759) \| 0.049061 / 0.004250 (0.044811) \| 0.040170 / 0.037052 (0.003117) \| 0.263305 / 0.258489 (0.004816) \| 0.286255 / 0.293841 (-0.007586) \| 0.028206 / 0.128546 (-0.100340) \| 0.010337 / 0.075646 (-0.065309) \| 0.206235 / 0.419271 (-0.213036) \| 0.038182 / 0.043533 (-0.005351) \| 0.246486 / 0.255139 (-0.008653) \| 0.263077 / 0.283200 (-0.020122) \| 0.017850 / 0.141683 (-0.123833) \| 1.173921 / 1.452155 (-0.278234) \| 1.255583 / 1.492716 (-0.237133) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090278 / 0.018006 (0.072272) \| 0.298146 / 0.000490 (0.297657) \| 0.000215 / 0.000200 (0.000015) \| 0.000043 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018021 / 0.037411 (-0.019390) \| 0.061434 / 0.014526 (0.046908) \| 0.072617 / 0.176557 (-0.103939) \| 0.119063 / 0.737135 (-0.618072) \| 0.073997 / 0.296338 (-0.222341) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288496 / 0.215209 (0.073287) \| 2.794943 / 2.077655 (0.717288) \| 1.538299 / 1.504120 (0.034179) \| 1.399164 / 1.541195 (-0.142031) \| 1.419104 / 1.468490 (-0.049386) \| 0.566147 / 4.584777 (-4.018630) \| 2.386687 / 3.745712 (-1.359025) \| 2.723584 / 5.269862 (-2.546278) \| 1.699161 / 4.565676 (-2.866515) \| 0.062526 / 0.424275 (-0.361750) \| 0.004927 / 0.007607 (-0.002680) \| 0.345132 / 0.226044 (0.119087) \| 3.389634 / 2.268929 (1.120706) \| 1.898012 / 55.444624 (-53.546612) \| 1.599050 / 6.876477 (-5.277427) \| 1.614289 / 2.142072 (-0.527783) \| 0.656716 / 4.805227 (-4.148511) \| 0.118480 / 6.500664 (-6.382184) \| 0.041913 / 0.075469 (-0.033557) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.968676 / 1.841788 (-0.873111) \| 11.184668 / 8.074308 (3.110360) \| 9.249912 / 10.191392 (-0.941480) \| 0.141139 / 0.680424 (-0.539285) \| 0.014207 / 0.534201 (-0.519994) \| 0.287603 / 0.579283 (-0.291680) \| 0.262792 / 0.434364 (-0.171572) \| 0.340239 / 0.540337 (-0.200099) \| 0.437471 / 1.386936 (-0.949465) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005268 / 0.011353 (-0.006085) \| 0.003142 / 0.011008 (-0.007866) \| 0.049333 / 0.038508 (0.010825) \| 0.029558 / 0.023109 (0.006449) \| 0.270716 / 0.275898 (-0.005182) \| 0.293834 / 0.323480 (-0.029646) \| 0.004285 / 0.007986 (-0.003701) \| 0.002703 / 0.004328 (-0.001626) \| 0.048857 / 0.004250 (0.044607) \| 0.043456 / 0.037052 (0.006404) \| 0.286058 / 0.258489 (0.027569) \| 0.313491 / 0.293841 (0.019650) \| 0.029336 / 0.128546 (-0.099210) \| 0.010287 / 0.075646 (-0.065360) \| 0.057753 / 0.419271 (-0.361518) \| 0.050867 / 0.043533 (0.007334) \| 0.271717 / 0.255139 (0.016578) \| 0.291468 / 0.283200 (0.008268) \| 0.018668 / 0.141683 (-0.123015) \| 1.137399 / 1.452155 (-0.314755) \| 1.186315 / 1.492716 (-0.306401) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090289 / 0.018006 (0.072283) \| 0.297987 / 0.000490 (0.297497) \| 0.000227 / 0.000200 (0.000027) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021012 / 0.037411 (-0.016399) \| 0.075046 / 0.014526 (0.060520) \| 0.085295 / 0.176557 (-0.091261) \| 0.123879 / 0.737135 (-0.613257) \| 0.086572 / 0.296338 (-0.209766) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293350 / 0.215209 (0.078141) \| 2.875958 / 2.077655 (0.798304) \| 1.586460 / 1.504120 (0.082340) \| 1.467950 / 1.541195 (-0.073245) \| 1.453478 / 1.468490 (-0.015012) \| 0.566083 / 4.584777 (-4.018693) \| 2.462582 / 3.745712 (-1.283130) \| 2.609367 / 5.269862 (-2.660495) \| 1.709691 / 4.565676 (-2.855985) \| 0.062928 / 0.424275 (-0.361347) \| 0.005040 / 0.007607 (-0.002567) \| 0.337997 / 0.226044 (0.111952) \| 3.347235 / 2.268929 (1.078306) \| 1.923940 / 55.444624 (-53.520684) \| 1.657731 / 6.876477 (-5.218746) \| 1.747469 / 2.142072 (-0.394604) \| 0.657061 / 4.805227 (-4.148167) \| 0.116655 / 6.500664 (-6.384009) \| 0.040363 / 0.075469 (-0.035106) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.011171 / 1.841788 (-0.830617) \| 11.705905 / 8.074308 (3.631597) \| 10.064391 / 10.191392 (-0.127001) \| 0.141681 / 0.680424 (-0.538743) \| 0.014763 / 0.534201 (-0.519438) \| 0.286425 / 0.579283 (-0.292858) \| 0.271036 / 0.434364 (-0.163328) \| 0.321393 / 0.540337 (-0.218944) \| 0.424539 / 1.386936 (-0.962397) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e52f4d0cf1cb89a9719b42fff13a891f92d51e04 \"CML watermark\")\n", "Thanks @alvarobartt. \r\n\r\nI am linking this PR to the corresponding issue (on the right column, under \"Development\") and closing the issue.\r\n\r\nFor future contributions, please add to the PR description the word \"fix\" followed by the issue number, e.g.:\r\n```\r\nFix #6086.\r\n```\r\n- I have edited the PR description to add this.", "> Thanks @alvarobartt.\r\n> \r\n> I am linking this PR to the corresponding issue (on the right column, under \"Development\") and closing the issue.\r\n> \r\n> For future contributions, please add to the PR description the word \"fix\" followed by the issue number, e.g.:\r\n> \r\n> ```\r\n> Fix #6086.\r\n> ```\r\n> \r\n> * I have edited the PR description to add this.\r\n\r\nHi @albertvillanova, fair, I missed that, thanks for the edit and the heads up!" ]
1,826,496,967	6,095	Fix deprecation of errors in TextConfig	closed	2023-07-28T14:08:37	2023-07-31T05:26:32	2023-07-31T05:17:38	https://github.com/huggingface/datasets/pull/6095	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6095", "html_url": "https://github.com/huggingface/datasets/pull/6095", "diff_url": "https://github.com/huggingface/datasets/pull/6095.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6095.patch", "merged_at": "2023-07-31T05:17:38" }	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) \| 0.012497 / 0.011353 (0.001144) \| 0.005355 / 0.011008 (-0.005654) \| 0.106018 / 0.038508 (0.067510) \| 0.093069 / 0.023109 (0.069960) \| 0.394699 / 0.275898 (0.118801) \| 0.449723 / 0.323480 (0.126243) \| 0.006434 / 0.007986 (-0.001552) \| 0.004187 / 0.004328 (-0.000141) \| 0.079620 / 0.004250 (0.075370) \| 0.062513 / 0.037052 (0.025460) \| 0.410305 / 0.258489 (0.151816) \| 0.467231 / 0.293841 (0.173390) \| 0.048130 / 0.128546 (-0.080416) \| 0.013747 / 0.075646 (-0.061899) \| 0.357979 / 0.419271 (-0.061293) \| 0.064764 / 0.043533 (0.021231) \| 0.411029 / 0.255139 (0.155890) \| 0.454734 / 0.283200 (0.171534) \| 0.037215 / 0.141683 (-0.104468) \| 1.801331 / 1.452155 (0.349176) \| 1.951628 / 1.492716 (0.458912) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.231073 / 0.018006 (0.213067) \| 0.564179 / 0.000490 (0.563689) \| 0.000947 / 0.000200 (0.000747) \| 0.000091 / 0.000054 (0.000036) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030629 / 0.037411 (-0.006783) \| 0.092522 / 0.014526 (0.077996) \| 0.109781 / 0.176557 (-0.066775) \| 0.183185 / 0.737135 (-0.553950) \| 0.109679 / 0.296338 (-0.186660) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.600095 / 0.215209 (0.384886) \| 6.072868 / 2.077655 (3.995213) \| 2.684109 / 1.504120 (1.179989) \| 2.436204 / 1.541195 (0.895010) \| 2.514667 / 1.468490 (1.046177) \| 0.865455 / 4.584777 (-3.719322) \| 5.245561 / 3.745712 (1.499849) \| 5.628688 / 5.269862 (0.358826) \| 3.457343 / 4.565676 (-1.108333) \| 0.107563 / 0.424275 (-0.316712) \| 0.008803 / 0.007607 (0.001196) \| 0.754014 / 0.226044 (0.527970) \| 7.341226 / 2.268929 (5.072297) \| 3.482090 / 55.444624 (-51.962534) \| 2.726071 / 6.876477 (-4.150406) \| 3.168494 / 2.142072 (1.026422) \| 1.023517 / 4.805227 (-3.781710) \| 0.207440 / 6.500664 (-6.293224) \| 0.073642 / 0.075469 (-0.001827) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.588636 / 1.841788 (-0.253152) \| 23.305257 / 8.074308 (15.230949) \| 22.071476 / 10.191392 (11.880084) \| 0.242044 / 0.680424 (-0.438379) \| 0.028830 / 0.534201 (-0.505371) \| 0.461414 / 0.579283 (-0.117869) \| 0.591024 / 0.434364 (0.156660) \| 0.548984 / 0.540337 (0.008646) \| 0.783318 / 1.386936 (-0.603618) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008724 / 0.011353 (-0.002629) \| 0.004638 / 0.011008 (-0.006371) \| 0.081024 / 0.038508 (0.042516) \| 0.077533 / 0.023109 (0.054423) \| 0.444827 / 0.275898 (0.168929) \| 0.507812 / 0.323480 (0.184332) \| 0.006017 / 0.007986 (-0.001968) \| 0.004204 / 0.004328 (-0.000124) \| 0.082154 / 0.004250 (0.077904) \| 0.063818 / 0.037052 (0.026765) \| 0.463468 / 0.258489 (0.204979) \| 0.536784 / 0.293841 (0.242943) \| 0.046393 / 0.128546 (-0.082153) \| 0.014349 / 0.075646 (-0.061298) \| 0.089213 / 0.419271 (-0.330059) \| 0.058313 / 0.043533 (0.014780) \| 0.463674 / 0.255139 (0.208535) \| 0.495865 / 0.283200 (0.212665) \| 0.036586 / 0.141683 (-0.105096) \| 1.801601 / 1.452155 (0.349447) \| 1.871219 / 1.492716 (0.378502) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.273411 / 0.018006 (0.255405) \| 0.531745 / 0.000490 (0.531255) \| 0.000424 / 0.000200 (0.000224) \| 0.000130 / 0.000054 (0.000076) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037689 / 0.037411 (0.000278) \| 0.109544 / 0.014526 (0.095019) \| 0.124053 / 0.176557 (-0.052504) \| 0.179960 / 0.737135 (-0.557175) \| 0.118218 / 0.296338 (-0.178120) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.639859 / 0.215209 (0.424650) \| 6.347385 / 2.077655 (4.269730) \| 2.910188 / 1.504120 (1.406068) \| 2.698821 / 1.541195 (1.157626) \| 2.802652 / 1.468490 (1.334161) \| 0.816109 / 4.584777 (-3.768668) \| 5.190313 / 3.745712 (1.444601) \| 4.642684 / 5.269862 (-0.627178) \| 2.948092 / 4.565676 (-1.617584) \| 0.095877 / 0.424275 (-0.328398) \| 0.009631 / 0.007607 (0.002024) \| 0.779136 / 0.226044 (0.553091) \| 7.611586 / 2.268929 (5.342658) \| 3.760804 / 55.444624 (-51.683820) \| 3.139355 / 6.876477 (-3.737122) \| 3.419660 / 2.142072 (1.277587) \| 1.036397 / 4.805227 (-3.768831) \| 0.224015 / 6.500664 (-6.276649) \| 0.084037 / 0.075469 (0.008568) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.710608 / 1.841788 (-0.131179) \| 24.447646 / 8.074308 (16.373338) \| 21.345322 / 10.191392 (11.153930) \| 0.232383 / 0.680424 (-0.448040) \| 0.026381 / 0.534201 (-0.507820) \| 0.475995 / 0.579283 (-0.103289) \| 0.611939 / 0.434364 (0.177575) \| 0.541441 / 0.540337 (0.001104) \| 0.742796 / 1.386936 (-0.644140) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7929929525e734f7232cfc68d1d22fb8d53c54a3 \"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) \| 0.006140 / 0.011353 (-0.005213) \| 0.003664 / 0.011008 (-0.007344) \| 0.080765 / 0.038508 (0.042257) \| 0.065009 / 0.023109 (0.041900) \| 0.312787 / 0.275898 (0.036889) \| 0.354637 / 0.323480 (0.031157) \| 0.004846 / 0.007986 (-0.003140) \| 0.003019 / 0.004328 (-0.001310) \| 0.062823 / 0.004250 (0.058573) \| 0.050446 / 0.037052 (0.013394) \| 0.314478 / 0.258489 (0.055989) \| 0.360206 / 0.293841 (0.066365) \| 0.027282 / 0.128546 (-0.101265) \| 0.008024 / 0.075646 (-0.067622) \| 0.262125 / 0.419271 (-0.157146) \| 0.045793 / 0.043533 (0.002260) \| 0.310508 / 0.255139 (0.055369) \| 0.340899 / 0.283200 (0.057699) \| 0.021850 / 0.141683 (-0.119833) \| 1.510791 / 1.452155 (0.058636) \| 1.570661 / 1.492716 (0.077944) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.192136 / 0.018006 (0.174130) \| 0.449310 / 0.000490 (0.448820) \| 0.004556 / 0.000200 (0.004356) \| 0.000078 / 0.000054 (0.000023) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023689 / 0.037411 (-0.013722) \| 0.076316 / 0.014526 (0.061791) \| 0.084800 / 0.176557 (-0.091757) \| 0.153154 / 0.737135 (-0.583981) \| 0.086467 / 0.296338 (-0.209871) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.432254 / 0.215209 (0.217045) \| 4.305098 / 2.077655 (2.227443) \| 2.304267 / 1.504120 (0.800147) \| 2.139503 / 1.541195 (0.598309) \| 2.220414 / 1.468490 (0.751924) \| 0.498595 / 4.584777 (-4.086182) \| 3.058593 / 3.745712 (-0.687119) \| 4.324501 / 5.269862 (-0.945361) \| 2.667731 / 4.565676 (-1.897946) \| 0.059917 / 0.424275 (-0.364358) \| 0.006829 / 0.007607 (-0.000778) \| 0.504608 / 0.226044 (0.278564) \| 5.044480 / 2.268929 (2.775552) \| 2.753080 / 55.444624 (-52.691545) \| 2.449265 / 6.876477 (-4.427212) \| 2.635113 / 2.142072 (0.493040) \| 0.590760 / 4.805227 (-4.214467) \| 0.130133 / 6.500664 (-6.370532) \| 0.062759 / 0.075469 (-0.012710) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.267014 / 1.841788 (-0.574773) \| 18.562890 / 8.074308 (10.488581) \| 13.991257 / 10.191392 (3.799865) \| 0.147108 / 0.680424 (-0.533315) \| 0.017216 / 0.534201 (-0.516985) \| 0.330317 / 0.579283 (-0.248966) \| 0.351328 / 0.434364 (-0.083036) \| 0.381097 / 0.540337 (-0.159241) \| 0.558718 / 1.386936 (-0.828218) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006385 / 0.011353 (-0.004967) \| 0.003668 / 0.011008 (-0.007340) \| 0.062581 / 0.038508 (0.024073) \| 0.067006 / 0.023109 (0.043896) \| 0.428465 / 0.275898 (0.152567) \| 0.466106 / 0.323480 (0.142626) \| 0.005806 / 0.007986 (-0.002180) \| 0.003117 / 0.004328 (-0.001212) \| 0.063554 / 0.004250 (0.059303) \| 0.054404 / 0.037052 (0.017352) \| 0.431168 / 0.258489 (0.172679) \| 0.467578 / 0.293841 (0.173737) \| 0.027779 / 0.128546 (-0.100767) \| 0.008055 / 0.075646 (-0.067592) \| 0.067718 / 0.419271 (-0.351554) \| 0.043042 / 0.043533 (-0.000491) \| 0.425926 / 0.255139 (0.170787) \| 0.453699 / 0.283200 (0.170500) \| 0.023495 / 0.141683 (-0.118187) \| 1.435356 / 1.452155 (-0.016799) \| 1.509340 / 1.492716 (0.016624) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.242322 / 0.018006 (0.224316) \| 0.446865 / 0.000490 (0.446376) \| 0.001079 / 0.000200 (0.000879) \| 0.000065 / 0.000054 (0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025376 / 0.037411 (-0.012035) \| 0.079373 / 0.014526 (0.064847) \| 0.088554 / 0.176557 (-0.088002) \| 0.141026 / 0.737135 (-0.596109) \| 0.090666 / 0.296338 (-0.205672) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.434800 / 0.215209 (0.219590) \| 4.314491 / 2.077655 (2.236836) \| 2.320688 / 1.504120 (0.816568) \| 2.163941 / 1.541195 (0.622747) \| 2.292576 / 1.468490 (0.824086) \| 0.500226 / 4.584777 (-4.084551) \| 3.114604 / 3.745712 (-0.631108) \| 4.206997 / 5.269862 (-1.062864) \| 2.461126 / 4.565676 (-2.104551) \| 0.057717 / 0.424275 (-0.366558) \| 0.006989 / 0.007607 (-0.000618) \| 0.515623 / 0.226044 (0.289579) \| 5.155301 / 2.268929 (2.886372) \| 2.733589 / 55.444624 (-52.711035) \| 2.542111 / 6.876477 (-4.334366) \| 2.697035 / 2.142072 (0.554963) \| 0.594213 / 4.805227 (-4.211014) \| 0.128537 / 6.500664 (-6.372127) \| 0.065223 / 0.075469 (-0.010246) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.306738 / 1.841788 (-0.535050) \| 19.065370 / 8.074308 (10.991062) \| 14.242096 / 10.191392 (4.050704) \| 0.146177 / 0.680424 (-0.534246) \| 0.017186 / 0.534201 (-0.517015) \| 0.337224 / 0.579283 (-0.242059) \| 0.349997 / 0.434364 (-0.084367) \| 0.390408 / 0.540337 (-0.149930) \| 0.524597 / 1.386936 (-0.862339) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#69ec36948b0ef1f194e9dcd43ec53a50b7708962 \"CML watermark\")\n" ]
1,826,293,414	6,094	Fix deprecation of use_auth_token in DownloadConfig	closed	2023-07-28T11:52:21	2023-07-31T05:08:41	2023-07-31T04:59:50	https://github.com/huggingface/datasets/pull/6094	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6094", "html_url": "https://github.com/huggingface/datasets/pull/6094", "diff_url": "https://github.com/huggingface/datasets/pull/6094.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6094.patch", "merged_at": "2023-07-31T04:59:50" }	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) \| 0.008996 / 0.011353 (-0.002357) \| 0.004976 / 0.011008 (-0.006033) \| 0.114495 / 0.038508 (0.075987) \| 0.083958 / 0.023109 (0.060849) \| 0.408395 / 0.275898 (0.132497) \| 0.456757 / 0.323480 (0.133278) \| 0.006396 / 0.007986 (-0.001589) \| 0.004315 / 0.004328 (-0.000014) \| 0.093558 / 0.004250 (0.089307) \| 0.062067 / 0.037052 (0.025014) \| 0.423452 / 0.258489 (0.164963) \| 0.463947 / 0.293841 (0.170106) \| 0.049934 / 0.128546 (-0.078613) \| 0.013937 / 0.075646 (-0.061709) \| 0.365809 / 0.419271 (-0.053463) \| 0.067382 / 0.043533 (0.023849) \| 0.418860 / 0.255139 (0.163721) \| 0.463264 / 0.283200 (0.180065) \| 0.034392 / 0.141683 (-0.107291) \| 1.870685 / 1.452155 (0.418530) \| 1.975313 / 1.492716 (0.482597) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.261748 / 0.018006 (0.243742) \| 0.645510 / 0.000490 (0.645020) \| 0.000376 / 0.000200 (0.000176) \| 0.000077 / 0.000054 (0.000023) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032129 / 0.037411 (-0.005282) \| 0.104309 / 0.014526 (0.089783) \| 0.113154 / 0.176557 (-0.063403) \| 0.186795 / 0.737135 (-0.550341) \| 0.115584 / 0.296338 (-0.180755) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.577755 / 0.215209 (0.362546) \| 5.984988 / 2.077655 (3.907333) \| 2.581967 / 1.504120 (1.077848) \| 2.305744 / 1.541195 (0.764549) \| 2.359618 / 1.468490 (0.891128) \| 0.882892 / 4.584777 (-3.701885) \| 5.755578 / 3.745712 (2.009866) \| 8.718373 / 5.269862 (3.448511) \| 5.217586 / 4.565676 (0.651909) \| 0.099785 / 0.424275 (-0.324490) \| 0.009008 / 0.007607 (0.001401) \| 0.730937 / 0.226044 (0.504892) \| 7.265309 / 2.268929 (4.996381) \| 3.487167 / 55.444624 (-51.957457) \| 2.750090 / 6.876477 (-4.126386) \| 3.060198 / 2.142072 (0.918125) \| 1.069945 / 4.805227 (-3.735282) \| 0.227143 / 6.500664 (-6.273521) \| 0.083601 / 0.075469 (0.008132) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.754375 / 1.841788 (-0.087412) \| 25.448731 / 8.074308 (17.374423) \| 22.385943 / 10.191392 (12.194551) \| 0.249921 / 0.680424 (-0.430503) \| 0.034138 / 0.534201 (-0.500063) \| 0.535170 / 0.579283 (-0.044113) \| 0.605474 / 0.434364 (0.171110) \| 0.580025 / 0.540337 (0.039688) \| 0.810537 / 1.386936 (-0.576399) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009117 / 0.011353 (-0.002236) \| 0.005029 / 0.011008 (-0.005979) \| 0.082200 / 0.038508 (0.043691) \| 0.082386 / 0.023109 (0.059277) \| 0.491869 / 0.275898 (0.215971) \| 0.546735 / 0.323480 (0.223255) \| 0.006893 / 0.007986 (-0.001093) \| 0.004571 / 0.004328 (0.000243) \| 0.085361 / 0.004250 (0.081111) \| 0.063342 / 0.037052 (0.026290) \| 0.522522 / 0.258489 (0.264033) \| 0.560784 / 0.293841 (0.266943) \| 0.047685 / 0.128546 (-0.080861) \| 0.017741 / 0.075646 (-0.057905) \| 0.098204 / 0.419271 (-0.321067) \| 0.062919 / 0.043533 (0.019386) \| 0.504005 / 0.255139 (0.248866) \| 0.547022 / 0.283200 (0.263823) \| 0.033731 / 0.141683 (-0.107952) \| 1.869765 / 1.452155 (0.417610) \| 1.935867 / 1.492716 (0.443151) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.304756 / 0.018006 (0.286750) \| 0.623647 / 0.000490 (0.623157) \| 0.000508 / 0.000200 (0.000308) \| 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) \| 0.043627 / 0.037411 (0.006216) \| 0.107183 / 0.014526 (0.092657) \| 0.119304 / 0.176557 (-0.057253) \| 0.192651 / 0.737135 (-0.544485) \| 0.125118 / 0.296338 (-0.171221) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.669980 / 0.215209 (0.454771) \| 6.566068 / 2.077655 (4.488413) \| 3.136271 / 1.504120 (1.632152) \| 2.964643 / 1.541195 (1.423448) \| 2.936772 / 1.468490 (1.468282) \| 0.885205 / 4.584777 (-3.699572) \| 5.539062 / 3.745712 (1.793350) \| 5.006133 / 5.269862 (-0.263729) \| 3.313697 / 4.565676 (-1.251979) \| 0.102975 / 0.424275 (-0.321301) \| 0.010759 / 0.007607 (0.003152) \| 0.791176 / 0.226044 (0.565132) \| 7.822195 / 2.268929 (5.553266) \| 3.982315 / 55.444624 (-51.462309) \| 3.357026 / 6.876477 (-3.519451) \| 3.561307 / 2.142072 (1.419234) \| 1.056966 / 4.805227 (-3.748261) \| 0.220476 / 6.500664 (-6.280188) \| 0.090535 / 0.075469 (0.015066) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.897984 / 1.841788 (0.056196) \| 26.411411 / 8.074308 (18.337103) \| 22.951939 / 10.191392 (12.760547) \| 0.216091 / 0.680424 (-0.464333) \| 0.037005 / 0.534201 (-0.497196) \| 0.505585 / 0.579283 (-0.073698) \| 0.617794 / 0.434364 (0.183430) \| 0.604631 / 0.540337 (0.064293) \| 0.826356 / 1.386936 (-0.560580) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ca6342c0177adc3a1d114740444e207b8525ed6e \"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) \| 0.006850 / 0.011353 (-0.004503) \| 0.004062 / 0.011008 (-0.006947) \| 0.086587 / 0.038508 (0.048079) \| 0.079587 / 0.023109 (0.056478) \| 0.353601 / 0.275898 (0.077702) \| 0.396399 / 0.323480 (0.072919) \| 0.004182 / 0.007986 (-0.003804) \| 0.004445 / 0.004328 (0.000117) \| 0.065100 / 0.004250 (0.060849) \| 0.057386 / 0.037052 (0.020334) \| 0.356945 / 0.258489 (0.098456) \| 0.407093 / 0.293841 (0.113252) \| 0.031949 / 0.128546 (-0.096597) \| 0.008525 / 0.075646 (-0.067121) \| 0.291310 / 0.419271 (-0.127961) \| 0.053638 / 0.043533 (0.010105) \| 0.359381 / 0.255139 (0.104242) \| 0.399473 / 0.283200 (0.116273) \| 0.025880 / 0.141683 (-0.115803) \| 1.487604 / 1.452155 (0.035449) \| 1.550528 / 1.492716 (0.057812) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.201106 / 0.018006 (0.183099) \| 0.457538 / 0.000490 (0.457048) \| 0.003995 / 0.000200 (0.003795) \| 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) \| 0.030365 / 0.037411 (-0.007046) \| 0.088064 / 0.014526 (0.073538) \| 0.096432 / 0.176557 (-0.080124) \| 0.158063 / 0.737135 (-0.579072) \| 0.098258 / 0.296338 (-0.198080) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.405351 / 0.215209 (0.190142) \| 4.032639 / 2.077655 (1.954984) \| 2.018357 / 1.504120 (0.514237) \| 1.848493 / 1.541195 (0.307298) \| 1.929401 / 1.468490 (0.460910) \| 0.488729 / 4.584777 (-4.096048) \| 3.586114 / 3.745712 (-0.159598) \| 5.279054 / 5.269862 (0.009193) \| 3.113275 / 4.565676 (-1.452402) \| 0.057373 / 0.424275 (-0.366902) \| 0.007416 / 0.007607 (-0.000191) \| 0.485514 / 0.226044 (0.259470) \| 4.854389 / 2.268929 (2.585461) \| 2.493113 / 55.444624 (-52.951512) \| 2.128836 / 6.876477 (-4.747641) \| 2.383669 / 2.142072 (0.241596) \| 0.588266 / 4.805227 (-4.216962) \| 0.133603 / 6.500664 (-6.367061) \| 0.061812 / 0.075469 (-0.013657) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.260841 / 1.841788 (-0.580947) \| 20.086954 / 8.074308 (12.012646) \| 14.620932 / 10.191392 (4.429540) \| 0.161525 / 0.680424 (-0.518899) \| 0.018102 / 0.534201 (-0.516099) \| 0.393810 / 0.579283 (-0.185473) \| 0.406974 / 0.434364 (-0.027390) \| 0.462732 / 0.540337 (-0.077606) \| 0.634221 / 1.386936 (-0.752715) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006692 / 0.011353 (-0.004661) \| 0.004068 / 0.011008 (-0.006940) \| 0.068009 / 0.038508 (0.029501) \| 0.081298 / 0.023109 (0.058189) \| 0.363531 / 0.275898 (0.087633) \| 0.408482 / 0.323480 (0.085002) \| 0.005601 / 0.007986 (-0.002384) \| 0.003385 / 0.004328 (-0.000943) \| 0.068043 / 0.004250 (0.063792) \| 0.059739 / 0.037052 (0.022687) \| 0.374043 / 0.258489 (0.115553) \| 0.407219 / 0.293841 (0.113378) \| 0.031194 / 0.128546 (-0.097352) \| 0.008630 / 0.075646 (-0.067017) \| 0.073755 / 0.419271 (-0.345517) \| 0.049831 / 0.043533 (0.006298) \| 0.363664 / 0.255139 (0.108525) \| 0.381515 / 0.283200 (0.098315) \| 0.026331 / 0.141683 (-0.115352) \| 1.507771 / 1.452155 (0.055617) \| 1.554403 / 1.492716 (0.061686) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.226309 / 0.018006 (0.208302) \| 0.452428 / 0.000490 (0.451938) \| 0.000937 / 0.000200 (0.000737) \| 0.000069 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031899 / 0.037411 (-0.005513) \| 0.092090 / 0.014526 (0.077564) \| 0.100838 / 0.176557 (-0.075718) \| 0.153722 / 0.737135 (-0.583413) \| 0.101950 / 0.296338 (-0.194389) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.417879 / 0.215209 (0.202669) \| 4.171939 / 2.077655 (2.094284) \| 2.312937 / 1.504120 (0.808817) \| 2.209991 / 1.541195 (0.668796) \| 2.329469 / 1.468490 (0.860979) \| 0.484576 / 4.584777 (-4.100201) \| 3.659198 / 3.745712 (-0.086514) \| 5.255227 / 5.269862 (-0.014634) \| 3.047430 / 4.565676 (-1.518247) \| 0.057029 / 0.424275 (-0.367246) \| 0.007735 / 0.007607 (0.000127) \| 0.499962 / 0.226044 (0.273918) \| 4.991655 / 2.268929 (2.722727) \| 2.755999 / 55.444624 (-52.688625) \| 2.374034 / 6.876477 (-4.502443) \| 2.599759 / 2.142072 (0.457687) \| 0.600319 / 4.805227 (-4.204908) \| 0.146176 / 6.500664 (-6.354488) \| 0.062328 / 0.075469 (-0.013142) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.346065 / 1.841788 (-0.495722) \| 20.430343 / 8.074308 (12.356035) \| 14.632959 / 10.191392 (4.441567) \| 0.167007 / 0.680424 (-0.513417) \| 0.018588 / 0.534201 (-0.515613) \| 0.396015 / 0.579283 (-0.183268) \| 0.429384 / 0.434364 (-0.004980) \| 0.467746 / 0.540337 (-0.072591) \| 0.615166 / 1.386936 (-0.771770) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#289bcc2ae9bf98c9414b6846ae603178a1816d3f \"CML watermark\")\n" ]
1,826,210,490	6,093	Deprecate `download_custom`	closed	2023-07-28T10:49:06	2023-08-21T17:51:34	2023-07-28T11:30:02	https://github.com/huggingface/datasets/pull/6093	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6093", "html_url": "https://github.com/huggingface/datasets/pull/6093", "diff_url": "https://github.com/huggingface/datasets/pull/6093.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6093.patch", "merged_at": "2023-07-28T11:30:02" }	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) \| 0.007498 / 0.011353 (-0.003855) \| 0.004158 / 0.011008 (-0.006850) \| 0.087568 / 0.038508 (0.049060) \| 0.083265 / 0.023109 (0.060156) \| 0.378505 / 0.275898 (0.102607) \| 0.399025 / 0.323480 (0.075545) \| 0.006173 / 0.007986 (-0.001813) \| 0.003743 / 0.004328 (-0.000586) \| 0.071958 / 0.004250 (0.067707) \| 0.059323 / 0.037052 (0.022271) \| 0.377084 / 0.258489 (0.118595) \| 0.408358 / 0.293841 (0.114517) \| 0.035191 / 0.128546 (-0.093356) \| 0.009408 / 0.075646 (-0.066238) \| 0.312587 / 0.419271 (-0.106685) \| 0.058073 / 0.043533 (0.014540) \| 0.381977 / 0.255139 (0.126838) \| 0.395611 / 0.283200 (0.112411) \| 0.024191 / 0.141683 (-0.117491) \| 1.572735 / 1.452155 (0.120581) \| 1.687186 / 1.492716 (0.194470) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.208886 / 0.018006 (0.190879) \| 0.474625 / 0.000490 (0.474135) \| 0.006261 / 0.000200 (0.006061) \| 0.000093 / 0.000054 (0.000038) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031401 / 0.037411 (-0.006011) \| 0.086433 / 0.014526 (0.071907) \| 0.108405 / 0.176557 (-0.068152) \| 0.174564 / 0.737135 (-0.562571) \| 0.099932 / 0.296338 (-0.196407) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.407059 / 0.215209 (0.191850) \| 4.102056 / 2.077655 (2.024401) \| 1.975397 / 1.504120 (0.471277) \| 1.807117 / 1.541195 (0.265922) \| 1.908667 / 1.468490 (0.440177) \| 0.525880 / 4.584777 (-4.058897) \| 3.899639 / 3.745712 (0.153927) \| 4.358664 / 5.269862 (-0.911198) \| 2.586185 / 4.565676 (-1.979492) \| 0.061967 / 0.424275 (-0.362308) \| 0.007656 / 0.007607 (0.000049) \| 0.504851 / 0.226044 (0.278807) \| 5.004429 / 2.268929 (2.735500) \| 2.515540 / 55.444624 (-52.929084) \| 2.183142 / 6.876477 (-4.693334) \| 2.369835 / 2.142072 (0.227763) \| 0.623527 / 4.805227 (-4.181700) \| 0.145105 / 6.500664 (-6.355559) \| 0.063924 / 0.075469 (-0.011546) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.472661 / 1.841788 (-0.369126) \| 21.781655 / 8.074308 (13.707347) \| 15.628820 / 10.191392 (5.437428) \| 0.182342 / 0.680424 (-0.498082) \| 0.021139 / 0.534201 (-0.513062) \| 0.438610 / 0.579283 (-0.140673) \| 0.451343 / 0.434364 (0.016979) \| 0.563320 / 0.540337 (0.022983) \| 0.740976 / 1.386936 (-0.645960) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007492 / 0.011353 (-0.003861) \| 0.004429 / 0.011008 (-0.006579) \| 0.068517 / 0.038508 (0.030008) \| 0.078533 / 0.023109 (0.055424) \| 0.383530 / 0.275898 (0.107632) \| 0.435061 / 0.323480 (0.111581) \| 0.005955 / 0.007986 (-0.002030) \| 0.003645 / 0.004328 (-0.000683) \| 0.068792 / 0.004250 (0.064541) \| 0.062452 / 0.037052 (0.025399) \| 0.408768 / 0.258489 (0.150279) \| 0.438538 / 0.293841 (0.144697) \| 0.032038 / 0.128546 (-0.096508) \| 0.009196 / 0.075646 (-0.066450) \| 0.074495 / 0.419271 (-0.344776) \| 0.051322 / 0.043533 (0.007789) \| 0.394458 / 0.255139 (0.139319) \| 0.424763 / 0.283200 (0.141564) \| 0.024890 / 0.141683 (-0.116793) \| 1.568322 / 1.452155 (0.116167) \| 1.703903 / 1.492716 (0.211187) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.249630 / 0.018006 (0.231624) \| 0.471412 / 0.000490 (0.470923) \| 0.000435 / 0.000200 (0.000235) \| 0.000060 / 0.000054 (0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033054 / 0.037411 (-0.004358) \| 0.100150 / 0.014526 (0.085624) \| 0.101704 / 0.176557 (-0.074853) \| 0.164031 / 0.737135 (-0.573104) \| 0.112497 / 0.296338 (-0.183841) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.487150 / 0.215209 (0.271941) \| 4.662335 / 2.077655 (2.584681) \| 2.477285 / 1.504120 (0.973165) \| 2.294033 / 1.541195 (0.752838) \| 2.380143 / 1.468490 (0.911653) \| 0.519182 / 4.584777 (-4.065595) \| 3.983589 / 3.745712 (0.237877) \| 3.669895 / 5.269862 (-1.599967) \| 2.267147 / 4.565676 (-2.298529) \| 0.063300 / 0.424275 (-0.360975) \| 0.008839 / 0.007607 (0.001232) \| 0.566766 / 0.226044 (0.340721) \| 5.533475 / 2.268929 (3.264546) \| 3.033412 / 55.444624 (-52.411212) \| 2.701793 / 6.876477 (-4.174684) \| 2.899444 / 2.142072 (0.757372) \| 0.614236 / 4.805227 (-4.190991) \| 0.139533 / 6.500664 (-6.361131) \| 0.067537 / 0.075469 (-0.007932) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.505572 / 1.841788 (-0.336216) \| 22.859062 / 8.074308 (14.784754) \| 15.044777 / 10.191392 (4.853385) \| 0.169153 / 0.680424 (-0.511271) \| 0.021027 / 0.534201 (-0.513174) \| 0.447979 / 0.579283 (-0.131304) \| 0.460676 / 0.434364 (0.026312) \| 0.506327 / 0.540337 (-0.034010) \| 0.737880 / 1.386936 (-0.649057) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#db7180eb7e3ebf52b9d1f2c6629db6d92d8a29ba \"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) \| 0.006118 / 0.011353 (-0.005235) \| 0.003692 / 0.011008 (-0.007316) \| 0.080606 / 0.038508 (0.042098) \| 0.062014 / 0.023109 (0.038905) \| 0.391886 / 0.275898 (0.115988) \| 0.423978 / 0.323480 (0.100498) \| 0.004968 / 0.007986 (-0.003017) \| 0.002911 / 0.004328 (-0.001417) \| 0.062867 / 0.004250 (0.058617) \| 0.049493 / 0.037052 (0.012441) \| 0.395656 / 0.258489 (0.137167) \| 0.432406 / 0.293841 (0.138565) \| 0.027242 / 0.128546 (-0.101304) \| 0.007938 / 0.075646 (-0.067709) \| 0.261703 / 0.419271 (-0.157569) \| 0.045922 / 0.043533 (0.002389) \| 0.391544 / 0.255139 (0.136405) \| 0.417902 / 0.283200 (0.134703) \| 0.021339 / 0.141683 (-0.120344) \| 1.508391 / 1.452155 (0.056236) \| 1.518970 / 1.492716 (0.026254) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.181159 / 0.018006 (0.163153) \| 0.431402 / 0.000490 (0.430912) \| 0.003849 / 0.000200 (0.003649) \| 0.000068 / 0.000054 (0.000014) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024498 / 0.037411 (-0.012914) \| 0.072758 / 0.014526 (0.058233) \| 0.084910 / 0.176557 (-0.091646) \| 0.148314 / 0.737135 (-0.588821) \| 0.085212 / 0.296338 (-0.211126) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.386693 / 0.215209 (0.171484) \| 3.852652 / 2.077655 (1.774997) \| 1.891758 / 1.504120 (0.387638) \| 1.718793 / 1.541195 (0.177598) \| 1.747595 / 1.468490 (0.279104) \| 0.498593 / 4.584777 (-4.086184) \| 3.057907 / 3.745712 (-0.687805) \| 4.728449 / 5.269862 (-0.541413) \| 2.966368 / 4.565676 (-1.599308) \| 0.057538 / 0.424275 (-0.366737) \| 0.006415 / 0.007607 (-0.001192) \| 0.461652 / 0.226044 (0.235608) \| 4.625944 / 2.268929 (2.357015) \| 2.306938 / 55.444624 (-53.137686) \| 1.974670 / 6.876477 (-4.901806) \| 2.146327 / 2.142072 (0.004254) \| 0.585033 / 4.805227 (-4.220195) \| 0.125936 / 6.500664 (-6.374728) \| 0.062365 / 0.075469 (-0.013104) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.263415 / 1.841788 (-0.578373) \| 18.380651 / 8.074308 (10.306343) \| 13.853410 / 10.191392 (3.662018) \| 0.144674 / 0.680424 (-0.535749) \| 0.016833 / 0.534201 (-0.517368) \| 0.330812 / 0.579283 (-0.248471) \| 0.357553 / 0.434364 (-0.076810) \| 0.383529 / 0.540337 (-0.156809) \| 0.558923 / 1.386936 (-0.828013) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006074 / 0.011353 (-0.005278) \| 0.003655 / 0.011008 (-0.007353) \| 0.062981 / 0.038508 (0.024473) \| 0.061457 / 0.023109 (0.038348) \| 0.366471 / 0.275898 (0.090573) \| 0.408463 / 0.323480 (0.084983) \| 0.004854 / 0.007986 (-0.003132) \| 0.002916 / 0.004328 (-0.001412) \| 0.062745 / 0.004250 (0.058494) \| 0.051136 / 0.037052 (0.014084) \| 0.380313 / 0.258489 (0.121824) \| 0.416945 / 0.293841 (0.123104) \| 0.027228 / 0.128546 (-0.101318) \| 0.008031 / 0.075646 (-0.067615) \| 0.067941 / 0.419271 (-0.351331) \| 0.042886 / 0.043533 (-0.000647) \| 0.370112 / 0.255139 (0.114973) \| 0.397111 / 0.283200 (0.113911) \| 0.023063 / 0.141683 (-0.118620) \| 1.476955 / 1.452155 (0.024800) \| 1.534783 / 1.492716 (0.042066) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.231462 / 0.018006 (0.213456) \| 0.439559 / 0.000490 (0.439069) \| 0.000364 / 0.000200 (0.000164) \| 0.000056 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026925 / 0.037411 (-0.010486) \| 0.079623 / 0.014526 (0.065097) \| 0.088694 / 0.176557 (-0.087862) \| 0.143163 / 0.737135 (-0.593972) \| 0.089900 / 0.296338 (-0.206438) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.451429 / 0.215209 (0.236220) \| 4.510723 / 2.077655 (2.433069) \| 2.491853 / 1.504120 (0.987733) \| 2.334670 / 1.541195 (0.793475) \| 2.395519 / 1.468490 (0.927029) \| 0.501369 / 4.584777 (-4.083408) \| 3.014019 / 3.745712 (-0.731693) \| 2.809199 / 5.269862 (-2.460662) \| 1.842195 / 4.565676 (-2.723481) \| 0.057675 / 0.424275 (-0.366600) \| 0.006742 / 0.007607 (-0.000865) \| 0.524402 / 0.226044 (0.298358) \| 5.245296 / 2.268929 (2.976367) \| 2.957990 / 55.444624 (-52.486634) \| 2.649807 / 6.876477 (-4.226670) \| 2.755909 / 2.142072 (0.613836) \| 0.589610 / 4.805227 (-4.215617) \| 0.125708 / 6.500664 (-6.374956) \| 0.062237 / 0.075469 (-0.013232) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.362758 / 1.841788 (-0.479030) \| 18.343694 / 8.074308 (10.269386) \| 13.621521 / 10.191392 (3.430129) \| 0.128866 / 0.680424 (-0.551558) \| 0.016608 / 0.534201 (-0.517593) \| 0.333071 / 0.579283 (-0.246212) \| 0.341917 / 0.434364 (-0.092447) \| 0.381075 / 0.540337 (-0.159263) \| 0.512485 / 1.386936 (-0.874451) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ab3f0165d4a2a8ab1aee1ebc4628893e17e27387 \"CML watermark\")\n", "I forgot to mention this in the initial comment, but only one public dataset (excluding gated) uses this method - `pg19`, which I just fixed.\r\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007838 / 0.011353 (-0.003515) \| 0.004791 / 0.011008 (-0.006217) \| 0.102596 / 0.038508 (0.064088) \| 0.087678 / 0.023109 (0.064569) \| 0.373858 / 0.275898 (0.097960) \| 0.416643 / 0.323480 (0.093163) \| 0.006147 / 0.007986 (-0.001839) \| 0.003837 / 0.004328 (-0.000491) \| 0.076706 / 0.004250 (0.072456) \| 0.063449 / 0.037052 (0.026396) \| 0.378392 / 0.258489 (0.119903) \| 0.431768 / 0.293841 (0.137927) \| 0.036648 / 0.128546 (-0.091898) \| 0.010042 / 0.075646 (-0.065604) \| 0.350277 / 0.419271 (-0.068995) \| 0.062892 / 0.043533 (0.019359) \| 0.376151 / 0.255139 (0.121012) \| 0.420929 / 0.283200 (0.137729) \| 0.027816 / 0.141683 (-0.113867) \| 1.791607 / 1.452155 (0.339452) \| 1.903045 / 1.492716 (0.410328) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224688 / 0.018006 (0.206682) \| 0.491941 / 0.000490 (0.491451) \| 0.004482 / 0.000200 (0.004282) \| 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) \| 0.033495 / 0.037411 (-0.003917) \| 0.099855 / 0.014526 (0.085329) \| 0.114593 / 0.176557 (-0.061964) \| 0.190947 / 0.737135 (-0.546189) \| 0.116202 / 0.296338 (-0.180136) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.488581 / 0.215209 (0.273372) \| 4.869531 / 2.077655 (2.791876) \| 2.527920 / 1.504120 (1.023800) \| 2.340021 / 1.541195 (0.798826) \| 2.432661 / 1.468490 (0.964171) \| 0.569646 / 4.584777 (-4.015131) \| 4.392036 / 3.745712 (0.646324) \| 4.987253 / 5.269862 (-0.282608) \| 2.866604 / 4.565676 (-1.699073) \| 0.067393 / 0.424275 (-0.356882) \| 0.008759 / 0.007607 (0.001152) \| 0.584327 / 0.226044 (0.358283) \| 5.853000 / 2.268929 (3.584072) \| 3.206721 / 55.444624 (-52.237904) \| 2.730867 / 6.876477 (-4.145610) \| 2.944814 / 2.142072 (0.802742) \| 0.703336 / 4.805227 (-4.101891) \| 0.173985 / 6.500664 (-6.326679) \| 0.075333 / 0.075469 (-0.000137) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.519755 / 1.841788 (-0.322033) \| 22.918038 / 8.074308 (14.843730) \| 17.211160 / 10.191392 (7.019768) \| 0.196941 / 0.680424 (-0.483483) \| 0.021833 / 0.534201 (-0.512368) \| 0.476835 / 0.579283 (-0.102448) \| 0.464513 / 0.434364 (0.030149) \| 0.559180 / 0.540337 (0.018843) \| 0.748232 / 1.386936 (-0.638704) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008461 / 0.011353 (-0.002892) \| 0.004799 / 0.011008 (-0.006209) \| 0.077466 / 0.038508 (0.038958) \| 0.103562 / 0.023109 (0.080453) \| 0.453661 / 0.275898 (0.177763) \| 0.531126 / 0.323480 (0.207647) \| 0.006618 / 0.007986 (-0.001367) \| 0.004048 / 0.004328 (-0.000280) \| 0.075446 / 0.004250 (0.071196) \| 0.072815 / 0.037052 (0.035762) \| 0.497145 / 0.258489 (0.238656) \| 0.533828 / 0.293841 (0.239987) \| 0.037657 / 0.128546 (-0.090890) \| 0.010139 / 0.075646 (-0.065507) \| 0.083759 / 0.419271 (-0.335512) \| 0.061401 / 0.043533 (0.017868) \| 0.441785 / 0.255139 (0.186646) \| 0.491678 / 0.283200 (0.208479) \| 0.033100 / 0.141683 (-0.108583) \| 1.753612 / 1.452155 (0.301458) \| 1.838956 / 1.492716 (0.346240) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.395023 / 0.018006 (0.377017) \| 0.509362 / 0.000490 (0.508872) \| 0.060742 / 0.000200 (0.060542) \| 0.000545 / 0.000054 (0.000491) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.039327 / 0.037411 (0.001916) \| 0.117345 / 0.014526 (0.102819) \| 0.124540 / 0.176557 (-0.052017) \| 0.200743 / 0.737135 (-0.536392) \| 0.126750 / 0.296338 (-0.169589) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.488597 / 0.215209 (0.273388) \| 4.875534 / 2.077655 (2.797880) \| 2.714364 / 1.504120 (1.210244) \| 2.603707 / 1.541195 (1.062513) \| 2.733547 / 1.468490 (1.265057) \| 0.575183 / 4.584777 (-4.009594) \| 4.126096 / 3.745712 (0.380384) \| 3.853803 / 5.269862 (-1.416058) \| 2.395160 / 4.565676 (-2.170516) \| 0.067391 / 0.424275 (-0.356884) \| 0.009108 / 0.007607 (0.001501) \| 0.585865 / 0.226044 (0.359820) \| 5.864878 / 2.268929 (3.595949) \| 3.153369 / 55.444624 (-52.291256) \| 2.759064 / 6.876477 (-4.117413) \| 3.032489 / 2.142072 (0.890416) \| 0.702615 / 4.805227 (-4.102613) \| 0.160034 / 6.500664 (-6.340630) \| 0.077294 / 0.075469 (0.001825) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.595069 / 1.841788 (-0.246719) \| 23.231191 / 8.074308 (15.156883) \| 16.365137 / 10.191392 (6.173745) \| 0.188360 / 0.680424 (-0.492063) \| 0.021704 / 0.534201 (-0.512497) \| 0.469996 / 0.579283 (-0.109287) \| 0.463255 / 0.434364 (0.028891) \| 0.560506 / 0.540337 (0.020169) \| 0.751006 / 1.386936 (-0.635930) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#50d9a70c666ff46ff9974c47cedc77d9f88d6471 \"CML watermark\")\n", "@mariosasko How would you stream a split zip file with just [download_and_extract or download](https://github.com/huggingface/datasets/blob/main/src/datasets/download/download_manager.py#L353)? With download_custom, it is possible to combine a split zip file. Perhaps add an option in [download](https://huggingface.co/docs/datasets/v2.2.1/en/package_reference/builder_classes#datasets.DownloadManager.download) to combine split zips. This issue may apply to other multipart file-types.\r\n\r\nEdit - \r\nIn case asked why I use split zips, I haven't been able to upload zips larger than 50 GB to HuggingFace.\r\n\r\nEdit2 -\r\nIssue is [tackled](https://discuss.huggingface.co/t/download-custom-method-of-streamingdownloadmanager-not-implemented/28298/8) for split zips. " ]
1,826,111,806	6,092	Minor fix in `iter_files` for hidden files	closed	2023-07-28T09:50:12	2023-07-28T10:59:28	2023-07-28T10:50:10	https://github.com/huggingface/datasets/pull/6092	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6092", "html_url": "https://github.com/huggingface/datasets/pull/6092", "diff_url": "https://github.com/huggingface/datasets/pull/6092.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6092.patch", "merged_at": "2023-07-28T10:50:09" }	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) \| 0.007873 / 0.011353 (-0.003480) \| 0.004585 / 0.011008 (-0.006423) \| 0.101622 / 0.038508 (0.063114) \| 0.092459 / 0.023109 (0.069350) \| 0.365157 / 0.275898 (0.089259) \| 0.405943 / 0.323480 (0.082463) \| 0.006229 / 0.007986 (-0.001756) \| 0.003811 / 0.004328 (-0.000518) \| 0.073831 / 0.004250 (0.069580) \| 0.065097 / 0.037052 (0.028045) \| 0.378912 / 0.258489 (0.120423) \| 0.422174 / 0.293841 (0.128333) \| 0.036244 / 0.128546 (-0.092302) \| 0.009677 / 0.075646 (-0.065970) \| 0.345164 / 0.419271 (-0.074107) \| 0.061632 / 0.043533 (0.018099) \| 0.370350 / 0.255139 (0.115211) \| 0.418245 / 0.283200 (0.135046) \| 0.027272 / 0.141683 (-0.114411) \| 1.774047 / 1.452155 (0.321892) \| 1.880278 / 1.492716 (0.387562) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.217238 / 0.018006 (0.199231) \| 0.489560 / 0.000490 (0.489071) \| 0.004013 / 0.000200 (0.003813) \| 0.000092 / 0.000054 (0.000038) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034139 / 0.037411 (-0.003272) \| 0.103831 / 0.014526 (0.089305) \| 0.114353 / 0.176557 (-0.062204) \| 0.182034 / 0.737135 (-0.555102) \| 0.116171 / 0.296338 (-0.180168) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.448658 / 0.215209 (0.233449) \| 4.520849 / 2.077655 (2.443195) \| 2.216121 / 1.504120 (0.712001) \| 2.034596 / 1.541195 (0.493402) \| 2.193216 / 1.468490 (0.724725) \| 0.568166 / 4.584777 (-4.016611) \| 4.133587 / 3.745712 (0.387875) \| 4.641117 / 5.269862 (-0.628744) \| 2.772913 / 4.565676 (-1.792764) \| 0.067664 / 0.424275 (-0.356611) \| 0.008719 / 0.007607 (0.001112) \| 0.547723 / 0.226044 (0.321678) \| 5.438325 / 2.268929 (3.169397) \| 2.877667 / 55.444624 (-52.566958) \| 2.477503 / 6.876477 (-4.398974) \| 2.688209 / 2.142072 (0.546136) \| 0.692593 / 4.805227 (-4.112634) \| 0.154549 / 6.500664 (-6.346115) \| 0.073286 / 0.075469 (-0.002183) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.610927 / 1.841788 (-0.230861) \| 23.413345 / 8.074308 (15.339037) \| 16.851819 / 10.191392 (6.660427) \| 0.170076 / 0.680424 (-0.510348) \| 0.021428 / 0.534201 (-0.512773) \| 0.468184 / 0.579283 (-0.111099) \| 0.491820 / 0.434364 (0.057456) \| 0.553453 / 0.540337 (0.013115) \| 0.762303 / 1.386936 (-0.624633) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008033 / 0.011353 (-0.003320) \| 0.004638 / 0.011008 (-0.006370) \| 0.077044 / 0.038508 (0.038536) \| 0.096529 / 0.023109 (0.073420) \| 0.428735 / 0.275898 (0.152837) \| 0.477303 / 0.323480 (0.153823) \| 0.006040 / 0.007986 (-0.001946) \| 0.003808 / 0.004328 (-0.000521) \| 0.076042 / 0.004250 (0.071791) \| 0.066123 / 0.037052 (0.029071) \| 0.445482 / 0.258489 (0.186993) \| 0.481350 / 0.293841 (0.187509) \| 0.036951 / 0.128546 (-0.091595) \| 0.009944 / 0.075646 (-0.065703) \| 0.082731 / 0.419271 (-0.336541) \| 0.057490 / 0.043533 (0.013958) \| 0.432668 / 0.255139 (0.177529) \| 0.461146 / 0.283200 (0.177947) \| 0.027330 / 0.141683 (-0.114353) \| 1.784195 / 1.452155 (0.332040) \| 1.834776 / 1.492716 (0.342059) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.254104 / 0.018006 (0.236097) \| 0.475810 / 0.000490 (0.475321) \| 0.000459 / 0.000200 (0.000259) \| 0.000069 / 0.000054 (0.000014) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037058 / 0.037411 (-0.000353) \| 0.114962 / 0.014526 (0.100436) \| 0.123725 / 0.176557 (-0.052832) \| 0.188885 / 0.737135 (-0.548251) \| 0.125668 / 0.296338 (-0.170670) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.492627 / 0.215209 (0.277418) \| 4.900625 / 2.077655 (2.822970) \| 2.546349 / 1.504120 (1.042229) \| 2.360350 / 1.541195 (0.819155) \| 2.477975 / 1.468490 (1.009485) \| 0.574042 / 4.584777 (-4.010735) \| 4.408414 / 3.745712 (0.662702) \| 3.836640 / 5.269862 (-1.433222) \| 2.438450 / 4.565676 (-2.127227) \| 0.067706 / 0.424275 (-0.356569) \| 0.009165 / 0.007607 (0.001558) \| 0.580313 / 0.226044 (0.354269) \| 5.798211 / 2.268929 (3.529283) \| 3.098480 / 55.444624 (-52.346145) \| 2.740180 / 6.876477 (-4.136296) \| 2.984548 / 2.142072 (0.842476) \| 0.702550 / 4.805227 (-4.102677) \| 0.158248 / 6.500664 (-6.342416) \| 0.073999 / 0.075469 (-0.001470) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.636034 / 1.841788 (-0.205754) \| 24.068000 / 8.074308 (15.993692) \| 17.123987 / 10.191392 (6.932595) \| 0.210101 / 0.680424 (-0.470323) \| 0.022555 / 0.534201 (-0.511646) \| 0.509354 / 0.579283 (-0.069929) \| 0.540739 / 0.434364 (0.106375) \| 0.546048 / 0.540337 (0.005711) \| 0.719155 / 1.386936 (-0.667781) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#40530382ba98f54445de8820943b1236d4a4704f \"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) \| 0.007342 / 0.011353 (-0.004010) \| 0.004579 / 0.011008 (-0.006429) \| 0.087050 / 0.038508 (0.048542) \| 0.089001 / 0.023109 (0.065892) \| 0.307319 / 0.275898 (0.031421) \| 0.377573 / 0.323480 (0.054093) \| 0.006472 / 0.007986 (-0.001514) \| 0.004287 / 0.004328 (-0.000041) \| 0.067226 / 0.004250 (0.062976) \| 0.063147 / 0.037052 (0.026094) \| 0.314541 / 0.258489 (0.056052) \| 0.369919 / 0.293841 (0.076078) \| 0.031283 / 0.128546 (-0.097263) \| 0.009175 / 0.075646 (-0.066471) \| 0.289211 / 0.419271 (-0.130061) \| 0.053444 / 0.043533 (0.009911) \| 0.307308 / 0.255139 (0.052169) \| 0.346221 / 0.283200 (0.063021) \| 0.027948 / 0.141683 (-0.113735) \| 1.475177 / 1.452155 (0.023022) \| 1.575971 / 1.492716 (0.083255) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291092 / 0.018006 (0.273086) \| 0.696951 / 0.000490 (0.696461) \| 0.005211 / 0.000200 (0.005011) \| 0.000094 / 0.000054 (0.000040) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031787 / 0.037411 (-0.005625) \| 0.084382 / 0.014526 (0.069857) \| 0.106474 / 0.176557 (-0.070083) \| 0.161472 / 0.737135 (-0.575663) \| 0.108650 / 0.296338 (-0.187688) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.379656 / 0.215209 (0.164447) \| 3.784072 / 2.077655 (1.706417) \| 1.826580 / 1.504120 (0.322460) \| 1.654916 / 1.541195 (0.113721) \| 1.730698 / 1.468490 (0.262208) \| 0.478003 / 4.584777 (-4.106774) \| 3.564920 / 3.745712 (-0.180792) \| 5.824873 / 5.269862 (0.555012) \| 3.454563 / 4.565676 (-1.111113) \| 0.056646 / 0.424275 (-0.367629) \| 0.007410 / 0.007607 (-0.000197) \| 0.461781 / 0.226044 (0.235737) \| 4.600928 / 2.268929 (2.331999) \| 2.351887 / 55.444624 (-53.092738) \| 1.986470 / 6.876477 (-4.890007) \| 2.311623 / 2.142072 (0.169551) \| 0.571247 / 4.805227 (-4.233980) \| 0.132191 / 6.500664 (-6.368473) \| 0.059943 / 0.075469 (-0.015526) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.253142 / 1.841788 (-0.588646) \| 21.294983 / 8.074308 (13.220675) \| 14.522429 / 10.191392 (4.331037) \| 0.166663 / 0.680424 (-0.513761) \| 0.019694 / 0.534201 (-0.514507) \| 0.395908 / 0.579283 (-0.183375) \| 0.413283 / 0.434364 (-0.021081) \| 0.457739 / 0.540337 (-0.082599) \| 0.664361 / 1.386936 (-0.722575) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007228 / 0.011353 (-0.004124) \| 0.004941 / 0.011008 (-0.006067) \| 0.065381 / 0.038508 (0.026873) \| 0.090790 / 0.023109 (0.067681) \| 0.391315 / 0.275898 (0.115417) \| 0.416518 / 0.323480 (0.093038) \| 0.007015 / 0.007986 (-0.000970) \| 0.004417 / 0.004328 (0.000089) \| 0.067235 / 0.004250 (0.062985) \| 0.068092 / 0.037052 (0.031039) \| 0.403031 / 0.258489 (0.144542) \| 0.434013 / 0.293841 (0.140172) \| 0.032004 / 0.128546 (-0.096542) \| 0.009242 / 0.075646 (-0.066404) \| 0.071222 / 0.419271 (-0.348050) \| 0.054207 / 0.043533 (0.010674) \| 0.386198 / 0.255139 (0.131059) \| 0.404350 / 0.283200 (0.121150) \| 0.036284 / 0.141683 (-0.105399) \| 1.488814 / 1.452155 (0.036660) \| 1.587785 / 1.492716 (0.095069) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.313760 / 0.018006 (0.295754) \| 0.747778 / 0.000490 (0.747289) \| 0.003307 / 0.000200 (0.003107) \| 0.000113 / 0.000054 (0.000058) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034321 / 0.037411 (-0.003090) \| 0.088266 / 0.014526 (0.073740) \| 0.112874 / 0.176557 (-0.063682) \| 0.171554 / 0.737135 (-0.565581) \| 0.111356 / 0.296338 (-0.184982) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.422624 / 0.215209 (0.207415) \| 4.212079 / 2.077655 (2.134425) \| 2.242742 / 1.504120 (0.738622) \| 2.072555 / 1.541195 (0.531360) \| 2.192648 / 1.468490 (0.724158) \| 0.488214 / 4.584777 (-4.096563) \| 3.597013 / 3.745712 (-0.148699) \| 3.477556 / 5.269862 (-1.792305) \| 2.184340 / 4.565676 (-2.381337) \| 0.057170 / 0.424275 (-0.367105) \| 0.007772 / 0.007607 (0.000165) \| 0.499455 / 0.226044 (0.273411) \| 4.988953 / 2.268929 (2.720024) \| 2.797894 / 55.444624 (-52.646731) \| 2.402215 / 6.876477 (-4.474262) \| 2.725069 / 2.142072 (0.582997) \| 0.596213 / 4.805227 (-4.209014) \| 0.136564 / 6.500664 (-6.364100) \| 0.061799 / 0.075469 (-0.013670) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.360739 / 1.841788 (-0.481049) \| 21.846457 / 8.074308 (13.772149) \| 14.568842 / 10.191392 (4.377450) \| 0.168980 / 0.680424 (-0.511444) \| 0.018795 / 0.534201 (-0.515406) \| 0.396173 / 0.579283 (-0.183110) \| 0.418651 / 0.434364 (-0.015713) \| 0.480042 / 0.540337 (-0.060295) \| 0.650803 / 1.386936 (-0.736133) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b7d460304487d4daab0a64ca0ca707e896367ca1 \"CML watermark\")\n" ]
1,826,086,487	6,091	Bump fsspec from 2021.11.1 to 2022.3.0	closed	2023-07-28T09:37:15	2023-07-28T10:16:11	2023-07-28T10:07:02	https://github.com/huggingface/datasets/pull/6091	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6091", "html_url": "https://github.com/huggingface/datasets/pull/6091", "diff_url": "https://github.com/huggingface/datasets/pull/6091.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6091.patch", "merged_at": "2023-07-28T10:07:02" }	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) \| 0.006640 / 0.011353 (-0.004713) \| 0.004077 / 0.011008 (-0.006931) \| 0.084905 / 0.038508 (0.046397) \| 0.074004 / 0.023109 (0.050895) \| 0.315968 / 0.275898 (0.040070) \| 0.351594 / 0.323480 (0.028114) \| 0.005623 / 0.007986 (-0.002362) \| 0.003476 / 0.004328 (-0.000852) \| 0.065089 / 0.004250 (0.060839) \| 0.054683 / 0.037052 (0.017631) \| 0.314983 / 0.258489 (0.056494) \| 0.371776 / 0.293841 (0.077935) \| 0.031727 / 0.128546 (-0.096819) \| 0.008786 / 0.075646 (-0.066860) \| 0.289905 / 0.419271 (-0.129367) \| 0.053340 / 0.043533 (0.009807) \| 0.311802 / 0.255139 (0.056663) \| 0.351927 / 0.283200 (0.068727) \| 0.024453 / 0.141683 (-0.117229) \| 1.491727 / 1.452155 (0.039572) \| 1.585027 / 1.492716 (0.092310) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.238757 / 0.018006 (0.220750) \| 0.557691 / 0.000490 (0.557202) \| 0.005158 / 0.000200 (0.004958) \| 0.000204 / 0.000054 (0.000149) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028435 / 0.037411 (-0.008977) \| 0.082219 / 0.014526 (0.067693) \| 0.096932 / 0.176557 (-0.079625) \| 0.153802 / 0.737135 (-0.583333) \| 0.098338 / 0.296338 (-0.198001) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.383448 / 0.215209 (0.168238) \| 3.816074 / 2.077655 (1.738420) \| 1.835111 / 1.504120 (0.330991) \| 1.662326 / 1.541195 (0.121131) \| 1.720202 / 1.468490 (0.251712) \| 0.483107 / 4.584777 (-4.101669) \| 3.648528 / 3.745712 (-0.097184) \| 4.020929 / 5.269862 (-1.248932) \| 2.433141 / 4.565676 (-2.132536) \| 0.057081 / 0.424275 (-0.367194) \| 0.007303 / 0.007607 (-0.000304) \| 0.461366 / 0.226044 (0.235322) \| 4.609090 / 2.268929 (2.340162) \| 2.355940 / 55.444624 (-53.088684) \| 1.989833 / 6.876477 (-4.886644) \| 2.201451 / 2.142072 (0.059378) \| 0.586156 / 4.805227 (-4.219071) \| 0.133486 / 6.500664 (-6.367178) \| 0.060062 / 0.075469 (-0.015407) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.247845 / 1.841788 (-0.593942) \| 19.624252 / 8.074308 (11.549944) \| 14.305975 / 10.191392 (4.114583) \| 0.168687 / 0.680424 (-0.511737) \| 0.018075 / 0.534201 (-0.516126) \| 0.393859 / 0.579283 (-0.185424) \| 0.407272 / 0.434364 (-0.027092) \| 0.463760 / 0.540337 (-0.076578) \| 0.629930 / 1.386936 (-0.757006) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated 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) \| 0.004345 / 0.011008 (-0.006663) \| 0.064379 / 0.038508 (0.025871) \| 0.078295 / 0.023109 (0.055186) \| 0.364532 / 0.275898 (0.088633) \| 0.395852 / 0.323480 (0.072372) \| 0.005659 / 0.007986 (-0.002327) \| 0.003515 / 0.004328 (-0.000813) \| 0.065030 / 0.004250 (0.060780) \| 0.059950 / 0.037052 (0.022898) \| 0.375420 / 0.258489 (0.116931) \| 0.411579 / 0.293841 (0.117738) \| 0.031575 / 0.128546 (-0.096972) \| 0.008737 / 0.075646 (-0.066910) \| 0.070350 / 0.419271 (-0.348922) \| 0.050607 / 0.043533 (0.007075) \| 0.359785 / 0.255139 (0.104646) \| 0.382638 / 0.283200 (0.099438) \| 0.025533 / 0.141683 (-0.116150) \| 1.564379 / 1.452155 (0.112225) \| 1.620642 / 1.492716 (0.127925) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.212779 / 0.018006 (0.194773) \| 0.563827 / 0.000490 (0.563337) \| 0.003767 / 0.000200 (0.003567) \| 0.000103 / 0.000054 (0.000049) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030275 / 0.037411 (-0.007136) \| 0.088108 / 0.014526 (0.073582) \| 0.102454 / 0.176557 (-0.074103) \| 0.156107 / 0.737135 (-0.581028) \| 0.103961 / 0.296338 (-0.192378) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.421395 / 0.215209 (0.206186) \| 4.204935 / 2.077655 (2.127280) \| 2.144929 / 1.504120 (0.640809) \| 1.999341 / 1.541195 (0.458147) \| 2.066966 / 1.468490 (0.598476) \| 0.486135 / 4.584777 (-4.098642) \| 3.628139 / 3.745712 (-0.117573) \| 5.652683 / 5.269862 (0.382821) \| 3.216721 / 4.565676 (-1.348956) \| 0.057513 / 0.424275 (-0.366762) \| 0.007553 / 0.007607 (-0.000055) \| 0.494470 / 0.226044 (0.268426) \| 4.949343 / 2.268929 (2.680414) \| 2.654222 / 55.444624 (-52.790402) \| 2.322257 / 6.876477 (-4.554220) \| 2.555633 / 2.142072 (0.413561) \| 0.588355 / 4.805227 (-4.216872) \| 0.134481 / 6.500664 (-6.366183) \| 0.062415 / 0.075469 (-0.013054) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.377578 / 1.841788 (-0.464209) \| 19.805201 / 8.074308 (11.730893) \| 14.128536 / 10.191392 (3.937144) \| 0.164343 / 0.680424 (-0.516081) \| 0.018553 / 0.534201 (-0.515648) \| 0.398191 / 0.579283 (-0.181093) \| 0.414268 / 0.434364 (-0.020096) \| 0.462270 / 0.540337 (-0.078068) \| 0.608497 / 1.386936 (-0.778439) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3af05ba487f361fae90a4c80af72de5c4ed70162 \"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) \| 0.006966 / 0.011353 (-0.004387) \| 0.004339 / 0.011008 (-0.006669) \| 0.086682 / 0.038508 (0.048174) \| 0.086143 / 0.023109 (0.063034) \| 0.316106 / 0.275898 (0.040208) \| 0.351422 / 0.323480 (0.027942) \| 0.005916 / 0.007986 (-0.002069) \| 0.003630 / 0.004328 (-0.000698) \| 0.066980 / 0.004250 (0.062730) \| 0.060031 / 0.037052 (0.022979) \| 0.317487 / 0.258489 (0.058998) \| 0.356280 / 0.293841 (0.062439) \| 0.031816 / 0.128546 (-0.096730) \| 0.008797 / 0.075646 (-0.066849) \| 0.289848 / 0.419271 (-0.129424) \| 0.055431 / 0.043533 (0.011898) \| 0.318881 / 0.255139 (0.063742) \| 0.332315 / 0.283200 (0.049116) \| 0.025946 / 0.141683 (-0.115737) \| 1.472904 / 1.452155 (0.020749) \| 1.577973 / 1.492716 (0.085257) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.239056 / 0.018006 (0.221050) \| 0.565406 / 0.000490 (0.564917) \| 0.003606 / 0.000200 (0.003406) \| 0.000080 / 0.000054 (0.000025) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029771 / 0.037411 (-0.007640) \| 0.085534 / 0.014526 (0.071008) \| 0.107008 / 0.176557 (-0.069548) \| 0.631583 / 0.737135 (-0.105552) \| 0.104210 / 0.296338 (-0.192128) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.390675 / 0.215209 (0.175466) \| 3.898746 / 2.077655 (1.821091) \| 1.933048 / 1.504120 (0.428928) \| 1.792162 / 1.541195 (0.250967) \| 1.958045 / 1.468490 (0.489555) \| 0.488632 / 4.584777 (-4.096144) \| 3.696306 / 3.745712 (-0.049406) \| 3.454600 / 5.269862 (-1.815262) \| 2.176292 / 4.565676 (-2.389385) \| 0.057617 / 0.424275 (-0.366658) \| 0.007603 / 0.007607 (-0.000004) \| 0.467843 / 0.226044 (0.241798) \| 4.672928 / 2.268929 (2.404000) \| 2.441096 / 55.444624 (-53.003529) \| 2.133506 / 6.876477 (-4.742970) \| 2.431167 / 2.142072 (0.289095) \| 0.588567 / 4.805227 (-4.216661) \| 0.136070 / 6.500664 (-6.364594) \| 0.063395 / 0.075469 (-0.012074) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.255003 / 1.841788 (-0.586784) \| 20.587656 / 8.074308 (12.513348) \| 15.147817 / 10.191392 (4.956425) \| 0.152039 / 0.680424 (-0.528384) \| 0.018815 / 0.534201 (-0.515386) \| 0.397458 / 0.579283 (-0.181825) \| 0.431433 / 0.434364 (-0.002931) \| 0.487890 / 0.540337 (-0.052448) \| 0.675367 / 1.386936 (-0.711569) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007209 / 0.011353 (-0.004144) \| 0.004372 / 0.011008 (-0.006636) \| 0.066288 / 0.038508 (0.027780) \| 0.091776 / 0.023109 (0.068667) \| 0.390724 / 0.275898 (0.114826) \| 0.434711 / 0.323480 (0.111231) \| 0.005790 / 0.007986 (-0.002196) \| 0.003562 / 0.004328 (-0.000767) \| 0.066155 / 0.004250 (0.061904) \| 0.062459 / 0.037052 (0.025406) \| 0.406622 / 0.258489 (0.148133) \| 0.433976 / 0.293841 (0.140135) \| 0.032590 / 0.128546 (-0.095957) \| 0.008856 / 0.075646 (-0.066790) \| 0.072327 / 0.419271 (-0.346945) \| 0.049958 / 0.043533 (0.006426) \| 0.400164 / 0.255139 (0.145025) \| 0.413339 / 0.283200 (0.130139) \| 0.025283 / 0.141683 (-0.116399) \| 1.487668 / 1.452155 (0.035514) \| 1.537679 / 1.492716 (0.044962) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.257814 / 0.018006 (0.239808) \| 0.571741 / 0.000490 (0.571251) \| 0.000412 / 0.000200 (0.000212) \| 0.000056 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033893 / 0.037411 (-0.003518) \| 0.094533 / 0.014526 (0.080008) \| 0.105876 / 0.176557 (-0.070680) \| 0.158675 / 0.737135 (-0.578460) \| 0.107790 / 0.296338 (-0.188548) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.425796 / 0.215209 (0.210587) \| 4.229159 / 2.077655 (2.151505) \| 2.239613 / 1.504120 (0.735493) \| 2.073830 / 1.541195 (0.532635) \| 2.185508 / 1.468490 (0.717018) \| 0.483984 / 4.584777 (-4.100793) \| 3.645575 / 3.745712 (-0.100137) \| 3.454767 / 5.269862 (-1.815095) \| 2.141387 / 4.565676 (-2.424290) \| 0.057570 / 0.424275 (-0.366705) \| 0.007901 / 0.007607 (0.000294) \| 0.501160 / 0.226044 (0.275116) \| 5.012283 / 2.268929 (2.743355) \| 2.701267 / 55.444624 (-52.743357) \| 2.465409 / 6.876477 (-4.411068) \| 2.696812 / 2.142072 (0.554739) \| 0.587160 / 4.805227 (-4.218067) \| 0.134175 / 6.500664 (-6.366489) \| 0.062028 / 0.075469 (-0.013441) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.345632 / 1.841788 (-0.496155) \| 21.077279 / 8.074308 (13.002971) \| 14.700826 / 10.191392 (4.509434) \| 0.156191 / 0.680424 (-0.524233) \| 0.018991 / 0.534201 (-0.515210) \| 0.400413 / 0.579283 (-0.178870) \| 0.420597 / 0.434364 (-0.013767) \| 0.486534 / 0.540337 (-0.053804) \| 0.646606 / 1.386936 (-0.740330) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5bb8fabb135ca8adf47151ad3de050e3a258ccab \"CML watermark\")\n" ]
1,825,865,043	6,090	FilesIterable skips all the files after a hidden file	closed	2023-07-28T07:25:57	2023-07-28T10:51:14	2023-07-28T10:50:11	https://github.com/huggingface/datasets/issues/6090	null	dkrivosic	false	[ "Thanks for reporting. We've merged a PR with a fix." ]
1,825,761,476	6,089	AssertionError: daemonic processes are not allowed to have children	open	2023-07-28T06:04:00	2023-07-31T02:34:02	null	https://github.com/huggingface/datasets/issues/6089	null	codingl2k1	false	[ "We could add a \"threads\" parallel backend to `datasets.parallel.parallel_backend` to support downloading with threads but note that `download_and_extract` also decompresses archives, and this is a CPU-intensive task, which is not ideal for (Python) threads (good for IO-intensive tasks).", "> We could add a \"threads\" parallel backend to `datasets.parallel.parallel_backend` to support downloading with threads but note that `download_and_extract` also decompresses archives, and this is a CPU-intensive task, which is not ideal for (Python) threads (good for IO-intensive tasks).\r\n\r\nGreat! Download takes more time than extract, multiple threads can download in parallel, which can speed up a lot." ]
1,825,665,235	6,088	Loading local data files initiates web requests	closed	2023-07-28T04:06:26	2023-07-28T05:02:22	2023-07-28T05:02:22	https://github.com/huggingface/datasets/issues/6088	null	lytning98	false	[]

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