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
1,771,588,158	5,985	Cannot reuse tokenizer object for dataset map	closed	2023-06-23T14:45:31	2023-07-21T14:09:14	2023-07-21T14:09:14	https://github.com/huggingface/datasets/issues/5985	null	vikigenius	false	[ "This is a known issue: https://github.com/huggingface/datasets/issues/3847.\r\n\r\nFixing this requires significant work - rewriting the `tokenizers` lib to make them immutable.\r\n\r\nThe current solution is to pass `cache_file_name` to `map` to use that file for caching or calling a tokenizer before `map` (with the same set of parameters as the ones in the map transform)", "Closing since this is a duplicate" ]
1,771,571,458	5,984	AutoSharding IterableDataset's when num_workers > 1	open	2023-06-23T14:34:20	2024-03-22T15:01:14	null	https://github.com/huggingface/datasets/issues/5984	null	mathephysicist	false	[ "For this to be possible, we would have to switch from the \"Streaming\" Arrow format to the \"Random Access\" (IPC/Feather) format, which allows reading arbitrary record batches (explained [here](https://arrow.apache.org/docs/python/ipc.html)). We could then use these batches to construct shards.\r\n\r\n@lhoestq @albertvillanova Do you think this use case is worth the switch? Also, we currently shard files, not inner row groups/chunks. Should we also support sharding row groups (e.g. if the number of input files is 1)?\r\n\r\nPS: I don't expect significant speed-up for local, uncompressed Arrow files.", "Alternatively we could support multiprocessing map for iterable datasets and let the user do the CPU intensive task there ?\r\n\r\nThis way it would work on arrow data but also on any iterable dataset", "> For this to be possible, we would have to switch from the \"Streaming\" Arrow format to the \"Random Access\" (IPC/Feather) format, which allows reading arbitrary record batches (explained [here](https://arrow.apache.org/docs/python/ipc.html)). We could then use these batches to construct shards.\r\n> \r\n> @lhoestq @albertvillanova Do you think this use case is worth the switch? Also, we currently shard files, not inner row groups/chunks. Should we also support sharding row groups (e.g. if the number of input files is 1)?\r\n> \r\n> PS: I don't expect significant speed-up for local, uncompressed Arrow files.\r\n\r\nCould you explain why you'd need to change the arrow format?\r\n\r\nWhen we use streaming datasets we simply determine the number of worker shards and then add some modulo logic at the appropriate place. Worst case scenario, you'd skip streaming entries according to the number of shards.\r\n\r\nFor PyTorch, I'd be happy to provide an implementation or a sketch thereof, if you point me toward what the testing requirements would be for such a PR.", "> Could you explain why you'd need to change the arrow format?\r\n\r\nThis way workers have random access to the location of the file where its dataset subset starts. Currently we're using the Arrow streaming format which doesn't include the metadata of the record batches offsets. This is needed here to efficiently split a dataset made of one single file.", "> > Could you explain why you'd need to change the arrow format?\r\n> \r\n> This way workers have random access to the location of the file where its dataset subset starts. Currently we're using the Arrow streaming format which doesn't include the metadata of the record batches offsets. This is needed here to efficiently split a dataset made of one single file.\r\n\r\nI guess I don't understand why you'd need to subset the dataset in the first place. \r\nIt seems sufficient to figure out how to offset or skip rows.\r\n\r\nFor instance, using pyArrow, you could use RecordBatchStreamReader to zero-copy iterate over records with read_next_batch and then only initiate the next step for records modulo worker shard.\r\nThat's one way to do it, where of course you'd need to account for gpu sharding as well.\r\n\r\n\r\nOtherwise, how did you implement worker/node/GPU sharding for iterable/streaming data where you do not have index information or prior splits (e.g. files)?", "> For instance, using pyArrow, you could use RecordBatchStreamReader to zero-copy iterate over records with read_next_batch and then only initiate the next step for records modulo worker shard.\r\n\r\nThat works indeed ! And what we meant is that you can make it even faster to instantiate. Indeed using RecordBatchStreamReader you need to get the list of all the record batches in each worker, whereas you could just get the list of record batches per worker if you use the record batches locations in the Arrow IPC file footer. This would be especially appreciated to have a fast instantiation in case you have tens of thousands of Arrow files for example.", "Any recent updates on this ? ", "I would also appreciate this feature" ]
1,770,578,804	5,983	replaced PathLike as a variable for save_to_disk for dataset_path wit…	closed	2023-06-23T00:57:05	2023-09-11T04:17:17	2023-09-11T04:17:17	https://github.com/huggingface/datasets/pull/5983	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5983", "html_url": "https://github.com/huggingface/datasets/pull/5983", "diff_url": "https://github.com/huggingface/datasets/pull/5983.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5983.patch", "merged_at": null }	benjaminbrown038	true	[]
1,770,333,296	5,982	404 on Datasets Documentation Page	closed	2023-06-22T20:14:57	2023-06-26T15:45:03	2023-06-26T15:45:03	https://github.com/huggingface/datasets/issues/5982	null	kmulka-bloomberg	false	[ "This wasn’t working for me a bit earlier, but it looks to be back up now", "We had a minor issue updating the docs after the latest release. It should work now :)." ]
1,770,310,087	5,981	Only two cores are getting used in sagemaker with pytorch 3.10 kernel	closed	2023-06-22T19:57:31	2023-10-30T06:17:40	2023-07-24T11:54:52	https://github.com/huggingface/datasets/issues/5981	null	mmr-crexi	false	[ "I think it's more likely that this issue is related to PyTorch than Datasets, as PyTorch (on import) registers functions to execute when forking a process. Maybe this is the culprit: https://github.com/pytorch/pytorch/issues/99625", "From reading that ticket, it may be down in mkl? Is it worth hotfixing in the meantime, with the express intention of turning it off? I know that's a horribly crufty solution, but it's also deeply frustrating to be limited to 2 cores for operations as simple as filtration.", "This is too specific and unrelated to `datasets`, so this shouldn't be fixed here.", "@mariosasko @mmr-crexi I had the exact same problem on my kubernetes cluster. the datasets subprocess only user 1 and 17 core" ]
1,770,255,973	5,980	Viewing dataset card returns “502 Bad Gateway”	closed	2023-06-22T19:14:48	2023-06-27T08:38:19	2023-06-26T14:42:45	https://github.com/huggingface/datasets/issues/5980	null	tbenthompson	false	[ "Can you try again? Maybe there was a minor outage.", "Yes, it seems to be working now. In case it's helpful, the outage lasted several days. It was failing as late as yesterday morning. ", "we fixed something on the server side, glad it's fixed now" ]
1,770,198,250	5,979	set dev version	closed	2023-06-22T18:32:14	2023-06-22T18:42:22	2023-06-22T18:32:22	https://github.com/huggingface/datasets/pull/5979	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5979", "html_url": "https://github.com/huggingface/datasets/pull/5979", "diff_url": "https://github.com/huggingface/datasets/pull/5979.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5979.patch", "merged_at": "2023-06-22T18:32:22" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5979). 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) \| 0.008087 / 0.011353 (-0.003266) \| 0.004691 / 0.011008 (-0.006317) \| 0.121545 / 0.038508 (0.083037) \| 0.057436 / 0.023109 (0.034326) \| 0.368864 / 0.275898 (0.092966) \| 0.457199 / 0.323480 (0.133719) \| 0.006745 / 0.007986 (-0.001241) \| 0.003689 / 0.004328 (-0.000640) \| 0.090480 / 0.004250 (0.086229) \| 0.071368 / 0.037052 (0.034316) \| 0.372788 / 0.258489 (0.114299) \| 0.429894 / 0.293841 (0.136053) \| 0.037544 / 0.128546 (-0.091002) \| 0.010142 / 0.075646 (-0.065505) \| 0.420467 / 0.419271 (0.001196) \| 0.064359 / 0.043533 (0.020826) \| 0.370345 / 0.255139 (0.115206) \| 0.405220 / 0.283200 (0.122020) \| 0.028410 / 0.141683 (-0.113273) \| 1.824845 / 1.452155 (0.372690) \| 1.888109 / 1.492716 (0.395392) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.234585 / 0.018006 (0.216578) \| 0.499965 / 0.000490 (0.499476) \| 0.000461 / 0.000200 (0.000261) \| 0.000064 / 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) \| 0.032294 / 0.037411 (-0.005117) \| 0.131769 / 0.014526 (0.117243) \| 0.146472 / 0.176557 (-0.030085) \| 0.210035 / 0.737135 (-0.527100) \| 0.145600 / 0.296338 (-0.150739) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.507455 / 0.215209 (0.292246) \| 5.080090 / 2.077655 (3.002435) \| 2.506104 / 1.504120 (1.001984) \| 2.297655 / 1.541195 (0.756460) \| 2.324920 / 1.468490 (0.856430) \| 0.645003 / 4.584777 (-3.939774) \| 4.677856 / 3.745712 (0.932144) \| 2.254179 / 5.269862 (-3.015683) \| 1.280663 / 4.565676 (-3.285013) \| 0.078809 / 0.424275 (-0.345466) \| 0.014059 / 0.007607 (0.006452) \| 0.628053 / 0.226044 (0.402009) \| 6.327289 / 2.268929 (4.058360) \| 2.957918 / 55.444624 (-52.486706) \| 2.571568 / 6.876477 (-4.304909) \| 2.708766 / 2.142072 (0.566694) \| 0.772868 / 4.805227 (-4.032360) \| 0.164835 / 6.500664 (-6.335829) \| 0.075334 / 0.075469 (-0.000135) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.471930 / 1.841788 (-0.369858) \| 17.917340 / 8.074308 (9.843032) \| 15.719327 / 10.191392 (5.527935) \| 0.191999 / 0.680424 (-0.488424) \| 0.022464 / 0.534201 (-0.511737) \| 0.511038 / 0.579283 (-0.068245) \| 0.512050 / 0.434364 (0.077686) \| 0.608711 / 0.540337 (0.068373) \| 0.749660 / 1.386936 (-0.637276) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008028 / 0.011353 (-0.003325) \| 0.004908 / 0.011008 (-0.006100) \| 0.092294 / 0.038508 (0.053786) \| 0.053051 / 0.023109 (0.029942) \| 0.453862 / 0.275898 (0.177964) \| 0.512548 / 0.323480 (0.189068) \| 0.004817 / 0.007986 (-0.003168) \| 0.005330 / 0.004328 (0.001002) \| 0.095600 / 0.004250 (0.091350) \| 0.068763 / 0.037052 (0.031710) \| 0.453654 / 0.258489 (0.195165) \| 0.504995 / 0.293841 (0.211154) \| 0.038123 / 0.128546 (-0.090423) \| 0.010650 / 0.075646 (-0.064996) \| 0.102854 / 0.419271 (-0.316417) \| 0.062973 / 0.043533 (0.019440) \| 0.430420 / 0.255139 (0.175281) \| 0.465448 / 0.283200 (0.182248) \| 0.029736 / 0.141683 (-0.111947) \| 1.844225 / 1.452155 (0.392070) \| 1.934685 / 1.492716 (0.441968) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227797 / 0.018006 (0.209791) \| 0.467868 / 0.000490 (0.467378) \| 0.004531 / 0.000200 (0.004331) \| 0.000105 / 0.000054 (0.000051) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035632 / 0.037411 (-0.001780) \| 0.145943 / 0.014526 (0.131417) \| 0.151944 / 0.176557 (-0.024613) \| 0.220519 / 0.737135 (-0.516616) \| 0.159732 / 0.296338 (-0.136606) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.520641 / 0.215209 (0.305432) \| 5.184740 / 2.077655 (3.107086) \| 2.538751 / 1.504120 (1.034631) \| 2.316571 / 1.541195 (0.775377) \| 2.387898 / 1.468490 (0.919408) \| 0.614515 / 4.584777 (-3.970262) \| 4.573142 / 3.745712 (0.827430) \| 4.657052 / 5.269862 (-0.612809) \| 2.159664 / 4.565676 (-2.406013) \| 0.079713 / 0.424275 (-0.344562) \| 0.014462 / 0.007607 (0.006855) \| 0.656611 / 0.226044 (0.430566) \| 6.481630 / 2.268929 (4.212702) \| 3.135047 / 55.444624 (-52.309577) \| 2.757502 / 6.876477 (-4.118975) \| 2.851488 / 2.142072 (0.709415) \| 0.790795 / 4.805227 (-4.014432) \| 0.172358 / 6.500664 (-6.328306) \| 0.080255 / 0.075469 (0.004786) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.571391 / 1.841788 (-0.270396) \| 19.025224 / 8.074308 (10.950916) \| 17.079230 / 10.191392 (6.887838) \| 0.172823 / 0.680424 (-0.507601) \| 0.021845 / 0.534201 (-0.512356) \| 0.522286 / 0.579283 (-0.056998) \| 0.510406 / 0.434364 (0.076042) \| 0.604830 / 0.540337 (0.064493) \| 0.735466 / 1.386936 (-0.651471) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4084609bdc40d173d1daa74ad2fe98f3ead72f8e \"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) \| 0.010025 / 0.011353 (-0.001328) \| 0.005699 / 0.011008 (-0.005310) \| 0.134194 / 0.038508 (0.095686) \| 0.056154 / 0.023109 (0.033045) \| 0.470091 / 0.275898 (0.194193) \| 0.539225 / 0.323480 (0.215745) \| 0.006659 / 0.007986 (-0.001326) \| 0.004468 / 0.004328 (0.000140) \| 0.110040 / 0.004250 (0.105790) \| 0.074172 / 0.037052 (0.037119) \| 0.497450 / 0.258489 (0.238961) \| 0.535048 / 0.293841 (0.241207) \| 0.051195 / 0.128546 (-0.077352) \| 0.014926 / 0.075646 (-0.060721) \| 0.461334 / 0.419271 (0.042062) \| 0.073773 / 0.043533 (0.030240) \| 0.450741 / 0.255139 (0.195602) \| 0.474853 / 0.283200 (0.191653) \| 0.036372 / 0.141683 (-0.105311) \| 1.982873 / 1.452155 (0.530719) \| 1.989912 / 1.492716 (0.497196) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287817 / 0.018006 (0.269811) \| 0.613415 / 0.000490 (0.612926) \| 0.007082 / 0.000200 (0.006882) \| 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) \| 0.031119 / 0.037411 (-0.006292) \| 0.129886 / 0.014526 (0.115361) \| 0.143492 / 0.176557 (-0.033065) \| 0.208536 / 0.737135 (-0.528600) \| 0.147081 / 0.296338 (-0.149257) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.668312 / 0.215209 (0.453103) \| 6.568609 / 2.077655 (4.490955) \| 2.708788 / 1.504120 (1.204668) \| 2.366737 / 1.541195 (0.825542) \| 2.392598 / 1.468490 (0.924108) \| 0.967582 / 4.584777 (-3.617195) \| 5.582743 / 3.745712 (1.837031) \| 3.021607 / 5.269862 (-2.248255) \| 1.866402 / 4.565676 (-2.699275) \| 0.115998 / 0.424275 (-0.308277) \| 0.015571 / 0.007607 (0.007964) \| 0.820069 / 0.226044 (0.594025) \| 8.229725 / 2.268929 (5.960797) \| 3.437068 / 55.444624 (-52.007557) \| 2.902312 / 6.876477 (-3.974164) \| 3.025874 / 2.142072 (0.883802) \| 1.230359 / 4.805227 (-3.574868) \| 0.237341 / 6.500664 (-6.263323) \| 0.089923 / 0.075469 (0.014453) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.670970 / 1.841788 (-0.170818) \| 19.667167 / 8.074308 (11.592859) \| 21.624423 / 10.191392 (11.433031) \| 0.231683 / 0.680424 (-0.448741) \| 0.029145 / 0.534201 (-0.505056) \| 0.543441 / 0.579283 (-0.035842) \| 0.617510 / 0.434364 (0.183146) \| 0.612662 / 0.540337 (0.072324) \| 0.790589 / 1.386936 (-0.596347) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010324 / 0.011353 (-0.001029) \| 0.005339 / 0.011008 (-0.005669) \| 0.104762 / 0.038508 (0.066254) \| 0.052631 / 0.023109 (0.029522) \| 0.485864 / 0.275898 (0.209966) \| 0.595768 / 0.323480 (0.272288) \| 0.007417 / 0.007986 (-0.000569) \| 0.005229 / 0.004328 (0.000900) \| 0.100775 / 0.004250 (0.096524) \| 0.067144 / 0.037052 (0.030092) \| 0.522269 / 0.258489 (0.263780) \| 0.592597 / 0.293841 (0.298756) \| 0.051101 / 0.128546 (-0.077446) \| 0.015277 / 0.075646 (-0.060369) \| 0.115530 / 0.419271 (-0.303741) \| 0.071922 / 0.043533 (0.028390) \| 0.490208 / 0.255139 (0.235069) \| 0.578936 / 0.283200 (0.295736) \| 0.040382 / 0.141683 (-0.101301) \| 1.986059 / 1.452155 (0.533904) \| 2.040600 / 1.492716 (0.547883) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300399 / 0.018006 (0.282393) \| 0.624702 / 0.000490 (0.624212) \| 0.004908 / 0.000200 (0.004708) \| 0.000155 / 0.000054 (0.000100) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.038031 / 0.037411 (0.000619) \| 0.140353 / 0.014526 (0.125828) \| 0.152600 / 0.176557 (-0.023956) \| 0.219165 / 0.737135 (-0.517970) \| 0.154232 / 0.296338 (-0.142106) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.698855 / 0.215209 (0.483646) \| 7.125543 / 2.077655 (5.047889) \| 3.251222 / 1.504120 (1.747102) \| 2.953404 / 1.541195 (1.412209) \| 3.051108 / 1.468490 (1.582618) \| 0.962068 / 4.584777 (-3.622709) \| 5.789579 / 3.745712 (2.043867) \| 5.193271 / 5.269862 (-0.076591) \| 2.757886 / 4.565676 (-1.807790) \| 0.111865 / 0.424275 (-0.312410) \| 0.014684 / 0.007607 (0.007077) \| 0.875967 / 0.226044 (0.649923) \| 8.818359 / 2.268929 (6.549430) \| 4.165216 / 55.444624 (-51.279408) \| 3.372059 / 6.876477 (-3.504418) \| 3.486886 / 2.142072 (1.344813) \| 1.232276 / 4.805227 (-3.572951) \| 0.238967 / 6.500664 (-6.261697) \| 0.091584 / 0.075469 (0.016115) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.850755 / 1.841788 (0.008968) \| 20.058756 / 8.074308 (11.984448) \| 23.761271 / 10.191392 (13.569879) \| 0.231826 / 0.680424 (-0.448598) \| 0.030119 / 0.534201 (-0.504082) \| 0.532614 / 0.579283 (-0.046669) \| 0.628968 / 0.434364 (0.194604) \| 0.628403 / 0.540337 (0.088066) \| 0.745648 / 1.386936 (-0.641288) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a8a797cc92e860c8d0df71e0aa826f4d2690713e \"CML watermark\")\n" ]
1,770,187,053	5,978	Release: 2.13.1	closed	2023-06-22T18:23:11	2023-06-22T18:40:24	2023-06-22T18:30:16	https://github.com/huggingface/datasets/pull/5978	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5978", "html_url": "https://github.com/huggingface/datasets/pull/5978", "diff_url": "https://github.com/huggingface/datasets/pull/5978.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5978.patch", "merged_at": "2023-06-22T18:30:16" }	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) \| 0.006173 / 0.011353 (-0.005180) \| 0.003773 / 0.011008 (-0.007235) \| 0.099499 / 0.038508 (0.060991) \| 0.037918 / 0.023109 (0.014809) \| 0.321329 / 0.275898 (0.045431) \| 0.379739 / 0.323480 (0.056259) \| 0.004664 / 0.007986 (-0.003322) \| 0.002943 / 0.004328 (-0.001385) \| 0.077759 / 0.004250 (0.073509) \| 0.055271 / 0.037052 (0.018219) \| 0.329428 / 0.258489 (0.070939) \| 0.378731 / 0.293841 (0.084890) \| 0.027737 / 0.128546 (-0.100810) \| 0.008566 / 0.075646 (-0.067081) \| 0.313220 / 0.419271 (-0.106052) \| 0.047101 / 0.043533 (0.003568) \| 0.316211 / 0.255139 (0.061072) \| 0.341826 / 0.283200 (0.058626) \| 0.020838 / 0.141683 (-0.120845) \| 1.550064 / 1.452155 (0.097909) \| 1.706518 / 1.492716 (0.213801) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.203093 / 0.018006 (0.185087) \| 0.425345 / 0.000490 (0.424856) \| 0.004800 / 0.000200 (0.004600) \| 0.000077 / 0.000054 (0.000022) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024590 / 0.037411 (-0.012821) \| 0.098115 / 0.014526 (0.083589) \| 0.108274 / 0.176557 (-0.068282) \| 0.170804 / 0.737135 (-0.566332) \| 0.110560 / 0.296338 (-0.185778) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.425251 / 0.215209 (0.210042) \| 4.239075 / 2.077655 (2.161421) \| 1.955601 / 1.504120 (0.451481) \| 1.774796 / 1.541195 (0.233602) \| 1.826641 / 1.468490 (0.358150) \| 0.558777 / 4.584777 (-4.026000) \| 3.361697 / 3.745712 (-0.384015) \| 1.764468 / 5.269862 (-3.505394) \| 1.032280 / 4.565676 (-3.533396) \| 0.067872 / 0.424275 (-0.356403) \| 0.010998 / 0.007607 (0.003391) \| 0.525682 / 0.226044 (0.299637) \| 5.254356 / 2.268929 (2.985427) \| 2.384332 / 55.444624 (-53.060292) \| 2.045578 / 6.876477 (-4.830898) \| 2.170914 / 2.142072 (0.028841) \| 0.674782 / 4.805227 (-4.130445) \| 0.135351 / 6.500664 (-6.365314) \| 0.066591 / 0.075469 (-0.008878) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.209181 / 1.841788 (-0.632606) \| 14.044518 / 8.074308 (5.970210) \| 13.184705 / 10.191392 (2.993313) \| 0.130836 / 0.680424 (-0.549588) \| 0.016582 / 0.534201 (-0.517619) \| 0.360005 / 0.579283 (-0.219279) \| 0.379519 / 0.434364 (-0.054845) \| 0.422174 / 0.540337 (-0.118164) \| 0.515546 / 1.386936 (-0.871390) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006293 / 0.011353 (-0.005060) \| 0.003784 / 0.011008 (-0.007224) \| 0.079248 / 0.038508 (0.040739) \| 0.038452 / 0.023109 (0.015343) \| 0.444727 / 0.275898 (0.168829) \| 0.500535 / 0.323480 (0.177055) \| 0.003455 / 0.007986 (-0.004531) \| 0.002873 / 0.004328 (-0.001455) \| 0.077439 / 0.004250 (0.073189) \| 0.047855 / 0.037052 (0.010803) \| 0.448049 / 0.258489 (0.189560) \| 0.509517 / 0.293841 (0.215676) \| 0.028359 / 0.128546 (-0.100188) \| 0.008503 / 0.075646 (-0.067143) \| 0.084961 / 0.419271 (-0.334310) \| 0.042880 / 0.043533 (-0.000653) \| 0.436628 / 0.255139 (0.181489) \| 0.456574 / 0.283200 (0.173375) \| 0.019539 / 0.141683 (-0.122144) \| 1.561273 / 1.452155 (0.109118) \| 1.572018 / 1.492716 (0.079301) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230250 / 0.018006 (0.212244) \| 0.415189 / 0.000490 (0.414700) \| 0.003213 / 0.000200 (0.003013) \| 0.000080 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025541 / 0.037411 (-0.011871) \| 0.102326 / 0.014526 (0.087800) \| 0.110258 / 0.176557 (-0.066298) \| 0.162488 / 0.737135 (-0.574647) \| 0.112782 / 0.296338 (-0.183556) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.457936 / 0.215209 (0.242727) \| 4.581503 / 2.077655 (2.503848) \| 2.237659 / 1.504120 (0.733540) \| 2.029960 / 1.541195 (0.488765) \| 2.082911 / 1.468490 (0.614421) \| 0.556485 / 4.584777 (-4.028292) \| 3.384418 / 3.745712 (-0.361295) \| 1.748809 / 5.269862 (-3.521053) \| 1.034759 / 4.565676 (-3.530917) \| 0.067500 / 0.424275 (-0.356776) \| 0.011425 / 0.007607 (0.003818) \| 0.561340 / 0.226044 (0.335295) \| 5.623629 / 2.268929 (3.354701) \| 2.733587 / 55.444624 (-52.711038) \| 2.401578 / 6.876477 (-4.474899) \| 2.524569 / 2.142072 (0.382496) \| 0.673170 / 4.805227 (-4.132057) \| 0.136681 / 6.500664 (-6.363983) \| 0.068060 / 0.075469 (-0.007409) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.318651 / 1.841788 (-0.523137) \| 14.362123 / 8.074308 (6.287815) \| 14.385964 / 10.191392 (4.194572) \| 0.149914 / 0.680424 (-0.530510) \| 0.016877 / 0.534201 (-0.517324) \| 0.358406 / 0.579283 (-0.220877) \| 0.394349 / 0.434364 (-0.040015) \| 0.422471 / 0.540337 (-0.117866) \| 0.513807 / 1.386936 (-0.873129) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1b9ce11d1b94e6178df663ff5fcad029849d10fb \"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) \| 0.006272 / 0.011353 (-0.005080) \| 0.003903 / 0.011008 (-0.007105) \| 0.100180 / 0.038508 (0.061672) \| 0.037799 / 0.023109 (0.014690) \| 0.385627 / 0.275898 (0.109729) \| 0.446518 / 0.323480 (0.123038) \| 0.004811 / 0.007986 (-0.003175) \| 0.003032 / 0.004328 (-0.001296) \| 0.077063 / 0.004250 (0.072812) \| 0.055564 / 0.037052 (0.018512) \| 0.397346 / 0.258489 (0.138857) \| 0.443242 / 0.293841 (0.149401) \| 0.027904 / 0.128546 (-0.100642) \| 0.008386 / 0.075646 (-0.067260) \| 0.315013 / 0.419271 (-0.104259) \| 0.047943 / 0.043533 (0.004410) \| 0.378443 / 0.255139 (0.123304) \| 0.411472 / 0.283200 (0.128272) \| 0.020465 / 0.141683 (-0.121218) \| 1.526594 / 1.452155 (0.074439) \| 1.547018 / 1.492716 (0.054301) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.219377 / 0.018006 (0.201370) \| 0.430254 / 0.000490 (0.429764) \| 0.003218 / 0.000200 (0.003018) \| 0.000072 / 0.000054 (0.000017) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023667 / 0.037411 (-0.013744) \| 0.099143 / 0.014526 (0.084617) \| 0.106044 / 0.176557 (-0.070513) \| 0.166186 / 0.737135 (-0.570949) \| 0.108736 / 0.296338 (-0.187603) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.437971 / 0.215209 (0.222762) \| 4.363675 / 2.077655 (2.286021) \| 2.011993 / 1.504120 (0.507873) \| 1.845189 / 1.541195 (0.303994) \| 1.831848 / 1.468490 (0.363358) \| 0.562402 / 4.584777 (-4.022375) \| 3.365259 / 3.745712 (-0.380453) \| 1.781491 / 5.269862 (-3.488371) \| 1.023454 / 4.565676 (-3.542223) \| 0.067857 / 0.424275 (-0.356418) \| 0.011076 / 0.007607 (0.003469) \| 0.532267 / 0.226044 (0.306223) \| 5.340344 / 2.268929 (3.071415) \| 2.388649 / 55.444624 (-53.055976) \| 2.055373 / 6.876477 (-4.821104) \| 2.205047 / 2.142072 (0.062975) \| 0.672909 / 4.805227 (-4.132318) \| 0.135244 / 6.500664 (-6.365420) \| 0.066184 / 0.075469 (-0.009285) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.206838 / 1.841788 (-0.634950) \| 13.967075 / 8.074308 (5.892767) \| 13.143971 / 10.191392 (2.952579) \| 0.143991 / 0.680424 (-0.536433) \| 0.016673 / 0.534201 (-0.517527) \| 0.376180 / 0.579283 (-0.203103) \| 0.386550 / 0.434364 (-0.047814) \| 0.440590 / 0.540337 (-0.099747) \| 0.529974 / 1.386936 (-0.856962) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006299 / 0.011353 (-0.005054) \| 0.003784 / 0.011008 (-0.007224) \| 0.077875 / 0.038508 (0.039367) \| 0.038689 / 0.023109 (0.015580) \| 0.421684 / 0.275898 (0.145786) \| 0.472649 / 0.323480 (0.149169) \| 0.003570 / 0.007986 (-0.004415) \| 0.004448 / 0.004328 (0.000120) \| 0.077867 / 0.004250 (0.073616) \| 0.049514 / 0.037052 (0.012462) \| 0.375983 / 0.258489 (0.117494) \| 0.470632 / 0.293841 (0.176791) \| 0.028238 / 0.128546 (-0.100308) \| 0.008462 / 0.075646 (-0.067185) \| 0.082452 / 0.419271 (-0.336819) \| 0.043617 / 0.043533 (0.000084) \| 0.400874 / 0.255139 (0.145735) \| 0.426191 / 0.283200 (0.142992) \| 0.020602 / 0.141683 (-0.121081) \| 1.567658 / 1.452155 (0.115504) \| 1.572610 / 1.492716 (0.079893) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.246144 / 0.018006 (0.228138) \| 0.419402 / 0.000490 (0.418913) \| 0.001691 / 0.000200 (0.001491) \| 0.000071 / 0.000054 (0.000017) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026105 / 0.037411 (-0.011306) \| 0.104734 / 0.014526 (0.090208) \| 0.110257 / 0.176557 (-0.066300) \| 0.161429 / 0.737135 (-0.575706) \| 0.114367 / 0.296338 (-0.181972) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.453352 / 0.215209 (0.238143) \| 4.537924 / 2.077655 (2.460269) \| 2.196193 / 1.504120 (0.692073) \| 2.002087 / 1.541195 (0.460892) \| 2.041722 / 1.468490 (0.573231) \| 0.561643 / 4.584777 (-4.023134) \| 3.449108 / 3.745712 (-0.296605) \| 2.862800 / 5.269862 (-2.407062) \| 1.387895 / 4.565676 (-3.177782) \| 0.068076 / 0.424275 (-0.356199) \| 0.011568 / 0.007607 (0.003961) \| 0.559279 / 0.226044 (0.333235) \| 5.598738 / 2.268929 (3.329809) \| 2.676649 / 55.444624 (-52.767975) \| 2.334588 / 6.876477 (-4.541889) \| 2.376215 / 2.142072 (0.234142) \| 0.673109 / 4.805227 (-4.132118) \| 0.137587 / 6.500664 (-6.363077) \| 0.069131 / 0.075469 (-0.006338) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.307332 / 1.841788 (-0.534456) \| 14.536036 / 8.074308 (6.461728) \| 14.173734 / 10.191392 (3.982342) \| 0.145143 / 0.680424 (-0.535281) \| 0.016662 / 0.534201 (-0.517539) \| 0.366901 / 0.579283 (-0.212383) \| 0.394498 / 0.434364 (-0.039866) \| 0.430546 / 0.540337 (-0.109792) \| 0.518950 / 1.386936 (-0.867986) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#682d21e94ab1e64c11b583de39dc4c93f0101c5a \"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) \| 0.008122 / 0.011353 (-0.003231) \| 0.005585 / 0.011008 (-0.005424) \| 0.121219 / 0.038508 (0.082711) \| 0.047616 / 0.023109 (0.024507) \| 0.440576 / 0.275898 (0.164678) \| 0.491053 / 0.323480 (0.167573) \| 0.004774 / 0.007986 (-0.003211) \| 0.006758 / 0.004328 (0.002430) \| 0.103852 / 0.004250 (0.099602) \| 0.071560 / 0.037052 (0.034508) \| 0.463107 / 0.258489 (0.204618) \| 0.516904 / 0.293841 (0.223063) \| 0.048052 / 0.128546 (-0.080494) \| 0.013679 / 0.075646 (-0.061968) \| 0.428383 / 0.419271 (0.009112) \| 0.069468 / 0.043533 (0.025936) \| 0.432593 / 0.255139 (0.177454) \| 0.471810 / 0.283200 (0.188611) \| 0.037541 / 0.141683 (-0.104142) \| 1.823490 / 1.452155 (0.371335) \| 1.922558 / 1.492716 (0.429842) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.252315 / 0.018006 (0.234309) \| 0.541757 / 0.000490 (0.541267) \| 0.000373 / 0.000200 (0.000173) \| 0.000083 / 0.000054 (0.000028) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030361 / 0.037411 (-0.007050) \| 0.125928 / 0.014526 (0.111402) \| 0.145102 / 0.176557 (-0.031455) \| 0.209798 / 0.737135 (-0.527337) \| 0.147349 / 0.296338 (-0.148990) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.627554 / 0.215209 (0.412345) \| 5.917422 / 2.077655 (3.839767) \| 2.491083 / 1.504120 (0.986963) \| 2.147078 / 1.541195 (0.605883) \| 2.167511 / 1.468490 (0.699021) \| 0.903061 / 4.584777 (-3.681716) \| 5.518537 / 3.745712 (1.772825) \| 2.654348 / 5.269862 (-2.615514) \| 1.645121 / 4.565676 (-2.920556) \| 0.103782 / 0.424275 (-0.320493) \| 0.013048 / 0.007607 (0.005441) \| 0.756732 / 0.226044 (0.530687) \| 7.622873 / 2.268929 (5.353945) \| 3.122689 / 55.444624 (-52.321936) \| 2.537735 / 6.876477 (-4.338742) \| 2.640090 / 2.142072 (0.498018) \| 1.128635 / 4.805227 (-3.676593) \| 0.228089 / 6.500664 (-6.272575) \| 0.086207 / 0.075469 (0.010738) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.561591 / 1.841788 (-0.280197) \| 18.110299 / 8.074308 (10.035991) \| 20.718017 / 10.191392 (10.526625) \| 0.225741 / 0.680424 (-0.454682) \| 0.031738 / 0.534201 (-0.502463) \| 0.530789 / 0.579283 (-0.048495) \| 0.607364 / 0.434364 (0.173000) \| 0.581593 / 0.540337 (0.041256) \| 0.726033 / 1.386936 (-0.660903) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009323 / 0.011353 (-0.002030) \| 0.005360 / 0.011008 (-0.005649) \| 0.103608 / 0.038508 (0.065100) \| 0.050158 / 0.023109 (0.027049) \| 0.499906 / 0.275898 (0.224008) \| 0.561005 / 0.323480 (0.237525) \| 0.005093 / 0.007986 (-0.002892) \| 0.008285 / 0.004328 (0.003956) \| 0.103446 / 0.004250 (0.099196) \| 0.061478 / 0.037052 (0.024426) \| 0.494016 / 0.258489 (0.235527) \| 0.537550 / 0.293841 (0.243709) \| 0.048829 / 0.128546 (-0.079717) \| 0.017032 / 0.075646 (-0.058614) \| 0.107748 / 0.419271 (-0.311524) \| 0.065607 / 0.043533 (0.022074) \| 0.488709 / 0.255139 (0.233570) \| 0.512023 / 0.283200 (0.228823) \| 0.032067 / 0.141683 (-0.109616) \| 1.907585 / 1.452155 (0.455431) \| 1.960994 / 1.492716 (0.468278) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278378 / 0.018006 (0.260371) \| 0.551474 / 0.000490 (0.550985) \| 0.006886 / 0.000200 (0.006686) \| 0.000106 / 0.000054 (0.000051) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030674 / 0.037411 (-0.006737) \| 0.135179 / 0.014526 (0.120654) \| 0.133703 / 0.176557 (-0.042853) \| 0.198923 / 0.737135 (-0.538212) \| 0.155108 / 0.296338 (-0.141231) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.690566 / 0.215209 (0.475357) \| 6.789594 / 2.077655 (4.711940) \| 2.940668 / 1.504120 (1.436549) \| 2.562431 / 1.541195 (1.021236) \| 2.554232 / 1.468490 (1.085742) \| 0.888470 / 4.584777 (-3.696307) \| 5.672318 / 3.745712 (1.926606) \| 2.741626 / 5.269862 (-2.528236) \| 1.818336 / 4.565676 (-2.747340) \| 0.110434 / 0.424275 (-0.313841) \| 0.014114 / 0.007607 (0.006507) \| 0.830632 / 0.226044 (0.604588) \| 8.270787 / 2.268929 (6.001859) \| 3.723486 / 55.444624 (-51.721139) \| 2.993671 / 6.876477 (-3.882806) \| 2.918273 / 2.142072 (0.776201) \| 1.105337 / 4.805227 (-3.699891) \| 0.222976 / 6.500664 (-6.277688) \| 0.085290 / 0.075469 (0.009820) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.816027 / 1.841788 (-0.025760) \| 18.496850 / 8.074308 (10.422541) \| 20.457032 / 10.191392 (10.265640) \| 0.243533 / 0.680424 (-0.436891) \| 0.027044 / 0.534201 (-0.507157) \| 0.500752 / 0.579283 (-0.078531) \| 0.620963 / 0.434364 (0.186599) \| 0.607995 / 0.540337 (0.067658) \| 0.722915 / 1.386936 (-0.664021) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#682d21e94ab1e64c11b583de39dc4c93f0101c5a \"CML watermark\")\n" ]
1,768,503,913	5,976	Avoid stuck map operation when subprocesses crashes	closed	2023-06-21T21:18:31	2023-07-10T09:58:39	2023-07-10T09:50:07	https://github.com/huggingface/datasets/pull/5976	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5976", "html_url": "https://github.com/huggingface/datasets/pull/5976", "diff_url": "https://github.com/huggingface/datasets/pull/5976.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5976.patch", "merged_at": "2023-07-10T09:50:07" }	pappacena	true	[ "Hi ! Do you think this can be fixed at the Pool level ? Ideally it should be the Pool responsibility to handle this, not the `map` code. We could even subclass Pool if needed (at least the one from `multiprocess`)", "@lhoestq it makes sense to me. Just pushed a refactoring creating a `class ProcessPool(multiprocess.pool.Pool)` to keep track of the PID changes.", "_The documentation is not available anymore as the PR was closed or merged._", "I managed to raise an error without subclassing Pool with two additions to `iflatmap_unordered`:\r\n\r\n1. at the beggining\r\n```python\r\noriginal_pool = list(pool._pool)\r\n```\r\n\r\n2. in the loop\r\n```python\r\nif any(async_result._pool != original_pool for async_result in async_results) and queue.empty():\r\n raise RuntimeError(\r\n \"One of the subprocesses has abruptly died during map operation.\"\r\n \"To debug the error, disable multiprocessing.\"\r\n )\r\n```\r\n\r\nIt's still a fix that only works for `iflatmap_unordered` (so not for map, imap etc) but is maybe simpler that subclassing. It also works for both multiprocessing.Pool and multiprocess.Pool", "@lhoestq sorry for the delay. Busy weeks here. \r\n\r\nI just pushed the change you requested. It looks closer to the original proposal, actually.\r\n\r\nIt seems that `map` actually uses `iflatmap_unordered` ([here](https://github.com/huggingface/datasets/blob/819bb4346434912eb405ce3f3e9f21dc25a2fe85/src/datasets/arrow_dataset.py#L1509)). I think this solution works fine for the `map` method (which is the one being tested by the new `tests/test_arrow_dataset.py::BaseDatasetTest::test_map_crash_subprocess`, right?).", "Yes fixing iflatmap_unordered does fix Dataset.map, but it won't fix any Pool.map that we may use elsewhere so we'll have to keep this in mind.", "It looks all good to me, feel free to fix code formatting by running `make style` and we can merge :)", "> Yes fixing iflatmap_unordered does fix Dataset.map, but it won't fix any Pool.map that we may use elsewhere so we'll have to keep this in mind.\r\n\r\nRight, I agree. The best way moving forward is probably not using the buggy `multiprocess.Pool` anymore, and replace it with `concurrent.futures.ProcessPoolExecutor` as much as possible.\r\n\r\nAnyway, I've run `make style` now. Thanks for the support!", "It looks like checking the async_result._pool doesn't always work - sorry about that. We might just go back to your original solution then. Would also be cool to open an issue in `multiprocess` to ask if they have a solution or if they plan to fix this.", "@lhoestq no problem! Reverted to the previous version.\r\n\r\nTBH, given the discussions [in this python issue](https://github.com/python/cpython/issues/66587), I don't think the error in `multiprocess` will be merged upstream any time soon...", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006060 / 0.011353 (-0.005293) \| 0.003695 / 0.011008 (-0.007313) \| 0.080484 / 0.038508 (0.041976) \| 0.061894 / 0.023109 (0.038785) \| 0.312510 / 0.275898 (0.036612) \| 0.352398 / 0.323480 (0.028918) \| 0.004638 / 0.007986 (-0.003348) \| 0.002918 / 0.004328 (-0.001410) \| 0.062932 / 0.004250 (0.058681) \| 0.050859 / 0.037052 (0.013807) \| 0.316812 / 0.258489 (0.058323) \| 0.357684 / 0.293841 (0.063843) \| 0.027622 / 0.128546 (-0.100924) \| 0.008012 / 0.075646 (-0.067634) \| 0.260970 / 0.419271 (-0.158302) \| 0.045807 / 0.043533 (0.002275) \| 0.321235 / 0.255139 (0.066096) \| 0.343162 / 0.283200 (0.059962) \| 0.021136 / 0.141683 (-0.120547) \| 1.465886 / 1.452155 (0.013731) \| 1.500216 / 1.492716 (0.007500) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.187286 / 0.018006 (0.169279) \| 0.428724 / 0.000490 (0.428235) \| 0.003029 / 0.000200 (0.002829) \| 0.000063 / 0.000054 (0.000008) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022703 / 0.037411 (-0.014708) \| 0.072740 / 0.014526 (0.058215) \| 0.083436 / 0.176557 (-0.093120) \| 0.144559 / 0.737135 (-0.592577) \| 0.083958 / 0.296338 (-0.212380) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.435729 / 0.215209 (0.220520) \| 4.351146 / 2.077655 (2.273491) \| 2.316627 / 1.504120 (0.812508) \| 2.144587 / 1.541195 (0.603393) \| 2.209182 / 1.468490 (0.740692) \| 0.501131 / 4.584777 (-4.083646) \| 3.077085 / 3.745712 (-0.668627) \| 4.353706 / 5.269862 (-0.916156) \| 2.621523 / 4.565676 (-1.944154) \| 0.058976 / 0.424275 (-0.365299) \| 0.006467 / 0.007607 (-0.001141) \| 0.506690 / 0.226044 (0.280646) \| 5.085787 / 2.268929 (2.816858) \| 2.731336 / 55.444624 (-52.713289) \| 2.419451 / 6.876477 (-4.457025) \| 2.583649 / 2.142072 (0.441577) \| 0.589869 / 4.805227 (-4.215359) \| 0.131040 / 6.500664 (-6.369624) \| 0.061332 / 0.075469 (-0.014137) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.220542 / 1.841788 (-0.621245) \| 18.169643 / 8.074308 (10.095335) \| 13.251704 / 10.191392 (3.060312) \| 0.142952 / 0.680424 (-0.537472) \| 0.016639 / 0.534201 (-0.517562) \| 0.334851 / 0.579283 (-0.244432) \| 0.361865 / 0.434364 (-0.072499) \| 0.380933 / 0.540337 (-0.159404) \| 0.527374 / 1.386936 (-0.859562) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006319 / 0.011353 (-0.005034) \| 0.003778 / 0.011008 (-0.007231) \| 0.062388 / 0.038508 (0.023880) \| 0.062228 / 0.023109 (0.039119) \| 0.373727 / 0.275898 (0.097829) \| 0.399442 / 0.323480 (0.075962) \| 0.005434 / 0.007986 (-0.002551) \| 0.003020 / 0.004328 (-0.001308) \| 0.062774 / 0.004250 (0.058524) \| 0.052784 / 0.037052 (0.015732) \| 0.376428 / 0.258489 (0.117939) \| 0.405039 / 0.293841 (0.111198) \| 0.027884 / 0.128546 (-0.100662) \| 0.008086 / 0.075646 (-0.067561) \| 0.067078 / 0.419271 (-0.352194) \| 0.042927 / 0.043533 (-0.000606) \| 0.372142 / 0.255139 (0.117003) \| 0.389604 / 0.283200 (0.106405) \| 0.021582 / 0.141683 (-0.120101) \| 1.473332 / 1.452155 (0.021177) \| 1.536018 / 1.492716 (0.043302) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.184729 / 0.018006 (0.166723) \| 0.421065 / 0.000490 (0.420575) \| 0.002681 / 0.000200 (0.002481) \| 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) \| 0.026067 / 0.037411 (-0.011344) \| 0.077138 / 0.014526 (0.062612) \| 0.085178 / 0.176557 (-0.091379) \| 0.139681 / 0.737135 (-0.597454) \| 0.087528 / 0.296338 (-0.208810) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.444899 / 0.215209 (0.229690) \| 4.459168 / 2.077655 (2.381513) \| 2.408792 / 1.504120 (0.904672) \| 2.237243 / 1.541195 (0.696048) \| 2.296298 / 1.468490 (0.827808) \| 0.498508 / 4.584777 (-4.086269) \| 3.067064 / 3.745712 (-0.678648) \| 4.470577 / 5.269862 (-0.799284) \| 2.701972 / 4.565676 (-1.863705) \| 0.057711 / 0.424275 (-0.366564) \| 0.006443 / 0.007607 (-0.001164) \| 0.524046 / 0.226044 (0.298002) \| 5.229928 / 2.268929 (2.961000) \| 2.862101 / 55.444624 (-52.582523) \| 2.545972 / 6.876477 (-4.330504) \| 2.606459 / 2.142072 (0.464387) \| 0.593285 / 4.805227 (-4.211942) \| 0.124913 / 6.500664 (-6.375751) \| 0.061942 / 0.075469 (-0.013527) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.322162 / 1.841788 (-0.519625) \| 18.745796 / 8.074308 (10.671488) \| 13.955443 / 10.191392 (3.764051) \| 0.145610 / 0.680424 (-0.534814) \| 0.016817 / 0.534201 (-0.517384) \| 0.331180 / 0.579283 (-0.248103) \| 0.343019 / 0.434364 (-0.091345) \| 0.379459 / 0.540337 (-0.160878) \| 0.526403 / 1.386936 (-0.860533) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aca4cdcc79f16ec5157a2a3a665fdef0e3aa176d \"CML watermark\")\n" ]
1,768,271,343	5,975	Streaming Dataset behind Proxy - FileNotFoundError	closed	2023-06-21T19:10:02	2023-06-30T05:55:39	2023-06-30T05:55:38	https://github.com/huggingface/datasets/issues/5975	null	Veluchs	false	[ "Duplicate of #", "Hi ! can you try to set the upper case environment variables `HTTP_PROXY` and `HTTPS_PROXY` ?\r\n\r\nWe use `aiohttp` for streaming and it uses case sensitive environment variables", "Hi, thanks for the quick reply.\r\n\r\nI set the uppercase env variables with\r\n\r\n`\r\nos.environ['HTTP_PROXY'] = \"http://example.com:xxxx\" \r\nos.environ['HTTPS_PROXY'] = \"http://example.com:xxxx\" \r\n`\r\n\r\nHowever, I still get the same error.\r\n\r\nOne thing that could be helpfull: When downloading a dataset without streaming i get the following message:\r\n_HF google storage unreachable. Downloading and preparing it from source_.\r\nThe download does however work as expected.\r\n", "Are you able to use `aiohttp` to get the file at `https://huggingface.co/datasets/facebook/voxpopuli/resolve/main/data/n_files.json` using your proxy ?", "It only works when passing trust_env=True when creating the ClientSession, as well as setting ssl=False.\r\n\r\nWorking Example:\r\n\r\n```\r\nimport os\r\n\r\nos.environ['HTTP_PROXY'] = \"xyz\"\r\nos.environ['HTTPS_PROXY'] = \"xyz\"\r\n\r\nimport asyncio\r\nimport aiohttp\r\n\r\nasync def download_pep(url):\r\n async with aiohttp.ClientSession(trust_env=True) as session:\r\n print(\"1\")\r\n async with session.get(url, ssl=False) as resp:\r\n print(\"2\")\r\n content = await resp.text()\r\n print(content)\r\n return content\r\n\r\nasyncio.run(download_pep(\"https://huggingface.co/datasets/facebook/voxpopuli/resolve/main/data/n_files.json\"))\r\n```\r\n\r\n\r\n\r\nSSL Verification has been a problem with other packages as well. Usually I circumvent the problem by setting\r\n```\r\nimport ssl\r\nssl._create_default_https_context = ssl._create_unverified_context\r\n```\r\n(probably not the best idea for security), although here aiohttp does not seem to use this default context.", "We do pass `trust_env` as well. Could you share the full stack trace you get when streaming using `datasets` ? That could help locate where we might have forgotten to pass `trust_env`", "Is there a way to disable ssl verification when streaming a dataset. I suspect this might be the isssue with my proxy.\r\n\r\n\r\nHere you go:\r\n\r\n```\r\nFileNotFoundError Traceback (most recent call last)\r\nCell In[8], line 3\r\n 1 from datasets import load_dataset\r\n----> 3 ds = load_dataset(\"facebook/voxpopuli\", name=\"de\", streaming=True)\r\n 5 sample = next(iter(ds))\r\n\r\nFile [~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/load.py:1790](https://vscode-remote+ssh-002dremote-002bml-002er-002dsoftware-002eat.vscode-resource.vscode-cdn.net/home/wrsbri/projects/audio_course/~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/load.py:1790), in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, *config_kwargs)\r\n 1788 # Return iterable dataset in case of streaming\r\n 1789 if streaming:\r\n-> 1790 return builder_instance.as_streaming_dataset(split=split)\r\n 1792 # Some datasets are already processed on the HF google storage\r\n 1793 # Don't try downloading from Google storage for the packaged datasets as text, json, csv or pandas\r\n 1794 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES\r\n\r\nFile [~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/builder.py:1281](https://vscode-remote+ssh-002dremote-002bml-002er-002dsoftware-002eat.vscode-resource.vscode-cdn.net/home/wrsbri/projects/audio_course/~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/builder.py:1281), in DatasetBuilder.as_streaming_dataset(self, split, base_path)\r\n 1274 dl_manager = StreamingDownloadManager(\r\n 1275 base_path=base_path or self.base_path,\r\n 1276 download_config=DownloadConfig(use_auth_token=self.use_auth_token, storage_options=self.storage_options),\r\n 1277 dataset_name=self.name,\r\n 1278 data_dir=self.config.data_dir,\r\n 1279 )\r\n 1280 self._check_manual_download(dl_manager)\r\n-> 1281 splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}\r\n 1282 # By default, return all splits\r\n 1283 if split is None:\r\n\r\nFile [~/.cache/huggingface/modules/datasets_modules/datasets/facebook--voxpopuli/b5ff837284f0778eefe0f642734e142d8c3f574eba8c9c8a4b13602297f73604/voxpopuli.py:120](https://vscode-remote+ssh-002dremote-002bml-002er-002dsoftware-002eat.vscode-resource.vscode-cdn.net/home/wrsbri/projects/audio_course/~/.cache/huggingface/modules/datasets_modules/datasets/facebook--voxpopuli/b5ff837284f0778eefe0f642734e142d8c3f574eba8c9c8a4b13602297f73604/voxpopuli.py:120), in Voxpopuli._split_generators(self, dl_manager)\r\n 118 def _split_generators(self, dl_manager):\r\n 119 n_shards_path = dl_manager.download_and_extract(_N_SHARDS_FILE)\r\n--> 120 with open(n_shards_path) as f:\r\n 121 n_shards = json.load(f)\r\n 123 if self.config.name == \"en_accented\":\r\n\r\nFile [~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/streaming.py:71](https://vscode-remote+ssh-002dremote-002bml-002er-002dsoftware-002eat.vscode-resource.vscode-cdn.net/home/wrsbri/projects/audio_course/~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/streaming.py:71), in extend_module_for_streaming..wrap_auth..wrapper(args, *kwargs)\r\n 69 @wraps(function)\r\n 70 def wrapper(args, *kwargs):\r\n---> 71 return function(args, use_auth_token=use_auth_token, *kwargs)\r\n\r\nFile [~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py:517](https://vscode-remote+ssh-002dremote-002bml-002er-002dsoftware-002eat.vscode-resource.vscode-cdn.net/home/wrsbri/projects/audio_course/~/.conda/envs/audio_hf/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py:517), in xopen(file, mode, use_auth_token, args, **kwargs)\r\n 515 except FileNotFoundError:\r\n 516 if file.startswith(config.HF_ENDPOINT):\r\n--> 517 raise FileNotFoundError(\r\n 518 file + \"\\nIf the repo is private or gated, make sure to log in with `huggingface-cli login`.\"\r\n 519 ) from None\r\n 520 else:\r\n 521 raise\r\n\r\nFileNotFoundError: https://huggingface.co/datasets/facebook/voxpopuli/resolve/main/data/n_files.json\r\nIf the repo is private or gated, make sure to log in with `huggingface-cli login`.\r\n```", "> Is there a way to disable ssl verification when streaming a dataset.\r\n\r\nI don't think so.\r\n\r\nWe use `fsspec` HTTPFileSystem implementation that is based on `aiohttp`. If you register a subclass of HTTPFileSystem that has SSL disabled by default it could work, but I wouldn't recommended it because it can raise security issues.", "Okay thanks for your help! I guess I have to figure out how to improve the proxy environment / see if I can make it work with ssl connections." ]
1,767,981,231	5,974	Deprecate `errors` param in favor of `encoding_errors` in text builder	closed	2023-06-21T16:31:38	2023-06-26T10:34:43	2023-06-26T10:27:40	https://github.com/huggingface/datasets/pull/5974	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5974", "html_url": "https://github.com/huggingface/datasets/pull/5974", "diff_url": "https://github.com/huggingface/datasets/pull/5974.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5974.patch", "merged_at": "2023-06-26T10:27:40" }	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) \| 0.006518 / 0.011353 (-0.004835) \| 0.004121 / 0.011008 (-0.006887) \| 0.103350 / 0.038508 (0.064842) \| 0.045030 / 0.023109 (0.021920) \| 0.351670 / 0.275898 (0.075772) \| 0.408110 / 0.323480 (0.084630) \| 0.003883 / 0.007986 (-0.004102) \| 0.003352 / 0.004328 (-0.000977) \| 0.078786 / 0.004250 (0.074535) \| 0.063977 / 0.037052 (0.026925) \| 0.369759 / 0.258489 (0.111270) \| 0.415103 / 0.293841 (0.121262) \| 0.033069 / 0.128546 (-0.095477) \| 0.008863 / 0.075646 (-0.066783) \| 0.353660 / 0.419271 (-0.065611) \| 0.055714 / 0.043533 (0.012181) \| 0.350458 / 0.255139 (0.095319) \| 0.369505 / 0.283200 (0.086305) \| 0.022822 / 0.141683 (-0.118861) \| 1.537588 / 1.452155 (0.085433) \| 1.590569 / 1.492716 (0.097853) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.206826 / 0.018006 (0.188819) \| 0.471625 / 0.000490 (0.471135) \| 0.005188 / 0.000200 (0.004988) \| 0.000316 / 0.000054 (0.000261) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028148 / 0.037411 (-0.009263) \| 0.111941 / 0.014526 (0.097415) \| 0.122106 / 0.176557 (-0.054451) \| 0.181127 / 0.737135 (-0.556009) \| 0.127534 / 0.296338 (-0.168805) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.409520 / 0.215209 (0.194311) \| 4.098455 / 2.077655 (2.020800) \| 1.852447 / 1.504120 (0.348327) \| 1.657036 / 1.541195 (0.115842) \| 1.709624 / 1.468490 (0.241134) \| 0.542806 / 4.584777 (-4.041970) \| 3.809352 / 3.745712 (0.063640) \| 1.855412 / 5.269862 (-3.414449) \| 1.109180 / 4.565676 (-3.456497) \| 0.066801 / 0.424275 (-0.357474) \| 0.011832 / 0.007607 (0.004225) \| 0.518338 / 0.226044 (0.292293) \| 5.190108 / 2.268929 (2.921179) \| 2.320602 / 55.444624 (-53.124023) \| 1.991416 / 6.876477 (-4.885060) \| 2.106989 / 2.142072 (-0.035084) \| 0.668914 / 4.805227 (-4.136313) \| 0.145325 / 6.500664 (-6.355340) \| 0.065145 / 0.075469 (-0.010324) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.254706 / 1.841788 (-0.587082) \| 14.707264 / 8.074308 (6.632956) \| 14.615423 / 10.191392 (4.424031) \| 0.170764 / 0.680424 (-0.509659) \| 0.017905 / 0.534201 (-0.516296) \| 0.435606 / 0.579283 (-0.143677) \| 0.434648 / 0.434364 (0.000284) \| 0.520813 / 0.540337 (-0.019524) \| 0.633902 / 1.386936 (-0.753034) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007212 / 0.011353 (-0.004141) \| 0.004301 / 0.011008 (-0.006707) \| 0.080767 / 0.038508 (0.042258) \| 0.051949 / 0.023109 (0.028840) \| 0.398473 / 0.275898 (0.122575) \| 0.465038 / 0.323480 (0.141558) \| 0.005580 / 0.007986 (-0.002406) \| 0.003556 / 0.004328 (-0.000773) \| 0.080682 / 0.004250 (0.076431) \| 0.059517 / 0.037052 (0.022464) \| 0.421171 / 0.258489 (0.162682) \| 0.459752 / 0.293841 (0.165911) \| 0.032960 / 0.128546 (-0.095586) \| 0.009107 / 0.075646 (-0.066539) \| 0.086382 / 0.419271 (-0.332889) \| 0.056053 / 0.043533 (0.012520) \| 0.393357 / 0.255139 (0.138218) \| 0.412972 / 0.283200 (0.129772) \| 0.031115 / 0.141683 (-0.110568) \| 1.576961 / 1.452155 (0.124806) \| 1.627249 / 1.492716 (0.134533) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227618 / 0.018006 (0.209612) \| 0.444640 / 0.000490 (0.444150) \| 0.004376 / 0.000200 (0.004176) \| 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) \| 0.030826 / 0.037411 (-0.006586) \| 0.117587 / 0.014526 (0.103062) \| 0.127467 / 0.176557 (-0.049089) \| 0.184440 / 0.737135 (-0.552695) \| 0.133664 / 0.296338 (-0.162675) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.443183 / 0.215209 (0.227974) \| 4.408312 / 2.077655 (2.330658) \| 2.132487 / 1.504120 (0.628367) \| 1.923632 / 1.541195 (0.382438) \| 1.967882 / 1.468490 (0.499392) \| 0.552954 / 4.584777 (-4.031823) \| 3.777701 / 3.745712 (0.031989) \| 1.857686 / 5.269862 (-3.412176) \| 1.104847 / 4.565676 (-3.460829) \| 0.068350 / 0.424275 (-0.355925) \| 0.012437 / 0.007607 (0.004830) \| 0.559258 / 0.226044 (0.333214) \| 5.593258 / 2.268929 (3.324330) \| 2.648059 / 55.444624 (-52.796565) \| 2.277428 / 6.876477 (-4.599049) \| 2.351685 / 2.142072 (0.209612) \| 0.678750 / 4.805227 (-4.126477) \| 0.145550 / 6.500664 (-6.355114) \| 0.066556 / 0.075469 (-0.008913) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.327128 / 1.841788 (-0.514659) \| 15.649079 / 8.074308 (7.574771) \| 14.478659 / 10.191392 (4.287267) \| 0.147633 / 0.680424 (-0.532791) \| 0.018502 / 0.534201 (-0.515699) \| 0.438556 / 0.579283 (-0.140727) \| 0.433381 / 0.434364 (-0.000983) \| 0.514367 / 0.540337 (-0.025970) \| 0.618347 / 1.386936 (-0.768589) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#16aa1c886c5b499641a4bb3d8ce4a4f7de8244b7 \"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) \| 0.006078 / 0.011353 (-0.005275) \| 0.003914 / 0.011008 (-0.007095) \| 0.102039 / 0.038508 (0.063531) \| 0.037660 / 0.023109 (0.014551) \| 0.348963 / 0.275898 (0.073065) \| 0.407284 / 0.323480 (0.083804) \| 0.004661 / 0.007986 (-0.003324) \| 0.003253 / 0.004328 (-0.001076) \| 0.078276 / 0.004250 (0.074025) \| 0.054144 / 0.037052 (0.017091) \| 0.376715 / 0.258489 (0.118225) \| 0.418499 / 0.293841 (0.124658) \| 0.027627 / 0.128546 (-0.100919) \| 0.008494 / 0.075646 (-0.067152) \| 0.316894 / 0.419271 (-0.102377) \| 0.046560 / 0.043533 (0.003027) \| 0.339835 / 0.255139 (0.084696) \| 0.374628 / 0.283200 (0.091428) \| 0.020729 / 0.141683 (-0.120954) \| 1.502769 / 1.452155 (0.050615) \| 1.548756 / 1.492716 (0.056040) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.229192 / 0.018006 (0.211186) \| 0.426245 / 0.000490 (0.425756) \| 0.005190 / 0.000200 (0.004990) \| 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) \| 0.024271 / 0.037411 (-0.013140) \| 0.098869 / 0.014526 (0.084343) \| 0.105079 / 0.176557 (-0.071477) \| 0.164707 / 0.737135 (-0.572428) \| 0.110337 / 0.296338 (-0.186002) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.426593 / 0.215209 (0.211383) \| 4.293977 / 2.077655 (2.216323) \| 1.928502 / 1.504120 (0.424382) \| 1.728623 / 1.541195 (0.187428) \| 1.792084 / 1.468490 (0.323594) \| 0.568737 / 4.584777 (-4.016040) \| 3.438534 / 3.745712 (-0.307178) \| 1.797798 / 5.269862 (-3.472063) \| 1.054078 / 4.565676 (-3.511598) \| 0.068711 / 0.424275 (-0.355564) \| 0.011250 / 0.007607 (0.003643) \| 0.529299 / 0.226044 (0.303255) \| 5.283965 / 2.268929 (3.015037) \| 2.358274 / 55.444624 (-53.086350) \| 2.012818 / 6.876477 (-4.863659) \| 2.109923 / 2.142072 (-0.032149) \| 0.679556 / 4.805227 (-4.125671) \| 0.138346 / 6.500664 (-6.362318) \| 0.066349 / 0.075469 (-0.009120) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.193994 / 1.841788 (-0.647794) \| 14.073158 / 8.074308 (5.998850) \| 13.488525 / 10.191392 (3.297133) \| 0.144536 / 0.680424 (-0.535888) \| 0.016748 / 0.534201 (-0.517453) \| 0.362703 / 0.579283 (-0.216580) \| 0.389511 / 0.434364 (-0.044853) \| 0.427296 / 0.540337 (-0.113041) \| 0.513227 / 1.386936 (-0.873709) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006215 / 0.011353 (-0.005138) \| 0.003834 / 0.011008 (-0.007174) \| 0.078001 / 0.038508 (0.039493) \| 0.036537 / 0.023109 (0.013428) \| 0.369724 / 0.275898 (0.093826) \| 0.426761 / 0.323480 (0.103281) \| 0.003602 / 0.007986 (-0.004383) \| 0.003001 / 0.004328 (-0.001327) \| 0.075989 / 0.004250 (0.071739) \| 0.048618 / 0.037052 (0.011566) \| 0.374296 / 0.258489 (0.115807) \| 0.430330 / 0.293841 (0.136489) \| 0.028299 / 0.128546 (-0.100247) \| 0.008537 / 0.075646 (-0.067109) \| 0.083275 / 0.419271 (-0.335997) \| 0.043136 / 0.043533 (-0.000397) \| 0.359072 / 0.255139 (0.103933) \| 0.387391 / 0.283200 (0.104192) \| 0.021202 / 0.141683 (-0.120481) \| 1.520832 / 1.452155 (0.068677) \| 1.567030 / 1.492716 (0.074313) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230944 / 0.018006 (0.212938) \| 0.422159 / 0.000490 (0.421669) \| 0.003447 / 0.000200 (0.003247) \| 0.000125 / 0.000054 (0.000071) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025442 / 0.037411 (-0.011969) \| 0.103944 / 0.014526 (0.089418) \| 0.110577 / 0.176557 (-0.065979) \| 0.161393 / 0.737135 (-0.575743) \| 0.113482 / 0.296338 (-0.182857) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.485765 / 0.215209 (0.270556) \| 4.845737 / 2.077655 (2.768083) \| 2.556732 / 1.504120 (1.052612) \| 2.348638 / 1.541195 (0.807443) \| 2.379289 / 1.468490 (0.910799) \| 0.561261 / 4.584777 (-4.023516) \| 3.482468 / 3.745712 (-0.263244) \| 3.061319 / 5.269862 (-2.208543) \| 1.483938 / 4.565676 (-3.081738) \| 0.067584 / 0.424275 (-0.356691) \| 0.011333 / 0.007607 (0.003726) \| 0.594342 / 0.226044 (0.368297) \| 5.935477 / 2.268929 (3.666548) \| 3.025029 / 55.444624 (-52.419595) \| 2.687032 / 6.876477 (-4.189445) \| 2.752470 / 2.142072 (0.610398) \| 0.674470 / 4.805227 (-4.130757) \| 0.136777 / 6.500664 (-6.363887) \| 0.068335 / 0.075469 (-0.007134) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.336456 / 1.841788 (-0.505332) \| 14.376007 / 8.074308 (6.301699) \| 14.171375 / 10.191392 (3.979983) \| 0.159620 / 0.680424 (-0.520804) \| 0.016685 / 0.534201 (-0.517516) \| 0.364344 / 0.579283 (-0.214939) \| 0.395358 / 0.434364 (-0.039006) \| 0.424876 / 0.540337 (-0.115461) \| 0.513267 / 1.386936 (-0.873669) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6ed837325cb539a5deb99129e5ad181d0269e050 \"CML watermark\")\n" ]
1,767,897,485	5,972	Filter unsupported extensions	closed	2023-06-21T15:43:01	2023-06-22T14:23:29	2023-06-22T14:16:26	https://github.com/huggingface/datasets/pull/5972	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5972", "html_url": "https://github.com/huggingface/datasets/pull/5972", "diff_url": "https://github.com/huggingface/datasets/pull/5972.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5972.patch", "merged_at": "2023-06-22T14:16:26" }	lhoestq	true	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006983 / 0.011353 (-0.004369) \| 0.004473 / 0.011008 (-0.006535) \| 0.105158 / 0.038508 (0.066650) \| 0.048973 / 0.023109 (0.025864) \| 0.358771 / 0.275898 (0.082873) \| 0.432389 / 0.323480 (0.108909) \| 0.005689 / 0.007986 (-0.002297) \| 0.003584 / 0.004328 (-0.000744) \| 0.080852 / 0.004250 (0.076601) \| 0.066133 / 0.037052 (0.029081) \| 0.370981 / 0.258489 (0.112492) \| 0.406942 / 0.293841 (0.113101) \| 0.032123 / 0.128546 (-0.096424) \| 0.009313 / 0.075646 (-0.066333) \| 0.355220 / 0.419271 (-0.064051) \| 0.055768 / 0.043533 (0.012235) \| 0.370545 / 0.255139 (0.115406) \| 0.375619 / 0.283200 (0.092419) \| 0.024258 / 0.141683 (-0.117425) \| 1.559073 / 1.452155 (0.106918) \| 1.616520 / 1.492716 (0.123804) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.277893 / 0.018006 (0.259887) \| 0.535447 / 0.000490 (0.534957) \| 0.004877 / 0.000200 (0.004677) \| 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) \| 0.029444 / 0.037411 (-0.007968) \| 0.114366 / 0.014526 (0.099841) \| 0.130957 / 0.176557 (-0.045599) \| 0.189604 / 0.737135 (-0.547531) \| 0.131682 / 0.296338 (-0.164656) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.412315 / 0.215209 (0.197106) \| 4.093879 / 2.077655 (2.016225) \| 1.856169 / 1.504120 (0.352050) \| 1.655358 / 1.541195 (0.114164) \| 1.758190 / 1.468490 (0.289699) \| 0.545829 / 4.584777 (-4.038948) \| 3.871436 / 3.745712 (0.125724) \| 1.938244 / 5.269862 (-3.331618) \| 1.122727 / 4.565676 (-3.442950) \| 0.067107 / 0.424275 (-0.357168) \| 0.012012 / 0.007607 (0.004405) \| 0.518868 / 0.226044 (0.292824) \| 5.235081 / 2.268929 (2.966153) \| 2.335115 / 55.444624 (-53.109509) \| 2.013074 / 6.876477 (-4.863402) \| 2.219808 / 2.142072 (0.077735) \| 0.674602 / 4.805227 (-4.130626) \| 0.147051 / 6.500664 (-6.353613) \| 0.068444 / 0.075469 (-0.007025) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.245600 / 1.841788 (-0.596188) \| 15.537727 / 8.074308 (7.463419) \| 15.074300 / 10.191392 (4.882908) \| 0.194217 / 0.680424 (-0.486207) \| 0.018536 / 0.534201 (-0.515665) \| 0.437085 / 0.579283 (-0.142198) \| 0.441123 / 0.434364 (0.006759) \| 0.530681 / 0.540337 (-0.009657) \| 0.649154 / 1.386936 (-0.737782) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007243 / 0.011353 (-0.004110) \| 0.004688 / 0.011008 (-0.006320) \| 0.079809 / 0.038508 (0.041301) \| 0.046915 / 0.023109 (0.023805) \| 0.415144 / 0.275898 (0.139246) \| 0.474867 / 0.323480 (0.151388) \| 0.004550 / 0.007986 (-0.003435) \| 0.004585 / 0.004328 (0.000257) \| 0.080837 / 0.004250 (0.076587) \| 0.061667 / 0.037052 (0.024614) \| 0.411321 / 0.258489 (0.152832) \| 0.464195 / 0.293841 (0.170354) \| 0.032510 / 0.128546 (-0.096037) \| 0.009306 / 0.075646 (-0.066340) \| 0.086637 / 0.419271 (-0.332635) \| 0.053335 / 0.043533 (0.009802) \| 0.402302 / 0.255139 (0.147163) \| 0.424864 / 0.283200 (0.141664) \| 0.026573 / 0.141683 (-0.115110) \| 1.566793 / 1.452155 (0.114639) \| 1.628118 / 1.492716 (0.135401) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.317802 / 0.018006 (0.299796) \| 0.544593 / 0.000490 (0.544103) \| 0.005690 / 0.000200 (0.005490) \| 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) \| 0.033015 / 0.037411 (-0.004397) \| 0.121940 / 0.014526 (0.107414) \| 0.132920 / 0.176557 (-0.043637) \| 0.191481 / 0.737135 (-0.545655) \| 0.139139 / 0.296338 (-0.157199) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.460382 / 0.215209 (0.245173) \| 4.610046 / 2.077655 (2.532392) \| 2.296573 / 1.504120 (0.792453) \| 2.099735 / 1.541195 (0.558540) \| 2.213913 / 1.468490 (0.745423) \| 0.544871 / 4.584777 (-4.039906) \| 3.814174 / 3.745712 (0.068462) \| 3.246397 / 5.269862 (-2.023464) \| 1.480236 / 4.565676 (-3.085440) \| 0.068464 / 0.424275 (-0.355811) \| 0.012651 / 0.007607 (0.005043) \| 0.564989 / 0.226044 (0.338944) \| 5.639188 / 2.268929 (3.370259) \| 2.827601 / 55.444624 (-52.617023) \| 2.473743 / 6.876477 (-4.402734) \| 2.567413 / 2.142072 (0.425340) \| 0.674351 / 4.805227 (-4.130876) \| 0.146248 / 6.500664 (-6.354416) \| 0.067553 / 0.075469 (-0.007916) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.346703 / 1.841788 (-0.495085) \| 16.494787 / 8.074308 (8.420479) \| 15.179487 / 10.191392 (4.988095) \| 0.181864 / 0.680424 (-0.498560) \| 0.018857 / 0.534201 (-0.515344) \| 0.437787 / 0.579283 (-0.141496) \| 0.431770 / 0.434364 (-0.002594) \| 0.507116 / 0.540337 (-0.033221) \| 0.608899 / 1.386936 (-0.778037) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0fd5b7412f907675e76b183a6e39ef6d176fdcc0 \"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) \| 0.005963 / 0.011353 (-0.005390) \| 0.003743 / 0.011008 (-0.007265) \| 0.098519 / 0.038508 (0.060011) \| 0.037392 / 0.023109 (0.014283) \| 0.322706 / 0.275898 (0.046808) \| 0.380032 / 0.323480 (0.056552) \| 0.004694 / 0.007986 (-0.003292) \| 0.002897 / 0.004328 (-0.001432) \| 0.078664 / 0.004250 (0.074414) \| 0.052646 / 0.037052 (0.015594) \| 0.335523 / 0.258489 (0.077034) \| 0.375464 / 0.293841 (0.081623) \| 0.027537 / 0.128546 (-0.101010) \| 0.008452 / 0.075646 (-0.067194) \| 0.313844 / 0.419271 (-0.105427) \| 0.047368 / 0.043533 (0.003835) \| 0.313833 / 0.255139 (0.058694) \| 0.342284 / 0.283200 (0.059085) \| 0.021136 / 0.141683 (-0.120547) \| 1.544764 / 1.452155 (0.092610) \| 1.563850 / 1.492716 (0.071134) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.188609 / 0.018006 (0.170603) \| 0.421686 / 0.000490 (0.421196) \| 0.003336 / 0.000200 (0.003136) \| 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) \| 0.023678 / 0.037411 (-0.013733) \| 0.099191 / 0.014526 (0.084665) \| 0.105819 / 0.176557 (-0.070738) \| 0.169654 / 0.737135 (-0.567481) \| 0.110240 / 0.296338 (-0.186099) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.425497 / 0.215209 (0.210288) \| 4.237165 / 2.077655 (2.159510) \| 1.902953 / 1.504120 (0.398833) \| 1.699012 / 1.541195 (0.157818) \| 1.751107 / 1.468490 (0.282617) \| 0.563326 / 4.584777 (-4.021451) \| 3.394189 / 3.745712 (-0.351523) \| 2.706129 / 5.269862 (-2.563732) \| 1.361522 / 4.565676 (-3.204155) \| 0.067776 / 0.424275 (-0.356499) \| 0.010959 / 0.007607 (0.003352) \| 0.530905 / 0.226044 (0.304860) \| 5.322467 / 2.268929 (3.053538) \| 2.384356 / 55.444624 (-53.060269) \| 2.044196 / 6.876477 (-4.832281) \| 2.119837 / 2.142072 (-0.022235) \| 0.682236 / 4.805227 (-4.122991) \| 0.136921 / 6.500664 (-6.363743) \| 0.066784 / 0.075469 (-0.008685) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.210642 / 1.841788 (-0.631146) \| 13.804572 / 8.074308 (5.730264) \| 13.309229 / 10.191392 (3.117837) \| 0.154356 / 0.680424 (-0.526068) \| 0.016833 / 0.534201 (-0.517368) \| 0.366503 / 0.579283 (-0.212780) \| 0.385201 / 0.434364 (-0.049163) \| 0.426713 / 0.540337 (-0.113624) \| 0.516795 / 1.386936 (-0.870141) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006144 / 0.011353 (-0.005209) \| 0.003723 / 0.011008 (-0.007285) \| 0.077427 / 0.038508 (0.038919) \| 0.037636 / 0.023109 (0.014527) \| 0.375048 / 0.275898 (0.099150) \| 0.442254 / 0.323480 (0.118774) \| 0.003506 / 0.007986 (-0.004480) \| 0.003751 / 0.004328 (-0.000577) \| 0.076771 / 0.004250 (0.072521) \| 0.047915 / 0.037052 (0.010862) \| 0.378918 / 0.258489 (0.120429) \| 0.435300 / 0.293841 (0.141459) \| 0.028317 / 0.128546 (-0.100230) \| 0.008413 / 0.075646 (-0.067233) \| 0.082774 / 0.419271 (-0.336497) \| 0.043211 / 0.043533 (-0.000321) \| 0.362022 / 0.255139 (0.106883) \| 0.404928 / 0.283200 (0.121728) \| 0.020692 / 0.141683 (-0.120991) \| 1.527303 / 1.452155 (0.075148) \| 1.596091 / 1.492716 (0.103375) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.225537 / 0.018006 (0.207530) \| 0.399901 / 0.000490 (0.399412) \| 0.000424 / 0.000200 (0.000224) \| 0.000058 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026483 / 0.037411 (-0.010928) \| 0.104373 / 0.014526 (0.089847) \| 0.111271 / 0.176557 (-0.065286) \| 0.163872 / 0.737135 (-0.573264) \| 0.113991 / 0.296338 (-0.182347) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.456484 / 0.215209 (0.241275) \| 4.572652 / 2.077655 (2.494998) \| 2.374908 / 1.504120 (0.870788) \| 2.207855 / 1.541195 (0.666661) \| 2.260009 / 1.468490 (0.791519) \| 0.562678 / 4.584777 (-4.022099) \| 3.441778 / 3.745712 (-0.303934) \| 1.729006 / 5.269862 (-3.540855) \| 1.024937 / 4.565676 (-3.540739) \| 0.068707 / 0.424275 (-0.355568) \| 0.011334 / 0.007607 (0.003727) \| 0.564293 / 0.226044 (0.338248) \| 5.638367 / 2.268929 (3.369438) \| 2.665654 / 55.444624 (-52.778970) \| 2.320033 / 6.876477 (-4.556444) \| 2.328706 / 2.142072 (0.186634) \| 0.677433 / 4.805227 (-4.127794) \| 0.137190 / 6.500664 (-6.363474) \| 0.068585 / 0.075469 (-0.006885) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.312476 / 1.841788 (-0.529312) \| 14.206685 / 8.074308 (6.132377) \| 14.217928 / 10.191392 (4.026536) \| 0.143416 / 0.680424 (-0.537007) \| 0.016647 / 0.534201 (-0.517554) \| 0.361228 / 0.579283 (-0.218055) \| 0.396185 / 0.434364 (-0.038178) \| 0.423275 / 0.540337 (-0.117063) \| 0.512966 / 1.386936 (-0.873970) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b424648fd68bd0b5279eb916cec4836d1220e268 \"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) \| 0.008913 / 0.011353 (-0.002440) \| 0.005142 / 0.011008 (-0.005866) \| 0.133958 / 0.038508 (0.095449) \| 0.049180 / 0.023109 (0.026071) \| 0.389169 / 0.275898 (0.113270) \| 0.481513 / 0.323480 (0.158033) \| 0.006555 / 0.007986 (-0.001430) \| 0.003806 / 0.004328 (-0.000522) \| 0.102056 / 0.004250 (0.097806) \| 0.083259 / 0.037052 (0.046207) \| 0.392536 / 0.258489 (0.134047) \| 0.447503 / 0.293841 (0.153662) \| 0.047472 / 0.128546 (-0.081074) \| 0.014748 / 0.075646 (-0.060899) \| 0.475619 / 0.419271 (0.056348) \| 0.107306 / 0.043533 (0.063773) \| 0.421942 / 0.255139 (0.166803) \| 0.419736 / 0.283200 (0.136536) \| 0.044195 / 0.141683 (-0.097488) \| 1.793840 / 1.452155 (0.341686) \| 1.960204 / 1.492716 (0.467488) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.252046 / 0.018006 (0.234040) \| 0.627725 / 0.000490 (0.627236) \| 0.007435 / 0.000200 (0.007235) \| 0.000526 / 0.000054 (0.000472) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034656 / 0.037411 (-0.002755) \| 0.114534 / 0.014526 (0.100008) \| 0.135804 / 0.176557 (-0.040753) \| 0.209309 / 0.737135 (-0.527826) \| 0.140369 / 0.296338 (-0.155969) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.636736 / 0.215209 (0.421527) \| 6.039985 / 2.077655 (3.962330) \| 2.640141 / 1.504120 (1.136021) \| 2.284492 / 1.541195 (0.743297) \| 2.324956 / 1.468490 (0.856466) \| 0.934499 / 4.584777 (-3.650278) \| 5.673415 / 3.745712 (1.927703) \| 5.184584 / 5.269862 (-0.085278) \| 2.661911 / 4.565676 (-1.903766) \| 0.150420 / 0.424275 (-0.273855) \| 0.015655 / 0.007607 (0.008048) \| 0.748290 / 0.226044 (0.522246) \| 7.579755 / 2.268929 (5.310827) \| 3.346732 / 55.444624 (-52.097892) \| 2.708212 / 6.876477 (-4.168264) \| 2.682423 / 2.142072 (0.540351) \| 1.170389 / 4.805227 (-3.634838) \| 0.215775 / 6.500664 (-6.284889) \| 0.076360 / 0.075469 (0.000891) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.516794 / 1.841788 (-0.324993) \| 18.709117 / 8.074308 (10.634809) \| 22.492542 / 10.191392 (12.301150) \| 0.237978 / 0.680424 (-0.442446) \| 0.027828 / 0.534201 (-0.506373) \| 0.499968 / 0.579283 (-0.079315) \| 0.645899 / 0.434364 (0.211535) \| 0.548599 / 0.540337 (0.008262) \| 0.675428 / 1.386936 (-0.711508) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008469 / 0.011353 (-0.002884) \| 0.005420 / 0.011008 (-0.005589) \| 0.093340 / 0.038508 (0.054832) \| 0.045896 / 0.023109 (0.022786) \| 0.533267 / 0.275898 (0.257369) \| 0.596034 / 0.323480 (0.272555) \| 0.004816 / 0.007986 (-0.003170) \| 0.004379 / 0.004328 (0.000051) \| 0.096356 / 0.004250 (0.092106) \| 0.058339 / 0.037052 (0.021287) \| 0.574464 / 0.258489 (0.315975) \| 0.649301 / 0.293841 (0.355461) \| 0.047599 / 0.128546 (-0.080947) \| 0.013759 / 0.075646 (-0.061887) \| 0.104672 / 0.419271 (-0.314599) \| 0.061658 / 0.043533 (0.018125) \| 0.560956 / 0.255139 (0.305817) \| 0.585328 / 0.283200 (0.302128) \| 0.034137 / 0.141683 (-0.107546) \| 1.844528 / 1.452155 (0.392373) \| 1.971398 / 1.492716 (0.478682) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278666 / 0.018006 (0.260660) \| 0.577342 / 0.000490 (0.576853) \| 0.005496 / 0.000200 (0.005296) \| 0.000131 / 0.000054 (0.000076) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029863 / 0.037411 (-0.007549) \| 0.161703 / 0.014526 (0.147177) \| 0.132279 / 0.176557 (-0.044277) \| 0.227345 / 0.737135 (-0.509791) \| 0.138047 / 0.296338 (-0.158291) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.651535 / 0.215209 (0.436326) \| 7.077949 / 2.077655 (5.000295) \| 2.926990 / 1.504120 (1.422871) \| 2.598872 / 1.541195 (1.057678) \| 2.614192 / 1.468490 (1.145702) \| 0.913845 / 4.584777 (-3.670932) \| 5.704301 / 3.745712 (1.958589) \| 2.796914 / 5.269862 (-2.472948) \| 1.836096 / 4.565676 (-2.729580) \| 0.106294 / 0.424275 (-0.317981) \| 0.012705 / 0.007607 (0.005098) \| 0.836336 / 0.226044 (0.610291) \| 8.234079 / 2.268929 (5.965150) \| 3.836410 / 55.444624 (-51.608215) \| 3.116752 / 6.876477 (-3.759724) \| 3.154258 / 2.142072 (1.012186) \| 1.195794 / 4.805227 (-3.609434) \| 0.240491 / 6.500664 (-6.260173) \| 0.087913 / 0.075469 (0.012444) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.724723 / 1.841788 (-0.117064) \| 19.492194 / 8.074308 (11.417885) \| 21.443341 / 10.191392 (11.251949) \| 0.245819 / 0.680424 (-0.434605) \| 0.027024 / 0.534201 (-0.507177) \| 0.481071 / 0.579283 (-0.098212) \| 0.596359 / 0.434364 (0.161995) \| 0.646462 / 0.540337 (0.106124) \| 0.706380 / 1.386936 (-0.680556) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#67ca664e6d5ef137127b238aae1d0aff54e22db2 \"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) \| 0.006634 / 0.011353 (-0.004719) \| 0.004003 / 0.011008 (-0.007005) \| 0.097874 / 0.038508 (0.059365) \| 0.043528 / 0.023109 (0.020419) \| 0.302293 / 0.275898 (0.026395) \| 0.357041 / 0.323480 (0.033561) \| 0.003761 / 0.007986 (-0.004225) \| 0.004312 / 0.004328 (-0.000016) \| 0.076253 / 0.004250 (0.072003) \| 0.062807 / 0.037052 (0.025755) \| 0.316737 / 0.258489 (0.058248) \| 0.356722 / 0.293841 (0.062881) \| 0.030816 / 0.128546 (-0.097730) \| 0.008691 / 0.075646 (-0.066955) \| 0.328366 / 0.419271 (-0.090906) \| 0.062299 / 0.043533 (0.018766) \| 0.293877 / 0.255139 (0.038738) \| 0.319832 / 0.283200 (0.036632) \| 0.024996 / 0.141683 (-0.116687) \| 1.473912 / 1.452155 (0.021758) \| 1.565439 / 1.492716 (0.072723) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.208428 / 0.018006 (0.190422) \| 0.435618 / 0.000490 (0.435128) \| 0.000695 / 0.000200 (0.000495) \| 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) \| 0.026253 / 0.037411 (-0.011158) \| 0.106908 / 0.014526 (0.092382) \| 0.117075 / 0.176557 (-0.059482) \| 0.177969 / 0.737135 (-0.559166) \| 0.123400 / 0.296338 (-0.172938) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.424970 / 0.215209 (0.209761) \| 4.203233 / 2.077655 (2.125578) \| 2.009679 / 1.504120 (0.505559) \| 1.825691 / 1.541195 (0.284496) \| 1.870639 / 1.468490 (0.402149) \| 0.530758 / 4.584777 (-4.054019) \| 3.718791 / 3.745712 (-0.026921) \| 1.800206 / 5.269862 (-3.469656) \| 1.071651 / 4.565676 (-3.494025) \| 0.065126 / 0.424275 (-0.359149) \| 0.011312 / 0.007607 (0.003704) \| 0.532503 / 0.226044 (0.306458) \| 5.353950 / 2.268929 (3.085021) \| 2.463548 / 55.444624 (-52.981076) \| 2.139832 / 6.876477 (-4.736645) \| 2.238722 / 2.142072 (0.096650) \| 0.655736 / 4.805227 (-4.149492) \| 0.141689 / 6.500664 (-6.358975) \| 0.063282 / 0.075469 (-0.012187) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.183523 / 1.841788 (-0.658265) \| 14.146428 / 8.074308 (6.072120) \| 14.312883 / 10.191392 (4.121491) \| 0.169286 / 0.680424 (-0.511138) \| 0.017343 / 0.534201 (-0.516858) \| 0.397934 / 0.579283 (-0.181349) \| 0.417791 / 0.434364 (-0.016573) \| 0.463639 / 0.540337 (-0.076698) \| 0.562787 / 1.386936 (-0.824149) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006594 / 0.011353 (-0.004759) \| 0.004086 / 0.011008 (-0.006922) \| 0.075122 / 0.038508 (0.036614) \| 0.041849 / 0.023109 (0.018740) \| 0.362645 / 0.275898 (0.086747) \| 0.464350 / 0.323480 (0.140870) \| 0.003760 / 0.007986 (-0.004226) \| 0.003327 / 0.004328 (-0.001001) \| 0.076154 / 0.004250 (0.071904) \| 0.053232 / 0.037052 (0.016180) \| 0.407863 / 0.258489 (0.149374) \| 0.460787 / 0.293841 (0.166946) \| 0.031917 / 0.128546 (-0.096630) \| 0.008770 / 0.075646 (-0.066876) \| 0.082612 / 0.419271 (-0.336660) \| 0.051311 / 0.043533 (0.007779) \| 0.354508 / 0.255139 (0.099369) \| 0.419533 / 0.283200 (0.136334) \| 0.023980 / 0.141683 (-0.117703) \| 1.491255 / 1.452155 (0.039100) \| 1.536101 / 1.492716 (0.043384) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.178261 / 0.018006 (0.160255) \| 0.444680 / 0.000490 (0.444190) \| 0.013761 / 0.000200 (0.013561) \| 0.000117 / 0.000054 (0.000063) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027875 / 0.037411 (-0.009536) \| 0.111269 / 0.014526 (0.096744) \| 0.121096 / 0.176557 (-0.055461) \| 0.174387 / 0.737135 (-0.562749) \| 0.124714 / 0.296338 (-0.171624) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.445422 / 0.215209 (0.230213) \| 4.435877 / 2.077655 (2.358222) \| 2.221895 / 1.504120 (0.717775) \| 2.030571 / 1.541195 (0.489376) \| 2.074863 / 1.468490 (0.606373) \| 0.543331 / 4.584777 (-4.041446) \| 3.753615 / 3.745712 (0.007903) \| 3.317074 / 5.269862 (-1.952787) \| 1.630390 / 4.565676 (-2.935286) \| 0.066726 / 0.424275 (-0.357549) \| 0.011556 / 0.007607 (0.003949) \| 0.546985 / 0.226044 (0.320941) \| 5.460634 / 2.268929 (3.191705) \| 2.705945 / 55.444624 (-52.738679) \| 2.373425 / 6.876477 (-4.503052) \| 2.401472 / 2.142072 (0.259399) \| 0.663225 / 4.805227 (-4.142002) \| 0.143694 / 6.500664 (-6.356970) \| 0.065283 / 0.075469 (-0.010186) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.264804 / 1.841788 (-0.576983) \| 14.803228 / 8.074308 (6.728919) \| 14.178514 / 10.191392 (3.987122) \| 0.162651 / 0.680424 (-0.517772) \| 0.017586 / 0.534201 (-0.516615) \| 0.398740 / 0.579283 (-0.180543) \| 0.414478 / 0.434364 (-0.019886) \| 0.465442 / 0.540337 (-0.074895) \| 0.563450 / 1.386936 (-0.823486) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#76f75a9a3b2aaad05ea0ea5ab77e01fd2ca66760 \"CML watermark\")\n" ]
1,767,053,635	5,971	Docs: make "repository structure" easier to find	open	2023-06-21T08:26:44	2023-07-05T06:51:38	null	https://github.com/huggingface/datasets/issues/5971	null	severo	false	[ "Loading a local dataset also works the same way when `data_files` are not specified, so I agree we should make this info easier to discover \r\n\r\ncc @stevhliu ", "Is this issue open? If so, I will self assign. ", "@benjaminbrown038 Yes, it is. Maybe @stevhliu can give some pointers on improving this doc page's discoverability.", "I think we can add a version of the [Main use-case](https://huggingface.co/docs/datasets/repository_structure#main-usecase) section to the [Share a dataset to the Hub](https://huggingface.co/docs/datasets/upload_dataset) tutorial. \r\n\r\nCurrently, it doesn't tell you how to structure the repository; it only tells you how to create it. So adding the \"main use-case\" will help bridge the gap and make it easier to find. We should also add a link to the [Structure your repository](https://huggingface.co/docs/datasets/repository_structure) guide for users who want to learn about the other options.", "#self-assign" ]
1,766,010,356	5,970	description disappearing from Info when Uploading a Dataset Created with `from_dict`	open	2023-06-20T19:18:26	2023-06-22T14:23:56	null	https://github.com/huggingface/datasets/issues/5970	null	balisujohn	false	[ "Here's a minimal way to reproduce the bug, for the sake of convenience.\r\n````\r\nfrom datasets import Dataset, DatasetInfo, load_dataset\r\n\r\n\r\nepisodes_dict = {\"test\":[1,2,3],\"test2\": [1,2,4]}\r\n\r\nhugging_face_dataset = Dataset.from_dict(\r\n episodes_dict, info=DatasetInfo(description=\"test_str\")\r\n)\r\nprint(hugging_face_dataset.info)\r\n\r\nhugging_face_dataset.push_to_hub(\"balisujohn/minari_test\", private=True)\r\n\r\nredownloaded_dataset= load_dataset(\"balisujohn/minari_test\")[\"train\"]\r\n\r\n\r\nprint(redownloaded_dataset.info)\r\n````\r\n", "Thanks for reporting !\r\n\r\nFor now I would recommend uploading a separate JSON file for your metadata.\r\n\r\nAlternatively you can upload a second configuration of the dataset containing your metadata but this feature is not released yet (though you can already use it from [here](https://github.com/huggingface/datasets/pull/5331), it will be released soon)" ]
1,765,529,905	5,969	Add `encoding` and `errors` params to JSON loader	closed	2023-06-20T14:28:35	2023-06-21T13:39:50	2023-06-21T13:32:22	https://github.com/huggingface/datasets/pull/5969	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5969", "html_url": "https://github.com/huggingface/datasets/pull/5969", "diff_url": "https://github.com/huggingface/datasets/pull/5969.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5969.patch", "merged_at": "2023-06-21T13:32:22" }	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) \| 0.006770 / 0.011353 (-0.004583) \| 0.004143 / 0.011008 (-0.006865) \| 0.098928 / 0.038508 (0.060420) \| 0.044893 / 0.023109 (0.021783) \| 0.302630 / 0.275898 (0.026732) \| 0.368173 / 0.323480 (0.044693) \| 0.005631 / 0.007986 (-0.002354) \| 0.003397 / 0.004328 (-0.000931) \| 0.075748 / 0.004250 (0.071497) \| 0.062582 / 0.037052 (0.025530) \| 0.329586 / 0.258489 (0.071097) \| 0.362625 / 0.293841 (0.068784) \| 0.033250 / 0.128546 (-0.095296) \| 0.008880 / 0.075646 (-0.066766) \| 0.329683 / 0.419271 (-0.089588) \| 0.054426 / 0.043533 (0.010893) \| 0.297940 / 0.255139 (0.042801) \| 0.319796 / 0.283200 (0.036597) \| 0.023296 / 0.141683 (-0.118387) \| 1.462142 / 1.452155 (0.009987) \| 1.495796 / 1.492716 (0.003079) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.201771 / 0.018006 (0.183765) \| 0.454514 / 0.000490 (0.454024) \| 0.003333 / 0.000200 (0.003133) \| 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) \| 0.028084 / 0.037411 (-0.009327) \| 0.109452 / 0.014526 (0.094926) \| 0.119200 / 0.176557 (-0.057357) \| 0.180302 / 0.737135 (-0.556834) \| 0.125653 / 0.296338 (-0.170686) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.409819 / 0.215209 (0.194610) \| 4.055117 / 2.077655 (1.977462) \| 1.855279 / 1.504120 (0.351159) \| 1.655281 / 1.541195 (0.114086) \| 1.687938 / 1.468490 (0.219448) \| 0.528352 / 4.584777 (-4.056425) \| 3.750250 / 3.745712 (0.004538) \| 3.386741 / 5.269862 (-1.883121) \| 1.572036 / 4.565676 (-2.993640) \| 0.065125 / 0.424275 (-0.359150) \| 0.011259 / 0.007607 (0.003652) \| 0.513449 / 0.226044 (0.287405) \| 5.139421 / 2.268929 (2.870492) \| 2.316973 / 55.444624 (-53.127651) \| 1.984109 / 6.876477 (-4.892368) \| 2.127915 / 2.142072 (-0.014158) \| 0.653238 / 4.805227 (-4.151989) \| 0.142686 / 6.500664 (-6.357978) \| 0.063666 / 0.075469 (-0.011803) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.185174 / 1.841788 (-0.656614) \| 14.790282 / 8.074308 (6.715974) \| 13.089222 / 10.191392 (2.897830) \| 0.146055 / 0.680424 (-0.534369) \| 0.017835 / 0.534201 (-0.516366) \| 0.399598 / 0.579283 (-0.179685) \| 0.425296 / 0.434364 (-0.009068) \| 0.478552 / 0.540337 (-0.061786) \| 0.579702 / 1.386936 (-0.807234) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006750 / 0.011353 (-0.004603) \| 0.004156 / 0.011008 (-0.006853) \| 0.074948 / 0.038508 (0.036440) \| 0.043368 / 0.023109 (0.020259) \| 0.355389 / 0.275898 (0.079491) \| 0.429167 / 0.323480 (0.105687) \| 0.003911 / 0.007986 (-0.004075) \| 0.004340 / 0.004328 (0.000012) \| 0.075940 / 0.004250 (0.071689) \| 0.054293 / 0.037052 (0.017241) \| 0.400317 / 0.258489 (0.141827) \| 0.432001 / 0.293841 (0.138160) \| 0.032340 / 0.128546 (-0.096206) \| 0.008876 / 0.075646 (-0.066770) \| 0.082284 / 0.419271 (-0.336987) \| 0.050819 / 0.043533 (0.007286) \| 0.351994 / 0.255139 (0.096855) \| 0.375917 / 0.283200 (0.092717) \| 0.022466 / 0.141683 (-0.119217) \| 1.538824 / 1.452155 (0.086669) \| 1.563995 / 1.492716 (0.071279) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227330 / 0.018006 (0.209323) \| 0.446380 / 0.000490 (0.445890) \| 0.000408 / 0.000200 (0.000208) \| 0.000058 / 0.000054 (0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028534 / 0.037411 (-0.008878) \| 0.113467 / 0.014526 (0.098941) \| 0.123590 / 0.176557 (-0.052966) \| 0.174309 / 0.737135 (-0.562827) \| 0.130631 / 0.296338 (-0.165707) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.441020 / 0.215209 (0.225811) \| 4.386564 / 2.077655 (2.308909) \| 2.100704 / 1.504120 (0.596584) \| 1.901484 / 1.541195 (0.360289) \| 1.963494 / 1.468490 (0.495004) \| 0.536838 / 4.584777 (-4.047939) \| 3.739071 / 3.745712 (-0.006642) \| 3.278981 / 5.269862 (-1.990881) \| 1.515476 / 4.565676 (-3.050201) \| 0.066388 / 0.424275 (-0.357887) \| 0.011857 / 0.007607 (0.004250) \| 0.545507 / 0.226044 (0.319463) \| 5.441479 / 2.268929 (3.172550) \| 2.602144 / 55.444624 (-52.842480) \| 2.235583 / 6.876477 (-4.640894) \| 2.293458 / 2.142072 (0.151385) \| 0.658535 / 4.805227 (-4.146692) \| 0.141327 / 6.500664 (-6.359337) \| 0.063726 / 0.075469 (-0.011743) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.247819 / 1.841788 (-0.593968) \| 15.234524 / 8.074308 (7.160216) \| 14.592700 / 10.191392 (4.401308) \| 0.141952 / 0.680424 (-0.538472) \| 0.017747 / 0.534201 (-0.516454) \| 0.396819 / 0.579283 (-0.182465) \| 0.415902 / 0.434364 (-0.018462) \| 0.464619 / 0.540337 (-0.075718) \| 0.560866 / 1.386936 (-0.826070) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4b7f6c59deb868e21f295917548fa2df10dd0158 \"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) \| 0.008278 / 0.011353 (-0.003075) \| 0.005044 / 0.011008 (-0.005964) \| 0.123382 / 0.038508 (0.084874) \| 0.054039 / 0.023109 (0.030929) \| 0.382338 / 0.275898 (0.106440) \| 0.453287 / 0.323480 (0.129807) \| 0.006342 / 0.007986 (-0.001644) \| 0.003930 / 0.004328 (-0.000398) \| 0.094039 / 0.004250 (0.089789) \| 0.076525 / 0.037052 (0.039472) \| 0.394066 / 0.258489 (0.135577) \| 0.445600 / 0.293841 (0.151759) \| 0.039348 / 0.128546 (-0.089199) \| 0.010485 / 0.075646 (-0.065161) \| 0.433730 / 0.419271 (0.014459) \| 0.082671 / 0.043533 (0.039138) \| 0.375250 / 0.255139 (0.120111) \| 0.416269 / 0.283200 (0.133070) \| 0.038397 / 0.141683 (-0.103286) \| 1.864834 / 1.452155 (0.412680) \| 2.010453 / 1.492716 (0.517737) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.240008 / 0.018006 (0.222002) \| 0.470975 / 0.000490 (0.470485) \| 0.004001 / 0.000200 (0.003801) \| 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) \| 0.031107 / 0.037411 (-0.006304) \| 0.129371 / 0.014526 (0.114846) \| 0.141559 / 0.176557 (-0.034997) \| 0.205571 / 0.737135 (-0.531564) \| 0.144611 / 0.296338 (-0.151728) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.506972 / 0.215209 (0.291763) \| 5.055951 / 2.077655 (2.978296) \| 2.397438 / 1.504120 (0.893318) \| 2.170435 / 1.541195 (0.629240) \| 2.240296 / 1.468490 (0.771806) \| 0.641559 / 4.584777 (-3.943218) \| 4.644772 / 3.745712 (0.899060) \| 4.064200 / 5.269862 (-1.205662) \| 1.946991 / 4.565676 (-2.618685) \| 0.086413 / 0.424275 (-0.337862) \| 0.015082 / 0.007607 (0.007475) \| 0.670413 / 0.226044 (0.444369) \| 6.331346 / 2.268929 (4.062418) \| 2.965813 / 55.444624 (-52.478812) \| 2.547952 / 6.876477 (-4.328524) \| 2.718390 / 2.142072 (0.576318) \| 0.796657 / 4.805227 (-4.008571) \| 0.173229 / 6.500664 (-6.327435) \| 0.079606 / 0.075469 (0.004137) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.568761 / 1.841788 (-0.273026) \| 18.485432 / 8.074308 (10.411124) \| 15.758513 / 10.191392 (5.567121) \| 0.170427 / 0.680424 (-0.509997) \| 0.021421 / 0.534201 (-0.512780) \| 0.518623 / 0.579283 (-0.060660) \| 0.525887 / 0.434364 (0.091523) \| 0.640331 / 0.540337 (0.099993) \| 0.766748 / 1.386936 (-0.620188) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007680 / 0.011353 (-0.003673) \| 0.005289 / 0.011008 (-0.005719) \| 0.093773 / 0.038508 (0.055265) \| 0.054997 / 0.023109 (0.031888) \| 0.456277 / 0.275898 (0.180379) \| 0.500642 / 0.323480 (0.177162) \| 0.005935 / 0.007986 (-0.002050) \| 0.004375 / 0.004328 (0.000047) \| 0.094131 / 0.004250 (0.089881) \| 0.063399 / 0.037052 (0.026347) \| 0.470546 / 0.258489 (0.212057) \| 0.504989 / 0.293841 (0.211148) \| 0.038541 / 0.128546 (-0.090006) \| 0.010403 / 0.075646 (-0.065244) \| 0.102469 / 0.419271 (-0.316802) \| 0.063105 / 0.043533 (0.019572) \| 0.466005 / 0.255139 (0.210866) \| 0.458677 / 0.283200 (0.175477) \| 0.028407 / 0.141683 (-0.113276) \| 1.893829 / 1.452155 (0.441675) \| 1.917954 / 1.492716 (0.425238) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.272760 / 0.018006 (0.254754) \| 0.476159 / 0.000490 (0.475669) \| 0.008467 / 0.000200 (0.008267) \| 0.000146 / 0.000054 (0.000091) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035755 / 0.037411 (-0.001656) \| 0.145038 / 0.014526 (0.130512) \| 0.148322 / 0.176557 (-0.028235) \| 0.210193 / 0.737135 (-0.526943) \| 0.156547 / 0.296338 (-0.139792) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.541204 / 0.215209 (0.325995) \| 5.382746 / 2.077655 (3.305091) \| 2.704229 / 1.504120 (1.200109) \| 2.468422 / 1.541195 (0.927227) \| 2.522672 / 1.468490 (1.054182) \| 0.644899 / 4.584777 (-3.939878) \| 4.654401 / 3.745712 (0.908689) \| 2.159223 / 5.269862 (-3.110638) \| 1.280098 / 4.565676 (-3.285578) \| 0.080053 / 0.424275 (-0.344222) \| 0.014383 / 0.007607 (0.006776) \| 0.662770 / 0.226044 (0.436725) \| 6.617651 / 2.268929 (4.348722) \| 3.234347 / 55.444624 (-52.210277) \| 2.861417 / 6.876477 (-4.015059) \| 2.888928 / 2.142072 (0.746856) \| 0.792854 / 4.805227 (-4.012374) \| 0.172553 / 6.500664 (-6.328111) \| 0.078402 / 0.075469 (0.002933) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.565351 / 1.841788 (-0.276436) \| 18.681916 / 8.074308 (10.607608) \| 17.264473 / 10.191392 (7.073081) \| 0.168461 / 0.680424 (-0.511963) \| 0.021353 / 0.534201 (-0.512848) \| 0.517843 / 0.579283 (-0.061440) \| 0.519907 / 0.434364 (0.085543) \| 0.623687 / 0.540337 (0.083350) \| 0.761796 / 1.386936 (-0.625140) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bbf58747f734a46e75937bdbcbc05b06ade0224a \"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) \| 0.006750 / 0.011353 (-0.004603) \| 0.004268 / 0.011008 (-0.006741) \| 0.098644 / 0.038508 (0.060136) \| 0.044643 / 0.023109 (0.021534) \| 0.309420 / 0.275898 (0.033522) \| 0.379294 / 0.323480 (0.055815) \| 0.005729 / 0.007986 (-0.002256) \| 0.003615 / 0.004328 (-0.000714) \| 0.076086 / 0.004250 (0.071835) \| 0.068994 / 0.037052 (0.031942) \| 0.325653 / 0.258489 (0.067164) \| 0.375187 / 0.293841 (0.081347) \| 0.032546 / 0.128546 (-0.096000) \| 0.009089 / 0.075646 (-0.066557) \| 0.329905 / 0.419271 (-0.089366) \| 0.066832 / 0.043533 (0.023300) \| 0.299247 / 0.255139 (0.044108) \| 0.323460 / 0.283200 (0.040260) \| 0.034226 / 0.141683 (-0.107457) \| 1.475659 / 1.452155 (0.023505) \| 1.556234 / 1.492716 (0.063518) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292305 / 0.018006 (0.274299) \| 0.542584 / 0.000490 (0.542094) \| 0.003047 / 0.000200 (0.002847) \| 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) \| 0.030096 / 0.037411 (-0.007315) \| 0.112341 / 0.014526 (0.097815) \| 0.124965 / 0.176557 (-0.051591) \| 0.183159 / 0.737135 (-0.553976) \| 0.131885 / 0.296338 (-0.164453) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.426437 / 0.215209 (0.211228) \| 4.260984 / 2.077655 (2.183330) \| 2.078358 / 1.504120 (0.574238) \| 1.877644 / 1.541195 (0.336449) \| 2.044036 / 1.468490 (0.575546) \| 0.532980 / 4.584777 (-4.051797) \| 3.749573 / 3.745712 (0.003860) \| 1.944155 / 5.269862 (-3.325706) \| 1.090307 / 4.565676 (-3.475370) \| 0.065445 / 0.424275 (-0.358830) \| 0.011237 / 0.007607 (0.003630) \| 0.521448 / 0.226044 (0.295403) \| 5.213118 / 2.268929 (2.944189) \| 2.507829 / 55.444624 (-52.936795) \| 2.177179 / 6.876477 (-4.699297) \| 2.351161 / 2.142072 (0.209088) \| 0.656775 / 4.805227 (-4.148452) \| 0.141207 / 6.500664 (-6.359457) \| 0.063286 / 0.075469 (-0.012183) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.190281 / 1.841788 (-0.651506) \| 15.327424 / 8.074308 (7.253116) \| 13.300695 / 10.191392 (3.109303) \| 0.190484 / 0.680424 (-0.489939) \| 0.017984 / 0.534201 (-0.516217) \| 0.405714 / 0.579283 (-0.173569) \| 0.435915 / 0.434364 (0.001551) \| 0.494083 / 0.540337 (-0.046254) \| 0.600616 / 1.386936 (-0.786320) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006740 / 0.011353 (-0.004613) \| 0.004289 / 0.011008 (-0.006719) \| 0.076532 / 0.038508 (0.038024) \| 0.043305 / 0.023109 (0.020196) \| 0.356111 / 0.275898 (0.080213) \| 0.434121 / 0.323480 (0.110641) \| 0.005599 / 0.007986 (-0.002387) \| 0.003461 / 0.004328 (-0.000868) \| 0.077097 / 0.004250 (0.072847) \| 0.055369 / 0.037052 (0.018317) \| 0.367093 / 0.258489 (0.108604) \| 0.418801 / 0.293841 (0.124960) \| 0.032057 / 0.128546 (-0.096489) \| 0.009048 / 0.075646 (-0.066599) \| 0.082897 / 0.419271 (-0.336374) \| 0.050287 / 0.043533 (0.006754) \| 0.352060 / 0.255139 (0.096921) \| 0.376278 / 0.283200 (0.093078) \| 0.023924 / 0.141683 (-0.117759) \| 1.522780 / 1.452155 (0.070626) \| 1.578938 / 1.492716 (0.086222) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287317 / 0.018006 (0.269311) \| 0.508490 / 0.000490 (0.508000) \| 0.000431 / 0.000200 (0.000231) \| 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) \| 0.031139 / 0.037411 (-0.006272) \| 0.113927 / 0.014526 (0.099401) \| 0.128147 / 0.176557 (-0.048409) \| 0.179712 / 0.737135 (-0.557424) \| 0.134364 / 0.296338 (-0.161975) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.452834 / 0.215209 (0.237625) \| 4.507944 / 2.077655 (2.430289) \| 2.287758 / 1.504120 (0.783638) \| 2.091145 / 1.541195 (0.549951) \| 2.196228 / 1.468490 (0.727738) \| 0.539306 / 4.584777 (-4.045471) \| 3.838941 / 3.745712 (0.093228) \| 1.908801 / 5.269862 (-3.361060) \| 1.139235 / 4.565676 (-3.426442) \| 0.066677 / 0.424275 (-0.357599) \| 0.011422 / 0.007607 (0.003815) \| 0.562966 / 0.226044 (0.336921) \| 5.633712 / 2.268929 (3.364784) \| 2.788622 / 55.444624 (-52.656002) \| 2.438465 / 6.876477 (-4.438012) \| 2.523479 / 2.142072 (0.381407) \| 0.668730 / 4.805227 (-4.136498) \| 0.143977 / 6.500664 (-6.356687) \| 0.064661 / 0.075469 (-0.010808) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.291708 / 1.841788 (-0.550080) \| 15.573316 / 8.074308 (7.499008) \| 14.435099 / 10.191392 (4.243707) \| 0.147745 / 0.680424 (-0.532679) \| 0.017602 / 0.534201 (-0.516599) \| 0.401560 / 0.579283 (-0.177723) \| 0.429861 / 0.434364 (-0.004502) \| 0.469800 / 0.540337 (-0.070538) \| 0.567515 / 1.386936 (-0.819421) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79c340f5dcfd06340f180f6c6ea2d5ef81f49d98 \"CML watermark\")\n" ]
1,765,252,561	5,968	Common Voice datasets still need `use_auth_token=True`	closed	2023-06-20T11:58:37	2023-07-29T16:08:59	2023-07-29T16:08:58	https://github.com/huggingface/datasets/issues/5968	null	patrickvonplaten	false	[ "cc @pcuenca as well. \r\n\r\nNot super urgent btw", "The issue commes from the dataset itself and is not related to the `datasets` lib\r\n\r\nsee https://huggingface.co/datasets/mozilla-foundation/common_voice_6_1/blob/2c475b3b88e0f2e5828f830a4b91618a25ff20b7/common_voice_6_1.py#L148-L152", "Let's remove these lines in the dataset no? cc @anton-l @Vaibhavs10 ", "Addressed in:\r\n\r\n* `mozilla-foundation/common_voice_1_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_1_0/discussions/4)\r\n* `mozilla-foundation/common_voice_2_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_2_0/discussions/3)\r\n* `mozilla-foundation/common_voice_3_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_3_0/discussions/3)\r\n* `mozilla-foundation/common_voice_4_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_4_0/discussions/3)\r\n* `mozilla-foundation/common_voice_5_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_5_0/discussions/3)\r\n* `mozilla-foundation/common_voice_5_1` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_5_1/discussions/3)\r\n* `mozilla-foundation/common_voice_6_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_6_0/discussions/3)\r\n* `mozilla-foundation/common_voice_6_1` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_6_1/discussions/3)\r\n* `mozilla-foundation/common_voice_7_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0/discussions/3)\r\n* `mozilla-foundation/common_voice_8_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0/discussions/7)\r\n* `mozilla-foundation/common_voice_9_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0/discussions/8)\r\n* `mozilla-foundation/common_voice_10_0` [PR](https://huggingface.co/datasets/mozilla-foundation/common_voice_10_0/discussions/7)" ]
1,763,926,520	5,967	Config name / split name lost after map with multiproc	open	2023-06-19T17:27:36	2023-06-28T08:55:25	null	https://github.com/huggingface/datasets/issues/5967	null	sanchit-gandhi	false	[ "This must be due to DatasetInfo.from_merge which drops them and is used in `concatenate_datasets`.\r\n\r\nAnd you're experiencing this issue because multiprocessing does concatenate the resulting datasets from each process.\r\n\r\nMaybe they should be kept if all the subdatasets share the same values for config_name and split", "That sounds like a clean workaround!" ]
1,763,885,914	5,966	Fix JSON generation in benchmarks CI	closed	2023-06-19T16:56:06	2023-06-19T17:29:11	2023-06-19T17:22:10	https://github.com/huggingface/datasets/pull/5966	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5966", "html_url": "https://github.com/huggingface/datasets/pull/5966", "diff_url": "https://github.com/huggingface/datasets/pull/5966.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5966.patch", "merged_at": "2023-06-19T17:22:10" }	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) \| 0.006186 / 0.011353 (-0.005167) \| 0.003744 / 0.011008 (-0.007264) \| 0.097295 / 0.038508 (0.058787) \| 0.037106 / 0.023109 (0.013997) \| 0.424154 / 0.275898 (0.148256) \| 0.474536 / 0.323480 (0.151057) \| 0.003454 / 0.007986 (-0.004532) \| 0.003865 / 0.004328 (-0.000463) \| 0.077348 / 0.004250 (0.073097) \| 0.051728 / 0.037052 (0.014675) \| 0.437120 / 0.258489 (0.178631) \| 0.478379 / 0.293841 (0.184538) \| 0.028939 / 0.128546 (-0.099608) \| 0.008376 / 0.075646 (-0.067270) \| 0.312002 / 0.419271 (-0.107270) \| 0.053723 / 0.043533 (0.010190) \| 0.424815 / 0.255139 (0.169676) \| 0.446203 / 0.283200 (0.163004) \| 0.026553 / 0.141683 (-0.115130) \| 1.479983 / 1.452155 (0.027828) \| 1.530613 / 1.492716 (0.037896) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.196627 / 0.018006 (0.178620) \| 0.422361 / 0.000490 (0.421871) \| 0.003442 / 0.000200 (0.003242) \| 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) \| 0.022913 / 0.037411 (-0.014499) \| 0.096011 / 0.014526 (0.081485) \| 0.104091 / 0.176557 (-0.072466) \| 0.163273 / 0.737135 (-0.573862) \| 0.109142 / 0.296338 (-0.187197) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.431032 / 0.215209 (0.215823) \| 4.314391 / 2.077655 (2.236737) \| 2.003812 / 1.504120 (0.499692) \| 1.799538 / 1.541195 (0.258344) \| 1.830026 / 1.468490 (0.361536) \| 0.560131 / 4.584777 (-4.024646) \| 3.368997 / 3.745712 (-0.376715) \| 1.703032 / 5.269862 (-3.566830) \| 1.026949 / 4.565676 (-3.538727) \| 0.067507 / 0.424275 (-0.356768) \| 0.010910 / 0.007607 (0.003303) \| 0.532606 / 0.226044 (0.306562) \| 5.345179 / 2.268929 (3.076250) \| 2.368077 / 55.444624 (-53.076548) \| 2.028913 / 6.876477 (-4.847564) \| 2.147621 / 2.142072 (0.005549) \| 0.675696 / 4.805227 (-4.129531) \| 0.134902 / 6.500664 (-6.365762) \| 0.065004 / 0.075469 (-0.010465) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.233412 / 1.841788 (-0.608376) \| 13.767465 / 8.074308 (5.693157) \| 13.933653 / 10.191392 (3.742261) \| 0.129010 / 0.680424 (-0.551414) \| 0.016708 / 0.534201 (-0.517493) \| 0.362341 / 0.579283 (-0.216942) \| 0.390902 / 0.434364 (-0.043462) \| 0.429156 / 0.540337 (-0.111182) \| 0.521166 / 1.386936 (-0.865770) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006169 / 0.011353 (-0.005184) \| 0.003839 / 0.011008 (-0.007169) \| 0.078784 / 0.038508 (0.040276) \| 0.040218 / 0.023109 (0.017109) \| 0.360439 / 0.275898 (0.084541) \| 0.423957 / 0.323480 (0.100477) \| 0.003456 / 0.007986 (-0.004529) \| 0.002900 / 0.004328 (-0.001428) \| 0.078820 / 0.004250 (0.074569) \| 0.047240 / 0.037052 (0.010187) \| 0.372081 / 0.258489 (0.113592) \| 0.424263 / 0.293841 (0.130422) \| 0.027977 / 0.128546 (-0.100569) \| 0.008400 / 0.075646 (-0.067246) \| 0.084399 / 0.419271 (-0.334872) \| 0.043303 / 0.043533 (-0.000230) \| 0.361583 / 0.255139 (0.106444) \| 0.394987 / 0.283200 (0.111787) \| 0.020006 / 0.141683 (-0.121677) \| 1.520208 / 1.452155 (0.068053) \| 1.587335 / 1.492716 (0.094619) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223847 / 0.018006 (0.205840) \| 0.402194 / 0.000490 (0.401704) \| 0.000384 / 0.000200 (0.000184) \| 0.000057 / 0.000054 (0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024902 / 0.037411 (-0.012509) \| 0.099076 / 0.014526 (0.084550) \| 0.108041 / 0.176557 (-0.068516) \| 0.159385 / 0.737135 (-0.577750) \| 0.111442 / 0.296338 (-0.184896) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.446232 / 0.215209 (0.231023) \| 4.464927 / 2.077655 (2.387272) \| 2.155234 / 1.504120 (0.651114) \| 1.953645 / 1.541195 (0.412450) \| 1.965991 / 1.468490 (0.497501) \| 0.553473 / 4.584777 (-4.031304) \| 3.321397 / 3.745712 (-0.424315) \| 1.693761 / 5.269862 (-3.576101) \| 1.006299 / 4.565676 (-3.559378) \| 0.067013 / 0.424275 (-0.357262) \| 0.011116 / 0.007607 (0.003509) \| 0.555014 / 0.226044 (0.328970) \| 5.535694 / 2.268929 (3.266765) \| 2.598339 / 55.444624 (-52.846285) \| 2.249298 / 6.876477 (-4.627179) \| 2.243419 / 2.142072 (0.101347) \| 0.667603 / 4.805227 (-4.137624) \| 0.133322 / 6.500664 (-6.367343) \| 0.065473 / 0.075469 (-0.009996) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.293051 / 1.841788 (-0.548737) \| 14.103731 / 8.074308 (6.029423) \| 14.215204 / 10.191392 (4.023812) \| 0.143990 / 0.680424 (-0.536434) \| 0.016805 / 0.534201 (-0.517396) \| 0.363264 / 0.579283 (-0.216019) \| 0.392769 / 0.434364 (-0.041594) \| 0.425291 / 0.540337 (-0.115046) \| 0.515479 / 1.386936 (-0.871457) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e03a58f3f5d7e6f07279fb833e62d859a0babaad \"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) \| 0.006346 / 0.011353 (-0.005006) \| 0.004130 / 0.011008 (-0.006878) \| 0.096898 / 0.038508 (0.058390) \| 0.042564 / 0.023109 (0.019455) \| 0.343748 / 0.275898 (0.067850) \| 0.412515 / 0.323480 (0.089035) \| 0.006153 / 0.007986 (-0.001833) \| 0.003345 / 0.004328 (-0.000984) \| 0.075314 / 0.004250 (0.071064) \| 0.061478 / 0.037052 (0.024426) \| 0.362948 / 0.258489 (0.104459) \| 0.401533 / 0.293841 (0.107692) \| 0.032363 / 0.128546 (-0.096184) \| 0.008780 / 0.075646 (-0.066867) \| 0.328691 / 0.419271 (-0.090580) \| 0.054253 / 0.043533 (0.010721) \| 0.340783 / 0.255139 (0.085644) \| 0.360705 / 0.283200 (0.077505) \| 0.023183 / 0.141683 (-0.118500) \| 1.484078 / 1.452155 (0.031924) \| 1.528581 / 1.492716 (0.035865) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.208732 / 0.018006 (0.190726) \| 0.452572 / 0.000490 (0.452082) \| 0.002936 / 0.000200 (0.002737) \| 0.000082 / 0.000054 (0.000028) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024616 / 0.037411 (-0.012795) \| 0.107547 / 0.014526 (0.093021) \| 0.114492 / 0.176557 (-0.062065) \| 0.171770 / 0.737135 (-0.565365) \| 0.122538 / 0.296338 (-0.173800) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.406140 / 0.215209 (0.190930) \| 4.062391 / 2.077655 (1.984736) \| 1.865962 / 1.504120 (0.361842) \| 1.682236 / 1.541195 (0.141041) \| 1.738119 / 1.468490 (0.269629) \| 0.532244 / 4.584777 (-4.052533) \| 3.816421 / 3.745712 (0.070709) \| 2.981205 / 5.269862 (-2.288656) \| 1.519497 / 4.565676 (-3.046179) \| 0.065904 / 0.424275 (-0.358371) \| 0.011277 / 0.007607 (0.003670) \| 0.512789 / 0.226044 (0.286745) \| 5.107618 / 2.268929 (2.838690) \| 2.419399 / 55.444624 (-53.025226) \| 2.079262 / 6.876477 (-4.797214) \| 2.150447 / 2.142072 (0.008375) \| 0.696737 / 4.805227 (-4.108490) \| 0.142497 / 6.500664 (-6.358167) \| 0.063521 / 0.075469 (-0.011949) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.180692 / 1.841788 (-0.661095) \| 14.343084 / 8.074308 (6.268776) \| 13.303719 / 10.191392 (3.112327) \| 0.164234 / 0.680424 (-0.516190) \| 0.017439 / 0.534201 (-0.516762) \| 0.399712 / 0.579283 (-0.179571) \| 0.428248 / 0.434364 (-0.006115) \| 0.471909 / 0.540337 (-0.068428) \| 0.573853 / 1.386936 (-0.813083) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006210 / 0.011353 (-0.005143) \| 0.004104 / 0.011008 (-0.006905) \| 0.075140 / 0.038508 (0.036632) \| 0.044647 / 0.023109 (0.021538) \| 0.370120 / 0.275898 (0.094222) \| 0.452936 / 0.323480 (0.129457) \| 0.003943 / 0.007986 (-0.004042) \| 0.003285 / 0.004328 (-0.001043) \| 0.075267 / 0.004250 (0.071017) \| 0.055517 / 0.037052 (0.018465) \| 0.396385 / 0.258489 (0.137896) \| 0.447870 / 0.293841 (0.154029) \| 0.031342 / 0.128546 (-0.097204) \| 0.008720 / 0.075646 (-0.066926) \| 0.082702 / 0.419271 (-0.336570) \| 0.051010 / 0.043533 (0.007477) \| 0.350546 / 0.255139 (0.095407) \| 0.425395 / 0.283200 (0.142195) \| 0.024483 / 0.141683 (-0.117200) \| 1.467341 / 1.452155 (0.015186) \| 1.537187 / 1.492716 (0.044471) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.218067 / 0.018006 (0.200061) \| 0.441603 / 0.000490 (0.441114) \| 0.003711 / 0.000200 (0.003512) \| 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) \| 0.028669 / 0.037411 (-0.008742) \| 0.112941 / 0.014526 (0.098415) \| 0.122584 / 0.176557 (-0.053972) \| 0.176494 / 0.737135 (-0.560641) \| 0.129369 / 0.296338 (-0.166970) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.434543 / 0.215209 (0.219334) \| 4.344056 / 2.077655 (2.266401) \| 2.079286 / 1.504120 (0.575166) \| 1.887264 / 1.541195 (0.346069) \| 1.910386 / 1.468490 (0.441896) \| 0.538824 / 4.584777 (-4.045953) \| 3.844786 / 3.745712 (0.099074) \| 2.902091 / 5.269862 (-2.367770) \| 1.270852 / 4.565676 (-3.294824) \| 0.066324 / 0.424275 (-0.357951) \| 0.011346 / 0.007607 (0.003739) \| 0.537122 / 0.226044 (0.311078) \| 5.367354 / 2.268929 (3.098426) \| 2.533672 / 55.444624 (-52.910952) \| 2.203260 / 6.876477 (-4.673217) \| 2.224310 / 2.142072 (0.082237) \| 0.663806 / 4.805227 (-4.141422) \| 0.142758 / 6.500664 (-6.357906) \| 0.063870 / 0.075469 (-0.011599) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.260487 / 1.841788 (-0.581301) \| 14.800106 / 8.074308 (6.725798) \| 13.993488 / 10.191392 (3.802096) \| 0.165829 / 0.680424 (-0.514595) \| 0.017347 / 0.534201 (-0.516854) \| 0.401819 / 0.579283 (-0.177464) \| 0.424577 / 0.434364 (-0.009787) \| 0.475161 / 0.540337 (-0.065176) \| 0.574659 / 1.386936 (-0.812277) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#02e1e9ab6df4720f57b2d08c0b800cecac79a7c8 \"CML watermark\")\n" ]
1,763,648,540	5,965	"Couldn't cast array of type" in complex datasets	closed	2023-06-19T14:16:14	2023-07-26T15:13:53	2023-07-26T15:13:53	https://github.com/huggingface/datasets/issues/5965	null	piercefreeman	false	[ "Thanks for reporting! \r\n\r\nSpecifying the target features explicitly should avoid this error:\r\n```python\r\ndataset = dataset.map(\r\n batch_process,\r\n batched=True,\r\n batch_size=1,\r\n num_proc=1,\r\n remove_columns=dataset.column_names,\r\n features=datasets.Features({\"texts\": datasets.Sequence(datasets.Value(\"string\"))})\r\n)\r\n```\r\n\r\nThis error stems from our type promotion not handling the nested case. But this promotion/casting allocates memory in most scenarios, which can be problematic for large datasets, so explicitly passing the features is the optimal solution.", "Hi @mariosasko thanks for the context, this is helpful to know. Would it be worth having some logic to generate this explicit feature specification automatically if a type annotation for a .map returns a dataclass that can be inferred?\r\n\r\nFeels like something that would be easy to implement and could save memory / deal with this case in a standardized way.", "> . Would it be worth having some logic to generate this explicit feature specification automatically if a type annotation for a .map returns a dataclass that can be inferred?\r\n\r\nInteresting proposal! Yes, we could consider doing this if the (return) type hint is `TypedDict`, and raise an error that type hints are incorrect if the cast using the inferred types fails.", "@mariosasko Put up an initial PR to implement this proposal. Let me know your thoughts on direction and what else should be in-scope here." ]
1,763,513,574	5,964	Always return list in `list_datasets`	closed	2023-06-19T13:07:08	2023-06-19T17:29:37	2023-06-19T17:22:41	https://github.com/huggingface/datasets/pull/5964	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5964", "html_url": "https://github.com/huggingface/datasets/pull/5964", "diff_url": "https://github.com/huggingface/datasets/pull/5964.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5964.patch", "merged_at": "2023-06-19T17:22:41" }	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) \| 0.006795 / 0.011353 (-0.004558) \| 0.004170 / 0.011008 (-0.006838) \| 0.098698 / 0.038508 (0.060190) \| 0.045393 / 0.023109 (0.022284) \| 0.309205 / 0.275898 (0.033307) \| 0.361333 / 0.323480 (0.037853) \| 0.006009 / 0.007986 (-0.001977) \| 0.003334 / 0.004328 (-0.000995) \| 0.075071 / 0.004250 (0.070821) \| 0.062587 / 0.037052 (0.025535) \| 0.322395 / 0.258489 (0.063906) \| 0.360499 / 0.293841 (0.066659) \| 0.032243 / 0.128546 (-0.096303) \| 0.008768 / 0.075646 (-0.066878) \| 0.329799 / 0.419271 (-0.089472) \| 0.062261 / 0.043533 (0.018728) \| 0.298112 / 0.255139 (0.042973) \| 0.322815 / 0.283200 (0.039615) \| 0.032348 / 0.141683 (-0.109335) \| 1.445807 / 1.452155 (-0.006347) \| 1.528768 / 1.492716 (0.036051) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.195701 / 0.018006 (0.177695) \| 0.437042 / 0.000490 (0.436552) \| 0.003867 / 0.000200 (0.003667) \| 0.000080 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026713 / 0.037411 (-0.010698) \| 0.109548 / 0.014526 (0.095022) \| 0.119216 / 0.176557 (-0.057341) \| 0.178947 / 0.737135 (-0.558188) \| 0.125224 / 0.296338 (-0.171114) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.400885 / 0.215209 (0.185676) \| 3.991223 / 2.077655 (1.913568) \| 1.818449 / 1.504120 (0.314329) \| 1.609285 / 1.541195 (0.068090) \| 1.666675 / 1.468490 (0.198184) \| 0.531486 / 4.584777 (-4.053291) \| 3.770142 / 3.745712 (0.024430) \| 3.057189 / 5.269862 (-2.212673) \| 1.517491 / 4.565676 (-3.048186) \| 0.065782 / 0.424275 (-0.358493) \| 0.011251 / 0.007607 (0.003644) \| 0.504277 / 0.226044 (0.278233) \| 5.038979 / 2.268929 (2.770050) \| 2.254717 / 55.444624 (-53.189908) \| 1.929743 / 6.876477 (-4.946734) \| 2.080051 / 2.142072 (-0.062022) \| 0.656831 / 4.805227 (-4.148396) \| 0.142860 / 6.500664 (-6.357804) \| 0.063057 / 0.075469 (-0.012412) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.208819 / 1.841788 (-0.632969) \| 14.456966 / 8.074308 (6.382658) \| 12.839799 / 10.191392 (2.648407) \| 0.164361 / 0.680424 (-0.516063) \| 0.017330 / 0.534201 (-0.516871) \| 0.397384 / 0.579283 (-0.181899) \| 0.422704 / 0.434364 (-0.011660) \| 0.472065 / 0.540337 (-0.068273) \| 0.576960 / 1.386936 (-0.809976) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006950 / 0.011353 (-0.004403) \| 0.004012 / 0.011008 (-0.006997) \| 0.076050 / 0.038508 (0.037542) \| 0.046646 / 0.023109 (0.023537) \| 0.353813 / 0.275898 (0.077915) \| 0.417111 / 0.323480 (0.093631) \| 0.005422 / 0.007986 (-0.002564) \| 0.003356 / 0.004328 (-0.000972) \| 0.076662 / 0.004250 (0.072411) \| 0.055018 / 0.037052 (0.017966) \| 0.371561 / 0.258489 (0.113072) \| 0.410471 / 0.293841 (0.116630) \| 0.031860 / 0.128546 (-0.096686) \| 0.008754 / 0.075646 (-0.066893) \| 0.083192 / 0.419271 (-0.336079) \| 0.050479 / 0.043533 (0.006946) \| 0.351725 / 0.255139 (0.096586) \| 0.371596 / 0.283200 (0.088396) \| 0.023042 / 0.141683 (-0.118641) \| 1.480533 / 1.452155 (0.028379) \| 1.545970 / 1.492716 (0.053254) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.220095 / 0.018006 (0.202089) \| 0.441550 / 0.000490 (0.441061) \| 0.000375 / 0.000200 (0.000175) \| 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) \| 0.029527 / 0.037411 (-0.007884) \| 0.111645 / 0.014526 (0.097119) \| 0.125732 / 0.176557 (-0.050825) \| 0.177322 / 0.737135 (-0.559813) \| 0.128620 / 0.296338 (-0.167718) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.432415 / 0.215209 (0.217206) \| 4.314381 / 2.077655 (2.236726) \| 2.079450 / 1.504120 (0.575331) \| 1.893139 / 1.541195 (0.351944) \| 1.951363 / 1.468490 (0.482873) \| 0.531466 / 4.584777 (-4.053311) \| 3.716860 / 3.745712 (-0.028852) \| 1.850111 / 5.269862 (-3.419750) \| 1.100676 / 4.565676 (-3.465000) \| 0.066247 / 0.424275 (-0.358028) \| 0.011503 / 0.007607 (0.003896) \| 0.537208 / 0.226044 (0.311164) \| 5.367560 / 2.268929 (3.098631) \| 2.543697 / 55.444624 (-52.900927) \| 2.221670 / 6.876477 (-4.654806) \| 2.252009 / 2.142072 (0.109937) \| 0.658509 / 4.805227 (-4.146718) \| 0.142345 / 6.500664 (-6.358319) \| 0.064701 / 0.075469 (-0.010768) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.266442 / 1.841788 (-0.575346) \| 15.105953 / 8.074308 (7.031645) \| 14.288229 / 10.191392 (4.096837) \| 0.161182 / 0.680424 (-0.519242) \| 0.017074 / 0.534201 (-0.517127) \| 0.399464 / 0.579283 (-0.179819) \| 0.419459 / 0.434364 (-0.014905) \| 0.467553 / 0.540337 (-0.072784) \| 0.566337 / 1.386936 (-0.820599) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53ac2d9662f9e5923ae7c52199eaa620d82f0043 \"CML watermark\")\n" ]
1,762,774,457	5,963	Got an error _pickle.PicklingError use Dataset.from_spark.	closed	2023-06-19T05:30:35	2023-07-24T11:55:46	2023-07-24T11:55:46	https://github.com/huggingface/datasets/issues/5963	null	yanzia12138	false	[ "i got error using method from_spark when using multi-node Spark cluster. seems could only use \"from_spark\" in local?", "@lhoestq ", "cc @maddiedawson it looks like there an issue with `_validate_cache_dir` ?\r\n\r\nIt looks like the function passed to mapPartitions has a reference to the Spark dataset builder, and therefore contains the SparkContext itself.\r\n\r\nI think it can be fixed by defining `create_cache_and_write_probe` outside the Spark dataset builder, and pass a `partial(create_cache_and_write_probe, cache_dir=self._cache_dir)` to `mapPartitions`", "Just saw this; thanks for flagging! Your proposed solution sounds good. I can prepare a PR", "@maddiedawson can you show me the demo ,so i can test in local .before your PR" ]
1,761,589,882	5,962	Issue with train_test_split maintaining the same underlying PyArrow Table	open	2023-06-17T02:19:58	2023-06-17T02:19:58	null	https://github.com/huggingface/datasets/issues/5962	null	Oziel14	false	[]
1,758,525,111	5,961	IterableDataset: split by node and map may preprocess samples that will be skipped anyway	open	2023-06-15T10:29:10	2023-09-01T10:35:11	null	https://github.com/huggingface/datasets/issues/5961	null	johnchienbronci	false	[ "Does \"number of shards\" refer to the total number of data?\r\n\r\nmy config:\r\nnproc_per_node=2\r\nds=ds['train'] = load_dataset(streaming=True).take(50000)\r\n\r\nI'm test again: in prepare_data(), data have the same for each GPU\r\n", "The number of shards is `ds.n_shards`. It corresponds generally to the number of files the dataset is made of, to be able to distribute to several nodes.\r\n\r\nYou don't end up with the same data per GPU. But all the samples are going through your preprocessing function you pass to map. They are just skipped afterwards to only keep 1 sample out of n(GPUs)", "For each GPU, although see the same data in prepare_data(), the actual training data will not be the same in the end. \r\nIs my understanding correct?\r\n\r\nWhere can I print the actual training data for each GPU?", "> For each GPU, although see the same data in prepare_data(), the actual training data will not be the same in the end.\r\nIs my understanding correct?\r\n\r\nYes exactly :)\r\n\r\n> Where can I print the actual training data for each GPU?\r\n\r\nYou should call print in the data_collator", "I print out n_shards, and under multiple GPUs, this value is always 1.\r\nIs this value correct?", "Yes it's correct, and it explains why you always have the same data passed to your map function (the data can't be split).\r\n\r\nBut after being passed to `map`, each GPU keeps one example out of n(GPUs) so that you don't end up with duplicate data across GPUs", "> > For each GPU, although see the same data in prepare_data(), the actual training data will not be the same in the end.\r\n> > Is my understanding correct?\r\n> \r\n> Yes exactly :)\r\n> \r\n> > Where can I print the actual training data for each GPU?\r\n> \r\n> You should call print in the data_collator\r\n\r\nOK, when printing the train data in the data collator, each GPU sees different data.\r\n\r\nThanks for your reply", "Do we have a solution for this one? Or it's required to get \"number of shards is a factor of number of GPUs: in that case the shards are evenly distributed per GPU\"", "For now it's required to have a number of shards that is a factor of the number of GPUs to not have all the workers process the same data (and then skip the right ones to not end up training on duplicate data).\r\n\r\nIt would be quite complex to implement a strategy that would utilize all the GPUs with an arbitrary number of shards even at the end of training" ]
1,757,397,507	5,959	read metric glue.py from local file	closed	2023-06-14T17:59:35	2023-06-14T18:04:16	2023-06-14T18:04:16	https://github.com/huggingface/datasets/issues/5959	null	JiazhaoLi	false	[ "Sorry, I solve this by call `evaluate.load('glue_metric.py','sst-2')`\r\n" ]
1,757,265,971	5,958	set dev version	closed	2023-06-14T16:26:34	2023-06-14T16:34:55	2023-06-14T16:26:51	https://github.com/huggingface/datasets/pull/5958	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5958", "html_url": "https://github.com/huggingface/datasets/pull/5958", "diff_url": "https://github.com/huggingface/datasets/pull/5958.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5958.patch", "merged_at": "2023-06-14T16:26:51" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5958). 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) \| 0.006232 / 0.011353 (-0.005121) \| 0.003788 / 0.011008 (-0.007220) \| 0.100014 / 0.038508 (0.061506) \| 0.036488 / 0.023109 (0.013379) \| 0.306255 / 0.275898 (0.030357) \| 0.363337 / 0.323480 (0.039857) \| 0.004765 / 0.007986 (-0.003221) \| 0.002935 / 0.004328 (-0.001394) \| 0.078897 / 0.004250 (0.074647) \| 0.052221 / 0.037052 (0.015169) \| 0.315169 / 0.258489 (0.056680) \| 0.353050 / 0.293841 (0.059209) \| 0.029059 / 0.128546 (-0.099488) \| 0.008599 / 0.075646 (-0.067047) \| 0.318770 / 0.419271 (-0.100502) \| 0.046631 / 0.043533 (0.003098) \| 0.303728 / 0.255139 (0.048589) \| 0.332379 / 0.283200 (0.049180) \| 0.021164 / 0.141683 (-0.120519) \| 1.576963 / 1.452155 (0.124808) \| 1.629575 / 1.492716 (0.136859) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.204246 / 0.018006 (0.186240) \| 0.426600 / 0.000490 (0.426110) \| 0.004336 / 0.000200 (0.004136) \| 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) \| 0.024039 / 0.037411 (-0.013372) \| 0.098240 / 0.014526 (0.083715) \| 0.108889 / 0.176557 (-0.067668) \| 0.170827 / 0.737135 (-0.566308) \| 0.111288 / 0.296338 (-0.185051) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.418103 / 0.215209 (0.202894) \| 4.190759 / 2.077655 (2.113104) \| 1.875978 / 1.504120 (0.371858) \| 1.679198 / 1.541195 (0.138003) \| 1.737965 / 1.468490 (0.269474) \| 0.556660 / 4.584777 (-4.028117) \| 3.413800 / 3.745712 (-0.331912) \| 3.004999 / 5.269862 (-2.264862) \| 1.464030 / 4.565676 (-3.101647) \| 0.067338 / 0.424275 (-0.356937) \| 0.011486 / 0.007607 (0.003879) \| 0.522589 / 0.226044 (0.296544) \| 5.214653 / 2.268929 (2.945724) \| 2.316903 / 55.444624 (-53.127722) \| 1.991941 / 6.876477 (-4.884536) \| 2.110601 / 2.142072 (-0.031471) \| 0.665400 / 4.805227 (-4.139828) \| 0.135755 / 6.500664 (-6.364910) \| 0.065980 / 0.075469 (-0.009489) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.197269 / 1.841788 (-0.644519) \| 14.085205 / 8.074308 (6.010897) \| 14.083360 / 10.191392 (3.891968) \| 0.148054 / 0.680424 (-0.532369) \| 0.016548 / 0.534201 (-0.517653) \| 0.371538 / 0.579283 (-0.207745) \| 0.391068 / 0.434364 (-0.043296) \| 0.430589 / 0.540337 (-0.109748) \| 0.529319 / 1.386936 (-0.857617) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006214 / 0.011353 (-0.005138) \| 0.003846 / 0.011008 (-0.007162) \| 0.078559 / 0.038508 (0.040051) \| 0.037855 / 0.023109 (0.014745) \| 0.437479 / 0.275898 (0.161581) \| 0.497588 / 0.323480 (0.174108) \| 0.003491 / 0.007986 (-0.004494) \| 0.003900 / 0.004328 (-0.000428) \| 0.078443 / 0.004250 (0.074193) \| 0.048019 / 0.037052 (0.010967) \| 0.452076 / 0.258489 (0.193587) \| 0.494597 / 0.293841 (0.200756) \| 0.028127 / 0.128546 (-0.100419) \| 0.008549 / 0.075646 (-0.067098) \| 0.082977 / 0.419271 (-0.336295) \| 0.043133 / 0.043533 (-0.000400) \| 0.441342 / 0.255139 (0.186203) \| 0.464339 / 0.283200 (0.181139) \| 0.020110 / 0.141683 (-0.121573) \| 1.485181 / 1.452155 (0.033026) \| 1.532019 / 1.492716 (0.039302) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.228014 / 0.018006 (0.210007) \| 0.416887 / 0.000490 (0.416397) \| 0.001133 / 0.000200 (0.000933) \| 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) \| 0.026452 / 0.037411 (-0.010960) \| 0.104328 / 0.014526 (0.089802) \| 0.110045 / 0.176557 (-0.066511) \| 0.164725 / 0.737135 (-0.572410) \| 0.116348 / 0.296338 (-0.179990) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.483502 / 0.215209 (0.268293) \| 4.829814 / 2.077655 (2.752159) \| 2.505271 / 1.504120 (1.001151) \| 2.305819 / 1.541195 (0.764624) \| 2.348633 / 1.468490 (0.880143) \| 0.562316 / 4.584777 (-4.022461) \| 3.426425 / 3.745712 (-0.319287) \| 1.737934 / 5.269862 (-3.531927) \| 1.042616 / 4.565676 (-3.523061) \| 0.068088 / 0.424275 (-0.356187) \| 0.011735 / 0.007607 (0.004128) \| 0.586339 / 0.226044 (0.360295) \| 5.861283 / 2.268929 (3.592354) \| 2.953956 / 55.444624 (-52.490668) \| 2.626611 / 6.876477 (-4.249865) \| 2.687978 / 2.142072 (0.545906) \| 0.672748 / 4.805227 (-4.132479) \| 0.137231 / 6.500664 (-6.363433) \| 0.068149 / 0.075469 (-0.007320) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.323139 / 1.841788 (-0.518649) \| 14.503102 / 8.074308 (6.428794) \| 14.092102 / 10.191392 (3.900710) \| 0.165395 / 0.680424 (-0.515028) \| 0.016898 / 0.534201 (-0.517303) \| 0.366905 / 0.579283 (-0.212378) \| 0.396671 / 0.434364 (-0.037692) \| 0.421831 / 0.540337 (-0.118506) \| 0.514075 / 1.386936 (-0.872861) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9d4238c132dd44b9a6e1dfe7101228bdeb538d57 \"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) \| 0.007778 / 0.011353 (-0.003575) \| 0.004624 / 0.011008 (-0.006384) \| 0.123426 / 0.038508 (0.084918) \| 0.052209 / 0.023109 (0.029100) \| 0.341084 / 0.275898 (0.065186) \| 0.421905 / 0.323480 (0.098425) \| 0.005768 / 0.007986 (-0.002217) \| 0.003647 / 0.004328 (-0.000682) \| 0.085569 / 0.004250 (0.081319) \| 0.070473 / 0.037052 (0.033421) \| 0.356626 / 0.258489 (0.098136) \| 0.407413 / 0.293841 (0.113572) \| 0.038800 / 0.128546 (-0.089746) \| 0.010289 / 0.075646 (-0.065357) \| 0.462707 / 0.419271 (0.043436) \| 0.060390 / 0.043533 (0.016858) \| 0.349805 / 0.255139 (0.094666) \| 0.355288 / 0.283200 (0.072088) \| 0.025364 / 0.141683 (-0.116318) \| 1.745720 / 1.452155 (0.293565) \| 1.852764 / 1.492716 (0.360048) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290582 / 0.018006 (0.272576) \| 0.480044 / 0.000490 (0.479554) \| 0.007658 / 0.000200 (0.007458) \| 0.000100 / 0.000054 (0.000046) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031529 / 0.037411 (-0.005882) \| 0.130441 / 0.014526 (0.115915) \| 0.147653 / 0.176557 (-0.028904) \| 0.215935 / 0.737135 (-0.521200) \| 0.149871 / 0.296338 (-0.146467) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.461662 / 0.215209 (0.246453) \| 4.570353 / 2.077655 (2.492698) \| 2.104416 / 1.504120 (0.600297) \| 1.936974 / 1.541195 (0.395779) \| 2.139167 / 1.468490 (0.670677) \| 0.645100 / 4.584777 (-3.939677) \| 4.361536 / 3.745712 (0.615824) \| 2.155960 / 5.269862 (-3.113902) \| 1.207854 / 4.565676 (-3.357822) \| 0.080162 / 0.424275 (-0.344113) \| 0.014265 / 0.007607 (0.006658) \| 0.606294 / 0.226044 (0.380250) \| 5.928093 / 2.268929 (3.659165) \| 2.701811 / 55.444624 (-52.742813) \| 2.344490 / 6.876477 (-4.531987) \| 2.435997 / 2.142072 (0.293925) \| 0.761020 / 4.805227 (-4.044207) \| 0.165860 / 6.500664 (-6.334804) \| 0.075666 / 0.075469 (0.000197) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.427318 / 1.841788 (-0.414469) \| 17.327468 / 8.074308 (9.253160) \| 15.323065 / 10.191392 (5.131673) \| 0.178518 / 0.680424 (-0.501905) \| 0.020888 / 0.534201 (-0.513313) \| 0.497891 / 0.579283 (-0.081393) \| 0.487717 / 0.434364 (0.053353) \| 0.581430 / 0.540337 (0.041093) \| 0.703430 / 1.386936 (-0.683506) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007954 / 0.011353 (-0.003399) \| 0.004442 / 0.011008 (-0.006566) \| 0.090950 / 0.038508 (0.052442) \| 0.054282 / 0.023109 (0.031173) \| 0.424474 / 0.275898 (0.148576) \| 0.531770 / 0.323480 (0.208290) \| 0.004492 / 0.007986 (-0.003493) \| 0.004745 / 0.004328 (0.000416) \| 0.088213 / 0.004250 (0.083962) \| 0.063967 / 0.037052 (0.026914) \| 0.454256 / 0.258489 (0.195767) \| 0.502870 / 0.293841 (0.209029) \| 0.038203 / 0.128546 (-0.090343) \| 0.010327 / 0.075646 (-0.065319) \| 0.097809 / 0.419271 (-0.321463) \| 0.062136 / 0.043533 (0.018604) \| 0.426148 / 0.255139 (0.171009) \| 0.467812 / 0.283200 (0.184612) \| 0.029148 / 0.141683 (-0.112535) \| 1.762307 / 1.452155 (0.310152) \| 1.814238 / 1.492716 (0.321521) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.195676 / 0.018006 (0.177670) \| 0.475382 / 0.000490 (0.474892) \| 0.003070 / 0.000200 (0.002870) \| 0.000112 / 0.000054 (0.000057) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033945 / 0.037411 (-0.003466) \| 0.134666 / 0.014526 (0.120140) \| 0.147585 / 0.176557 (-0.028971) \| 0.209472 / 0.737135 (-0.527664) \| 0.154471 / 0.296338 (-0.141867) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.518132 / 0.215209 (0.302923) \| 5.103423 / 2.077655 (3.025768) \| 2.565207 / 1.504120 (1.061087) \| 2.389454 / 1.541195 (0.848259) \| 2.391706 / 1.468490 (0.923216) \| 0.606463 / 4.584777 (-3.978314) \| 4.392227 / 3.745712 (0.646515) \| 2.067121 / 5.269862 (-3.202741) \| 1.217551 / 4.565676 (-3.348125) \| 0.074304 / 0.424275 (-0.349971) \| 0.013418 / 0.007607 (0.005811) \| 0.623327 / 0.226044 (0.397282) \| 6.340233 / 2.268929 (4.071304) \| 3.153948 / 55.444624 (-52.290677) \| 2.824548 / 6.876477 (-4.051929) \| 2.938402 / 2.142072 (0.796329) \| 0.774305 / 4.805227 (-4.030922) \| 0.170681 / 6.500664 (-6.329983) \| 0.075895 / 0.075469 (0.000426) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.473491 / 1.841788 (-0.368296) \| 17.372294 / 8.074308 (9.297986) \| 15.550201 / 10.191392 (5.358809) \| 0.191402 / 0.680424 (-0.489022) \| 0.021401 / 0.534201 (-0.512800) \| 0.484377 / 0.579283 (-0.094906) \| 0.488844 / 0.434364 (0.054480) \| 0.563336 / 0.540337 (0.022999) \| 0.694210 / 1.386936 (-0.692726) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b96da7f51d81e52d7b587685f820b5e55f71e07d \"CML watermark\")\n" ]
1,757,252,466	5,957	Release: 2.13.0	closed	2023-06-14T16:17:26	2023-06-14T16:33:39	2023-06-14T16:24:39	https://github.com/huggingface/datasets/pull/5957	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5957", "html_url": "https://github.com/huggingface/datasets/pull/5957", "diff_url": "https://github.com/huggingface/datasets/pull/5957.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5957.patch", "merged_at": "2023-06-14T16:24:39" }	lhoestq	true	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006498 / 0.011353 (-0.004855) \| 0.003970 / 0.011008 (-0.007038) \| 0.099242 / 0.038508 (0.060734) \| 0.044363 / 0.023109 (0.021254) \| 0.313900 / 0.275898 (0.038002) \| 0.386562 / 0.323480 (0.063082) \| 0.003837 / 0.007986 (-0.004149) \| 0.004203 / 0.004328 (-0.000125) \| 0.076191 / 0.004250 (0.071940) \| 0.058823 / 0.037052 (0.021771) \| 0.333838 / 0.258489 (0.075349) \| 0.368235 / 0.293841 (0.074394) \| 0.030774 / 0.128546 (-0.097772) \| 0.008787 / 0.075646 (-0.066860) \| 0.326474 / 0.419271 (-0.092798) \| 0.050903 / 0.043533 (0.007370) \| 0.303928 / 0.255139 (0.048789) \| 0.321532 / 0.283200 (0.038333) \| 0.024162 / 0.141683 (-0.117520) \| 1.479662 / 1.452155 (0.027507) \| 1.520300 / 1.492716 (0.027584) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.212403 / 0.018006 (0.194397) \| 0.448019 / 0.000490 (0.447529) \| 0.005465 / 0.000200 (0.005265) \| 0.000388 / 0.000054 (0.000334) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027533 / 0.037411 (-0.009878) \| 0.117477 / 0.014526 (0.102952) \| 0.121182 / 0.176557 (-0.055374) \| 0.181150 / 0.737135 (-0.555985) \| 0.128557 / 0.296338 (-0.167782) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.397763 / 0.215209 (0.182554) \| 3.959460 / 2.077655 (1.881805) \| 1.822057 / 1.504120 (0.317937) \| 1.627020 / 1.541195 (0.085826) \| 1.695394 / 1.468490 (0.226904) \| 0.536848 / 4.584777 (-4.047929) \| 3.765205 / 3.745712 (0.019493) \| 3.196300 / 5.269862 (-2.073561) \| 1.623583 / 4.565676 (-2.942094) \| 0.065823 / 0.424275 (-0.358452) \| 0.011062 / 0.007607 (0.003455) \| 0.500428 / 0.226044 (0.274384) \| 5.008816 / 2.268929 (2.739888) \| 2.314660 / 55.444624 (-53.129965) \| 2.007429 / 6.876477 (-4.869047) \| 2.141438 / 2.142072 (-0.000635) \| 0.656697 / 4.805227 (-4.148530) \| 0.143555 / 6.500664 (-6.357109) \| 0.063928 / 0.075469 (-0.011541) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.169038 / 1.841788 (-0.672750) \| 15.027186 / 8.074308 (6.952878) \| 13.571484 / 10.191392 (3.380092) \| 0.166437 / 0.680424 (-0.513986) \| 0.017656 / 0.534201 (-0.516545) \| 0.397725 / 0.579283 (-0.181558) \| 0.451019 / 0.434364 (0.016655) \| 0.469134 / 0.540337 (-0.071203) \| 0.575885 / 1.386936 (-0.811051) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006887 / 0.011353 (-0.004465) \| 0.004166 / 0.011008 (-0.006842) \| 0.077137 / 0.038508 (0.038629) \| 0.055631 / 0.023109 (0.032522) \| 0.397658 / 0.275898 (0.121760) \| 0.473981 / 0.323480 (0.150502) \| 0.005365 / 0.007986 (-0.002621) \| 0.003401 / 0.004328 (-0.000928) \| 0.076481 / 0.004250 (0.072231) \| 0.056014 / 0.037052 (0.018961) \| 0.415253 / 0.258489 (0.156764) \| 0.457620 / 0.293841 (0.163779) \| 0.031850 / 0.128546 (-0.096696) \| 0.008869 / 0.075646 (-0.066777) \| 0.083475 / 0.419271 (-0.335796) \| 0.049232 / 0.043533 (0.005699) \| 0.392947 / 0.255139 (0.137808) \| 0.417243 / 0.283200 (0.134043) \| 0.024554 / 0.141683 (-0.117129) \| 1.508081 / 1.452155 (0.055926) \| 1.541845 / 1.492716 (0.049129) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.228470 / 0.018006 (0.210464) \| 0.450933 / 0.000490 (0.450443) \| 0.001508 / 0.000200 (0.001308) \| 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) \| 0.030189 / 0.037411 (-0.007222) \| 0.118853 / 0.014526 (0.104327) \| 0.124809 / 0.176557 (-0.051747) \| 0.175066 / 0.737135 (-0.562069) \| 0.129819 / 0.296338 (-0.166519) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.451830 / 0.215209 (0.236621) \| 4.505352 / 2.077655 (2.427698) \| 2.309303 / 1.504120 (0.805183) \| 2.120983 / 1.541195 (0.579789) \| 2.198808 / 1.468490 (0.730317) \| 0.543836 / 4.584777 (-4.040940) \| 3.836650 / 3.745712 (0.090938) \| 1.872293 / 5.269862 (-3.397568) \| 1.122335 / 4.565676 (-3.443342) \| 0.067463 / 0.424275 (-0.356812) \| 0.012143 / 0.007607 (0.004536) \| 0.553674 / 0.226044 (0.327630) \| 5.572101 / 2.268929 (3.303173) \| 2.772151 / 55.444624 (-52.672473) \| 2.451557 / 6.876477 (-4.424920) \| 2.521241 / 2.142072 (0.379169) \| 0.665799 / 4.805227 (-4.139428) \| 0.143842 / 6.500664 (-6.356822) \| 0.065373 / 0.075469 (-0.010096) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.271013 / 1.841788 (-0.570775) \| 15.290054 / 8.074308 (7.215746) \| 14.807044 / 10.191392 (4.615652) \| 0.163767 / 0.680424 (-0.516657) \| 0.017383 / 0.534201 (-0.516818) \| 0.393046 / 0.579283 (-0.186237) \| 0.423056 / 0.434364 (-0.011308) \| 0.459193 / 0.540337 (-0.081145) \| 0.559964 / 1.386936 (-0.826972) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#011b75f044ef7fa6b8981ef3496615296aeb315b \"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) \| 0.006112 / 0.011353 (-0.005241) \| 0.003712 / 0.011008 (-0.007297) \| 0.099996 / 0.038508 (0.061488) \| 0.037526 / 0.023109 (0.014417) \| 0.305834 / 0.275898 (0.029936) \| 0.361368 / 0.323480 (0.037888) \| 0.004849 / 0.007986 (-0.003136) \| 0.002912 / 0.004328 (-0.001417) \| 0.077729 / 0.004250 (0.073479) \| 0.053203 / 0.037052 (0.016151) \| 0.318088 / 0.258489 (0.059599) \| 0.371745 / 0.293841 (0.077904) \| 0.029384 / 0.128546 (-0.099162) \| 0.008504 / 0.075646 (-0.067142) \| 0.318472 / 0.419271 (-0.100799) \| 0.046043 / 0.043533 (0.002510) \| 0.310418 / 0.255139 (0.055279) \| 0.335044 / 0.283200 (0.051844) \| 0.020364 / 0.141683 (-0.121319) \| 1.503201 / 1.452155 (0.051047) \| 1.556408 / 1.492716 (0.063692) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.210245 / 0.018006 (0.192239) \| 0.418918 / 0.000490 (0.418428) \| 0.002552 / 0.000200 (0.002352) \| 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) \| 0.022295 / 0.037411 (-0.015116) \| 0.099534 / 0.014526 (0.085008) \| 0.106432 / 0.176557 (-0.070124) \| 0.165110 / 0.737135 (-0.572026) \| 0.109851 / 0.296338 (-0.186488) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.423947 / 0.215209 (0.208738) \| 4.232978 / 2.077655 (2.155323) \| 2.004849 / 1.504120 (0.500729) \| 1.814345 / 1.541195 (0.273151) \| 1.809192 / 1.468490 (0.340702) \| 0.561146 / 4.584777 (-4.023631) \| 3.385043 / 3.745712 (-0.360669) \| 1.708265 / 5.269862 (-3.561597) \| 1.030290 / 4.565676 (-3.535387) \| 0.067095 / 0.424275 (-0.357180) \| 0.011052 / 0.007607 (0.003445) \| 0.522416 / 0.226044 (0.296371) \| 5.207003 / 2.268929 (2.938075) \| 2.367067 / 55.444624 (-53.077558) \| 1.998705 / 6.876477 (-4.877772) \| 2.068633 / 2.142072 (-0.073439) \| 0.672396 / 4.805227 (-4.132831) \| 0.135818 / 6.500664 (-6.364846) \| 0.065229 / 0.075469 (-0.010240) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.187079 / 1.841788 (-0.654709) \| 13.893153 / 8.074308 (5.818845) \| 13.951328 / 10.191392 (3.759936) \| 0.142519 / 0.680424 (-0.537905) \| 0.016546 / 0.534201 (-0.517655) \| 0.364008 / 0.579283 (-0.215275) \| 0.385957 / 0.434364 (-0.048407) \| 0.425218 / 0.540337 (-0.115120) \| 0.519586 / 1.386936 (-0.867350) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated 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 (-0.005439) \| 0.003619 / 0.011008 (-0.007389) \| 0.077806 / 0.038508 (0.039298) \| 0.037254 / 0.023109 (0.014144) \| 0.378976 / 0.275898 (0.103078) \| 0.433620 / 0.323480 (0.110140) \| 0.003291 / 0.007986 (-0.004694) \| 0.004523 / 0.004328 (0.000194) \| 0.077604 / 0.004250 (0.073353) \| 0.047493 / 0.037052 (0.010441) \| 0.396027 / 0.258489 (0.137538) \| 0.453345 / 0.293841 (0.159504) \| 0.028170 / 0.128546 (-0.100376) \| 0.008431 / 0.075646 (-0.067215) \| 0.083985 / 0.419271 (-0.335286) \| 0.045149 / 0.043533 (0.001617) \| 0.369364 / 0.255139 (0.114225) \| 0.407191 / 0.283200 (0.123991) \| 0.024033 / 0.141683 (-0.117649) \| 1.516838 / 1.452155 (0.064683) \| 1.564260 / 1.492716 (0.071544) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.200848 / 0.018006 (0.182842) \| 0.407818 / 0.000490 (0.407328) \| 0.003971 / 0.000200 (0.003771) \| 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) \| 0.025033 / 0.037411 (-0.012378) \| 0.103585 / 0.014526 (0.089059) \| 0.108741 / 0.176557 (-0.067816) \| 0.161061 / 0.737135 (-0.576075) \| 0.112763 / 0.296338 (-0.183576) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.479913 / 0.215209 (0.264704) \| 4.801904 / 2.077655 (2.724249) \| 2.511433 / 1.504120 (1.007313) \| 2.307523 / 1.541195 (0.766328) \| 2.338343 / 1.468490 (0.869853) \| 0.557731 / 4.584777 (-4.027046) \| 3.386261 / 3.745712 (-0.359451) \| 2.999978 / 5.269862 (-2.269883) \| 1.463058 / 4.565676 (-3.102619) \| 0.067645 / 0.424275 (-0.356630) \| 0.011224 / 0.007607 (0.003617) \| 0.596854 / 0.226044 (0.370810) \| 5.940946 / 2.268929 (3.672017) \| 2.980194 / 55.444624 (-52.464430) \| 2.634961 / 6.876477 (-4.241516) \| 2.648160 / 2.142072 (0.506088) \| 0.669728 / 4.805227 (-4.135499) \| 0.135536 / 6.500664 (-6.365128) \| 0.066865 / 0.075469 (-0.008604) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.287151 / 1.841788 (-0.554637) \| 14.491681 / 8.074308 (6.417373) \| 14.185752 / 10.191392 (3.994360) \| 0.129391 / 0.680424 (-0.551032) \| 0.016650 / 0.534201 (-0.517551) \| 0.380111 / 0.579283 (-0.199172) \| 0.392877 / 0.434364 (-0.041487) \| 0.439402 / 0.540337 (-0.100935) \| 0.530865 / 1.386936 (-0.856071) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9aaee6fd0b2bcbe18e4829602084bcd83d669c5e \"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) \| 0.011446 / 0.011353 (0.000093) \| 0.006623 / 0.011008 (-0.004386) \| 0.131915 / 0.038508 (0.093407) \| 0.047364 / 0.023109 (0.024255) \| 0.369203 / 0.275898 (0.093305) \| 0.451509 / 0.323480 (0.128029) \| 0.006265 / 0.007986 (-0.001720) \| 0.004072 / 0.004328 (-0.000257) \| 0.098626 / 0.004250 (0.094375) \| 0.079523 / 0.037052 (0.042470) \| 0.406038 / 0.258489 (0.147549) \| 0.450564 / 0.293841 (0.156723) \| 0.050793 / 0.128546 (-0.077753) \| 0.014667 / 0.075646 (-0.060979) \| 0.401359 / 0.419271 (-0.017913) \| 0.072299 / 0.043533 (0.028767) \| 0.404456 / 0.255139 (0.149317) \| 0.396223 / 0.283200 (0.113023) \| 0.037048 / 0.141683 (-0.104635) \| 1.869123 / 1.452155 (0.416968) \| 1.953621 / 1.492716 (0.460905) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.237246 / 0.018006 (0.219240) \| 0.533207 / 0.000490 (0.532717) \| 0.007392 / 0.000200 (0.007192) \| 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) \| 0.029458 / 0.037411 (-0.007954) \| 0.112438 / 0.014526 (0.097912) \| 0.139115 / 0.176557 (-0.037441) \| 0.215225 / 0.737135 (-0.521911) \| 0.134440 / 0.296338 (-0.161898) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.616783 / 0.215209 (0.401574) \| 6.113925 / 2.077655 (4.036270) \| 2.403465 / 1.504120 (0.899345) \| 1.967523 / 1.541195 (0.426329) \| 2.042144 / 1.468490 (0.573654) \| 0.927447 / 4.584777 (-3.657330) \| 5.280413 / 3.745712 (1.534701) \| 2.715335 / 5.269862 (-2.554527) \| 1.755640 / 4.565676 (-2.810036) \| 0.114370 / 0.424275 (-0.309905) \| 0.013583 / 0.007607 (0.005976) \| 0.761701 / 0.226044 (0.535657) \| 7.466049 / 2.268929 (5.197120) \| 3.041943 / 55.444624 (-52.402682) \| 2.314477 / 6.876477 (-4.562000) \| 2.469285 / 2.142072 (0.327213) \| 1.216055 / 4.805227 (-3.589172) \| 0.214205 / 6.500664 (-6.286459) \| 0.080901 / 0.075469 (0.005432) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.565185 / 1.841788 (-0.276603) \| 18.387986 / 8.074308 (10.313678) \| 19.665109 / 10.191392 (9.473717) \| 0.226670 / 0.680424 (-0.453754) \| 0.028430 / 0.534201 (-0.505771) \| 0.510526 / 0.579283 (-0.068757) \| 0.623178 / 0.434364 (0.188814) \| 0.592039 / 0.540337 (0.051702) \| 0.728462 / 1.386936 (-0.658474) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009161 / 0.011353 (-0.002192) \| 0.004891 / 0.011008 (-0.006117) \| 0.106502 / 0.038508 (0.067994) \| 0.048234 / 0.023109 (0.025125) \| 0.451173 / 0.275898 (0.175275) \| 0.557948 / 0.323480 (0.234468) \| 0.005350 / 0.007986 (-0.002635) \| 0.004559 / 0.004328 (0.000230) \| 0.110393 / 0.004250 (0.106142) \| 0.060624 / 0.037052 (0.023572) \| 0.459265 / 0.258489 (0.200776) \| 0.575302 / 0.293841 (0.281461) \| 0.051379 / 0.128546 (-0.077167) \| 0.015576 / 0.075646 (-0.060070) \| 0.116650 / 0.419271 (-0.302621) \| 0.065534 / 0.043533 (0.022001) \| 0.461431 / 0.255139 (0.206292) \| 0.487677 / 0.283200 (0.204477) \| 0.037773 / 0.141683 (-0.103910) \| 1.992416 / 1.452155 (0.540261) \| 1.991280 / 1.492716 (0.498564) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.233607 / 0.018006 (0.215601) \| 0.507539 / 0.000490 (0.507049) \| 0.001307 / 0.000200 (0.001107) \| 0.000096 / 0.000054 (0.000042) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032897 / 0.037411 (-0.004514) \| 0.126549 / 0.014526 (0.112023) \| 0.137893 / 0.176557 (-0.038663) \| 0.192124 / 0.737135 (-0.545012) \| 0.147300 / 0.296338 (-0.149038) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.679371 / 0.215209 (0.464162) \| 6.673249 / 2.077655 (4.595595) \| 2.979141 / 1.504120 (1.475022) \| 2.568789 / 1.541195 (1.027594) \| 2.537540 / 1.468490 (1.069050) \| 0.973555 / 4.584777 (-3.611222) \| 5.313536 / 3.745712 (1.567824) \| 2.693283 / 5.269862 (-2.576579) \| 1.819483 / 4.565676 (-2.746194) \| 0.111644 / 0.424275 (-0.312631) \| 0.013218 / 0.007607 (0.005611) \| 0.776114 / 0.226044 (0.550070) \| 7.758907 / 2.268929 (5.489978) \| 3.417611 / 55.444624 (-52.027013) \| 2.859502 / 6.876477 (-4.016975) \| 2.927726 / 2.142072 (0.785653) \| 1.163671 / 4.805227 (-3.641556) \| 0.228636 / 6.500664 (-6.272028) \| 0.082077 / 0.075469 (0.006607) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.746150 / 1.841788 (-0.095637) \| 17.961955 / 8.074308 (9.887647) \| 21.590545 / 10.191392 (11.399153) \| 0.210017 / 0.680424 (-0.470406) \| 0.028435 / 0.534201 (-0.505766) \| 0.509253 / 0.579283 (-0.070030) \| 0.606993 / 0.434364 (0.172629) \| 0.587189 / 0.540337 (0.046851) \| 0.684023 / 1.386936 (-0.702913) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9aaee6fd0b2bcbe18e4829602084bcd83d669c5e \"CML watermark\")\n" ]
1,756,959,367	5,956	Fix ArrowExamplesIterable.shard_data_sources	closed	2023-06-14T13:50:38	2023-06-14T14:43:12	2023-06-14T14:33:45	https://github.com/huggingface/datasets/pull/5956	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5956", "html_url": "https://github.com/huggingface/datasets/pull/5956", "diff_url": "https://github.com/huggingface/datasets/pull/5956.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5956.patch", "merged_at": "2023-06-14T14:33:45" }	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) \| 0.005893 / 0.011353 (-0.005460) \| 0.003682 / 0.011008 (-0.007327) \| 0.098358 / 0.038508 (0.059850) \| 0.028130 / 0.023109 (0.005020) \| 0.305960 / 0.275898 (0.030062) \| 0.334869 / 0.323480 (0.011390) \| 0.003522 / 0.007986 (-0.004463) \| 0.003683 / 0.004328 (-0.000645) \| 0.079418 / 0.004250 (0.075168) \| 0.037662 / 0.037052 (0.000609) \| 0.310893 / 0.258489 (0.052404) \| 0.341347 / 0.293841 (0.047506) \| 0.027450 / 0.128546 (-0.101096) \| 0.008381 / 0.075646 (-0.067265) \| 0.316020 / 0.419271 (-0.103252) \| 0.045079 / 0.043533 (0.001546) \| 0.307806 / 0.255139 (0.052667) \| 0.331804 / 0.283200 (0.048604) \| 0.091806 / 0.141683 (-0.049877) \| 1.492611 / 1.452155 (0.040457) \| 1.551762 / 1.492716 (0.059046) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.201640 / 0.018006 (0.183634) \| 0.422776 / 0.000490 (0.422286) \| 0.003734 / 0.000200 (0.003535) \| 0.000080 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025429 / 0.037411 (-0.011982) \| 0.104699 / 0.014526 (0.090173) \| 0.110505 / 0.176557 (-0.066051) \| 0.171252 / 0.737135 (-0.565883) \| 0.113131 / 0.296338 (-0.183208) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.419914 / 0.215209 (0.204705) \| 4.184414 / 2.077655 (2.106760) \| 1.999263 / 1.504120 (0.495143) \| 1.828669 / 1.541195 (0.287474) \| 1.940366 / 1.468490 (0.471876) \| 0.556939 / 4.584777 (-4.027838) \| 3.389164 / 3.745712 (-0.356548) \| 1.796323 / 5.269862 (-3.473538) \| 1.048843 / 4.565676 (-3.516833) \| 0.067315 / 0.424275 (-0.356960) \| 0.011531 / 0.007607 (0.003923) \| 0.517226 / 0.226044 (0.291182) \| 5.167255 / 2.268929 (2.898326) \| 2.431129 / 55.444624 (-53.013495) \| 2.133913 / 6.876477 (-4.742564) \| 2.359021 / 2.142072 (0.216948) \| 0.666390 / 4.805227 (-4.138838) \| 0.135147 / 6.500664 (-6.365517) \| 0.064855 / 0.075469 (-0.010614) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.166530 / 1.841788 (-0.675258) \| 14.060551 / 8.074308 (5.986242) \| 14.171663 / 10.191392 (3.980271) \| 0.285821 / 0.680424 (-0.394603) \| 0.016867 / 0.534201 (-0.517334) \| 0.369102 / 0.579283 (-0.210181) \| 0.393580 / 0.434364 (-0.040784) \| 0.423721 / 0.540337 (-0.116616) \| 0.512559 / 1.386936 (-0.874377) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006674 / 0.011353 (-0.004679) \| 0.004006 / 0.011008 (-0.007002) \| 0.080160 / 0.038508 (0.041652) \| 0.032508 / 0.023109 (0.009399) \| 0.378168 / 0.275898 (0.102270) \| 0.417796 / 0.323480 (0.094316) \| 0.003706 / 0.007986 (-0.004280) \| 0.002995 / 0.004328 (-0.001333) \| 0.079275 / 0.004250 (0.075025) \| 0.043690 / 0.037052 (0.006638) \| 0.377717 / 0.258489 (0.119228) \| 0.439801 / 0.293841 (0.145961) \| 0.028438 / 0.128546 (-0.100108) \| 0.008661 / 0.075646 (-0.066985) \| 0.085280 / 0.419271 (-0.333991) \| 0.043716 / 0.043533 (0.000183) \| 0.370086 / 0.255139 (0.114947) \| 0.403763 / 0.283200 (0.120563) \| 0.095022 / 0.141683 (-0.046661) \| 1.534376 / 1.452155 (0.082221) \| 1.597658 / 1.492716 (0.104942) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.240229 / 0.018006 (0.222223) \| 0.496281 / 0.000490 (0.495792) \| 0.002165 / 0.000200 (0.001965) \| 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) \| 0.025330 / 0.037411 (-0.012081) \| 0.102414 / 0.014526 (0.087888) \| 0.112733 / 0.176557 (-0.063824) \| 0.161181 / 0.737135 (-0.575955) \| 0.114196 / 0.296338 (-0.182143) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.456808 / 0.215209 (0.241599) \| 4.534937 / 2.077655 (2.457283) \| 2.318834 / 1.504120 (0.814714) \| 2.074085 / 1.541195 (0.532890) \| 2.117409 / 1.468490 (0.648919) \| 0.559110 / 4.584777 (-4.025667) \| 3.371695 / 3.745712 (-0.374017) \| 2.543154 / 5.269862 (-2.726708) \| 1.360552 / 4.565676 (-3.205125) \| 0.067602 / 0.424275 (-0.356674) \| 0.011396 / 0.007607 (0.003789) \| 0.561666 / 0.226044 (0.335622) \| 5.607666 / 2.268929 (3.338737) \| 2.802775 / 55.444624 (-52.641849) \| 2.486162 / 6.876477 (-4.390315) \| 2.390885 / 2.142072 (0.248813) \| 0.667407 / 4.805227 (-4.137820) \| 0.135948 / 6.500664 (-6.364717) \| 0.067272 / 0.075469 (-0.008197) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.279664 / 1.841788 (-0.562124) \| 15.188099 / 8.074308 (7.113791) \| 14.380355 / 10.191392 (4.188963) \| 0.140344 / 0.680424 (-0.540080) \| 0.016832 / 0.534201 (-0.517369) \| 0.364631 / 0.579283 (-0.214652) \| 0.400306 / 0.434364 (-0.034058) \| 0.430793 / 0.540337 (-0.109545) \| 0.525923 / 1.386936 (-0.861013) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#48ca19cf1f4d1c99765a1f847c1f6b849496d99d \"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) \| 0.008502 / 0.011353 (-0.002851) \| 0.005946 / 0.011008 (-0.005062) \| 0.131279 / 0.038508 (0.092771) \| 0.035400 / 0.023109 (0.012291) \| 0.423240 / 0.275898 (0.147342) \| 0.470248 / 0.323480 (0.146768) \| 0.004949 / 0.007986 (-0.003037) \| 0.004544 / 0.004328 (0.000215) \| 0.106856 / 0.004250 (0.102605) \| 0.046579 / 0.037052 (0.009527) \| 0.441135 / 0.258489 (0.182646) \| 0.470401 / 0.293841 (0.176561) \| 0.047231 / 0.128546 (-0.081315) \| 0.017278 / 0.075646 (-0.058368) \| 0.401937 / 0.419271 (-0.017335) \| 0.067151 / 0.043533 (0.023619) \| 0.453908 / 0.255139 (0.198769) \| 0.422171 / 0.283200 (0.138971) \| 0.123583 / 0.141683 (-0.018100) \| 1.852895 / 1.452155 (0.400740) \| 1.827282 / 1.492716 (0.334566) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.246419 / 0.018006 (0.228413) \| 0.576930 / 0.000490 (0.576440) \| 0.007511 / 0.000200 (0.007312) \| 0.000165 / 0.000054 (0.000111) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032732 / 0.037411 (-0.004680) \| 0.130266 / 0.014526 (0.115740) \| 0.150537 / 0.176557 (-0.026019) \| 0.218554 / 0.737135 (-0.518582) \| 0.148572 / 0.296338 (-0.147766) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.598611 / 0.215209 (0.383402) \| 6.181219 / 2.077655 (4.103564) \| 2.473468 / 1.504120 (0.969348) \| 2.206374 / 1.541195 (0.665179) \| 2.216707 / 1.468490 (0.748217) \| 0.981295 / 4.584777 (-3.603482) \| 5.716384 / 3.745712 (1.970672) \| 5.882327 / 5.269862 (0.612466) \| 2.761081 / 4.565676 (-1.804595) \| 0.113544 / 0.424275 (-0.310731) \| 0.015131 / 0.007607 (0.007524) \| 0.850939 / 0.226044 (0.624894) \| 8.046611 / 2.268929 (5.777682) \| 3.340542 / 55.444624 (-52.104083) \| 2.673692 / 6.876477 (-4.202785) \| 2.926330 / 2.142072 (0.784257) \| 1.176164 / 4.805227 (-3.629064) \| 0.226745 / 6.500664 (-6.273919) \| 0.085910 / 0.075469 (0.010441) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.483792 / 1.841788 (-0.357995) \| 18.895009 / 8.074308 (10.820701) \| 20.982461 / 10.191392 (10.791069) \| 0.253085 / 0.680424 (-0.427339) \| 0.031284 / 0.534201 (-0.502917) \| 0.516569 / 0.579283 (-0.062714) \| 0.635781 / 0.434364 (0.201417) \| 0.604359 / 0.540337 (0.064022) \| 0.725278 / 1.386936 (-0.661658) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009220 / 0.011353 (-0.002133) \| 0.005792 / 0.011008 (-0.005216) \| 0.099795 / 0.038508 (0.061287) \| 0.033812 / 0.023109 (0.010703) \| 0.459386 / 0.275898 (0.183488) \| 0.518067 / 0.323480 (0.194587) \| 0.005083 / 0.007986 (-0.002902) \| 0.004145 / 0.004328 (-0.000183) \| 0.103506 / 0.004250 (0.099255) \| 0.050429 / 0.037052 (0.013377) \| 0.478149 / 0.258489 (0.219660) \| 0.531280 / 0.293841 (0.237440) \| 0.047373 / 0.128546 (-0.081173) \| 0.013647 / 0.075646 (-0.061999) \| 0.115174 / 0.419271 (-0.304098) \| 0.061099 / 0.043533 (0.017566) \| 0.455002 / 0.255139 (0.199863) \| 0.507765 / 0.283200 (0.224565) \| 0.112219 / 0.141683 (-0.029464) \| 1.873591 / 1.452155 (0.421436) \| 1.952061 / 1.492716 (0.459345) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283587 / 0.018006 (0.265581) \| 0.587562 / 0.000490 (0.587073) \| 0.001252 / 0.000200 (0.001052) \| 0.000095 / 0.000054 (0.000040) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032706 / 0.037411 (-0.004705) \| 0.137715 / 0.014526 (0.123189) \| 0.131932 / 0.176557 (-0.044625) \| 0.200042 / 0.737135 (-0.537094) \| 0.159327 / 0.296338 (-0.137011) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.624061 / 0.215209 (0.408852) \| 6.386235 / 2.077655 (4.308580) \| 2.908786 / 1.504120 (1.404666) \| 2.589855 / 1.541195 (1.048660) \| 2.387988 / 1.468490 (0.919498) \| 0.952625 / 4.584777 (-3.632152) \| 5.571641 / 3.745712 (1.825929) \| 2.711154 / 5.269862 (-2.558708) \| 1.788015 / 4.565676 (-2.777662) \| 0.104488 / 0.424275 (-0.319787) \| 0.015213 / 0.007607 (0.007606) \| 0.798446 / 0.226044 (0.572401) \| 8.011614 / 2.268929 (5.742686) \| 3.711951 / 55.444624 (-51.732673) \| 2.896881 / 6.876477 (-3.979595) \| 3.172116 / 2.142072 (1.030043) \| 1.136816 / 4.805227 (-3.668411) \| 0.239254 / 6.500664 (-6.261410) \| 0.081136 / 0.075469 (0.005667) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.798246 / 1.841788 (-0.043542) \| 19.497108 / 8.074308 (11.422800) \| 23.450258 / 10.191392 (13.258866) \| 0.250021 / 0.680424 (-0.430403) \| 0.029138 / 0.534201 (-0.505063) \| 0.532984 / 0.579283 (-0.046299) \| 0.638161 / 0.434364 (0.203797) \| 0.615720 / 0.540337 (0.075382) \| 0.770621 / 1.386936 (-0.616315) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7d8345c5f8a844ff44cfbb30cbda514ffe89bfd7 \"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) \| 0.009120 / 0.011353 (-0.002233) \| 0.005381 / 0.011008 (-0.005627) \| 0.139719 / 0.038508 (0.101211) \| 0.037229 / 0.023109 (0.014120) \| 0.414633 / 0.275898 (0.138734) \| 0.480313 / 0.323480 (0.156833) \| 0.005027 / 0.007986 (-0.002959) \| 0.005015 / 0.004328 (0.000687) \| 0.108513 / 0.004250 (0.104263) \| 0.056167 / 0.037052 (0.019115) \| 0.407588 / 0.258489 (0.149099) \| 0.518899 / 0.293841 (0.225058) \| 0.048857 / 0.128546 (-0.079689) \| 0.013694 / 0.075646 (-0.061952) \| 0.418035 / 0.419271 (-0.001237) \| 0.067755 / 0.043533 (0.024222) \| 0.417740 / 0.255139 (0.162601) \| 0.478622 / 0.283200 (0.195422) \| 0.118290 / 0.141683 (-0.023393) \| 1.901473 / 1.452155 (0.449319) \| 1.978126 / 1.492716 (0.485409) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.271960 / 0.018006 (0.253954) \| 0.602745 / 0.000490 (0.602255) \| 0.005371 / 0.000200 (0.005171) \| 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) \| 0.029620 / 0.037411 (-0.007791) \| 0.122402 / 0.014526 (0.107877) \| 0.132645 / 0.176557 (-0.043911) \| 0.212635 / 0.737135 (-0.524500) \| 0.136901 / 0.296338 (-0.159438) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.644017 / 0.215209 (0.428808) \| 6.597151 / 2.077655 (4.519496) \| 2.454471 / 1.504120 (0.950351) \| 2.151357 / 1.541195 (0.610163) \| 2.290748 / 1.468490 (0.822258) \| 0.970194 / 4.584777 (-3.614583) \| 5.475275 / 3.745712 (1.729563) \| 2.772658 / 5.269862 (-2.497204) \| 1.785311 / 4.565676 (-2.780366) \| 0.114503 / 0.424275 (-0.309772) \| 0.015374 / 0.007607 (0.007767) \| 0.768413 / 0.226044 (0.542368) \| 7.956219 / 2.268929 (5.687290) \| 3.272138 / 55.444624 (-52.172486) \| 2.539638 / 6.876477 (-4.336839) \| 2.713526 / 2.142072 (0.571454) \| 1.181221 / 4.805227 (-3.624006) \| 0.236327 / 6.500664 (-6.264337) \| 0.089815 / 0.075469 (0.014345) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.521805 / 1.841788 (-0.319983) \| 18.196529 / 8.074308 (10.122221) \| 20.287324 / 10.191392 (10.095932) \| 0.256959 / 0.680424 (-0.423465) \| 0.028846 / 0.534201 (-0.505355) \| 0.522354 / 0.579283 (-0.056929) \| 0.600216 / 0.434364 (0.165852) \| 0.607668 / 0.540337 (0.067331) \| 0.762101 / 1.386936 (-0.624835) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009227 / 0.011353 (-0.002126) \| 0.005398 / 0.011008 (-0.005610) \| 0.094998 / 0.038508 (0.056490) \| 0.036633 / 0.023109 (0.013524) \| 0.493317 / 0.275898 (0.217419) \| 0.517216 / 0.323480 (0.193736) \| 0.005510 / 0.007986 (-0.002476) \| 0.004249 / 0.004328 (-0.000079) \| 0.107936 / 0.004250 (0.103685) \| 0.050223 / 0.037052 (0.013171) \| 0.580275 / 0.258489 (0.321786) \| 0.551477 / 0.293841 (0.257636) \| 0.048758 / 0.128546 (-0.079788) \| 0.013954 / 0.075646 (-0.061692) \| 0.107021 / 0.419271 (-0.312250) \| 0.064416 / 0.043533 (0.020884) \| 0.485225 / 0.255139 (0.230086) \| 0.513862 / 0.283200 (0.230663) \| 0.118848 / 0.141683 (-0.022835) \| 1.755396 / 1.452155 (0.303241) \| 1.970349 / 1.492716 (0.477633) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290743 / 0.018006 (0.272737) \| 0.603293 / 0.000490 (0.602803) \| 0.006814 / 0.000200 (0.006614) \| 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) \| 0.029862 / 0.037411 (-0.007550) \| 0.136530 / 0.014526 (0.122005) \| 0.133728 / 0.176557 (-0.042829) \| 0.194709 / 0.737135 (-0.542427) \| 0.151080 / 0.296338 (-0.145258) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.649202 / 0.215209 (0.433993) \| 6.637578 / 2.077655 (4.559923) \| 3.040135 / 1.504120 (1.536015) \| 2.671308 / 1.541195 (1.130113) \| 2.722412 / 1.468490 (1.253922) \| 0.953029 / 4.584777 (-3.631748) \| 5.805002 / 3.745712 (2.059290) \| 5.049939 / 5.269862 (-0.219922) \| 2.284053 / 4.565676 (-2.281623) \| 0.130399 / 0.424275 (-0.293876) \| 0.014726 / 0.007607 (0.007119) \| 0.932570 / 0.226044 (0.706526) \| 8.576693 / 2.268929 (6.307765) \| 4.032738 / 55.444624 (-51.411886) \| 3.274715 / 6.876477 (-3.601762) \| 3.513788 / 2.142072 (1.371716) \| 1.130624 / 4.805227 (-3.674603) \| 0.219597 / 6.500664 (-6.281067) \| 0.081425 / 0.075469 (0.005956) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.735312 / 1.841788 (-0.106476) \| 18.438587 / 8.074308 (10.364279) \| 21.582310 / 10.191392 (11.390918) \| 0.224040 / 0.680424 (-0.456384) \| 0.027590 / 0.534201 (-0.506611) \| 0.503598 / 0.579283 (-0.075685) \| 0.624379 / 0.434364 (0.190015) \| 0.571911 / 0.540337 (0.031574) \| 0.723215 / 1.386936 (-0.663721) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9e40d28f2b0060a429c70827191fa5ff3ce8cf27 \"CML watermark\")\n" ]
1,756,827,133	5,955	Strange bug in loading local JSON files, using load_dataset	closed	2023-06-14T12:46:00	2023-06-21T14:42:15	2023-06-21T14:42:15	https://github.com/huggingface/datasets/issues/5955	null	Night-Quiet	false	[ "This is the actual error:\r\n```\r\nFailed to read file '/home/lakala/hjc/code/pycode/glm/temp.json' with error <class 'pyarrow.lib.ArrowInvalid'>: cannot mix list and non-list, non-null values\r\n```\r\nWhich means some samples are incorrectly formatted.\r\n\r\nPyArrow, a storage backend that we use under the hood, requires that all the list elements have the same level of nesting (same number of dimensions) or are `None`.\r\n```python\r\nimport pyarrow as pa\r\npa.array([[1, 2, 3], 2]) # ArrowInvalid: cannot mix list and non-list, non-null values\r\npa.array([[1, 2, 3], [2]]) # works\r\n``` ", "@mariosasko \r\nI used the same operation to check the original data before and after slicing.\r\nThis is reflected in my code.\r\n160000 is not a specific number.\r\nI can also get output using 150000.\r\nThis doesn't seem to align very well with what you said.\r\nBecause if only some sample formats are incorrect.\r\nSo there should be an error in one of the front and back slices.\r\nthank you for your reply.", "Our JSON loader does the following in your case:\r\n\r\n```python\r\nimport json\r\nimport pyarrow as pa\r\n\r\nwith open(file, encoding=\"utf-8\") as f:\r\n dataset = json.load(f)\r\nkeys = set().union(*[row.keys() for row in dataset])\r\nmapping = {col: [row.get(col) for row in dataset] for col in keys}\r\npa_table = pa.Table.from_pydict(mapping) # the ArrowInvalid error comes from here\r\n```\r\n\r\nSo if this code throws an error with correctly-formatted JSON, then this is an Arrow bug and should be reported in their repo.\r\n\r\n> I used the same operation to check the original data before and after slicing.\r\nThis is reflected in my code.\r\n160000 is not a specific number.\r\nI can also get output using 150000.\r\nThis doesn't seem to align very well with what you said.\r\nBecause if only some sample formats are incorrect.\r\nSo there should be an error in one of the front and back slices.\r\n\r\nYou should shuffle the data to make sure that's not the case", "@mariosasko \r\nThank you.\r\nI will try again." ]
1,756,572,994	5,954	Better filenotfound for gated	closed	2023-06-14T10:33:10	2023-06-14T12:33:27	2023-06-14T12:26:31	https://github.com/huggingface/datasets/pull/5954	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5954", "html_url": "https://github.com/huggingface/datasets/pull/5954", "diff_url": "https://github.com/huggingface/datasets/pull/5954.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5954.patch", "merged_at": "2023-06-14T12:26:31" }	lhoestq	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006374 / 0.011353 (-0.004979) \| 0.004100 / 0.011008 (-0.006909) \| 0.104031 / 0.038508 (0.065523) \| 0.035186 / 0.023109 (0.012076) \| 0.328904 / 0.275898 (0.053006) \| 0.361409 / 0.323480 (0.037929) \| 0.003855 / 0.007986 (-0.004130) \| 0.004140 / 0.004328 (-0.000189) \| 0.080406 / 0.004250 (0.076156) \| 0.045658 / 0.037052 (0.008606) \| 0.341133 / 0.258489 (0.082644) \| 0.372688 / 0.293841 (0.078847) \| 0.032025 / 0.128546 (-0.096521) \| 0.008877 / 0.075646 (-0.066769) \| 0.354784 / 0.419271 (-0.064488) \| 0.068874 / 0.043533 (0.025341) \| 0.335441 / 0.255139 (0.080302) \| 0.356498 / 0.283200 (0.073298) \| 0.113367 / 0.141683 (-0.028316) \| 1.522458 / 1.452155 (0.070304) \| 1.608046 / 1.492716 (0.115329) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.231653 / 0.018006 (0.213647) \| 0.446678 / 0.000490 (0.446188) \| 0.003246 / 0.000200 (0.003046) \| 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) \| 0.025299 / 0.037411 (-0.012112) \| 0.111440 / 0.014526 (0.096914) \| 0.118758 / 0.176557 (-0.057799) \| 0.175037 / 0.737135 (-0.562098) \| 0.124583 / 0.296338 (-0.171755) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.418694 / 0.215209 (0.203484) \| 4.174695 / 2.077655 (2.097041) \| 1.890323 / 1.504120 (0.386203) \| 1.683300 / 1.541195 (0.142106) \| 1.781954 / 1.468490 (0.313464) \| 0.546131 / 4.584777 (-4.038645) \| 3.768055 / 3.745712 (0.022343) \| 1.839878 / 5.269862 (-3.429983) \| 1.111877 / 4.565676 (-3.453800) \| 0.068568 / 0.424275 (-0.355707) \| 0.011950 / 0.007607 (0.004343) \| 0.527469 / 0.226044 (0.301425) \| 5.274887 / 2.268929 (3.005958) \| 2.391274 / 55.444624 (-53.053351) \| 2.063837 / 6.876477 (-4.812640) \| 2.140627 / 2.142072 (-0.001445) \| 0.681508 / 4.805227 (-4.123719) \| 0.148203 / 6.500664 (-6.352461) \| 0.064456 / 0.075469 (-0.011013) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.221478 / 1.841788 (-0.620310) \| 14.713705 / 8.074308 (6.639397) \| 14.674184 / 10.191392 (4.482792) \| 0.148411 / 0.680424 (-0.532012) \| 0.017858 / 0.534201 (-0.516343) \| 0.436166 / 0.579283 (-0.143117) \| 0.437290 / 0.434364 (0.002926) \| 0.521994 / 0.540337 (-0.018343) \| 0.635488 / 1.386936 (-0.751448) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006108 / 0.011353 (-0.005245) \| 0.003888 / 0.011008 (-0.007120) \| 0.078424 / 0.038508 (0.039916) \| 0.033618 / 0.023109 (0.010509) \| 0.376284 / 0.275898 (0.100386) \| 0.396957 / 0.323480 (0.073477) \| 0.003799 / 0.007986 (-0.004187) \| 0.003160 / 0.004328 (-0.001168) \| 0.078358 / 0.004250 (0.074107) \| 0.045597 / 0.037052 (0.008545) \| 0.386396 / 0.258489 (0.127907) \| 0.412985 / 0.293841 (0.119144) \| 0.031610 / 0.128546 (-0.096936) \| 0.008720 / 0.075646 (-0.066926) \| 0.085944 / 0.419271 (-0.333328) \| 0.050780 / 0.043533 (0.007247) \| 0.378099 / 0.255139 (0.122960) \| 0.381894 / 0.283200 (0.098694) \| 0.098926 / 0.141683 (-0.042756) \| 1.513842 / 1.452155 (0.061688) \| 1.595040 / 1.492716 (0.102323) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.208169 / 0.018006 (0.190163) \| 0.431653 / 0.000490 (0.431163) \| 0.000935 / 0.000200 (0.000735) \| 0.000088 / 0.000054 (0.000034) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029600 / 0.037411 (-0.007812) \| 0.116936 / 0.014526 (0.102410) \| 0.125603 / 0.176557 (-0.050953) \| 0.177007 / 0.737135 (-0.560129) \| 0.130602 / 0.296338 (-0.165736) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.457158 / 0.215209 (0.241949) \| 4.563254 / 2.077655 (2.485599) \| 2.303549 / 1.504120 (0.799429) \| 2.107269 / 1.541195 (0.566074) \| 2.130861 / 1.468490 (0.662371) \| 0.548931 / 4.584777 (-4.035846) \| 3.745578 / 3.745712 (-0.000134) \| 1.820372 / 5.269862 (-3.449490) \| 1.099316 / 4.565676 (-3.466361) \| 0.068218 / 0.424275 (-0.356057) \| 0.012336 / 0.007607 (0.004728) \| 0.569721 / 0.226044 (0.343676) \| 5.691312 / 2.268929 (3.422384) \| 2.797483 / 55.444624 (-52.647141) \| 2.422621 / 6.876477 (-4.453855) \| 2.426187 / 2.142072 (0.284115) \| 0.674777 / 4.805227 (-4.130451) \| 0.144855 / 6.500664 (-6.355809) \| 0.065805 / 0.075469 (-0.009664) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.305078 / 1.841788 (-0.536709) \| 14.874315 / 8.074308 (6.800007) \| 14.541301 / 10.191392 (4.349909) \| 0.175818 / 0.680424 (-0.504606) \| 0.018169 / 0.534201 (-0.516032) \| 0.435836 / 0.579283 (-0.143447) \| 0.458397 / 0.434364 (0.024033) \| 0.506232 / 0.540337 (-0.034106) \| 0.605306 / 1.386936 (-0.781630) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7e0c1ceab96821c7c6557482d25a9bd2078d716a \"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) \| 0.006138 / 0.011353 (-0.005215) \| 0.003792 / 0.011008 (-0.007216) \| 0.099417 / 0.038508 (0.060908) \| 0.028739 / 0.023109 (0.005630) \| 0.302835 / 0.275898 (0.026937) \| 0.336397 / 0.323480 (0.012918) \| 0.003537 / 0.007986 (-0.004449) \| 0.002973 / 0.004328 (-0.001355) \| 0.077461 / 0.004250 (0.073211) \| 0.039493 / 0.037052 (0.002440) \| 0.302367 / 0.258489 (0.043878) \| 0.344936 / 0.293841 (0.051095) \| 0.027813 / 0.128546 (-0.100733) \| 0.008591 / 0.075646 (-0.067055) \| 0.318975 / 0.419271 (-0.100297) \| 0.045971 / 0.043533 (0.002438) \| 0.301672 / 0.255139 (0.046533) \| 0.328202 / 0.283200 (0.045003) \| 0.091400 / 0.141683 (-0.050282) \| 1.487215 / 1.452155 (0.035060) \| 1.557730 / 1.492716 (0.065014) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.208343 / 0.018006 (0.190336) \| 0.426764 / 0.000490 (0.426275) \| 0.001196 / 0.000200 (0.000996) \| 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) \| 0.024332 / 0.037411 (-0.013079) \| 0.101861 / 0.014526 (0.087335) \| 0.108669 / 0.176557 (-0.067888) \| 0.172042 / 0.737135 (-0.565093) \| 0.113048 / 0.296338 (-0.183290) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.421419 / 0.215209 (0.206210) \| 4.200816 / 2.077655 (2.123162) \| 1.913516 / 1.504120 (0.409396) \| 1.712167 / 1.541195 (0.170972) \| 1.762129 / 1.468490 (0.293639) \| 0.561616 / 4.584777 (-4.023161) \| 3.398122 / 3.745712 (-0.347590) \| 1.744323 / 5.269862 (-3.525538) \| 1.036023 / 4.565676 (-3.529653) \| 0.067658 / 0.424275 (-0.356617) \| 0.011145 / 0.007607 (0.003538) \| 0.522803 / 0.226044 (0.296759) \| 5.226245 / 2.268929 (2.957317) \| 2.355148 / 55.444624 (-53.089476) \| 2.014939 / 6.876477 (-4.861538) \| 2.140028 / 2.142072 (-0.002044) \| 0.695049 / 4.805227 (-4.110178) \| 0.138428 / 6.500664 (-6.362236) \| 0.066721 / 0.075469 (-0.008748) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.219610 / 1.841788 (-0.622177) \| 14.239576 / 8.074308 (6.165268) \| 14.381955 / 10.191392 (4.190563) \| 0.131208 / 0.680424 (-0.549216) \| 0.016698 / 0.534201 (-0.517503) \| 0.361373 / 0.579283 (-0.217910) \| 0.382560 / 0.434364 (-0.051804) \| 0.419427 / 0.540337 (-0.120911) \| 0.508314 / 1.386936 (-0.878622) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006174 / 0.011353 (-0.005179) \| 0.003893 / 0.011008 (-0.007115) \| 0.079614 / 0.038508 (0.041106) \| 0.028685 / 0.023109 (0.005576) \| 0.368627 / 0.275898 (0.092729) \| 0.411599 / 0.323480 (0.088119) \| 0.003573 / 0.007986 (-0.004413) \| 0.002989 / 0.004328 (-0.001340) \| 0.078653 / 0.004250 (0.074402) \| 0.041146 / 0.037052 (0.004094) \| 0.362387 / 0.258489 (0.103898) \| 0.417234 / 0.293841 (0.123393) \| 0.027958 / 0.128546 (-0.100589) \| 0.008695 / 0.075646 (-0.066952) \| 0.084637 / 0.419271 (-0.334635) \| 0.044188 / 0.043533 (0.000655) \| 0.358514 / 0.255139 (0.103375) \| 0.392314 / 0.283200 (0.109114) \| 0.093986 / 0.141683 (-0.047697) \| 1.535366 / 1.452155 (0.083212) \| 1.605978 / 1.492716 (0.113262) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.196215 / 0.018006 (0.178209) \| 0.429403 / 0.000490 (0.428913) \| 0.003736 / 0.000200 (0.003536) \| 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) \| 0.025281 / 0.037411 (-0.012130) \| 0.104325 / 0.014526 (0.089799) \| 0.111548 / 0.176557 (-0.065009) \| 0.162326 / 0.737135 (-0.574809) \| 0.113853 / 0.296338 (-0.182486) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.447600 / 0.215209 (0.232391) \| 4.463422 / 2.077655 (2.385767) \| 2.168028 / 1.504120 (0.663908) \| 1.968699 / 1.541195 (0.427504) \| 2.035531 / 1.468490 (0.567041) \| 0.564575 / 4.584777 (-4.020202) \| 3.435338 / 3.745712 (-0.310374) \| 2.981930 / 5.269862 (-2.287932) \| 1.492172 / 4.565676 (-3.073505) \| 0.067981 / 0.424275 (-0.356294) \| 0.011254 / 0.007607 (0.003647) \| 0.544385 / 0.226044 (0.318340) \| 5.441694 / 2.268929 (3.172765) \| 2.650168 / 55.444624 (-52.794456) \| 2.333974 / 6.876477 (-4.542503) \| 2.383424 / 2.142072 (0.241351) \| 0.669814 / 4.805227 (-4.135414) \| 0.135456 / 6.500664 (-6.365209) \| 0.067067 / 0.075469 (-0.008402) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.313275 / 1.841788 (-0.528513) \| 14.527636 / 8.074308 (6.453328) \| 14.470957 / 10.191392 (4.279565) \| 0.144361 / 0.680424 (-0.536063) \| 0.016847 / 0.534201 (-0.517354) \| 0.365158 / 0.579283 (-0.214125) \| 0.393809 / 0.434364 (-0.040555) \| 0.428527 / 0.540337 (-0.111810) \| 0.515816 / 1.386936 (-0.871120) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7845d4c3c301226b3f8941ac90aaa123bfd7c69e \"CML watermark\")\n" ]
1,756,520,523	5,953	Bad error message when trying to download gated dataset	closed	2023-06-14T10:03:39	2023-06-14T16:36:51	2023-06-14T12:26:32	https://github.com/huggingface/datasets/issues/5953	null	patrickvonplaten	false	[ "cc @sanchit-gandhi @Vaibhavs10 @lhoestq - this is mainly for demos that use Common Voice datasets as done here: https://github.com/facebookresearch/fairseq/tree/main/examples/mms#-transformers\r\n", "Hi ! the error for me is\r\n\r\n```\r\nFileNotFoundError: Couldn't find a dataset script at /content/mozilla-foundation/common_voice_13_0/common_voice_13_0.py or any data file in the same directory. Couldn't find 'mozilla-foundation/common_voice_13_0' on the Hugging Face Hub either: FileNotFoundError: Dataset 'mozilla-foundation/common_voice_13_0' doesn't exist on the Hub. If the repo is private or gated, make sure to log in with `huggingface-cli login`.\r\n```\r\n\r\nAnd tbh idk how you managed to get your error. \"n_shards.json\" is not even a thing in `datasets`", "Okay, I am able to reproduce @patrickvonplaten's original error: https://github.com/Vaibhavs10/scratchpad/blob/main/cv13_datasets_test.ipynb\r\n\r\nAlso not sure why it looks for `n_shards.json`", "Ok I see, this file is downloaded from the CV dataset script - let me investigate", "Ok I see: when you log out you no longer have access to the repository.\r\n\r\nTherefore the dataset script is loaded from cache:\r\n```\r\nWARNING:datasets.load:Using the latest cached version of the module from /root/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_13_0/22809012aac1fc9803eaffc44122e4149043748e93933935d5ea19898587e4d7 (last modified on Wed Jun 14 10:13:17 2023) since it couldn't be found locally at mozilla-foundation/common_voice_13_0., or remotely on the Hugging Face Hub.\r\n```\r\n\r\nand the script tries to download the n_shards.json but fails", "Is this ok for you https://github.com/huggingface/datasets/pull/5954 ?\r\n\r\nI'll do a release this afternoon", "Cool! ", "this is included in the new release 2.13.0" ]
1,756,481,591	5,952	Add Arrow builder docs	closed	2023-06-14T09:42:46	2023-06-14T14:42:31	2023-06-14T14:34:39	https://github.com/huggingface/datasets/pull/5952	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5952", "html_url": "https://github.com/huggingface/datasets/pull/5952", "diff_url": "https://github.com/huggingface/datasets/pull/5952.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5952.patch", "merged_at": "2023-06-14T14:34:39" }	lhoestq	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006522 / 0.011353 (-0.004831) \| 0.004319 / 0.011008 (-0.006690) \| 0.099280 / 0.038508 (0.060772) \| 0.033117 / 0.023109 (0.010007) \| 0.339392 / 0.275898 (0.063494) \| 0.366219 / 0.323480 (0.042739) \| 0.003896 / 0.007986 (-0.004090) \| 0.003412 / 0.004328 (-0.000916) \| 0.076655 / 0.004250 (0.072404) \| 0.045203 / 0.037052 (0.008150) \| 0.355800 / 0.258489 (0.097311) \| 0.372533 / 0.293841 (0.078692) \| 0.032318 / 0.128546 (-0.096229) \| 0.009030 / 0.075646 (-0.066616) \| 0.328701 / 0.419271 (-0.090571) \| 0.052891 / 0.043533 (0.009358) \| 0.341131 / 0.255139 (0.085992) \| 0.351593 / 0.283200 (0.068393) \| 0.105136 / 0.141683 (-0.036546) \| 1.475953 / 1.452155 (0.023798) \| 1.566074 / 1.492716 (0.073357) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.216671 / 0.018006 (0.198664) \| 0.446952 / 0.000490 (0.446462) \| 0.006340 / 0.000200 (0.006140) \| 0.000096 / 0.000054 (0.000042) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028293 / 0.037411 (-0.009118) \| 0.112298 / 0.014526 (0.097773) \| 0.118634 / 0.176557 (-0.057923) \| 0.175542 / 0.737135 (-0.561593) \| 0.124773 / 0.296338 (-0.171565) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.435209 / 0.215209 (0.220000) \| 4.344361 / 2.077655 (2.266706) \| 2.128943 / 1.504120 (0.624823) \| 1.945465 / 1.541195 (0.404271) \| 2.049932 / 1.468490 (0.581442) \| 0.547126 / 4.584777 (-4.037651) \| 3.768698 / 3.745712 (0.022986) \| 1.924441 / 5.269862 (-3.345420) \| 1.146364 / 4.565676 (-3.419312) \| 0.067466 / 0.424275 (-0.356809) \| 0.011175 / 0.007607 (0.003568) \| 0.540978 / 0.226044 (0.314933) \| 5.393120 / 2.268929 (3.124191) \| 2.639027 / 55.444624 (-52.805597) \| 2.327216 / 6.876477 (-4.549261) \| 2.500532 / 2.142072 (0.358460) \| 0.679120 / 4.805227 (-4.126107) \| 0.148824 / 6.500664 (-6.351840) \| 0.064195 / 0.075469 (-0.011274) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.158387 / 1.841788 (-0.683401) \| 14.880751 / 8.074308 (6.806443) \| 14.725249 / 10.191392 (4.533857) \| 0.149785 / 0.680424 (-0.530639) \| 0.017338 / 0.534201 (-0.516863) \| 0.390980 / 0.579283 (-0.188303) \| 0.425611 / 0.434364 (-0.008753) \| 0.458851 / 0.540337 (-0.081487) \| 0.559209 / 1.386936 (-0.827727) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated 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) \| 0.004318 / 0.011008 (-0.006690) \| 0.076715 / 0.038508 (0.038207) \| 0.033528 / 0.023109 (0.010419) \| 0.411986 / 0.275898 (0.136087) \| 0.438752 / 0.323480 (0.115272) \| 0.004039 / 0.007986 (-0.003947) \| 0.003509 / 0.004328 (-0.000819) \| 0.077924 / 0.004250 (0.073673) \| 0.049519 / 0.037052 (0.012467) \| 0.420595 / 0.258489 (0.162106) \| 0.450536 / 0.293841 (0.156695) \| 0.032817 / 0.128546 (-0.095729) \| 0.008963 / 0.075646 (-0.066684) \| 0.083818 / 0.419271 (-0.335454) \| 0.057591 / 0.043533 (0.014058) \| 0.404605 / 0.255139 (0.149466) \| 0.423661 / 0.283200 (0.140462) \| 0.110698 / 0.141683 (-0.030984) \| 1.512515 / 1.452155 (0.060361) \| 1.569207 / 1.492716 (0.076490) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.200795 / 0.018006 (0.182789) \| 0.448853 / 0.000490 (0.448363) \| 0.003657 / 0.000200 (0.003457) \| 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) \| 0.031612 / 0.037411 (-0.005799) \| 0.116712 / 0.014526 (0.102186) \| 0.126162 / 0.176557 (-0.050395) \| 0.180522 / 0.737135 (-0.556614) \| 0.129768 / 0.296338 (-0.166570) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.433797 / 0.215209 (0.218588) \| 4.353099 / 2.077655 (2.275444) \| 2.117582 / 1.504120 (0.613462) \| 1.934487 / 1.541195 (0.393292) \| 2.016988 / 1.468490 (0.548498) \| 0.531387 / 4.584777 (-4.053390) \| 3.843520 / 3.745712 (0.097807) \| 1.879560 / 5.269862 (-3.390301) \| 1.129445 / 4.565676 (-3.436231) \| 0.065952 / 0.424275 (-0.358323) \| 0.011566 / 0.007607 (0.003959) \| 0.533949 / 0.226044 (0.307904) \| 5.327447 / 2.268929 (3.058518) \| 2.572202 / 55.444624 (-52.872422) \| 2.240723 / 6.876477 (-4.635753) \| 2.329290 / 2.142072 (0.187217) \| 0.662162 / 4.805227 (-4.143066) \| 0.143191 / 6.500664 (-6.357473) \| 0.065273 / 0.075469 (-0.010196) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.274945 / 1.841788 (-0.566843) \| 15.444511 / 8.074308 (7.370203) \| 14.793524 / 10.191392 (4.602132) \| 0.175607 / 0.680424 (-0.504817) \| 0.017324 / 0.534201 (-0.516877) \| 0.396172 / 0.579283 (-0.183111) \| 0.437334 / 0.434364 (0.002970) \| 0.472621 / 0.540337 (-0.067716) \| 0.574888 / 1.386936 (-0.812048) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b4ab1b3ed7257b0e0ad075d7271a51835f320a5e \"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) \| 0.006976 / 0.011353 (-0.004377) \| 0.004541 / 0.011008 (-0.006467) \| 0.106085 / 0.038508 (0.067577) \| 0.029148 / 0.023109 (0.006039) \| 0.306386 / 0.275898 (0.030488) \| 0.351474 / 0.323480 (0.027994) \| 0.003924 / 0.007986 (-0.004062) \| 0.004588 / 0.004328 (0.000260) \| 0.090479 / 0.004250 (0.086229) \| 0.041195 / 0.037052 (0.004142) \| 0.346020 / 0.258489 (0.087531) \| 0.362526 / 0.293841 (0.068685) \| 0.041020 / 0.128546 (-0.087526) \| 0.012536 / 0.075646 (-0.063110) \| 0.333247 / 0.419271 (-0.086024) \| 0.059786 / 0.043533 (0.016253) \| 0.318094 / 0.255139 (0.062955) \| 0.343879 / 0.283200 (0.060679) \| 0.110083 / 0.141683 (-0.031600) \| 1.514027 / 1.452155 (0.061872) \| 1.551435 / 1.492716 (0.058719) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.235401 / 0.018006 (0.217395) \| 0.544292 / 0.000490 (0.543803) \| 0.005284 / 0.000200 (0.005084) \| 0.000112 / 0.000054 (0.000057) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025008 / 0.037411 (-0.012403) \| 0.102235 / 0.014526 (0.087709) \| 0.105523 / 0.176557 (-0.071034) \| 0.180846 / 0.737135 (-0.556289) \| 0.107078 / 0.296338 (-0.189261) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.502374 / 0.215209 (0.287165) \| 5.224254 / 2.077655 (3.146600) \| 1.987193 / 1.504120 (0.483073) \| 1.694680 / 1.541195 (0.153485) \| 1.663907 / 1.468490 (0.195417) \| 0.786470 / 4.584777 (-3.798307) \| 4.977895 / 3.745712 (1.232183) \| 4.713451 / 5.269862 (-0.556410) \| 2.298763 / 4.565676 (-2.266913) \| 0.090225 / 0.424275 (-0.334051) \| 0.011427 / 0.007607 (0.003820) \| 0.640686 / 0.226044 (0.414641) \| 6.351727 / 2.268929 (4.082798) \| 2.636912 / 55.444624 (-52.807712) \| 2.075566 / 6.876477 (-4.800911) \| 2.080260 / 2.142072 (-0.061812) \| 0.952727 / 4.805227 (-3.852500) \| 0.188651 / 6.500664 (-6.312013) \| 0.068997 / 0.075469 (-0.006472) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.258878 / 1.841788 (-0.582910) \| 15.444724 / 8.074308 (7.370416) \| 17.521918 / 10.191392 (7.330526) \| 0.189732 / 0.680424 (-0.490692) \| 0.031084 / 0.534201 (-0.503117) \| 0.445150 / 0.579283 (-0.134133) \| 0.575844 / 0.434364 (0.141480) \| 0.498162 / 0.540337 (-0.042176) \| 0.635885 / 1.386936 (-0.751051) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007402 / 0.011353 (-0.003951) \| 0.005058 / 0.011008 (-0.005950) \| 0.077659 / 0.038508 (0.039151) \| 0.034934 / 0.023109 (0.011825) \| 0.373139 / 0.275898 (0.097241) \| 0.411857 / 0.323480 (0.088377) \| 0.003751 / 0.007986 (-0.004235) \| 0.003634 / 0.004328 (-0.000695) \| 0.075914 / 0.004250 (0.071663) \| 0.037555 / 0.037052 (0.000503) \| 0.387482 / 0.258489 (0.128993) \| 0.434407 / 0.293841 (0.140566) \| 0.040540 / 0.128546 (-0.088006) \| 0.013458 / 0.075646 (-0.062189) \| 0.096129 / 0.419271 (-0.323143) \| 0.055369 / 0.043533 (0.011836) \| 0.386564 / 0.255139 (0.131425) \| 0.410417 / 0.283200 (0.127218) \| 0.093265 / 0.141683 (-0.048418) \| 1.432841 / 1.452155 (-0.019314) \| 1.533180 / 1.492716 (0.040463) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281051 / 0.018006 (0.263045) \| 0.547635 / 0.000490 (0.547146) \| 0.004434 / 0.000200 (0.004234) \| 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) \| 0.026409 / 0.037411 (-0.011002) \| 0.098586 / 0.014526 (0.084060) \| 0.109223 / 0.176557 (-0.067334) \| 0.165958 / 0.737135 (-0.571177) \| 0.111751 / 0.296338 (-0.184587) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.542717 / 0.215209 (0.327508) \| 5.530075 / 2.077655 (3.452420) \| 2.351141 / 1.504120 (0.847022) \| 2.021659 / 1.541195 (0.480464) \| 1.964900 / 1.468490 (0.496410) \| 0.819698 / 4.584777 (-3.765079) \| 4.917412 / 3.745712 (1.171700) \| 2.425149 / 5.269862 (-2.844712) \| 1.561953 / 4.565676 (-3.003724) \| 0.098417 / 0.424275 (-0.325858) \| 0.012594 / 0.007607 (0.004986) \| 0.717212 / 0.226044 (0.491168) \| 6.994833 / 2.268929 (4.725904) \| 2.997347 / 55.444624 (-52.447277) \| 2.388366 / 6.876477 (-4.488111) \| 2.502913 / 2.142072 (0.360841) \| 1.030545 / 4.805227 (-3.774682) \| 0.184844 / 6.500664 (-6.315820) \| 0.076889 / 0.075469 (0.001420) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.371647 / 1.841788 (-0.470141) \| 15.522995 / 8.074308 (7.448687) \| 17.349823 / 10.191392 (7.158431) \| 0.229709 / 0.680424 (-0.450714) \| 0.023303 / 0.534201 (-0.510898) \| 0.413874 / 0.579283 (-0.165409) \| 0.567552 / 0.434364 (0.133188) \| 0.491722 / 0.540337 (-0.048615) \| 0.590640 / 1.386936 (-0.796296) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f1911ffa5d1f58f509d04fe1ddeb9d00a63f94d5 \"CML watermark\")\n" ]
1,756,363,546	5,951	What is the Right way to use discofuse dataset??	closed	2023-06-14T08:38:39	2023-06-14T13:25:06	2023-06-14T12:10:16	https://github.com/huggingface/datasets/issues/5951	null	akesh1235	false	[ "Thanks for opening https://huggingface.co/datasets/discofuse/discussions/3, let's continue the discussion over there if you don't mind", "I have posted there also sir, please check\r\n@lhoestq" ]
1,755,197,946	5,950	Support for data with instance-wise dictionary as features	open	2023-06-13T15:49:00	2025-04-07T13:20:37	null	https://github.com/huggingface/datasets/issues/5950	null	richardwth	false	[ "Hi ! We use the Arrow columnar format under the hood, which doesn't support such dictionaries: each field must have a fixed type and exist in each sample.\r\n\r\nInstead you can restructure your data like\r\n```\r\n{\r\n \"index\": 0,\r\n \"keys\": [\"2 * x + y >= 3\"],\r\n \"values\": [[\"2 * x + y >= 3\", \"4 * x + 2 * y >= 6\"]],\r\n }\r\n},\r\n...\r\n{\r\n \"index\": 9999,\r\n \"keys\": [\"x >= 6\"],\r\n \"values\": [[\"x >= 6\", \"x >= 0\", \"x >= -1\"]],\r\n},\r\n...\r\n```", "Maybe there could be some type of automated conversion from dicts to tuples. I am also trying to wrangle a json-based dataset into `datasets` and it's awful because of this issue.", "Alternatively we can maybe support the [Json extension type](https://arrow.apache.org/docs/python/generated/pyarrow.JsonType.html#pyarrow.JsonType) in `pyarrow` ?\n\nbtw `datasets` is open to contributions on this subject if you'd like to take a look", "Hmm, I'll think about this a bit.\n\nhttps://arrow.apache.org/docs/python/json.html\n\n> Nested JSON objects convert to a struct type, and inference proceeds recursively on the JSON objects’ values.\n\nhttps://arrow.apache.org/docs/dev/python/generated/pyarrow.JsonType.html\n\nHmm... AFAICT from reading the docs, the `JsonType` seems like a string that is annotated as JSON. So when you read it, it's literally just the encoded JSON. So that's not ideal.\n\nI guess there are conceptually two components to using this:\n1. Modifying schema inference to use JsonType when \"appropriate\". More on that below.\n2. Handling JsonType when reading. I guess we would want to call `json.loads` for the user on any JsonType columns?\n\n### Schema inference\n\nI can think of a few ways forward:\n1. Add a JSON builder option that converts nested JSON objects to JsonTypes instead.\n2. Use some heuristic to detect when a struct type is bad (e.g., average number of `None` values) and convert those to JsonTypes instead.\n\nThe first option is the easiest, but also would remove the nested structure from the arrow schema. Does this matter?", "The first option sounds good indeed, and more explicit / flexible.\n\n> but also would remove the nested structure from the arrow schema. Does this matter?\n\nWell I expect some `pyarrow.compute` functions for nested data to not work for JsonType, and maybe exporting to a pandas / polars dataframe can have a few issues. But it's ok as a first step and we can iterate imo", "So this will be harder than I thought. I thought pyarrow would provide a separate type inference function, but it seems like it doesn't. Type inference is wrapped into the conversion/loading functions, which are, of course, failing.", "Since we use pandas to load the JSON before converting to arrow, maybe we can do some conversion there.\n\n```\nIn [29]: df = pd.read_json(\"/tmp/wtf.json\")\n\nIn [30]: df\nOut[30]: \n lol\n0 [42, []]\n\nIn [31]: pa.Table.from_pandas(df)\n---------------------------------------------------------------------------\nArrowInvalid Traceback (most recent call last)\n```\n\nBut if we convert the problem column to a string, we can proceed:\n\n```\nIn [38]: df['lol'] = df['lol'].astype(str)\n\nIn [39]: df\nOut[39]: \n lol\n0 [42, []]\n\nIn [40]: pa.Table.from_pandas(df)\nOut[40]: \npyarrow.Table\nlol: string\n----\nlol: [[\"[42, []]\"]]\n```\n\nSo I guess we could coerce columns to strings until we're able to convert to arrow, and then convert those coerced columns to JsonType in the final arrow. This feels kind of icky to me though. But I think it might work.", "Makes sense, yes it's maybe the way to go to have it working short term.\n\nLonger term `pyarrow` should handle it though IMO via its JSON reader, have you opened an issue there already by any chance ?", "> Longer term pyarrow should handle it though IMO via its JSON reader, have you opened an issue there already by any chance ?\n\nI agree that `pyarrow` changes are a better solution long term. I haven't opened any issues yet. Were you thinking:\n \n* To separate the inference function\n* Open an issue with an incompatible JSON file and see what they suggest?", "I was thinking of seeing what they suggest in case of incompatible JSON, it's also possible that other members of the community have asked for for help / requested such a feature already. But sharing about the inference function idea can be interesting as well", "Thanks, I will do this and see what they suggest.\r\n\r\nOn Mon, Apr 7, 2025, 9:18 AM Quentin Lhoest - *@.\r\n@.> wrote:\r\n\r\n> I was thinking of seeing what they suggest in case of incompatible JSON,\r\n> it's also possible that other members of the community have asked for for\r\n> help / requested such a feature already. But sharing about the inference\r\n> function idea can be interesting as well\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/5950#issuecomment-2783312654>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AAHYKZKUPOK5IBQBMMVDCTT2YJ3LXAVCNFSM6AAAAAB2H43DQKVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDOOBTGMYTENRVGQ>\r\n> .\r\n> You are receiving this because you are subscribed to this thread.Message\r\n> ID: @.*>\r\n> [image: lhoestq]lhoestq* left a comment (huggingface/datasets#5950)\r\n> <https://github.com/huggingface/datasets/issues/5950#issuecomment-2783312654>\r\n>\r\n> I was thinking of seeing what they suggest in case of incompatible JSON,\r\n> it's also possible that other members of the community have asked for for\r\n> help / requested such a feature already. But sharing about the inference\r\n> function idea can be interesting as well\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/5950#issuecomment-2783312654>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AAHYKZKUPOK5IBQBMMVDCTT2YJ3LXAVCNFSM6AAAAAB2H43DQKVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDOOBTGMYTENRVGQ>\r\n> .\r\n> You are receiving this because you are subscribed to this thread.Message\r\n> ID: *@.**>\r\n>\r\n" ]
1,754,843,717	5,949	Replace metadata utils with `huggingface_hub`'s RepoCard API	closed	2023-06-13T13:03:19	2023-06-27T16:47:51	2023-06-27T16:38:32	https://github.com/huggingface/datasets/pull/5949	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5949", "html_url": "https://github.com/huggingface/datasets/pull/5949", "diff_url": "https://github.com/huggingface/datasets/pull/5949.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5949.patch", "merged_at": "2023-06-27T16:38:32" }	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) \| 0.006635 / 0.011353 (-0.004718) \| 0.004439 / 0.011008 (-0.006570) \| 0.107831 / 0.038508 (0.069323) \| 0.035664 / 0.023109 (0.012555) \| 0.393733 / 0.275898 (0.117835) \| 0.418336 / 0.323480 (0.094856) \| 0.005739 / 0.007986 (-0.002247) \| 0.005737 / 0.004328 (0.001408) \| 0.079820 / 0.004250 (0.075569) \| 0.045402 / 0.037052 (0.008349) \| 0.396108 / 0.258489 (0.137619) \| 0.422951 / 0.293841 (0.129110) \| 0.030506 / 0.128546 (-0.098040) \| 0.009785 / 0.075646 (-0.065861) \| 0.375302 / 0.419271 (-0.043969) \| 0.054355 / 0.043533 (0.010823) \| 0.399652 / 0.255139 (0.144513) \| 0.410825 / 0.283200 (0.127625) \| 0.109238 / 0.141683 (-0.032445) \| 1.687532 / 1.452155 (0.235378) \| 1.736829 / 1.492716 (0.244113) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.226514 / 0.018006 (0.208508) \| 0.487010 / 0.000490 (0.486520) \| 0.006436 / 0.000200 (0.006236) \| 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) \| 0.029097 / 0.037411 (-0.008315) \| 0.122979 / 0.014526 (0.108453) \| 0.129454 / 0.176557 (-0.047103) \| 0.194006 / 0.737135 (-0.543129) \| 0.137968 / 0.296338 (-0.158370) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.466425 / 0.215209 (0.251216) \| 4.627307 / 2.077655 (2.549652) \| 2.108840 / 1.504120 (0.604720) \| 1.882547 / 1.541195 (0.341353) \| 1.891077 / 1.468490 (0.422587) \| 0.590646 / 4.584777 (-3.994131) \| 4.176918 / 3.745712 (0.431205) \| 2.071475 / 5.269862 (-3.198386) \| 1.173815 / 4.565676 (-3.391862) \| 0.075330 / 0.424275 (-0.348945) \| 0.012944 / 0.007607 (0.005337) \| 0.587080 / 0.226044 (0.361036) \| 5.827053 / 2.268929 (3.558125) \| 2.694258 / 55.444624 (-52.750366) \| 2.276997 / 6.876477 (-4.599480) \| 2.329678 / 2.142072 (0.187605) \| 0.721860 / 4.805227 (-4.083367) \| 0.159238 / 6.500664 (-6.341426) \| 0.073013 / 0.075469 (-0.002456) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.345396 / 1.841788 (-0.496391) \| 16.619283 / 8.074308 (8.544975) \| 14.754754 / 10.191392 (4.563362) \| 0.180784 / 0.680424 (-0.499639) \| 0.020376 / 0.534201 (-0.513825) \| 0.451010 / 0.579283 (-0.128273) \| 0.481524 / 0.434364 (0.047160) \| 0.564777 / 0.540337 (0.024440) \| 0.683232 / 1.386936 (-0.703704) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007243 / 0.011353 (-0.004110) \| 0.005262 / 0.011008 (-0.005746) \| 0.084090 / 0.038508 (0.045581) \| 0.037429 / 0.023109 (0.014320) \| 0.404038 / 0.275898 (0.128140) \| 0.445040 / 0.323480 (0.121560) \| 0.006220 / 0.007986 (-0.001766) \| 0.004256 / 0.004328 (-0.000072) \| 0.083794 / 0.004250 (0.079544) \| 0.052655 / 0.037052 (0.015603) \| 0.414083 / 0.258489 (0.155594) \| 0.458190 / 0.293841 (0.164349) \| 0.032719 / 0.128546 (-0.095828) \| 0.010063 / 0.075646 (-0.065583) \| 0.092281 / 0.419271 (-0.326990) \| 0.053888 / 0.043533 (0.010355) \| 0.407813 / 0.255139 (0.152674) \| 0.431692 / 0.283200 (0.148493) \| 0.119799 / 0.141683 (-0.021884) \| 1.709853 / 1.452155 (0.257698) \| 1.771592 / 1.492716 (0.278876) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.246540 / 0.018006 (0.228534) \| 0.483199 / 0.000490 (0.482709) \| 0.002514 / 0.000200 (0.002315) \| 0.000096 / 0.000054 (0.000042) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031576 / 0.037411 (-0.005835) \| 0.130020 / 0.014526 (0.115495) \| 0.140285 / 0.176557 (-0.036272) \| 0.196164 / 0.737135 (-0.540972) \| 0.143924 / 0.296338 (-0.152414) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled 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) \| 4.888055 / 2.077655 (2.810400) \| 2.389163 / 1.504120 (0.885043) \| 2.184626 / 1.541195 (0.643431) \| 2.260227 / 1.468490 (0.791737) \| 0.601331 / 4.584777 (-3.983446) \| 4.386159 / 3.745712 (0.640447) \| 3.345814 / 5.269862 (-1.924048) \| 1.734360 / 4.565676 (-2.831317) \| 0.073199 / 0.424275 (-0.351076) \| 0.012397 / 0.007607 (0.004790) \| 0.601411 / 0.226044 (0.375366) \| 6.135000 / 2.268929 (3.866072) \| 2.930169 / 55.444624 (-52.514456) \| 2.532631 / 6.876477 (-4.343845) \| 2.619351 / 2.142072 (0.477279) \| 0.740954 / 4.805227 (-4.064274) \| 0.162936 / 6.500664 (-6.337728) \| 0.073885 / 0.075469 (-0.001585) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.502493 / 1.841788 (-0.339294) \| 17.026756 / 8.074308 (8.952448) \| 15.880958 / 10.191392 (5.689566) \| 0.167261 / 0.680424 (-0.513163) \| 0.020347 / 0.534201 (-0.513854) \| 0.452902 / 0.579283 (-0.126381) \| 0.481614 / 0.434364 (0.047250) \| 0.539893 / 0.540337 (-0.000445) \| 0.653401 / 1.386936 (-0.733535) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6a5781212e968e2515afdf29370a6eab6f657120 \"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) \| 0.008268 / 0.011353 (-0.003084) \| 0.005538 / 0.011008 (-0.005470) \| 0.126136 / 0.038508 (0.087628) \| 0.046100 / 0.023109 (0.022991) \| 0.366882 / 0.275898 (0.090984) \| 0.408912 / 0.323480 (0.085432) \| 0.007090 / 0.007986 (-0.000895) \| 0.004820 / 0.004328 (0.000491) \| 0.091432 / 0.004250 (0.087181) \| 0.058390 / 0.037052 (0.021338) \| 0.368787 / 0.258489 (0.110298) \| 0.419429 / 0.293841 (0.125588) \| 0.034958 / 0.128546 (-0.093588) \| 0.010526 / 0.075646 (-0.065120) \| 0.463063 / 0.419271 (0.043791) \| 0.070544 / 0.043533 (0.027011) \| 0.366182 / 0.255139 (0.111043) \| 0.390851 / 0.283200 (0.107652) \| 0.128377 / 0.141683 (-0.013306) \| 1.819385 / 1.452155 (0.367231) \| 1.928834 / 1.492716 (0.436117) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.228413 / 0.018006 (0.210407) \| 0.485511 / 0.000490 (0.485021) \| 0.005395 / 0.000200 (0.005195) \| 0.000119 / 0.000054 (0.000064) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035209 / 0.037411 (-0.002203) \| 0.144492 / 0.014526 (0.129967) \| 0.150467 / 0.176557 (-0.026089) \| 0.223861 / 0.737135 (-0.513274) \| 0.156363 / 0.296338 (-0.139975) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.517751 / 0.215209 (0.302542) \| 5.150438 / 2.077655 (3.072783) \| 2.483601 / 1.504120 (0.979481) \| 2.279786 / 1.541195 (0.738592) \| 2.374510 / 1.468490 (0.906020) \| 0.637547 / 4.584777 (-3.947230) \| 4.845393 / 3.745712 (1.099681) \| 2.241554 / 5.269862 (-3.028307) \| 1.290105 / 4.565676 (-3.275572) \| 0.079791 / 0.424275 (-0.344484) \| 0.014915 / 0.007607 (0.007308) \| 0.640468 / 0.226044 (0.414423) \| 6.394810 / 2.268929 (4.125881) \| 3.012748 / 55.444624 (-52.431876) \| 2.625565 / 6.876477 (-4.250912) \| 2.792435 / 2.142072 (0.650363) \| 0.782284 / 4.805227 (-4.022944) \| 0.171628 / 6.500664 (-6.329036) \| 0.081714 / 0.075469 (0.006245) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.592411 / 1.841788 (-0.249377) \| 18.999604 / 8.074308 (10.925295) \| 18.469946 / 10.191392 (8.278554) \| 0.200878 / 0.680424 (-0.479546) \| 0.021595 / 0.534201 (-0.512606) \| 0.519247 / 0.579283 (-0.060036) \| 0.534940 / 0.434364 (0.100576) \| 0.656325 / 0.540337 (0.115987) \| 0.789658 / 1.386936 (-0.597278) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008093 / 0.011353 (-0.003260) \| 0.005524 / 0.011008 (-0.005484) \| 0.092339 / 0.038508 (0.053831) \| 0.045619 / 0.023109 (0.022510) \| 0.449376 / 0.275898 (0.173478) \| 0.478587 / 0.323480 (0.155107) \| 0.006978 / 0.007986 (-0.001007) \| 0.004622 / 0.004328 (0.000294) \| 0.090618 / 0.004250 (0.086368) \| 0.059321 / 0.037052 (0.022269) \| 0.450989 / 0.258489 (0.192500) \| 0.491652 / 0.293841 (0.197811) \| 0.033308 / 0.128546 (-0.095238) \| 0.010677 / 0.075646 (-0.064969) \| 0.099836 / 0.419271 (-0.319435) \| 0.055937 / 0.043533 (0.012404) \| 0.440560 / 0.255139 (0.185421) \| 0.475305 / 0.283200 (0.192105) \| 0.130829 / 0.141683 (-0.010854) \| 1.857943 / 1.452155 (0.405789) \| 1.989534 / 1.492716 (0.496818) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.244715 / 0.018006 (0.226709) \| 0.482866 / 0.000490 (0.482377) \| 0.001100 / 0.000200 (0.000900) \| 0.000095 / 0.000054 (0.000041) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036288 / 0.037411 (-0.001124) \| 0.147903 / 0.014526 (0.133377) \| 0.154141 / 0.176557 (-0.022416) \| 0.221863 / 0.737135 (-0.515272) \| 0.162319 / 0.296338 (-0.134019) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.536972 / 0.215209 (0.321763) \| 5.382866 / 2.077655 (3.305211) \| 2.719575 / 1.504120 (1.215456) \| 2.516596 / 1.541195 (0.975401) \| 2.699602 / 1.468490 (1.231112) \| 0.639886 / 4.584777 (-3.944891) \| 5.109746 / 3.745712 (1.364034) \| 2.260206 / 5.269862 (-3.009656) \| 1.305506 / 4.565676 (-3.260170) \| 0.080262 / 0.424275 (-0.344013) \| 0.014801 / 0.007607 (0.007194) \| 0.661228 / 0.226044 (0.435184) \| 6.596485 / 2.268929 (4.327557) \| 3.226114 / 55.444624 (-52.218510) \| 2.859776 / 6.876477 (-4.016701) \| 3.059355 / 2.142072 (0.917282) \| 0.793413 / 4.805227 (-4.011814) \| 0.176521 / 6.500664 (-6.324143) \| 0.084062 / 0.075469 (0.008593) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.642085 / 1.841788 (-0.199703) \| 20.355459 / 8.074308 (12.281151) \| 17.979620 / 10.191392 (7.788228) \| 0.229329 / 0.680424 (-0.451094) \| 0.025681 / 0.534201 (-0.508520) \| 0.534142 / 0.579283 (-0.045141) \| 0.623439 / 0.434364 (0.189075) \| 0.621938 / 0.540337 (0.081601) \| 0.759038 / 1.386936 (-0.627898) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6a98ff43225df344139023a5b7eb9caef610b677 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007703 / 0.011353 (-0.003649) \| 0.005362 / 0.011008 (-0.005646) \| 0.113111 / 0.038508 (0.074602) \| 0.038891 / 0.023109 (0.015782) \| 0.348938 / 0.275898 (0.073040) \| 0.398079 / 0.323480 (0.074599) \| 0.006707 / 0.007986 (-0.001278) \| 0.004489 / 0.004328 (0.000160) \| 0.087194 / 0.004250 (0.082943) \| 0.054268 / 0.037052 (0.017216) \| 0.359949 / 0.258489 (0.101460) \| 0.402959 / 0.293841 (0.109118) \| 0.032508 / 0.128546 (-0.096038) \| 0.010224 / 0.075646 (-0.065422) \| 0.387007 / 0.419271 (-0.032264) \| 0.058971 / 0.043533 (0.015439) \| 0.345085 / 0.255139 (0.089946) \| 0.384306 / 0.283200 (0.101107) \| 0.122253 / 0.141683 (-0.019430) \| 1.706353 / 1.452155 (0.254199) \| 1.840780 / 1.492716 (0.348063) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.254374 / 0.018006 (0.236368) \| 0.497387 / 0.000490 (0.496897) \| 0.012294 / 0.000200 (0.012094) \| 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) \| 0.030902 / 0.037411 (-0.006509) \| 0.132098 / 0.014526 (0.117573) \| 0.140311 / 0.176557 (-0.036245) \| 0.205887 / 0.737135 (-0.531249) \| 0.143992 / 0.296338 (-0.152347) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.467367 / 0.215209 (0.252158) \| 4.669936 / 2.077655 (2.592281) \| 2.155358 / 1.504120 (0.651238) \| 1.984132 / 1.541195 (0.442937) \| 2.102352 / 1.468490 (0.633861) \| 0.607014 / 4.584777 (-3.977763) \| 4.396479 / 3.745712 (0.650767) \| 4.666056 / 5.269862 (-0.603806) \| 2.176649 / 4.565676 (-2.389028) \| 0.072657 / 0.424275 (-0.351619) \| 0.012367 / 0.007607 (0.004759) \| 0.569706 / 0.226044 (0.343661) \| 5.749083 / 2.268929 (3.480154) \| 2.640824 / 55.444624 (-52.803801) \| 2.310253 / 6.876477 (-4.566224) \| 2.486748 / 2.142072 (0.344676) \| 0.737891 / 4.805227 (-4.067336) \| 0.163507 / 6.500664 (-6.337157) \| 0.075776 / 0.075469 (0.000307) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.362710 / 1.841788 (-0.479078) \| 17.010705 / 8.074308 (8.936396) \| 15.084231 / 10.191392 (4.892839) \| 0.218274 / 0.680424 (-0.462150) \| 0.019555 / 0.534201 (-0.514646) \| 0.456013 / 0.579283 (-0.123270) \| 0.502772 / 0.434364 (0.068408) \| 0.581480 / 0.540337 (0.041142) \| 0.686952 / 1.386936 (-0.699984) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007976 / 0.011353 (-0.003377) \| 0.005141 / 0.011008 (-0.005868) \| 0.086629 / 0.038508 (0.048121) \| 0.039553 / 0.023109 (0.016444) \| 0.433028 / 0.275898 (0.157130) \| 0.463444 / 0.323480 (0.139964) \| 0.006967 / 0.007986 (-0.001018) \| 0.005814 / 0.004328 (0.001485) \| 0.086266 / 0.004250 (0.082015) \| 0.055384 / 0.037052 (0.018332) \| 0.428733 / 0.258489 (0.170243) \| 0.475670 / 0.293841 (0.181829) \| 0.032872 / 0.128546 (-0.095674) \| 0.010664 / 0.075646 (-0.064983) \| 0.094357 / 0.419271 (-0.324915) \| 0.058386 / 0.043533 (0.014854) \| 0.431114 / 0.255139 (0.175975) \| 0.441728 / 0.283200 (0.158528) \| 0.131942 / 0.141683 (-0.009740) \| 1.782214 / 1.452155 (0.330060) \| 1.843185 / 1.492716 (0.350469) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.247047 / 0.018006 (0.229041) \| 0.488931 / 0.000490 (0.488441) \| 0.002657 / 0.000200 (0.002457) \| 0.000106 / 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) \| 0.033893 / 0.037411 (-0.003518) \| 0.131021 / 0.014526 (0.116495) \| 0.142892 / 0.176557 (-0.033665) \| 0.200955 / 0.737135 (-0.536180) \| 0.151329 / 0.296338 (-0.145010) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.521138 / 0.215209 (0.305929) \| 5.085207 / 2.077655 (3.007552) \| 2.652901 / 1.504120 (1.148781) \| 2.401545 / 1.541195 (0.860350) \| 2.553461 / 1.468490 (1.084971) \| 0.615347 / 4.584777 (-3.969430) \| 4.448038 / 3.745712 (0.702326) \| 2.049997 / 5.269862 (-3.219865) \| 1.190602 / 4.565676 (-3.375075) \| 0.073356 / 0.424275 (-0.350919) \| 0.013685 / 0.007607 (0.006078) \| 0.626705 / 0.226044 (0.400660) \| 6.391941 / 2.268929 (4.123012) \| 3.218864 / 55.444624 (-52.225760) \| 2.858808 / 6.876477 (-4.017669) \| 3.005808 / 2.142072 (0.863736) \| 0.740725 / 4.805227 (-4.064502) \| 0.161904 / 6.500664 (-6.338760) \| 0.073727 / 0.075469 (-0.001742) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.488623 / 1.841788 (-0.353164) \| 17.584367 / 8.074308 (9.510059) \| 16.281818 / 10.191392 (6.090426) \| 0.164482 / 0.680424 (-0.515942) \| 0.020197 / 0.534201 (-0.514003) \| 0.456750 / 0.579283 (-0.122533) \| 0.501156 / 0.434364 (0.066792) \| 0.549779 / 0.540337 (0.009442) \| 0.650156 / 1.386936 (-0.736780) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2b6cc63b868ea4ee60502845ebec68abb943958b \"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) \| 0.008337 / 0.011353 (-0.003016) \| 0.005911 / 0.011008 (-0.005097) \| 0.129037 / 0.038508 (0.090529) \| 0.046071 / 0.023109 (0.022962) \| 0.418657 / 0.275898 (0.142759) \| 0.490340 / 0.323480 (0.166860) \| 0.006387 / 0.007986 (-0.001598) \| 0.004724 / 0.004328 (0.000396) \| 0.097953 / 0.004250 (0.093702) \| 0.069025 / 0.037052 (0.031972) \| 0.431178 / 0.258489 (0.172689) \| 0.458363 / 0.293841 (0.164522) \| 0.049341 / 0.128546 (-0.079205) \| 0.014637 / 0.075646 (-0.061009) \| 0.439800 / 0.419271 (0.020529) \| 0.069905 / 0.043533 (0.026373) \| 0.406775 / 0.255139 (0.151636) \| 0.441989 / 0.283200 (0.158790) \| 0.046009 / 0.141683 (-0.095674) \| 1.847630 / 1.452155 (0.395475) \| 1.904067 / 1.492716 (0.411351) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288305 / 0.018006 (0.270299) \| 0.594547 / 0.000490 (0.594058) \| 0.005600 / 0.000200 (0.005400) \| 0.000106 / 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) \| 0.033847 / 0.037411 (-0.003564) \| 0.125139 / 0.014526 (0.110613) \| 0.147982 / 0.176557 (-0.028574) \| 0.208396 / 0.737135 (-0.528739) \| 0.144005 / 0.296338 (-0.152334) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.669175 / 0.215209 (0.453966) \| 6.605289 / 2.077655 (4.527634) \| 2.720468 / 1.504120 (1.216348) \| 2.341355 / 1.541195 (0.800160) \| 2.402069 / 1.468490 (0.933578) \| 0.939303 / 4.584777 (-3.645474) \| 5.718545 / 3.745712 (1.972833) \| 2.856235 / 5.269862 (-2.413627) \| 1.821555 / 4.565676 (-2.744121) \| 0.105473 / 0.424275 (-0.318802) \| 0.014490 / 0.007607 (0.006883) \| 0.774349 / 0.226044 (0.548305) \| 8.065048 / 2.268929 (5.796120) \| 3.508482 / 55.444624 (-51.936143) \| 2.822881 / 6.876477 (-4.053596) \| 2.962947 / 2.142072 (0.820875) \| 1.138944 / 4.805227 (-3.666284) \| 0.248414 / 6.500664 (-6.252250) \| 0.095665 / 0.075469 (0.020196) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.688231 / 1.841788 (-0.153557) \| 18.673305 / 8.074308 (10.598997) \| 22.768663 / 10.191392 (12.577271) \| 0.211238 / 0.680424 (-0.469186) \| 0.031380 / 0.534201 (-0.502821) \| 0.517175 / 0.579283 (-0.062108) \| 0.626437 / 0.434364 (0.192073) \| 0.624225 / 0.540337 (0.083888) \| 0.743746 / 1.386936 (-0.643191) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008888 / 0.011353 (-0.002464) \| 0.005491 / 0.011008 (-0.005517) \| 0.105013 / 0.038508 (0.066505) \| 0.049456 / 0.023109 (0.026347) \| 0.528989 / 0.275898 (0.253091) \| 0.651871 / 0.323480 (0.328391) \| 0.006683 / 0.007986 (-0.001302) \| 0.004365 / 0.004328 (0.000037) \| 0.098161 / 0.004250 (0.093911) \| 0.075615 / 0.037052 (0.038563) \| 0.543746 / 0.258489 (0.285257) \| 0.650855 / 0.293841 (0.357014) \| 0.050220 / 0.128546 (-0.078327) \| 0.014471 / 0.075646 (-0.061175) \| 0.115903 / 0.419271 (-0.303368) \| 0.065925 / 0.043533 (0.022392) \| 0.527797 / 0.255139 (0.272658) \| 0.543834 / 0.283200 (0.260634) \| 0.043005 / 0.141683 (-0.098678) \| 1.842846 / 1.452155 (0.390691) \| 1.970615 / 1.492716 (0.477899) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287350 / 0.018006 (0.269343) \| 0.591139 / 0.000490 (0.590649) \| 0.006423 / 0.000200 (0.006223) \| 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) \| 0.034594 / 0.037411 (-0.002818) \| 0.137155 / 0.014526 (0.122629) \| 0.154662 / 0.176557 (-0.021894) \| 0.217834 / 0.737135 (-0.519301) \| 0.159642 / 0.296338 (-0.136696) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.664288 / 0.215209 (0.449079) \| 6.926912 / 2.077655 (4.849257) \| 3.028957 / 1.504120 (1.524837) \| 2.625178 / 1.541195 (1.083983) \| 2.725316 / 1.468490 (1.256826) \| 1.015715 / 4.584777 (-3.569062) \| 5.834694 / 3.745712 (2.088982) \| 5.105269 / 5.269862 (-0.164593) \| 2.316194 / 4.565676 (-2.249483) \| 0.113802 / 0.424275 (-0.310473) \| 0.014079 / 0.007607 (0.006472) \| 0.893727 / 0.226044 (0.667683) \| 8.577701 / 2.268929 (6.308772) \| 3.706907 / 55.444624 (-51.737717) \| 3.087530 / 6.876477 (-3.788947) \| 3.295004 / 2.142072 (1.152931) \| 1.204172 / 4.805227 (-3.601055) \| 0.248720 / 6.500664 (-6.251944) \| 0.107208 / 0.075469 (0.031739) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.800058 / 1.841788 (-0.041730) \| 19.253646 / 8.074308 (11.179338) \| 22.590804 / 10.191392 (12.399412) \| 0.270687 / 0.680424 (-0.409737) \| 0.028678 / 0.534201 (-0.505522) \| 0.534670 / 0.579283 (-0.044613) \| 0.642881 / 0.434364 (0.208518) \| 0.615521 / 0.540337 (0.075184) \| 0.723733 / 1.386936 (-0.663203) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2591cd45a002a06bd551343ec785abf16f1433e2 \"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) \| 0.017236 / 0.011353 (0.005883) \| 0.005341 / 0.011008 (-0.005667) \| 0.131471 / 0.038508 (0.092963) \| 0.048868 / 0.023109 (0.025758) \| 0.448942 / 0.275898 (0.173044) \| 0.498721 / 0.323480 (0.175241) \| 0.006825 / 0.007986 (-0.001161) \| 0.004587 / 0.004328 (0.000259) \| 0.104142 / 0.004250 (0.099891) \| 0.075521 / 0.037052 (0.038469) \| 0.439538 / 0.258489 (0.181049) \| 0.498720 / 0.293841 (0.204879) \| 0.051352 / 0.128546 (-0.077194) \| 0.015070 / 0.075646 (-0.060576) \| 0.441752 / 0.419271 (0.022480) \| 0.089166 / 0.043533 (0.045633) \| 0.428909 / 0.255139 (0.173770) \| 0.446648 / 0.283200 (0.163448) \| 0.042371 / 0.141683 (-0.099312) \| 1.993948 / 1.452155 (0.541793) \| 2.065756 / 1.492716 (0.573039) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.257279 / 0.018006 (0.239273) \| 0.575453 / 0.000490 (0.574964) \| 0.004120 / 0.000200 (0.003920) \| 0.000114 / 0.000054 (0.000060) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034012 / 0.037411 (-0.003399) \| 0.141737 / 0.014526 (0.127211) \| 0.145241 / 0.176557 (-0.031316) \| 0.226196 / 0.737135 (-0.510939) \| 0.149526 / 0.296338 (-0.146813) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.665762 / 0.215209 (0.450553) \| 6.683737 / 2.077655 (4.606083) \| 2.869485 / 1.504120 (1.365365) \| 2.462808 / 1.541195 (0.921613) \| 2.526808 / 1.468490 (1.058318) \| 0.957518 / 4.584777 (-3.627259) \| 5.926261 / 3.745712 (2.180548) \| 5.027822 / 5.269862 (-0.242040) \| 2.643185 / 4.565676 (-1.922491) \| 0.117014 / 0.424275 (-0.307261) \| 0.015142 / 0.007607 (0.007535) \| 0.835694 / 0.226044 (0.609650) \| 8.427356 / 2.268929 (6.158427) \| 3.649597 / 55.444624 (-51.795027) \| 2.989607 / 6.876477 (-3.886870) \| 3.043160 / 2.142072 (0.901088) \| 1.158872 / 4.805227 (-3.646355) \| 0.240456 / 6.500664 (-6.260208) \| 0.089196 / 0.075469 (0.013726) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.689361 / 1.841788 (-0.152427) \| 18.842158 / 8.074308 (10.767850) \| 22.604249 / 10.191392 (12.412857) \| 0.248487 / 0.680424 (-0.431936) \| 0.029668 / 0.534201 (-0.504533) \| 0.536283 / 0.579283 (-0.043001) \| 0.663253 / 0.434364 (0.228890) \| 0.622973 / 0.540337 (0.082635) \| 0.735297 / 1.386936 (-0.651639) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009296 / 0.011353 (-0.002057) \| 0.005955 / 0.011008 (-0.005053) \| 0.105723 / 0.038508 (0.067215) \| 0.051184 / 0.023109 (0.028074) \| 0.527095 / 0.275898 (0.251197) \| 0.631697 / 0.323480 (0.308217) \| 0.006577 / 0.007986 (-0.001408) \| 0.004452 / 0.004328 (0.000124) \| 0.105921 / 0.004250 (0.101670) \| 0.071951 / 0.037052 (0.034899) \| 0.572518 / 0.258489 (0.314029) \| 0.623957 / 0.293841 (0.330116) \| 0.050861 / 0.128546 (-0.077686) \| 0.014897 / 0.075646 (-0.060749) \| 0.122013 / 0.419271 (-0.297258) \| 0.067194 / 0.043533 (0.023661) \| 0.530352 / 0.255139 (0.275213) \| 0.563912 / 0.283200 (0.280712) \| 0.034756 / 0.141683 (-0.106927) \| 1.961580 / 1.452155 (0.509425) \| 2.052412 / 1.492716 (0.559696) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.304996 / 0.018006 (0.286990) \| 0.584899 / 0.000490 (0.584409) \| 0.010444 / 0.000200 (0.010244) \| 0.000134 / 0.000054 (0.000080) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032540 / 0.037411 (-0.004871) \| 0.137349 / 0.014526 (0.122823) \| 0.146233 / 0.176557 (-0.030323) \| 0.206978 / 0.737135 (-0.530157) \| 0.154380 / 0.296338 (-0.141959) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.705438 / 0.215209 (0.490229) \| 7.042159 / 2.077655 (4.964504) \| 3.285501 / 1.504120 (1.781381) \| 2.904710 / 1.541195 (1.363515) \| 2.952838 / 1.468490 (1.484348) \| 0.987784 / 4.584777 (-3.596993) \| 5.949550 / 3.745712 (2.203838) \| 2.927148 / 5.269862 (-2.342714) \| 1.870054 / 4.565676 (-2.695622) \| 0.119548 / 0.424275 (-0.304727) \| 0.014565 / 0.007607 (0.006958) \| 0.858311 / 0.226044 (0.632266) \| 8.721679 / 2.268929 (6.452750) \| 4.100825 / 55.444624 (-51.343800) \| 3.358093 / 6.876477 (-3.518383) \| 3.499637 / 2.142072 (1.357564) \| 1.208932 / 4.805227 (-3.596295) \| 0.232961 / 6.500664 (-6.267703) \| 0.089727 / 0.075469 (0.014258) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.780143 / 1.841788 (-0.061645) \| 19.074991 / 8.074308 (11.000683) \| 21.218487 / 10.191392 (11.027095) \| 0.258690 / 0.680424 (-0.421734) \| 0.029514 / 0.534201 (-0.504687) \| 0.541764 / 0.579283 (-0.037519) \| 0.640603 / 0.434364 (0.206239) \| 0.635336 / 0.540337 (0.094999) \| 0.756309 / 1.386936 (-0.630627) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1b525c199e6352aa8aac55f1dcddeb55a80db373 \"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) \| 0.009619 / 0.011353 (-0.001734) \| 0.005683 / 0.011008 (-0.005325) \| 0.136971 / 0.038508 (0.098463) \| 0.051607 / 0.023109 (0.028497) \| 0.439716 / 0.275898 (0.163818) \| 0.486193 / 0.323480 (0.162713) \| 0.006304 / 0.007986 (-0.001681) \| 0.004489 / 0.004328 (0.000160) \| 0.103837 / 0.004250 (0.099587) \| 0.082954 / 0.037052 (0.045901) \| 0.447286 / 0.258489 (0.188797) \| 0.495434 / 0.293841 (0.201593) \| 0.049244 / 0.128546 (-0.079302) \| 0.015176 / 0.075646 (-0.060470) \| 0.444406 / 0.419271 (0.025134) \| 0.074766 / 0.043533 (0.031233) \| 0.438585 / 0.255139 (0.183446) \| 0.438232 / 0.283200 (0.155032) \| 0.043372 / 0.141683 (-0.098311) \| 2.057286 / 1.452155 (0.605131) \| 2.049540 / 1.492716 (0.556824) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298038 / 0.018006 (0.280031) \| 0.630771 / 0.000490 (0.630281) \| 0.008287 / 0.000200 (0.008087) \| 0.000123 / 0.000054 (0.000068) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033637 / 0.037411 (-0.003775) \| 0.128327 / 0.014526 (0.113801) \| 0.150672 / 0.176557 (-0.025885) \| 0.228521 / 0.737135 (-0.508614) \| 0.142733 / 0.296338 (-0.153606) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.629072 / 0.215209 (0.413863) \| 6.612047 / 2.077655 (4.534392) \| 2.715594 / 1.504120 (1.211474) \| 2.327823 / 1.541195 (0.786628) \| 2.417508 / 1.468490 (0.949018) \| 0.959134 / 4.584777 (-3.625643) \| 5.669921 / 3.745712 (1.924209) \| 2.977920 / 5.269862 (-2.291941) \| 1.814564 / 4.565676 (-2.751112) \| 0.120233 / 0.424275 (-0.304042) \| 0.015859 / 0.007607 (0.008252) \| 0.822618 / 0.226044 (0.596574) \| 8.440306 / 2.268929 (6.171377) \| 3.721611 / 55.444624 (-51.723013) \| 2.954867 / 6.876477 (-3.921610) \| 3.135364 / 2.142072 (0.993292) \| 1.226475 / 4.805227 (-3.578752) \| 0.246658 / 6.500664 (-6.254006) \| 0.093920 / 0.075469 (0.018451) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.665631 / 1.841788 (-0.176157) \| 19.136369 / 8.074308 (11.062061) \| 23.659564 / 10.191392 (13.468172) \| 0.273430 / 0.680424 (-0.406994) \| 0.028180 / 0.534201 (-0.506021) \| 0.559588 / 0.579283 (-0.019695) \| 0.649203 / 0.434364 (0.214840) \| 0.647113 / 0.540337 (0.106776) \| 0.737978 / 1.386936 (-0.648958) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009104 / 0.011353 (-0.002249) \| 0.006838 / 0.011008 (-0.004171) \| 0.104516 / 0.038508 (0.066008) \| 0.047986 / 0.023109 (0.024877) \| 0.521849 / 0.275898 (0.245951) \| 0.586281 / 0.323480 (0.262801) \| 0.006225 / 0.007986 (-0.001760) \| 0.005713 / 0.004328 (0.001384) \| 0.111507 / 0.004250 (0.107257) \| 0.072320 / 0.037052 (0.035267) \| 0.551061 / 0.258489 (0.292572) \| 0.628034 / 0.293841 (0.334193) \| 0.055417 / 0.128546 (-0.073129) \| 0.019613 / 0.075646 (-0.056034) \| 0.123958 / 0.419271 (-0.295314) \| 0.066132 / 0.043533 (0.022600) \| 0.504461 / 0.255139 (0.249322) \| 0.560428 / 0.283200 (0.277229) \| 0.036098 / 0.141683 (-0.105585) \| 1.927398 / 1.452155 (0.475243) \| 2.015952 / 1.492716 (0.523235) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.313065 / 0.018006 (0.295059) \| 0.609174 / 0.000490 (0.608684) \| 0.008755 / 0.000200 (0.008555) \| 0.000120 / 0.000054 (0.000066) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.040042 / 0.037411 (0.002630) \| 0.136053 / 0.014526 (0.121527) \| 0.143406 / 0.176557 (-0.033150) \| 0.213080 / 0.737135 (-0.524055) \| 0.154730 / 0.296338 (-0.141609) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.692706 / 0.215209 (0.477497) \| 6.952968 / 2.077655 (4.875314) \| 3.232023 / 1.504120 (1.727903) \| 2.835450 / 1.541195 (1.294256) \| 2.933821 / 1.468490 (1.465331) \| 0.984712 / 4.584777 (-3.600065) \| 6.127651 / 3.745712 (2.381939) \| 2.956781 / 5.269862 (-2.313081) \| 1.879928 / 4.565676 (-2.685748) \| 0.111069 / 0.424275 (-0.313206) \| 0.014598 / 0.007607 (0.006991) \| 0.871486 / 0.226044 (0.645442) \| 8.588500 / 2.268929 (6.319572) \| 3.910740 / 55.444624 (-51.533885) \| 3.115781 / 6.876477 (-3.760695) \| 3.222367 / 2.142072 (1.080294) \| 1.229680 / 4.805227 (-3.575547) \| 0.232092 / 6.500664 (-6.268572) \| 0.097717 / 0.075469 (0.022248) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.774193 / 1.841788 (-0.067595) \| 19.863087 / 8.074308 (11.788779) \| 24.058856 / 10.191392 (13.867464) \| 0.214917 / 0.680424 (-0.465507) \| 0.028771 / 0.534201 (-0.505430) \| 0.544548 / 0.579283 (-0.034735) \| 0.655882 / 0.434364 (0.221518) \| 0.629110 / 0.540337 (0.088773) \| 0.749246 / 1.386936 (-0.637690) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f4a5ea6a42dcfef1577288b51beeccc0eb124cee \"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) \| 0.007075 / 0.011353 (-0.004278) \| 0.005195 / 0.011008 (-0.005813) \| 0.113043 / 0.038508 (0.074535) \| 0.038442 / 0.023109 (0.015333) \| 0.336310 / 0.275898 (0.060412) \| 0.381888 / 0.323480 (0.058409) \| 0.005990 / 0.007986 (-0.001996) \| 0.003893 / 0.004328 (-0.000435) \| 0.093123 / 0.004250 (0.088872) \| 0.058449 / 0.037052 (0.021397) \| 0.359463 / 0.258489 (0.100974) \| 0.427485 / 0.293841 (0.133644) \| 0.041454 / 0.128546 (-0.087092) \| 0.013016 / 0.075646 (-0.062630) \| 0.372849 / 0.419271 (-0.046422) \| 0.059386 / 0.043533 (0.015853) \| 0.381398 / 0.255139 (0.126259) \| 0.367603 / 0.283200 (0.084403) \| 0.033907 / 0.141683 (-0.107775) \| 1.628903 / 1.452155 (0.176749) \| 1.764131 / 1.492716 (0.271415) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298329 / 0.018006 (0.280322) \| 0.593030 / 0.000490 (0.592540) \| 0.007653 / 0.000200 (0.007453) \| 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) \| 0.025445 / 0.037411 (-0.011966) \| 0.112062 / 0.014526 (0.097536) \| 0.119863 / 0.176557 (-0.056693) \| 0.178389 / 0.737135 (-0.558746) \| 0.129934 / 0.296338 (-0.166404) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.532834 / 0.215209 (0.317625) \| 5.250908 / 2.077655 (3.173253) \| 2.086920 / 1.504120 (0.582800) \| 1.799745 / 1.541195 (0.258550) \| 1.909648 / 1.468490 (0.441158) \| 0.825382 / 4.584777 (-3.759395) \| 5.268304 / 3.745712 (1.522592) \| 2.533347 / 5.269862 (-2.736515) \| 1.730187 / 4.565676 (-2.835490) \| 0.099824 / 0.424275 (-0.324451) \| 0.012969 / 0.007607 (0.005362) \| 0.732234 / 0.226044 (0.506189) \| 6.989066 / 2.268929 (4.720138) \| 2.873486 / 55.444624 (-52.571138) \| 2.274351 / 6.876477 (-4.602125) \| 2.311060 / 2.142072 (0.168987) \| 1.125366 / 4.805227 (-3.679861) \| 0.214522 / 6.500664 (-6.286142) \| 0.077579 / 0.075469 (0.002110) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.670950 / 1.841788 (-0.170838) \| 18.131528 / 8.074308 (10.057220) \| 21.277823 / 10.191392 (11.086431) \| 0.238807 / 0.680424 (-0.441617) \| 0.032251 / 0.534201 (-0.501950) \| 0.503859 / 0.579283 (-0.075424) \| 0.604825 / 0.434364 (0.170461) \| 0.555623 / 0.540337 (0.015286) \| 0.647301 / 1.386936 (-0.739635) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010857 / 0.011353 (-0.000496) \| 0.005581 / 0.011008 (-0.005427) \| 0.094346 / 0.038508 (0.055838) \| 0.053084 / 0.023109 (0.029975) \| 0.457586 / 0.275898 (0.181688) \| 0.545475 / 0.323480 (0.221995) \| 0.006761 / 0.007986 (-0.001225) \| 0.005094 / 0.004328 (0.000765) \| 0.095509 / 0.004250 (0.091258) \| 0.077182 / 0.037052 (0.040130) \| 0.498717 / 0.258489 (0.240228) \| 0.542433 / 0.293841 (0.248592) \| 0.051547 / 0.128546 (-0.076999) \| 0.014633 / 0.075646 (-0.061014) \| 0.106843 / 0.419271 (-0.312428) \| 0.068459 / 0.043533 (0.024926) \| 0.435793 / 0.255139 (0.180654) \| 0.475484 / 0.283200 (0.192285) \| 0.039495 / 0.141683 (-0.102188) \| 1.684906 / 1.452155 (0.232751) \| 1.798693 / 1.492716 (0.305976) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279853 / 0.018006 (0.261847) \| 0.601016 / 0.000490 (0.600526) \| 0.002055 / 0.000200 (0.001855) \| 0.000219 / 0.000054 (0.000165) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030935 / 0.037411 (-0.006477) \| 0.121197 / 0.014526 (0.106671) \| 0.143360 / 0.176557 (-0.033197) \| 0.200862 / 0.737135 (-0.536274) \| 0.138656 / 0.296338 (-0.157683) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.613904 / 0.215209 (0.398695) \| 6.155422 / 2.077655 (4.077767) \| 2.777238 / 1.504120 (1.273118) \| 2.473045 / 1.541195 (0.931851) \| 2.604470 / 1.468490 (1.135980) \| 0.898871 / 4.584777 (-3.685906) \| 5.739666 / 3.745712 (1.993954) \| 4.719822 / 5.269862 (-0.550040) \| 2.727354 / 4.565676 (-1.838322) \| 0.108232 / 0.424275 (-0.316043) \| 0.013632 / 0.007607 (0.006025) \| 0.771802 / 0.226044 (0.545757) \| 7.987466 / 2.268929 (5.718537) \| 3.609856 / 55.444624 (-51.834768) \| 2.974421 / 6.876477 (-3.902056) \| 2.956567 / 2.142072 (0.814495) \| 1.093792 / 4.805227 (-3.711435) \| 0.213369 / 6.500664 (-6.287295) \| 0.084486 / 0.075469 (0.009017) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.693855 / 1.841788 (-0.147933) \| 18.055027 / 8.074308 (9.980719) \| 21.397964 / 10.191392 (11.206571) \| 0.240549 / 0.680424 (-0.439875) \| 0.031212 / 0.534201 (-0.502989) \| 0.513657 / 0.579283 (-0.065626) \| 0.651348 / 0.434364 (0.216985) \| 0.603740 / 0.540337 (0.063402) \| 0.752287 / 1.386936 (-0.634649) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6f3f38d00dd40a444ae54c18caa28304ae36b9c3 \"CML watermark\")\n" ]
1,754,794,611	5,948	Fix sequence of array support for most dtype	closed	2023-06-13T12:38:59	2023-06-14T15:11:55	2023-06-14T15:03:33	https://github.com/huggingface/datasets/pull/5948	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5948", "html_url": "https://github.com/huggingface/datasets/pull/5948", "diff_url": "https://github.com/huggingface/datasets/pull/5948.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5948.patch", "merged_at": "2023-06-14T15:03:33" }	qgallouedec	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007220 / 0.011353 (-0.004133) \| 0.004558 / 0.011008 (-0.006451) \| 0.116647 / 0.038508 (0.078139) \| 0.046845 / 0.023109 (0.023736) \| 0.352429 / 0.275898 (0.076531) \| 0.429739 / 0.323480 (0.106259) \| 0.006620 / 0.007986 (-0.001366) \| 0.003731 / 0.004328 (-0.000597) \| 0.088683 / 0.004250 (0.084433) \| 0.070583 / 0.037052 (0.033530) \| 0.366699 / 0.258489 (0.108210) \| 0.420730 / 0.293841 (0.126889) \| 0.037342 / 0.128546 (-0.091204) \| 0.010041 / 0.075646 (-0.065605) \| 0.383477 / 0.419271 (-0.035795) \| 0.060279 / 0.043533 (0.016746) \| 0.349988 / 0.255139 (0.094849) \| 0.371423 / 0.283200 (0.088224) \| 0.026725 / 0.141683 (-0.114958) \| 1.736886 / 1.452155 (0.284731) \| 1.812874 / 1.492716 (0.320157) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.253256 / 0.018006 (0.235250) \| 0.563470 / 0.000490 (0.562980) \| 0.010475 / 0.000200 (0.010275) \| 0.000164 / 0.000054 (0.000110) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030518 / 0.037411 (-0.006893) \| 0.133324 / 0.014526 (0.118798) \| 0.137095 / 0.176557 (-0.039461) \| 0.202227 / 0.737135 (-0.534909) \| 0.144195 / 0.296338 (-0.152143) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.480870 / 0.215209 (0.265661) \| 4.822713 / 2.077655 (2.745058) \| 2.124183 / 1.504120 (0.620064) \| 1.910733 / 1.541195 (0.369538) \| 1.970266 / 1.468490 (0.501776) \| 0.624695 / 4.584777 (-3.960082) \| 4.459659 / 3.745712 (0.713947) \| 2.210123 / 5.269862 (-3.059739) \| 1.300520 / 4.565676 (-3.265157) \| 0.077096 / 0.424275 (-0.347180) \| 0.013333 / 0.007607 (0.005726) \| 0.596841 / 0.226044 (0.370797) \| 5.917397 / 2.268929 (3.648469) \| 2.699397 / 55.444624 (-52.745228) \| 2.274833 / 6.876477 (-4.601644) \| 2.525376 / 2.142072 (0.383304) \| 0.755718 / 4.805227 (-4.049510) \| 0.163587 / 6.500664 (-6.337077) \| 0.072817 / 0.075469 (-0.002653) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.524306 / 1.841788 (-0.317481) \| 18.843312 / 8.074308 (10.769004) \| 15.694644 / 10.191392 (5.503252) \| 0.177400 / 0.680424 (-0.503024) \| 0.020104 / 0.534201 (-0.514097) \| 0.466421 / 0.579283 (-0.112862) \| 0.537274 / 0.434364 (0.102910) \| 0.576920 / 0.540337 (0.036583) \| 0.718889 / 1.386936 (-0.668047) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007671 / 0.011353 (-0.003682) \| 0.004850 / 0.011008 (-0.006158) \| 0.090085 / 0.038508 (0.051576) \| 0.052023 / 0.023109 (0.028914) \| 0.508575 / 0.275898 (0.232677) \| 0.590024 / 0.323480 (0.266544) \| 0.004564 / 0.007986 (-0.003422) \| 0.005345 / 0.004328 (0.001017) \| 0.087904 / 0.004250 (0.083653) \| 0.064446 / 0.037052 (0.027394) \| 0.525625 / 0.258489 (0.267136) \| 0.584307 / 0.293841 (0.290466) \| 0.037221 / 0.128546 (-0.091325) \| 0.010588 / 0.075646 (-0.065059) \| 0.098612 / 0.419271 (-0.320659) \| 0.059597 / 0.043533 (0.016064) \| 0.488064 / 0.255139 (0.232925) \| 0.522330 / 0.283200 (0.239131) \| 0.030004 / 0.141683 (-0.111679) \| 1.732512 / 1.452155 (0.280357) \| 1.809027 / 1.492716 (0.316310) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.218741 / 0.018006 (0.200735) \| 0.494946 / 0.000490 (0.494456) \| 0.004580 / 0.000200 (0.004380) \| 0.000104 / 0.000054 (0.000049) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034916 / 0.037411 (-0.002495) \| 0.133695 / 0.014526 (0.119169) \| 0.147964 / 0.176557 (-0.028592) \| 0.213210 / 0.737135 (-0.523926) \| 0.148850 / 0.296338 (-0.147488) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.508855 / 0.215209 (0.293646) \| 5.065088 / 2.077655 (2.987433) \| 2.473110 / 1.504120 (0.968990) \| 2.259765 / 1.541195 (0.718570) \| 2.359189 / 1.468490 (0.890699) \| 0.639082 / 4.584777 (-3.945695) \| 4.768195 / 3.745712 (1.022482) \| 2.253803 / 5.269862 (-3.016059) \| 1.442996 / 4.565676 (-3.122680) \| 0.078761 / 0.424275 (-0.345514) \| 0.013936 / 0.007607 (0.006329) \| 0.625977 / 0.226044 (0.399933) \| 6.260817 / 2.268929 (3.991888) \| 3.149640 / 55.444624 (-52.294985) \| 2.753555 / 6.876477 (-4.122921) \| 2.831872 / 2.142072 (0.689799) \| 0.781294 / 4.805227 (-4.023933) \| 0.169109 / 6.500664 (-6.331555) \| 0.075810 / 0.075469 (0.000341) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.533282 / 1.841788 (-0.308506) \| 19.460579 / 8.074308 (11.386271) \| 17.250424 / 10.191392 (7.059032) \| 0.193485 / 0.680424 (-0.486939) \| 0.020650 / 0.534201 (-0.513551) \| 0.472110 / 0.579283 (-0.107173) \| 0.532276 / 0.434364 (0.097912) \| 0.613152 / 0.540337 (0.072814) \| 0.684684 / 1.386936 (-0.702252) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#650a86ee122209d4a8c8e8068c01ebfd3ba553f5 \"CML watermark\")\n" ]
1,754,359,316	5,947	Return the audio filename when decoding fails due to corrupt files	open	2023-06-13T08:44:09	2023-06-14T12:45:01	null	https://github.com/huggingface/datasets/issues/5947	null	wetdog	false	[ "Hi ! The audio data don't always exist as files on disk - the blobs are often stored in the Arrow files. For now I'd suggest disabling decoding with `.cast_column(\"audio\", Audio(decode=False))` and apply your own decoding that handles corrupted files (maybe to filter them out ?)\r\n\r\ncc @sanchit-gandhi since it's related to our discussion about allowing users to make decoding return `None` and show a warning when there are corrupted files", "Thanks @lhoestq, I wasn't aware of the decode flag. It makes more sense as you say to show a warning when there are corrupted files together with some metadata of the file that allows to filter them from the dataset.\r\n\r\nMy workaround was to catch the LibsndfileError and generate a dummy audio with an unsual sample rate to filter it later. However returning `None` seems better. \r\n\r\n`try:\r\n array, sampling_rate = sf.read(file)\r\nexcept sf.LibsndfileError:\r\n print(\"bad file\")\r\n array = np.array([0.0])\r\n sampling_rate = 99.000` \r\n\r\n" ]
1,754,234,469	5,946	IndexError Not Solving -> IndexError: Invalid key: ?? is out of bounds for size 0 or ??	open	2023-06-13T07:34:15	2023-07-14T12:04:48	null	https://github.com/huggingface/datasets/issues/5946	null	syngokhan	false	[ "https://colab.research.google.com/#scrollTo=AQ_HCYruWIHU&fileId=https%3A//huggingface.co/dfurman/falcon-40b-chat-oasst1/blob/main/finetune_falcon40b_oasst1_with_bnb_peft.ipynb\r\n\r\nI ran the same administration exactly the same but got the same error", "Looks related to https://discuss.huggingface.co/t/indexerror-invalid-key-16-is-out-of-bounds-for-size-0/14298/4?u=lhoestq", "> Looks related to https://discuss.huggingface.co/t/indexerror-invalid-key-16-is-out-of-bounds-for-size-0/14298/4?u=lhoestq\n\nThe problem has not been solved, I have tried this before, but the problem is the same", "> \r\n\r\n@syngokhan did u solve it? \r\nI am desperate ", "data = data[\"train\"].shuffle().map(generate_and_tokenize_prompt, batched = False) # change this line to -\r\n\r\ndata[\"train\"] = data[\"train\"].shuffle().map(generate_and_tokenize_prompt, batched = False)\r\nAfter doing this change you code should run fine.", "> > \r\n> \r\n> @syngokhan did u solve it? I am desperate\r\n\r\nrefer to my earlier comment. you will find the solution." ]
1,754,084,577	5,945	Failing to upload dataset to the hub	closed	2023-06-13T05:46:46	2023-07-24T11:56:40	2023-07-24T11:56:40	https://github.com/huggingface/datasets/issues/5945	null	Ar770	false	[ "Hi ! Feel free to re-run your code later, it will resume automatically where you left", "Tried many times in the last 2 weeks, problem remains.", "Alternatively you can save your dataset in parquet files locally and upload them to the hub manually\r\n\r\n```python\r\nfrom tqdm import tqdm\r\nnum_shards = 60\r\nfor index in tqdm(range(num_shards)):\r\n ds.shard(num_shards=num_shards, index=index, contiguous=True).to_parquet(f\"{index:05d}.parquet\")\r\n````" ]
1,752,882,200	5,944	Arrow dataset builder to be able to load and stream Arrow datasets	closed	2023-06-12T14:21:49	2023-06-13T17:36:02	2023-06-13T17:29:01	https://github.com/huggingface/datasets/pull/5944	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5944", "html_url": "https://github.com/huggingface/datasets/pull/5944", "diff_url": "https://github.com/huggingface/datasets/pull/5944.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5944.patch", "merged_at": "2023-06-13T17:29:01" }	mariusz-jachimowicz-83	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq tips applied. Thanks for a review. :smile: It's a lot of fun to improve this project. ", "Let's add some documentation in a subsequent PR :)\r\n\r\nIn particular @mariosasko and I think it's important to note to users that local arrow data are copied to cache according to the way load_dataset works, but if they want they can use Dataset.from_file instead", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006384 / 0.011353 (-0.004969) \| 0.003788 / 0.011008 (-0.007220) \| 0.098524 / 0.038508 (0.060016) \| 0.031786 / 0.023109 (0.008677) \| 0.307799 / 0.275898 (0.031901) \| 0.337329 / 0.323480 (0.013849) \| 0.003650 / 0.007986 (-0.004336) \| 0.003731 / 0.004328 (-0.000598) \| 0.076816 / 0.004250 (0.072566) \| 0.041888 / 0.037052 (0.004835) \| 0.310702 / 0.258489 (0.052213) \| 0.343846 / 0.293841 (0.050005) \| 0.027841 / 0.128546 (-0.100705) \| 0.008312 / 0.075646 (-0.067334) \| 0.320230 / 0.419271 (-0.099042) \| 0.047378 / 0.043533 (0.003845) \| 0.308683 / 0.255139 (0.053544) \| 0.335129 / 0.283200 (0.051930) \| 0.096294 / 0.141683 (-0.045389) \| 1.485521 / 1.452155 (0.033366) \| 1.559868 / 1.492716 (0.067152) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.197376 / 0.018006 (0.179370) \| 0.430461 / 0.000490 (0.429972) \| 0.004152 / 0.000200 (0.003953) \| 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) \| 0.023660 / 0.037411 (-0.013751) \| 0.103128 / 0.014526 (0.088602) \| 0.107549 / 0.176557 (-0.069008) \| 0.175934 / 0.737135 (-0.561201) \| 0.112210 / 0.296338 (-0.184129) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.415804 / 0.215209 (0.200595) \| 4.216333 / 2.077655 (2.138679) \| 1.910354 / 1.504120 (0.406234) \| 1.712689 / 1.541195 (0.171494) \| 1.754705 / 1.468490 (0.286215) \| 0.554647 / 4.584777 (-4.030130) \| 3.393592 / 3.745712 (-0.352120) \| 1.737504 / 5.269862 (-3.532358) \| 1.021213 / 4.565676 (-3.544464) \| 0.066908 / 0.424275 (-0.357367) \| 0.011446 / 0.007607 (0.003839) \| 0.524630 / 0.226044 (0.298585) \| 5.243005 / 2.268929 (2.974077) \| 2.349685 / 55.444624 (-53.094939) \| 2.027457 / 6.876477 (-4.849020) \| 2.131053 / 2.142072 (-0.011020) \| 0.669070 / 4.805227 (-4.136157) \| 0.136317 / 6.500664 (-6.364347) \| 0.065924 / 0.075469 (-0.009545) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.254102 / 1.841788 (-0.587686) \| 13.790492 / 8.074308 (5.716184) \| 14.197772 / 10.191392 (4.006380) \| 0.143989 / 0.680424 (-0.536434) \| 0.016577 / 0.534201 (-0.517624) \| 0.375437 / 0.579283 (-0.203846) \| 0.398995 / 0.434364 (-0.035369) \| 0.445287 / 0.540337 (-0.095050) \| 0.538632 / 1.386936 (-0.848304) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006251 / 0.011353 (-0.005101) \| 0.004019 / 0.011008 (-0.006989) \| 0.077985 / 0.038508 (0.039477) \| 0.028705 / 0.023109 (0.005596) \| 0.417360 / 0.275898 (0.141462) \| 0.463964 / 0.323480 (0.140484) \| 0.003489 / 0.007986 (-0.004497) \| 0.003032 / 0.004328 (-0.001296) \| 0.077953 / 0.004250 (0.073702) \| 0.040104 / 0.037052 (0.003051) \| 0.405242 / 0.258489 (0.146753) \| 0.475029 / 0.293841 (0.181188) \| 0.028113 / 0.128546 (-0.100433) \| 0.008610 / 0.075646 (-0.067036) \| 0.084847 / 0.419271 (-0.334424) \| 0.048227 / 0.043533 (0.004694) \| 0.417235 / 0.255139 (0.162096) \| 0.450470 / 0.283200 (0.167270) \| 0.096978 / 0.141683 (-0.044705) \| 1.514688 / 1.452155 (0.062533) \| 1.560205 / 1.492716 (0.067488) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.235125 / 0.018006 (0.217119) \| 0.409904 / 0.000490 (0.409414) \| 0.002474 / 0.000200 (0.002275) \| 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) \| 0.025152 / 0.037411 (-0.012259) \| 0.103517 / 0.014526 (0.088991) \| 0.110154 / 0.176557 (-0.066402) \| 0.161431 / 0.737135 (-0.575704) \| 0.114891 / 0.296338 (-0.181448) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.456077 / 0.215209 (0.240868) \| 4.541171 / 2.077655 (2.463517) \| 2.297912 / 1.504120 (0.793792) \| 2.079337 / 1.541195 (0.538143) \| 2.121291 / 1.468490 (0.652801) \| 0.560172 / 4.584777 (-4.024605) \| 3.421122 / 3.745712 (-0.324590) \| 1.764675 / 5.269862 (-3.505186) \| 1.043482 / 4.565676 (-3.522195) \| 0.067652 / 0.424275 (-0.356623) \| 0.011181 / 0.007607 (0.003574) \| 0.557232 / 0.226044 (0.331188) \| 5.607851 / 2.268929 (3.338922) \| 2.783715 / 55.444624 (-52.660909) \| 2.380943 / 6.876477 (-4.495534) \| 2.378316 / 2.142072 (0.236244) \| 0.674356 / 4.805227 (-4.130871) \| 0.135912 / 6.500664 (-6.364752) \| 0.067009 / 0.075469 (-0.008460) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.309002 / 1.841788 (-0.532786) \| 14.464073 / 8.074308 (6.389765) \| 14.418727 / 10.191392 (4.227335) \| 0.148486 / 0.680424 (-0.531938) \| 0.016650 / 0.534201 (-0.517551) \| 0.368786 / 0.579283 (-0.210497) \| 0.395026 / 0.434364 (-0.039338) \| 0.433565 / 0.540337 (-0.106772) \| 0.526603 / 1.386936 (-0.860333) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#443fc92700b4f9e12421e8082e205535314a67d5 \"CML watermark\")\n" ]
1,752,021,681	5,942	Pass datasets-cli additional args as kwargs to DatasetBuilder in `run_beam.py`	open	2023-06-12T06:50:50	2023-06-30T09:15:00	null	https://github.com/huggingface/datasets/pull/5942	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5942", "html_url": "https://github.com/huggingface/datasets/pull/5942", "diff_url": "https://github.com/huggingface/datasets/pull/5942.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5942.patch", "merged_at": null }	graelo	true	[]
1,751,838,897	5,941	Load Data Sets Too Slow In Train Seq2seq Model	closed	2023-06-12T03:58:43	2023-08-15T02:52:22	2023-08-15T02:52:22	https://github.com/huggingface/datasets/issues/5941	null	xyx361100238	false	[ "Hi ! you can speed it up using multiprocessing by passing `num_proc=` to `load_dataset()`", "already did，but not useful for step Generating train split，it works in step \"Resolving data files\" & \"Downloading data files\" ", "@mariosasko some advice ， thanks！", "I met the same problem, terrible experience", "@mariosasko ", "We need more info about the issue to provide help. \r\n\r\nCan you interrupt the process (with `num_proc=None`) after the `load_dataset` call when the slowdown occurs? So we can know what part of the code is causing it.\r\n\r\nThe `audiofolder` \\ `imagefolder` with metadata is not performant for large datasets. Luckily, we can make them much faster if drop the nested metadata files feature (not that useful). I plan to work on this soon.\r\n\r\nIn the meantime, it's better to use `Dataset.from_generator` (requires replacing the `load_dataset` calls in the transformers script with `Dataset.from_generator`) or write a dataset loading script for large datasets.", "Can you interrupt the process (with num_proc=None) after the load_dataset call when the slowdown occurs? So we can know what part of the code is causing it.\r\n（I'll try this operation）\r\nThe audiofolder \\ imagefolder with metadata is not performant for large datasets. Luckily, we can make them much faster if drop the nested metadata files feature (not that useful). I plan to work on this soon.\r\n(My data is indeed a bit large, exceeding 10000 hours of audio data. Looking forward to your improvement work very much)\r\n\r\nIn the meantime, it's better to use Dataset.from_generator (requires replacing the load_dataset calls in the transformers script with Dataset.from_generator) or write a dataset loading script for large datasets.\r\n（I want to use Dataset.from_generator instead of load_dataset ，where can i found sample code to load audio&label dataset， I was to do asr task）", "Can you interrupt the process (with num_proc=None) after the load_dataset call when the slowdown occurs? So we can know what part of the code is causing it.\r\n================================================================================\r\nHere is the log：\r\n[load_dataset.log](https://github.com/huggingface/datasets/files/12169362/load_dataset.log)\r\n（The larger my training data, the slower it loads）\r\n![image](https://github.com/huggingface/datasets/assets/19569322/381b73e4-0a54-4240-b95e-cb8164584047)\r\n\r\n", "In the meantime, it's better to use Dataset.from_generator (requires replacing the load_dataset calls in the transformers script with Dataset.from_generator) or write a dataset loading script for large datasets.\r\n================================================================================\r\nI tried ‘Dataset. from_generator’ implements data loading, but the testing results show no improvement", "I have already solved this problem, referring to #5990 : read audio frist, then use data_generator to change format ." ]
1,774,389,854	5,990	Pushing a large dataset on the hub consistently hangs	open	2023-06-10T14:46:47	2025-02-15T09:29:10	null	https://github.com/huggingface/datasets/issues/5990	null	AntreasAntoniou	false	[ "Hi @AntreasAntoniou , sorry to know you are facing this issue. To help debugging it, could you tell me:\r\n- What is the total dataset size?\r\n- Is it always failing on the same shard or is the hanging problem happening randomly?\r\n- Were you able to save the dataset as parquet locally? This would help us determine if the problem comes from the upload or the file generation.\r\n\r\nI'm cc-ing @lhoestq who might have some insights from a `datasets` perspective.", "One trick that can also help is to check the traceback when you kill your python process: it will show where in the code it was hanging", "Right. So I did the trick @lhoestq suggested. Here is where things seem to hang\r\n\r\n```\r\nError while uploading 'data/train-00120-of-00195-466c2dbab2eb9989.parquet' to the Hub. \r\nPushing split train to the Hub. \r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:03<00:00, 1.15s/ba]\r\nUpload 1 LFS files: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:52<00:00, 52.12s/it]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:03<00:00, 1.08s/ba]\r\nUpload 1 LFS files: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:45<00:00, 45.54s/it]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:03<00:00, 1.08s/ba]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:03<00:00, 1.03s/ba^Upload 1 LFS files: 0%\| \| 0/1 [\r\n21:27:35<?, ?it/s] \r\nPushing dataset shards to the dataset hub: 63%\|█████████████████████████████████████████████████████████████▎ \| 122/195 [23:37:11<14:07:59, 696.98s/it]\r\n^CError in sys.excepthook: \r\nTraceback (most recent call last): \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1699, in print \r\n extend(render(renderable, render_options)) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1335, in render \r\n yield from self.render(render_output, _options) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1331, in render \r\n for render_output in iter_render: \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/constrain.py\", line 29, in __rich_console__ \r\n yield from console.render(self.renderable, child_options) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1331, in render \r\n for render_output in iter_render: \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/panel.py\", line 220, in __rich_console__ \r\n lines = console.render_lines(renderable, child_options, style=style) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1371, in render_lines \r\n lines = list( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/segment.py\", line 292, in split_and_crop_lines \r\n for segment in segments: \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1331, in render \r\n for render_output in iter_render: \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/padding.py\", line 97, in __rich_console__ \r\n lines = console.render_lines( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1371, in render_lines \r\n lines = list( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/segment.py\", line 292, in split_and_crop_lines \r\n for segment in segments: \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1335, in render \r\n yield from self.render(render_output, _options) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1331, in render \r\n for render_output in iter_render: \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/syntax.py\", line 611, in __rich_console__ \r\n segments = Segments(self._get_syntax(console, options)) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/segment.py\", line 668, in __init__ \r\n self.segments = list(segments) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/syntax.py\", line 674, in _get_syntax \r\n lines: Union[List[Text], Lines] = text.split(\"\\n\", allow_blank=ends_on_nl) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/text.py\", line 1042, in split \r\n lines = Lines( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/containers.py\", line 70, in __init__ \r\n self._lines: List[\"Text\"] = list(lines) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/text.py\", line 1043, in <genexpr> \r\n line for line in self.divide(flatten_spans()) if line.plain != separator \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/text.py\", line 385, in plain \r\n if len(self._text) != 1: \r\nKeyboardInterrupt \r\n \r\nOriginal exception was: \r\nTraceback (most recent call last): \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/tqdm/contrib/concurrent.py\", line 51, in _executor_map \r\n return list(tqdm_class(ex.map(fn, iterables, chunksize=chunksize), kwargs)) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/tqdm/std.py\", line 1178, in __iter__ \r\n for obj in iterable: \r\n File \"/opt/conda/envs/main/lib/python3.10/concurrent/futures/_base.py\", line 621, in result_iterator \r\n yield _result_or_cancel(fs.pop()) \r\n File \"/opt/conda/envs/main/lib/python3.10/concurrent/futures/_base.py\", line 319, in _result_or_cancel \r\n return fut.result(timeout) \r\n File \"/opt/conda/envs/main/lib/python3.10/concurrent/futures/_base.py\", line 453, in result \r\n self._condition.wait(timeout) \r\n File \"/opt/conda/envs/main/lib/python3.10/threading.py\", line 320, in wait \r\n waiter.acquire() \r\nKeyboardInterrupt \r\n \r\nDuring handling of the above exception, another exception occurred: \r\n \r\nTraceback (most recent call last): \r\n File \"/TALI/tali/scripts/validate_dataset.py\", line 127, in <module> \r\n train_dataset.push_to_hub(repo_id=\"Antreas/TALI-base\", max_shard_size=\"5GB\") \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/datasets/dataset_dict.py\", line 1583, in push_to_hub \r\n repo_id, split, uploaded_size, dataset_nbytes, _, _ = self[split]._push_parquet_shards_to_hub( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 5275, in _push_parquet_shards_to_hub \r\n _retry( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/datasets/utils/file_utils.py\", line 282, in _retry \r\n return func(func_args, *func_kwargs) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn \r\n return fn(args, *kwargs) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/hf_api.py\", line 826, in _inner \r\n return fn(self, args, *kwargs) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/hf_api.py\", line 3205, in upload_file \r\n commit_info = self.create_commit( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn \r\n return fn(args, *kwargs) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/hf_api.py\", line 826, in _inner \r\n return fn(self, args, *kwargs) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/hf_api.py\", line 2680, in create_commit \r\n upload_lfs_files( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn \r\n return fn(args, *kwargs) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/_commit_api.py\", line 353, in upload_lfs_files \r\n thread_map( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/tqdm/contrib/concurrent.py\", line 69, in thread_map \r\n return _executor_map(ThreadPoolExecutor, fn, iterables, *tqdm_kwargs) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/tqdm/contrib/concurrent.py\", line 49, in _executor_map \r\n with PoolExecutor(max_workers=max_workers, initializer=tqdm_class.set_lock, \r\n File \"/opt/conda/envs/main/lib/python3.10/concurrent/futures/_base.py\", line 649, in __exit__ \r\n self.shutdown(wait=True) \r\n File \"/opt/conda/envs/main/lib/python3.10/concurrent/futures/thread.py\", line 235, in shutdown \r\n t.join() \r\n File \"/opt/conda/envs/main/lib/python3.10/threading.py\", line 1096, in join \r\n self._wait_for_tstate_lock() \r\n File \"/opt/conda/envs/main/lib/python3.10/threading.py\", line 1116, in _wait_for_tstate_lock \r\n if lock.acquire(block, timeout): \r\nKeyboardInterrupt \r\n```", "@Wauplin \r\n\r\n>What is the total dataset size?\r\n\r\nThere are three variants, and the random hanging happens on all three. The sizes are 2TB, 1TB, and 200GB. \r\n\r\n>Is it always failing on the same shard or is the hanging problem happening randomly?\r\n\r\nIt seems to be very much random, as restarting can help move past the previous hang, only to find a new one, or not. \r\n\r\n>Were you able to save the dataset as parquet locally? This would help us determine if the problem comes from the upload or the file generation.\r\n\r\nYes. The dataset seems to be locally stored as parquet. ", "Hmm it looks like an issue with TQDM lock. Maybe you can try updating TQDM ?", "I am using the latest version of tqdm\r\n\r\n```\r\n⬢ [Docker] ❯ pip install tqdm --upgrade\r\nRequirement already satisfied: tqdm in /opt/conda/envs/main/lib/python3.10/site-packages (4.65.0)\r\nWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\r\n```", "I tried trying to catch the hanging issue in action again\r\n\r\n```\r\nPushing dataset shards to the dataset hub: 65%\|█████████████████████████████████████████████████████████████████▊ \| 127/195 [2:28:02<1:19:15, 69.94s/it] \r\nError while uploading 'data/train-00127-of-00195-3f8d036ade107c27.parquet' to the Hub. \r\nPushing split train to the Hub. \r\nPushing dataset shards to the dataset hub: 64%\|████████████████████████████████████████████████████████████████▏ \| 124/195 [2:06:10<1:12:14, 61.05s/it]C^[^C^C^C \r\n╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮ \r\n│ /TALI/tali/scripts/validate_dataset.py:127 in <module> │ \r\n│ │ \r\n│ 124 │ │ \r\n│ 125 │ while not succesful_competion: │ \r\n│ 126 │ │ try: │ \r\n│ ❱ 127 │ │ │ train_dataset.push_to_hub(repo_id=\"Antreas/TALI-base\", max_shard_size=\"5GB\") │ \r\n│ 128 │ │ │ succesful_competion = True │ \r\n│ 129 │ │ except Exception as e: │ \r\n│ 130 │ │ │ print(e) │ \r\n│ │ \r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/dataset_dict.py:1583 in push_to_hub │ \r\n│ │ \r\n│ 1580 │ │ for split in self.keys(): │ \r\n│ 1581 │ │ │ logger.warning(f\"Pushing split {split} to the Hub.\") │ \r\n│ 1582 │ │ │ # The split=key needs to be removed before merging │ \r\n│ ❱ 1583 │ │ │ repo_id, split, uploaded_size, dataset_nbytes, _, _ = self[split]._push_parq │ \r\n│ 1584 │ │ │ │ repo_id, │ \r\n│ 1585 │ │ │ │ split=split, │ \r\n│ 1586 │ │ │ │ private=private, │ \r\n│ │ \r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_dataset.py:5263 in │ \r\n│ _push_parquet_shards_to_hub │ \r\n│ │ \r\n│ 5260 │ │ │ \r\n│ 5261 │ │ uploaded_size = 0 │ \r\n│ 5262 │ │ shards_path_in_repo = [] │ \r\n│ ❱ 5263 │ │ for index, shard in logging.tqdm( │ \r\n│ 5264 │ │ │ enumerate(itertools.chain([first_shard], shards_iter)), │ \r\n│ 5265 │ │ │ desc=\"Pushing dataset shards to the dataset hub\", │ \r\n│ 5266 │ │ │ total=num_shards, │ \r\n│ │ \r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/tqdm/std.py:1178 in __iter__ │ \r\n│ │ \r\n│ 1175 │ │ time = self._time │ \r\n│ 1176 │ │ │ \r\n│ 1177 │ │ try: │\r\n│ ❱ 1178 │ │ │ for obj in iterable: │\r\n│ 1179 │ │ │ │ yield obj │\r\n│ 1180 │ │ │ │ # Update and possibly print the progressbar. │\r\n│ 1181 │ │ │ │ # Note: does not call self.update(1) for speed optimisation. │\r\n│ │\r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_dataset.py:5238 in │\r\n│ shards_with_embedded_external_files │\r\n│ │\r\n│ 5235 │ │ │ │ for shard in shards: │\r\n│ 5236 │ │ │ │ │ format = shard.format │\r\n│ 5237 │ │ │ │ │ shard = shard.with_format(\"arrow\") │\r\n│ ❱ 5238 │ │ │ │ │ shard = shard.map( │\r\n│ 5239 │ │ │ │ │ │ embed_table_storage, │\r\n│ 5240 │ │ │ │ │ │ batched=True, │\r\n│ 5241 │ │ │ │ │ │ batch_size=1000, │\r\n│ │\r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_dataset.py:578 in wrapper │\r\n│ │\r\n│ 575 │ │ else: │\r\n│ 576 │ │ │ self: \"Dataset\" = kwargs.pop(\"self\") │\r\n│ 577 │ │ # apply actual function │\r\n│ ❱ 578 │ │ out: Union[\"Dataset\", \"DatasetDict\"] = func(self, args, *kwargs) │ \r\n│ 579 │ │ datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [ou │ \r\n│ 580 │ │ for dataset in datasets: │ \r\n│ 581 │ │ │ # Remove task templates if a column mapping of the template is no longer val │ \r\n│ │ \r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_dataset.py:543 in wrapper │ \r\n│ │ \r\n│ 540 │ │ │ \"output_all_columns\": self._output_all_columns, │ \r\n│ 541 │ │ } │ \r\n│ 542 │ │ # apply actual function │ \r\n│ ❱ 543 │ │ out: Union[\"Dataset\", \"DatasetDict\"] = func(self, args, kwargs) │ \r\n│ 544 │ │ datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [ou │ \r\n│ 545 │ │ # re-apply format to the output │ \r\n│ 546 │ │ for dataset in datasets: │ \r\n│ │ \r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_dataset.py:3073 in map │ \r\n│ │ \r\n│ 3070 │ │ │ │ │ leave=False, │ \r\n│ 3071 │ │ │ │ │ desc=desc or \"Map\", │ \r\n│ 3072 │ │ │ │ ) as pbar: │ \r\n│ ❱ 3073 │ │ │ │ │ for rank, done, content in Dataset._map_single(dataset_kwargs): │ \r\n│ 3074 │ │ │ │ │ │ if done: │ \r\n│ 3075 │ │ │ │ │ │ │ shards_done += 1 │ \r\n│ 3076 │ │ │ │ │ │ │ logger.debug(f\"Finished processing shard number {rank} of {n │ \r\n│ │ \r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_dataset.py:3464 in _map_single │ \r\n│ │ \r\n│ 3461 │ │ │ │ │ │ │ │ buf_writer, writer, tmp_file = init_buffer_and_writer() │ \r\n│ 3462 │ │ │ │ │ │ │ │ stack.enter_context(writer) │ \r\n│ 3463 │ │ │ │ │ │ │ if isinstance(batch, pa.Table): │ \r\n│ ❱ 3464 │ │ │ │ │ │ │ │ writer.write_table(batch) │ \r\n│ 3465 │ │ │ │ │ │ │ else: │ \r\n│ 3466 │ │ │ │ │ │ │ │ writer.write_batch(batch) │ \r\n│ 3467 │ │ │ │ │ │ num_examples_progress_update += num_examples_in_batch │ \r\n│ │ \r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_writer.py:567 in write_table │ \r\n│ │ \r\n│ 564 │ │ │ writer_batch_size = self.writer_batch_size │ \r\n│ 565 │ │ if self.pa_writer is None: │ \r\n│ 566 │ │ │ self._build_writer(inferred_schema=pa_table.schema) │ \r\n│ ❱ 567 │ │ pa_table = pa_table.combine_chunks() │ \r\n│ 568 │ │ pa_table = table_cast(pa_table, self._schema) │ \r\n│ 569 │ │ if self.embed_local_files: │ \r\n│ 570 │ │ │ pa_table = embed_table_storage(pa_table) │ \r\n╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ \r\nKeyboardInterrupt \r\n```", "I'm on my phone so can't help that much. What I'd advice to do is to [save_to_disk](https://huggingface.co/docs/datasets/package_reference/main_classes#save_to_disk) if it's not already done and then upload the files/folder to the Hub separately. You can find what you need in the [upload guide](https://huggingface.co/docs/huggingface_hub/guides/upload). It might not help finding the exact issue for now but at least it can unblock you. ", "In your last stacktrace it interrupted while embedding external content - in case your dataset in made of images or audio files that live on your disk. Is it the case ?", "Yeah, the dataset has images, audio, video and text. ", "It's maybe related to https://github.com/apache/arrow/issues/34455: are you using ArrayND features ?\r\n\r\nAlso what's your `pyarrow` version ? Could you try updating to >= 12.0.1 ?", "I was using pyarrow == 12.0.0\r\n\r\nI am not explicitly using ArrayND features, unless the hub API automatically converts my files to such. ", "I have now updated to pyarrow == 12.0.1 and retrying", "You can also try to reduce the `max_shard_size` - Sometimes parquet has a hard time working with data bigger than 2GB", "So, updating the pyarrow seems to help. It can still throw errors here and there but I can retry when that happens. It's better than hanging. \r\n\r\nHowever, I am a bit confused about something. I have uploaded my datasets, but while earlier I could see all three sets, now I can only see 1. What's going on? \r\nhttps://huggingface.co/datasets/Antreas/TALI-base\r\n\r\nI have seen this happen before as well, so I deleted and reuploaded, but this dataset is way too large for me to do this. ", "It's a bug on our side, I'll update the dataset viewer ;)\r\n\r\nThanks for reporting !", "Apparently this happened because of bad modifications in the README.md split metadata.\r\n\r\nI fixed them in this PR: https://huggingface.co/datasets/Antreas/TALI-base/discussions/1", "@lhoestq It's a bit odd that when uploading a dataset, one set at a time \"train\", \"val\", \"test\", the push_to_hub function overwrites the readme and removes differently named sets from previous commits. i.e., you push \"val\", all is well. Then you push \"test\", and the \"val\" entry disappears from the readme, while the data remain intact. ", "Also, just found another related issue. One of the many that make things hang or fail when pushing to hub. \r\n\r\nIn the following code:\r\n\r\n```python\r\ntrain_generator = lambda: data_generator(\"train\", percentage=1.0)\r\n val_generator = lambda: data_generator(\"val\")\r\n test_generator = lambda: data_generator(\"test\")\r\n\r\n train_data = datasets.Dataset.from_generator(\r\n train_generator,\r\n num_proc=mp.cpu_count(),\r\n writer_batch_size=5000,\r\n cache_dir=tali_dataset_dir,\r\n )\r\n\r\n val_data = datasets.Dataset.from_generator(\r\n val_generator,\r\n writer_batch_size=5000,\r\n num_proc=mp.cpu_count(),\r\n cache_dir=tali_dataset_dir,\r\n )\r\n\r\n test_data = datasets.Dataset.from_generator(\r\n test_generator,\r\n writer_batch_size=5000,\r\n num_proc=mp.cpu_count(),\r\n cache_dir=tali_dataset_dir,\r\n )\r\n\r\n print(f\"Pushing TALI-large to hub\")\r\n\r\n dataset = datasets.DatasetDict(\r\n {\"train\": train_data, \"val\": val_data, \"test\": test_data}\r\n )\r\n succesful_competion = False\r\n\r\n while not succesful_competion:\r\n try:\r\n dataset.push_to_hub(repo_id=\"Antreas/TALI-large\", max_shard_size=\"2GB\")\r\n succesful_competion = True\r\n except Exception as e:\r\n print(e)\r\n ```\r\n \r\n \r\n Things keep failing in the push_to_repo step, at random places, with the following error:\r\n \r\n ```bash\r\n Pushing dataset shards to the dataset hub: 7%\|██████████▋ \| 67/950 [42:41<9:22:37, 38.23s/it]\r\nError while uploading 'data/train-00067-of-00950-a4d179ed5a593486.parquet' to the Hub.\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:01<00:00, 1.81ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:11<00:00, 11.20s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.48ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:15<00:00, 15.30s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.39ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:11<00:00, 11.52s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.47ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:10<00:00, 10.39s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.26ba/s]\r\nUpload 1 LFS files: 0%\| \| 0/1 [16:38<?, ?it/s]\r\nPushing dataset shards to the dataset hub: 7%\|███████████▎ \| 71/950 [44:37<9:12:28, 37.71s/it]\r\nError while uploading 'data/train-00071-of-00950-72bab6e5cb223aee.parquet' to the Hub.\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.18ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:10<00:00, 10.94s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.36ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:10<00:00, 10.67s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.57ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:10<00:00, 10.16s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.68ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:09<00:00, 9.63s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.36ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:10<00:00, 10.67s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.37ba/s]\r\nUpload 1 LFS files: 0%\| \| 0/1 [16:39<?, ?it/s]\r\nPushing dataset shards to the dataset hub: 8%\|████████████ \| 76/950 [46:21<8:53:08, 36.60s/it]\r\nError while uploading 'data/train-00076-of-00950-b90e4e3b433db179.parquet' to the Hub.\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.21ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:25<00:00, 25.40s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:01<00:00, 1.56ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:10<00:00, 10.40s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.49ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:23<00:00, 23.53s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.27ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:10<00:00, 10.25s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.42ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:11<00:00, 11.03s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.39ba/s]\r\nUpload 1 LFS files: 0%\| \| 0/1 [16:39<?, ?it/s]\r\nPushing dataset shards to the dataset hub: 9%\|████████████▊ \| 81/950 [48:30<8:40:22, 35.93s/it]\r\nError while uploading 'data/train-00081-of-00950-84b0450a1df093a9.parquet' to the Hub.\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.18ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:11<00:00, 11.65s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:01<00:00, 1.92ba/s]\r\nUpload 1 LFS files: 0%\| \| 0/1 [16:38<?, ?it/s]\r\nPushing dataset shards to the dataset hub: 9%\|█████████████ \| 82/950 [48:55<8:37:57, 35.80s/it]\r\nError while uploading 'data/train-00082-of-00950-0a1f52da35653e08.parquet' to the Hub.\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.31ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:26<00:00, 26.29s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.42ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:10<00:00, 10.57s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.64ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:10<00:00, 10.35s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.64ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:11<00:00, 11.74s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.31ba/s]\r\nUpload 1 LFS files: 0%\| \| 0/1 [16:40<?, ?it/s]\r\nPushing dataset shards to the dataset hub: 9%\|█████████████▋ \| 86/950 [50:48<8:30:25, 35.45s/it]\r\nError while uploading 'data/train-00086-of-00950-e1cc80dd17191b20.parquet' to the Hub.\r\n```\r\n\r\nI have a while loop that forces retries, but it seems that the progress itself is randomly getting lost as well. Any ideas on how to improve this? It has been blocking me for way too long. \r\n\r\nShould I build the parquet manually and then push manually as well? If I do things manually, how can I ensure my dataset works properly with \"stream=True\"? \r\n\r\nThank you for your help and time. ", "> @lhoestq It's a bit odd that when uploading a dataset, one set at a time \"train\", \"val\", \"test\", the push_to_hub function overwrites the readme and removes differently named sets from previous commits. i.e., you push \"val\", all is well. Then you push \"test\", and the \"val\" entry disappears from the readme, while the data remain intact.\r\n\r\nHmm this shouldn't happen. What code did you run exactly ? Using which version of `datasets` ?", "> I have a while loop that forces retries, but it seems that the progress itself is randomly getting lost as well. Any ideas on how to improve this? It has been blocking me for way too long.\r\n\r\nCould you also print the cause of the error (`e.__cause__`) ? Or show the full stack trace when the error happens ?\r\nThis would give more details about why it failed and would help investigate.", "> Should I build the parquet manually and then push manually as well? If I do things manually, how can I ensure my dataset works properly with \"stream=True\"?\r\n\r\nParquet is supported out of the box ^^\r\n\r\nIf you want to make sure it works as expected you can try locally first:\r\n```python\r\nds = load_dataset(\"path/to/local\", streaming=True)\r\n```", "@lhoestq @AntreasAntoniou I transferred this issue to the `datasets` repository as the questions and answers are more related to this repo. Hope it can help other users find the bug and fixes more easily (like updating [tqdm](https://github.com/huggingface/datasets/issues/5990#issuecomment-1607120204) and [pyarrow](https://github.com/huggingface/datasets/issues/5990#issuecomment-1607120278) or [setting a lower `max_shard_size`](https://github.com/huggingface/datasets/issues/5990#issuecomment-1607120328)).\r\n\r\n~For the initial \"pushing large dataset consistently hangs\"-issue, I still think it's best to try to `save_to_disk` first and then upload it manually/with a script (see [upload_folder](https://huggingface.co/docs/huggingface_hub/guides/upload#upload-a-folder)). It's not the most satisfying solution but at least it would confirm from where the problem comes from.~\r\n\r\nEDIT: removed suggestion about saving to disk first (see https://github.com/huggingface/datasets/issues/5990#issuecomment-1607186914).", "> @lhoestq @AntreasAntoniou I transferred this issue to the datasets repository as the questions and answers are more related to this repo. Hope it can help other users find the bug and fixes more easily (like updating https://github.com/huggingface/datasets/issues/5990#issuecomment-1607120204 and https://github.com/huggingface/datasets/issues/5990#issuecomment-1607120278 or https://github.com/huggingface/datasets/issues/5990#issuecomment-1607120328).\r\n\r\nthanks :)\r\n\r\n> For the initial \"pushing large dataset consistently hangs\"-issue, I still think it's best to try to save_to_disk first and then upload it manually/with a script (see [upload_folder](https://huggingface.co/docs/huggingface_hub/guides/upload#upload-a-folder)). It's not the most satisfying solution but at least it would confirm from where the problem comes from.\r\n\r\nAs I've already said in other discussions, I would not recommend pushing files saved with `save_to_disk` to the Hub but save to parquet shards and upload them instead. The Hub does not support datasets saved with `save_to_disk`, which is meant for disk only.", "> As I've already said in other discussions, I would not recommend pushing files saved with save_to_disk to the Hub but save to parquet shards and upload them instead. The Hub does not support datasets saved with save_to_disk, which is meant for disk only.\r\n\r\nWell noted, thanks. That part was not clear to me :)", "Sorry for not replying in a few days, I was on leave. :) \r\n\r\nSo, here are more information as to the error that causes some of the delay\r\n\r\n```bash\r\nPushing Antreas/TALI-tiny to hub\r\nAttempting to push to hub\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 6/6 [00:24<00:00, 4.06s/ba]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 6/6 [00:24<00:00, 4.15s/ba]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 6/6 [00:26<00:00, 4.45s/ba]\r\n/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/lfs.py:310: UserWarning: hf_transfer is enabled but does not support uploading from bytes or BinaryIO, falling back to regular upload\r\n warnings.warn(\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 6/6 [00:25<00:00, 4.26s/ba]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 6/6 [00:27<00:00, 4.58s/ba]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 6/6 [00:24<00:00, 4.10s/ba]\r\nPushing dataset shards to the dataset hub: 22%\|████████████████████████▎ \| 5/23 [52:23<3:08:37, 628.74s/it]\r\nException: Error while uploading 'data/train-00005-of-00023-e224d901fd65e062.parquet' to the Hub., with stacktrace: <traceback object at 0x7f745458d0c0>, and type: <class 'RuntimeError'>, and \r\ncause: HTTPSConnectionPool(host='s3.us-east-1.amazonaws.com', port=443): Max retries exceeded with url: \r\n/lfs.huggingface.co/repos/7c/d3/7cd385d9324302dc13e3986331d72d9be6fa0174c63dcfe0e08cd474f7f1e8b7/3415166ae28c0beccbbc692f38742b8dea2c197f5c805321104e888d21d7eb90?X-Amz-Algorithm=AWS4-HMAC-SHA256\r\n&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA4N7VTDGO27GPWFUO%2F20230627%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230627T003349Z&X-Amz-Expires=86400&X-Amz-Signature=5a12ff96f2\r\n91f644134170992a6628e5f3c4e7b2e7fc3e940b4378fe11ae5390&X-Amz-SignedHeaders=host&partNumber=1&uploadId=JSsK8r63XSF.VlKQx3Vf8OW4DEVp5YIIY7LPnuapNIegsxs5EHgM1p4u0.Nn6_wlPlQnvxm8HKMxZhczKE9KB74t0etB\r\noLcxqBIvsgey3uXBTZMAEGwU6y7CDUADiEIO&x-id=UploadPart (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:2426)')))\r\nPush failed, retrying\r\nAttempting to push to hub\r\nPushing split train to the Hub.\r\n```\r\n\r\nOne issue is that the uploading does not continue from the chunk it failed off. It often continues from a very old chunk. e.g. if it failed on chunk 192/250, it will continue from say 53/250, and this behaviour appears almost random. ", "Are you using a proxy of some sort ?", "I am using a kubernetes cluster built into a university VPN. ", "So, other than the random connection drops here and there, any idea why the progress does not continue where it left off?\r\n\r\n```bash\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 28/28 [00:02<00:00, 10.79ba/s]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 28/28 [00:02<00:00, 13.65ba/s]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 28/28 [00:02<00:00, 13.39ba/s]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 28/28 [00:02<00:00, 13.04ba/s]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 28/28 [00:02<00:00, 13.52ba/s]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 28/28 [00:02<00:00, 12.28ba/s]\r\nPushing dataset shards to the dataset hub: 20%\|██████████████████████ \| 75/381 [1:34:39<6:26:11, 75.72s/it]\r\nException: Error while uploading 'data/train-00075-of-00381-1614bc251b778766.parquet' to the Hub., with stacktrace: <traceback object at 0x7fab6d9a4980>, and type: <class 'RuntimeError'>, and \r\ncause: HTTPSConnectionPool(host='s3.us-east-1.amazonaws.com', port=443): Max retries exceeded with url: \r\n/lfs.huggingface.co/repos/3b/31/3b311464573d8d63b137fcd5b40af1e7a5b1306843c88e80372d0117157504e5/ed8dae933fb79ae1ef5fb1f698f5125d3e1c02977ac69438631f152bb3bfdd1e?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-\r\nAmz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA4N7VTDGO27GPWFUO%2F20230629%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230629T053004Z&X-Amz-Expires=86400&X-Amz-Signature=da2b26270edfd6d0\r\nd069c015a5a432031107a8664c3f0917717e5e40c688183c&X-Amz-SignedHeaders=host&partNumber=1&uploadId=2erWGHTh3ICqBLU_QvHfnygZ2tkMWbL0rEqpJdYohCKHUHnfwMjvoBIg0TI_KSGn4rSKxUxOyqSIzFUFSRSzixZeLeneaXJOw.Qx8\r\nzLKSV5xV7HRQDj4RBesNve6cSoo&x-id=UploadPart (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:2426)')))\r\nPush failed, retrying\r\nAttempting to push to hub\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 28/28 [00:02<00:00, 12.09ba/s]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 28/28 [00:02<00:00, 11.51ba/s]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 28/28 [00:02<00:00, 10.77ba/s]\r\nPushing dataset shards to the dataset hub: 20%\|██████████████████████▋ \| 77/381 [1:32:50<6:06:34, 72.35s/it]\r\nException: Error while uploading 'data/train-00077-of-00381-368b2327a9908aab.parquet' to the Hub., with stacktrace: <traceback object at 0x7fab45b27f80>, and type: <class 'RuntimeError'>, and \r\ncause: HTTPSConnectionPool(host='s3.us-east-1.amazonaws.com', port=443): Max retries exceeded with url: \r\n/lfs.huggingface.co/repos/3b/31/3b311464573d8d63b137fcd5b40af1e7a5b1306843c88e80372d0117157504e5/9462ff2c5e61283b53b091984a22de2f41a2f6e37b681171e2eca4a998f979cb?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-\r\nAmz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA4N7VTDGO27GPWFUO%2F20230629%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230629T070510Z&X-Amz-Expires=86400&X-Amz-Signature=9ab8487b93d443cd\r\n21f05476405855d46051a0771b4986bbb20f770ded21b1a4&X-Amz-SignedHeaders=host&partNumber=1&uploadId=UiHX1B.DcoAO2QmIHpWpCuNPwhXU_o1dsTkTGPqZt1P51o9k0yz.EsFD9eKpQMwgAST3jOatRG78I_JWRBeLBDYYVNp8r0TpIdeSg\r\neUg8uwPZOCPw9y5mWOw8MWJrnBo&x-id=UploadPart (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:2426)')))\r\nPush failed, retrying\r\nAttempting to push to hub\r\nPushing split train to the Hub.\r\nPushing dataset shards to the dataset hub: 8%\|████████▋ \| 29/381 [27:39<5:50:03, 59.67s/it]\r\nMap: 36%\|████████████████████████████████████████████████████ \| 1000/2764 [00:35<00:34, 51.63 examples/Map: 72%\|████████████████████████████████████████████████████████████████████████████████████████████████████████▏ \| 2000/2764 [00:40<00:15, 49.06 examples/Map: 72%\|████████████████████████████████████████████████████████████████████████████████████████████████████████▏ \| 2000/2764 [00:55<00:15, 49.06 examples/Map: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2764/2764 [00:56<00:00, 48.82 examples/Pushing dataset shards to the dataset hub: 8%\|████████▉ \| 30/381 [28:35<5:43:03, 58.64s/iPushing dataset shards to the dataset hub: 8%\|█████████▎ \| 31/381 [29:40<5:52:18, 60.40s/iPushing dataset shards to the dataset hub: 8%\|█████████▌ \| 32/381 [30:46<6:02:20, 62.29s/it] \r\nMap: 36%\|███████████████████████████████████████████████████▎ \r\n```\r\n\r\nThis is actually the issue that wastes the most time for me, and I need it fixed. Please advice on how I can go about it.\r\n\r\nNotice how the progress goes from \r\n\| 77/381 to 30/381", "If the any shard is missing on the Hub, it will re-upload it. It looks like the 30th shard was missing on the Hub in your case. \r\n\r\nIt also means that the other files up to the 77th that were successfully uploaded won't be uploaded again.\r\n\r\ncc @mariosasko who might know better" ]
1,749,955,883	5,939	.	closed	2023-06-09T14:01:34	2023-06-12T12:19:34	2023-06-12T12:19:19	https://github.com/huggingface/datasets/issues/5939	null	flckv	false	[]
1,749,462,851	5,938	Make get_from_cache use custom temp filename that is locked	closed	2023-06-09T09:01:13	2023-06-14T13:35:38	2023-06-14T13:27:24	https://github.com/huggingface/datasets/pull/5938	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5938", "html_url": "https://github.com/huggingface/datasets/pull/5938", "diff_url": "https://github.com/huggingface/datasets/pull/5938.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5938.patch", "merged_at": "2023-06-14T13:27:24" }	albertvillanova	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007241 / 0.011353 (-0.004112) \| 0.004574 / 0.011008 (-0.006434) \| 0.120481 / 0.038508 (0.081973) \| 0.040492 / 0.023109 (0.017383) \| 0.391399 / 0.275898 (0.115501) \| 0.422844 / 0.323480 (0.099365) \| 0.004441 / 0.007986 (-0.003545) \| 0.004544 / 0.004328 (0.000216) \| 0.089482 / 0.004250 (0.085231) \| 0.052939 / 0.037052 (0.015887) \| 0.393649 / 0.258489 (0.135160) \| 0.433852 / 0.293841 (0.140011) \| 0.035882 / 0.128546 (-0.092664) \| 0.010172 / 0.075646 (-0.065474) \| 0.410331 / 0.419271 (-0.008940) \| 0.061481 / 0.043533 (0.017948) \| 0.405066 / 0.255139 (0.149927) \| 0.417732 / 0.283200 (0.134532) \| 0.121647 / 0.141683 (-0.020035) \| 1.790624 / 1.452155 (0.338469) \| 1.863398 / 1.492716 (0.370681) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.250650 / 0.018006 (0.232644) \| 0.489044 / 0.000490 (0.488554) \| 0.010421 / 0.000200 (0.010222) \| 0.000106 / 0.000054 (0.000051) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030340 / 0.037411 (-0.007071) \| 0.128318 / 0.014526 (0.113792) \| 0.140463 / 0.176557 (-0.036093) \| 0.205762 / 0.737135 (-0.531373) \| 0.147996 / 0.296338 (-0.148342) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.493158 / 0.215209 (0.277949) \| 4.858346 / 2.077655 (2.780691) \| 2.242942 / 1.504120 (0.738822) \| 2.010092 / 1.541195 (0.468897) \| 2.076765 / 1.468490 (0.608275) \| 0.636669 / 4.584777 (-3.948108) \| 4.478027 / 3.745712 (0.732314) \| 2.157843 / 5.269862 (-3.112019) \| 1.305133 / 4.565676 (-3.260543) \| 0.079220 / 0.424275 (-0.345055) \| 0.013858 / 0.007607 (0.006251) \| 0.604501 / 0.226044 (0.378457) \| 5.950071 / 2.268929 (3.681143) \| 2.738373 / 55.444624 (-52.706251) \| 2.380275 / 6.876477 (-4.496201) \| 2.517108 / 2.142072 (0.375035) \| 0.772249 / 4.805227 (-4.032979) \| 0.169874 / 6.500664 (-6.330790) \| 0.078026 / 0.075469 (0.002557) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.450200 / 1.841788 (-0.391588) \| 17.810965 / 8.074308 (9.736657) \| 15.518998 / 10.191392 (5.327606) \| 0.200469 / 0.680424 (-0.479954) \| 0.020777 / 0.534201 (-0.513424) \| 0.504556 / 0.579283 (-0.074727) \| 0.518493 / 0.434364 (0.084129) \| 0.615335 / 0.540337 (0.074998) \| 0.754065 / 1.386936 (-0.632871) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007224 / 0.011353 (-0.004129) \| 0.004663 / 0.011008 (-0.006345) \| 0.092151 / 0.038508 (0.053643) \| 0.038359 / 0.023109 (0.015250) \| 0.486413 / 0.275898 (0.210515) \| 0.521596 / 0.323480 (0.198116) \| 0.004207 / 0.007986 (-0.003778) \| 0.003745 / 0.004328 (-0.000583) \| 0.089840 / 0.004250 (0.085589) \| 0.050996 / 0.037052 (0.013943) \| 0.498090 / 0.258489 (0.239601) \| 0.533647 / 0.293841 (0.239806) \| 0.035151 / 0.128546 (-0.093395) \| 0.010293 / 0.075646 (-0.065354) \| 0.099056 / 0.419271 (-0.320215) \| 0.057365 / 0.043533 (0.013833) \| 0.470652 / 0.255139 (0.215513) \| 0.509801 / 0.283200 (0.226602) \| 0.115650 / 0.141683 (-0.026033) \| 1.810860 / 1.452155 (0.358705) \| 1.896775 / 1.492716 (0.404059) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.261887 / 0.018006 (0.243880) \| 0.489919 / 0.000490 (0.489430) \| 0.006117 / 0.000200 (0.005917) \| 0.000134 / 0.000054 (0.000079) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035033 / 0.037411 (-0.002378) \| 0.141093 / 0.014526 (0.126567) \| 0.152613 / 0.176557 (-0.023943) \| 0.218351 / 0.737135 (-0.518785) \| 0.158366 / 0.296338 (-0.137972) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.542219 / 0.215209 (0.327010) \| 5.479358 / 2.077655 (3.401703) \| 2.749586 / 1.504120 (1.245466) \| 2.537686 / 1.541195 (0.996491) \| 2.582351 / 1.468490 (1.113861) \| 0.636750 / 4.584777 (-3.948027) \| 4.537501 / 3.745712 (0.791789) \| 2.141392 / 5.269862 (-3.128469) \| 1.279711 / 4.565676 (-3.285965) \| 0.079227 / 0.424275 (-0.345048) \| 0.014141 / 0.007607 (0.006534) \| 0.662070 / 0.226044 (0.436025) \| 6.572144 / 2.268929 (4.303215) \| 3.321349 / 55.444624 (-52.123275) \| 2.928219 / 6.876477 (-3.948258) \| 3.002732 / 2.142072 (0.860659) \| 0.773808 / 4.805227 (-4.031419) \| 0.166017 / 6.500664 (-6.334647) \| 0.076424 / 0.075469 (0.000955) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.584325 / 1.841788 (-0.257463) \| 18.359247 / 8.074308 (10.284938) \| 16.977875 / 10.191392 (6.786483) \| 0.195381 / 0.680424 (-0.485043) \| 0.021048 / 0.534201 (-0.513153) \| 0.512237 / 0.579283 (-0.067047) \| 0.511435 / 0.434364 (0.077071) \| 0.592856 / 0.540337 (0.052518) \| 0.711905 / 1.386936 (-0.675031) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d536e37b21a6dd5c122b6d8113994ec50846c5b5 \"CML watermark\")\n" ]
1,749,388,597	5,937	Avoid parallel redownload in cache	closed	2023-06-09T08:18:36	2023-06-14T12:30:59	2023-06-14T12:23:57	https://github.com/huggingface/datasets/pull/5937	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5937", "html_url": "https://github.com/huggingface/datasets/pull/5937", "diff_url": "https://github.com/huggingface/datasets/pull/5937.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5937.patch", "merged_at": "2023-06-14T12:23:57" }	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) \| 0.006157 / 0.011353 (-0.005196) \| 0.003790 / 0.011008 (-0.007219) \| 0.097889 / 0.038508 (0.059381) \| 0.029038 / 0.023109 (0.005929) \| 0.306918 / 0.275898 (0.031020) \| 0.339637 / 0.323480 (0.016157) \| 0.003526 / 0.007986 (-0.004460) \| 0.003102 / 0.004328 (-0.001227) \| 0.076908 / 0.004250 (0.072658) \| 0.039254 / 0.037052 (0.002201) \| 0.309197 / 0.258489 (0.050708) \| 0.345635 / 0.293841 (0.051794) \| 0.027954 / 0.128546 (-0.100593) \| 0.008510 / 0.075646 (-0.067136) \| 0.314674 / 0.419271 (-0.104598) \| 0.057102 / 0.043533 (0.013569) \| 0.307495 / 0.255139 (0.052356) \| 0.329501 / 0.283200 (0.046302) \| 0.098450 / 0.141683 (-0.043233) \| 1.480102 / 1.452155 (0.027948) \| 1.550554 / 1.492716 (0.057838) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.207440 / 0.018006 (0.189434) \| 0.426560 / 0.000490 (0.426071) \| 0.003250 / 0.000200 (0.003050) \| 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) \| 0.023777 / 0.037411 (-0.013634) \| 0.103905 / 0.014526 (0.089379) \| 0.108324 / 0.176557 (-0.068233) \| 0.167223 / 0.737135 (-0.569913) \| 0.113529 / 0.296338 (-0.182810) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.426770 / 0.215209 (0.211561) \| 4.251806 / 2.077655 (2.174151) \| 2.010426 / 1.504120 (0.506306) \| 1.858630 / 1.541195 (0.317435) \| 1.941318 / 1.468490 (0.472828) \| 0.558056 / 4.584777 (-4.026721) \| 3.399107 / 3.745712 (-0.346606) \| 1.758386 / 5.269862 (-3.511476) \| 1.036305 / 4.565676 (-3.529372) \| 0.067094 / 0.424275 (-0.357182) \| 0.011167 / 0.007607 (0.003560) \| 0.526705 / 0.226044 (0.300661) \| 5.250319 / 2.268929 (2.981390) \| 2.496723 / 55.444624 (-52.947902) \| 2.154013 / 6.876477 (-4.722464) \| 2.394724 / 2.142072 (0.252652) \| 0.669723 / 4.805227 (-4.135504) \| 0.136367 / 6.500664 (-6.364297) \| 0.067080 / 0.075469 (-0.008389) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.269700 / 1.841788 (-0.572088) \| 14.099775 / 8.074308 (6.025467) \| 14.422936 / 10.191392 (4.231544) \| 0.132344 / 0.680424 (-0.548080) \| 0.016744 / 0.534201 (-0.517457) \| 0.378286 / 0.579283 (-0.200997) \| 0.392282 / 0.434364 (-0.042082) \| 0.437648 / 0.540337 (-0.102689) \| 0.528554 / 1.386936 (-0.858382) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006086 / 0.011353 (-0.005267) \| 0.003769 / 0.011008 (-0.007239) \| 0.077414 / 0.038508 (0.038906) \| 0.027806 / 0.023109 (0.004697) \| 0.360333 / 0.275898 (0.084434) \| 0.404725 / 0.323480 (0.081245) \| 0.003443 / 0.007986 (-0.004543) \| 0.004434 / 0.004328 (0.000106) \| 0.077309 / 0.004250 (0.073059) \| 0.040441 / 0.037052 (0.003388) \| 0.358627 / 0.258489 (0.100138) \| 0.415246 / 0.293841 (0.121405) \| 0.027718 / 0.128546 (-0.100829) \| 0.008495 / 0.075646 (-0.067151) \| 0.082874 / 0.419271 (-0.336397) \| 0.042323 / 0.043533 (-0.001210) \| 0.354895 / 0.255139 (0.099756) \| 0.390032 / 0.283200 (0.106832) \| 0.092377 / 0.141683 (-0.049306) \| 1.492817 / 1.452155 (0.040662) \| 1.551859 / 1.492716 (0.059143) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.198921 / 0.018006 (0.180915) \| 0.417699 / 0.000490 (0.417209) \| 0.001349 / 0.000200 (0.001149) \| 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) \| 0.026349 / 0.037411 (-0.011062) \| 0.105712 / 0.014526 (0.091186) \| 0.111792 / 0.176557 (-0.064765) \| 0.163677 / 0.737135 (-0.573459) \| 0.116864 / 0.296338 (-0.179474) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.447532 / 0.215209 (0.232323) \| 4.468770 / 2.077655 (2.391116) \| 2.403820 / 1.504120 (0.899700) \| 2.273640 / 1.541195 (0.732445) \| 2.337505 / 1.468490 (0.869015) \| 0.560729 / 4.584777 (-4.024048) \| 3.389165 / 3.745712 (-0.356547) \| 2.697614 / 5.269862 (-2.572247) \| 1.351909 / 4.565676 (-3.213768) \| 0.068089 / 0.424275 (-0.356186) \| 0.011639 / 0.007607 (0.004032) \| 0.555277 / 0.226044 (0.329233) \| 5.559291 / 2.268929 (3.290363) \| 2.657609 / 55.444624 (-52.787015) \| 2.346667 / 6.876477 (-4.529809) \| 2.615823 / 2.142072 (0.473751) \| 0.668662 / 4.805227 (-4.136566) \| 0.136593 / 6.500664 (-6.364071) \| 0.068384 / 0.075469 (-0.007085) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.312089 / 1.841788 (-0.529699) \| 14.477510 / 8.074308 (6.403202) \| 14.231432 / 10.191392 (4.040040) \| 0.132015 / 0.680424 (-0.548409) \| 0.016908 / 0.534201 (-0.517293) \| 0.368315 / 0.579283 (-0.210968) \| 0.397964 / 0.434364 (-0.036400) \| 0.432446 / 0.540337 (-0.107891) \| 0.526349 / 1.386936 (-0.860587) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#78b4d55c3cfc60e309eb033d3ed0aba5e796b6ce \"CML watermark\")\n" ]
1,748,424,388	5,936	Sequence of array not supported for most dtype	closed	2023-06-08T18:18:07	2023-06-14T15:03:34	2023-06-14T15:03:34	https://github.com/huggingface/datasets/issues/5936	null	qgallouedec	false	[ "Related, `float16` is the only dtype not supported by `Array2D` (probably by every `ArrayND`):\r\n\r\n```python\r\nfrom datasets import Array2D, Features, Dataset\r\n\r\nimport numpy as np\r\n\r\nfor dtype in [\r\n \"bool\", # ok\r\n \"int8\", # ok\r\n \"int16\", # ok\r\n \"int32\", # ok\r\n \"int64\", # ok\r\n \"uint8\", # ok\r\n \"uint16\", # ok\r\n \"uint32\", # ok\r\n \"uint64\", # ok\r\n \"float16\", # failed\r\n \"float32\", # ok\r\n \"float64\", # ok\r\n]:\r\n features = Features({\"foo\": Array2D(dtype=dtype, shape=(3, 4))})\r\n array = np.zeros((3, 4), dtype=dtype)\r\n try:\r\n dataset = Dataset.from_dict({\"foo\": [array]}, features=features)\r\n except Exception as e:\r\n print(f\"Failed for dtype={dtype}\")\r\n```", "Here's something I can't explain:\r\n\r\nWhen an array is encoded in the `from_dict` method, the numpy array is converted to a list (thus losing the original dtype, which is transfromed to the nearest builtin Python type)\r\n\r\nhttps://github.com/huggingface/datasets/blob/6ee61e6e695b1df9f232d47faf3a5e2b30b33737/src/datasets/features/features.py#L524-L525\r\n\r\nHowever, later on, this same data is written to memory, and it seems authorized that the data is an array (or in this case, a list of arrays). \r\n\r\nhttps://github.com/huggingface/datasets/blob/6ee61e6e695b1df9f232d47faf3a5e2b30b33737/src/datasets/arrow_writer.py#L185-L186\r\n\r\nSo the question is: why convert it to a Python list? This seems to be quite expensive both in terms of write time (all data is copied) and memory (e.g., an int8 is converted to an int64).\r\n\r\nFinally, if I try to remove this step, it solves all the previous problems, and it seems to me that it doesn't break anything (the CI passes without problem).", "Arrow only support 1d numpy arrays, so we convert multidim arrays to lists of 1s arrays (and keep the dtype).\r\n\r\nThough you noticed that it's concerting to lists and lose the dtype. If it's the case then it's a bug.", "Ok the conversion to list shouldn't be there indeed ! Could you open a PR to remove it ?" ]
1,748,090,220	5,935	Better row group size in push_to_hub	closed	2023-06-08T15:01:15	2023-06-09T17:47:37	2023-06-09T17:40:09	https://github.com/huggingface/datasets/pull/5935	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5935", "html_url": "https://github.com/huggingface/datasets/pull/5935", "diff_url": "https://github.com/huggingface/datasets/pull/5935.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5935.patch", "merged_at": "2023-06-09T17:40:09" }	lhoestq	true	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007489 / 0.011353 (-0.003864) \| 0.004914 / 0.011008 (-0.006095) \| 0.111626 / 0.038508 (0.073117) \| 0.037920 / 0.023109 (0.014811) \| 0.350571 / 0.275898 (0.074673) \| 0.389667 / 0.323480 (0.066187) \| 0.006309 / 0.007986 (-0.001676) \| 0.005488 / 0.004328 (0.001160) \| 0.083962 / 0.004250 (0.079712) \| 0.050728 / 0.037052 (0.013675) \| 0.360997 / 0.258489 (0.102508) \| 0.392736 / 0.293841 (0.098895) \| 0.031975 / 0.128546 (-0.096571) \| 0.009941 / 0.075646 (-0.065705) \| 0.379840 / 0.419271 (-0.039432) \| 0.056522 / 0.043533 (0.012989) \| 0.359379 / 0.255139 (0.104240) \| 0.384487 / 0.283200 (0.101287) \| 0.117523 / 0.141683 (-0.024160) \| 1.683639 / 1.452155 (0.231485) \| 1.791645 / 1.492716 (0.298929) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.236862 / 0.018006 (0.218856) \| 0.481208 / 0.000490 (0.480719) \| 0.007455 / 0.000200 (0.007255) \| 0.000111 / 0.000054 (0.000057) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030854 / 0.037411 (-0.006557) \| 0.126892 / 0.014526 (0.112367) \| 0.139207 / 0.176557 (-0.037350) \| 0.206447 / 0.737135 (-0.530689) \| 0.143095 / 0.296338 (-0.153244) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.474677 / 0.215209 (0.259468) \| 4.699534 / 2.077655 (2.621879) \| 2.152102 / 1.504120 (0.647983) \| 1.934815 / 1.541195 (0.393620) \| 1.986448 / 1.468490 (0.517958) \| 0.607184 / 4.584777 (-3.977593) \| 4.480385 / 3.745712 (0.734673) \| 2.074729 / 5.269862 (-3.195132) \| 1.182383 / 4.565676 (-3.383294) \| 0.075624 / 0.424275 (-0.348651) \| 0.014046 / 0.007607 (0.006439) \| 0.598859 / 0.226044 (0.372814) \| 5.959551 / 2.268929 (3.690622) \| 2.700851 / 55.444624 (-52.743773) \| 2.303775 / 6.876477 (-4.572702) \| 2.456441 / 2.142072 (0.314369) \| 0.747185 / 4.805227 (-4.058042) \| 0.165787 / 6.500664 (-6.334878) \| 0.075817 / 0.075469 (0.000348) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.411859 / 1.841788 (-0.429928) \| 17.375495 / 8.074308 (9.301187) \| 15.187098 / 10.191392 (4.995706) \| 0.169953 / 0.680424 (-0.510471) \| 0.020204 / 0.534201 (-0.513997) \| 0.461424 / 0.579283 (-0.117859) \| 0.494443 / 0.434364 (0.060080) \| 0.544583 / 0.540337 (0.004246) \| 0.648231 / 1.386936 (-0.738705) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007785 / 0.011353 (-0.003568) \| 0.005314 / 0.011008 (-0.005694) \| 0.087273 / 0.038508 (0.048765) \| 0.037810 / 0.023109 (0.014701) \| 0.425473 / 0.275898 (0.149575) \| 0.459976 / 0.323480 (0.136497) \| 0.007270 / 0.007986 (-0.000716) \| 0.004631 / 0.004328 (0.000303) \| 0.087063 / 0.004250 (0.082812) \| 0.052630 / 0.037052 (0.015578) \| 0.432384 / 0.258489 (0.173895) \| 0.500291 / 0.293841 (0.206450) \| 0.033144 / 0.128546 (-0.095402) \| 0.010101 / 0.075646 (-0.065545) \| 0.096068 / 0.419271 (-0.323204) \| 0.062750 / 0.043533 (0.019217) \| 0.419308 / 0.255139 (0.164169) \| 0.437099 / 0.283200 (0.153900) \| 0.122289 / 0.141683 (-0.019394) \| 1.737829 / 1.452155 (0.285674) \| 1.851481 / 1.492716 (0.358765) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.014277 / 0.018006 (-0.003729) \| 0.489835 / 0.000490 (0.489345) \| 0.008423 / 0.000200 (0.008223) \| 0.000188 / 0.000054 (0.000134) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032966 / 0.037411 (-0.004445) \| 0.130069 / 0.014526 (0.115544) \| 0.144372 / 0.176557 (-0.032185) \| 0.200400 / 0.737135 (-0.536735) \| 0.149384 / 0.296338 (-0.146954) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.511542 / 0.215209 (0.296333) \| 5.093879 / 2.077655 (3.016225) \| 2.572088 / 1.504120 (1.067968) \| 2.339118 / 1.541195 (0.797923) \| 2.441637 / 1.468490 (0.973147) \| 0.614818 / 4.584777 (-3.969959) \| 4.724441 / 3.745712 (0.978729) \| 5.431978 / 5.269862 (0.162116) \| 2.257794 / 4.565676 (-2.307883) \| 0.078109 / 0.424275 (-0.346166) \| 0.013821 / 0.007607 (0.006214) \| 0.639232 / 0.226044 (0.413188) \| 6.424623 / 2.268929 (4.155694) \| 3.163018 / 55.444624 (-52.281606) \| 2.756786 / 6.876477 (-4.119690) \| 2.808655 / 2.142072 (0.666583) \| 0.745843 / 4.805227 (-4.059385) \| 0.165562 / 6.500664 (-6.335102) \| 0.076610 / 0.075469 (0.001141) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.738630 / 1.841788 (-0.103158) \| 18.073573 / 8.074308 (9.999265) \| 16.482820 / 10.191392 (6.291428) \| 0.213233 / 0.680424 (-0.467191) \| 0.022839 / 0.534201 (-0.511362) \| 0.487043 / 0.579283 (-0.092240) \| 0.512518 / 0.434364 (0.078154) \| 0.549365 / 0.540337 (0.009028) \| 0.656612 / 1.386936 (-0.730324) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#288e92b03bd4ec91c10c8a529b32631cfaba9fb7 \"CML watermark\")\n", "Good idea!\r\n\r\nI was wondering: if we want to optimize the balance between the size of downloading a row group, and the number of rows in the group, would it make sense to compute the row group size by checking the average size of the rows?\r\n\r\neg. 32x32 images could have a larger row group size than full HD images, no? Relying on the size would even remove the need to check the column types.\r\n\r\n(in this proposal, we could use the computed row group size, eg 837, or use the nearest row group size in a list of values: 10, 100, 1000, 10000)", "Probably, but I would go for a simpler solution first :p", "Sure! I wanted to understand if the idea made sense or not, but it's not for this PR.", "I think it will be more useful for people who use the viewer and won't impact sequential io that much.", "DuckDB [paragraph](https://duckdb.org/docs/data/parquet/tips.html#selecting-a-row_group_size) that explains how to choose the `row_group_size`. Our default shard size is 500 MB in `push_to_hub`, so, ideally, we should aim for 64 MB row groups (and make this part configurable for power users 🙂).\r\n\r\nSo, before merging this PR, let's add a TODO or open an issue as a reminder that this can be improved.", "I moved the config values, improved the features check and mentioned the improvements we could do in the docstring :)", "_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) \| 0.006211 / 0.011353 (-0.005141) \| 0.004244 / 0.011008 (-0.006764) \| 0.097941 / 0.038508 (0.059433) \| 0.028564 / 0.023109 (0.005455) \| 0.299651 / 0.275898 (0.023753) \| 0.340694 / 0.323480 (0.017214) \| 0.005161 / 0.007986 (-0.002824) \| 0.004764 / 0.004328 (0.000435) \| 0.075505 / 0.004250 (0.071255) \| 0.039656 / 0.037052 (0.002603) \| 0.309242 / 0.258489 (0.050753) \| 0.350783 / 0.293841 (0.056942) \| 0.025145 / 0.128546 (-0.103401) \| 0.008498 / 0.075646 (-0.067148) \| 0.317657 / 0.419271 (-0.101615) \| 0.043926 / 0.043533 (0.000394) \| 0.305915 / 0.255139 (0.050776) \| 0.331630 / 0.283200 (0.048430) \| 0.088564 / 0.141683 (-0.053119) \| 1.533175 / 1.452155 (0.081021) \| 1.581017 / 1.492716 (0.088301) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.206032 / 0.018006 (0.188025) \| 0.433446 / 0.000490 (0.432956) \| 0.003955 / 0.000200 (0.003755) \| 0.000095 / 0.000054 (0.000040) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023468 / 0.037411 (-0.013943) \| 0.103292 / 0.014526 (0.088766) \| 0.107234 / 0.176557 (-0.069322) \| 0.168525 / 0.737135 (-0.568610) \| 0.113218 / 0.296338 (-0.183120) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.431085 / 0.215209 (0.215875) \| 4.302082 / 2.077655 (2.224427) \| 2.068290 / 1.504120 (0.564171) \| 1.850718 / 1.541195 (0.309523) \| 1.964261 / 1.468490 (0.495771) \| 0.547562 / 4.584777 (-4.037215) \| 3.410739 / 3.745712 (-0.334974) \| 1.779640 / 5.269862 (-3.490221) \| 1.005466 / 4.565676 (-3.560210) \| 0.066250 / 0.424275 (-0.358025) \| 0.011877 / 0.007607 (0.004270) \| 0.525185 / 0.226044 (0.299141) \| 5.234786 / 2.268929 (2.965857) \| 2.398045 / 55.444624 (-53.046580) \| 2.073020 / 6.876477 (-4.803457) \| 2.210753 / 2.142072 (0.068680) \| 0.654897 / 4.805227 (-4.150331) \| 0.134639 / 6.500664 (-6.366025) \| 0.067050 / 0.075469 (-0.008419) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.180210 / 1.841788 (-0.661577) \| 13.613091 / 8.074308 (5.538783) \| 13.441837 / 10.191392 (3.250445) \| 0.146048 / 0.680424 (-0.534376) \| 0.016505 / 0.534201 (-0.517696) \| 0.363210 / 0.579283 (-0.216073) \| 0.405484 / 0.434364 (-0.028880) \| 0.428712 / 0.540337 (-0.111625) \| 0.522300 / 1.386936 (-0.864636) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006147 / 0.011353 (-0.005206) \| 0.004161 / 0.011008 (-0.006847) \| 0.075861 / 0.038508 (0.037353) \| 0.027948 / 0.023109 (0.004839) \| 0.362466 / 0.275898 (0.086568) \| 0.398227 / 0.323480 (0.074747) \| 0.005014 / 0.007986 (-0.002972) \| 0.004772 / 0.004328 (0.000444) \| 0.075674 / 0.004250 (0.071423) \| 0.039158 / 0.037052 (0.002106) \| 0.363567 / 0.258489 (0.105078) \| 0.410378 / 0.293841 (0.116537) \| 0.025510 / 0.128546 (-0.103036) \| 0.008528 / 0.075646 (-0.067118) \| 0.081803 / 0.419271 (-0.337468) \| 0.040954 / 0.043533 (-0.002579) \| 0.358492 / 0.255139 (0.103353) \| 0.381345 / 0.283200 (0.098145) \| 0.092347 / 0.141683 (-0.049336) \| 1.567695 / 1.452155 (0.115540) \| 1.668412 / 1.492716 (0.175696) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.203367 / 0.018006 (0.185360) \| 0.424642 / 0.000490 (0.424152) \| 0.002451 / 0.000200 (0.002251) \| 0.000071 / 0.000054 (0.000017) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026129 / 0.037411 (-0.011282) \| 0.102564 / 0.014526 (0.088039) \| 0.110583 / 0.176557 (-0.065973) \| 0.164332 / 0.737135 (-0.572804) \| 0.115706 / 0.296338 (-0.180632) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.468925 / 0.215209 (0.253716) \| 4.657266 / 2.077655 (2.579612) \| 2.423280 / 1.504120 (0.919160) \| 2.236284 / 1.541195 (0.695089) \| 2.323019 / 1.468490 (0.854529) \| 0.548120 / 4.584777 (-4.036657) \| 3.455602 / 3.745712 (-0.290110) \| 1.730421 / 5.269862 (-3.539441) \| 1.006089 / 4.565676 (-3.559588) \| 0.067478 / 0.424275 (-0.356797) \| 0.011465 / 0.007607 (0.003857) \| 0.574235 / 0.226044 (0.348190) \| 5.744404 / 2.268929 (3.475475) \| 2.882225 / 55.444624 (-52.562400) \| 2.618246 / 6.876477 (-4.258231) \| 2.642920 / 2.142072 (0.500847) \| 0.661441 / 4.805227 (-4.143787) \| 0.137358 / 6.500664 (-6.363306) \| 0.070372 / 0.075469 (-0.005097) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.333815 / 1.841788 (-0.507973) \| 14.689667 / 8.074308 (6.615359) \| 14.362294 / 10.191392 (4.170902) \| 0.152011 / 0.680424 (-0.528413) \| 0.016869 / 0.534201 (-0.517332) \| 0.370433 / 0.579283 (-0.208851) \| 0.399642 / 0.434364 (-0.034722) \| 0.433759 / 0.540337 (-0.106578) \| 0.525443 / 1.386936 (-0.861493) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#09e9f9a88edd9055b5c540e3d83b5a11d48f8ba8 \"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) \| 0.006564 / 0.011353 (-0.004789) \| 0.004350 / 0.011008 (-0.006658) \| 0.096277 / 0.038508 (0.057769) \| 0.032956 / 0.023109 (0.009847) \| 0.303675 / 0.275898 (0.027777) \| 0.336384 / 0.323480 (0.012904) \| 0.005789 / 0.007986 (-0.002197) \| 0.003957 / 0.004328 (-0.000371) \| 0.073990 / 0.004250 (0.069740) \| 0.050974 / 0.037052 (0.013922) \| 0.321754 / 0.258489 (0.063265) \| 0.349489 / 0.293841 (0.055648) \| 0.031138 / 0.128546 (-0.097409) \| 0.009000 / 0.075646 (-0.066646) \| 0.325445 / 0.419271 (-0.093826) \| 0.070173 / 0.043533 (0.026640) \| 0.304706 / 0.255139 (0.049567) \| 0.321803 / 0.283200 (0.038603) \| 0.109405 / 0.141683 (-0.032278) \| 1.489812 / 1.452155 (0.037657) \| 1.577729 / 1.492716 (0.085013) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287187 / 0.018006 (0.269181) \| 0.527625 / 0.000490 (0.527135) \| 0.006533 / 0.000200 (0.006333) \| 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) \| 0.026659 / 0.037411 (-0.010752) \| 0.106236 / 0.014526 (0.091710) \| 0.118615 / 0.176557 (-0.057941) \| 0.173156 / 0.737135 (-0.563979) \| 0.122883 / 0.296338 (-0.173456) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.407189 / 0.215209 (0.191980) \| 4.055732 / 2.077655 (1.978078) \| 1.865594 / 1.504120 (0.361474) \| 1.664325 / 1.541195 (0.123130) \| 1.668961 / 1.468490 (0.200471) \| 0.521207 / 4.584777 (-4.063570) \| 3.740424 / 3.745712 (-0.005288) \| 3.431973 / 5.269862 (-1.837889) \| 1.636669 / 4.565676 (-2.929008) \| 0.065271 / 0.424275 (-0.359005) \| 0.012151 / 0.007607 (0.004544) \| 0.514233 / 0.226044 (0.288189) \| 5.110150 / 2.268929 (2.841222) \| 2.264340 / 55.444624 (-53.180284) \| 1.940428 / 6.876477 (-4.936049) \| 2.042286 / 2.142072 (-0.099787) \| 0.639200 / 4.805227 (-4.166028) \| 0.139537 / 6.500664 (-6.361127) \| 0.063195 / 0.075469 (-0.012274) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.179501 / 1.841788 (-0.662286) \| 14.600133 / 8.074308 (6.525825) \| 14.902137 / 10.191392 (4.710745) \| 0.144509 / 0.680424 (-0.535915) \| 0.017449 / 0.534201 (-0.516752) \| 0.393135 / 0.579283 (-0.186148) \| 0.413103 / 0.434364 (-0.021261) \| 0.459897 / 0.540337 (-0.080440) \| 0.552602 / 1.386936 (-0.834334) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006891 / 0.011353 (-0.004462) \| 0.004633 / 0.011008 (-0.006375) \| 0.073093 / 0.038508 (0.034585) \| 0.032509 / 0.023109 (0.009399) \| 0.348332 / 0.275898 (0.072434) \| 0.381920 / 0.323480 (0.058440) \| 0.005978 / 0.007986 (-0.002007) \| 0.005360 / 0.004328 (0.001032) \| 0.074307 / 0.004250 (0.070056) \| 0.049668 / 0.037052 (0.012615) \| 0.354713 / 0.258489 (0.096224) \| 0.398521 / 0.293841 (0.104681) \| 0.032013 / 0.128546 (-0.096534) \| 0.008890 / 0.075646 (-0.066756) \| 0.080013 / 0.419271 (-0.339259) \| 0.051820 / 0.043533 (0.008288) \| 0.349730 / 0.255139 (0.094591) \| 0.369267 / 0.283200 (0.086067) \| 0.103874 / 0.141683 (-0.037809) \| 1.484148 / 1.452155 (0.031993) \| 1.573927 / 1.492716 (0.081211) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009699 / 0.018006 (-0.008307) \| 0.511176 / 0.000490 (0.510686) \| 0.002938 / 0.000200 (0.002738) \| 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) \| 0.027847 / 0.037411 (-0.009564) \| 0.111565 / 0.014526 (0.097039) \| 0.120625 / 0.176557 (-0.055932) \| 0.172130 / 0.737135 (-0.565006) \| 0.125949 / 0.296338 (-0.170389) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.430634 / 0.215209 (0.215424) \| 4.315377 / 2.077655 (2.237722) \| 2.070764 / 1.504120 (0.566644) \| 1.881962 / 1.541195 (0.340767) \| 1.904053 / 1.468490 (0.435563) \| 0.524973 / 4.584777 (-4.059804) \| 3.718359 / 3.745712 (-0.027353) \| 3.415344 / 5.269862 (-1.854518) \| 1.224568 / 4.565676 (-3.341108) \| 0.065593 / 0.424275 (-0.358682) \| 0.011643 / 0.007607 (0.004036) \| 0.537050 / 0.226044 (0.311006) \| 5.352155 / 2.268929 (3.083226) \| 2.557361 / 55.444624 (-52.887263) \| 2.217770 / 6.876477 (-4.658707) \| 2.194975 / 2.142072 (0.052902) \| 0.635142 / 4.805227 (-4.170085) \| 0.140642 / 6.500664 (-6.360022) \| 0.064690 / 0.075469 (-0.010779) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.266125 / 1.841788 (-0.575663) \| 14.836413 / 8.074308 (6.762105) \| 14.446870 / 10.191392 (4.255478) \| 0.191545 / 0.680424 (-0.488878) \| 0.017433 / 0.534201 (-0.516768) \| 0.392296 / 0.579283 (-0.186987) \| 0.420698 / 0.434364 (-0.013666) \| 0.463225 / 0.540337 (-0.077112) \| 0.556127 / 1.386936 (-0.830809) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7fcbe5b1575c8d162b65b9397b3dfda995a4e048 \"CML watermark\")\n" ]
1,747,904,840	5,934	Modify levels of some logging messages	closed	2023-06-08T13:31:44	2023-07-12T18:21:03	2023-07-12T18:21:02	https://github.com/huggingface/datasets/pull/5934	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5934", "html_url": "https://github.com/huggingface/datasets/pull/5934", "diff_url": "https://github.com/huggingface/datasets/pull/5934.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5934.patch", "merged_at": null }	Laurent2916	true	[ "I've addressed this as part of #6019, so feel free to close this PR. ", "Thanks !" ]
1,747,382,500	5,933	Fix `to_numpy` when None values in the sequence	closed	2023-06-08T08:38:56	2023-06-09T13:49:41	2023-06-09T13:23:48	https://github.com/huggingface/datasets/pull/5933	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5933", "html_url": "https://github.com/huggingface/datasets/pull/5933", "diff_url": "https://github.com/huggingface/datasets/pull/5933.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5933.patch", "merged_at": "2023-06-09T13:23:48" }	qgallouedec	true	[ "I just added the same test with dynamic shape", "_The documentation is not available anymore as the PR was closed or merged._", "Awesome ! I'm merging now if you don't mind :)\r\nWe should probably give you permissions to merge your own PRs when you have an approval", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009980 / 0.011353 (-0.001373) \| 0.005709 / 0.011008 (-0.005300) \| 0.132185 / 0.038508 (0.093677) \| 0.039299 / 0.023109 (0.016190) \| 0.400168 / 0.275898 (0.124270) \| 0.470582 / 0.323480 (0.147102) \| 0.007753 / 0.007986 (-0.000233) \| 0.005196 / 0.004328 (0.000868) \| 0.093698 / 0.004250 (0.089448) \| 0.052631 / 0.037052 (0.015579) \| 0.430347 / 0.258489 (0.171858) \| 0.460162 / 0.293841 (0.166321) \| 0.057511 / 0.128546 (-0.071035) \| 0.013944 / 0.075646 (-0.061702) \| 0.459008 / 0.419271 (0.039737) \| 0.075532 / 0.043533 (0.031999) \| 0.405165 / 0.255139 (0.150026) \| 0.456142 / 0.283200 (0.172942) \| 0.117309 / 0.141683 (-0.024374) \| 1.945787 / 1.452155 (0.493633) \| 2.067162 / 1.492716 (0.574446) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285755 / 0.018006 (0.267749) \| 0.619965 / 0.000490 (0.619476) \| 0.005071 / 0.000200 (0.004871) \| 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) \| 0.031112 / 0.037411 (-0.006299) \| 0.128514 / 0.014526 (0.113988) \| 0.137161 / 0.176557 (-0.039396) \| 0.211363 / 0.737135 (-0.525772) \| 0.151045 / 0.296338 (-0.145293) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.609361 / 0.215209 (0.394152) \| 6.124844 / 2.077655 (4.047189) \| 2.440757 / 1.504120 (0.936637) \| 2.034495 / 1.541195 (0.493300) \| 2.047192 / 1.468490 (0.578702) \| 0.883171 / 4.584777 (-3.701606) \| 5.470552 / 3.745712 (1.724840) \| 4.401696 / 5.269862 (-0.868165) \| 2.378674 / 4.565676 (-2.187003) \| 0.108065 / 0.424275 (-0.316210) \| 0.013239 / 0.007607 (0.005632) \| 0.830957 / 0.226044 (0.604913) \| 8.090659 / 2.268929 (5.821731) \| 3.289203 / 55.444624 (-52.155422) \| 2.500777 / 6.876477 (-4.375700) \| 2.561440 / 2.142072 (0.419367) \| 1.064893 / 4.805227 (-3.740334) \| 0.220486 / 6.500664 (-6.280178) \| 0.079507 / 0.075469 (0.004038) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.544334 / 1.841788 (-0.297454) \| 17.878997 / 8.074308 (9.804689) \| 18.952191 / 10.191392 (8.760799) \| 0.245166 / 0.680424 (-0.435258) \| 0.028022 / 0.534201 (-0.506179) \| 0.517828 / 0.579283 (-0.061455) \| 0.618988 / 0.434364 (0.184624) \| 0.589742 / 0.540337 (0.049405) \| 0.670902 / 1.386936 (-0.716034) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009616 / 0.011353 (-0.001737) \| 0.006098 / 0.011008 (-0.004911) \| 0.100301 / 0.038508 (0.061793) \| 0.037792 / 0.023109 (0.014683) \| 0.484667 / 0.275898 (0.208769) \| 0.519286 / 0.323480 (0.195806) \| 0.007427 / 0.007986 (-0.000558) \| 0.007172 / 0.004328 (0.002844) \| 0.104429 / 0.004250 (0.100179) \| 0.056567 / 0.037052 (0.019515) \| 0.502641 / 0.258489 (0.244152) \| 0.549629 / 0.293841 (0.255788) \| 0.049574 / 0.128546 (-0.078972) \| 0.015223 / 0.075646 (-0.060424) \| 0.113947 / 0.419271 (-0.305324) \| 0.064585 / 0.043533 (0.021053) \| 0.512962 / 0.255139 (0.257823) \| 0.507218 / 0.283200 (0.224019) \| 0.122194 / 0.141683 (-0.019488) \| 1.927821 / 1.452155 (0.475667) \| 2.051161 / 1.492716 (0.558445) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291350 / 0.018006 (0.273344) \| 0.588099 / 0.000490 (0.587610) \| 0.001368 / 0.000200 (0.001168) \| 0.000153 / 0.000054 (0.000099) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030604 / 0.037411 (-0.006807) \| 0.126810 / 0.014526 (0.112285) \| 0.139309 / 0.176557 (-0.037248) \| 0.208030 / 0.737135 (-0.529105) \| 0.138985 / 0.296338 (-0.157353) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.681254 / 0.215209 (0.466045) \| 6.753856 / 2.077655 (4.676201) \| 2.780704 / 1.504120 (1.276585) \| 2.475205 / 1.541195 (0.934010) \| 2.486784 / 1.468490 (1.018294) \| 0.879223 / 4.584777 (-3.705554) \| 5.662294 / 3.745712 (1.916582) \| 2.698705 / 5.269862 (-2.571156) \| 1.660620 / 4.565676 (-2.905057) \| 0.112218 / 0.424275 (-0.312057) \| 0.014211 / 0.007607 (0.006604) \| 0.796957 / 0.226044 (0.570913) \| 8.180897 / 2.268929 (5.911969) \| 3.540419 / 55.444624 (-51.904205) \| 2.899467 / 6.876477 (-3.977010) \| 2.870306 / 2.142072 (0.728233) \| 1.069537 / 4.805227 (-3.735690) \| 0.211281 / 6.500664 (-6.289383) \| 0.078898 / 0.075469 (0.003429) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.666790 / 1.841788 (-0.174998) \| 18.302127 / 8.074308 (10.227819) \| 21.317546 / 10.191392 (11.126153) \| 0.242795 / 0.680424 (-0.437629) \| 0.026754 / 0.534201 (-0.507447) \| 0.493375 / 0.579283 (-0.085908) \| 0.605400 / 0.434364 (0.171036) \| 0.586888 / 0.540337 (0.046550) \| 0.722809 / 1.386936 (-0.664127) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ce2328e7b1d62998b22510492530af55d4493b73 \"CML watermark\")\n" ]
1,746,249,161	5,932	[doc build] Use secrets	closed	2023-06-07T16:09:39	2023-06-09T10:16:58	2023-06-09T09:53:16	https://github.com/huggingface/datasets/pull/5932	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5932", "html_url": "https://github.com/huggingface/datasets/pull/5932", "diff_url": "https://github.com/huggingface/datasets/pull/5932.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5932.patch", "merged_at": "2023-06-09T09:53:16" }	mishig25	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) \| 0.008499 / 0.011353 (-0.002854) \| 0.006155 / 0.011008 (-0.004853) \| 0.124032 / 0.038508 (0.085524) \| 0.037337 / 0.023109 (0.014228) \| 0.389274 / 0.275898 (0.113376) \| 0.427736 / 0.323480 (0.104257) \| 0.006929 / 0.007986 (-0.001057) \| 0.005017 / 0.004328 (0.000689) \| 0.096356 / 0.004250 (0.092105) \| 0.055694 / 0.037052 (0.018642) \| 0.391417 / 0.258489 (0.132928) \| 0.448098 / 0.293841 (0.154257) \| 0.042442 / 0.128546 (-0.086105) \| 0.013456 / 0.075646 (-0.062190) \| 0.423502 / 0.419271 (0.004230) \| 0.062919 / 0.043533 (0.019386) \| 0.384317 / 0.255139 (0.129178) \| 0.410851 / 0.283200 (0.127652) \| 0.112807 / 0.141683 (-0.028875) \| 1.746050 / 1.452155 (0.293895) \| 1.977974 / 1.492716 (0.485257) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.306382 / 0.018006 (0.288375) \| 0.620310 / 0.000490 (0.619820) \| 0.009309 / 0.000200 (0.009109) \| 0.000106 / 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) \| 0.026900 / 0.037411 (-0.010511) \| 0.140125 / 0.014526 (0.125599) \| 0.136295 / 0.176557 (-0.040261) \| 0.207721 / 0.737135 (-0.529414) \| 0.146328 / 0.296338 (-0.150011) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.616712 / 0.215209 (0.401503) \| 6.237820 / 2.077655 (4.160166) \| 2.503809 / 1.504120 (0.999689) \| 2.129739 / 1.541195 (0.588544) \| 2.160768 / 1.468490 (0.692277) \| 0.971273 / 4.584777 (-3.613504) \| 5.687161 / 3.745712 (1.941449) \| 2.738148 / 5.269862 (-2.531713) \| 1.692695 / 4.565676 (-2.872981) \| 0.113701 / 0.424275 (-0.310574) \| 0.014809 / 0.007607 (0.007202) \| 0.774795 / 0.226044 (0.548750) \| 7.660012 / 2.268929 (5.391083) \| 3.253036 / 55.444624 (-52.191588) \| 2.607498 / 6.876477 (-4.268979) \| 2.681678 / 2.142072 (0.539606) \| 1.095275 / 4.805227 (-3.709952) \| 0.239078 / 6.500664 (-6.261586) \| 0.081034 / 0.075469 (0.005565) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.574547 / 1.841788 (-0.267240) \| 18.323566 / 8.074308 (10.249258) \| 19.274482 / 10.191392 (9.083090) \| 0.210275 / 0.680424 (-0.470149) \| 0.031843 / 0.534201 (-0.502358) \| 0.514843 / 0.579283 (-0.064440) \| 0.633782 / 0.434364 (0.199418) \| 0.588569 / 0.540337 (0.048232) \| 0.721401 / 1.386936 (-0.665535) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008866 / 0.011353 (-0.002487) \| 0.006460 / 0.011008 (-0.004548) \| 0.121337 / 0.038508 (0.082829) \| 0.033896 / 0.023109 (0.010786) \| 0.455702 / 0.275898 (0.179804) \| 0.509685 / 0.323480 (0.186205) \| 0.007650 / 0.007986 (-0.000336) \| 0.005578 / 0.004328 (0.001250) \| 0.098505 / 0.004250 (0.094255) \| 0.056122 / 0.037052 (0.019069) \| 0.478483 / 0.258489 (0.219994) \| 0.560008 / 0.293841 (0.266167) \| 0.044926 / 0.128546 (-0.083620) \| 0.014562 / 0.075646 (-0.061085) \| 0.115027 / 0.419271 (-0.304244) \| 0.066494 / 0.043533 (0.022961) \| 0.463434 / 0.255139 (0.208296) \| 0.513856 / 0.283200 (0.230656) \| 0.126436 / 0.141683 (-0.015247) \| 1.874729 / 1.452155 (0.422575) \| 1.925080 / 1.492716 (0.432364) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.012672 / 0.018006 (-0.005334) \| 0.615797 / 0.000490 (0.615307) \| 0.001606 / 0.000200 (0.001406) \| 0.000118 / 0.000054 (0.000064) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031104 / 0.037411 (-0.006307) \| 0.130107 / 0.014526 (0.115581) \| 0.140587 / 0.176557 (-0.035970) \| 0.205081 / 0.737135 (-0.532054) \| 0.144068 / 0.296338 (-0.152270) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.646549 / 0.215209 (0.431340) \| 6.403962 / 2.077655 (4.326307) \| 2.812594 / 1.504120 (1.308474) \| 2.478480 / 1.541195 (0.937285) \| 2.552385 / 1.468490 (1.083895) \| 0.991987 / 4.584777 (-3.592790) \| 5.777917 / 3.745712 (2.032205) \| 5.697830 / 5.269862 (0.427969) \| 2.370583 / 4.565676 (-2.195094) \| 0.109905 / 0.424275 (-0.314370) \| 0.013801 / 0.007607 (0.006193) \| 0.799932 / 0.226044 (0.573888) \| 8.155672 / 2.268929 (5.886743) \| 3.711662 / 55.444624 (-51.732963) \| 3.042164 / 6.876477 (-3.834312) \| 3.073549 / 2.142072 (0.931477) \| 1.137515 / 4.805227 (-3.667712) \| 0.231266 / 6.500664 (-6.269398) \| 0.080893 / 0.075469 (0.005424) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.669210 / 1.841788 (-0.172577) \| 18.747144 / 8.074308 (10.672836) \| 21.084589 / 10.191392 (10.893197) \| 0.241379 / 0.680424 (-0.439045) \| 0.029473 / 0.534201 (-0.504728) \| 0.524605 / 0.579283 (-0.054678) \| 0.622852 / 0.434364 (0.188488) \| 0.604941 / 0.540337 (0.064604) \| 0.715978 / 1.386936 (-0.670958) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#142484a60b1330359d7713e906fc9e5e30aa9f64 \"CML watermark\")\n", "Cool ! what about `.github/workflows/build_pr_documentation.yml` and `.github/workflows/delete_doc_comment.yml` ?", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005973 / 0.011353 (-0.005380) \| 0.004389 / 0.011008 (-0.006620) \| 0.096076 / 0.038508 (0.057568) \| 0.031569 / 0.023109 (0.008460) \| 0.328300 / 0.275898 (0.052402) \| 0.359356 / 0.323480 (0.035876) \| 0.005378 / 0.007986 (-0.002607) \| 0.003703 / 0.004328 (-0.000625) \| 0.075251 / 0.004250 (0.071000) \| 0.042340 / 0.037052 (0.005287) \| 0.346103 / 0.258489 (0.087614) \| 0.379896 / 0.293841 (0.086055) \| 0.027493 / 0.128546 (-0.101053) \| 0.009033 / 0.075646 (-0.066613) \| 0.327829 / 0.419271 (-0.091442) \| 0.064074 / 0.043533 (0.020541) \| 0.337703 / 0.255139 (0.082564) \| 0.355335 / 0.283200 (0.072136) \| 0.101179 / 0.141683 (-0.040504) \| 1.471738 / 1.452155 (0.019584) \| 1.539031 / 1.492716 (0.046315) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.194097 / 0.018006 (0.176091) \| 0.434190 / 0.000490 (0.433701) \| 0.005730 / 0.000200 (0.005530) \| 0.000088 / 0.000054 (0.000033) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025634 / 0.037411 (-0.011778) \| 0.105080 / 0.014526 (0.090555) \| 0.116508 / 0.176557 (-0.060049) \| 0.173867 / 0.737135 (-0.563269) \| 0.117749 / 0.296338 (-0.178590) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.401566 / 0.215209 (0.186357) \| 4.003558 / 2.077655 (1.925903) \| 1.802756 / 1.504120 (0.298636) \| 1.604222 / 1.541195 (0.063027) \| 1.656617 / 1.468490 (0.188127) \| 0.523385 / 4.584777 (-4.061392) \| 3.744292 / 3.745712 (-0.001420) \| 1.794295 / 5.269862 (-3.475567) \| 1.044690 / 4.565676 (-3.520987) \| 0.064992 / 0.424275 (-0.359284) \| 0.011542 / 0.007607 (0.003935) \| 0.507830 / 0.226044 (0.281785) \| 5.061574 / 2.268929 (2.792645) \| 2.252896 / 55.444624 (-53.191729) \| 1.912551 / 6.876477 (-4.963926) \| 2.073510 / 2.142072 (-0.068562) \| 0.642148 / 4.805227 (-4.163079) \| 0.140151 / 6.500664 (-6.360513) \| 0.062623 / 0.075469 (-0.012846) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.180367 / 1.841788 (-0.661421) \| 14.263475 / 8.074308 (6.189167) \| 12.917251 / 10.191392 (2.725859) \| 0.143815 / 0.680424 (-0.536608) \| 0.017286 / 0.534201 (-0.516915) \| 0.388411 / 0.579283 (-0.190872) \| 0.430512 / 0.434364 (-0.003851) \| 0.466595 / 0.540337 (-0.073742) \| 0.564545 / 1.386936 (-0.822391) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006059 / 0.011353 (-0.005294) \| 0.004419 / 0.011008 (-0.006590) \| 0.074206 / 0.038508 (0.035697) \| 0.031180 / 0.023109 (0.008071) \| 0.380031 / 0.275898 (0.104133) \| 0.410373 / 0.323480 (0.086893) \| 0.005397 / 0.007986 (-0.002589) \| 0.003952 / 0.004328 (-0.000376) \| 0.074426 / 0.004250 (0.070176) \| 0.046256 / 0.037052 (0.009203) \| 0.385543 / 0.258489 (0.127054) \| 0.430724 / 0.293841 (0.136883) \| 0.028052 / 0.128546 (-0.100494) \| 0.008810 / 0.075646 (-0.066836) \| 0.080749 / 0.419271 (-0.338522) \| 0.046746 / 0.043533 (0.003214) \| 0.380325 / 0.255139 (0.125186) \| 0.398901 / 0.283200 (0.115701) \| 0.099607 / 0.141683 (-0.042076) \| 1.433343 / 1.452155 (-0.018812) \| 1.520447 / 1.492716 (0.027730) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.202232 / 0.018006 (0.184225) \| 0.431342 / 0.000490 (0.430852) \| 0.001020 / 0.000200 (0.000820) \| 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) \| 0.028762 / 0.037411 (-0.008649) \| 0.111777 / 0.014526 (0.097251) \| 0.119283 / 0.176557 (-0.057273) \| 0.168151 / 0.737135 (-0.568985) \| 0.126093 / 0.296338 (-0.170245) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.442689 / 0.215209 (0.227480) \| 4.369202 / 2.077655 (2.291547) \| 2.167703 / 1.504120 (0.663583) \| 1.960580 / 1.541195 (0.419385) \| 2.001459 / 1.468490 (0.532969) \| 0.527169 / 4.584777 (-4.057608) \| 3.738987 / 3.745712 (-0.006726) \| 1.819002 / 5.269862 (-3.450860) \| 1.082786 / 4.565676 (-3.482891) \| 0.066209 / 0.424275 (-0.358066) \| 0.011549 / 0.007607 (0.003942) \| 0.545959 / 0.226044 (0.319915) \| 5.466655 / 2.268929 (3.197727) \| 2.671448 / 55.444624 (-52.773176) \| 2.340968 / 6.876477 (-4.535509) \| 2.358805 / 2.142072 (0.216733) \| 0.649456 / 4.805227 (-4.155771) \| 0.142009 / 6.500664 (-6.358655) \| 0.064199 / 0.075469 (-0.011270) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.259819 / 1.841788 (-0.581969) \| 14.456988 / 8.074308 (6.382680) \| 14.478982 / 10.191392 (4.287590) \| 0.163156 / 0.680424 (-0.517268) \| 0.017090 / 0.534201 (-0.517111) \| 0.391339 / 0.579283 (-0.187944) \| 0.422021 / 0.434364 (-0.012343) \| 0.465340 / 0.540337 (-0.074997) \| 0.564517 / 1.386936 (-0.822419) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#97358c88f996a65f49923ec215358044e4146a95 \"CML watermark\")\n", "> .github/workflows/delete_doc_comment.yml \r\n\r\nis already updated https://github.com/huggingface/datasets/pull/5932/files\r\n\r\n> .github/workflows/build_pr_documentation.yml\r\n\r\nindeed no changes are needed" ]
1,745,408,784	5,931	`datasets.map` not reusing cached copy by default	closed	2023-06-07T09:03:33	2023-06-21T16:15:40	2023-06-21T16:15:40	https://github.com/huggingface/datasets/issues/5931	null	bhavitvyamalik	false	[ "This can happen when a map transform cannot be hashed deterministically (e.g., an object referenced by the transform changes its state after the first call - an issue with fast tokenizers). The solution is to provide `cache_file_name` in the `map` call to check this file for the cached result instead of relying on the default caching mechanism." ]
1,745,184,395	5,930	loading private custom dataset script - authentication error	closed	2023-06-07T06:58:23	2023-06-15T14:49:21	2023-06-15T14:49:20	https://github.com/huggingface/datasets/issues/5930	null	flckv	false	[ "This issue seems to have been resolved, so I'm closing it." ]
1,744,478,456	5,929	Importing PyTorch reduces multiprocessing performance for map	closed	2023-06-06T19:42:25	2023-06-16T13:09:12	2023-06-16T13:09:12	https://github.com/huggingface/datasets/issues/5929	null	Maxscha	false	[ "Hi! The times match when I run this code locally or on Colab.\r\n\r\nAlso, we use `multiprocess`, not `multiprocessing`, for parallelization, and torch's `__init__.py` (executed on `import torch` ) slightly modifies the latter.", "Hey Mariosasko,\r\n\r\nThanks for looking into it. We further did some investigations after your comment and figured out it's only affecting some hardware/software configurations with the `pytorch` installation of `conda-forge`. Based on this we found the following issue in PyTorch: https://github.com/pytorch/pytorch/issues/102269 with a quick fix for now.\r\n\r\nSince it seems to be a deeper issue with forking processes, the difference between`multiprocess` and `multiprocessing` didn't make a difference.\r\n\r\nClosing this, since the issue comes from `pytorch` not `dataset`. \r\n" ]
1,744,098,371	5,928	Fix link to quickstart docs in README.md	closed	2023-06-06T15:23:01	2023-06-06T15:52:34	2023-06-06T15:43:53	https://github.com/huggingface/datasets/pull/5928	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5928", "html_url": "https://github.com/huggingface/datasets/pull/5928", "diff_url": "https://github.com/huggingface/datasets/pull/5928.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5928.patch", "merged_at": "2023-06-06T15:43:53" }	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) \| 0.006693 / 0.011353 (-0.004660) \| 0.004331 / 0.011008 (-0.006677) \| 0.098022 / 0.038508 (0.059514) \| 0.032764 / 0.023109 (0.009654) \| 0.295812 / 0.275898 (0.019914) \| 0.325029 / 0.323480 (0.001550) \| 0.005779 / 0.007986 (-0.002206) \| 0.005381 / 0.004328 (0.001052) \| 0.075785 / 0.004250 (0.071535) \| 0.048759 / 0.037052 (0.011707) \| 0.308986 / 0.258489 (0.050497) \| 0.348000 / 0.293841 (0.054159) \| 0.027686 / 0.128546 (-0.100860) \| 0.008839 / 0.075646 (-0.066807) \| 0.328389 / 0.419271 (-0.090883) \| 0.062173 / 0.043533 (0.018640) \| 0.312257 / 0.255139 (0.057119) \| 0.325024 / 0.283200 (0.041824) \| 0.103886 / 0.141683 (-0.037797) \| 1.440215 / 1.452155 (-0.011940) \| 1.528665 / 1.492716 (0.035948) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.210082 / 0.018006 (0.192076) \| 0.442480 / 0.000490 (0.441990) \| 0.006559 / 0.000200 (0.006359) \| 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) \| 0.026774 / 0.037411 (-0.010637) \| 0.108362 / 0.014526 (0.093837) \| 0.117631 / 0.176557 (-0.058926) \| 0.176657 / 0.737135 (-0.560478) \| 0.124154 / 0.296338 (-0.172184) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.428136 / 0.215209 (0.212927) \| 4.270287 / 2.077655 (2.192632) \| 2.014728 / 1.504120 (0.510608) \| 1.806772 / 1.541195 (0.265577) \| 1.946284 / 1.468490 (0.477794) \| 0.525542 / 4.584777 (-4.059235) \| 3.667025 / 3.745712 (-0.078687) \| 1.878751 / 5.269862 (-3.391111) \| 1.048321 / 4.565676 (-3.517356) \| 0.065550 / 0.424275 (-0.358725) \| 0.011881 / 0.007607 (0.004274) \| 0.529873 / 0.226044 (0.303829) \| 5.289641 / 2.268929 (3.020712) \| 2.489403 / 55.444624 (-52.955221) \| 2.141037 / 6.876477 (-4.735440) \| 2.230735 / 2.142072 (0.088662) \| 0.639781 / 4.805227 (-4.165447) \| 0.141410 / 6.500664 (-6.359254) \| 0.064374 / 0.075469 (-0.011095) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.159462 / 1.841788 (-0.682325) \| 14.524730 / 8.074308 (6.450422) \| 13.578070 / 10.191392 (3.386678) \| 0.152138 / 0.680424 (-0.528286) \| 0.017255 / 0.534201 (-0.516946) \| 0.387607 / 0.579283 (-0.191676) \| 0.413652 / 0.434364 (-0.020712) \| 0.453644 / 0.540337 (-0.086693) \| 0.550051 / 1.386936 (-0.836885) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006668 / 0.011353 (-0.004685) \| 0.004677 / 0.011008 (-0.006331) \| 0.075950 / 0.038508 (0.037442) \| 0.032439 / 0.023109 (0.009329) \| 0.381839 / 0.275898 (0.105941) \| 0.419411 / 0.323480 (0.095931) \| 0.005813 / 0.007986 (-0.002172) \| 0.004090 / 0.004328 (-0.000238) \| 0.075052 / 0.004250 (0.070802) \| 0.048453 / 0.037052 (0.011401) \| 0.388076 / 0.258489 (0.129587) \| 0.431793 / 0.293841 (0.137952) \| 0.028408 / 0.128546 (-0.100138) \| 0.009028 / 0.075646 (-0.066618) \| 0.082569 / 0.419271 (-0.336702) \| 0.046772 / 0.043533 (0.003239) \| 0.380182 / 0.255139 (0.125043) \| 0.401828 / 0.283200 (0.118629) \| 0.105388 / 0.141683 (-0.036294) \| 1.453356 / 1.452155 (0.001201) \| 1.561483 / 1.492716 (0.068767) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008922 / 0.018006 (-0.009084) \| 0.444112 / 0.000490 (0.443623) \| 0.002756 / 0.000200 (0.002556) \| 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) \| 0.030408 / 0.037411 (-0.007003) \| 0.112924 / 0.014526 (0.098399) \| 0.124625 / 0.176557 (-0.051932) \| 0.176915 / 0.737135 (-0.560220) \| 0.129141 / 0.296338 (-0.167198) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.448197 / 0.215209 (0.232987) \| 4.476548 / 2.077655 (2.398893) \| 2.243977 / 1.504120 (0.739857) \| 2.054060 / 1.541195 (0.512865) \| 2.130680 / 1.468490 (0.662190) \| 0.526815 / 4.584777 (-4.057962) \| 3.759312 / 3.745712 (0.013600) \| 3.333618 / 5.269862 (-1.936244) \| 1.579611 / 4.565676 (-2.986065) \| 0.065714 / 0.424275 (-0.358561) \| 0.011939 / 0.007607 (0.004332) \| 0.550313 / 0.226044 (0.324269) \| 5.476946 / 2.268929 (3.208018) \| 2.726521 / 55.444624 (-52.718104) \| 2.364977 / 6.876477 (-4.511499) \| 2.450624 / 2.142072 (0.308551) \| 0.647174 / 4.805227 (-4.158053) \| 0.141265 / 6.500664 (-6.359399) \| 0.065493 / 0.075469 (-0.009976) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.249702 / 1.841788 (-0.592085) \| 15.205647 / 8.074308 (7.131338) \| 14.678310 / 10.191392 (4.486918) \| 0.141539 / 0.680424 (-0.538884) \| 0.017323 / 0.534201 (-0.516878) \| 0.387602 / 0.579283 (-0.191681) \| 0.415106 / 0.434364 (-0.019258) \| 0.458146 / 0.540337 (-0.082192) \| 0.553318 / 1.386936 (-0.833618) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#55127d7bf399fd2f3a8713db9822e8cb47cdbbed \"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) \| 0.008567 / 0.011353 (-0.002786) \| 0.005245 / 0.011008 (-0.005763) \| 0.115074 / 0.038508 (0.076566) \| 0.032567 / 0.023109 (0.009458) \| 0.352297 / 0.275898 (0.076399) \| 0.393403 / 0.323480 (0.069923) \| 0.006402 / 0.007986 (-0.001583) \| 0.004353 / 0.004328 (0.000025) \| 0.087903 / 0.004250 (0.083653) \| 0.048424 / 0.037052 (0.011372) \| 0.370078 / 0.258489 (0.111588) \| 0.410192 / 0.293841 (0.116351) \| 0.042396 / 0.128546 (-0.086150) \| 0.014426 / 0.075646 (-0.061220) \| 0.411358 / 0.419271 (-0.007914) \| 0.059546 / 0.043533 (0.016013) \| 0.364721 / 0.255139 (0.109582) \| 0.385100 / 0.283200 (0.101901) \| 0.100572 / 0.141683 (-0.041111) \| 1.741457 / 1.452155 (0.289302) \| 1.933134 / 1.492716 (0.440418) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.217177 / 0.018006 (0.199171) \| 0.510399 / 0.000490 (0.509909) \| 0.005542 / 0.000200 (0.005342) \| 0.000120 / 0.000054 (0.000065) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026852 / 0.037411 (-0.010559) \| 0.125580 / 0.014526 (0.111054) \| 0.132164 / 0.176557 (-0.044392) \| 0.189073 / 0.737135 (-0.548063) \| 0.135980 / 0.296338 (-0.160358) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.601924 / 0.215209 (0.386715) \| 5.891397 / 2.077655 (3.813743) \| 2.389494 / 1.504120 (0.885375) \| 2.044013 / 1.541195 (0.502818) \| 2.019367 / 1.468490 (0.550877) \| 0.883807 / 4.584777 (-3.700970) \| 5.141349 / 3.745712 (1.395636) \| 2.607415 / 5.269862 (-2.662446) \| 1.567268 / 4.565676 (-2.998409) \| 0.102738 / 0.424275 (-0.321537) \| 0.013480 / 0.007607 (0.005873) \| 0.744979 / 0.226044 (0.518934) \| 7.404182 / 2.268929 (5.135254) \| 2.983406 / 55.444624 (-52.461219) \| 2.331847 / 6.876477 (-4.544630) \| 2.465119 / 2.142072 (0.323047) \| 1.106725 / 4.805227 (-3.698502) \| 0.205779 / 6.500664 (-6.294885) \| 0.081019 / 0.075469 (0.005550) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.527840 / 1.841788 (-0.313947) \| 16.989487 / 8.074308 (8.915179) \| 18.016123 / 10.191392 (7.824731) \| 0.216157 / 0.680424 (-0.464266) \| 0.025393 / 0.534201 (-0.508808) \| 0.496743 / 0.579283 (-0.082540) \| 0.575365 / 0.434364 (0.141002) \| 0.559978 / 0.540337 (0.019641) \| 0.677474 / 1.386936 (-0.709462) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008913 / 0.011353 (-0.002440) \| 0.005540 / 0.011008 (-0.005469) \| 0.100001 / 0.038508 (0.061493) \| 0.034432 / 0.023109 (0.011323) \| 0.419824 / 0.275898 (0.143926) \| 0.443566 / 0.323480 (0.120086) \| 0.006372 / 0.007986 (-0.001614) \| 0.004405 / 0.004328 (0.000077) \| 0.094927 / 0.004250 (0.090677) \| 0.050300 / 0.037052 (0.013248) \| 0.424806 / 0.258489 (0.166317) \| 0.480793 / 0.293841 (0.186952) \| 0.050869 / 0.128546 (-0.077677) \| 0.015899 / 0.075646 (-0.059747) \| 0.111413 / 0.419271 (-0.307859) \| 0.058093 / 0.043533 (0.014560) \| 0.430575 / 0.255139 (0.175436) \| 0.483786 / 0.283200 (0.200586) \| 0.106878 / 0.141683 (-0.034805) \| 1.763576 / 1.452155 (0.311422) \| 1.837750 / 1.492716 (0.345033) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.011565 / 0.018006 (-0.006441) \| 0.484411 / 0.000490 (0.483922) \| 0.004869 / 0.000200 (0.004669) \| 0.000111 / 0.000054 (0.000057) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030706 / 0.037411 (-0.006706) \| 0.126901 / 0.014526 (0.112375) \| 0.130367 / 0.176557 (-0.046190) \| 0.206568 / 0.737135 (-0.530567) \| 0.146505 / 0.296338 (-0.149834) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.627266 / 0.215209 (0.412057) \| 6.314049 / 2.077655 (4.236394) \| 2.582920 / 1.504120 (1.078800) \| 2.249401 / 1.541195 (0.708206) \| 2.244960 / 1.468490 (0.776470) \| 0.907770 / 4.584777 (-3.677007) \| 5.349622 / 3.745712 (1.603910) \| 4.591244 / 5.269862 (-0.678618) \| 2.301612 / 4.565676 (-2.264064) \| 0.108813 / 0.424275 (-0.315462) \| 0.013187 / 0.007607 (0.005580) \| 0.806071 / 0.226044 (0.580027) \| 7.843903 / 2.268929 (5.574974) \| 3.405968 / 55.444624 (-52.038656) \| 2.564301 / 6.876477 (-4.312176) \| 2.652208 / 2.142072 (0.510135) \| 1.168142 / 4.805227 (-3.637086) \| 0.218551 / 6.500664 (-6.282113) \| 0.078120 / 0.075469 (0.002651) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.562517 / 1.841788 (-0.279271) \| 17.519325 / 8.074308 (9.445017) \| 20.727083 / 10.191392 (10.535691) \| 0.207135 / 0.680424 (-0.473288) \| 0.028208 / 0.534201 (-0.505993) \| 0.496157 / 0.579283 (-0.083126) \| 0.569239 / 0.434364 (0.134875) \| 0.566137 / 0.540337 (0.025799) \| 0.704208 / 1.386936 (-0.682728) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8eb3f34d876da98e722d866be90d7f26135ea9e3 \"CML watermark\")\n" ]
1,744,009,032	5,927	`IndexError` when indexing `Sequence` of `Array2D` with `None` values	closed	2023-06-06T14:36:22	2023-06-13T12:39:39	2023-06-09T13:23:50	https://github.com/huggingface/datasets/issues/5927	null	qgallouedec	false	[ "Easy fix would be to add:\r\n\r\n```python\r\nnull_indices -= np.arange(len(null_indices))\r\n```\r\n\r\nbefore L279, but I'm not sure it's the most intuitive way to fix it.", "Same issue here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/7fcbe5b1575c8d162b65b9397b3dfda995a4e048/src/datasets/features/features.py#L1398\r\n\r\nFixed in #5948 " ]
1,743,922,028	5,926	Uncaught exception when generating the splits from a dataset that miss data	open	2023-06-06T13:51:01	2023-06-07T07:53:16	null	https://github.com/huggingface/datasets/issues/5926	null	severo	false	[ "Thanks for reporting, @severo.\r\n\r\nThis is a known issue with `fsspec`:\r\n- #5862\r\n- https://github.com/fsspec/filesystem_spec/issues/1265" ]
1,741,941,436	5,925	Breaking API change in datasets.list_datasets caused by change in HfApi.list_datasets	closed	2023-06-05T14:46:04	2023-06-19T17:22:43	2023-06-19T17:22:43	https://github.com/huggingface/datasets/issues/5925	null	mtkinit	false	[]
1,738,889,236	5,924	Add parallel module using joblib for Spark	closed	2023-06-02T22:25:25	2023-06-14T10:25:10	2023-06-14T10:15:46	https://github.com/huggingface/datasets/pull/5924	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5924", "html_url": "https://github.com/huggingface/datasets/pull/5924", "diff_url": "https://github.com/huggingface/datasets/pull/5924.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5924.patch", "merged_at": "2023-06-14T10:15:46" }	es94129	true	[ "Hi @lhoestq, I added the `parallel` part according to the discussion we had. Could you take a look to see if this is aligned with your proposal?\r\n\r\nMeanwhile I'm working on adding a `parallel_backend` parameter to `load_datasets` so that it can be used like:\r\n```python\r\nwith parallel_backend('spark', steps=['downloading']) as backend:\r\n ds = load_dataset(..., parallel_backend=backend)\r\n```\r\nwhere `parallel_backend` is a `ParallelBackend` class.", "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq Thanks for the comments!\r\nWith your suggestion, no changes made to `load_dataset` and I validated that downloading with spark is working now with this:\r\n```py\r\nwith parallel_backend('spark', steps=[\"download\"]):\r\n dataset = load_dataset(..., num_proc=2)\r\n```", "@lhoestq Can a maintainer help trigger the tests again?\r\n> One idea is to decorate the download method to set the current global step to \"download\", and then only use joblib if the current step is one of the steps provided in parallel_backend.\r\n\r\nYes I think this is doable in a subsequent PR.\r\nFor throwing `NotImplementedError` I also think it can be done in a subsequent PR, because I'm not sure if `Dataset.map` is the only function that a user would expect to run using `with parallel_backend`.", "Just triggered the tests :)\r\n\r\n> Yes I think this is doable in a subsequent PR.\r\nFor throwing NotImplementedError I also think it can be done in a subsequent PR, because I'm not sure if Dataset.map is the only function that a user would expect to run using with parallel_backend.\r\n\r\nI think any Dataset method that has a `num_proc` argument: Dataset.map (the other methods like filter or cast or based on map), and later we can see for the to_xxx methods (to_csv, to_parquet, etc.)", "Hi maintainers, I've just addressed most of the comments, please take another look, thank you.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008422 / 0.011353 (-0.002931) \| 0.005658 / 0.011008 (-0.005350) \| 0.135372 / 0.038508 (0.096864) \| 0.044766 / 0.023109 (0.021657) \| 0.417876 / 0.275898 (0.141978) \| 0.462785 / 0.323480 (0.139305) \| 0.005485 / 0.007986 (-0.002501) \| 0.005640 / 0.004328 (0.001311) \| 0.105020 / 0.004250 (0.100770) \| 0.049114 / 0.037052 (0.012062) \| 0.490450 / 0.258489 (0.231961) \| 0.467693 / 0.293841 (0.173852) \| 0.050929 / 0.128546 (-0.077617) \| 0.014644 / 0.075646 (-0.061002) \| 0.452373 / 0.419271 (0.033101) \| 0.074897 / 0.043533 (0.031364) \| 0.425816 / 0.255139 (0.170677) \| 0.420415 / 0.283200 (0.137215) \| 0.134121 / 0.141683 (-0.007561) \| 1.927744 / 1.452155 (0.475589) \| 2.014417 / 1.492716 (0.521701) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.254811 / 0.018006 (0.236805) \| 0.550011 / 0.000490 (0.549521) \| 0.004913 / 0.000200 (0.004714) \| 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) \| 0.032644 / 0.037411 (-0.004768) \| 0.135672 / 0.014526 (0.121146) \| 0.158984 / 0.176557 (-0.017572) \| 0.218267 / 0.737135 (-0.518869) \| 0.150348 / 0.296338 (-0.145991) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.625723 / 0.215209 (0.410514) \| 6.247559 / 2.077655 (4.169905) \| 2.626785 / 1.504120 (1.122666) \| 2.195224 / 1.541195 (0.654030) \| 2.232140 / 1.468490 (0.763650) \| 0.943082 / 4.584777 (-3.641695) \| 5.799262 / 3.745712 (2.053550) \| 2.849411 / 5.269862 (-2.420450) \| 1.744160 / 4.565676 (-2.821516) \| 0.119056 / 0.424275 (-0.305219) \| 0.014233 / 0.007607 (0.006626) \| 0.795238 / 0.226044 (0.569194) \| 7.569586 / 2.268929 (5.300657) \| 3.179481 / 55.444624 (-52.265143) \| 2.519772 / 6.876477 (-4.356704) \| 2.714570 / 2.142072 (0.572498) \| 1.107197 / 4.805227 (-3.698030) \| 0.229986 / 6.500664 (-6.270678) \| 0.087993 / 0.075469 (0.012524) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.535610 / 1.841788 (-0.306178) \| 18.639369 / 8.074308 (10.565061) \| 21.081844 / 10.191392 (10.890452) \| 0.253247 / 0.680424 (-0.427177) \| 0.026711 / 0.534201 (-0.507490) \| 0.503790 / 0.579283 (-0.075493) \| 0.600124 / 0.434364 (0.165760) \| 0.617944 / 0.540337 (0.077607) \| 0.766947 / 1.386936 (-0.619989) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007885 / 0.011353 (-0.003468) \| 0.004761 / 0.011008 (-0.006248) \| 0.097995 / 0.038508 (0.059487) \| 0.033624 / 0.023109 (0.010515) \| 0.504307 / 0.275898 (0.228409) \| 0.534803 / 0.323480 (0.211323) \| 0.006048 / 0.007986 (-0.001937) \| 0.005042 / 0.004328 (0.000714) \| 0.102288 / 0.004250 (0.098038) \| 0.048695 / 0.037052 (0.011643) \| 0.559086 / 0.258489 (0.300597) \| 0.553233 / 0.293841 (0.259392) \| 0.044596 / 0.128546 (-0.083950) \| 0.013696 / 0.075646 (-0.061950) \| 0.109875 / 0.419271 (-0.309397) \| 0.059993 / 0.043533 (0.016460) \| 0.485579 / 0.255139 (0.230440) \| 0.519835 / 0.283200 (0.236635) \| 0.123504 / 0.141683 (-0.018179) \| 1.820506 / 1.452155 (0.368351) \| 1.963448 / 1.492716 (0.470732) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292663 / 0.018006 (0.274656) \| 0.557783 / 0.000490 (0.557293) \| 0.001330 / 0.000200 (0.001130) \| 0.000112 / 0.000054 (0.000057) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036890 / 0.037411 (-0.000522) \| 0.140373 / 0.014526 (0.125847) \| 0.140176 / 0.176557 (-0.036381) \| 0.237378 / 0.737135 (-0.499757) \| 0.160186 / 0.296338 (-0.136152) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.673599 / 0.215209 (0.458390) \| 6.510280 / 2.077655 (4.432625) \| 2.981617 / 1.504120 (1.477497) \| 2.684664 / 1.541195 (1.143469) \| 2.760471 / 1.468490 (1.291981) \| 0.975413 / 4.584777 (-3.609364) \| 5.708933 / 3.745712 (1.963220) \| 2.772069 / 5.269862 (-2.497793) \| 1.763627 / 4.565676 (-2.802049) \| 0.111632 / 0.424275 (-0.312643) \| 0.013223 / 0.007607 (0.005616) \| 0.791545 / 0.226044 (0.565500) \| 8.063287 / 2.268929 (5.794359) \| 3.671920 / 55.444624 (-51.772704) \| 3.057248 / 6.876477 (-3.819229) \| 3.083569 / 2.142072 (0.941497) \| 1.118136 / 4.805227 (-3.687092) \| 0.214655 / 6.500664 (-6.286009) \| 0.083074 / 0.075469 (0.007605) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.761731 / 1.841788 (-0.080056) \| 18.874200 / 8.074308 (10.799892) \| 22.383693 / 10.191392 (12.192301) \| 0.240292 / 0.680424 (-0.440132) \| 0.028850 / 0.534201 (-0.505351) \| 0.557334 / 0.579283 (-0.021949) \| 0.627732 / 0.434364 (0.193369) \| 0.634484 / 0.540337 (0.094146) \| 0.767372 / 1.386936 (-0.619564) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#accaaf2e69fbb5dc5e50229d2eb1591b8ad982b6 \"CML watermark\")\n" ]
1,737,436,227	5,923	Cannot import datasets - ValueError: pyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility	closed	2023-06-02T04:16:32	2024-06-27T10:07:49	2024-02-25T16:38:03	https://github.com/huggingface/datasets/issues/5923	null	ehuangc	false	[ "Based on https://github.com/rapidsai/cudf/issues/10187, this probably means your `pyarrow` installation is not compatible with `datasets`.\r\n\r\nCan you please execute the following commands in the terminal and paste the output here?\r\n```\r\nconda list \| grep arrow\r\n``` \r\n```\r\npython -c \"import pyarrow; print(pyarrow.__file__)\"\r\n```\r\n\r\n\r\n", "> Based on [rapidsai/cudf#10187](https://github.com/rapidsai/cudf/issues/10187), this probably means your `pyarrow` installation is not compatible with `datasets`.\r\n> \r\n> Can you please execute the following commands in the terminal and paste the output here?\r\n> \r\n> ```\r\n> conda list \| grep arrow\r\n> ```\r\n> \r\n> ```\r\n> python -c \"import pyarrow; print(pyarrow.__file__)\"\r\n> ```\r\n\r\n\r\nHere is the output to the first command:\r\n```\r\narrow-cpp 11.0.0 py39h7f74497_0 \r\npyarrow 12.0.0 pypi_0 pypi\r\n```\r\nand the second:\r\n```\r\n/Users/edward/opt/anaconda3/envs/cs235/lib/python3.9/site-packages/pyarrow/__init__.py\r\n```\r\nThanks!\r\n\r\n\r\n\r\n", "after installing pytesseract 0.3.10, I got the above error. FYI ", "RuntimeError: Failed to import transformers.trainer because of the following error (look up to see its traceback):\r\npyarrow.lib.IpcWriteOptions size changed, may indicate binary incompatibility. Expected 88 from C header, got 72 from PyObject", "I got the same error, pyarrow 12.0.0 released May/2023 (https://pypi.org/project/pyarrow/) is not compatible, running `pip install pyarrow==11.0.0` to force install the previous version solved the problem.\r\n\r\nDo we need to update dependencies? ", "Please note that our CI properly passes all tests with `pyarrow-12.0.0`, for Python 3.7 and Python 3.10, for Ubuntu and Windows: see for example https://github.com/huggingface/datasets/actions/runs/5157324334/jobs/9289582291", "For conda with python3.8.16 this solved my problem! thanks!\r\n\r\n> I got the same error, pyarrow 12.0.0 released May/2023 (https://pypi.org/project/pyarrow/) is not compatible, running `pip install pyarrow==11.0.0` to force install the previous version solved the problem.\r\n> \r\n> Do we need to update dependencies? I can work on that if no one else is working on it.\r\n\r\n", "Thanks for replying. I am not sure about those environments but it seems like pyarrow-12.0.0 does not work for conda with python 3.8.16. \r\n\r\n> Please note that our CI properly passes all tests with `pyarrow-12.0.0`, for Python 3.7 and Python 3.10, for Ubuntu and Windows: see for example https://github.com/huggingface/datasets/actions/runs/5157324334/jobs/9289582291\r\n\r\n", "Got the same error with:\r\n\r\n```\r\narrow-cpp 11.0.0 py310h7516544_0 \r\npyarrow 12.0.0 pypi_0 pypi\r\n\r\npython 3.10.11 h7a1cb2a_2 \r\n\r\ndatasets 2.13.0 pyhd8ed1ab_0 conda-forge\r\n```", "> I got the same error, pyarrow 12.0.0 released May/2023 (https://pypi.org/project/pyarrow/) is not compatible, running `pip install pyarrow==11.0.0` to force install the previous version solved the problem.\r\n> \r\n> Do we need to update dependencies?\r\n\r\nThis solved the issue for me as well.", "> I got the same error, pyarrow 12.0.0 released May/2023 (https://pypi.org/project/pyarrow/) is not compatible, running `pip install pyarrow==11.0.0` to force install the previous version solved the problem.\r\n> \r\n> Do we need to update dependencies?\r\n\r\nSolved it for me also", "> 基于 [rapidsai/cudf#10187](https://github.com/rapidsai/cudf/issues/10187)，这可能意味着您的安装与不兼容。`pyarrow``datasets`\r\n> \r\n> 您能否在终端中执行以下命令并将输出粘贴到此处？\r\n> \r\n> ```\r\n> conda list \| grep arrow\r\n> ```\r\n> \r\n> ```\r\n> python -c \"import pyarrow; print(pyarrow.__file__)\"\r\n> ```\r\n\r\narrow-cpp 11.0.0 py310h7516544_0 \r\npyarrow 12.0.1 pypi_0 pypi\r\n\r\n/root/miniconda3/lib/python3.10/site-packages/pyarrow/__init__.py", "Got the same problem with\r\n\r\narrow-cpp 11.0.0 py310h1fc3239_0 \r\npyarrow 12.0.1 pypi_0 pypi\r\n\r\nminiforge3/envs/mlp/lib/python3.10/site-packages/pyarrow/__init__.py\r\n\r\nReverting back to pyarrow 11 solved the problem.\r\n", "Solved with `pip install pyarrow==11.0.0`", "I got different. Solved with\r\npip install pyarrow==12.0.1\r\npip install cchardet\r\n\r\nenv:\r\nPython 3.9.16\r\ntransformers 4.32.1", "> I got the same error, pyarrow 12.0.0 released May/2023 (https://pypi.org/project/pyarrow/) is not compatible, running `pip install pyarrow==11.0.0` to force install the previous version solved the problem.\r\n> \r\n> Do we need to update dependencies?\r\n\r\nThis works for me as well", "> I got different. Solved with pip install pyarrow==12.0.1 pip install cchardet\r\n> \r\n> env: Python 3.9.16 transformers 4.32.1\r\n\r\nI guess it also depends on the Python version. I got Python 3.11.5 and pyarrow==12.0.0. \r\nIt works! ", "Hi, if this helps anyone, pip install pyarrow==11.0.0 did not work for me (I'm using Colab) but this worked: \r\n!pip install --extra-index-url=https://pypi.nvidia.com cudf-cu11", "> Hi, if this helps anyone, pip install pyarrow==11.0.0 did not work for me (I'm using Colab) but this worked: !pip install --extra-index-url=https://pypi.nvidia.com cudf-cu11\r\n\r\nthanks! I met the same problem and your suggestion solved it.", "(I was doing quiet install so I didn't notice it initially)\r\nI've been loading the same dataset for months on Colab, just now I got this error as well. I think Colab has changed their image recently (I had some errors regarding CUDA previously as well). beware of this and restart runtime if you're doing quite pip installs.\r\nmoreover installing stable version of datasets on pypi gives this:\r\n\r\n```\r\nERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\nibis-framework 7.1.0 requires pyarrow<15,>=2, but you have pyarrow 15.0.0 which is incompatible.\r\nSuccessfully installed datasets-2.17.0 dill-0.3.8 multiprocess-0.70.16 pyarrow-15.0.0\r\nWARNING: The following packages were previously imported in this runtime:\r\n [pyarrow]\r\nYou must restart the runtime in order to use newly installed versions.\r\n``` \r\n", "for colab - pip install pyarrow==11.0.0", "The above methods didn't help me. So I installed an older version: `!pip install datasets==2.16.1`\r\nand `import datasets` worked!!", "@rasith1998 @PennlaineChu You can avoid this issue by restarting the session after the `datasets` installation (see https://github.com/huggingface/datasets/issues/6661 for more info)\r\n\r\nAlso, we've contacted Google Colab folks to update the default PyArrow installation, so the issue should soon be \"officially\" resolved on their side.", "> Also, we've contacted Google Colab folks to update the default PyArrow installation, so the issue should soon be \"officially\" resolved on their side.\r\n\r\nThis has been done! Google Colab now pre-installs PyArrow 14.0.2, which makes this issue unlikely to happen, so I'm closing it.", "I am facing this issue outside of Colab, in a normal Python (3.10.14) environment:\r\n```\r\npyarrow==11.0.0\r\ndatasets=2.20.0\r\ntransformers==4.41.2\r\n```\r\n\r\nWhat can I do to solve it?\r\n\r\nI am somewhat bound to `pyarrow==11.0.0`. Is there a version of `datasets` that supports this?" ]
1,736,898,953	5,922	Length of table does not accurately reflect the split	closed	2023-06-01T18:56:26	2023-06-02T16:13:31	2023-06-02T16:13:31	https://github.com/huggingface/datasets/issues/5922	null	amogkam	false	[ "As already replied by @lhoestq (private channel):\r\n> `.train_test_split` (as well as `.shard`, `.select`) doesn't create a new arrow table to save time and disk space. Instead, it uses an indices mapping on top of the table that locate which examples are part of train or test.", "This is an optimization that we don't plan to \"fix\", so I'm closing this issue." ]
1,736,563,023	5,921	Fix streaming parquet with image feature in schema	closed	2023-06-01T15:23:10	2023-06-02T10:02:54	2023-06-02T09:53:11	https://github.com/huggingface/datasets/pull/5921	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5921", "html_url": "https://github.com/huggingface/datasets/pull/5921", "diff_url": "https://github.com/huggingface/datasets/pull/5921.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5921.patch", "merged_at": "2023-06-02T09:53:11" }	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) \| 0.007088 / 0.011353 (-0.004265) \| 0.005216 / 0.011008 (-0.005793) \| 0.097572 / 0.038508 (0.059064) \| 0.036510 / 0.023109 (0.013401) \| 0.316885 / 0.275898 (0.040987) \| 0.348541 / 0.323480 (0.025061) \| 0.006513 / 0.007986 (-0.001473) \| 0.004579 / 0.004328 (0.000251) \| 0.073779 / 0.004250 (0.069529) \| 0.057500 / 0.037052 (0.020448) \| 0.329840 / 0.258489 (0.071351) \| 0.357530 / 0.293841 (0.063690) \| 0.028515 / 0.128546 (-0.100031) \| 0.009156 / 0.075646 (-0.066491) \| 0.328340 / 0.419271 (-0.090932) \| 0.068400 / 0.043533 (0.024867) \| 0.313692 / 0.255139 (0.058553) \| 0.329170 / 0.283200 (0.045971) \| 0.111969 / 0.141683 (-0.029714) \| 1.422096 / 1.452155 (-0.030059) \| 1.550042 / 1.492716 (0.057326) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285113 / 0.018006 (0.267107) \| 0.546788 / 0.000490 (0.546298) \| 0.006992 / 0.000200 (0.006792) \| 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) \| 0.026841 / 0.037411 (-0.010570) \| 0.108413 / 0.014526 (0.093887) \| 0.118375 / 0.176557 (-0.058181) \| 0.174889 / 0.737135 (-0.562246) \| 0.122781 / 0.296338 (-0.173558) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.404187 / 0.215209 (0.188978) \| 4.039673 / 2.077655 (1.962019) \| 1.894616 / 1.504120 (0.390496) \| 1.729182 / 1.541195 (0.187987) \| 1.772917 / 1.468490 (0.304427) \| 0.524046 / 4.584777 (-4.060731) \| 3.628111 / 3.745712 (-0.117601) \| 1.866075 / 5.269862 (-3.403787) \| 1.026435 / 4.565676 (-3.539242) \| 0.065328 / 0.424275 (-0.358947) \| 0.012717 / 0.007607 (0.005110) \| 0.505821 / 0.226044 (0.279777) \| 5.049518 / 2.268929 (2.780589) \| 2.338486 / 55.444624 (-53.106139) \| 2.002874 / 6.876477 (-4.873602) \| 2.193049 / 2.142072 (0.050976) \| 0.664638 / 4.805227 (-4.140589) \| 0.151323 / 6.500664 (-6.349341) \| 0.063774 / 0.075469 (-0.011695) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.168168 / 1.841788 (-0.673620) \| 15.289200 / 8.074308 (7.214891) \| 13.614249 / 10.191392 (3.422857) \| 0.167950 / 0.680424 (-0.512474) \| 0.017522 / 0.534201 (-0.516679) \| 0.393480 / 0.579283 (-0.185803) \| 0.420549 / 0.434364 (-0.013815) \| 0.461425 / 0.540337 (-0.078912) \| 0.563583 / 1.386936 (-0.823353) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006859 / 0.011353 (-0.004493) \| 0.004864 / 0.011008 (-0.006144) \| 0.075084 / 0.038508 (0.036576) \| 0.033989 / 0.023109 (0.010880) \| 0.372512 / 0.275898 (0.096614) \| 0.394725 / 0.323480 (0.071246) \| 0.006382 / 0.007986 (-0.001604) \| 0.004521 / 0.004328 (0.000193) \| 0.076422 / 0.004250 (0.072172) \| 0.055383 / 0.037052 (0.018331) \| 0.400974 / 0.258489 (0.142485) \| 0.411570 / 0.293841 (0.117729) \| 0.028264 / 0.128546 (-0.100282) \| 0.009123 / 0.075646 (-0.066523) \| 0.081257 / 0.419271 (-0.338015) \| 0.048147 / 0.043533 (0.004614) \| 0.390735 / 0.255139 (0.135596) \| 0.376426 / 0.283200 (0.093226) \| 0.108164 / 0.141683 (-0.033518) \| 1.429667 / 1.452155 (-0.022488) \| 1.556291 / 1.492716 (0.063575) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289514 / 0.018006 (0.271508) \| 0.532860 / 0.000490 (0.532370) \| 0.003810 / 0.000200 (0.003611) \| 0.000121 / 0.000054 (0.000066) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031292 / 0.037411 (-0.006119) \| 0.116530 / 0.014526 (0.102005) \| 0.127624 / 0.176557 (-0.048932) \| 0.178276 / 0.737135 (-0.558859) \| 0.133742 / 0.296338 (-0.162597) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.431505 / 0.215209 (0.216296) \| 4.309206 / 2.077655 (2.231551) \| 2.174779 / 1.504120 (0.670659) \| 1.998122 / 1.541195 (0.456927) \| 2.126478 / 1.468490 (0.657988) \| 0.528971 / 4.584777 (-4.055806) \| 3.797608 / 3.745712 (0.051895) \| 1.876275 / 5.269862 (-3.393586) \| 1.087458 / 4.565676 (-3.478218) \| 0.066940 / 0.424275 (-0.357335) \| 0.012432 / 0.007607 (0.004825) \| 0.538346 / 0.226044 (0.312301) \| 5.370968 / 2.268929 (3.102039) \| 2.613718 / 55.444624 (-52.830906) \| 2.246585 / 6.876477 (-4.629892) \| 2.375695 / 2.142072 (0.233622) \| 0.652227 / 4.805227 (-4.153001) \| 0.143246 / 6.500664 (-6.357418) \| 0.066163 / 0.075469 (-0.009306) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.291263 / 1.841788 (-0.550524) \| 16.532281 / 8.074308 (8.457973) \| 15.038471 / 10.191392 (4.847079) \| 0.168139 / 0.680424 (-0.512285) \| 0.017724 / 0.534201 (-0.516477) \| 0.391636 / 0.579283 (-0.187648) \| 0.429690 / 0.434364 (-0.004674) \| 0.474941 / 0.540337 (-0.065396) \| 0.579461 / 1.386936 (-0.807475) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#db690affa0373b08f7cef04e25fe2113ee831ef5 \"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) \| 0.006083 / 0.011353 (-0.005269) \| 0.004085 / 0.011008 (-0.006923) \| 0.098337 / 0.038508 (0.059829) \| 0.027573 / 0.023109 (0.004464) \| 0.305688 / 0.275898 (0.029790) \| 0.341767 / 0.323480 (0.018287) \| 0.005143 / 0.007986 (-0.002842) \| 0.003396 / 0.004328 (-0.000932) \| 0.076925 / 0.004250 (0.072674) \| 0.041027 / 0.037052 (0.003975) \| 0.307877 / 0.258489 (0.049388) \| 0.346559 / 0.293841 (0.052718) \| 0.025183 / 0.128546 (-0.103363) \| 0.008575 / 0.075646 (-0.067071) \| 0.319449 / 0.419271 (-0.099823) \| 0.043378 / 0.043533 (-0.000154) \| 0.304563 / 0.255139 (0.049424) \| 0.332019 / 0.283200 (0.048819) \| 0.087725 / 0.141683 (-0.053958) \| 1.484904 / 1.452155 (0.032749) \| 1.582780 / 1.492716 (0.090064) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.197503 / 0.018006 (0.179497) \| 0.410370 / 0.000490 (0.409880) \| 0.003840 / 0.000200 (0.003640) \| 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) \| 0.024179 / 0.037411 (-0.013232) \| 0.098876 / 0.014526 (0.084350) \| 0.106189 / 0.176557 (-0.070367) \| 0.168964 / 0.737135 (-0.568171) \| 0.109723 / 0.296338 (-0.186616) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.429453 / 0.215209 (0.214244) \| 4.295584 / 2.077655 (2.217929) \| 2.014330 / 1.504120 (0.510210) \| 1.841119 / 1.541195 (0.299924) \| 1.928378 / 1.468490 (0.459888) \| 0.554571 / 4.584777 (-4.030206) \| 3.431769 / 3.745712 (-0.313943) \| 1.716204 / 5.269862 (-3.553658) \| 0.995054 / 4.565676 (-3.570622) \| 0.067374 / 0.424275 (-0.356902) \| 0.012557 / 0.007607 (0.004950) \| 0.533785 / 0.226044 (0.307740) \| 5.363360 / 2.268929 (3.094431) \| 2.535190 / 55.444624 (-52.909434) \| 2.191646 / 6.876477 (-4.684831) \| 2.400799 / 2.142072 (0.258727) \| 0.663961 / 4.805227 (-4.141266) \| 0.135992 / 6.500664 (-6.364672) \| 0.067378 / 0.075469 (-0.008092) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.235110 / 1.841788 (-0.606678) \| 13.820695 / 8.074308 (5.746387) \| 13.667202 / 10.191392 (3.475810) \| 0.143025 / 0.680424 (-0.537399) \| 0.016757 / 0.534201 (-0.517444) \| 0.356262 / 0.579283 (-0.223021) \| 0.401871 / 0.434364 (-0.032493) \| 0.423928 / 0.540337 (-0.116410) \| 0.514598 / 1.386936 (-0.872338) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006260 / 0.011353 (-0.005093) \| 0.004159 / 0.011008 (-0.006850) \| 0.076780 / 0.038508 (0.038272) \| 0.027899 / 0.023109 (0.004789) \| 0.412756 / 0.275898 (0.136858) \| 0.455145 / 0.323480 (0.131665) \| 0.005029 / 0.007986 (-0.002956) \| 0.003482 / 0.004328 (-0.000847) \| 0.076148 / 0.004250 (0.071898) \| 0.038969 / 0.037052 (0.001917) \| 0.429975 / 0.258489 (0.171486) \| 0.465880 / 0.293841 (0.172039) \| 0.025555 / 0.128546 (-0.102991) \| 0.008612 / 0.075646 (-0.067034) \| 0.082604 / 0.419271 (-0.336667) \| 0.039690 / 0.043533 (-0.003842) \| 0.403644 / 0.255139 (0.148505) \| 0.440438 / 0.283200 (0.157238) \| 0.090984 / 0.141683 (-0.050699) \| 1.465915 / 1.452155 (0.013760) \| 1.564227 / 1.492716 (0.071511) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010502 / 0.018006 (-0.007504) \| 0.410573 / 0.000490 (0.410083) \| 0.000384 / 0.000200 (0.000184) \| 0.000059 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025726 / 0.037411 (-0.011686) \| 0.101760 / 0.014526 (0.087235) \| 0.110102 / 0.176557 (-0.066454) \| 0.161321 / 0.737135 (-0.575815) \| 0.112507 / 0.296338 (-0.183832) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.469925 / 0.215209 (0.254716) \| 4.718740 / 2.077655 (2.641085) \| 2.466272 / 1.504120 (0.962152) \| 2.267357 / 1.541195 (0.726162) \| 2.331343 / 1.468490 (0.862853) \| 0.553448 / 4.584777 (-4.031329) \| 3.464228 / 3.745712 (-0.281484) \| 3.060957 / 5.269862 (-2.208905) \| 1.387261 / 4.565676 (-3.178415) \| 0.067989 / 0.424275 (-0.356286) \| 0.012349 / 0.007607 (0.004741) \| 0.575046 / 0.226044 (0.349001) \| 5.740322 / 2.268929 (3.471394) \| 2.925666 / 55.444624 (-52.518958) \| 2.606535 / 6.876477 (-4.269942) \| 2.658144 / 2.142072 (0.516072) \| 0.655157 / 4.805227 (-4.150071) \| 0.138520 / 6.500664 (-6.362144) \| 0.069442 / 0.075469 (-0.006027) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.306523 / 1.841788 (-0.535265) \| 14.400380 / 8.074308 (6.326072) \| 14.231519 / 10.191392 (4.040127) \| 0.146194 / 0.680424 (-0.534230) \| 0.016632 / 0.534201 (-0.517569) \| 0.361151 / 0.579283 (-0.218132) \| 0.388838 / 0.434364 (-0.045526) \| 0.419337 / 0.540337 (-0.121001) \| 0.500483 / 1.386936 (-0.886453) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c0429e9806bf7065d03dc5858c039a30c5af716c \"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) \| 0.009430 / 0.011353 (-0.001923) \| 0.006673 / 0.011008 (-0.004335) \| 0.125151 / 0.038508 (0.086643) \| 0.038258 / 0.023109 (0.015149) \| 0.426383 / 0.275898 (0.150485) \| 0.432327 / 0.323480 (0.108847) \| 0.006964 / 0.007986 (-0.001022) \| 0.005140 / 0.004328 (0.000811) \| 0.100767 / 0.004250 (0.096517) \| 0.058663 / 0.037052 (0.021610) \| 0.424709 / 0.258489 (0.166220) \| 0.453049 / 0.293841 (0.159208) \| 0.051042 / 0.128546 (-0.077505) \| 0.015291 / 0.075646 (-0.060355) \| 0.456549 / 0.419271 (0.037278) \| 0.067106 / 0.043533 (0.023573) \| 0.408959 / 0.255139 (0.153820) \| 0.445067 / 0.283200 (0.161867) \| 0.115590 / 0.141683 (-0.026092) \| 1.929439 / 1.452155 (0.477284) \| 2.045709 / 1.492716 (0.552992) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.250726 / 0.018006 (0.232720) \| 0.598976 / 0.000490 (0.598486) \| 0.007542 / 0.000200 (0.007342) \| 0.000101 / 0.000054 (0.000046) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030317 / 0.037411 (-0.007094) \| 0.133177 / 0.014526 (0.118651) \| 0.152761 / 0.176557 (-0.023795) \| 0.233708 / 0.737135 (-0.503428) \| 0.147303 / 0.296338 (-0.149036) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.633562 / 0.215209 (0.418353) \| 6.235021 / 2.077655 (4.157366) \| 2.652573 / 1.504120 (1.148454) \| 2.223363 / 1.541195 (0.682168) \| 2.231022 / 1.468490 (0.762531) \| 0.942218 / 4.584777 (-3.642559) \| 6.068661 / 3.745712 (2.322949) \| 2.778604 / 5.269862 (-2.491257) \| 1.787939 / 4.565676 (-2.777737) \| 0.117749 / 0.424275 (-0.306526) \| 0.015613 / 0.007607 (0.008006) \| 0.810222 / 0.226044 (0.584177) \| 7.931509 / 2.268929 (5.662581) \| 3.260679 / 55.444624 (-52.183945) \| 2.609085 / 6.876477 (-4.267391) \| 2.867838 / 2.142072 (0.725766) \| 1.144672 / 4.805227 (-3.660555) \| 0.224379 / 6.500664 (-6.276285) \| 0.084490 / 0.075469 (0.009021) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.650608 / 1.841788 (-0.191179) \| 18.919748 / 8.074308 (10.845440) \| 20.163162 / 10.191392 (9.971770) \| 0.229427 / 0.680424 (-0.450997) \| 0.033090 / 0.534201 (-0.501111) \| 0.535549 / 0.579283 (-0.043734) \| 0.658629 / 0.434364 (0.224265) \| 0.631526 / 0.540337 (0.091189) \| 0.748701 / 1.386936 (-0.638235) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009157 / 0.011353 (-0.002196) \| 0.006153 / 0.011008 (-0.004856) \| 0.106294 / 0.038508 (0.067786) \| 0.040947 / 0.023109 (0.017837) \| 0.493242 / 0.275898 (0.217344) \| 0.563525 / 0.323480 (0.240045) \| 0.007256 / 0.007986 (-0.000730) \| 0.006757 / 0.004328 (0.002429) \| 0.105151 / 0.004250 (0.100901) \| 0.056262 / 0.037052 (0.019209) \| 0.573341 / 0.258489 (0.314852) \| 0.591125 / 0.293841 (0.297284) \| 0.047935 / 0.128546 (-0.080611) \| 0.015385 / 0.075646 (-0.060262) \| 0.119457 / 0.419271 (-0.299814) \| 0.066510 / 0.043533 (0.022977) \| 0.485622 / 0.255139 (0.230483) \| 0.540929 / 0.283200 (0.257730) \| 0.132619 / 0.141683 (-0.009064) \| 1.916905 / 1.452155 (0.464750) \| 2.152722 / 1.492716 (0.660006) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294823 / 0.018006 (0.276817) \| 0.569371 / 0.000490 (0.568882) \| 0.000642 / 0.000200 (0.000442) \| 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) \| 0.034321 / 0.037411 (-0.003090) \| 0.134165 / 0.014526 (0.119639) \| 0.157871 / 0.176557 (-0.018685) \| 0.210753 / 0.737135 (-0.526382) \| 0.152961 / 0.296338 (-0.143377) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.686810 / 0.215209 (0.471601) \| 6.890432 / 2.077655 (4.812778) \| 3.182875 / 1.504120 (1.678755) \| 2.770836 / 1.541195 (1.229641) \| 2.790785 / 1.468490 (1.322295) \| 0.938145 / 4.584777 (-3.646632) \| 5.861093 / 3.745712 (2.115381) \| 2.719862 / 5.269862 (-2.550000) \| 1.760834 / 4.565676 (-2.804842) \| 0.111317 / 0.424275 (-0.312958) \| 0.015722 / 0.007607 (0.008115) \| 0.863032 / 0.226044 (0.636988) \| 8.482433 / 2.268929 (6.213504) \| 3.892621 / 55.444624 (-51.552003) \| 3.207370 / 6.876477 (-3.669106) \| 3.344412 / 2.142072 (1.202339) \| 1.133903 / 4.805227 (-3.671324) \| 0.223456 / 6.500664 (-6.277209) \| 0.084335 / 0.075469 (0.008866) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.794116 / 1.841788 (-0.047672) \| 19.077447 / 8.074308 (11.003139) \| 23.102309 / 10.191392 (12.910917) \| 0.268806 / 0.680424 (-0.411617) \| 0.027709 / 0.534201 (-0.506492) \| 0.540488 / 0.579283 (-0.038796) \| 0.658478 / 0.434364 (0.224114) \| 0.604769 / 0.540337 (0.064431) \| 0.722768 / 1.386936 (-0.664168) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7e52021c66666e6953d5be0bd45a079e3ddb8c3f \"CML watermark\")\n" ]
1,736,196,991	5,920	Optimize IterableDataset.from_file using ArrowExamplesIterable	closed	2023-06-01T12:14:36	2023-06-01T12:42:10	2023-06-01T12:35:14	https://github.com/huggingface/datasets/pull/5920	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5920", "html_url": "https://github.com/huggingface/datasets/pull/5920", "diff_url": "https://github.com/huggingface/datasets/pull/5920.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5920.patch", "merged_at": "2023-06-01T12:35:14" }	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) \| 0.007439 / 0.011353 (-0.003914) \| 0.004884 / 0.011008 (-0.006124) \| 0.098750 / 0.038508 (0.060242) \| 0.040723 / 0.023109 (0.017613) \| 0.347242 / 0.275898 (0.071344) \| 0.381202 / 0.323480 (0.057722) \| 0.006814 / 0.007986 (-0.001171) \| 0.004543 / 0.004328 (0.000215) \| 0.075338 / 0.004250 (0.071088) \| 0.058976 / 0.037052 (0.021924) \| 0.344746 / 0.258489 (0.086257) \| 0.406761 / 0.293841 (0.112920) \| 0.028961 / 0.128546 (-0.099585) \| 0.009531 / 0.075646 (-0.066115) \| 0.337324 / 0.419271 (-0.081947) \| 0.051071 / 0.043533 (0.007538) \| 0.341251 / 0.255139 (0.086112) \| 0.362773 / 0.283200 (0.079573) \| 0.109423 / 0.141683 (-0.032260) \| 1.457420 / 1.452155 (0.005266) \| 1.588824 / 1.492716 (0.096108) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288620 / 0.018006 (0.270614) \| 0.568975 / 0.000490 (0.568485) \| 0.003350 / 0.000200 (0.003150) \| 0.000088 / 0.000054 (0.000034) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028732 / 0.037411 (-0.008680) \| 0.117820 / 0.014526 (0.103294) \| 0.120180 / 0.176557 (-0.056376) \| 0.178736 / 0.737135 (-0.558399) \| 0.126399 / 0.296338 (-0.169939) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.428357 / 0.215209 (0.213148) \| 4.251989 / 2.077655 (2.174334) \| 2.005239 / 1.504120 (0.501119) \| 1.784009 / 1.541195 (0.242815) \| 1.883763 / 1.468490 (0.415272) \| 0.555429 / 4.584777 (-4.029348) \| 3.868146 / 3.745712 (0.122434) \| 2.081896 / 5.269862 (-3.187965) \| 1.126047 / 4.565676 (-3.439629) \| 0.069496 / 0.424275 (-0.354779) \| 0.012926 / 0.007607 (0.005318) \| 0.536989 / 0.226044 (0.310944) \| 5.256052 / 2.268929 (2.987124) \| 2.526802 / 55.444624 (-52.917822) \| 2.233346 / 6.876477 (-4.643131) \| 2.389063 / 2.142072 (0.246990) \| 0.677107 / 4.805227 (-4.128120) \| 0.147212 / 6.500664 (-6.353452) \| 0.067061 / 0.075469 (-0.008408) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.210651 / 1.841788 (-0.631137) \| 17.236898 / 8.074308 (9.162589) \| 14.427301 / 10.191392 (4.235909) \| 0.207194 / 0.680424 (-0.473229) \| 0.018079 / 0.534201 (-0.516122) \| 0.398355 / 0.579283 (-0.180929) \| 0.462453 / 0.434364 (0.028089) \| 0.484544 / 0.540337 (-0.055794) \| 0.590119 / 1.386936 (-0.796817) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007392 / 0.011353 (-0.003961) \| 0.005614 / 0.011008 (-0.005394) \| 0.075587 / 0.038508 (0.037079) \| 0.040429 / 0.023109 (0.017320) \| 0.389901 / 0.275898 (0.114003) \| 0.429466 / 0.323480 (0.105986) \| 0.006790 / 0.007986 (-0.001196) \| 0.006627 / 0.004328 (0.002299) \| 0.075227 / 0.004250 (0.070976) \| 0.060298 / 0.037052 (0.023246) \| 0.391905 / 0.258489 (0.133416) \| 0.449385 / 0.293841 (0.155544) \| 0.028794 / 0.128546 (-0.099753) \| 0.009461 / 0.075646 (-0.066185) \| 0.083386 / 0.419271 (-0.335886) \| 0.057968 / 0.043533 (0.014435) \| 0.377327 / 0.255139 (0.122188) \| 0.402825 / 0.283200 (0.119626) \| 0.125477 / 0.141683 (-0.016206) \| 1.462986 / 1.452155 (0.010832) \| 1.595959 / 1.492716 (0.103243) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.304179 / 0.018006 (0.286173) \| 0.543113 / 0.000490 (0.542623) \| 0.004136 / 0.000200 (0.003936) \| 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) \| 0.032617 / 0.037411 (-0.004794) \| 0.123596 / 0.014526 (0.109070) \| 0.128714 / 0.176557 (-0.047842) \| 0.176344 / 0.737135 (-0.560792) \| 0.132525 / 0.296338 (-0.163813) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.446041 / 0.215209 (0.230832) \| 4.438799 / 2.077655 (2.361144) \| 2.210815 / 1.504120 (0.706695) \| 2.052025 / 1.541195 (0.510830) \| 2.204687 / 1.468490 (0.736197) \| 0.535219 / 4.584777 (-4.049558) \| 3.858407 / 3.745712 (0.112695) \| 3.826043 / 5.269862 (-1.443819) \| 1.334149 / 4.565676 (-3.231527) \| 0.067454 / 0.424275 (-0.356821) \| 0.012566 / 0.007607 (0.004958) \| 0.551597 / 0.226044 (0.325553) \| 5.520054 / 2.268929 (3.251126) \| 2.817976 / 55.444624 (-52.626649) \| 2.528074 / 6.876477 (-4.348403) \| 2.622391 / 2.142072 (0.480319) \| 0.657632 / 4.805227 (-4.147595) \| 0.147039 / 6.500664 (-6.353625) \| 0.069603 / 0.075469 (-0.005866) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.300140 / 1.841788 (-0.541648) \| 17.303907 / 8.074308 (9.229599) \| 15.657887 / 10.191392 (5.466495) \| 0.168991 / 0.680424 (-0.511433) \| 0.021332 / 0.534201 (-0.512869) \| 0.487261 / 0.579283 (-0.092022) \| 0.450073 / 0.434364 (0.015709) \| 0.465865 / 0.540337 (-0.074473) \| 0.565501 / 1.386936 (-0.821435) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f1723ab75a6b3a5e156ea0a41651e80e91fa9cc6 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006536 / 0.011353 (-0.004817) \| 0.004254 / 0.011008 (-0.006755) \| 0.095387 / 0.038508 (0.056878) \| 0.032885 / 0.023109 (0.009776) \| 0.298580 / 0.275898 (0.022682) \| 0.319771 / 0.323480 (-0.003709) \| 0.005510 / 0.007986 (-0.002476) \| 0.003891 / 0.004328 (-0.000437) \| 0.073763 / 0.004250 (0.069513) \| 0.041625 / 0.037052 (0.004573) \| 0.294896 / 0.258489 (0.036407) \| 0.341308 / 0.293841 (0.047467) \| 0.027898 / 0.128546 (-0.100648) \| 0.008837 / 0.075646 (-0.066809) \| 0.325055 / 0.419271 (-0.094216) \| 0.050652 / 0.043533 (0.007119) \| 0.298756 / 0.255139 (0.043617) \| 0.318261 / 0.283200 (0.035061) \| 0.098927 / 0.141683 (-0.042756) \| 1.450356 / 1.452155 (-0.001798) \| 1.508034 / 1.492716 (0.015318) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.209009 / 0.018006 (0.191003) \| 0.439154 / 0.000490 (0.438665) \| 0.004299 / 0.000200 (0.004099) \| 0.000142 / 0.000054 (0.000087) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025938 / 0.037411 (-0.011473) \| 0.105954 / 0.014526 (0.091429) \| 0.113858 / 0.176557 (-0.062698) \| 0.168887 / 0.737135 (-0.568249) \| 0.121292 / 0.296338 (-0.175046) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.402050 / 0.215209 (0.186841) \| 4.002310 / 2.077655 (1.924655) \| 1.816190 / 1.504120 (0.312070) \| 1.634404 / 1.541195 (0.093209) \| 1.713632 / 1.468490 (0.245142) \| 0.519633 / 4.584777 (-4.065144) \| 3.740291 / 3.745712 (-0.005421) \| 1.787602 / 5.269862 (-3.482260) \| 1.038844 / 4.565676 (-3.526833) \| 0.064973 / 0.424275 (-0.359302) \| 0.012475 / 0.007607 (0.004868) \| 0.498152 / 0.226044 (0.272108) \| 4.970941 / 2.268929 (2.702013) \| 2.287429 / 55.444624 (-53.157195) \| 1.998050 / 6.876477 (-4.878427) \| 2.091903 / 2.142072 (-0.050169) \| 0.630363 / 4.805227 (-4.174864) \| 0.138623 / 6.500664 (-6.362041) \| 0.063293 / 0.075469 (-0.012176) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.201802 / 1.841788 (-0.639986) \| 14.073836 / 8.074308 (5.999528) \| 12.968665 / 10.191392 (2.777273) \| 0.144653 / 0.680424 (-0.535771) \| 0.017613 / 0.534201 (-0.516588) \| 0.392067 / 0.579283 (-0.187216) \| 0.416955 / 0.434364 (-0.017409) \| 0.471492 / 0.540337 (-0.068845) \| 0.554576 / 1.386936 (-0.832360) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006408 / 0.011353 (-0.004945) \| 0.004452 / 0.011008 (-0.006556) \| 0.073648 / 0.038508 (0.035140) \| 0.032536 / 0.023109 (0.009427) \| 0.358546 / 0.275898 (0.082648) \| 0.387330 / 0.323480 (0.063850) \| 0.005542 / 0.007986 (-0.002444) \| 0.003882 / 0.004328 (-0.000447) \| 0.073867 / 0.004250 (0.069617) \| 0.044798 / 0.037052 (0.007746) \| 0.362303 / 0.258489 (0.103814) \| 0.400496 / 0.293841 (0.106655) \| 0.028244 / 0.128546 (-0.100302) \| 0.008931 / 0.075646 (-0.066715) \| 0.080617 / 0.419271 (-0.338654) \| 0.046575 / 0.043533 (0.003043) \| 0.364283 / 0.255139 (0.109145) \| 0.373215 / 0.283200 (0.090015) \| 0.100080 / 0.141683 (-0.041603) \| 1.430047 / 1.452155 (-0.022108) \| 1.530957 / 1.492716 (0.038240) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.221061 / 0.018006 (0.203055) \| 0.441753 / 0.000490 (0.441263) \| 0.003626 / 0.000200 (0.003426) \| 0.000088 / 0.000054 (0.000033) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029509 / 0.037411 (-0.007902) \| 0.109578 / 0.014526 (0.095053) \| 0.121009 / 0.176557 (-0.055548) \| 0.168950 / 0.737135 (-0.568185) \| 0.124475 / 0.296338 (-0.171864) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.431355 / 0.215209 (0.216146) \| 4.295507 / 2.077655 (2.217852) \| 2.167514 / 1.504120 (0.663394) \| 2.013073 / 1.541195 (0.471879) \| 1.973730 / 1.468490 (0.505240) \| 0.529778 / 4.584777 (-4.054999) \| 3.794702 / 3.745712 (0.048989) \| 3.062940 / 5.269862 (-2.206922) \| 1.503426 / 4.565676 (-3.062251) \| 0.066692 / 0.424275 (-0.357583) \| 0.011682 / 0.007607 (0.004075) \| 0.539311 / 0.226044 (0.313266) \| 5.406342 / 2.268929 (3.137414) \| 2.652709 / 55.444624 (-52.791916) \| 2.260066 / 6.876477 (-4.616410) \| 2.295752 / 2.142072 (0.153680) \| 0.647199 / 4.805227 (-4.158029) \| 0.142981 / 6.500664 (-6.357683) \| 0.065082 / 0.075469 (-0.010387) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.279788 / 1.841788 (-0.562000) \| 14.982845 / 8.074308 (6.908536) \| 14.277166 / 10.191392 (4.085774) \| 0.145082 / 0.680424 (-0.535342) \| 0.017885 / 0.534201 (-0.516316) \| 0.392071 / 0.579283 (-0.187212) \| 0.420425 / 0.434364 (-0.013939) \| 0.461244 / 0.540337 (-0.079093) \| 0.559956 / 1.386936 (-0.826980) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#651d96c1c4083a206c65f11602712d75f1f0453d \"CML watermark\")\n" ]
1,735,519,227	5,919	add support for storage_options for load_dataset API	closed	2023-06-01T05:52:32	2023-07-18T06:14:32	2023-07-17T17:02:00	https://github.com/huggingface/datasets/pull/5919	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5919", "html_url": "https://github.com/huggingface/datasets/pull/5919", "diff_url": "https://github.com/huggingface/datasets/pull/5919.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5919.patch", "merged_at": null }	janineguo	true	[ "hi @lhoestq,\r\nI saw some errors in my test and found all the failed reasons are `FileNotFoundError` about `test_load_streaming_private_dataset_with_zipped_data` and `test_load_dataset_private_zipped_images` in `test_load.py `, I run pytest on my own Wins and Ubuntu system all the test in `test_load.py ` are succeed. could you help me to check the test environment of our server?\r\n\r\n`2023-06-08T16:50:48.0828281Z FAILED tests/test_load.py::test_load_streaming_private_dataset_with_zipped_data - FileNotFoundError: Couldn't find a dataset script at D:\\a\\datasets\\datasets\\__DUMMY_TRANSFORMERS_USER__\\repo_zipped_txt_data-16862429577813\\repo_zipped_txt_data-16862429577813.py or any data file in the same directory. Couldn't find '__DUMMY_TRANSFORMERS_USER__/repo_zipped_txt_data-16862429577813' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in __DUMMY_TRANSFORMERS_USER__/repo_zipped_txt_data-16862429577813`\r\n`2023-06-08T16:50:48.0830602Z FAILED tests/test_load.py::test_load_dataset_private_zipped_images[False-False] - FileNotFoundError: Couldn't find a dataset script at D:\\a\\datasets\\datasets\\__DUMMY_TRANSFORMERS_USER__\\repo_zipped_img_data-16862429594168\\repo_zipped_img_data-16862429594168.py or any data file in the same directory. Couldn't find '__DUMMY_TRANSFORMERS_USER__/repo_zipped_img_data-16862429594168' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in __DUMMY_TRANSFORMERS_USER__/repo_zipped_img_data-16862429594168`", "I just re-ran the CI, hopefully it's fixed", "_The documentation is not available anymore as the PR was closed or merged._", "> I just re-ran the CI, hopefully it's fixed\r\n\r\nI just checked, still has the same error, maybe need someone to fix it", "I think the issue comes from this PR somehow, since the CI fail is related to loading private repositories and this PR touches authentication related code. Let me check what's the issue, and I'll also review your PR later (sorry I don't have a ton of bandwidth atm)", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5919). All of your documentation changes will be reflected on that endpoint.", "@lhoestq Hi sorry to bother you, the CI check_code_quality failed and it said `would reformat /home/runner/work/datasets/datasets/src/datasets/download/streaming_download_manager.py` but I cant see any changes when I run `python3 -m black --check tests src benchmarks metrics` and `python3 -m ruff tests src benchmarks metrics` on my own computer, is there any version requirements on the tools? I didn't specific the version.", "I just ran `make style` and pushed the changes.\r\nYou can install the right versions of black and ruff using `pip install -e .[quality]` ;)", "I am working on this issue right now https://github.com/huggingface/datasets/issues/6017 which is strongly connected to your PR, and I might end up cherry-picking some of your commits (keeping attribution of course !). Would you be ok with that ?", "it's totally ok for me, I just wish the S3 File system could support streaming too.\r\n", "\r\nI already adjust the code and test on my local Mac, you can check it now, and you can make any changes to it.", "Closing this PR in favor of https://github.com/huggingface/datasets/pull/6028 which includes your contribution :)" ]
1,735,313,549	5,918	File not found for audio dataset	open	2023-06-01T02:15:29	2023-06-11T06:02:25	null	https://github.com/huggingface/datasets/issues/5918	null	RobertBaruch	false	[ "load_dataset () did not work for loading local files either " ]
1,733,661,588	5,917	Refactor extensions	closed	2023-05-31T08:33:02	2023-05-31T13:34:35	2023-05-31T13:25:57	https://github.com/huggingface/datasets/pull/5917	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5917", "html_url": "https://github.com/huggingface/datasets/pull/5917", "diff_url": "https://github.com/huggingface/datasets/pull/5917.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5917.patch", "merged_at": "2023-05-31T13:25:57" }	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) \| 0.008358 / 0.011353 (-0.002995) \| 0.005673 / 0.011008 (-0.005335) \| 0.124034 / 0.038508 (0.085526) \| 0.037550 / 0.023109 (0.014441) \| 0.331301 / 0.275898 (0.055403) \| 0.383542 / 0.323480 (0.060062) \| 0.006940 / 0.007986 (-0.001046) \| 0.005959 / 0.004328 (0.001631) \| 0.084670 / 0.004250 (0.080419) \| 0.054214 / 0.037052 (0.017162) \| 0.359897 / 0.258489 (0.101408) \| 0.383260 / 0.293841 (0.089419) \| 0.047642 / 0.128546 (-0.080904) \| 0.013902 / 0.075646 (-0.061744) \| 0.380232 / 0.419271 (-0.039040) \| 0.077790 / 0.043533 (0.034257) \| 0.376648 / 0.255139 (0.121509) \| 0.387536 / 0.283200 (0.104336) \| 0.104644 / 0.141683 (-0.037038) \| 1.618560 / 1.452155 (0.166406) \| 1.742569 / 1.492716 (0.249853) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.257218 / 0.018006 (0.239212) \| 0.636801 / 0.000490 (0.636311) \| 0.000634 / 0.000200 (0.000434) \| 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) \| 0.037874 / 0.037411 (0.000462) \| 0.107454 / 0.014526 (0.092928) \| 0.117855 / 0.176557 (-0.058702) \| 0.204067 / 0.737135 (-0.533068) \| 0.134029 / 0.296338 (-0.162310) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.583657 / 0.215209 (0.368447) \| 5.761289 / 2.077655 (3.683635) \| 2.280201 / 1.504120 (0.776081) \| 2.033442 / 1.541195 (0.492247) \| 2.035343 / 1.468490 (0.566853) \| 0.868122 / 4.584777 (-3.716655) \| 5.352591 / 3.745712 (1.606879) \| 2.432814 / 5.269862 (-2.837047) \| 1.560765 / 4.565676 (-3.004911) \| 0.098793 / 0.424275 (-0.325482) \| 0.017327 / 0.007607 (0.009720) \| 0.734676 / 0.226044 (0.508631) \| 7.070318 / 2.268929 (4.801390) \| 2.972701 / 55.444624 (-52.471924) \| 2.442189 / 6.876477 (-4.434288) \| 2.604379 / 2.142072 (0.462307) \| 1.028853 / 4.805227 (-3.776374) \| 0.210390 / 6.500664 (-6.290274) \| 0.069329 / 0.075469 (-0.006140) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.469586 / 1.841788 (-0.372202) \| 16.570305 / 8.074308 (8.495997) \| 19.187845 / 10.191392 (8.996453) \| 0.219162 / 0.680424 (-0.461262) \| 0.026356 / 0.534201 (-0.507845) \| 0.447370 / 0.579283 (-0.131913) \| 0.555893 / 0.434364 (0.121529) \| 0.574958 / 0.540337 (0.034621) \| 0.639166 / 1.386936 (-0.747770) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008166 / 0.011353 (-0.003187) \| 0.005577 / 0.011008 (-0.005431) \| 0.103578 / 0.038508 (0.065070) \| 0.040563 / 0.023109 (0.017454) \| 0.441996 / 0.275898 (0.166098) \| 0.483594 / 0.323480 (0.160114) \| 0.007329 / 0.007986 (-0.000657) \| 0.004546 / 0.004328 (0.000218) \| 0.090471 / 0.004250 (0.086220) \| 0.052740 / 0.037052 (0.015688) \| 0.442197 / 0.258489 (0.183708) \| 0.524310 / 0.293841 (0.230469) \| 0.042487 / 0.128546 (-0.086060) \| 0.012917 / 0.075646 (-0.062730) \| 0.103992 / 0.419271 (-0.315280) \| 0.060570 / 0.043533 (0.017037) \| 0.441956 / 0.255139 (0.186817) \| 0.477084 / 0.283200 (0.193885) \| 0.103815 / 0.141683 (-0.037868) \| 1.696963 / 1.452155 (0.244809) \| 1.747849 / 1.492716 (0.255132) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292465 / 0.018006 (0.274458) \| 0.571518 / 0.000490 (0.571028) \| 0.000476 / 0.000200 (0.000276) \| 0.000077 / 0.000054 (0.000022) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028697 / 0.037411 (-0.008714) \| 0.111671 / 0.014526 (0.097145) \| 0.138826 / 0.176557 (-0.037731) \| 0.189697 / 0.737135 (-0.547439) \| 0.125454 / 0.296338 (-0.170884) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.619273 / 0.215209 (0.404064) \| 6.138669 / 2.077655 (4.061015) \| 2.558622 / 1.504120 (1.054502) \| 2.201550 / 1.541195 (0.660356) \| 2.279034 / 1.468490 (0.810544) \| 0.850752 / 4.584777 (-3.734025) \| 5.438185 / 3.745712 (1.692473) \| 2.529343 / 5.269862 (-2.740518) \| 1.572178 / 4.565676 (-2.993499) \| 0.100768 / 0.424275 (-0.323507) \| 0.013902 / 0.007607 (0.006295) \| 0.726660 / 0.226044 (0.500616) \| 7.794918 / 2.268929 (5.525990) \| 3.311695 / 55.444624 (-52.132930) \| 2.729167 / 6.876477 (-4.147310) \| 2.630984 / 2.142072 (0.488911) \| 1.018534 / 4.805227 (-3.786693) \| 0.194602 / 6.500664 (-6.306062) \| 0.070876 / 0.075469 (-0.004593) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.573005 / 1.841788 (-0.268783) \| 17.042710 / 8.074308 (8.968401) \| 19.615320 / 10.191392 (9.423928) \| 0.229405 / 0.680424 (-0.451019) \| 0.027560 / 0.534201 (-0.506641) \| 0.447984 / 0.579283 (-0.131299) \| 0.598392 / 0.434364 (0.164028) \| 0.571769 / 0.540337 (0.031431) \| 0.653025 / 1.386936 (-0.733911) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9dca2ff89a8589595313e9535d16597ce10e3700 \"CML watermark\")\n" ]
1,732,456,392	5,916	Unpin responses	closed	2023-05-30T14:59:48	2023-05-30T18:03:10	2023-05-30T17:53:29	https://github.com/huggingface/datasets/pull/5916	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5916", "html_url": "https://github.com/huggingface/datasets/pull/5916", "diff_url": "https://github.com/huggingface/datasets/pull/5916.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5916.patch", "merged_at": "2023-05-30T17:53:29" }	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) \| 0.006113 / 0.011353 (-0.005239) \| 0.004195 / 0.011008 (-0.006813) \| 0.098103 / 0.038508 (0.059595) \| 0.027970 / 0.023109 (0.004860) \| 0.300992 / 0.275898 (0.025094) \| 0.335402 / 0.323480 (0.011922) \| 0.005079 / 0.007986 (-0.002906) \| 0.003516 / 0.004328 (-0.000813) \| 0.077311 / 0.004250 (0.073061) \| 0.037863 / 0.037052 (0.000810) \| 0.302638 / 0.258489 (0.044149) \| 0.346554 / 0.293841 (0.052713) \| 0.025218 / 0.128546 (-0.103328) \| 0.008630 / 0.075646 (-0.067017) \| 0.319748 / 0.419271 (-0.099523) \| 0.049182 / 0.043533 (0.005650) \| 0.306233 / 0.255139 (0.051094) \| 0.331040 / 0.283200 (0.047840) \| 0.089203 / 0.141683 (-0.052480) \| 1.496104 / 1.452155 (0.043949) \| 1.567878 / 1.492716 (0.075162) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.215774 / 0.018006 (0.197768) \| 0.436810 / 0.000490 (0.436320) \| 0.000307 / 0.000200 (0.000107) \| 0.000059 / 0.000054 (0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024102 / 0.037411 (-0.013310) \| 0.095459 / 0.014526 (0.080933) \| 0.106564 / 0.176557 (-0.069992) \| 0.169894 / 0.737135 (-0.567241) \| 0.109152 / 0.296338 (-0.187186) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.429066 / 0.215209 (0.213857) \| 4.297385 / 2.077655 (2.219730) \| 2.054854 / 1.504120 (0.550734) \| 1.846844 / 1.541195 (0.305649) \| 1.840807 / 1.468490 (0.372317) \| 0.553193 / 4.584777 (-4.031584) \| 3.366788 / 3.745712 (-0.378924) \| 1.727337 / 5.269862 (-3.542525) \| 0.994357 / 4.565676 (-3.571319) \| 0.067790 / 0.424275 (-0.356485) \| 0.012002 / 0.007607 (0.004395) \| 0.533335 / 0.226044 (0.307291) \| 5.341341 / 2.268929 (3.072412) \| 2.543581 / 55.444624 (-52.901043) \| 2.220374 / 6.876477 (-4.656103) \| 2.321656 / 2.142072 (0.179583) \| 0.654408 / 4.805227 (-4.150819) \| 0.134693 / 6.500664 (-6.365971) \| 0.066926 / 0.075469 (-0.008544) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.209463 / 1.841788 (-0.632325) \| 13.568221 / 8.074308 (5.493913) \| 13.965418 / 10.191392 (3.774026) \| 0.145049 / 0.680424 (-0.535375) \| 0.016936 / 0.534201 (-0.517265) \| 0.371587 / 0.579283 (-0.207696) \| 0.386363 / 0.434364 (-0.048001) \| 0.437137 / 0.540337 (-0.103201) \| 0.514779 / 1.386936 (-0.872157) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006245 / 0.011353 (-0.005108) \| 0.004232 / 0.011008 (-0.006776) \| 0.075682 / 0.038508 (0.037174) \| 0.027858 / 0.023109 (0.004749) \| 0.425325 / 0.275898 (0.149427) \| 0.466732 / 0.323480 (0.143253) \| 0.005240 / 0.007986 (-0.002745) \| 0.003506 / 0.004328 (-0.000823) \| 0.075294 / 0.004250 (0.071044) \| 0.041677 / 0.037052 (0.004624) \| 0.426552 / 0.258489 (0.168063) \| 0.469452 / 0.293841 (0.175611) \| 0.025443 / 0.128546 (-0.103104) \| 0.008526 / 0.075646 (-0.067120) \| 0.082190 / 0.419271 (-0.337081) \| 0.040906 / 0.043533 (-0.002626) \| 0.428406 / 0.255139 (0.173267) \| 0.446795 / 0.283200 (0.163595) \| 0.093837 / 0.141683 (-0.047846) \| 1.518639 / 1.452155 (0.066484) \| 1.620214 / 1.492716 (0.127498) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223259 / 0.018006 (0.205253) \| 0.425077 / 0.000490 (0.424588) \| 0.001980 / 0.000200 (0.001780) \| 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) \| 0.025813 / 0.037411 (-0.011599) \| 0.103062 / 0.014526 (0.088536) \| 0.108958 / 0.176557 (-0.067598) \| 0.161591 / 0.737135 (-0.575544) \| 0.112130 / 0.296338 (-0.184209) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.472843 / 0.215209 (0.257634) \| 4.713281 / 2.077655 (2.635626) \| 2.458216 / 1.504120 (0.954096) \| 2.272467 / 1.541195 (0.731273) \| 2.324456 / 1.468490 (0.855965) \| 0.554686 / 4.584777 (-4.030091) \| 3.445079 / 3.745712 (-0.300634) \| 3.451896 / 5.269862 (-1.817966) \| 1.431065 / 4.565676 (-3.134612) \| 0.067868 / 0.424275 (-0.356407) \| 0.012093 / 0.007607 (0.004486) \| 0.573571 / 0.226044 (0.347526) \| 5.820452 / 2.268929 (3.551523) \| 2.934858 / 55.444624 (-52.509767) \| 2.602719 / 6.876477 (-4.273758) \| 2.645999 / 2.142072 (0.503927) \| 0.660688 / 4.805227 (-4.144540) \| 0.137490 / 6.500664 (-6.363174) \| 0.068311 / 0.075469 (-0.007158) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.321709 / 1.841788 (-0.520079) \| 14.592346 / 8.074308 (6.518038) \| 14.520748 / 10.191392 (4.329356) \| 0.132689 / 0.680424 (-0.547735) \| 0.016422 / 0.534201 (-0.517779) \| 0.370071 / 0.579283 (-0.209212) \| 0.397091 / 0.434364 (-0.037273) \| 0.431979 / 0.540337 (-0.108358) \| 0.509965 / 1.386936 (-0.876971) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8bcd061ab2082a0862f30329bc52f6e0d321805c \"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) \| 0.006182 / 0.011353 (-0.005171) \| 0.004153 / 0.011008 (-0.006855) \| 0.095715 / 0.038508 (0.057207) \| 0.032457 / 0.023109 (0.009347) \| 0.314961 / 0.275898 (0.039063) \| 0.353696 / 0.323480 (0.030216) \| 0.005256 / 0.007986 (-0.002729) \| 0.004870 / 0.004328 (0.000541) \| 0.072442 / 0.004250 (0.068192) \| 0.046102 / 0.037052 (0.009050) \| 0.324410 / 0.258489 (0.065921) \| 0.366861 / 0.293841 (0.073020) \| 0.027088 / 0.128546 (-0.101458) \| 0.008572 / 0.075646 (-0.067075) \| 0.325988 / 0.419271 (-0.093284) \| 0.049494 / 0.043533 (0.005961) \| 0.311221 / 0.255139 (0.056082) \| 0.359720 / 0.283200 (0.076521) \| 0.095101 / 0.141683 (-0.046581) \| 1.472821 / 1.452155 (0.020667) \| 1.516157 / 1.492716 (0.023441) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.210456 / 0.018006 (0.192450) \| 0.439440 / 0.000490 (0.438950) \| 0.003764 / 0.000200 (0.003564) \| 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) \| 0.024076 / 0.037411 (-0.013335) \| 0.104886 / 0.014526 (0.090360) \| 0.114164 / 0.176557 (-0.062393) \| 0.167289 / 0.737135 (-0.569847) \| 0.116457 / 0.296338 (-0.179882) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.400039 / 0.215209 (0.184830) \| 3.973243 / 2.077655 (1.895588) \| 1.801991 / 1.504120 (0.297871) \| 1.592017 / 1.541195 (0.050822) \| 1.612564 / 1.468490 (0.144074) \| 0.527475 / 4.584777 (-4.057302) \| 3.676246 / 3.745712 (-0.069466) \| 1.806423 / 5.269862 (-3.463438) \| 1.176921 / 4.565676 (-3.388756) \| 0.065902 / 0.424275 (-0.358373) \| 0.012245 / 0.007607 (0.004638) \| 0.490883 / 0.226044 (0.264838) \| 4.905270 / 2.268929 (2.636341) \| 2.218694 / 55.444624 (-53.225930) \| 1.903074 / 6.876477 (-4.973403) \| 1.979505 / 2.142072 (-0.162567) \| 0.644415 / 4.805227 (-4.160812) \| 0.142433 / 6.500664 (-6.358231) \| 0.063564 / 0.075469 (-0.011905) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.193756 / 1.841788 (-0.648032) \| 14.673103 / 8.074308 (6.598795) \| 13.410951 / 10.191392 (3.219559) \| 0.159175 / 0.680424 (-0.521249) \| 0.017076 / 0.534201 (-0.517125) \| 0.388880 / 0.579283 (-0.190403) \| 0.409974 / 0.434364 (-0.024390) \| 0.454494 / 0.540337 (-0.085844) \| 0.556873 / 1.386936 (-0.830063) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006107 / 0.011353 (-0.005246) \| 0.004433 / 0.011008 (-0.006575) \| 0.073892 / 0.038508 (0.035384) \| 0.032386 / 0.023109 (0.009277) \| 0.370339 / 0.275898 (0.094441) \| 0.388996 / 0.323480 (0.065516) \| 0.005438 / 0.007986 (-0.002548) \| 0.003875 / 0.004328 (-0.000454) \| 0.073867 / 0.004250 (0.069617) \| 0.048350 / 0.037052 (0.011298) \| 0.380328 / 0.258489 (0.121839) \| 0.411373 / 0.293841 (0.117532) \| 0.028183 / 0.128546 (-0.100363) \| 0.008924 / 0.075646 (-0.066723) \| 0.082484 / 0.419271 (-0.336787) \| 0.047321 / 0.043533 (0.003788) \| 0.371702 / 0.255139 (0.116563) \| 0.380535 / 0.283200 (0.097335) \| 0.100772 / 0.141683 (-0.040911) \| 1.475038 / 1.452155 (0.022883) \| 1.564293 / 1.492716 (0.071577) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.214589 / 0.018006 (0.196583) \| 0.437193 / 0.000490 (0.436703) \| 0.003676 / 0.000200 (0.003476) \| 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) \| 0.027991 / 0.037411 (-0.009421) \| 0.111154 / 0.014526 (0.096628) \| 0.120365 / 0.176557 (-0.056191) \| 0.173601 / 0.737135 (-0.563535) \| 0.126244 / 0.296338 (-0.170094) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.442848 / 0.215209 (0.227639) \| 4.398336 / 2.077655 (2.320681) \| 2.217058 / 1.504120 (0.712938) \| 2.011155 / 1.541195 (0.469960) \| 2.123086 / 1.468490 (0.654596) \| 0.525857 / 4.584777 (-4.058920) \| 3.730191 / 3.745712 (-0.015521) \| 3.517680 / 5.269862 (-1.752181) \| 1.557940 / 4.565676 (-3.007736) \| 0.066309 / 0.424275 (-0.357967) \| 0.011788 / 0.007607 (0.004181) \| 0.548506 / 0.226044 (0.322462) \| 5.483615 / 2.268929 (3.214687) \| 2.663784 / 55.444624 (-52.780840) \| 2.325744 / 6.876477 (-4.550732) \| 2.344179 / 2.142072 (0.202106) \| 0.644217 / 4.805227 (-4.161010) \| 0.141546 / 6.500664 (-6.359118) \| 0.063730 / 0.075469 (-0.011739) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.296032 / 1.841788 (-0.545756) \| 14.903729 / 8.074308 (6.829421) \| 14.505409 / 10.191392 (4.314017) \| 0.170478 / 0.680424 (-0.509946) \| 0.017876 / 0.534201 (-0.516325) \| 0.401047 / 0.579283 (-0.178236) \| 0.417855 / 0.434364 (-0.016509) \| 0.472138 / 0.540337 (-0.068200) \| 0.570859 / 1.386936 (-0.816077) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5a4d530965eb35c66955ef89df79210c66b7f5e6 \"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) \| 0.008495 / 0.011353 (-0.002858) \| 0.005322 / 0.011008 (-0.005686) \| 0.125471 / 0.038508 (0.086962) \| 0.034604 / 0.023109 (0.011495) \| 0.419831 / 0.275898 (0.143933) \| 0.415707 / 0.323480 (0.092227) \| 0.007471 / 0.007986 (-0.000515) \| 0.005441 / 0.004328 (0.001112) \| 0.095412 / 0.004250 (0.091162) \| 0.053865 / 0.037052 (0.016812) \| 0.375257 / 0.258489 (0.116768) \| 0.438114 / 0.293841 (0.144273) \| 0.046183 / 0.128546 (-0.082363) \| 0.013663 / 0.075646 (-0.061984) \| 0.438317 / 0.419271 (0.019045) \| 0.065665 / 0.043533 (0.022133) \| 0.387640 / 0.255139 (0.132501) \| 0.431350 / 0.283200 (0.148150) \| 0.112841 / 0.141683 (-0.028842) \| 1.778639 / 1.452155 (0.326484) \| 1.891948 / 1.492716 (0.399232) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284371 / 0.018006 (0.266365) \| 0.598247 / 0.000490 (0.597758) \| 0.013674 / 0.000200 (0.013474) \| 0.000483 / 0.000054 (0.000428) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032437 / 0.037411 (-0.004974) \| 0.120547 / 0.014526 (0.106021) \| 0.129845 / 0.176557 (-0.046711) \| 0.203455 / 0.737135 (-0.533680) \| 0.140039 / 0.296338 (-0.156300) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.596549 / 0.215209 (0.381340) \| 6.138766 / 2.077655 (4.061111) \| 2.515506 / 1.504120 (1.011386) \| 2.124472 / 1.541195 (0.583277) \| 2.160812 / 1.468490 (0.692322) \| 0.898965 / 4.584777 (-3.685812) \| 5.588152 / 3.745712 (1.842440) \| 2.717580 / 5.269862 (-2.552282) \| 1.683641 / 4.565676 (-2.882036) \| 0.108045 / 0.424275 (-0.316230) \| 0.014089 / 0.007607 (0.006481) \| 0.749567 / 0.226044 (0.523523) \| 7.518051 / 2.268929 (5.249123) \| 3.198238 / 55.444624 (-52.246386) \| 2.575156 / 6.876477 (-4.301321) \| 2.725818 / 2.142072 (0.583745) \| 1.149338 / 4.805227 (-3.655889) \| 0.220443 / 6.500664 (-6.280221) \| 0.081452 / 0.075469 (0.005983) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.624462 / 1.841788 (-0.217325) \| 18.204963 / 8.074308 (10.130655) \| 21.379169 / 10.191392 (11.187777) \| 0.248520 / 0.680424 (-0.431903) \| 0.030121 / 0.534201 (-0.504080) \| 0.499542 / 0.579283 (-0.079741) \| 0.599783 / 0.434364 (0.165419) \| 0.597642 / 0.540337 (0.057305) \| 0.681948 / 1.386936 (-0.704988) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008431 / 0.011353 (-0.002921) \| 0.006143 / 0.011008 (-0.004865) \| 0.107531 / 0.038508 (0.069023) \| 0.036308 / 0.023109 (0.013199) \| 0.480555 / 0.275898 (0.204657) \| 0.556407 / 0.323480 (0.232927) \| 0.007614 / 0.007986 (-0.000372) \| 0.004749 / 0.004328 (0.000421) \| 0.105734 / 0.004250 (0.101484) \| 0.051619 / 0.037052 (0.014567) \| 0.514821 / 0.258489 (0.256332) \| 0.562143 / 0.293841 (0.268302) \| 0.042957 / 0.128546 (-0.085589) \| 0.015142 / 0.075646 (-0.060505) \| 0.143161 / 0.419271 (-0.276111) \| 0.061910 / 0.043533 (0.018377) \| 0.496923 / 0.255139 (0.241784) \| 0.556302 / 0.283200 (0.273102) \| 0.136700 / 0.141683 (-0.004983) \| 1.886184 / 1.452155 (0.434029) \| 2.004087 / 1.492716 (0.511371) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.235530 / 0.018006 (0.217523) \| 0.600796 / 0.000490 (0.600306) \| 0.009074 / 0.000200 (0.008874) \| 0.000203 / 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) \| 0.036345 / 0.037411 (-0.001066) \| 0.126112 / 0.014526 (0.111586) \| 0.143369 / 0.176557 (-0.033188) \| 0.211381 / 0.737135 (-0.525755) \| 0.151095 / 0.296338 (-0.145243) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.695022 / 0.215209 (0.479813) \| 6.685981 / 2.077655 (4.608326) \| 3.104521 / 1.504120 (1.600401) \| 2.758323 / 1.541195 (1.217128) \| 2.706286 / 1.468490 (1.237796) \| 0.941182 / 4.584777 (-3.643595) \| 5.715839 / 3.745712 (1.970127) \| 5.089636 / 5.269862 (-0.180226) \| 2.594739 / 4.565676 (-1.970937) \| 0.112621 / 0.424275 (-0.311655) \| 0.014001 / 0.007607 (0.006394) \| 0.812990 / 0.226044 (0.586945) \| 8.060890 / 2.268929 (5.791961) \| 3.832506 / 55.444624 (-51.612119) \| 3.148051 / 6.876477 (-3.728425) \| 3.110096 / 2.142072 (0.968023) \| 1.105050 / 4.805227 (-3.700178) \| 0.219835 / 6.500664 (-6.280829) \| 0.078600 / 0.075469 (0.003131) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.707551 / 1.841788 (-0.134237) \| 19.238194 / 8.074308 (11.163885) \| 22.167076 / 10.191392 (11.975684) \| 0.233458 / 0.680424 (-0.446966) \| 0.025131 / 0.534201 (-0.509070) \| 0.525241 / 0.579283 (-0.054042) \| 0.649666 / 0.434364 (0.215303) \| 0.602941 / 0.540337 (0.062603) \| 0.718472 / 1.386936 (-0.668464) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ac3a42c525d91cb630273702a0c110a71c9bf54b \"CML watermark\")\n" ]
1,732,389,984	5,915	Raise error in `DatasetBuilder.as_dataset` when `file_format` is not `"arrow"`	closed	2023-05-30T14:27:55	2023-05-31T13:31:21	2023-05-31T13:23:54	https://github.com/huggingface/datasets/pull/5915	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5915", "html_url": "https://github.com/huggingface/datasets/pull/5915", "diff_url": "https://github.com/huggingface/datasets/pull/5915.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5915.patch", "merged_at": "2023-05-31T13:23:54" }	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) \| 0.006416 / 0.011353 (-0.004937) \| 0.004278 / 0.011008 (-0.006731) \| 0.097562 / 0.038508 (0.059054) \| 0.029488 / 0.023109 (0.006379) \| 0.308648 / 0.275898 (0.032750) \| 0.339879 / 0.323480 (0.016399) \| 0.005288 / 0.007986 (-0.002697) \| 0.005033 / 0.004328 (0.000704) \| 0.074666 / 0.004250 (0.070416) \| 0.034888 / 0.037052 (-0.002164) \| 0.309960 / 0.258489 (0.051471) \| 0.344276 / 0.293841 (0.050435) \| 0.025564 / 0.128546 (-0.102982) \| 0.008579 / 0.075646 (-0.067067) \| 0.319796 / 0.419271 (-0.099476) \| 0.044786 / 0.043533 (0.001253) \| 0.308888 / 0.255139 (0.053749) \| 0.334001 / 0.283200 (0.050802) \| 0.089917 / 0.141683 (-0.051766) \| 1.456696 / 1.452155 (0.004541) \| 1.542273 / 1.492716 (0.049557) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.213236 / 0.018006 (0.195230) \| 0.425139 / 0.000490 (0.424650) \| 0.008831 / 0.000200 (0.008631) \| 0.000209 / 0.000054 (0.000155) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023990 / 0.037411 (-0.013421) \| 0.096787 / 0.014526 (0.082261) \| 0.105783 / 0.176557 (-0.070774) \| 0.167182 / 0.737135 (-0.569954) \| 0.108896 / 0.296338 (-0.187442) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.419844 / 0.215209 (0.204635) \| 4.201909 / 2.077655 (2.124254) \| 1.910784 / 1.504120 (0.406664) \| 1.685183 / 1.541195 (0.143988) \| 1.716927 / 1.468490 (0.248437) \| 0.548261 / 4.584777 (-4.036516) \| 3.414168 / 3.745712 (-0.331544) \| 1.695446 / 5.269862 (-3.574415) \| 0.989668 / 4.565676 (-3.576008) \| 0.067328 / 0.424275 (-0.356948) \| 0.012084 / 0.007607 (0.004477) \| 0.523799 / 0.226044 (0.297754) \| 5.240589 / 2.268929 (2.971661) \| 2.331618 / 55.444624 (-53.113007) \| 1.996094 / 6.876477 (-4.880383) \| 2.105450 / 2.142072 (-0.036623) \| 0.654614 / 4.805227 (-4.150613) \| 0.134721 / 6.500664 (-6.365943) \| 0.066227 / 0.075469 (-0.009242) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.196266 / 1.841788 (-0.645521) \| 13.990045 / 8.074308 (5.915737) \| 13.928126 / 10.191392 (3.736734) \| 0.142600 / 0.680424 (-0.537824) \| 0.016462 / 0.534201 (-0.517739) \| 0.363113 / 0.579283 (-0.216170) \| 0.428590 / 0.434364 (-0.005773) \| 0.452594 / 0.540337 (-0.087743) \| 0.551678 / 1.386936 (-0.835258) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005992 / 0.011353 (-0.005361) \| 0.004161 / 0.011008 (-0.006847) \| 0.076098 / 0.038508 (0.037589) \| 0.028559 / 0.023109 (0.005450) \| 0.411696 / 0.275898 (0.135798) \| 0.444519 / 0.323480 (0.121040) \| 0.004965 / 0.007986 (-0.003021) \| 0.003452 / 0.004328 (-0.000876) \| 0.075107 / 0.004250 (0.070857) \| 0.037305 / 0.037052 (0.000252) \| 0.429728 / 0.258489 (0.171239) \| 0.444313 / 0.293841 (0.150472) \| 0.025278 / 0.128546 (-0.103268) \| 0.008527 / 0.075646 (-0.067120) \| 0.081502 / 0.419271 (-0.337770) \| 0.041237 / 0.043533 (-0.002296) \| 0.417848 / 0.255139 (0.162709) \| 0.426615 / 0.283200 (0.143415) \| 0.094641 / 0.141683 (-0.047041) \| 1.525141 / 1.452155 (0.072987) \| 1.615608 / 1.492716 (0.122892) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.192867 / 0.018006 (0.174861) \| 0.414979 / 0.000490 (0.414490) \| 0.000815 / 0.000200 (0.000615) \| 0.000068 / 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) \| 0.025354 / 0.037411 (-0.012058) \| 0.102085 / 0.014526 (0.087559) \| 0.107930 / 0.176557 (-0.068626) \| 0.160483 / 0.737135 (-0.576652) \| 0.112341 / 0.296338 (-0.183997) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.446938 / 0.215209 (0.231728) \| 4.480057 / 2.077655 (2.402402) \| 2.154825 / 1.504120 (0.650705) \| 1.942774 / 1.541195 (0.401580) \| 1.996418 / 1.468490 (0.527928) \| 0.556728 / 4.584777 (-4.028049) \| 3.441228 / 3.745712 (-0.304484) \| 3.004179 / 5.269862 (-2.265683) \| 1.314104 / 4.565676 (-3.251573) \| 0.068670 / 0.424275 (-0.355606) \| 0.011972 / 0.007607 (0.004365) \| 0.556604 / 0.226044 (0.330560) \| 5.561783 / 2.268929 (3.292855) \| 2.631262 / 55.444624 (-52.813363) \| 2.262143 / 6.876477 (-4.614333) \| 2.364243 / 2.142072 (0.222170) \| 0.660621 / 4.805227 (-4.144607) \| 0.137371 / 6.500664 (-6.363293) \| 0.069104 / 0.075469 (-0.006365) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.305706 / 1.841788 (-0.536081) \| 14.015932 / 8.074308 (5.941624) \| 14.353580 / 10.191392 (4.162187) \| 0.146172 / 0.680424 (-0.534251) \| 0.016699 / 0.534201 (-0.517502) \| 0.357970 / 0.579283 (-0.221313) \| 0.389067 / 0.434364 (-0.045297) \| 0.415470 / 0.540337 (-0.124867) \| 0.501359 / 1.386936 (-0.885577) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b2b837b4e7267db9e32d2613d8bf8d70d2ce0b47 \"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) \| 0.006800 / 0.011353 (-0.004552) \| 0.004721 / 0.011008 (-0.006287) \| 0.097760 / 0.038508 (0.059252) \| 0.034192 / 0.023109 (0.011083) \| 0.298240 / 0.275898 (0.022342) \| 0.331119 / 0.323480 (0.007639) \| 0.005826 / 0.007986 (-0.002160) \| 0.003968 / 0.004328 (-0.000360) \| 0.073833 / 0.004250 (0.069582) \| 0.046288 / 0.037052 (0.009236) \| 0.303018 / 0.258489 (0.044529) \| 0.342163 / 0.293841 (0.048322) \| 0.028504 / 0.128546 (-0.100042) \| 0.009031 / 0.075646 (-0.066615) \| 0.331617 / 0.419271 (-0.087655) \| 0.060911 / 0.043533 (0.017379) \| 0.304044 / 0.255139 (0.048905) \| 0.328959 / 0.283200 (0.045759) \| 0.113174 / 0.141683 (-0.028509) \| 1.424652 / 1.452155 (-0.027502) \| 1.531392 / 1.492716 (0.038676) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.206175 / 0.018006 (0.188169) \| 0.435916 / 0.000490 (0.435426) \| 0.002587 / 0.000200 (0.002387) \| 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) \| 0.026996 / 0.037411 (-0.010415) \| 0.106722 / 0.014526 (0.092196) \| 0.117655 / 0.176557 (-0.058902) \| 0.176969 / 0.737135 (-0.560166) \| 0.122577 / 0.296338 (-0.173762) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.396086 / 0.215209 (0.180877) \| 3.972465 / 2.077655 (1.894811) \| 1.800798 / 1.504120 (0.296678) \| 1.616747 / 1.541195 (0.075552) \| 1.680711 / 1.468490 (0.212221) \| 0.526479 / 4.584777 (-4.058298) \| 3.791528 / 3.745712 (0.045816) \| 2.989518 / 5.269862 (-2.280344) \| 1.463221 / 4.565676 (-3.102455) \| 0.065649 / 0.424275 (-0.358626) \| 0.012155 / 0.007607 (0.004548) \| 0.500241 / 0.226044 (0.274197) \| 5.008895 / 2.268929 (2.739966) \| 2.315288 / 55.444624 (-53.129336) \| 1.959409 / 6.876477 (-4.917067) \| 2.102371 / 2.142072 (-0.039701) \| 0.639611 / 4.805227 (-4.165617) \| 0.140101 / 6.500664 (-6.360563) \| 0.063599 / 0.075469 (-0.011870) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.206729 / 1.841788 (-0.635059) \| 15.127250 / 8.074308 (7.052942) \| 14.397228 / 10.191392 (4.205836) \| 0.148802 / 0.680424 (-0.531622) \| 0.017628 / 0.534201 (-0.516573) \| 0.396150 / 0.579283 (-0.183133) \| 0.435826 / 0.434364 (0.001462) \| 0.471215 / 0.540337 (-0.069122) \| 0.559413 / 1.386936 (-0.827523) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006479 / 0.011353 (-0.004874) \| 0.004520 / 0.011008 (-0.006488) \| 0.074395 / 0.038508 (0.035887) \| 0.033400 / 0.023109 (0.010291) \| 0.388411 / 0.275898 (0.112513) \| 0.396714 / 0.323480 (0.073234) \| 0.005736 / 0.007986 (-0.002250) \| 0.004038 / 0.004328 (-0.000291) \| 0.073595 / 0.004250 (0.069345) \| 0.045207 / 0.037052 (0.008155) \| 0.378096 / 0.258489 (0.119607) \| 0.417830 / 0.293841 (0.123989) \| 0.028365 / 0.128546 (-0.100181) \| 0.008887 / 0.075646 (-0.066760) \| 0.080766 / 0.419271 (-0.338505) \| 0.046923 / 0.043533 (0.003390) \| 0.376190 / 0.255139 (0.121051) \| 0.385875 / 0.283200 (0.102675) \| 0.107542 / 0.141683 (-0.034141) \| 1.409257 / 1.452155 (-0.042898) \| 1.518475 / 1.492716 (0.025759) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223299 / 0.018006 (0.205292) \| 0.440640 / 0.000490 (0.440150) \| 0.000397 / 0.000200 (0.000197) \| 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) \| 0.031388 / 0.037411 (-0.006024) \| 0.113078 / 0.014526 (0.098552) \| 0.124398 / 0.176557 (-0.052159) \| 0.173802 / 0.737135 (-0.563333) \| 0.129555 / 0.296338 (-0.166783) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.440220 / 0.215209 (0.225011) \| 4.398052 / 2.077655 (2.320398) \| 2.188396 / 1.504120 (0.684276) \| 1.997811 / 1.541195 (0.456616) \| 2.093338 / 1.468490 (0.624847) \| 0.519597 / 4.584777 (-4.065180) \| 3.885795 / 3.745712 (0.140083) \| 2.896327 / 5.269862 (-2.373534) \| 1.245785 / 4.565676 (-3.319891) \| 0.065675 / 0.424275 (-0.358600) \| 0.011729 / 0.007607 (0.004121) \| 0.541526 / 0.226044 (0.315482) \| 5.406763 / 2.268929 (3.137834) \| 2.722914 / 55.444624 (-52.721711) \| 2.471111 / 6.876477 (-4.405366) \| 2.541488 / 2.142072 (0.399415) \| 0.633566 / 4.805227 (-4.171661) \| 0.139622 / 6.500664 (-6.361042) \| 0.064220 / 0.075469 (-0.011249) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.296097 / 1.841788 (-0.545690) \| 15.095320 / 8.074308 (7.021012) \| 14.300821 / 10.191392 (4.109429) \| 0.145470 / 0.680424 (-0.534954) \| 0.017496 / 0.534201 (-0.516705) \| 0.400589 / 0.579283 (-0.178694) \| 0.423091 / 0.434364 (-0.011273) \| 0.468258 / 0.540337 (-0.072079) \| 0.570873 / 1.386936 (-0.816063) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aee6c67034d6ff298b2153a2fcdab97f14ee6d66 \"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) \| 0.005918 / 0.011353 (-0.005435) \| 0.004393 / 0.011008 (-0.006615) \| 0.091677 / 0.038508 (0.053169) \| 0.033546 / 0.023109 (0.010437) \| 0.344682 / 0.275898 (0.068784) \| 0.388906 / 0.323480 (0.065426) \| 0.005412 / 0.007986 (-0.002574) \| 0.004909 / 0.004328 (0.000580) \| 0.082589 / 0.004250 (0.078339) \| 0.045242 / 0.037052 (0.008190) \| 0.339191 / 0.258489 (0.080702) \| 0.349673 / 0.293841 (0.055832) \| 0.026805 / 0.128546 (-0.101742) \| 0.007529 / 0.075646 (-0.068117) \| 0.319108 / 0.419271 (-0.100164) \| 0.049482 / 0.043533 (0.005949) \| 0.320013 / 0.255139 (0.064874) \| 0.342059 / 0.283200 (0.058859) \| 0.096623 / 0.141683 (-0.045060) \| 1.458204 / 1.452155 (0.006049) \| 1.571172 / 1.492716 (0.078455) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.235171 / 0.018006 (0.217165) \| 0.479678 / 0.000490 (0.479188) \| 0.006627 / 0.000200 (0.006427) \| 0.000257 / 0.000054 (0.000202) \|\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) \| 0.107730 / 0.014526 (0.093204) \| 0.111595 / 0.176557 (-0.064962) \| 0.171316 / 0.737135 (-0.565819) \| 0.118962 / 0.296338 (-0.177377) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.376318 / 0.215209 (0.161109) \| 4.039484 / 2.077655 (1.961829) \| 1.811548 / 1.504120 (0.307428) \| 1.646728 / 1.541195 (0.105533) \| 1.688071 / 1.468490 (0.219581) \| 0.551256 / 4.584777 (-4.033520) \| 4.153931 / 3.745712 (0.408218) \| 3.424154 / 5.269862 (-1.845707) \| 1.734860 / 4.565676 (-2.830816) \| 0.067753 / 0.424275 (-0.356522) \| 0.012699 / 0.007607 (0.005092) \| 0.505722 / 0.226044 (0.279677) \| 4.997321 / 2.268929 (2.728392) \| 2.258755 / 55.444624 (-53.185869) \| 1.954382 / 6.876477 (-4.922095) \| 1.967545 / 2.142072 (-0.174527) \| 0.630489 / 4.805227 (-4.174738) \| 0.138738 / 6.500664 (-6.361926) \| 0.064907 / 0.075469 (-0.010562) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.209634 / 1.841788 (-0.632154) \| 15.055062 / 8.074308 (6.980754) \| 12.721606 / 10.191392 (2.530214) \| 0.164908 / 0.680424 (-0.515516) \| 0.019528 / 0.534201 (-0.514673) \| 0.400136 / 0.579283 (-0.179147) \| 0.451640 / 0.434364 (0.017276) \| 0.466272 / 0.540337 (-0.074065) \| 0.553258 / 1.386936 (-0.833679) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006341 / 0.011353 (-0.005011) \| 0.004617 / 0.011008 (-0.006391) \| 0.077953 / 0.038508 (0.039445) \| 0.031104 / 0.023109 (0.007995) \| 0.360328 / 0.275898 (0.084430) \| 0.408403 / 0.323480 (0.084923) \| 0.005704 / 0.007986 (-0.002282) \| 0.003588 / 0.004328 (-0.000741) \| 0.071441 / 0.004250 (0.067190) \| 0.043520 / 0.037052 (0.006468) \| 0.375798 / 0.258489 (0.117309) \| 0.400955 / 0.293841 (0.107114) \| 0.028166 / 0.128546 (-0.100381) \| 0.008578 / 0.075646 (-0.067068) \| 0.086673 / 0.419271 (-0.332598) \| 0.046424 / 0.043533 (0.002891) \| 0.367276 / 0.255139 (0.112137) \| 0.414550 / 0.283200 (0.131351) \| 0.097355 / 0.141683 (-0.044328) \| 1.465191 / 1.452155 (0.013036) \| 1.555028 / 1.492716 (0.062312) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.196642 / 0.018006 (0.178636) \| 0.464221 / 0.000490 (0.463731) \| 0.002726 / 0.000200 (0.002526) \| 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) \| 0.028078 / 0.037411 (-0.009333) \| 0.110762 / 0.014526 (0.096236) \| 0.122212 / 0.176557 (-0.054344) \| 0.164758 / 0.737135 (-0.572377) \| 0.133969 / 0.296338 (-0.162370) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.448134 / 0.215209 (0.232925) \| 4.339335 / 2.077655 (2.261680) \| 2.129209 / 1.504120 (0.625089) \| 1.957805 / 1.541195 (0.416611) \| 1.994038 / 1.468490 (0.525548) \| 0.497101 / 4.584777 (-4.087676) \| 4.114432 / 3.745712 (0.368720) \| 3.437305 / 5.269862 (-1.832556) \| 1.692810 / 4.565676 (-2.872866) \| 0.071077 / 0.424275 (-0.353198) \| 0.012735 / 0.007607 (0.005128) \| 0.534393 / 0.226044 (0.308348) \| 5.217445 / 2.268929 (2.948517) \| 2.594858 / 55.444624 (-52.849766) \| 2.317464 / 6.876477 (-4.559012) \| 2.337974 / 2.142072 (0.195902) \| 0.622291 / 4.805227 (-4.182936) \| 0.144934 / 6.500664 (-6.355730) \| 0.068524 / 0.075469 (-0.006945) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.310601 / 1.841788 (-0.531187) \| 15.771527 / 8.074308 (7.697219) \| 13.952032 / 10.191392 (3.760640) \| 0.212473 / 0.680424 (-0.467951) \| 0.017963 / 0.534201 (-0.516238) \| 0.400755 / 0.579283 (-0.178528) \| 0.439817 / 0.434364 (0.005453) \| 0.472614 / 0.540337 (-0.067724) \| 0.558410 / 1.386936 (-0.828526) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1b51429d02a0da1ff798873afe655309136c5689 \"CML watermark\")\n" ]
1,731,483,996	5,914	array is too big; `arr.size * arr.dtype.itemsize` is larger than the maximum possible size in Datasets	open	2023-05-30T04:25:00	2024-10-27T04:09:18	null	https://github.com/huggingface/datasets/issues/5914	null	ravenouse	false	[ "Was a fix for this identified?", "> Was a fix for this identified?\r\n\r\nHi @pranav-sridhar \r\nHave you encountered a similar issue with this dataset?\r\nI’ve modified the dataset construction script to address the problem. Feel free to use this updated version to avoid the issue.\r\n\r\n[Ericwang/samromur_children_test](https://huggingface.co/datasets/Ericwang/samromur_children_test)\r\n" ]
1,731,427,484	5,913	I tried to load a custom dataset using the following statement: dataset = load_dataset('json', data_files=data_files). The dataset contains 50 million text-image pairs, but an error occurred.	closed	2023-05-30T02:55:26	2023-07-24T12:00:38	2023-07-24T12:00:38	https://github.com/huggingface/datasets/issues/5913	null	cjt222	false	[ "Thanks for reporting, @cjt222.\r\n\r\nWhat is the structure of your JSON files. Please note that it is normally simpler if the data file format is JSON-Lines instead. ", "> Thanks for reporting, @cjt222.\r\n> \r\n> What is the structure of your JSON files. Please note that it is normally simpler if the data file format is JSON-Lines instead.\r\n\r\nThanks! I have encountered similar problems. I modify the json format from list to line and works!" ]
1,730,299,852	5,912	Missing elements in `map` a batched dataset	closed	2023-05-29T08:09:19	2023-07-26T15:48:15	2023-07-26T15:48:15	https://github.com/huggingface/datasets/issues/5912	null	sachinruk	false	[ "Hi ! in your code batching is only used within `map`, to process examples in batch. The dataset itself however is not batched and returns elements one by one.\r\n\r\nTo iterate on batches, you can do\r\n```python\r\nfor batch in dataset.iter(batch_size=8):\r\n ...\r\n```" ]
1,728,909,790	5,910	Cannot use both set_format and set_transform	closed	2023-05-27T19:22:23	2023-07-09T21:40:54	2023-06-16T14:41:24	https://github.com/huggingface/datasets/issues/5910	null	ybouane	false	[ "Currently, it's not possible to chain `set_format`/`set_transform` calls (plus, this is a breaking change if we decide to implement it), so I see two possible solutions:\r\n* using `set_format`/`set_transform` for the 1st transform and then passing the transformed example/batch to the 2nd transform\r\n* implementing and registering a custom formatter (the relevant code is [here](https://github.com/huggingface/datasets/tree/main/src/datasets/formatting))\r\n\r\nBtw, your example requires a single `set_format` call:\r\n```python\r\nds.set_format(\"torch\", columns=[\"image\"], output_all_columns=True, dtype=torch.double)\r\n```", "Hey Mario,\r\nThanks, for getting back to me. the toDouble was just an example my real life case requires many more transforms.\r\n\r\nWhat do you mean by:\r\n> using set_format/set_transform for the 1st transform and then passing the transformed example/batch to the 2nd transform\r\n\r\nHow would that go, I thought you can't chain them?\r\n\r\nAs for the custom formatter, is it possible to reference an existing formatter, in my case `torch_formatter` inside of my custom formatter?\r\n\r\nmaybe I can inherit from it and just call `super.recursive_tensorize()`?", "> How would that go, I thought you can't chain them?\r\n\r\nYes, they cannot be chained. This is what I meant:\r\n```python\r\nds.set_transform(first_transform)\r\n# calling the 2nd transform on each accessed batch\r\nsecond_transform(ds[2:3])\r\n```\r\n\r\n> As for the custom formatter, is it possible to reference an existing formatter, in my case torch_formatter inside of my custom formatter?\r\n>\r\n>maybe I can inherit from it and just call super.recursive_tensorize()?\r\n\r\nYes, subclassing makes the most sense.", "Great, thank you for the details.", "https://github.com/huggingface/datasets/issues/6012" ]
1,728,900,068	5,909	Use more efficient and idiomatic way to construct list.	closed	2023-05-27T18:54:47	2023-05-31T15:37:11	2023-05-31T13:28:29	https://github.com/huggingface/datasets/pull/5909	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5909", "html_url": "https://github.com/huggingface/datasets/pull/5909", "diff_url": "https://github.com/huggingface/datasets/pull/5909.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5909.patch", "merged_at": "2023-05-31T13:28:28" }	ttsugriy	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) \| 0.008156 / 0.011353 (-0.003197) \| 0.005563 / 0.011008 (-0.005445) \| 0.118319 / 0.038508 (0.079810) \| 0.044305 / 0.023109 (0.021195) \| 0.366221 / 0.275898 (0.090323) \| 0.407585 / 0.323480 (0.084105) \| 0.006961 / 0.007986 (-0.001024) \| 0.004841 / 0.004328 (0.000513) \| 0.089949 / 0.004250 (0.085698) \| 0.062197 / 0.037052 (0.025144) \| 0.360721 / 0.258489 (0.102232) \| 0.415332 / 0.293841 (0.121491) \| 0.035709 / 0.128546 (-0.092837) \| 0.010617 / 0.075646 (-0.065030) \| 0.397454 / 0.419271 (-0.021817) \| 0.063490 / 0.043533 (0.019958) \| 0.374289 / 0.255139 (0.119150) \| 0.382827 / 0.283200 (0.099628) \| 0.121014 / 0.141683 (-0.020669) \| 1.729933 / 1.452155 (0.277779) \| 1.896222 / 1.492716 (0.403506) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.254030 / 0.018006 (0.236023) \| 0.491225 / 0.000490 (0.490736) \| 0.018933 / 0.000200 (0.018734) \| 0.000413 / 0.000054 (0.000358) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033085 / 0.037411 (-0.004327) \| 0.132837 / 0.014526 (0.118311) \| 0.143275 / 0.176557 (-0.033282) \| 0.215800 / 0.737135 (-0.521335) \| 0.149802 / 0.296338 (-0.146536) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.474688 / 0.215209 (0.259479) \| 4.743223 / 2.077655 (2.665569) \| 2.163107 / 1.504120 (0.658988) \| 1.946396 / 1.541195 (0.405201) \| 2.057538 / 1.468490 (0.589047) \| 0.618836 / 4.584777 (-3.965941) \| 4.605934 / 3.745712 (0.860222) \| 2.201537 / 5.269862 (-3.068324) \| 1.275758 / 4.565676 (-3.289919) \| 0.077782 / 0.424275 (-0.346493) \| 0.014830 / 0.007607 (0.007223) \| 0.593372 / 0.226044 (0.367328) \| 5.927000 / 2.268929 (3.658072) \| 2.687293 / 55.444624 (-52.757331) \| 2.301797 / 6.876477 (-4.574679) \| 2.489928 / 2.142072 (0.347856) \| 0.756779 / 4.805227 (-4.048449) \| 0.168065 / 6.500664 (-6.332600) \| 0.077276 / 0.075469 (0.001807) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.608169 / 1.841788 (-0.233619) \| 19.048790 / 8.074308 (10.974482) \| 16.100228 / 10.191392 (5.908836) \| 0.215346 / 0.680424 (-0.465077) \| 0.022293 / 0.534201 (-0.511907) \| 0.535899 / 0.579283 (-0.043384) \| 0.533729 / 0.434364 (0.099365) \| 0.562697 / 0.540337 (0.022360) \| 0.764082 / 1.386936 (-0.622854) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010087 / 0.011353 (-0.001266) \| 0.005357 / 0.011008 (-0.005651) \| 0.092678 / 0.038508 (0.054170) \| 0.041207 / 0.023109 (0.018098) \| 0.437464 / 0.275898 (0.161566) \| 0.527867 / 0.323480 (0.204387) \| 0.006861 / 0.007986 (-0.001125) \| 0.006131 / 0.004328 (0.001802) \| 0.093741 / 0.004250 (0.089490) \| 0.064142 / 0.037052 (0.027090) \| 0.433577 / 0.258489 (0.175088) \| 0.537148 / 0.293841 (0.243307) \| 0.035339 / 0.128546 (-0.093207) \| 0.010432 / 0.075646 (-0.065214) \| 0.102838 / 0.419271 (-0.316434) \| 0.057905 / 0.043533 (0.014372) \| 0.437956 / 0.255139 (0.182817) \| 0.509562 / 0.283200 (0.226362) \| 0.120620 / 0.141683 (-0.021063) \| 1.798686 / 1.452155 (0.346531) \| 2.013290 / 1.492716 (0.520574) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.249067 / 0.018006 (0.231061) \| 0.462219 / 0.000490 (0.461729) \| 0.000476 / 0.000200 (0.000276) \| 0.000068 / 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) \| 0.033988 / 0.037411 (-0.003424) \| 0.135863 / 0.014526 (0.121337) \| 0.144082 / 0.176557 (-0.032474) \| 0.201715 / 0.737135 (-0.535421) \| 0.152079 / 0.296338 (-0.144259) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.522820 / 0.215209 (0.307611) \| 5.216723 / 2.077655 (3.139068) \| 2.582355 / 1.504120 (1.078235) \| 2.352799 / 1.541195 (0.811604) \| 2.451943 / 1.468490 (0.983453) \| 0.620381 / 4.584777 (-3.964396) \| 4.537841 / 3.745712 (0.792129) \| 2.206431 / 5.269862 (-3.063431) \| 1.269865 / 4.565676 (-3.295811) \| 0.078744 / 0.424275 (-0.345531) \| 0.014375 / 0.007607 (0.006768) \| 0.648215 / 0.226044 (0.422171) \| 6.482809 / 2.268929 (4.213881) \| 3.210670 / 55.444624 (-52.233954) \| 2.847485 / 6.876477 (-4.028992) \| 2.820946 / 2.142072 (0.678873) \| 0.762711 / 4.805227 (-4.042516) \| 0.171235 / 6.500664 (-6.329429) \| 0.080230 / 0.075469 (0.004761) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.646840 / 1.841788 (-0.194948) \| 19.400451 / 8.074308 (11.326142) \| 16.758845 / 10.191392 (6.567453) \| 0.171377 / 0.680424 (-0.509046) \| 0.020400 / 0.534201 (-0.513801) \| 0.467675 / 0.579283 (-0.111608) \| 0.529745 / 0.434364 (0.095381) \| 0.605989 / 0.540337 (0.065652) \| 0.694659 / 1.386936 (-0.692277) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#006bf33ac5c308f9c70f4df4868abd539eb6c366 \"CML watermark\")\n", "It's faster because all the items are the same object, but this also means modifying one of them will alter each unless these items are immutable, and they are in this case (tuples). So we should be careful when using this idiom." ]
1,728,653,935	5,908	Unbearably slow sorting on big mapped datasets	open	2023-05-27T11:08:32	2023-06-13T17:45:10	null	https://github.com/huggingface/datasets/issues/5908	null	maximxlss	false	[ "Hi ! `shard` currently returns a slow dataset by default, with examples evenly distributed in the dataset.\r\n\r\nYou can get a fast dataset using `contiguous=True` (which should be the default imo):\r\n\r\n```python\r\ndataset = dataset.shard(10, 0, contiguous=True)\r\n```\r\n\r\nThis way you don't need to flatten_indices() and sort should be fast as well", "@lhoestq \r\n\r\n> contiguous=True (which should be the default imo)\r\n\r\nFor `IterableDataset`, it's not possible to implement contiguous sharding without knowing the number of examples in advance, so setting the default value to `contiguous=True` would result in an inconsistency between `Dataset` and `IterableDataset` (when we add `IterableDataset.shard`)", "Actually sharded iterable datasets are made of sub iterables that generally yield contiguous data no ? So in a way it's possible to shard an iterable dataset contiguously.\r\n\r\nIf the dataset is made of one shard it's indeed not possible to shard it contiguously though", "> Actually sharded iterable datasets are made of sub iterables that generally yield contiguous data no ? So in a way it's possible to shard an iterable dataset contiguously.\r\n\r\nBut sharding an iterable dataset by sharding its `gen_kwargs` would still yield approximate shards(not equal to `Dataset.shard`), no? ", "Yes indeed !", "I understand the issue doesn't exist with non-mapped datasets, but if flattening is so much more efficient than sorting the indices, that's an issue in itself.\n\nThere are plenty of issues people posted for which the root cause turns out to be the same. It seems like mapped datasets are terribly inefficient. I think I saw some issue like that somewhere (about the mapped datasets in general), but can't find it now.\n\nMaybe indices should be flattened before any additional processing, then." ]
1,728,648,560	5,907	Add `flatten_indices` to `DatasetDict`	closed	2023-05-27T10:55:44	2023-06-01T11:46:35	2023-06-01T11:39:36	https://github.com/huggingface/datasets/pull/5907	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5907", "html_url": "https://github.com/huggingface/datasets/pull/5907", "diff_url": "https://github.com/huggingface/datasets/pull/5907.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5907.patch", "merged_at": "2023-06-01T11:39:35" }	maximxlss	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) \| 0.006192 / 0.011353 (-0.005161) \| 0.004410 / 0.011008 (-0.006598) \| 0.095990 / 0.038508 (0.057482) \| 0.032662 / 0.023109 (0.009553) \| 0.322827 / 0.275898 (0.046929) \| 0.352542 / 0.323480 (0.029062) \| 0.005398 / 0.007986 (-0.002588) \| 0.003926 / 0.004328 (-0.000403) \| 0.075131 / 0.004250 (0.070880) \| 0.046205 / 0.037052 (0.009153) \| 0.330957 / 0.258489 (0.072468) \| 0.360166 / 0.293841 (0.066325) \| 0.027880 / 0.128546 (-0.100666) \| 0.008813 / 0.075646 (-0.066833) \| 0.327316 / 0.419271 (-0.091955) \| 0.050071 / 0.043533 (0.006539) \| 0.319939 / 0.255139 (0.064800) \| 0.331593 / 0.283200 (0.048393) \| 0.096745 / 0.141683 (-0.044938) \| 1.445165 / 1.452155 (-0.006990) \| 1.515538 / 1.492716 (0.022821) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.209365 / 0.018006 (0.191358) \| 0.437007 / 0.000490 (0.436518) \| 0.003207 / 0.000200 (0.003007) \| 0.000088 / 0.000054 (0.000033) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027261 / 0.037411 (-0.010151) \| 0.105101 / 0.014526 (0.090575) \| 0.117163 / 0.176557 (-0.059394) \| 0.176237 / 0.737135 (-0.560898) \| 0.122559 / 0.296338 (-0.173779) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.406792 / 0.215209 (0.191583) \| 4.060831 / 2.077655 (1.983176) \| 1.829691 / 1.504120 (0.325571) \| 1.633155 / 1.541195 (0.091960) \| 1.704817 / 1.468490 (0.236327) \| 0.525325 / 4.584777 (-4.059452) \| 3.752907 / 3.745712 (0.007194) \| 1.857513 / 5.269862 (-3.412349) \| 1.222237 / 4.565676 (-3.343439) \| 0.065941 / 0.424275 (-0.358334) \| 0.012498 / 0.007607 (0.004891) \| 0.495009 / 0.226044 (0.268965) \| 4.968074 / 2.268929 (2.699145) \| 2.277898 / 55.444624 (-53.166727) \| 1.936656 / 6.876477 (-4.939821) \| 1.970698 / 2.142072 (-0.171374) \| 0.635221 / 4.805227 (-4.170006) \| 0.140539 / 6.500664 (-6.360125) \| 0.064111 / 0.075469 (-0.011358) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.238151 / 1.841788 (-0.603637) \| 14.681262 / 8.074308 (6.606954) \| 13.405525 / 10.191392 (3.214133) \| 0.163225 / 0.680424 (-0.517199) \| 0.017282 / 0.534201 (-0.516918) \| 0.395526 / 0.579283 (-0.183757) \| 0.429156 / 0.434364 (-0.005208) \| 0.470806 / 0.540337 (-0.069531) \| 0.571290 / 1.386936 (-0.815646) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006444 / 0.011353 (-0.004909) \| 0.004388 / 0.011008 (-0.006621) \| 0.075004 / 0.038508 (0.036496) \| 0.032904 / 0.023109 (0.009795) \| 0.375360 / 0.275898 (0.099462) \| 0.413684 / 0.323480 (0.090204) \| 0.005854 / 0.007986 (-0.002132) \| 0.005504 / 0.004328 (0.001175) \| 0.075049 / 0.004250 (0.070799) \| 0.047973 / 0.037052 (0.010920) \| 0.377943 / 0.258489 (0.119454) \| 0.427039 / 0.293841 (0.133198) \| 0.028248 / 0.128546 (-0.100298) \| 0.008972 / 0.075646 (-0.066674) \| 0.081848 / 0.419271 (-0.337424) \| 0.047935 / 0.043533 (0.004402) \| 0.377980 / 0.255139 (0.122841) \| 0.407856 / 0.283200 (0.124656) \| 0.103454 / 0.141683 (-0.038229) \| 1.469051 / 1.452155 (0.016896) \| 1.590657 / 1.492716 (0.097941) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.192380 / 0.018006 (0.174374) \| 0.440995 / 0.000490 (0.440505) \| 0.004082 / 0.000200 (0.003882) \| 0.000096 / 0.000054 (0.000042) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029584 / 0.037411 (-0.007828) \| 0.110051 / 0.014526 (0.095525) \| 0.121196 / 0.176557 (-0.055361) \| 0.172249 / 0.737135 (-0.564886) \| 0.125380 / 0.296338 (-0.170958) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.435218 / 0.215209 (0.220009) \| 4.354811 / 2.077655 (2.277156) \| 2.102050 / 1.504120 (0.597930) \| 1.913454 / 1.541195 (0.372260) \| 1.974624 / 1.468490 (0.506134) \| 0.529975 / 4.584777 (-4.054802) \| 3.801605 / 3.745712 (0.055893) \| 3.162408 / 5.269862 (-2.107454) \| 1.599576 / 4.565676 (-2.966101) \| 0.066710 / 0.424275 (-0.357565) \| 0.012158 / 0.007607 (0.004551) \| 0.549187 / 0.226044 (0.323142) \| 5.489930 / 2.268929 (3.221002) \| 2.646787 / 55.444624 (-52.797837) \| 2.311915 / 6.876477 (-4.564562) \| 2.335645 / 2.142072 (0.193572) \| 0.641067 / 4.805227 (-4.164160) \| 0.142227 / 6.500664 (-6.358437) \| 0.065303 / 0.075469 (-0.010166) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.283209 / 1.841788 (-0.558579) \| 15.241809 / 8.074308 (7.167501) \| 14.131471 / 10.191392 (3.940079) \| 0.143921 / 0.680424 (-0.536503) \| 0.017497 / 0.534201 (-0.516704) \| 0.402236 / 0.579283 (-0.177047) \| 0.418917 / 0.434364 (-0.015447) \| 0.461745 / 0.540337 (-0.078593) \| 0.560212 / 1.386936 (-0.826724) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7098922130cabfbfa6b8a3885ff2e6f032d6203d \"CML watermark\")\n" ]
1,728,171,113	5,906	Could you unpin responses version?	closed	2023-05-26T20:02:14	2023-05-30T17:53:31	2023-05-30T17:53:31	https://github.com/huggingface/datasets/issues/5906	null	kenimou	false	[]
1,727,541,392	5,905	Offer an alternative to Iterable Dataset that allows lazy loading and processing while skipping batches efficiently	open	2023-05-26T12:33:02	2023-06-15T13:34:18	null	https://github.com/huggingface/datasets/issues/5905	null	bruno-hays	false	[ "We plan to improve this eventually (see https://github.com/huggingface/datasets/issues/5454 and https://github.com/huggingface/datasets/issues/5380).\r\n\r\n> Is it possible to lazily load samples of a mapped dataset ? I'm used to [dataset scripts](https://huggingface.co/docs/datasets/dataset_script), maybe something can be done there.\r\nIf not, I could do it using a plain Pytorch dataset. Then I would need to convert it to a datasets' dataset to get all the features of datasets. Is it something possible ?\r\n\r\nYes, by creating a mapped dataset that stores audio URLs. Indexing a dataset in such format only downloads and decodes the bytes of the accessed samples (without storing them on disk).\r\n\r\nYou can do the following to create this dataset:\r\n```python\r\n\r\ndef gen():\r\n # Generator that yields (audio URL, text) pairs as dict\r\n ...\r\n yield {\"audio\": \"audio_url\", \"text\": \"some text\"}\r\n\r\nfeatures = Features({\"audio\": datasets.Audio(), \"text\": datasets.Value(\"string\")})\r\nds = Dataset.from_generator(gen, features=features)\r\nds[2:5] # downloads and decodes the samples each time they are accessed\r\n```" ]
1,727,415,626	5,904	Validate name parameter in make_file_instructions	closed	2023-05-26T11:12:46	2023-05-31T07:43:32	2023-05-31T07:34:57	https://github.com/huggingface/datasets/pull/5904	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5904", "html_url": "https://github.com/huggingface/datasets/pull/5904", "diff_url": "https://github.com/huggingface/datasets/pull/5904.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5904.patch", "merged_at": "2023-05-31T07:34:57" }	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) \| 0.007401 / 0.011353 (-0.003952) \| 0.005198 / 0.011008 (-0.005810) \| 0.112317 / 0.038508 (0.073809) \| 0.038406 / 0.023109 (0.015297) \| 0.358008 / 0.275898 (0.082110) \| 0.395350 / 0.323480 (0.071870) \| 0.006201 / 0.007986 (-0.001785) \| 0.004368 / 0.004328 (0.000039) \| 0.087718 / 0.004250 (0.083467) \| 0.055299 / 0.037052 (0.018247) \| 0.350481 / 0.258489 (0.091992) \| 0.419876 / 0.293841 (0.126035) \| 0.032459 / 0.128546 (-0.096087) \| 0.010635 / 0.075646 (-0.065011) \| 0.383282 / 0.419271 (-0.035989) \| 0.059241 / 0.043533 (0.015708) \| 0.365101 / 0.255139 (0.109962) \| 0.378144 / 0.283200 (0.094944) \| 0.114287 / 0.141683 (-0.027396) \| 1.680870 / 1.452155 (0.228715) \| 1.788183 / 1.492716 (0.295467) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.242919 / 0.018006 (0.224913) \| 0.489850 / 0.000490 (0.489360) \| 0.011408 / 0.000200 (0.011208) \| 0.000444 / 0.000054 (0.000389) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030742 / 0.037411 (-0.006669) \| 0.123092 / 0.014526 (0.108566) \| 0.138246 / 0.176557 (-0.038311) \| 0.207299 / 0.737135 (-0.529836) \| 0.142647 / 0.296338 (-0.153691) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.472553 / 0.215209 (0.257344) \| 4.671763 / 2.077655 (2.594108) \| 2.119986 / 1.504120 (0.615866) \| 1.891851 / 1.541195 (0.350656) \| 1.979094 / 1.468490 (0.510604) \| 0.617956 / 4.584777 (-3.966821) \| 4.969418 / 3.745712 (1.223706) \| 4.672083 / 5.269862 (-0.597779) \| 2.119049 / 4.565676 (-2.446627) \| 0.077466 / 0.424275 (-0.346809) \| 0.014434 / 0.007607 (0.006827) \| 0.580746 / 0.226044 (0.354701) \| 5.805458 / 2.268929 (3.536530) \| 2.622498 / 55.444624 (-52.822126) \| 2.259499 / 6.876477 (-4.616978) \| 2.362078 / 2.142072 (0.220006) \| 0.719911 / 4.805227 (-4.085317) \| 0.164939 / 6.500664 (-6.335725) \| 0.074762 / 0.075469 (-0.000707) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.496709 / 1.841788 (-0.345079) \| 18.247499 / 8.074308 (10.173191) \| 15.397075 / 10.191392 (5.205683) \| 0.181163 / 0.680424 (-0.499261) \| 0.022604 / 0.534201 (-0.511597) \| 0.462791 / 0.579283 (-0.116492) \| 0.504473 / 0.434364 (0.070109) \| 0.582254 / 0.540337 (0.041917) \| 0.673849 / 1.386936 (-0.713087) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007633 / 0.011353 (-0.003720) \| 0.004859 / 0.011008 (-0.006149) \| 0.091194 / 0.038508 (0.052686) \| 0.038255 / 0.023109 (0.015146) \| 0.460972 / 0.275898 (0.185074) \| 0.470441 / 0.323480 (0.146961) \| 0.006482 / 0.007986 (-0.001504) \| 0.004500 / 0.004328 (0.000172) \| 0.089998 / 0.004250 (0.085748) \| 0.055470 / 0.037052 (0.018418) \| 0.459188 / 0.258489 (0.200699) \| 0.491255 / 0.293841 (0.197414) \| 0.032200 / 0.128546 (-0.096346) \| 0.010372 / 0.075646 (-0.065274) \| 0.097429 / 0.419271 (-0.321843) \| 0.052469 / 0.043533 (0.008936) \| 0.452492 / 0.255139 (0.197353) \| 0.475210 / 0.283200 (0.192010) \| 0.116976 / 0.141683 (-0.024707) \| 1.752742 / 1.452155 (0.300587) \| 1.849535 / 1.492716 (0.356819) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.229822 / 0.018006 (0.211816) \| 0.472259 / 0.000490 (0.471770) \| 0.000455 / 0.000200 (0.000255) \| 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) \| 0.033796 / 0.037411 (-0.003615) \| 0.136151 / 0.014526 (0.121625) \| 0.144015 / 0.176557 (-0.032542) \| 0.199337 / 0.737135 (-0.537798) \| 0.150024 / 0.296338 (-0.146315) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.522737 / 0.215209 (0.307528) \| 5.165223 / 2.077655 (3.087568) \| 2.630334 / 1.504120 (1.126214) \| 2.392383 / 1.541195 (0.851188) \| 2.488966 / 1.468490 (1.020476) \| 0.608981 / 4.584777 (-3.975796) \| 4.711545 / 3.745712 (0.965833) \| 2.121537 / 5.269862 (-3.148325) \| 1.205477 / 4.565676 (-3.360199) \| 0.078277 / 0.424275 (-0.345998) \| 0.014175 / 0.007607 (0.006568) \| 0.640720 / 0.226044 (0.414675) \| 6.391173 / 2.268929 (4.122245) \| 3.265131 / 55.444624 (-52.179493) \| 2.939188 / 6.876477 (-3.937289) \| 2.919217 / 2.142072 (0.777145) \| 0.745095 / 4.805227 (-4.060132) \| 0.164065 / 6.500664 (-6.336599) \| 0.076993 / 0.075469 (0.001524) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.539971 / 1.841788 (-0.301817) \| 18.597296 / 8.074308 (10.522988) \| 16.899330 / 10.191392 (6.707938) \| 0.169005 / 0.680424 (-0.511419) \| 0.020447 / 0.534201 (-0.513754) \| 0.465862 / 0.579283 (-0.113421) \| 0.522819 / 0.434364 (0.088455) \| 0.547111 / 0.540337 (0.006773) \| 0.657777 / 1.386936 (-0.729159) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#56aff9ecb4e565eb95faad525558914648cc22f1 \"CML watermark\")\n" ]
1,727,372,549	5,903	Relax `ci.yml` trigger for `pull_request` based on modified paths	open	2023-05-26T10:46:52	2023-09-07T15:52:36	null	https://github.com/huggingface/datasets/pull/5903	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5903", "html_url": "https://github.com/huggingface/datasets/pull/5903", "diff_url": "https://github.com/huggingface/datasets/pull/5903.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5903.patch", "merged_at": null }	alvarobartt	true	[ "Also this could be extended to the rest of the GitHub Action `yml` files, so let me know whether you want me to have a look into it! 🤗", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5903). All of your documentation changes will be reflected on that endpoint.", "Maybe we can add\r\n```python\r\npaths-ignore:\r\n - \"docs/**\"\r\n```\r\nto `ci.yml` and `benchmarks.yml`. The other supporting files are not modified often, so leaving them out is fine." ]
1,727,342,194	5,902	Fix `Overview.ipynb` & detach Jupyter Notebooks from `datasets` repository	closed	2023-05-26T10:25:01	2023-07-25T13:50:06	2023-07-25T13:38:33	https://github.com/huggingface/datasets/pull/5902	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5902", "html_url": "https://github.com/huggingface/datasets/pull/5902", "diff_url": "https://github.com/huggingface/datasets/pull/5902.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5902.patch", "merged_at": "2023-07-25T13:38:33" }	alvarobartt	true	[ "Random fact: previous run was showing that the Hub was hosting 13336 datasets, while the most recent run shows 36662 👀🎉", "_The documentation is not available anymore as the PR was closed or merged._", "Thanks! \r\n\r\nHowever, I think we should stop linking this notebook and use the notebook version of the Quickstart doc page instead of it for easier maintenance (we would have the \"Open in Colab\" button in the Quickstart doc as Transformers [does](https://huggingface.co/docs/transformers/quicktour)). \r\n\r\n@stevhliu should be able to help with this. If I'm not mistaken, this can be done by adding the `[[open in colab]]` marker to the doc page.\r\n\r\nAlso, if some useful info from the Overview notebook is not in the docs, feel free to add it so we don't lose it 🙂.", "Cool, makes sense @mariosasko, then I'll check both notebooks and see whether there's something in `Overview.ipynb` worth including in the `docs/source/quickstart.mdx` and remove `Overview.ipynb` and update references in favour of `docs/source/quickstart.mdx`\r\n\r\nAre you OK if I do that @stevhliu @mariosasko? Thanks 🤗 ", "For the moment I've just updated the `quickstart.mdx` to be more similar to [quicktour.mdx](https://github.com/huggingface/transformers/blob/main/docs/source/en/quicktour.mdx), but regarding the `Overview.ipynb` notebook I was planning to create a PR in https://github.com/huggingface/notebooks to add it there, does that make sense @stevhliu? And then to create a `README.md` in this repository in `notebooks/` as `transformers` does to point to the related notebooks hosted in https://github.com/huggingface/notebooks, WDYT? 🤗 ", "Hi @stevhliu thanks for the feedback! Already applied your suggestions, I'll also add the pointers to both audio and image datasets in the \"What's next\" section.\r\n\r\nBesides that, let me know if I can help with the notebook being hosted in `huggingface/notebooks` instead, and I'll happily do so!", "Thanks a lot for the detailed feedback @mariosasko, I'll apply the changes today!", "> Besides that, let me know if I can help with the notebook being hosted in `huggingface/notebooks` instead, and I'll happily do so!\r\n\r\nAwesome! If you're up for it, I think you can go ahead and open a PR with the changes I've outlined [here](https://github.com/huggingface/datasets/pull/5902#pullrequestreview-1475236887) to add the notebook building workflow. ", "Hi @stevhliu @mariosasko, sorry for the delay I had a busy week, I'll tackle this either today or tomorrow to ideally close it before the weekend, thanks again for the help and guidance 😄 ", "Hi guys @stevhliu @mariosasko sorry for the delay! I've resolved all the comments and applied your reviews 👍🏻 Let me know if this works and we can finally close this PR, thanks for the help in the meantime!", "> Thanks for iterating on this and wrapping it up! 🤗\r\n\r\nNo need to! Always a pleasure to collaborate with you guys 🤗 ", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009814 / 0.011353 (-0.001539) \| 0.004632 / 0.011008 (-0.006376) \| 0.103059 / 0.038508 (0.064551) \| 0.090277 / 0.023109 (0.067167) \| 0.389344 / 0.275898 (0.113446) \| 0.464536 / 0.323480 (0.141056) \| 0.008196 / 0.007986 (0.000210) \| 0.003872 / 0.004328 (-0.000457) \| 0.081912 / 0.004250 (0.077662) \| 0.073197 / 0.037052 (0.036145) \| 0.407545 / 0.258489 (0.149056) \| 0.458035 / 0.293841 (0.164194) \| 0.037485 / 0.128546 (-0.091061) \| 0.010141 / 0.075646 (-0.065505) \| 0.365998 / 0.419271 (-0.053273) \| 0.065218 / 0.043533 (0.021685) \| 0.414091 / 0.255139 (0.158952) \| 0.435617 / 0.283200 (0.152417) \| 0.028850 / 0.141683 (-0.112833) \| 1.883510 / 1.452155 (0.431355) \| 1.979986 / 1.492716 (0.487269) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.236623 / 0.018006 (0.218616) \| 0.467128 / 0.000490 (0.466638) \| 0.008273 / 0.000200 (0.008074) \| 0.000699 / 0.000054 (0.000645) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033061 / 0.037411 (-0.004350) \| 0.101381 / 0.014526 (0.086856) \| 0.110862 / 0.176557 (-0.065695) \| 0.180982 / 0.737135 (-0.556154) \| 0.113791 / 0.296338 (-0.182548) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.450805 / 0.215209 (0.235596) \| 4.478374 / 2.077655 (2.400719) \| 2.190814 / 1.504120 (0.686694) \| 1.976726 / 1.541195 (0.435532) \| 2.078527 / 1.468490 (0.610037) \| 0.569150 / 4.584777 (-4.015627) \| 4.557790 / 3.745712 (0.812078) \| 3.794964 / 5.269862 (-1.474898) \| 2.555689 / 4.565676 (-2.009987) \| 0.067380 / 0.424275 (-0.356896) \| 0.008741 / 0.007607 (0.001134) \| 0.536913 / 0.226044 (0.310868) \| 5.364588 / 2.268929 (3.095659) \| 2.725602 / 55.444624 (-52.719022) \| 2.332012 / 6.876477 (-4.544465) \| 2.560550 / 2.142072 (0.418477) \| 0.672490 / 4.805227 (-4.132738) \| 0.153629 / 6.500664 (-6.347035) \| 0.070583 / 0.075469 (-0.004886) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.620083 / 1.841788 (-0.221704) \| 23.094248 / 8.074308 (15.019939) \| 17.797625 / 10.191392 (7.606233) \| 0.167993 / 0.680424 (-0.512430) \| 0.021151 / 0.534201 (-0.513050) \| 0.470216 / 0.579283 (-0.109067) \| 0.515492 / 0.434364 (0.081128) \| 0.666359 / 0.540337 (0.126021) \| 0.772928 / 1.386936 (-0.614008) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007853 / 0.011353 (-0.003500) \| 0.004627 / 0.011008 (-0.006381) \| 0.079803 / 0.038508 (0.041295) \| 0.091562 / 0.023109 (0.068453) \| 0.488537 / 0.275898 (0.212639) \| 0.579207 / 0.323480 (0.255728) \| 0.006579 / 0.007986 (-0.001406) \| 0.003946 / 0.004328 (-0.000382) \| 0.080224 / 0.004250 (0.075973) \| 0.074499 / 0.037052 (0.037446) \| 0.488292 / 0.258489 (0.229803) \| 0.569246 / 0.293841 (0.275405) \| 0.039994 / 0.128546 (-0.088553) \| 0.012867 / 0.075646 (-0.062780) \| 0.092563 / 0.419271 (-0.326709) \| 0.061656 / 0.043533 (0.018124) \| 0.488271 / 0.255139 (0.233132) \| 0.550651 / 0.283200 (0.267451) \| 0.032078 / 0.141683 (-0.109605) \| 1.874440 / 1.452155 (0.422286) \| 1.973480 / 1.492716 (0.480763) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.238789 / 0.018006 (0.220782) \| 0.460237 / 0.000490 (0.459748) \| 0.000500 / 0.000200 (0.000300) \| 0.000067 / 0.000054 (0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034961 / 0.037411 (-0.002450) \| 0.102696 / 0.014526 (0.088170) \| 0.117772 / 0.176557 (-0.058784) \| 0.183865 / 0.737135 (-0.553270) \| 0.119216 / 0.296338 (-0.177122) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.528894 / 0.215209 (0.313685) \| 5.303954 / 2.077655 (3.226300) \| 2.897505 / 1.504120 (1.393385) \| 2.475898 / 1.541195 (0.934703) \| 2.553479 / 1.468490 (1.084988) \| 0.625847 / 4.584777 (-3.958930) \| 4.656595 / 3.745712 (0.910882) \| 3.745170 / 5.269862 (-1.524691) \| 2.470922 / 4.565676 (-2.094755) \| 0.066908 / 0.424275 (-0.357367) \| 0.009172 / 0.007607 (0.001565) \| 0.572695 / 0.226044 (0.346650) \| 5.753428 / 2.268929 (3.484499) \| 3.033226 / 55.444624 (-52.411398) \| 2.677280 / 6.876477 (-4.199197) \| 2.908857 / 2.142072 (0.766785) \| 0.681595 / 4.805227 (-4.123632) \| 0.154602 / 6.500664 (-6.346062) \| 0.072608 / 0.075469 (-0.002861) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.738550 / 1.841788 (-0.103237) \| 25.090637 / 8.074308 (17.016329) \| 18.371478 / 10.191392 (8.180086) \| 0.207357 / 0.680424 (-0.473067) \| 0.023396 / 0.534201 (-0.510805) \| 0.505663 / 0.579283 (-0.073620) \| 0.503137 / 0.434364 (0.068773) \| 0.598015 / 0.540337 (0.057678) \| 0.714122 / 1.386936 (-0.672814) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#971e33ec81b1013654e845b1c2e33cb43cda5558 \"CML watermark\")\n", "Just as a heads up @mariosasko, the `quickstart.ipynb` Jupyter Notebook has been built at https://github.com/huggingface/notebooks/blob/main/datasets_doc/en/quickstart.ipynb, while the URLs in here point to https://github.com/huggingface/notebooks/blob/main/datasets_doc/quickstart.ipynb instead, should we update that?" ]
1,727,179,016	5,901	Make prepare_split more robust if errors in metadata dataset_info splits	closed	2023-05-26T08:48:22	2023-06-02T06:06:38	2023-06-01T13:39:40	https://github.com/huggingface/datasets/pull/5901	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5901", "html_url": "https://github.com/huggingface/datasets/pull/5901", "diff_url": "https://github.com/huggingface/datasets/pull/5901.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5901.patch", "merged_at": "2023-06-01T13:39:39" }	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) \| 0.008809 / 0.011353 (-0.002544) \| 0.005641 / 0.011008 (-0.005367) \| 0.124986 / 0.038508 (0.086477) \| 0.037311 / 0.023109 (0.014202) \| 0.388915 / 0.275898 (0.113017) \| 0.430123 / 0.323480 (0.106643) \| 0.007447 / 0.007986 (-0.000538) \| 0.009593 / 0.004328 (0.005264) \| 0.099148 / 0.004250 (0.094898) \| 0.052393 / 0.037052 (0.015341) \| 0.399779 / 0.258489 (0.141290) \| 0.439109 / 0.293841 (0.145268) \| 0.043409 / 0.128546 (-0.085137) \| 0.016286 / 0.075646 (-0.059360) \| 0.431198 / 0.419271 (0.011927) \| 0.064932 / 0.043533 (0.021400) \| 0.390650 / 0.255139 (0.135511) \| 0.432883 / 0.283200 (0.149684) \| 0.110978 / 0.141683 (-0.030705) \| 1.796121 / 1.452155 (0.343967) \| 1.960097 / 1.492716 (0.467381) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286292 / 0.018006 (0.268286) \| 0.659495 / 0.000490 (0.659005) \| 0.008294 / 0.000200 (0.008094) \| 0.000485 / 0.000054 (0.000431) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029325 / 0.037411 (-0.008086) \| 0.125454 / 0.014526 (0.110928) \| 0.136459 / 0.176557 (-0.040097) \| 0.221075 / 0.737135 (-0.516060) \| 0.140281 / 0.296338 (-0.156058) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.602401 / 0.215209 (0.387192) \| 6.124553 / 2.077655 (4.046898) \| 2.453141 / 1.504120 (0.949021) \| 2.038611 / 1.541195 (0.497416) \| 2.073611 / 1.468490 (0.605121) \| 0.938040 / 4.584777 (-3.646737) \| 5.755972 / 3.745712 (2.010260) \| 4.450935 / 5.269862 (-0.818926) \| 2.337219 / 4.565676 (-2.228457) \| 0.107118 / 0.424275 (-0.317157) \| 0.015201 / 0.007607 (0.007594) \| 0.785833 / 0.226044 (0.559788) \| 7.732984 / 2.268929 (5.464055) \| 3.236892 / 55.444624 (-52.207733) \| 2.696402 / 6.876477 (-4.180074) \| 2.805036 / 2.142072 (0.662964) \| 1.108612 / 4.805227 (-3.696616) \| 0.221067 / 6.500664 (-6.279597) \| 0.085538 / 0.075469 (0.010068) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.600311 / 1.841788 (-0.241476) \| 18.528118 / 8.074308 (10.453810) \| 21.107199 / 10.191392 (10.915807) \| 0.219489 / 0.680424 (-0.460934) \| 0.028927 / 0.534201 (-0.505274) \| 0.503446 / 0.579283 (-0.075837) \| 0.619833 / 0.434364 (0.185469) \| 0.582454 / 0.540337 (0.042117) \| 0.709154 / 1.386936 (-0.677782) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008516 / 0.011353 (-0.002837) \| 0.006090 / 0.011008 (-0.004918) \| 0.104574 / 0.038508 (0.066066) \| 0.042676 / 0.023109 (0.019566) \| 0.458623 / 0.275898 (0.182725) \| 0.568479 / 0.323480 (0.244999) \| 0.008374 / 0.007986 (0.000389) \| 0.004677 / 0.004328 (0.000349) \| 0.105946 / 0.004250 (0.101695) \| 0.055256 / 0.037052 (0.018204) \| 0.511036 / 0.258489 (0.252547) \| 0.598383 / 0.293841 (0.304542) \| 0.043612 / 0.128546 (-0.084934) \| 0.014707 / 0.075646 (-0.060940) \| 0.116350 / 0.419271 (-0.302921) \| 0.061413 / 0.043533 (0.017880) \| 0.477785 / 0.255139 (0.222646) \| 0.542643 / 0.283200 (0.259443) \| 0.120431 / 0.141683 (-0.021252) \| 1.994083 / 1.452155 (0.541928) \| 2.100600 / 1.492716 (0.607883) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298480 / 0.018006 (0.280474) \| 0.601921 / 0.000490 (0.601432) \| 0.000445 / 0.000200 (0.000245) \| 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) \| 0.034784 / 0.037411 (-0.002627) \| 0.133555 / 0.014526 (0.119029) \| 0.138541 / 0.176557 (-0.038015) \| 0.203114 / 0.737135 (-0.534021) \| 0.153477 / 0.296338 (-0.142861) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.780484 / 0.215209 (0.565275) \| 7.150876 / 2.077655 (5.073222) \| 3.168590 / 1.504120 (1.664470) \| 2.698746 / 1.541195 (1.157552) \| 2.695678 / 1.468490 (1.227188) \| 1.037706 / 4.584777 (-3.547071) \| 5.672631 / 3.745712 (1.926918) \| 2.798137 / 5.269862 (-2.471725) \| 1.738588 / 4.565676 (-2.827088) \| 0.111160 / 0.424275 (-0.313115) \| 0.013878 / 0.007607 (0.006271) \| 0.800191 / 0.226044 (0.574146) \| 8.546676 / 2.268929 (6.277748) \| 4.116852 / 55.444624 (-51.327773) \| 3.331271 / 6.876477 (-3.545206) \| 3.307410 / 2.142072 (1.165337) \| 1.191019 / 4.805227 (-3.614208) \| 0.248953 / 6.500664 (-6.251711) \| 0.086632 / 0.075469 (0.011162) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.795057 / 1.841788 (-0.046730) \| 18.038785 / 8.074308 (9.964476) \| 21.865566 / 10.191392 (11.674174) \| 0.211058 / 0.680424 (-0.469366) \| 0.026956 / 0.534201 (-0.507245) \| 0.518855 / 0.579283 (-0.060428) \| 0.618105 / 0.434364 (0.183741) \| 0.569227 / 0.540337 (0.028889) \| 0.705431 / 1.386936 (-0.681505) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#074925b9b7c1dfd33b8675aa99c07cc26375665c \"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) \| 0.008900 / 0.011353 (-0.002453) \| 0.005726 / 0.011008 (-0.005283) \| 0.131747 / 0.038508 (0.093239) \| 0.040585 / 0.023109 (0.017476) \| 0.420531 / 0.275898 (0.144633) \| 0.459430 / 0.323480 (0.135950) \| 0.007642 / 0.007986 (-0.000344) \| 0.006750 / 0.004328 (0.002421) \| 0.099147 / 0.004250 (0.094897) \| 0.055852 / 0.037052 (0.018799) \| 0.423653 / 0.258489 (0.165164) \| 0.453304 / 0.293841 (0.159463) \| 0.045247 / 0.128546 (-0.083300) \| 0.016034 / 0.075646 (-0.059612) \| 0.443115 / 0.419271 (0.023843) \| 0.078853 / 0.043533 (0.035320) \| 0.417508 / 0.255139 (0.162369) \| 0.440936 / 0.283200 (0.157736) \| 0.115603 / 0.141683 (-0.026080) \| 1.844610 / 1.452155 (0.392456) \| 1.998497 / 1.492716 (0.505781) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.272622 / 0.018006 (0.254616) \| 0.598045 / 0.000490 (0.597556) \| 0.007088 / 0.000200 (0.006888) \| 0.000159 / 0.000054 (0.000105) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032976 / 0.037411 (-0.004436) \| 0.143970 / 0.014526 (0.129444) \| 0.142172 / 0.176557 (-0.034384) \| 0.216747 / 0.737135 (-0.520389) \| 0.146004 / 0.296338 (-0.150334) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.687507 / 0.215209 (0.472298) \| 6.549524 / 2.077655 (4.471870) \| 2.924142 / 1.504120 (1.420022) \| 2.504471 / 1.541195 (0.963277) \| 2.496280 / 1.468490 (1.027790) \| 0.959054 / 4.584777 (-3.625723) \| 5.851742 / 3.745712 (2.106030) \| 4.983357 / 5.269862 (-0.286504) \| 2.627403 / 4.565676 (-1.938274) \| 0.112955 / 0.424275 (-0.311320) \| 0.016206 / 0.007607 (0.008599) \| 0.819158 / 0.226044 (0.593114) \| 8.416949 / 2.268929 (6.148020) \| 3.776765 / 55.444624 (-51.667859) \| 3.002397 / 6.876477 (-3.874080) \| 3.158852 / 2.142072 (1.016779) \| 1.197099 / 4.805227 (-3.608129) \| 0.280654 / 6.500664 (-6.220010) \| 0.099471 / 0.075469 (0.024002) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.687007 / 1.841788 (-0.154781) \| 19.411976 / 8.074308 (11.337668) \| 22.053482 / 10.191392 (11.862090) \| 0.228038 / 0.680424 (-0.452386) \| 0.028226 / 0.534201 (-0.505975) \| 0.527695 / 0.579283 (-0.051588) \| 0.635911 / 0.434364 (0.201547) \| 0.618205 / 0.540337 (0.077868) \| 0.735164 / 1.386936 (-0.651772) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009450 / 0.011353 (-0.001903) \| 0.006566 / 0.011008 (-0.004442) \| 0.108919 / 0.038508 (0.070411) \| 0.050010 / 0.023109 (0.026900) \| 0.505168 / 0.275898 (0.229270) \| 0.552190 / 0.323480 (0.228710) \| 0.007569 / 0.007986 (-0.000417) \| 0.006807 / 0.004328 (0.002478) \| 0.116621 / 0.004250 (0.112371) \| 0.060374 / 0.037052 (0.023321) \| 0.515165 / 0.258489 (0.256676) \| 0.572125 / 0.293841 (0.278284) \| 0.046561 / 0.128546 (-0.081986) \| 0.016159 / 0.075646 (-0.059487) \| 0.114568 / 0.419271 (-0.304704) \| 0.064689 / 0.043533 (0.021157) \| 0.497870 / 0.255139 (0.242731) \| 0.567332 / 0.283200 (0.284132) \| 0.126254 / 0.141683 (-0.015429) \| 1.954074 / 1.452155 (0.501919) \| 2.057682 / 1.492716 (0.564966) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.013857 / 0.018006 (-0.004149) \| 0.601561 / 0.000490 (0.601071) \| 0.002897 / 0.000200 (0.002697) \| 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) \| 0.038480 / 0.037411 (0.001069) \| 0.142480 / 0.014526 (0.127954) \| 0.160479 / 0.176557 (-0.016077) \| 0.217942 / 0.737135 (-0.519194) \| 0.159908 / 0.296338 (-0.136431) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.697926 / 0.215209 (0.482717) \| 6.869754 / 2.077655 (4.792100) \| 3.125463 / 1.504120 (1.621343) \| 2.729123 / 1.541195 (1.187928) \| 2.855747 / 1.468490 (1.387257) \| 1.015345 / 4.584777 (-3.569432) \| 5.839176 / 3.745712 (2.093463) \| 5.019678 / 5.269862 (-0.250184) \| 2.080489 / 4.565676 (-2.485187) \| 0.118884 / 0.424275 (-0.305391) \| 0.021381 / 0.007607 (0.013774) \| 0.877847 / 0.226044 (0.651803) \| 8.714561 / 2.268929 (6.445633) \| 3.933399 / 55.444624 (-51.511226) \| 3.281809 / 6.876477 (-3.594668) \| 3.330342 / 2.142072 (1.188269) \| 1.235005 / 4.805227 (-3.570222) \| 0.239686 / 6.500664 (-6.260978) \| 0.093546 / 0.075469 (0.018077) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.787916 / 1.841788 (-0.053872) \| 20.094828 / 8.074308 (12.020520) \| 22.902101 / 10.191392 (12.710709) \| 0.249315 / 0.680424 (-0.431109) \| 0.028058 / 0.534201 (-0.506143) \| 0.524960 / 0.579283 (-0.054323) \| 0.643881 / 0.434364 (0.209517) \| 0.621203 / 0.540337 (0.080866) \| 0.723337 / 1.386936 (-0.663599) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#074925b9b7c1dfd33b8675aa99c07cc26375665c \"CML watermark\")\n" ]
1,727,129,617	5,900	Fix minor typo in docs loading.mdx	closed	2023-05-26T08:10:54	2023-05-26T09:34:15	2023-05-26T09:25:12	https://github.com/huggingface/datasets/pull/5900	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5900", "html_url": "https://github.com/huggingface/datasets/pull/5900", "diff_url": "https://github.com/huggingface/datasets/pull/5900.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5900.patch", "merged_at": "2023-05-26T09:25:12" }	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) \| 0.006763 / 0.011353 (-0.004589) \| 0.004548 / 0.011008 (-0.006460) \| 0.095631 / 0.038508 (0.057123) \| 0.034046 / 0.023109 (0.010936) \| 0.298064 / 0.275898 (0.022166) \| 0.330391 / 0.323480 (0.006911) \| 0.006058 / 0.007986 (-0.001928) \| 0.004163 / 0.004328 (-0.000165) \| 0.073260 / 0.004250 (0.069010) \| 0.048885 / 0.037052 (0.011832) \| 0.304651 / 0.258489 (0.046162) \| 0.345882 / 0.293841 (0.052042) \| 0.028061 / 0.128546 (-0.100485) \| 0.008823 / 0.075646 (-0.066823) \| 0.325620 / 0.419271 (-0.093651) \| 0.064480 / 0.043533 (0.020948) \| 0.303373 / 0.255139 (0.048234) \| 0.321672 / 0.283200 (0.038472) \| 0.116353 / 0.141683 (-0.025330) \| 1.442327 / 1.452155 (-0.009827) \| 1.567553 / 1.492716 (0.074837) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.213042 / 0.018006 (0.195035) \| 0.457646 / 0.000490 (0.457156) \| 0.003989 / 0.000200 (0.003789) \| 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) \| 0.028068 / 0.037411 (-0.009344) \| 0.114791 / 0.014526 (0.100265) \| 0.120870 / 0.176557 (-0.055686) \| 0.183006 / 0.737135 (-0.554130) \| 0.126772 / 0.296338 (-0.169567) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.406438 / 0.215209 (0.191229) \| 4.041890 / 2.077655 (1.964235) \| 1.839967 / 1.504120 (0.335847) \| 1.646857 / 1.541195 (0.105662) \| 1.729372 / 1.468490 (0.260882) \| 0.525540 / 4.584777 (-4.059237) \| 3.809996 / 3.745712 (0.064284) \| 1.842598 / 5.269862 (-3.427263) \| 1.062815 / 4.565676 (-3.502862) \| 0.065301 / 0.424275 (-0.358974) \| 0.012027 / 0.007607 (0.004420) \| 0.505459 / 0.226044 (0.279415) \| 5.051177 / 2.268929 (2.782248) \| 2.354368 / 55.444624 (-53.090256) \| 2.035482 / 6.876477 (-4.840995) \| 2.120493 / 2.142072 (-0.021579) \| 0.642233 / 4.805227 (-4.162994) \| 0.141690 / 6.500664 (-6.358974) \| 0.063933 / 0.075469 (-0.011536) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.186261 / 1.841788 (-0.655527) \| 14.919653 / 8.074308 (6.845345) \| 14.534003 / 10.191392 (4.342611) \| 0.183165 / 0.680424 (-0.497259) \| 0.017581 / 0.534201 (-0.516620) \| 0.397284 / 0.579283 (-0.181999) \| 0.431363 / 0.434364 (-0.003001) \| 0.510774 / 0.540337 (-0.029564) \| 0.614421 / 1.386936 (-0.772516) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006682 / 0.011353 (-0.004671) \| 0.004558 / 0.011008 (-0.006450) \| 0.076272 / 0.038508 (0.037764) \| 0.034285 / 0.023109 (0.011176) \| 0.395594 / 0.275898 (0.119696) \| 0.402702 / 0.323480 (0.079222) \| 0.006093 / 0.007986 (-0.001893) \| 0.005538 / 0.004328 (0.001209) \| 0.075797 / 0.004250 (0.071547) \| 0.051638 / 0.037052 (0.014585) \| 0.396071 / 0.258489 (0.137582) \| 0.409282 / 0.293841 (0.115441) \| 0.028193 / 0.128546 (-0.100354) \| 0.008827 / 0.075646 (-0.066819) \| 0.083182 / 0.419271 (-0.336089) \| 0.047605 / 0.043533 (0.004072) \| 0.391148 / 0.255139 (0.136009) \| 0.386784 / 0.283200 (0.103584) \| 0.115303 / 0.141683 (-0.026380) \| 1.463666 / 1.452155 (0.011512) \| 1.566147 / 1.492716 (0.073431) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.213846 / 0.018006 (0.195839) \| 0.454769 / 0.000490 (0.454279) \| 0.004767 / 0.000200 (0.004567) \| 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) \| 0.030369 / 0.037411 (-0.007042) \| 0.115585 / 0.014526 (0.101059) \| 0.125181 / 0.176557 (-0.051376) \| 0.179247 / 0.737135 (-0.557888) \| 0.129336 / 0.296338 (-0.167003) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.446040 / 0.215209 (0.230831) \| 4.462644 / 2.077655 (2.384989) \| 2.254511 / 1.504120 (0.750392) \| 2.062679 / 1.541195 (0.521484) \| 2.180766 / 1.468490 (0.712276) \| 0.530928 / 4.584777 (-4.053849) \| 3.781392 / 3.745712 (0.035680) \| 3.522539 / 5.269862 (-1.747322) \| 1.506960 / 4.565676 (-3.058717) \| 0.067101 / 0.424275 (-0.357174) \| 0.012011 / 0.007607 (0.004404) \| 0.546407 / 0.226044 (0.320362) \| 5.429894 / 2.268929 (3.160965) \| 2.702244 / 55.444624 (-52.742381) \| 2.367559 / 6.876477 (-4.508917) \| 2.556032 / 2.142072 (0.413960) \| 0.639690 / 4.805227 (-4.165538) \| 0.144538 / 6.500664 (-6.356126) \| 0.067822 / 0.075469 (-0.007647) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.284977 / 1.841788 (-0.556811) \| 15.546489 / 8.074308 (7.472181) \| 14.747519 / 10.191392 (4.556127) \| 0.160044 / 0.680424 (-0.520380) \| 0.017746 / 0.534201 (-0.516454) \| 0.390140 / 0.579283 (-0.189143) \| 0.420342 / 0.434364 (-0.014021) \| 0.459788 / 0.540337 (-0.080549) \| 0.556360 / 1.386936 (-0.830576) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d646afbac7ea3dc0996fa2cb6ffd8a98e158e742 \"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) \| 0.006493 / 0.011353 (-0.004860) \| 0.004532 / 0.011008 (-0.006476) \| 0.096509 / 0.038508 (0.058001) \| 0.033084 / 0.023109 (0.009974) \| 0.297802 / 0.275898 (0.021904) \| 0.345880 / 0.323480 (0.022400) \| 0.005461 / 0.007986 (-0.002525) \| 0.005282 / 0.004328 (0.000954) \| 0.073719 / 0.004250 (0.069469) \| 0.045035 / 0.037052 (0.007983) \| 0.295504 / 0.258489 (0.037015) \| 0.345400 / 0.293841 (0.051559) \| 0.027880 / 0.128546 (-0.100666) \| 0.008804 / 0.075646 (-0.066842) \| 0.328017 / 0.419271 (-0.091255) \| 0.050169 / 0.043533 (0.006637) \| 0.299642 / 0.255139 (0.044503) \| 0.313573 / 0.283200 (0.030374) \| 0.103359 / 0.141683 (-0.038323) \| 1.482145 / 1.452155 (0.029990) \| 1.554584 / 1.492716 (0.061867) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.212860 / 0.018006 (0.194853) \| 0.444823 / 0.000490 (0.444334) \| 0.003014 / 0.000200 (0.002815) \| 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) \| 0.026906 / 0.037411 (-0.010506) \| 0.108056 / 0.014526 (0.093530) \| 0.118721 / 0.176557 (-0.057835) \| 0.176646 / 0.737135 (-0.560489) \| 0.123285 / 0.296338 (-0.173053) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.430157 / 0.215209 (0.214948) \| 4.279362 / 2.077655 (2.201707) \| 1.999732 / 1.504120 (0.495612) \| 1.803787 / 1.541195 (0.262592) \| 1.868322 / 1.468490 (0.399832) \| 0.529314 / 4.584777 (-4.055463) \| 3.785101 / 3.745712 (0.039389) \| 2.812608 / 5.269862 (-2.457254) \| 1.373460 / 4.565676 (-3.192216) \| 0.066208 / 0.424275 (-0.358067) \| 0.012173 / 0.007607 (0.004566) \| 0.528716 / 0.226044 (0.302672) \| 5.295003 / 2.268929 (3.026074) \| 2.450188 / 55.444624 (-52.994437) \| 2.114560 / 6.876477 (-4.761917) \| 2.268468 / 2.142072 (0.126395) \| 0.651706 / 4.805227 (-4.153521) \| 0.142185 / 6.500664 (-6.358479) \| 0.064862 / 0.075469 (-0.010607) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.184933 / 1.841788 (-0.656854) \| 14.503903 / 8.074308 (6.429595) \| 13.928965 / 10.191392 (3.737573) \| 0.156788 / 0.680424 (-0.523636) \| 0.017320 / 0.534201 (-0.516881) \| 0.391366 / 0.579283 (-0.187918) \| 0.416261 / 0.434364 (-0.018103) \| 0.461951 / 0.540337 (-0.078387) \| 0.553496 / 1.386936 (-0.833440) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006623 / 0.011353 (-0.004730) \| 0.004617 / 0.011008 (-0.006392) \| 0.075579 / 0.038508 (0.037071) \| 0.033863 / 0.023109 (0.010754) \| 0.357097 / 0.275898 (0.081199) \| 0.396177 / 0.323480 (0.072697) \| 0.005712 / 0.007986 (-0.002274) \| 0.004232 / 0.004328 (-0.000097) \| 0.074669 / 0.004250 (0.070418) \| 0.048253 / 0.037052 (0.011201) \| 0.362453 / 0.258489 (0.103964) \| 0.405423 / 0.293841 (0.111582) \| 0.028709 / 0.128546 (-0.099837) \| 0.008884 / 0.075646 (-0.066763) \| 0.083042 / 0.419271 (-0.336230) \| 0.048074 / 0.043533 (0.004541) \| 0.355314 / 0.255139 (0.100175) \| 0.372536 / 0.283200 (0.089336) \| 0.111548 / 0.141683 (-0.030135) \| 1.466353 / 1.452155 (0.014198) \| 1.555077 / 1.492716 (0.062361) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.217016 / 0.018006 (0.199010) \| 0.450145 / 0.000490 (0.449655) \| 0.001910 / 0.000200 (0.001711) \| 0.000098 / 0.000054 (0.000044) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029787 / 0.037411 (-0.007624) \| 0.115282 / 0.014526 (0.100756) \| 0.121962 / 0.176557 (-0.054595) \| 0.173424 / 0.737135 (-0.563711) \| 0.127519 / 0.296338 (-0.168819) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.438211 / 0.215209 (0.223002) \| 4.346352 / 2.077655 (2.268697) \| 2.140197 / 1.504120 (0.636077) \| 1.957890 / 1.541195 (0.416696) \| 2.044300 / 1.468490 (0.575810) \| 0.527958 / 4.584777 (-4.056819) \| 3.805079 / 3.745712 (0.059367) \| 2.601763 / 5.269862 (-2.668098) \| 1.359469 / 4.565676 (-3.206208) \| 0.065358 / 0.424275 (-0.358917) \| 0.011571 / 0.007607 (0.003964) \| 0.538513 / 0.226044 (0.312469) \| 5.363508 / 2.268929 (3.094580) \| 2.640495 / 55.444624 (-52.804129) \| 2.335930 / 6.876477 (-4.540547) \| 2.407782 / 2.142072 (0.265710) \| 0.641637 / 4.805227 (-4.163590) \| 0.142196 / 6.500664 (-6.358468) \| 0.065041 / 0.075469 (-0.010428) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.296031 / 1.841788 (-0.545757) \| 14.950424 / 8.074308 (6.876115) \| 14.371304 / 10.191392 (4.179912) \| 0.148157 / 0.680424 (-0.532267) \| 0.017506 / 0.534201 (-0.516695) \| 0.392037 / 0.579283 (-0.187246) \| 0.423238 / 0.434364 (-0.011126) \| 0.464608 / 0.540337 (-0.075730) \| 0.563876 / 1.386936 (-0.823060) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#04b1d0371408beb0c7bc587a69c382bd8d0bec36 \"CML watermark\")\n" ]
1,726,279,011	5,899	canonicalize data dir in config ID hash	closed	2023-05-25T18:17:10	2023-06-02T16:02:15	2023-06-02T15:52:04	https://github.com/huggingface/datasets/pull/5899	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5899", "html_url": "https://github.com/huggingface/datasets/pull/5899", "diff_url": "https://github.com/huggingface/datasets/pull/5899.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5899.patch", "merged_at": "2023-06-02T15:52:04" }	kylrth	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) \| 0.009137 / 0.011353 (-0.002216) \| 0.006119 / 0.011008 (-0.004889) \| 0.136530 / 0.038508 (0.098022) \| 0.038434 / 0.023109 (0.015325) \| 0.427900 / 0.275898 (0.152002) \| 0.449757 / 0.323480 (0.126277) \| 0.007673 / 0.007986 (-0.000313) \| 0.007147 / 0.004328 (0.002818) \| 0.108029 / 0.004250 (0.103778) \| 0.055072 / 0.037052 (0.018020) \| 0.439245 / 0.258489 (0.180756) \| 0.477285 / 0.293841 (0.183444) \| 0.044838 / 0.128546 (-0.083708) \| 0.020814 / 0.075646 (-0.054832) \| 0.436098 / 0.419271 (0.016826) \| 0.067459 / 0.043533 (0.023926) \| 0.427470 / 0.255139 (0.172331) \| 0.443260 / 0.283200 (0.160060) \| 0.125466 / 0.141683 (-0.016216) \| 1.996756 / 1.452155 (0.544601) \| 2.100679 / 1.492716 (0.607962) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278407 / 0.018006 (0.260401) \| 0.625855 / 0.000490 (0.625365) \| 0.005544 / 0.000200 (0.005344) \| 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) \| 0.033495 / 0.037411 (-0.003916) \| 0.134718 / 0.014526 (0.120192) \| 0.150151 / 0.176557 (-0.026406) \| 0.221385 / 0.737135 (-0.515751) \| 0.150932 / 0.296338 (-0.145406) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.668845 / 0.215209 (0.453636) \| 6.678436 / 2.077655 (4.600781) \| 2.714074 / 1.504120 (1.209954) \| 2.275784 / 1.541195 (0.734589) \| 2.332852 / 1.468490 (0.864361) \| 1.014877 / 4.584777 (-3.569900) \| 6.086455 / 3.745712 (2.340743) \| 2.990029 / 5.269862 (-2.279832) \| 1.862236 / 4.565676 (-2.703441) \| 0.122179 / 0.424275 (-0.302096) \| 0.015706 / 0.007607 (0.008099) \| 0.873473 / 0.226044 (0.647429) \| 8.580109 / 2.268929 (6.311180) \| 3.458360 / 55.444624 (-51.986264) \| 2.738801 / 6.876477 (-4.137676) \| 2.918428 / 2.142072 (0.776356) \| 1.224910 / 4.805227 (-3.580317) \| 0.243006 / 6.500664 (-6.257658) \| 0.087121 / 0.075469 (0.011652) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.757802 / 1.841788 (-0.083986) \| 19.447999 / 8.074308 (11.373691) \| 24.518157 / 10.191392 (14.326765) \| 0.245013 / 0.680424 (-0.435411) \| 0.032290 / 0.534201 (-0.501911) \| 0.542043 / 0.579283 (-0.037240) \| 0.708154 / 0.434364 (0.273790) \| 0.660584 / 0.540337 (0.120247) \| 0.794868 / 1.386936 (-0.592068) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009496 / 0.011353 (-0.001857) \| 0.005842 / 0.011008 (-0.005166) \| 0.112813 / 0.038508 (0.074305) \| 0.039120 / 0.023109 (0.016011) \| 0.489717 / 0.275898 (0.213819) \| 0.532586 / 0.323480 (0.209107) \| 0.007681 / 0.007986 (-0.000304) \| 0.005337 / 0.004328 (0.001009) \| 0.107244 / 0.004250 (0.102994) \| 0.056847 / 0.037052 (0.019794) \| 0.499447 / 0.258489 (0.240958) \| 0.548995 / 0.293841 (0.255154) \| 0.058047 / 0.128546 (-0.070499) \| 0.015468 / 0.075646 (-0.060179) \| 0.124600 / 0.419271 (-0.294671) \| 0.060940 / 0.043533 (0.017407) \| 0.488370 / 0.255139 (0.233231) \| 0.518540 / 0.283200 (0.235341) \| 0.124147 / 0.141683 (-0.017536) \| 1.902922 / 1.452155 (0.450767) \| 2.033519 / 1.492716 (0.540803) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.319527 / 0.018006 (0.301521) \| 0.629641 / 0.000490 (0.629152) \| 0.000721 / 0.000200 (0.000521) \| 0.000101 / 0.000054 (0.000046) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033150 / 0.037411 (-0.004262) \| 0.134250 / 0.014526 (0.119724) \| 0.161273 / 0.176557 (-0.015283) \| 0.211471 / 0.737135 (-0.525664) \| 0.155326 / 0.296338 (-0.141012) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.705244 / 0.215209 (0.490035) \| 7.043040 / 2.077655 (4.965386) \| 3.308948 / 1.504120 (1.804828) \| 2.885050 / 1.541195 (1.343855) \| 2.810260 / 1.468490 (1.341770) \| 1.027095 / 4.584777 (-3.557682) \| 6.111398 / 3.745712 (2.365686) \| 5.385545 / 5.269862 (0.115684) \| 2.521668 / 4.565676 (-2.044009) \| 0.122419 / 0.424275 (-0.301856) \| 0.016376 / 0.007607 (0.008768) \| 0.830856 / 0.226044 (0.604811) \| 8.952199 / 2.268929 (6.683271) \| 4.207875 / 55.444624 (-51.236749) \| 3.346624 / 6.876477 (-3.529853) \| 3.395316 / 2.142072 (1.253244) \| 1.351816 / 4.805227 (-3.453411) \| 0.303056 / 6.500664 (-6.197608) \| 0.098713 / 0.075469 (0.023244) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.841903 / 1.841788 (0.000116) \| 20.472125 / 8.074308 (12.397817) \| 23.433200 / 10.191392 (13.241808) \| 0.242599 / 0.680424 (-0.437825) \| 0.030701 / 0.534201 (-0.503500) \| 0.541614 / 0.579283 (-0.037669) \| 0.657827 / 0.434364 (0.223463) \| 0.652448 / 0.540337 (0.112111) \| 0.773743 / 1.386936 (-0.613193) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#02ee418831aba68d0be93227bce8b3f42ef8980f \"CML watermark\")\n" ]
1,726,190,481	5,898	Loading The flores data set for specific language	closed	2023-05-25T17:08:55	2023-05-25T17:21:38	2023-05-25T17:21:37	https://github.com/huggingface/datasets/issues/5898	null	106AbdulBasit	false	[ "got that the syntax is like this\r\n\r\ndataset = load_dataset(\"facebook/flores\", \"ace_Arab\")" ]
1,726,135,494	5,897	Fix `FixedSizeListArray` casting	closed	2023-05-25T16:26:33	2023-05-26T12:22:04	2023-05-26T11:57:16	https://github.com/huggingface/datasets/pull/5897	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5897", "html_url": "https://github.com/huggingface/datasets/pull/5897", "diff_url": "https://github.com/huggingface/datasets/pull/5897.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5897.patch", "merged_at": "2023-05-26T11:57:16" }	mariosasko	true	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006213 / 0.011353 (-0.005140) \| 0.004230 / 0.011008 (-0.006778) \| 0.098014 / 0.038508 (0.059506) \| 0.028659 / 0.023109 (0.005550) \| 0.303272 / 0.275898 (0.027374) \| 0.337186 / 0.323480 (0.013706) \| 0.005126 / 0.007986 (-0.002860) \| 0.003563 / 0.004328 (-0.000765) \| 0.075295 / 0.004250 (0.071045) \| 0.036836 / 0.037052 (-0.000216) \| 0.309612 / 0.258489 (0.051123) \| 0.346484 / 0.293841 (0.052643) \| 0.025714 / 0.128546 (-0.102832) \| 0.008562 / 0.075646 (-0.067085) \| 0.323475 / 0.419271 (-0.095796) \| 0.044072 / 0.043533 (0.000539) \| 0.308261 / 0.255139 (0.053122) \| 0.330903 / 0.283200 (0.047703) \| 0.091805 / 0.141683 (-0.049878) \| 1.517011 / 1.452155 (0.064856) \| 1.570815 / 1.492716 (0.078099) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.211265 / 0.018006 (0.193259) \| 0.438860 / 0.000490 (0.438370) \| 0.001127 / 0.000200 (0.000927) \| 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) \| 0.023337 / 0.037411 (-0.014074) \| 0.096243 / 0.014526 (0.081717) \| 0.103529 / 0.176557 (-0.073028) \| 0.161171 / 0.737135 (-0.575964) \| 0.105904 / 0.296338 (-0.190435) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.417042 / 0.215209 (0.201833) \| 4.155067 / 2.077655 (2.077412) \| 1.879657 / 1.504120 (0.375537) \| 1.669341 / 1.541195 (0.128146) \| 1.717623 / 1.468490 (0.249133) \| 0.556246 / 4.584777 (-4.028531) \| 3.484535 / 3.745712 (-0.261177) \| 1.728845 / 5.269862 (-3.541017) \| 0.997477 / 4.565676 (-3.568199) \| 0.068355 / 0.424275 (-0.355920) \| 0.012445 / 0.007607 (0.004837) \| 0.519023 / 0.226044 (0.292978) \| 5.173506 / 2.268929 (2.904577) \| 2.332435 / 55.444624 (-53.112190) \| 1.986348 / 6.876477 (-4.890129) \| 2.076885 / 2.142072 (-0.065187) \| 0.656738 / 4.805227 (-4.148489) \| 0.135308 / 6.500664 (-6.365356) \| 0.065486 / 0.075469 (-0.009984) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.208874 / 1.841788 (-0.632914) \| 13.994200 / 8.074308 (5.919892) \| 14.160978 / 10.191392 (3.969586) \| 0.146009 / 0.680424 (-0.534415) \| 0.016573 / 0.534201 (-0.517628) \| 0.356082 / 0.579283 (-0.223202) \| 0.387766 / 0.434364 (-0.046598) \| 0.419130 / 0.540337 (-0.121208) \| 0.508634 / 1.386936 (-0.878302) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006238 / 0.011353 (-0.005115) \| 0.004221 / 0.011008 (-0.006788) \| 0.075155 / 0.038508 (0.036646) \| 0.028491 / 0.023109 (0.005382) \| 0.355606 / 0.275898 (0.079708) \| 0.388986 / 0.323480 (0.065506) \| 0.005941 / 0.007986 (-0.002044) \| 0.003510 / 0.004328 (-0.000819) \| 0.074905 / 0.004250 (0.070655) \| 0.039111 / 0.037052 (0.002059) \| 0.358492 / 0.258489 (0.100003) \| 0.398763 / 0.293841 (0.104922) \| 0.025535 / 0.128546 (-0.103012) \| 0.008580 / 0.075646 (-0.067067) \| 0.080461 / 0.419271 (-0.338811) \| 0.041381 / 0.043533 (-0.002152) \| 0.355498 / 0.255139 (0.100359) \| 0.379163 / 0.283200 (0.095963) \| 0.096450 / 0.141683 (-0.045233) \| 1.503248 / 1.452155 (0.051093) \| 1.595616 / 1.492716 (0.102900) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.238065 / 0.018006 (0.220058) \| 0.422800 / 0.000490 (0.422311) \| 0.002274 / 0.000200 (0.002074) \| 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) \| 0.025746 / 0.037411 (-0.011665) \| 0.103319 / 0.014526 (0.088793) \| 0.112155 / 0.176557 (-0.064401) \| 0.163034 / 0.737135 (-0.574101) \| 0.113377 / 0.296338 (-0.182962) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.440522 / 0.215209 (0.225313) \| 4.398123 / 2.077655 (2.320468) \| 2.143538 / 1.504120 (0.639418) \| 1.946084 / 1.541195 (0.404890) \| 1.996556 / 1.468490 (0.528066) \| 0.550108 / 4.584777 (-4.034669) \| 3.455774 / 3.745712 (-0.289938) \| 2.862474 / 5.269862 (-2.407387) \| 1.213446 / 4.565676 (-3.352230) \| 0.067987 / 0.424275 (-0.356288) \| 0.012413 / 0.007607 (0.004806) \| 0.543990 / 0.226044 (0.317945) \| 5.454807 / 2.268929 (3.185879) \| 2.669195 / 55.444624 (-52.775429) \| 2.332948 / 6.876477 (-4.543528) \| 2.383870 / 2.142072 (0.241797) \| 0.652017 / 4.805227 (-4.153210) \| 0.135508 / 6.500664 (-6.365156) \| 0.068238 / 0.075469 (-0.007231) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.322669 / 1.841788 (-0.519118) \| 14.368136 / 8.074308 (6.293828) \| 14.167431 / 10.191392 (3.976039) \| 0.159371 / 0.680424 (-0.521052) \| 0.016638 / 0.534201 (-0.517563) \| 0.357106 / 0.579283 (-0.222177) \| 0.392491 / 0.434364 (-0.041873) \| 0.419458 / 0.540337 (-0.120880) \| 0.504662 / 1.386936 (-0.882274) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bf764819ba6754cb7edf15899db517be0548676f \"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) \| 0.006296 / 0.011353 (-0.005057) \| 0.004185 / 0.011008 (-0.006823) \| 0.096170 / 0.038508 (0.057662) \| 0.029212 / 0.023109 (0.006102) \| 0.315356 / 0.275898 (0.039458) \| 0.335214 / 0.323480 (0.011734) \| 0.005108 / 0.007986 (-0.002877) \| 0.003634 / 0.004328 (-0.000694) \| 0.074186 / 0.004250 (0.069936) \| 0.038716 / 0.037052 (0.001663) \| 0.311041 / 0.258489 (0.052551) \| 0.341202 / 0.293841 (0.047361) \| 0.025584 / 0.128546 (-0.102962) \| 0.008499 / 0.075646 (-0.067148) \| 0.318660 / 0.419271 (-0.100611) \| 0.043745 / 0.043533 (0.000212) \| 0.314824 / 0.255139 (0.059685) \| 0.328117 / 0.283200 (0.044917) \| 0.093425 / 0.141683 (-0.048258) \| 1.478732 / 1.452155 (0.026578) \| 1.531743 / 1.492716 (0.039027) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.203484 / 0.018006 (0.185478) \| 0.416131 / 0.000490 (0.415641) \| 0.007352 / 0.000200 (0.007152) \| 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) \| 0.022908 / 0.037411 (-0.014503) \| 0.098641 / 0.014526 (0.084115) \| 0.103426 / 0.176557 (-0.073131) \| 0.161658 / 0.737135 (-0.575477) \| 0.106506 / 0.296338 (-0.189832) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.430781 / 0.215209 (0.215572) \| 4.315677 / 2.077655 (2.238022) \| 2.022302 / 1.504120 (0.518182) \| 1.832043 / 1.541195 (0.290849) \| 1.789302 / 1.468490 (0.320812) \| 0.560484 / 4.584777 (-4.024293) \| 3.448204 / 3.745712 (-0.297508) \| 1.725016 / 5.269862 (-3.544846) \| 1.002649 / 4.565676 (-3.563027) \| 0.068480 / 0.424275 (-0.355795) \| 0.012617 / 0.007607 (0.005010) \| 0.532291 / 0.226044 (0.306246) \| 5.319352 / 2.268929 (3.050423) \| 2.520730 / 55.444624 (-52.923894) \| 2.213881 / 6.876477 (-4.662596) \| 2.352477 / 2.142072 (0.210404) \| 0.662516 / 4.805227 (-4.142711) \| 0.136481 / 6.500664 (-6.364183) \| 0.066597 / 0.075469 (-0.008872) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.224537 / 1.841788 (-0.617251) \| 13.849920 / 8.074308 (5.775612) \| 14.026358 / 10.191392 (3.834966) \| 0.131018 / 0.680424 (-0.549405) \| 0.016756 / 0.534201 (-0.517445) \| 0.358091 / 0.579283 (-0.221192) \| 0.397709 / 0.434364 (-0.036655) \| 0.450024 / 0.540337 (-0.090314) \| 0.542609 / 1.386936 (-0.844327) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006179 / 0.011353 (-0.005174) \| 0.004145 / 0.011008 (-0.006863) \| 0.077482 / 0.038508 (0.038974) \| 0.028005 / 0.023109 (0.004896) \| 0.400010 / 0.275898 (0.124112) \| 0.408206 / 0.323480 (0.084726) \| 0.005049 / 0.007986 (-0.002937) \| 0.003608 / 0.004328 (-0.000721) \| 0.076841 / 0.004250 (0.072590) \| 0.036714 / 0.037052 (-0.000338) \| 0.406020 / 0.258489 (0.147531) \| 0.412392 / 0.293841 (0.118551) \| 0.025626 / 0.128546 (-0.102920) \| 0.008560 / 0.075646 (-0.067087) \| 0.084088 / 0.419271 (-0.335183) \| 0.039707 / 0.043533 (-0.003826) \| 0.396909 / 0.255139 (0.141770) \| 0.403623 / 0.283200 (0.120424) \| 0.095137 / 0.141683 (-0.046546) \| 1.515670 / 1.452155 (0.063515) \| 1.568379 / 1.492716 (0.075662) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.181802 / 0.018006 (0.163795) \| 0.408778 / 0.000490 (0.408289) \| 0.000393 / 0.000200 (0.000193) \| 0.000060 / 0.000054 (0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025940 / 0.037411 (-0.011471) \| 0.099992 / 0.014526 (0.085466) \| 0.106280 / 0.176557 (-0.070276) \| 0.161729 / 0.737135 (-0.575406) \| 0.108625 / 0.296338 (-0.187713) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.459802 / 0.215209 (0.244593) \| 4.603002 / 2.077655 (2.525347) \| 2.406851 / 1.504120 (0.902732) \| 2.265422 / 1.541195 (0.724227) \| 2.306305 / 1.468490 (0.837815) \| 0.553903 / 4.584777 (-4.030874) \| 3.482052 / 3.745712 (-0.263660) \| 2.969855 / 5.269862 (-2.300007) \| 1.309285 / 4.565676 (-3.256391) \| 0.068130 / 0.424275 (-0.356145) \| 0.012189 / 0.007607 (0.004582) \| 0.571299 / 0.226044 (0.345254) \| 5.711420 / 2.268929 (3.442492) \| 2.716748 / 55.444624 (-52.727876) \| 2.369869 / 6.876477 (-4.506608) \| 2.544240 / 2.142072 (0.402167) \| 0.659955 / 4.805227 (-4.145272) \| 0.136684 / 6.500664 (-6.363980) \| 0.068962 / 0.075469 (-0.006507) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.297659 / 1.841788 (-0.544129) \| 14.012758 / 8.074308 (5.938449) \| 14.324644 / 10.191392 (4.133252) \| 0.144894 / 0.680424 (-0.535530) \| 0.016751 / 0.534201 (-0.517450) \| 0.361547 / 0.579283 (-0.217736) \| 0.396595 / 0.434364 (-0.037769) \| 0.422375 / 0.540337 (-0.117962) \| 0.508209 / 1.386936 (-0.878727) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ba5f81357b53099b1bedfbb277211dba3952257b \"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) \| 0.006303 / 0.011353 (-0.005050) \| 0.004043 / 0.011008 (-0.006965) \| 0.096239 / 0.038508 (0.057731) \| 0.029608 / 0.023109 (0.006498) \| 0.321058 / 0.275898 (0.045160) \| 0.367066 / 0.323480 (0.043587) \| 0.005236 / 0.007986 (-0.002749) \| 0.003342 / 0.004328 (-0.000987) \| 0.074407 / 0.004250 (0.070157) \| 0.038810 / 0.037052 (0.001757) \| 0.332597 / 0.258489 (0.074108) \| 0.363562 / 0.293841 (0.069721) \| 0.025460 / 0.128546 (-0.103086) \| 0.008426 / 0.075646 (-0.067221) \| 0.316998 / 0.419271 (-0.102273) \| 0.043621 / 0.043533 (0.000088) \| 0.338043 / 0.255139 (0.082904) \| 0.366441 / 0.283200 (0.083241) \| 0.092061 / 0.141683 (-0.049622) \| 1.461531 / 1.452155 (0.009376) \| 1.538047 / 1.492716 (0.045331) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.206796 / 0.018006 (0.188790) \| 0.517959 / 0.000490 (0.517469) \| 0.002745 / 0.000200 (0.002545) \| 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) \| 0.022902 / 0.037411 (-0.014510) \| 0.097901 / 0.014526 (0.083375) \| 0.103664 / 0.176557 (-0.072893) \| 0.163516 / 0.737135 (-0.573619) \| 0.108561 / 0.296338 (-0.187778) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.418964 / 0.215209 (0.203755) \| 4.159113 / 2.077655 (2.081458) \| 1.843946 / 1.504120 (0.339827) \| 1.641083 / 1.541195 (0.099888) \| 1.686848 / 1.468490 (0.218358) \| 0.554583 / 4.584777 (-4.030194) \| 3.409862 / 3.745712 (-0.335850) \| 2.647904 / 5.269862 (-2.621958) \| 1.355424 / 4.565676 (-3.210253) \| 0.068229 / 0.424275 (-0.356046) \| 0.012217 / 0.007607 (0.004610) \| 0.515895 / 0.226044 (0.289851) \| 5.144920 / 2.268929 (2.875991) \| 2.298046 / 55.444624 (-53.146579) \| 1.964735 / 6.876477 (-4.911741) \| 2.075580 / 2.142072 (-0.066492) \| 0.657104 / 4.805227 (-4.148123) \| 0.134759 / 6.500664 (-6.365905) \| 0.067545 / 0.075469 (-0.007924) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.233075 / 1.841788 (-0.608713) \| 13.896762 / 8.074308 (5.822454) \| 14.055143 / 10.191392 (3.863751) \| 0.145507 / 0.680424 (-0.534917) \| 0.016702 / 0.534201 (-0.517499) \| 0.365157 / 0.579283 (-0.214126) \| 0.385842 / 0.434364 (-0.048522) \| 0.459993 / 0.540337 (-0.080344) \| 0.547115 / 1.386936 (-0.839821) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006174 / 0.011353 (-0.005179) \| 0.004191 / 0.011008 (-0.006817) \| 0.078311 / 0.038508 (0.039803) \| 0.028038 / 0.023109 (0.004928) \| 0.360056 / 0.275898 (0.084158) \| 0.398081 / 0.323480 (0.074602) \| 0.005069 / 0.007986 (-0.002916) \| 0.003464 / 0.004328 (-0.000864) \| 0.077858 / 0.004250 (0.073608) \| 0.039420 / 0.037052 (0.002367) \| 0.361743 / 0.258489 (0.103254) \| 0.404829 / 0.293841 (0.110988) \| 0.025604 / 0.128546 (-0.102943) \| 0.008573 / 0.075646 (-0.067074) \| 0.084944 / 0.419271 (-0.334328) \| 0.042652 / 0.043533 (-0.000881) \| 0.368549 / 0.255139 (0.113410) \| 0.385682 / 0.283200 (0.102482) \| 0.099085 / 0.141683 (-0.042598) \| 1.495815 / 1.452155 (0.043661) \| 1.548168 / 1.492716 (0.055452) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.193737 / 0.018006 (0.175730) \| 0.421871 / 0.000490 (0.421381) \| 0.002306 / 0.000200 (0.002106) \| 0.000073 / 0.000054 (0.000018) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025928 / 0.037411 (-0.011483) \| 0.103410 / 0.014526 (0.088885) \| 0.107931 / 0.176557 (-0.068626) \| 0.157127 / 0.737135 (-0.580008) \| 0.111892 / 0.296338 (-0.184446) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.477562 / 0.215209 (0.262353) \| 4.772711 / 2.077655 (2.695056) \| 2.458725 / 1.504120 (0.954605) \| 2.269871 / 1.541195 (0.728676) \| 2.365502 / 1.468490 (0.897012) \| 0.556182 / 4.584777 (-4.028595) \| 3.408016 / 3.745712 (-0.337697) \| 1.730639 / 5.269862 (-3.539222) \| 1.000973 / 4.565676 (-3.564704) \| 0.068293 / 0.424275 (-0.355982) \| 0.012119 / 0.007607 (0.004512) \| 0.581281 / 0.226044 (0.355236) \| 5.811930 / 2.268929 (3.543001) \| 2.890337 / 55.444624 (-52.554288) \| 2.592156 / 6.876477 (-4.284321) \| 2.687764 / 2.142072 (0.545691) \| 0.664282 / 4.805227 (-4.140946) \| 0.136029 / 6.500664 (-6.364635) \| 0.067493 / 0.075469 (-0.007976) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.330723 / 1.841788 (-0.511064) \| 14.379172 / 8.074308 (6.304864) \| 14.153286 / 10.191392 (3.961894) \| 0.142942 / 0.680424 (-0.537482) \| 0.016698 / 0.534201 (-0.517503) \| 0.361044 / 0.579283 (-0.218239) \| 0.393174 / 0.434364 (-0.041190) \| 0.423107 / 0.540337 (-0.117231) \| 0.514299 / 1.386936 (-0.872637) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1cb02285358ab4be6386e0a2aae40d267ff561fc \"CML watermark\")\n" ]
1,726,022,500	5,896	HuggingFace does not cache downloaded files aggressively/early enough	closed	2023-05-25T15:14:36	2024-03-15T15:36:07	2024-03-15T15:36:07	https://github.com/huggingface/datasets/issues/5896	null	jack-jjm	false	[ "I also faced this. Any update?", "We've dropped the `apache-beam` dependency in https://huggingface.co/datasets/wikipedia/discussions/19, so you should no longer get this error." ]
1,725,467,252	5,895	The dir name and split strings are confused when loading ArmelR/stack-exchange-instruction dataset	closed	2023-05-25T09:39:06	2023-05-29T02:32:12	2023-05-29T02:32:12	https://github.com/huggingface/datasets/issues/5895	null	DongHande	false	[ "Thanks for reporting, @DongHande.\r\n\r\nI think the issue is caused by the metadata in the dataset card: in the header of the `README.md`, they state that the dataset has 4 splits (\"finetune\", \"reward\", \"rl\", \"evaluation\"). \r\n```yaml\r\n splits:\r\n - name: finetune\r\n num_bytes: 6674567576\r\n num_examples: 3000000\r\n - name: reward\r\n num_bytes: 6674341521\r\n num_examples: 3000000\r\n - name: rl\r\n num_bytes: 6679279968\r\n num_examples: 3000000\r\n - name: evaluation\r\n num_bytes: 4022714493\r\n num_examples: 1807695\r\n```\r\n\r\n\r\nI guess the user wanted to define these as configs, instead of splits. This is not yet supported for no-script datasets, but will be soon supported. See:\r\n- #5331\r\n\r\nI think we should contact the dataset author to inform about the issue with the split names, as you already did: https://huggingface.co/datasets/ArmelR/stack-exchange-instruction/discussions/1\r\nLet's continue the discussion there!", "Thank you! It has been fixed. " ]
1,724,774,910	5,894	Force overwrite existing filesystem protocol	closed	2023-05-24T21:41:53	2023-05-25T06:52:08	2023-05-25T06:42:33	https://github.com/huggingface/datasets/pull/5894	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5894", "html_url": "https://github.com/huggingface/datasets/pull/5894", "diff_url": "https://github.com/huggingface/datasets/pull/5894.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5894.patch", "merged_at": "2023-05-25T06:42:33" }	baskrahmer	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) \| 0.009139 / 0.011353 (-0.002214) \| 0.005634 / 0.011008 (-0.005374) \| 0.129587 / 0.038508 (0.091079) \| 0.038298 / 0.023109 (0.015189) \| 0.428149 / 0.275898 (0.152251) \| 0.443744 / 0.323480 (0.120264) \| 0.007501 / 0.007986 (-0.000485) \| 0.005999 / 0.004328 (0.001671) \| 0.100796 / 0.004250 (0.096546) \| 0.053236 / 0.037052 (0.016184) \| 0.423868 / 0.258489 (0.165379) \| 0.460110 / 0.293841 (0.166269) \| 0.041255 / 0.128546 (-0.087291) \| 0.013790 / 0.075646 (-0.061856) \| 0.438398 / 0.419271 (0.019127) \| 0.063086 / 0.043533 (0.019553) \| 0.414826 / 0.255139 (0.159687) \| 0.460652 / 0.283200 (0.177453) \| 0.121223 / 0.141683 (-0.020460) \| 1.754430 / 1.452155 (0.302275) \| 1.900037 / 1.492716 (0.407320) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027222 / 0.018006 (0.009216) \| 0.617666 / 0.000490 (0.617176) \| 0.022443 / 0.000200 (0.022243) \| 0.000820 / 0.000054 (0.000766) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030397 / 0.037411 (-0.007014) \| 0.125732 / 0.014526 (0.111206) \| 0.149805 / 0.176557 (-0.026752) \| 0.234048 / 0.737135 (-0.503087) \| 0.143108 / 0.296338 (-0.153231) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.631189 / 0.215209 (0.415980) \| 6.182871 / 2.077655 (4.105216) \| 2.635730 / 1.504120 (1.131610) \| 2.231429 / 1.541195 (0.690235) \| 2.438360 / 1.468490 (0.969870) \| 0.861170 / 4.584777 (-3.723607) \| 5.785984 / 3.745712 (2.040272) \| 2.758358 / 5.269862 (-2.511504) \| 1.678095 / 4.565676 (-2.887582) \| 0.105961 / 0.424275 (-0.318314) \| 0.013659 / 0.007607 (0.006052) \| 0.762943 / 0.226044 (0.536898) \| 7.774399 / 2.268929 (5.505471) \| 3.319027 / 55.444624 (-52.125598) \| 2.700248 / 6.876477 (-4.176229) \| 3.008581 / 2.142072 (0.866509) \| 1.122522 / 4.805227 (-3.682705) \| 0.214832 / 6.500664 (-6.285832) \| 0.085281 / 0.075469 (0.009811) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.647610 / 1.841788 (-0.194177) \| 18.178316 / 8.074308 (10.104008) \| 21.199177 / 10.191392 (11.007785) \| 0.247063 / 0.680424 (-0.433361) \| 0.030443 / 0.534201 (-0.503758) \| 0.512527 / 0.579283 (-0.066757) \| 0.640758 / 0.434364 (0.206394) \| 0.639986 / 0.540337 (0.099649) \| 0.760113 / 1.386936 (-0.626823) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008293 / 0.011353 (-0.003060) \| 0.005360 / 0.011008 (-0.005648) \| 0.102932 / 0.038508 (0.064424) \| 0.037457 / 0.023109 (0.014347) \| 0.444114 / 0.275898 (0.168216) \| 0.512855 / 0.323480 (0.189375) \| 0.007030 / 0.007986 (-0.000956) \| 0.004954 / 0.004328 (0.000625) \| 0.095757 / 0.004250 (0.091507) \| 0.051239 / 0.037052 (0.014187) \| 0.471118 / 0.258489 (0.212629) \| 0.517764 / 0.293841 (0.223923) \| 0.041953 / 0.128546 (-0.086593) \| 0.013748 / 0.075646 (-0.061898) \| 0.118089 / 0.419271 (-0.301182) \| 0.060159 / 0.043533 (0.016626) \| 0.466011 / 0.255139 (0.210872) \| 0.489180 / 0.283200 (0.205980) \| 0.123250 / 0.141683 (-0.018433) \| 1.714738 / 1.452155 (0.262584) \| 1.838571 / 1.492716 (0.345855) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.267792 / 0.018006 (0.249785) \| 0.624313 / 0.000490 (0.623824) \| 0.007315 / 0.000200 (0.007115) \| 0.000136 / 0.000054 (0.000082) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033751 / 0.037411 (-0.003661) \| 0.122819 / 0.014526 (0.108293) \| 0.148270 / 0.176557 (-0.028286) \| 0.198581 / 0.737135 (-0.538554) \| 0.144845 / 0.296338 (-0.151494) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.620631 / 0.215209 (0.405422) \| 6.224665 / 2.077655 (4.147010) \| 2.856592 / 1.504120 (1.352473) \| 2.525089 / 1.541195 (0.983894) \| 2.600198 / 1.468490 (1.131708) \| 0.872038 / 4.584777 (-3.712739) \| 5.571650 / 3.745712 (1.825937) \| 5.907643 / 5.269862 (0.637782) \| 2.348770 / 4.565676 (-2.216906) \| 0.111665 / 0.424275 (-0.312610) \| 0.013886 / 0.007607 (0.006278) \| 0.762154 / 0.226044 (0.536109) \| 7.792686 / 2.268929 (5.523758) \| 3.601122 / 55.444624 (-51.843503) \| 2.939412 / 6.876477 (-3.937064) \| 2.973430 / 2.142072 (0.831358) \| 1.065016 / 4.805227 (-3.740211) \| 0.221701 / 6.500664 (-6.278963) \| 0.088157 / 0.075469 (0.012688) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.771061 / 1.841788 (-0.070727) \| 18.826926 / 8.074308 (10.752618) \| 21.283830 / 10.191392 (11.092438) \| 0.239233 / 0.680424 (-0.441191) \| 0.026159 / 0.534201 (-0.508042) \| 0.487074 / 0.579283 (-0.092209) \| 0.623241 / 0.434364 (0.188877) \| 0.600506 / 0.540337 (0.060169) \| 0.691271 / 1.386936 (-0.695665) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1bbe2c3496498a6415765b517ac4bc600a02ad06 \"CML watermark\")\n" ]
1,722,519,056	5,893	Load cached dataset as iterable	closed	2023-05-23T17:40:35	2023-06-01T11:58:24	2023-06-01T11:51:29	https://github.com/huggingface/datasets/pull/5893	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5893", "html_url": "https://github.com/huggingface/datasets/pull/5893", "diff_url": "https://github.com/huggingface/datasets/pull/5893.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5893.patch", "merged_at": "2023-06-01T11:51:29" }	mariusz-jachimowicz-83	true	[ "@lhoestq Could you please look into that and review?", "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq I refactored the code. Could you please check is it what you requested?", "@lhoestq Thanks for a review. Excellent tips. All tips applied. ", "I think there is just PythonFormatter that needs to be imported in the test file and we should be good to merge", "@lhoestq that is weird. I have linter error when I do it.", "@lhoestq Now it should work properly.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006152 / 0.011353 (-0.005201) \| 0.004169 / 0.011008 (-0.006839) \| 0.097968 / 0.038508 (0.059460) \| 0.028325 / 0.023109 (0.005216) \| 0.308958 / 0.275898 (0.033060) \| 0.341832 / 0.323480 (0.018352) \| 0.005098 / 0.007986 (-0.002887) \| 0.004721 / 0.004328 (0.000393) \| 0.075067 / 0.004250 (0.070817) \| 0.040514 / 0.037052 (0.003462) \| 0.308355 / 0.258489 (0.049866) \| 0.351063 / 0.293841 (0.057222) \| 0.025261 / 0.128546 (-0.103285) \| 0.008483 / 0.075646 (-0.067163) \| 0.321219 / 0.419271 (-0.098052) \| 0.058258 / 0.043533 (0.014725) \| 0.312572 / 0.255139 (0.057433) \| 0.330667 / 0.283200 (0.047467) \| 0.091047 / 0.141683 (-0.050635) \| 1.536541 / 1.452155 (0.084387) \| 1.606566 / 1.492716 (0.113850) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.213234 / 0.018006 (0.195228) \| 0.494801 / 0.000490 (0.494311) \| 0.003764 / 0.000200 (0.003564) \| 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) \| 0.023653 / 0.037411 (-0.013758) \| 0.097176 / 0.014526 (0.082650) \| 0.102961 / 0.176557 (-0.073595) \| 0.164285 / 0.737135 (-0.572851) \| 0.107586 / 0.296338 (-0.188753) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.421402 / 0.215209 (0.206193) \| 4.195828 / 2.077655 (2.118174) \| 1.884664 / 1.504120 (0.380544) \| 1.679750 / 1.541195 (0.138556) \| 1.719725 / 1.468490 (0.251235) \| 0.552290 / 4.584777 (-4.032486) \| 3.386337 / 3.745712 (-0.359375) \| 1.771527 / 5.269862 (-3.498334) \| 1.133327 / 4.565676 (-3.432349) \| 0.067911 / 0.424275 (-0.356364) \| 0.012572 / 0.007607 (0.004965) \| 0.518004 / 0.226044 (0.291960) \| 5.192381 / 2.268929 (2.923453) \| 2.316032 / 55.444624 (-53.128592) \| 1.993264 / 6.876477 (-4.883212) \| 2.071009 / 2.142072 (-0.071063) \| 0.655062 / 4.805227 (-4.150165) \| 0.135488 / 6.500664 (-6.365177) \| 0.067273 / 0.075469 (-0.008196) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.217731 / 1.841788 (-0.624056) \| 13.812927 / 8.074308 (5.738619) \| 13.137886 / 10.191392 (2.946494) \| 0.143102 / 0.680424 (-0.537322) \| 0.016884 / 0.534201 (-0.517317) \| 0.370106 / 0.579283 (-0.209178) \| 0.392349 / 0.434364 (-0.042015) \| 0.424501 / 0.540337 (-0.115837) \| 0.509830 / 1.386936 (-0.877106) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006210 / 0.011353 (-0.005142) \| 0.004215 / 0.011008 (-0.006793) \| 0.076129 / 0.038508 (0.037621) \| 0.027825 / 0.023109 (0.004716) \| 0.403973 / 0.275898 (0.128075) \| 0.441089 / 0.323480 (0.117609) \| 0.005420 / 0.007986 (-0.002566) \| 0.004870 / 0.004328 (0.000542) \| 0.075558 / 0.004250 (0.071308) \| 0.039464 / 0.037052 (0.002411) \| 0.404329 / 0.258489 (0.145840) \| 0.447213 / 0.293841 (0.153372) \| 0.025877 / 0.128546 (-0.102669) \| 0.008660 / 0.075646 (-0.066987) \| 0.081849 / 0.419271 (-0.337422) \| 0.044551 / 0.043533 (0.001018) \| 0.379102 / 0.255139 (0.123963) \| 0.403104 / 0.283200 (0.119905) \| 0.094754 / 0.141683 (-0.046929) \| 1.460772 / 1.452155 (0.008617) \| 1.569531 / 1.492716 (0.076815) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.183923 / 0.018006 (0.165917) \| 0.420708 / 0.000490 (0.420219) \| 0.002091 / 0.000200 (0.001891) \| 0.000080 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026180 / 0.037411 (-0.011231) \| 0.101529 / 0.014526 (0.087003) \| 0.108739 / 0.176557 (-0.067818) \| 0.160702 / 0.737135 (-0.576433) \| 0.111739 / 0.296338 (-0.184600) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.448671 / 0.215209 (0.233462) \| 4.469287 / 2.077655 (2.391632) \| 2.244335 / 1.504120 (0.740215) \| 2.107495 / 1.541195 (0.566301) \| 2.224763 / 1.468490 (0.756272) \| 0.554006 / 4.584777 (-4.030771) \| 3.390109 / 3.745712 (-0.355603) \| 1.744189 / 5.269862 (-3.525673) \| 1.008515 / 4.565676 (-3.557161) \| 0.067904 / 0.424275 (-0.356371) \| 0.012243 / 0.007607 (0.004636) \| 0.557635 / 0.226044 (0.331590) \| 5.610383 / 2.268929 (3.341454) \| 2.687326 / 55.444624 (-52.757298) \| 2.405262 / 6.876477 (-4.471214) \| 2.527300 / 2.142072 (0.385227) \| 0.662282 / 4.805227 (-4.142945) \| 0.136225 / 6.500664 (-6.364439) \| 0.068136 / 0.075469 (-0.007334) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.310791 / 1.841788 (-0.530997) \| 14.370381 / 8.074308 (6.296072) \| 14.122675 / 10.191392 (3.931283) \| 0.152302 / 0.680424 (-0.528122) \| 0.016624 / 0.534201 (-0.517577) \| 0.359395 / 0.579283 (-0.219888) \| 0.392131 / 0.434364 (-0.042233) \| 0.423796 / 0.540337 (-0.116542) \| 0.511387 / 1.386936 (-0.875549) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d6a61a1af1502677a6f2333896a6ffeede9ca21b \"CML watermark\")\n" ]
1,722,503,824	5,892	User access requests with manual review do not notify the dataset owner	closed	2023-05-23T17:27:46	2023-07-21T13:55:37	2023-07-21T13:55:36	https://github.com/huggingface/datasets/issues/5892	null	leondz	false	[ "cc @SBrandeis", "I think this has been addressed.\r\n\r\nPlease open a new issue if you are still not getting notified." ]
1,722,384,135	5,891	Make split slicing consistent with list slicing	closed	2023-05-23T16:04:33	2024-01-31T16:00:26	2024-01-31T15:54:17	https://github.com/huggingface/datasets/pull/5891	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5891", "html_url": "https://github.com/huggingface/datasets/pull/5891", "diff_url": "https://github.com/huggingface/datasets/pull/5891.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5891.patch", "merged_at": "2024-01-31T15:54:17" }	mariosasko	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5891). 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) \| 0.006916 / 0.011353 (-0.004437) \| 0.004749 / 0.011008 (-0.006259) \| 0.096086 / 0.038508 (0.057578) \| 0.035448 / 0.023109 (0.012338) \| 0.299645 / 0.275898 (0.023747) \| 0.331279 / 0.323480 (0.007799) \| 0.006018 / 0.007986 (-0.001968) \| 0.004210 / 0.004328 (-0.000118) \| 0.072998 / 0.004250 (0.068747) \| 0.050082 / 0.037052 (0.013030) \| 0.297714 / 0.258489 (0.039225) \| 0.365523 / 0.293841 (0.071682) \| 0.028081 / 0.128546 (-0.100465) \| 0.009072 / 0.075646 (-0.066574) \| 0.327628 / 0.419271 (-0.091643) \| 0.051165 / 0.043533 (0.007633) \| 0.295091 / 0.255139 (0.039952) \| 0.320052 / 0.283200 (0.036852) \| 0.109841 / 0.141683 (-0.031842) \| 1.467867 / 1.452155 (0.015712) \| 1.572600 / 1.492716 (0.079884) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281490 / 0.018006 (0.263484) \| 0.499259 / 0.000490 (0.498770) \| 0.000691 / 0.000200 (0.000491) \| 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) \| 0.027548 / 0.037411 (-0.009863) \| 0.106592 / 0.014526 (0.092066) \| 0.118654 / 0.176557 (-0.057902) \| 0.174313 / 0.737135 (-0.562822) \| 0.124491 / 0.296338 (-0.171848) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.399674 / 0.215209 (0.184465) \| 3.984092 / 2.077655 (1.906437) \| 1.790935 / 1.504120 (0.286815) \| 1.593612 / 1.541195 (0.052417) \| 1.694595 / 1.468490 (0.226105) \| 0.517588 / 4.584777 (-4.067189) \| 3.724353 / 3.745712 (-0.021359) \| 3.244807 / 5.269862 (-2.025054) \| 1.602929 / 4.565676 (-2.962748) \| 0.065334 / 0.424275 (-0.358941) \| 0.012259 / 0.007607 (0.004652) \| 0.501355 / 0.226044 (0.275311) \| 4.996546 / 2.268929 (2.727618) \| 2.279333 / 55.444624 (-53.165291) \| 1.940126 / 6.876477 (-4.936351) \| 2.122945 / 2.142072 (-0.019128) \| 0.626104 / 4.805227 (-4.179123) \| 0.141278 / 6.500664 (-6.359386) \| 0.064522 / 0.075469 (-0.010947) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.195351 / 1.841788 (-0.646436) \| 15.258932 / 8.074308 (7.184624) \| 14.627623 / 10.191392 (4.436231) \| 0.266897 / 0.680424 (-0.413527) \| 0.017557 / 0.534201 (-0.516644) \| 0.392932 / 0.579283 (-0.186351) \| 0.416409 / 0.434364 (-0.017955) \| 0.469100 / 0.540337 (-0.071237) \| 0.556247 / 1.386936 (-0.830689) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006880 / 0.011353 (-0.004473) \| 0.004837 / 0.011008 (-0.006171) \| 0.074518 / 0.038508 (0.036010) \| 0.034204 / 0.023109 (0.011095) \| 0.365100 / 0.275898 (0.089202) \| 0.394976 / 0.323480 (0.071496) \| 0.006364 / 0.007986 (-0.001621) \| 0.004269 / 0.004328 (-0.000060) \| 0.073531 / 0.004250 (0.069281) \| 0.051334 / 0.037052 (0.014281) \| 0.373904 / 0.258489 (0.115415) \| 0.413662 / 0.293841 (0.119821) \| 0.028779 / 0.128546 (-0.099767) \| 0.009292 / 0.075646 (-0.066354) \| 0.081574 / 0.419271 (-0.337698) \| 0.046531 / 0.043533 (0.002998) \| 0.368995 / 0.255139 (0.113856) \| 0.376938 / 0.283200 (0.093739) \| 0.112576 / 0.141683 (-0.029107) \| 1.458880 / 1.452155 (0.006725) \| 1.550918 / 1.492716 (0.058202) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.319521 / 0.018006 (0.301515) \| 0.510146 / 0.000490 (0.509656) \| 0.000438 / 0.000200 (0.000238) \| 0.000059 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033082 / 0.037411 (-0.004329) \| 0.118009 / 0.014526 (0.103483) \| 0.127108 / 0.176557 (-0.049448) \| 0.176600 / 0.737135 (-0.560535) \| 0.133790 / 0.296338 (-0.162549) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.437360 / 0.215209 (0.222151) \| 4.367426 / 2.077655 (2.289771) \| 2.193646 / 1.504120 (0.689526) \| 2.025002 / 1.541195 (0.483808) \| 2.142347 / 1.468490 (0.673856) \| 0.525497 / 4.584777 (-4.059280) \| 3.751275 / 3.745712 (0.005563) \| 1.912271 / 5.269862 (-3.357590) \| 1.087286 / 4.565676 (-3.478390) \| 0.066328 / 0.424275 (-0.357947) \| 0.011904 / 0.007607 (0.004297) \| 0.545870 / 0.226044 (0.319825) \| 5.434481 / 2.268929 (3.165552) \| 2.719745 / 55.444624 (-52.724880) \| 2.445001 / 6.876477 (-4.431476) \| 2.500205 / 2.142072 (0.358133) \| 0.645735 / 4.805227 (-4.159492) \| 0.144210 / 6.500664 (-6.356455) \| 0.065688 / 0.075469 (-0.009781) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.273522 / 1.841788 (-0.568265) \| 15.771778 / 8.074308 (7.697470) \| 14.685261 / 10.191392 (4.493869) \| 0.176523 / 0.680424 (-0.503900) \| 0.017877 / 0.534201 (-0.516324) \| 0.392687 / 0.579283 (-0.186596) \| 0.449992 / 0.434364 (0.015628) \| 0.462851 / 0.540337 (-0.077487) \| 0.560178 / 1.386936 (-0.826758) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0fa3ef6eba906ee1214e0596d15a78fc358909f4 \"CML watermark\")\n", "Just curious how's this PR going? I was facing similar issues.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005182 / 0.011353 (-0.006171) \| 0.003642 / 0.011008 (-0.007366) \| 0.063225 / 0.038508 (0.024717) \| 0.030534 / 0.023109 (0.007425) \| 0.247135 / 0.275898 (-0.028763) \| 0.269880 / 0.323480 (-0.053600) \| 0.003029 / 0.007986 (-0.004956) \| 0.002656 / 0.004328 (-0.001673) \| 0.048647 / 0.004250 (0.044397) \| 0.043300 / 0.037052 (0.006247) \| 0.261586 / 0.258489 (0.003097) \| 0.288003 / 0.293841 (-0.005838) \| 0.029556 / 0.128546 (-0.098990) \| 0.010604 / 0.075646 (-0.065042) \| 0.208228 / 0.419271 (-0.211043) \| 0.036079 / 0.043533 (-0.007454) \| 0.255650 / 0.255139 (0.000511) \| 0.283756 / 0.283200 (0.000556) \| 0.017992 / 0.141683 (-0.123691) \| 1.134861 / 1.452155 (-0.317293) \| 1.165310 / 1.492716 (-0.327406) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090709 / 0.018006 (0.072702) \| 0.301131 / 0.000490 (0.300641) \| 0.000211 / 0.000200 (0.000011) \| 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) \| 0.018186 / 0.037411 (-0.019225) \| 0.061704 / 0.014526 (0.047178) \| 0.074085 / 0.176557 (-0.102471) \| 0.119107 / 0.737135 (-0.618029) \| 0.074166 / 0.296338 (-0.222172) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287430 / 0.215209 (0.072221) \| 2.832602 / 2.077655 (0.754947) \| 1.485971 / 1.504120 (-0.018149) \| 1.366806 / 1.541195 (-0.174388) \| 1.359044 / 1.468490 (-0.109446) \| 0.583573 / 4.584777 (-4.001204) \| 2.376348 / 3.745712 (-1.369364) \| 2.766067 / 5.269862 (-2.503795) \| 1.732066 / 4.565676 (-2.833610) \| 0.064489 / 0.424275 (-0.359786) \| 0.004974 / 0.007607 (-0.002633) \| 0.343600 / 0.226044 (0.117555) \| 3.392277 / 2.268929 (1.123349) \| 1.840875 / 55.444624 (-53.603750) \| 1.543068 / 6.876477 (-5.333409) \| 1.573766 / 2.142072 (-0.568307) \| 0.651920 / 4.805227 (-4.153308) \| 0.117797 / 6.500664 (-6.382867) \| 0.042248 / 0.075469 (-0.033221) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.976276 / 1.841788 (-0.865511) \| 11.386207 / 8.074308 (3.311899) \| 10.473297 / 10.191392 (0.281905) \| 0.155482 / 0.680424 (-0.524942) \| 0.014182 / 0.534201 (-0.520019) \| 0.288501 / 0.579283 (-0.290782) \| 0.263505 / 0.434364 (-0.170859) \| 0.325396 / 0.540337 (-0.214942) \| 0.428070 / 1.386936 (-0.958866) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005310 / 0.011353 (-0.006043) \| 0.003510 / 0.011008 (-0.007498) \| 0.049418 / 0.038508 (0.010910) \| 0.031668 / 0.023109 (0.008559) \| 0.266345 / 0.275898 (-0.009553) \| 0.289230 / 0.323480 (-0.034249) \| 0.004168 / 0.007986 (-0.003818) \| 0.002769 / 0.004328 (-0.001559) \| 0.049786 / 0.004250 (0.045536) \| 0.044009 / 0.037052 (0.006957) \| 0.281882 / 0.258489 (0.023393) \| 0.309962 / 0.293841 (0.016121) \| 0.047216 / 0.128546 (-0.081330) \| 0.010661 / 0.075646 (-0.064986) \| 0.058619 / 0.419271 (-0.360652) \| 0.034658 / 0.043533 (-0.008875) \| 0.269676 / 0.255139 (0.014537) \| 0.288581 / 0.283200 (0.005381) \| 0.018159 / 0.141683 (-0.123523) \| 1.177047 / 1.452155 (-0.275107) \| 1.206391 / 1.492716 (-0.286325) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091422 / 0.018006 (0.073416) \| 0.301936 / 0.000490 (0.301446) \| 0.000204 / 0.000200 (0.000004) \| 0.000045 / 0.000054 (-0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022347 / 0.037411 (-0.015064) \| 0.075856 / 0.014526 (0.061330) \| 0.086459 / 0.176557 (-0.090097) \| 0.124683 / 0.737135 (-0.612452) \| 0.087559 / 0.296338 (-0.208779) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287423 / 0.215209 (0.072214) \| 2.840060 / 2.077655 (0.762405) \| 1.561290 / 1.504120 (0.057170) \| 1.442124 / 1.541195 (-0.099071) \| 1.458619 / 1.468490 (-0.009871) \| 0.578217 / 4.584777 (-4.006560) \| 2.450982 / 3.745712 (-1.294731) \| 2.685603 / 5.269862 (-2.584259) \| 1.750036 / 4.565676 (-2.815640) \| 0.063797 / 0.424275 (-0.360478) \| 0.005158 / 0.007607 (-0.002449) \| 0.342598 / 0.226044 (0.116553) \| 3.356456 / 2.268929 (1.087527) \| 1.913493 / 55.444624 (-53.531132) \| 1.638930 / 6.876477 (-5.237547) \| 1.751691 / 2.142072 (-0.390382) \| 0.662609 / 4.805227 (-4.142619) \| 0.117465 / 6.500664 (-6.383199) \| 0.041316 / 0.075469 (-0.034153) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.019255 / 1.841788 (-0.822533) \| 12.084293 / 8.074308 (4.009985) \| 10.957918 / 10.191392 (0.766526) \| 0.142433 / 0.680424 (-0.537991) \| 0.015969 / 0.534201 (-0.518232) \| 0.292411 / 0.579283 (-0.286872) \| 0.278925 / 0.434364 (-0.155439) \| 0.329967 / 0.540337 (-0.210370) \| 0.421786 / 1.386936 (-0.965150) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f9975f636542df7f95c27065ea93147440d690b7 \"CML watermark\")\n" ]
1,722,373,618	5,889	Token Alignment for input and output data over train and test batch/dataset.	open	2023-05-23T15:58:55	2023-05-23T15:58:55	null	https://github.com/huggingface/datasets/issues/5889	null	akesh1235	false	[]
1,722,166,382	5,887	HuggingsFace dataset example give error	closed	2023-05-23T14:09:05	2023-07-25T14:01:01	2023-07-25T14:01:00	https://github.com/huggingface/datasets/issues/5887	null	donhuvy	false	[ "Nice catch @donhuvy, that's because some models don't need the `token_type_ids`, as in this case, as the example is using `distilbert-base-cased`, and according to the DistilBert documentation at https://huggingface.co/transformers/v3.0.2/model_doc/distilbert.html, `DistilBert doesn’t have token_type_ids, you don’t need to indicate which token belongs to which segment. Just separate your segments with the separation token tokenizer.sep_token (or [SEP])`. `token_type_ids` are neither required in some other well known models such as RoBERTa. \r\n\r\nHere the issue comes due to a mismatch between the tokenizer and the model, as the Colab is using a BERT tokenizer (`bert-base-cased`), while the model is a DistilBERT (`distilbert-base-cased`), so aligning the tokenizer and the model solves it!", "#self-assign", "@donhuvy I've created https://github.com/huggingface/datasets/pull/5902 to solve it! 🤗", "This has been addressed in #5902.\r\n\r\nThe Quicktour notebook is deprecated now - please use the notebook version of the [Quickstart doc page](https://huggingface.co/docs/datasets/main/en/quickstart) instead (\"Open in Colab\" button)." ]
1,721,070,225	5,886	Use work-stealing algorithm when parallel computing	open	2023-05-23T03:08:44	2023-05-24T15:30:09	null	https://github.com/huggingface/datasets/issues/5886	null	1014661165	false	[ "Alternatively we could set the number of shards to be a factor than the number of processes (current they're equal) - this way it will be less likely to end up with a shard that is significantly slower than all the other ones." ]
1,720,954,440	5,885	Modify `is_remote_filesystem` to return True for FUSE-mounted paths	closed	2023-05-23T01:04:54	2024-01-08T18:31:00	2024-01-08T18:31:00	https://github.com/huggingface/datasets/pull/5885	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5885", "html_url": "https://github.com/huggingface/datasets/pull/5885", "diff_url": "https://github.com/huggingface/datasets/pull/5885.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5885.patch", "merged_at": null }	maddiedawson	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5885). All of your documentation changes will be reflected on that endpoint.", "@lhoestq would you or another maintainer be able to review please? :)", "Why you do need to support FUSE mounted paths ?\r\n\r\n`datasets` uses data that live on disk for fast lookups - FUSE mounted disks would lead to poor performance and I wouldn't recomment using it.", "Fuse is commonly used to mount remote file systems (e.g. S3, DBFS) as a local directory. Since it's slower than using an actual local device, it's better to treat it as remote to reduce latency.", "I think people would be confused if they don't have the same dataset behavior depending on the disk type.\r\n\r\nIf they want to use a remote bucket they should use the remote URI instead, e.g. `s3://...`. Advancements on this are tracked at #5281 " ]
1,722,290,363	5,888	A way to upload and visualize .mp4 files (millions of them) as part of a dataset	open	2023-05-22T18:05:26	2023-06-23T03:37:16	null	https://github.com/huggingface/datasets/issues/5888	null	AntreasAntoniou	false	[ "Hi! \r\n\r\nYou want to use `push_to_hub` (creates Parquet files) instead of `save_to_disk` (creates Arrow files) when creating a Hub dataset. Parquet is designed for long-term storage and takes less space than the Arrow format, and, most importantly, `load_dataset` can parse it, which should fix the viewer. \r\n\r\nRegarding the dataset generation, `Dataset.from_generator` with the video data represented as `datasets.Value(\"binary\")` followed by `push_to_hub` should work (if the `push_to_hub` step times out, restart it to resume uploading)\r\n\r\nPS: Once the dataset is uploaded, to make working with the dataset easier, it's a good idea to add a [transform](https://huggingface.co/docs/datasets/main/en/process#format-transform) to the README that shows how to decode the binary video data into something a model can understand. Also, if you get an `ArrowInvalid` error (can happen when working with large binary data) in `Dataset.from_generator`, reduce the value of `writer_batch_size` (the default is 1000) to fix it.", "One issue here is that Dataset.from_generator can work well for the non 'infinite sampling' version of the dataset. The training set for example is often sampled dynamically given the video files that I have uploaded. I worry that storing the video data as binary means that I'll end up duplicating a lot of the data. Furthermore, storing video data as anything but .mp4 would quickly make the dataset size from 1.9TB to 1PB. ", "> storing video data as anything but .mp4\r\n\r\nWhat I mean by storing as `datasets.Value(\"binary\")` is embedding raw MP4 bytes in the Arrow table, but, indeed, this would waste a lot of space if there are duplicates.\r\n\r\nSo I see two options:\r\n* if one video is not mapped to too many samples, you can embed the video bytes and do \"group by\" on the rest of the columns (this would turn them into lists) to avoid duplicating them (then, it should be easy to define a `map` in the README that samples the video data to \"unpack\" the samples)\r\n* you can create a dataset script that downloads the video files and embeds their file paths into the Arrow file\r\n\r\nAlso, I misread MP4 as MP3. We need to add a `Video` feature to the `datasets` lib to support MP4 files in the viewer (a bit trickier to implement than the `Image` feature due to the Arrow limitations).", "I'm transferring this issue to the `datasets` repo, as it's not related to `huggingface_hub`", "@mariosasko Right. If I want my dataset to be streamable, what are the necessary requirements to achieve that within the context of .mp4 binaries like we have here? I guess your second point here would not support that right?", "The streaming would work, but the video paths would require using `fsspec.open` to get the content.", "Are there any plans to make video playable on the hub?", "Not yet. The (open source) tooling for video is not great in terms of ease of use/performance, so we are discussing internally the best way to support it (one option is creating a new library for video IO, but this will require a lot of work)", "True. I spend a good 4 months just mixing and matching existing solutions so I could get performance that would not IO bound my model training. \r\n\r\nThis is what I ended up with, in case it's useful\r\n\r\nhttps://github.com/AntreasAntoniou/TALI/blob/045cf9e5aa75b1bf2c6d5351fb910fa10e3ff32c/tali/data/data_plus.py#L85" ]
1,719,548,172	5,884	`Dataset.to_tf_dataset` fails when strings cannot be encoded as `np.bytes_`	closed	2023-05-22T12:03:06	2023-06-09T16:04:56	2023-06-09T16:04:55	https://github.com/huggingface/datasets/issues/5884	null	alvarobartt	false	[ "May eventually be solved in #5883 ", "#self-assign" ]
1,719,527,597	5,883	Fix string-encoding, make `batch_size` optional, and minor improvements in `Dataset.to_tf_dataset`	closed	2023-05-22T11:51:07	2023-06-08T11:09:03	2023-06-06T16:49:15	https://github.com/huggingface/datasets/pull/5883	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5883", "html_url": "https://github.com/huggingface/datasets/pull/5883", "diff_url": "https://github.com/huggingface/datasets/pull/5883.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5883.patch", "merged_at": "2023-06-06T16:49:15" }	alvarobartt	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "To showcase the current issue, here's a Colab Gist, that shows that the `imdb` dataset cannot be read/iterated, since one or more samples contain a non-ascii character that is being converted to `numpy.bytes_`, and so on fails.\r\n\r\nColab Gist at https://gist.github.com/alvarobartt/1746959d1abb9a33e0c593f3bd82a2fb\r\n\r\nAlso, here's a quick sample of what's happening:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"imdb\", split=\"train\")\r\ntfds = ds.to_tf_dataset(batch_size=16)\r\nfor batch in tfds:\r\n print(batch)\r\n>>> UnicodeEncodeError: 'ascii' codec can't encode character '\\xe9' in position 0: ordinal not in range(128)\r\n```\r\n\r\nA more detailed version of it:\r\n\r\n```python\r\nfrom datasets import Dataset\r\n\r\nds = Dataset.from_dict(\r\n {\r\n \"a\": [1],\r\n \"b\": [\"é\"],\r\n }\r\n)\r\ntfds = ds.to_tf_dataset(batch_size=1)\r\nfor batch in tfds:\r\n print(batch)\r\n>>> UnicodeEncodeError: 'ascii' codec can't encode character '\\xe9' in position 0: ordinal not in range(128)\r\n```\r\n\r\nThe original issue comes from https://github.com/tensorflow/tensorflow/blob/388d952114e59a1aeda440ed4737b29f8b7c6e8a/tensorflow/python/ops/script_ops.py#LL234C4-L234C4, which could easily be solved by replacing that line with `return result.astype(np.unicode_)` but they are mentioning that it may lead to issues.\r\n\r\nEven the following fails in `numpy`:\r\n\r\n```python\r\nimport numpy as np\r\n\r\nx = np.array([\"é\"]).astype(np.bytes_)\r\n```", "cc. @lhoestq :hugs:", "cc @Rocketknight1 ", "> Nice ! Could you add some tests to make sure that batch_size=None works as expected ?\r\n\r\nSure, I'll add the tests for everything, including the string-encoding issue to make sure it's solved!", "Thanks for the review @lhoestq and @Rocketknight1! I do understand that processing it in batches is always more efficient than processing it one-by-one, it was just to make `batch_size` optional. What we can do is default it to a certain batch size e.g. 16 as before, and that's it, but I think it can still remain optional.", "@Rocketknight1 then I'll add the integration tests for the optional `batch_size` as well as for the encoding of non-ASCII compatible characters 😄 Do we set the default `batch_size` to 16 instead of `None`?", "@alvarobartt I think 16 is a reasonable default, yep!", "I think default should be None, not 16.\r\nUsers won't expect to have it batched by default.", "Then I'll leave it as is, and add the unit/integration tests, thanks @Rocketknight1 and @lhoestq ", "Hi @Rocketknight1 @lhoestq! So the string-encoding issue is already solved, but I've got one doubt about the `batch_size` being optional in the multiprocessing approach, since in that case I assume the `batch_size` should be mandatory, for the moment I'm assuming it is/should be mandatory, but let me know if you want me to add a check to disallow `batch_size=None` when `num_workers>1`. Thanks!", "> To showcase the current issue, here's a Colab Gist, that shows that the `imdb` dataset cannot be read/iterated, since one or more samples contain a non-ascii character that is being converted to `numpy.bytes_`, and so on fails.\r\n> \r\n> Colab Gist at https://gist.github.com/alvarobartt/1746959d1abb9a33e0c593f3bd82a2fb\r\n\r\nI've used the Colab shared above for testing purposes, and it works fine, plus the unit/integration tests are passing. I've also trained a `KerasNLP` model with incoming data from 🤗`datasets` with no issue at all!", "> in the multiprocessing approach, since in that case I assume the batch_size should be mandatory,\r\n\r\nNo I think they're quite orthogonal, no need to have it mandatory", "> No I think they're quite orthogonal, no need to have it mandatory\r\n\r\nBut it will break if `batch_size=None` as the multiprocessing approach will aim to prepare batches and distribute those to every worker, and assuming `batch_size=1` when `batch_size=None` I guess is not a good assumption, right?", "Ah I see. Multiprocessing should support batch_size=None indeed. If you have ideas you can do it in this PR, or raise a NotImplementedError and we can see later", "Sure @lhoestq, I can add a `NotImplementedError` for the moment, and prepare the next PR straight-away to tackle the multiprocessing approach with `batch_size=None`, but not sure if that may eventually collide with @Rocketknight1 PR at https://github.com/huggingface/datasets/pull/5863", "Yes, let me merge the PR at #5863 after this one, and then we can open another to improve the behaviour with multiprocessing and `batch_size=None`!", "Sure @Rocketknight1 makes complete sense to me! Do you want me to add the `raise NotImplementedError` and then we merge this PR? Or you prefer to directly merge the current?", "`raise NotImplementedError` for now with an error telling the user that multiprocessing needs them to specify a batch size, I think!", "Since you recently approved @Rocketknight1, are we ready to merge? Thanks 🤗", "Ah actually it looks like `minimal_tf_collate_fn` doesn't support batch_size=None", "Hi @lhoestq so I didn't include the call to `collate_fn`, as we won't need to collate the incoming data e.g. \"str\" should remain a \"str\" not a [\"str\"], and the `minimal_collate_fn` was indeed putting everything into a list, so the output was not un-batched, but batched with size 1", "What if the user passes a collate_fn ? The torch DataLoader still applies it if batch_size=None for example.\r\n\r\nDoes my last change look of to you ? If so I think we can merge", "> What if the user passes a collate_fn ? The torch DataLoader still applies it if batch_size=None for example.\r\n> \r\n> Does my last change look of to you ? If so I think we can merge\r\n\r\nI think we're good, since it won't batch it under the scenario of `str` being provided instead of `List[str]`, and the unit/integration tests are passing, so I'm OK to merge. Maybe we can double check with Matt? cc @Rocketknight1 ", "Yes, and sorry for the delay! I'm happy to merge.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006555 / 0.011353 (-0.004798) \| 0.004521 / 0.011008 (-0.006487) \| 0.096633 / 0.038508 (0.058125) \| 0.032859 / 0.023109 (0.009750) \| 0.294632 / 0.275898 (0.018734) \| 0.325140 / 0.323480 (0.001660) \| 0.005676 / 0.007986 (-0.002310) \| 0.005252 / 0.004328 (0.000924) \| 0.074349 / 0.004250 (0.070099) \| 0.045836 / 0.037052 (0.008784) \| 0.302919 / 0.258489 (0.044430) \| 0.340686 / 0.293841 (0.046845) \| 0.028398 / 0.128546 (-0.100148) \| 0.008942 / 0.075646 (-0.066704) \| 0.326994 / 0.419271 (-0.092278) \| 0.049556 / 0.043533 (0.006023) \| 0.293883 / 0.255139 (0.038744) \| 0.316522 / 0.283200 (0.033322) \| 0.097385 / 0.141683 (-0.044298) \| 1.405334 / 1.452155 (-0.046821) \| 1.521529 / 1.492716 (0.028812) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.212269 / 0.018006 (0.194263) \| 0.445692 / 0.000490 (0.445203) \| 0.004930 / 0.000200 (0.004730) \| 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) \| 0.026907 / 0.037411 (-0.010504) \| 0.108607 / 0.014526 (0.094081) \| 0.116806 / 0.176557 (-0.059751) \| 0.178428 / 0.737135 (-0.558707) \| 0.122326 / 0.296338 (-0.174012) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.404211 / 0.215209 (0.189002) \| 4.045374 / 2.077655 (1.967719) \| 1.877237 / 1.504120 (0.373117) \| 1.706276 / 1.541195 (0.165081) \| 1.750610 / 1.468490 (0.282120) \| 0.522331 / 4.584777 (-4.062446) \| 3.742286 / 3.745712 (-0.003426) \| 1.791285 / 5.269862 (-3.478577) \| 1.043872 / 4.565676 (-3.521805) \| 0.065176 / 0.424275 (-0.359099) \| 0.011821 / 0.007607 (0.004214) \| 0.507374 / 0.226044 (0.281329) \| 5.088803 / 2.268929 (2.819875) \| 2.282742 / 55.444624 (-53.161882) \| 1.950737 / 6.876477 (-4.925740) \| 2.042262 / 2.142072 (-0.099810) \| 0.636525 / 4.805227 (-4.168702) \| 0.140837 / 6.500664 (-6.359827) \| 0.063223 / 0.075469 (-0.012246) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.188070 / 1.841788 (-0.653718) \| 14.622681 / 8.074308 (6.548372) \| 13.247988 / 10.191392 (3.056596) \| 0.165858 / 0.680424 (-0.514566) \| 0.017476 / 0.534201 (-0.516725) \| 0.391973 / 0.579283 (-0.187310) \| 0.433326 / 0.434364 (-0.001038) \| 0.467163 / 0.540337 (-0.073175) \| 0.568359 / 1.386936 (-0.818577) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated 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 (-0.005276) \| 0.004439 / 0.011008 (-0.006570) \| 0.074496 / 0.038508 (0.035988) \| 0.031396 / 0.023109 (0.008287) \| 0.372237 / 0.275898 (0.096339) \| 0.403412 / 0.323480 (0.079932) \| 0.005430 / 0.007986 (-0.002555) \| 0.003846 / 0.004328 (-0.000483) \| 0.074403 / 0.004250 (0.070153) \| 0.045398 / 0.037052 (0.008346) \| 0.394133 / 0.258489 (0.135644) \| 0.421769 / 0.293841 (0.127928) \| 0.027936 / 0.128546 (-0.100610) \| 0.008962 / 0.075646 (-0.066685) \| 0.083158 / 0.419271 (-0.336113) \| 0.044863 / 0.043533 (0.001331) \| 0.393834 / 0.255139 (0.138695) \| 0.391537 / 0.283200 (0.108337) \| 0.097971 / 0.141683 (-0.043712) \| 1.496632 / 1.452155 (0.044477) \| 1.585511 / 1.492716 (0.092795) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010094 / 0.018006 (-0.007913) \| 0.437811 / 0.000490 (0.437321) \| 0.000963 / 0.000200 (0.000763) \| 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) \| 0.028864 / 0.037411 (-0.008547) \| 0.112480 / 0.014526 (0.097954) \| 0.120938 / 0.176557 (-0.055619) \| 0.170888 / 0.737135 (-0.566247) \| 0.125903 / 0.296338 (-0.170435) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.426716 / 0.215209 (0.211507) \| 4.238380 / 2.077655 (2.160725) \| 2.052889 / 1.504120 (0.548769) \| 1.871043 / 1.541195 (0.329848) \| 1.890405 / 1.468490 (0.421915) \| 0.522059 / 4.584777 (-4.062718) \| 3.813331 / 3.745712 (0.067619) \| 2.891651 / 5.269862 (-2.378210) \| 1.323836 / 4.565676 (-3.241841) \| 0.065124 / 0.424275 (-0.359151) \| 0.011498 / 0.007607 (0.003891) \| 0.525102 / 0.226044 (0.299057) \| 5.245190 / 2.268929 (2.976261) \| 2.531149 / 55.444624 (-52.913476) \| 2.197323 / 6.876477 (-4.679153) \| 2.197314 / 2.142072 (0.055241) \| 0.633423 / 4.805227 (-4.171804) \| 0.140248 / 6.500664 (-6.360416) \| 0.064432 / 0.075469 (-0.011037) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.270639 / 1.841788 (-0.571149) \| 14.856678 / 8.074308 (6.782369) \| 14.337631 / 10.191392 (4.146239) \| 0.195319 / 0.680424 (-0.485105) \| 0.017628 / 0.534201 (-0.516573) \| 0.393984 / 0.579283 (-0.185299) \| 0.421987 / 0.434364 (-0.012376) \| 0.459245 / 0.540337 (-0.081092) \| 0.557786 / 1.386936 (-0.829150) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a129219a48c1b07c06d4bc1db32c317bf513089d \"CML watermark\")\n", "Will you eventually need help with your PR @Rocketknight1? I'll be happy to help if needed 😄 ", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007577 / 0.011353 (-0.003776) \| 0.004960 / 0.011008 (-0.006048) \| 0.113622 / 0.038508 (0.075114) \| 0.037981 / 0.023109 (0.014872) \| 0.355312 / 0.275898 (0.079414) \| 0.393384 / 0.323480 (0.069904) \| 0.006575 / 0.007986 (-0.001411) \| 0.005941 / 0.004328 (0.001612) \| 0.085976 / 0.004250 (0.081726) \| 0.053784 / 0.037052 (0.016732) \| 0.369358 / 0.258489 (0.110869) \| 0.399402 / 0.293841 (0.105561) \| 0.032155 / 0.128546 (-0.096391) \| 0.010448 / 0.075646 (-0.065199) \| 0.389009 / 0.419271 (-0.030263) \| 0.057377 / 0.043533 (0.013844) \| 0.354968 / 0.255139 (0.099829) \| 0.382404 / 0.283200 (0.099204) \| 0.111056 / 0.141683 (-0.030627) \| 1.807986 / 1.452155 (0.355832) \| 1.866070 / 1.492716 (0.373354) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.244439 / 0.018006 (0.226432) \| 0.491942 / 0.000490 (0.491452) \| 0.001910 / 0.000200 (0.001710) \| 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) \| 0.031024 / 0.037411 (-0.006387) \| 0.129674 / 0.014526 (0.115148) \| 0.142974 / 0.176557 (-0.033583) \| 0.213568 / 0.737135 (-0.523568) \| 0.147794 / 0.296338 (-0.148545) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.480333 / 0.215209 (0.265124) \| 4.792901 / 2.077655 (2.715246) \| 2.233145 / 1.504120 (0.729025) \| 2.036291 / 1.541195 (0.495096) \| 2.109631 / 1.468490 (0.641140) \| 0.624546 / 4.584777 (-3.960231) \| 4.543511 / 3.745712 (0.797799) \| 3.961345 / 5.269862 (-1.308517) \| 1.903634 / 4.565676 (-2.662042) \| 0.076584 / 0.424275 (-0.347691) \| 0.014590 / 0.007607 (0.006983) \| 0.593195 / 0.226044 (0.367151) \| 5.928740 / 2.268929 (3.659811) \| 2.781164 / 55.444624 (-52.663460) \| 2.364303 / 6.876477 (-4.512173) \| 2.510139 / 2.142072 (0.368067) \| 0.770886 / 4.805227 (-4.034341) \| 0.167995 / 6.500664 (-6.332669) \| 0.076622 / 0.075469 (0.001153) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.402398 / 1.841788 (-0.439390) \| 17.921233 / 8.074308 (9.846925) \| 17.036738 / 10.191392 (6.845346) \| 0.168997 / 0.680424 (-0.511427) \| 0.020259 / 0.534201 (-0.513941) \| 0.465322 / 0.579283 (-0.113962) \| 0.500435 / 0.434364 (0.066071) \| 0.546846 / 0.540337 (0.006509) \| 0.658130 / 1.386936 (-0.728806) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007624 / 0.011353 (-0.003729) \| 0.005265 / 0.011008 (-0.005744) \| 0.086886 / 0.038508 (0.048377) \| 0.038235 / 0.023109 (0.015126) \| 0.463969 / 0.275898 (0.188071) \| 0.502451 / 0.323480 (0.178971) \| 0.006285 / 0.007986 (-0.001701) \| 0.004525 / 0.004328 (0.000197) \| 0.086557 / 0.004250 (0.082307) \| 0.052414 / 0.037052 (0.015362) \| 0.482167 / 0.258489 (0.223678) \| 0.513684 / 0.293841 (0.219843) \| 0.032929 / 0.128546 (-0.095618) \| 0.010249 / 0.075646 (-0.065397) \| 0.093377 / 0.419271 (-0.325895) \| 0.054114 / 0.043533 (0.010582) \| 0.466116 / 0.255139 (0.210977) \| 0.488977 / 0.283200 (0.205777) \| 0.115446 / 0.141683 (-0.026237) \| 1.762912 / 1.452155 (0.310757) \| 1.874191 / 1.492716 (0.381475) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.012666 / 0.018006 (-0.005341) \| 0.485962 / 0.000490 (0.485473) \| 0.002621 / 0.000200 (0.002421) \| 0.000128 / 0.000054 (0.000074) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033661 / 0.037411 (-0.003751) \| 0.135395 / 0.014526 (0.120869) \| 0.147230 / 0.176557 (-0.029326) \| 0.205847 / 0.737135 (-0.531288) \| 0.151496 / 0.296338 (-0.144842) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.514097 / 0.215209 (0.298887) \| 5.134093 / 2.077655 (3.056438) \| 2.496775 / 1.504120 (0.992655) \| 2.268078 / 1.541195 (0.726883) \| 2.342153 / 1.468490 (0.873663) \| 0.623130 / 4.584777 (-3.961647) \| 4.601787 / 3.745712 (0.856075) \| 3.414249 / 5.269862 (-1.855613) \| 1.849603 / 4.565676 (-2.716073) \| 0.078350 / 0.424275 (-0.345925) \| 0.013785 / 0.007607 (0.006178) \| 0.638783 / 0.226044 (0.412739) \| 6.378356 / 2.268929 (4.109427) \| 3.072867 / 55.444624 (-52.371757) \| 2.668123 / 6.876477 (-4.208354) \| 2.693905 / 2.142072 (0.551833) \| 0.764583 / 4.805227 (-4.040644) \| 0.166854 / 6.500664 (-6.333810) \| 0.076883 / 0.075469 (0.001414) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.502003 / 1.841788 (-0.339784) \| 18.674205 / 8.074308 (10.599897) \| 16.837759 / 10.191392 (6.646367) \| 0.176995 / 0.680424 (-0.503428) \| 0.020126 / 0.534201 (-0.514075) \| 0.464480 / 0.579283 (-0.114803) \| 0.516477 / 0.434364 (0.082113) \| 0.549818 / 0.540337 (0.009481) \| 0.659927 / 1.386936 (-0.727009) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a129219a48c1b07c06d4bc1db32c317bf513089d \"CML watermark\")\n", "@alvarobartt Yes, I'll ping you for a review once it's ready!" ]
1,719,402,643	5,881	Split dataset by node: index error when sharding iterable dataset	open	2023-05-22T10:36:13	2025-01-31T16:36:30	null	https://github.com/huggingface/datasets/issues/5881	null	sanchit-gandhi	false	[ "cc @lhoestq in case you have any ideas here! Might need a multi-host set-up to debug (can give you access to a JAX one if you need)", "I am also facing the same problem. Could you let me know if you found a solution for this?", "I couldn't reproduce with the latest version of `datasets` 2.16.1, can you update `datasets` and try again ?", "I have a similar issue when sharding for multiple nodes. I am using datasets 3.2.0.\n\n```\nProcessing shard 2/129\nShard has 10000 entries\nTraceback (most recent call last):\n File \"/pfss/mlde/workspaces/mlde_wsp_KIServiceCenter/finngu/LlavaGuard/src/experiments/datasets/imagenet/entrypoint_download.py\", line 39, in <module>\n shard = iterable_ds.shard(num_shards, shard_idx, contiguous=True)\n File \"/pfss/mlde/workspaces/mlde_wsp_KIServiceCenter/finngu/envs/dataset_download/lib/python3.10/site-packages/datasets/iterable_dataset.py\", line 2709, in shard\n ex_iterable = self._ex_iterable.shard_data_sources(num_shards=num_shards, index=index, contiguous=contiguous)\n File \"/pfss/mlde/workspaces/mlde_wsp_KIServiceCenter/finngu/envs/dataset_download/lib/python3.10/site-packages/datasets/iterable_dataset.py\", line 318, in shard_data_sources\n requested_gen_kwargs = _merge_gen_kwargs([gen_kwargs_list[i] for i in shard_indices])\n File \"/pfss/mlde/workspaces/mlde_wsp_KIServiceCenter/finngu/envs/dataset_download/lib/python3.10/site-packages/datasets/utils/sharding.py\", line 76, in _merge_gen_kwargs\n for key in gen_kwargs_list[0]\nIndexError: list index out of range\n```", "Hi ! on which dataset ? can you share a code example that reproduces the issue ?" ]
1,719,090,101	5,880	load_dataset from s3 file system through streaming can't not iterate data	open	2023-05-22T07:40:27	2023-05-26T12:52:08	null	https://github.com/huggingface/datasets/issues/5880	null	janineguo	false	[ "This sounds related to #5281.\r\n\r\nCan you try passing `storage_options=s3_client.storage_options` instead passing it to `use_auth_token=` ?", "I tried `storage_options` before, but it doesn't work, I checked our source code and I found that we even didn't pass this parameter to the following process. if I use `storage_options` instead of `use_auth_token`, then I also need to change another place of the code. the last line of `streaming_download_manager.py`. our code only passes the `use_auth_token` to the following handler, but does nothing to the `storage_options`\r\n<img width=\"1050\" alt=\"image\" src=\"https://github.com/huggingface/datasets/assets/59083384/5be90933-3331-4ecf-9e11-34f9852d8f92\">\r\n", "Cloud storage support is still experimental indeed and you can expect some bugs.\r\n\r\nI think we need to pass the storage options anywhere use_auth_token is passed in indeed. Let me know if you'd be interested in contributing a fix !", "Oh, that's great, I really like to fix it. because datasets is really useful and most of our projects need to use it, but we can store our data on the internet due to security reasons. fix it not only make our own work more efficient but also can benefit others who use it." ]
1,718,203,843	5,878	Prefetching for IterableDataset	open	2023-05-20T15:25:40	2025-01-24T17:13:55	null	https://github.com/huggingface/datasets/issues/5878	null	vyeevani	false	[ "Very cool! Do you have a link to the code that you're using to eagerly fetch the data? Would also be interested in hacking around something here for pre-fetching iterable datasets", "I ended up just switching back to the pytorch dataloader and using it's multiprocessing functionality to handle this :(. I'm just not that familiar with python multiprocessing to get something to work in jupyter (kept having weird behaviors happening with zombies living after the cell finished).", "Ultimately settled on using webdataset to circumvent huggingface datasets entirely. Would definitely switch back if: https://github.com/huggingface/datasets/issues/5337 was resolved.", "Hi! You can combine `datasets` with `torchdata` to prefetch `IterableDataset`'s samples:\r\n```python\r\nfrom datasets import load_dataset\r\nfrom torchdata.datapipes.iter import IterableWrapper, HuggingFaceHubReader\r\nfrom torch.utils.data import DataLoader\r\n\r\nds = load_dataset(\"sst\", split=\"train\", streaming=True)\r\n# processing...\r\ndp = IterableWrapper(ds)\r\ndp = dp.prefetch(100)\r\ndl = DataLoader(dp, batch_size=8)\r\n\r\ni = iter(dl)\r\nnext(i)\r\n```", "Hey @mariosasko! Thanks for the tip here - introducing prefetch with `torchdata` didn't really give me any performance difference vs not prefetching, but the concept is definitely one that could be really beneficial. Are there any benchmarks that show the speed-up you can get with `torchdata`'s prefetch just for comparison?", "I want to perform mapping() in advance by prefetching within the IterableDataset. Are there any recent updates?", "> Ultimately settled on using webdataset to circumvent huggingface datasets entirely. Would definitely switch back if: [#5337](https://github.com/huggingface/datasets/issues/5337) was resolved.\n\nThe HF dataset now supports the webdataset format. On a custom dataset with multiple .tar files that is on a single HDD. The read speed is ~180 MB/s when building the buffer.\n\nUsing the following setup, I observe ~170MB/s read speed when training with hf accelerate (multi-gpu)\n\n```\n train_dataset = load_dataset(\n \"path_to_py\",\n split=\"train\",\n streaming=True,\n trust_remote_code=True\n )\n train_dataset = train_dataset.shuffle(seed=general_config.seed, buffer_size=1000)\n train_dataset = train_dataset.with_format(\"torch\")\n train_dataloader = DataLoader(train_dataset, num_workers=1, batch_size=8, prefetch_factor=4)\n # Then accelerator.prepare (I used dispatch_batches=False as my custom dataset does not return tuple of tensors)\n```\n" ]

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