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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
1,676,716,662	5,774	Fix style	closed	2023-04-20T13:21:32	2023-04-20T13:34:26	2023-04-20T13:24:28	https://github.com/huggingface/datasets/pull/5774	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5774", "html_url": "https://github.com/huggingface/datasets/pull/5774", "diff_url": "https://github.com/huggingface/datasets/pull/5774.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5774.patch", "merged_at": "2023-04-20T13:24:28" }	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) \| 0.010336 / 0.011353 (-0.001017) \| 0.007085 / 0.011008 (-0.003924) \| 0.135577 / 0.038508 (0.097069) \| 0.038301 / 0.023109 (0.015192) \| 0.427919 / 0.275898 (0.152021) \| 0.461451 / 0.323480 (0.137971) \| 0.008929 / 0.007986 (0.000944) \| 0.005260 / 0.004328 (0.000931) \| 0.103481 / 0.004250 (0.099231) \| 0.054885 / 0.037052 (0.017833) \| 0.434956 / 0.258489 (0.176467) \| 0.466915 / 0.293841 (0.173074) \| 0.052403 / 0.128546 (-0.076144) \| 0.021128 / 0.075646 (-0.054518) \| 0.466847 / 0.419271 (0.047576) \| 0.085096 / 0.043533 (0.041563) \| 0.439935 / 0.255139 (0.184796) \| 0.453613 / 0.283200 (0.170413) \| 0.123913 / 0.141683 (-0.017769) \| 1.930114 / 1.452155 (0.477959) \| 2.052083 / 1.492716 (0.559366) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280612 / 0.018006 (0.262606) \| 0.583937 / 0.000490 (0.583447) \| 0.004542 / 0.000200 (0.004342) \| 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) \| 0.035901 / 0.037411 (-0.001510) \| 0.160357 / 0.014526 (0.145831) \| 0.141661 / 0.176557 (-0.034896) \| 0.234915 / 0.737135 (-0.502220) \| 0.164110 / 0.296338 (-0.132228) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.659901 / 0.215209 (0.444692) \| 6.529102 / 2.077655 (4.451447) \| 2.635324 / 1.504120 (1.131204) \| 2.275777 / 1.541195 (0.734583) \| 2.343205 / 1.468490 (0.874715) \| 1.241310 / 4.584777 (-3.343467) \| 5.683784 / 3.745712 (1.938072) \| 3.377162 / 5.269862 (-1.892700) \| 2.176404 / 4.565676 (-2.389273) \| 0.144303 / 0.424275 (-0.279972) \| 0.016352 / 0.007607 (0.008745) \| 0.817383 / 0.226044 (0.591339) \| 8.148356 / 2.268929 (5.879428) \| 3.489277 / 55.444624 (-51.955347) \| 2.848086 / 6.876477 (-4.028391) \| 2.973304 / 2.142072 (0.831232) \| 1.517821 / 4.805227 (-3.287407) \| 0.278794 / 6.500664 (-6.221870) \| 0.096385 / 0.075469 (0.020916) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.631693 / 1.841788 (-0.210095) \| 19.564716 / 8.074308 (11.490408) \| 23.583081 / 10.191392 (13.391689) \| 0.252363 / 0.680424 (-0.428061) \| 0.027644 / 0.534201 (-0.506557) \| 0.579634 / 0.579283 (0.000351) \| 0.645702 / 0.434364 (0.211338) \| 0.667302 / 0.540337 (0.126965) \| 0.766425 / 1.386936 (-0.620511) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.011186 / 0.011353 (-0.000167) \| 0.007327 / 0.011008 (-0.003681) \| 0.105441 / 0.038508 (0.066933) \| 0.040293 / 0.023109 (0.017184) \| 0.480557 / 0.275898 (0.204659) \| 0.522049 / 0.323480 (0.198569) \| 0.007779 / 0.007986 (-0.000207) \| 0.007338 / 0.004328 (0.003009) \| 0.104744 / 0.004250 (0.100494) \| 0.059463 / 0.037052 (0.022411) \| 0.494055 / 0.258489 (0.235566) \| 0.534340 / 0.293841 (0.240499) \| 0.062800 / 0.128546 (-0.065746) \| 0.020687 / 0.075646 (-0.054959) \| 0.135833 / 0.419271 (-0.283439) \| 0.087472 / 0.043533 (0.043939) \| 0.465019 / 0.255139 (0.209880) \| 0.526713 / 0.283200 (0.243513) \| 0.131424 / 0.141683 (-0.010259) \| 1.884759 / 1.452155 (0.432605) \| 2.015817 / 1.492716 (0.523101) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.237032 / 0.018006 (0.219026) \| 0.605209 / 0.000490 (0.604719) \| 0.006653 / 0.000200 (0.006453) \| 0.000264 / 0.000054 (0.000210) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034982 / 0.037411 (-0.002430) \| 0.141409 / 0.014526 (0.126883) \| 0.151635 / 0.176557 (-0.024922) \| 0.217298 / 0.737135 (-0.519837) \| 0.171945 / 0.296338 (-0.124393) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.678596 / 0.215209 (0.463387) \| 6.802432 / 2.077655 (4.724777) \| 3.021617 / 1.504120 (1.517497) \| 2.722508 / 1.541195 (1.181313) \| 2.728194 / 1.468490 (1.259704) \| 1.245863 / 4.584777 (-3.338914) \| 5.762676 / 3.745712 (2.016963) \| 5.497855 / 5.269862 (0.227994) \| 2.855764 / 4.565676 (-1.709912) \| 0.157359 / 0.424275 (-0.266916) \| 0.015562 / 0.007607 (0.007955) \| 0.865559 / 0.226044 (0.639515) \| 8.553052 / 2.268929 (6.284123) \| 3.905544 / 55.444624 (-51.539081) \| 3.272528 / 6.876477 (-3.603949) \| 3.399481 / 2.142072 (1.257408) \| 1.540155 / 4.805227 (-3.265072) \| 0.275871 / 6.500664 (-6.224793) \| 0.092346 / 0.075469 (0.016877) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.753646 / 1.841788 (-0.088142) \| 20.074050 / 8.074308 (11.999742) \| 23.920391 / 10.191392 (13.728999) \| 0.257161 / 0.680424 (-0.423263) \| 0.027805 / 0.534201 (-0.506396) \| 0.565605 / 0.579283 (-0.013678) \| 0.643277 / 0.434364 (0.208914) \| 0.633504 / 0.540337 (0.093167) \| 0.754317 / 1.386936 (-0.632619) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2d34c7968ea1a3fe7d4fa7cdf23673e0354f69ac \"CML watermark\")\n" ]
1,675,984,633	5,773	train_dataset does not implement __len__	open	2023-04-20T04:37:05	2023-07-19T20:33:13	null	https://github.com/huggingface/datasets/issues/5773	null	ben-8543	false	[ "Thanks for reporting, @v-yunbin.\r\n\r\nCould you please give more details, the steps to reproduce the bug, the complete error back trace and the environment information (`datasets-cli env`)?", "this is a detail error info from transformers：\r\n```\r\nTraceback (most recent call last):\r\n File \"finetune.py\", line 177, in <module>\r\n whisper_finetune(traindir,devdir,outdir)\r\n File \"finetune.py\", line 161, in whisper_finetune\r\n trainer = Seq2SeqTrainer(\r\n File \"/home/ybZhang/miniconda3/envs/whister/lib/python3.8/site-packages/transformers/trainer_seq2seq.py\", line 56, in __init__\r\n super().__init__(\r\n File \"/home/ybZhang/miniconda3/envs/whister/lib/python3.8/site-packages/transformers/trainer.py\", line 567, in __init__\r\n raise ValueError(\r\nValueError: The train_dataset does not implement __len__, max_steps has to be specified. The number of steps needs to be known in advance for the learning rate scheduler.\r\n```\r\n", "How did you create `train_dataset`? The `datasets` library does not appear in your stack trace.\r\n\r\nWe need more information in order to reproduce the issue...", "```\r\ndef asr_dataset(traindir,devdir):\r\n we_voice = IterableDatasetDict()\r\n #we_voice[\"train\"] = load_from_disk(traindir,streaming=True)\r\n #we_voice[\"test\"]= load_from_disk(devdir,streaming=True)\r\n we_voice[\"train\"] = load_dataset(\"csv\",data_files=os.path.join(traindir,\"train.csv\"),split=\"train\",streaming=True)\r\n #print(load_dataset(\"csv\",data_files=os.path.join(traindir,\"train.csv\"),split=\"train\"))\r\n we_voice[\"test\"] = load_dataset(\"csv\",data_files=os.path.join(devdir,\"dev.csv\"), split=\"train\",streaming=True)\r\n we_voice = we_voice.remove_columns([\"id\"])\r\n we_voice = we_voice.cast_column(\"audio\", Audio(sampling_rate=16000))\r\n return we_voice\r\n\r\n```", "As you are using iterable datasets (`streaming=True`), their length is not defined.\r\n\r\nYou should:\r\n- Either use non-iterable datasets, which have a defined length: use `DatasetDict` and not passing `streaming=True`\r\n- Or pass `args.max_steps` to the `Trainer`", "I don't know how to give a reasonable args.max_steps...........................", "Then you should not use streaming.", "@albertvillanova I think @v-yunbin, myself, and others might be slightly confused about max_steps and how it differs from num_train_epochs.", "@lkurlandski A step is referring to optimizer's update after back propagation, and it's associated with a batch of data. For example, if a dataset contains 64 examples and you have an overall batch size of 4, then an epoch will have 64/4=16 batches. Therefore, in one epoch, you will have 16 optimizer steps." ]
1,675,033,510	5,772	Fix JSON builder when missing keys in first row	closed	2023-04-19T14:32:57	2023-04-21T06:45:13	2023-04-21T06:35:27	https://github.com/huggingface/datasets/pull/5772	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5772", "html_url": "https://github.com/huggingface/datasets/pull/5772", "diff_url": "https://github.com/huggingface/datasets/pull/5772.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5772.patch", "merged_at": "2023-04-21T06:35:27" }	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) \| 0.009262 / 0.011353 (-0.002091) \| 0.006157 / 0.011008 (-0.004851) \| 0.125960 / 0.038508 (0.087451) \| 0.036213 / 0.023109 (0.013104) \| 0.399331 / 0.275898 (0.123433) \| 0.453597 / 0.323480 (0.130117) \| 0.006990 / 0.007986 (-0.000995) \| 0.007320 / 0.004328 (0.002991) \| 0.100321 / 0.004250 (0.096070) \| 0.048870 / 0.037052 (0.011818) \| 0.396284 / 0.258489 (0.137795) \| 0.475619 / 0.293841 (0.181778) \| 0.052329 / 0.128546 (-0.076217) \| 0.019564 / 0.075646 (-0.056083) \| 0.430942 / 0.419271 (0.011670) \| 0.063224 / 0.043533 (0.019692) \| 0.391717 / 0.255139 (0.136578) \| 0.448342 / 0.283200 (0.165142) \| 0.114055 / 0.141683 (-0.027628) \| 1.793204 / 1.452155 (0.341049) \| 1.895151 / 1.492716 (0.402435) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283699 / 0.018006 (0.265693) \| 0.597194 / 0.000490 (0.596704) \| 0.007143 / 0.000200 (0.006944) \| 0.000602 / 0.000054 (0.000548) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034761 / 0.037411 (-0.002651) \| 0.124555 / 0.014526 (0.110030) \| 0.149126 / 0.176557 (-0.027430) \| 0.220335 / 0.737135 (-0.516801) \| 0.153109 / 0.296338 (-0.143229) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.620210 / 0.215209 (0.405001) \| 6.229937 / 2.077655 (4.152282) \| 2.615203 / 1.504120 (1.111083) \| 2.239337 / 1.541195 (0.698143) \| 2.262138 / 1.468490 (0.793648) \| 1.196498 / 4.584777 (-3.388279) \| 5.609932 / 3.745712 (1.864220) \| 3.031347 / 5.269862 (-2.238515) \| 2.025530 / 4.565676 (-2.540146) \| 0.139828 / 0.424275 (-0.284447) \| 0.015476 / 0.007607 (0.007869) \| 0.768964 / 0.226044 (0.542920) \| 7.728677 / 2.268929 (5.459748) \| 3.336407 / 55.444624 (-52.108217) \| 2.700055 / 6.876477 (-4.176422) \| 2.765223 / 2.142072 (0.623151) \| 1.409073 / 4.805227 (-3.396155) \| 0.246849 / 6.500664 (-6.253815) \| 0.081231 / 0.075469 (0.005762) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.593836 / 1.841788 (-0.247952) \| 18.020525 / 8.074308 (9.946216) \| 21.766822 / 10.191392 (11.575430) \| 0.258615 / 0.680424 (-0.421809) \| 0.026895 / 0.534201 (-0.507306) \| 0.529823 / 0.579283 (-0.049460) \| 0.623470 / 0.434364 (0.189106) \| 0.628171 / 0.540337 (0.087833) \| 0.745249 / 1.386936 (-0.641687) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008624 / 0.011353 (-0.002729) \| 0.006317 / 0.011008 (-0.004691) \| 0.097315 / 0.038508 (0.058807) \| 0.035217 / 0.023109 (0.012108) \| 0.440197 / 0.275898 (0.164299) \| 0.473863 / 0.323480 (0.150383) \| 0.006722 / 0.007986 (-0.001264) \| 0.006444 / 0.004328 (0.002116) \| 0.102056 / 0.004250 (0.097806) \| 0.047142 / 0.037052 (0.010089) \| 0.452476 / 0.258489 (0.193986) \| 0.487619 / 0.293841 (0.193778) \| 0.052456 / 0.128546 (-0.076090) \| 0.018735 / 0.075646 (-0.056911) \| 0.114656 / 0.419271 (-0.304616) \| 0.062577 / 0.043533 (0.019044) \| 0.444471 / 0.255139 (0.189332) \| 0.494264 / 0.283200 (0.211065) \| 0.117112 / 0.141683 (-0.024571) \| 1.848965 / 1.452155 (0.396810) \| 1.984008 / 1.492716 (0.491292) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290494 / 0.018006 (0.272488) \| 0.588415 / 0.000490 (0.587925) \| 0.000459 / 0.000200 (0.000259) \| 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) \| 0.032873 / 0.037411 (-0.004538) \| 0.131139 / 0.014526 (0.116614) \| 0.140268 / 0.176557 (-0.036289) \| 0.204561 / 0.737135 (-0.532574) \| 0.147443 / 0.296338 (-0.148895) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.636899 / 0.215209 (0.421690) \| 6.236139 / 2.077655 (4.158484) \| 2.801468 / 1.504120 (1.297348) \| 2.398808 / 1.541195 (0.857613) \| 2.493150 / 1.468490 (1.024659) \| 1.228845 / 4.584777 (-3.355932) \| 5.675874 / 3.745712 (1.930162) \| 3.084939 / 5.269862 (-2.184922) \| 2.061310 / 4.565676 (-2.504367) \| 0.142285 / 0.424275 (-0.281990) \| 0.014972 / 0.007607 (0.007365) \| 0.786599 / 0.226044 (0.560555) \| 7.876036 / 2.268929 (5.607107) \| 3.476136 / 55.444624 (-51.968489) \| 2.847922 / 6.876477 (-4.028555) \| 3.040326 / 2.142072 (0.898253) \| 1.448538 / 4.805227 (-3.356690) \| 0.257230 / 6.500664 (-6.243434) \| 0.085137 / 0.075469 (0.009668) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.668173 / 1.841788 (-0.173615) \| 18.668520 / 8.074308 (10.594212) \| 20.535542 / 10.191392 (10.344150) \| 0.244580 / 0.680424 (-0.435844) \| 0.026364 / 0.534201 (-0.507837) \| 0.531753 / 0.579283 (-0.047530) \| 0.616578 / 0.434364 (0.182214) \| 0.618906 / 0.540337 (0.078569) \| 0.738785 / 1.386936 (-0.648151) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f7265cafa3103d77d6d52aa897088faefcd96659 \"CML watermark\")\n" ]
1,674,828,380	5,771	Support cloud storage for loading datasets	closed	2023-04-19T12:43:53	2023-05-07T17:47:41	2023-05-07T17:47:41	https://github.com/huggingface/datasets/issues/5771	null	eli-osherovich	false	[ "A duplicate of https://github.com/huggingface/datasets/issues/5281" ]
1,673,581,555	5,770	Add IterableDataset.from_spark	closed	2023-04-18T17:47:53	2023-05-17T14:07:32	2023-05-17T14:00:38	https://github.com/huggingface/datasets/pull/5770	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5770", "html_url": "https://github.com/huggingface/datasets/pull/5770", "diff_url": "https://github.com/huggingface/datasets/pull/5770.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5770.patch", "merged_at": "2023-05-17T14:00:38" }	maddiedawson	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "Hi again @lhoestq this is ready for review! Not sure I have permission to add people to the reviewers list...", "Cool ! I think you can define `IterableDataset.from_spark` instead of adding `streaming=` in `Dataset.from_spark`, it can be more intuitive IMO :)", "Thanks for reviewing! I moved the streaming behavior to IterableDataset.from_spark", "Thanks Quentin! I'll flesh out the docs in a follow-up PR", "Friendly ping @lhoestq ", "Thanks @lhoestq ! I fixed the partition order thing and added more unit tests.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006165 / 0.011353 (-0.005188) \| 0.004497 / 0.011008 (-0.006511) \| 0.099142 / 0.038508 (0.060634) \| 0.027479 / 0.023109 (0.004369) \| 0.352491 / 0.275898 (0.076593) \| 0.402993 / 0.323480 (0.079513) \| 0.004885 / 0.007986 (-0.003100) \| 0.003315 / 0.004328 (-0.001013) \| 0.075787 / 0.004250 (0.071537) \| 0.035320 / 0.037052 (-0.001732) \| 0.368401 / 0.258489 (0.109912) \| 0.409090 / 0.293841 (0.115249) \| 0.030125 / 0.128546 (-0.098421) \| 0.011670 / 0.075646 (-0.063976) \| 0.324381 / 0.419271 (-0.094890) \| 0.050815 / 0.043533 (0.007283) \| 0.352598 / 0.255139 (0.097460) \| 0.389189 / 0.283200 (0.105989) \| 0.092873 / 0.141683 (-0.048810) \| 1.485140 / 1.452155 (0.032986) \| 1.545586 / 1.492716 (0.052869) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.199522 / 0.018006 (0.181516) \| 0.404576 / 0.000490 (0.404087) \| 0.003322 / 0.000200 (0.003122) \| 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) \| 0.022945 / 0.037411 (-0.014466) \| 0.095512 / 0.014526 (0.080987) \| 0.103077 / 0.176557 (-0.073480) \| 0.163918 / 0.737135 (-0.573217) \| 0.105560 / 0.296338 (-0.190779) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.417360 / 0.215209 (0.202151) \| 4.161693 / 2.077655 (2.084039) \| 1.851941 / 1.504120 (0.347821) \| 1.649872 / 1.541195 (0.108677) \| 1.682099 / 1.468490 (0.213609) \| 0.693187 / 4.584777 (-3.891590) \| 3.462528 / 3.745712 (-0.283184) \| 1.839893 / 5.269862 (-3.429968) \| 1.155945 / 4.565676 (-3.409731) \| 0.082611 / 0.424275 (-0.341664) \| 0.012076 / 0.007607 (0.004469) \| 0.514325 / 0.226044 (0.288280) \| 5.155052 / 2.268929 (2.886123) \| 2.307280 / 55.444624 (-53.137345) \| 1.966483 / 6.876477 (-4.909994) \| 2.018892 / 2.142072 (-0.123181) \| 0.803068 / 4.805227 (-4.002159) \| 0.152213 / 6.500664 (-6.348451) \| 0.066320 / 0.075469 (-0.009149) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.218578 / 1.841788 (-0.623209) \| 13.563869 / 8.074308 (5.489561) \| 13.954596 / 10.191392 (3.763204) \| 0.151527 / 0.680424 (-0.528897) \| 0.016655 / 0.534201 (-0.517546) \| 0.380637 / 0.579283 (-0.198646) \| 0.395854 / 0.434364 (-0.038509) \| 0.459111 / 0.540337 (-0.081226) \| 0.560219 / 1.386936 (-0.826717) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006427 / 0.011353 (-0.004926) \| 0.004728 / 0.011008 (-0.006280) \| 0.080525 / 0.038508 (0.042017) \| 0.027294 / 0.023109 (0.004185) \| 0.414688 / 0.275898 (0.138790) \| 0.449882 / 0.323480 (0.126402) \| 0.004771 / 0.007986 (-0.003214) \| 0.003402 / 0.004328 (-0.000926) \| 0.078748 / 0.004250 (0.074497) \| 0.037046 / 0.037052 (-0.000007) \| 0.417398 / 0.258489 (0.158909) \| 0.462921 / 0.293841 (0.169080) \| 0.030364 / 0.128546 (-0.098182) \| 0.011810 / 0.075646 (-0.063837) \| 0.089787 / 0.419271 (-0.329485) \| 0.039806 / 0.043533 (-0.003727) \| 0.403401 / 0.255139 (0.148262) \| 0.439477 / 0.283200 (0.156278) \| 0.088431 / 0.141683 (-0.053252) \| 1.534373 / 1.452155 (0.082219) \| 1.592316 / 1.492716 (0.099600) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.217701 / 0.018006 (0.199695) \| 0.384770 / 0.000490 (0.384280) \| 0.000437 / 0.000200 (0.000237) \| 0.000061 / 0.000054 (0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024952 / 0.037411 (-0.012459) \| 0.098728 / 0.014526 (0.084202) \| 0.106324 / 0.176557 (-0.070233) \| 0.155484 / 0.737135 (-0.581651) \| 0.109503 / 0.296338 (-0.186836) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.450639 / 0.215209 (0.235430) \| 4.523110 / 2.077655 (2.445455) \| 2.224810 / 1.504120 (0.720690) \| 2.119516 / 1.541195 (0.578321) \| 2.225192 / 1.468490 (0.756702) \| 0.695397 / 4.584777 (-3.889380) \| 3.433559 / 3.745712 (-0.312153) \| 2.633127 / 5.269862 (-2.636735) \| 1.448471 / 4.565676 (-3.117206) \| 0.082262 / 0.424275 (-0.342013) \| 0.012246 / 0.007607 (0.004639) \| 0.561243 / 0.226044 (0.335199) \| 5.652711 / 2.268929 (3.383782) \| 2.689771 / 55.444624 (-52.754853) \| 2.359512 / 6.876477 (-4.516965) \| 2.471098 / 2.142072 (0.329026) \| 0.802955 / 4.805227 (-4.002272) \| 0.151142 / 6.500664 (-6.349522) \| 0.067494 / 0.075469 (-0.007975) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.306879 / 1.841788 (-0.534909) \| 14.030775 / 8.074308 (5.956467) \| 12.917790 / 10.191392 (2.726398) \| 0.141269 / 0.680424 (-0.539155) \| 0.016264 / 0.534201 (-0.517937) \| 0.411957 / 0.579283 (-0.167326) \| 0.393235 / 0.434364 (-0.041129) \| 0.505144 / 0.540337 (-0.035193) \| 0.590660 / 1.386936 (-0.796276) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7790ebd7072eafff755fb575b392f3efa74069e4 \"CML watermark\")\n" ]
1,673,441,182	5,769	Tiktoken tokenizers are not pickable	closed	2023-04-18T16:07:40	2023-05-04T18:55:57	2023-05-04T18:55:57	https://github.com/huggingface/datasets/issues/5769	null	markovalexander	false	[ "Thanks for reporting, @markovalexander.\r\n\r\nUnfortunately, I'm not able to reproduce the issue: the `tiktoken` tokenizer can be used within `Dataset.map`, both in my local machine and in a Colab notebook: https://colab.research.google.com/drive/1DhJroZgk0sNFJ2Mrz-jYgrmh9jblXaCG?usp=sharing\r\n\r\nAre you sure you are using `datasets` version 2.11.0?" ]
1,672,494,561	5,768	load_dataset("squad") doesn't work in 2.7.1 and 2.10.1	closed	2023-04-18T07:10:56	2023-04-20T10:27:23	2023-04-20T10:27:22	https://github.com/huggingface/datasets/issues/5768	null	yaseen157	false	[ "Thanks for reporting, @yaseen157.\r\n\r\nCould you please give the complete error stack trace?", "I am not able to reproduce your issue: the dataset loads perfectly on my local machine and on a Colab notebook: https://colab.research.google.com/drive/1Fbdoa1JdNz8DOdX6gmIsOK1nCT8Abj4O?usp=sharing\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"squad\")\r\nDownloading builder script: 100%\|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 5.27k/5.27k [00:00<00:00, 3.22MB/s]\r\nDownloading metadata: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2.36k/2.36k [00:00<00:00, 1.60MB/s]\r\nDownloading readme: 100%\|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 7.67k/7.67k [00:00<00:00, 4.58MB/s]\r\nDownloading and preparing dataset squad/plain_text to ...t/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453...\r\nDownloading data: 30.3MB [00:00, 91.8MB/s] \| 0/2 [00:00<?, ?it/s]\r\nDownloading data: 4.85MB [00:00, 75.3MB/s] \r\nDownloading data files: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.31it/s]\r\nExtracting data files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2157.01it/s]\r\nDataset squad downloaded and prepared to .../.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453. Subsequent calls will reuse this data.\r\n100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 463.95it/s]\r\n\r\nIn [3]: ds\r\nOut[3]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['id', 'title', 'context', 'question', 'answers'],\r\n num_rows: 87599\r\n })\r\n validation: Dataset({\r\n features: ['id', 'title', 'context', 'question', 'answers'],\r\n num_rows: 10570\r\n })\r\n})\r\n```", "I am at a complete loss for what's happening here. A quick summary, I have 3 machines to try this with:\r\n1) My windows 10 laptop\r\n2) Linux machine1, super computer login node\r\n3) Linux machine2, super computer compute node\r\n\r\nLet's define the following as a test script for the machines:\r\n\r\n```\r\nimport traceback\r\nimport datasets\r\nprint(f\"{datasets.__version__=}\")\r\ntry:\r\n ds = datasets.load_dataset(\"squad\")\r\nexcept:\r\n traceback.print_exc()\r\nelse:\r\n print(\"Success!\")\r\n```\r\n\r\nThe Windows laptop enters some sort of traceback recursion loop:\r\n\r\n> datasets.__version__='2.7.1'\r\n> Downloading and preparing dataset squad/plain_text to C:/Users/yr3g17/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453...\r\n> Downloading data files: 100%\|██████████\| 2/2 [00:00<?, ?it/s]\r\n> Traceback (most recent call last):\r\n> File \"<string>\", line 1, in <module>\r\n> File \"C:\\Users\\yr3g17\\AppData\\Local\\Programs\\Python\\Python39\\lib\\multiprocessing\\spawn.py\", line 116, in spawn_main\r\n> exitcode = _main(fd, parent_sentinel)\r\n> File \"C:\\Users\\yr3g17\\AppData\\Local\\Programs\\Python\\Python39\\lib\\multiprocessing\\spawn.py\", line 125, in _main\r\n> prepare(preparation_data)\r\n> File \"C:\\Users\\yr3g17\\AppData\\Local\\Programs\\Python\\Python39\\lib\\multiprocessing\\spawn.py\", line 236, in prepare\r\n> _fixup_main_from_path(data['init_main_from_path'])\r\n> File \"C:\\Users\\yr3g17\\AppData\\Local\\Programs\\Python\\Python39\\lib\\multiprocessing\\spawn.py\", line 287, in _fixup_main_from_path\r\n> main_content = runpy.run_path(main_path,\r\n> File \"C:\\Users\\yr3g17\\AppData\\Local\\Programs\\Python\\Python39\\lib\\runpy.py\", line 267, in run_path\r\n> code, fname = _get_code_from_file(run_name, path_name)\r\n> File \"C:\\Users\\yr3g17\\AppData\\Local\\Programs\\Python\\Python39\\lib\\runpy.py\", line 237, in _get_code_from_file\r\n> with io.open_code(decoded_path) as f:\r\n> OSError: [Errno 22] Invalid argument: 'C:\\\\Users\\\\yr3g17\\\\OneDrive - University of Southampton\\\\Documents\\\\PhD-repository\\\\<input>'\r\n> Traceback (most recent call last):\r\n> File \"<string>\", line 1, in <module>\r\n> File \"C:\\Users\\yr3g17\\AppData\\Local\\Programs\\Python\\Python39\\lib\\multiprocessing\\spawn.py\", line 116, in spawn_main\r\n> exitcode = _main(fd, parent_sentinel)\r\n> File \"C:\\Users\\yr3g17\\AppData\\Local\\Programs\\Python\\Python39\\lib\\multiprocessing\\spawn.py\", line 125, in _main\r\n> prepare(preparation_data)\r\nthis error traceback is endlessly recursive\r\n\r\nThis is a brand new issue that started today and I didn't even realise was a thing, as I had been using my windows machine to follow tracebacks for the other machines...\r\n\r\nI suspect this issue had something to do with my filepath naming, but I couldn't confirm this when I spent time trying to debug this myself weeks ago, something to do with files being locked and never released. I'm not too concerned about my laptop not working here because I've had so many issues with Microsoft OneDrive and my filesystem.\r\n\r\nLinux machines 1 and 2 were working fine for months, but have all of a sudden stopped working. Trying to run linux machine 1 (login node), I get:\r\n\r\n> datasets.__version__='2.10.1'\r\n> Downloading and preparing dataset json/squad to /home/yr3g17/.cache/hugg\r\ningface/datasets/json/squad-d733af945be1d2c2/0.0.0/0f7e3662623656454fcd2\r\nb650f34e886a7db4b9104504885bd462096cc7a9f51...\r\n> Downloading data files: 100%\|███████████████████████████████████████████\r\n█████████████████████████████████████████████\| 2/2 [00:00<00:00, 4042.70\r\nit/s]\r\n>Extracting data files: 100%\|███████████████████████████████████████\r\n███████████████████████████████████████████████████\| 2/2 [00:00<00:00, 1\r\n11.15it/s]\r\n> Generating train split: 0 examples [00:00, ? examples/s]\r\n\r\n and hangs here. This has not happened to me before on the Linux machine. If I forcefully keyboard interrupt, I get:\r\n \r\n> Traceback (most recent call last):\r\n> File \"<stdin>\", line 2, in <module>\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/d\r\n> atasets/load.py\", line 1782, in load_dataset\r\n> builder_instance.download_and_prepare(\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/d\r\n> atasets/builder.py\", line 793, in download_and_prepare\r\n> with FileLock(lock_path) if is_local else contextlib.nullcontext():\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/d\r\n> atasets/utils/filelock.py\", line 320, in __enter__\r\n> self.acquire()\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/d\r\n> atasets/utils/filelock.py\", line 282, in acquire\r\n> time.sleep(poll_intervall)\r\n\r\nWhich also appears to be file lock related! I resolved this by navigating to my ~/.cache/huggingface/datasets directory and wiping out anything to do with the squad dataset in .lock files. Now I get:\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset_load(\"squad\")\r\n\r\n```\r\n> Downloading and preparing dataset squad/plain_text to /home/yr3g17/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb\r\n> 2511d223b9150cce08a837ef62ffea453...\r\n> Downloading data files: 100%\|██████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 44.75it/s]\r\n> Extracting data files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 8.54it/s]\r\n> Dataset squad downloaded and prepared to /home/yr3g17/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150\r\n> cce08a837ef62ffea453. Subsequent calls will reuse this data.\r\n> 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 19.77it/s]\r\n> DatasetDict({\r\n> train: Dataset({\r\n> features: ['id', 'title', 'context', 'question', 'answers'],\r\n> num_rows: 87599\r\n> })\r\n> validation: Dataset({\r\n> features: ['id', 'title', 'context', 'question', 'answers'],\r\n> num_rows: 10570\r\n> })\r\n> })\r\n> \r\n\r\nWhich all seems fine right, it's doing what it should be. But now, without ever leaving the IDE, I \"make a subsequent call\" to reuse the data by repeating the command. I encounter the following traceback\r\n\r\n`load_dataset(\"squad\")`\r\n\r\n> Traceback (most recent call last):\r\n> File \"<stdin>\", line 1, in <module>\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/load.py\", line 1759, in load_dataset\r\n> builder_instance = load_dataset_builder(\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/load.py\", line 1496, in load_dataset_builder\r\n> dataset_module = dataset_module_factory(\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/load.py\", line 1151, in dataset_module_factory\r\n> ).get_module()\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/load.py\", line 631, in get_module\r\n> data_files = DataFilesDict.from_local_or_remote(\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/data_files.py\", line 796, in from_local_or_remote\r\n> DataFilesList.from_local_or_remote(\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/data_files.py\", line 764, in from_local_or_remote\r\n> data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/data_files.py\", line 369, in resolve_patterns_locally_or_by_urls\r\n> raise FileNotFoundError(error_msg)\r\n> FileNotFoundError: Unable to resolve any data file that matches '['train[-._ 0-9/]', '[-._ 0-9/]train[-._ 0-9/]', 'training[-._ 0-9/]', '[-\r\n> ._ 0-9/]training[-._ 0-9/]*']' at /mainfs/home/yr3g17/.cache/huggingface/datasets/squad with any supported extension ['csv', 'tsv', 'json', 'jsonl',\r\n> 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'gr\r\n> ib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', '\r\n> mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', '\r\n> emf', 'xbm', 'xpm', 'BLP', 'BMP', 'DIB', 'BUFR', 'CUR', 'PCX', 'DCX', 'DDS', 'PS', 'EPS', 'FIT', 'FITS', 'FLI', 'FLC', 'FTC', 'FTU', 'GBR', 'GIF', 'G\r\n> RIB', 'H5', 'HDF', 'PNG', 'APNG', 'JP2', 'J2K', 'JPC', 'JPF', 'JPX', 'J2C', 'ICNS', 'ICO', 'IM', 'IIM', 'TIF', 'TIFF', 'JFIF', 'JPE', 'JPG', 'JPEG',\r\n> 'MPG', 'MPEG', 'MSP', 'PCD', 'PXR', 'PBM', 'PGM', 'PPM', 'PNM', 'PSD', 'BW', 'RGB', 'RGBA', 'SGI', 'RAS', 'TGA', 'ICB', 'VDA', 'VST', 'WEBP', 'WMF',\r\n> 'EMF', 'XBM', 'XPM', 'aiff', 'au', 'avr', 'caf', 'flac', 'htk', 'svx', 'mat4', 'mat5', 'mpc2k', 'ogg', 'paf', 'pvf', 'raw', 'rf64', 'sd2', 'sds', 'ir\r\n> cam', 'voc', 'w64', 'wav', 'nist', 'wavex', 'wve', 'xi', 'mp3', 'opus', 'AIFF', 'AU', 'AVR', 'CAF', 'FLAC', 'HTK', 'SVX', 'MAT4', 'MAT5', 'MPC2K', 'O\r\n> GG', 'PAF', 'PVF', 'RAW', 'RF64', 'SD2', 'SDS', 'IRCAM', 'VOC', 'W64', 'WAV', 'NIST', 'WAVEX', 'WVE', 'XI', 'MP3', 'OPUS', 'zip']\r\n\r\nIt doesn't even appear like I can reliably repeat this process. I'll nuke squad files in my dataset cache and run the Python code again (which downloads a new copy of the dataset to cache). It will either fail (as it just did in the quote above), or it will successfully recall the dataset.\r\n\r\nI repeated this nuking process a few times until calling load_dataset was reliably giving me the correct result (no filelocking issues or tracebacks). I then sent the test script as a job to the supercomputer compute nodes (which do not have internet access and therefore depend on cached data from Linux machine 1 login nodes)\r\n\r\n> Using the latest cached version of the module from /home/yr3g17/.cache/huggingface/modules/datasets_modules/datasets/squad/8730650fed465361f38ac4d810\r\n> ccdd16e8fc87b56498e52fb7e2cadaefc1f177 (last modified on Tue Feb 14 10:12:56 2023) since it couldn't be found locally at squad., or remotely on the Hugging Face Hub.\r\n> Traceback (most recent call last):\r\n> File \"/mainfs/scratch/yr3g17/squad_qanswering/3054408/0/../../main.py\", line 5, in <module>\r\n> dataset = load_dataset(\"squad\")\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/load.py\", line 1759, in load_dataset\r\n> builder_instance = load_dataset_builder(\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/load.py\", line 1522, in load_dataset_builder\r\n> builder_instance: DatasetBuilder = builder_cls(\r\n> TypeError: 'NoneType' object is not callable\r\n\r\nand I have absolutely no idea why the second and third machines are producing different tracebacks. I have previously run these exact scripts successfully on the login and compute nodes of the supercomputer, this issue I'm raising has appeared fairly recently for me. This, is where I encounter the TypeError that I opened this issue with, which I was able to traceback (using my laptop before it too started not working) to whatever was dynamically importing \"builder_cls\". That bit of code wasn't doing importing builder_cls correctly and would effectively make the assignment \"builder_cls=None\" resulting in the TypeError. Does any of this help?", "I'm back on linux machine 1 (login node) now. After submitting that as a job to machine 2 and it failing with TypeError, linux machine 1 now produces identical traceback to machine 2:\r\n\r\n> (arkroyal) [yr3g17@cyan52 squad_qanswering]$ python\r\n> Python 3.10.8 (main, Nov 24 2022, 14:13:03) [GCC 11.2.0] on linux\r\n> Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>\r\n> from datasets import load_dataset\r\n> load_dataset(\"squad\")\r\n>\r\n> Traceback (most recent call last):\r\n> File \"<stdin>\", line 1, in <module>\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/load.py\", line 1759, in load_dataset\r\n> builder_instance = load_dataset_builder(\r\n> File \"/home/yr3g17/.conda/envs/arkroyal/lib/python3.10/site-packages/datasets/load.py\", line 1522, in load_dataset_builder\r\n> builder_instance: DatasetBuilder = builder_cls(\r\n> TypeError: 'NoneType' object is not callable\r\n\r\nI thought it might be useful to provide you with my cache file structure:\r\n\r\n>_home_yr3g17_.cache_huggingface_datasets_casino_default_1.1.0_302c3b1ac78c48091deabe83a11f4003c7b472a4e11a8eb92799653785bd5da1.lock\r\n>_home_yr3g17_.cache_huggingface_datasets_imdb_plain_text_1.0.0_2fdd8b9bcadd6e7055e742a706876ba43f19faee861df134affd7a3f60fc38a1.lock\r\n>_home_yr3g17_.cache_huggingface_datasets_squad_plain_text_1.0.0_d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453.lock\r\n>_home_yr3g17_.cache_huggingface_datasets_yelp_review_full_yelp_review_full_1.0.0_e8e18e19d7be9e75642fc66b198abadb116f73599ec89a69ba5dd8d1e57ba0bf.lock\r\n> casino\r\n> downloads\r\n> imdb\r\n> json\r\n> squad\r\n> squad_v2\r\n> yelp_review_full\r\n\r\nThe inside of squad/plain_text/1.0.0/ looks like\r\n\r\n> d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453\r\n> d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453.incomplete_info.lock\r\n> d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453_builder.lock\r\n", "I see this is quite a complex use case...\r\n\r\nLet's try multiple things:\r\n- First, update `datasets` and make sure you use the same version in all machines, so that we can easily compare different behaviors.\r\n ```\r\n pip install -U datasets\r\n ```\r\n- Second, wherever you run the `load_dataset(\"squad\")` command, make sure there is not a local directory named \"squad\". The datasets library gives priority to any local file/directory over the datasets on the Hugging Face Hub\r\n - I tell you this, because in one of your trace backs, it seems it refers to a local directory:\r\n ```\r\n Downloading and preparing dataset json/squad to /home/yr3g17/.cache/huggingface/datasets/json/squad-d733af945be1d2c2/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51...\r\n ```\r\n- Third, to use the \"squad\" dataset from the Hub, you need to have internet connection, so that you can download the \"squad\" Python loading script from the Hub. Do all your machines have internet connection?\r\n - I ask this because of this error message:\r\n ```\r\n Using the latest cached version of the module from /home/yr3g17/.cache/huggingface/modules/datasets_modules/datasets/squad/8730650fed465361f38ac4d810ccdd16e8fc87b56498e52fb7e2cadaefc1f177 (last modified on Tue Feb 14 10:12:56 2023) since it couldn't be found locally at squad., or remotely on the Hugging Face Hub.\r\n ```\r\n- Fourth, to be sure that we avoid any issues with the cache, it is a good idea to remove it and regenerate it. Remove `.cache/huggingface/datasets` and also `.cache/huggingface/modules`\r\n- Fifth, as an additional debugging tool, let's be sure we use the latest \"squad\" Python loading script by passing the revision parameter:\r\n ```\r\n ds = load_dataset(\"squad\", revision=\"5fe18c4c680f9922d794e3f4dd673a751c74ee37\")\r\n ```", "Additionally, we just had an infrastructure issue on the Hugging Face Hub at around 11:30 today. That might have contributed to the connectivity issue... It is fixed now.\r\n\r\nhttps://status.huggingface.co/", "Hi again, thanks for your help and insight Albert Villanova.\r\n\r\nIt's all working now, so thank you for that. For the benefit of anyone else who ends up in this thread, I solved the problem by addressing Albert's advice:\r\n\r\n(1) Both Windows and Linux machine 1 (have internet access) and can now access the SQuAD dataset. The supercomputer login node can only access version 2.7.1, but my Windows laptop is running on datasets 2.11.0 just fine. I suspect it was just a perfect storm alongside the aforementioned \"infrastructure issue\".\r\n\r\n(2) I did have a local directory called squad, because I was using a local copy of evaluate's \"SQuAD\" metric. The supercomputer compute nodes do not have internet access and treat `metric = evaluate.load('<x>')` as a way of loading a metric at the local path `./<x>/<x>.py`, which could've been a related issue as I was storing the metric under `squad/squad.py`. Don't be lazy like me and store the evaluation code under a path with a name that can be misinterpreted.\r\n\r\n(3) I can't give internet access to the supercomputer compute nodes, so local files do just fine here.\r\n\r\n(4) The windows and Linux machine 1 can both access the internet and were getting fresh copies of the dataset from the huggingface hub. Linux machine 2 was working after I cleared the contents of ~/.cache/huggingface/....\r\n\r\nI feel silly now, knowing it was all so simple! Sorry about that Albert, and thanks again for the help. I've not raised a Github issue like this before, so I'm not sure if I should be close my own issues or if this is something you guys do?", "Thanks for your detailed feedback which for sure will be useful to other community members." ]
1,672,433,979	5,767	How to use Distill-BERT with different datasets?	closed	2023-04-18T06:25:12	2023-04-20T16:52:05	2023-04-20T16:52:05	https://github.com/huggingface/datasets/issues/5767	null	sauravtii	false	[ "Closing this one in favor of the same issue opened in the `transformers` repo." ]
1,671,485,882	5,766	Support custom feature types	open	2023-04-17T15:46:41	2024-03-10T11:11:22	null	https://github.com/huggingface/datasets/issues/5766	null	jmontalt	false	[ "Hi ! Interesting :) What kind of new types would you like to use ?\r\n\r\nNote that you can already implement your own decoding by using `set_transform` that can decode data on-the-fly when rows are accessed", "An interesting proposal indeed. \r\n\r\nPandas and Polars have the \"extension API\", so doing something similar on our side could be useful, too. However, this requires defining a common interface for the existing feature types before discussing the API/workflow for defining/sharing custom feature types, and this could take some time.\r\n\r\nIt would also be nice if the datasets viewer could render these custom types.", "Thank you for your replies! @lhoestq I have a use case involving whole-slide images in digital pathology. These are very large images (potentially gigapixel scale), so standard image tools are not suitable. Essentially, encoding/decoding can be done from/to [`OpenSlide`](https://openslide.org/api/python/) objects. Though there may be interest in this use case from the digital pathology community, it may not be sufficiently useful to suggest adding the feature type, but there will likely be many other use cases for a generic custom feature type.\r\n\r\nThank you for pointing out `set_transform`! I will make sure to keep this in mind in the future.\r\n\r\n@mariosasko An \"extension API\" sounds like a good idea, though I understand that this needs to be properly defined, and that you will need to discuss it internally. Support from the viewer would be awesome, too, though the generalization to arbitrary types sounds challenging.\r\n\r\nFor now, happy to know that you're considering the feature. Feel free to let me know if I can do anything to support the process.", "Not a beautiful solution, but we use this for now\r\n\r\n\r\n```\r\nimport datasets.features.features\r\nold_decode_fn = datasets.features.features.decode_nested_example\r\ndef decode_ext_fn(schema, obj, token_per_repo_id = None):\r\n #Decode new type here\r\n\r\n return old_decode_fn(schema, obj, token_per_repo_id)\r\ndatasets.features.features.decode_nested_example = decode_ext_fn\r\n\r\n```\r\n" ]
1,671,388,824	5,765	ValueError: You should supply an encoding or a list of encodings to this method that includes input_ids, but you provided ['text']	open	2023-04-17T15:00:50	2023-04-25T13:50:45	null	https://github.com/huggingface/datasets/issues/5765	null	sauravtii	false	[ "You need to remove the `text` and `text_en` columns before passing the dataset to the `DataLoader` to avoid this error:\r\n```python\r\ntokenized_datasets = tokenized_datasets.remove_columns([\"text\", \"text_en\"])\r\n```\r\n", "Thanks @mariosasko. Now I am getting this error:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"client_2.py\", line 138, in <module>\r\n main()\r\n File \"client_2.py\", line 134, in main\r\n fl.client.start_numpy_client(server_address=\"localhost:8080\", client=IMDBClient())\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/flwr/client/app.py\", line 208, in start_numpy_client\r\n start_client(\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/flwr/client/app.py\", line 142, in start_client\r\n client_message, sleep_duration, keep_going = handle(\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/flwr/client/grpc_client/message_handler.py\", line 68, in handle\r\n return _fit(client, server_msg.fit_ins), 0, True\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/flwr/client/grpc_client/message_handler.py\", line 157, in _fit\r\n fit_res = client.fit(fit_ins)\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/flwr/client/app.py\", line 252, in _fit\r\n results = self.numpy_client.fit(parameters, ins.config) # type: ignore\r\n File \"client_2.py\", line 124, in fit\r\n train(net, trainloader, epochs=1)\r\n File \"client_2.py\", line 78, in train\r\n for batch in trainloader:\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/torch/utils/data/dataloader.py\", line 652, in __next__\r\n data = self._next_data()\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/torch/utils/data/dataloader.py\", line 692, in _next_data\r\n data = self._dataset_fetcher.fetch(index) # may raise StopIteration\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py\", line 49, in fetch\r\n data = [self.dataset[idx] for idx in possibly_batched_index]\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py\", line 49, in <listcomp>\r\n data = [self.dataset[idx] for idx in possibly_batched_index]\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 1525, in __getitem__\r\n return self._getitem(\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 1517, in _getitem\r\n pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/formatting/formatting.py\", line 373, in query_table\r\n pa_subtable = _query_table_with_indices_mapping(table, key, indices=indices)\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/formatting/formatting.py\", line 55, in _query_table_with_indices_mapping\r\n return _query_table(table, key)\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/formatting/formatting.py\", line 79, in _query_table\r\n return table.fast_slice(key % table.num_rows, 1)\r\nZeroDivisionError: integer division or modulo by zero\r\n```\r\n\r\nThis is my code:\r\n\r\n```\r\nfrom collections import OrderedDict\r\nimport warnings\r\n\r\nimport flwr as fl\r\nimport torch\r\nimport numpy as np\r\n\r\nimport random\r\nfrom torch.utils.data import DataLoader\r\n\r\nfrom datasets import load_dataset, load_metric\r\n\r\nfrom transformers import AutoTokenizer, DataCollatorWithPadding\r\nfrom transformers import AutoModelForSequenceClassification\r\nfrom transformers import AdamW\r\n#from transformers import tokenized_datasets\r\n\r\n\r\nwarnings.filterwarnings(\"ignore\", category=UserWarning)\r\n# DEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\r\n\r\nDEVICE = \"cpu\"\r\n\r\nCHECKPOINT = \"distilbert-base-uncased\" # transformer model checkpoint\r\n\r\n\r\ndef load_data():\r\n \"\"\"Load IMDB data (training and eval)\"\"\"\r\n raw_datasets = load_dataset(\"yhavinga/imdb_dutch\")\r\n raw_datasets = raw_datasets.shuffle(seed=42)\r\n\r\n # remove unnecessary data split\r\n del raw_datasets[\"unsupervised\"]\r\n\r\n tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT)\r\n\r\n def tokenize_function(examples):\r\n return tokenizer(examples[\"text\"], truncation=True)\r\n\r\n # random 100 samples\r\n population = random.sample(range(len(raw_datasets[\"train\"])), 100)\r\n\r\n tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n tokenized_datasets[\"train\"] = tokenized_datasets[\"train\"].select(population)\r\n tokenized_datasets[\"test\"] = tokenized_datasets[\"test\"].select(population)\r\n\r\n # tokenized_datasets = tokenized_datasets.remove_columns(\"text\")\r\n # tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\r\n\r\n tokenized_datasets = tokenized_datasets.remove_columns(\"attention_mask\")\r\n tokenized_datasets = tokenized_datasets.remove_columns(\"input_ids\")\r\n tokenized_datasets = tokenized_datasets.remove_columns(\"label\")\r\n # tokenized_datasets = tokenized_datasets.remove_columns(\"text_en\")\r\n\r\n # tokenized_datasets = tokenized_datasets.remove_columns(raw_datasets[\"train\"].column_names)\r\n \r\n tokenized_datasets = tokenized_datasets.remove_columns([\"text\", \"text_en\"])\r\n \r\n data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\r\n trainloader = DataLoader(\r\n tokenized_datasets[\"train\"],\r\n shuffle=True,\r\n batch_size=32,\r\n collate_fn=data_collator,\r\n )\r\n\r\n testloader = DataLoader(\r\n tokenized_datasets[\"test\"], batch_size=32, collate_fn=data_collator\r\n )\r\n\r\n return trainloader, testloader\r\n\r\n\r\ndef train(net, trainloader, epochs):\r\n optimizer = AdamW(net.parameters(), lr=5e-4)\r\n net.train()\r\n for _ in range(epochs):\r\n for batch in trainloader:\r\n batch = {k: v.to(DEVICE) for k, v in batch.items()}\r\n outputs = net(batch)\r\n loss = outputs.loss\r\n loss.backward()\r\n optimizer.step()\r\n optimizer.zero_grad()\r\n\r\n\r\ndef test(net, testloader):\r\n metric = load_metric(\"accuracy\")\r\n loss = 0\r\n net.eval()\r\n for batch in testloader:\r\n batch = {k: v.to(DEVICE) for k, v in batch.items()}\r\n with torch.no_grad():\r\n outputs = net(batch)\r\n logits = outputs.logits\r\n loss += outputs.loss.item()\r\n predictions = torch.argmax(logits, dim=-1)\r\n metric.add_batch(predictions=predictions, references=batch[\"labels\"])\r\n loss /= len(testloader.dataset)\r\n accuracy = metric.compute()[\"accuracy\"]\r\n return loss, accuracy\r\n\r\n\r\ndef main():\r\n net = AutoModelForSequenceClassification.from_pretrained(\r\n CHECKPOINT, num_labels=2\r\n ).to(DEVICE)\r\n\r\n trainloader, testloader = load_data()\r\n\r\n # Flower client\r\n class IMDBClient(fl.client.NumPyClient):\r\n def get_parameters(self, config):\r\n return [val.cpu().numpy() for _, val in net.state_dict().items()]\r\n\r\n def set_parameters(self, parameters):\r\n params_dict = zip(net.state_dict().keys(), parameters)\r\n state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})\r\n net.load_state_dict(state_dict, strict=True)\r\n\r\n def fit(self, parameters, config):\r\n self.set_parameters(parameters)\r\n print(\"Training Started...\")\r\n train(net, trainloader, epochs=1)\r\n print(\"Training Finished.\")\r\n return self.get_parameters(config={}), len(trainloader), {}\r\n\r\n def evaluate(self, parameters, config):\r\n self.set_parameters(parameters)\r\n loss, accuracy = test(net, testloader)\r\n return float(loss), len(testloader), {\"accuracy\": float(accuracy)}\r\n\r\n # Start client\r\n fl.client.start_numpy_client(server_address=\"localhost:8080\", client=IMDBClient())\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n```", "Please also remove/comment these lines:\r\n```python\r\ntokenized_datasets = tokenized_datasets.remove_columns(\"attention_mask\")\r\ntokenized_datasets = tokenized_datasets.remove_columns(\"input_ids\")\r\ntokenized_datasets = tokenized_datasets.remove_columns(\"label\")\r\n```", "Thanks @mariosasko .\r\n\r\nNow, I am trying out this [tutorial](https://flower.dev/docs/quickstart-huggingface.html) which basically trains distil-BERT with IMDB dataset (very similar to this [tutorial](https://huggingface.co/docs/transformers/main/tasks/sequence_classification)). But I don't know why my accuracy isn't increasing even after training for a significant amount of time and also by using the entire dataset. Below I have attached `client.py` file:\r\n\r\n`client.py`:\r\n\r\n```\r\nfrom collections import OrderedDict\r\nimport warnings\r\n\r\nimport flwr as fl\r\nimport torch\r\nimport numpy as np\r\n\r\nimport random\r\nfrom torch.utils.data import DataLoader\r\n\r\nfrom datasets import load_dataset, load_metric\r\n\r\nfrom transformers import AutoTokenizer, DataCollatorWithPadding\r\nfrom transformers import AutoModelForSequenceClassification\r\nfrom transformers import AdamW\r\n\r\nwarnings.filterwarnings(\"ignore\", category=UserWarning)\r\n\r\nDEVICE = \"cuda:1\"\r\n\r\nCHECKPOINT = \"distilbert-base-uncased\" # transformer model checkpoint\r\n\r\n\r\ndef load_data():\r\n \"\"\"Load IMDB data (training and eval)\"\"\"\r\n raw_datasets = load_dataset(\"imdb\")\r\n raw_datasets = raw_datasets.shuffle(seed=42)\r\n\r\n # remove unnecessary data split\r\n del raw_datasets[\"unsupervised\"]\r\n\r\n tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT)\r\n\r\n def tokenize_function(examples):\r\n return tokenizer(examples[\"text\"], truncation=True)\r\n\r\n tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\r\n\r\n tokenized_datasets = tokenized_datasets.remove_columns(\"text\")\r\n tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\r\n\r\n data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\r\n trainloader = DataLoader(\r\n tokenized_datasets[\"train\"],\r\n shuffle=True,\r\n batch_size=32,\r\n collate_fn=data_collator,\r\n )\r\n\r\n testloader = DataLoader(\r\n tokenized_datasets[\"test\"], batch_size=32, collate_fn=data_collator\r\n )\r\n\r\n return trainloader, testloader\r\n\r\n\r\ndef train(net, trainloader, epochs):\r\n optimizer = AdamW(net.parameters(), lr=5e-5)\r\n net.train()\r\n for i in range(epochs):\r\n print(\"Epoch: \", i+1)\r\n j = 1\r\n print(\"####################### The length of the trainloader is: \", len(trainloader)) \r\n for batch in trainloader:\r\n print(\"####################### The batch number is: \", j)\r\n batch = {k: v.to(DEVICE) for k, v in batch.items()}\r\n outputs = net(batch)\r\n loss = outputs.loss\r\n loss.backward()\r\n optimizer.step()\r\n optimizer.zero_grad()\r\n j += 1\r\n\r\n\r\ndef test(net, testloader):\r\n metric = load_metric(\"accuracy\")\r\n loss = 0\r\n net.eval()\r\n for batch in testloader:\r\n batch = {k: v.to(DEVICE) for k, v in batch.items()}\r\n with torch.no_grad():\r\n outputs = net(batch)\r\n logits = outputs.logits\r\n loss += outputs.loss.item()\r\n predictions = torch.argmax(logits, dim=-1)\r\n metric.add_batch(predictions=predictions, references=batch[\"labels\"])\r\n loss /= len(testloader.dataset)\r\n accuracy = metric.compute()[\"accuracy\"]\r\n return loss, accuracy\r\n\r\n\r\ndef main():\r\n net = AutoModelForSequenceClassification.from_pretrained(\r\n CHECKPOINT, num_labels=2\r\n ).to(DEVICE)\r\n\r\n trainloader, testloader = load_data()\r\n\r\n # Flower client\r\n class IMDBClient(fl.client.NumPyClient):\r\n def get_parameters(self, config):\r\n return [val.cpu().numpy() for _, val in net.state_dict().items()]\r\n\r\n def set_parameters(self, parameters):\r\n params_dict = zip(net.state_dict().keys(), parameters)\r\n state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})\r\n net.load_state_dict(state_dict, strict=True)\r\n\r\n def fit(self, parameters, config):\r\n self.set_parameters(parameters)\r\n print(\"Training Started...\")\r\n train(net, trainloader, epochs=1)\r\n print(\"Training Finished.\")\r\n return self.get_parameters(config={}), len(trainloader), {}\r\n\r\n def evaluate(self, parameters, config):\r\n self.set_parameters(parameters)\r\n loss, accuracy = test(net, testloader)\r\n print({\"loss\": float(loss), \"accuracy\": float(accuracy)})\r\n return float(loss), len(testloader), {\"loss\": float(loss), \"accuracy\": float(accuracy)}\r\n\r\n # Start client\r\n fl.client.start_numpy_client(server_address=\"localhost:5040\", client=IMDBClient())\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n```\r\n\r\nCan I get any help, please?" ]
1,670,740,198	5,764	ConnectionError: Couldn't reach https://www.dropbox.com/s/zts98j4vkqtsns6/aclImdb_v2.tar?dl=1	closed	2023-04-17T09:08:18	2023-04-18T07:18:20	2023-04-18T07:18:20	https://github.com/huggingface/datasets/issues/5764	null	sauravtii	false	[ "Thanks for reporting, @sauravtii.\r\n\r\nUnfortunately, I'm not able to reproduce the issue:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"josianem/imdb\")\r\n\r\nIn [2]: ds\r\nOut[2]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['text', 'label'],\r\n num_rows: 25799\r\n })\r\n test: Dataset({\r\n features: ['text', 'label'],\r\n num_rows: 25000\r\n })\r\n unsupervised: Dataset({\r\n features: ['text', 'label'],\r\n num_rows: 50000\r\n })\r\n})\r\n```\r\n\r\nCould you please retry to load the dataset? Maybe there was a temporary connection issue to Dropbox.", "Thanks @albertvillanova. I am facing another issue now\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"sample.py\", line 4, in <module>\r\n dataset = load_dataset(\"josianem/imdb\")\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/builder.py\", line 738, in _download_and_prepare\r\n verify_splits(self.info.splits, split_dict)\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/utils/info_utils.py\", line 74, in verify_splits\r\n raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=34501348, num_examples=25799, dataset_name='imdb'), 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='imdb')}, {'expected': SplitInfo(name='test', num_bytes=32650697, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='test', num_bytes=0, num_examples=0, dataset_name='imdb')}, {'expected': SplitInfo(name='unsupervised', num_bytes=67106814, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=0, num_examples=0, dataset_name='imdb')}]\r\n```\r\n\r\nThis is my code\r\n\r\n```\r\nfrom datasets import load_dataset, load_metric\r\n\r\ndataset = load_dataset(\"josianem/imdb\")\r\n```", "Your connection didn't work and you got an empty dataset (`num_bytes=0, num_examples=0`):\r\n```\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: \r\n[\r\n {\r\n 'expected': SplitInfo(name='train', num_bytes=34501348, num_examples=25799, dataset_name='imdb'), \r\n 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='imdb')\r\n }, \r\n {\r\n 'expected': SplitInfo(name='test', num_bytes=32650697, num_examples=25000, dataset_name='imdb'), \r\n 'recorded': SplitInfo(name='test', num_bytes=0, num_examples=0, dataset_name='imdb')\r\n }, \r\n {\r\n 'expected': SplitInfo(name='unsupervised', num_bytes=67106814, num_examples=50000, dataset_name='imdb'), \r\n 'recorded': SplitInfo(name='unsupervised', num_bytes=0, num_examples=0, dataset_name='imdb')\r\n }\r\n]\r\n```\r\n\r\nCould you please try the link in your browser and see if it works? https://www.dropbox.com/s/zts98j4vkqtsns6/aclImdb_v2.tar?dl=1\r\n- If it does not work, you should contact the author of the dataset in their Community tab (https://huggingface.co/datasets/josianem/imdb/discussions) and inform them, so that they can host their data elsewhere, for example on the Hugging Face Hub itself\r\n\r\nIf the link works, you should try to load the dataset but forcing the re-download of the data files (so that the cache is refreshed with the actual data file), by passing `download_mode=\"force_redownload\"`:\r\n```python\r\ndataset = load_dataset(\"josianem/imdb\", download_mode=\"force_redownload\")\r\n```", "After pasting the link in the browser, it did start the download so it seems that the link is working. But even after including the `download_mode` in my code I am facing the same issue:\r\n\r\nError:\r\n```\r\nTraceback (most recent call last):\r\n File \"sample.py\", line 4, in <module>\r\n dataset = load_dataset(\"josianem/imdb\", download_mode=\"force_redownload\")\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/load.py\", line 1112, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/builder.py\", line 636, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/builder.py\", line 704, in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n File \"/home/saurav/.cache/huggingface/modules/datasets_modules/datasets/imdb/cc6ab4acab2799be15d5d217c24548b856156dafdc850165fdc4f2031f27ff2f/imdb.py\", line 79, in _split_generators\r\n archive = dl_manager.download(_DOWNLOAD_URL)\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/utils/download_manager.py\", line 196, in download\r\n downloaded_path_or_paths = map_nested(\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 197, in map_nested\r\n return function(data_struct)\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/utils/download_manager.py\", line 217, in _download\r\n return cached_path(url_or_filename, download_config=download_config)\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 289, in cached_path\r\n output_path = get_from_cache(\r\n File \"/home/saurav/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 606, in get_from_cache\r\n raise ConnectionError(\"Couldn't reach {}\".format(url))\r\nConnectionError: Couldn't reach https://www.dropbox.com/s/zts98j4vkqtsns6/aclImdb_v2.tar?dl=1\r\n```\r\n\r\nMy code:\r\n```\r\nfrom datasets import load_dataset, load_metric\r\n\r\ndataset = load_dataset(\"josianem/imdb\", download_mode=\"force_redownload\")\r\n```", "I have tried again to reproduce your issue without success: the dataset loads perfectly, both in my local machine and in a Colab notebook.\r\n- See: https://colab.research.google.com/drive/1dky3T0XGFuldggy22NNQQN-UqOFqvnuY?usp=sharing\r\n\r\nI think the cause maight be that you are using a very old version of `datasets`. Please, could you update it and retry?\r\n```\r\npip install -U datasets\r\n```", "That worked!! Thanks @albertvillanova : )\r\n\r\n```\r\nDownloading builder script: 100%\|███████\| 4.20k/4.20k [00:00<00:00, 6.69MB/s]\r\nDownloading metadata: 100%\|█████████████\| 2.60k/2.60k [00:00<00:00, 3.41MB/s]\r\nDownloading readme: 100%\|███████████████\| 7.52k/7.52k [00:00<00:00, 12.6MB/s]\r\nDownloading and preparing dataset imdb/plain_text to /home/saurav/.cache/huggingface/datasets/josianem___imdb/plain_text/1.0.0/cc6ab4acab2799be15d5d217c24548b856156dafdc850165fdc4f2031f27ff2f...\r\nDownloading data: 100%\|███████████████████\| 301M/301M [01:32<00:00, 3.25MB/s]\r\nDataset imdb downloaded and prepared to /home/saurav/.cache/huggingface/datasets/josianem___imdb/plain_text/1.0.0/cc6ab4acab2799be15d5d217c24548b856156dafdc850165fdc4f2031f27ff2f. Subsequent calls will reuse this data.\r\n100%\|█████████████████████████████████████████\| 3/3 [00:00<00:00, 794.83it/s]\r\n```\r\n\r\nThe code I used:\r\n```\r\nfrom datasets import load_dataset, load_metric\r\n\r\ndataset = load_dataset(\"josianem/imdb\", download_mode=\"force_redownload\")\r\n\r\n```\r\n\r\nBut when I remove `download_mode=\"force_redownload\"` I get the same error. Any guess on that?", "That is because the cache got the \"empty\" download file the first time you tried and got the connection error.\r\n\r\nThen, once you no longer get the connection error, you need to refresh the cache by passing `download_mode=\"force_redownload\"`." ]
1,670,476,302	5,763	fix typo: "mow" -> "now"	closed	2023-04-17T06:03:44	2023-04-17T15:01:53	2023-04-17T14:54:46	https://github.com/huggingface/datasets/pull/5763	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5763", "html_url": "https://github.com/huggingface/datasets/pull/5763", "diff_url": "https://github.com/huggingface/datasets/pull/5763.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5763.patch", "merged_at": "2023-04-17T14:54:46" }	csris	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) \| 0.006804 / 0.011353 (-0.004549) \| 0.004984 / 0.011008 (-0.006024) \| 0.096781 / 0.038508 (0.058273) \| 0.033049 / 0.023109 (0.009939) \| 0.297681 / 0.275898 (0.021783) \| 0.329553 / 0.323480 (0.006073) \| 0.005697 / 0.007986 (-0.002289) \| 0.004019 / 0.004328 (-0.000310) \| 0.072691 / 0.004250 (0.068441) \| 0.046921 / 0.037052 (0.009868) \| 0.311467 / 0.258489 (0.052978) \| 0.337616 / 0.293841 (0.043775) \| 0.042400 / 0.128546 (-0.086146) \| 0.011919 / 0.075646 (-0.063727) \| 0.331390 / 0.419271 (-0.087881) \| 0.051004 / 0.043533 (0.007471) \| 0.295317 / 0.255139 (0.040178) \| 0.316570 / 0.283200 (0.033371) \| 0.099283 / 0.141683 (-0.042400) \| 1.430583 / 1.452155 (-0.021572) \| 1.493550 / 1.492716 (0.000834) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.213634 / 0.018006 (0.195628) \| 0.432557 / 0.000490 (0.432067) \| 0.001586 / 0.000200 (0.001386) \| 0.000079 / 0.000054 (0.000025) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025249 / 0.037411 (-0.012162) \| 0.105433 / 0.014526 (0.090908) \| 0.113474 / 0.176557 (-0.063082) \| 0.168799 / 0.737135 (-0.568336) \| 0.119363 / 0.296338 (-0.176975) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.412450 / 0.215209 (0.197241) \| 4.117432 / 2.077655 (2.039777) \| 1.935176 / 1.504120 (0.431056) \| 1.745674 / 1.541195 (0.204479) \| 1.853872 / 1.468490 (0.385382) \| 0.703429 / 4.584777 (-3.881348) \| 3.756981 / 3.745712 (0.011269) \| 3.730607 / 5.269862 (-1.539255) \| 1.839052 / 4.565676 (-2.726624) \| 0.087574 / 0.424275 (-0.336701) \| 0.012293 / 0.007607 (0.004686) \| 0.517234 / 0.226044 (0.291190) \| 5.189759 / 2.268929 (2.920831) \| 2.418739 / 55.444624 (-53.025885) \| 2.081424 / 6.876477 (-4.795053) \| 2.204464 / 2.142072 (0.062392) \| 0.842768 / 4.805227 (-3.962459) \| 0.169014 / 6.500664 (-6.331650) \| 0.063711 / 0.075469 (-0.011758) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.180636 / 1.841788 (-0.661152) \| 14.816088 / 8.074308 (6.741779) \| 14.290085 / 10.191392 (4.098693) \| 0.165267 / 0.680424 (-0.515156) \| 0.017290 / 0.534201 (-0.516911) \| 0.419678 / 0.579283 (-0.159605) \| 0.418164 / 0.434364 (-0.016200) \| 0.492210 / 0.540337 (-0.048127) \| 0.588528 / 1.386936 (-0.798408) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007144 / 0.011353 (-0.004209) \| 0.005223 / 0.011008 (-0.005785) \| 0.073583 / 0.038508 (0.035075) \| 0.033534 / 0.023109 (0.010425) \| 0.339020 / 0.275898 (0.063122) \| 0.366546 / 0.323480 (0.043066) \| 0.006245 / 0.007986 (-0.001741) \| 0.004081 / 0.004328 (-0.000247) \| 0.073089 / 0.004250 (0.068839) \| 0.047024 / 0.037052 (0.009971) \| 0.342540 / 0.258489 (0.084051) \| 0.379743 / 0.293841 (0.085902) \| 0.037551 / 0.128546 (-0.090995) \| 0.012246 / 0.075646 (-0.063400) \| 0.084796 / 0.419271 (-0.334476) \| 0.052256 / 0.043533 (0.008723) \| 0.342675 / 0.255139 (0.087536) \| 0.367157 / 0.283200 (0.083957) \| 0.102939 / 0.141683 (-0.038744) \| 1.409039 / 1.452155 (-0.043115) \| 1.526137 / 1.492716 (0.033420) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.208143 / 0.018006 (0.190136) \| 0.437940 / 0.000490 (0.437450) \| 0.000424 / 0.000200 (0.000224) \| 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) \| 0.028321 / 0.037411 (-0.009091) \| 0.110417 / 0.014526 (0.095891) \| 0.119449 / 0.176557 (-0.057107) \| 0.168081 / 0.737135 (-0.569054) \| 0.126658 / 0.296338 (-0.169681) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.429302 / 0.215209 (0.214093) \| 4.270547 / 2.077655 (2.192892) \| 2.061323 / 1.504120 (0.557203) \| 1.857877 / 1.541195 (0.316682) \| 1.873317 / 1.468490 (0.404827) \| 0.688750 / 4.584777 (-3.896027) \| 3.767951 / 3.745712 (0.022239) \| 2.011436 / 5.269862 (-3.258426) \| 1.299965 / 4.565676 (-3.265712) \| 0.084799 / 0.424275 (-0.339476) \| 0.012082 / 0.007607 (0.004475) \| 0.521981 / 0.226044 (0.295937) \| 5.265333 / 2.268929 (2.996405) \| 2.494326 / 55.444624 (-52.950298) \| 2.144672 / 6.876477 (-4.731804) \| 2.365624 / 2.142072 (0.223551) \| 0.839868 / 4.805227 (-3.965359) \| 0.166614 / 6.500664 (-6.334050) \| 0.063804 / 0.075469 (-0.011665) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.264623 / 1.841788 (-0.577164) \| 14.946515 / 8.074308 (6.872207) \| 14.450115 / 10.191392 (4.258723) \| 0.163878 / 0.680424 (-0.516546) \| 0.017501 / 0.534201 (-0.516700) \| 0.420992 / 0.579283 (-0.158291) \| 0.423005 / 0.434364 (-0.011359) \| 0.489505 / 0.540337 (-0.050832) \| 0.594631 / 1.386936 (-0.792305) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fd893098627230cc734f6009ad04cf885c979ac4 \"CML watermark\")\n" ]
1,670,326,470	5,762	Not able to load the pile	closed	2023-04-17T03:09:10	2023-04-17T09:37:27	2023-04-17T09:37:27	https://github.com/huggingface/datasets/issues/5762	null	surya-narayanan	false	[ "Thanks for reporting, @surya-narayanan.\r\n\r\nI see you already started a discussion about this on the Community tab of the corresponding dataset: https://huggingface.co/datasets/EleutherAI/the_pile/discussions/10\r\nLet's continue the discussion there!" ]
1,670,034,582	5,761	One or several metadata.jsonl were found, but not in the same directory or in a parent directory	open	2023-04-16T16:21:55	2023-04-19T11:53:24	null	https://github.com/huggingface/datasets/issues/5761	null	blghtr	false	[ "Also, when generated from a zip archive, the dataset contains only a few images. In my case, 20 versus 2000+ contained in the archive. The generation from folders works as expected.", "Thanks for reporting, @blghtr.\r\n\r\nYou should include the `metadata.jsonl` in your ZIP archives, at the root level directory.\r\n\r\nI agree that our documentation is not clear enough. Maybe we could improve it.", "You can find a dummy dataset example here: https://huggingface.co/datasets/albertvillanova/tmp-imagefolder-metadata\r\n\r\n```\r\ntmp-imagefolder-metadata/\r\n└── data/\r\n ├── train.zip\r\n └── valid.zip\r\n```\r\nwhere, the directory structure within the `train.zip` archive is:\r\n```\r\nmetadata.jsonl\r\ntrain/\r\n ├── bharatanatyam/\r\n └── bharatanatyam_original_113.jpg_70c297a2-e2f2-4ed8-b93c-0c03d0809fe2.jpg\r\n └── kathak/\r\n └── kathak_original_10.jpg_2c4a2c3d-47fc-4b33-9c09-38b542826632.jpg\r\n```\r\nand the metadata file contains:\r\n```\r\n{\"file_name\": \"train/bharatanatyam/bharatanatyam_original_113.jpg_70c297a2-e2f2-4ed8-b93c-0c03d0809fe2.jpg\", \"text\": \"first\"}\r\n{\"file_name\": \"train/kathak/kathak_original_10.jpg_2c4a2c3d-47fc-4b33-9c09-38b542826632.jpg\", \"text\": \"second\"}\r\n```" ]
1,670,028,072	5,760	Multi-image loading in Imagefolder dataset	open	2023-04-16T16:01:05	2024-12-01T11:16:09	null	https://github.com/huggingface/datasets/issues/5760	null	vvvm23	false	[ "Supporting this could be useful (I remember a use-case for this on the Hub). Do you agree @polinaeterna? \r\n\r\nImplementing this should be possible if we iterate over metadata files and build image/audio file paths instead of iterating over image/audio files and looking for the corresponding entries in metadata files.", "I've build a similar feature from scratch and would be interested to combine it with the datasets package.\r\n\r\nMy solution works something like this:\r\nInterpret the first element of each column as a file path. If the path exists and is a file, (try to) load the files for the entire column. Thereby, one isn't restricted to a particular column name, with comes in handy when dealing with multiple file columns.\r\n\r\nI've looked into the code to try to implement this, but didn't find the right places. I'm also open to contribute, but will need some guidance.", "Required here: https://discuss.huggingface.co/t/dataset-repo-requires-arbitrary-python-code-execution/59346/14", "+1\r\n\r\nIs the only way to do this right now to write a custom dataset loader script?", "Also: be able to have input and output images for each row. Asked here: https://discuss.huggingface.co/t/how-to-structure-image-files-for-datasets-load-dataset-imagefolder-when-you-have-input-and-output-images-like-in-instruct-pix2pix/82467", "👀 I encountered the same problem. Is the only way to solve it by writing a custom dataset loader script?", "Yes I had to use script at the end\r\n\r\nSent from Outlook for iOS<https://aka.ms/o0ukef>\r\n________________________________\r\nFrom: Yunzhuo Hao *@.>\r\nSent: Sunday, December 1, 2024 11:52:21 AM\r\nTo: huggingface/datasets @.>\r\nCc: Sushant Gautam @.>; Manual @.>\r\nSubject: Re: [huggingface/datasets] Multi-image loading in Imagefolder dataset (Issue #5760)\r\n\r\n\r\n👀 I encountered the same problem. Is the only way to solve it by writing a custom dataset loader script?\r\n\r\n—\r\nReply to this email directly, view it on GitHub<https://github.com/huggingface/datasets/issues/5760#issuecomment-2509684773>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AD7SQP5KY67NTZ7ELAGSIJ32DLS6LAVCNFSM6AAAAABSZRPAQOVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDKMBZGY4DINZXGM>.\r\nYou are receiving this because you are subscribed to this thread.Message ID: @.*>\r\n", "@SushantGautam Thanks for your reply!! In addition, can the dataset shared by writing such a script be displayed in the Viewer? And if it is convenient, can you please show me your script? Thanks again!!", "Yes will be visible!\r\nThe script docs is at\r\nhttps://huggingface.co/docs/datasets/en/dataset_script\r\n\r\n\r\n\r\nSincerely,\r\nSushant Gautam\r\n\r\n📧: @. \| @.\r\n🌐: sushant.info.np \| simula.no/people/sushant\r\n<https://www.simula.no/people/sushant>\r\n[image: Simula Metropolitan Center for Digital Engineering]\r\n<https://www.simulamet.no/>\r\nStensberggata 27, 0170 Oslo\r\nFind me on: LinkedIn <https://www.linkedin.com/in/eSushant> \| Facebook\r\n<https://www.facebook.com/eSushant> \| Twitter\r\n<https://twitter.com/esushant> \r\n\r\n\r\nOn Sun, 1 Dec 2024 at 12:02, Yunzhuo Hao @.> wrote:\r\n\r\n> @SushantGautam <https://github.com/SushantGautam> Thanks for your reply!!\r\n> In addition, can the dataset shared by writing such a script be displayed\r\n> in the Viewer? And if it is convenient, can you please show me your script?\r\n> Thanks again!!\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/5760#issuecomment-2509692544>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AD7SQP2XGIKBCSSSNW2S6K32DLUFDAVCNFSM6AAAAABSZRPAQOVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDKMBZGY4TENJUGQ>\r\n> .\r\n> You are receiving this because you were mentioned.Message ID:\r\n> @.>\r\n>\r\n", "I see. Thank you very very much!!!\r\n\r\nSushant Gautam @.>于2024年12月1日周日18:54写道：\r\n\r\n> Yes I had to use script at the end\r\n>\r\n> Sent from Outlook for iOS<https://aka.ms/o0ukef>\r\n> ________________________________\r\n> From: Yunzhuo Hao @.>\r\n> Sent: Sunday, December 1, 2024 11:52:21 AM\r\n> To: huggingface/datasets @.>\r\n> Cc: Sushant Gautam @.>; Manual @.>\r\n> Subject: Re: [huggingface/datasets] Multi-image loading in Imagefolder\r\n> dataset (Issue #5760)\r\n>\r\n>\r\n> 👀 I encountered the same problem. Is the only way to solve it by writing\r\n> a custom dataset loader script?\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub<\r\n> https://github.com/huggingface/datasets/issues/5760#issuecomment-2509684773>,\r\n> or unsubscribe<\r\n> https://github.com/notifications/unsubscribe-auth/AD7SQP5KY67NTZ7ELAGSIJ32DLS6LAVCNFSM6AAAAABSZRPAQOVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDKMBZGY4DINZXGM>.\r\n>\r\n> You are receiving this because you are subscribed to this thread.Message\r\n> ID: @.>\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/5760#issuecomment-2509685427>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AVYJNSJ6UQZ6G6VGSB5RGXD2DLTGFAVCNFSM6AAAAABSZRPAQOVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDKMBZGY4DKNBSG4>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> @.**>\r\n>\r\n" ]
1,669,977,848	5,759	Can I load in list of list of dict format?	open	2023-04-16T13:50:14	2023-04-19T12:04:36	null	https://github.com/huggingface/datasets/issues/5759	null	LZY-the-boys	false	[ "Thanks for reporting, @LZY-the-boys.\r\n\r\nCould you please give more details about what is your intended dataset structure? What are the names of the columns and the value of each row?\r\n\r\nCurrently, the JSON-Lines format is supported:\r\n- Each line correspond to one row of the dataset\r\n- Each line is composed of one JSON object, where the names are the names of the columns, and the values are the values for the row-column pair." ]
1,669,920,923	5,758	Fixes #5757	closed	2023-04-16T11:56:01	2023-04-20T15:37:49	2023-04-20T15:30:48	https://github.com/huggingface/datasets/pull/5758	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5758", "html_url": "https://github.com/huggingface/datasets/pull/5758", "diff_url": "https://github.com/huggingface/datasets/pull/5758.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5758.patch", "merged_at": "2023-04-20T15:30:48" }	eli-osherovich	true	[ "The CI can be fixed by merging `main` into your branch. Can you do that before we merge ?", "_The documentation is not available anymore as the PR was closed or merged._", "Done.\n\nOn Thu, Apr 20, 2023 at 6:01 PM Quentin Lhoest *@.>\nwrote:\n\n> The CI can be fixed by merging main into your branch. Can you do that\n> before we merge ?\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/pull/5758#issuecomment-1516488124>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AASS73QPLA735AMN4PFDYRTXCFFTJANCNFSM6AAAAAAXACBUQU>\n> .\n> You are receiving this because you authored the thread.Message ID:\n> @.*>\n>\n", "Nice thanks !", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007161 / 0.011353 (-0.004192) \| 0.005099 / 0.011008 (-0.005909) \| 0.099301 / 0.038508 (0.060793) \| 0.034144 / 0.023109 (0.011034) \| 0.298273 / 0.275898 (0.022375) \| 0.329009 / 0.323480 (0.005529) \| 0.005486 / 0.007986 (-0.002500) \| 0.003887 / 0.004328 (-0.000441) \| 0.074769 / 0.004250 (0.070518) \| 0.047505 / 0.037052 (0.010453) \| 0.306550 / 0.258489 (0.048061) \| 0.335380 / 0.293841 (0.041540) \| 0.034796 / 0.128546 (-0.093750) \| 0.012152 / 0.075646 (-0.063495) \| 0.332194 / 0.419271 (-0.087077) \| 0.049661 / 0.043533 (0.006128) \| 0.296832 / 0.255139 (0.041693) \| 0.316417 / 0.283200 (0.033218) \| 0.098234 / 0.141683 (-0.043449) \| 1.494114 / 1.452155 (0.041959) \| 1.566468 / 1.492716 (0.073751) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.221309 / 0.018006 (0.203303) \| 0.440855 / 0.000490 (0.440365) \| 0.003025 / 0.000200 (0.002825) \| 0.000076 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026594 / 0.037411 (-0.010817) \| 0.110406 / 0.014526 (0.095880) \| 0.116117 / 0.176557 (-0.060439) \| 0.173502 / 0.737135 (-0.563633) \| 0.121988 / 0.296338 (-0.174351) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.403307 / 0.215209 (0.188098) \| 4.034146 / 2.077655 (1.956492) \| 1.852162 / 1.504120 (0.348042) \| 1.675643 / 1.541195 (0.134448) \| 1.748851 / 1.468490 (0.280360) \| 0.703458 / 4.584777 (-3.881319) \| 3.809055 / 3.745712 (0.063343) \| 2.118060 / 5.269862 (-3.151801) \| 1.338394 / 4.565676 (-3.227282) \| 0.086319 / 0.424275 (-0.337956) \| 0.012195 / 0.007607 (0.004588) \| 0.520814 / 0.226044 (0.294769) \| 5.201074 / 2.268929 (2.932145) \| 2.418384 / 55.444624 (-53.026240) \| 2.085496 / 6.876477 (-4.790980) \| 2.245638 / 2.142072 (0.103565) \| 0.849042 / 4.805227 (-3.956185) \| 0.171912 / 6.500664 (-6.328752) \| 0.065691 / 0.075469 (-0.009778) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.159985 / 1.841788 (-0.681803) \| 14.910867 / 8.074308 (6.836559) \| 14.473926 / 10.191392 (4.282534) \| 0.181532 / 0.680424 (-0.498891) \| 0.017203 / 0.534201 (-0.516998) \| 0.420805 / 0.579283 (-0.158479) \| 0.426455 / 0.434364 (-0.007909) \| 0.497086 / 0.540337 (-0.043251) \| 0.593909 / 1.386936 (-0.793027) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007688 / 0.011353 (-0.003665) \| 0.005353 / 0.011008 (-0.005656) \| 0.076869 / 0.038508 (0.038361) \| 0.035030 / 0.023109 (0.011921) \| 0.344649 / 0.275898 (0.068751) \| 0.387669 / 0.323480 (0.064190) \| 0.005913 / 0.007986 (-0.002072) \| 0.004107 / 0.004328 (-0.000221) \| 0.074111 / 0.004250 (0.069860) \| 0.049351 / 0.037052 (0.012299) \| 0.346061 / 0.258489 (0.087572) \| 0.395499 / 0.293841 (0.101658) \| 0.035549 / 0.128546 (-0.092997) \| 0.012340 / 0.075646 (-0.063307) \| 0.087031 / 0.419271 (-0.332241) \| 0.049088 / 0.043533 (0.005556) \| 0.342774 / 0.255139 (0.087635) \| 0.362037 / 0.283200 (0.078837) \| 0.100329 / 0.141683 (-0.041354) \| 1.442349 / 1.452155 (-0.009806) \| 1.551079 / 1.492716 (0.058363) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.228458 / 0.018006 (0.210452) \| 0.446190 / 0.000490 (0.445701) \| 0.000413 / 0.000200 (0.000213) \| 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) \| 0.029884 / 0.037411 (-0.007527) \| 0.117527 / 0.014526 (0.103002) \| 0.123221 / 0.176557 (-0.053335) \| 0.172290 / 0.737135 (-0.564845) \| 0.128682 / 0.296338 (-0.167657) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.420905 / 0.215209 (0.205696) \| 4.199342 / 2.077655 (2.121687) \| 2.007327 / 1.504120 (0.503207) \| 1.814732 / 1.541195 (0.273537) \| 1.893999 / 1.468490 (0.425509) \| 0.712259 / 4.584777 (-3.872518) \| 3.843402 / 3.745712 (0.097690) \| 3.198514 / 5.269862 (-2.071348) \| 1.678732 / 4.565676 (-2.886945) \| 0.086435 / 0.424275 (-0.337840) \| 0.012233 / 0.007607 (0.004626) \| 0.526121 / 0.226044 (0.300077) \| 5.190578 / 2.268929 (2.921650) \| 2.473259 / 55.444624 (-52.971366) \| 2.142795 / 6.876477 (-4.733682) \| 2.277594 / 2.142072 (0.135521) \| 0.846117 / 4.805227 (-3.959110) \| 0.169458 / 6.500664 (-6.331206) \| 0.065017 / 0.075469 (-0.010452) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.272479 / 1.841788 (-0.569309) \| 15.086473 / 8.074308 (7.012165) \| 14.659728 / 10.191392 (4.468336) \| 0.163915 / 0.680424 (-0.516509) \| 0.017561 / 0.534201 (-0.516640) \| 0.422074 / 0.579283 (-0.157209) \| 0.421963 / 0.434364 (-0.012401) \| 0.490321 / 0.540337 (-0.050016) \| 0.586854 / 1.386936 (-0.800083) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e7ce0ac60c7efc10886471932854903a7c19f172 \"CML watermark\")\n" ]
1,669,910,503	5,757	Tilde (~) is not supported	closed	2023-04-16T11:48:10	2023-04-20T15:30:51	2023-04-20T15:30:51	https://github.com/huggingface/datasets/issues/5757	null	eli-osherovich	false	[]
1,669,678,080	5,756	Calling shuffle on a IterableDataset with streaming=True, gives "ValueError: cannot reshape array"	closed	2023-04-16T04:59:47	2023-04-18T03:40:56	2023-04-18T03:40:56	https://github.com/huggingface/datasets/issues/5756	null	rohfle	false	[ "Hi! I've merged a PR on the Hub with a fix: https://huggingface.co/datasets/fashion_mnist/discussions/3", "Thanks, this appears to have fixed the issue.\r\n\r\nI've created a PR for the same change in the mnist dataset: https://huggingface.co/datasets/mnist/discussions/3/files" ]
1,669,048,438	5,755	ImportError: cannot import name 'DeprecatedEnum' from 'datasets.utils.deprecation_utils'	closed	2023-04-14T23:28:54	2023-04-14T23:36:19	2023-04-14T23:36:19	https://github.com/huggingface/datasets/issues/5755	null	fivejjs	false	[ "update the version. fix" ]
1,668,755,035	5,754	Minor tqdm fixes	closed	2023-04-14T18:15:14	2023-04-20T15:27:58	2023-04-20T15:21:00	https://github.com/huggingface/datasets/pull/5754	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5754", "html_url": "https://github.com/huggingface/datasets/pull/5754", "diff_url": "https://github.com/huggingface/datasets/pull/5754.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5754.patch", "merged_at": "2023-04-20T15:21:00" }	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) \| 0.006479 / 0.011353 (-0.004874) \| 0.004592 / 0.011008 (-0.006416) \| 0.097239 / 0.038508 (0.058731) \| 0.028609 / 0.023109 (0.005499) \| 0.309225 / 0.275898 (0.033327) \| 0.340015 / 0.323480 (0.016535) \| 0.004857 / 0.007986 (-0.003129) \| 0.004649 / 0.004328 (0.000320) \| 0.074770 / 0.004250 (0.070520) \| 0.038351 / 0.037052 (0.001299) \| 0.313360 / 0.258489 (0.054871) \| 0.350256 / 0.293841 (0.056416) \| 0.030770 / 0.128546 (-0.097776) \| 0.011591 / 0.075646 (-0.064055) \| 0.322444 / 0.419271 (-0.096828) \| 0.043704 / 0.043533 (0.000171) \| 0.311790 / 0.255139 (0.056651) \| 0.339183 / 0.283200 (0.055984) \| 0.088041 / 0.141683 (-0.053642) \| 1.490649 / 1.452155 (0.038494) \| 1.561789 / 1.492716 (0.069072) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.208984 / 0.018006 (0.190978) \| 0.406105 / 0.000490 (0.405616) \| 0.003152 / 0.000200 (0.002952) \| 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) \| 0.022622 / 0.037411 (-0.014790) \| 0.095819 / 0.014526 (0.081294) \| 0.105132 / 0.176557 (-0.071424) \| 0.165684 / 0.737135 (-0.571451) \| 0.106706 / 0.296338 (-0.189632) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.426126 / 0.215209 (0.210917) \| 4.233864 / 2.077655 (2.156209) \| 1.918727 / 1.504120 (0.414607) \| 1.729905 / 1.541195 (0.188710) \| 1.760342 / 1.468490 (0.291852) \| 0.695449 / 4.584777 (-3.889328) \| 3.413531 / 3.745712 (-0.332181) \| 1.904557 / 5.269862 (-3.365305) \| 1.270604 / 4.565676 (-3.295072) \| 0.083018 / 0.424275 (-0.341257) \| 0.012760 / 0.007607 (0.005152) \| 0.523991 / 0.226044 (0.297947) \| 5.236132 / 2.268929 (2.967204) \| 2.360959 / 55.444624 (-53.083665) \| 1.996533 / 6.876477 (-4.879943) \| 2.072934 / 2.142072 (-0.069138) \| 0.804133 / 4.805227 (-4.001094) \| 0.150976 / 6.500664 (-6.349688) \| 0.065503 / 0.075469 (-0.009966) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.211828 / 1.841788 (-0.629960) \| 13.657743 / 8.074308 (5.583435) \| 13.887148 / 10.191392 (3.695756) \| 0.145996 / 0.680424 (-0.534428) \| 0.016562 / 0.534201 (-0.517639) \| 0.380359 / 0.579283 (-0.198924) \| 0.388698 / 0.434364 (-0.045666) \| 0.440373 / 0.540337 (-0.099965) \| 0.531753 / 1.386936 (-0.855183) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated 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) \| 0.004569 / 0.011008 (-0.006439) \| 0.076239 / 0.038508 (0.037731) \| 0.028462 / 0.023109 (0.005352) \| 0.365540 / 0.275898 (0.089642) \| 0.398242 / 0.323480 (0.074762) \| 0.005785 / 0.007986 (-0.002200) \| 0.003346 / 0.004328 (-0.000982) \| 0.076296 / 0.004250 (0.072046) \| 0.039853 / 0.037052 (0.002800) \| 0.367684 / 0.258489 (0.109195) \| 0.409570 / 0.293841 (0.115730) \| 0.030536 / 0.128546 (-0.098010) \| 0.011534 / 0.075646 (-0.064112) \| 0.084962 / 0.419271 (-0.334309) \| 0.042708 / 0.043533 (-0.000825) \| 0.344058 / 0.255139 (0.088919) \| 0.389096 / 0.283200 (0.105897) \| 0.090559 / 0.141683 (-0.051124) \| 1.507101 / 1.452155 (0.054946) \| 1.563977 / 1.492716 (0.071260) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.228740 / 0.018006 (0.210734) \| 0.396890 / 0.000490 (0.396400) \| 0.000392 / 0.000200 (0.000192) \| 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) \| 0.025052 / 0.037411 (-0.012360) \| 0.099951 / 0.014526 (0.085426) \| 0.106847 / 0.176557 (-0.069710) \| 0.156666 / 0.737135 (-0.580469) \| 0.110344 / 0.296338 (-0.185994) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.442363 / 0.215209 (0.227154) \| 4.429571 / 2.077655 (2.351917) \| 2.076501 / 1.504120 (0.572381) \| 1.875226 / 1.541195 (0.334031) \| 1.909093 / 1.468490 (0.440603) \| 0.703047 / 4.584777 (-3.881730) \| 3.457036 / 3.745712 (-0.288676) \| 2.866648 / 5.269862 (-2.403214) \| 1.524430 / 4.565676 (-3.041246) \| 0.083687 / 0.424275 (-0.340588) \| 0.012251 / 0.007607 (0.004643) \| 0.543945 / 0.226044 (0.317901) \| 5.440559 / 2.268929 (3.171630) \| 2.522924 / 55.444624 (-52.921700) \| 2.188770 / 6.876477 (-4.687707) \| 2.249632 / 2.142072 (0.107559) \| 0.813499 / 4.805227 (-3.991728) \| 0.152861 / 6.500664 (-6.347803) \| 0.067189 / 0.075469 (-0.008280) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.284255 / 1.841788 (-0.557533) \| 14.207864 / 8.074308 (6.133556) \| 14.279691 / 10.191392 (4.088299) \| 0.167027 / 0.680424 (-0.513396) \| 0.016455 / 0.534201 (-0.517746) \| 0.380798 / 0.579283 (-0.198485) \| 0.390013 / 0.434364 (-0.044351) \| 0.445493 / 0.540337 (-0.094845) \| 0.526278 / 1.386936 (-0.860658) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3fdb46c526b9d070df0eb2d56b0ecacdace7cb9a \"CML watermark\")\n" ]
1,668,659,536	5,753	[IterableDatasets] Add column followed by interleave datasets gives bogus outputs	closed	2023-04-14T17:32:31	2025-07-04T05:22:53	2023-04-14T17:36:37	https://github.com/huggingface/datasets/issues/5753	null	sanchit-gandhi	false	[ "Problem with the code snippet! Using global vars and functions was not a good idea with iterable datasets!\r\n\r\nIf we update to:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\noriginal_dataset = load_dataset(\"librispeech_asr\", \"clean\", split=\"validation\", streaming=True)\r\n\r\n# now add a new column to our streaming dataset using our hack\r\nname = \"new_column\"\r\ncolumn_1 = [f\"new dataset 1, row {i}\" for i in range(50)]\r\n\r\nnew_features = original_dataset.features.copy()\r\nnew_features[name] = new_features[\"file\"] # I know that \"file\" has the right column type to match our new feature\r\n\r\ndef add_column_fn_1(example, idx):\r\n if name in example:\r\n raise ValueError(f\"Error when adding {name}: column {name} is already in the dataset.\")\r\n return {name: column_1[idx]}\r\n\r\nmodified_dataset_1 = original_dataset.map(add_column_fn_1, with_indices=True, features=new_features)\r\n\r\n# now create a second modified dataset using the same trick\r\ncolumn_2 = [f\"new dataset 2, row {i}\" for i in range(50)]\r\n\r\ndef add_column_fn_2(example, idx):\r\n if name in example:\r\n raise ValueError(f\"Error when adding {name}: column {name} is already in the dataset.\")\r\n return {name: column_2[idx]}\r\n\r\nmodified_dataset_2 = original_dataset.map(add_column_fn_2, with_indices=True, features=new_features)\r\n\r\ninterleaved_dataset = interleave_datasets([modified_dataset_1, modified_dataset_2])\r\n\r\nfor i, sample in enumerate(interleaved_dataset):\r\n print(sample[\"new_column\"])\r\n if i == 10:\r\n break\r\n```\r\nwe get the correct outputs:\r\n```python\r\nnew dataset 1, row 0\r\nnew dataset 2, row 0\r\nnew dataset 1, row 1\r\nnew dataset 2, row 1\r\nnew dataset 1, row 2\r\nnew dataset 2, row 2\r\nnew dataset 1, row 3\r\nnew dataset 2, row 3\r\nnew dataset 1, row 4\r\nnew dataset 2, row 4\r\nnew dataset 1, row 5\r\n```\r\n", "Thanks @sanchit-gandhi, this solo performance is very helpful! :)" ]
1,668,574,209	5,752	Streaming dataset looses `.feature` method after `.add_column`	open	2023-04-14T16:39:50	2024-01-18T10:15:20	null	https://github.com/huggingface/datasets/issues/5752	null	sanchit-gandhi	false	[ "I believe the issue resides in this line:\r\nhttps://github.com/huggingface/datasets/blob/7c3a9b057c476c40d157bd7a5d57f49066239df0/src/datasets/iterable_dataset.py#L1415\r\n\r\nIf we pass the new features of the dataset to the `.map` method we can return the features after adding a column, e.g.:\r\n```python\r\nfrom datasets import load_dataset, Value\r\n\r\noriginal_dataset = load_dataset(\"librispeech_asr\", \"clean\", split=\"validation\", streaming=True)\r\nprint(original_dataset.features.keys())\r\n\r\n# now add a new column to our streaming dataset using our hack\r\nname = \"new_column\"\r\ncolumn = [\"some random text\" for _ in range(50)]\r\n\r\nnew_features = original_dataset.features.copy()\r\nnew_features[name] = Value(dtype=\"string\", id=None) # I know the correct column type for this feature\r\n\r\ndef add_column_fn(example, idx):\r\n if name in example:\r\n raise ValueError(f\"Error when adding {name}: column {name} is already in the dataset.\")\r\n return {name: column[idx]}\r\n\r\nmodified_dataset = original_dataset.map(add_column_fn, with_indices=True, features=new_features)\r\n\r\nprint(modified_dataset.features.keys())\r\n```\r\nPrint Output:\r\n```\r\ndict_keys(['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'])\r\ndict_keys(['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id', 'new_column'])\r\n```\r\n", "It seems that map will also cause this issue\r\n\r\n### Steps to reproduce the bug\r\n```python\r\nfrom datasets import load_dataset\r\n\r\noriginal_dataset = load_dataset(\"librispeech_asr\", \"clean\", split=\"validation\", streaming=True)\r\nprint(original_dataset.features.keys())\r\n\r\ndef test(data):\r\n return data\r\n\r\nmodified_dataset = original_dataset.map(test)\r\nprint(modified_dataset.features.keys())\r\n```\r\n\r\n### Output\r\n```\r\ndict_keys(['file', 'audio', 'text', 'speaker_id', 'chapter_id', 'id'])\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\nCell In[5], line 10\r\n 7 return data\r\n 9 modified_dataset = original_dataset.map(test)\r\n---> 10 print(modified_dataset.features.keys())\r\n\r\nAttributeError: 'NoneType' object has no attribute 'keys'\r\n```" ]
1,668,333,316	5,751	Consistent ArrayXD Python formatting + better NumPy/Pandas formatting	closed	2023-04-14T14:13:59	2023-04-20T14:43:20	2023-04-20T14:40:34	https://github.com/huggingface/datasets/pull/5751	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5751", "html_url": "https://github.com/huggingface/datasets/pull/5751", "diff_url": "https://github.com/huggingface/datasets/pull/5751.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5751.patch", "merged_at": "2023-04-20T14:40:34" }	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) \| 0.010459 / 0.011353 (-0.000894) \| 0.007009 / 0.011008 (-0.003999) \| 0.153885 / 0.038508 (0.115377) \| 0.037308 / 0.023109 (0.014199) \| 0.431931 / 0.275898 (0.156033) \| 0.452940 / 0.323480 (0.129461) \| 0.008572 / 0.007986 (0.000586) \| 0.007479 / 0.004328 (0.003150) \| 0.093835 / 0.004250 (0.089584) \| 0.050172 / 0.037052 (0.013120) \| 0.428855 / 0.258489 (0.170366) \| 0.517814 / 0.293841 (0.223974) \| 0.058558 / 0.128546 (-0.069988) \| 0.019550 / 0.075646 (-0.056096) \| 0.449837 / 0.419271 (0.030566) \| 0.069710 / 0.043533 (0.026177) \| 0.444163 / 0.255139 (0.189024) \| 0.469003 / 0.283200 (0.185803) \| 0.114665 / 0.141683 (-0.027018) \| 1.822415 / 1.452155 (0.370261) \| 1.956360 / 1.492716 (0.463644) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.237489 / 0.018006 (0.219483) \| 0.556947 / 0.000490 (0.556457) \| 0.006988 / 0.000200 (0.006789) \| 0.000499 / 0.000054 (0.000444) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037047 / 0.037411 (-0.000364) \| 0.133973 / 0.014526 (0.119447) \| 0.137072 / 0.176557 (-0.039485) \| 0.201520 / 0.737135 (-0.535615) \| 0.144177 / 0.296338 (-0.152161) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.694853 / 0.215209 (0.479644) \| 6.805746 / 2.077655 (4.728091) \| 2.717864 / 1.504120 (1.213744) \| 2.360529 / 1.541195 (0.819335) \| 2.384403 / 1.468490 (0.915913) \| 1.337512 / 4.584777 (-3.247265) \| 5.734090 / 3.745712 (1.988378) \| 5.344909 / 5.269862 (0.075047) \| 2.906218 / 4.565676 (-1.659458) \| 0.160148 / 0.424275 (-0.264127) \| 0.015159 / 0.007607 (0.007551) \| 0.871356 / 0.226044 (0.645312) \| 8.550965 / 2.268929 (6.282037) \| 3.613522 / 55.444624 (-51.831103) \| 2.868508 / 6.876477 (-4.007969) \| 2.912263 / 2.142072 (0.770190) \| 1.652548 / 4.805227 (-3.152680) \| 0.274117 / 6.500664 (-6.226547) \| 0.085911 / 0.075469 (0.010442) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.624798 / 1.841788 (-0.216989) \| 18.413303 / 8.074308 (10.338995) \| 21.742854 / 10.191392 (11.551462) \| 0.255937 / 0.680424 (-0.424487) \| 0.029492 / 0.534201 (-0.504709) \| 0.541932 / 0.579283 (-0.037351) \| 0.638594 / 0.434364 (0.204230) \| 0.607427 / 0.540337 (0.067090) \| 0.763046 / 1.386936 (-0.623890) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020543 / 0.011353 (0.009190) \| 0.006079 / 0.011008 (-0.004929) \| 0.100558 / 0.038508 (0.062050) \| 0.039474 / 0.023109 (0.016365) \| 0.468889 / 0.275898 (0.192991) \| 0.477731 / 0.323480 (0.154251) \| 0.006999 / 0.007986 (-0.000987) \| 0.005845 / 0.004328 (0.001516) \| 0.110022 / 0.004250 (0.105772) \| 0.056885 / 0.037052 (0.019833) \| 0.447296 / 0.258489 (0.188807) \| 0.489007 / 0.293841 (0.195166) \| 0.055086 / 0.128546 (-0.073460) \| 0.020623 / 0.075646 (-0.055024) \| 0.129599 / 0.419271 (-0.289672) \| 0.064316 / 0.043533 (0.020784) \| 0.446681 / 0.255139 (0.191542) \| 0.488897 / 0.283200 (0.205698) \| 0.119121 / 0.141683 (-0.022562) \| 1.836248 / 1.452155 (0.384093) \| 2.002456 / 1.492716 (0.509740) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.249344 / 0.018006 (0.231338) \| 0.544320 / 0.000490 (0.543830) \| 0.000459 / 0.000200 (0.000259) \| 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) \| 0.038771 / 0.037411 (0.001359) \| 0.129527 / 0.014526 (0.115002) \| 0.144681 / 0.176557 (-0.031876) \| 0.208237 / 0.737135 (-0.528898) \| 0.149502 / 0.296338 (-0.146836) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.668457 / 0.215209 (0.453248) \| 6.729550 / 2.077655 (4.651895) \| 2.741076 / 1.504120 (1.236956) \| 2.394737 / 1.541195 (0.853542) \| 2.415242 / 1.468490 (0.946752) \| 1.322334 / 4.584777 (-3.262442) \| 5.787454 / 3.745712 (2.041742) \| 3.309847 / 5.269862 (-1.960015) \| 2.199181 / 4.565676 (-2.366495) \| 0.170740 / 0.424275 (-0.253535) \| 0.015095 / 0.007607 (0.007487) \| 0.864157 / 0.226044 (0.638112) \| 8.701858 / 2.268929 (6.432929) \| 3.617966 / 55.444624 (-51.826658) \| 2.847144 / 6.876477 (-4.029332) \| 3.011391 / 2.142072 (0.869319) \| 1.595466 / 4.805227 (-3.209762) \| 0.284010 / 6.500664 (-6.216654) \| 0.091054 / 0.075469 (0.015585) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.702404 / 1.841788 (-0.139384) \| 19.427130 / 8.074308 (11.352822) \| 21.900446 / 10.191392 (11.709053) \| 0.244088 / 0.680424 (-0.436336) \| 0.027428 / 0.534201 (-0.506773) \| 0.552226 / 0.579283 (-0.027057) \| 0.653102 / 0.434364 (0.218738) \| 0.635379 / 0.540337 (0.095042) \| 0.771842 / 1.386936 (-0.615094) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#efde2a0b9ad937defc83e0ac3f14bbb90fb5f345 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006547 / 0.011353 (-0.004806) \| 0.004569 / 0.011008 (-0.006439) \| 0.097782 / 0.038508 (0.059274) \| 0.028157 / 0.023109 (0.005048) \| 0.319017 / 0.275898 (0.043119) \| 0.340758 / 0.323480 (0.017278) \| 0.005078 / 0.007986 (-0.002907) \| 0.003343 / 0.004328 (-0.000985) \| 0.074194 / 0.004250 (0.069944) \| 0.037918 / 0.037052 (0.000866) \| 0.310298 / 0.258489 (0.051809) \| 0.349441 / 0.293841 (0.055600) \| 0.030375 / 0.128546 (-0.098171) \| 0.011527 / 0.075646 (-0.064119) \| 0.320499 / 0.419271 (-0.098773) \| 0.042639 / 0.043533 (-0.000894) \| 0.312182 / 0.255139 (0.057043) \| 0.329058 / 0.283200 (0.045858) \| 0.085517 / 0.141683 (-0.056165) \| 1.532603 / 1.452155 (0.080448) \| 1.583996 / 1.492716 (0.091279) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.208286 / 0.018006 (0.190280) \| 0.418696 / 0.000490 (0.418206) \| 0.007051 / 0.000200 (0.006851) \| 0.000409 / 0.000054 (0.000354) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024055 / 0.037411 (-0.013356) \| 0.098420 / 0.014526 (0.083894) \| 0.104785 / 0.176557 (-0.071771) \| 0.163618 / 0.737135 (-0.573517) \| 0.110006 / 0.296338 (-0.186332) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.418756 / 0.215209 (0.203547) \| 4.179557 / 2.077655 (2.101902) \| 1.881708 / 1.504120 (0.377588) \| 1.683393 / 1.541195 (0.142198) \| 1.731909 / 1.468490 (0.263419) \| 0.696674 / 4.584777 (-3.888103) \| 3.384167 / 3.745712 (-0.361545) \| 3.173479 / 5.269862 (-2.096382) \| 1.620019 / 4.565676 (-2.945658) \| 0.082850 / 0.424275 (-0.341426) \| 0.012396 / 0.007607 (0.004789) \| 0.519743 / 0.226044 (0.293699) \| 5.208480 / 2.268929 (2.939552) \| 2.312917 / 55.444624 (-53.131708) \| 1.963486 / 6.876477 (-4.912991) \| 2.084553 / 2.142072 (-0.057519) \| 0.805486 / 4.805227 (-3.999742) \| 0.153429 / 6.500664 (-6.347235) \| 0.069451 / 0.075469 (-0.006018) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.197185 / 1.841788 (-0.644603) \| 14.341005 / 8.074308 (6.266696) \| 14.476162 / 10.191392 (4.284770) \| 0.157372 / 0.680424 (-0.523052) \| 0.016444 / 0.534201 (-0.517757) \| 0.383721 / 0.579283 (-0.195562) \| 0.380800 / 0.434364 (-0.053564) \| 0.441137 / 0.540337 (-0.099200) \| 0.524778 / 1.386936 (-0.862158) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006728 / 0.011353 (-0.004625) \| 0.004536 / 0.011008 (-0.006472) \| 0.076266 / 0.038508 (0.037757) \| 0.028133 / 0.023109 (0.005024) \| 0.351072 / 0.275898 (0.075174) \| 0.375823 / 0.323480 (0.052344) \| 0.005166 / 0.007986 (-0.002819) \| 0.004717 / 0.004328 (0.000388) \| 0.076130 / 0.004250 (0.071880) \| 0.041354 / 0.037052 (0.004301) \| 0.345904 / 0.258489 (0.087415) \| 0.384119 / 0.293841 (0.090278) \| 0.030759 / 0.128546 (-0.097787) \| 0.011659 / 0.075646 (-0.063988) \| 0.085269 / 0.419271 (-0.334002) \| 0.042161 / 0.043533 (-0.001372) \| 0.340806 / 0.255139 (0.085667) \| 0.366832 / 0.283200 (0.083632) \| 0.092187 / 0.141683 (-0.049495) \| 1.520035 / 1.452155 (0.067880) \| 1.603856 / 1.492716 (0.111140) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.237763 / 0.018006 (0.219757) \| 0.413406 / 0.000490 (0.412916) \| 0.000415 / 0.000200 (0.000215) \| 0.000060 / 0.000054 (0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026095 / 0.037411 (-0.011317) \| 0.105775 / 0.014526 (0.091249) \| 0.108452 / 0.176557 (-0.068105) \| 0.160014 / 0.737135 (-0.577122) \| 0.112385 / 0.296338 (-0.183953) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.437327 / 0.215209 (0.222118) \| 4.374949 / 2.077655 (2.297294) \| 2.090292 / 1.504120 (0.586172) \| 1.885946 / 1.541195 (0.344752) \| 1.946768 / 1.468490 (0.478278) \| 0.704124 / 4.584777 (-3.880653) \| 3.394994 / 3.745712 (-0.350718) \| 1.905189 / 5.269862 (-3.364673) \| 1.182300 / 4.565676 (-3.383376) \| 0.082920 / 0.424275 (-0.341355) \| 0.012781 / 0.007607 (0.005174) \| 0.535467 / 0.226044 (0.309423) \| 5.362799 / 2.268929 (3.093870) \| 2.504825 / 55.444624 (-52.939799) \| 2.180458 / 6.876477 (-4.696019) \| 2.317750 / 2.142072 (0.175677) \| 0.811182 / 4.805227 (-3.994045) \| 0.151654 / 6.500664 (-6.349010) \| 0.067925 / 0.075469 (-0.007544) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.290746 / 1.841788 (-0.551042) \| 14.799309 / 8.074308 (6.725001) \| 14.439722 / 10.191392 (4.248330) \| 0.144358 / 0.680424 (-0.536066) \| 0.016688 / 0.534201 (-0.517513) \| 0.392907 / 0.579283 (-0.186376) \| 0.383109 / 0.434364 (-0.051255) \| 0.450069 / 0.540337 (-0.090269) \| 0.532534 / 1.386936 (-0.854402) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#87c061032972509a2a1b4103763e62fb74912128 \"CML watermark\")\n", "I turned it into a draft to fix the failing tests, but CI is now green, so there is no good reason for it :)" ]
1,668,289,067	5,750	Fail to create datasets from a generator when using Google Big Query	closed	2023-04-14T13:50:59	2023-04-17T12:20:43	2023-04-17T12:20:43	https://github.com/huggingface/datasets/issues/5750	null	ivanprado	false	[ "`from_generator` expects a generator function, not a generator object, so this should work:\r\n```python\r\nfrom datasets import Dataset\r\nfrom google.cloud import bigquery\r\n\r\nclient = bigquery.Client()\r\n\r\ndef gen()\r\n # Perform a query.\r\n QUERY = (\r\n 'SELECT name FROM `bigquery-public-data.usa_names.usa_1910_2013` '\r\n 'WHERE state = \"TX\" '\r\n 'LIMIT 100')\r\n query_job = client.query(QUERY) # API request\r\n yield from query_job.result() # Waits for query to finish\r\n\r\nds = Dataset.from_generator(rows)\r\n\r\nfor r in ds:\r\n print(r)\r\n```", "@mariosasko your code was incomplete, so I tried to fix it:\r\n\r\n```py\r\nfrom datasets import Dataset\r\nfrom google.cloud import bigquery\r\n\r\nclient = bigquery.Client()\r\n\r\ndef gen():\r\n # Perform a query.\r\n QUERY = (\r\n 'SELECT name FROM `bigquery-public-data.usa_names.usa_1910_2013` '\r\n 'WHERE state = \"TX\" '\r\n 'LIMIT 100')\r\n query_job = client.query(QUERY) # API request\r\n yield from query_job.result() # Waits for query to finish\r\n\r\nds = Dataset.from_generator(gen)\r\n\r\nfor r in ds:\r\n print(r)\r\n```\r\n\r\nThe error is also present in this case:\r\n\r\n```\r\n_pickle.PicklingError: Pickling client objects is explicitly not supported.\r\nClients have non-trivial state that is local and unpickleable.\r\n```\r\n\r\nI think it doesn't matter if the generator is an object or a function. The problem is that the generator is referencing an object that is not pickable (the client in this case). ", "It does matter: this function expects a generator function, as stated in the docs.\r\n\r\nThis should work:\r\n```python\r\nfrom datasets import Dataset\r\nfrom google.cloud import bigquery\r\n\r\ndef gen():\r\n client = bigquery.Client()\r\n # Perform a query.\r\n QUERY = (\r\n 'SELECT name FROM `bigquery-public-data.usa_names.usa_1910_2013` '\r\n 'WHERE state = \"TX\" '\r\n 'LIMIT 100')\r\n query_job = client.query(QUERY) # API request\r\n yield from query_job.result() # Waits for query to finish\r\n\r\nds = Dataset.from_generator(gen)\r\n\r\nfor r in ds:\r\n print(r)\r\n```\r\n\r\nWe could allow passing non-picklable objects and use a random hash for the generated arrow file. In that case, the caching mechanism would not work, meaning repeated calls with the same set of arguments would generate new datasets instead of reusing the cached version, but this behavior is still better than raising an error.", "Thank you @mariosasko . Your last code is working indeed. Curiously, the important detail here was to wrap the client instantiation within the generator itself. If the line `client = bigquery.Client()` is moved outside, then the error is back.\r\n\r\nI see now also your point in regard to the generator being a generator function. We can close the issue if you want." ]
1,668,016,321	5,749	AttributeError: 'Version' object has no attribute 'match'	closed	2023-04-14T10:48:06	2023-06-30T11:31:17	2023-04-18T12:57:08	https://github.com/huggingface/datasets/issues/5749	null	gulnaz-zh	false	[ "I got the same error, and the official website for visual genome is down. Did you solve this problem? ", "I am in the same situation now :( ", "Thanks for reporting, @gulnaz-zh.\r\n\r\nI am investigating it.", "The host server is down: https://visualgenome.org/\r\n\r\nWe are contacting the dataset authors.", "Apart form data host server being down, there is an additional issue with the `datasets` library introduced by this PR:\r\n- #5238\r\n\r\nI am working to fix it.", "PR that fixes the AttributeError: https://huggingface.co/datasets/visual_genome/discussions/2", "For the issue with their data host server being down, I have opened a discussion in the \"Community\" tab of the Hub dataset: https://huggingface.co/datasets/visual_genome/discussions/3\r\nLet's continue the discussion there.", "The authors just replied to us with their new URL: https://homes.cs.washington.edu/~ranjay/visualgenome/\r\n\r\nWe have fixed the datasets loading script, which is operative again." ]
1,667,517,024	5,748	[BUG FIX] Issue 5739	open	2023-04-14T05:07:31	2023-04-14T05:07:31	null	https://github.com/huggingface/datasets/pull/5748	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5748", "html_url": "https://github.com/huggingface/datasets/pull/5748", "diff_url": "https://github.com/huggingface/datasets/pull/5748.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5748.patch", "merged_at": null }	airlsyn	true	[]
1,667,270,412	5,747	[WIP] Add Dataset.to_spark	closed	2023-04-13T23:20:03	2024-01-08T18:31:50	2024-01-08T18:31:50	https://github.com/huggingface/datasets/pull/5747	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5747", "html_url": "https://github.com/huggingface/datasets/pull/5747", "diff_url": "https://github.com/huggingface/datasets/pull/5747.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5747.patch", "merged_at": null }	maddiedawson	true	[]
1,667,102,459	5,746	Fix link in docs	closed	2023-04-13T20:45:19	2023-04-14T13:15:38	2023-04-14T13:08:42	https://github.com/huggingface/datasets/pull/5746	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5746", "html_url": "https://github.com/huggingface/datasets/pull/5746", "diff_url": "https://github.com/huggingface/datasets/pull/5746.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5746.patch", "merged_at": "2023-04-14T13:08:42" }	bbbxyz	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) \| 0.006461 / 0.011353 (-0.004892) \| 0.004671 / 0.011008 (-0.006337) \| 0.097329 / 0.038508 (0.058821) \| 0.028380 / 0.023109 (0.005270) \| 0.369892 / 0.275898 (0.093994) \| 0.398244 / 0.323480 (0.074764) \| 0.004795 / 0.007986 (-0.003190) \| 0.004866 / 0.004328 (0.000538) \| 0.075060 / 0.004250 (0.070809) \| 0.035678 / 0.037052 (-0.001374) \| 0.372197 / 0.258489 (0.113708) \| 0.407509 / 0.293841 (0.113668) \| 0.031557 / 0.128546 (-0.096989) \| 0.011608 / 0.075646 (-0.064038) \| 0.325467 / 0.419271 (-0.093805) \| 0.042590 / 0.043533 (-0.000943) \| 0.373738 / 0.255139 (0.118599) \| 0.395793 / 0.283200 (0.112593) \| 0.082335 / 0.141683 (-0.059348) \| 1.471582 / 1.452155 (0.019427) \| 1.535834 / 1.492716 (0.043117) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.192432 / 0.018006 (0.174426) \| 0.404423 / 0.000490 (0.403933) \| 0.003252 / 0.000200 (0.003052) \| 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) \| 0.025312 / 0.037411 (-0.012099) \| 0.099964 / 0.014526 (0.085438) \| 0.108779 / 0.176557 (-0.067777) \| 0.170438 / 0.737135 (-0.566697) \| 0.110116 / 0.296338 (-0.186223) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.420402 / 0.215209 (0.205193) \| 4.179142 / 2.077655 (2.101487) \| 1.858114 / 1.504120 (0.353994) \| 1.674452 / 1.541195 (0.133257) \| 1.697839 / 1.468490 (0.229349) \| 0.694707 / 4.584777 (-3.890070) \| 3.394321 / 3.745712 (-0.351391) \| 1.918437 / 5.269862 (-3.351425) \| 1.277954 / 4.565676 (-3.287723) \| 0.082357 / 0.424275 (-0.341918) \| 0.012206 / 0.007607 (0.004598) \| 0.522093 / 0.226044 (0.296049) \| 5.239604 / 2.268929 (2.970675) \| 2.347764 / 55.444624 (-53.096860) \| 1.996864 / 6.876477 (-4.879613) \| 2.050820 / 2.142072 (-0.091253) \| 0.806110 / 4.805227 (-3.999118) \| 0.151061 / 6.500664 (-6.349603) \| 0.066438 / 0.075469 (-0.009031) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.211233 / 1.841788 (-0.630554) \| 14.054422 / 8.074308 (5.980114) \| 14.110141 / 10.191392 (3.918749) \| 0.129962 / 0.680424 (-0.550462) \| 0.017271 / 0.534201 (-0.516930) \| 0.386410 / 0.579283 (-0.192873) \| 0.392648 / 0.434364 (-0.041716) \| 0.444940 / 0.540337 (-0.095398) \| 0.533535 / 1.386936 (-0.853401) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006865 / 0.011353 (-0.004488) \| 0.004662 / 0.011008 (-0.006346) \| 0.077837 / 0.038508 (0.039329) \| 0.028258 / 0.023109 (0.005149) \| 0.346136 / 0.275898 (0.070238) \| 0.380414 / 0.323480 (0.056934) \| 0.005039 / 0.007986 (-0.002947) \| 0.004967 / 0.004328 (0.000638) \| 0.077774 / 0.004250 (0.073523) \| 0.037504 / 0.037052 (0.000452) \| 0.341550 / 0.258489 (0.083061) \| 0.382494 / 0.293841 (0.088653) \| 0.031881 / 0.128546 (-0.096665) \| 0.011746 / 0.075646 (-0.063901) \| 0.087087 / 0.419271 (-0.332185) \| 0.043108 / 0.043533 (-0.000425) \| 0.344103 / 0.255139 (0.088964) \| 0.366613 / 0.283200 (0.083413) \| 0.090399 / 0.141683 (-0.051284) \| 1.492675 / 1.452155 (0.040520) \| 1.588666 / 1.492716 (0.095950) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.191859 / 0.018006 (0.173853) \| 0.412514 / 0.000490 (0.412025) \| 0.001953 / 0.000200 (0.001753) \| 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) \| 0.025159 / 0.037411 (-0.012252) \| 0.100125 / 0.014526 (0.085599) \| 0.106000 / 0.176557 (-0.070556) \| 0.160710 / 0.737135 (-0.576425) \| 0.110449 / 0.296338 (-0.185889) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.436636 / 0.215209 (0.221427) \| 4.364597 / 2.077655 (2.286942) \| 2.077492 / 1.504120 (0.573372) \| 1.868248 / 1.541195 (0.327053) \| 1.911218 / 1.468490 (0.442728) \| 0.700306 / 4.584777 (-3.884471) \| 3.385428 / 3.745712 (-0.360284) \| 2.965384 / 5.269862 (-2.304478) \| 1.522093 / 4.565676 (-3.043583) \| 0.082805 / 0.424275 (-0.341470) \| 0.012432 / 0.007607 (0.004825) \| 0.538478 / 0.226044 (0.312433) \| 5.383207 / 2.268929 (3.114278) \| 2.525177 / 55.444624 (-52.919447) \| 2.179632 / 6.876477 (-4.696845) \| 2.280768 / 2.142072 (0.138695) \| 0.805869 / 4.805227 (-3.999358) \| 0.152716 / 6.500664 (-6.347948) \| 0.067848 / 0.075469 (-0.007621) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.318899 / 1.841788 (-0.522889) \| 14.416310 / 8.074308 (6.342002) \| 14.172804 / 10.191392 (3.981412) \| 0.141729 / 0.680424 (-0.538695) \| 0.016785 / 0.534201 (-0.517416) \| 0.378626 / 0.579283 (-0.200657) \| 0.387153 / 0.434364 (-0.047211) \| 0.439950 / 0.540337 (-0.100388) \| 0.523958 / 1.386936 (-0.862978) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7c3a9b057c476c40d157bd7a5d57f49066239df0 \"CML watermark\")\n" ]
1,667,086,143	5,745	[BUG FIX] Issue 5744	open	2023-04-13T20:29:55	2023-04-21T15:22:43	null	https://github.com/huggingface/datasets/pull/5745	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5745", "html_url": "https://github.com/huggingface/datasets/pull/5745", "diff_url": "https://github.com/huggingface/datasets/pull/5745.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5745.patch", "merged_at": null }	keyboardAnt	true	[ "Have met the same problem with datasets==2.8.0, pandas==2.0.0. It could be solved by installing the latest version of datasets or using datasets==2.8.0, pandas==1.5.3.", "Pandas 2.0.0 has removed support to passing `mangle_dupe_cols`.\r\n\r\nHowever, our `datasets` library does not use this parameter: it only passes it to pandas if the user passes it to `load_dataset`.\r\n\r\nYou should better:\r\n- Either \"take steps to stop the use of 'mangle_dupe_cols'\" (as it was suggested in the deprecation warning in pandas-1.5.3)\r\n- Or pin pandas (< 2.0.0) in your local requirements file\r\n\r\nPlease note that from `datasets` library, we don't want to force users to use a specific pandas version. We would like to support users as well:\r\n- that use pandas < 1.5.3\r\n- that use pandas >= 2.0.0 and that do not pass the 'mangle_dupe_cols' parameter", "`datasets` 2.11 doesn't pass `mangle_dupe_cols` unless the user specifies it indeed, so I think we're fine" ]
1,667,076,620	5,744	[BUG] With Pandas 2.0.0, `load_dataset` raises `TypeError: read_csv() got an unexpected keyword argument 'mangle_dupe_cols'`	closed	2023-04-13T20:21:28	2024-04-09T16:13:59	2023-07-06T17:01:59	https://github.com/huggingface/datasets/issues/5744	null	keyboardAnt	false	[ "Thanks for reporting, @keyboardAnt.\r\n\r\nWe haven't noticed any crash in our CI tests. Could you please indicate specifically the `load_dataset` command that crashes in your side, so that we can reproduce it?", "This has been fixed in `datasets` 2.11", "I am still getting this bug with the latest pandas and datasets lib installed. Anyone else?\r\n```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"csv\", data_files={\"train\":\"/kaggle/working/train.csv\", \"test\":\"/kaggle/working/test.csv\"})\r\nprint(dataset)\r\n\r\n\r\n\r\n---------------------------------------------------------------------------\r\nTypeError Traceback (most recent call last)\r\nCell In[5], line 3\r\n 1 from datasets import load_dataset\r\n----> 3 dataset = load_dataset(\"csv\", data_files={\"train\":\"/kaggle/working/train.csv\", \"test\":\"/kaggle/working/test.csv\"})\r\n 4 print(dataset)\r\n\r\nFile /opt/conda/lib/python3.10/site-packages/datasets/load.py:1691, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, config_kwargs)\r\n 1688 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES\r\n 1690 # Download and prepare data\r\n-> 1691 builder_instance.download_and_prepare(\r\n 1692 download_config=download_config,\r\n 1693 download_mode=download_mode,\r\n 1694 ignore_verifications=ignore_verifications,\r\n 1695 try_from_hf_gcs=try_from_hf_gcs,\r\n 1696 use_auth_token=use_auth_token,\r\n 1697 )\r\n 1699 # Build dataset for splits\r\n 1700 keep_in_memory = (\r\n 1701 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)\r\n 1702 )\r\n\r\nFile /opt/conda/lib/python3.10/site-packages/datasets/builder.py:605, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, download_and_prepare_kwargs)\r\n 603 logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n 604 if not downloaded_from_gcs:\r\n--> 605 self._download_and_prepare(\r\n 606 dl_manager=dl_manager, verify_infos=verify_infos, download_and_prepare_kwargs\r\n 607 )\r\n 608 # Sync info\r\n 609 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())\r\n\r\nFile /opt/conda/lib/python3.10/site-packages/datasets/builder.py:694, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, prepare_split_kwargs)\r\n 690 split_dict.add(split_generator.split_info)\r\n 692 try:\r\n 693 # Prepare split will record examples associated to the split\r\n--> 694 self._prepare_split(split_generator, prepare_split_kwargs)\r\n 695 except OSError as e:\r\n 696 raise OSError(\r\n 697 \"Cannot find data file. \"\r\n 698 + (self.manual_download_instructions or \"\")\r\n 699 + \"\\nOriginal error:\\n\"\r\n 700 + str(e)\r\n 701 ) from None\r\n\r\nFile /opt/conda/lib/python3.10/site-packages/datasets/builder.py:1151, in ArrowBasedBuilder._prepare_split(self, split_generator)\r\n 1149 generator = self._generate_tables(split_generator.gen_kwargs)\r\n 1150 with ArrowWriter(features=self.info.features, path=fpath) as writer:\r\n-> 1151 for key, table in logging.tqdm(\r\n 1152 generator, unit=\" tables\", leave=False, disable=True # not logging.is_progress_bar_enabled()\r\n 1153 ):\r\n 1154 writer.write_table(table)\r\n 1155 num_examples, num_bytes = writer.finalize()\r\n\r\nFile /opt/conda/lib/python3.10/site-packages/tqdm/notebook.py:249, in tqdm_notebook.__iter__(self)\r\n 247 try:\r\n 248 it = super(tqdm_notebook, self).__iter__()\r\n--> 249 for obj in it:\r\n 250 # return super(tqdm...) will not catch exception\r\n 251 yield obj\r\n 252 # NB: except ... [ as ...] breaks IPython async KeyboardInterrupt\r\n\r\nFile /opt/conda/lib/python3.10/site-packages/tqdm/std.py:1170, in tqdm.__iter__(self)\r\n 1167 # If the bar is disabled, then just walk the iterable\r\n 1168 # (note: keep this check outside the loop for performance)\r\n 1169 if self.disable:\r\n-> 1170 for obj in iterable:\r\n 1171 yield obj\r\n 1172 return\r\n\r\nFile /opt/conda/lib/python3.10/site-packages/datasets/packaged_modules/csv/csv.py:154, in Csv._generate_tables(self, files)\r\n 152 dtype = {name: dtype.to_pandas_dtype() for name, dtype in zip(schema.names, schema.types)} if schema else None\r\n 153 for file_idx, file in enumerate(files):\r\n--> 154 csv_file_reader = pd.read_csv(file, iterator=True, dtype=dtype, **self.config.read_csv_kwargs)\r\n 155 try:\r\n 156 for batch_idx, df in enumerate(csv_file_reader):\r\n\r\nTypeError: read_csv() got an unexpected keyword argument 'mangle_dupe_cols'```", "Feel free to update `datasets` to fix this issue\r\n\r\n```\r\npip install -U datasets\r\n```", "I am still having the same issue with the version >= 2.14", "Edit: Sorry, I found that our version is 2.2.1. Please ignore the following comment. This issue was already solved by this line:\r\nhttps://github.com/huggingface/datasets/blob/bf02cff8d70180a9e89328961ded9e3d8510fd22/src/datasets/packaged_modules/csv/csv.py#L18\r\n\r\n> This issue still exists as you can see in version 2.14:\r\n> https://github.com/huggingface/datasets/blob/bf02cff8d70180a9e89328961ded9e3d8510fd22/src/datasets/packaged_modules/csv/csv.py#L35\r\n> https://github.com/huggingface/datasets/blob/bf02cff8d70180a9e89328961ded9e3d8510fd22/src/datasets/packaged_modules/csv/csv.py#L84\r\n> that \"mangle_dupe_cols\" still exists in the arguments.\r\n> \r\n> And this error occurs at this line:\r\n> https://github.com/huggingface/datasets/blob/bf02cff8d70180a9e89328961ded9e3d8510fd22/src/datasets/packaged_modules/csv/csv.py#L185\r\n> where\r\n> ```python\r\n> file == '~/llama/llama-recipes/recipes/finetuning/gtrain_10k.csv'\r\n> dtype == None\r\n> self.config.pd_read_csv_kwargs == {\r\n> \"sep\": \",\",\r\n> \"header\": \"infer\",\r\n> \"index_col\": None,\r\n> \"usecols\": None,\r\n> \"mangle_dupe_cols\": True,\r\n> \"engine\": None,\r\n> \"true_values\": None,\r\n> \"false_values\": None,\r\n> \"skipinitialspace\": False,\r\n> \"skiprows\": None,\r\n> \"nrows\": None,\r\n> \"na_values\": None,\r\n> \"keep_default_na\": True,\r\n> \"na_filter\": True,\r\n> \"verbose\": False,\r\n> \"skip_blank_lines\": True,\r\n> \"thousands\": None,\r\n> \"decimal\": \".\",\r\n> \"lineterminator\": None,\r\n> \"quotechar\": '\"',\r\n> \"quoting\": 0,\r\n> \"escapechar\": None,\r\n> \"comment\": None,\r\n> \"encoding\": None,\r\n> \"dialect\": None,\r\n> \"skipfooter\": 0,\r\n> \"doublequote\": True,\r\n> \"memory_map\": False,\r\n> \"float_precision\": None,\r\n> \"chunksize\": 10000,\r\n> }\r\n> ```\r\n> for me.\r\n> \r\n> Here is where we got the error: https://github.com/meta-llama/llama-recipes/issues/426" ]
1,666,843,832	5,743	dataclass.py in virtual environment is overriding the stdlib module "dataclasses"	closed	2023-04-13T17:28:33	2023-04-17T12:23:18	2023-04-17T12:23:18	https://github.com/huggingface/datasets/issues/5743	null	syedabdullahhassan	false	[ "We no longer depend on `dataclasses` (for almost a year), so I don't think our package is the problematic one. \r\n\r\nI think it makes more sense to raise this issue in the `dataclasses` repo: https://github.com/ericvsmith/dataclasses." ]
1,666,209,738	5,742	Warning specifying future change in to_tf_dataset behaviour	closed	2023-04-13T11:10:00	2023-04-21T13:18:14	2023-04-21T13:11:09	https://github.com/huggingface/datasets/pull/5742	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5742", "html_url": "https://github.com/huggingface/datasets/pull/5742", "diff_url": "https://github.com/huggingface/datasets/pull/5742.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5742.patch", "merged_at": "2023-04-21T13:11:09" }	amyeroberts	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) \| 0.004586 / 0.011008 (-0.006422) \| 0.097238 / 0.038508 (0.058730) \| 0.027912 / 0.023109 (0.004802) \| 0.347339 / 0.275898 (0.071441) \| 0.393847 / 0.323480 (0.070368) \| 0.005105 / 0.007986 (-0.002880) \| 0.004750 / 0.004328 (0.000422) \| 0.074671 / 0.004250 (0.070421) \| 0.037912 / 0.037052 (0.000860) \| 0.368973 / 0.258489 (0.110483) \| 0.403983 / 0.293841 (0.110142) \| 0.030817 / 0.128546 (-0.097730) \| 0.011813 / 0.075646 (-0.063833) \| 0.324470 / 0.419271 (-0.094802) \| 0.044232 / 0.043533 (0.000699) \| 0.347623 / 0.255139 (0.092484) \| 0.382458 / 0.283200 (0.099259) \| 0.086603 / 0.141683 (-0.055080) \| 1.485778 / 1.452155 (0.033623) \| 1.549776 / 1.492716 (0.057059) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.200154 / 0.018006 (0.182147) \| 0.440645 / 0.000490 (0.440155) \| 0.003664 / 0.000200 (0.003464) \| 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) \| 0.023635 / 0.037411 (-0.013776) \| 0.094969 / 0.014526 (0.080443) \| 0.103630 / 0.176557 (-0.072927) \| 0.168655 / 0.737135 (-0.568480) \| 0.105850 / 0.296338 (-0.190488) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.425224 / 0.215209 (0.210015) \| 4.236618 / 2.077655 (2.158963) \| 1.917091 / 1.504120 (0.412971) \| 1.746984 / 1.541195 (0.205789) \| 1.817766 / 1.468490 (0.349276) \| 0.700989 / 4.584777 (-3.883788) \| 3.412577 / 3.745712 (-0.333135) \| 3.049311 / 5.269862 (-2.220551) \| 1.607692 / 4.565676 (-2.957984) \| 0.083410 / 0.424275 (-0.340865) \| 0.012601 / 0.007607 (0.004994) \| 0.528244 / 0.226044 (0.302200) \| 5.284134 / 2.268929 (3.015206) \| 2.391885 / 55.444624 (-53.052740) \| 2.020018 / 6.876477 (-4.856459) \| 2.105908 / 2.142072 (-0.036164) \| 0.801262 / 4.805227 (-4.003965) \| 0.151467 / 6.500664 (-6.349197) \| 0.066529 / 0.075469 (-0.008940) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.203894 / 1.841788 (-0.637894) \| 13.827561 / 8.074308 (5.753253) \| 14.136730 / 10.191392 (3.945338) \| 0.143829 / 0.680424 (-0.536595) \| 0.016410 / 0.534201 (-0.517791) \| 0.378194 / 0.579283 (-0.201089) \| 0.391235 / 0.434364 (-0.043129) \| 0.439261 / 0.540337 (-0.101076) \| 0.527181 / 1.386936 (-0.859755) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006639 / 0.011353 (-0.004714) \| 0.004469 / 0.011008 (-0.006540) \| 0.076495 / 0.038508 (0.037987) \| 0.027880 / 0.023109 (0.004771) \| 0.342807 / 0.275898 (0.066909) \| 0.374258 / 0.323480 (0.050778) \| 0.005543 / 0.007986 (-0.002443) \| 0.003362 / 0.004328 (-0.000966) \| 0.075064 / 0.004250 (0.070813) \| 0.039209 / 0.037052 (0.002156) \| 0.342490 / 0.258489 (0.084001) \| 0.382135 / 0.293841 (0.088294) \| 0.030356 / 0.128546 (-0.098191) \| 0.011762 / 0.075646 (-0.063884) \| 0.086031 / 0.419271 (-0.333241) \| 0.041991 / 0.043533 (-0.001542) \| 0.340323 / 0.255139 (0.085184) \| 0.364160 / 0.283200 (0.080961) \| 0.088483 / 0.141683 (-0.053200) \| 1.502836 / 1.452155 (0.050681) \| 1.570438 / 1.492716 (0.077722) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.218486 / 0.018006 (0.200480) \| 0.405251 / 0.000490 (0.404761) \| 0.000398 / 0.000200 (0.000198) \| 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) \| 0.025738 / 0.037411 (-0.011673) \| 0.100390 / 0.014526 (0.085864) \| 0.109913 / 0.176557 (-0.066644) \| 0.161310 / 0.737135 (-0.575826) \| 0.113269 / 0.296338 (-0.183069) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.438083 / 0.215209 (0.222874) \| 4.377742 / 2.077655 (2.300087) \| 2.069949 / 1.504120 (0.565829) \| 1.857807 / 1.541195 (0.316613) \| 1.881315 / 1.468490 (0.412825) \| 0.695373 / 4.584777 (-3.889404) \| 3.440287 / 3.745712 (-0.305425) \| 1.842888 / 5.269862 (-3.426973) \| 1.146655 / 4.565676 (-3.419022) \| 0.083386 / 0.424275 (-0.340889) \| 0.012290 / 0.007607 (0.004683) \| 0.545672 / 0.226044 (0.319628) \| 5.469568 / 2.268929 (3.200639) \| 2.511886 / 55.444624 (-52.932739) \| 2.184210 / 6.876477 (-4.692267) \| 2.329822 / 2.142072 (0.187749) \| 0.804114 / 4.805227 (-4.001114) \| 0.151651 / 6.500664 (-6.349013) \| 0.067269 / 0.075469 (-0.008200) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.272564 / 1.841788 (-0.569223) \| 14.180708 / 8.074308 (6.106400) \| 14.181657 / 10.191392 (3.990265) \| 0.131443 / 0.680424 (-0.548981) \| 0.016513 / 0.534201 (-0.517688) \| 0.383786 / 0.579283 (-0.195497) \| 0.397678 / 0.434364 (-0.036686) \| 0.447003 / 0.540337 (-0.093334) \| 0.539453 / 1.386936 (-0.847483) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#649d5a3315f9e7666713b6affe318ee00c7163a0 \"CML watermark\")\n" ]
1,665,860,919	5,741	Fix CI warnings	closed	2023-04-13T07:17:02	2023-04-13T09:48:10	2023-04-13T09:40:50	https://github.com/huggingface/datasets/pull/5741	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5741", "html_url": "https://github.com/huggingface/datasets/pull/5741", "diff_url": "https://github.com/huggingface/datasets/pull/5741.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5741.patch", "merged_at": "2023-04-13T09:40:50" }	albertvillanova	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007448 / 0.011353 (-0.003905) \| 0.005182 / 0.011008 (-0.005826) \| 0.098718 / 0.038508 (0.060210) \| 0.034594 / 0.023109 (0.011485) \| 0.317301 / 0.275898 (0.041403) \| 0.357800 / 0.323480 (0.034320) \| 0.005860 / 0.007986 (-0.002126) \| 0.004267 / 0.004328 (-0.000061) \| 0.074876 / 0.004250 (0.070626) \| 0.048002 / 0.037052 (0.010950) \| 0.333360 / 0.258489 (0.074871) \| 0.362080 / 0.293841 (0.068239) \| 0.035957 / 0.128546 (-0.092589) \| 0.012245 / 0.075646 (-0.063401) \| 0.332970 / 0.419271 (-0.086301) \| 0.050825 / 0.043533 (0.007293) \| 0.313936 / 0.255139 (0.058797) \| 0.340684 / 0.283200 (0.057485) \| 0.106630 / 0.141683 (-0.035053) \| 1.427898 / 1.452155 (-0.024257) \| 1.547518 / 1.492716 (0.054801) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296952 / 0.018006 (0.278945) \| 0.515708 / 0.000490 (0.515218) \| 0.004225 / 0.000200 (0.004025) \| 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) \| 0.029365 / 0.037411 (-0.008046) \| 0.111142 / 0.014526 (0.096616) \| 0.124414 / 0.176557 (-0.052142) \| 0.185227 / 0.737135 (-0.551908) \| 0.129545 / 0.296338 (-0.166793) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.403303 / 0.215209 (0.188094) \| 4.044138 / 2.077655 (1.966483) \| 1.803622 / 1.504120 (0.299502) \| 1.615436 / 1.541195 (0.074242) \| 1.703576 / 1.468490 (0.235086) \| 0.706398 / 4.584777 (-3.878379) \| 3.912995 / 3.745712 (0.167283) \| 4.004575 / 5.269862 (-1.265287) \| 2.101592 / 4.565676 (-2.464085) \| 0.087280 / 0.424275 (-0.336995) \| 0.012564 / 0.007607 (0.004957) \| 0.508484 / 0.226044 (0.282440) \| 5.089351 / 2.268929 (2.820422) \| 2.269022 / 55.444624 (-53.175602) \| 1.933375 / 6.876477 (-4.943102) \| 2.136783 / 2.142072 (-0.005289) \| 0.862624 / 4.805227 (-3.942603) \| 0.172107 / 6.500664 (-6.328557) \| 0.066694 / 0.075469 (-0.008775) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.172513 / 1.841788 (-0.669275) \| 15.877519 / 8.074308 (7.803211) \| 14.687476 / 10.191392 (4.496084) \| 0.189392 / 0.680424 (-0.491032) \| 0.017334 / 0.534201 (-0.516866) \| 0.420201 / 0.579283 (-0.159082) \| 0.418502 / 0.434364 (-0.015862) \| 0.489130 / 0.540337 (-0.051207) \| 0.580678 / 1.386936 (-0.806258) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007942 / 0.011353 (-0.003411) \| 0.005312 / 0.011008 (-0.005696) \| 0.074684 / 0.038508 (0.036176) \| 0.035952 / 0.023109 (0.012843) \| 0.349672 / 0.275898 (0.073774) \| 0.377157 / 0.323480 (0.053678) \| 0.006399 / 0.007986 (-0.001586) \| 0.005769 / 0.004328 (0.001441) \| 0.074283 / 0.004250 (0.070032) \| 0.053217 / 0.037052 (0.016165) \| 0.342545 / 0.258489 (0.084056) \| 0.383663 / 0.293841 (0.089822) \| 0.037234 / 0.128546 (-0.091312) \| 0.012349 / 0.075646 (-0.063298) \| 0.086522 / 0.419271 (-0.332749) \| 0.049888 / 0.043533 (0.006355) \| 0.337686 / 0.255139 (0.082547) \| 0.361564 / 0.283200 (0.078365) \| 0.104902 / 0.141683 (-0.036781) \| 1.478259 / 1.452155 (0.026104) \| 1.576376 / 1.492716 (0.083660) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.339760 / 0.018006 (0.321753) \| 0.530946 / 0.000490 (0.530456) \| 0.000474 / 0.000200 (0.000274) \| 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) \| 0.029685 / 0.037411 (-0.007726) \| 0.109409 / 0.014526 (0.094883) \| 0.125579 / 0.176557 (-0.050978) \| 0.175378 / 0.737135 (-0.561757) \| 0.130672 / 0.296338 (-0.165667) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.428456 / 0.215209 (0.213247) \| 4.238731 / 2.077655 (2.161077) \| 2.046703 / 1.504120 (0.542583) \| 1.850701 / 1.541195 (0.309506) \| 1.909290 / 1.468490 (0.440800) \| 0.714314 / 4.584777 (-3.870463) \| 3.816056 / 3.745712 (0.070344) \| 2.118567 / 5.269862 (-3.151295) \| 1.348017 / 4.565676 (-3.217659) \| 0.087140 / 0.424275 (-0.337135) \| 0.012546 / 0.007607 (0.004938) \| 0.538041 / 0.226044 (0.311997) \| 5.381822 / 2.268929 (3.112893) \| 2.525685 / 55.444624 (-52.918939) \| 2.178659 / 6.876477 (-4.697817) \| 2.381054 / 2.142072 (0.238981) \| 0.844404 / 4.805227 (-3.960823) \| 0.171802 / 6.500664 (-6.328862) \| 0.065630 / 0.075469 (-0.009839) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.262187 / 1.841788 (-0.579600) \| 16.197668 / 8.074308 (8.123360) \| 15.148636 / 10.191392 (4.957244) \| 0.152601 / 0.680424 (-0.527823) \| 0.020238 / 0.534201 (-0.513963) \| 0.420141 / 0.579283 (-0.159142) \| 0.416295 / 0.434364 (-0.018068) \| 0.487051 / 0.540337 (-0.053286) \| 0.581942 / 1.386936 (-0.804994) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9615e5af75b190c4e7b66792f9ba444f352765a0 \"CML watermark\")\n" ]
1,664,132,130	5,740	Fix CI mock filesystem fixtures	closed	2023-04-12T08:52:35	2023-04-13T11:01:24	2023-04-13T10:54:13	https://github.com/huggingface/datasets/pull/5740	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5740", "html_url": "https://github.com/huggingface/datasets/pull/5740", "diff_url": "https://github.com/huggingface/datasets/pull/5740.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5740.patch", "merged_at": "2023-04-13T10:54:13" }	albertvillanova	true	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007003 / 0.011353 (-0.004350) \| 0.004854 / 0.011008 (-0.006154) \| 0.096982 / 0.038508 (0.058474) \| 0.033218 / 0.023109 (0.010109) \| 0.314088 / 0.275898 (0.038190) \| 0.351315 / 0.323480 (0.027835) \| 0.005679 / 0.007986 (-0.002307) \| 0.005404 / 0.004328 (0.001075) \| 0.071773 / 0.004250 (0.067522) \| 0.044593 / 0.037052 (0.007540) \| 0.323643 / 0.258489 (0.065154) \| 0.357172 / 0.293841 (0.063331) \| 0.036782 / 0.128546 (-0.091764) \| 0.012146 / 0.075646 (-0.063501) \| 0.334874 / 0.419271 (-0.084397) \| 0.051475 / 0.043533 (0.007942) \| 0.305949 / 0.255139 (0.050810) \| 0.339326 / 0.283200 (0.056126) \| 0.101509 / 0.141683 (-0.040174) \| 1.458254 / 1.452155 (0.006099) \| 1.535252 / 1.492716 (0.042535) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.264837 / 0.018006 (0.246831) \| 0.441444 / 0.000490 (0.440955) \| 0.003331 / 0.000200 (0.003131) \| 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) \| 0.026529 / 0.037411 (-0.010882) \| 0.105924 / 0.014526 (0.091398) \| 0.117191 / 0.176557 (-0.059365) \| 0.176606 / 0.737135 (-0.560529) \| 0.123452 / 0.296338 (-0.172887) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.412351 / 0.215209 (0.197142) \| 4.135468 / 2.077655 (2.057813) \| 1.912820 / 1.504120 (0.408700) \| 1.738993 / 1.541195 (0.197798) \| 1.754228 / 1.468490 (0.285738) \| 0.692239 / 4.584777 (-3.892538) \| 3.765672 / 3.745712 (0.019959) \| 2.081141 / 5.269862 (-3.188720) \| 1.425153 / 4.565676 (-3.140523) \| 0.085055 / 0.424275 (-0.339220) \| 0.011918 / 0.007607 (0.004311) \| 0.517573 / 0.226044 (0.291529) \| 5.179809 / 2.268929 (2.910881) \| 2.471620 / 55.444624 (-52.973005) \| 2.140634 / 6.876477 (-4.735843) \| 2.200150 / 2.142072 (0.058077) \| 0.831662 / 4.805227 (-3.973566) \| 0.168828 / 6.500664 (-6.331836) \| 0.062755 / 0.075469 (-0.012714) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.196890 / 1.841788 (-0.644898) \| 14.826423 / 8.074308 (6.752114) \| 14.020782 / 10.191392 (3.829390) \| 0.161275 / 0.680424 (-0.519149) \| 0.017467 / 0.534201 (-0.516734) \| 0.422278 / 0.579283 (-0.157005) \| 0.424053 / 0.434364 (-0.010311) \| 0.490768 / 0.540337 (-0.049570) \| 0.584490 / 1.386936 (-0.802446) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007102 / 0.011353 (-0.004250) \| 0.005145 / 0.011008 (-0.005863) \| 0.073823 / 0.038508 (0.035315) \| 0.032947 / 0.023109 (0.009838) \| 0.336978 / 0.275898 (0.061080) \| 0.368961 / 0.323480 (0.045481) \| 0.006052 / 0.007986 (-0.001934) \| 0.003970 / 0.004328 (-0.000358) \| 0.072925 / 0.004250 (0.068674) \| 0.044502 / 0.037052 (0.007450) \| 0.340849 / 0.258489 (0.082360) \| 0.381487 / 0.293841 (0.087646) \| 0.037207 / 0.128546 (-0.091339) \| 0.012095 / 0.075646 (-0.063551) \| 0.085206 / 0.419271 (-0.334065) \| 0.056236 / 0.043533 (0.012703) \| 0.334048 / 0.255139 (0.078909) \| 0.360442 / 0.283200 (0.077242) \| 0.104402 / 0.141683 (-0.037281) \| 1.446907 / 1.452155 (-0.005248) \| 1.542430 / 1.492716 (0.049713) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.238720 / 0.018006 (0.220714) \| 0.445857 / 0.000490 (0.445367) \| 0.009280 / 0.000200 (0.009080) \| 0.000150 / 0.000054 (0.000095) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028414 / 0.037411 (-0.008998) \| 0.110506 / 0.014526 (0.095981) \| 0.124593 / 0.176557 (-0.051964) \| 0.170951 / 0.737135 (-0.566184) \| 0.128033 / 0.296338 (-0.168305) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.426206 / 0.215209 (0.210997) \| 4.267289 / 2.077655 (2.189634) \| 2.026880 / 1.504120 (0.522760) \| 1.844052 / 1.541195 (0.302858) \| 1.897697 / 1.468490 (0.429207) \| 0.713545 / 4.584777 (-3.871232) \| 3.815052 / 3.745712 (0.069339) \| 3.217091 / 5.269862 (-2.052770) \| 1.790546 / 4.565676 (-2.775130) \| 0.087501 / 0.424275 (-0.336774) \| 0.012136 / 0.007607 (0.004529) \| 0.534495 / 0.226044 (0.308451) \| 5.325913 / 2.268929 (3.056984) \| 2.484309 / 55.444624 (-52.960315) \| 2.149721 / 6.876477 (-4.726756) \| 2.158764 / 2.142072 (0.016692) \| 0.855273 / 4.805227 (-3.949954) \| 0.170374 / 6.500664 (-6.330290) \| 0.064053 / 0.075469 (-0.011416) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.253171 / 1.841788 (-0.588617) \| 15.254562 / 8.074308 (7.180254) \| 14.242119 / 10.191392 (4.050727) \| 0.159298 / 0.680424 (-0.521126) \| 0.017504 / 0.534201 (-0.516696) \| 0.419710 / 0.579283 (-0.159574) \| 0.417879 / 0.434364 (-0.016485) \| 0.486328 / 0.540337 (-0.054009) \| 0.578933 / 1.386936 (-0.808003) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bc38663c8e2c2b0b246791c3ed8bddbff163dd64 \"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) \| 0.008476 / 0.011353 (-0.002877) \| 0.005745 / 0.011008 (-0.005263) \| 0.115307 / 0.038508 (0.076799) \| 0.039356 / 0.023109 (0.016247) \| 0.367155 / 0.275898 (0.091257) \| 0.422147 / 0.323480 (0.098667) \| 0.006817 / 0.007986 (-0.001168) \| 0.004652 / 0.004328 (0.000323) \| 0.084045 / 0.004250 (0.079795) \| 0.055483 / 0.037052 (0.018431) \| 0.364249 / 0.258489 (0.105760) \| 0.415975 / 0.293841 (0.122134) \| 0.041322 / 0.128546 (-0.087224) \| 0.014178 / 0.075646 (-0.061469) \| 0.392658 / 0.419271 (-0.026614) \| 0.060156 / 0.043533 (0.016623) \| 0.373938 / 0.255139 (0.118799) \| 0.397494 / 0.283200 (0.114294) \| 0.113811 / 0.141683 (-0.027872) \| 1.688581 / 1.452155 (0.236427) \| 1.790374 / 1.492716 (0.297658) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.222203 / 0.018006 (0.204196) \| 0.471109 / 0.000490 (0.470619) \| 0.007071 / 0.000200 (0.006871) \| 0.000156 / 0.000054 (0.000102) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032112 / 0.037411 (-0.005299) \| 0.118726 / 0.014526 (0.104200) \| 0.134918 / 0.176557 (-0.041639) \| 0.207766 / 0.737135 (-0.529369) \| 0.139756 / 0.296338 (-0.156582) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.479858 / 0.215209 (0.264649) \| 4.798428 / 2.077655 (2.720773) \| 2.221573 / 1.504120 (0.717453) \| 1.964956 / 1.541195 (0.423761) \| 2.021763 / 1.468490 (0.553273) \| 0.820401 / 4.584777 (-3.764376) \| 4.533887 / 3.745712 (0.788175) \| 4.121332 / 5.269862 (-1.148529) \| 2.195807 / 4.565676 (-2.369869) \| 0.103133 / 0.424275 (-0.321142) \| 0.014620 / 0.007607 (0.007013) \| 0.605012 / 0.226044 (0.378967) \| 5.966623 / 2.268929 (3.697694) \| 2.844118 / 55.444624 (-52.600506) \| 2.463569 / 6.876477 (-4.412907) \| 2.597177 / 2.142072 (0.455105) \| 0.983201 / 4.805227 (-3.822026) \| 0.199500 / 6.500664 (-6.301164) \| 0.078387 / 0.075469 (0.002918) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.401083 / 1.841788 (-0.440705) \| 17.258725 / 8.074308 (9.184417) \| 16.825992 / 10.191392 (6.634600) \| 0.216762 / 0.680424 (-0.463662) \| 0.021135 / 0.534201 (-0.513066) \| 0.513688 / 0.579283 (-0.065595) \| 0.488892 / 0.434364 (0.054529) \| 0.566745 / 0.540337 (0.026408) \| 0.688958 / 1.386936 (-0.697978) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007948 / 0.011353 (-0.003405) \| 0.005981 / 0.011008 (-0.005027) \| 0.084474 / 0.038508 (0.045966) \| 0.037952 / 0.023109 (0.014843) \| 0.383359 / 0.275898 (0.107461) \| 0.409324 / 0.323480 (0.085844) \| 0.006641 / 0.007986 (-0.001344) \| 0.004785 / 0.004328 (0.000456) \| 0.083214 / 0.004250 (0.078964) \| 0.053177 / 0.037052 (0.016125) \| 0.393147 / 0.258489 (0.134658) \| 0.438496 / 0.293841 (0.144655) \| 0.042090 / 0.128546 (-0.086456) \| 0.013373 / 0.075646 (-0.062273) \| 0.097585 / 0.419271 (-0.321686) \| 0.056359 / 0.043533 (0.012826) \| 0.378113 / 0.255139 (0.122974) \| 0.403874 / 0.283200 (0.120674) \| 0.123503 / 0.141683 (-0.018180) \| 1.639557 / 1.452155 (0.187403) \| 1.759787 / 1.492716 (0.267071) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.242534 / 0.018006 (0.224528) \| 0.459040 / 0.000490 (0.458550) \| 0.000454 / 0.000200 (0.000254) \| 0.000066 / 0.000054 (0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031747 / 0.037411 (-0.005664) \| 0.125823 / 0.014526 (0.111297) \| 0.138985 / 0.176557 (-0.037571) \| 0.194371 / 0.737135 (-0.542764) \| 0.148905 / 0.296338 (-0.147433) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.508201 / 0.215209 (0.292992) \| 5.007519 / 2.077655 (2.929865) \| 2.412956 / 1.504120 (0.908836) \| 2.143378 / 1.541195 (0.602183) \| 2.192966 / 1.468490 (0.724476) \| 0.828497 / 4.584777 (-3.756280) \| 4.496457 / 3.745712 (0.750745) \| 2.397546 / 5.269862 (-2.872315) \| 1.522889 / 4.565676 (-3.042787) \| 0.099904 / 0.424275 (-0.324371) \| 0.014561 / 0.007607 (0.006954) \| 0.627417 / 0.226044 (0.401373) \| 6.296441 / 2.268929 (4.027512) \| 2.962858 / 55.444624 (-52.481767) \| 2.543083 / 6.876477 (-4.333394) \| 2.711884 / 2.142072 (0.569811) \| 0.997969 / 4.805227 (-3.807259) \| 0.200283 / 6.500664 (-6.300382) \| 0.075934 / 0.075469 (0.000465) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.541707 / 1.841788 (-0.300081) \| 17.791559 / 8.074308 (9.717251) \| 16.782877 / 10.191392 (6.591485) \| 0.171954 / 0.680424 (-0.508470) \| 0.020506 / 0.534201 (-0.513695) \| 0.504189 / 0.579283 (-0.075094) \| 0.501655 / 0.434364 (0.067291) \| 0.583120 / 0.540337 (0.042782) \| 0.694931 / 1.386936 (-0.692005) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53355f308f4ffb9b4071f5d420b5c6767799ef1c \"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) \| 0.007613 / 0.011353 (-0.003740) \| 0.005057 / 0.011008 (-0.005951) \| 0.099147 / 0.038508 (0.060639) \| 0.035358 / 0.023109 (0.012249) \| 0.303442 / 0.275898 (0.027544) \| 0.336898 / 0.323480 (0.013418) \| 0.006216 / 0.007986 (-0.001770) \| 0.004085 / 0.004328 (-0.000244) \| 0.074567 / 0.004250 (0.070317) \| 0.050917 / 0.037052 (0.013865) \| 0.301786 / 0.258489 (0.043297) \| 0.341362 / 0.293841 (0.047521) \| 0.037019 / 0.128546 (-0.091528) \| 0.011977 / 0.075646 (-0.063669) \| 0.334688 / 0.419271 (-0.084583) \| 0.051326 / 0.043533 (0.007793) \| 0.299878 / 0.255139 (0.044739) \| 0.325571 / 0.283200 (0.042371) \| 0.110744 / 0.141683 (-0.030939) \| 1.480898 / 1.452155 (0.028743) \| 1.566917 / 1.492716 (0.074201) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.253249 / 0.018006 (0.235242) \| 0.558576 / 0.000490 (0.558086) \| 0.003838 / 0.000200 (0.003638) \| 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) \| 0.028731 / 0.037411 (-0.008681) \| 0.110643 / 0.014526 (0.096117) \| 0.119560 / 0.176557 (-0.056996) \| 0.178010 / 0.737135 (-0.559126) \| 0.130286 / 0.296338 (-0.166053) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.400190 / 0.215209 (0.184981) \| 3.999326 / 2.077655 (1.921672) \| 1.797332 / 1.504120 (0.293212) \| 1.610808 / 1.541195 (0.069613) \| 1.679949 / 1.468490 (0.211459) \| 0.696539 / 4.584777 (-3.888238) \| 3.784766 / 3.745712 (0.039054) \| 2.205008 / 5.269862 (-3.064854) \| 1.501697 / 4.565676 (-3.063979) \| 0.085553 / 0.424275 (-0.338723) \| 0.012223 / 0.007607 (0.004616) \| 0.494858 / 0.226044 (0.268813) \| 4.968535 / 2.268929 (2.699606) \| 2.258759 / 55.444624 (-53.185865) \| 1.926236 / 6.876477 (-4.950241) \| 2.072155 / 2.142072 (-0.069917) \| 0.838354 / 4.805227 (-3.966873) \| 0.168810 / 6.500664 (-6.331854) \| 0.064347 / 0.075469 (-0.011122) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.166696 / 1.841788 (-0.675091) \| 14.721287 / 8.074308 (6.646979) \| 14.319272 / 10.191392 (4.127880) \| 0.144534 / 0.680424 (-0.535890) \| 0.017502 / 0.534201 (-0.516699) \| 0.422682 / 0.579283 (-0.156601) \| 0.424426 / 0.434364 (-0.009938) \| 0.493561 / 0.540337 (-0.046777) \| 0.586765 / 1.386936 (-0.800171) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007764 / 0.011353 (-0.003589) \| 0.005516 / 0.011008 (-0.005492) \| 0.074745 / 0.038508 (0.036237) \| 0.034364 / 0.023109 (0.011255) \| 0.344318 / 0.275898 (0.068420) \| 0.374779 / 0.323480 (0.051299) \| 0.005904 / 0.007986 (-0.002082) \| 0.004323 / 0.004328 (-0.000005) \| 0.073191 / 0.004250 (0.068941) \| 0.051549 / 0.037052 (0.014496) \| 0.341792 / 0.258489 (0.083303) \| 0.387576 / 0.293841 (0.093735) \| 0.037483 / 0.128546 (-0.091063) \| 0.012410 / 0.075646 (-0.063237) \| 0.086480 / 0.419271 (-0.332791) \| 0.050035 / 0.043533 (0.006502) \| 0.335475 / 0.255139 (0.080336) \| 0.361436 / 0.283200 (0.078236) \| 0.106890 / 0.141683 (-0.034792) \| 1.464032 / 1.452155 (0.011877) \| 1.563490 / 1.492716 (0.070774) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.268765 / 0.018006 (0.250758) \| 0.563811 / 0.000490 (0.563321) \| 0.004904 / 0.000200 (0.004704) \| 0.000096 / 0.000054 (0.000041) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029885 / 0.037411 (-0.007526) \| 0.113885 / 0.014526 (0.099359) \| 0.124283 / 0.176557 (-0.052274) \| 0.173619 / 0.737135 (-0.563517) \| 0.131781 / 0.296338 (-0.164557) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.420296 / 0.215209 (0.205087) \| 4.167656 / 2.077655 (2.090001) \| 1.982356 / 1.504120 (0.478237) \| 1.792181 / 1.541195 (0.250986) \| 1.871459 / 1.468490 (0.402969) \| 0.707066 / 4.584777 (-3.877711) \| 3.835922 / 3.745712 (0.090210) \| 3.506796 / 5.269862 (-1.763066) \| 1.857172 / 4.565676 (-2.708505) \| 0.086219 / 0.424275 (-0.338056) \| 0.012404 / 0.007607 (0.004796) \| 0.512393 / 0.226044 (0.286348) \| 5.111623 / 2.268929 (2.842695) \| 2.493523 / 55.444624 (-52.951101) \| 2.188220 / 6.876477 (-4.688257) \| 2.319096 / 2.142072 (0.177024) \| 0.844084 / 4.805227 (-3.961144) \| 0.171130 / 6.500664 (-6.329534) \| 0.065913 / 0.075469 (-0.009556) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.284768 / 1.841788 (-0.557020) \| 15.334610 / 8.074308 (7.260301) \| 14.724436 / 10.191392 (4.533044) \| 0.188425 / 0.680424 (-0.491999) \| 0.017984 / 0.534201 (-0.516217) \| 0.428150 / 0.579283 (-0.151133) \| 0.429013 / 0.434364 (-0.005351) \| 0.500818 / 0.540337 (-0.039519) \| 0.592879 / 1.386936 (-0.794057) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ee68da958c2fab3a26d9f0efb1e207ecbcf7ce15 \"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) \| 0.006870 / 0.011353 (-0.004483) \| 0.004702 / 0.011008 (-0.006306) \| 0.099258 / 0.038508 (0.060750) \| 0.029008 / 0.023109 (0.005899) \| 0.330599 / 0.275898 (0.054701) \| 0.361163 / 0.323480 (0.037683) \| 0.005020 / 0.007986 (-0.002965) \| 0.003474 / 0.004328 (-0.000855) \| 0.075902 / 0.004250 (0.071651) \| 0.037462 / 0.037052 (0.000410) \| 0.336213 / 0.258489 (0.077724) \| 0.370645 / 0.293841 (0.076804) \| 0.032435 / 0.128546 (-0.096111) \| 0.011686 / 0.075646 (-0.063960) \| 0.326040 / 0.419271 (-0.093232) \| 0.043750 / 0.043533 (0.000217) \| 0.332629 / 0.255139 (0.077490) \| 0.353302 / 0.283200 (0.070102) \| 0.090421 / 0.141683 (-0.051262) \| 1.470097 / 1.452155 (0.017942) \| 1.544908 / 1.492716 (0.052191) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.213418 / 0.018006 (0.195411) \| 0.434808 / 0.000490 (0.434319) \| 0.005949 / 0.000200 (0.005749) \| 0.000072 / 0.000054 (0.000018) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023085 / 0.037411 (-0.014327) \| 0.098222 / 0.014526 (0.083696) \| 0.104543 / 0.176557 (-0.072013) \| 0.165423 / 0.737135 (-0.571713) \| 0.108732 / 0.296338 (-0.187606) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.433933 / 0.215209 (0.218724) \| 4.334358 / 2.077655 (2.256704) \| 2.013984 / 1.504120 (0.509864) \| 1.862981 / 1.541195 (0.321787) \| 1.873936 / 1.468490 (0.405446) \| 0.699857 / 4.584777 (-3.884920) \| 3.417815 / 3.745712 (-0.327897) \| 1.946403 / 5.269862 (-3.323459) \| 1.308683 / 4.565676 (-3.256994) \| 0.083297 / 0.424275 (-0.340978) \| 0.012610 / 0.007607 (0.005003) \| 0.540877 / 0.226044 (0.314832) \| 5.408293 / 2.268929 (3.139365) \| 2.529574 / 55.444624 (-52.915050) \| 2.201047 / 6.876477 (-4.675429) \| 2.392966 / 2.142072 (0.250894) \| 0.812719 / 4.805227 (-3.992509) \| 0.154013 / 6.500664 (-6.346651) \| 0.067614 / 0.075469 (-0.007855) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.228150 / 1.841788 (-0.613638) \| 14.037090 / 8.074308 (5.962782) \| 14.259416 / 10.191392 (4.068024) \| 0.155554 / 0.680424 (-0.524870) \| 0.016521 / 0.534201 (-0.517680) \| 0.379615 / 0.579283 (-0.199668) \| 0.421352 / 0.434364 (-0.013012) \| 0.446512 / 0.540337 (-0.093825) \| 0.531802 / 1.386936 (-0.855134) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006629 / 0.011353 (-0.004724) \| 0.004432 / 0.011008 (-0.006577) \| 0.076662 / 0.038508 (0.038154) \| 0.027674 / 0.023109 (0.004565) \| 0.341667 / 0.275898 (0.065769) \| 0.376493 / 0.323480 (0.053014) \| 0.005076 / 0.007986 (-0.002910) \| 0.004655 / 0.004328 (0.000326) \| 0.075698 / 0.004250 (0.071448) \| 0.036905 / 0.037052 (-0.000147) \| 0.342394 / 0.258489 (0.083905) \| 0.383330 / 0.293841 (0.089489) \| 0.031729 / 0.128546 (-0.096817) \| 0.011582 / 0.075646 (-0.064064) \| 0.085721 / 0.419271 (-0.333551) \| 0.042012 / 0.043533 (-0.001521) \| 0.342063 / 0.255139 (0.086924) \| 0.367335 / 0.283200 (0.084136) \| 0.089641 / 0.141683 (-0.052042) \| 1.520353 / 1.452155 (0.068198) \| 1.643653 / 1.492716 (0.150937) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.178995 / 0.018006 (0.160989) \| 0.436544 / 0.000490 (0.436055) \| 0.002311 / 0.000200 (0.002111) \| 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) \| 0.025386 / 0.037411 (-0.012026) \| 0.099717 / 0.014526 (0.085192) \| 0.110809 / 0.176557 (-0.065747) \| 0.162931 / 0.737135 (-0.574204) \| 0.110430 / 0.296338 (-0.185909) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.438592 / 0.215209 (0.223382) \| 4.372560 / 2.077655 (2.294905) \| 2.069686 / 1.504120 (0.565567) \| 1.860576 / 1.541195 (0.319382) \| 1.898161 / 1.468490 (0.429671) \| 0.698353 / 4.584777 (-3.886424) \| 3.462440 / 3.745712 (-0.283272) \| 1.868602 / 5.269862 (-3.401260) \| 1.160498 / 4.565676 (-3.405179) \| 0.082869 / 0.424275 (-0.341406) \| 0.012690 / 0.007607 (0.005083) \| 0.533278 / 0.226044 (0.307233) \| 5.386214 / 2.268929 (3.117285) \| 2.519243 / 55.444624 (-52.925382) \| 2.171109 / 6.876477 (-4.705368) \| 2.272617 / 2.142072 (0.130544) \| 0.805843 / 4.805227 (-3.999384) \| 0.152275 / 6.500664 (-6.348389) \| 0.068038 / 0.075469 (-0.007431) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.291967 / 1.841788 (-0.549821) \| 14.386474 / 8.074308 (6.312166) \| 14.180693 / 10.191392 (3.989301) \| 0.131714 / 0.680424 (-0.548710) \| 0.016596 / 0.534201 (-0.517605) \| 0.384293 / 0.579283 (-0.194990) \| 0.404051 / 0.434364 (-0.030313) \| 0.452167 / 0.540337 (-0.088170) \| 0.542718 / 1.386936 (-0.844218) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f9c770bb1a43fa7fe390286d7535266d3964d067 \"CML watermark\")\n" ]
1,663,762,901	5,739	weird result during dataset split when data path starts with `/data`	open	2023-04-12T04:51:35	2023-04-21T14:20:59	null	https://github.com/huggingface/datasets/issues/5739	null	airlsyn	false	[ "Same problem.", "hi! \r\nI think you can run python from `/data/train/raw/` directory and load dataset as `load_dataset(\"code_contests\")` to mitigate this issue as a workaround. \r\n@ericxsun Do you want to open a PR to fix the regex? As you already found the solution :) ", "> hi! I think you can run python from `/data/train/raw/` directory and load dataset as `load_dataset(\"code_contests\")` to mitigate this issue as a workaround. @ericxsun Do you want to open a PR to fix the regex? As you already found the solution :)\r\n\r\nSure, please see https://github.com/huggingface/datasets/pull/5748 @polinaeterna ", "I think `string_to_dict` is ok, and that the issue is that it gets `'/data2/train/raw/code_contests/data/test-00000-of-00001-9c49eeff30aacaa8.parquet'` as input instead of `'data/test-00000-of-00001-9c49eeff30aacaa8.parquet'`. The path should be relative to the directory being loaded by `load_dataset`" ]
1,663,477,690	5,738	load_dataset("text","dataset.txt") loads the wrong dataset!	closed	2023-04-12T01:07:46	2023-04-19T12:08:27	2023-04-19T12:08:27	https://github.com/huggingface/datasets/issues/5738	null	Tylersuard	false	[ "You need to provide a text file as `data_files`, not as a configuration:\r\n\r\n```python\r\nmy_dataset = load_dataset(\"text\", data_files=\"TextFile.txt\")\r\n```\r\n\r\nOtherwise, since `data_files` is `None`, it picks up Colab's sample datasets from the `content` dir." ]
1,662,919,811	5,737	ClassLabel Error	closed	2023-04-11T17:14:13	2023-04-13T16:49:57	2023-04-13T16:49:57	https://github.com/huggingface/datasets/issues/5737	null	mrcaelumn	false	[ "Hi, you can use the `cast_column` function to change the feature type from a `Value(int64)` to `ClassLabel`:\r\n\r\n```py\r\ndataset = dataset.cast_column(\"label\", ClassLabel(names=[\"label_1\", \"label_2\", \"label_3\"]))\r\nprint(dataset.features)\r\n{'text': Value(dtype='string', id=None),\r\n 'label': ClassLabel(names=['label_1', 'label_2', 'label_3'], id=None)}\r\n```", "thank you @stevhliu, its worked. " ]
1,662,286,061	5,736	FORCE_REDOWNLOAD raises "Directory not empty" exception on second run	open	2023-04-11T11:29:15	2023-11-30T07:16:58	null	https://github.com/huggingface/datasets/issues/5736	null	rcasero	false	[ "Hi ! I couldn't reproduce your issue :/\r\n\r\nIt seems that `shutil.rmtree` failed. It is supposed to work even if the directory is not empty, but you still end up with `OSError: [Errno 39] Directory not empty:`. Can you make sure another process is not using this directory at the same time ?", "I have the same error with `datasets==2.14.5` and `pyarrow==13.0.0`. Python 3.10.13", "I have same error. Any workaround?" ]
1,662,150,903	5,735	Implement sharding on merged iterable datasets	closed	2023-04-11T10:02:25	2023-04-27T16:39:04	2023-04-27T16:32:09	https://github.com/huggingface/datasets/pull/5735	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5735", "html_url": "https://github.com/huggingface/datasets/pull/5735", "diff_url": "https://github.com/huggingface/datasets/pull/5735.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5735.patch", "merged_at": "2023-04-27T16:32:09" }	bruno-hays	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "Hi ! What if one of the sub-iterables only has one shard ? In that case I don't think we'd end up with a correctly interleaved dataset, since only rank 0 would yield examples from this sub-iterable", "Hi ! \r\nI just tested this out with the code below and it seems to be ok. Both datasets are alternating and we get all the examples with no duplicates.\r\n\r\nOn thing to keep in mind is that the max amount of workers is equal to the lowest amount of shard amongst the datasets to be merged (1 in this example).\r\n\r\n ```python\r\nfrom torch.utils.data import DataLoader\r\n\r\nfrom datasets import load_dataset, interleave_datasets\r\n\r\n\r\ndef process_dataset_train(batch):\r\n return {\"input\": f'train: {batch[\"review\"][:20]}'}\r\n\r\n\r\ndef process_dataset_test(batch):\r\n return {\"input\": f'test: {batch[\"review\"][:20]}'}\r\n\r\n\r\ndef identity_collator(x):\r\n return x\r\n\r\n\r\nif __name__ == \"__main__\":\r\n ds = load_dataset(\"lhoestq/demo1\")\r\n ds[\"train\"] = ds[\"train\"].map(process_dataset_train, remove_columns=ds[\"train\"].column_names)\r\n ds[\"test\"] = ds[\"test\"].map(process_dataset_test, remove_columns=ds[\"test\"].column_names)\r\n\r\n ds1 = ds[\"train\"].to_iterable_dataset(num_shards=5)\r\n ds2 = ds[\"test\"].to_iterable_dataset(num_shards=1)\r\n\r\n ds_merged = interleave_datasets([ds1, ds2], stopping_strategy=\"all_exhausted\")\r\n\r\n dataloader = DataLoader(ds_merged, collate_fn=identity_collator, num_workers=1, batch_size=1)\r\n\r\n for i, element in enumerate(dataloader):\r\n print(i, element)\r\n\r\n```\r\n\r\n```\r\n0 [{'input': 'train: Great app! The new v'}]\r\n1 [{'input': 'test: Works with RTL and N'}]\r\n2 [{'input': \"train: Great It's not fully\"}]\r\n3 [{'input': 'test: Works with RTL SDR W'}]\r\n4 [{'input': 'train: Works on a Nexus 6p '}]\r\n5 [{'input': 'test: Awsome App! Easy to '}]\r\n6 [{'input': 'train: The bandwidth seemed'}]\r\n7 [{'input': \"test: I'll forgo the refun\"}]\r\n8 [{'input': 'train: Works well with my H'}]\r\n9 [{'input': 'test: looks like a great p'}]\r\n```", "<s> Could you try with `num_workers>1` ? </s>\r\n\r\nedit: Oh I see\r\n\r\n> On thing to keep in mind is that the max amount of workers is equal to the lowest amount of shard amongst the datasets to be merged (1 in this example).", "Great ! It's ok to have the max amount of workers is equal to the lowest amount of shard :)\r\n\r\nSo in the case of `num_workers>min(n_shards_per_dataset)` maybe some workers should turn off, and a warning can probably be shown. This is already the case if you use a single dataset with a single shard and `num_workers>1`.\r\n\r\n\r\nRight now it seems to raise an error:\r\n\r\n```python\r\n File \"/Users/quentinlhoest/hf/datasets/src/datasets/iterable_dataset.py\", line 979, in __iter__\r\n yield from self._iter_pytorch(ex_iterable)\r\n File \"/Users/quentinlhoest/hf/datasets/src/datasets/iterable_dataset.py\", line 912, in _iter_pytorch\r\n for key, example in ex_iterable.shard_data_sources(worker_info.id, worker_info.num_workers):\r\n File \"/Users/quentinlhoest/hf/datasets/src/datasets/iterable_dataset.py\", line 259, in shard_data_sources\r\n [iterable.shard_data_sources(worker_id, num_workers) for iterable in self.ex_iterables],\r\n File \"/Users/quentinlhoest/hf/datasets/src/datasets/iterable_dataset.py\", line 259, in <listcomp>\r\n [iterable.shard_data_sources(worker_id, num_workers) for iterable in self.ex_iterables],\r\n File \"/Users/quentinlhoest/hf/datasets/src/datasets/iterable_dataset.py\", line 125, in shard_data_sources\r\n requested_gen_kwargs = _merge_gen_kwargs([gen_kwargs_list[i] for i in shard_indices])\r\n File \"/Users/quentinlhoest/hf/datasets/src/datasets/utils/sharding.py\", line 76, in _merge_gen_kwargs\r\n for key in gen_kwargs_list[0]\r\nIndexError: list index out of range\r\n```", "Good point. I have fixed the n_shards property of merged iterable datasets so that this warning is raised properly", "Hey @lhoestq, what do you think of the last modifications ? ", "Hello! No problem :)\r\n\r\n- About HorizontallyConcatenatedMultiSourcesExamplesIterable, I've haven't been able to create a bug with sharding. So either I missed something or it's working somehow:\r\n\r\n```python\r\nfrom torch.utils.data import DataLoader\r\n\r\nfrom datasets import load_dataset, interleave_datasets, concatenate_datasets\r\n\r\n\r\ndef process_dataset_train(batch):\r\n return {\"input\": f'train: {batch[\"review\"][:20]}'}\r\n\r\n\r\ndef process_dataset_test(batch):\r\n return {\"input\": f'test: {batch[\"review\"][:20]}'}\r\n\r\n\r\ndef identity_collator(x):\r\n return x\r\n\r\n\r\nif __name__ == \"__main__\":\r\n ds = load_dataset(\"lhoestq/demo1\")\r\n ds[\"train\"] = ds[\"train\"].map(process_dataset_train, remove_columns=ds[\"train\"].column_names)\r\n ds[\"test\"] = ds[\"test\"].map(process_dataset_test, remove_columns=ds[\"test\"].column_names)\r\n ds[\"test\"] = ds[\"test\"].rename_columns({\"input\": \"input2\"})\r\n\r\n ds1 = ds[\"train\"].to_iterable_dataset(num_shards=5)\r\n ds2 = ds[\"test\"].to_iterable_dataset(num_shards=3)\r\n\r\n ds_merged = concatenate_datasets([ds1, ds2], axis=1)\r\n\r\n #n_shards is always 1 for HorizontallyConcatenatedMultiSourcesExamplesIterable\r\n dataloader = DataLoader(ds_merged, collate_fn=identity_collator, num_workers=1, batch_size=1)\r\n\r\n for i, element in enumerate(dataloader):\r\n print(i, element)\r\n```\r\n\r\n```\r\n0 [{'input': 'train: Great app! The new v', 'input2': 'test: Works with RTL and N'}]\r\n1 [{'input': \"train: Great It's not fully\", 'input2': 'test: Works with RTL SDR W'}]\r\n2 [{'input': 'train: Works on a Nexus 6p ', 'input2': 'test: Awsome App! Easy to '}]\r\n3 [{'input': 'train: The bandwidth seemed', 'input2': \"test: I'll forgo the refun\"}]\r\n4 [{'input': 'train: Works well with my H', 'input2': 'test: looks like a great p'}]\r\n```\r\n\r\n- I've added a test but I'm not completely happy with it. My issue is that multiprocessing makes interleaving not completely deterministic as samples are yielded whenever ready by each process, if I'm correct.\r\nAs a result I opted to check for the amount of samples yielded and make that they are all unique, which should be equivalent.\r\nBut now my issue is that the \"first_exhausted\" method breaks the loop when one of the datasets of one of the shards is empty which means that all shards stop yielding and we could be missing up to n_workers samples. I don't know if this is the behaviour expected, but I had to modify the test to accomodate this.\r\n\r\nWhat are your thoughts about this ?", "Ah indeed it works because it's set to be only 1 shard - my bad :)", "> But now my issue is that the \"first_exhausted\" method breaks the loop when one of the datasets of one of the shards is empty which means that all shards stop yielding and we could be missing up to n_workers samples. I don't know if this is the behaviour expected, but I had to modify the test to accomodate this.\r\n\r\nThis looks reasonable, maybe this can be documented in the `interleave_datasets` docstring ?\r\n```\r\nNote for iterable datasets:\r\n\r\nIn a distributed setup or in PyTorch DataLoader workers, the stopping strategy is applied per process.\r\nTherefore the \"first_exhausted\" strategy on an sharded iterable dataset can generate less samples in total (up to 1 missing sample per subdataset per worker).\r\n```", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006441 / 0.011353 (-0.004912) \| 0.004551 / 0.011008 (-0.006457) \| 0.099144 / 0.038508 (0.060636) \| 0.028163 / 0.023109 (0.005054) \| 0.386342 / 0.275898 (0.110444) \| 0.398347 / 0.323480 (0.074867) \| 0.004836 / 0.007986 (-0.003150) \| 0.004724 / 0.004328 (0.000395) \| 0.076277 / 0.004250 (0.072027) \| 0.036305 / 0.037052 (-0.000747) \| 0.377179 / 0.258489 (0.118690) \| 0.410694 / 0.293841 (0.116853) \| 0.030196 / 0.128546 (-0.098351) \| 0.011436 / 0.075646 (-0.064211) \| 0.325911 / 0.419271 (-0.093360) \| 0.043709 / 0.043533 (0.000177) \| 0.375801 / 0.255139 (0.120662) \| 0.396511 / 0.283200 (0.113311) \| 0.088346 / 0.141683 (-0.053337) \| 1.483427 / 1.452155 (0.031272) \| 1.553708 / 1.492716 (0.060992) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.190974 / 0.018006 (0.172968) \| 0.451309 / 0.000490 (0.450819) \| 0.004045 / 0.000200 (0.003845) \| 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) \| 0.023814 / 0.037411 (-0.013597) \| 0.096922 / 0.014526 (0.082396) \| 0.101506 / 0.176557 (-0.075050) \| 0.164694 / 0.737135 (-0.572441) \| 0.106899 / 0.296338 (-0.189439) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.432164 / 0.215209 (0.216954) \| 4.308076 / 2.077655 (2.230421) \| 2.092434 / 1.504120 (0.588314) \| 1.937405 / 1.541195 (0.396210) \| 1.988030 / 1.468490 (0.519540) \| 0.695476 / 4.584777 (-3.889301) \| 3.436413 / 3.745712 (-0.309299) \| 2.892954 / 5.269862 (-2.376908) \| 1.519906 / 4.565676 (-3.045771) \| 0.082579 / 0.424275 (-0.341696) \| 0.012233 / 0.007607 (0.004626) \| 0.531329 / 0.226044 (0.305284) \| 5.365272 / 2.268929 (3.096344) \| 2.391452 / 55.444624 (-53.053172) \| 2.051116 / 6.876477 (-4.825361) \| 2.140663 / 2.142072 (-0.001410) \| 0.807262 / 4.805227 (-3.997966) \| 0.151290 / 6.500664 (-6.349374) \| 0.066137 / 0.075469 (-0.009333) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.193106 / 1.841788 (-0.648682) \| 13.577240 / 8.074308 (5.502932) \| 14.280126 / 10.191392 (4.088734) \| 0.142538 / 0.680424 (-0.537886) \| 0.016641 / 0.534201 (-0.517560) \| 0.386318 / 0.579283 (-0.192965) \| 0.385991 / 0.434364 (-0.048373) \| 0.440712 / 0.540337 (-0.099625) \| 0.524189 / 1.386936 (-0.862747) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006628 / 0.011353 (-0.004725) \| 0.004664 / 0.011008 (-0.006344) \| 0.077254 / 0.038508 (0.038746) \| 0.028369 / 0.023109 (0.005259) \| 0.343076 / 0.275898 (0.067178) \| 0.376491 / 0.323480 (0.053011) \| 0.005298 / 0.007986 (-0.002687) \| 0.004853 / 0.004328 (0.000524) \| 0.075927 / 0.004250 (0.071677) \| 0.039951 / 0.037052 (0.002899) \| 0.346225 / 0.258489 (0.087736) \| 0.382367 / 0.293841 (0.088526) \| 0.031133 / 0.128546 (-0.097413) \| 0.011666 / 0.075646 (-0.063981) \| 0.086383 / 0.419271 (-0.332889) \| 0.042885 / 0.043533 (-0.000647) \| 0.343885 / 0.255139 (0.088746) \| 0.366840 / 0.283200 (0.083640) \| 0.095942 / 0.141683 (-0.045741) \| 1.528972 / 1.452155 (0.076817) \| 1.586392 / 1.492716 (0.093676) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223952 / 0.018006 (0.205946) \| 0.410767 / 0.000490 (0.410277) \| 0.001014 / 0.000200 (0.000814) \| 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) \| 0.024210 / 0.037411 (-0.013201) \| 0.100308 / 0.014526 (0.085782) \| 0.106899 / 0.176557 (-0.069658) \| 0.156514 / 0.737135 (-0.580621) \| 0.109548 / 0.296338 (-0.186790) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.434763 / 0.215209 (0.219554) \| 4.348485 / 2.077655 (2.270831) \| 2.064255 / 1.504120 (0.560135) \| 1.864394 / 1.541195 (0.323199) \| 1.899732 / 1.468490 (0.431242) \| 0.694147 / 4.584777 (-3.890630) \| 3.357898 / 3.745712 (-0.387815) \| 2.909155 / 5.269862 (-2.360707) \| 1.424790 / 4.565676 (-3.140886) \| 0.082597 / 0.424275 (-0.341678) \| 0.012442 / 0.007607 (0.004835) \| 0.538758 / 0.226044 (0.312713) \| 5.390288 / 2.268929 (3.121359) \| 2.532016 / 55.444624 (-52.912609) \| 2.185724 / 6.876477 (-4.690753) \| 2.274176 / 2.142072 (0.132104) \| 0.804785 / 4.805227 (-4.000442) \| 0.152649 / 6.500664 (-6.348015) \| 0.067707 / 0.075469 (-0.007762) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.285219 / 1.841788 (-0.556568) \| 13.958098 / 8.074308 (5.883790) \| 14.043653 / 10.191392 (3.852261) \| 0.144526 / 0.680424 (-0.535898) \| 0.016813 / 0.534201 (-0.517388) \| 0.390286 / 0.579283 (-0.188997) \| 0.389184 / 0.434364 (-0.045180) \| 0.470810 / 0.540337 (-0.069527) \| 0.562391 / 1.386936 (-0.824545) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4bb172c9772858c188f85ffc9a51f8cb1da292a0 \"CML watermark\")\n" ]
1,662,058,028	5,734	Remove temporary pin of fsspec	closed	2023-04-11T09:04:17	2023-04-11T11:04:52	2023-04-11T11:04:52	https://github.com/huggingface/datasets/issues/5734	null	albertvillanova	false	[]
1,662,039,191	5,733	Unpin fsspec	closed	2023-04-11T08:52:12	2023-04-11T11:11:45	2023-04-11T11:04:51	https://github.com/huggingface/datasets/pull/5733	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5733", "html_url": "https://github.com/huggingface/datasets/pull/5733", "diff_url": "https://github.com/huggingface/datasets/pull/5733.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5733.patch", "merged_at": "2023-04-11T11:04:51" }	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) \| 0.006240 / 0.011353 (-0.005113) \| 0.004392 / 0.011008 (-0.006616) \| 0.097276 / 0.038508 (0.058768) \| 0.027262 / 0.023109 (0.004153) \| 0.303203 / 0.275898 (0.027305) \| 0.331878 / 0.323480 (0.008398) \| 0.004706 / 0.007986 (-0.003279) \| 0.004428 / 0.004328 (0.000100) \| 0.074666 / 0.004250 (0.070416) \| 0.036154 / 0.037052 (-0.000899) \| 0.302997 / 0.258489 (0.044508) \| 0.340350 / 0.293841 (0.046509) \| 0.031011 / 0.128546 (-0.097535) \| 0.011616 / 0.075646 (-0.064031) \| 0.323671 / 0.419271 (-0.095601) \| 0.042062 / 0.043533 (-0.001471) \| 0.311381 / 0.255139 (0.056242) \| 0.324697 / 0.283200 (0.041498) \| 0.084248 / 0.141683 (-0.057435) \| 1.471651 / 1.452155 (0.019496) \| 1.533414 / 1.492716 (0.040697) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.193555 / 0.018006 (0.175549) \| 0.393452 / 0.000490 (0.392962) \| 0.002348 / 0.000200 (0.002148) \| 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) \| 0.022523 / 0.037411 (-0.014889) \| 0.096552 / 0.014526 (0.082026) \| 0.101746 / 0.176557 (-0.074810) \| 0.163145 / 0.737135 (-0.573990) \| 0.106417 / 0.296338 (-0.189921) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.448589 / 0.215209 (0.233380) \| 4.467803 / 2.077655 (2.390148) \| 2.178745 / 1.504120 (0.674625) \| 1.983339 / 1.541195 (0.442145) \| 2.056554 / 1.468490 (0.588064) \| 0.697571 / 4.584777 (-3.887206) \| 3.363967 / 3.745712 (-0.381745) \| 1.872526 / 5.269862 (-3.397336) \| 1.258245 / 4.565676 (-3.307432) \| 0.082954 / 0.424275 (-0.341321) \| 0.012306 / 0.007607 (0.004699) \| 0.545096 / 0.226044 (0.319052) \| 5.468706 / 2.268929 (3.199777) \| 2.645333 / 55.444624 (-52.799292) \| 2.287659 / 6.876477 (-4.588818) \| 2.346768 / 2.142072 (0.204696) \| 0.803730 / 4.805227 (-4.001497) \| 0.151037 / 6.500664 (-6.349627) \| 0.066404 / 0.075469 (-0.009065) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.192982 / 1.841788 (-0.648806) \| 13.631225 / 8.074308 (5.556917) \| 13.830053 / 10.191392 (3.638661) \| 0.141901 / 0.680424 (-0.538523) \| 0.016500 / 0.534201 (-0.517701) \| 0.373268 / 0.579283 (-0.206015) \| 0.380123 / 0.434364 (-0.054241) \| 0.430786 / 0.540337 (-0.109551) \| 0.512669 / 1.386936 (-0.874267) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006161 / 0.011353 (-0.005192) \| 0.004399 / 0.011008 (-0.006609) \| 0.076210 / 0.038508 (0.037702) \| 0.026791 / 0.023109 (0.003681) \| 0.341523 / 0.275898 (0.065625) \| 0.370400 / 0.323480 (0.046920) \| 0.004495 / 0.007986 (-0.003491) \| 0.003204 / 0.004328 (-0.001125) \| 0.075444 / 0.004250 (0.071194) \| 0.035914 / 0.037052 (-0.001138) \| 0.343806 / 0.258489 (0.085317) \| 0.384320 / 0.293841 (0.090479) \| 0.031438 / 0.128546 (-0.097109) \| 0.011253 / 0.075646 (-0.064393) \| 0.085364 / 0.419271 (-0.333908) \| 0.041407 / 0.043533 (-0.002126) \| 0.338831 / 0.255139 (0.083692) \| 0.364357 / 0.283200 (0.081158) \| 0.087417 / 0.141683 (-0.054266) \| 1.520624 / 1.452155 (0.068470) \| 1.572432 / 1.492716 (0.079716) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.232403 / 0.018006 (0.214396) \| 0.388187 / 0.000490 (0.387698) \| 0.001158 / 0.000200 (0.000958) \| 0.000069 / 0.000054 (0.000014) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024596 / 0.037411 (-0.012816) \| 0.101203 / 0.014526 (0.086677) \| 0.105243 / 0.176557 (-0.071314) \| 0.158215 / 0.737135 (-0.578920) \| 0.110277 / 0.296338 (-0.186061) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.435661 / 0.215209 (0.220452) \| 4.350151 / 2.077655 (2.272496) \| 2.072372 / 1.504120 (0.568252) \| 1.870675 / 1.541195 (0.329480) \| 1.910883 / 1.468490 (0.442393) \| 0.697384 / 4.584777 (-3.887393) \| 3.399377 / 3.745712 (-0.346335) \| 2.685008 / 5.269862 (-2.584854) \| 1.476843 / 4.565676 (-3.088834) \| 0.083177 / 0.424275 (-0.341098) \| 0.012413 / 0.007607 (0.004806) \| 0.542543 / 0.226044 (0.316498) \| 5.431422 / 2.268929 (3.162494) \| 2.506419 / 55.444624 (-52.938206) \| 2.166342 / 6.876477 (-4.710135) \| 2.164421 / 2.142072 (0.022348) \| 0.800609 / 4.805227 (-4.004618) \| 0.150527 / 6.500664 (-6.350137) \| 0.065780 / 0.075469 (-0.009689) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.293409 / 1.841788 (-0.548379) \| 13.814898 / 8.074308 (5.740590) \| 13.940416 / 10.191392 (3.749024) \| 0.149377 / 0.680424 (-0.531047) \| 0.016462 / 0.534201 (-0.517739) \| 0.393748 / 0.579283 (-0.185535) \| 0.384327 / 0.434364 (-0.050037) \| 0.489900 / 0.540337 (-0.050437) \| 0.574608 / 1.386936 (-0.812328) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f2607935c4e45c70c44fcb698db0363ca7ba83d4 \"CML watermark\")\n" ]
1,662,020,571	5,732	Enwik8 should support the standard split	closed	2023-04-11T08:38:53	2023-04-11T09:28:17	2023-04-11T09:28:16	https://github.com/huggingface/datasets/issues/5732	null	lucaslingle	false	[ "#self-assign", "The Enwik8 pipeline is not present in this codebase, and is hosted elsewhere. I have opened a PR [there](https://huggingface.co/datasets/enwik8/discussions/4) instead. " ]
1,662,012,913	5,731	Temporarily pin fsspec	closed	2023-04-11T08:33:15	2023-04-11T08:57:45	2023-04-11T08:47:55	https://github.com/huggingface/datasets/pull/5731	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5731", "html_url": "https://github.com/huggingface/datasets/pull/5731", "diff_url": "https://github.com/huggingface/datasets/pull/5731.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5731.patch", "merged_at": "2023-04-11T08:47:55" }	albertvillanova	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009735 / 0.011353 (-0.001618) \| 0.010410 / 0.011008 (-0.000598) \| 0.134986 / 0.038508 (0.096478) \| 0.038392 / 0.023109 (0.015283) \| 0.414451 / 0.275898 (0.138553) \| 0.447775 / 0.323480 (0.124295) \| 0.007223 / 0.007986 (-0.000763) \| 0.006373 / 0.004328 (0.002045) \| 0.102631 / 0.004250 (0.098381) \| 0.048516 / 0.037052 (0.011464) \| 0.410179 / 0.258489 (0.151690) \| 0.467773 / 0.293841 (0.173932) \| 0.053163 / 0.128546 (-0.075384) \| 0.019801 / 0.075646 (-0.055845) \| 0.452708 / 0.419271 (0.033436) \| 0.068691 / 0.043533 (0.025159) \| 0.405482 / 0.255139 (0.150343) \| 0.457669 / 0.283200 (0.174470) \| 0.113464 / 0.141683 (-0.028219) \| 1.918143 / 1.452155 (0.465988) \| 2.033123 / 1.492716 (0.540407) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.274564 / 0.018006 (0.256557) \| 0.608855 / 0.000490 (0.608366) \| 0.006266 / 0.000200 (0.006066) \| 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) \| 0.033704 / 0.037411 (-0.003708) \| 0.130982 / 0.014526 (0.116456) \| 0.143862 / 0.176557 (-0.032694) \| 0.212622 / 0.737135 (-0.524513) \| 0.148899 / 0.296338 (-0.147439) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.670968 / 0.215209 (0.455759) \| 6.602911 / 2.077655 (4.525256) \| 2.644290 / 1.504120 (1.140171) \| 2.268593 / 1.541195 (0.727399) \| 2.325393 / 1.468490 (0.856903) \| 1.388156 / 4.584777 (-3.196621) \| 5.958569 / 3.745712 (2.212857) \| 3.310756 / 5.269862 (-1.959106) \| 2.390953 / 4.565676 (-2.174724) \| 0.147416 / 0.424275 (-0.276859) \| 0.015201 / 0.007607 (0.007594) \| 0.794109 / 0.226044 (0.568064) \| 7.984855 / 2.268929 (5.715926) \| 3.382275 / 55.444624 (-52.062349) \| 2.676102 / 6.876477 (-4.200375) \| 2.846743 / 2.142072 (0.704671) \| 1.467523 / 4.805227 (-3.337704) \| 0.283184 / 6.500664 (-6.217480) \| 0.088655 / 0.075469 (0.013186) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.632765 / 1.841788 (-0.209022) \| 19.102473 / 8.074308 (11.028165) \| 25.632535 / 10.191392 (15.441143) \| 0.255628 / 0.680424 (-0.424795) \| 0.034655 / 0.534201 (-0.499546) \| 0.564593 / 0.579283 (-0.014690) \| 0.668339 / 0.434364 (0.233975) \| 0.648414 / 0.540337 (0.108076) \| 0.766735 / 1.386936 (-0.620201) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009658 / 0.011353 (-0.001695) \| 0.006690 / 0.011008 (-0.004318) \| 0.099151 / 0.038508 (0.060643) \| 0.037092 / 0.023109 (0.013983) \| 0.470354 / 0.275898 (0.194456) \| 0.525863 / 0.323480 (0.202383) \| 0.007593 / 0.007986 (-0.000393) \| 0.006637 / 0.004328 (0.002308) \| 0.098782 / 0.004250 (0.094532) \| 0.058524 / 0.037052 (0.021471) \| 0.502569 / 0.258489 (0.244080) \| 0.526410 / 0.293841 (0.232569) \| 0.059486 / 0.128546 (-0.069060) \| 0.019742 / 0.075646 (-0.055904) \| 0.119715 / 0.419271 (-0.299556) \| 0.065269 / 0.043533 (0.021736) \| 0.483327 / 0.255139 (0.228188) \| 0.506148 / 0.283200 (0.222948) \| 0.123178 / 0.141683 (-0.018505) \| 1.916624 / 1.452155 (0.464470) \| 2.051410 / 1.492716 (0.558694) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286481 / 0.018006 (0.268475) \| 0.597300 / 0.000490 (0.596810) \| 0.008906 / 0.000200 (0.008706) \| 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) \| 0.031406 / 0.037411 (-0.006005) \| 0.146748 / 0.014526 (0.132222) \| 0.152898 / 0.176557 (-0.023658) \| 0.212535 / 0.737135 (-0.524600) \| 0.155577 / 0.296338 (-0.140761) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.660989 / 0.215209 (0.445780) \| 6.688530 / 2.077655 (4.610875) \| 3.039278 / 1.504120 (1.535159) \| 2.660357 / 1.541195 (1.119162) \| 2.696912 / 1.468490 (1.228422) \| 1.259760 / 4.584777 (-3.325017) \| 5.922452 / 3.745712 (2.176740) \| 5.304200 / 5.269862 (0.034338) \| 2.823928 / 4.565676 (-1.741748) \| 0.148118 / 0.424275 (-0.276157) \| 0.015575 / 0.007607 (0.007968) \| 0.794404 / 0.226044 (0.568360) \| 8.233651 / 2.268929 (5.964722) \| 3.777482 / 55.444624 (-51.667142) \| 3.064924 / 6.876477 (-3.811552) \| 3.117803 / 2.142072 (0.975731) \| 1.479559 / 4.805227 (-3.325668) \| 0.254070 / 6.500664 (-6.246594) \| 0.086806 / 0.075469 (0.011337) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.735515 / 1.841788 (-0.106273) \| 18.934157 / 8.074308 (10.859848) \| 22.645248 / 10.191392 (12.453856) \| 0.227073 / 0.680424 (-0.453351) \| 0.030650 / 0.534201 (-0.503551) \| 0.594619 / 0.579283 (0.015336) \| 0.653304 / 0.434364 (0.218940) \| 0.707484 / 0.540337 (0.167147) \| 0.823327 / 1.386936 (-0.563610) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#273392966e434286f4f5ba2ad596730bff11056d \"CML watermark\")\n" ]
1,662,007,926	5,730	CI is broken: ValueError: Name (mock) already in the registry and clobber is False	closed	2023-04-11T08:29:46	2023-04-11T08:47:56	2023-04-11T08:47:56	https://github.com/huggingface/datasets/issues/5730	null	albertvillanova	false	[]
1,661,929,923	5,729	Fix nondeterministic sharded data split order	closed	2023-04-11T07:34:20	2023-04-26T15:12:25	2023-04-26T15:05:12	https://github.com/huggingface/datasets/pull/5729	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5729", "html_url": "https://github.com/huggingface/datasets/pull/5729", "diff_url": "https://github.com/huggingface/datasets/pull/5729.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5729.patch", "merged_at": "2023-04-26T15:05:12" }	albertvillanova	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "The error in the CI was unrelated to this PR. I have merged main branch once that has been fixed.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006954 / 0.011353 (-0.004399) \| 0.004947 / 0.011008 (-0.006061) \| 0.086564 / 0.038508 (0.048056) \| 0.031167 / 0.023109 (0.008058) \| 0.262285 / 0.275898 (-0.013613) \| 0.295753 / 0.323480 (-0.027727) \| 0.005389 / 0.007986 (-0.002596) \| 0.004130 / 0.004328 (-0.000198) \| 0.065127 / 0.004250 (0.060877) \| 0.042511 / 0.037052 (0.005458) \| 0.263497 / 0.258489 (0.005008) \| 0.307456 / 0.293841 (0.013615) \| 0.031338 / 0.128546 (-0.097209) \| 0.011023 / 0.075646 (-0.064623) \| 0.295625 / 0.419271 (-0.123647) \| 0.045813 / 0.043533 (0.002280) \| 0.259369 / 0.255139 (0.004230) \| 0.279325 / 0.283200 (-0.003875) \| 0.099748 / 0.141683 (-0.041934) \| 1.252572 / 1.452155 (-0.199583) \| 1.347069 / 1.492716 (-0.145647) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.249726 / 0.018006 (0.231720) \| 0.556882 / 0.000490 (0.556392) \| 0.008237 / 0.000200 (0.008037) \| 0.000294 / 0.000054 (0.000239) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026879 / 0.037411 (-0.010533) \| 0.105141 / 0.014526 (0.090615) \| 0.115473 / 0.176557 (-0.061084) \| 0.172989 / 0.737135 (-0.564147) \| 0.120433 / 0.296338 (-0.175906) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.400022 / 0.215209 (0.184812) \| 3.965402 / 2.077655 (1.887747) \| 1.805257 / 1.504120 (0.301138) \| 1.610136 / 1.541195 (0.068941) \| 1.661162 / 1.468490 (0.192672) \| 0.695311 / 4.584777 (-3.889466) \| 3.753757 / 3.745712 (0.008045) \| 2.060609 / 5.269862 (-3.209253) \| 1.333251 / 4.565676 (-3.232426) \| 0.085790 / 0.424275 (-0.338485) \| 0.012256 / 0.007607 (0.004649) \| 0.502133 / 0.226044 (0.276088) \| 5.040979 / 2.268929 (2.772051) \| 2.310919 / 55.444624 (-53.133705) \| 2.010534 / 6.876477 (-4.865943) \| 2.132961 / 2.142072 (-0.009111) \| 0.837636 / 4.805227 (-3.967592) \| 0.169838 / 6.500664 (-6.330826) \| 0.065003 / 0.075469 (-0.010466) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.218674 / 1.841788 (-0.623114) \| 14.696076 / 8.074308 (6.621768) \| 14.559492 / 10.191392 (4.368100) \| 0.167761 / 0.680424 (-0.512663) \| 0.017747 / 0.534201 (-0.516454) \| 0.421624 / 0.579283 (-0.157659) \| 0.414086 / 0.434364 (-0.020278) \| 0.501398 / 0.540337 (-0.038940) \| 0.596099 / 1.386936 (-0.790837) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007230 / 0.011353 (-0.004123) \| 0.005345 / 0.011008 (-0.005664) \| 0.073739 / 0.038508 (0.035231) \| 0.033440 / 0.023109 (0.010330) \| 0.339790 / 0.275898 (0.063892) \| 0.367857 / 0.323480 (0.044377) \| 0.005927 / 0.007986 (-0.002058) \| 0.004279 / 0.004328 (-0.000049) \| 0.074247 / 0.004250 (0.069996) \| 0.048971 / 0.037052 (0.011918) \| 0.340235 / 0.258489 (0.081746) \| 0.380521 / 0.293841 (0.086680) \| 0.035322 / 0.128546 (-0.093225) \| 0.012416 / 0.075646 (-0.063230) \| 0.086060 / 0.419271 (-0.333212) \| 0.049331 / 0.043533 (0.005799) \| 0.342871 / 0.255139 (0.087732) \| 0.355673 / 0.283200 (0.072473) \| 0.111976 / 0.141683 (-0.029707) \| 1.462530 / 1.452155 (0.010375) \| 1.550336 / 1.492716 (0.057620) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.266560 / 0.018006 (0.248554) \| 0.550886 / 0.000490 (0.550396) \| 0.001069 / 0.000200 (0.000869) \| 0.000085 / 0.000054 (0.000031) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028701 / 0.037411 (-0.008711) \| 0.110535 / 0.014526 (0.096010) \| 0.122846 / 0.176557 (-0.053711) \| 0.176395 / 0.737135 (-0.560740) \| 0.128653 / 0.296338 (-0.167685) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.431693 / 0.215209 (0.216484) \| 4.283691 / 2.077655 (2.206036) \| 2.013967 / 1.504120 (0.509847) \| 1.823914 / 1.541195 (0.282719) \| 1.872055 / 1.468490 (0.403565) \| 0.703318 / 4.584777 (-3.881459) \| 3.783412 / 3.745712 (0.037699) \| 2.950147 / 5.269862 (-2.319715) \| 1.826159 / 4.565676 (-2.739518) \| 0.086897 / 0.424275 (-0.337379) \| 0.012512 / 0.007607 (0.004905) \| 0.526730 / 0.226044 (0.300685) \| 5.263871 / 2.268929 (2.994943) \| 2.552163 / 55.444624 (-52.892462) \| 2.276216 / 6.876477 (-4.600261) \| 2.419934 / 2.142072 (0.277862) \| 0.848235 / 4.805227 (-3.956993) \| 0.170405 / 6.500664 (-6.330259) \| 0.064979 / 0.075469 (-0.010491) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.276780 / 1.841788 (-0.565008) \| 15.100829 / 8.074308 (7.026521) \| 15.117531 / 10.191392 (4.926139) \| 0.147129 / 0.680424 (-0.533295) \| 0.017806 / 0.534201 (-0.516395) \| 0.422975 / 0.579283 (-0.156308) \| 0.430286 / 0.434364 (-0.004078) \| 0.501405 / 0.540337 (-0.038932) \| 0.596810 / 1.386936 (-0.790126) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f6ee2e6603fe81638256d37a6aa7ad0400e31a83 \"CML watermark\")\n" ]
1,661,925,932	5,728	The order of data split names is nondeterministic	closed	2023-04-11T07:31:25	2023-04-26T15:05:13	2023-04-26T15:05:13	https://github.com/huggingface/datasets/issues/5728	null	albertvillanova	false	[]
1,661,536,363	5,727	load_dataset fails with FileNotFound error on Windows	closed	2023-04-10T23:21:12	2023-07-21T14:08:20	2023-07-21T14:08:19	https://github.com/huggingface/datasets/issues/5727	null	joelkowalewski	false	[ "Hi! Can you please paste the entire error stack trace, not only the last few lines?", "`----> 1 dataset = datasets.load_dataset(\"glue\", \"ax\")\r\n\r\nFile ~\\anaconda3\\envs\\huggingface\\Lib\\site-packages\\datasets\\load.py:1767, 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 1762 verification_mode = VerificationMode(\r\n 1763 (verification_mode or VerificationMode.BASIC_CHECKS) if not save_infos else VerificationMode.ALL_CHECKS\r\n 1764 )\r\n 1766 # Create a dataset builder\r\n-> 1767 builder_instance = load_dataset_builder(\r\n 1768 path=path,\r\n 1769 name=name,\r\n 1770 data_dir=data_dir,\r\n 1771 data_files=data_files,\r\n 1772 cache_dir=cache_dir,\r\n 1773 features=features,\r\n 1774 download_config=download_config,\r\n 1775 download_mode=download_mode,\r\n 1776 revision=revision,\r\n 1777 use_auth_token=use_auth_token,\r\n 1778 storage_options=storage_options,\r\n 1779 config_kwargs,\r\n 1780 )\r\n 1782 # Return iterable dataset in case of streaming\r\n 1783 if streaming:\r\n\r\nFile ~\\anaconda3\\envs\\huggingface\\Lib\\site-packages\\datasets\\load.py:1498, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, storage_options, config_kwargs)\r\n 1496 download_config = download_config.copy() if download_config else DownloadConfig()\r\n 1497 download_config.use_auth_token = use_auth_token\r\n-> 1498 dataset_module = dataset_module_factory(\r\n 1499 path,\r\n 1500 revision=revision,\r\n 1501 download_config=download_config,\r\n 1502 download_mode=download_mode,\r\n 1503 data_dir=data_dir,\r\n 1504 data_files=data_files,\r\n 1505 )\r\n 1507 # Get dataset builder class from the processing script\r\n 1508 builder_cls = import_main_class(dataset_module.module_path)\r\n\r\nFile ~\\anaconda3\\envs\\huggingface\\Lib\\site-packages\\datasets\\load.py:1211, in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, download_kwargs)\r\n 1209 raise e1 from None\r\n 1210 if isinstance(e1, FileNotFoundError):\r\n-> 1211 raise FileNotFoundError(\r\n 1212 f\"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. \"\r\n 1213 f\"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}\"\r\n 1214 ) from None\r\n 1215 raise e1 from None\r\n 1216 else:`", "Okay, this is the issue:\r\n```\r\nFileNotFoundError: [WinError 3] The system cannot find the path specified: \r\n'C:\\\\Users\\\\...\\\\.cache\\\\huggingface'\r\n``` \r\n\r\nI don't remember seeing this error before.\r\n\r\nI guess it could happen in a multi-process environment if one of the processes deletes the `datasets` cache as the other one is loading a dataset (with `load_dataset`), so make sure that's not the case. Also, you can disable the Windows max path length limit (if enabled), but this is most likely not the problem.", "Closing due to inactivity." ]
1,660,944,807	5,726	Fallback JSON Dataset loading does not load all values when features specified manually	closed	2023-04-10T15:22:14	2023-04-21T06:35:28	2023-04-21T06:35:28	https://github.com/huggingface/datasets/issues/5726	null	myluki2000	false	[ "Thanks for reporting, @myluki2000.\r\n\r\nI am working on a fix." ]
1,660,455,202	5,725	How to limit the number of examples in dataset, for testing?	closed	2023-04-10T08:41:43	2023-04-21T06:16:24	2023-04-21T06:16:24	https://github.com/huggingface/datasets/issues/5725	null	ndvbd	false	[ "Hi! You can use the `nrows` parameter for this:\r\n```python\r\ndata = load_dataset(\"json\", data_files=data_path, nrows=10)\r\n```", "@mariosasko I get:\r\n\r\n`TypeError: __init__() got an unexpected keyword argument 'nrows'`", "I misread the format in which the dataset is stored - the `nrows` parameter works for CSV, but not JSON.\r\n\r\nThis means the only option is first to create a DataFrame and then convert it to a Dataset object:\r\n```python\r\nimport pandas as pd\r\nfrom datasets import Dataset\r\n\r\ndf = pd.read_json(data_path, lines=True, nrows=10)\r\nds = Dataset.from_pandas(df)\r\n```" ]
1,659,938,135	5,724	Error after shuffling streaming IterableDatasets with downloaded dataset	closed	2023-04-09T16:58:44	2023-04-20T20:37:30	2023-04-20T20:37:30	https://github.com/huggingface/datasets/issues/5724	null	szxiangjn	false	[ "Moving `\"en\"` to the end of the path instead of passing it as a config name should fix the error:\r\n```python\r\nimport datasets\r\ndataset = datasets.load_dataset('/path/to/your/data/dir/en', streaming=True, split='train')\r\ndataset = dataset.shuffle(buffer_size=10_000, seed=42)\r\nnext(iter(dataset))\r\n```\r\n\r\nPS: https://github.com/huggingface/datasets/pull/5331, once merged, will allow us to define C4's configs in its README, making downloading it much more user-friendly." ]
1,659,837,510	5,722	Distributed Training Error on Customized Dataset	closed	2023-04-09T11:04:59	2023-07-24T14:50:46	2023-07-24T14:50:46	https://github.com/huggingface/datasets/issues/5722	null	wlhgtc	false	[ "Hmm the error doesn't seem related to data loading.\r\n\r\nRegarding `split_dataset_by_node`: it's generally used to split an iterable dataset (e.g. when streaming) in pytorch DDP. It's not needed if you use a regular dataset since the pytorch DataLoader already assigns a subset of the dataset indices to each node." ]
1,659,680,682	5,721	Calling datasets.load_dataset("text" ...) results in a wrong split.	open	2023-04-08T23:55:12	2023-04-08T23:55:12	null	https://github.com/huggingface/datasets/issues/5721	null	cyrilzakka	false	[]
1,659,610,705	5,720	Streaming IterableDatasets do not work with torch DataLoaders	open	2023-04-08T18:45:48	2025-03-19T14:06:47	null	https://github.com/huggingface/datasets/issues/5720	null	jlehrer1	false	[ "Edit: This behavior is true even without `.take/.set`", "I'm experiencing the same problem that @jlehrer1. I was able to reproduce it with a very small example:\r\n\r\n```py\r\nfrom datasets import Dataset, load_dataset, load_dataset_builder\r\nfrom torch.utils.data import DataLoader\r\n\r\n\r\ndef my_gen():\r\n for i in range(1, 4):\r\n yield {\"a\": i}\r\n\r\n# Saving the dataset as a parquet file\r\ndataset = Dataset.from_generator(my_gen)\r\ntrain_path = \"/tmp/test.parquet\"\r\ndataset.to_parquet(train_path)\r\n\r\n# Creating a local dataset from the parquet file\r\ndata_files = {\"train\": [str(train_path)]}\r\nbuilder = load_dataset_builder(\"parquet\", data_files=data_files)\r\nbuilder.download_and_prepare(\"/tmp/test_ds\", file_format=\"parquet\")\r\n\r\n# Loading the dataset from the local directory as streaming\r\ndataset = load_dataset(\"parquet\", data_dir=\"/tmp/test_ds\", split=\"train\", streaming=True)\r\ndataset.with_format(\"torch\")\r\n\r\ndl = DataLoader(dataset, batch_size=2, num_workers=1)\r\nfor row in dl:\r\n print(row)\r\n```\r\n\r\nMy env info:\r\n```\r\ndatasets 2.11.0\r\ntorch 2.0.0\r\ntorchvision 0.15.1\r\nPython 3.9.16\r\n```\r\n\r\nNote that the example above doesn't fail if the number of workers used is `0`", "I cannot reproduce this error, not even with your MRE @ivanprado (your env appears to be the same as Colab's, and your code runs there without issues). ", "@mariosasko you are right, it works on Colab. I digged deeper and found that the problem arises when the multiprocessing method is set to be `spawn`. This code reproduces the problem in Colab:\r\n\r\n```py\r\nfrom datasets import Dataset, load_dataset, load_dataset_builder\r\nfrom torch.utils.data import DataLoader\r\nimport multiprocessing as mp\r\n\r\nmp.set_start_method('spawn')\r\n\r\ndef my_gen():\r\n for i in range(1, 4):\r\n yield {\"a\": i}\r\n\r\n\r\ndef main():\r\n # Saving the dataset as a parquet file\r\n dataset = Dataset.from_generator(my_gen)\r\n train_path = \"/tmp/test.parquet\"\r\n dataset.to_parquet(train_path)\r\n\r\n # Creating a local dataset from the parquet file\r\n data_files = {\"train\": [str(train_path)]}\r\n builder = load_dataset_builder(\"parquet\", data_files=data_files)\r\n builder.download_and_prepare(\"/tmp/test_ds\", file_format=\"parquet\")\r\n\r\n # Loading the dataset from the local directory as streaming\r\n dataset = load_dataset(\"parquet\", data_dir=\"/tmp/test_ds\", split=\"train\", streaming=True)\r\n dataset.with_format(\"torch\")\r\n\r\n dl = DataLoader(dataset, batch_size=2, num_workers=1)\r\n for row in dl:\r\n print(row)\r\n\r\nmain()\r\n```", "So is there a way to fix this by changing the `mp` method? This is blocking any usage of the `datasets` library for me", "@jlehrer1 can you try adding `mp.set_start_method('fork')` at the beginning of your code? Maybe this helps you. Keep us posted. ", "I have a similar issue: \r\n> mp.set_start_method('fork')\r\n\r\n\r\nDidnt work", "What if I want to use GPU? spawn is a must have @ivanprado ", "@ivanprado you're right, this problem gets solved in case number of workers is set to 0, but this essentially destroys any level parallelism we can get.", "Exactly guys, agree with you. I'm just one like yours here. I'm not a datasets contributor. This issue prevented me to use this library." ]
1,659,203,222	5,719	Array2D feature creates a list of list instead of a numpy array	closed	2023-04-07T21:04:08	2023-04-20T15:34:41	2023-04-20T15:34:41	https://github.com/huggingface/datasets/issues/5719	null	offchan42	false	[ "Hi! \r\n\r\nYou need to set the format to `np` before indexing the dataset to get NumPy arrays:\r\n```python\r\nfeatures = Features(dict(seq=Array2D((2,2), 'float32'))) \r\nds = Dataset.from_dict(dict(seq=[np.random.rand(2,2)]), features=features)\r\nds.set_format(\"np\")\r\na = ds[0]['seq']\r\n```\r\n\r\n> I think it should not be the expected behavior especially when I feed a numpy array as input to the data creation function. Why is it converting my array into a list?\r\n\r\nThe same dataset can have examples in different types (Numpy arrays, Torch tensors, Pandas series, etc.), so recovering them all would be slow and impractical. Instead, the design of our formatting API is similar to Arrow's (the lib we use internally to store data on disk/ in RAM), which allows converting a batch of data to Python/Numpy/Pandas in a single call (and uses C++ to do so to make it faster).\r\n\r\n> Also if I change the first dimension of the Array2D shape to None, it's returning array correctly.\r\n\r\nSetting the first dimension to `None` makes it variable-length (allows passing arrays with the first dimensions of differing lengths).\r\n", "Current behavior when indexing the dataset:\r\n- Using `Array((2,2))` returns a list of lists.\r\n- Using `Array((None,2))` returns a numpy array.\r\n\r\nDon't you think this is kind of unexpected behavior from end-user perspective? \r\nAs a user, I expect that when I use `Array2D`, the behavior needs to be consistent even if I specify None or not. It should either return a list or an array. It needs to choose one. Let's say if it always return a list, then I will call `ds.set_format('np')` no problem.\r\n\r\nThe consistency can be in any of these aspects:\r\n1. preserves the type of the input data (in this case, a numpy array)\r\n2. ensure the output type is always the same (it can be either list or array, but it needs to be one of them)\r\n\r\nRight now the API doesn't conform to any of these aspects. But I think it needs to conform to one.", "I thought we made this consistent by returning lists in both scenarios...", "Fixed in #5751 " ]
1,658,958,406	5,718	Reorder default data splits to have validation before test	closed	2023-04-07T16:01:26	2023-04-27T14:43:13	2023-04-27T14:35:52	https://github.com/huggingface/datasets/pull/5718	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5718", "html_url": "https://github.com/huggingface/datasets/pull/5718", "diff_url": "https://github.com/huggingface/datasets/pull/5718.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5718.patch", "merged_at": "2023-04-27T14:35:52" }	albertvillanova	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "After this CI error: https://github.com/huggingface/datasets/actions/runs/4639528358/jobs/8210492953?pr=5718\r\n```\r\nFAILED tests/test_data_files.py::test_get_data_files_patterns[data_file_per_split4] - AssertionError: assert ['random', 'train'] == ['train', 'random']\r\n At index 0 diff: 'random' != 'train'\r\n Full diff:\r\n - ['train', 'random']\r\n + ['random', 'train']\r\n```\r\nI have checked locally and found out that the data split order is nondeterministic. I am addressing this in a separate issue.\r\n\r\nWe should first address:\r\n- #5728 \r\n- #5729", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007728 / 0.011353 (-0.003624) \| 0.005275 / 0.011008 (-0.005734) \| 0.097708 / 0.038508 (0.059199) \| 0.039851 / 0.023109 (0.016741) \| 0.333360 / 0.275898 (0.057462) \| 0.376135 / 0.323480 (0.052655) \| 0.006355 / 0.007986 (-0.001630) \| 0.004193 / 0.004328 (-0.000135) \| 0.072882 / 0.004250 (0.068631) \| 0.052668 / 0.037052 (0.015615) \| 0.347359 / 0.258489 (0.088870) \| 0.382280 / 0.293841 (0.088440) \| 0.035996 / 0.128546 (-0.092550) \| 0.012517 / 0.075646 (-0.063129) \| 0.334520 / 0.419271 (-0.084751) \| 0.051969 / 0.043533 (0.008436) \| 0.335735 / 0.255139 (0.080596) \| 0.359921 / 0.283200 (0.076722) \| 0.113971 / 0.141683 (-0.027712) \| 1.465636 / 1.452155 (0.013481) \| 1.559824 / 1.492716 (0.067108) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223997 / 0.018006 (0.205991) \| 0.499041 / 0.000490 (0.498551) \| 0.009697 / 0.000200 (0.009497) \| 0.000245 / 0.000054 (0.000190) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027031 / 0.037411 (-0.010381) \| 0.110271 / 0.014526 (0.095745) \| 0.115848 / 0.176557 (-0.060709) \| 0.174253 / 0.737135 (-0.562883) \| 0.122616 / 0.296338 (-0.173723) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.417275 / 0.215209 (0.202066) \| 4.158678 / 2.077655 (2.081023) \| 1.917585 / 1.504120 (0.413465) \| 1.722219 / 1.541195 (0.181025) \| 1.813284 / 1.468490 (0.344793) \| 0.707193 / 4.584777 (-3.877584) \| 3.853545 / 3.745712 (0.107833) \| 3.369240 / 5.269862 (-1.900621) \| 1.820264 / 4.565676 (-2.745412) \| 0.087340 / 0.424275 (-0.336936) \| 0.012305 / 0.007607 (0.004698) \| 0.520326 / 0.226044 (0.294281) \| 5.107383 / 2.268929 (2.838455) \| 2.413977 / 55.444624 (-53.030647) \| 2.074356 / 6.876477 (-4.802121) \| 2.255959 / 2.142072 (0.113887) \| 0.849850 / 4.805227 (-3.955377) \| 0.170116 / 6.500664 (-6.330548) \| 0.067203 / 0.075469 (-0.008267) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.168158 / 1.841788 (-0.673629) \| 15.046312 / 8.074308 (6.972004) \| 15.113924 / 10.191392 (4.922532) \| 0.145288 / 0.680424 (-0.535136) \| 0.017959 / 0.534201 (-0.516242) \| 0.424666 / 0.579283 (-0.154617) \| 0.422560 / 0.434364 (-0.011804) \| 0.526386 / 0.540337 (-0.013952) \| 0.623755 / 1.386936 (-0.763181) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007676 / 0.011353 (-0.003677) \| 0.005240 / 0.011008 (-0.005769) \| 0.074668 / 0.038508 (0.036160) \| 0.035570 / 0.023109 (0.012461) \| 0.348524 / 0.275898 (0.072626) \| 0.378157 / 0.323480 (0.054677) \| 0.006112 / 0.007986 (-0.001873) \| 0.005641 / 0.004328 (0.001312) \| 0.073536 / 0.004250 (0.069286) \| 0.048651 / 0.037052 (0.011599) \| 0.359282 / 0.258489 (0.100793) \| 0.385961 / 0.293841 (0.092120) \| 0.035417 / 0.128546 (-0.093129) \| 0.012227 / 0.075646 (-0.063419) \| 0.085725 / 0.419271 (-0.333546) \| 0.049651 / 0.043533 (0.006118) \| 0.344122 / 0.255139 (0.088983) \| 0.364795 / 0.283200 (0.081595) \| 0.112711 / 0.141683 (-0.028972) \| 1.426823 / 1.452155 (-0.025332) \| 1.534745 / 1.492716 (0.042029) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.201728 / 0.018006 (0.183721) \| 0.448533 / 0.000490 (0.448043) \| 0.003554 / 0.000200 (0.003354) \| 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) \| 0.030917 / 0.037411 (-0.006494) \| 0.117966 / 0.014526 (0.103440) \| 0.125954 / 0.176557 (-0.050602) \| 0.176382 / 0.737135 (-0.560753) \| 0.130757 / 0.296338 (-0.165582) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.422167 / 0.215209 (0.206958) \| 4.213948 / 2.077655 (2.136294) \| 2.040049 / 1.504120 (0.535929) \| 1.858317 / 1.541195 (0.317122) \| 1.937108 / 1.468490 (0.468618) \| 0.707797 / 4.584777 (-3.876979) \| 3.831061 / 3.745712 (0.085349) \| 3.373711 / 5.269862 (-1.896151) \| 1.590343 / 4.565676 (-2.975333) \| 0.086672 / 0.424275 (-0.337603) \| 0.012429 / 0.007607 (0.004821) \| 0.520269 / 0.226044 (0.294225) \| 5.207285 / 2.268929 (2.938357) \| 2.518107 / 55.444624 (-52.926517) \| 2.230696 / 6.876477 (-4.645781) \| 2.363164 / 2.142072 (0.221091) \| 0.836749 / 4.805227 (-3.968479) \| 0.169676 / 6.500664 (-6.330988) \| 0.065766 / 0.075469 (-0.009703) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.251195 / 1.841788 (-0.590592) \| 15.196091 / 8.074308 (7.121782) \| 14.991600 / 10.191392 (4.800208) \| 0.165335 / 0.680424 (-0.515089) \| 0.017789 / 0.534201 (-0.516412) \| 0.433863 / 0.579283 (-0.145420) \| 0.428660 / 0.434364 (-0.005704) \| 0.527385 / 0.540337 (-0.012952) \| 0.628067 / 1.386936 (-0.758869) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d06b8c21ba98ae85971a2b1d135ac2ef035b59c9 \"CML watermark\")\n" ]
1,658,729,866	5,717	Errror when saving to disk a dataset of images	open	2023-04-07T11:59:17	2025-07-13T08:27:47	null	https://github.com/huggingface/datasets/issues/5717	null	jplu	false	[ "Looks like as long as the number of shards makes a batch lower than 1000 images it works. In my training set I have 40K images. If I use `num_shards=40` (batch of 1000 images) I get the error, but if I update it to `num_shards=50` (batch of 800 images) it works.\r\n\r\nI will be happy to share my dataset privately if it can help to better debug.", "Hi! I didn't manage to reproduce this behavior, so sharing the dataset with us would help a lot. \r\n\r\n> My dataset is around 50K images, is this error might be due to a bad image?\r\n\r\nThis shouldn't be the case as we save raw data to disk without decoding it.", "OK, thanks! The dataset is currently hosted on a gcs bucket. How would you like to proceed for sharing the link? ", "You could follow [this](https://cloud.google.com/storage/docs/collaboration#browser) procedure or upload the dataset to Google Drive (50K images is not that much unless high-res) and send me an email with the link.", "Thanks @mariosasko. I just sent you the GDrive link.", "Thanks @jplu! I managed to reproduce the `TypeError` - it stems from [this](https://github.com/huggingface/datasets/blob/e3f4f124a1b118a5bfff5bae76b25a68aedbebbc/src/datasets/features/image.py#L258-L264) line, which can return a `ChunkedArray` (its mask is also chunked then, which Arrow does not allow) when the embedded data is too big to fit in a standard `Array`.\r\n\r\nI'm working on a fix.", "@yairl-dn You should be able to bypass this issue by reducing `datasets.config.DEFAULT_MAX_BATCH_SIZE` (1000 by default)\r\n\r\nIn Datasets 3.0, the Image storage format will be simplified, so this should be easier to fix then.", "The same error occurs with my save_to_disk() of Audio() items. I still get it with:\r\n```python\r\nimport datasets\r\ndatasets.config.DEFAULT_MAX_BATCH_SIZE=35\r\nfrom datasets import Features, Array2D, Value, Dataset, Sequence, Audio\r\n```\r\n\r\n```\r\nSaving the dataset (41/47 shards): 88%\|██████████████████████████████████████████▉ \| 297/339 [01:21<00:11, 3.65 examples/s]\r\nTraceback (most recent call last):\r\nFile \"/mnt/ddrive/prj/voice/voice-training-dataset-create/./dataset.py\", line 155, in <module>\r\ncreate_dataset(args)\r\nFile \"/mnt/ddrive/prj/voice/voice-training-dataset-create/./dataset.py\", line 137, in create_dataset\r\nhf_dataset.save_to_disk(args.outds)\r\nFile \"/home/j/src/py/datasets/src/datasets/arrow_dataset.py\", line 1532, in save_to_disk\r\nfor job_id, done, content in Dataset._save_to_disk_single(*kwargs):\r\nFile \"/home/j/src/py/datasets/src/datasets/arrow_dataset.py\", line 1563, in _save_to_disk_single\r\nwriter.write_table(pa_table)\r\nFile \"/home/j/src/py/datasets/src/datasets/arrow_writer.py\", line 574, in write_table\r\npa_table = embed_table_storage(pa_table)\r\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nFile \"/home/j/src/py/datasets/src/datasets/table.py\", line 2307, in embed_table_storage\r\narrays = [\r\n^\r\nFile \"/home/j/src/py/datasets/src/datasets/table.py\", line 2308, in <listcomp>\r\nembed_array_storage(table[name], feature) if require_storage_embed(feature) else table[name]\r\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nFile \"/home/j/src/py/datasets/src/datasets/table.py\", line 1831, in wrapper\r\nreturn pa.chunked_array([func(chunk, args, *kwargs) for chunk in array.chunks])\r\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nFile \"/home/j/src/py/datasets/src/datasets/table.py\", line 1831, in <listcomp>\r\nreturn pa.chunked_array([func(chunk, args, kwargs) for chunk in array.chunks])\r\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nFile \"/home/j/src/py/datasets/src/datasets/table.py\", line 2177, in embed_array_storage\r\nreturn feature.embed_storage(array)\r\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nFile \"/home/j/src/py/datasets/src/datasets/features/audio.py\", line 276, in embed_storage\r\nstorage = pa.StructArray.from_arrays([bytes_array, path_array], [\"bytes\", \"path\"], mask=bytes_array.is_null())\r\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nFile \"pyarrow/array.pxi\", line 2850, in pyarrow.lib.StructArray.from_arrays\r\nFile \"pyarrow/array.pxi\", line 3290, in pyarrow.lib.c_mask_inverted_from_obj\r\nTypeError: Mask must be a pyarrow.Array of type boolean\r\n```", "Similar to @jaggzh, setting `datasets.config.DEFAULT_MAX_BATCH_SIZE` did not help in my case (same error here but for different dataset: https://github.com/Stanford-AIMI/RRG24/issues/2).\r\n\r\nThis is also reproducible with this open dataset: https://huggingface.co/datasets/nlphuji/winogavil/discussions/1\r\n\r\nHere's some code to do so:\r\n```python\r\nimport datasets\r\n\r\ndatasets.config.DEFAULT_MAX_BATCH_SIZE = 1\r\n\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"nlphuji/winogavil\")\r\n\r\nds.save_to_disk(\"temp\")\r\n```\r\n\r\nI've done some more debugging with `datasets==2.18.0` (which incorporates PR #6283 as suggested by @lhoestq in the above dataset discussion), and it seems like the culprit might now be these lines: https://github.com/huggingface/datasets/blob/ca8409a8bec4508255b9c3e808d0751eb1005260/src/datasets/table.py#L2111-L2115\r\n\r\nFrom what I understand (and apologies I'm new to pyarrow), for an Image or Audio feature, these lines recursively call `embed_array_storage` for a list of either feature, ending up in the feature's `embed_storage` function. For all values in the list, `embed_storage` reads the bytes if they're not already loaded. The issue is the list being passed to the first recursive call is `array.values` which are the underlying values of `array` regardless of `array`'s slicing (as influenced by parameters such as `datasets.config.DEFAULT_MAX_BATCH_SIZE`). This results in the same overflowing list of bytes that result in the ChunkedArray being returned in `embed_storage`. Even if the array weren't to overflow and this code ran without throwing an exception, it still seems incorrect to load all values if you ultimately only want some subset with `ListArray.from_arrays(offsets, values)`; it seems some wasted effort if those values thrown out will get loaded again in the next batch and vice versa for the current batch of values during later batches.\r\n\r\nMaybe there's a fix where you could pass a mask to `embed_storage` such that it only loads the values you ultimately want for the current batch? Curious to see if you agree with this diagnosis of the problem and if you think this fix is viable @mariosasko?", "Would be nice if they have something similar to Dagshub's S3 sync; it worked like a charm for my bigger datasets.", "I guess also the proposed masking solution simply enables `datasets.config.DEFAULT_MAX_BATCH_SIZE` by reducing the number of elements loaded, it does not address the underlying problem of trying to load all the images as bytes into a pyarrow array.\r\n\r\nI'm happy to turn this into an actual PR but here's what I've implemented locally at `tables.py:embed_array_storage` to fix the above test case (`nlphuji/winogavil`) and my own use case:\r\n```python\r\n elif pa.types.is_list(array.type):\r\n # feature must be either [subfeature] or Sequence(subfeature)\r\n # Merge offsets with the null bitmap to avoid the \"Null bitmap with offsets slice not supported\" ArrowNotImplementedError\r\n array_offsets = _combine_list_array_offsets_with_mask(array)\r\n\r\n # mask underlying struct array so array_values.to_pylist()\r\n # fills None (see feature.embed_storage)\r\n idxs = np.arange(len(array.values))\r\n idxs = pa.ListArray.from_arrays(array_offsets, idxs).flatten()\r\n mask = np.ones(len(array.values)).astype(bool)\r\n mask[idxs] = False\r\n mask = pa.array(mask)\r\n # indexing 0 might be problematic but not sure\r\n # how else to get arbitrary keys from a struct array\r\n array_keys = array.values[0].keys()\r\n # is array.values always a struct array?\r\n array_values = pa.StructArray.from_arrays(\r\n arrays=[array.values.field(k) for k in array_keys],\r\n names=array_keys,\r\n mask=mask,\r\n )\r\n if isinstance(feature, list):\r\n return pa.ListArray.from_arrays(array_offsets, _e(array_values, feature[0]))\r\n if isinstance(feature, Sequence) and feature.length == -1:\r\n return pa.ListArray.from_arrays(array_offsets, _e(array_values, feature.feature))\r\n```\r\n\r\nAgain though I'm new to pyarrow so this might not be the cleanest implementation, also I'm really not sure if there are other cases where this solution doesn't work. Would love to get some feedback from the hf folks!", "I have the same issue, with an audio dataset where file sizes vary significantly (~0.2-200 mb). Reducing `datasets.config.DEFAULT_MAX_BATCH_SIZE` doesn't help.", "Still the problem is occured.\r\nHuggingface is sucks 🤮🤮🤮🤮", "Came across this issue myself, with the same symptoms and reasons as everyone else; `pa.array` is returning a `ChunkedArray` in `features.audio.Audio.embed_storage` for my audio which varies between ~1MB and ~10MB in size.\r\n\r\nI would rather remove a troublesome file from my dataset than have to switch off this library, but it would be difficult to identify which file(s) caused the issue, and it may just shift the issue down to another shard or another file anyway. So, I took the path of least resistance and simply dropped anything beyond the first chunk when this issue occurred, and added a warning to indicate what was dropped.\r\n\r\nIn the end I lost one** file out of 105,024 samples and was able to complete the 1,479 shard dataset after only the one issue on shard 228.\r\n\r\nWhile this is certainly not an ideal solution, it does represent a much better user experience, and was acceptable for my use case. I'm going to test the Image portion and then open a pull request to propose this \"lossy\" behavior become the way these edge cases are handled (maybe behind an environment flag?) until someone like @mariosasko or others can formulate a more holistic solution.\r\n\r\nMy work-in-progress \"fix\": https://github.com/huggingface/datasets/compare/main...painebenjamin:datasets:main (https://github.com/painebenjamin/datasets)", "Another option could be to use `pa.large_binary` instead of `pa.binary` in certain cases ?", "For my large audio dataset, what seems to work for me is to locally change `pa_type` to `pa.large_binary` in both\r\n\r\nhttps://github.com/huggingface/datasets/blob/01f91bae037c98f2e05456287bab21470adb8f07/src/datasets/features/audio.py#L71\r\n\r\nand\r\n\r\nhttps://github.com/huggingface/datasets/blob/01f91bae037c98f2e05456287bab21470adb8f07/src/datasets/features/audio.py#L270\r\n\r\nprior to uploading the dataset. Before downloading it, I just remove both changes to make sure any user with latest `datasets` can use it.\r\n\r\nAs a side note, the other proposed workarounds did not work for me.", "Hey @fdschmidt93 I am not sure to follow. Can users downloading your dataset from the hub read it if you created the files with large_binary? It sounds like it will not be casted properly for them? ", "Yes, that should work. In full detail -\r\n\r\nI have two separate conda environments. \r\n\r\n1. The one I prepare the data with for which I apply the above changes.\r\n2. Another one I actually run my experiments with that uses latest available `datasets` from pip\r\n\r\nThis seems to work just fine. More concretely, I'm downloading my own data uploaded with environment 1 from a private HF datasets repo in environment 2 (i.e., `load_dataset(...)`) and run the experiments.\r\n\r\nE: The private HF repo I was referring to now is public: https://huggingface.co/datasets/WueNLP/belebele-fleurs", "Interesting ... so it's not even relevant at reading time, only writting ... Thanks I'll try this out.", "I'm experiencing the same issue with the image dataset. Has this problem been resolved? Neither reducing the number of images per batch nor decreasing the DEFAULT_MAX_BATCH_SIZE has solved the issue.\n\nWhat I discovered is that in [line 272](https://github.com/huggingface/datasets/blob/3a4e74a9ace62ecd5c9cde7dcb6bcabd65cc7857/src/datasets/features/image.py#L272), when the storage length becomes large (probably over 1000), it returns a chunked array, which causes an error. \n\n Therefore, I tried to explicitly flatten it using `.combine_chunks()`, but encountered errors like `pyarrow.lib.ArrowInvalid: offset overflow while concatenating arrays`. \n```python\n bytes_array = pa.array(\n [\n (path_to_bytes(x[\"path\"]) if x[\"bytes\"] is None else x[\"bytes\"]) if x is not None else None\n for x in storage.to_pylist()\n ],\n type=pa.binary(),\n ).combine_chunks() # <- HERE\n```\n\nThis makes me feel there are limitations in processing images with arrow.\n\nWhen constructing an image dataset, I think storing image paths as strings might be the most straightforward bypass rather than using `Image()`. I'm curious if there would be any performance disadvantages to this approach.\n", "Absurd, after months I encountered the same error with datasets 3.6.0 and an Image-Text dataset.\nAny solution?", "I end up by splitting the dataset in chunks, and uploading them with different commits and config names. Starting from ~ 30k images, each chunk has ~ 2.5k images. \nThis is the only effective solution I found.\n\nI wonder how the HF team has successfully uploaded datasets like the_cauldron [localized_narratives] with more then 200k lines of image-text. I mean, for sure they can do more then push_to_hub, eventually they can explain @lhoestq ? (thanks in advance) " ]
1,658,613,092	5,716	Handle empty audio	closed	2023-04-07T09:51:40	2023-09-27T17:47:08	2023-09-27T17:47:08	https://github.com/huggingface/datasets/issues/5716	null	ben-8543	false	[ "Hi! Can you share one of the problematic audio files with us?\r\n\r\nI tried to reproduce the error with the following code: \r\n```python\r\nimport soundfile as sf\r\nimport numpy as np\r\nfrom datasets import Audio\r\n\r\nsf.write(\"empty.wav\", np.array([]), 16000)\r\nAudio(sampling_rate=24000).decode_example({\"path\": \"empty.wav\", \"bytes\": None})\r\n```\r\nBut without success.\r\n\r\nAlso, what version of `librosa` is installed in your env? (You can get this info with `python -c \"import librosa; print(librosa.__version__)`)\r\n\r\n", "I'm closing this issue as the reproducer hasn't been provided." ]
1,657,479,788	5,715	Return Numpy Array (fixed length) Mode, in __get_item__, Instead of List	closed	2023-04-06T13:57:48	2023-04-20T17:16:26	2023-04-20T17:16:26	https://github.com/huggingface/datasets/issues/5715	null	jungbaepark	false	[ "Hi! \r\n\r\nYou can use [`.set_format(\"np\")`](https://huggingface.co/docs/datasets/process#format) to get NumPy arrays (or Pytorch tensors with `.set_format(\"torch\")`) in `__getitem__`.\r\n\r\nAlso, have you been able to reproduce the linked PyTorch issue with a HF dataset?\r\n " ]
1,657,388,033	5,714	Fix xnumpy_load for .npz files	closed	2023-04-06T13:01:45	2023-04-07T09:23:54	2023-04-07T09:16:57	https://github.com/huggingface/datasets/pull/5714	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5714", "html_url": "https://github.com/huggingface/datasets/pull/5714", "diff_url": "https://github.com/huggingface/datasets/pull/5714.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5714.patch", "merged_at": "2023-04-07T09:16:57" }	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) \| 0.006498 / 0.011353 (-0.004855) \| 0.004406 / 0.011008 (-0.006602) \| 0.097136 / 0.038508 (0.058628) \| 0.027711 / 0.023109 (0.004601) \| 0.303092 / 0.275898 (0.027194) \| 0.336804 / 0.323480 (0.013324) \| 0.004838 / 0.007986 (-0.003148) \| 0.004533 / 0.004328 (0.000204) \| 0.075062 / 0.004250 (0.070812) \| 0.035105 / 0.037052 (-0.001947) \| 0.310245 / 0.258489 (0.051756) \| 0.347086 / 0.293841 (0.053245) \| 0.030867 / 0.128546 (-0.097679) \| 0.011436 / 0.075646 (-0.064211) \| 0.320728 / 0.419271 (-0.098544) \| 0.042303 / 0.043533 (-0.001230) \| 0.308177 / 0.255139 (0.053038) \| 0.333673 / 0.283200 (0.050473) \| 0.084736 / 0.141683 (-0.056947) \| 1.477391 / 1.452155 (0.025237) \| 1.530399 / 1.492716 (0.037682) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.212698 / 0.018006 (0.194692) \| 0.409098 / 0.000490 (0.408608) \| 0.004202 / 0.000200 (0.004002) \| 0.000070 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022725 / 0.037411 (-0.014686) \| 0.095866 / 0.014526 (0.081340) \| 0.104153 / 0.176557 (-0.072404) \| 0.162964 / 0.737135 (-0.574171) \| 0.106505 / 0.296338 (-0.189834) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.431336 / 0.215209 (0.216127) \| 4.283290 / 2.077655 (2.205635) \| 1.982418 / 1.504120 (0.478298) \| 1.762104 / 1.541195 (0.220909) \| 1.807528 / 1.468490 (0.339038) \| 0.695507 / 4.584777 (-3.889270) \| 3.376299 / 3.745712 (-0.369413) \| 1.856642 / 5.269862 (-3.413219) \| 1.154258 / 4.565676 (-3.411419) \| 0.082749 / 0.424275 (-0.341526) \| 0.012289 / 0.007607 (0.004682) \| 0.525842 / 0.226044 (0.299798) \| 5.285764 / 2.268929 (3.016835) \| 2.389926 / 55.444624 (-53.054698) \| 2.021830 / 6.876477 (-4.854646) \| 2.107460 / 2.142072 (-0.034612) \| 0.808118 / 4.805227 (-3.997109) \| 0.150791 / 6.500664 (-6.349873) \| 0.065825 / 0.075469 (-0.009644) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.206939 / 1.841788 (-0.634849) \| 13.795902 / 8.074308 (5.721594) \| 14.107950 / 10.191392 (3.916558) \| 0.144300 / 0.680424 (-0.536124) \| 0.016478 / 0.534201 (-0.517723) \| 0.379395 / 0.579283 (-0.199888) \| 0.388437 / 0.434364 (-0.045927) \| 0.451443 / 0.540337 (-0.088894) \| 0.523142 / 1.386936 (-0.863794) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006503 / 0.011353 (-0.004850) \| 0.004578 / 0.011008 (-0.006430) \| 0.076278 / 0.038508 (0.037770) \| 0.028052 / 0.023109 (0.004943) \| 0.337873 / 0.275898 (0.061975) \| 0.371368 / 0.323480 (0.047888) \| 0.005086 / 0.007986 (-0.002899) \| 0.003354 / 0.004328 (-0.000975) \| 0.076876 / 0.004250 (0.072625) \| 0.039146 / 0.037052 (0.002093) \| 0.340299 / 0.258489 (0.081810) \| 0.381209 / 0.293841 (0.087368) \| 0.031771 / 0.128546 (-0.096775) \| 0.011670 / 0.075646 (-0.063976) \| 0.085156 / 0.419271 (-0.334116) \| 0.041990 / 0.043533 (-0.001543) \| 0.338644 / 0.255139 (0.083505) \| 0.362461 / 0.283200 (0.079262) \| 0.089772 / 0.141683 (-0.051911) \| 1.480341 / 1.452155 (0.028187) \| 1.562815 / 1.492716 (0.070099) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.205700 / 0.018006 (0.187694) \| 0.402206 / 0.000490 (0.401716) \| 0.001212 / 0.000200 (0.001012) \| 0.000070 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025172 / 0.037411 (-0.012240) \| 0.100959 / 0.014526 (0.086433) \| 0.108464 / 0.176557 (-0.068093) \| 0.161321 / 0.737135 (-0.575814) \| 0.114245 / 0.296338 (-0.182093) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.437425 / 0.215209 (0.222216) \| 4.362212 / 2.077655 (2.284557) \| 2.068815 / 1.504120 (0.564695) \| 1.864089 / 1.541195 (0.322894) \| 1.909038 / 1.468490 (0.440548) \| 0.696097 / 4.584777 (-3.888680) \| 3.358628 / 3.745712 (-0.387084) \| 2.999085 / 5.269862 (-2.270777) \| 1.533917 / 4.565676 (-3.031760) \| 0.083010 / 0.424275 (-0.341266) \| 0.012372 / 0.007607 (0.004765) \| 0.539926 / 0.226044 (0.313882) \| 5.438326 / 2.268929 (3.169397) \| 2.498581 / 55.444624 (-52.946043) \| 2.153359 / 6.876477 (-4.723117) \| 2.177891 / 2.142072 (0.035819) \| 0.803169 / 4.805227 (-4.002059) \| 0.151079 / 6.500664 (-6.349585) \| 0.065981 / 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) \| 1.336682 / 1.841788 (-0.505106) \| 14.133055 / 8.074308 (6.058747) \| 14.033972 / 10.191392 (3.842580) \| 0.152109 / 0.680424 (-0.528315) \| 0.016475 / 0.534201 (-0.517726) \| 0.387808 / 0.579283 (-0.191475) \| 0.378347 / 0.434364 (-0.056017) \| 0.484732 / 0.540337 (-0.055606) \| 0.569907 / 1.386936 (-0.817029) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1c4ec00511868bd881e84a6f7e0333648d833b8e \"CML watermark\")\n" ]
1,657,141,251	5,713	ArrowNotImplementedError when loading dataset from the hub	closed	2023-04-06T10:27:22	2023-04-06T13:06:22	2023-04-06T13:06:21	https://github.com/huggingface/datasets/issues/5713	null	jplu	false	[ "Hi Julien ! This sounds related to https://github.com/huggingface/datasets/issues/5695 - TL;DR: you need to have shards smaller than 2GB to avoid this issue\r\n\r\nThe number of rows per shard is computed using an estimated size of the full dataset, which can sometimes lead to shards bigger than `max_shard_size`. The estimation is currently done using the first samples of the dataset (which can surely be improved). We should probably open an issue to fix this once and for all.\r\n\r\nAnyway for your specific dataset I'd suggest you to pass `num_shards` instead of `max_shard_size` for now, and make sure to have enough shards to end up with shards smaller than 2GB", "Hi Quentin! Thanks a lot! Using `num_shards` instead of `max_shard_size` works as expected.\r\n\r\nIndeed the way you describe how the size is computed cannot really work with the dataset I'm building as all the image doesn't have the same resolution and then size. Opening an issue on this might be a good idea." ]
1,655,972,106	5,712	load_dataset in v2.11.0 raises "ValueError: seek of closed file" in np.load()	closed	2023-04-05T16:47:10	2023-04-06T08:32:37	2023-04-05T17:17:44	https://github.com/huggingface/datasets/issues/5712	null	rcasero	false	[ "Closing since this is a duplicate of #5711", "> Closing since this is a duplicate of #5711\r\n\r\nSorry @mariosasko , my internet went down went submitting the issue, and somehow it ended up creating a duplicate" ]
1,655,971,647	5,711	load_dataset in v2.11.0 raises "ValueError: seek of closed file" in np.load()	closed	2023-04-05T16:46:49	2023-04-07T09:16:59	2023-04-07T09:16:59	https://github.com/huggingface/datasets/issues/5711	null	rcasero	false	[ "It seems like https://github.com/huggingface/datasets/pull/5626 has introduced this error. \r\n\r\ncc @albertvillanova \r\n\r\nI think replacing:\r\nhttps://github.com/huggingface/datasets/blob/0803a006db1c395ac715662cc6079651f77c11ea/src/datasets/download/streaming_download_manager.py#L777-L778\r\nwith:\r\n```python\r\nreturn np.load(xopen(filepath_or_buffer, \"rb\", use_auth_token=use_auth_token), args, *kwargs)\r\n```\r\nshould fix the issue.\r\n\r\n(Maybe this is also worth doing a patch release afterward)", "Thanks for reporting, @rcasero.\r\n\r\nI can have a look..." ]
1,655,703,534	5,710	OSError: Memory mapping file failed: Cannot allocate memory	closed	2023-04-05T14:11:26	2023-04-20T17:16:40	2023-04-20T17:16:40	https://github.com/huggingface/datasets/issues/5710	null	Saibo-creator	false	[ "Hi! This error means that PyArrow's internal [`mmap`](https://man7.org/linux/man-pages/man2/mmap.2.html) call failed to allocate memory, which can be tricky to debug. Since this error is more related to PyArrow than us, I think it's best to report this issue in their [repo](https://github.com/apache/arrow) (they are more experienced on this matter). Also, googling \"mmap cannot allocate memory\" returns some approaches to solving this problem." ]
1,655,423,503	5,709	Manually dataset info made not taken into account	closed	2023-04-05T11:15:17	2023-04-06T08:52:20	2023-04-06T08:52:19	https://github.com/huggingface/datasets/issues/5709	null	jplu	false	[ "hi @jplu ! Did I understand you correctly that you create the dataset, push it to the Hub with `.push_to_hub` and you see a `dataset_infos.json` file there, then you edit this file, load the dataset with `load_dataset` and you don't see any changes in `.info` attribute of a dataset object? \r\n\r\nThis is actually weird that when you push your dataset to the Hub, a `dataset_infos.json` file is created, because this file is deprecated and it should create `README.md` with the `dataset_info` field instead. Some keys are also deprecated, like \"supervised_keys\" and \"task_templates\".\r\n\r\nCan you please provide a toy reproducible example of how you create and push the dataset? And also why do you want to change this file, especially the number of bytes and examples?", "Hi @polinaeterna Yes I have created the dataset with `Dataset.from_dict` applied some updates afterward and when I pushed to the hub I had a `dataset_infos.json` file and there was a `README.md` file as well.\r\n\r\nI didn't know that the JSON file was deprecated. So I have built my dataset with `ImageBuilder` instead and now it works like a charm without having to touch anything.\r\n\r\nI haven't succeed to reproduce the creation of the JSON file with a toy example, hence, I certainly did some mistakes when I have manipulated my dataset manually at first. My bad." ]
1,655,023,642	5,708	Dataset sizes are in MiB instead of MB in dataset cards	closed	2023-04-05T06:36:03	2023-12-21T10:20:28	2023-12-21T10:20:27	https://github.com/huggingface/datasets/issues/5708	null	albertvillanova	false	[ "Example of bulk edit: https://huggingface.co/datasets/aeslc/discussions/5", "looks great! \r\n\r\nDo you encode the fact that you've already converted a dataset? (to not convert it twice) or do you base yourself on the info contained in `dataset_info`", "I am only looping trough the dataset cards, assuming that all of them were created with MiB.\r\n\r\nI agree we should only run the bulk edit once for all canonical datasets: I'm using a for-loop over canonical datasets.", "yes, worst case, we have this in structured data:\r\n\r\n<img width=\"337\" alt=\"image\" src=\"https://user-images.githubusercontent.com/326577/230037051-06caddcb-08c8-4953-a710-f3d122917db3.png\">\r\n", "I have just included as well the conversion from MB to GB if necessary. See: \r\n- https://huggingface.co/datasets/bookcorpus/discussions/2/files\r\n- https://huggingface.co/datasets/asnq/discussions/2/files", "Nice. Is it another loop? Because in https://huggingface.co/datasets/amazon_us_reviews/discussions/2/files we have `32377.29 MB` for example", "First, I tested some batches to check the changes made. Then I incorporated the MB to GB conversion. Now I'm running the rest.", "The bulk edit parsed 751 canonical datasets and updated 166.", "Thanks a lot!\r\n\r\nThe sizes now match as expected!\r\n\r\n<img width=\"1446\" alt=\"Capture d’écran 2023-04-05 à 16 10 15\" src=\"https://user-images.githubusercontent.com/1676121/230107044-ac2a76ea-a4fe-4e81-a925-f464b85f5edd.png\">\r\n", "I made another bulk edit of ancient canonical datasets that were moved to community organization. I have parsed 11 datasets and opened a PR on 3 of them:\r\n- [x] \"allenai/scicite\": https://huggingface.co/datasets/allenai/scicite/discussions/3\r\n- [x] \"allenai/scifact\": https://huggingface.co/datasets/allenai/scifact/discussions/2\r\n- [x] \"dair-ai/emotion\": https://huggingface.co/datasets/dair-ai/emotion/discussions/6", "should we force merge the PR and close this issue?", "I merged the PRs for \"scicite\" and \"scifact\"." ]
1,653,545,835	5,706	Support categorical data types for Parquet	closed	2023-04-04T09:45:35	2024-06-07T12:20:43	2024-06-07T12:20:43	https://github.com/huggingface/datasets/issues/5706	null	kklemon	false	[ "Hi ! We could definitely a type that holds the categories and uses a DictionaryType storage. There's a ClassLabel type that is similar with a 'names' parameter (similar to a id2label in deep learning frameworks) that uses an integer array as storage.\r\n\r\nIt can be added in `features.py`. Here are some pointers:\r\n- the conversion from HF type to PyArrow type is done in `get_nested_type`\r\n- the conversion from Pyarrow type to HF type is done in `generate_from_arrow_type`\r\n- `encode_nested_example` and `decode_nested_example` are used to do user's value (what users see) <-> storage value (what is in the pyarrow.array) if there's any conversion to do", "@kklemon did you implement this? Otherwise I would like to give it a try", "@mhattingpete no, I hadn't time for this so far. Feel free to work on this.", "#self-assign", "This would be super useful, so +1. \r\n\r\nAlso, these prior issues/PRs seem relevant: \r\nhttps://github.com/huggingface/datasets/issues/1906\r\nhttps://github.com/huggingface/datasets/pull/1936", "Hi, this is a really useful feature, has this been implemented yet? ", "Hey folks -- I'm thinking about trying a PR for this. As far as I can tell the only sticky point is that auto-generation of features from a pyarrow schema will fail under the current `generate_from_arrow_type` function because there is no encoding of the categorical string label -> int map in the pa.dictionary type itself; that is stored with the full array. \r\n\r\nI see two ways to solve this. Option 1 is to require datasets with categorical types to use pyarrow schema metadata to encode the entire HF feature dictionary, that way categorical types don't ever need to be inferred from the pa type alone. The downside to this is that it means that these datasets will be a bit brittle, as if the feature encoding API ever changes, they will suddenly be unloadable. \r\n\r\nThe other option is to modify `generate_from_arrow_type` to take per-field metadata, and include just that metadata (the category labels) in the schema metadata. \r\n\r\nDoes anyone at HF have any preference on these two (or alternate) approaches?", "Maybe we don't need to store the string label -> int map in the categorical for the corresponding `datasets` feature ?", "I think that does need to be stored in the Feature object. Similar to how\r\n`ClassLabel` needs the class names for some of the provided default\r\nfunctionality (e.g., encoding or decoding values) here, a categorical\r\nfeature needs the same. Without storing that information, would you suggest\r\nthat categorical features just be stored internally as integer arrays?\r\n\r\nOn Fri, Sep 8, 2023, 5:37 AM Quentin Lhoest *@.>\r\nwrote:\r\n\r\n> Maybe we don't need to store the string label -> int map in the\r\n> categorical for the corresponding datasets feature ?\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/5706#issuecomment-1711375652>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AADS5XZV3RA4GBRVBLJN72LXZLROZANCNFSM6AAAAAAWSOUTJ4>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> @.>\r\n>\r\n", "Well IIRC you can concatenate two Arrow arrays with different dictionaries together. But for `datasets` would mean updating the `datasets` features when concatenating two arrays of the same type, which is not supported right now. That's why if there is a way to have it without storing the mapping in the feature object it would be nice.\r\n\r\nFor decoding we do have the string<->integer mapping from the array `dictionary` attribute so we're fine. For encoding I think it can work if we only encode when converting python objects to pyarrow in `TypedSequence.__arrow_array__` in `arow_writer.py`. It can work by converting the python objects to a pyarrow array and then use the `dictionary_encode` method.\r\n\r\nAnother concern about concatenation: I noticed pyarrow creates the new dictionary and indices in memory* when concatenating two dictionary encoded arrays. This can be a problem for big datastets, and we should probably use ChunkedArray objects instead. This can surely be taken care of in `array_concat` in `table.py`\r\n\r\ncc @mariosasko in case you have other ideas\r\n\r\n", "Hmm, that is a good point. What if we implemented this feature first in a\r\nmanner that didn't allow concatenation of arrays with different index to\r\ncategory maps? Then concatenation would be very straightforward, and I\r\nthink this is reasonable if the index to category map is stored in the\r\nschema as well. Obviously, this is limited in how folks could use the\r\nfeature, but they can always fall back to raw strings if needed, and as\r\nusage increases we'll have more data to see what the right solution here\r\nis.\r\n\r\nOn Fri, Sep 8, 2023, 6:49 AM Quentin Lhoest *@.*>\r\nwrote:\r\n\r\n> Well IIRC you can concatenate two Arrow arrays with different dictionaries\r\n> together. But for datasets would mean updating the datasets features when\r\n> concatenating two arrays of the same type, which is not supported right\r\n> now. That's why if there is a way to have it without storing the mapping in\r\n> the feature object it would be nice.\r\n>\r\n> For decoding we do have the string<->integer mapping from the array\r\n> dictionary attribute so we're fine. For encoding I think it can work if\r\n> we only encode when converting python objects to pyarrow in\r\n> TypedSequence.__arrow_array__ in arow_writer.py. It can work by\r\n> converting the python objects to a pyarrow array and then use the\r\n> dictionary_encode method.\r\n>\r\n> Another concern about concatenation: I noticed pyarrow creates the new\r\n> dictionary and indices in memory* when concatenating two dictionary\r\n> encoded arrays. This can be a problem for big datastets, and we should\r\n> probably use ChunkedArray objects instead. This can surely be taken care of\r\n> in array_concat in table.py\r\n>\r\n> cc @mariosasko <https://github.com/mariosasko> in case you have other\r\n> ideas\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/5706#issuecomment-1711468806>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AADS5X4E2KC2IXLDPYR3XZLXZLZ2FANCNFSM6AAAAAAWSOUTJ4>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> *@.>\r\n>\r\n", "@lhoestq @mariosasko just re-pinging on this so I can push forward further here. What are your thoughts on disallowing concatenation of categorical arrays for now such that the index to category map can be stored in the schema metadata? And/or other approaches that should be taken here?\r\n", "I think the easiest for now would be to add a `dictionary_decode` argument to the parquet loaders that would convert the dictionary type back to strings when set to `True`, and make `dictionary_decode=False` raise `NotImplementedError` for now if there are dictionary type columns. Would that be ok as a first step ?", "I mean, that would certainly be easiest but I don't think it really solves this issue in a meaningful way. This just changes the burden from string conversion from the user to HF Datasets, but doesn't actually enable HF Datasets to take advantage of the (very significant) storage and associated speed/memory savings offered by using categorical types. Given that those savings are what is of real interest here, I think keeping it explicit that it is not supported (and forcing the user to do the conversion) might actually be better that way this problem stays top of mind.\r\n\r\nIs there an objection with supporting categorical types explicitly through the medium I outlined above, where the error is raised if you try to concat two differently typed categorical columns?", "> This just changes the burden from string conversion from the user to HF Datasets, but doesn't actually enable HF Datasets to take advantage of the (very significant) storage and associated speed/memory savings offered by using categorical types.\r\n\r\nThere's already a ClassLabel type that does pretty much the same thing (store as integer instead of string) if it can help\r\n\r\n> Is there an objection with supporting categorical types explicitly through the medium I outlined above, where the error is raised if you try to concat two differently typed categorical columns?\r\n\r\nYea we do concatenation quite often (e.g. in `map`) so I don't think this is a viable option", "But how often in the cases where concatenation is done now would the\r\ncategorical label vocabulary actually change? I think it would be in\r\nbasically none of them. And in such cases, concatenation remains very easy,\r\nno?\r\n\r\nOn Fri, Sep 22, 2023, 12:02 PM Quentin Lhoest @.>\r\nwrote:\r\n\r\n> This just changes the burden from string conversion from the user to HF\r\n> Datasets, but doesn't actually enable HF Datasets to take advantage of the\r\n> (very significant) storage and associated speed/memory savings offered by\r\n> using categorical types.\r\n>\r\n> There's already a ClassLabel type that does pretty much the same thing\r\n> (store as integer instead of string) if it can help\r\n>\r\n> Is there an objection with supporting categorical types explicitly through\r\n> the medium I outlined above, where the error is raised if you try to concat\r\n> two differently typed categorical columns?\r\n>\r\n> Yea we do concatenation quite often (e.g. in map) so I don't think this\r\n> is a viable option\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/5706#issuecomment-1731667012>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AADS5X5CGWFXDCML6UKCWYLX3WZBXANCNFSM6AAAAAAWSOUTJ4>\r\n> .\r\n> You are receiving this because you commented.Message ID:\r\n> @.**>\r\n>\r\n", "Arrow IPC seems to require unified dictionaries anyway so actually we could surely focus only on this use case indeed @mmcdermott \r\n\r\nSo defining a new Feature type in `datasets` that contains the dictionary mapping should be fine (and concatenation would work out of the box), and it should also take care of checking that the data it encodes/decodes has the right dictionary. Do you think it can be done without impacting iterating speed for the other types @mariosasko ?\r\n\r\nRight now we have little bandwidth to work in this kind of things though" ]
1,653,500,383	5,705	Getting next item from IterableDataset took forever.	closed	2023-04-04T09:16:17	2023-04-05T23:35:41	2023-04-05T23:35:41	https://github.com/huggingface/datasets/issues/5705	null	HongtaoYang	false	[ "Hi! It can take some time to iterate over Parquet files as big as yours, convert the samples to Python, and find the first one that matches a filter predicate before yielding it...", "Thanks @mariosasko, I figured it was the filter operation. I'm closing this issue because it is not a bug, it is the expected beheaviour." ]
1,653,471,356	5,704	5537 speedup load	open	2023-04-04T08:58:14	2023-04-07T16:10:55	null	https://github.com/huggingface/datasets/pull/5704	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5704", "html_url": "https://github.com/huggingface/datasets/pull/5704", "diff_url": "https://github.com/huggingface/datasets/pull/5704.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5704.patch", "merged_at": null }	semajyllek	true	[ "Awesome ! cc @mariosasko :)", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5704). All of your documentation changes will be reflected on that endpoint.", "Hi, thanks for working on this!\r\n\r\nYour solution only works if the `root` is `\"\"`, e.g., this would yield an incorrect result:\r\n```python\r\ndset = load_dataset(\"user/hf-dataset-repo\", data_dir=\"path/to/data_dir\")\r\n```\r\n\r\nAlso, the `HfFileSystem` implementation in `datasets` will be replaced with the more powerful [one](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/hf_file_system.py) from `huggingface_hub` soon (I plan to open a PR that makes `find` much faster in the coming days). \r\n\r\nSo I don't think we want to merge this PR in the current state, but thanks again for the effort.\r\n\r\n (I'll comment on the original issue to propose a cleaner solution)", "Ooof. Sorry, I should have checked that more thoroughly then! I would say we could just add that check and only use my approach if the root is \"\", which would still be faster in many cases, but it sounds like you have a better solution on the way. Thanks for the feedback Mario." ]
1,653,158,955	5,703	[WIP][Test, Please ignore] Investigate performance impact of using multiprocessing only	closed	2023-04-04T04:37:49	2023-04-20T03:17:37	2023-04-20T03:17:32	https://github.com/huggingface/datasets/pull/5703	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5703", "html_url": "https://github.com/huggingface/datasets/pull/5703", "diff_url": "https://github.com/huggingface/datasets/pull/5703.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5703.patch", "merged_at": null }	hvaara	true	[ "`multiprocess` uses `dill` instead of `pickle` for pickling shared objects and, as such, can pickle more types than `multiprocessing`. And I don't think this is something we want to change :).", "That makes sense to me, and I don't think you should merge this change. I was only curious about the performance impact. I saw the benchmarks that was produced in other PRs, and wanted to get a better understanding of it. I created this PR to see if it got automatically added here.\r\n\r\nIs there a way I can generate those benchmarks myself?", "You can find some speed comparisons between dill and pickle on SO if you google \"dill vs pickle speed\".\r\n\r\nAnd for the benchmarks, you can generate them locally with DVC running this code from the repo root: https://github.com/huggingface/datasets/blob/0803a006db1c395ac715662cc6079651f77c11ea/.github/workflows/benchmarks.yaml#L23-L47.", "Thanks for the help @mariosasko!" ]
1,653,104,720	5,702	Is it possible or how to define a `datasets.Sequence` that could potentially be either a dict, a str, or None?	closed	2023-04-04T03:20:43	2023-04-05T14:15:18	2023-04-05T14:15:17	https://github.com/huggingface/datasets/issues/5702	null	gitforziio	false	[ "Hi ! `datasets` uses Apache Arrow as backend to store the data, and it requires each column to have a fixed type. Therefore a column can't have a mix of dicts/lists/strings.\r\n\r\nThough it's possible to have one (nullable) field for each type:\r\n```python\r\nfeatures = Features({\r\n \"text_alone\": Value(\"string\"),\r\n \"text_with_idxes\": {\r\n \"text\": Value(\"string\"),\r\n \"idxes\": Value(\"int64\")\r\n }\r\n})\r\n```\r\n\r\nbut you'd have to reformat your data fiels or define a [dataset loading script](https://huggingface.co/docs/datasets/dataset_script) to apply the appropriate parsing.\r\n\r\nAlternatively we could explore supporting the Arrow [Union](https://arrow.apache.org/docs/python/generated/pyarrow.UnionType.html) type which could solve this issue, but I don't know if it's well supported in python and with the rest of the ecosystem like Parquet", "@lhoestq Thank you! I further wonder if it's possible to use list subscripts as keys of a feature? Like\r\n```python\r\nfeatures = Features({\r\n 0: Value(\"string\"),\r\n 1: {\r\n \"text\": Value(\"string\"),\r\n \"idxes\": [Value(\"int64\")]\r\n },\r\n 2: Value(\"string\"),\r\n # ...\r\n})\r\n```", "Column names need to be strings, so you could use \"1\", \"2\", etc. or give appropriate column names", "@lhoestq Got it. Thank you!" ]
1,652,931,399	5,701	Add Dataset.from_spark	closed	2023-04-03T23:51:29	2023-06-16T16:39:32	2023-04-26T15:43:39	https://github.com/huggingface/datasets/pull/5701	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5701", "html_url": "https://github.com/huggingface/datasets/pull/5701", "diff_url": "https://github.com/huggingface/datasets/pull/5701.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5701.patch", "merged_at": "2023-04-26T15:43:39" }	maddiedawson	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "@mariosasko Would you or another HF datasets maintainer be able to review this, please?", "Amazing ! Great job @maddiedawson \r\n\r\nDo you know if it's possible to also support writing to Parquet using the HF ParquetWriter if `file_format=\"parquet\"` ?\r\n\r\nParquet is often used when people want to stream the data to train models - which is suitable for big datasets. On the other hand Arrow is generally used for local memory mapping with random access.\r\n\r\n> Please note there was a previous PR adding this functionality\r\n\r\nAm I right to say that it uses the spark workers to prepare the Arrow files ? If so this should make the data preparation fast and won't fill up the executor's memory as in the previously proposed PR", "Thanks for taking a look! Unlike the previous PR's approach, this implementation takes advantage of Spark mapping to distribute file writing over multiple tasks. (Also it doesn't load the entire dataset into memory :) )\r\n\r\nSupporting Parquet here sgtm; I'll modify the PR.\r\n\r\nI also updated the PR description with a common Spark-HF use case that we want to improve.", "Hey @albertvillanova @lhoestq , would one of you be able to re-review please? Thank you!", "@lhoestq this is ready for another pass! Thanks so much 🙏 ", "Friendly ping @lhoestq , also cc @polinaeterna who may be able to help take a look?", "Merging `main` into this branch should fix the CI", "Just rebased @lhoestq ", "Thanks @lhoestq ! Is there a way for me to trigger the github workflow myself to triage the test failure? I'm not able to repro the test failures locally.", "There were two test issues in the workflow that I wasn't able to reproduce locally:\r\n\r\n- Python 3.7: createDataFrame fails due to a pickling error. I modified the tests to instead write and read from json files\r\n- Python 3.10: A worker crashes for unknown reasons. I modified the spark setup to explicitly specify local mode in case it was trying to do something else; let's see if that fixes the issue", "Also one more question @lhoestq when is the next datasets release? We're hoping this can make it in", "I just re-ran the CI.\r\nI think we can do a release right after this PR is merged ;)", "Thanks all! @lhoestq could we re-run CI again please? I think we have to disable this feature on python 3.7 due to the pickling error. The other failure was due to https://issues.apache.org/jira/browse/SPARK-30952 so I rewrote the df processing", "Thanks @lhoestq , this is ready for another CI run. I pinned the pyspark version to see if that fixes the pickling issue", "The remaining CI issues have been addressed! They were\r\n\r\n- dill=0.3.1.1 is incompatible with cloudpickle, used by Spark. The min-dependency tests use this dill version, and those were failing. I added a skip-test annotation to skip Spark tests when using this dill version. This shouldn't be a production issue since if users are using that version of dill, they won't really be able to do anything with Spark anyway.\r\n- One of the Spark APIs used in this feature (mapInArrow) is incompatible with Windows. I filed a Spark ticket for the team to investigate. For the tests, I added another annotation to skip Spark tests on Windows. In the next PR (adding streaming mode), we should be able to support Windows since that won't use mapInArrow.\r\n\r\nI ran the CI on my forked branch: https://github.com/maddiedawson/datasets/pull/2 Everything passes except one instance of tests/test_metric_common.py::LocalMetricTest::test_load_metric_frugalscore; it looks like a flake.\r\n\r\n@lhoestq granted that the CI passes here, is this ok to merge and release? We'd like to put out a blog post tomorrow to broadcast this to Spark users!", "Thanks @lhoestq ! Could you help take a look at the error please? Seems unrelated...\r\n\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_multiprocessing_on_disk - NotADirectoryError: [WinError 267] The directory name is invalid: 'C:\\\\Users\\\\RUNNER~1\\\\AppData\\\\Local\\\\Temp\\\\tmptfnrdj4x\\\\cache-5c5687cf5629c97a_00000_of_00002.arrow'\r\n===== 1 failed, 2152 passed, 23 skipped, 20 warnings in 461.68s (0:07:41) =====", "The blog is live btw! https://www.databricks.com/blog/contributing-spark-loader-for-hugging-face-datasets Hopefully there can be a release today?", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.012686 / 0.011353 (0.001333) \| 0.006051 / 0.011008 (-0.004957) \| 0.123057 / 0.038508 (0.084549) \| 0.033238 / 0.023109 (0.010128) \| 0.388207 / 0.275898 (0.112309) \| 0.393972 / 0.323480 (0.070492) \| 0.006645 / 0.007986 (-0.001340) \| 0.006715 / 0.004328 (0.002386) \| 0.098348 / 0.004250 (0.094097) \| 0.041410 / 0.037052 (0.004358) \| 0.380123 / 0.258489 (0.121634) \| 0.427982 / 0.293841 (0.134141) \| 0.052194 / 0.128546 (-0.076352) \| 0.018775 / 0.075646 (-0.056871) \| 0.399063 / 0.419271 (-0.020209) \| 0.061019 / 0.043533 (0.017487) \| 0.370943 / 0.255139 (0.115804) \| 0.398326 / 0.283200 (0.115127) \| 0.136893 / 0.141683 (-0.004790) \| 1.777431 / 1.452155 (0.325276) \| 1.844354 / 1.492716 (0.351638) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.267296 / 0.018006 (0.249289) \| 0.565133 / 0.000490 (0.564643) \| 0.005811 / 0.000200 (0.005611) \| 0.000122 / 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) \| 0.027009 / 0.037411 (-0.010402) \| 0.125907 / 0.014526 (0.111381) \| 0.122111 / 0.176557 (-0.054445) \| 0.189023 / 0.737135 (-0.548112) \| 0.140510 / 0.296338 (-0.155829) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.589269 / 0.215209 (0.374060) \| 6.038038 / 2.077655 (3.960384) \| 2.394681 / 1.504120 (0.890561) \| 2.099268 / 1.541195 (0.558073) \| 2.105146 / 1.468490 (0.636656) \| 1.216304 / 4.584777 (-3.368473) \| 5.823110 / 3.745712 (2.077397) \| 4.999323 / 5.269862 (-0.270539) \| 2.781554 / 4.565676 (-1.784122) \| 0.148370 / 0.424275 (-0.275905) \| 0.015163 / 0.007607 (0.007556) \| 0.775153 / 0.226044 (0.549109) \| 7.425314 / 2.268929 (5.156385) \| 3.320254 / 55.444624 (-52.124370) \| 2.718595 / 6.876477 (-4.157881) \| 2.696215 / 2.142072 (0.554142) \| 1.452249 / 4.805227 (-3.352978) \| 0.281355 / 6.500664 (-6.219309) \| 0.088146 / 0.075469 (0.012677) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.495718 / 1.841788 (-0.346070) \| 17.498714 / 8.074308 (9.424405) \| 20.109705 / 10.191392 (9.918313) \| 0.233053 / 0.680424 (-0.447371) \| 0.028336 / 0.534201 (-0.505865) \| 0.538146 / 0.579283 (-0.041137) \| 0.642106 / 0.434364 (0.207742) \| 0.597214 / 0.540337 (0.056876) \| 0.732219 / 1.386936 (-0.654717) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008153 / 0.011353 (-0.003200) \| 0.005605 / 0.011008 (-0.005403) \| 0.096159 / 0.038508 (0.057651) \| 0.034102 / 0.023109 (0.010992) \| 0.428091 / 0.275898 (0.152193) \| 0.476535 / 0.323480 (0.153056) \| 0.006278 / 0.007986 (-0.001708) \| 0.006752 / 0.004328 (0.002424) \| 0.100553 / 0.004250 (0.096302) \| 0.045546 / 0.037052 (0.008494) \| 0.463236 / 0.258489 (0.204747) \| 0.502512 / 0.293841 (0.208671) \| 0.051014 / 0.128546 (-0.077533) \| 0.018499 / 0.075646 (-0.057148) \| 0.127587 / 0.419271 (-0.291685) \| 0.059254 / 0.043533 (0.015722) \| 0.432248 / 0.255139 (0.177109) \| 0.462002 / 0.283200 (0.178802) \| 0.124918 / 0.141683 (-0.016765) \| 1.689740 / 1.452155 (0.237585) \| 1.871546 / 1.492716 (0.378830) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.274844 / 0.018006 (0.256838) \| 0.570522 / 0.000490 (0.570032) \| 0.004008 / 0.000200 (0.003808) \| 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) \| 0.025323 / 0.037411 (-0.012088) \| 0.116323 / 0.014526 (0.101797) \| 0.129434 / 0.176557 (-0.047122) \| 0.187069 / 0.737135 (-0.550067) \| 0.134459 / 0.296338 (-0.161880) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.633551 / 0.215209 (0.418341) \| 6.290078 / 2.077655 (4.212423) \| 2.692071 / 1.504120 (1.187951) \| 2.354344 / 1.541195 (0.813149) \| 2.409260 / 1.468490 (0.940770) \| 1.270515 / 4.584777 (-3.314261) \| 5.552982 / 3.745712 (1.807270) \| 3.041417 / 5.269862 (-2.228444) \| 1.920634 / 4.565676 (-2.645043) \| 0.142500 / 0.424275 (-0.281775) \| 0.014378 / 0.007607 (0.006770) \| 0.786444 / 0.226044 (0.560399) \| 7.711558 / 2.268929 (5.442630) \| 3.439688 / 55.444624 (-52.004936) \| 2.742314 / 6.876477 (-4.134163) \| 2.800531 / 2.142072 (0.658458) \| 1.405843 / 4.805227 (-3.399385) \| 0.245322 / 6.500664 (-6.255342) \| 0.076662 / 0.075469 (0.001193) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.592961 / 1.841788 (-0.248827) \| 18.165647 / 8.074308 (10.091339) \| 20.011433 / 10.191392 (9.820041) \| 0.240558 / 0.680424 (-0.439866) \| 0.026045 / 0.534201 (-0.508156) \| 0.529610 / 0.579283 (-0.049674) \| 0.652494 / 0.434364 (0.218130) \| 0.612284 / 0.540337 (0.071947) \| 0.733180 / 1.386936 (-0.653756) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ea251c726c73bd076a1bef7e39e2ac4e97c8d166 \"CML watermark\")\n", "python 3.9.2\r\nGot an error _pickle.PicklingError use Dataset.from_spark.\r\n\r\nDid the dataset import load data from spark dataframe using multi-node Spark cluster\r\ndf = spark.read.parquet(args.input_data).repartition(50)\r\nds = Dataset.from_spark(df, keep_in_memory=True,\r\n cache_dir=\"/pnc-data/data/nuplan/t5_spark/cache_data\")\r\nds.save_to_disk(args.output_data)\r\n\r\nError : \r\n_pickle.PicklingError: Could not serialize object: RuntimeError: It appears that you are attempting to reference SparkContext from a broadcast variable, action, or transforma\r\ntion. SparkContext can only be used on the driver, not in code that it run on workers. For more information, see SPARK-5063.\r\n23/06/16 21:17:20 WARN ExecutorPodsWatchSnapshotSource: Kubernetes client has been closed (this is expected if the application is shutting down.)\r\n", "Hi @yanzia12138 ! Could you open a new issue please and share the full stack trace ? This will help to know what happened exactly" ]
1,652,527,530	5,700	fix: fix wrong modification of the 'cache_file_name' -related paramet…	open	2023-04-03T18:05:26	2023-04-06T17:17:27	null	https://github.com/huggingface/datasets/pull/5700	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5700", "html_url": "https://github.com/huggingface/datasets/pull/5700", "diff_url": "https://github.com/huggingface/datasets/pull/5700.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5700.patch", "merged_at": null }	FrancoisNoyez	true	[ "Have you tried to set the cache file names if `keep_in_memory`is True ?\r\n\r\n```diff\r\n- if self.cache_files:\r\n+ if self.cache_files and not keep_in_memory:\r\n```\r\n\r\nThis way it doesn't change the indice cache arguments and leave them as `None`", "@lhoestq \r\nRegarding what you suggest:\r\nThe thing is, if cached files already exist and do correspond to the split that we are currently trying to perform, then it would be a shame not to use them, would it not? So I don't think that we should necessarily bypass this step in the method (corresponding to the reading of already existing data), if 'keep_in_memory' = True. For me, 'keep_in_memory' = True is supposed to mean \"don't cache the output of this method\", but it should say nothing regarding what to do with potentially already existing cached data, should it?\r\nBesides, even if we do what you suggest, and do only that (so, not the modifs that I suggested), then, assuming that 'keep_in_memory' = False and that there exist cached files, if the following check on the existence of cached files with specific name fails, we will still have ended up modifying an input value which will be then used in the remaining of the method, potentially altering the behavior that the user intended the method's call to have. Basically, the issue with what you suggest is that we can't guaranty that we won't continue with the remaining of the method even if this condition is met. Because of that, in my opinion, the best way to not have to worry about potential, unwanted side effects in the rest of the code is to not modify those variables in place, and so, here, to use other variables.\r\nSo, I'm sorry, but for those two reasons, I don't think that what you are suggesting addresses the problems which are described in the opened issue.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5700). All of your documentation changes will be reflected on that endpoint.", "Makes sense ! Therefore removing the ValueError messages sounds good to me, thanks for detailing.\r\n\r\nThen I think it's fine to keep using the same variables for the cache file names is enough instead of defining new ones - it doesn't alter the behavior of the function. Otherwise it would feel a bit confusing to have similar variables with slightly modified names just for that", "Ok for the removing the ValueError exceptions, thanks.\r\n\r\nThat said, it seems to me like we should still find a way not to modify the values input by the user, insofar as they can be used elsewhere down the line in the program. Sure, here, by removing the raising of those ValueError exceptions, we have fixed one use cases were allowing this modification actually caused an issue, but maybe there are other use cases where this would also caused an issue? Also, maybe in the future we will add other functionalities which will depend on the values of those input parameters, with then new risks of such an issue occurring?\r\nThat's why, in order not to have to worry about that, and in order to make the code a bit more future -proof, I suggest that make sure those input values are not modified.\r\n\r\nOne way that I did this is to create different but similar looking variable names. If you find this confusing, we can always add a comment.\r\nAnother way would be to not store the result of the conditional definition of the values (the '\\_cache_file_name = (... if condition else ...)' in my proposition of code), and to use it every time we need. But since we use those new variables at least twice, that creates code redundancy, which is not great either.\r\nFinally, a third way that I can imagine would be to put all this logic into its own method, which would then encapsulate it, and protect the remaining of the 'train_test_split' code from all unintended side effect that this logic can currently cause. This one is probably best. Also, maybe it could be used to remove some code redundancy elsewhere in the definition of the Dataset class? I have not checked if such a code redundancy exists.", "We're already replacing the user's input by default values automatically in other methods, it's fine to do it here as well and actually fits the library's style.\r\n\r\nNote that the case where it would reload the cache even if `keep_in_memory=True` is not implemented though, but it should be easy to add in `_select_with_indices_mapping`:\r\n- add keep_in_memory in `_new_dataset_with_indices` that uses InMemoryTable.from_file\r\n- inside `_select_with_indices_mapping` return the dataset from `_new_dataset_with_indices` if:\r\n - `keep_in_memory=True`\r\n - and `indices_cache_file_name` is not None and exists \r\n - and `is_caching_enabled()`\r\n\r\nBecause if we let it this way it would recreate the cache file unfortunately", "> We're already replacing the user's input by default values automatically in other methods, it's fine to do it here as well and actually fits the library's style.\r\n\r\nI think the fact that it's a style of the library is not really an argument in itself; however, after thinking through it several times, I think I know see why your solution is acceptable: as soon as the user specifies that 'keep_in_memory=True', they should not care anymore about the value of the '\\_indices_cache_file_name' variables, since from their point of view those are now irrelevant. So it's \"fine\" if we allow ourselves to modify the value of those variables, if it helps the internal code being more concise.\r\nStill, I find that it's a bit unintuitive, and a risk as far as future evolution of the method / of the code is concerned; someone tasked with doing that would need to have the knowledge of a lot of, if not all, the other methods of the class, in order to understand the potentially far-reaching impact of some modifications made to this portion of the code. But I guess that's a choice which is the library's owners to make. Also, if we use your proposed solution, as I explained, we can't get the benefit of potentially reusing possibly already existing cached data.\r\nOn that note...\r\n\r\n> Note that the case where it would reload the cache even if `keep_in_memory=True` is not implemented though\r\n\r\nI'm not sure what you mean here:\r\nWithin the current code trying to load up the potentially already existing split data, there is no trace of the 'keep_in_memory' variable. So why do you say that 'the case where it would reload the cache even if keep_in_memory=True is not implemented' (I assume that you mean 'currently implemented')? Surely, currently, this bit of code works regardless of the value of the 'keep_in_memory' variable', does it not?" ]
1,652,437,419	5,699	Issue when wanting to split in memory a cached dataset	open	2023-04-03T17:00:07	2024-05-15T13:12:18	null	https://github.com/huggingface/datasets/issues/5699	null	FrancoisNoyez	false	[ "Hi ! Good catch, this is wrong indeed and thanks for opening a PR :)", "Facing the same issue. Kindly fix this bug." ]
1,652,183,611	5,698	Add Qdrant as another search index	open	2023-04-03T14:25:19	2023-04-11T10:28:40	null	https://github.com/huggingface/datasets/issues/5698	null	kacperlukawski	false	[ "@mariosasko I'd appreciate your feedback on this. " ]
1,651,812,614	5,697	Raise an error on missing distributed seed	closed	2023-04-03T10:44:58	2023-04-04T15:05:24	2023-04-04T14:58:16	https://github.com/huggingface/datasets/pull/5697	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5697", "html_url": "https://github.com/huggingface/datasets/pull/5697", "diff_url": "https://github.com/huggingface/datasets/pull/5697.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5697.patch", "merged_at": "2023-04-04T14:58:16" }	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) \| 0.009644 / 0.011353 (-0.001709) \| 0.006407 / 0.011008 (-0.004601) \| 0.148353 / 0.038508 (0.109845) \| 0.037537 / 0.023109 (0.014428) \| 0.379697 / 0.275898 (0.103799) \| 0.466260 / 0.323480 (0.142780) \| 0.007884 / 0.007986 (-0.000102) \| 0.005140 / 0.004328 (0.000812) \| 0.111078 / 0.004250 (0.106827) \| 0.049429 / 0.037052 (0.012377) \| 0.364766 / 0.258489 (0.106277) \| 0.453809 / 0.293841 (0.159968) \| 0.051918 / 0.128546 (-0.076628) \| 0.020081 / 0.075646 (-0.055566) \| 0.616041 / 0.419271 (0.196770) \| 0.059834 / 0.043533 (0.016301) \| 0.373104 / 0.255139 (0.117965) \| 0.419304 / 0.283200 (0.136104) \| 0.113526 / 0.141683 (-0.028156) \| 1.827160 / 1.452155 (0.375006) \| 1.912092 / 1.492716 (0.419376) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.269584 / 0.018006 (0.251578) \| 0.554100 / 0.000490 (0.553610) \| 0.006618 / 0.000200 (0.006418) \| 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) \| 0.025280 / 0.037411 (-0.012131) \| 0.123116 / 0.014526 (0.108591) \| 0.127674 / 0.176557 (-0.048883) \| 0.189106 / 0.737135 (-0.548030) \| 0.142072 / 0.296338 (-0.154267) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.602201 / 0.215209 (0.386992) \| 5.959610 / 2.077655 (3.881956) \| 2.404856 / 1.504120 (0.900736) \| 2.175017 / 1.541195 (0.633823) \| 2.154360 / 1.468490 (0.685870) \| 1.265339 / 4.584777 (-3.319438) \| 5.598429 / 3.745712 (1.852716) \| 5.130249 / 5.269862 (-0.139612) \| 2.764922 / 4.565676 (-1.800754) \| 0.143232 / 0.424275 (-0.281043) \| 0.014721 / 0.007607 (0.007114) \| 0.764734 / 0.226044 (0.538689) \| 7.518810 / 2.268929 (5.249882) \| 3.344734 / 55.444624 (-52.099890) \| 2.601158 / 6.876477 (-4.275319) \| 2.726018 / 2.142072 (0.583945) \| 1.397918 / 4.805227 (-3.407309) \| 0.253277 / 6.500664 (-6.247387) \| 0.077772 / 0.075469 (0.002303) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.499535 / 1.841788 (-0.342253) \| 17.782490 / 8.074308 (9.708182) \| 21.953064 / 10.191392 (11.761672) \| 0.248753 / 0.680424 (-0.431671) \| 0.029194 / 0.534201 (-0.505007) \| 0.529700 / 0.579283 (-0.049583) \| 0.618412 / 0.434364 (0.184048) \| 0.605062 / 0.540337 (0.064725) \| 0.725661 / 1.386936 (-0.661275) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009489 / 0.011353 (-0.001864) \| 0.006423 / 0.011008 (-0.004585) \| 0.096789 / 0.038508 (0.058281) \| 0.034639 / 0.023109 (0.011530) \| 0.403875 / 0.275898 (0.127977) \| 0.439368 / 0.323480 (0.115888) \| 0.006354 / 0.007986 (-0.001631) \| 0.006794 / 0.004328 (0.002466) \| 0.095537 / 0.004250 (0.091287) \| 0.047749 / 0.037052 (0.010697) \| 0.424157 / 0.258489 (0.165668) \| 0.487825 / 0.293841 (0.193984) \| 0.054675 / 0.128546 (-0.073872) \| 0.021349 / 0.075646 (-0.054297) \| 0.108917 / 0.419271 (-0.310354) \| 0.075891 / 0.043533 (0.032358) \| 0.412889 / 0.255139 (0.157750) \| 0.464512 / 0.283200 (0.181312) \| 0.118832 / 0.141683 (-0.022850) \| 1.721215 / 1.452155 (0.269060) \| 1.857195 / 1.492716 (0.364478) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.248308 / 0.018006 (0.230302) \| 0.559496 / 0.000490 (0.559006) \| 0.007136 / 0.000200 (0.006936) \| 0.000160 / 0.000054 (0.000106) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031772 / 0.037411 (-0.005639) \| 0.123565 / 0.014526 (0.109039) \| 0.132660 / 0.176557 (-0.043896) \| 0.201428 / 0.737135 (-0.535707) \| 0.135238 / 0.296338 (-0.161101) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.646978 / 0.215209 (0.431769) \| 6.183477 / 2.077655 (4.105822) \| 2.782117 / 1.504120 (1.277997) \| 2.294093 / 1.541195 (0.752898) \| 2.346932 / 1.468490 (0.878442) \| 1.239085 / 4.584777 (-3.345692) \| 5.696364 / 3.745712 (1.950652) \| 4.980102 / 5.269862 (-0.289759) \| 2.278116 / 4.565676 (-2.287560) \| 0.157339 / 0.424275 (-0.266936) \| 0.014936 / 0.007607 (0.007329) \| 0.778001 / 0.226044 (0.551957) \| 7.708066 / 2.268929 (5.439138) \| 3.412235 / 55.444624 (-52.032389) \| 2.670670 / 6.876477 (-4.205806) \| 2.731802 / 2.142072 (0.589730) \| 1.446516 / 4.805227 (-3.358712) \| 0.263689 / 6.500664 (-6.236975) \| 0.086359 / 0.075469 (0.010890) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.573169 / 1.841788 (-0.268619) \| 17.690842 / 8.074308 (9.616534) \| 20.343336 / 10.191392 (10.151944) \| 0.231028 / 0.680424 (-0.449396) \| 0.025954 / 0.534201 (-0.508247) \| 0.570554 / 0.579283 (-0.008729) \| 0.610453 / 0.434364 (0.176089) \| 0.675830 / 0.540337 (0.135493) \| 0.790650 / 1.386936 (-0.596286) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d094ed07823bfb3271f3a9006daa1f92a64967a5 \"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) \| 0.007553 / 0.011353 (-0.003800) \| 0.005426 / 0.011008 (-0.005582) \| 0.096550 / 0.038508 (0.058042) \| 0.034393 / 0.023109 (0.011284) \| 0.322297 / 0.275898 (0.046399) \| 0.340943 / 0.323480 (0.017463) \| 0.006350 / 0.007986 (-0.001635) \| 0.005700 / 0.004328 (0.001372) \| 0.074929 / 0.004250 (0.070678) \| 0.054819 / 0.037052 (0.017767) \| 0.320151 / 0.258489 (0.061662) \| 0.346957 / 0.293841 (0.053116) \| 0.036659 / 0.128546 (-0.091887) \| 0.012443 / 0.075646 (-0.063204) \| 0.332232 / 0.419271 (-0.087040) \| 0.051467 / 0.043533 (0.007934) \| 0.310952 / 0.255139 (0.055813) \| 0.325617 / 0.283200 (0.042417) \| 0.104908 / 0.141683 (-0.036775) \| 1.446752 / 1.452155 (-0.005403) \| 1.558773 / 1.492716 (0.066056) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300639 / 0.018006 (0.282633) \| 0.499901 / 0.000490 (0.499411) \| 0.007340 / 0.000200 (0.007140) \| 0.000255 / 0.000054 (0.000201) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027206 / 0.037411 (-0.010206) \| 0.105603 / 0.014526 (0.091077) \| 0.118669 / 0.176557 (-0.057887) \| 0.174050 / 0.737135 (-0.563086) \| 0.125099 / 0.296338 (-0.171239) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.404285 / 0.215209 (0.189076) \| 4.034587 / 2.077655 (1.956933) \| 1.812639 / 1.504120 (0.308519) \| 1.625745 / 1.541195 (0.084551) \| 1.735523 / 1.468490 (0.267033) \| 0.709699 / 4.584777 (-3.875078) \| 3.802196 / 3.745712 (0.056484) \| 3.656984 / 5.269862 (-1.612877) \| 1.968470 / 4.565676 (-2.597206) \| 0.086612 / 0.424275 (-0.337663) \| 0.012368 / 0.007607 (0.004761) \| 0.502622 / 0.226044 (0.276577) \| 5.017876 / 2.268929 (2.748948) \| 2.279794 / 55.444624 (-53.164831) \| 1.956938 / 6.876477 (-4.919538) \| 2.150430 / 2.142072 (0.008357) \| 0.847691 / 4.805227 (-3.957536) \| 0.170157 / 6.500664 (-6.330507) \| 0.064141 / 0.075469 (-0.011328) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.172246 / 1.841788 (-0.669542) \| 15.229444 / 8.074308 (7.155136) \| 14.715913 / 10.191392 (4.524521) \| 0.192501 / 0.680424 (-0.487923) \| 0.017972 / 0.534201 (-0.516229) \| 0.423834 / 0.579283 (-0.155449) \| 0.423019 / 0.434364 (-0.011345) \| 0.493298 / 0.540337 (-0.047039) \| 0.589833 / 1.386936 (-0.797103) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007773 / 0.011353 (-0.003580) \| 0.005449 / 0.011008 (-0.005560) \| 0.075180 / 0.038508 (0.036672) \| 0.035221 / 0.023109 (0.012111) \| 0.338169 / 0.275898 (0.062271) \| 0.374002 / 0.323480 (0.050522) \| 0.006391 / 0.007986 (-0.001595) \| 0.004406 / 0.004328 (0.000078) \| 0.074925 / 0.004250 (0.070675) \| 0.056527 / 0.037052 (0.019475) \| 0.338071 / 0.258489 (0.079582) \| 0.391882 / 0.293841 (0.098041) \| 0.037241 / 0.128546 (-0.091305) \| 0.012546 / 0.075646 (-0.063100) \| 0.087331 / 0.419271 (-0.331940) \| 0.049851 / 0.043533 (0.006318) \| 0.335264 / 0.255139 (0.080125) \| 0.354813 / 0.283200 (0.071614) \| 0.110614 / 0.141683 (-0.031069) \| 1.432782 / 1.452155 (-0.019372) \| 1.548800 / 1.492716 (0.056083) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.307892 / 0.018006 (0.289886) \| 0.518809 / 0.000490 (0.518319) \| 0.004058 / 0.000200 (0.003858) \| 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) \| 0.029155 / 0.037411 (-0.008256) \| 0.111706 / 0.014526 (0.097180) \| 0.122964 / 0.176557 (-0.053592) \| 0.170939 / 0.737135 (-0.566196) \| 0.128538 / 0.296338 (-0.167801) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.426529 / 0.215209 (0.211320) \| 4.254218 / 2.077655 (2.176563) \| 2.011455 / 1.504120 (0.507335) \| 1.817397 / 1.541195 (0.276202) \| 1.952915 / 1.468490 (0.484425) \| 0.705052 / 4.584777 (-3.879725) \| 3.844458 / 3.745712 (0.098746) \| 3.592754 / 5.269862 (-1.677107) \| 1.573567 / 4.565676 (-2.992109) \| 0.086834 / 0.424275 (-0.337441) \| 0.012389 / 0.007607 (0.004782) \| 0.541695 / 0.226044 (0.315650) \| 5.224492 / 2.268929 (2.955564) \| 2.473648 / 55.444624 (-52.970976) \| 2.167458 / 6.876477 (-4.709019) \| 2.253319 / 2.142072 (0.111246) \| 0.836322 / 4.805227 (-3.968905) \| 0.168680 / 6.500664 (-6.331984) \| 0.065699 / 0.075469 (-0.009770) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.281886 / 1.841788 (-0.559902) \| 15.451741 / 8.074308 (7.377433) \| 14.906870 / 10.191392 (4.715478) \| 0.168554 / 0.680424 (-0.511870) \| 0.017365 / 0.534201 (-0.516836) \| 0.434183 / 0.579283 (-0.145100) \| 0.421891 / 0.434364 (-0.012473) \| 0.538993 / 0.540337 (-0.001344) \| 0.636212 / 1.386936 (-0.750724) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1f428b8172319a6bfe95d7a4356b1d14a8d386d8 \"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) \| 0.007362 / 0.011353 (-0.003991) \| 0.004992 / 0.011008 (-0.006016) \| 0.098730 / 0.038508 (0.060222) \| 0.033673 / 0.023109 (0.010563) \| 0.296334 / 0.275898 (0.020436) \| 0.328208 / 0.323480 (0.004728) \| 0.005658 / 0.007986 (-0.002327) \| 0.004130 / 0.004328 (-0.000199) \| 0.074596 / 0.004250 (0.070346) \| 0.048230 / 0.037052 (0.011178) \| 0.295631 / 0.258489 (0.037142) \| 0.347176 / 0.293841 (0.053335) \| 0.036359 / 0.128546 (-0.092187) \| 0.011889 / 0.075646 (-0.063758) \| 0.332889 / 0.419271 (-0.086382) \| 0.049708 / 0.043533 (0.006175) \| 0.291207 / 0.255139 (0.036068) \| 0.311066 / 0.283200 (0.027867) \| 0.098418 / 0.141683 (-0.043265) \| 1.415450 / 1.452155 (-0.036705) \| 1.526928 / 1.492716 (0.034212) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.212636 / 0.018006 (0.194630) \| 0.432337 / 0.000490 (0.431847) \| 0.006839 / 0.000200 (0.006639) \| 0.000205 / 0.000054 (0.000150) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026045 / 0.037411 (-0.011366) \| 0.107427 / 0.014526 (0.092901) \| 0.114634 / 0.176557 (-0.061922) \| 0.169943 / 0.737135 (-0.567192) \| 0.123290 / 0.296338 (-0.173048) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.409432 / 0.215209 (0.194223) \| 4.097910 / 2.077655 (2.020255) \| 1.857177 / 1.504120 (0.353057) \| 1.672355 / 1.541195 (0.131160) \| 1.740130 / 1.468490 (0.271640) \| 0.706520 / 4.584777 (-3.878257) \| 3.773606 / 3.745712 (0.027893) \| 2.101635 / 5.269862 (-3.168226) \| 1.326295 / 4.565676 (-3.239382) \| 0.085672 / 0.424275 (-0.338604) \| 0.012142 / 0.007607 (0.004534) \| 0.501168 / 0.226044 (0.275123) \| 5.049784 / 2.268929 (2.780855) \| 2.322477 / 55.444624 (-53.122148) \| 1.990105 / 6.876477 (-4.886372) \| 2.115003 / 2.142072 (-0.027070) \| 0.837518 / 4.805227 (-3.967709) \| 0.168457 / 6.500664 (-6.332207) \| 0.064622 / 0.075469 (-0.010847) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.188152 / 1.841788 (-0.653635) \| 14.991585 / 8.074308 (6.917276) \| 14.635187 / 10.191392 (4.443795) \| 0.183708 / 0.680424 (-0.496716) \| 0.017452 / 0.534201 (-0.516749) \| 0.418963 / 0.579283 (-0.160320) \| 0.428893 / 0.434364 (-0.005471) \| 0.502108 / 0.540337 (-0.038229) \| 0.596345 / 1.386936 (-0.790591) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007404 / 0.011353 (-0.003949) \| 0.005148 / 0.011008 (-0.005860) \| 0.074785 / 0.038508 (0.036277) \| 0.033815 / 0.023109 (0.010706) \| 0.332752 / 0.275898 (0.056854) \| 0.368018 / 0.323480 (0.044538) \| 0.005642 / 0.007986 (-0.002344) \| 0.004041 / 0.004328 (-0.000287) \| 0.073455 / 0.004250 (0.069205) \| 0.047380 / 0.037052 (0.010328) \| 0.337017 / 0.258489 (0.078528) \| 0.384185 / 0.293841 (0.090344) \| 0.036592 / 0.128546 (-0.091954) \| 0.012109 / 0.075646 (-0.063537) \| 0.086862 / 0.419271 (-0.332410) \| 0.049030 / 0.043533 (0.005497) \| 0.336542 / 0.255139 (0.081403) \| 0.350295 / 0.283200 (0.067096) \| 0.100998 / 0.141683 (-0.040685) \| 1.469749 / 1.452155 (0.017594) \| 1.588355 / 1.492716 (0.095639) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227552 / 0.018006 (0.209546) \| 0.438087 / 0.000490 (0.437598) \| 0.000394 / 0.000200 (0.000194) \| 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) \| 0.030575 / 0.037411 (-0.006836) \| 0.111914 / 0.014526 (0.097388) \| 0.124583 / 0.176557 (-0.051973) \| 0.175471 / 0.737135 (-0.561665) \| 0.129535 / 0.296338 (-0.166803) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.425625 / 0.215209 (0.210416) \| 4.228328 / 2.077655 (2.150673) \| 2.021087 / 1.504120 (0.516967) \| 1.832550 / 1.541195 (0.291355) \| 1.925572 / 1.468490 (0.457082) \| 0.690772 / 4.584777 (-3.894005) \| 3.724900 / 3.745712 (-0.020813) \| 2.080286 / 5.269862 (-3.189576) \| 1.316854 / 4.565676 (-3.248822) \| 0.085123 / 0.424275 (-0.339152) \| 0.012078 / 0.007607 (0.004471) \| 0.525802 / 0.226044 (0.299758) \| 5.242598 / 2.268929 (2.973670) \| 2.491596 / 55.444624 (-52.953028) \| 2.125156 / 6.876477 (-4.751320) \| 2.185922 / 2.142072 (0.043850) \| 0.823116 / 4.805227 (-3.982111) \| 0.165188 / 6.500664 (-6.335476) \| 0.063970 / 0.075469 (-0.011499) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.256948 / 1.841788 (-0.584840) \| 14.981990 / 8.074308 (6.907682) \| 14.565266 / 10.191392 (4.373874) \| 0.175064 / 0.680424 (-0.505360) \| 0.017628 / 0.534201 (-0.516573) \| 0.429979 / 0.579283 (-0.149304) \| 0.422509 / 0.434364 (-0.011855) \| 0.546262 / 0.540337 (0.005924) \| 0.647103 / 1.386936 (-0.739833) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0803a006db1c395ac715662cc6079651f77c11ea \"CML watermark\")\n" ]
1,651,707,008	5,696	Shuffle a sharded iterable dataset without seed can lead to duplicate data	closed	2023-04-03T09:40:03	2023-04-04T14:58:18	2023-04-04T14:58:18	https://github.com/huggingface/datasets/issues/5696	null	lhoestq	false	[]
1,650,974,156	5,695	Loading big dataset raises pyarrow.lib.ArrowNotImplementedError	closed	2023-04-02T14:42:44	2024-05-15T12:04:47	2023-04-10T08:04:04	https://github.com/huggingface/datasets/issues/5695	null	amariucaitheodor	false	[ "Hi ! It looks like an issue with PyArrow: https://issues.apache.org/jira/browse/ARROW-5030\r\n\r\nIt appears it can happen when you have parquet files with row groups larger than 2GB.\r\nI can see that your parquet files are around 10GB. It is usually advised to keep a value around the default value 500MB to avoid these issues.\r\n\r\nNote that currently the row group size is simply defined by the number of rows `datasets.config.DEFAULT_MAX_BATCH_SIZE`, so reducing this value could let you have parquet files bigger than 2GB and with row groups lower than 2GB.\r\n\r\nWould it be possible for you to re-upload the dataset with the default shard size 500MB ?", "Hey, thanks for the reply! I've since switched to working with the locally-saved dataset (which works).\r\nMaybe it makes sense to show a warning for uploads with large shard sizes? Since the functionality completely breaks (due to the PyArrow bug).", "Just tried uploading the same dataset with 500MB shards, I get an errors 4 hours in:\r\n\r\n```\r\nPushing dataset shards to the dataset hub: 25%\|██▍ \| 358/1453 [4:40:31<14:18:00, 47.01s/it]\r\nTraceback (most recent call last):\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 344, in _inner_upload_lfs_object\r\n return _upload_lfs_object(operation=operation, lfs_batch_action=batch_action, token=token)\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 391, in _upload_lfs_object\r\n lfs_upload(\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 254, in lfs_upload\r\n _upload_multi_part(\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 374, in _upload_multi_part\r\n hf_raise_for_status(part_upload_res)\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 301, in hf_raise_for_status\r\n raise HfHubHTTPError(str(e), response=response) from e\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 46, in __init__\r\n server_data = response.json()\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/requests/models.py\", line 899, in json\r\n return complexjson.loads(\r\n File \"/cluster/work/cotterell/tamariucai/miniconda3/envs/torch-multimodal/lib/python3.8/json/__init__.py\", line 357, in loads\r\n return _default_decoder.decode(s)\r\n File \"/cluster/work/cotterell/tamariucai/miniconda3/envs/torch-multimodal/lib/python3.8/json/decoder.py\", line 337, in decode\r\n obj, end = self.raw_decode(s, idx=_w(s, 0).end())\r\n File \"/cluster/work/cotterell/tamariucai/miniconda3/envs/torch-multimodal/lib/python3.8/json/decoder.py\", line 355, in raw_decode\r\n raise JSONDecodeError(\"Expecting value\", s, err.value) from None\r\njson.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"process_wit.py\", line 146, in <module>\r\n dataset.push_to_hub(FINAL_PATH, max_shard_size=\"500MB\", private=False)\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/datasets/dataset_dict.py\", line 1534, in push_to_hub\r\n repo_id, split, uploaded_size, dataset_nbytes, _, _ = self[split]._push_parquet_shards_to_hub(\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 4804, in _push_parquet_shards_to_hub\r\n _retry(\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 281, in _retry\r\n return func(func_args, func_kwargs)\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py\", line 120, in _inner_fn\r\n return fn(args, *kwargs)\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 2593, in upload_file\r\n commit_info = self.create_commit(\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py\", line 120, in _inner_fn\r\n return fn(args, *kwargs)\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 2411, in create_commit\r\n upload_lfs_files(\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py\", line 120, in _inner_fn\r\n return fn(args, *kwargs)\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 351, in upload_lfs_files\r\n thread_map(\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/tqdm/contrib/concurrent.py\", line 69, in thread_map\r\n return _executor_map(ThreadPoolExecutor, fn, iterables, *tqdm_kwargs)\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/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 \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/tqdm/std.py\", line 1178, in __iter__\r\n for obj in iterable:\r\n File \"/cluster/work/cotterell/tamariucai/miniconda3/envs/torch-multimodal/lib/python3.8/concurrent/futures/_base.py\", line 619, in result_iterator\r\n yield fs.pop().result()\r\n File \"/cluster/work/cotterell/tamariucai/miniconda3/envs/torch-multimodal/lib/python3.8/concurrent/futures/_base.py\", line 444, in result\r\n return self.__get_result()\r\n File \"/cluster/work/cotterell/tamariucai/miniconda3/envs/torch-multimodal/lib/python3.8/concurrent/futures/_base.py\", line 389, in __get_result\r\n raise self._exception\r\n File \"/cluster/work/cotterell/tamariucai/miniconda3/envs/torch-multimodal/lib/python3.8/concurrent/futures/thread.py\", line 57, in run\r\n result = self.fn(self.args, **self.kwargs)\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 346, in _inner_upload_lfs_object\r\n raise RuntimeError(f\"Error while uploading '{operation.path_in_repo}' to the Hub.\") from exc\r\nRuntimeError: Error while uploading 'data/train-00358-of-01453-22a5cc8b3eb12be3.parquet' to the Hub.\r\n```\r\nLocal saves do work, however.", "Hmmm that was probably an intermitent bug, you can resume the upload by re-running push_to_hub", "Leaving this other error here for the record, which occurs when I load the +700GB dataset from the hub with shard sizes of 500MB:\r\n\r\n```\r\n Traceback (most recent call last): \r\n File \"/cluster/home/tamariucai/.local/lib/python3.10/site-packages/datasets/builder.py\", line 1860, in _prepare_split_single\r\n for _, table in generator:\r\n File \"/cluster/home/tamariucai/.local/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py\", line 69, in _generate_tables\r\n for batch_idx, record_batch in enumerate(\r\n File \"pyarrow/_parquet.pyx\", line 1323, in iter_batches\r\n File \"pyarrow/error.pxi\", line 115, in pyarrow.lib.check_status\r\nOSError: Corrupt snappy compressed data.\r\n```\r\nI will probably switch back to the local big dataset or shrink it.", "I am having this same issue trying to load my Audio dataset of about 520GB of audio files and about 1.8 million rows: https://github.com/huggingface/datasets/issues/5695#issuecomment-1500738729\r\n\r\n\r\nI also tried the default shard size of 500MB and still hit this issue after about 4 hours. When I re-run the code, it restarts the uploading from scratch. I don't know how to resume it as @lhoestq suggested [here] (https://github.com/huggingface/datasets/issues/5695#issuecomment-1500829320).", "`push_to_hub` has a fast resuming, though for audio/image there is this PR to fix a speed issue: https://github.com/huggingface/datasets/pull/6056" ]
1,650,467,793	5,694	Dataset configuration	open	2023-04-01T13:08:05	2023-04-04T14:54:37	null	https://github.com/huggingface/datasets/issues/5694	null	lhoestq	false	[ "Originally we also though about adding it to the YAML part of the README.md:\r\n\r\n```yaml\r\nbuilder_config:\r\n data_dir: data\r\n data_files:\r\n - split: train\r\n pattern: \"train-[0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9].\"\r\n```\r\n\r\nHaving it in the README.md could make it easier to modify it in the UI on HF, and for validation on commit", "From internal discussions we agreed to go with the YAML approach, since it's the one that seems more appropriate to be modified by a human on the Hub or locally (while JSON e.g. for models are usually created programmatically).", "Current format:\r\n```yaml\r\nbuilder_config:\r\n data_files:\r\n - split: train\r\n pattern: data/train-*\r\n```" ]
1,649,934,749	5,693	[docs] Split pattern search order	closed	2023-03-31T19:51:38	2023-04-03T18:43:30	2023-04-03T18:29:58	https://github.com/huggingface/datasets/pull/5693	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5693", "html_url": "https://github.com/huggingface/datasets/pull/5693", "diff_url": "https://github.com/huggingface/datasets/pull/5693.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5693.patch", "merged_at": "2023-04-03T18:29:58" }	stevhliu	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007841 / 0.011353 (-0.003512) \| 0.005640 / 0.011008 (-0.005368) \| 0.096465 / 0.038508 (0.057957) \| 0.036476 / 0.023109 (0.013367) \| 0.306431 / 0.275898 (0.030533) \| 0.339545 / 0.323480 (0.016065) \| 0.006064 / 0.007986 (-0.001922) \| 0.004404 / 0.004328 (0.000076) \| 0.073130 / 0.004250 (0.068879) \| 0.052765 / 0.037052 (0.015713) \| 0.309895 / 0.258489 (0.051406) \| 0.354037 / 0.293841 (0.060196) \| 0.037127 / 0.128546 (-0.091420) \| 0.012387 / 0.075646 (-0.063260) \| 0.333503 / 0.419271 (-0.085769) \| 0.059799 / 0.043533 (0.016266) \| 0.305496 / 0.255139 (0.050358) \| 0.324122 / 0.283200 (0.040922) \| 0.107007 / 0.141683 (-0.034676) \| 1.416743 / 1.452155 (-0.035411) \| 1.520772 / 1.492716 (0.028055) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.261233 / 0.018006 (0.243227) \| 0.573806 / 0.000490 (0.573316) \| 0.000390 / 0.000200 (0.000190) \| 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) \| 0.027672 / 0.037411 (-0.009740) \| 0.112803 / 0.014526 (0.098278) \| 0.121085 / 0.176557 (-0.055471) \| 0.176056 / 0.737135 (-0.561080) \| 0.127171 / 0.296338 (-0.169167) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.414756 / 0.215209 (0.199547) \| 4.148743 / 2.077655 (2.071088) \| 1.883940 / 1.504120 (0.379820) \| 1.698771 / 1.541195 (0.157576) \| 1.811926 / 1.468490 (0.343436) \| 0.708293 / 4.584777 (-3.876484) \| 3.780456 / 3.745712 (0.034744) \| 2.098556 / 5.269862 (-3.171306) \| 1.323512 / 4.565676 (-3.242164) \| 0.086253 / 0.424275 (-0.338022) \| 0.012587 / 0.007607 (0.004980) \| 0.514824 / 0.226044 (0.288779) \| 5.157415 / 2.268929 (2.888487) \| 2.382519 / 55.444624 (-53.062105) \| 2.014539 / 6.876477 (-4.861938) \| 2.215239 / 2.142072 (0.073166) \| 0.847178 / 4.805227 (-3.958049) \| 0.170053 / 6.500664 (-6.330611) \| 0.066461 / 0.075469 (-0.009008) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.199056 / 1.841788 (-0.642732) \| 15.244999 / 8.074308 (7.170691) \| 14.661593 / 10.191392 (4.470201) \| 0.168855 / 0.680424 (-0.511569) \| 0.017889 / 0.534201 (-0.516312) \| 0.424961 / 0.579283 (-0.154322) \| 0.428632 / 0.434364 (-0.005732) \| 0.502680 / 0.540337 (-0.037658) \| 0.597827 / 1.386936 (-0.789109) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007749 / 0.011353 (-0.003604) \| 0.005527 / 0.011008 (-0.005482) \| 0.074774 / 0.038508 (0.036266) \| 0.035367 / 0.023109 (0.012258) \| 0.340594 / 0.275898 (0.064696) \| 0.373970 / 0.323480 (0.050490) \| 0.006094 / 0.007986 (-0.001892) \| 0.004428 / 0.004328 (0.000100) \| 0.074120 / 0.004250 (0.069869) \| 0.054852 / 0.037052 (0.017800) \| 0.357173 / 0.258489 (0.098684) \| 0.388877 / 0.293841 (0.095036) \| 0.037002 / 0.128546 (-0.091545) \| 0.012337 / 0.075646 (-0.063309) \| 0.086962 / 0.419271 (-0.332310) \| 0.050370 / 0.043533 (0.006837) \| 0.342989 / 0.255139 (0.087850) \| 0.358065 / 0.283200 (0.074865) \| 0.111063 / 0.141683 (-0.030620) \| 1.516704 / 1.452155 (0.064549) \| 1.634359 / 1.492716 (0.141643) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.261493 / 0.018006 (0.243487) \| 0.566288 / 0.000490 (0.565799) \| 0.000439 / 0.000200 (0.000239) \| 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) \| 0.030426 / 0.037411 (-0.006985) \| 0.114606 / 0.014526 (0.100080) \| 0.126134 / 0.176557 (-0.050423) \| 0.175324 / 0.737135 (-0.561812) \| 0.132766 / 0.296338 (-0.163573) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.426785 / 0.215209 (0.211576) \| 4.243555 / 2.077655 (2.165900) \| 2.089631 / 1.504120 (0.585511) \| 1.994562 / 1.541195 (0.453367) \| 2.140284 / 1.468490 (0.671794) \| 0.698645 / 4.584777 (-3.886132) \| 3.807471 / 3.745712 (0.061759) \| 3.275343 / 5.269862 (-1.994519) \| 1.796756 / 4.565676 (-2.768921) \| 0.085986 / 0.424275 (-0.338289) \| 0.012213 / 0.007607 (0.004606) \| 0.536815 / 0.226044 (0.310771) \| 5.344611 / 2.268929 (3.075683) \| 2.498578 / 55.444624 (-52.946047) \| 2.153260 / 6.876477 (-4.723217) \| 2.251310 / 2.142072 (0.109237) \| 0.839104 / 4.805227 (-3.966123) \| 0.169639 / 6.500664 (-6.331025) \| 0.065880 / 0.075469 (-0.009589) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.268610 / 1.841788 (-0.573178) \| 15.624915 / 8.074308 (7.550606) \| 15.163684 / 10.191392 (4.972292) \| 0.172992 / 0.680424 (-0.507432) \| 0.018154 / 0.534201 (-0.516047) \| 0.440485 / 0.579283 (-0.138798) \| 0.431949 / 0.434364 (-0.002415) \| 0.547935 / 0.540337 (0.007597) \| 0.662442 / 1.386936 (-0.724494) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5c8a6ba43c4aaa0ca0665d8dadd87ef33e28e8e4 \"CML watermark\")\n" ]
1,649,818,644	5,692	pyarrow.lib.ArrowInvalid: Unable to merge: Field <field> has incompatible types	open	2023-03-31T18:19:40	2024-01-14T07:24:21	null	https://github.com/huggingface/datasets/issues/5692	null	cyanic-selkie	false	[ "Hi! The link pointing to the code that generated the dataset is broken. Can you please fix it to make debugging easier?", "> Hi! The link pointing to the code that generated the dataset is broken. Can you please fix it to make debugging easier?\r\n\r\nSorry about that, it's fixed now.\r\n", "@cyanic-selkie could you explain how you fixed it? I met the same error in loading other datasets, is it due to the version of the library enviroment? ", "@MingsYang I never fixed it. If you're referring to my comment above, I only meant I fixed the link to my code.\r\n\r\nAnyway, I managed to work around the issue by using `streaming` when loading the dataset.", "@cyanic-selkie Emm, I get it. I just tried to use a new version python enviroment, and it show no errors anymore.", "Upgrade pyarrow to the latest version solves this problem in my case." ]
1,649,737,526	5,691	[docs] Compress data files	closed	2023-03-31T17:17:26	2023-04-19T13:37:32	2023-04-19T07:25:58	https://github.com/huggingface/datasets/pull/5691	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5691", "html_url": "https://github.com/huggingface/datasets/pull/5691", "diff_url": "https://github.com/huggingface/datasets/pull/5691.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5691.patch", "merged_at": "2023-04-19T07:25:58" }	stevhliu	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "[Confirmed](https://huggingface.slack.com/archives/C02EMARJ65P/p1680541667004199) with the Hub team the file size limit for the Hugging Face Hub is 10MB :)", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006789 / 0.011353 (-0.004564) \| 0.004935 / 0.011008 (-0.006073) \| 0.096796 / 0.038508 (0.058288) \| 0.032485 / 0.023109 (0.009376) \| 0.335342 / 0.275898 (0.059444) \| 0.354999 / 0.323480 (0.031519) \| 0.005467 / 0.007986 (-0.002519) \| 0.005267 / 0.004328 (0.000939) \| 0.073988 / 0.004250 (0.069737) \| 0.044402 / 0.037052 (0.007350) \| 0.331156 / 0.258489 (0.072666) \| 0.363595 / 0.293841 (0.069754) \| 0.035301 / 0.128546 (-0.093245) \| 0.012141 / 0.075646 (-0.063505) \| 0.333164 / 0.419271 (-0.086107) \| 0.048818 / 0.043533 (0.005286) \| 0.331458 / 0.255139 (0.076319) \| 0.343567 / 0.283200 (0.060367) \| 0.094963 / 0.141683 (-0.046720) \| 1.444383 / 1.452155 (-0.007772) \| 1.520093 / 1.492716 (0.027377) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.212311 / 0.018006 (0.194305) \| 0.436413 / 0.000490 (0.435923) \| 0.000333 / 0.000200 (0.000133) \| 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) \| 0.026670 / 0.037411 (-0.010742) \| 0.105774 / 0.014526 (0.091248) \| 0.115796 / 0.176557 (-0.060760) \| 0.176504 / 0.737135 (-0.560631) \| 0.121883 / 0.296338 (-0.174456) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.400783 / 0.215209 (0.185574) \| 4.006608 / 2.077655 (1.928953) \| 1.817659 / 1.504120 (0.313539) \| 1.619777 / 1.541195 (0.078582) \| 1.684247 / 1.468490 (0.215757) \| 0.701116 / 4.584777 (-3.883661) \| 3.684056 / 3.745712 (-0.061656) \| 2.065258 / 5.269862 (-3.204603) \| 1.425460 / 4.565676 (-3.140217) \| 0.084519 / 0.424275 (-0.339757) \| 0.011949 / 0.007607 (0.004342) \| 0.496793 / 0.226044 (0.270749) \| 4.978864 / 2.268929 (2.709935) \| 2.303388 / 55.444624 (-53.141237) \| 1.978341 / 6.876477 (-4.898135) \| 2.055744 / 2.142072 (-0.086329) \| 0.832022 / 4.805227 (-3.973206) \| 0.164715 / 6.500664 (-6.335949) \| 0.062701 / 0.075469 (-0.012768) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.178723 / 1.841788 (-0.663065) \| 14.583986 / 8.074308 (6.509678) \| 14.189402 / 10.191392 (3.998010) \| 0.183867 / 0.680424 (-0.496557) \| 0.017565 / 0.534201 (-0.516636) \| 0.421345 / 0.579283 (-0.157938) \| 0.420235 / 0.434364 (-0.014129) \| 0.496758 / 0.540337 (-0.043580) \| 0.591558 / 1.386936 (-0.795378) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007019 / 0.011353 (-0.004334) \| 0.004996 / 0.011008 (-0.006012) \| 0.073345 / 0.038508 (0.034836) \| 0.033077 / 0.023109 (0.009968) \| 0.335954 / 0.275898 (0.060056) \| 0.372616 / 0.323480 (0.049136) \| 0.005678 / 0.007986 (-0.002308) \| 0.003906 / 0.004328 (-0.000423) \| 0.072841 / 0.004250 (0.068591) \| 0.046829 / 0.037052 (0.009777) \| 0.335177 / 0.258489 (0.076688) \| 0.382862 / 0.293841 (0.089021) \| 0.038406 / 0.128546 (-0.090141) \| 0.012110 / 0.075646 (-0.063536) \| 0.085796 / 0.419271 (-0.333476) \| 0.049896 / 0.043533 (0.006363) \| 0.338232 / 0.255139 (0.083093) \| 0.361054 / 0.283200 (0.077855) \| 0.103171 / 0.141683 (-0.038512) \| 1.556692 / 1.452155 (0.104538) \| 1.540023 / 1.492716 (0.047306) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223705 / 0.018006 (0.205699) \| 0.438771 / 0.000490 (0.438282) \| 0.002838 / 0.000200 (0.002639) \| 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) \| 0.028423 / 0.037411 (-0.008988) \| 0.110560 / 0.014526 (0.096035) \| 0.121629 / 0.176557 (-0.054928) \| 0.173638 / 0.737135 (-0.563498) \| 0.127062 / 0.296338 (-0.169277) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.425806 / 0.215209 (0.210597) \| 4.251051 / 2.077655 (2.173397) \| 2.059735 / 1.504120 (0.555615) \| 1.864886 / 1.541195 (0.323692) \| 1.941553 / 1.468490 (0.473063) \| 0.700084 / 4.584777 (-3.884693) \| 3.753150 / 3.745712 (0.007438) \| 3.218606 / 5.269862 (-2.051256) \| 1.439648 / 4.565676 (-3.126028) \| 0.085239 / 0.424275 (-0.339037) \| 0.012026 / 0.007607 (0.004419) \| 0.521564 / 0.226044 (0.295520) \| 5.217902 / 2.268929 (2.948973) \| 2.557831 / 55.444624 (-52.886793) \| 2.240223 / 6.876477 (-4.636254) \| 2.364664 / 2.142072 (0.222591) \| 0.825884 / 4.805227 (-3.979343) \| 0.167800 / 6.500664 (-6.332864) \| 0.063552 / 0.075469 (-0.011917) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.255532 / 1.841788 (-0.586256) \| 14.747783 / 8.074308 (6.673475) \| 14.352263 / 10.191392 (4.160871) \| 0.143659 / 0.680424 (-0.536765) \| 0.017517 / 0.534201 (-0.516684) \| 0.419863 / 0.579283 (-0.159421) \| 0.416674 / 0.434364 (-0.017690) \| 0.485694 / 0.540337 (-0.054643) \| 0.584810 / 1.386936 (-0.802126) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#61db0e9c936bc67c18b37b0960e2f0bb1f8ffdcd \"CML watermark\")\n" ]
1,648,956,349	5,689	Support streaming Beam datasets from HF GCS preprocessed data	closed	2023-03-31T08:44:24	2023-04-12T05:57:55	2023-04-12T05:50:31	https://github.com/huggingface/datasets/pull/5689	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5689", "html_url": "https://github.com/huggingface/datasets/pull/5689", "diff_url": "https://github.com/huggingface/datasets/pull/5689.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5689.patch", "merged_at": "2023-04-12T05:50:30" }	albertvillanova	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"wikipedia\", \"20220301.en\", split=\"train\", streaming=True); item = next(iter(ds)); item\r\nOut[2]: \r\n{'id': '12',\r\n 'url': 'https://en.wikipedia.org/wiki/Anarchism',\r\n 'title': 'Anarchism',\r\n 'text': 'Anarchism is a political philosophy and movement that is sceptical of authority and rejects all involuntary, coercive forms of hierarchy. Anarchism calls for the abolition of the state, which it holds to be unnecessary, undesirable, and harmful. As a historically left-wing movement, placed on the farthest left of the political spectrum, it is usually described alongside communalism and libertarian Marxism as the libertarian wing (libertarian socialism) of the socialist movement,...}\r\n```", "I love your example 🏴‍🅰️", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007859 / 0.011353 (-0.003493) \| 0.005129 / 0.011008 (-0.005879) \| 0.098070 / 0.038508 (0.059562) \| 0.036500 / 0.023109 (0.013391) \| 0.311575 / 0.275898 (0.035677) \| 0.338351 / 0.323480 (0.014872) \| 0.005962 / 0.007986 (-0.002024) \| 0.004060 / 0.004328 (-0.000268) \| 0.072970 / 0.004250 (0.068719) \| 0.049289 / 0.037052 (0.012237) \| 0.310303 / 0.258489 (0.051814) \| 0.347449 / 0.293841 (0.053608) \| 0.046912 / 0.128546 (-0.081634) \| 0.011952 / 0.075646 (-0.063694) \| 0.333600 / 0.419271 (-0.085671) \| 0.052700 / 0.043533 (0.009167) \| 0.325486 / 0.255139 (0.070347) \| 0.326920 / 0.283200 (0.043720) \| 0.107683 / 0.141683 (-0.034000) \| 1.416679 / 1.452155 (-0.035476) \| 1.502418 / 1.492716 (0.009702) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.216520 / 0.018006 (0.198514) \| 0.448450 / 0.000490 (0.447960) \| 0.004213 / 0.000200 (0.004013) \| 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) \| 0.027081 / 0.037411 (-0.010331) \| 0.110989 / 0.014526 (0.096463) \| 0.116087 / 0.176557 (-0.060470) \| 0.173771 / 0.737135 (-0.563364) \| 0.121240 / 0.296338 (-0.175099) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.399938 / 0.215209 (0.184729) \| 4.017665 / 2.077655 (1.940010) \| 1.782327 / 1.504120 (0.278207) \| 1.612955 / 1.541195 (0.071761) \| 1.698839 / 1.468490 (0.230349) \| 0.706702 / 4.584777 (-3.878075) \| 4.533425 / 3.745712 (0.787713) \| 2.102611 / 5.269862 (-3.167250) \| 1.461429 / 4.565676 (-3.104248) \| 0.085719 / 0.424275 (-0.338556) \| 0.012104 / 0.007607 (0.004497) \| 0.507397 / 0.226044 (0.281352) \| 5.061572 / 2.268929 (2.792643) \| 2.272106 / 55.444624 (-53.172518) \| 1.935575 / 6.876477 (-4.940901) \| 2.102541 / 2.142072 (-0.039532) \| 0.838395 / 4.805227 (-3.966832) \| 0.168573 / 6.500664 (-6.332091) \| 0.064234 / 0.075469 (-0.011235) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.190077 / 1.841788 (-0.651710) \| 15.765587 / 8.074308 (7.691279) \| 14.694626 / 10.191392 (4.503234) \| 0.142912 / 0.680424 (-0.537512) \| 0.017669 / 0.534201 (-0.516532) \| 0.421502 / 0.579283 (-0.157781) \| 0.452732 / 0.434364 (0.018368) \| 0.497480 / 0.540337 (-0.042857) \| 0.586310 / 1.386936 (-0.800626) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007629 / 0.011353 (-0.003724) \| 0.005330 / 0.011008 (-0.005679) \| 0.076366 / 0.038508 (0.037858) \| 0.034703 / 0.023109 (0.011593) \| 0.356300 / 0.275898 (0.080402) \| 0.392909 / 0.323480 (0.069429) \| 0.005959 / 0.007986 (-0.002026) \| 0.004140 / 0.004328 (-0.000188) \| 0.075289 / 0.004250 (0.071039) \| 0.047880 / 0.037052 (0.010828) \| 0.357289 / 0.258489 (0.098800) \| 0.404554 / 0.293841 (0.110714) \| 0.037182 / 0.128546 (-0.091365) \| 0.012266 / 0.075646 (-0.063380) \| 0.088554 / 0.419271 (-0.330718) \| 0.049698 / 0.043533 (0.006165) \| 0.353453 / 0.255139 (0.098314) \| 0.373252 / 0.283200 (0.090052) \| 0.101892 / 0.141683 (-0.039791) \| 1.481534 / 1.452155 (0.029380) \| 1.553818 / 1.492716 (0.061102) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.229891 / 0.018006 (0.211884) \| 0.452444 / 0.000490 (0.451954) \| 0.000434 / 0.000200 (0.000234) \| 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) \| 0.030170 / 0.037411 (-0.007241) \| 0.115097 / 0.014526 (0.100571) \| 0.122094 / 0.176557 (-0.054463) \| 0.171352 / 0.737135 (-0.565784) \| 0.128441 / 0.296338 (-0.167898) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.428347 / 0.215209 (0.213138) \| 4.266243 / 2.077655 (2.188588) \| 2.148327 / 1.504120 (0.644207) \| 1.874141 / 1.541195 (0.332946) \| 1.968737 / 1.468490 (0.500246) \| 0.715320 / 4.584777 (-3.869457) \| 4.166097 / 3.745712 (0.420384) \| 2.169550 / 5.269862 (-3.100312) \| 1.377441 / 4.565676 (-3.188236) \| 0.086376 / 0.424275 (-0.337899) \| 0.012018 / 0.007607 (0.004411) \| 0.517433 / 0.226044 (0.291388) \| 5.167327 / 2.268929 (2.898398) \| 2.545822 / 55.444624 (-52.898803) \| 2.241726 / 6.876477 (-4.634751) \| 2.327220 / 2.142072 (0.185147) \| 0.841618 / 4.805227 (-3.963609) \| 0.169473 / 6.500664 (-6.331191) \| 0.065505 / 0.075469 (-0.009964) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.270476 / 1.841788 (-0.571312) \| 17.049885 / 8.074308 (8.975577) \| 14.847615 / 10.191392 (4.656223) \| 0.168671 / 0.680424 (-0.511753) \| 0.017564 / 0.534201 (-0.516637) \| 0.424780 / 0.579283 (-0.154503) \| 0.517392 / 0.434364 (0.083028) \| 0.561197 / 0.540337 (0.020859) \| 0.697792 / 1.386936 (-0.689144) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ce06edf0afb70027ffbd3c2ddec5d28037e9bd31 \"CML watermark\")\n" ]
1,649,289,883	5,690	raise AttributeError(f"No {package_name} attribute {name}") AttributeError: No huggingface_hub attribute hf_api	closed	2023-03-31T08:22:22	2023-07-21T14:21:57	2023-07-21T14:21:57	https://github.com/huggingface/datasets/issues/5690	null	wccccp	false	[ "Hi @wccccp, thanks for reporting. \r\nThat's weird since `huggingface_hub` _has_ a module called `hf_api` and you are using a recent version of it. \r\n\r\nWhich version of `datasets` are you using? And is it a bug that you experienced only recently? (cc @lhoestq can it be somehow related to the recent release of `datasets`?)\r\n\r\n~@wccccp what I can suggest you is to uninstall and reinstall completely huggingface_hub and datasets? My first guess is that there is a discrepancy somewhere in your setup 😕~", "@wccccp Actually I have also been able to reproduce the error so it's not an issue with your setup.\r\n\r\n@huggingface/datasets I found this issue quite weird. Is this a module that is not used very often?\r\nThe problematic line is [this one](https://github.com/huggingface/datasets/blame/c33e8ce68b5000988bf6b2e4bca27ffaa469acea/src/datasets/data_files.py#L476) where `huggingface_hub.hf_api.DatasetInfo` is used. `huggingface_hub` is imported [here](https://github.com/huggingface/datasets/blame/c33e8ce68b5000988bf6b2e4bca27ffaa469acea/src/datasets/data_files.py#L6) as `import huggingface_hub`. However since modules are lazy-loaded in `hfh` you need to explicitly import them (i.e. `import huggingface_hub.hf_api`).\r\n\r\nWhat's weird is that nothing has changed for months. Datasets code seems that it didn't change for 2 years when I git-blame this part. And lazy-loading was introduced 1 year ago in `huggingface_hub`. Could it be that `data_files.py` is a file almost never used?\r\n", "For context, I tried to run `import huggingface_hub; huggingface_hub.hf_api.DatasetInfo` in the terminal with different versions of `hfh` and I need to go back to `huggingface_hub==0.7.0` to make it work (latest is 0.13.3).", "Before the error happens at line 120 in `data_files.py`, `datasets.filesystems.hffilesystem` is imported at the top of `data_files.py` and this file does `from huggingface_hub.hf_api import DatasetInfo` - so `huggingface_hub.hf_api` is imported. Not sure how the error could happen, what version of `datasets` are you using @wccccp ?", "Closing due to inactivity." ]
1,648,463,504	5,688	Wikipedia download_and_prepare for GCS	closed	2023-03-30T23:43:22	2024-03-15T15:59:18	2024-03-15T15:59:18	https://github.com/huggingface/datasets/issues/5688	null	adrianfagerland	false	[ "Hi @adrianfagerland, thanks for reporting.\r\n\r\nPlease note that \"wikipedia\" is a special dataset, with an Apache Beam builder: https://beam.apache.org/\r\nYou can find more info about Beam datasets in our docs: https://huggingface.co/docs/datasets/beam\r\n\r\nIt was implemented to be run in parallel processing, using one of the distributed back-ends supported by Apache Beam: https://beam.apache.org/get-started/beam-overview/#apache-beam-pipeline-runners\r\n\r\nThat is, you are trying to process the source wikipedia data on your machine (not distributed) when passing `beam_runner=\"DirectRunner\"`.\r\n\r\nAs documented in the wikipedia dataset page (https://huggingface.co/datasets/wikipedia):\r\n\r\n Some subsets of Wikipedia have already been processed by HuggingFace, and you can load them just with:\r\n \r\n from datasets import load_dataset\r\n \r\n load_dataset(\"wikipedia\", \"20220301.en\")\r\n\r\n The list of pre-processed subsets is:\r\n - \"20220301.de\"\r\n - \"20220301.en\"\r\n - \"20220301.fr\"\r\n - \"20220301.frr\"\r\n - \"20220301.it\"\r\n - \"20220301.simple\"\r\n\r\nTo download the available processed data (in Arrow format):\r\n```python\r\nbuilder = datasets.load_dataset_builder(\"wikipedia\", \"20220301.en\")\r\nbuilder.download_and_prepare(your_path)\r\n```", "When running this using :\r\n```\r\nimport datasets\r\nfrom apache_beam.options.pipeline_options import PipelineOptions\r\nfrom gcsfs import GCSFileSystem\r\n\r\nstorage_options = {\"project\":\"tdt4310\", \"token\":\"cloud\"}\r\nfs = GCSFileSystem(storage_options)\r\n\r\noutput_dir = \"gcs://quiz_transformer/\"\r\nbeam_options = PipelineOptions(\r\n region=\"europe-west4\",\r\n project=\"tdt4310\",\r\n temp_location=output_dir+\"tmp/\")\r\n\r\n\r\nbuilder = datasets.load_dataset_builder(\"wikipedia\", \"20220301.en\", beam_runner=\"dataflow\", beam_options=beam_options)\r\nbuilder.download_and_prepare(\r\n output_dir, storage_options=storage_options, file_format=\"parquet\")\r\n```\r\nI now get this error:\r\n```\r\nraise FileNotFoundError(f\"Couldn't find file at {url}\")\r\nFileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/enwiki/20220301/dumpstatus.json\r\nDownloading data files: 0%\| \| 0/1 [00:00<?, ?it/s]\r\n```\r\n\r\nI get the same error for this:\r\n```\r\nimport datasets\r\nfrom gcsfs import GCSFileSystem\r\n\r\nstorage_options = {\"project\":\"tdt4310\", \"token\":\"cloud\"}\r\nfs = GCSFileSystem(storage_options)\r\n\r\noutput_dir = \"gcs://quiz_transformer/\"\r\nbuilder = datasets.load_dataset_builder(\"wikipedia\", \"20220301.en\")\r\nbuilder.download_and_prepare(\r\n output_dir, storage_options=storage_options, file_format=\"parquet\")\r\n```\r\n\r\n\r\n\r\n", "`wikipedia` is no longer a Beam dataset, so the above code should work now.\r\n\r\nPS: You can use [these files](https://huggingface.co/datasets/wikipedia/tree/main/data/20220301.en) (or a newer dump at https://huggingface.co/datasets/wikimedia/wikipedia/tree/main/20231101.en) instead of generating the Parquet version yourself" ]
1,647,009,018	5,687	Document to compress data files before uploading	closed	2023-03-30T06:41:07	2023-04-19T07:25:59	2023-04-19T07:25:59	https://github.com/huggingface/datasets/issues/5687	null	albertvillanova	false	[ "Great idea!\r\n\r\nShould we also take this opportunity to include some audio/image file formats? Currently, it still reads very text heavy. Something like:\r\n\r\n> We support many text, audio, and image data extensions such as `.zip`, `.rar`, `.mp3`, and `.jpg` among many others. For data extensions like `.csv`, `.json`, `.jsonl`, and `txt`, we recommend compressing them before uploading to the Hub. These file extensions are not tracked by Git LFS by default, and if they're too large, they will not be committed and uploaded. Take a look at the `.gitattributes` file in your repository for a complete list of supported file extensions.", "Hi @stevhliu, thanks for your suggestion.\r\n\r\nI agree it is a good opportunity to mention that audio/image file formats are also supported.\r\n\r\nNit:\r\nI would not mention .zip, .rar after \"text, audio, and image data extensions\". Those are \"compression\" extensions and not \"text, audio, and image data extensions\".\r\n\r\nWhat about something similar to:\r\n> We support many text, audio, and image data extensions such as `.csv`, `.mp3`, and `.jpg` among many others. For text data extensions like `.csv`, `.json`, `.jsonl`, and `.txt`, we recommend compressing them before uploading to the Hub (to `.zip` or `.gz` file extension for example). \r\n>\r\n> Note that text file extensions are not tracked by Git LFS by default, and if they're too large, they will not be committed and uploaded. Take a look at the `.gitattributes` file in your repository for a complete list of tracked file extensions by default.\r\n\r\nNote that for compressions I have mentioned:\r\n- gz, to compress individual files\r\n- zip, to compress and archive multiple files; zip is preferred rather than tar because it supports streaming out of the box", "Perfect, thanks for making the distinction between compression and data extensions!" ]
1,646,308,228	5,686	set dev version	closed	2023-03-29T18:24:13	2023-03-29T18:33:49	2023-03-29T18:24:22	https://github.com/huggingface/datasets/pull/5686	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5686", "html_url": "https://github.com/huggingface/datasets/pull/5686", "diff_url": "https://github.com/huggingface/datasets/pull/5686.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5686.patch", "merged_at": "2023-03-29T18:24:22" }	lhoestq	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5686). 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) \| 0.008460 / 0.011353 (-0.002893) \| 0.006114 / 0.011008 (-0.004894) \| 0.121496 / 0.038508 (0.082987) \| 0.035030 / 0.023109 (0.011920) \| 0.397778 / 0.275898 (0.121880) \| 0.429020 / 0.323480 (0.105540) \| 0.007811 / 0.007986 (-0.000174) \| 0.006269 / 0.004328 (0.001940) \| 0.098895 / 0.004250 (0.094645) \| 0.045407 / 0.037052 (0.008355) \| 0.413679 / 0.258489 (0.155189) \| 0.437491 / 0.293841 (0.143650) \| 0.053207 / 0.128546 (-0.075339) \| 0.018471 / 0.075646 (-0.057175) \| 0.414800 / 0.419271 (-0.004472) \| 0.060864 / 0.043533 (0.017332) \| 0.398501 / 0.255139 (0.143362) \| 0.421142 / 0.283200 (0.137942) \| 0.114908 / 0.141683 (-0.026775) \| 1.678630 / 1.452155 (0.226475) \| 1.782313 / 1.492716 (0.289596) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280783 / 0.018006 (0.262777) \| 0.591573 / 0.000490 (0.591083) \| 0.005797 / 0.000200 (0.005597) \| 0.000115 / 0.000054 (0.000060) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030431 / 0.037411 (-0.006981) \| 0.117342 / 0.014526 (0.102816) \| 0.128456 / 0.176557 (-0.048101) \| 0.198782 / 0.737135 (-0.538354) \| 0.128501 / 0.296338 (-0.167838) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.603073 / 0.215209 (0.387864) \| 6.101354 / 2.077655 (4.023699) \| 2.527812 / 1.504120 (1.023692) \| 2.101468 / 1.541195 (0.560273) \| 2.092813 / 1.468490 (0.624323) \| 1.182150 / 4.584777 (-3.402627) \| 5.389278 / 3.745712 (1.643566) \| 5.041001 / 5.269862 (-0.228860) \| 2.650581 / 4.565676 (-1.915095) \| 0.138761 / 0.424275 (-0.285514) \| 0.014209 / 0.007607 (0.006602) \| 0.748596 / 0.226044 (0.522552) \| 7.373937 / 2.268929 (5.105008) \| 3.245882 / 55.444624 (-52.198742) \| 2.523569 / 6.876477 (-4.352908) \| 2.581343 / 2.142072 (0.439270) \| 1.340436 / 4.805227 (-3.464791) \| 0.241388 / 6.500664 (-6.259276) \| 0.076634 / 0.075469 (0.001164) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.480237 / 1.841788 (-0.361551) \| 16.781338 / 8.074308 (8.707030) \| 19.735028 / 10.191392 (9.543636) \| 0.256872 / 0.680424 (-0.423551) \| 0.029211 / 0.534201 (-0.504990) \| 0.503292 / 0.579283 (-0.075991) \| 0.584510 / 0.434364 (0.150146) \| 0.580293 / 0.540337 (0.039955) \| 0.678863 / 1.386936 (-0.708073) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009972 / 0.011353 (-0.001381) \| 0.006107 / 0.011008 (-0.004902) \| 0.096188 / 0.038508 (0.057680) \| 0.033320 / 0.023109 (0.010210) \| 0.420789 / 0.275898 (0.144891) \| 0.460488 / 0.323480 (0.137008) \| 0.006492 / 0.007986 (-0.001493) \| 0.005325 / 0.004328 (0.000997) \| 0.094974 / 0.004250 (0.090723) \| 0.047708 / 0.037052 (0.010655) \| 0.426689 / 0.258489 (0.168200) \| 0.476440 / 0.293841 (0.182599) \| 0.052776 / 0.128546 (-0.075770) \| 0.018779 / 0.075646 (-0.056868) \| 0.119598 / 0.419271 (-0.299673) \| 0.061800 / 0.043533 (0.018267) \| 0.421305 / 0.255139 (0.166166) \| 0.441125 / 0.283200 (0.157925) \| 0.114221 / 0.141683 (-0.027462) \| 1.712681 / 1.452155 (0.260526) \| 1.852316 / 1.492716 (0.359600) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.272412 / 0.018006 (0.254405) \| 0.583996 / 0.000490 (0.583506) \| 0.000505 / 0.000200 (0.000305) \| 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) \| 0.029553 / 0.037411 (-0.007858) \| 0.124921 / 0.014526 (0.110395) \| 0.133338 / 0.176557 (-0.043218) \| 0.193811 / 0.737135 (-0.543325) \| 0.147973 / 0.296338 (-0.148365) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.595241 / 0.215209 (0.380032) \| 6.012015 / 2.077655 (3.934360) \| 2.611295 / 1.504120 (1.107175) \| 2.290127 / 1.541195 (0.748932) \| 2.300366 / 1.468490 (0.831876) \| 1.197602 / 4.584777 (-3.387175) \| 5.439064 / 3.745712 (1.693352) \| 2.906088 / 5.269862 (-2.363773) \| 1.919183 / 4.565676 (-2.646493) \| 0.132166 / 0.424275 (-0.292109) \| 0.014544 / 0.007607 (0.006937) \| 0.726377 / 0.226044 (0.500333) \| 7.361023 / 2.268929 (5.092094) \| 3.289266 / 55.444624 (-52.155358) \| 2.635570 / 6.876477 (-4.240907) \| 2.595691 / 2.142072 (0.453619) \| 1.329458 / 4.805227 (-3.475769) \| 0.239419 / 6.500664 (-6.261245) \| 0.076316 / 0.075469 (0.000847) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.547616 / 1.841788 (-0.294172) \| 17.374315 / 8.074308 (9.300007) \| 20.216275 / 10.191392 (10.024883) \| 0.252102 / 0.680424 (-0.428322) \| 0.027535 / 0.534201 (-0.506665) \| 0.524618 / 0.579283 (-0.054666) \| 0.596803 / 0.434364 (0.162439) \| 0.652632 / 0.540337 (0.112294) \| 0.762272 / 1.386936 (-0.624664) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8c7d4b2f981f8cf639dcbd80f40a41aa5b1693c6 \"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) \| 0.008236 / 0.011353 (-0.003117) \| 0.006186 / 0.011008 (-0.004822) \| 0.117852 / 0.038508 (0.079344) \| 0.034711 / 0.023109 (0.011602) \| 0.447564 / 0.275898 (0.171666) \| 0.438727 / 0.323480 (0.115247) \| 0.006576 / 0.007986 (-0.001410) \| 0.005903 / 0.004328 (0.001574) \| 0.094309 / 0.004250 (0.090059) \| 0.042760 / 0.037052 (0.005708) \| 0.393269 / 0.258489 (0.134780) \| 0.438061 / 0.293841 (0.144220) \| 0.059029 / 0.128546 (-0.069517) \| 0.020296 / 0.075646 (-0.055350) \| 0.412057 / 0.419271 (-0.007215) \| 0.059808 / 0.043533 (0.016275) \| 0.407243 / 0.255139 (0.152104) \| 0.414290 / 0.283200 (0.131090) \| 0.107701 / 0.141683 (-0.033981) \| 1.671522 / 1.452155 (0.219367) \| 1.775055 / 1.492716 (0.282338) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275242 / 0.018006 (0.257236) \| 0.599698 / 0.000490 (0.599208) \| 0.001289 / 0.000200 (0.001089) \| 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) \| 0.029579 / 0.037411 (-0.007832) \| 0.127249 / 0.014526 (0.112723) \| 0.137431 / 0.176557 (-0.039126) \| 0.220330 / 0.737135 (-0.516805) \| 0.133540 / 0.296338 (-0.162798) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.571989 / 0.215209 (0.356780) \| 5.931503 / 2.077655 (3.853848) \| 2.526646 / 1.504120 (1.022527) \| 2.189476 / 1.541195 (0.648281) \| 2.151935 / 1.468490 (0.683444) \| 1.242440 / 4.584777 (-3.342337) \| 5.599675 / 3.745712 (1.853963) \| 3.242035 / 5.269862 (-2.027826) \| 2.368361 / 4.565676 (-2.197315) \| 0.145659 / 0.424275 (-0.278616) \| 0.013813 / 0.007607 (0.006206) \| 0.782495 / 0.226044 (0.556451) \| 7.861619 / 2.268929 (5.592690) \| 3.241001 / 55.444624 (-52.203623) \| 2.611025 / 6.876477 (-4.265452) \| 2.667263 / 2.142072 (0.525191) \| 1.429992 / 4.805227 (-3.375235) \| 0.243008 / 6.500664 (-6.257656) \| 0.083686 / 0.075469 (0.008217) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.565526 / 1.841788 (-0.276262) \| 18.260815 / 8.074308 (10.186507) \| 22.586133 / 10.191392 (12.394741) \| 0.231864 / 0.680424 (-0.448559) \| 0.030877 / 0.534201 (-0.503324) \| 0.569726 / 0.579283 (-0.009557) \| 0.678638 / 0.434364 (0.244274) \| 0.611810 / 0.540337 (0.071472) \| 0.718771 / 1.386936 (-0.668165) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009398 / 0.011353 (-0.001955) \| 0.006452 / 0.011008 (-0.004556) \| 0.103352 / 0.038508 (0.064844) \| 0.034773 / 0.023109 (0.011664) \| 0.523782 / 0.275898 (0.247884) \| 0.523554 / 0.323480 (0.200074) \| 0.006990 / 0.007986 (-0.000996) \| 0.004994 / 0.004328 (0.000666) \| 0.102199 / 0.004250 (0.097949) \| 0.050087 / 0.037052 (0.013035) \| 0.496662 / 0.258489 (0.238173) \| 0.563130 / 0.293841 (0.269289) \| 0.052851 / 0.128546 (-0.075695) \| 0.019824 / 0.075646 (-0.055822) \| 0.122657 / 0.419271 (-0.296614) \| 0.057714 / 0.043533 (0.014181) \| 0.470502 / 0.255139 (0.215363) \| 0.518908 / 0.283200 (0.235708) \| 0.114374 / 0.141683 (-0.027309) \| 1.795918 / 1.452155 (0.343763) \| 1.957461 / 1.492716 (0.464744) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.303921 / 0.018006 (0.285915) \| 0.584406 / 0.000490 (0.583916) \| 0.000444 / 0.000200 (0.000244) \| 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) \| 0.032254 / 0.037411 (-0.005158) \| 0.129966 / 0.014526 (0.115440) \| 0.151000 / 0.176557 (-0.025557) \| 0.234060 / 0.737135 (-0.503076) \| 0.149444 / 0.296338 (-0.146895) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.666627 / 0.215209 (0.451418) \| 7.054701 / 2.077655 (4.977046) \| 2.836895 / 1.504120 (1.332775) \| 2.561994 / 1.541195 (1.020799) \| 2.672460 / 1.468490 (1.203970) \| 1.411929 / 4.584777 (-3.172848) \| 6.026918 / 3.745712 (2.281206) \| 3.341745 / 5.269862 (-1.928116) \| 2.280317 / 4.565676 (-2.285359) \| 0.156635 / 0.424275 (-0.267641) \| 0.014256 / 0.007607 (0.006649) \| 0.804830 / 0.226044 (0.578786) \| 8.106960 / 2.268929 (5.838031) \| 3.597452 / 55.444624 (-51.847172) \| 3.002847 / 6.876477 (-3.873630) \| 2.931160 / 2.142072 (0.789088) \| 1.484172 / 4.805227 (-3.321056) \| 0.254166 / 6.500664 (-6.246498) \| 0.080554 / 0.075469 (0.005085) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.809909 / 1.841788 (-0.031879) \| 18.988994 / 8.074308 (10.914686) \| 23.153442 / 10.191392 (12.962050) \| 0.250554 / 0.680424 (-0.429870) \| 0.048677 / 0.534201 (-0.485524) \| 0.574109 / 0.579283 (-0.005174) \| 0.640917 / 0.434364 (0.206553) \| 0.725215 / 0.540337 (0.184878) \| 0.878234 / 1.386936 (-0.508702) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e3667d6e17d68503469c8e88ec344b7cccfa2346 \"CML watermark\")\n" ]
1,646,048,667	5,685	Broken Image render on the hub website	closed	2023-03-29T15:25:30	2023-03-30T07:54:25	2023-03-30T07:54:25	https://github.com/huggingface/datasets/issues/5685	null	FrancescoSaverioZuppichini	false	[ "Hi! \r\n\r\nYou can fix the viewer by adding the `dataset_info` YAML field deleted in https://huggingface.co/datasets/Francesco/cell-towers/commit/b95b59ddd91ebe9c12920f0efe0ed415cd0d4298 back to the metadata section of the card. \r\n\r\nTo avoid this issue in the feature, you can use `huggingface_hub`'s [RepoCard](https://huggingface.co/docs/huggingface_hub/package_reference/cards) API to update the dataset card instead of `upload_file`:\r\n```python\r\nfrom huggingface_hub import DatasetCard\r\n# Load card\r\ncard = DatasetCard.load(\"<namespace>/<repo_id>\")\r\n# Modify card content\r\ncard.content = ...\r\n# Push card to the Hub\r\ncard.push_to_hub(\"<namespace>/<repo_id>\")\r\n```\r\n\r\nHowever, the best solution would be to use the features info stored in the header of the Parquet shards generated with `push_to_hub` on the viewer side to avoid unexpected issues such as this one. This shouldn't be too hard to address.", "Thanks for reporting @FrancescoSaverioZuppichini.\r\n\r\nFor future issues with your specific dataset, you can use its \"Community\" tab to start a conversation: https://huggingface.co/datasets/Francesco/cell-towers/discussions/new", "Thanks @albertvillanova , @mariosasko I was not aware of this requirement from the doc (must have skipped :sweat_smile: )\r\n\r\nConfirmed, adding back `dataset_info` fixed the issu" ]
1,646,013,226	5,684	Release: 2.11.0	closed	2023-03-29T15:06:07	2023-03-29T18:30:34	2023-03-29T18:15:54	https://github.com/huggingface/datasets/pull/5684	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5684", "html_url": "https://github.com/huggingface/datasets/pull/5684", "diff_url": "https://github.com/huggingface/datasets/pull/5684.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5684.patch", "merged_at": "2023-03-29T18:15:54" }	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) \| 0.007017 / 0.011353 (-0.004335) \| 0.004917 / 0.011008 (-0.006091) \| 0.098391 / 0.038508 (0.059883) \| 0.032677 / 0.023109 (0.009568) \| 0.312126 / 0.275898 (0.036227) \| 0.352477 / 0.323480 (0.028998) \| 0.005960 / 0.007986 (-0.002025) \| 0.003801 / 0.004328 (-0.000528) \| 0.073916 / 0.004250 (0.069666) \| 0.045610 / 0.037052 (0.008557) \| 0.319626 / 0.258489 (0.061137) \| 0.370575 / 0.293841 (0.076734) \| 0.035888 / 0.128546 (-0.092658) \| 0.012012 / 0.075646 (-0.063635) \| 0.338290 / 0.419271 (-0.080982) \| 0.049452 / 0.043533 (0.005919) \| 0.301226 / 0.255139 (0.046087) \| 0.336744 / 0.283200 (0.053545) \| 0.100835 / 0.141683 (-0.040847) \| 1.500008 / 1.452155 (0.047853) \| 1.566757 / 1.492716 (0.074041) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.220668 / 0.018006 (0.202662) \| 0.449273 / 0.000490 (0.448784) \| 0.003861 / 0.000200 (0.003661) \| 0.000126 / 0.000054 (0.000072) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026847 / 0.037411 (-0.010565) \| 0.105916 / 0.014526 (0.091390) \| 0.116245 / 0.176557 (-0.060312) \| 0.172617 / 0.737135 (-0.564519) \| 0.122846 / 0.296338 (-0.173492) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.417906 / 0.215209 (0.202697) \| 4.169092 / 2.077655 (2.091437) \| 1.934439 / 1.504120 (0.430319) \| 1.735718 / 1.541195 (0.194523) \| 1.828205 / 1.468490 (0.359715) \| 0.697446 / 4.584777 (-3.887331) \| 3.802830 / 3.745712 (0.057118) \| 3.686464 / 5.269862 (-1.583398) \| 1.863924 / 4.565676 (-2.701752) \| 0.086520 / 0.424275 (-0.337755) \| 0.012101 / 0.007607 (0.004493) \| 0.521252 / 0.226044 (0.295208) \| 5.200937 / 2.268929 (2.932009) \| 2.414290 / 55.444624 (-53.030334) \| 2.070890 / 6.876477 (-4.805587) \| 2.237693 / 2.142072 (0.095621) \| 0.843417 / 4.805227 (-3.961811) \| 0.167856 / 6.500664 (-6.332809) \| 0.064997 / 0.075469 (-0.010472) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.212334 / 1.841788 (-0.629454) \| 14.710632 / 8.074308 (6.636324) \| 14.877489 / 10.191392 (4.686097) \| 0.151268 / 0.680424 (-0.529156) \| 0.018663 / 0.534201 (-0.515538) \| 0.429678 / 0.579283 (-0.149605) \| 0.425054 / 0.434364 (-0.009310) \| 0.502804 / 0.540337 (-0.037533) \| 0.587932 / 1.386936 (-0.799004) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007462 / 0.011353 (-0.003891) \| 0.005307 / 0.011008 (-0.005701) \| 0.074309 / 0.038508 (0.035801) \| 0.033437 / 0.023109 (0.010328) \| 0.355087 / 0.275898 (0.079189) \| 0.391417 / 0.323480 (0.067937) \| 0.005904 / 0.007986 (-0.002082) \| 0.004062 / 0.004328 (-0.000266) \| 0.073801 / 0.004250 (0.069550) \| 0.048503 / 0.037052 (0.011451) \| 0.359547 / 0.258489 (0.101058) \| 0.405325 / 0.293841 (0.111484) \| 0.036615 / 0.128546 (-0.091931) \| 0.012185 / 0.075646 (-0.063461) \| 0.086829 / 0.419271 (-0.332443) \| 0.049101 / 0.043533 (0.005569) \| 0.334259 / 0.255139 (0.079120) \| 0.376317 / 0.283200 (0.093117) \| 0.099935 / 0.141683 (-0.041748) \| 1.483166 / 1.452155 (0.031011) \| 1.569092 / 1.492716 (0.076375) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.207528 / 0.018006 (0.189521) \| 0.437473 / 0.000490 (0.436983) \| 0.004915 / 0.000200 (0.004715) \| 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) \| 0.028632 / 0.037411 (-0.008780) \| 0.111782 / 0.014526 (0.097256) \| 0.122545 / 0.176557 (-0.054011) \| 0.171191 / 0.737135 (-0.565945) \| 0.128999 / 0.296338 (-0.167339) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.424422 / 0.215209 (0.209213) \| 4.239488 / 2.077655 (2.161833) \| 2.027969 / 1.504120 (0.523849) \| 1.800667 / 1.541195 (0.259473) \| 1.898701 / 1.468490 (0.430211) \| 0.711453 / 4.584777 (-3.873324) \| 3.766696 / 3.745712 (0.020984) \| 2.107530 / 5.269862 (-3.162331) \| 1.347137 / 4.565676 (-3.218540) \| 0.086823 / 0.424275 (-0.337452) \| 0.012137 / 0.007607 (0.004530) \| 0.523143 / 0.226044 (0.297099) \| 5.273434 / 2.268929 (3.004505) \| 2.545463 / 55.444624 (-52.899161) \| 2.246683 / 6.876477 (-4.629793) \| 2.296862 / 2.142072 (0.154789) \| 0.855690 / 4.805227 (-3.949538) \| 0.168526 / 6.500664 (-6.332138) \| 0.063392 / 0.075469 (-0.012078) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.248926 / 1.841788 (-0.592862) \| 14.676308 / 8.074308 (6.602000) \| 14.524364 / 10.191392 (4.332972) \| 0.184138 / 0.680424 (-0.496286) \| 0.017259 / 0.534201 (-0.516942) \| 0.433875 / 0.579283 (-0.145408) \| 0.416787 / 0.434364 (-0.017577) \| 0.532391 / 0.540337 (-0.007947) \| 0.628572 / 1.386936 (-0.758364) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3929cc227a474ce0c716146c8d14ae94f8a7625b \"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) \| 0.006469 / 0.011353 (-0.004884) \| 0.004499 / 0.011008 (-0.006510) \| 0.098856 / 0.038508 (0.060348) \| 0.027753 / 0.023109 (0.004644) \| 0.321348 / 0.275898 (0.045450) \| 0.351480 / 0.323480 (0.028000) \| 0.004949 / 0.007986 (-0.003036) \| 0.004655 / 0.004328 (0.000327) \| 0.076732 / 0.004250 (0.072482) \| 0.036175 / 0.037052 (-0.000878) \| 0.310111 / 0.258489 (0.051622) \| 0.372427 / 0.293841 (0.078586) \| 0.031947 / 0.128546 (-0.096599) \| 0.011669 / 0.075646 (-0.063977) \| 0.323086 / 0.419271 (-0.096186) \| 0.043578 / 0.043533 (0.000045) \| 0.325549 / 0.255139 (0.070410) \| 0.363827 / 0.283200 (0.080627) \| 0.087819 / 0.141683 (-0.053864) \| 1.479429 / 1.452155 (0.027274) \| 1.549797 / 1.492716 (0.057080) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.178502 / 0.018006 (0.160496) \| 0.415954 / 0.000490 (0.415465) \| 0.008767 / 0.000200 (0.008567) \| 0.000429 / 0.000054 (0.000375) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023639 / 0.037411 (-0.013772) \| 0.096266 / 0.014526 (0.081740) \| 0.106406 / 0.176557 (-0.070151) \| 0.168819 / 0.737135 (-0.568317) \| 0.109158 / 0.296338 (-0.187181) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.420729 / 0.215209 (0.205520) \| 4.219469 / 2.077655 (2.141814) \| 1.885673 / 1.504120 (0.381553) \| 1.681868 / 1.541195 (0.140674) \| 1.709240 / 1.468490 (0.240749) \| 0.694763 / 4.584777 (-3.890014) \| 3.395377 / 3.745712 (-0.350335) \| 1.846811 / 5.269862 (-3.423051) \| 1.158381 / 4.565676 (-3.407296) \| 0.082717 / 0.424275 (-0.341558) \| 0.012302 / 0.007607 (0.004695) \| 0.518148 / 0.226044 (0.292103) \| 5.189590 / 2.268929 (2.920661) \| 2.294127 / 55.444624 (-53.150498) \| 1.960080 / 6.876477 (-4.916397) \| 2.045359 / 2.142072 (-0.096713) \| 0.803739 / 4.805227 (-4.001488) \| 0.152322 / 6.500664 (-6.348342) \| 0.067051 / 0.075469 (-0.008418) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.206582 / 1.841788 (-0.635206) \| 13.590515 / 8.074308 (5.516207) \| 14.083739 / 10.191392 (3.892347) \| 0.128738 / 0.680424 (-0.551686) \| 0.016577 / 0.534201 (-0.517624) \| 0.375499 / 0.579283 (-0.203784) \| 0.383256 / 0.434364 (-0.051108) \| 0.439441 / 0.540337 (-0.100896) \| 0.518102 / 1.386936 (-0.868834) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006708 / 0.011353 (-0.004645) \| 0.004591 / 0.011008 (-0.006417) \| 0.076512 / 0.038508 (0.038004) \| 0.027977 / 0.023109 (0.004868) \| 0.341915 / 0.275898 (0.066017) \| 0.374381 / 0.323480 (0.050901) \| 0.004985 / 0.007986 (-0.003001) \| 0.003374 / 0.004328 (-0.000954) \| 0.075334 / 0.004250 (0.071083) \| 0.037522 / 0.037052 (0.000470) \| 0.341702 / 0.258489 (0.083213) \| 0.384342 / 0.293841 (0.090501) \| 0.032231 / 0.128546 (-0.096315) \| 0.011494 / 0.075646 (-0.064153) \| 0.084897 / 0.419271 (-0.334375) \| 0.041914 / 0.043533 (-0.001619) \| 0.342030 / 0.255139 (0.086891) \| 0.371024 / 0.283200 (0.087825) \| 0.089936 / 0.141683 (-0.051746) \| 1.497242 / 1.452155 (0.045087) \| 1.585203 / 1.492716 (0.092486) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227681 / 0.018006 (0.209674) \| 0.398995 / 0.000490 (0.398505) \| 0.003232 / 0.000200 (0.003032) \| 0.000073 / 0.000054 (0.000019) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024705 / 0.037411 (-0.012706) \| 0.099906 / 0.014526 (0.085380) \| 0.106806 / 0.176557 (-0.069750) \| 0.157521 / 0.737135 (-0.579614) \| 0.110803 / 0.296338 (-0.185535) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.457442 / 0.215209 (0.242233) \| 4.580101 / 2.077655 (2.502446) \| 2.094687 / 1.504120 (0.590567) \| 1.880722 / 1.541195 (0.339528) \| 1.938746 / 1.468490 (0.470256) \| 0.700933 / 4.584777 (-3.883844) \| 3.416278 / 3.745712 (-0.329434) \| 2.852183 / 5.269862 (-2.417679) \| 1.602659 / 4.565676 (-2.963017) \| 0.083949 / 0.424275 (-0.340326) \| 0.012255 / 0.007607 (0.004648) \| 0.551631 / 0.226044 (0.325586) \| 5.539225 / 2.268929 (3.270296) \| 2.707298 / 55.444624 (-52.737326) \| 2.354720 / 6.876477 (-4.521757) \| 2.320790 / 2.142072 (0.178717) \| 0.807152 / 4.805227 (-3.998075) \| 0.152048 / 6.500664 (-6.348616) \| 0.067723 / 0.075469 (-0.007746) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.295690 / 1.841788 (-0.546097) \| 13.738082 / 8.074308 (5.663774) \| 14.129549 / 10.191392 (3.938157) \| 0.161568 / 0.680424 (-0.518855) \| 0.016678 / 0.534201 (-0.517522) \| 0.386609 / 0.579283 (-0.192674) \| 0.383538 / 0.434364 (-0.050826) \| 0.477872 / 0.540337 (-0.062465) \| 0.564547 / 1.386936 (-0.822389) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2ab4c98618bce7c1f60ce96d4a853a940ae4b250 \"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) \| 0.007247 / 0.011353 (-0.004106) \| 0.005044 / 0.011008 (-0.005964) \| 0.095135 / 0.038508 (0.056627) \| 0.033622 / 0.023109 (0.010513) \| 0.309969 / 0.275898 (0.034071) \| 0.340354 / 0.323480 (0.016875) \| 0.005635 / 0.007986 (-0.002351) \| 0.003938 / 0.004328 (-0.000391) \| 0.072089 / 0.004250 (0.067838) \| 0.045592 / 0.037052 (0.008539) \| 0.316620 / 0.258489 (0.058131) \| 0.358174 / 0.293841 (0.064333) \| 0.036446 / 0.128546 (-0.092100) \| 0.011961 / 0.075646 (-0.063685) \| 0.332299 / 0.419271 (-0.086973) \| 0.049955 / 0.043533 (0.006422) \| 0.307638 / 0.255139 (0.052499) \| 0.331719 / 0.283200 (0.048519) \| 0.095115 / 0.141683 (-0.046568) \| 1.457960 / 1.452155 (0.005806) \| 1.502812 / 1.492716 (0.010096) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223747 / 0.018006 (0.205740) \| 0.444837 / 0.000490 (0.444347) \| 0.002583 / 0.000200 (0.002383) \| 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) \| 0.026461 / 0.037411 (-0.010951) \| 0.103946 / 0.014526 (0.089420) \| 0.114355 / 0.176557 (-0.062201) \| 0.170076 / 0.737135 (-0.567059) \| 0.121087 / 0.296338 (-0.175252) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.403252 / 0.215209 (0.188043) \| 4.016911 / 2.077655 (1.939257) \| 1.787168 / 1.504120 (0.283048) \| 1.605206 / 1.541195 (0.064012) \| 1.657012 / 1.468490 (0.188522) \| 0.701425 / 4.584777 (-3.883352) \| 3.818308 / 3.745712 (0.072596) \| 3.493757 / 5.269862 (-1.776105) \| 1.860534 / 4.565676 (-2.705142) \| 0.084994 / 0.424275 (-0.339281) \| 0.011904 / 0.007607 (0.004297) \| 0.534199 / 0.226044 (0.308155) \| 4.992703 / 2.268929 (2.723774) \| 2.286231 / 55.444624 (-53.158393) \| 1.918163 / 6.876477 (-4.958314) \| 2.029811 / 2.142072 (-0.112262) \| 0.837532 / 4.805227 (-3.967695) \| 0.168545 / 6.500664 (-6.332119) \| 0.062866 / 0.075469 (-0.012604) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.172862 / 1.841788 (-0.668926) \| 14.966793 / 8.074308 (6.892485) \| 14.202079 / 10.191392 (4.010687) \| 0.144688 / 0.680424 (-0.535736) \| 0.017499 / 0.534201 (-0.516702) \| 0.443081 / 0.579283 (-0.136202) \| 0.427496 / 0.434364 (-0.006868) \| 0.525182 / 0.540337 (-0.015155) \| 0.611849 / 1.386936 (-0.775087) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007264 / 0.011353 (-0.004089) \| 0.005106 / 0.011008 (-0.005902) \| 0.074101 / 0.038508 (0.035593) \| 0.033388 / 0.023109 (0.010279) \| 0.337108 / 0.275898 (0.061210) \| 0.369820 / 0.323480 (0.046340) \| 0.005701 / 0.007986 (-0.002284) \| 0.003976 / 0.004328 (-0.000353) \| 0.073517 / 0.004250 (0.069267) \| 0.048741 / 0.037052 (0.011688) \| 0.339118 / 0.258489 (0.080629) \| 0.398687 / 0.293841 (0.104846) \| 0.036661 / 0.128546 (-0.091886) \| 0.012082 / 0.075646 (-0.063564) \| 0.086743 / 0.419271 (-0.332529) \| 0.050150 / 0.043533 (0.006617) \| 0.335572 / 0.255139 (0.080433) \| 0.354306 / 0.283200 (0.071107) \| 0.102074 / 0.141683 (-0.039609) \| 1.442911 / 1.452155 (-0.009244) \| 1.531564 / 1.492716 (0.038848) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.183163 / 0.018006 (0.165157) \| 0.439273 / 0.000490 (0.438783) \| 0.002765 / 0.000200 (0.002565) \| 0.000225 / 0.000054 (0.000171) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028185 / 0.037411 (-0.009227) \| 0.107337 / 0.014526 (0.092811) \| 0.119925 / 0.176557 (-0.056631) \| 0.172120 / 0.737135 (-0.565015) \| 0.124332 / 0.296338 (-0.172007) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.428750 / 0.215209 (0.213541) \| 4.268933 / 2.077655 (2.191279) \| 2.050135 / 1.504120 (0.546015) \| 1.837567 / 1.541195 (0.296372) \| 1.907040 / 1.468490 (0.438549) \| 0.694162 / 4.584777 (-3.890615) \| 3.831542 / 3.745712 (0.085830) \| 3.476580 / 5.269862 (-1.793281) \| 1.855097 / 4.565676 (-2.710580) \| 0.085816 / 0.424275 (-0.338459) \| 0.012195 / 0.007607 (0.004588) \| 0.544920 / 0.226044 (0.318876) \| 5.332977 / 2.268929 (3.064049) \| 2.592097 / 55.444624 (-52.852527) \| 2.295411 / 6.876477 (-4.581065) \| 2.330803 / 2.142072 (0.188730) \| 0.833268 / 4.805227 (-3.971959) \| 0.177698 / 6.500664 (-6.322966) \| 0.063780 / 0.075469 (-0.011689) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.273361 / 1.841788 (-0.568427) \| 14.981380 / 8.074308 (6.907072) \| 14.395166 / 10.191392 (4.203774) \| 0.186590 / 0.680424 (-0.493834) \| 0.017676 / 0.534201 (-0.516525) \| 0.432100 / 0.579283 (-0.147183) \| 0.422490 / 0.434364 (-0.011874) \| 0.531421 / 0.540337 (-0.008916) \| 0.628548 / 1.386936 (-0.758388) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3b16e08dd599f4646a77a5ca88b6445467e1e7e9 \"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) \| 0.009005 / 0.011353 (-0.002348) \| 0.005803 / 0.011008 (-0.005205) \| 0.103491 / 0.038508 (0.064983) \| 0.048099 / 0.023109 (0.024990) \| 0.304026 / 0.275898 (0.028128) \| 0.340840 / 0.323480 (0.017360) \| 0.006782 / 0.007986 (-0.001204) \| 0.004625 / 0.004328 (0.000296) \| 0.076695 / 0.004250 (0.072445) \| 0.057541 / 0.037052 (0.020489) \| 0.304015 / 0.258489 (0.045526) \| 0.347822 / 0.293841 (0.053981) \| 0.037904 / 0.128546 (-0.090642) \| 0.012686 / 0.075646 (-0.062960) \| 0.368093 / 0.419271 (-0.051179) \| 0.051795 / 0.043533 (0.008262) \| 0.302553 / 0.255139 (0.047415) \| 0.328581 / 0.283200 (0.045381) \| 0.108947 / 0.141683 (-0.032736) \| 1.449770 / 1.452155 (-0.002385) \| 1.541944 / 1.492716 (0.049227) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.207529 / 0.018006 (0.189523) \| 0.455313 / 0.000490 (0.454823) \| 0.008276 / 0.000200 (0.008076) \| 0.000322 / 0.000054 (0.000268) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030564 / 0.037411 (-0.006848) \| 0.122790 / 0.014526 (0.108264) \| 0.126981 / 0.176557 (-0.049576) \| 0.187203 / 0.737135 (-0.549932) \| 0.129931 / 0.296338 (-0.166408) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.402680 / 0.215209 (0.187471) \| 4.017505 / 2.077655 (1.939850) \| 1.801480 / 1.504120 (0.297360) \| 1.647984 / 1.541195 (0.106790) \| 1.702596 / 1.468490 (0.234106) \| 0.717469 / 4.584777 (-3.867308) \| 3.793813 / 3.745712 (0.048101) \| 2.288014 / 5.269862 (-2.981848) \| 1.497545 / 4.565676 (-3.068132) \| 0.091241 / 0.424275 (-0.333034) \| 0.013115 / 0.007607 (0.005508) \| 0.498567 / 0.226044 (0.272522) \| 4.990203 / 2.268929 (2.721275) \| 2.334983 / 55.444624 (-53.109642) \| 2.047888 / 6.876477 (-4.828589) \| 2.167825 / 2.142072 (0.025753) \| 0.863769 / 4.805227 (-3.941459) \| 0.172699 / 6.500664 (-6.327965) \| 0.069285 / 0.075469 (-0.006184) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.397331 / 1.841788 (-0.444457) \| 16.678240 / 8.074308 (8.603932) \| 16.665143 / 10.191392 (6.473751) \| 0.151011 / 0.680424 (-0.529412) \| 0.018303 / 0.534201 (-0.515898) \| 0.445389 / 0.579283 (-0.133894) \| 0.444644 / 0.434364 (0.010280) \| 0.524647 / 0.540337 (-0.015690) \| 0.629747 / 1.386936 (-0.757189) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008853 / 0.011353 (-0.002499) \| 0.006196 / 0.011008 (-0.004813) \| 0.078595 / 0.038508 (0.040087) \| 0.048348 / 0.023109 (0.025239) \| 0.347038 / 0.275898 (0.071140) \| 0.385807 / 0.323480 (0.062327) \| 0.007047 / 0.007986 (-0.000938) \| 0.004772 / 0.004328 (0.000443) \| 0.076116 / 0.004250 (0.071866) \| 0.058805 / 0.037052 (0.021752) \| 0.345731 / 0.258489 (0.087242) \| 0.401589 / 0.293841 (0.107748) \| 0.039349 / 0.128546 (-0.089197) \| 0.012949 / 0.075646 (-0.062697) \| 0.089761 / 0.419271 (-0.329511) \| 0.060001 / 0.043533 (0.016468) \| 0.351587 / 0.255139 (0.096448) \| 0.377708 / 0.283200 (0.094509) \| 0.117391 / 0.141683 (-0.024292) \| 1.471622 / 1.452155 (0.019467) \| 1.568759 / 1.492716 (0.076042) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.191390 / 0.018006 (0.173384) \| 0.469033 / 0.000490 (0.468544) \| 0.003615 / 0.000200 (0.003415) \| 0.000113 / 0.000054 (0.000059) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032706 / 0.037411 (-0.004706) \| 0.127095 / 0.014526 (0.112569) \| 0.128755 / 0.176557 (-0.047801) \| 0.182590 / 0.737135 (-0.554545) \| 0.136939 / 0.296338 (-0.159400) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.427392 / 0.215209 (0.212183) \| 4.246708 / 2.077655 (2.169053) \| 2.115557 / 1.504120 (0.611437) \| 2.021221 / 1.541195 (0.480026) \| 2.177559 / 1.468490 (0.709069) \| 0.713930 / 4.584777 (-3.870847) \| 4.192467 / 3.745712 (0.446755) \| 3.645437 / 5.269862 (-1.624424) \| 1.964986 / 4.565676 (-2.600690) \| 0.089436 / 0.424275 (-0.334839) \| 0.012917 / 0.007607 (0.005310) \| 0.530468 / 0.226044 (0.304423) \| 5.310759 / 2.268929 (3.041831) \| 2.613566 / 55.444624 (-52.831058) \| 2.350443 / 6.876477 (-4.526034) \| 2.385278 / 2.142072 (0.243205) \| 0.862838 / 4.805227 (-3.942389) \| 0.172246 / 6.500664 (-6.328418) \| 0.069570 / 0.075469 (-0.005899) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.310008 / 1.841788 (-0.531780) \| 16.557079 / 8.074308 (8.482771) \| 15.818145 / 10.191392 (5.626752) \| 0.180337 / 0.680424 (-0.500087) \| 0.018117 / 0.534201 (-0.516083) \| 0.433189 / 0.579283 (-0.146095) \| 0.429276 / 0.434364 (-0.005088) \| 0.539757 / 0.540337 (-0.000580) \| 0.640905 / 1.386936 (-0.746031) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3b16e08dd599f4646a77a5ca88b6445467e1e7e9 \"CML watermark\")\n" ]
1,646,001,197	5,683	Fix verification_mode when ignore_verifications is passed	closed	2023-03-29T15:00:50	2023-03-29T17:36:06	2023-03-29T17:28:57	https://github.com/huggingface/datasets/pull/5683	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5683", "html_url": "https://github.com/huggingface/datasets/pull/5683", "diff_url": "https://github.com/huggingface/datasets/pull/5683.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5683.patch", "merged_at": "2023-03-29T17:28:57" }	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) \| 0.006935 / 0.011353 (-0.004418) \| 0.004711 / 0.011008 (-0.006297) \| 0.098461 / 0.038508 (0.059953) \| 0.028889 / 0.023109 (0.005780) \| 0.332167 / 0.275898 (0.056269) \| 0.363309 / 0.323480 (0.039829) \| 0.005179 / 0.007986 (-0.002807) \| 0.004783 / 0.004328 (0.000455) \| 0.074293 / 0.004250 (0.070043) \| 0.038778 / 0.037052 (0.001726) \| 0.318871 / 0.258489 (0.060382) \| 0.362975 / 0.293841 (0.069134) \| 0.032897 / 0.128546 (-0.095649) \| 0.011685 / 0.075646 (-0.063961) \| 0.322824 / 0.419271 (-0.096447) \| 0.043842 / 0.043533 (0.000309) \| 0.334789 / 0.255139 (0.079650) \| 0.352922 / 0.283200 (0.069723) \| 0.089692 / 0.141683 (-0.051991) \| 1.490110 / 1.452155 (0.037955) \| 1.601530 / 1.492716 (0.108813) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.201882 / 0.018006 (0.183875) \| 0.410875 / 0.000490 (0.410385) \| 0.002472 / 0.000200 (0.002272) \| 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) \| 0.023636 / 0.037411 (-0.013775) \| 0.102168 / 0.014526 (0.087642) \| 0.107247 / 0.176557 (-0.069310) \| 0.171858 / 0.737135 (-0.565278) \| 0.110619 / 0.296338 (-0.185720) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.433740 / 0.215209 (0.218531) \| 4.332121 / 2.077655 (2.254466) \| 2.075398 / 1.504120 (0.571278) \| 1.941074 / 1.541195 (0.399879) \| 2.033331 / 1.468490 (0.564841) \| 0.697134 / 4.584777 (-3.887643) \| 3.463855 / 3.745712 (-0.281857) \| 3.080446 / 5.269862 (-2.189416) \| 1.575020 / 4.565676 (-2.990656) \| 0.083054 / 0.424275 (-0.341221) \| 0.012454 / 0.007607 (0.004847) \| 0.537996 / 0.226044 (0.311951) \| 5.366765 / 2.268929 (3.097836) \| 2.464398 / 55.444624 (-52.980227) \| 2.143912 / 6.876477 (-4.732564) \| 2.245706 / 2.142072 (0.103634) \| 0.801397 / 4.805227 (-4.003831) \| 0.150954 / 6.500664 (-6.349710) \| 0.066758 / 0.075469 (-0.008711) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.216412 / 1.841788 (-0.625376) \| 13.679322 / 8.074308 (5.605014) \| 14.055286 / 10.191392 (3.863894) \| 0.130264 / 0.680424 (-0.550160) \| 0.016566 / 0.534201 (-0.517635) \| 0.379126 / 0.579283 (-0.200157) \| 0.390815 / 0.434364 (-0.043549) \| 0.437586 / 0.540337 (-0.102751) \| 0.526822 / 1.386936 (-0.860114) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006898 / 0.011353 (-0.004455) \| 0.004705 / 0.011008 (-0.006304) \| 0.078592 / 0.038508 (0.040084) \| 0.028635 / 0.023109 (0.005525) \| 0.340143 / 0.275898 (0.064245) \| 0.377526 / 0.323480 (0.054047) \| 0.005645 / 0.007986 (-0.002340) \| 0.003533 / 0.004328 (-0.000796) \| 0.078441 / 0.004250 (0.074191) \| 0.039408 / 0.037052 (0.002356) \| 0.342303 / 0.258489 (0.083814) \| 0.386837 / 0.293841 (0.092996) \| 0.032427 / 0.128546 (-0.096119) \| 0.011763 / 0.075646 (-0.063883) \| 0.087984 / 0.419271 (-0.331287) \| 0.042126 / 0.043533 (-0.001406) \| 0.339951 / 0.255139 (0.084812) \| 0.366165 / 0.283200 (0.082966) \| 0.091414 / 0.141683 (-0.050269) \| 1.502034 / 1.452155 (0.049880) \| 1.597901 / 1.492716 (0.105184) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.232122 / 0.018006 (0.214115) \| 0.410205 / 0.000490 (0.409715) \| 0.000418 / 0.000200 (0.000218) \| 0.000063 / 0.000054 (0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026013 / 0.037411 (-0.011399) \| 0.105520 / 0.014526 (0.090995) \| 0.108649 / 0.176557 (-0.067908) \| 0.159324 / 0.737135 (-0.577811) \| 0.114033 / 0.296338 (-0.182306) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.455634 / 0.215209 (0.240425) \| 4.508544 / 2.077655 (2.430889) \| 2.087065 / 1.504120 (0.582945) \| 1.872622 / 1.541195 (0.331427) \| 1.935617 / 1.468490 (0.467127) \| 0.696909 / 4.584777 (-3.887868) \| 3.449365 / 3.745712 (-0.296348) \| 3.008399 / 5.269862 (-2.261462) \| 1.459245 / 4.565676 (-3.106431) \| 0.083637 / 0.424275 (-0.340638) \| 0.012358 / 0.007607 (0.004750) \| 0.547232 / 0.226044 (0.321187) \| 5.522395 / 2.268929 (3.253466) \| 2.691019 / 55.444624 (-52.753605) \| 2.408083 / 6.876477 (-4.468394) \| 2.369239 / 2.142072 (0.227166) \| 0.807148 / 4.805227 (-3.998080) \| 0.152030 / 6.500664 (-6.348634) \| 0.067883 / 0.075469 (-0.007586) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.336956 / 1.841788 (-0.504832) \| 14.403730 / 8.074308 (6.329422) \| 14.854084 / 10.191392 (4.662692) \| 0.146530 / 0.680424 (-0.533894) \| 0.016611 / 0.534201 (-0.517590) \| 0.398557 / 0.579283 (-0.180726) \| 0.393194 / 0.434364 (-0.041170) \| 0.486824 / 0.540337 (-0.053513) \| 0.572844 / 1.386936 (-0.814092) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#411f9cc281e50954ea0c903e7a0a6618b3d31b9e \"CML watermark\")\n" ]
1,646,000,571	5,682	ValueError when passing ignore_verifications	closed	2023-03-29T15:00:30	2023-03-29T17:28:58	2023-03-29T17:28:58	https://github.com/huggingface/datasets/issues/5682	null	albertvillanova	false	[]
1,645,630,784	5,681	Add information about patterns search order to the doc about structuring repo	closed	2023-03-29T11:44:49	2023-04-03T18:31:11	2023-04-03T18:31:11	https://github.com/huggingface/datasets/issues/5681	null	polinaeterna	false	[ "Good idea, I think I've seen this a couple of times before too on the forums. I can work on this :)", "Closed in #5693 " ]
1,645,430,103	5,680	Fix a description error for interleave_datasets.	closed	2023-03-29T09:50:23	2023-03-30T13:14:19	2023-03-30T13:07:18	https://github.com/huggingface/datasets/pull/5680	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5680", "html_url": "https://github.com/huggingface/datasets/pull/5680", "diff_url": "https://github.com/huggingface/datasets/pull/5680.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5680.patch", "merged_at": "2023-03-30T13:07:18" }	QizhiPei	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "_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) \| 0.006772 / 0.011353 (-0.004581) \| 0.004674 / 0.011008 (-0.006335) \| 0.098702 / 0.038508 (0.060194) \| 0.028257 / 0.023109 (0.005148) \| 0.368008 / 0.275898 (0.092110) \| 0.402825 / 0.323480 (0.079345) \| 0.005158 / 0.007986 (-0.002828) \| 0.003470 / 0.004328 (-0.000858) \| 0.075541 / 0.004250 (0.071291) \| 0.039755 / 0.037052 (0.002702) \| 0.373431 / 0.258489 (0.114942) \| 0.410159 / 0.293841 (0.116318) \| 0.031355 / 0.128546 (-0.097192) \| 0.011632 / 0.075646 (-0.064014) \| 0.325475 / 0.419271 (-0.093797) \| 0.042574 / 0.043533 (-0.000958) \| 0.373629 / 0.255139 (0.118490) \| 0.393921 / 0.283200 (0.110721) \| 0.084669 / 0.141683 (-0.057013) \| 1.459947 / 1.452155 (0.007792) \| 1.529593 / 1.492716 (0.036877) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.189994 / 0.018006 (0.171988) \| 0.409091 / 0.000490 (0.408602) \| 0.003693 / 0.000200 (0.003493) \| 0.000072 / 0.000054 (0.000018) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024649 / 0.037411 (-0.012762) \| 0.097702 / 0.014526 (0.083177) \| 0.103650 / 0.176557 (-0.072906) \| 0.167141 / 0.737135 (-0.569994) \| 0.108460 / 0.296338 (-0.187879) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.429544 / 0.215209 (0.214335) \| 4.277106 / 2.077655 (2.199451) \| 2.018745 / 1.504120 (0.514625) \| 1.814782 / 1.541195 (0.273587) \| 1.897030 / 1.468490 (0.428540) \| 0.700332 / 4.584777 (-3.884445) \| 3.421761 / 3.745712 (-0.323951) \| 3.008281 / 5.269862 (-2.261581) \| 1.554230 / 4.565676 (-3.011446) \| 0.082922 / 0.424275 (-0.341353) \| 0.012312 / 0.007607 (0.004705) \| 0.527757 / 0.226044 (0.301713) \| 5.287450 / 2.268929 (3.018522) \| 2.329083 / 55.444624 (-53.115542) \| 2.016651 / 6.876477 (-4.859826) \| 2.214510 / 2.142072 (0.072437) \| 0.807676 / 4.805227 (-3.997551) \| 0.151752 / 6.500664 (-6.348912) \| 0.066819 / 0.075469 (-0.008651) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.239522 / 1.841788 (-0.602266) \| 13.923672 / 8.074308 (5.849364) \| 14.317394 / 10.191392 (4.126002) \| 0.159379 / 0.680424 (-0.521045) \| 0.016537 / 0.534201 (-0.517664) \| 0.376808 / 0.579283 (-0.202475) \| 0.376351 / 0.434364 (-0.058012) \| 0.437124 / 0.540337 (-0.103213) \| 0.520589 / 1.386936 (-0.866347) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006892 / 0.011353 (-0.004461) \| 0.004671 / 0.011008 (-0.006337) \| 0.075841 / 0.038508 (0.037333) \| 0.028713 / 0.023109 (0.005604) \| 0.345105 / 0.275898 (0.069207) \| 0.380694 / 0.323480 (0.057214) \| 0.005155 / 0.007986 (-0.002830) \| 0.003379 / 0.004328 (-0.000949) \| 0.075134 / 0.004250 (0.070883) \| 0.039990 / 0.037052 (0.002938) \| 0.345540 / 0.258489 (0.087051) \| 0.389913 / 0.293841 (0.096072) \| 0.032089 / 0.128546 (-0.096458) \| 0.011583 / 0.075646 (-0.064063) \| 0.085169 / 0.419271 (-0.334102) \| 0.041847 / 0.043533 (-0.001686) \| 0.341504 / 0.255139 (0.086365) \| 0.367582 / 0.283200 (0.084382) \| 0.092684 / 0.141683 (-0.048999) \| 1.498647 / 1.452155 (0.046492) \| 1.549056 / 1.492716 (0.056339) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.228643 / 0.018006 (0.210637) \| 0.410680 / 0.000490 (0.410191) \| 0.000398 / 0.000200 (0.000198) \| 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) \| 0.025354 / 0.037411 (-0.012057) \| 0.101567 / 0.014526 (0.087041) \| 0.108340 / 0.176557 (-0.068217) \| 0.157804 / 0.737135 (-0.579332) \| 0.113985 / 0.296338 (-0.182354) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.436427 / 0.215209 (0.221218) \| 4.359331 / 2.077655 (2.281676) \| 2.047877 / 1.504120 (0.543757) \| 1.844242 / 1.541195 (0.303047) \| 1.924553 / 1.468490 (0.456063) \| 0.695986 / 4.584777 (-3.888791) \| 3.435571 / 3.745712 (-0.310141) \| 1.905189 / 5.269862 (-3.364673) \| 1.198542 / 4.565676 (-3.367134) \| 0.083386 / 0.424275 (-0.340889) \| 0.012442 / 0.007607 (0.004835) \| 0.542562 / 0.226044 (0.316517) \| 5.416554 / 2.268929 (3.147625) \| 2.499496 / 55.444624 (-52.945128) \| 2.160658 / 6.876477 (-4.715819) \| 2.210535 / 2.142072 (0.068462) \| 0.803324 / 4.805227 (-4.001903) \| 0.151735 / 6.500664 (-6.348929) \| 0.068392 / 0.075469 (-0.007078) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.319915 / 1.841788 (-0.521873) \| 14.176755 / 8.074308 (6.102446) \| 14.376366 / 10.191392 (4.184974) \| 0.141219 / 0.680424 (-0.539204) \| 0.017181 / 0.534201 (-0.517020) \| 0.383589 / 0.579283 (-0.195694) \| 0.389352 / 0.434364 (-0.045012) \| 0.474465 / 0.540337 (-0.065873) \| 0.563047 / 1.386936 (-0.823889) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c33e8ce68b5000988bf6b2e4bca27ffaa469acea \"CML watermark\")\n" ]
1,645,184,622	5,679	Allow load_dataset to take a working dir for intermediate data	open	2023-03-29T07:21:09	2023-04-12T22:30:25	null	https://github.com/huggingface/datasets/issues/5679	null	lu-wang-dl	false	[ "Hi ! AFAIK a dataset must be present on a local disk to be able to efficiently memory map the datasets Arrow files. What makes you think that it is possible to load from a cloud storage and have good performance ?\r\n\r\nAnyway it's already possible to download_and_prepare a dataset as Arrow files in a cloud storage with:\r\n```python\r\nbuilder = load_dataset_builder(..., cache_dir=\"/temp/dir\")\r\nbuilder.download_and_prepare(\"/cloud_dir\")\r\n```\r\n\r\nbut then \r\n```python\r\nds = builder.as_dataset()\r\n```\r\nwould fail if \"/cloud_dir\" is not a local directory.", "In my use case, I am trying to mount the S3 bucket as local system with S3FS-FUSE / [goofys](https://github.com/kahing/goofys). I want to use S3 to save the download data and save checkpoint for training for persistent. Setting the s3 location as cache directory is not fast enough. That is why I want to set a work directory for temp data for memory map and only save the final result to s3 cache. ", "You can try setting `HF_DATASETS_DOWNLOADED_DATASETS_PATH` and `HF_DATASETS_EXTRACTED_DATASETS_PATH` to S3, and `HF_DATASETS_CACHE` to your local disk.\r\n\r\nThis way all your downloaded and extracted data are on your mounted S3, but the datasets Arrow files are on your local disk", "If we hope to also persist the Arrow files on the mounted S3 but work with the efficiency of local disk, is there any recommended way to do this, other than copying the Arrow files from local disk to S3?" ]
1,645,018,359	5,678	Add support to create a Dataset from spark dataframe	closed	2023-03-29T04:36:28	2024-08-27T14:43:19	2023-07-21T14:15:38	https://github.com/huggingface/datasets/issues/5678	null	lu-wang-dl	false	[ "if i read spark Dataframe , got an error on multi-node Spark cluster.\r\nDid the Api (Dataset.from_spark) support Spark cluster, read dataframe and save_to_disk?\r\n\r\nError: \r\n_pickle.PicklingError: Could not serialize object: RuntimeError: It appears that you are attempting to reference SparkContext from a broadcast variable, action, or transforma\r\ntion. SparkContext can only be used on the driver, not in code that it run on workers. For more information, see SPARK-5063.\r\n23/06/16 21:17:20 WARN ExecutorPodsWatchSnapshotSource: Kubernetes client has been closed (this is expected if the application is shutting down.)\r\n\r\n", "How to perform predictions on Dataset object in Spark with multi-node cluster parallelism?", "Addressed in #5701", "Hi ! for your information we are working on some more documentation on how to use Spark with HF Datasets repositories (without the need for the `datasets` library) <s>https://github.com/huggingface/datasets/issues/5678</s>\r\nCc @lu-wang-dl @maddiedawson let me know what you think !", "sorry, wrong link: https://github.com/huggingface/hub-docs/pull/1392" ]
1,644,828,606	5,677	Dataset.map() crashes when any column contains more than 1000 empty dictionaries	closed	2023-03-29T00:01:31	2023-07-07T14:01:14	2023-07-07T14:01:14	https://github.com/huggingface/datasets/issues/5677	null	mtoles	false	[]
1,641,763,478	5,675	Filter datasets by language code	closed	2023-03-27T09:42:28	2023-03-30T08:08:15	2023-03-30T08:08:15	https://github.com/huggingface/datasets/issues/5675	null	named-entity	false	[ "The dataset still can be found, if instead of using the search form you just enter the language code in the url, like https://huggingface.co/datasets?language=language:myv. \r\n\r\nBut of course having a more complete list of languages in the search form (or just a fallback to the language codes, if they are missing from the code=>language mapping) would be much more convenient!", "Hi! I've opened a PR to make these languages searchable on the Hub.", "Thanks @mariosasko!\r\nDo you think it is possible to turn this into a more scalable pipeline? Such as:\r\n1. Looping through all the datasets on the hub and collecting the set of all their language codes;\r\n2. Selecting the codes not covered yet in `Language.ts`\r\n3. Looking up their codes at https://iso639-3.sil.org/code_tables/639/data\r\n4. Adding all the newly found language codes to `Language.ts`", "@avidale This has been discussed in https://github.com/huggingface/datasets/issues/4881, so also feel free to share your opinion there." ]
1,641,084,105	5,674	Stored XSS	closed	2023-03-26T20:55:58	2024-04-30T22:56:41	2023-03-27T21:01:55	https://github.com/huggingface/datasets/issues/5674	null	Fadavvi	false	[ "Hi! You can contact `security@huggingface.co` to report this vulnerability." ]
1,641,066,352	5,673	Pass down storage options	closed	2023-03-26T20:09:37	2023-03-28T15:03:38	2023-03-28T14:54:17	https://github.com/huggingface/datasets/pull/5673	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5673", "html_url": "https://github.com/huggingface/datasets/pull/5673", "diff_url": "https://github.com/huggingface/datasets/pull/5673.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5673.patch", "merged_at": "2023-03-28T14:54:17" }	dwyatte	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "> download_and_prepare is not called when streaming a dataset, so we may need to have storage_options in the DatasetBuilder.__init__ ? This way it could also be passed later to as_streaming_dataset and the StreamingDownloadManager\r\n\r\n> Currently the storage_options parameter in download_and_prepare are for the target filesystem where the dataset must be downloaded and prepared as arrow files\r\n\r\nAh, I noted this when looking for ways to plumb down `storage_options` although I think I was looking at adding to `BuilderConfig`. The `DatasetBuilder` constructor looks more appropriate for this, will get that added in a future commit", "Noting as experimental SGTM. The only tests I can think of to add at the moment would be mocks that assert the storage options get passed all the way down using `mock.assert_called_with` but if Hugging Face has some S3/GCS buckets for testing, maybe those would be better in a future PR. Let me know what you think", "I think adding tests with the mockfs fixture will do the job. Tests and docs can be added when request_etag and is_remote_url support fsspec (right now they would fail with mockfs).\r\n\r\nLet's see in a subsequent PR, this is exciting ! :)", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009217 / 0.011353 (-0.002136) \| 0.006275 / 0.011008 (-0.004733) \| 0.124361 / 0.038508 (0.085853) \| 0.035680 / 0.023109 (0.012570) \| 0.395255 / 0.275898 (0.119357) \| 0.426104 / 0.323480 (0.102624) \| 0.006822 / 0.007986 (-0.001163) \| 0.004467 / 0.004328 (0.000138) \| 0.099404 / 0.004250 (0.095153) \| 0.051919 / 0.037052 (0.014867) \| 0.388286 / 0.258489 (0.129797) \| 0.426361 / 0.293841 (0.132520) \| 0.053100 / 0.128546 (-0.075446) \| 0.019453 / 0.075646 (-0.056194) \| 0.433139 / 0.419271 (0.013867) \| 0.063240 / 0.043533 (0.019707) \| 0.381175 / 0.255139 (0.126036) \| 0.411686 / 0.283200 (0.128487) \| 0.104843 / 0.141683 (-0.036840) \| 1.853582 / 1.452155 (0.401427) \| 1.935644 / 1.492716 (0.442928) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.218969 / 0.018006 (0.200963) \| 0.515011 / 0.000490 (0.514522) \| 0.004017 / 0.000200 (0.003818) \| 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) \| 0.028975 / 0.037411 (-0.008437) \| 0.125239 / 0.014526 (0.110713) \| 0.131371 / 0.176557 (-0.045185) \| 0.203864 / 0.737135 (-0.533271) \| 0.140784 / 0.296338 (-0.155554) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.620701 / 0.215209 (0.405492) \| 6.263557 / 2.077655 (4.185903) \| 2.510058 / 1.504120 (1.005938) \| 2.085892 / 1.541195 (0.544697) \| 2.170362 / 1.468490 (0.701872) \| 1.325600 / 4.584777 (-3.259177) \| 5.583355 / 3.745712 (1.837642) \| 5.092791 / 5.269862 (-0.177071) \| 2.814766 / 4.565676 (-1.750911) \| 0.153568 / 0.424275 (-0.270707) \| 0.014850 / 0.007607 (0.007243) \| 0.787011 / 0.226044 (0.560967) \| 7.948813 / 2.268929 (5.679885) \| 3.320831 / 55.444624 (-52.123793) \| 2.526327 / 6.876477 (-4.350150) \| 2.691651 / 2.142072 (0.549579) \| 1.521199 / 4.805227 (-3.284028) \| 0.269738 / 6.500664 (-6.230926) \| 0.082959 / 0.075469 (0.007490) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.740056 / 1.841788 (-0.101732) \| 17.699732 / 8.074308 (9.625424) \| 22.450689 / 10.191392 (12.259297) \| 0.229350 / 0.680424 (-0.451073) \| 0.027486 / 0.534201 (-0.506715) \| 0.536153 / 0.579283 (-0.043130) \| 0.608166 / 0.434364 (0.173802) \| 0.629144 / 0.540337 (0.088807) \| 0.732671 / 1.386936 (-0.654265) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010147 / 0.011353 (-0.001206) \| 0.006484 / 0.011008 (-0.004524) \| 0.098664 / 0.038508 (0.060156) \| 0.036400 / 0.023109 (0.013291) \| 0.432895 / 0.275898 (0.156997) \| 0.466433 / 0.323480 (0.142954) \| 0.008102 / 0.007986 (0.000117) \| 0.004554 / 0.004328 (0.000225) \| 0.100466 / 0.004250 (0.096216) \| 0.054066 / 0.037052 (0.017013) \| 0.439177 / 0.258489 (0.180688) \| 0.502907 / 0.293841 (0.209066) \| 0.059210 / 0.128546 (-0.069336) \| 0.020220 / 0.075646 (-0.055426) \| 0.124671 / 0.419271 (-0.294600) \| 0.064278 / 0.043533 (0.020746) \| 0.435659 / 0.255139 (0.180520) \| 0.459670 / 0.283200 (0.176471) \| 0.115574 / 0.141683 (-0.026109) \| 1.826360 / 1.452155 (0.374205) \| 1.943199 / 1.492716 (0.450483) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.238463 / 0.018006 (0.220457) \| 0.534889 / 0.000490 (0.534400) \| 0.000404 / 0.000200 (0.000204) \| 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) \| 0.033210 / 0.037411 (-0.004201) \| 0.133529 / 0.014526 (0.119003) \| 0.143813 / 0.176557 (-0.032743) \| 0.213079 / 0.737135 (-0.524056) \| 0.148427 / 0.296338 (-0.147912) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.656819 / 0.215209 (0.441610) \| 6.414860 / 2.077655 (4.337205) \| 2.756182 / 1.504120 (1.252062) \| 2.405268 / 1.541195 (0.864073) \| 2.436418 / 1.468490 (0.967928) \| 1.289828 / 4.584777 (-3.294949) \| 5.572731 / 3.745712 (1.827018) \| 3.185432 / 5.269862 (-2.084429) \| 2.093220 / 4.565676 (-2.472457) \| 0.144817 / 0.424275 (-0.279458) \| 0.015674 / 0.007607 (0.008067) \| 0.801238 / 0.226044 (0.575194) \| 7.955925 / 2.268929 (5.686996) \| 3.605670 / 55.444624 (-51.838955) \| 2.837568 / 6.876477 (-4.038908) \| 2.873848 / 2.142072 (0.731775) \| 1.493512 / 4.805227 (-3.311715) \| 0.266251 / 6.500664 (-6.234413) \| 0.082417 / 0.075469 (0.006948) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.608685 / 1.841788 (-0.233103) \| 18.587875 / 8.074308 (10.513567) \| 21.786119 / 10.191392 (11.594727) \| 0.261748 / 0.680424 (-0.418675) \| 0.026228 / 0.534201 (-0.507973) \| 0.553538 / 0.579283 (-0.025745) \| 0.599780 / 0.434364 (0.165416) \| 0.665663 / 0.540337 (0.125325) \| 0.792785 / 1.386936 (-0.594151) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1520e017a9bb6f80e82a38b578213e418ad7e845 \"CML watermark\")\n" ]
1,641,005,322	5,672	Pushing dataset to hub crash	closed	2023-03-26T17:42:13	2023-03-30T08:11:05	2023-03-30T08:11:05	https://github.com/huggingface/datasets/issues/5672	null	tzvc	false	[ "Hi ! It's been fixed by https://github.com/huggingface/datasets/pull/5598. We're doing a new release tomorrow with the fix and you'll be able to push your 100k images ;)\r\n\r\nBasically `push_to_hub` used to fail if the remote repository already exists and has a README.md without dataset_info in the YAML tags.\r\n\r\nIn the meantime you can install datasets from source", "Hi @lhoestq ,\r\n\r\nWhat version of datasets library fix this case? I am using the last `v2.10.1` and I get the same error.", "We just released 2.11 which includes a fix :)" ]

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