Datasets:

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Francesco-A

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github-issues_huggingface-datasets

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]

[ "Hi ! This is an issue with `datasets` 1.12. Sorry for the inconvenience. Can you update to `>=1.13` ?\r\nsee https://github.com/huggingface/datasets/issues/3190\r\n\r\nMaybe we should also backport the bug fix to `1.12` (in a new version `1.12.2`)" ]

[ { "color": "008672", "default": true, "description": "Extra attention is needed", "id": 1935892884, "name": "help wanted", "node_id": "MDU6TGFiZWwxOTM1ODkyODg0", "url": "https://api.github.com/repos/huggingface/datasets/labels/help%20wanted" }, { "color": "e99695", "default": false, "description": "Requesting to add a new dataset", "id": 2067376369, "name": "dataset request", "node_id": "MDU6TGFiZWwyMDY3Mzc2MzY5", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20request" } ]

[ "Pinging @mariamabarham as well", "- `nlp` has MR! It's called `rotten_tomatoes`\r\n- SST is part of GLUE, or is that just SST-2?\r\n- `nlp` also has `ag_news`, a popular news classification dataset\r\n\r\nI'd also like to see:\r\n- the Yahoo Answers topic classification dataset\r\n- the Kaggle Fake News classification dataset", "Thanks @jxmorris12 for pointing this out. \r\n\r\nIn glue we only have SST-2 maybe we can add separately SST-1.\r\n", "This is the homepage for the Amazon dataset: https://www.kaggle.com/datafiniti/consumer-reviews-of-amazon-products\r\n\r\nIs there an easy way to download kaggle datasets programmatically? If so, I can add this one!", "Hi @jxmorris12 for now I think our `dl_manager` does not download from Kaggle.\r\n@thomwolf , @lhoestq", "Pretty sure the quora dataset is the same one I implemented here: https://github.com/huggingface/nlp/pull/366", "Great list. Any idea if Amazon Reviews has been added?\r\n\r\n- ~40 GB of text (sadly no emoji)\r\n- popular MLM pre-training dataset before bigger datasets like WebText https://arxiv.org/abs/1808.01371\r\n- turns out that binarizing the 1-5 star rating leads to great Pos/Neg/Neutral dataset, T5 paper claims to get very high accuracy (98%!) on this with small amount of finetuning https://arxiv.org/abs/2004.14546\r\n\r\nApologies if it's been included (great to see where) and if not, it's one of the better medium/large NLP dataset for semi-supervised learning, albeit a bit out of date. \r\n\r\nThanks!! \r\n\r\ncc @sshleifer ", "On the Amazon Reviews dataset, the original UCSD website has noted these are now updated to include product reviews through 2018 -- actually quite recent compared to many other datasets. Almost certainly the largest NLP dataset out there with labels!\r\nhttps://jmcauley.ucsd.edu/data/amazon/ \r\n\r\nAny chance someone has time to onboard this dataset in a HF way?\r\n\r\ncc @sshleifer " ]

[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]

[ "I'm not able to reproduce on my side.\r\nCan you provide the full stacktrace please ?\r\nWhat version of `python` and `dill` do you have ? Which OS are you using ?", "```\r\nco_filename = '<ipython-input-2-e0383a102aae>', returned_obj = [0]\r\n \r\n def create_ipython_func(co_filename, returned_obj):\r\n def func():\r\n return returned_obj\r\n \r\n code = func.__code__\r\n> code = CodeType(*[getattr(code, k) if k != \"co_filename\" else co_filename for k in code_args])\r\nE TypeError: an integer is required (got type bytes)\r\n\r\ntests/test_caching.py:152: TypeError\r\n```\r\n\r\nPython 3.8.8 \r\ndill==0.3.1.1\r\n", "I managed to reproduce. This comes from the CodeType init signature that is different in python 3.8.8\r\nI opened a PR to fix this test\r\nThanks !" ]

[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]

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[ "_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) | 0.007167 / 0.011353 (-0.004185) | 0.004914 / 0.011008 (-0.006094) | 0.096858 / 0.038508 (0.058350) | 0.033468 / 0.023109 (0.010359) | 0.297276 / 0.275898 (0.021378) | 0.344289 / 0.323480 (0.020809) | 0.005703 / 0.007986 (-0.002282) | 0.003972 / 0.004328 (-0.000357) | 0.075191 / 0.004250 (0.070940) | 0.046247 / 0.037052 (0.009194) | 0.317857 / 0.258489 (0.059368) | 0.347263 / 0.293841 (0.053422) | 0.035017 / 0.128546 (-0.093529) | 0.012036 / 0.075646 (-0.063611) | 0.332522 / 0.419271 (-0.086750) | 0.050188 / 0.043533 (0.006655) | 0.296627 / 0.255139 (0.041488) | 0.319196 / 0.283200 (0.035997) | 0.101100 / 0.141683 (-0.040583) | 1.484536 / 1.452155 (0.032382) | 1.606364 / 1.492716 (0.113648) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203954 / 0.018006 (0.185948) | 0.436505 / 0.000490 (0.436015) | 0.003853 / 0.000200 (0.003654) | 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) | 0.025834 / 0.037411 (-0.011578) | 0.105759 / 0.014526 (0.091233) | 0.114289 / 0.176557 (-0.062268) | 0.174388 / 0.737135 (-0.562748) | 0.122248 / 0.296338 (-0.174090) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.404218 / 0.215209 (0.189009) | 4.027900 / 2.077655 (1.950245) | 1.854757 / 1.504120 (0.350637) | 1.668882 / 1.541195 (0.127687) | 1.731451 / 1.468490 (0.262961) | 0.707843 / 4.584777 (-3.876934) | 3.756386 / 3.745712 (0.010674) | 2.067751 / 5.269862 (-3.202110) | 1.313039 / 4.565676 (-3.252638) | 0.086442 / 0.424275 (-0.337833) | 0.012329 / 0.007607 (0.004722) | 0.505964 / 0.226044 (0.279919) | 5.050788 / 2.268929 (2.781860) | 2.353936 / 55.444624 (-53.090688) | 2.055560 / 6.876477 (-4.820917) | 2.162948 / 2.142072 (0.020876) | 0.850532 / 4.805227 (-3.954696) | 0.168560 / 6.500664 (-6.332104) | 0.063143 / 0.075469 (-0.012326) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.182723 / 1.841788 (-0.659065) | 14.779342 / 8.074308 (6.705034) | 14.461572 / 10.191392 (4.270180) | 0.163120 / 0.680424 (-0.517303) | 0.017978 / 0.534201 (-0.516223) | 0.419168 / 0.579283 (-0.160115) | 0.420955 / 0.434364 (-0.013409) | 0.509710 / 0.540337 (-0.030628) | 0.619586 / 1.386936 (-0.767350) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated 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) | 0.005136 / 0.011008 (-0.005872) | 0.074910 / 0.038508 (0.036402) | 0.032552 / 0.023109 (0.009443) | 0.374998 / 0.275898 (0.099100) | 0.399219 / 0.323480 (0.075739) | 0.005615 / 0.007986 (-0.002371) | 0.004118 / 0.004328 (-0.000210) | 0.074219 / 0.004250 (0.069969) | 0.045924 / 0.037052 (0.008871) | 0.383228 / 0.258489 (0.124739) | 0.407195 / 0.293841 (0.113354) | 0.035460 / 0.128546 (-0.093086) | 0.012460 / 0.075646 (-0.063187) | 0.087077 / 0.419271 (-0.332195) | 0.050507 / 0.043533 (0.006974) | 0.369001 / 0.255139 (0.113862) | 0.385761 / 0.283200 (0.102561) | 0.106999 / 0.141683 (-0.034684) | 1.465456 / 1.452155 (0.013302) | 1.556962 / 1.492716 (0.064246) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.214926 / 0.018006 (0.196920) | 0.436893 / 0.000490 (0.436403) | 0.003388 / 0.000200 (0.003188) | 0.000093 / 0.000054 (0.000038) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029919 / 0.037411 (-0.007492) | 0.110859 / 0.014526 (0.096333) | 0.120617 / 0.176557 (-0.055939) | 0.171781 / 0.737135 (-0.565355) | 0.125627 / 0.296338 (-0.170712) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436024 / 0.215209 (0.220815) | 4.359167 / 2.077655 (2.281512) | 2.188399 / 1.504120 (0.684279) | 2.001196 / 1.541195 (0.460001) | 2.023710 / 1.468490 (0.555220) | 0.713799 / 4.584777 (-3.870978) | 3.832217 / 3.745712 (0.086504) | 3.269351 / 5.269862 (-2.000510) | 1.534608 / 4.565676 (-3.031068) | 0.088505 / 0.424275 (-0.335770) | 0.012345 / 0.007607 (0.004738) | 0.542446 / 0.226044 (0.316401) | 5.377757 / 2.268929 (3.108828) | 2.659837 / 55.444624 (-52.784787) | 2.272356 / 6.876477 (-4.604120) | 2.297289 / 2.142072 (0.155217) | 0.855276 / 4.805227 (-3.949952) | 0.170666 / 6.500664 (-6.329998) | 0.064549 / 0.075469 (-0.010920) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.255938 / 1.841788 (-0.585850) | 15.151471 / 8.074308 (7.077163) | 12.905762 / 10.191392 (2.714370) | 0.162425 / 0.680424 (-0.517999) | 0.017504 / 0.534201 (-0.516697) | 0.448671 / 0.579283 (-0.130612) | 0.422424 / 0.434364 (-0.011940) | 0.551772 / 0.540337 (0.011434) | 0.649115 / 1.386936 (-0.737821) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#be73d9f192149727c5542ff257df81b03024fa39 \"CML watermark\")\n", "Having those different checks helps providing an appropriate error message.\r\n\r\nIf the input is a dict, we suggest to select a split. If the input lists is a mix of iterable and non-iterable, we mention that it must be one or the other.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006559 / 0.011353 (-0.004794) | 0.004569 / 0.011008 (-0.006439) | 0.104503 / 0.038508 (0.065995) | 0.028220 / 0.023109 (0.005111) | 0.365507 / 0.275898 (0.089609) | 0.400238 / 0.323480 (0.076758) | 0.004968 / 0.007986 (-0.003017) | 0.003271 / 0.004328 (-0.001057) | 0.082804 / 0.004250 (0.078554) | 0.036299 / 0.037052 (-0.000754) | 0.361201 / 0.258489 (0.102712) | 0.410962 / 0.293841 (0.117121) | 0.030423 / 0.128546 (-0.098123) | 0.011612 / 0.075646 (-0.064034) | 0.331820 / 0.419271 (-0.087452) | 0.043822 / 0.043533 (0.000289) | 0.356242 / 0.255139 (0.101103) | 0.393035 / 0.283200 (0.109836) | 0.088426 / 0.141683 (-0.053257) | 1.484139 / 1.452155 (0.031984) | 1.566712 / 1.492716 (0.073995) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.195887 / 0.018006 (0.177880) | 0.402720 / 0.000490 (0.402231) | 0.003516 / 0.000200 (0.003316) | 0.000075 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023270 / 0.037411 (-0.014141) | 0.095834 / 0.014526 (0.081308) | 0.102924 / 0.176557 (-0.073632) | 0.161397 / 0.737135 (-0.575738) | 0.105225 / 0.296338 (-0.191114) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.451701 / 0.215209 (0.236491) | 4.495171 / 2.077655 (2.417517) | 2.223203 / 1.504120 (0.719083) | 2.035533 / 1.541195 (0.494338) | 2.076182 / 1.468490 (0.607692) | 0.697317 / 4.584777 (-3.887460) | 3.406309 / 3.745712 (-0.339403) | 1.847179 / 5.269862 (-3.422683) | 1.158762 / 4.565676 (-3.406914) | 0.083067 / 0.424275 (-0.341208) | 0.012453 / 0.007607 (0.004846) | 0.546502 / 0.226044 (0.320458) | 5.455712 / 2.268929 (3.186784) | 2.654142 / 55.444624 (-52.790483) | 2.298722 / 6.876477 (-4.577755) | 2.383467 / 2.142072 (0.241395) | 0.805950 / 4.805227 (-3.999278) | 0.152479 / 6.500664 (-6.348185) | 0.066784 / 0.075469 (-0.008685) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.239129 / 1.841788 (-0.602659) | 13.603707 / 8.074308 (5.529398) | 14.062004 / 10.191392 (3.870612) | 0.130928 / 0.680424 (-0.549495) | 0.016907 / 0.534201 (-0.517294) | 0.381614 / 0.579283 (-0.197670) | 0.386770 / 0.434364 (-0.047594) | 0.455792 / 0.540337 (-0.084545) | 0.526092 / 1.386936 (-0.860844) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006202 / 0.011353 (-0.005151) | 0.004478 / 0.011008 (-0.006531) | 0.076492 / 0.038508 (0.037984) | 0.026703 / 0.023109 (0.003594) | 0.355134 / 0.275898 (0.079236) | 0.391207 / 0.323480 (0.067727) | 0.004852 / 0.007986 (-0.003133) | 0.003271 / 0.004328 (-0.001057) | 0.075080 / 0.004250 (0.070830) | 0.038803 / 0.037052 (0.001750) | 0.359530 / 0.258489 (0.101041) | 0.409044 / 0.293841 (0.115203) | 0.030366 / 0.128546 (-0.098180) | 0.011544 / 0.075646 (-0.064102) | 0.084849 / 0.419271 (-0.334423) | 0.040076 / 0.043533 (-0.003457) | 0.357359 / 0.255139 (0.102220) | 0.384075 / 0.283200 (0.100875) | 0.089130 / 0.141683 (-0.052552) | 1.520400 / 1.452155 (0.068246) | 1.604403 / 1.492716 (0.111687) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.257127 / 0.018006 (0.239121) | 0.403691 / 0.000490 (0.403202) | 0.006894 / 0.000200 (0.006694) | 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) | 0.024653 / 0.037411 (-0.012758) | 0.098834 / 0.014526 (0.084309) | 0.107276 / 0.176557 (-0.069281) | 0.158256 / 0.737135 (-0.578879) | 0.111339 / 0.296338 (-0.184999) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.445006 / 0.215209 (0.229797) | 4.452953 / 2.077655 (2.375299) | 2.168291 / 1.504120 (0.664171) | 1.969457 / 1.541195 (0.428262) | 2.003505 / 1.468490 (0.535015) | 0.695857 / 4.584777 (-3.888920) | 3.433424 / 3.745712 (-0.312288) | 2.466977 / 5.269862 (-2.802885) | 1.528167 / 4.565676 (-3.037509) | 0.082425 / 0.424275 (-0.341850) | 0.012470 / 0.007607 (0.004863) | 0.559039 / 0.226044 (0.332995) | 5.609496 / 2.268929 (3.340568) | 2.602898 / 55.444624 (-52.841726) | 2.273971 / 6.876477 (-4.602506) | 2.303370 / 2.142072 (0.161298) | 0.803875 / 4.805227 (-4.001352) | 0.151069 / 6.500664 (-6.349595) | 0.067956 / 0.075469 (-0.007513) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.334443 / 1.841788 (-0.507345) | 13.773252 / 8.074308 (5.698944) | 13.007042 / 10.191392 (2.815650) | 0.127939 / 0.680424 (-0.552485) | 0.016412 / 0.534201 (-0.517789) | 0.374744 / 0.579283 (-0.204539) | 0.396912 / 0.434364 (-0.037452) | 0.443197 / 0.540337 (-0.097140) | 0.528338 / 1.386936 (-0.858598) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#51d9f2a3064aa89a780e3d02c6cc34000c51c4fb \"CML watermark\")\n", "Just modified it to use only one loop. I think I managed to keep it readable as well", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007382 / 0.011353 (-0.003971) | 0.005143 / 0.011008 (-0.005865) | 0.097635 / 0.038508 (0.059127) | 0.034726 / 0.023109 (0.011616) | 0.315556 / 0.275898 (0.039658) | 0.355951 / 0.323480 (0.032472) | 0.006055 / 0.007986 (-0.001931) | 0.004264 / 0.004328 (-0.000065) | 0.073636 / 0.004250 (0.069386) | 0.050480 / 0.037052 (0.013428) | 0.316031 / 0.258489 (0.057542) | 0.363933 / 0.293841 (0.070092) | 0.035138 / 0.128546 (-0.093408) | 0.012407 / 0.075646 (-0.063239) | 0.333677 / 0.419271 (-0.085595) | 0.050586 / 0.043533 (0.007053) | 0.309507 / 0.255139 (0.054369) | 0.327043 / 0.283200 (0.043844) | 0.108975 / 0.141683 (-0.032708) | 1.447778 / 1.452155 (-0.004377) | 1.519971 / 1.492716 (0.027255) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.248770 / 0.018006 (0.230764) | 0.603036 / 0.000490 (0.602546) | 0.000383 / 0.000200 (0.000183) | 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) | 0.027094 / 0.037411 (-0.010317) | 0.104427 / 0.014526 (0.089901) | 0.120627 / 0.176557 (-0.055929) | 0.178790 / 0.737135 (-0.558346) | 0.124877 / 0.296338 (-0.171461) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.414442 / 0.215209 (0.199233) | 4.138009 / 2.077655 (2.060355) | 1.964642 / 1.504120 (0.460523) | 1.775940 / 1.541195 (0.234745) | 1.899719 / 1.468490 (0.431228) | 0.695406 / 4.584777 (-3.889371) | 3.760470 / 3.745712 (0.014758) | 3.906958 / 5.269862 (-1.362904) | 2.028164 / 4.565676 (-2.537513) | 0.086704 / 0.424275 (-0.337571) | 0.012465 / 0.007607 (0.004857) | 0.512336 / 0.226044 (0.286292) | 5.108587 / 2.268929 (2.839659) | 2.435273 / 55.444624 (-53.009352) | 2.142387 / 6.876477 (-4.734090) | 2.258234 / 2.142072 (0.116162) | 0.854035 / 4.805227 (-3.951193) | 0.170443 / 6.500664 (-6.330222) | 0.065762 / 0.075469 (-0.009707) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.187529 / 1.841788 (-0.654259) | 15.151164 / 8.074308 (7.076856) | 14.577545 / 10.191392 (4.386153) | 0.166973 / 0.680424 (-0.513450) | 0.017883 / 0.534201 (-0.516318) | 0.427607 / 0.579283 (-0.151676) | 0.417050 / 0.434364 (-0.017314) | 0.508116 / 0.540337 (-0.032221) | 0.590173 / 1.386936 (-0.796763) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007499 / 0.011353 (-0.003854) | 0.005195 / 0.011008 (-0.005813) | 0.073600 / 0.038508 (0.035091) | 0.033574 / 0.023109 (0.010464) | 0.377506 / 0.275898 (0.101608) | 0.432752 / 0.323480 (0.109272) | 0.006042 / 0.007986 (-0.001944) | 0.006427 / 0.004328 (0.002098) | 0.071666 / 0.004250 (0.067416) | 0.053243 / 0.037052 (0.016190) | 0.363972 / 0.258489 (0.105483) | 0.454988 / 0.293841 (0.161147) | 0.035118 / 0.128546 (-0.093428) | 0.012395 / 0.075646 (-0.063251) | 0.084308 / 0.419271 (-0.334963) | 0.048589 / 0.043533 (0.005057) | 0.368036 / 0.255139 (0.112897) | 0.399414 / 0.283200 (0.116215) | 0.109043 / 0.141683 (-0.032640) | 1.462972 / 1.452155 (0.010817) | 1.574443 / 1.492716 (0.081726) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.215107 / 0.018006 (0.197101) | 0.550255 / 0.000490 (0.549765) | 0.004630 / 0.000200 (0.004430) | 0.000104 / 0.000054 (0.000049) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029948 / 0.037411 (-0.007463) | 0.111866 / 0.014526 (0.097340) | 0.126559 / 0.176557 (-0.049997) | 0.181443 / 0.737135 (-0.555693) | 0.130559 / 0.296338 (-0.165779) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441410 / 0.215209 (0.226201) | 4.403406 / 2.077655 (2.325752) | 2.180276 / 1.504120 (0.676156) | 2.003729 / 1.541195 (0.462534) | 2.079394 / 1.468490 (0.610904) | 0.706061 / 4.584777 (-3.878716) | 3.805668 / 3.745712 (0.059956) | 3.864941 / 5.269862 (-1.404921) | 1.970468 / 4.565676 (-2.595208) | 0.086033 / 0.424275 (-0.338242) | 0.012261 / 0.007607 (0.004654) | 0.550427 / 0.226044 (0.324383) | 5.542270 / 2.268929 (3.273342) | 2.717047 / 55.444624 (-52.727577) | 2.449022 / 6.876477 (-4.427455) | 2.549567 / 2.142072 (0.407495) | 0.854981 / 4.805227 (-3.950247) | 0.169756 / 6.500664 (-6.330908) | 0.067082 / 0.075469 (-0.008387) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.281369 / 1.841788 (-0.560419) | 15.445090 / 8.074308 (7.370781) | 13.205652 / 10.191392 (3.014260) | 0.170070 / 0.680424 (-0.510354) | 0.017815 / 0.534201 (-0.516385) | 0.425193 / 0.579283 (-0.154090) | 0.425205 / 0.434364 (-0.009159) | 0.493561 / 0.540337 (-0.046776) | 0.588994 / 1.386936 (-0.797942) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e427105fc68fce04d0f3c74efb942cbf3a65d166 \"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) | 0.006345 / 0.011353 (-0.005008) | 0.004330 / 0.011008 (-0.006678) | 0.096327 / 0.038508 (0.057819) | 0.032964 / 0.023109 (0.009855) | 0.335600 / 0.275898 (0.059702) | 0.365635 / 0.323480 (0.042155) | 0.005435 / 0.007986 (-0.002551) | 0.005005 / 0.004328 (0.000677) | 0.071107 / 0.004250 (0.066856) | 0.044363 / 0.037052 (0.007311) | 0.339988 / 0.258489 (0.081498) | 0.375575 / 0.293841 (0.081734) | 0.028343 / 0.128546 (-0.100203) | 0.008587 / 0.075646 (-0.067059) | 0.324349 / 0.419271 (-0.094922) | 0.050105 / 0.043533 (0.006573) | 0.327398 / 0.255139 (0.072259) | 0.348479 / 0.283200 (0.065279) | 0.102357 / 0.141683 (-0.039326) | 1.419905 / 1.452155 (-0.032250) | 1.534887 / 1.492716 (0.042171) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212418 / 0.018006 (0.194412) | 0.433183 / 0.000490 (0.432693) | 0.000595 / 0.000200 (0.000395) | 0.000062 / 0.000054 (0.000007) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027520 / 0.037411 (-0.009891) | 0.109503 / 0.014526 (0.094977) | 0.118202 / 0.176557 (-0.058355) | 0.177236 / 0.737135 (-0.559899) | 0.123736 / 0.296338 (-0.172602) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.405734 / 0.215209 (0.190525) | 4.039566 / 2.077655 (1.961911) | 1.838211 / 1.504120 (0.334091) | 1.652650 / 1.541195 (0.111456) | 1.753488 / 1.468490 (0.284998) | 0.525258 / 4.584777 (-4.059519) | 3.704509 / 3.745712 (-0.041203) | 1.826794 / 5.269862 (-3.443067) | 1.236361 / 4.565676 (-3.329315) | 0.065619 / 0.424275 (-0.358656) | 0.011606 / 0.007607 (0.003999) | 0.505954 / 0.226044 (0.279910) | 5.054140 / 2.268929 (2.785211) | 2.352587 / 55.444624 (-53.092037) | 2.050601 / 6.876477 (-4.825875) | 2.097222 / 2.142072 (-0.044850) | 0.641044 / 4.805227 (-4.164183) | 0.140676 / 6.500664 (-6.359988) | 0.063217 / 0.075469 (-0.012253) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.177750 / 1.841788 (-0.664038) | 14.819346 / 8.074308 (6.745038) | 14.085937 / 10.191392 (3.894545) | 0.168618 / 0.680424 (-0.511806) | 0.017189 / 0.534201 (-0.517011) | 0.393415 / 0.579283 (-0.185868) | 0.422879 / 0.434364 (-0.011485) | 0.477289 / 0.540337 (-0.063048) | 0.569078 / 1.386936 (-0.817858) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006502 / 0.011353 (-0.004850) | 0.004640 / 0.011008 (-0.006368) | 0.073272 / 0.038508 (0.034764) | 0.033225 / 0.023109 (0.010116) | 0.359165 / 0.275898 (0.083267) | 0.391659 / 0.323480 (0.068179) | 0.005684 / 0.007986 (-0.002302) | 0.004045 / 0.004328 (-0.000284) | 0.072880 / 0.004250 (0.068629) | 0.046260 / 0.037052 (0.009208) | 0.361772 / 0.258489 (0.103283) | 0.402905 / 0.293841 (0.109064) | 0.027732 / 0.128546 (-0.100814) | 0.008864 / 0.075646 (-0.066783) | 0.081961 / 0.419271 (-0.337310) | 0.046170 / 0.043533 (0.002637) | 0.364198 / 0.255139 (0.109059) | 0.387468 / 0.283200 (0.104269) | 0.105456 / 0.141683 (-0.036227) | 1.457176 / 1.452155 (0.005021) | 1.564899 / 1.492716 (0.072183) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.179129 / 0.018006 (0.161123) | 0.439699 / 0.000490 (0.439209) | 0.002882 / 0.000200 (0.002682) | 0.000090 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029123 / 0.037411 (-0.008288) | 0.112046 / 0.014526 (0.097520) | 0.122773 / 0.176557 (-0.053784) | 0.178404 / 0.737135 (-0.558732) | 0.127904 / 0.296338 (-0.168434) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440413 / 0.215209 (0.225204) | 4.407334 / 2.077655 (2.329680) | 2.112932 / 1.504120 (0.608812) | 1.911034 / 1.541195 (0.369840) | 2.057168 / 1.468490 (0.588677) | 0.525472 / 4.584777 (-4.059305) | 3.738894 / 3.745712 (-0.006818) | 1.807592 / 5.269862 (-3.462270) | 1.053837 / 4.565676 (-3.511839) | 0.066203 / 0.424275 (-0.358072) | 0.011965 / 0.007607 (0.004358) | 0.541137 / 0.226044 (0.315093) | 5.415040 / 2.268929 (3.146112) | 2.580476 / 55.444624 (-52.864148) | 2.234144 / 6.876477 (-4.642333) | 2.306014 / 2.142072 (0.163942) | 0.644221 / 4.805227 (-4.161006) | 0.142870 / 6.500664 (-6.357794) | 0.065015 / 0.075469 (-0.010454) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.303465 / 1.841788 (-0.538323) | 14.949683 / 8.074308 (6.875375) | 14.370871 / 10.191392 (4.179478) | 0.142714 / 0.680424 (-0.537710) | 0.017372 / 0.534201 (-0.516829) | 0.403898 / 0.579283 (-0.175385) | 0.424781 / 0.434364 (-0.009583) | 0.465984 / 0.540337 (-0.074353) | 0.570863 / 1.386936 (-0.816074) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#22d1d533e8ab831b1aa1aab3e7d3c72ba42a83e8 \"CML watermark\")\n" ]

[]

[ "cc @lhoestq in case you have any ideas here! Might need a multi-host set-up to debug (can give you access to a JAX one if you need)" ]

[]

[ "_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) | 0.007697 / 0.011353 (-0.003656) | 0.004233 / 0.011008 (-0.006776) | 0.087890 / 0.038508 (0.049382) | 0.065305 / 0.023109 (0.042196) | 0.366919 / 0.275898 (0.091020) | 0.399656 / 0.323480 (0.076176) | 0.006753 / 0.007986 (-0.001232) | 0.003428 / 0.004328 (-0.000900) | 0.070180 / 0.004250 (0.065930) | 0.054164 / 0.037052 (0.017112) | 0.377130 / 0.258489 (0.118641) | 0.403456 / 0.293841 (0.109615) | 0.042639 / 0.128546 (-0.085907) | 0.012396 / 0.075646 (-0.063250) | 0.314235 / 0.419271 (-0.105036) | 0.061976 / 0.043533 (0.018443) | 0.376959 / 0.255139 (0.121820) | 0.433313 / 0.283200 (0.150113) | 0.031253 / 0.141683 (-0.110430) | 1.555749 / 1.452155 (0.103594) | 1.643905 / 1.492716 (0.151189) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208630 / 0.018006 (0.190624) | 0.519532 / 0.000490 (0.519042) | 0.003719 / 0.000200 (0.003519) | 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) | 0.027403 / 0.037411 (-0.010008) | 0.080990 / 0.014526 (0.066464) | 0.090424 / 0.176557 (-0.086133) | 0.153922 / 0.737135 (-0.583213) | 0.098156 / 0.296338 (-0.198183) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.519453 / 0.215209 (0.304244) | 5.100089 / 2.077655 (3.022434) | 2.212165 / 1.504120 (0.708045) | 1.894405 / 1.541195 (0.353210) | 1.922914 / 1.468490 (0.454424) | 0.762443 / 4.584777 (-3.822334) | 4.669214 / 3.745712 (0.923502) | 5.016066 / 5.269862 (-0.253796) | 3.128821 / 4.565676 (-1.436856) | 0.091541 / 0.424275 (-0.332734) | 0.007582 / 0.007607 (-0.000026) | 0.652753 / 0.226044 (0.426709) | 6.601375 / 2.268929 (4.332446) | 3.076948 / 55.444624 (-52.367677) | 2.250544 / 6.876477 (-4.625933) | 2.404059 / 2.142072 (0.261987) | 0.994917 / 4.805227 (-3.810311) | 0.200318 / 6.500664 (-6.300346) | 0.069354 / 0.075469 (-0.006115) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.482559 / 1.841788 (-0.359229) | 20.722092 / 8.074308 (12.647784) | 17.703217 / 10.191392 (7.511825) | 0.215370 / 0.680424 (-0.465053) | 0.028208 / 0.534201 (-0.505993) | 0.425992 / 0.579283 (-0.153291) | 0.492785 / 0.434364 (0.058421) | 0.474154 / 0.540337 (-0.066183) | 0.644599 / 1.386936 (-0.742337) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008372 / 0.011353 (-0.002981) | 0.004543 / 0.011008 (-0.006465) | 0.070564 / 0.038508 (0.032056) | 0.066855 / 0.023109 (0.043746) | 0.386724 / 0.275898 (0.110826) | 0.432184 / 0.323480 (0.108704) | 0.005250 / 0.007986 (-0.002736) | 0.003630 / 0.004328 (-0.000698) | 0.069310 / 0.004250 (0.065060) | 0.055759 / 0.037052 (0.018707) | 0.375789 / 0.258489 (0.117299) | 0.417335 / 0.293841 (0.123494) | 0.043424 / 0.128546 (-0.085122) | 0.013106 / 0.075646 (-0.062541) | 0.087836 / 0.419271 (-0.331436) | 0.057770 / 0.043533 (0.014237) | 0.396694 / 0.255139 (0.141555) | 0.439350 / 0.283200 (0.156150) | 0.031660 / 0.141683 (-0.110023) | 1.571339 / 1.452155 (0.119185) | 1.667169 / 1.492716 (0.174452) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.180534 / 0.018006 (0.162528) | 0.540027 / 0.000490 (0.539537) | 0.003573 / 0.000200 (0.003373) | 0.000141 / 0.000054 (0.000086) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031380 / 0.037411 (-0.006032) | 0.083762 / 0.014526 (0.069236) | 0.098166 / 0.176557 (-0.078390) | 0.160761 / 0.737135 (-0.576374) | 0.097683 / 0.296338 (-0.198656) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.568074 / 0.215209 (0.352865) | 5.660544 / 2.077655 (3.582889) | 2.416698 / 1.504120 (0.912578) | 2.177096 / 1.541195 (0.635901) | 2.206178 / 1.468490 (0.737688) | 0.844864 / 4.584777 (-3.739912) | 4.793636 / 3.745712 (1.047923) | 7.062387 / 5.269862 (1.792525) | 4.201228 / 4.565676 (-0.364449) | 0.091997 / 0.424275 (-0.332279) | 0.007881 / 0.007607 (0.000274) | 0.679466 / 0.226044 (0.453422) | 6.580268 / 2.268929 (4.311340) | 3.229907 / 55.444624 (-52.214717) | 2.524877 / 6.876477 (-4.351600) | 2.463796 / 2.142072 (0.321723) | 0.975627 / 4.805227 (-3.829600) | 0.186670 / 6.500664 (-6.313994) | 0.065307 / 0.075469 (-0.010163) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.501447 / 1.841788 (-0.340340) | 21.231037 / 8.074308 (13.156729) | 17.591671 / 10.191392 (7.400279) | 0.212745 / 0.680424 (-0.467679) | 0.026100 / 0.534201 (-0.508101) | 0.428391 / 0.579283 (-0.150892) | 0.535268 / 0.434364 (0.100904) | 0.506733 / 0.540337 (-0.033604) | 0.660832 / 1.386936 (-0.726104) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#962537d7ee9191438ef47a4185d0ba626b2ee949 \"CML watermark\")\n" ]

[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]

[ "Yes that's definitely something we plan to add ^^", "Yes, that would be nice. We could take a look at what tensorflow `tf.data` does under the hood for instance.", "So `tf.data.Dataset.map()` returns a `ParallelMapDataset` if `num_parallel_calls is not None` [link](https://github.com/tensorflow/tensorflow/blob/2b96f3662bd776e277f86997659e61046b56c315/tensorflow/python/data/ops/dataset_ops.py#L1623).\r\n\r\nThere, `num_parallel_calls` is turned into a tensor and and fed to `gen_dataset_ops.parallel_map_dataset` where it looks like tensorflow takes over.\r\n\r\nWe could start with something simple like a thread or process pool that `imap`s over some shards.\r\n ", "Multiprocessing was added in #552 . You can set the number of processes with `.map(..., num_proc=...)`. It also works for `filter`\r\n\r\nClosing this one, but feel free to reo-open if you have other questions", "@lhoestq Great feature implemented! Do you have plans to add it to official tutorials [Processing data in a Dataset](https://huggingface.co/docs/datasets/processing.html?highlight=save#augmenting-the-dataset)? It took me sometime to find this parallel processing api.", "Thanks for the heads up !\r\n\r\nI just added a paragraph about multiprocessing:\r\nhttps://huggingface.co/docs/datasets/master/processing.html#multiprocessing" ]

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[ "Thanks for noticing !\r\n#2020 fixed this earlier today though ^^'\r\n\r\nClosing this one" ]

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[ "We could add a \"threads\" parallel backend to `datasets.parallel.parallel_backend` to support downloading with threads but note that `download_and_extract` also decompresses archives, and this is a CPU-intensive task, which is not ideal for (Python) threads (good for IO-intensive tasks).", "> We could add a \"threads\" parallel backend to `datasets.parallel.parallel_backend` to support downloading with threads but note that `download_and_extract` also decompresses archives, and this is a CPU-intensive task, which is not ideal for (Python) threads (good for IO-intensive tasks).\r\n\r\nGreat! Download takes more time than extract, multiple threads can download in parallel, which can speed up a lot." ]

[ { "color": "d4c5f9", "default": false, "description": "Maintenance tasks", "id": 4296013012, "name": "maintenance", "node_id": "LA_kwDODunzps8AAAABAA_01A", "url": "https://api.github.com/repos/huggingface/datasets/labels/maintenance" } ]

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[ { "color": "0e8a16", "default": false, "description": "Contribution to a dataset script", "id": 4564477500, "name": "dataset contribution", "node_id": "LA_kwDODunzps8AAAABEBBmPA", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20contribution" } ]

[ "Thanks for your contribution, @arampacha. Are you still interested in adding this dataset?\r\n\r\nWe are removing the dataset scripts from this GitHub repo and moving them to the Hugging Face Hub: https://huggingface.co/datasets\r\n\r\nWe would suggest you create this dataset there. Please, feel free to tell us if you need some help." ]

[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" }, { "color": "E5583E", "default": false, "description": "Related to the dataset viewer on huggingface.co", "id": 3470211881, "name": "dataset-viewer", "node_id": "LA_kwDODunzps7O1zsp", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset-viewer" } ]

[ "Done by @lhoestq here: https://github.com/huggingface/datasets/pull/4500 (https://github.com/huggingface/datasets/pull/4500/files#diff-02930e1d966f4b41f9ddf15d961f16f5466d9bee583138657018c7329f71aa43R1255 in particular)\r\n" ]

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[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]

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[ { "color": "e99695", "default": false, "description": "Requesting to add a new dataset", "id": 2067376369, "name": "dataset request", "node_id": "MDU6TGFiZWwyMDY3Mzc2MzY5", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20request" }, { "color": "bfdadc", "default": false, "description": "Vision datasets", "id": 3608941089, "name": "vision", "node_id": "LA_kwDODunzps7XHBIh", "url": "https://api.github.com/repos/huggingface/datasets/labels/vision" } ]

[ "I'm currently adding it, the entire dataset is quite big around 30 GB so I add splits separately. You can take a look here https://huggingface.co/datasets/merve/coco", "I talked to @lhoestq and it's best if I download this dataset through TensorFlow datasets instead, so I'll be implementing that one really soon.\r\n@NielsRogge ", "I started adding COCO, will be done tomorrow EOD\r\nmy work so far https://github.com/merveenoyan/datasets (my fork)", "Hi Merve @merveenoyan , thank you so much for your great contribution! May I ask about the current progress of your implementation? Cuz I see the pull request is still in progess here. Or can I just run the COCO scripts in your fork repo?", "Hello @yixuanren I had another prioritized project about to be merged, but I'll start continuing today will finish up soon. ", "> Hello @yixuanren I had another prioritized project about to be merged, but I'll start continuing today will finish up soon.\r\n\r\nIt's really nice of you!! I see you've commited another version just now", "@yixuanren we're working on it, will be available soon, thanks a lot for your patience", "Hi @NielsRogge and @merveenoyan, did you find a way to load a dataset with COCO annotations to HF's hub?\r\nI have a panoptic segmentation dataset in COCO format and would like to share it with the community.\r\nThanks in advance :)", "The COCO format is not supported out of the box in the HF's hub - you'd need to reformat it to an [ImageFolder](https://huggingface.co/docs/datasets/image_dataset#imagefolder) with metadata format, or write a [loading script](https://huggingface.co/docs/datasets/image_dataset#loading-script)", "> The COCO format is not supported out of the box in the HF's hub - you'd need to reformat it to an [ImageFolder](https://huggingface.co/docs/datasets/image_dataset#imagefolder) with metadata format, or write a [loading script](https://huggingface.co/docs/datasets/image_dataset#loading-script)\r\n\r\nHi @lhoestq , thank you for your quick reply.\r\nI've correctly created a metadata.jsonl file for a dataset with instance segmentation annotations [here](https://huggingface.co/datasets/lombardata/data_2017)\r\nbut do not understand how I can integrate panoptic annotations with the metadata format of ImageFolder datasets. The \"problem\" with panoptic annotations is that we have a folder with images, a json file with annotations and another folder with png annotations.\r\n\r\nI checked between all the datasets already published on HuggingFace and, the only one who has uploaded a correct panoptic dataset is @NielsRogge [here](https://huggingface.co/datasets/nielsr/coco-panoptic-val2017) and [here](https://huggingface.co/datasets/nielsr/ade20k-panoptic-demo). Indeed he accomplished to have three fields : \r\n1.image (image)\r\n2.label (image)\r\n3.segments_info (list)\r\nbut I not find the corresponding code that allows to upload a panoptic dataset from this 3 sources.\r\nCan you please share an example code?\r\nThanks !", "Both were uploaded using `ds.push_to_hub()` :)\r\n\r\nYou can get a Dataset from a python dictionary using `ds = Dataset.from_dict(...)` and casts the paths to images to the `Image()` type using `ds = ds.cast_column(\"image\", Image())`.\r\n\r\n```python\r\nfrom datasets import Dataset, Image\r\n\r\nds = Dataset.from_dict(...)\r\nds = ds.cast_column(\"image\", Image())\r\nds = ds.cast_column(\"label\", Image())\r\nds.push_to_hub(...)\r\n```", "> Both were uploaded using `ds.push_to_hub()` :)\r\n> \r\n> You can get a Dataset from a python dictionary using `ds = Dataset.from_dict(...)` and casts the paths to images to the `Image()` type using `ds = ds.cast_column(\"image\", Image())`.\r\n> \r\n> ```python\r\n> from datasets import Dataset, Image\r\n> \r\n> ds = Dataset.from_dict(...)\r\n> ds = ds.cast_column(\"image\", Image())\r\n> ds = ds.cast_column(\"label\", Image())\r\n> ds.push_to_hub(...)\r\n> ```\r\n\r\nThank you very much @lhoestq , I succesfully created a hf dataset [here](https://huggingface.co/datasets/lombardata/panoptic_2023_06_21) with the two fields :\r\n1.image (image)\r\n2.label (image)\r\nfollowing your suggestions. Now still remain the problem of uploading **segments_info** information to the dataset.\r\nThere is a function that easily imports the _panoptic_coco_annotation.json_ file to a segment_info field?\r\nI think we must define a **list_of_segment**, i.e. a list of lists of this type : \r\n```python\r\n[ { \"area\": 214858, \"bbox\": [ 0, 0, 511, 760 ], \"category_id\": 0, \"id\": 7895160, \"iscrowd\": 0 }, { \"area\": 73067, \"bbox\": [ 98, 719, 413, 253 ], \"category_id\": 3, \"id\": 3289680, \"iscrowd\": 0 }, { \"area\": 832, \"bbox\": [ 53, 0, 101, 16 ], \"category_id\": 5, \"id\": 5273720, \"iscrowd\": 0 }, { \"area\": 70668, \"bbox\": [ 318, 60, 191, 392 ], \"category_id\": 8, \"id\": 15132390, \"iscrowd\": 0 }, { \"area\": 32696, \"bbox\": [ 0, 100, 78, 872 ], \"category_id\": 18, \"id\": 472063, \"iscrowd\": 0 }, { \"area\": 76045, \"bbox\": [ 42, 48, 264, 924 ], \"category_id\": 37, \"id\": 16713830, \"iscrowd\": 0 }, { \"area\": 27103, \"bbox\": [ 288, 482, 216, 306 ], \"category_id\": 47, \"id\": 16753408, \"iscrowd\": 0 } ]\r\n```\r\nand then apply again the **cast_column** function [here](https://github.com/huggingface/datasets/blob/2.13.0/src/datasets/arrow_dataset.py#L2060) but with a list as a second argument, like : \r\n```python\r\nfrom datasets import Dataset, Image\r\nds = ds.cast_column(\"image\", Image())\r\nds = ds.cast_column(\"label\", Image())\r\nds = ds.cast_column(\"segments_info\", list)\r\n```\r\nbut I do not see how to transfer the information of the _panoptic_coco_annotation.json_ to a list of lists of this type : \r\n```python\r\n[ { \"area\": 214858, \"bbox\": [ 0, 0, 511, 760 ], \"category_id\": 0, \"id\": 7895160, \"iscrowd\": 0 }, { \"area\": 73067, \"bbox\": [ 98, 719, 413, 253 ], \"category_id\": 3, \"id\": 3289680, \"iscrowd\": 0 }, { \"area\": 832, \"bbox\": [ 53, 0, 101, 16 ], \"category_id\": 5, \"id\": 5273720, \"iscrowd\": 0 }, { \"area\": 70668, \"bbox\": [ 318, 60, 191, 392 ], \"category_id\": 8, \"id\": 15132390, \"iscrowd\": 0 }, { \"area\": 32696, \"bbox\": [ 0, 100, 78, 872 ], \"category_id\": 18, \"id\": 472063, \"iscrowd\": 0 }, { \"area\": 76045, \"bbox\": [ 42, 48, 264, 924 ], \"category_id\": 37, \"id\": 16713830, \"iscrowd\": 0 }, { \"area\": 27103, \"bbox\": [ 288, 482, 216, 306 ], \"category_id\": 47, \"id\": 16753408, \"iscrowd\": 0 } ]\r\n```\r\nlike @NielsRogge has done [here](https://huggingface.co/datasets/nielsr/coco-panoptic-val2017) and [here](https://huggingface.co/datasets/nielsr/ade20k-panoptic-demo).\r\nThank you again for your help and have a good day !", "You can pass this data in .from_dict() - no need to cast anything for this column\r\n\r\n```python\r\nds = Dataset.from_dict({\r\n \"image\": [...],\r\n \"label\": [...],\r\n \"segments_info\": [...],\r\n)}\r\n```\r\n\r\nwhere `segments_info` is the list of the segment_infos of all the examples in the dataset, and therefore is a list of lists of dicts.", "> You can pass this data in .from_dict() - no need to cast anything for this column\r\n> \r\n> ```python\r\n> ds = Dataset.from_dict({\r\n> \"image\": [...],\r\n> \"label\": [...],\r\n> \"segments_info\": [...],\r\n> )}\r\n> ```\r\n> \r\n> where `segments_info` is the list of the segment_infos of all the examples in the dataset, and therefore is a list of lists of dicts.\r\n\r\nThank you for the quick reply @lhoestq , but then how to generate the `segments_info` list of lists of dicts starting from a _panoptic_coco_annotation.json_ file ?\r\n\r\n\r\n", "You read the JSON file and transform the data yourself. I don't think there's an automatic converter somewhere", "> You read the JSON file and transform the data yourself. I don't think there's an automatic converter somewhere\r\n\r\nPerfect, I've done it and succesfully uploaded a new dataset [here](https://huggingface.co/datasets/lombardata/panoptic_2023_06_22), but I've (I hope) a last problem.\r\nThe dataset has currently 302 images and, when I upload it to the hub, only the first page of images is correctly uploaded.\r\nWhen I try to see the second/third/fourth page of items of my dataset, I can see that the fields **segments_info** and **image_name** are correctly uploaded, while the images are not (the \"null\" string is shown everywhere).\r\n\r\nI've checked the path of images that are not uploaded and they exists, is there a problem with the size of the dataset ?\r\nHow can I upload the whole dataset to the hub ?\r\nThank you again @lhoestq and have a good day !", "Awesome ! Your dataset looks all good 🤗 \r\n\r\nThe `null` in the viewer is a bug on our side, let me investigate" ]

[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]

[ "\r\n> \r\n> Input:\r\n> \r\n> ```\r\n> tokenizer = AutoTokenizer.from_pretrained(checkpoint)\r\n> data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")\r\n> ```\r\n> \r\n> Output:\r\n> \r\n> ```\r\n> TypeError Traceback (most recent call last)\r\n> /tmp/ipykernel_42/1563280798.py in <module>\r\n> 1 checkpoint = 'bert-base-uncased'\r\n> 2 tokenizer = AutoTokenizer.from_pretrained(checkpoint)\r\n> ----> 3 data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"pt\")\r\n> TypeError: __init__() got an unexpected keyword argument 'return_tensors'\r\n> ```\r\n> \r\n\r\nThe issue is due to the older version of transformers and datasets. It has been resolved by upgrading their versions.\r\n\r\n`# upgrade transformers and datasets to latest versions`\r\n`!pip install --upgrade transformers`\r\n`!pip install --upgrade datasets`\r\n\r\nCheers!" ]

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[ "Since the failure was originally intermittent, there is no 100% telling that the problem is gone. \r\nBut if my artificial race condition setup https://github.com/huggingface/datasets/issues/1942#issuecomment-787124529 is to be the litmus test then the problem has been fixed, as with this PR branch that particular race condition is taken care of correctly.\r\n\r\nThank you for taking care of this, @lhoestq - locking can be very tricky to do right!" ]

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[ "Thanks for reporting @lewtun.\r\n\r\nI confirm there is an issue with streaming: it does not stream locally. ", "Oh, after investigation, the source of the issue is in the Hub dataset loading script.\r\n\r\nI'm opening a PR on the Hub dataset.", "I've opened a PR on their Hub dataset to support streaming: https://huggingface.co/datasets/ccdv/pubmed-summarization/discussions/2" ]

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[ "Hello ! Thanks for noticing this bug, let me fix that.\r\n\r\nAlso for information, as specified in the changelog of the latest release, C4 currently needs to have a runtime for apache beam to work on. Apache beam is used to process this very big dataset and it can work on dataflow, spark, flink, apex, etc. You can find more info on beam datasets [here](https://github.com/huggingface/nlp/blob/master/docs/beam_dataset.md).\r\n\r\nOur goal in the future is to make available an already-processed version of C4 (as we do for wikipedia for example) so that users without apache beam runtimes can load it.", "@lhoestq I am facing `IsADirectoryError` while downloading with this command.\r\nCan you pls look into it & help me.\r\nI'm using version 0.4.0 of `nlp`.\r\n\r\n```\r\ndataset = load_dataset(\"c4\", 'en', data_dir='.', beam_runner='DirectRunner')\r\n```\r\n\r\nHere's the complete stack trace.\r\n\r\n```\r\nDownloading and preparing dataset c4/en (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to /home/devops/.cache/huggingface/datasets/c4/en/2.3.0/096df5a27756d51957c959a2499453e60a08154971fceb017bbb29f54b11bef7...\r\n\r\n---------------------------------------------------------------------------\r\nIsADirectoryError Traceback (most recent call last)\r\n<ipython-input-11-f622e6705e03> in <module>\r\n----> 1 dataset = load_dataset(\"c4\", 'en', data_dir='.', beam_runner='DirectRunner')\r\n\r\n/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n 547 # Download and prepare data\r\n 548 builder_instance.download_and_prepare(\r\n--> 549 download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,\r\n 550 )\r\n 551 \r\n\r\n/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n 461 if not downloaded_from_gcs:\r\n 462 self._download_and_prepare(\r\n--> 463 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n 464 )\r\n 465 # Sync info\r\n\r\n/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos)\r\n 964 pipeline = beam_utils.BeamPipeline(runner=beam_runner, options=beam_options,)\r\n 965 super(BeamBasedBuilder, self)._download_and_prepare(\r\n--> 966 dl_manager, verify_infos=False, pipeline=pipeline,\r\n 967 ) # TODO handle verify_infos in beam datasets\r\n 968 # Run pipeline\r\n\r\n/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n 516 split_dict = SplitDict(dataset_name=self.name)\r\n 517 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 518 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n 519 # Checksums verification\r\n 520 if verify_infos:\r\n\r\n/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/datasets/c4/096df5a27756d51957c959a2499453e60a08154971fceb017bbb29f54b11bef7/c4.py in _split_generators(self, dl_manager, pipeline)\r\n 187 if self.config.realnewslike:\r\n 188 files_to_download[\"realnews_domains\"] = _REALNEWS_DOMAINS_URL\r\n--> 189 file_paths = dl_manager.download_and_extract(files_to_download)\r\n 190 \r\n 191 if self.config.webtextlike:\r\n\r\n/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/utils/download_manager.py in download_and_extract(self, url_or_urls)\r\n 218 extracted_path(s): `str`, extracted paths of given URL(s).\r\n 219 \"\"\"\r\n--> 220 return self.extract(self.download(url_or_urls))\r\n 221 \r\n 222 def get_recorded_sizes_checksums(self):\r\n\r\n/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/utils/download_manager.py in download(self, url_or_urls)\r\n 156 lambda url: cached_path(url, download_config=self._download_config,), url_or_urls,\r\n 157 )\r\n--> 158 self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths)\r\n 159 return downloaded_path_or_paths\r\n 160 \r\n\r\n/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/utils/download_manager.py in _record_sizes_checksums(self, url_or_urls, downloaded_path_or_paths)\r\n 106 flattened_downloaded_path_or_paths = flatten_nested(downloaded_path_or_paths)\r\n 107 for url, path in zip(flattened_urls_or_urls, flattened_downloaded_path_or_paths):\r\n--> 108 self._recorded_sizes_checksums[url] = get_size_checksum_dict(path)\r\n 109 \r\n 110 def download_custom(self, url_or_urls, custom_download):\r\n\r\n/data/anaconda/envs/hf/lib/python3.6/site-packages/nlp/utils/info_utils.py in get_size_checksum_dict(path)\r\n 77 \"\"\"Compute the file size and the sha256 checksum of a file\"\"\"\r\n 78 m = sha256()\r\n---> 79 with open(path, \"rb\") as f:\r\n 80 for chunk in iter(lambda: f.read(1 << 20), b\"\"):\r\n 81 m.update(chunk)\r\n\r\nIsADirectoryError: [Errno 21] Is a directory: '/'\r\n\r\n```\r\n\r\nCan anyone please try to see what I am doing wrong or is this a bug?", "I have the same problem as @prashant-kikani", "Looks like a bug in the dataset script, can you open an issue ?", "I see the same issue as @prashant-kikani. I'm using `datasets` version 1.2.0 to download C4." ]

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[ "Hi @J-shel, thanks for reporting.\r\n\r\nI think the issue comes from your call to `load_dataset`. As first argument, you should pass:\r\n- either the name of your dataset (\"mrf\") if this is already published on the Hub\r\n- or the path to the loading script of your dataset (\"path/to/your/local/mrf.py\").", "Hi, following your suggestion, I changed my call to load_dataset. Below is the latest:\r\nreader = load_dataset('data/mrf.py',\"default\", input_size=1024, split=split, streaming=True, keep_in_memory=None)\r\nHowever, I still got the same error.\r\nI have one question that is if I only define input_size=2048 in BUILDER_CONFIGS, may I specify input_size=1024 when loading the dataset? Cause I found that I could only specify name=\"default\" since I only define name=\"default\" in BUILDER_CONFIGS." ]

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[ "LGTM, don't forget the tags ;)" ]

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[ "Not relevant for our current docs :)." ]

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[ "I am experiencing the same problem with the following environment:\r\n\r\n* `datasets` version: 2.11.0\r\n* Platform: `Linux 5.19.0-41-generic x86_64 GNU/Linux`\r\n* Python version: `3.8.5`\r\n* Huggingface_hub version: 0.13.3\r\n* PyArrow version: `11.0.0`\r\n* Pandas version: `1.5.3`\r\n\r\nTrying to get some diagnostics, I got the following: \r\n\r\n```python\r\n>>> from huggingface_hub import scan_cache_dir\r\n>>> sd = scan_cache_dir()\r\n>>> sd\r\nHFCacheInfo(size_on_disk=0, repos=frozenset(), warnings=[CorruptedCacheException('Repo path is not a directory: /home/myname/.cache/huggingface/hub/version_diffusers_cache.txt')])\r\n\r\n```\r\nHowever, that might also be because I had tried to manually specify the `cache_dir` and that resulted in trying to download the dataset again ... but into a folder one level higher up than it should have.\r\n\r\nNote that my issue is with the `huggan/wikiart` dataset, so it is not a dataset-specific issue.", "same problem with a private dataset repo, seems the huggingface hub server got some connection problem?", "Yes, dataset server seems down for now", "@SingL3 You can avoid this error by setting the [`HF_DATASETS_OFFLINE`](https://huggingface.co/docs/datasets/v2.12.0/en/loading#offline) env variable to 1. By default, if an internet connection is available, we check whether the cache of a cached dataset is up-to-date.\r\n\r\n@lucidBrot `datasets`' cache is still not aligned with `huggigface_hub`'s. We plan to align it eventually.", "Today we had a big issue affecting the Hugging Face Hub, thus all the `504 Server Error: Gateway Time-out` errors.\r\n\r\nIt is fixed now and loading your datasets should work as expected.", "Hi, @albertvillanova.\r\nIf there is a locally cached version of datasets or something cache using huggingface_hub, when a network problem(either client or server) occurs, is it a better way to fallback to use the current cached version rather than raise a exception and exit?" ]

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[ "Awesome!" ]

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[ "Cleaned-up version of previous PR: https://github.com/huggingface/datasets/pull/1129", "@lhoestq FYI", "Before merging I might tweak a little bit the dummy data to avoid having to check if the `document_parses` and `embeddings` directories exist or not. I'll do that later today", "Looks all good now ! Thanks a lot @ggdupont :)\r\nMerging" ]

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[ "DUPLICATED comment from https://github.com/huggingface/datasets/issues/3807:\r\n\r\nmy code:\r\n```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"irc_disentangle\", download_mode=\"force_redownload\")\r\n```\r\nhowever, it produces the same error\r\n```\r\n[38](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=37) if len(bad_urls) > 0:\r\n [39](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=38) error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> [40](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=39) raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n [41](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=40) logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://github.com/jkkummerfeld/irc-disentanglement/tarball/master']\r\n```\r\nI attempted to use the `ignore_verifications' as such:\r\n\r\n```\r\nds = datasets.load_dataset('irc_disentangle', download_mode=\"force_redownload\", ignore_verifications=True)\r\n\r\nDownloading builder script: 12.0kB [00:00, 5.92MB/s] \r\nDownloading metadata: 7.58kB [00:00, 3.48MB/s] \r\nNo config specified, defaulting to: irc_disentangle/ubuntu\r\nDownloading and preparing dataset irc_disentangle/ubuntu (download: 112.98 MiB, generated: 60.05 MiB, post-processed: Unknown size, total: 173.03 MiB) to /Users/laylabouzoubaa/.cache/huggingface/datasets/irc_disentangle/ubuntu/1.0.0/0f24ab262a21d8c1d989fa53ed20caa928f5880be26c162bfbc02445dbade7e5...\r\nDownloading data: 118MB [00:09, 12.1MB/s] \r\n \r\nDataset irc_disentangle downloaded and prepared to /Users/laylabouzoubaa/.cache/huggingface/datasets/irc_disentangle/ubuntu/1.0.0/0f24ab262a21d8c1d989fa53ed20caa928f5880be26c162bfbc02445dbade7e5. Subsequent calls will reuse this data.\r\n100%|██████████| 3/3 [00:00<00:00, 675.38it/s]\r\n```\r\nbut, this returns an empty set?\r\n```\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'],\r\n num_rows: 0\r\n })\r\n test: Dataset({\r\n features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'],\r\n num_rows: 0\r\n })\r\n validation: Dataset({\r\n features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'],\r\n num_rows: 0\r\n })\r\n})\r\n```\r\nnot sure what else to try at this point?\r\nThanks in advanced🤗", "Thanks for reporting, @labouz. I'm addressing it. ", "The issue with checksum and empty dataset has been fixed by:\r\n- #4377\r\n\r\nTo load the dataset, you should force the re-generation of the dataset from the downloaded file by passing `download_mode=\"reuse_cache_if_exists\"` to `load_dataset`.\r\n\r\nIn relation with the issue with the dataset viewer, first the dataset should be refactored to support streaming.", "parfait!\r\nit works now, thank you 🙏 ", "Hi there, \r\nI see this issue is closed, but I am wondering if there is any chance the source files have been moved since this fix? I am stumbling into the same NonMatchingChecksumError noted by lebouz's second post once 118MB of data has been downloaded, and have tried the solutions noted in the various fix checksum posts linked here and in other posts regarding passing in \"reuse_cache_if_exists\" to download_mode. Any suggestions? Thank you!\r\n\r\n" ]

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[ "Can you give us stats/information on your pandas DataFrame?", "```\r\n<class 'pandas.core.frame.DataFrame'>\r\nInt64Index: 17136104 entries, 0 to 17136103\r\nData columns (total 6 columns):\r\n # Column Dtype \r\n--- ------ ----- \r\n 0 item_id int64 \r\n 1 item_titl object \r\n 2 start_price float64\r\n 3 shipping_fee float64\r\n 4 picture_url object \r\n 5 embeddings object \r\ndtypes: float64(2), int64(1), object(3)\r\nmemory usage: 915.2+ MB\r\n```", "Thanks and some more on the `embeddings` and `picture_url` would be nice as well (type and max lengths of the elements)", "`embedding` is `np.array` of shape `(128,)`. `picture_url` is url, such as 'https://i.ebayimg.com/00/s/MTE5OVgxNjAw/z/ZOsAAOSwAG9fHQq5/$_12.JPG?set_id=880000500F;https://i.ebayimg.com/00/s/MTE5OVgxNjAw/z/OSgAAOSwokBfHQq8/$_12.JPG?set_id=880000500F'", "It looks like a Pyarrow limitation.\r\nI was able to reproduce the error with \r\n\r\n```python\r\nimport pandas as pd\r\nimport numpy as np\r\nimport pyarrow as pa\r\n\r\n n = 1713614\r\ndf = pd.DataFrame.from_dict({\"a\": list(np.zeros((n, 128))), \"b\": range(n)})\r\npa.Table.from_pandas(df)\r\n```\r\n\r\nI also tried with 50% of the dataframe and it actually works.\r\nI created an issue on Apache Arrow's JIRA [here](https://issues.apache.org/jira/browse/ARROW-9976)\r\n\r\nOne way to fix that would be to chunk the dataframe and concatenate arrow tables.", "It looks like it's going to be fixed in pyarrow 2.0.0 :)\r\n\r\nIn the meantime I suggest to chunk big dataframes to create several small datasets, and then concatenate them using [concatenate_datasets](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=concatenate#datasets.concatenate_datasets)" ]

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[ "Hi ! According to the DPR paper, the wikipedia dump is the one from Dec. 20, 2018.\r\nEach instance contains a paragraph of at most 100 word, as well as the title of the wikipedia page it comes from and the DPR embedding (a 768-d vector).", "Closed by:\r\n- #3534" ]

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[ "uploaded dataset [here](https://huggingface.co/datasets/embedding-data/altlex)." ]

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[ "Same issue here. I will be digging further, but it looks like the [script](https://github.com/huggingface/datasets/blob/master/datasets/wiki_dpr/wiki_dpr.py#L132) is attempting to open a file that is not downloaded yet. \r\n\r\n```\r\n99dcbca09109e58502e6b9271d4d3f3791b43f61f3161a76b25d2775ab1a4498.lock\r\n```\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nUnpicklingError Traceback (most recent call last)\r\n~/anaconda3/envs/eqa/lib/python3.7/site-packages/numpy/lib/npyio.py in load(file, mmap_mode, allow_pickle, fix_imports, encoding)\r\n 446 try:\r\n--> 447 return pickle.load(fid, **pickle_kwargs)\r\n 448 except Exception:\r\n\r\nUnpicklingError: pickle data was truncated\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nOSError Traceback (most recent call last)\r\n~/src/datasets/src/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n 559 \r\n--> 560 if verify_infos:\r\n 561 verify_splits(self.info.splits, split_dict)\r\n\r\n~/src/datasets/src/datasets/builder.py in _prepare_split(self, split_generator)\r\n 847 writer.write(example)\r\n--> 848 finally:\r\n 849 num_examples, num_bytes = writer.finalize()\r\n\r\n~/anaconda3/envs/eqa/lib/python3.7/site-packages/tqdm/notebook.py in __iter__(self, *args, **kwargs)\r\n 227 try:\r\n--> 228 for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):\r\n 229 # return super(tqdm...) will not catch exception\r\n\r\n~/anaconda3/envs/eqa/lib/python3.7/site-packages/tqdm/std.py in __iter__(self)\r\n 1132 try:\r\n-> 1133 for obj in iterable:\r\n 1134 yield obj\r\n\r\n/hdd/rag/cache/huggingface/modules/datasets_modules/datasets/wiki_dpr/14b973bf2a456087ff69c0fd34526684eed22e48e0dfce4338f9a22b965ce7c2/wiki_dpr.py in _generate_examples(self, data_file, vectors_files)\r\n 131 break\r\n--> 132 vecs = np.load(open(vectors_files.pop(0), \"rb\"), allow_pickle=True)\r\n 133 vec_idx = 0\r\n\r\n~/anaconda3/envs/eqa/lib/python3.7/site-packages/numpy/lib/npyio.py in load(file, mmap_mode, allow_pickle, fix_imports, encoding)\r\n 449 raise IOError(\r\n--> 450 \"Failed to interpret file %s as a pickle\" % repr(file))\r\n 451 \r\n\r\nOSError: Failed to interpret file <_io.BufferedReader name='/hdd/rag/downloads/99dcbca09109e58502e6b9271d4d3f3791b43f61f3161a76b25d2775ab1a4498'> as a pickle\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nOSError Traceback (most recent call last)\r\n<ipython-input-8-24351ff8ce44> in <module>\r\n 4 retriever = RagRetriever.from_pretrained(\"facebook/rag-sequence-nq\", \r\n 5 index_name=\"exact\",\r\n----> 6 use_dummy_dataset=False)\r\n\r\n~/src/transformers/src/transformers/retrieval_rag.py in from_pretrained(cls, retriever_name_or_path, **kwargs)\r\n 321 generator_tokenizer = rag_tokenizer.generator\r\n 322 return cls(\r\n--> 323 config, question_encoder_tokenizer=question_encoder_tokenizer, generator_tokenizer=generator_tokenizer\r\n 324 )\r\n 325 \r\n\r\n~/src/transformers/src/transformers/retrieval_rag.py in __init__(self, config, question_encoder_tokenizer, generator_tokenizer)\r\n 310 self.config = config\r\n 311 if self._init_retrieval:\r\n--> 312 self.init_retrieval()\r\n 313 \r\n 314 @classmethod\r\n\r\n~/src/transformers/src/transformers/retrieval_rag.py in init_retrieval(self)\r\n 338 \r\n 339 logger.info(\"initializing retrieval\")\r\n--> 340 self.index.init_index()\r\n 341 \r\n 342 def postprocess_docs(self, docs, input_strings, prefix, n_docs, return_tensors=None):\r\n\r\n~/src/transformers/src/transformers/retrieval_rag.py in init_index(self)\r\n 248 split=self.dataset_split,\r\n 249 index_name=self.index_name,\r\n--> 250 dummy=self.use_dummy_dataset,\r\n 251 )\r\n 252 self.dataset.set_format(\"numpy\", columns=[\"embeddings\"], output_all_columns=True)\r\n\r\n~/src/datasets/src/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n 615 builder_instance.download_and_prepare(\r\n 616 download_config=download_config,\r\n--> 617 download_mode=download_mode,\r\n 618 ignore_verifications=ignore_verifications,\r\n 619 )\r\n\r\n~/src/datasets/src/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n 481 # Sync info\r\n 482 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())\r\n--> 483 self.info.download_checksums = dl_manager.get_recorded_sizes_checksums()\r\n 484 self.info.size_in_bytes = self.info.dataset_size + self.info.download_size\r\n 485 # Save info\r\n\r\n~/src/datasets/src/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n 560 if verify_infos:\r\n 561 verify_splits(self.info.splits, split_dict)\r\n--> 562 \r\n 563 # Update the info object with the splits.\r\n 564 self.info.splits = split_dict\r\n\r\nOSError: Cannot find data file.\r\n```\r\n\r\nThank you.", "An update on my end. This seems like a transient issue. Reran the script from scratch overnight with no errors. ", "Closing this one. Feel free to re-open if you have other questions about this issue" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "### Update:\r\nI wrote a bit cleaner code snippet (without transforming to json) that can expose leaking.\r\n```python\r\nimport datasets\r\nimport json\r\n\r\nitems = ['ab', 'c', 'df']\r\n\r\nds = datasets.Dataset.from_dict({'col': items})\r\nprint(list(ds))\r\n# > Prints: [{'col': 'ab'}, {'col': 'c'}, {'col': 'df'}]\r\n\r\nfiltered = ds\r\n\r\n# get all items that are starting with a character with ascii code bigger than 'a'\r\nfiltered = filtered.filter(lambda x: x['col'][0] > 'a', load_from_cache_file=False)\r\nprint(list(filtered))\r\n# > Prints: [{'col': 'c'}, {'col': 'df'}] as expected\r\n\r\n# get all items that are shorter than 2\r\nfiltered = filtered.filter(lambda x: len(x['col']) < 2, load_from_cache_file=False)\r\nprint(list(filtered))\r\n# > Prints: [{'col': 'ab'}] -> this is a leaked item from the first filter\r\n# > Should be: [{'col': 'c'}]\r\n```", "Thanks for reporting. I'm looking into it", "I just pushed a fix ! We'll do a new release soon.\r\nIn the meantime feel free to install `datasets` from source to play with it", "Thanks, I'm already using it from your branch!" ]

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[ "The metrics API in `datasets` is deprecated as of version 2.0, and `evaulate` is our new library for metrics. You can add a new metric to it by following [these steps](https://huggingface.co/docs/evaluate/creating_and_sharing)." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "@Wauplin I was testing it in a dedicated branch without opening a PR: https://github.com/huggingface/datasets/commits/test-hfh-0.12.0rc0", "Oops, sorry @albertvillanova. I thought for next time I'll start the CIs before pinging everyone.\r\nI'm closing this one.", "@Wauplin in your Slack message, you asked people from every major dependent library to check that our CI work. That is why I am checking it... :)\r\n\r\nAlso, I think for this purpose it is better to test it in a dedicated branch, rather than opening and closing a PR.", "Yes, yes I know. Completely my fault on this one" ]

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[ "Thnaks for reporting @jaketae. We are fixing it. " ]

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[ "Looks all good to me thanks :)\r\nJust did some minor corrections in the docstring" ]

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[ "Even if a dataset is cached, we still make requests to check whether the cache is up-to-date. [This](https://huggingface.co/docs/datasets/v2.13.1/en/loading#offline) section in the docs explains how to avoid them and directly load the cached version.", "Thanks - that works! However it doesn't resolve the original issue (but I am not sure if it is a WSL problem)", "We use `requests` to make HTTP requests (and `aiohttp` in the streaming mode), so I don't think we can provide much help regarding the socket issue (it probably has something to do with WSL). " ]

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[ "Hi @lewtun!\r\n \r\nThis one was tricky to debug. Initially, I tought there is a bug in the recently-added (by @lhoestq ) `cast_array_to_feature` function because `git bisect` points to the https://github.com/huggingface/datasets/commit/6ca96c707502e0689f9b58d94f46d871fa5a3c9c commit. Then, I noticed that the feature tpye of the `dialogue` field is `list`, which explains why you didn't get an error in earlier versions. Is there a specific reason why you use `list` instead of `Sequence` in the script? Maybe to avoid turning list of dicts to dicts of lists as it's done by `Sequence` for compatibility with TFDS or for performance reasons? If the field was `Sequence`, you would get an error in `encode_nested_example` because **the scripts yields some additional (nested) columns which are not specified in the `features` dictionary**. Previously, these additional columns would've been ignored by PyArrow (1), but now we have a check for them (2).\r\n(1) See PyArrow behavior:\r\n```\r\n>>> pa.array([{\"a\": 2, \"b\": 3}], type=pa.struct({\"a\": pa.int32()})) # pyarrow ignores the extra column\r\n-- is_valid: all not null\r\n-- child 0 type: int32\r\n [\r\n 2\r\n ]\r\n ```\r\n\r\n(2) Check:\r\nhttps://github.com/huggingface/datasets/blob/4c417d52def6e20359ca16c6723e0a2855e5c3fd/src/datasets/table.py#L1059\r\n\r\nThe fix is very simple: just add the missing columns to the _EMPTY_BELIEF_STATE list:\r\n```python\r\n_EMPTY_BELIEF_STATE.extend(['通用-产品类别', '火车-舱位档次', '通用-系列', '通用-价格区间', '通用-品牌'])\r\n```", "Hey @mariosasko, thank you so much for figuring this one out - it certainly looks like a tricky bug 😱 ! I don't think there's a specific reason to use `list` instead of `Sequence` with the script, but I'll let the dataset creators know to see if your suggestion is acceptable.\r\n\r\nThank you again!", "Thanks, this was indeed the fix! Would it make sense to produce a more informative error message in such cases? \r\n\r\nThe issue can be closed. \r\n\r\n" ]

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[ "Oh yes you can now do that with the `dataset.filter()` method that was added in #214 " ]

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[ "> It would be really useful to be able to run `datasets-cli test`for datasets that don't have scripts attached to them (whether the datasets are private or not).\r\n> \r\n> I wasn't able to run the script for a private test dataset that I had created on the hub (https://huggingface.co/datasets/huggingface/DataMeasurementsTest/tree/main) -- although @lhoestq came to save the day!\r\n\r\nwhy don't you try to share that info with people, so you can also save some days.", "Hi ! You can run the command if you download the repository\r\n```\r\ngit clone https://huggingface.co/datasets/huggingface/DataMeasurementsTest\r\n```\r\nand run the command\r\n```\r\ndatasets-cli test DataMeasurementsTest/DataMeasurementsTest.py\r\n```\r\n\r\n(though on my side it doesn't manage to download the data since the dataset is private ^^)", "> Hi ! You can run the command if you download the repository\r\n> \r\n> ```\r\n> git clone https://huggingface.co/datasets/huggingface/DataMeasurementsTest\r\n> ```\r\n> \r\n> and run the command\r\n> \r\n> ```\r\n> datasets-cli test DataMeasurementsTest/DataMeasurementsTest.py\r\n> ```\r\n> \r\n> (though on my side it doesn't manage to download the data since the dataset is private ^^)\r\n\r\nHi! Thanks for the info. \r\ngit cannot find the repository. Do you know if they have depreciated these tests and created a new one?", "I think it's become private, but feel free to try with any other dataset like `lhoestq/test` for example at `https://huggingface.co/datasets/lhoestq/test`", "> I think it's become private, but feel free to try with any other dataset like `lhoestq/test` for example at `https://huggingface.co/datasets/lhoestq/test`\r\n\r\nyour example repo and this page `https://huggingface.co/docs/datasets/add_dataset.html` helped me to solve.. thanks a lot" ]

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[ "Hi! Instead of @nateraw's `image-folder`, I suggest using the newly released `imagefolder` dataset:\r\n```python\r\n>>> from datasets import load_dataset\r\n>>> data_files={'train': ['/ssd/datasets/imagenet/pytorch/train/**'], 'validation': ['/ssd/datasets/imagenet/pytorch/val/**']}\r\n>>> ds = load_dataset('imagefolder', data_files=data_files, cache_dir='./', task='image-classification')\r\n```\r\n\r\n\r\nLet us know if that resolves the issue.", "> Hi! Instead of @nateraw's `image-folder`, I suggest using the newly released `imagefolder` dataset:\r\n> \r\n> ```python\r\n> >>> from datasets import load_dataset\r\n> >>> data_files={'train': ['/ssd/datasets/imagenet/pytorch/train/**'], 'validation': ['/ssd/datasets/imagenet/pytorch/val/**']}\r\n> >>> ds = load_dataset('imagefolder', data_files=data_files, cache_dir='./', task='image-classification')\r\n> ```\r\n> \r\n> Let us know if that resolves the issue.\r\n\r\nSorry, replied late.\r\nThanks a lot! It's worked for me. But it seems much slower than before, and now gets stuck.....\r\n\r\n```\r\n>>> from datasets import load_dataset\r\n>>> data_files={'train': ['/ssd/datasets/imagenet/pytorch/train/**'], 'validation': ['/ssd/datasets/imagenet/pytorch/val/**']}\r\n>>> ds = load_dataset('imagefolder', data_files=data_files, cache_dir='./', task='image-classification')\r\nResolving data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1281167/1281167 [00:02<00:00, 437283.97it/s]\r\nResolving data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50001/50001 [00:00<00:00, 89094.29it/s]\r\nUsing custom data configuration default-baebca6347576b33\r\nDownloading and preparing dataset image_folder/default to ./image_folder/default-baebca6347576b33/0.0.0/ee92df8e96c6907f3c851a987be3fd03d4b93b247e727b69a8e23ac94392a091...\r\nDownloading data files #0: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 80073/80073 [00:00<00:00, 82289.56obj/s]\r\nDownloading data files #1: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 80073/80073 [00:01<00:00, 73559.11obj/s]\r\nDownloading data files #2: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 80073/80073 [00:00<00:00, 81600.46obj/s]\r\nDownloading data files #3: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 80073/80073 [00:01<00:00, 79691.56obj/s]\r\nDownloading data files #4: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 80073/80073 [00:00<00:00, 82341.37obj/s]\r\nDownloading data files #5: 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100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 80073/80073 [00:00<00:00, 83082.31obj/s]\r\nDownloading data files #11: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 80073/80073 [00:01<00:00, 79944.21obj/s]\r\nDownloading data files #12: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 80073/80073 [00:00<00:00, 84569.77obj/s]\r\nDownloading data files #13: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 80073/80073 [00:00<00:00, 84949.63obj/s]\r\nDownloading data files #14: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 80073/80073 [00:00<00:00, 80666.53obj/s]\r\nDownloading data files #15: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 80072/80072 [00:01<00:00, 76723.20obj/s]\r\n^[[Bloading data files #8: 94%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 75061/80073 [00:00<00:00, 82609.89obj/s]\r\nDownloading data files #9: 85%|████████████████████████████████████████████████████████████████████████████████████████████████████▍ | 68120/80073 [00:00<00:00, 83868.54obj/s]\r\nDownloading data files #9: 96%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 76784/80073 [00:00<00:00, 84722.34obj/s]\r\nDownloading data files #10: 75%|███████████████████████████████████████████████████████████████████████████████████████▋ | 59995/80073 [00:00<00:00, 84148.19obj/s]\r\nDownloading data files #10: 97%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 77412/80073 [00:00<00:00, 85724.53obj/s]\r\nDownloading data files #11: 71%|███████████████████████████████████████████████████████████████████████████████████▎ | 57032/80073 [00:00<00:00, 79930.58obj/s]\r\nDownloading data files #11: 92%|███████████████████████████████████████████████████████████████████████████████████████████████████████████ | 73277/80073 [00:00<00:00, 78091.27obj/s]\r\nDownloading data files #12: 86%|█████████████████████████████████████████████████████████████████████████████████████████████████████ | 69125/80073 [00:00<00:00, 84723.02obj/s]\r\nDownloading data files #12: 97%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋ | 77803/80073 [00:00<00:00, 85351.59obj/s]\r\nDownloading data files #13: 75%|████████████████████████████████████████████████████████████████████████████████████████▏ | 60356/80073 [00:00<00:00, 84833.35obj/s]\r\nDownloading data files #13: 97%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 77368/80073 [00:00<00:00, 84475.10obj/s]\r\nDownloading data files #14: 72%|████████████████████████████████████████████████████████████████████████████████████▍ | 57751/80073 [00:00<00:00, 80727.33obj/s]\r\nDownloading data files #14: 92%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 74022/80073 [00:00<00:00, 78703.16obj/s]\r\nDownloading data files #15: 78%|███████████████████████████████████████████████████████████████████████████████████████████▋ | 62724/80072 [00:00<00:00, 78387.33obj/s]\r\nDownloading data files #15: 99%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 78933/80072 [00:01<00:00, 79353.63obj/s]\r\n```", "Wait a long time, it completed. I don't know why it's so slow...", "You can pass `ignore_verifications=True` in `load_dataset` to make it fast (to skip checksum verification). I'll add this tip to the docs.", "> You can pass `ignore_verifications=True` in `load_dataset` to make it fast (to skip checksum verification). I'll add this tip to the docs.\r\n\r\nThanks！It's worked well.", "> You can pass `ignore_verifications=True` in `load_dataset` to make it fast (to skip checksum verification). I'll add this tip to the docs.\r\n\r\nI find current `load_dataset` loads ImageNet still slowly, even add `ignore_verifications=True`.\r\nFirst loading, it costs about 20 min in my servers.\r\n```\r\nreal\t19m23.023s\r\nuser\t21m18.360s\r\nsys\t7m59.080s\r\n```\r\n\r\nSecond reusing, it costs about 15 min in my servers.\r\n```\r\nreal\t15m20.735s\r\nuser\t12m22.979s\r\nsys\t5m46.960s\r\n```\r\n\r\nI think it's too much slow, is there other method to make it faster?", "And in transformers the [ViT example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/image-classification/run_image_classification.py), could you make some changes ? Like the `collect_fn`\r\n```python\r\ndef collate_fn(examples):\r\n pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\r\n labels = torch.tensor([example[\"labels\"] for example in examples])\r\n return {\"pixel_values\": pixel_values, \"labels\": labels}\r\n```\r\nHow to know the keys of example?", "Loading the image files slowly, is it because the multiple processes load files at the same time?", "Could you please share the output you get after the second loading? Also, feel free to interrupt (`KeyboardInterrupt`) the process while waiting for it to end and share a traceback to show us where the process hangs. \r\n\r\n> And in transformers the [ViT example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/image-classification/run_image_classification.py), could you make some changes ? Like the `collect_fn`\r\n> \r\n> ```python\r\n> def collate_fn(examples):\r\n> pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\r\n> labels = torch.tensor([example[\"labels\"] for example in examples])\r\n> return {\"pixel_values\": pixel_values, \"labels\": labels}\r\n> ```\r\n> \r\n> How to know the keys of example?\r\n\r\nWhat do you mean by \"could you make some changes\".The `ViT` script doesn't remove unused columns by default, so the keys of an example are equal to the columns of the given dataset.\r\n\r\n", "> Could you please share the output you get after the second loading? Also, feel free to interrupt (`KeyboardInterrupt`) the process while waiting for it to end and share a traceback to show us where the process hangs.\r\n> \r\n> > And in transformers the [ViT example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/image-classification/run_image_classification.py), could you make some changes ? Like the `collect_fn`\r\n> > ```python\r\n> > def collate_fn(examples):\r\n> > pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\r\n> > labels = torch.tensor([example[\"labels\"] for example in examples])\r\n> > return {\"pixel_values\": pixel_values, \"labels\": labels}\r\n> > ```\r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > How to know the keys of example?\r\n> \r\n> What do you mean by \"could you make some changes\".The `ViT` script doesn't remove unused columns by default, so the keys of an example are equal to the columns of the given dataset.\r\n\r\nThanks for your reply!\r\n\r\n1. I did not record the second output, so I run it again. \r\n```\r\n(merak) txacs@master:/dat/txacs/test$ time python test.py \r\nResolving data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1281167/1281167 [00:02<00:00, 469497.89it/s]\r\nResolving data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50001/50001 [00:00<00:00, 70123.73it/s]\r\nUsing custom data configuration default-baebca6347576b33\r\nReusing dataset image_folder (./image_folder/default-baebca6347576b33/0.0.0/ee92df8e96c6907f3c851a987be3fd03d4b93b247e727b69a8e23ac94392a091)\r\n100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:10<00:00, 5.37s/it]\r\nLoading cached processed dataset at ./image_folder/default-baebca6347576b33/0.0.0/ee92df8e96c6907f3c851a987be3fd03d4b93b247e727b69a8e23ac94392a091/cache-cd3fbdc025e03f8c.arrow\r\nLoading cached processed dataset at ./image_folder/default-baebca6347576b33/0.0.0/ee92df8e96c6907f3c851a987be3fd03d4b93b247e727b69a8e23ac94392a091/cache-b5a9de701bbdbb2b.arrow\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['image', 'labels'],\r\n num_rows: 1281167\r\n })\r\n validation: Dataset({\r\n features: ['image', 'labels'],\r\n num_rows: 50000\r\n })\r\n})\r\n\r\nreal\t10m10.413s\r\nuser\t9m33.195s\r\nsys\t2m47.528s\r\n```\r\nAlthough it cost less time than the last, but still slowly.\r\n\r\n2. Sorry, forgive my poor statement. I solved it, updating to new script 'run_image_classification.py'.", "Thanks for rerunning the code to record the output. Is it the `\"Resolving data files\"` part on your machine that takes a long time to complete, or is it `\"Loading cached processed dataset at ...\"˙`? We plan to speed up the latter by splitting bigger Arrow files into smaller ones, but your dataset doesn't seem that big, so not sure if that's the issue.", "> Thanks for rerunning the code to record the output. Is it the `\"Resolving data files\"` part on your machine that takes a long time to complete, or is it `\"Loading cached processed dataset at ...\"˙`? We plan to speed up the latter by splitting bigger Arrow files into smaller ones, but your dataset doesn't seem that big, so not sure if that's the issue.\r\n\r\nSounds good! The main position, which costs long time, is from program starting to `\"Resolving data files\"`. I hope you can solve it early, thanks!", "I'm getting this problem. Script has been stuck at this part for the past 15 or so minutes:\r\n \r\n`Resolving data files: 100%|█████████████████████████████████████████| 107/107 [00:00<00:00, 472.74it/s]`\r\n\r\nI had everything working fine on an AWS EC2 node with a single GPU. Then I created an image based on the single GPU machine, and spun up a new one with 4 GPUs, so I got all of the training data ready at .cache. \r\n\r\nTurned off all checks with `verification_mode='no_checks'`. Logged in with huggingface-cli again just to be sure.\r\n\r\nInterrupting shows the code is stuck here:\r\n\r\n```\r\nFile \"/opt/conda/envs/pytorch/lib/python3.10/site-packages/datasets/arrow_reader.py\", line 200, in _read_files\r\n pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n File \"/opt/conda/envs/pytorch/lib/python3.10/site-packages/datasets/arrow_reader.py\", line 336, in _get_table_from_filename\r\n table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n File \"/opt/conda/envs/pytorch/lib/python3.10/site-packages/datasets/arrow_reader.py\", line 357, in read_table\r\n return table_cls.from_file(filename)\r\n File \"/opt/conda/envs/pytorch/lib/python3.10/site-packages/datasets/table.py\", line 1059, in from_file\r\n table = _memory_mapped_arrow_table_from_file(filename)\r\n File \"/opt/conda/envs/pytorch/lib/python3.10/site-packages/datasets/table.py\", line 66, in _memory_mapped_arrow_table_from_file\r\n pa_table = opened_stream.read_all()\r\n```\r\n\r\nIs it just going to take a while or am I going to run out of money? :sweat_smile: \r\n\r\nedit: ping @mariosasko " ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "I noticed that datasets like `cnn_dailymail` need the `version` parameter to be passed to its `config_kwargs`.\r\nShall we rename the `version` paramater in `load_dataset` ? Maybe `repo_version` or `script_version` ?" ]

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[ "Oops I merged without linking to the hacktoberfest issue - not sure if it counts in this case\r\n\r\nsorry about that..\r\n\r\nNext time you can just mention \"Close #XXXX\" in your issue to link it", "It should :) (the `hacktoberfest` repo topic is all that matters)" ]

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[ "It'll be new pull request with new commits." ]

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[ "As the survey is finished, can we close this issue, @LysandreJik ?", "Yes! I'll post a public summary on the forums shortly.", "Is the summary available? I would be interested in reading your findings." ]

[ { "color": "e99695", "default": false, "description": "Requesting to add a new dataset", "id": 2067376369, "name": "dataset request", "node_id": "MDU6TGFiZWwyMDY3Mzc2MzY5", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20request" } ]

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[ "Merging since the CI error is unrelated to this PR and fixed on master" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

[ { "color": "e99695", "default": false, "description": "Requesting to add a new dataset", "id": 2067376369, "name": "dataset request", "node_id": "MDU6TGFiZWwyMDY3Mzc2MzY5", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20request" } ]

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[ "That was fast! \r\n" ]

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