Datasets:

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[ "_The documentation is not available anymore as the PR was closed or merged._", "> We've been using `null` to specify that we checked on pwc but the dataset doesn't exist there.\r\n\r\n@lhoestq maybe it's better to accept it on the Hub side then? Let me know if you want us to do it Hub-side", "Yup it's maybe better to support it on the Hub side then indeed, thanks ! Closing this one" ]

"2022-07-04T12:11:32Z"

"2023-09-24T10:05:19Z"

"2022-07-04T13:10:38Z"

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[ "Hi! `load_dataset` is intended to be used to load a canonical dataset (`wikipedia`), a packaged dataset (`csv`, `json`, ...) or a dataset hosted on the Hub. For local datasets saved with `save_to_disk(\"path/to/dataset\")`, use `load_from_disk(\"path/to/dataset\")`.", "Maybe we should raise an informative error message in this case..." ]

"2022-02-10T15:05:53Z"

"2022-02-11T22:56:39Z"

"2022-02-11T22:56:39Z"

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[ "Yes this has the right files!!!\r\n\r\nI'll close the previous one then :) \r\n\r\nNow to pass the tests, you will need to:\r\n- `make style` and run `flake8 datasets` from your repository root directory\r\n- fix the dummy data\r\n\r\nDid you generate the dummy data with the auto-generation tool (see the guide) or manually?", "manually with the tool, it is not able to create", "Cool, in that case you need to pay special attention to the directory structure given to you by the tool, most failures are because the files are in the wrong directory or at the wrong level :) \r\n\r\nAlso, make sure that the tests pass locally before pushing to the branch, it should help you get the structure right ;) ", "yes I have give proper directory structure datasets/hind_encorp/dummy/0.0.0/dummy_data.zip but in my dummy_data.zip only 1 file hind_encorp.plaintext is present because the dataset I got has only 1 file with both English and Hindi languages on 1 file itself may be this is causing issue", "Looks like the name of the file is the issue here: you have a file called `hindencorp05.plaintext`, but it should be called `hindencorp05.plaintext.gz%3Fsequence%3D3%26isAllowed%3Dy`. You just have to rename it to pass the test:\r\n```\r\ncd datasets/hind_encorp/dummy/0.0.0\r\nrm -rf dummy_data\r\nunzip dummy_data.zip\r\nrm dummy_data.zip\r\nmv dummy_data/hindencorp05.plaintext \"dummy_data/hindencorp05.plaintext.gz%3Fsequence%3D3%26isAllowed%3Dy\"\r\nzip -r dummy_data.zip dummy_data \r\n```\r\n\r\nThen **once you pass the tests locally** you just have to remember to `make style` and `flake8 datasets` to pass the style checks, and you should be good to go :hugs: \r\n\r\nFor reference, here are the instructions given by the tool:\r\n```\r\n$ python datasets-cli dummy_data datasets/hind_encorp/\r\n2020-12-14 13:16:26.824828: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'libcudart.so.10.1'; dlerror: libcudart.so.10.1: cannot open shared object file: No such file or directory\r\n2020-12-14 13:16:26.824846: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\r\nUsing custom data configuration default\r\n\r\n==============================DUMMY DATA INSTRUCTIONS==============================\r\n- In order to create the dummy data for , please go into the folder 'datasets/hind_encorp/dummy/0.0.0' with `cd datasets/hind_encorp/dummy/0.0.0` . \r\n\r\n- Please create the following dummy data files 'dummy_data/hindencorp05.plaintext.gz%3Fsequence%3D3%26isAllowed%3Dy' from the folder 'datasets/hind_encorp/dummy/0.0.0'\r\n\r\n- For each of the splits 'train', make sure that one or more of the dummy data files provide at least one example \r\n\r\n- If the method `_generate_examples(...)` includes multiple `open()` statements, you might have to create other files in addition to 'dummy_data/hindencorp05.plaintext.gz%3Fsequence%3D3%26isAllowed%3Dy'. In this case please refer to the `_generate_examples(...)` method \r\n\r\n-After all dummy data files are created, they should be zipped recursively to 'dummy_data.zip' with the command `zip -r dummy_data.zip dummy_data/` \r\n\r\n-You can now delete the folder 'dummy_data' with the command `rm -r dummy_data` \r\n\r\n- To get the folder 'dummy_data' back for further changes to the dummy data, simply unzip dummy_data.zip with the command `unzip dummy_data.zip` \r\n\r\n- Make sure you have created the file 'dummy_data.zip' in 'datasets/hind_encorp/dummy/0.0.0' \r\n===================================================================================\r\n```\r\n", "all test passed locally. my new PR #1575 ", "Closing this one in favor of #1575 " ]

"2020-12-13T23:09:02Z"

"2020-12-15T10:37:05Z"

"2020-12-15T10:37:04Z"

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[ "Thanks for the suggestion, @Dref360.\r\n\r\nWe are adding that argument. " ]

"2022-03-25T15:55:52Z"

"2022-03-28T09:53:06Z"

"2022-03-28T09:53:06Z"

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"2020-12-09T04:24:40Z"

"2020-12-10T13:45:15Z"

"2020-12-10T13:45:15Z"

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[ "On a similar note it would be nice to differentiate between train-easy, train-medium, and train-hard", "Hi @tylerroost, we don't have a timeline for this at the moment.\r\nIf you want to give it a look we would be happy to review a PR on it.\r\nAlso, the library is one week old so everything is quite barebones, in particular the doc.\r\nYou should expect some bumps on the road.\r\n\r\nTo get you started, you can check the datasets scripts in the `./datasets` folder on the repo and find the one on math_datasets that will need to be modified. Then you should check the original repository on the math_dataset to see where the other files to download are located and what is the expected format for the various parts of the dataset.\r\n\r\nTo get a general overview on how datasets scripts are written and used, you can read the nice tutorial on how to add a new dataset for TensorFlow Dataset [here](https://www.tensorflow.org/datasets/add_dataset), our API is not exactly identical but it can give you a high-level overview.", "Thanks I'll give it a look" ]

"2020-05-24T15:46:52Z"

"2020-05-24T18:53:48Z"

"2020-05-24T18:53:48Z"

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"2023-03-15T15:20:45Z"

"2023-03-16T13:40:14Z"

"2023-03-16T13:40:14Z"

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[ "Hi ! Looks related to #861 \r\n\r\nYou are right: tokenizing a dataset using map takes a lot of space since it can store `input_ids` but also `token_type_ids`, `attention_mask` and `special_tokens_mask`. Moreover if your tokenization function returns python integers then by default they'll be stored as int64 which can take a lot of space. Padding can also increase the size of the tokenized dataset.\r\n\r\nTo make things more convenient, we recently added a \"lazy map\" feature that allows to tokenize each batch at training time as you mentioned. For example you'll be able to do\r\n```python\r\nfrom transformers import BertTokenizer\r\n\r\ntokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\r\n\r\ndef encode(batch):\r\n return tokenizer(batch[\"text\"], padding=\"longest\", truncation=True, max_length=512, return_tensors=\"pt\")\r\n\r\ndataset.set_transform(encode)\r\nprint(dataset.format)\r\n# {'type': 'custom', 'format_kwargs': {'transform': <function __main__.encode(batch)>}, 'columns': ['idx', 'label', 'sentence1', 'sentence2'], 'output_all_columns': False}\r\nprint(dataset[:2])\r\n# {'input_ids': tensor([[ 101, 2572, 3217, ... 102]]), 'token_type_ids': tensor([[0, 0, 0, ... 0]]), 'attention_mask': tensor([[1, 1, 1, ... 1]])}\r\n\r\n```\r\nIn this example the `encode` transform is applied on-the-fly on the \"text\" column.\r\n\r\nThis feature will be available in the next release 2.0 which will happen in a few days.\r\nYou can already play with it by installing `datasets` from source if you want :)\r\n\r\nHope that helps !", "How recently was `set_transform` added? I am actually trying to implement it and getting an error:\r\n\r\n`AttributeError: 'Dataset' object has no attribute 'set_transform'\r\n`\r\n\r\nI'm on v.1.2.1.\r\n\r\nEDIT: Oh, wait I see now it's in the v.2.0. Whoops! This should be really useful.", "Yes indeed it was added a few days ago. The code is available on master\r\nWe'll do a release next week :)\r\n\r\nFeel free to install `datasets` from source to try it out though, I would love to have some feedbacks", "For information: it's now available in `datasets` 1.3.0.\r\nThe 2.0 is reserved for even cooler features ;)", "Hi @alexvaca0 , we have optimized Datasets' disk usage in the latest release v1.5.\r\n\r\nFeel free to update your Datasets version\r\n```shell\r\npip install -U datasets\r\n```\r\nand see if it better suits your needs." ]

"2021-02-05T11:06:50Z"

"2021-03-30T14:04:01Z"

"2021-03-16T09:44:00Z"

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"2021-08-31T18:05:43Z"

"2021-09-06T13:33:35Z"

"2021-08-31T21:29:51Z"

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[ "@lhoestq, I think this reaches some memory limit on Linux instances... (?)", "It looks like the `comet` metric test fails because it tries to load a model in memory.\r\nIn the tests I think we have `patch_comet` that mocks the model download + inference. Not sure why it didn't work though.\r\nI can take a look tomorrow (this afternoon is the pytorch ecosystem day)", "@lhoestq thanks for the hint: I'm going to have a look at that mock... ;)", "@lhoestq finally I did not find out why the mock is not used... If you can give me some other hint tomorrow..." ]

"2021-04-20T17:06:22Z"

"2022-07-06T15:19:48Z"

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[ "Hi so there are indeed two configurations in the datasets as you can see [here](https://github.com/huggingface/nlp/blob/master/datasets/scientific_papers/scientific_papers.py#L81-L82).\r\n\r\nYou can load either one with:\r\n```python\r\ndataset = nlp.load_dataset('scientific_papers', 'pubmed')\r\ndataset = nlp.load_dataset('scientific_papers', 'arxiv')\r\n```\r\n\r\nThis issues is actually related to a similar user-experience issue with GLUE. When several configurations are available and the first configuration is loaded by default (see issue #152 and #130), it seems to be unexpected for users.\r\n\r\nI think we should maybe raise a (very explicit) error when there are several configurations available and the user doesn't specify one.\r\n\r\nWhat do you think @lhoestq @patrickvonplaten @mariamabarham ?", "Yes, it looks like the right thing to do ", "Now if you don't specify which part you want, it raises an error:\r\n```\r\nValueError: Config name is missing.\r\nPlease pick one among the available configs: ['pubmed', 'arxiv']\r\nExample of usage:\r\n\t`load_dataset('scientific_papers', 'pubmed')`\r\n```" ]

"2020-05-26T15:18:47Z"

"2020-05-28T08:19:28Z"

"2020-05-28T08:19:28Z"

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[ "Fixed. Please close.", "how to fix?i need your help" ]

"2022-11-10T21:57:06Z"

"2023-10-07T05:04:41Z"

"2022-11-10T22:05:43Z"

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"2023-05-26T20:02:14Z"

"2023-05-30T17:53:31Z"

"2023-05-30T17:53:31Z"

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[ "Nice ! :) \r\nCan we group them into one dataset with two versions, instead of having two datasets ?", "Yes sure, I can use the version as config name", "@lhoestq can you check? I grouped them", "Awesome :) feel free to merge after fixing the test in the CI", "@mariamabarham - feel free to merge when you're ready. I only checked the dummy files. I did not run the SLOW tests. " ]

"2020-05-25T08:08:40Z"

"2020-05-25T16:28:46Z"

"2020-05-25T16:28:45Z"

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"2021-12-23T11:18:46Z"

"2021-12-23T15:34:30Z"

"2021-12-23T15:34:30Z"

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"2020-12-14T23:28:51Z"

"2020-12-14T23:29:32Z"

"2020-12-14T23:29:32Z"

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"2021-07-27T14:17:09Z"

"2021-10-22T09:36:08Z"

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

"2022-09-26T11:55:36Z"

"2022-09-28T07:56:18Z"

"2022-09-28T07:54:19Z"

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[ "Hi @lhoestq,\r\n\r\nI have added changes from review.", "Thanks for approving :)" ]

"2021-03-14T00:01:05Z"

"2021-03-19T11:15:44Z"

"2021-03-19T10:31:59Z"

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[ "Hi @aleSuglia, thanks for reporting.\r\n\r\nWe are having a look at it. \r\n\r\nWe transfer this issue to the Community tab of the corresponding Hub dataset: https://huggingface.co/datasets/Heriot-WattUniversity/dialog-babi/discussions" ]

"2022-07-09T11:45:46Z"

"2022-07-11T07:10:09Z"

"2022-07-11T07:10:01Z"

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"2020-12-07T05:46:45Z"

"2020-12-08T13:03:19Z"

"2020-12-08T13:03:19Z"

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[]

[]

[ "This looks like a problem with your environment rather than `datasets`." ]

"2023-11-19T13:10:20Z"

"2023-11-29T16:28:34Z"

"2023-11-29T16:28:34Z"

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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) | 0.010459 / 0.011353 (-0.000894) | 0.007009 / 0.011008 (-0.003999) | 0.153885 / 0.038508 (0.115377) | 0.037308 / 0.023109 (0.014199) | 0.431931 / 0.275898 (0.156033) | 0.452940 / 0.323480 (0.129461) | 0.008572 / 0.007986 (0.000586) | 0.007479 / 0.004328 (0.003150) | 0.093835 / 0.004250 (0.089584) | 0.050172 / 0.037052 (0.013120) | 0.428855 / 0.258489 (0.170366) | 0.517814 / 0.293841 (0.223974) | 0.058558 / 0.128546 (-0.069988) | 0.019550 / 0.075646 (-0.056096) | 0.449837 / 0.419271 (0.030566) | 0.069710 / 0.043533 (0.026177) | 0.444163 / 0.255139 (0.189024) | 0.469003 / 0.283200 (0.185803) | 0.114665 / 0.141683 (-0.027018) | 1.822415 / 1.452155 (0.370261) | 1.956360 / 1.492716 (0.463644) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237489 / 0.018006 (0.219483) | 0.556947 / 0.000490 (0.556457) | 0.006988 / 0.000200 (0.006789) | 0.000499 / 0.000054 (0.000444) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037047 / 0.037411 (-0.000364) | 0.133973 / 0.014526 (0.119447) | 0.137072 / 0.176557 (-0.039485) | 0.201520 / 0.737135 (-0.535615) | 0.144177 / 0.296338 (-0.152161) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.694853 / 0.215209 (0.479644) | 6.805746 / 2.077655 (4.728091) | 2.717864 / 1.504120 (1.213744) | 2.360529 / 1.541195 (0.819335) | 2.384403 / 1.468490 (0.915913) | 1.337512 / 4.584777 (-3.247265) | 5.734090 / 3.745712 (1.988378) | 5.344909 / 5.269862 (0.075047) | 2.906218 / 4.565676 (-1.659458) | 0.160148 / 0.424275 (-0.264127) | 0.015159 / 0.007607 (0.007551) | 0.871356 / 0.226044 (0.645312) | 8.550965 / 2.268929 (6.282037) | 3.613522 / 55.444624 (-51.831103) | 2.868508 / 6.876477 (-4.007969) | 2.912263 / 2.142072 (0.770190) | 1.652548 / 4.805227 (-3.152680) | 0.274117 / 6.500664 (-6.226547) | 0.085911 / 0.075469 (0.010442) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.624798 / 1.841788 (-0.216989) | 18.413303 / 8.074308 (10.338995) | 21.742854 / 10.191392 (11.551462) | 0.255937 / 0.680424 (-0.424487) | 0.029492 / 0.534201 (-0.504709) | 0.541932 / 0.579283 (-0.037351) | 0.638594 / 0.434364 (0.204230) | 0.607427 / 0.540337 (0.067090) | 0.763046 / 1.386936 (-0.623890) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.020543 / 0.011353 (0.009190) | 0.006079 / 0.011008 (-0.004929) | 0.100558 / 0.038508 (0.062050) | 0.039474 / 0.023109 (0.016365) | 0.468889 / 0.275898 (0.192991) | 0.477731 / 0.323480 (0.154251) | 0.006999 / 0.007986 (-0.000987) | 0.005845 / 0.004328 (0.001516) | 0.110022 / 0.004250 (0.105772) | 0.056885 / 0.037052 (0.019833) | 0.447296 / 0.258489 (0.188807) | 0.489007 / 0.293841 (0.195166) | 0.055086 / 0.128546 (-0.073460) | 0.020623 / 0.075646 (-0.055024) | 0.129599 / 0.419271 (-0.289672) | 0.064316 / 0.043533 (0.020784) | 0.446681 / 0.255139 (0.191542) | 0.488897 / 0.283200 (0.205698) | 0.119121 / 0.141683 (-0.022562) | 1.836248 / 1.452155 (0.384093) | 2.002456 / 1.492716 (0.509740) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.249344 / 0.018006 (0.231338) | 0.544320 / 0.000490 (0.543830) | 0.000459 / 0.000200 (0.000259) | 0.000088 / 0.000054 (0.000034) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038771 / 0.037411 (0.001359) | 0.129527 / 0.014526 (0.115002) | 0.144681 / 0.176557 (-0.031876) | 0.208237 / 0.737135 (-0.528898) | 0.149502 / 0.296338 (-0.146836) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.668457 / 0.215209 (0.453248) | 6.729550 / 2.077655 (4.651895) | 2.741076 / 1.504120 (1.236956) | 2.394737 / 1.541195 (0.853542) | 2.415242 / 1.468490 (0.946752) | 1.322334 / 4.584777 (-3.262442) | 5.787454 / 3.745712 (2.041742) | 3.309847 / 5.269862 (-1.960015) | 2.199181 / 4.565676 (-2.366495) | 0.170740 / 0.424275 (-0.253535) | 0.015095 / 0.007607 (0.007487) | 0.864157 / 0.226044 (0.638112) | 8.701858 / 2.268929 (6.432929) | 3.617966 / 55.444624 (-51.826658) | 2.847144 / 6.876477 (-4.029332) | 3.011391 / 2.142072 (0.869319) | 1.595466 / 4.805227 (-3.209762) | 0.284010 / 6.500664 (-6.216654) | 0.091054 / 0.075469 (0.015585) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.702404 / 1.841788 (-0.139384) | 19.427130 / 8.074308 (11.352822) | 21.900446 / 10.191392 (11.709053) | 0.244088 / 0.680424 (-0.436336) | 0.027428 / 0.534201 (-0.506773) | 0.552226 / 0.579283 (-0.027057) | 0.653102 / 0.434364 (0.218738) | 0.635379 / 0.540337 (0.095042) | 0.771842 / 1.386936 (-0.615094) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#efde2a0b9ad937defc83e0ac3f14bbb90fb5f345 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006547 / 0.011353 (-0.004806) | 0.004569 / 0.011008 (-0.006439) | 0.097782 / 0.038508 (0.059274) | 0.028157 / 0.023109 (0.005048) | 0.319017 / 0.275898 (0.043119) | 0.340758 / 0.323480 (0.017278) | 0.005078 / 0.007986 (-0.002907) | 0.003343 / 0.004328 (-0.000985) | 0.074194 / 0.004250 (0.069944) | 0.037918 / 0.037052 (0.000866) | 0.310298 / 0.258489 (0.051809) | 0.349441 / 0.293841 (0.055600) | 0.030375 / 0.128546 (-0.098171) | 0.011527 / 0.075646 (-0.064119) | 0.320499 / 0.419271 (-0.098773) | 0.042639 / 0.043533 (-0.000894) | 0.312182 / 0.255139 (0.057043) | 0.329058 / 0.283200 (0.045858) | 0.085517 / 0.141683 (-0.056165) | 1.532603 / 1.452155 (0.080448) | 1.583996 / 1.492716 (0.091279) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208286 / 0.018006 (0.190280) | 0.418696 / 0.000490 (0.418206) | 0.007051 / 0.000200 (0.006851) | 0.000409 / 0.000054 (0.000354) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024055 / 0.037411 (-0.013356) | 0.098420 / 0.014526 (0.083894) | 0.104785 / 0.176557 (-0.071771) | 0.163618 / 0.737135 (-0.573517) | 0.110006 / 0.296338 (-0.186332) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418756 / 0.215209 (0.203547) | 4.179557 / 2.077655 (2.101902) | 1.881708 / 1.504120 (0.377588) | 1.683393 / 1.541195 (0.142198) | 1.731909 / 1.468490 (0.263419) | 0.696674 / 4.584777 (-3.888103) | 3.384167 / 3.745712 (-0.361545) | 3.173479 / 5.269862 (-2.096382) | 1.620019 / 4.565676 (-2.945658) | 0.082850 / 0.424275 (-0.341426) | 0.012396 / 0.007607 (0.004789) | 0.519743 / 0.226044 (0.293699) | 5.208480 / 2.268929 (2.939552) | 2.312917 / 55.444624 (-53.131708) | 1.963486 / 6.876477 (-4.912991) | 2.084553 / 2.142072 (-0.057519) | 0.805486 / 4.805227 (-3.999742) | 0.153429 / 6.500664 (-6.347235) | 0.069451 / 0.075469 (-0.006018) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.197185 / 1.841788 (-0.644603) | 14.341005 / 8.074308 (6.266696) | 14.476162 / 10.191392 (4.284770) | 0.157372 / 0.680424 (-0.523052) | 0.016444 / 0.534201 (-0.517757) | 0.383721 / 0.579283 (-0.195562) | 0.380800 / 0.434364 (-0.053564) | 0.441137 / 0.540337 (-0.099200) | 0.524778 / 1.386936 (-0.862158) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006728 / 0.011353 (-0.004625) | 0.004536 / 0.011008 (-0.006472) | 0.076266 / 0.038508 (0.037757) | 0.028133 / 0.023109 (0.005024) | 0.351072 / 0.275898 (0.075174) | 0.375823 / 0.323480 (0.052344) | 0.005166 / 0.007986 (-0.002819) | 0.004717 / 0.004328 (0.000388) | 0.076130 / 0.004250 (0.071880) | 0.041354 / 0.037052 (0.004301) | 0.345904 / 0.258489 (0.087415) | 0.384119 / 0.293841 (0.090278) | 0.030759 / 0.128546 (-0.097787) | 0.011659 / 0.075646 (-0.063988) | 0.085269 / 0.419271 (-0.334002) | 0.042161 / 0.043533 (-0.001372) | 0.340806 / 0.255139 (0.085667) | 0.366832 / 0.283200 (0.083632) | 0.092187 / 0.141683 (-0.049495) | 1.520035 / 1.452155 (0.067880) | 1.603856 / 1.492716 (0.111140) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237763 / 0.018006 (0.219757) | 0.413406 / 0.000490 (0.412916) | 0.000415 / 0.000200 (0.000215) | 0.000060 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026095 / 0.037411 (-0.011317) | 0.105775 / 0.014526 (0.091249) | 0.108452 / 0.176557 (-0.068105) | 0.160014 / 0.737135 (-0.577122) | 0.112385 / 0.296338 (-0.183953) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437327 / 0.215209 (0.222118) | 4.374949 / 2.077655 (2.297294) | 2.090292 / 1.504120 (0.586172) | 1.885946 / 1.541195 (0.344752) | 1.946768 / 1.468490 (0.478278) | 0.704124 / 4.584777 (-3.880653) | 3.394994 / 3.745712 (-0.350718) | 1.905189 / 5.269862 (-3.364673) | 1.182300 / 4.565676 (-3.383376) | 0.082920 / 0.424275 (-0.341355) | 0.012781 / 0.007607 (0.005174) | 0.535467 / 0.226044 (0.309423) | 5.362799 / 2.268929 (3.093870) | 2.504825 / 55.444624 (-52.939799) | 2.180458 / 6.876477 (-4.696019) | 2.317750 / 2.142072 (0.175677) | 0.811182 / 4.805227 (-3.994045) | 0.151654 / 6.500664 (-6.349010) | 0.067925 / 0.075469 (-0.007544) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.290746 / 1.841788 (-0.551042) | 14.799309 / 8.074308 (6.725001) | 14.439722 / 10.191392 (4.248330) | 0.144358 / 0.680424 (-0.536066) | 0.016688 / 0.534201 (-0.517513) | 0.392907 / 0.579283 (-0.186376) | 0.383109 / 0.434364 (-0.051255) | 0.450069 / 0.540337 (-0.090269) | 0.532534 / 1.386936 (-0.854402) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#87c061032972509a2a1b4103763e62fb74912128 \"CML watermark\")\n", "I turned it into a draft to fix the failing tests, but CI is now green, so there is no good reason for it :)" ]

"2023-04-14T14:13:59Z"

"2023-04-20T14:43:20Z"

"2023-04-20T14:40:34Z"

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[ "Transferring this issue as it's related to the 🤗 Datasets library ", "Hi @surya-narayanan! Could you provide some code snippet?", "This method has been recently added to the `IterableDataset`, so you need to update the `datasets`' installation (`pip install -U datasets`) to use it." ]

"2023-05-11T03:48:46Z"

"2023-06-21T16:36:42Z"

"2023-06-21T16:36:41Z"

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"2021-01-15T22:58:34Z"

"2021-01-20T13:41:26Z"

"2021-01-20T13:41:26Z"

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[ "Amazing, @LysandreJik!", "Really did my best!" ]

"2020-05-26T22:18:21Z"

"2020-05-26T23:40:31Z"

"2020-05-26T23:00:56Z"

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"2022-05-13T04:55:17Z"

"2022-05-13T05:47:41Z"

"2022-05-13T05:47:41Z"

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[ "Thank you for clarifying the precedence, @albertvillanova \r\n\r\nIsn't it typically the case where env vars have the highest precedence? \r\n\r\nIn my understanding the point of env vars is to be able to override software w/o needing to touch the code. \r\n\r\nPlease correct me if this is not so in the general case.", "Hi @stas00, \r\n\r\nWell, I'm not an expert on this topic, but the precedence hierarchy I have normally found is from higher to lower:\r\n- command line parameters\r\n- env vars\r\n- config files\r\nSo yes, normally env vars have precedence over configuration files.\r\n\r\nAnyway, for Datasets, there are no configuration files. The _in-memory_ config is set from default values or env vars (which have precedence over default values). But this is done at import.\r\n\r\nHowever, once the library is imported, the user can modify the in-memory config, and this will have precedence over the rest of mechanisms (which take place only at import).", "In my limited experience env vars are typically above cmd line args, so that one can override scripts with cmd lines using env vars, but usually one then uses env vars inside cmd line args, so it's loud and clear.\r\n\r\nFor example specifying a specific gpu number on a command line will depend on `CUDA_VISIBLE_DEVICES` so gpu0 will be different if someone sets `CUDA_VISIBLE_DEVICES=2,3` vs `CUDA_VISIBLE_DEVICES=0,1`.\r\n\r\n> However, once the library is imported, the user can modify the in-memory config, and this will have precedence over the rest of mechanisms (which take place only at import).\r\n\r\nAnd this is exactly the problem we are trying to solve here. For a good reason HF examples don't want to use `keep_in_memory=False`, and they may choose to now set `datasets.config.MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES` and which means we again can't override it via env var.\r\n\r\n", "oops, sorry, didn't think earlier - do we need to prefix this with `HF_DATASETS` or `HF_` like all the other env vars? or is it long enough already to be unique - it's just not telling the user in the config file what projet this variable is for...", "You're right, I just opened https://github.com/huggingface/datasets/pull/2409" ]

"2021-05-24T17:19:15Z"

"2021-05-27T09:07:15Z"

"2021-05-26T16:07:54Z"

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[ "Awesome that's great!" ]

"2020-05-14T16:35:39Z"

"2020-05-14T16:50:30Z"

"2020-05-14T16:50:29Z"

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[ "Hi ! Thanks for reporting, I opened a PR to fix this\r\n\r\nWhat filesystem are you using ?", "Hi @lhoestq ! Thank you so much 🙌 \r\n\r\nIt's a bit of a custom setup, but in practice I am using a [pyarrow.fs.S3FileSystem](https://arrow.apache.org/docs/python/generated/pyarrow.fs.S3FileSystem.html) (wrapped in a `fsspec.implementations.arrow.ArrowFSWrapper` [to make it](https://arrow.apache.org/docs/python/filesystems.html#using-arrow-filesystems-with-fsspec) `fsspec` compatible). I also register it as an entrypoint with `fsspec` so that it's the one that gets automatically resolved when looking for filesystems for the `s3` protocol\r\n\r\nIn my case the `storage_option` that seemed not getting piped through was the filesystem's `endpoint_override` that I use in some tests to point at a mock S3 bucket" ]

"2023-07-26T09:37:20Z"

"2023-07-27T12:42:58Z"

"2023-07-27T12:42:58Z"

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[ "Hi @patrickvonplaten \r\nI can have a look to this dataset later since I am trying to add the OpenSLR dataset https://github.com/huggingface/datasets/pull/2173\r\nHopefully I have enough space since the compressed file is 21GB. The release 3 is even bigger: 54GB :-0", "Resolved via https://github.com/huggingface/datasets/pull/4309" ]

"2021-02-08T13:17:52Z"

"2022-10-04T14:34:12Z"

"2022-10-04T14:34:12Z"

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"2020-12-01T12:33:34Z"

"2020-12-01T15:44:00Z"

"2020-12-01T15:44:00Z"

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[ "@lhoestq Thank you for the review. I have made all the changes you mentioned. PTAL! " ]

"2020-12-13T05:16:37Z"

"2020-12-21T14:39:49Z"

"2020-12-21T14:39:49Z"

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[ "By the way, the reason this is an issue for me is because I want to be able to \"save\" changes made to a dataset by writing something to disk. In this case, I would like to pre-process my dataset once, and then train multiple models on the dataset later without having to re-process the data. \r\n\r\nIs pickling/unpickling the Dataset object the \"sanctioned\" way of doing this? Or is there a better way that I'm missing?", "I've had success with saving datasets to disk via:\r\n\r\n```python\r\ncache_file = \"/my/dset.cache\"\r\ndset = dset.map(whatever, cache_file_name=cache_file)\r\n# then, later\r\ndset = nlp.Dataset.from_file(cache_file)\r\n```\r\n\r\nThis restores the dataset with all the attributes I need.", "Thanks @jarednielsen, that makes sense. I'm a little wary of messing with the cache files, since I still don't really understand what's going on under the hood with Apache Arrow. \r\n\r\nRelated question: I'd like to do parallel pre-processing of the dataset. I know how to break the dataset up via sharding, but is there any way to combine the shards back together again once the processing is done? Right now I'm probably just going to iterate over each shard, write the contexts to a txt file, and then cat the txt files, but it feels like there ought to be a nicer way to concatenate datasets.", "Haha, opened a PR for that functionality about an hour ago: https://github.com/huggingface/nlp/pull/390. Glad we're on the same page :)", "Datasets are not supposed to be pickled as pickle tries to put all the dataset in memory if I'm not wrong (and write all the data on disk).\r\nThe concatenate method however is a very cool feature, looking forward to having it merged :)", "Ah, yes, you are correct. The pickle file contains the whole dataset, not just the cache names, which is not quite what I expected.\r\n\r\nI tried adding a warning when pickling a Dataset, to prevent others like me from trying it. Interestingly, however, the warning is raised whenever any function on the dataset is called (select, shard, etc.). \r\n\r\n```\r\nimport nlp\r\nwiki = nlp.load_dataset('wikipedia', split='train')\r\nwiki = wiki.shard(16, 0) # Triggers pickling of dataset\r\n```\r\n\r\nI believe this is because [this line](https://github.com/huggingface/nlp/blob/master/src/nlp/arrow_dataset.py#L626), which gets the function signature, is actually pickling the whole dataset (and thereby serializing all the data to text). I checked by printing that string, and sure enough it was full of Wikipedia articles.\r\n\r\nI don't think the whole pickling thing is worth the effort, so I'll close the PR. But I did want to mention this serialization behavior in case it's not intended.", "Thanks for reporting. Indeed this line shouldn't serialize the data but only the function itself.\r\n", "Keeping this open because I would like to keep brainstorming a bit on this.\r\n\r\nOne note on this is that we should have a clean serialization workflow, probably one that could serialize to a few formats (arrow, parquet and tfrecords come to mind).", "This PR could be useful. My specific use case is `multiprocessing.Pool` for parallel preprocessing (because of the Python tokenization bottleneck at https://github.com/huggingface/transformers/issues/5729). I shard a large dataset, run map on each shard within a multiprocessing pool, and then concatenate them back together. This is only possible if a dataset can be pickled, otherwise the logic is much more complex. There's no reason to make it un-picklable, even if it's not the recommended usage.\r\n\r\n```python\r\nimport nlp\r\nimport multiprocessing\r\n\r\ndef func(ex):\r\n return {\"text\": \"Prefix: \" + ex[\"text\"]}\r\n\r\ndef map_helper(dset):\r\n return dset.map(func)\r\n\r\nn_shards = 16\r\ndset = nlp.load_dataset(\"wikitext-2-raw-v1\", split=\"train\")\r\nwith multiprocessing.Pool(processes=n_shards) as pool:\r\n shards = pool.map(map_helper, [dset.shard(n_shards, i, contiguous=True) for i in range(n_shards)])\r\ndset = nlp.concatenate_datasets(shards)\r\n```\r\n", "Yes I agree.\r\n#423 just got merged and should allow serialization of `SplitDict`. Could you try it and see if it'ok on your side now ?", "Closing this, assuming it was fixed in #423." ]

"2020-07-14T21:53:39Z"

"2020-08-04T14:38:10Z"

"2020-08-04T14:38:10Z"

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[ "Hi! `audiofolder` requires `datasets>=2.5.0`, so please update the `datasets`' installation (`pip install -U datasets`) in the environment (and restart the env for the update to take effect) to resolve the issue.", "> Hi! `audiofolder` requires `datasets>=2.5.0`, so please update the `datasets`' installation (`pip install -U datasets`) in the environment to resolve the issue.\r\n\r\nI don't think it is a problem of the version. It runs ok on colab or local machine. Only on kaggle will has this bug.", "Based on your dataset info, the installed version is `2.1.0`, which does not include `audiofolder`.\r\n\r\nBy default, Kaggle preinstalls `datasets` into a new env, but the version it installs is outdated and does not contain newer features such as `audiofolder`" ]

"2023-05-14T00:50:47Z"

"2023-08-16T13:35:36Z"

"2023-07-21T13:53:45Z"

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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) | 0.006795 / 0.011353 (-0.004558) | 0.004170 / 0.011008 (-0.006838) | 0.098698 / 0.038508 (0.060190) | 0.045393 / 0.023109 (0.022284) | 0.309205 / 0.275898 (0.033307) | 0.361333 / 0.323480 (0.037853) | 0.006009 / 0.007986 (-0.001977) | 0.003334 / 0.004328 (-0.000995) | 0.075071 / 0.004250 (0.070821) | 0.062587 / 0.037052 (0.025535) | 0.322395 / 0.258489 (0.063906) | 0.360499 / 0.293841 (0.066659) | 0.032243 / 0.128546 (-0.096303) | 0.008768 / 0.075646 (-0.066878) | 0.329799 / 0.419271 (-0.089472) | 0.062261 / 0.043533 (0.018728) | 0.298112 / 0.255139 (0.042973) | 0.322815 / 0.283200 (0.039615) | 0.032348 / 0.141683 (-0.109335) | 1.445807 / 1.452155 (-0.006347) | 1.528768 / 1.492716 (0.036051) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.195701 / 0.018006 (0.177695) | 0.437042 / 0.000490 (0.436552) | 0.003867 / 0.000200 (0.003667) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026713 / 0.037411 (-0.010698) | 0.109548 / 0.014526 (0.095022) | 0.119216 / 0.176557 (-0.057341) | 0.178947 / 0.737135 (-0.558188) | 0.125224 / 0.296338 (-0.171114) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400885 / 0.215209 (0.185676) | 3.991223 / 2.077655 (1.913568) | 1.818449 / 1.504120 (0.314329) | 1.609285 / 1.541195 (0.068090) | 1.666675 / 1.468490 (0.198184) | 0.531486 / 4.584777 (-4.053291) | 3.770142 / 3.745712 (0.024430) | 3.057189 / 5.269862 (-2.212673) | 1.517491 / 4.565676 (-3.048186) | 0.065782 / 0.424275 (-0.358493) | 0.011251 / 0.007607 (0.003644) | 0.504277 / 0.226044 (0.278233) | 5.038979 / 2.268929 (2.770050) | 2.254717 / 55.444624 (-53.189908) | 1.929743 / 6.876477 (-4.946734) | 2.080051 / 2.142072 (-0.062022) | 0.656831 / 4.805227 (-4.148396) | 0.142860 / 6.500664 (-6.357804) | 0.063057 / 0.075469 (-0.012412) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.208819 / 1.841788 (-0.632969) | 14.456966 / 8.074308 (6.382658) | 12.839799 / 10.191392 (2.648407) | 0.164361 / 0.680424 (-0.516063) | 0.017330 / 0.534201 (-0.516871) | 0.397384 / 0.579283 (-0.181899) | 0.422704 / 0.434364 (-0.011660) | 0.472065 / 0.540337 (-0.068273) | 0.576960 / 1.386936 (-0.809976) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006950 / 0.011353 (-0.004403) | 0.004012 / 0.011008 (-0.006997) | 0.076050 / 0.038508 (0.037542) | 0.046646 / 0.023109 (0.023537) | 0.353813 / 0.275898 (0.077915) | 0.417111 / 0.323480 (0.093631) | 0.005422 / 0.007986 (-0.002564) | 0.003356 / 0.004328 (-0.000972) | 0.076662 / 0.004250 (0.072411) | 0.055018 / 0.037052 (0.017966) | 0.371561 / 0.258489 (0.113072) | 0.410471 / 0.293841 (0.116630) | 0.031860 / 0.128546 (-0.096686) | 0.008754 / 0.075646 (-0.066893) | 0.083192 / 0.419271 (-0.336079) | 0.050479 / 0.043533 (0.006946) | 0.351725 / 0.255139 (0.096586) | 0.371596 / 0.283200 (0.088396) | 0.023042 / 0.141683 (-0.118641) | 1.480533 / 1.452155 (0.028379) | 1.545970 / 1.492716 (0.053254) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220095 / 0.018006 (0.202089) | 0.441550 / 0.000490 (0.441061) | 0.000375 / 0.000200 (0.000175) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029527 / 0.037411 (-0.007884) | 0.111645 / 0.014526 (0.097119) | 0.125732 / 0.176557 (-0.050825) | 0.177322 / 0.737135 (-0.559813) | 0.128620 / 0.296338 (-0.167718) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.432415 / 0.215209 (0.217206) | 4.314381 / 2.077655 (2.236726) | 2.079450 / 1.504120 (0.575331) | 1.893139 / 1.541195 (0.351944) | 1.951363 / 1.468490 (0.482873) | 0.531466 / 4.584777 (-4.053311) | 3.716860 / 3.745712 (-0.028852) | 1.850111 / 5.269862 (-3.419750) | 1.100676 / 4.565676 (-3.465000) | 0.066247 / 0.424275 (-0.358028) | 0.011503 / 0.007607 (0.003896) | 0.537208 / 0.226044 (0.311164) | 5.367560 / 2.268929 (3.098631) | 2.543697 / 55.444624 (-52.900927) | 2.221670 / 6.876477 (-4.654806) | 2.252009 / 2.142072 (0.109937) | 0.658509 / 4.805227 (-4.146718) | 0.142345 / 6.500664 (-6.358319) | 0.064701 / 0.075469 (-0.010768) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.266442 / 1.841788 (-0.575346) | 15.105953 / 8.074308 (7.031645) | 14.288229 / 10.191392 (4.096837) | 0.161182 / 0.680424 (-0.519242) | 0.017074 / 0.534201 (-0.517127) | 0.399464 / 0.579283 (-0.179819) | 0.419459 / 0.434364 (-0.014905) | 0.467553 / 0.540337 (-0.072784) | 0.566337 / 1.386936 (-0.820599) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53ac2d9662f9e5923ae7c52199eaa620d82f0043 \"CML watermark\")\n" ]

"2023-06-19T13:07:08Z"

"2023-06-19T17:29:37Z"

"2023-06-19T17:22:41Z"

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[ "This code behaves as expected on my local machine or in Colab. Which version of `soundfile` do you have installed? MP3 requires `soundfile>=0.12.1`.", "I upgraded the `soundfile` and it's working now! \r\nThanks @mariosasko for the help!" ]

"2023-07-26T12:44:05Z"

"2023-07-26T13:08:08Z"

"2023-07-26T13:08:08Z"

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[ "Hi @fdtomasi, thanks for reporting.\r\n\r\nYou are right: the original host data URL does no longer exist.\r\n\r\nI've contacted the authors of the dataset to ask them if they host this dataset in another URL.", "Hi, @fdtomasi, the URL is fixed.\r\n\r\nThe fix is already in our master branch and it will be accessible in our next release.\r\n\r\nIn the meantime, you can include the fix if you install the `datasets` library from the master branch:\r\n```\r\npip install -U git+ssh://[email protected]/huggingface/datasets.git@master#egg=datasest\r\n```\r\nor\r\n```\r\npip install -U git+https://github.com/huggingface/datasets.git@master#egg=datasets\r\n```", "Awesome thank you so much for the quick fix!" ]

"2021-10-15T09:20:40Z"

"2021-10-19T13:06:10Z"

"2021-10-19T12:50:39Z"

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[]

[ "I will add this directly on the hub (same as #4486)—in https://huggingface.co/lapix" ]

"2022-06-24T20:07:54Z"

"2022-07-06T19:03:02Z"

"2022-07-06T19:03:02Z"

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[ "@lhoestq looks like a lot of files were updated... shall I create a new PR?", "Hi @chaitnayabasava ! you can try rebasing and see if that fixes the number of files changed, otherwise please do open a new PR with only the relevant files and close this one :) ", "Created a new PR #1230.\r\nclosing this one :)" ]

"2020-12-04T15:38:17Z"

"2020-12-07T03:21:05Z"

"2020-12-07T03:21:05Z"

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[ "Yes this was documented in the PR that added this hf->paperswithcode mapping (https://github.com/huggingface/datasets/pull/2404) and AFAICT those are slightly distinct datasets so I think it's a wontfix\r\n\r\nFor context on the paperswithcode mapping you can also refer to https://github.com/huggingface/huggingface_hub/pull/43 which contains a lot of background discussion ", "Thanks for the antecedents. I close." ]

"2021-07-20T14:25:00Z"

"2021-07-20T15:44:17Z"

"2021-07-20T15:44:17Z"

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

"2022-08-09T13:47:38Z"

"2022-08-09T17:52:15Z"

"2022-08-09T17:38:18Z"

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[ "merging since the CI is fixed on master" ]

"2020-12-10T00:59:34Z"

"2020-12-11T15:01:23Z"

"2020-12-11T15:01:23Z"

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[ "Hi ! Could it be an Out of Memory issue that could have killed one of the processes ? can you check your memory ?", "Hi! I don't think it is a memory issue. I'm monitoring the main and spawn python processes and threads with `htop` and the memory does not peak. Besides, the example I've posted above should not be that demanding in terms of memory, right? (I have 32GB of RAM). ", "Indeed it should be fine. I couldn't reproduce the error though - I ran your script on my side and it works fine. What version of pytorch are you using ?", "Interesting.. I'm using `torch 1.12.1`", "I also tried on colab and it works fine 🤔 \r\nMaybe something is wrong with your installation of pytorch ?", "Oh actually I just saw that you're using python 3.9\r\n\r\nThis could be related to https://github.com/huggingface/datasets/issues/4113\r\n\r\nWe'll fix that as soon as we can, in the meantime you can try to use use single process, or use an older version of python maybe ?", "I tried with python 3.7 and the issue persists. In collab, which also uses 3.7 I don't get the issue, so yes I guess is something on mu side... will post it here if I manage to fix it", "Hi! Which version of transformers are you using? I test the code on Colab (so python 3.7) with transformers 4.23.1, torch 1.12.1 and pyarrow 9.0.0 (also 6.x), it worked without stuck.", "Hi, I have the same problem in use **datasets.IterableDatasetDict.map()**\r\nmy pytorch is 2.0.0a0+gitc263bd4\r\nmy python is 3.8.16(default, Jun 12 2023, 17:37:21)\r\nwork on aarch64 in 16 node, each node with 4*nVidia-A100-40G\r\nevery node have 4 process execute code as ↓\r\n\r\n```\r\nfrom datasets import load_dataset, interleave_datasets, IterableDatasetDict, concatenate_datasets\r\n```\r\n...\r\n```\r\n model_args.cache_dir = '/home/scx/.cache'\r\n for dataset_name in data_args.datasets_name:\r\n train_datasets.append(\r\n load_dataset(\r\n dataset_name,\r\n cache_dir=model_args.cache_dir,\r\n use_auth_token=True if model_args.use_auth_token else None,\r\n streaming=data_args.streaming,\r\n split='train'\r\n ).select_columns('text')\r\n )\r\n valid_datasets.append(\r\n load_dataset(\r\n dataset_name,\r\n cache_dir=model_args.cache_dir,\r\n use_auth_token=True if model_args.use_auth_token else None,\r\n streaming=data_args.streaming,\r\n split='validation'\r\n ).select_columns('text')\r\n )\r\n train_dataset = interleave_datasets(train_datasets,\r\n probabilities=data_args.datasets_probabilities, \r\n seed=training_args.seed,\r\n stopping_strategy='all_exhausted')\r\n raw_datasets = IterableDatasetDict({'train': train_dataset, 'validation': valid_dataset})\r\n```\r\n...\r\n\r\n```\r\n tokenized_datasets = None\r\n with training_args.main_process_first(desc=\"dataset map tokenization\"):\r\n if not data_args.streaming:\r\n tokenized_datasets = raw_datasets.map(\r\n tokenize_function,\r\n batched=True,\r\n num_proc=data_args.preprocessing_num_workers,\r\n load_from_cache_file=not data_args.overwrite_cache,\r\n desc=\"Running tokenizer on dataset\",\r\n remove_columns=column_names,\r\n )\r\n else:\r\n #TODO 20230722\r\n logger.info('{}: {}'.format(__file__, 'tokenized_datasets = raw_datasets.map('))\r\n logger.info('len raw_datasets: {}'.format(len(raw_datasets.items())))\r\n logger.info('raw_datasets:{}'.format(raw_datasets.items()))\r\n tokenized_datasets = raw_datasets.map(\r\n tokenize_function,\r\n batched=True,\r\n batch_size=1000,\r\n remove_columns=column_names\r\n )\r\n logger.info('map ok!')\r\n logger.info('show train: {}'.format(next(iter(tokenized_datasets['train']))))\r\n logger.info('ok')\r\n # ### RAW CODE ###\r\n # tokenized_datasets = raw_datasets.map(\r\n # tokenize_function,\r\n # batched=True,\r\n # batch_size=1000,\r\n # remove_columns=column_names\r\n # )\r\n #TODO 20230722\r\n logger.info(\"Finish tokenization\")\r\n```\r\nthe output of my code is\r\n```\r\n07/22/2023 21:57:09 - INFO - __main__ - /demo/run_blue_space.py: tokenized_datasets = raw_datasets.map(\r\n07/22/2023 21:57:09 - INFO - __main__ - len raw_datasets: 2\r\n07/22/2023 21:57:09 - INFO - __main__ - raw_datasets:dict_items([('train', <datasets.iterable_dataset.IterableDataset object at 0x4005ee301190>), ('validation', <datasets.iterable_dataset.IterableDataset object at 0x4005ee5427f0>)])\r\n07/22/2023 21:57:09 - INFO - __main__ - map ok!\r\n07/22/2023 22:01:07 - INFO - __main__ - show train: {'input_ids': [14608, 26797, 31891, 34260, 12227, 33207, 5, 5, 31632, 26797, 31891, 34260, 12227, 33207, 7398, 28561, 31236, 31177, 31253, 33558, 31556, 31377, 72, 20732, 32383, 32295, 14027, 31178, 53, 61, 53, 55, 31189, 31146, 31321, 31235, 53, 61, 56, 58, 31189, 31145, 72, 53, 61, 58, 54, 31189, 54, 31245, 53, 60, 31224, 31896, 31178, 28561, 29331, 20732, 31888, 32637, 4426, 2824, 72, 53, 61, 60, 55, 31189, 53, 54, 31245, 53, 31224, 31896, 31178, 28561, 29331, 26137, 20732, 4426, 2824, 73, 54, 52, 52, 52, 31189, 61, 31245, 59, 31224, 31896, 31178, 29331, 28561, 20732, 4426, 2824, 73, 5], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\r\n07/22/2023 22:01:07 - INFO - __main__ - ok\r\n```\r\n\r\n", "@bio-punk `IterableDatasetDict.map` does not support multiprocessing (only `DatasetDict.map` and `Dataset.map` do), so please open a new issue as this doesn't seem to be related to the original issue. ", "Closing as this issue doesn't seem to be related to `datasets`." ]

"2022-10-10T13:50:56Z"

"2023-07-24T15:29:13Z"

"2023-07-24T15:29:13Z"

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"2021-04-09T12:56:19Z"

"2021-04-12T13:32:17Z"

"2021-04-12T13:32:16Z"

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[ "Thank you for all the feedback! I have updated the dummy data with a zip under 30KB, which needs to include at least one data instance from both document domain and dialog domain. Please let me know if it is still too big. Thanks!", "Thank you again for the feedback! I am not too sure what the preferable style for data instance in readme, but still added my edits. Thanks!" ]

"2020-12-21T22:40:34Z"

"2020-12-28T10:32:53Z"

"2020-12-28T10:32:53Z"

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[ "Closing since this is a duplicate of #5711", "> Closing since this is a duplicate of #5711\r\n\r\nSorry @mariosasko , my internet went down went submitting the issue, and somehow it ended up creating a duplicate" ]

"2023-04-05T16:47:10Z"

"2023-04-06T08:32:37Z"

"2023-04-05T17:17:44Z"

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"2021-10-14T09:06:58Z"

"2021-10-14T09:14:56Z"

"2021-10-14T09:14:55Z"

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[ "`iter_archive` is about to support ZIP archives. I think we should use this no ?\r\n\r\nsee #3347 https://github.com/huggingface/datasets/pull/3379", "I was interested in the support for isfile/dir in remote.\r\n\r\nAnyway, `iter_files` will be available for community users.", "I'm not a big fan of having two functions that do the same thing. What do you think ?", "They do not do the same thing:\r\n- One iterates over files in a directory\r\n- The other I guess will iterate over the members of an archive", "Makes sense ! Sounds good then - sorry for my misunderstanding\r\n\r\nNote that `iter_archive` will be more performant for data streaming that `iter_files` thanks to the buffering so maybe in the future we can `iter_archive` for some of these datasets", "Yes, @lhoestq I agree with you: once `iter_archive` supports zip files, it will be more suitable than `iter_files` for these 2 datasets.\r\n\r\nAnyway, this PR also implements `isfile`/`isdir` in streaming mode, besides fixing `iter_files`. And I'm interested in having those in master.\r\n\r\nMaybe, could we merge this PR into master and take note to refactor the datasets to use `iter_archive` once zip is supported?\r\nOther option could be to split this PR into 2..." ]

"2021-12-23T16:26:55Z"

"2021-12-28T15:15:02Z"

"2021-12-28T15:15:02Z"

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[ "similar #3611 " ]

"2022-01-19T13:36:50Z"

"2022-01-28T15:35:57Z"

"2022-01-28T15:35:57Z"

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

"2022-08-19T23:44:39Z"

"2022-08-22T09:14:03Z"

"2022-08-22T08:58:31Z"

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"2020-07-31T11:11:12Z"

"2023-09-24T09:48:40Z"

"2020-07-31T11:13:15Z"

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[ "That’s a good point that we started to discuss internally as well. We should pin the dataset en metrics code by default indeed.\r\nLet’s update this in the coming release.", "Ok closed this with #567 and we are working on a more general solution to pin dataset version in #562 (should be in the coming release)." ]

"2020-09-01T03:01:25Z"

"2020-09-03T18:04:46Z"

"2020-09-03T09:04:20Z"

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[ "Once this is merged, let's update `transformers` examples to use this new code. As currently all those tqdm bars are who knows what they are....\r\n\r\nhttps://github.com/huggingface/transformers/issues/11797", "Sure @stas00! Once this is merged let's discuss what all changes can be done on `transformers` side", "@bhavitvyamalik, as it has been merged would you like to tackle https://github.com/huggingface/transformers/issues/11797?\r\n", "Definitely @stas00. From what I could gather, you guys want more meaningful `.map` calls for all examples [here](https://github.com/huggingface/transformers/tree/master/examples/pytorch)?", "That's exactly right, @bhavitvyamalik \r\n\r\nPerhaps the best approach is to do one example, see that other maintainers agree on it. and then replicate to other." ]

"2021-05-18T16:44:29Z"

"2021-05-27T15:44:04Z"

"2021-05-26T14:59:21Z"

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

"2022-10-04T08:51:46Z"

"2022-10-07T16:42:57Z"

"2022-10-07T16:40:16Z"

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[ "Hello, @lhoestq / @gaceladri : We have been seeing similar behavior with bigger datasets, where querying time increases. Are you folks aware of any solution that fixes this problem yet? ", "Hi ! I'm pretty sure that it can be fixed by using the Arrow IPC file format instead of the raw streaming format but I haven't tested yet.\r\nI'll take a look at it soon and let you know", "My workaround is to shard the dataset into splits in my ssd disk and feed the data in different training sessions. But it is a bit of a pain when we need to reload the last training session with the rest of the split with the Trainer in transformers.\r\n\r\nI mean, when I split the training and then reloads the model and optimizer, it not gets the correct global_status of the optimizer, so I need to hardcode some things. I'm planning to open an issue in transformers and think about it.\r\n```\r\nfrom datasets import load_dataset\r\n\r\nbook_corpus = load_dataset(\"bookcorpus\", split=\"train[:25%]\")\r\nwikicorpus = load_dataset(\"wikicorpus\", split=\"train[:25%]\")\r\nopenwebtext = load_dataset(\"openwebtext\", split=\"train[:25%]\")\r\n\r\nbig_dataset = datasets.concatenate_datasets([wikicorpus, openwebtext, book_corpus])\r\nbig_dataset.shuffle(seed=42)\r\nbig_dataset = big_dataset.map(encode, batched=True, num_proc=20, load_from_cache_file=True, writer_batch_size=5000)\r\nbig_dataset.set_format(type='torch', columns=[\"text\", \"input_ids\", \"attention_mask\", \"token_type_ids\"])\r\n\r\n\r\ntraining_args = TrainingArguments(\r\n output_dir=\"./linear_bert\",\r\n overwrite_output_dir=True,\r\n per_device_train_batch_size=71,\r\n save_steps=500,\r\n save_total_limit=10,\r\n logging_first_step=True,\r\n logging_steps=100,\r\n gradient_accumulation_steps=9,\r\n fp16=True,\r\n dataloader_num_workers=20,\r\n warmup_steps=24000,\r\n learning_rate=0.000545205002870214,\r\n adam_epsilon=1e-6,\r\n adam_beta2=0.98,\r\n weight_decay=0.01,\r\n max_steps=138974, # the total number of steps after concatenating 100% datasets\r\n max_grad_norm=1.0,\r\n)\r\n\r\ntrainer = Trainer(\r\n model=model,\r\n args=training_args,\r\n data_collator=data_collator,\r\n train_dataset=big_dataset,\r\n tokenizer=tokenizer))\r\n```\r\n\r\nI do one training pass with the total steps of this shard and I use len(bbig)/batchsize to stop the training (hardcoded in the trainer.py) when I pass over all the examples in this split.\r\n\r\nNow Im working, I will edit the comment with a more elaborated answer when I left the work.", "I just tested and using the Arrow File format doesn't improve the speed... This will need further investigation.\r\n\r\nMy guess is that it has to iterate over the record batches or chunks of a ChunkedArray in order to retrieve elements.\r\n\r\nHowever if we know in advance in which chunk the element is, and at what index it is, then we can access it instantaneously. But this requires dealing with the chunked arrays instead of the pyarrow Table directly which is not practical.", "I have a dataset with about 2.7 million rows (which I'm loading via `load_from_disk`), and I need to fetch around 300k (particular) rows of it, by index. Currently this is taking a really long time (~8 hours). I tried sharding the large dataset but overall it doesn't change how long it takes to fetch the desired rows.\r\n\r\nI actually have enough RAM that I could fit the large dataset in memory. Would having the large dataset in memory speed up querying? To find out, I tried to load (a column of) the large dataset into memory like this:\r\n```\r\ncolumn_data = large_ds['column_name']\r\n```\r\nbut in itself this takes a really long time.\r\n\r\nI'm pretty stuck - do you have any ideas what I should do? ", "Hi ! Feel free to post a message on the [forum](https://discuss.huggingface.co/c/datasets/10). I'd be happy to help you with this.\r\n\r\nIn your post on the forum, feel free to add more details about your setup:\r\nWhat are column names and types of your dataset ?\r\nHow was the dataset constructed ?\r\nIs the dataset shuffled ?\r\nIs the dataset tokenized ?\r\nAre you on a SSD or an HDD ?\r\n\r\nI'm sure we can figure something out.\r\nFor example on my laptop I can access the 6 millions articles from wikipedia in less than a minute.", "Thanks @lhoestq, I've [posted on the forum](https://discuss.huggingface.co/t/fetching-rows-of-a-large-dataset-by-index/4271?u=abisee).", "Fixed by #2122." ]

"2021-02-01T11:08:23Z"

"2021-08-04T18:11:01Z"

"2021-08-04T18:10:42Z"

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[ "It's a bug in the viewer, thanks for reporting it. We're hoping to update to a new version in the next few days which should fix it.", "Fixed \r\n\r\nhttps://huggingface.co/datasets/subjqa\r\n\r\n<img width=\"1040\" alt=\"Capture d’écran 2022-09-08 à 10 23 26\" src=\"https://user-images.githubusercontent.com/1676121/189073210-2a57ff88-8bb1-44bd-851e-0e75473cea3f.png\">\r\n" ]

"2022-08-24T13:26:20Z"

"2022-09-08T08:23:42Z"

"2022-09-08T08:23:42Z"

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[]

[ "Hi,\r\n\r\nThis is a known issue. More info on this issue can be found in #2061. If you are looking for an open-source contribution, there are step-by-step instructions in the linked issue that you can follow to fix it.", "Closed by #2466." ]

"2021-06-04T09:10:26Z"

"2021-06-18T11:53:43Z"

"2021-06-18T11:53:43Z"

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"2021-06-27T16:18:42Z"

"2021-06-28T13:09:55Z"

"2021-06-28T13:09:54Z"

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[]

[ "Cool, I think we can do both :)", "@lhoestq now the 2 are implemented.\r\n\r\nPlease note that for the the second protection, finally I have chosen to protect the master branch only from **merge commits** (see update comment above), so no need to disable/re-enable the protection on each release (direct commits, different from merge commits, can be pushed to the remote master branch; and eventually reverted without messing up the repo history)." ]

"2021-09-20T06:47:01Z"

"2021-09-20T12:01:27Z"

"2021-09-20T12:00:16Z"

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[ "@lhoestq note that the segfault also occurs on Linux.", "Created the ticket at\r\nhttps://issues.apache.org/jira/browse/ARROW-12568", "@lhoestq the ticket you mentioned is now in state resolved. Pyarrow supports AArch64 after version 4.0.0. Because of this restriction `datasets` is not installing in AArch64 systems." ]

"2021-04-27T11:58:28Z"

"2021-06-12T12:44:49Z"

"2021-04-27T13:43:20Z"

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[]

[ "I have started working on adding this dataset.", "Hey @BirgerMoell - awesome that you started working on Common Voice. Common Voice is a bit special since, there is no direct download link to download the data. In these cases we usually consider two options:\r\n\r\n1) Find a hacky solution to extract the download link somehow from the XLM tree of the website \r\n2) If this doesn't work we force the user to download the data himself and add a `\"data_dir\"` as an input parameter. E.g. you can take a look at how it is done for [this](https://github.com/huggingface/datasets/blob/66f2a7eece98d2778bd22bb5034cb7c2376032d4/datasets/arxiv_dataset/arxiv_dataset.py#L66) \r\n\r\nAlso the documentation here: https://huggingface.co/docs/datasets/add_dataset.html?highlight=data_dir#downloading-data-files-and-organizing-splits (especially the \"note\") might be helpful.", "Let me know if you have any other questions", "I added a Work in Progress pull request (hope that is ok). I've made a card for the dataset and filled out the common_voice.py file with information about the datset (not completely).\r\n\r\nI didn't manage to get the tagging tool working locally on my machine but will look into that later.\r\n\r\nLeft to do.\r\n\r\n- Tag the dataset\r\n- Add missing information and update common_voice.py\r\n\r\nhttps://github.com/huggingface/datasets/pull/1886", "Awesome! I left a longer comment on the PR :-)", "I saw that this current datasets package holds common voice version 6.1, how to add the new version 7.0 that is already available?", "Will me merged next week - we're working on it :-)", "Common voice still appears to be a 6.1. Is the plan still to upgrade to 7.0?", "We actually already have the code and everything ready to add Common Voice 7.0 to `datasets` but are still waiting for the common voice authors to give us the green light :-) \r\n\r\nAlso gently pinging @phirework and @milupo here", "Common Voice 7.0 is available here now: https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0", "For anyone else stumbling upon this thread, the 8.0 version is also available now: https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0" ]

"2021-02-08T13:21:05Z"

"2022-03-20T15:23:40Z"

"2021-03-15T05:56:21Z"

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[ "This'll be a really helpful fix, thank you!" ]

"2021-10-22T09:17:46Z"

"2021-10-22T16:04:15Z"

"2021-10-22T16:04:14Z"

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[ "I found a solution \r\n\r\nIf you specifically install datasets==1.18 and then run\r\n\r\nimport datasets\r\nwiki = datasets.load_dataset('wikipedia', '20200501.en')\r\nthen this should work (it worked for me.)", "I have the same problem here but installing datasets==1.18 wont work for me\r\n" ]

"2022-11-23T14:54:15Z"

"2023-10-03T19:46:44Z"

"2022-11-25T11:33:14Z"

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[ "can you also reference the `--organization` flag like in https://github.com/huggingface/transformers/blob/master/docs/source/model_sharing.rst#upload-your-model-with-the-cli ?", "done!", "Not sure if the changes in `datasets/wmt_t2t/wmt_utils.py` are intentional.\r\nIf you want to add more configs to wmt, could you do it in a serapate PR ?", "I don't think I changed wmt_utils (I think github is wrong or my setup is poorly configured).\r\n\r\nLocally git diff master --name-only says one file. Master is up to date.\r\nTried to make a new PR #755 and the same thing happened.", "Trying new fork." ]

"2020-10-15T13:41:39Z"

"2020-10-23T13:15:28Z"

"2020-10-23T13:15:28Z"

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[ "Looks like this PR includes changes about many other files than the ones for un_ga\r\n\r\nCan you create another branch an another PR please ?", "@lhoestq, Thank you for suggestions. I have made the changes and raised the new PR https://github.com/huggingface/datasets/pull/1569. " ]

"2020-12-08T17:58:38Z"

"2020-12-14T17:52:34Z"

"2020-12-14T17:52:34Z"

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[ "Feel free to merge once the CI is all green ;)" ]

"2021-02-08T18:41:15Z"

"2021-02-09T17:53:21Z"

"2021-02-09T17:53:21Z"

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

"2022-10-04T06:36:54Z"

"2022-10-04T08:58:15Z"

"2022-10-04T08:42:31Z"

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[ "Hi @dpressel, \r\n\r\nthanks for posting your issue! Can you maybe add a complete code snippet that we can copy paste to reproduce the error? For example, I'm not sure where the variable `train_set` comes from in your code and it seems like you are loading multiple datasets at once? ", "Hi, the full example was listed in the PR above, but here is the exact link:\r\n\r\nhttps://github.com/dpressel/mead-baseline/blob/3c1aa3ca062cb23f303ca98ac40b6652b37ee971/api-examples/layers-classify-hf-datasets.py\r\n\r\nThe problem is coming from\r\n```\r\n if cache_file_name is None:\r\n # we create a unique hash from the function, current dataset file and the mapping args\r\n cache_kwargs = {\r\n \"with_indices\": with_indices,\r\n \"batched\": batched,\r\n \"batch_size\": batch_size,\r\n \"remove_columns\": remove_columns,\r\n \"keep_in_memory\": keep_in_memory,\r\n \"load_from_cache_file\": load_from_cache_file,\r\n \"cache_file_name\": cache_file_name,\r\n \"writer_batch_size\": writer_batch_size,\r\n \"arrow_schema\": arrow_schema,\r\n \"disable_nullable\": disable_nullable,\r\n }\r\n cache_file_name = self._get_cache_file_path(function, cache_kwargs)\r\n```\r\nThe cached value is always the same, but I was able to change that by just renaming the function each time which seems to fix the issue.", "Ok, I think @lhoestq has already found a solution :-) Maybe you can chime in @lhoestq ", "This fixed my issue (I think)\r\n\r\nhttps://github.com/dpressel/mead-baseline/commit/48aa8ecde4b307bd3e7dde5fe71e43a1d4769ee1", "> Ok, I think @lhoestq has already found a solution :-) Maybe you can chime in @lhoestq\r\n\r\nOh, awesome! I see the PR, Ill check it out", "The PR should prevent the cache from losing track of the of the dataset type (based on the location of its data). Not sure about your second problem though (cache off).", "Yes, with caching on, it seems to work without the function renaming hack, I mentioned this also in the PR. Thanks!" ]

"2020-05-18T19:22:03Z"

"2020-05-18T21:36:20Z"

"2020-05-18T21:36:20Z"

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[ "Hi @DrMatters, thanks for reporting.\r\n\r\nThis issue was fixed 14 days ago: #2950.\r\n\r\nCurrently, the fix is only in the master branch and will be made available in our next library release.\r\n\r\nIn the meantime, you can incorporate the fix by installing datasets from the master branch:\r\n```shell\r\npip install -U git+ssh://[email protected]/huggingface/datasets.git@master#egg=datasest\r\n```\r\nor\r\n```shell\r\npip install -U git+https://github.com/huggingface/datasets.git@master#egg=datasets\r\n```", "Thanks, sorry for bothering" ]

"2021-10-04T00:49:29Z"

"2021-10-11T10:18:01Z"

"2021-10-04T08:46:13Z"

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